diff --git a/.travis.yml b/.travis.yml index e6684d0abe..c39ca4f42e 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,9 +1,25 @@ +os: linux language: python -sudo: false + +env: + global: + # GH_TOKEN ActivitySim/activitysim public_repo token + - secure: WZeCAmI08hBRgtVWUlerfizvSpOVlBxQDa/Z6HJiDUlIXdSAOIi7TAwnluOgs3lHbSqACegbLCU9CyIQa/ytwmmuwzltkSQN14EcnKea0bXyygd8DFdx4x8st8M3a4nh2svgp4BDM9PCu6T1XIZ1rYM46JsKzNk9X8GpWOVN498= + jobs: + # Add new TEST_SUITE jobs as needed via Travis build matrix expansion + - TEST_SUITE=activitysim/abm/models + - TEST_SUITE=activitysim/abm/test/test_misc.py + - TEST_SUITE=activitysim/abm/test/test_mp_pipeline.py + - TEST_SUITE=activitysim/abm/test/test_multi_zone.py + - TEST_SUITE=activitysim/abm/test/test_multi_zone_mp.py + - TEST_SUITE=activitysim/abm/test/test_pipeline.py + - TEST_SUITE=activitysim/cli + - TEST_SUITE=activitysim/core + python: - - '3.6' - '3.7' - '3.8' + install: - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - bash miniconda.sh -b -p $HOME/miniconda @@ -17,9 +33,18 @@ install: - conda install pytest pytest-cov coveralls pycodestyle - pip install . - pip freeze + script: +# build 2 and 3 zone test data twice since the Python test code on Linux sees these as different locations +- python activitysim/examples/example_multiple_zone/two_zone_example_data.py +- python activitysim/examples/example_multiple_zone/three_zone_example_data.py +- python /home/travis/miniconda/envs/test-environment/lib/python$TRAVIS_PYTHON_VERSION/site-packages/activitysim/examples/example_multiple_zone/two_zone_example_data.py +- python /home/travis/miniconda/envs/test-environment/lib/python$TRAVIS_PYTHON_VERSION/site-packages/activitysim/examples/example_multiple_zone/three_zone_example_data.py +# pycodestyle - pycodestyle activitysim -- py.test --cov activitysim --cov-report term-missing +# run specific TEST_SUITE job on travis to avoid job max time +- travis_wait 50 py.test $TEST_SUITE --cov activitysim --cov-report term-missing --durations=0 + after_success: - coveralls # Build docs @@ -43,7 +68,3 @@ notifications: on_success: never # default: change on_failure: always # default: always secure: Dpp+zBrnPGBHXrYWjwHy/bnHvhINfepSIiViwKfBZizBvTDvzSJfu6gCH+/lQ3squF3D4qTWwxB+LQ9V6KTYhuma8vQVisyneI6ARjUI/qgX6aJjuvmDDGPk6DVeDow7+aCLZ8VEHRhSjwy+dv0Ij0rxI6I94xPVwXUkk7ZjcK0= -env: - global: - # GH_TOKEN ActivitySim/activitysim public_repo token - - secure: WZeCAmI08hBRgtVWUlerfizvSpOVlBxQDa/Z6HJiDUlIXdSAOIi7TAwnluOgs3lHbSqACegbLCU9CyIQa/ytwmmuwzltkSQN14EcnKea0bXyygd8DFdx4x8st8M3a4nh2svgp4BDM9PCu6T1XIZ1rYM46JsKzNk9X8GpWOVN498= diff --git a/activitysim/__init__.py b/activitysim/__init__.py index 466ba31276..543d754739 100644 --- a/activitysim/__init__.py +++ b/activitysim/__init__.py @@ -1,5 +1,5 @@ # ActivitySim # See full license in LICENSE.txt. -__version__ = '0.9.5.2' +__version__ = '0.9.7' __doc__ = 'Activity-Based Travel Modeling' diff --git a/activitysim/abm/misc.py b/activitysim/abm/misc.py index 2660697ef5..b9d0cfe651 100644 --- a/activitysim/abm/misc.py +++ b/activitysim/abm/misc.py @@ -20,7 +20,7 @@ def households_sample_size(settings, override_hh_ids): if override_hh_ids is None: return settings.get('households_sample_size', 0) else: - return len(override_hh_ids) + return 0 if override_hh_ids is None else len(override_hh_ids) @inject.injectable(cache=True) @@ -79,7 +79,7 @@ def trace_od(settings): @inject.injectable(cache=True) def chunk_size(settings): - return int(settings.get('chunk_size', 0)) + return int(settings.get('chunk_size', 0) or 0) @inject.injectable(cache=True) diff --git a/activitysim/abm/models/__init__.py b/activitysim/abm/models/__init__.py index a855f1ca74..005496d4d2 100644 --- a/activitysim/abm/models/__init__.py +++ b/activitysim/abm/models/__init__.py @@ -9,6 +9,8 @@ from . import cdap from . import free_parking from . import initialize +from . import initialize_tours +from . import initialize_los from . import joint_tour_composition from . import joint_tour_destination from . import joint_tour_frequency @@ -20,6 +22,7 @@ from . import non_mandatory_destination from . import non_mandatory_scheduling from . import non_mandatory_tour_frequency +from . import parking_location_choice from . import stop_frequency from . import tour_mode_choice from . import trip_destination @@ -27,4 +30,7 @@ from . import trip_purpose from . import trip_purpose_and_destination from . import trip_scheduling +from . import trip_departure_choice +from . import trip_scheduling_choice from . import trip_matrices +from . import summarize diff --git a/activitysim/abm/models/accessibility.py b/activitysim/abm/models/accessibility.py index 6989d50701..6aa497822c 100644 --- a/activitysim/abm/models/accessibility.py +++ b/activitysim/abm/models/accessibility.py @@ -10,95 +10,99 @@ from activitysim.core import config from activitysim.core import inject from activitysim.core import pipeline +from activitysim.core import mem +from activitysim.core import los +from activitysim.core.pathbuilder import TransitVirtualPathBuilder logger = logging.getLogger(__name__) -class AccessibilitySkims(object): - """ - Wrapper for skim arrays to facilitate use of skims by accessibility model - - Parameters - ---------- - skims : 2D array - omx: open omx file object - this is only used to load skims on demand that were not preloaded - length: int - number of zones in skim to return in skim matrix - in case the skims contain additional external zones that should be trimmed out so skim - array is correct shape to match (flattened) O-D tiled columns in the od dataframe - transpose: bool - whether to transpose the matrix before flattening. (i.e. act as a D-O instead of O-D skim) - """ - - def __init__(self, skim_dict, orig_zones, dest_zones, transpose=False): - - omx_shape = skim_dict.skim_info['omx_shape'] - logger.info("init AccessibilitySkims with %d dest zones %d orig zones omx_shape %s" % - (len(dest_zones), len(orig_zones), omx_shape, )) - - assert len(orig_zones) <= len(dest_zones) - assert np.isin(orig_zones, dest_zones).all() - assert len(np.unique(orig_zones)) == len(orig_zones) - assert len(np.unique(dest_zones)) == len(dest_zones) - - self.skim_dict = skim_dict - self.transpose = transpose - - if omx_shape[0] == len(orig_zones) and skim_dict.offset_mapper.offset_series is None: - # no slicing required because whatever the offset_int, the skim data aligns with zone list - self.map_data = False - else: - - if omx_shape[0] == len(orig_zones): - logger.debug("AccessibilitySkims - applying offset_mapper") - - skim_index = list(range(omx_shape[0])) - orig_map = skim_dict.offset_mapper.map(orig_zones) - dest_map = skim_dict.offset_mapper.map(dest_zones) - - # (we might be sliced multiprocessing) - # assert np.isin(skim_index, orig_map).all() - - if np.isin(skim_index, dest_map).all(): - # not using the whole skim matrix - logger.info("%s skim zones not in dest_map: %s" % - ((~dest_map).sum(), np.ix_(~dest_map))) - - self.map_data = True - self.orig_map = orig_map - self.dest_map = dest_map - - def __getitem__(self, key): - """ - accessor to return flattened skim array with specified key - flattened array will have length length*length and will match tiled OD df used by assign - - this allows the skim array to be accessed from expressions as - skim['DISTANCE'] or skim[('SOVTOLL_TIME', 'MD')] - """ - - data = self.skim_dict.get(key).data - - if self.transpose: - data = data.transpose() - - if self.map_data: - - # slice skim to include only orig rows and dest columns - # 2-d boolean slicing in numpy is a bit tricky - # data = data[orig_map, dest_map] # <- WRONG! - # data = data[orig_map, :][:, dest_map] # <- RIGHT - # data = data[np.ix_(orig_map, dest_map)] # <- ALSO RIGHT - - data = data[self.orig_map, :][:, self.dest_map] - - return data.flatten() +# class AccessibilitySkims(object): +# """ +# Wrapper for skim arrays to facilitate use of skims by accessibility model +# +# Parameters +# ---------- +# skims : 2D array +# omx: open omx file object +# this is only used to load skims on demand that were not preloaded +# length: int +# number of zones in skim to return in skim matrix +# in case the skims contain additional external zones that should be trimmed out so skim +# array is correct shape to match (flattened) O-D tiled columns in the od dataframe +# transpose: bool +# whether to transpose the matrix before flattening. (i.e. act as a D-O instead of O-D skim) +# """ +# +# def __init__(self, skim_dict, orig_zones, dest_zones, transpose=False): +# +# logger.info(f"init AccessibilitySkims with {len(dest_zones)} dest zones {len(orig_zones)} orig zones") +# +# assert len(orig_zones) <= len(dest_zones) +# assert np.isin(orig_zones, dest_zones).all() +# assert len(np.unique(orig_zones)) == len(orig_zones) +# assert len(np.unique(dest_zones)) == len(dest_zones) +# +# self.skim_dict = skim_dict +# self.transpose = transpose +# +# num_skim_zones = skim_dict.get_skim_info('omx_shape')[0] +# if num_skim_zones == len(orig_zones) and skim_dict.offset_mapper.offset_series is None: +# # no slicing required because whatever the offset_int, the skim data aligns with zone list +# self.map_data = False +# else: +# +# logger.debug("AccessibilitySkims - applying offset_mapper") +# +# skim_index = list(range(num_skim_zones)) +# orig_map = skim_dict.offset_mapper.map(orig_zones) +# dest_map = skim_dict.offset_mapper.map(dest_zones) +# +# # (we might be sliced multiprocessing) +# # assert np.isin(skim_index, orig_map).all() +# +# out_of_bounds = ~np.isin(skim_index, dest_map) +# # if out_of_bounds.any(): +# # print(f"{(out_of_bounds).sum()} skim zones not in dest_map") +# # print(f"dest_zones {dest_zones}") +# # print(f"dest_map {dest_map}") +# # print(f"skim_index {skim_index}") +# assert not out_of_bounds.any(), \ +# f"AccessibilitySkims {(out_of_bounds).sum()} skim zones not in dest_map: {np.ix_(out_of_bounds)[0]}" +# +# self.map_data = True +# self.orig_map = orig_map +# self.dest_map = dest_map +# +# def __getitem__(self, key): +# """ +# accessor to return flattened skim array with specified key +# flattened array will have length length*length and will match tiled OD df used by assign +# +# this allows the skim array to be accessed from expressions as +# skim['DISTANCE'] or skim[('SOVTOLL_TIME', 'MD')] +# """ +# +# data = self.skim_dict.get(key).data +# +# if self.transpose: +# data = data.transpose() +# +# if self.map_data: +# # slice skim to include only orig rows and dest columns +# # 2-d boolean slicing in numpy is a bit tricky +# # data = data[orig_map, dest_map] # <- WRONG! +# # data = data[orig_map, :][:, dest_map] # <- RIGHT +# # data = data[np.ix_(orig_map, dest_map)] # <- ALSO RIGHT +# +# data = data[self.orig_map, :][:, self.dest_map] +# +# return data.flatten() @inject.step() -def compute_accessibility(accessibility, skim_dict, land_use, trace_od): +def compute_accessibility(accessibility, network_los, land_use, trace_od): """ Compute accessibility for each zone in land use file using expressions from accessibility_spec @@ -143,8 +147,8 @@ def compute_accessibility(accessibility, skim_dict, land_use, trace_od): # create OD dataframe od_df = pd.DataFrame( data={ - 'orig': np.repeat(np.asanyarray(accessibility_df.index), dest_zone_count), - 'dest': np.tile(np.asanyarray(land_use_df.index), orig_zone_count) + 'orig': np.repeat(orig_zones, dest_zone_count), + 'dest': np.tile(dest_zones, orig_zone_count) } ) @@ -160,9 +164,16 @@ def compute_accessibility(accessibility, skim_dict, land_use, trace_od): locals_d = { 'log': np.log, 'exp': np.exp, - 'skim_od': AccessibilitySkims(skim_dict, orig_zones, dest_zones), - 'skim_do': AccessibilitySkims(skim_dict, orig_zones, dest_zones, transpose=True) + 'network_los': network_los, } + + skim_dict = network_los.get_default_skim_dict() + locals_d['skim_od'] = skim_dict.wrap('orig', 'dest').set_df(od_df) + locals_d['skim_do'] = skim_dict.wrap('dest', 'orig').set_df(od_df) + + if network_los.zone_system == los.THREE_ZONE: + locals_d['tvpb'] = TransitVirtualPathBuilder(network_los) + if constants is not None: locals_d.update(constants) @@ -174,13 +185,15 @@ def compute_accessibility(accessibility, skim_dict, land_use, trace_od): data.shape = (orig_zone_count, dest_zone_count) # (o,d) accessibility_df[column] = np.log(np.sum(data, axis=1) + 1) + logger.info("{trace_label} added {len(results.columns} columns") + # - write table to pipeline pipeline.replace_table("accessibility", accessibility_df) if trace_od: if not trace_od_rows.any(): - logger.warning("trace_od not found origin = %s, dest = %s" % (trace_orig, trace_dest)) + logger.warning(f"trace_od not found origin = {trace_orig}, dest = {trace_dest}") else: # add OD columns to trace results diff --git a/activitysim/abm/models/atwork_subtour_destination.py b/activitysim/abm/models/atwork_subtour_destination.py index 12cbb6da77..90de4e816f 100644 --- a/activitysim/abm/models/atwork_subtour_destination.py +++ b/activitysim/abm/models/atwork_subtour_destination.py @@ -27,7 +27,7 @@ def atwork_subtour_destination_sample( tours, persons_merged, model_settings, - skim_dict, + network_los, destination_size_terms, estimator, chunk_size, trace_label): @@ -54,10 +54,12 @@ def atwork_subtour_destination_sample( logger.info("Running atwork_subtour_location_sample with %d tours", len(choosers)) - # create wrapper with keys for this lookup - in this case there is a workplace_taz - # in the choosers and a TAZ in the alternatives which get merged during interaction + # create wrapper with keys for this lookup - in this case there is a workplace_zone_id + # in the choosers and a zone_id in the alternatives which get merged during interaction # the skims will be available under the name "skims" for any @ expressions - skims = skim_dict.wrap('workplace_taz', 'TAZ') + dest_column_name = destination_size_terms.index.name + skim_dict = network_los.get_default_skim_dict() + skims = skim_dict.wrap('workplace_zone_id', dest_column_name) locals_d = { 'skims': skims @@ -86,16 +88,16 @@ def atwork_subtour_destination_logsums( persons_merged, destination_sample, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_label): """ add logsum column to existing atwork_subtour_destination_sample table - logsum is calculated by running the mode_choice model for each sample (person, dest_taz) pair + logsum is calculated by running the mode_choice model for each sample (person, dest_zone_id) pair in atwork_subtour_destination_sample, and computing the logsum of all the utilities +-----------+--------------+----------------+------------+----------------+ - | person_id | dest_TAZ | rand | pick_count | logsum (added) | + | person_id | dest_zone_id | rand | pick_count | logsum (added) | +===========+==============+================+============+================+ | 23750 | 14 | 0.565502716034 | 4 | 1.85659498857 | +-----------+--------------+----------------+------------+----------------+ @@ -132,7 +134,7 @@ def atwork_subtour_destination_logsums( choosers, tour_purpose, logsum_settings, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_label) @@ -147,7 +149,7 @@ def atwork_subtour_destination_simulate( destination_sample, want_logsums, model_settings, - skim_dict, + network_los, destination_size_terms, estimator, chunk_size, trace_label): @@ -175,7 +177,7 @@ def atwork_subtour_destination_simulate( estimator.write_choosers(choosers) alt_dest_col_name = model_settings['ALT_DEST_COL_NAME'] - chooser_col_name = 'workplace_taz' + chooser_col_name = 'workplace_zone_id' # alternatives are pre-sampled and annotated with logsums and pick_count # but we have to merge destination_size_terms columns into alt sample list @@ -189,9 +191,10 @@ def atwork_subtour_destination_simulate( logger.info("Running atwork_subtour_destination_simulate with %d persons", len(choosers)) - # create wrapper with keys for this lookup - in this case there is a TAZ in the choosers - # and a TAZ in the alternatives which get merged during interaction + # create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers + # and a zone_id in the alternatives which get merged during interaction # the skims will be available under the name "skims" for any @ expressions + skim_dict = network_los.get_default_skim_dict() skims = skim_dict.wrap(chooser_col_name, alt_dest_col_name) locals_d = { @@ -212,7 +215,7 @@ def atwork_subtour_destination_simulate( locals_d=locals_d, chunk_size=chunk_size, trace_label=trace_label, - trace_choice_name='workplace_location', + trace_choice_name='atwork_subtour', estimator=estimator) if not want_logsums: @@ -227,8 +230,7 @@ def atwork_subtour_destination_simulate( def atwork_subtour_destination( tours, persons_merged, - skim_dict, - skim_stack, + network_los, land_use, size_terms, chunk_size, trace_hh_id): @@ -271,7 +273,7 @@ def atwork_subtour_destination( subtours, persons_merged, model_settings, - skim_dict, + network_los, destination_size_terms, estimator=estimator, chunk_size=chunk_size, @@ -281,7 +283,7 @@ def atwork_subtour_destination( persons_merged, destination_sample_df, model_settings, - skim_dict, skim_stack, + network_los, chunk_size=chunk_size, trace_label=tracing.extend_trace_label(trace_label, 'logsums')) @@ -291,7 +293,7 @@ def atwork_subtour_destination( destination_sample_df, want_logsums, model_settings, - skim_dict, + network_los, destination_size_terms, estimator=estimator, chunk_size=chunk_size, diff --git a/activitysim/abm/models/atwork_subtour_frequency.py b/activitysim/abm/models/atwork_subtour_frequency.py index 103479c6ee..9cc7b02a9c 100644 --- a/activitysim/abm/models/atwork_subtour_frequency.py +++ b/activitysim/abm/models/atwork_subtour_frequency.py @@ -10,11 +10,11 @@ from activitysim.core import pipeline from activitysim.core import config from activitysim.core import inject +from activitysim.core import expressions from .util import estimation from .util.tour_frequency import process_atwork_subtours -from .util.expressions import assign_columns logger = logging.getLogger(__name__) @@ -69,7 +69,7 @@ def atwork_subtour_frequency(tours, preprocessor_settings = model_settings.get('preprocessor', None) if preprocessor_settings: - assign_columns( + expressions.assign_columns( df=work_tours, model_settings=preprocessor_settings, trace_label=trace_label) diff --git a/activitysim/abm/models/atwork_subtour_mode_choice.py b/activitysim/abm/models/atwork_subtour_mode_choice.py index 5d6c4de0de..517c0cfcd6 100644 --- a/activitysim/abm/models/atwork_subtour_mode_choice.py +++ b/activitysim/abm/models/atwork_subtour_mode_choice.py @@ -10,13 +10,14 @@ from activitysim.core import pipeline from activitysim.core import simulate -from activitysim.core.mem import force_garbage_collect - -from activitysim.core.util import assign_in_place +from activitysim.core import los +from activitysim.core.pathbuilder import TransitVirtualPathBuilder from .util.mode import run_tour_mode_choice_simulate from .util import estimation +from activitysim.core.mem import force_garbage_collect +from activitysim.core.util import assign_in_place logger = logging.getLogger(__name__) @@ -25,7 +26,7 @@ def atwork_subtour_mode_choice( tours, persons_merged, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id): """ @@ -52,26 +53,29 @@ def atwork_subtour_mode_choice( pd.merge(subtours, persons_merged.to_frame(), left_on='person_id', right_index=True, how='left') - constants = config.get_model_constants(model_settings) - logger.info("Running %s with %d subtours" % (trace_label, subtours_merged.shape[0])) tracing.print_summary('%s tour_type' % trace_label, subtours_merged.tour_type, value_counts=True) + constants = {} + constants.update(config.get_model_constants(model_settings)) + + skim_dict = network_los.get_default_skim_dict() + # setup skim keys - orig_col_name = 'workplace_taz' + orig_col_name = 'workplace_zone_id' dest_col_name = 'destination' out_time_col_name = 'start' in_time_col_name = 'end' - odt_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name, - skim_key='out_period') - dot_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name, - skim_key='in_period') - odr_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name, - skim_key='in_period') - dor_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name, - skim_key='out_period') + odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='out_period') + dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='in_period') + odr_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='in_period') + dor_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='out_period') od_skim_stack_wrapper = skim_dict.wrap(orig_col_name, dest_col_name) skims = { @@ -86,6 +90,27 @@ def atwork_subtour_mode_choice( 'in_time_col_name': in_time_col_name } + if network_los.zone_system == los.THREE_ZONE: + # fixme - is this a lightweight object? + tvpb = network_los.tvpb + + tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=orig_col_name, dest_key=dest_col_name, + tod_key='out_period', segment_key='demographic_segment', + cache_choices=True, + trace_label=trace_label, tag='tvpb_logsum_odt') + tvpb_logsum_dot = tvpb.wrap_logsum(orig_key=dest_col_name, dest_key=orig_col_name, + tod_key='in_period', segment_key='demographic_segment', + cache_choices=True, + trace_label=trace_label, tag='tvpb_logsum_dot') + + skims.update({ + 'tvpb_logsum_odt': tvpb_logsum_odt, + 'tvpb_logsum_dot': tvpb_logsum_dot + }) + + # TVPB constants can appear in expressions + constants.update(network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS')) + estimator = estimation.manager.begin_estimation('atwork_subtour_mode_choice') if estimator: estimator.write_coefficients(simulate.read_model_coefficients(model_settings)) @@ -99,6 +124,7 @@ def atwork_subtour_mode_choice( tour_purpose='atwork', model_settings=model_settings, mode_column_name=mode_column_name, logsum_column_name=logsum_column_name, + network_los=network_los, skims=skims, constants=constants, estimator=estimator, @@ -106,6 +132,27 @@ def atwork_subtour_mode_choice( trace_label=trace_label, trace_choice_name='tour_mode_choice') + # add cached tvpb_logsum tap choices for modes specified in tvpb_mode_path_types + if network_los.zone_system == los.THREE_ZONE: + + tvpb_mode_path_types = model_settings.get('tvpb_mode_path_types') + for mode, path_types in tvpb_mode_path_types.items(): + + for direction, skim in zip(['od', 'do'], [tvpb_logsum_odt, tvpb_logsum_dot]): + + path_type = path_types[direction] + skim_cache = skim.cache[path_type] + + print(f"mode {mode} direction {direction} path_type {path_type}") + + for c in skim_cache: + + dest_col = f'{direction}_{c}' + + if dest_col not in choices_df: + choices_df[dest_col] = 0 if pd.api.types.is_numeric_dtype(skim_cache[c]) else '' + choices_df[dest_col].where(choices_df.tour_mode != mode, skim_cache[c], inplace=True) + if estimator: estimator.write_choices(choices_df[mode_column_name]) choices_df[mode_column_name] = \ diff --git a/activitysim/abm/models/atwork_subtour_scheduling.py b/activitysim/abm/models/atwork_subtour_scheduling.py index ae9c2138dd..ae7e1900ba 100644 --- a/activitysim/abm/models/atwork_subtour_scheduling.py +++ b/activitysim/abm/models/atwork_subtour_scheduling.py @@ -11,8 +11,9 @@ from activitysim.core import config from activitysim.core import inject from activitysim.core import timetable as tt +from activitysim.core import expressions + from .util.vectorize_tour_scheduling import vectorize_subtour_scheduling -from .util.expressions import annotate_preprocessors from .util import estimation @@ -60,12 +61,10 @@ def atwork_subtour_scheduling( # preprocessor constants = config.get_model_constants(model_settings) od_skim_wrapper = skim_dict.wrap('origin', 'destination') - do_skim_wrapper = skim_dict.wrap('destination', 'origin') skims = { "od_skims": od_skim_wrapper, - "do_skims": do_skim_wrapper, } - annotate_preprocessors( + expressions.annotate_preprocessors( subtours, constants, skims, model_settings, trace_label) diff --git a/activitysim/abm/models/cdap.py b/activitysim/abm/models/cdap.py index fe90bc201d..9fd9dd7c81 100644 --- a/activitysim/abm/models/cdap.py +++ b/activitysim/abm/models/cdap.py @@ -9,9 +9,11 @@ from activitysim.core import pipeline from activitysim.core import config from activitysim.core import inject +from activitysim.core import expressions + +from activitysim.core.util import reindex from .util import cdap -from .util import expressions from .util import estimation logger = logging.getLogger(__name__) @@ -32,7 +34,7 @@ def cdap_simulate(persons_merged, persons, households, trace_label = 'cdap' model_settings = config.read_model_settings('cdap.yaml') - + person_type_map = model_settings.get('PERSON_TYPE_MAP', {}) cdap_indiv_spec = simulate.read_model_spec(file_name=model_settings['INDIV_AND_HHSIZE1_SPEC']) # Rules and coefficients for generating interaction specs for different household sizes @@ -51,6 +53,12 @@ def cdap_simulate(persons_merged, persons, households, persons_merged = persons_merged.to_frame() + # add tour-based chunk_id so we can chunk all trips in tour together + assert 'chunk_id' not in persons_merged.columns + unique_household_ids = persons_merged.household_id.unique() + household_chunk_ids = pd.Series(range(len(unique_household_ids)), index=unique_household_ids) + persons_merged['chunk_id'] = reindex(household_chunk_ids, persons_merged.household_id) + constants = config.get_model_constants(model_settings) cdap_interaction_coefficients = \ @@ -80,6 +88,7 @@ def cdap_simulate(persons_merged, persons, households, choices = cdap.run_cdap( persons=persons_merged, + person_type_map=person_type_map, cdap_indiv_spec=cdap_indiv_spec, cdap_interaction_coefficients=cdap_interaction_coefficients, cdap_fixed_relative_proportions=cdap_fixed_relative_proportions, diff --git a/activitysim/abm/models/free_parking.py b/activitysim/abm/models/free_parking.py index f33ba841b8..c19ed0cd76 100644 --- a/activitysim/abm/models/free_parking.py +++ b/activitysim/abm/models/free_parking.py @@ -7,8 +7,8 @@ from activitysim.core import pipeline from activitysim.core import simulate from activitysim.core import inject +from activitysim.core import expressions -from .util import expressions from .util import estimation logger = logging.getLogger(__name__) @@ -16,9 +16,8 @@ @inject.step() def free_parking( - persons_merged, persons, households, - skim_dict, skim_stack, - chunk_size, trace_hh_id, locutor): + persons_merged, persons, + chunk_size, trace_hh_id): """ """ @@ -27,7 +26,7 @@ def free_parking( model_settings_file_name = 'free_parking.yaml' choosers = persons_merged.to_frame() - choosers = choosers[choosers.workplace_taz > -1] + choosers = choosers[choosers.workplace_zone_id > -1] logger.info("Running %s with %d persons", trace_label, len(choosers)) model_settings = config.read_model_settings(model_settings_file_name) diff --git a/activitysim/abm/models/initialize.py b/activitysim/abm/models/initialize.py index bda6ac76a9..d2cb230d1e 100644 --- a/activitysim/abm/models/initialize.py +++ b/activitysim/abm/models/initialize.py @@ -9,16 +9,15 @@ from activitysim.core import config from activitysim.core import inject from activitysim.core import pipeline +from activitysim.core import expressions +from activitysim.core import mem from activitysim.core.steps.output import write_data_dictionary from activitysim.core.steps.output import write_tables from activitysim.core.steps.output import track_skim_usage -from .util import expressions - from activitysim.abm.tables import shadow_pricing - # We are using the naming conventions in the mtc_asim.h5 example # file for our default list. This provides backwards compatibility # with previous versions of ActivitySim in which only 'input_store' @@ -43,31 +42,35 @@ def annotate_tables(model_settings, trace_label): annotate_tables = model_settings.get('annotate_tables', []) if not annotate_tables: - logger.warning("annotate_tables setting is empty - nothing to do!") + logger.warning(f"{trace_label} - annotate_tables setting is empty - nothing to do!") + + assert isinstance(annotate_tables, list), \ + f"annotate_tables settings should be a list but is {type(annotate_tables)}" t0 = tracing.print_elapsed_time() for table_info in annotate_tables: tablename = table_info['tablename'] + df = inject.get_table(tablename).to_frame() # - rename columns column_map = table_info.get('column_map', None) if column_map: - warnings.warn("annotate_tables option 'column_map' renamed 'rename_columns' and moved" - "to settings.yaml. Support for 'column_map' in annotate_tables will be " - "removed in future versions.", + warnings.warn(f"{trace_label} - annotate_tables option 'column_map' renamed 'rename_columns' " + f"and moved to global settings file. Support for 'column_map' in annotate_tables " + f"will be removed in future versions.", FutureWarning) - logger.info("renaming %s columns %s" % (tablename, column_map,)) + logger.info(f"{trace_label} - renaming {tablename} columns {column_map}") df.rename(columns=column_map, inplace=True) # - annotate annotate = table_info.get('annotate', None) if annotate: - logger.info("annotated %s SPEC %s" % (tablename, annotate['SPEC'],)) + logger.info(f"{trace_label} - annotating {tablename} SPEC {annotate['SPEC']}") expressions.assign_columns( df=df, model_settings=annotate, @@ -105,6 +108,7 @@ def initialize_households(): # - initialize shadow_pricing size tables after annotating household and person tables # since these are scaled to model size, they have to be created while single-process shadow_pricing.add_size_tables() + mem.trace_memory_info(f"initialize_households after shadow_pricing.add_size_tables") # - preload person_windows t0 = tracing.print_elapsed_time() diff --git a/activitysim/abm/models/initialize_los.py b/activitysim/abm/models/initialize_los.py new file mode 100644 index 0000000000..aa7a83b572 --- /dev/null +++ b/activitysim/abm/models/initialize_los.py @@ -0,0 +1,288 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging +import os +import time +import multiprocessing +import numba + +from contextlib import contextmanager + +import pandas as pd +import numpy as np + +from activitysim.core import assign +from activitysim.core import config +from activitysim.core import simulate +from activitysim.core import pipeline +from activitysim.core import tracing +from activitysim.core import chunk +from activitysim.core import inject +from activitysim.core import los + +from activitysim.core import pathbuilder + +logger = logging.getLogger(__name__) + + +@contextmanager +def lock_data(lock): + if lock is not None: + with lock: + yield + else: + yield + + +@numba.njit(nogil=True) +def any_nans(data): + # equivalent to np.isnan(x).any() + # short circuit in any nans - but the real point is to save memory (np.flat is a nop-copy iterator) + for x in data.flat: + if np.isnan(x): + return True + return False + + +@numba.njit(nogil=True) +def num_nans(data): + # equivalent to np.isnan(x).sum() but with no transient memory overhead (np.flat is a nop-copy iterator) + n = 0 + for x in data.flat: + if np.isnan(x): + n += 1 + return n + + +def any_uninitialized(data, lock=None): + + with lock_data(lock): + result = any_nans(data) + return result + + +def num_uninitialized(data, lock=None): + + with lock_data(lock): + result = num_nans(data) + return result + + +@inject.step() +def initialize_los(network_los): + """ + Currently, this step is only needed for THREE_ZONE systems in which the tap_tap_utilities are precomputed + in the (presumably subsequent) initialize_tvpb step. + + Adds attribute_combinations_df table to the pipeline so that it can be used to as the slicer + for multiprocessing the initialize_tvpb s.tep + + FIXME - this step is only strictly necessary when multiprocessing, but initialize_tvpb would need to be tweaked + FIXME - to instantiate attribute_combinations_df if the pipeline table version were not available. + """ + + trace_label = 'initialize_los' + + if network_los.zone_system == los.THREE_ZONE: + + tap_cache = network_los.tvpb.tap_cache + uid_calculator = network_los.tvpb.uid_calculator + attribute_combinations_df = uid_calculator.scalar_attribute_combinations() + + # - write table to pipeline (so we can slice it, when multiprocessing) + pipeline.replace_table('attribute_combinations', attribute_combinations_df) + + # clean up any unwanted cache files from previous run + if network_los.rebuild_tvpb_cache: + network_los.tvpb.tap_cache.cleanup() + + # if multiprocessing make sure shared cache is filled with np.nan + # so that initialize_tvpb subprocesses can detect when cache is fully populated + if network_los.multiprocess(): + data, lock = tap_cache.get_data_and_lock_from_buffers() # don't need lock here since single process + + if os.path.isfile(tap_cache.cache_path): + # fully populated cache should have been loaded from saved cache + assert not network_los.rebuild_tvpb_cache + assert not any_uninitialized(data, lock=None) + else: + # shared cache should be filled with np.nan so that initialize_tvpb + # subprocesses can detect when cache is fully populated + with lock_data(lock): + np.copyto(data, np.nan) + + +def initialize_tvpb_calc_row_size(choosers, network_los, trace_label): + """ + rows_per_chunk calculator for trip_purpose + """ + + sizer = chunk.RowSizeEstimator(trace_label) + + model_settings = \ + network_los.setting(f'TVPB_SETTINGS.tour_mode_choice.tap_tap_settings') + attributes_as_columns = \ + network_los.setting('TVPB_SETTINGS.tour_mode_choice.tap_tap_settings.attributes_as_columns', []) + + # expression_values for each spec row + sizer.add_elements(len(choosers.columns), 'choosers') + + # expression_values for each spec row + sizer.add_elements(len(attributes_as_columns), 'attributes_as_columns') + + preprocessor_settings = model_settings.get('PREPROCESSOR') + if preprocessor_settings: + + preprocessor_spec_name = preprocessor_settings.get('SPEC', None) + + if not preprocessor_spec_name.endswith(".csv"): + preprocessor_spec_name = f'{preprocessor_spec_name}.csv' + expressions_spec = assign.read_assignment_spec(config.config_file_path(preprocessor_spec_name)) + + sizer.add_elements(expressions_spec.shape[0], 'preprocessor') + + # expression_values for each spec row + spec = simulate.read_model_spec(file_name=model_settings['SPEC']) + sizer.add_elements(spec.shape[0], 'expression_values') + + # expression_values for each spec row + sizer.add_elements(spec.shape[1], 'utilities') + + row_size = sizer.get_hwm() + + return row_size + + +def compute_utilities_for_atttribute_tuple(network_los, scalar_attributes, data, chunk_size, trace_label): + + # scalar_attributes is a dict of attribute name/value pairs for this combination + # (e.g. {'demographic_segment': 0, 'tod': 'AM', 'access_mode': 'walk'}) + + logger.info(f"{trace_label} scalar_attributes: {scalar_attributes}") + + uid_calculator = network_los.tvpb.uid_calculator + + attributes_as_columns = \ + network_los.setting('TVPB_SETTINGS.tour_mode_choice.tap_tap_settings.attributes_as_columns', []) + model_settings = \ + network_los.setting(f'TVPB_SETTINGS.tour_mode_choice.tap_tap_settings') + model_constants = \ + network_los.setting(f'TVPB_SETTINGS.tour_mode_choice.CONSTANTS').copy() + model_constants.update(scalar_attributes) + + data = data.reshape(uid_calculator.fully_populated_shape) + + # get od skim_offset dataframe with uid index corresponding to scalar_attributes + choosers_df = uid_calculator.get_od_dataframe(scalar_attributes) + + row_size = chunk_size and initialize_tvpb_calc_row_size(choosers_df, network_los, trace_label) + for i, chooser_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers(choosers_df, chunk_size, row_size, trace_label): + + # we should count choosers_df as chunk overhead since its pretty big and was custom made for compute_utilities + # (call log_df from inside yield loop so it is visible to adaptive_chunked_choosers chunk_log) + chunk.log_df(trace_label, 'choosers_df', choosers_df) + + # add any attribute columns specified as column attributes in settings (the rest will be scalars in locals_dict) + for attribute_name in attributes_as_columns: + chooser_chunk[attribute_name] = scalar_attributes[attribute_name] + + chunk.log_df(trace_label, 'chooser_chunk', chooser_chunk) + + utilities_df = \ + pathbuilder.compute_utilities(network_los, + model_settings=model_settings, + choosers=chooser_chunk, + model_constants=model_constants, + trace_label=trace_label) + + chunk.log_df(trace_label, 'utilities_df', utilities_df) + + assert len(utilities_df) == len(chooser_chunk) + assert len(utilities_df.columns) == data.shape[1] + assert not any_uninitialized(utilities_df.values) + + data[chooser_chunk.index.values, :] = utilities_df.values + + logger.debug(f"{trace_label} updated utilities") + + +@inject.step() +def initialize_tvpb(network_los, attribute_combinations, chunk_size): + """ + Initialize STATIC tap_tap_utility cache and write mmap to disk. + + uses pipeline attribute_combinations table created in initialize_los to determine which attribute tuples + to compute utilities for. + + if we are single-processing, this will be the entire set of attribute tuples required to fully populate cache + + if we are multiprocessing, then the attribute_combinations will have been sliced and we compute only a subset + of the tuples (and the other processes will compute the rest). All process wait until the cache is fully + populated before returning, and the spokesman/locutor process writes the results. + + + FIXME - if we did not close this, we could avoid having to reload it from mmap when single-process? + """ + + trace_label = 'initialize_tvpb' + + if network_los.zone_system != los.THREE_ZONE: + logger.info(f"{trace_label} - skipping step because zone_system is not THREE_ZONE") + return + + attribute_combinations_df = attribute_combinations.to_frame() + multiprocess = network_los.multiprocess() + uid_calculator = network_los.tvpb.uid_calculator + + tap_cache = network_los.tvpb.tap_cache + assert not tap_cache.is_open + + # if cache already exists, + if os.path.isfile(tap_cache.cache_path): + # otherwise should have been deleted by TVPBCache.cleanup in initialize_los step + assert not network_los.rebuild_tvpb_cache + logger.info(f"{trace_label} skipping rebuild of STATIC cache because rebuild_tvpb_cache setting is False" + f" and cache already exists: {tap_cache.cache_path}") + return + + if multiprocess: + # we will compute 'skim' chunks at offsets specified by our slice of attribute_combinations_df + data, lock = tap_cache.get_data_and_lock_from_buffers() + else: + data = tap_cache.allocate_data_buffer(shared=False) + lock = None + + logger.debug(f"{trace_label} processing {len(attribute_combinations_df)} attribute_combinations") + logger.debug(f"{trace_label} compute utilities for attribute_combinations_df\n{attribute_combinations_df}") + + for offset, scalar_attributes in attribute_combinations_df.to_dict('index').items(): + # compute utilities for this 'skim' with a single full set of scalar attributes + + offset = network_los.tvpb.uid_calculator.get_skim_offset(scalar_attributes) + tuple_trace_label = tracing.extend_trace_label(trace_label, f'offset{offset}') + + compute_utilities_for_atttribute_tuple(network_los, scalar_attributes, data, chunk_size, tuple_trace_label) + + # make sure we populated the entire offset + assert not any_uninitialized(data.reshape(uid_calculator.skim_shape)[offset], lock) + + if multiprocess and not inject.get_injectable('locutor', False): + return + + write_results = not multiprocess or inject.get_injectable('locutor', False) + if write_results: + + if multiprocess: + # if multiprocessing, wait for all processes to fully populate share data before writing results + # (the other processes don't have to wait, since we were sliced by attribute combination + # and they must wait to coalesce at the end of the multiprocessing_step) + # FIXME testing entire array is costly in terms of RAM) + while any_uninitialized(data, lock): + logger.debug(f"{trace_label}.{multiprocessing.current_process().name} waiting for other processes" + f" to populate {num_uninitialized(data, lock)} uninitialized data values") + time.sleep(5) + + logger.info(f"{trace_label} writing static cache.") + with lock_data(lock): + tap_cache.write_static_cache(data) diff --git a/activitysim/abm/models/initialize_tours.py b/activitysim/abm/models/initialize_tours.py new file mode 100644 index 0000000000..576cd85eec --- /dev/null +++ b/activitysim/abm/models/initialize_tours.py @@ -0,0 +1,120 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging +import warnings +import os +import pandas as pd + +from activitysim.core import config +from activitysim.core import inject +from activitysim.core import tracing +from activitysim.core import pipeline +from activitysim.core import expressions + +from activitysim.core.input import read_input_table + +from activitysim.abm.models.util import tour_frequency as tf + +logger = logging.getLogger(__name__) + +SURVEY_TOUR_ID = 'external_tour_id' +SURVEY_PARENT_TOUR_ID = 'external_parent_tour_id' +SURVEY_PARTICIPANT_ID = 'external_participant_id' +ASIM_TOUR_ID = 'tour_id' +ASIM_PARENT_TOUR_ID = 'parent_tour_id' +REQUIRED_TOUR_COLUMNS = set(['person_id', 'tour_category', 'tour_type']) + + +def patch_tour_ids(tours): + + def set_tour_index(tours, parent_tour_num_col, is_joint): + group_cols = ['person_id', 'tour_category', 'tour_type'] + + if 'parent_tour_num' in tours: + group_cols += ['parent_tour_num'] + + tours['tour_type_num'] = \ + tours.sort_values(by=group_cols).groupby(group_cols).cumcount() + 1 + + return tf.set_tour_index(tours, parent_tour_num_col=parent_tour_num_col, is_joint=is_joint) + + assert REQUIRED_TOUR_COLUMNS.issubset(set(tours.columns)), \ + f"Required columns missing from tours table: {REQUIRED_TOUR_COLUMNS.difference(set(tours.columns))}" + + # replace tour index with asim standard tour_ids (which are based on person_id and tour_type) + if tours.index.name is not None: + tours.insert(loc=0, column='legacy_index', value=tours.index) + + # FIXME - for now, only grok simple tours + assert set(tours.tour_category.unique()).issubset({'mandatory', 'non_mandatory'}) + + # mandatory tours + mandatory_tours = \ + set_tour_index(tours[tours.tour_category == 'mandatory'], parent_tour_num_col=None, is_joint=False) + + assert mandatory_tours.index.name == 'tour_id' + + # FIXME joint tours not implemented + assert not (tours.tour_category == 'joint').any() + + # non_mandatory tours + non_mandatory_tours = \ + set_tour_index(tours[tours.tour_category == 'non_mandatory'], parent_tour_num_col=None, is_joint=False) + + # FIXME atwork tours ot implemented + assert not (tours.tour_category == 'atwork').any() + + patched_tours = pd.concat([mandatory_tours, non_mandatory_tours]) + del patched_tours['tour_type_num'] + + return patched_tours + + +@inject.step() +def initialize_tours(network_los, households, persons, trace_hh_id): + + trace_label = 'initialize_tours' + + tours = read_input_table("tours") + + # FIXME can't use households_sliced injectable as flag like persons table does in case of resume_after. + # FIXME could just always slice... + slice_happened = \ + inject.get_injectable('households_sample_size', 0) > 0 \ + or inject.get_injectable('households_sample_size', 0) > 0 + if slice_happened: + logger.info("slicing tours %s" % (tours.shape,)) + # keep all persons in the sampled households + tours = tours[tours.person_id.isin(persons.index)] + + # annotate before patching tour_id to allow addition of REQUIRED_TOUR_COLUMNS defined above + model_settings = config.read_model_settings('initialize_tours.yaml', mandatory=True) + expressions.assign_columns( + df=tours, + model_settings=model_settings.get('annotate_tours'), + trace_label=tracing.extend_trace_label(trace_label, 'annotate_tours')) + + tours = patch_tour_ids(tours) + assert tours.index.name == 'tour_id' + + # replace table function with dataframe + inject.add_table('tours', tours) + + pipeline.get_rn_generator().add_channel('tours', tours) + + tracing.register_traceable_table('tours', tours) + + print(f"{len(tours.household_id.unique())} unique household_ids in tours") + print(f"{len(households.index.unique())} unique household_ids in households") + assert not tours.index.duplicated().any() + + tours_without_persons = ~tours.person_id.isin(persons.index) + if tours_without_persons.any(): + logger.error(f"{tours_without_persons.sum()} tours out of {len(persons)} without persons\n" + f"{pd.Series({'person_id': tours_without_persons.index.values})}") + raise RuntimeError(f"{tours_without_persons.sum()} tours with bad person_id") + + if trace_hh_id: + tracing.trace_df(tours, + label='initialize_tours', + warn_if_empty=True) diff --git a/activitysim/abm/models/joint_tour_composition.py b/activitysim/abm/models/joint_tour_composition.py index 8b9c8df9df..7449da7a9f 100644 --- a/activitysim/abm/models/joint_tour_composition.py +++ b/activitysim/abm/models/joint_tour_composition.py @@ -9,8 +9,8 @@ from activitysim.core import pipeline from activitysim.core import config from activitysim.core import inject +from activitysim.core import expressions -from .util import expressions from .util import estimation from .util.overlap import hh_time_window_overlap diff --git a/activitysim/abm/models/joint_tour_destination.py b/activitysim/abm/models/joint_tour_destination.py index e10fa2c346..deb011411b 100644 --- a/activitysim/abm/models/joint_tour_destination.py +++ b/activitysim/abm/models/joint_tour_destination.py @@ -31,7 +31,7 @@ def run_destination_sample( tours, households_merged, model_settings, - skim_dict, + network_los, destination_size_terms, estimator, chunk_size, trace_label): @@ -56,14 +56,18 @@ def run_destination_sample( logger.info("Estimation mode for %s using unsampled alternatives short_circuit_choices" % (trace_label,)) sample_size = 0 - # create wrapper with keys for this lookup - in this case there is a workplace_taz - # in the choosers and a TAZ in the alternatives which get merged during interaction - # (logit.interaction_dataset suffixes duplicate chooser column with '_chooser') + # create wrapper with keys for this lookup - in this case there is a workplace_zone_id + # in the choosers and a zone_id in the alternatives which ge t merged during interaction # the skims will be available under the name "skims" for any @ expressions origin_col_name = model_settings['CHOOSER_ORIG_COL_NAME'] - if origin_col_name == 'TAZ': - origin_col_name = 'TAZ_chooser' - skims = skim_dict.wrap(origin_col_name, 'TAZ') + dest_column_name = destination_size_terms.index.name + + # (logit.interaction_dataset suffixes duplicate chooser column with '_chooser') + if (origin_col_name == dest_column_name): + origin_col_name = f'{origin_col_name}_chooser' + + skim_dict = network_los.get_default_skim_dict() + skims = skim_dict.wrap(origin_col_name, dest_column_name) locals_d = { 'skims': skims @@ -96,12 +100,12 @@ def run_destination_logsums( persons_merged, destination_sample, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id, trace_label): """ add logsum column to existing tour_destination_sample table - logsum is calculated by running the mode_choice model for each sample (person, dest_taz) pair + logsum is calculated by running the mode_choice model for each sample (person, dest_zone_id) pair in destination_sample, and computing the logsum of all the utilities """ @@ -123,7 +127,7 @@ def run_destination_logsums( choosers, tour_purpose, logsum_settings, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_label) @@ -139,7 +143,7 @@ def run_destination_simulate( destination_sample, want_logsums, model_settings, - skim_dict, + network_los, destination_size_terms, estimator, chunk_size, trace_label): @@ -173,9 +177,10 @@ def run_destination_simulate( logger.info("Running tour_destination_simulate with %d persons", len(choosers)) - # create wrapper with keys for this lookup - in this case there is a TAZ in the choosers - # and a TAZ in the alternatives which get merged during interaction + # create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers + # and a zone_id in the alternatives which get merged during interaction # the skims will be available under the name "skims" for any @ expressions + skim_dict = network_los.get_default_skim_dict() skims = skim_dict.wrap(origin_col_name, alt_dest_col_name) locals_d = { @@ -197,6 +202,11 @@ def run_destination_simulate( trace_choice_name='destination', estimator=estimator) + if not want_logsums: + # for consistency, always return a dataframe with canonical column name + assert isinstance(choices, pd.Series) + choices = choices.to_frame('choice') + return choices @@ -207,8 +217,7 @@ def run_joint_tour_destination( want_logsums, want_sample_table, model_settings, - skim_dict, - skim_stack, + network_los, estimator, chunk_size, trace_hh_id, trace_label): @@ -249,7 +258,7 @@ def run_joint_tour_destination( choosers, households_merged, model_settings, - skim_dict, + network_los, segment_destination_size_terms, estimator, chunk_size, @@ -263,7 +272,7 @@ def run_joint_tour_destination( persons_merged, location_sample_df, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id, tracing.extend_trace_label(trace_label, 'logsums.%s' % segment_name)) @@ -277,7 +286,7 @@ def run_joint_tour_destination( destination_sample=location_sample_df, want_logsums=want_logsums, model_settings=model_settings, - skim_dict=skim_dict, + network_los=network_los, destination_size_terms=segment_destination_size_terms, estimator=estimator, chunk_size=chunk_size, @@ -316,7 +325,7 @@ def joint_tour_destination( tours, persons_merged, households_merged, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id): @@ -326,8 +335,8 @@ def joint_tour_destination( person that's making the tour) """ - trace_label = 'non_mandatory_tour_destination' - model_settings_file_name = 'non_mandatory_tour_destination.yaml' + trace_label = 'joint_tour_destination' + model_settings_file_name = 'joint_tour_destination.yaml' model_settings = config.read_model_settings(model_settings_file_name) logsum_column_name = model_settings.get('DEST_CHOICE_LOGSUM_COLUMN_NAME') @@ -368,8 +377,7 @@ def joint_tour_destination( want_logsums, want_sample_table, model_settings, - skim_dict, - skim_stack, + network_los, estimator, chunk_size, trace_hh_id, trace_label) diff --git a/activitysim/abm/models/joint_tour_frequency.py b/activitysim/abm/models/joint_tour_frequency.py index a690342686..f5645216b3 100644 --- a/activitysim/abm/models/joint_tour_frequency.py +++ b/activitysim/abm/models/joint_tour_frequency.py @@ -10,8 +10,8 @@ from activitysim.core import pipeline from activitysim.core import config from activitysim.core import inject +from activitysim.core import expressions -from .util import expressions from .util import estimation from .util.overlap import hh_time_window_overlap @@ -107,7 +107,7 @@ def joint_tour_frequency( temp_point_persons = persons.loc[persons.PNUM == 1] temp_point_persons['person_id'] = temp_point_persons.index temp_point_persons = temp_point_persons.set_index('household_id') - temp_point_persons = temp_point_persons[['person_id', 'home_taz']] + temp_point_persons = temp_point_persons[['person_id', 'home_zone_id']] joint_tours = \ process_joint_tours(choices, alternatives, temp_point_persons) diff --git a/activitysim/abm/models/joint_tour_participation.py b/activitysim/abm/models/joint_tour_participation.py index adaff27542..b7eb7d821f 100644 --- a/activitysim/abm/models/joint_tour_participation.py +++ b/activitysim/abm/models/joint_tour_participation.py @@ -10,10 +10,11 @@ from activitysim.core import config from activitysim.core import inject from activitysim.core import logit +from activitysim.core import expressions +from activitysim.core import chunk from activitysim.core.util import assign_in_place -from .util import expressions from .util import estimation from activitysim.core.util import reindex @@ -285,7 +286,13 @@ def joint_tour_participation( estimator.write_coefficients(coefficients_df) estimator.write_choosers(candidates) - choices = simulate.simple_simulate( + # add tour-based chunk_id so we can chunk all trips in tour together + assert 'chunk_id' not in candidates.columns + unique_household_ids = candidates.household_id.unique() + household_chunk_ids = pd.Series(range(len(unique_household_ids)), index=unique_household_ids) + candidates['chunk_id'] = reindex(household_chunk_ids, candidates.household_id) + + choices = simulate.simple_simulate_by_chunk_id( choosers=candidates, spec=model_spec, nest_spec=nest_spec, diff --git a/activitysim/abm/models/joint_tour_scheduling.py b/activitysim/abm/models/joint_tour_scheduling.py index 54f1aabfca..a25e8216d8 100644 --- a/activitysim/abm/models/joint_tour_scheduling.py +++ b/activitysim/abm/models/joint_tour_scheduling.py @@ -9,8 +9,8 @@ from activitysim.core import config from activitysim.core import inject from activitysim.core import pipeline +from activitysim.core import expressions -from .util import expressions from .util import estimation from .util.vectorize_tour_scheduling import vectorize_joint_tour_scheduling diff --git a/activitysim/abm/models/location_choice.py b/activitysim/abm/models/location_choice.py index 2b41b8fa97..e9f0c033ac 100644 --- a/activitysim/abm/models/location_choice.py +++ b/activitysim/abm/models/location_choice.py @@ -11,12 +11,12 @@ from activitysim.core import pipeline from activitysim.core import simulate from activitysim.core import inject -from activitysim.core.mem import force_garbage_collect +from activitysim.core import mem +from activitysim.core import expressions from activitysim.core.interaction_sample_simulate import interaction_sample_simulate from activitysim.core.interaction_sample import interaction_sample -from .util import expressions from .util import logsums as logsum from .util import estimation @@ -96,7 +96,7 @@ def write_estimation_specs(estimator, model_settings, settings_file): def run_location_sample( segment_name, persons_merged, - skim_dict, + network_los, dest_size_terms, estimator, model_settings, @@ -111,12 +111,12 @@ def run_location_sample( which results in sample containing up to choices for each choose (e.g. person) and a pick_count indicating how many times that choice was selected for that chooser.) - person_id, dest_TAZ, rand, pick_count - 23750, 14, 0.565502716034, 4 - 23750, 16, 0.711135838871, 6 + person_id, dest_zone_id, rand, pick_count + 23750, 14, 0.565502716034, 4 + 23750, 16, 0.711135838871, 6 ... - 23751, 12, 0.408038878552, 1 - 23751, 14, 0.972732479292, 2 + 23751, 12, 0.408038878552, 1 + 23751, 14, 0.972732479292, 2 """ assert not persons_merged.empty @@ -135,11 +135,12 @@ def run_location_sample( logger.info("Estimation mode for %s using unsampled alternatives short_circuit_choices" % (trace_label,)) sample_size = 0 - # create wrapper with keys for this lookup - in this case there is a TAZ in the choosers - # and a TAZ in the alternatives which get merged during interaction + # create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers + # and a zone_id in the alternatives which get merged during interaction # (logit.interaction_dataset suffixes duplicate chooser column with '_chooser') # the skims will be available under the name "skims" for any @ expressions - skims = skim_dict.wrap('TAZ_chooser', 'TAZ') + skim_dict = network_los.get_default_skim_dict() + skims = skim_dict.wrap('home_zone_id', 'zone_id') locals_d = { 'skims': skims, @@ -168,18 +169,18 @@ def run_location_sample( def run_location_logsums( segment_name, persons_merged_df, - skim_dict, skim_stack, + network_los, location_sample_df, model_settings, chunk_size, trace_hh_id, trace_label): """ add logsum column to existing location_sample table - logsum is calculated by running the mode_choice model for each sample (person, dest_taz) pair + logsum is calculated by running the mode_choice model for each sample (person, dest_zone_id) pair in location_sample, and computing the logsum of all the utilities +-----------+--------------+----------------+------------+----------------+ - | PERID | dest_TAZ | rand | pick_count | logsum (added) | + | PERID | dest_zone_id | rand | pick_count | logsum (added) | +===========+==============+================+============+================+ | 23750 | 14 | 0.565502716034 | 4 | 1.85659498857 | +-----------+--------------+----------------+------------+----------------+ @@ -213,7 +214,7 @@ def run_location_logsums( choosers, tour_purpose, logsum_settings, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_label) @@ -230,7 +231,7 @@ def run_location_simulate( segment_name, persons_merged, location_sample_df, - skim_dict, + network_los, dest_size_terms, want_logsums, estimator, @@ -264,11 +265,11 @@ def run_location_simulate( logger.info("Running %s with %d persons" % (trace_label, len(choosers))) - # create wrapper with keys for this lookup - in this case there is a TAZ in the choosers - # and a TAZ in the alternatives which get merged during interaction + # create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers + # and a zone_id in the alternatives which get merged during interaction # the skims will be available under the name "skims" for any @ expressions - orig_col_name = "TAZ_chooser" - skims = skim_dict.wrap(orig_col_name, alt_dest_col_name) + skim_dict = network_los.get_default_skim_dict() + skims = skim_dict.wrap('home_zone_id', alt_dest_col_name) locals_d = { 'skims': skims, @@ -311,7 +312,7 @@ def run_location_simulate( def run_location_choice( persons_merged_df, - skim_dict, skim_stack, + network_los, shadow_price_calculator, want_logsums, want_sample_table, @@ -328,8 +329,7 @@ def run_location_choice( ---------- persons_merged_df : pandas.DataFrame persons table merged with households and land_use - skim_dict : skim.SkimDict - skim_stack : skim.SkimStack + network_los : los.Network_LOS shadow_price_calculator : ShadowPriceCalculator to get size terms want_logsums : boolean @@ -372,7 +372,7 @@ def run_location_choice( run_location_sample( segment_name, choosers, - skim_dict, + network_los, dest_size_terms, estimator, model_settings, @@ -384,7 +384,7 @@ def run_location_choice( run_location_logsums( segment_name, choosers, - skim_dict, skim_stack, + network_los, location_sample_df, model_settings, chunk_size, @@ -397,7 +397,7 @@ def run_location_choice( segment_name, choosers, location_sample_df, - skim_dict, + network_los, dest_size_terms, want_logsums, estimator, @@ -421,7 +421,7 @@ def run_location_choice( # FIXME - want to do this here? del location_sample_df - force_garbage_collect() + mem.force_garbage_collect() if len(choices_list) > 0: choices_df = pd.concat(choices_list) @@ -442,7 +442,7 @@ def run_location_choice( def iterate_location_choice( model_settings, persons_merged, persons, households, - skim_dict, skim_stack, + network_los, estimator, chunk_size, trace_hh_id, locutor, trace_label): @@ -457,8 +457,7 @@ def iterate_location_choice( model_settings : dict persons_merged : injected table persons : injected table - skim_dict : skim.SkimDict - skim_stack : skim.SkimStack + network_los : los.Network_LOS chunk_size : int trace_hh_id : int locutor : bool @@ -472,9 +471,6 @@ def iterate_location_choice( adds annotations to persons table """ - # column containing segment id - chooser_segment_column = model_settings['CHOOSER_SEGMENT_COLUMN_NAME'] - # boolean to filter out persons not needing location modeling (e.g. is_worker, is_student) chooser_filter_column = model_settings['CHOOSER_FILTER_COLUMN_NAME'] @@ -490,6 +486,29 @@ def iterate_location_choice( persons_merged_df.sort_index(inplace=True) # interaction_sample expects chooser index to be monotonic increasing + # chooser segmentation allows different sets coefficients for e.g. different income_segments or tour_types + chooser_segment_column = model_settings['CHOOSER_SEGMENT_COLUMN_NAME'] + + # - run segment preprocessor to assign chooser_segment_column if it is not already in chooser df + segment_preprocessor_settings = model_settings.get('segment_preprocessor') + if segment_preprocessor_settings: + + assert chooser_segment_column not in persons_merged_df, \ + f"CHOOSER_SEGMENT_COLUMN '{chooser_segment_column}' already in persons " \ + f"but segment_preprocessor was specified in model settings." + + expressions.assign_columns( + df=persons_merged_df, + model_settings=segment_preprocessor_settings, + trace_label=tracing.extend_trace_label(trace_label, 'segment_preprocessor')) + + assert chooser_segment_column in persons_merged_df, \ + f"segment_preprocessor failed to add CHOOSER_SEGMENT_COLUMN '{chooser_segment_column}' to persons table. " + else: + assert chooser_segment_column in persons_merged_df, \ + f"CHOOSER_SEGMENT_COLUMN '{chooser_segment_column}' not already in persons table " \ + f"and no segment_preprocessor specified in model settings fiel to add it." + spc = shadow_pricing.load_shadow_price_calculator(model_settings) max_iterations = spc.max_iterations assert not (spc.use_shadow_pricing and estimator) @@ -503,7 +522,7 @@ def iterate_location_choice( choices_df, save_sample_df = run_location_choice( persons_merged_df, - skim_dict, skim_stack, + network_los, shadow_price_calculator=spc, want_logsums=logsum_column_name is not None, want_sample_table=want_sample_table, @@ -541,9 +560,9 @@ def iterate_location_choice( # We only chose school locations for the subset of persons who go to school # so we backfill the empty choices with -1 to code as no school location # names for location choice and (optional) logsums columns - NO_DEST_TAZ = -1 + NO_DEST_ZONE = -1 persons_df[dest_choice_column_name] = \ - choices_df['choice'].reindex(persons_df.index).fillna(NO_DEST_TAZ).astype(int) + choices_df['choice'].reindex(persons_df.index).fillna(NO_DEST_ZONE).astype(int) # add the dest_choice_logsum column to persons dataframe if logsum_column_name: @@ -596,7 +615,7 @@ def iterate_location_choice( @inject.step() def workplace_location( persons_merged, persons, households, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id, locutor): """ workplace location choice model @@ -619,7 +638,7 @@ def workplace_location( iterate_location_choice( model_settings, persons_merged, persons, households, - skim_dict, skim_stack, + network_los, estimator, chunk_size, trace_hh_id, locutor, trace_label ) @@ -631,7 +650,7 @@ def workplace_location( @inject.step() def school_location( persons_merged, persons, households, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id, locutor ): """ @@ -650,7 +669,7 @@ def school_location( iterate_location_choice( model_settings, persons_merged, persons, households, - skim_dict, skim_stack, + network_los, estimator, chunk_size, trace_hh_id, locutor, trace_label ) diff --git a/activitysim/abm/models/mandatory_scheduling.py b/activitysim/abm/models/mandatory_scheduling.py index 76903b495a..57533da416 100644 --- a/activitysim/abm/models/mandatory_scheduling.py +++ b/activitysim/abm/models/mandatory_scheduling.py @@ -10,10 +10,10 @@ from activitysim.core import inject from activitysim.core import pipeline from activitysim.core import timetable as tt +from activitysim.core import expressions from activitysim.core.util import reindex -from .util import expressions from .util import estimation from .util import vectorize_tour_scheduling as vts diff --git a/activitysim/abm/models/mandatory_tour_frequency.py b/activitysim/abm/models/mandatory_tour_frequency.py index 8ba55cdfc0..19432c877f 100644 --- a/activitysim/abm/models/mandatory_tour_frequency.py +++ b/activitysim/abm/models/mandatory_tour_frequency.py @@ -9,9 +9,9 @@ from activitysim.core import pipeline from activitysim.core import config from activitysim.core import inject +from activitysim.core import expressions from .util.tour_frequency import process_mandatory_tours -from .util import expressions from .util import estimation logger = logging.getLogger(__name__) diff --git a/activitysim/abm/models/non_mandatory_destination.py b/activitysim/abm/models/non_mandatory_destination.py index 2d0bc475e1..9580e78612 100644 --- a/activitysim/abm/models/non_mandatory_destination.py +++ b/activitysim/abm/models/non_mandatory_destination.py @@ -23,7 +23,7 @@ def non_mandatory_tour_destination( tours, persons_merged, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id): @@ -71,8 +71,7 @@ def non_mandatory_tour_destination( want_logsums, want_sample_table, model_settings, - skim_dict, - skim_stack, + network_los, estimator, chunk_size, trace_hh_id, trace_label) diff --git a/activitysim/abm/models/non_mandatory_scheduling.py b/activitysim/abm/models/non_mandatory_scheduling.py index f7c113452c..9680cc3add 100644 --- a/activitysim/abm/models/non_mandatory_scheduling.py +++ b/activitysim/abm/models/non_mandatory_scheduling.py @@ -10,8 +10,8 @@ from activitysim.core import pipeline from activitysim.core import timetable as tt from activitysim.core import simulate +from activitysim.core import expressions -from .util import expressions from .util import estimation from .util.vectorize_tour_scheduling import vectorize_tour_scheduling diff --git a/activitysim/abm/models/non_mandatory_tour_frequency.py b/activitysim/abm/models/non_mandatory_tour_frequency.py index 3503d391c0..70d39cf549 100644 --- a/activitysim/abm/models/non_mandatory_tour_frequency.py +++ b/activitysim/abm/models/non_mandatory_tour_frequency.py @@ -13,10 +13,10 @@ from activitysim.core import inject from activitysim.core import simulate from activitysim.core import logit +from activitysim.core import expressions from activitysim.core.mem import force_garbage_collect -from .util import expressions from .util import estimation from .util.overlap import person_max_window diff --git a/activitysim/abm/models/parking_location_choice.py b/activitysim/abm/models/parking_location_choice.py new file mode 100644 index 0000000000..42bc8bb145 --- /dev/null +++ b/activitysim/abm/models/parking_location_choice.py @@ -0,0 +1,311 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import config +from activitysim.core import inject +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import tracing + +from activitysim.core import expressions +from activitysim.core.interaction_sample_simulate import interaction_sample_simulate +from activitysim.core.logit import interaction_dataset +from activitysim.core.util import assign_in_place +from activitysim.core.tracing import print_elapsed_time + +from .util import estimation + + +logger = logging.getLogger(__name__) + +NO_DESTINATION = -1 + + +def wrap_skims(model_settings): + """ + wrap skims of trip destination using origin, dest column names from model settings. + Various of these are used by destination_sample, compute_logsums, and destination_simulate + so we create them all here with canonical names. + + Note that compute_logsums aliases their names so it can use the same equations to compute + logsums from origin to alt_dest, and from alt_dest to primarly destination + + odt_skims - SkimStackWrapper: trip origin, trip alt_dest, time_of_day + dot_skims - SkimStackWrapper: trip alt_dest, trip origin, time_of_day + dpt_skims - SkimStackWrapper: trip alt_dest, trip primary_dest, time_of_day + pdt_skims - SkimStackWrapper: trip primary_dest,trip alt_dest, time_of_day + od_skims - SkimDictWrapper: trip origin, trip alt_dest + dp_skims - SkimDictWrapper: trip alt_dest, trip primary_dest + + Parameters + ---------- + model_settings + + Returns + ------- + dict containing skims, keyed by canonical names relative to tour orientation + """ + + network_los = inject.get_injectable('network_los') + skim_dict = network_los.get_default_skim_dict() + + origin = model_settings['TRIP_ORIGIN'] + park_zone = model_settings['ALT_DEST_COL_NAME'] + destination = model_settings['TRIP_DESTINATION'] + time_period = model_settings['TRIP_DEPARTURE_PERIOD'] + + skims = { + "odt_skims": skim_dict.wrap_3d(orig_key=origin, dest_key=destination, dim3_key=time_period), + "dot_skims": skim_dict.wrap_3d(orig_key=destination, dest_key=origin, dim3_key=time_period), + "opt_skims": skim_dict.wrap_3d(orig_key=origin, dest_key=park_zone, dim3_key=time_period), + "pdt_skims": skim_dict.wrap_3d(orig_key=park_zone, dest_key=destination, dim3_key=time_period), + "od_skims": skim_dict.wrap(origin, destination), + "do_skims": skim_dict.wrap(destination, origin), + "op_skims": skim_dict.wrap(origin, park_zone), + "pd_skims": skim_dict.wrap(park_zone, destination), + } + + return skims + + +def get_spec_for_segment(model_settings, spec_name, segment): + + omnibus_spec = simulate.read_model_spec(file_name=model_settings[spec_name]) + + spec = omnibus_spec[[segment]] + + # might as well ignore any spec rows with 0 utility + spec = spec[spec.iloc[:, 0] != 0] + assert spec.shape[0] > 0 + + return spec + + +def parking_destination_simulate( + segment_name, + trips, + destination_sample, + model_settings, + skims, + chunk_size, trace_hh_id, + trace_label): + """ + Chose destination from destination_sample (with od_logsum and dp_logsum columns added) + + + Returns + ------- + choices - pandas.Series + destination alt chosen + """ + trace_label = tracing.extend_trace_label(trace_label, 'trip_destination_simulate') + + spec = get_spec_for_segment(model_settings, 'SPECIFICATION', segment_name) + + alt_dest_col_name = model_settings['ALT_DEST_COL_NAME'] + + logger.info("Running trip_destination_simulate with %d trips", len(trips)) + + locals_dict = config.get_model_constants(model_settings).copy() + locals_dict.update(skims) + + parking_locations = interaction_sample_simulate( + choosers=trips, + alternatives=destination_sample, + spec=spec, + choice_column=alt_dest_col_name, + want_logsums=False, + allow_zero_probs=True, zero_prob_choice_val=NO_DESTINATION, + skims=skims, + locals_d=locals_dict, + chunk_size=chunk_size, + trace_label=trace_label, + trace_choice_name='parking_loc') + + # drop any failed zero_prob destinations + if (parking_locations == NO_DESTINATION).any(): + logger.debug("dropping %s failed parking locations", (parking_locations == NO_DESTINATION).sum()) + parking_locations = parking_locations[parking_locations != NO_DESTINATION] + + return parking_locations + + +def choose_parking_location( + segment_name, + trips, + alternatives, + model_settings, + want_sample_table, + skims, + chunk_size, trace_hh_id, + trace_label): + + logger.info("choose_parking_location %s with %d trips", trace_label, trips.shape[0]) + + t0 = print_elapsed_time() + + alt_dest_col_name = model_settings['ALT_DEST_COL_NAME'] + destination_sample = interaction_dataset(trips, alternatives, alt_index_id=alt_dest_col_name) + destination_sample.index = np.repeat(trips.index.values, len(alternatives)) + destination_sample.index.name = trips.index.name + destination_sample = destination_sample[[alt_dest_col_name]].copy() + + # # - trip_destination_simulate + destinations = parking_destination_simulate( + segment_name=segment_name, + trips=trips, + destination_sample=destination_sample, + model_settings=model_settings, + skims=skims, + chunk_size=chunk_size, trace_hh_id=trace_hh_id, + trace_label=trace_label) + + if want_sample_table: + # FIXME - sample_table + destination_sample.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) + else: + destination_sample = None + + t0 = print_elapsed_time("%s.parking_location_simulate" % trace_label, t0) + + return destinations, destination_sample + + +def run_parking_destination( + model_settings, + trips, land_use, + chunk_size, trace_hh_id, + trace_label, + fail_some_trips_for_testing=False): + + chooser_filter_column = model_settings.get('CHOOSER_FILTER_COLUMN_NAME') + chooser_segment_column = model_settings.get('CHOOSER_SEGMENT_COLUMN_NAME') + + parking_location_column_name = model_settings['ALT_DEST_COL_NAME'] + sample_table_name = model_settings.get('DEST_CHOICE_SAMPLE_TABLE_NAME') + want_sample_table = config.setting('want_dest_choice_sample_tables') and sample_table_name is not None + + choosers = trips[trips[chooser_filter_column]] + choosers = choosers.sort_index() + + # Placeholder for trips without a parking choice + trips[parking_location_column_name] = -1 + + skims = wrap_skims(model_settings) + + alt_column_filter_name = model_settings.get('ALTERNATIVE_FILTER_COLUMN_NAME') + alternatives = land_use[land_use[alt_column_filter_name]] + + # don't need size terms in alternatives, just TAZ index + alternatives = alternatives.drop(alternatives.columns, axis=1) + alternatives.index.name = parking_location_column_name + + choices_list = [] + sample_list = [] + for segment_name, chooser_segment in choosers.groupby(chooser_segment_column): + if chooser_segment.shape[0] == 0: + logger.info("%s skipping segment %s: no choosers", trace_label, segment_name) + continue + + choices, destination_sample = choose_parking_location( + segment_name, + chooser_segment, + alternatives, + model_settings, + want_sample_table, + skims, + chunk_size, trace_hh_id, + trace_label=tracing.extend_trace_label(trace_label, segment_name)) + + choices_list.append(choices) + if want_sample_table: + assert destination_sample is not None + sample_list.append(destination_sample) + + if len(choices_list) > 0: + parking_df = pd.concat(choices_list) + + if fail_some_trips_for_testing: + parking_df = parking_df.drop(parking_df.index[0]) + + assign_in_place(trips, parking_df.to_frame(parking_location_column_name)) + trips[parking_location_column_name] = trips[parking_location_column_name].fillna(-1) + else: + trips[parking_location_column_name] = -1 + + save_sample_df = pd.concat(sample_list) if len(sample_list) > 0 else None + + return trips[parking_location_column_name], save_sample_df + + +@inject.step() +def parking_location( + trips, + trips_merged, + land_use, + network_los, + chunk_size, + trace_hh_id): + """ + Given a set of trips, each trip needs to have a parking location if + it is eligible for remote parking. + """ + + trace_label = 'parking_location' + model_settings = config.read_model_settings('parking_location_choice.yaml') + alt_destination_col_name = model_settings['ALT_DEST_COL_NAME'] + + preprocessor_settings = model_settings.get('PREPROCESSOR', None) + + trips_df = trips.to_frame() + trips_merged_df = trips_merged.to_frame() + land_use_df = land_use.to_frame() + + locals_dict = { + 'network_los': network_los + } + locals_dict.update(config.get_model_constants(model_settings)) + + if preprocessor_settings: + expressions.assign_columns( + df=trips_merged_df, + model_settings=preprocessor_settings, + locals_dict=locals_dict, + trace_label=trace_label) + + parking_locations, save_sample_df = run_parking_destination( + model_settings, + trips_merged_df, land_use_df, + chunk_size=chunk_size, + trace_hh_id=trace_hh_id, + trace_label=trace_label, + ) + + assign_in_place(trips_df, parking_locations.to_frame(alt_destination_col_name)) + + pipeline.replace_table("trips", trips_df) + + if trace_hh_id: + tracing.trace_df(trips_df, + label=trace_label, + slicer='trip_id', + index_label='trip_id', + warn_if_empty=True) + + if save_sample_df is not None: + assert len(save_sample_df.index.get_level_values(0).unique()) == \ + len(trips_df[trips_df.trip_num < trips_df.trip_count]) + + sample_table_name = model_settings.get('PARKING_LOCATION_SAMPLE_TABLE_NAME') + assert sample_table_name is not None + + logger.info("adding %s samples to %s" % (len(save_sample_df), sample_table_name)) + + # lest they try to put tour samples into the same table + if pipeline.is_table(sample_table_name): + raise RuntimeError("sample table %s already exists" % sample_table_name) + pipeline.extend_table(sample_table_name, save_sample_df) diff --git a/activitysim/abm/models/stop_frequency.py b/activitysim/abm/models/stop_frequency.py index bdab18e799..988c60e102 100644 --- a/activitysim/abm/models/stop_frequency.py +++ b/activitysim/abm/models/stop_frequency.py @@ -10,9 +10,9 @@ from activitysim.core import pipeline from activitysim.core import config from activitysim.core import inject +from activitysim.core import expressions from activitysim.core.util import assign_in_place -from .util import expressions from activitysim.core.util import reindex logger = logging.getLogger(__name__) @@ -114,7 +114,7 @@ def process_trips(tours, stop_frequency_alts): def stop_frequency( tours, tours_merged, stop_frequency_alts, - skim_dict, + network_los, chunk_size, trace_hh_id): """ @@ -162,14 +162,16 @@ def stop_frequency( if preprocessor_settings: # hack: preprocessor adds origin column in place if it does not exist already - od_skim_stack_wrapper = skim_dict.wrap('origin', 'destination') + assert 'origin' in tours_merged + assert 'destination' in tours_merged + od_skim_stack_wrapper = network_los.get_default_skim_dict().wrap('origin', 'destination') skims = [od_skim_stack_wrapper] locals_dict = { - "od_skims": od_skim_stack_wrapper + "od_skims": od_skim_stack_wrapper, + 'network_los': network_los } - if constants is not None: - locals_dict.update(constants) + locals_dict.update(constants) simulate.set_skim_wrapper_targets(tours_merged, skims) diff --git a/activitysim/abm/models/summarize.py b/activitysim/abm/models/summarize.py new file mode 100644 index 0000000000..8d224c8754 --- /dev/null +++ b/activitysim/abm/models/summarize.py @@ -0,0 +1,43 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging +import sys +import pandas as pd + +from activitysim.core import pipeline +from activitysim.core import inject +from activitysim.core import config + +from activitysim.core.config import setting + +logger = logging.getLogger(__name__) + + +@inject.step() +def write_summaries(output_dir): + + summary_settings_name = 'output_summaries' + summary_file_name = 'summaries.txt' + + summary_settings = setting(summary_settings_name) + + if summary_settings is None: + logger.info("No {summary_settings_name} specified in settings file. Nothing to write.") + return + + summary_dict = summary_settings + + mode = 'wb' if sys.version_info < (3,) else 'w' + with open(config.output_file_path(summary_file_name), mode) as output_file: + + for table_name, column_names in summary_dict.items(): + + df = pipeline.get_table(table_name) + + for c in column_names: + n = 100 + empty = (df[c] == '') | df[c].isnull() + + print(f"\n### {table_name}.{c} type: {df.dtypes[c]} rows: {len(df)} ({empty.sum()} empty)\n\n", + file=output_file) + print(df[c].value_counts().nlargest(n), file=output_file) diff --git a/activitysim/abm/models/tour_mode_choice.py b/activitysim/abm/models/tour_mode_choice.py index d28c509878..8c9424a501 100644 --- a/activitysim/abm/models/tour_mode_choice.py +++ b/activitysim/abm/models/tour_mode_choice.py @@ -13,6 +13,9 @@ from activitysim.core.mem import force_garbage_collect from activitysim.core.util import assign_in_place +from activitysim.core import los +from activitysim.core.pathbuilder import TransitVirtualPathBuilder + from .util.mode import run_tour_mode_choice_simulate from .util import estimation @@ -68,7 +71,7 @@ def write_coefficient_template(model_settings): @inject.step() def tour_mode_choice_simulate(tours, persons_merged, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id): """ @@ -84,38 +87,42 @@ def tour_mode_choice_simulate(tours, persons_merged, primary_tours = tours.to_frame() assert not (primary_tours.tour_category == 'atwork').any() - persons_merged = persons_merged.to_frame() - - constants = config.get_model_constants(model_settings) - logger.info("Running %s with %d tours" % (trace_label, primary_tours.shape[0])) tracing.print_summary('tour_types', primary_tours.tour_type, value_counts=True) + persons_merged = persons_merged.to_frame() primary_tours_merged = pd.merge(primary_tours, persons_merged, left_on='person_id', right_index=True, how='left', suffixes=('', '_r')) + constants = {} + # model_constants can appear in expressions + constants.update(config.get_model_constants(model_settings)) + + skim_dict = network_los.get_default_skim_dict() + # setup skim keys - orig_col_name = 'TAZ' + orig_col_name = 'home_zone_id' dest_col_name = 'destination' + out_time_col_name = 'start' in_time_col_name = 'end' - odt_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name, - skim_key='out_period') - dot_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name, - skim_key='in_period') - odr_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name, - skim_key='in_period') - dor_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name, - skim_key='out_period') + odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='out_period') + dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='in_period') + odr_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='in_period') + dor_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='out_period') od_skim_stack_wrapper = skim_dict.wrap(orig_col_name, dest_col_name) skims = { "odt_skims": odt_skim_stack_wrapper, "dot_skims": dot_skim_stack_wrapper, - "odr_skims": odr_skim_stack_wrapper, - "dor_skims": dor_skim_stack_wrapper, + "odr_skims": odr_skim_stack_wrapper, # dot return skims for e.g. TNC bridge return fare + "dor_skims": dor_skim_stack_wrapper, # odt return skims for e.g. TNC bridge return fare "od_skims": od_skim_stack_wrapper, 'orig_col_name': orig_col_name, 'dest_col_name': dest_col_name, @@ -123,40 +130,70 @@ def tour_mode_choice_simulate(tours, persons_merged, 'in_time_col_name': in_time_col_name } + if network_los.zone_system == los.THREE_ZONE: + # fixme - is this a lightweight object? + + tvpb = network_los.tvpb + + tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=orig_col_name, dest_key=dest_col_name, + tod_key='out_period', segment_key='demographic_segment', + cache_choices=True, + trace_label=trace_label, tag='tvpb_logsum_odt') + tvpb_logsum_dot = tvpb.wrap_logsum(orig_key=dest_col_name, dest_key=orig_col_name, + tod_key='in_period', segment_key='demographic_segment', + cache_choices=True, + trace_label=trace_label, tag='tvpb_logsum_dot') + + skims.update({ + 'tvpb_logsum_odt': tvpb_logsum_odt, + 'tvpb_logsum_dot': tvpb_logsum_dot + }) + + # TVPB constants can appear in expressions + constants.update(network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS')) + estimator = estimation.manager.begin_estimation('tour_mode_choice') if estimator: estimator.write_coefficients(simulate.read_model_coefficients(model_settings)) estimator.write_coefficients_template(simulate.read_model_coefficient_template(model_settings)) estimator.write_spec(model_settings) estimator.write_model_settings(model_settings, model_settings_file_name) - # FIXME run_tour_mode_choice_simulate writes choosers post-annotation + # (run_tour_mode_choice_simulate writes choosers post-annotation) + + # FIXME should normalize handling of tour_type and tour_purpose + # mtctm1 school tour_type includes univ, which has different coefficients from elementary and HS + # we should either add this column when tours created or add univ to tour_types + not_university = (primary_tours_merged.tour_type != 'school') | ~primary_tours_merged.is_university + primary_tours_merged['tour_purpose'] = \ + primary_tours_merged.tour_type.where(not_university, 'univ') choices_list = [] - primary_tours_merged['primary_purpose'] = \ - primary_tours_merged.tour_type.where((primary_tours_merged.tour_type != 'school') | - ~primary_tours_merged.is_university, 'univ') + for tour_purpose, tours_segment in primary_tours_merged.groupby('tour_purpose'): - for primary_purpose, tours_segment in primary_tours_merged.groupby('primary_purpose'): + logger.info("tour_mode_choice_simulate tour_type '%s' (%s tours)" % + (tour_purpose, len(tours_segment.index), )) - logger.info("tour_mode_choice_simulate primary_purpose '%s' (%s tours)" % - (primary_purpose, len(tours_segment.index), )) + if network_los.zone_system == los.THREE_ZONE: + tvpb_logsum_odt.extend_trace_label(tour_purpose) + tvpb_logsum_dot.extend_trace_label(tour_purpose) # name index so tracing knows how to slice assert tours_segment.index.name == 'tour_id' choices_df = run_tour_mode_choice_simulate( tours_segment, - primary_purpose, model_settings, + tour_purpose, model_settings, mode_column_name=mode_column_name, logsum_column_name=logsum_column_name, + network_los=network_los, skims=skims, constants=constants, estimator=estimator, chunk_size=chunk_size, - trace_label=tracing.extend_trace_label(trace_label, primary_purpose), + trace_label=tracing.extend_trace_label(trace_label, tour_purpose), trace_choice_name='tour_mode_choice') - tracing.print_summary('tour_mode_choice_simulate %s choices_df' % primary_purpose, + tracing.print_summary('tour_mode_choice_simulate %s choices_df' % tour_purpose, choices_df.tour_mode, value_counts=True) choices_list.append(choices_df) @@ -166,6 +203,27 @@ def tour_mode_choice_simulate(tours, persons_merged, choices_df = pd.concat(choices_list) + # add cached tvpb_logsum tap choices for modes specified in tvpb_mode_path_types + if network_los.zone_system == los.THREE_ZONE: + + tvpb_mode_path_types = model_settings.get('tvpb_mode_path_types') + for mode, path_types in tvpb_mode_path_types.items(): + + for direction, skim in zip(['od', 'do'], [tvpb_logsum_odt, tvpb_logsum_dot]): + + path_type = path_types[direction] + skim_cache = skim.cache[path_type] + + print(f"mode {mode} direction {direction} path_type {path_type}") + + for c in skim_cache: + + dest_col = f'{direction}_{c}' + + if dest_col not in choices_df: + choices_df[dest_col] = 0 if pd.api.types.is_numeric_dtype(skim_cache[c]) else '' + choices_df[dest_col].where(choices_df.tour_mode != mode, skim_cache[c], inplace=True) + if estimator: estimator.write_choices(choices_df.tour_mode) choices_df.tour_mode = estimator.get_survey_values(choices_df.tour_mode, 'tours', 'tour_mode') @@ -178,7 +236,7 @@ def tour_mode_choice_simulate(tours, persons_merged, # so we can trace with annotations assign_in_place(primary_tours, choices_df) - # but only keep mode choice col + # update tours table with mode choice (and optionally logsums) all_tours = tours.to_frame() assign_in_place(all_tours, choices_df) diff --git a/activitysim/abm/models/trip_departure_choice.py b/activitysim/abm/models/trip_departure_choice.py new file mode 100644 index 0000000000..3fe50fb99f --- /dev/null +++ b/activitysim/abm/models/trip_departure_choice.py @@ -0,0 +1,461 @@ +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import chunk +from activitysim.core import config +from activitysim.core import expressions +from activitysim.core import inject +from activitysim.core import logit +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import tracing + +from activitysim.abm.models.util.trip import get_time_windows +from activitysim.core.interaction_sample_simulate import eval_interaction_utilities +from activitysim.core.simulate import set_skim_wrapper_targets +from activitysim.core.util import reindex + + +logger = logging.getLogger(__name__) + +MAIN_LEG_DURATION = 'main_leg_duration' +IB_DURATION = 'inbound_duration' +OB_DURATION = 'outbound_duration' + +TOUR_ID = 'tour_id' +TRIP_ID = 'trip_id' +TOUR_LEG_ID = 'tour_leg_id' +PATTERN_ID = 'pattern_id' +TRIP_DURATION = 'trip_duration' +STOP_TIME_DURATION = 'stop_time_duration' +TRIP_NUM = 'trip_num' +TRIP_COUNT = 'trip_count' +OUTBOUND = 'outbound' + +MAX_TOUR_ID = int(1e9) + + +def generate_tour_leg_id(tour_leg_row): + return tour_leg_row.tour_id + (int(MAX_TOUR_ID) if tour_leg_row.outbound else int(2 * MAX_TOUR_ID)) + + +def get_tour_legs(trips): + tour_legs = trips.groupby([TOUR_ID, OUTBOUND], as_index=False)[TRIP_NUM].max() + tour_legs[TOUR_LEG_ID] = tour_legs.apply(generate_tour_leg_id, axis=1) + tour_legs = tour_legs.set_index(TOUR_LEG_ID) + return tour_legs + + +def trip_departure_rpc(chunk_size, choosers, trace_label): + + # NOTE we chunk chunk_id + num_choosers = choosers['chunk_id'].max() + 1 + + chooser_row_size = choosers.shape[1] + 1 + + # scale row_size by average number of chooser rows per chunk_id + rows_per_chunk_id = choosers.shape[0] / num_choosers + row_size = (rows_per_chunk_id * chooser_row_size) + + return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + + +def generate_alternatives(trips, alternative_col_name): + """ + This method creates an alternatives list of all possible + trip durations less than the total trip leg duration. If + the trip only has one trip on the leg, the trip alternative + only has one alternative for that trip equal to the trip + duration. + :param trips: pd.DataFrame + :param alternative_col_name: column name for the alternative column + :return: pd.DataFrame + """ + legs = trips[trips[TRIP_COUNT] > 1] + + leg_alts = None + durations = np.where(legs[OUTBOUND], legs[OB_DURATION], legs[IB_DURATION]) + if len(durations) > 0: + leg_alts = pd.Series(np.concatenate([np.arange(0, duration + 1) for duration in durations]), + np.repeat(legs.index, durations + 1), + name=alternative_col_name).to_frame() + + single_trips = trips[trips[TRIP_COUNT] == 1] + single_alts = None + durations = np.where(single_trips[OUTBOUND], single_trips[OB_DURATION], single_trips[IB_DURATION]) + if len(durations) > 0: + single_alts = pd.Series(durations, single_trips.index, + name=alternative_col_name).to_frame() + + if not legs.empty and not single_trips.empty: + return pd.concat([leg_alts, single_alts]) + + return leg_alts if not legs.empty else single_alts + + +def build_patterns(trips, time_windows): + tours = trips.groupby([TOUR_ID])[[TRIP_DURATION, TRIP_COUNT]].first() + duration_and_counts = tours[[TRIP_DURATION, TRIP_COUNT]].values + + # We subtract 1 here, because we already know + # the one trip of the tour leg based on main tour + # leg duration + max_trip_count = trips[TRIP_COUNT].max() - 1 + + patterns = [] + pattern_sizes = [] + + for duration, trip_count in duration_and_counts: + possible_windows = time_windows[:trip_count-1, np.where(time_windows[:trip_count-1].sum(axis=0) == duration)[0]] + possible_windows = np.unique(possible_windows, axis=1).transpose() + filler = np.full((possible_windows.shape[0], max_trip_count), np.nan) + filler[:possible_windows.shape[0], :possible_windows.shape[1]] = possible_windows + patterns.append(filler) + pattern_sizes.append(filler.shape[0]) + + patterns = np.concatenate(patterns) + pattern_names = ['_'.join('%0.0f' % x for x in y[~np.isnan(y)]) for y in patterns] + indexes = np.repeat(tours.index, pattern_sizes) + + # If we've done everything right, the indexes + # calculated above should be the same length as + # the pattern options + assert patterns.shape[0] == len(indexes) + + patterns = pd.DataFrame(index=indexes, data=patterns) + patterns.index.name = tours.index.name + patterns[PATTERN_ID] = pattern_names + + patterns = patterns.melt(id_vars=PATTERN_ID, value_name=STOP_TIME_DURATION, + var_name=TRIP_NUM, ignore_index=False).reset_index() + patterns = patterns[~patterns[STOP_TIME_DURATION].isnull()].copy() + + patterns[TRIP_NUM] = patterns[TRIP_NUM] + 1 + patterns[STOP_TIME_DURATION] = patterns[STOP_TIME_DURATION].astype(np.int) + + patterns = pd.merge(patterns, trips.reset_index()[[TOUR_ID, TRIP_ID, TRIP_NUM, OUTBOUND]], + on=[TOUR_ID, TRIP_NUM]) + + patterns.index = patterns.apply(generate_tour_leg_id, axis=1) + patterns.index.name = TOUR_LEG_ID + + return patterns + + +def get_spec_for_segment(omnibus_spec, segment): + + spec = omnibus_spec[[segment]] + + # might as well ignore any spec rows with 0 utility + spec = spec[spec.iloc[:, 0] != 0] + assert spec.shape[0] > 0 + + return spec + + +def trip_departure_calc_row_size(choosers, trace_label): + """ + rows_per_chunk calculator for trip_scheduler + """ + + sizer = chunk.RowSizeEstimator(trace_label) + + chooser_row_size = len(choosers.columns) + spec_columns = 3 + + sizer.add_elements(chooser_row_size + spec_columns, 'choosers') + + row_size = sizer.get_hwm() + return row_size + + +def choose_tour_leg_pattern(trip_segment, + patterns, spec, + trace_label='trace_label'): + alternatives = generate_alternatives(trip_segment, STOP_TIME_DURATION).sort_index() + have_trace_targets = tracing.has_trace_targets(trip_segment) + + if have_trace_targets: + tracing.trace_df(trip_segment, tracing.extend_trace_label(trace_label, 'choosers')) + tracing.trace_df(alternatives, tracing.extend_trace_label(trace_label, 'alternatives'), + transpose=False) + + if len(spec.columns) > 1: + raise RuntimeError('spec must have only one column') + + # - join choosers and alts + # in vanilla interaction_simulate interaction_df is cross join of choosers and alternatives + # interaction_df = logit.interaction_dataset(choosers, alternatives, sample_size) + # here, alternatives is sparsely repeated once for each (non-dup) sample + # we expect alternatives to have same index of choosers (but with duplicate index values) + # so we just need to left join alternatives with choosers + assert alternatives.index.name == trip_segment.index.name + + interaction_df = alternatives.join(trip_segment, how='left', rsuffix='_chooser') + + chunk.log_df(trace_label, 'interaction_df', interaction_df) + + if have_trace_targets: + trace_rows, trace_ids = tracing.interaction_trace_rows(interaction_df, trip_segment) + + tracing.trace_df(interaction_df, + tracing.extend_trace_label(trace_label, 'interaction_df'), + transpose=False) + else: + trace_rows = trace_ids = None + + interaction_utilities, trace_eval_results \ + = eval_interaction_utilities(spec, interaction_df, None, trace_label, trace_rows, None) + + interaction_utilities = pd.concat([interaction_df[STOP_TIME_DURATION], interaction_utilities], axis=1) + chunk.log_df(trace_label, 'interaction_utilities', interaction_utilities) + + interaction_utilities = pd.merge(interaction_utilities.reset_index(), + patterns[patterns[TRIP_ID].isin(interaction_utilities.index)], + on=[TRIP_ID, STOP_TIME_DURATION], how='left') + + if have_trace_targets: + tracing.trace_interaction_eval_results(trace_eval_results, trace_ids, + tracing.extend_trace_label(trace_label, 'eval')) + + tracing.trace_df(interaction_utilities, + tracing.extend_trace_label(trace_label, 'interaction_utilities'), + transpose=False) + + del interaction_df + chunk.log_df(trace_label, 'interaction_df', None) + + interaction_utilities = interaction_utilities.groupby([TOUR_ID, OUTBOUND, PATTERN_ID], + as_index=False)[['utility']].sum() + + interaction_utilities[TOUR_LEG_ID] = \ + interaction_utilities.apply(generate_tour_leg_id, axis=1) + + tour_choosers = interaction_utilities.set_index(TOUR_LEG_ID) + interaction_utilities = tour_choosers[['utility']].copy() + + # reshape utilities (one utility column and one row per row in model_design) + # to a dataframe with one row per chooser and one column per alternative + # interaction_utilities is sparse because duplicate sampled alternatives were dropped + # so we need to pad with dummy utilities so low that they are never chosen + + # number of samples per chooser + sample_counts = interaction_utilities.groupby(interaction_utilities.index).size().values + chunk.log_df(trace_label, 'sample_counts', sample_counts) + + # max number of alternatvies for any chooser + max_sample_count = sample_counts.max() + + # offsets of the first and last rows of each chooser in sparse interaction_utilities + last_row_offsets = sample_counts.cumsum() + first_row_offsets = np.insert(last_row_offsets[:-1], 0, 0) + + # repeat the row offsets once for each dummy utility to insert + # (we want to insert dummy utilities at the END of the list of alternative utilities) + # inserts is a list of the indices at which we want to do the insertions + inserts = np.repeat(last_row_offsets, max_sample_count - sample_counts) + + del sample_counts + chunk.log_df(trace_label, 'sample_counts', None) + + # insert the zero-prob utilities to pad each alternative set to same size + padded_utilities = np.insert(interaction_utilities.utility.values, inserts, -999) + del inserts + + del interaction_utilities + chunk.log_df(trace_label, 'interaction_utilities', None) + + # reshape to array with one row per chooser, one column per alternative + padded_utilities = padded_utilities.reshape(-1, max_sample_count) + chunk.log_df(trace_label, 'padded_utilities', padded_utilities) + + # convert to a dataframe with one row per chooser and one column per alternative + utilities_df = pd.DataFrame( + padded_utilities, + index=tour_choosers.index.unique()) + chunk.log_df(trace_label, 'utilities_df', utilities_df) + + del padded_utilities + chunk.log_df(trace_label, 'padded_utilities', None) + + if have_trace_targets: + tracing.trace_df(utilities_df, tracing.extend_trace_label(trace_label, 'utilities'), + column_labels=['alternative', 'utility']) + + # convert to probabilities (utilities exponentiated and normalized to probs) + # probs is same shape as utilities, one row per chooser and one column for alternative + probs = logit.utils_to_probs(utilities_df, + trace_label=trace_label, trace_choosers=trip_segment) + + chunk.log_df(trace_label, 'probs', probs) + + del utilities_df + chunk.log_df(trace_label, 'utilities_df', None) + + if have_trace_targets: + tracing.trace_df(probs, tracing.extend_trace_label(trace_label, 'probs'), + column_labels=['alternative', 'probability']) + + # make choices + # positions is series with the chosen alternative represented as a column index in probs + # which is an integer between zero and num alternatives in the alternative sample + positions, rands = \ + logit.make_choices(probs, trace_label=trace_label, trace_choosers=trip_segment) + + chunk.log_df(trace_label, 'positions', positions) + chunk.log_df(trace_label, 'rands', rands) + + del probs + chunk.log_df(trace_label, 'probs', None) + + # shouldn't have chosen any of the dummy pad utilities + assert positions.max() < max_sample_count + + # need to get from an integer offset into the alternative sample to the alternative index + # that is, we want the index value of the row that is offset by rows into the + # tranche of this choosers alternatives created by cross join of alternatives and choosers + + # resulting pandas Int64Index has one element per chooser row and is in same order as choosers + choices = tour_choosers[PATTERN_ID].take(positions + first_row_offsets) + + chunk.log_df(trace_label, 'choices', choices) + + if have_trace_targets: + tracing.trace_df(choices, tracing.extend_trace_label(trace_label, 'choices'), + columns=[None, PATTERN_ID]) + tracing.trace_df(rands, tracing.extend_trace_label(trace_label, 'rands'), + columns=[None, 'rand']) + + return choices + + +def apply_stage_two_model(omnibus_spec, trips, chunk_size, trace_label): + + if not trips.index.is_monotonic: + trips = trips.sort_index() + + # Assign the duration of the appropriate leg to the trip + trips[TRIP_DURATION] = np.where(trips[OUTBOUND], trips[OB_DURATION], trips[IB_DURATION]) + + trips['depart'] = -1 + + # If this is the first outbound trip, the choice is easy, assign the depart time + # to equal the tour start time. + trips.loc[(trips['trip_num'] == 1) & (trips[OUTBOUND]), 'depart'] = trips['start'] + + # If its the first return leg, it is easy too. Just assign the trip start time to the + # end time minus the IB duration + trips.loc[(trips['trip_num'] == 1) & (~trips[OUTBOUND]), 'depart'] = trips['end'] - trips[IB_DURATION] + + # The last leg of the outbound tour needs to begin at the start plus OB duration + trips.loc[(trips['trip_count'] == trips['trip_num']) & (trips[OUTBOUND]), 'depart'] = \ + trips['start'] + trips[OB_DURATION] + + # The last leg of the inbound tour needs to begin at the end time of the tour + trips.loc[(trips['trip_count'] == trips['trip_num']) & (~trips[OUTBOUND]), 'depart'] = \ + trips['end'] + + # Slice off the remaining trips with an intermediate stops to deal with. + # Hopefully, with the tricks above we've sliced off a lot of choices. + # This slice should only include trip numbers greater than 2 since the + side_trips = trips[(trips['trip_num'] != 1) & (trips['trip_count'] != trips['trip_num'])] + + # No processing needs to be done because we have simple trips / tours + if side_trips.empty: + assert trips['depart'].notnull().all + return trips['depart'].astype(int) + + # Get the potential time windows + time_windows = get_time_windows(side_trips[TRIP_DURATION].max(), side_trips[TRIP_COUNT].max() - 1) + + row_size = chunk_size and trip_departure_calc_row_size(trips, trace_label) + + trip_list = [] + + for i, chooser_chunk, chunk_trace_label in \ + chunk.adaptive_chunked_choosers_by_chunk_id(side_trips, chunk_size, row_size, trace_label): + + for is_outbound, trip_segment in chooser_chunk.groupby(OUTBOUND): + direction = OUTBOUND if is_outbound else 'inbound' + spec = get_spec_for_segment(omnibus_spec, direction) + segment_trace_label = '{}_{}'.format(direction, chunk_trace_label) + + patterns = build_patterns(trip_segment, time_windows) + + choices = choose_tour_leg_pattern(trip_segment, + patterns, spec, trace_label=segment_trace_label) + + choices = pd.merge(choices.reset_index(), patterns.reset_index(), + on=[TOUR_LEG_ID, PATTERN_ID], how='left') + + choices = choices[['trip_id', 'stop_time_duration']].copy() + + trip_list.append(choices) + + trip_list = pd.concat(trip_list, sort=True).set_index('trip_id') + trips['stop_time_duration'] = 0 + trips.update(trip_list) + trips.loc[trips['trip_num'] == 1, 'stop_time_duration'] = trips['depart'] + trips.sort_values(['tour_id', 'outbound', 'trip_num']) + trips['stop_time_duration'] = trips.groupby(['tour_id', 'outbound'])['stop_time_duration'].cumsum() + trips.loc[trips['trip_num'] != trips['trip_count'], 'depart'] = trips['stop_time_duration'] + return trips['depart'].astype(int) + + +@inject.step() +def trip_departure_choice( + trips, + trips_merged, + skim_dict, + chunk_size, + trace_hh_id): + + trace_label = 'trip_departure_choice' + model_settings = config.read_model_settings('trip_departure_choice.yaml') + + spec = simulate.read_model_spec(file_name=model_settings['SPECIFICATION']) + + trips_merged_df = trips_merged.to_frame() + # add tour-based chunk_id so we can chunk all trips in tour together + tour_ids = trips_merged[TOUR_ID].unique() + trips_merged_df['chunk_id'] = reindex(pd.Series(list(range(len(tour_ids))), tour_ids), trips_merged_df.tour_id) + + max_tour_id = trips_merged[TOUR_ID].max() + + trip_departure_choice.MAX_TOUR_ID = int(np.power(10, np.ceil(np.log10(max_tour_id)))) + locals_d = config.get_model_constants(model_settings).copy() + + preprocessor_settings = model_settings.get('PREPROCESSOR', None) + tour_legs = get_tour_legs(trips_merged_df) + pipeline.get_rn_generator().add_channel('tour_legs', tour_legs) + + if preprocessor_settings: + od_skim = skim_dict.wrap('origin', 'destination') + do_skim = skim_dict.wrap('destination', 'origin') + + skims = [od_skim, do_skim] + + simulate.set_skim_wrapper_targets(trips_merged_df, skims) + + locals_d.update({ + "od_skims": od_skim, + "do_skims": do_skim, + }) + + expressions.assign_columns( + df=trips_merged_df, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label) + + choices = apply_stage_two_model(spec, trips_merged_df, chunk_size, trace_label) + + trips_df = trips.to_frame() + trip_length = len(trips_df) + trips_df = pd.concat([trips_df, choices], axis=1) + assert len(trips_df) == trip_length + assert trips_df[trips_df['depart'].isnull()].empty + + pipeline.replace_table("trips", trips_df) diff --git a/activitysim/abm/models/trip_destination.py b/activitysim/abm/models/trip_destination.py index 4ece500ad2..3a2cd90fa8 100644 --- a/activitysim/abm/models/trip_destination.py +++ b/activitysim/abm/models/trip_destination.py @@ -12,19 +12,20 @@ from activitysim.core import pipeline from activitysim.core import simulate from activitysim.core import inject +from activitysim.core import los +from activitysim.core import assign +from activitysim.core import expressions from activitysim.core.tracing import print_elapsed_time from activitysim.core.util import reindex from activitysim.core.util import assign_in_place -from .util import expressions - -from activitysim.core import assign +from activitysim.core.pathbuilder import TransitVirtualPathBuilder from activitysim.abm.tables.size_terms import tour_destination_size_terms -from activitysim.core.skim import DataFrameMatrix +from activitysim.core.skim_dictionary import DataFrameMatrix from activitysim.core.interaction_sample_simulate import interaction_sample_simulate from activitysim.core.interaction_sample import interaction_sample @@ -66,10 +67,10 @@ def trip_destination_sample( destination_sample: pandas.dataframe choices_df from interaction_sample with (up to) sample_size alts for each chooser row index (non unique) is trip_id from trips (duplicated for each alt) - and columns dest_taz, prob, and pick_count + and columns dest_zone_id, prob, and pick_count - dest_taz: int - alt identifier (dest_taz) from alternatives[] + dest_zone_id: int + alt identifier from alternatives[] prob: float the probability of the chosen alternative pick_count : int @@ -166,6 +167,9 @@ def compute_logsums( trace_label = tracing.extend_trace_label(trace_label, 'compute_logsums') logger.info("Running %s with %d samples", trace_label, destination_sample.shape[0]) + # FIXME should pass this in? + network_los = inject.get_injectable('network_los') + # - trips_merged - merge trips and tours_merged trips_merged = pd.merge( trips, @@ -196,6 +200,10 @@ def compute_logsums( locals_dict = assign.evaluate_constants(coefficient_spec, constants=constants) locals_dict.update(constants) + if network_los.zone_system == los.THREE_ZONE: + # TVPB constants can appear in expressions + locals_dict.update(network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS')) + # - od_logsums od_skims = { 'ORIGIN': model_settings['TRIP_ORIGIN'], @@ -204,6 +212,11 @@ def compute_logsums( "dot_skims": skims['dot_skims'], "od_skims": skims['od_skims'], } + if network_los.zone_system == los.THREE_ZONE: + od_skims.update({ + 'tvpb_logsum_odt': skims['tvpb_logsum_odt'], + 'tvpb_logsum_dot': skims['tvpb_logsum_dot'] + }) destination_sample['od_logsum'] = compute_ood_logsums( choosers, logsum_settings, @@ -220,6 +233,12 @@ def compute_logsums( "dot_skims": skims['pdt_skims'], "od_skims": skims['dp_skims'], } + if network_los.zone_system == los.THREE_ZONE: + dp_skims.update({ + 'tvpb_logsum_odt': skims['tvpb_logsum_dpt'], + 'tvpb_logsum_dot': skims['tvpb_logsum_pdt'] + }) + destination_sample['dp_logsum'] = compute_ood_logsums( choosers, logsum_settings, @@ -249,7 +268,7 @@ def trip_destination_simulate( choices - pandas.Series destination alt chosen """ - trace_label = tracing.extend_trace_label(trace_label, 'trip_destination_simulate') + trace_label = tracing.extend_trace_label(trace_label, 'trip_dest_simulate') spec = get_spec_for_purpose(model_settings, 'DESTINATION_SPEC', primary_purpose) @@ -368,7 +387,7 @@ def choose_trip_destination( return destinations, destination_sample -def wrap_skims(model_settings): +def wrap_skims(model_settings, trace_label): """ wrap skims of trip destination using origin, dest column names from model settings. Various of these are used by destination_sample, compute_logsums, and destination_simulate @@ -377,12 +396,12 @@ def wrap_skims(model_settings): Note that compute_logsums aliases their names so it can use the same equations to compute logsums from origin to alt_dest, and from alt_dest to primarly destination - odt_skims - SkimStackWrapper: trip origin, trip alt_dest, time_of_day - dot_skims - SkimStackWrapper: trip alt_dest, trip origin, time_of_day - dpt_skims - SkimStackWrapper: trip alt_dest, trip primary_dest, time_of_day - pdt_skims - SkimStackWrapper: trip primary_dest,trip alt_dest, time_of_day - od_skims - SkimDictWrapper: trip origin, trip alt_dest - dp_skims - SkimDictWrapper: trip alt_dest, trip primary_dest + odt_skims - Skim3dWrapper: trip origin, trip alt_dest, time_of_day + dot_skims - Skim3dWrapper: trip alt_dest, trip origin, time_of_day + dpt_skims - Skim3dWrapper: trip alt_dest, trip primary_dest, time_of_day + pdt_skims - Skim3dWrapper: trip primary_dest,trip alt_dest, time_of_day + od_skims - SkimWrapper: trip origin, trip alt_dest + dp_skims - SkimWrapper: trip alt_dest, trip primary_dest Parameters ---------- @@ -393,22 +412,46 @@ def wrap_skims(model_settings): dict containing skims, keyed by canonical names relative to tour orientation """ - skim_dict = inject.get_injectable('skim_dict') - skim_stack = inject.get_injectable('skim_stack') + network_los = inject.get_injectable('network_los') + skim_dict = network_los.get_default_skim_dict() o = model_settings['TRIP_ORIGIN'] d = model_settings['ALT_DEST_COL_NAME'] p = model_settings['PRIMARY_DEST'] skims = { - "odt_skims": skim_stack.wrap(left_key=o, right_key=d, skim_key='trip_period'), - "dot_skims": skim_stack.wrap(left_key=d, right_key=o, skim_key='trip_period'), - "dpt_skims": skim_stack.wrap(left_key=d, right_key=p, skim_key='trip_period'), - "pdt_skims": skim_stack.wrap(left_key=p, right_key=d, skim_key='trip_period'), + "odt_skims": skim_dict.wrap_3d(orig_key=o, dest_key=d, dim3_key='trip_period'), + "dot_skims": skim_dict.wrap_3d(orig_key=d, dest_key=o, dim3_key='trip_period'), + "dpt_skims": skim_dict.wrap_3d(orig_key=d, dest_key=p, dim3_key='trip_period'), + "pdt_skims": skim_dict.wrap_3d(orig_key=p, dest_key=d, dim3_key='trip_period'), "od_skims": skim_dict.wrap(o, d), "dp_skims": skim_dict.wrap(d, p), } + if network_los.zone_system == los.THREE_ZONE: + # fixme - is this a lightweight object? + tvpb = network_los.tvpb + + tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=o, dest_key=d, + tod_key='trip_period', segment_key='demographic_segment', + trace_label=trace_label, tag='tvpb_logsum_odt') + tvpb_logsum_dot = tvpb.wrap_logsum(orig_key=d, dest_key=o, + tod_key='trip_period', segment_key='demographic_segment', + trace_label=trace_label, tag='tvpb_logsum_dot') + tvpb_logsum_dpt = tvpb.wrap_logsum(orig_key=d, dest_key=p, + tod_key='trip_period', segment_key='demographic_segment', + trace_label=trace_label, tag='tvpb_logsum_dpt') + tvpb_logsum_pdt = tvpb.wrap_logsum(orig_key=p, dest_key=d, + tod_key='trip_period', segment_key='demographic_segment', + trace_label=trace_label, tag='tvpb_logsum_pdt') + + skims.update({ + 'tvpb_logsum_odt': tvpb_logsum_odt, + 'tvpb_logsum_dot': tvpb_logsum_dot, + 'tvpb_logsum_dpt': tvpb_logsum_dpt, + 'tvpb_logsum_pdt': tvpb_logsum_pdt + }) + return skims @@ -453,6 +496,7 @@ def run_trip_destination( land_use = inject.get_table('land_use') size_terms = inject.get_injectable('size_terms') + network_los = inject.get_injectable('network_los') # - initialize trip origin and destination to those of half-tour # (we will sequentially adjust intermediate trips origin and destination as we choose them) @@ -475,17 +519,17 @@ def run_trip_destination( tours_merged = tours_merged[tours_merged_cols] # - skims - skims = wrap_skims(model_settings) + skims = wrap_skims(model_settings, trace_label) # - size_terms and alternatives alternatives = tour_destination_size_terms(land_use, size_terms, 'trip') - # DataFrameMatrix alows us to treat dataframe as virtual a 2-D array, indexed by TAZ, purpose - # e.g. size_terms.get(df.dest_taz, df.purpose) - # returns a series of size_terms for each chooser's dest_taz and purpose with chooser index + # DataFrameMatrix alows us to treat dataframe as virtual a 2-D array, indexed by zone_id, purpose + # e.g. size_terms.get(df.dest_zone_id, df.purpose) + # returns a series of size_terms for each chooser's dest_zone_id and purpose with chooser index size_term_matrix = DataFrameMatrix(alternatives) - # don't need size terms in alternatives, just TAZ index + # don't need size terms in alternatives, just zone_id index alternatives = alternatives.drop(alternatives.columns, axis=1) alternatives.index.name = model_settings['ALT_DEST_COL_NAME'] @@ -504,12 +548,17 @@ def run_trip_destination( nth_trips = trips[intermediate & (trips.trip_num == trip_num)] nth_trace_label = tracing.extend_trace_label(trace_label, 'trip_num_%s' % trip_num) + locals_dict = { + 'network_los': network_los + } + locals_dict.update(config.get_model_constants(model_settings)) + # - annotate nth_trips if preprocessor_settings: expressions.assign_columns( df=nth_trips, model_settings=preprocessor_settings, - locals_dict=config.get_model_constants(model_settings), + locals_dict=locals_dict, trace_label=nth_trace_label) logger.info("Running %s with %d trips", nth_trace_label, nth_trips.shape[0]) diff --git a/activitysim/abm/models/trip_matrices.py b/activitysim/abm/models/trip_matrices.py index 77699abb5e..661e97541c 100644 --- a/activitysim/abm/models/trip_matrices.py +++ b/activitysim/abm/models/trip_matrices.py @@ -10,15 +10,13 @@ from activitysim.core import config from activitysim.core import inject from activitysim.core import pipeline - -from .util import expressions -from .util.expressions import skim_time_period_label +from activitysim.core import expressions logger = logging.getLogger(__name__) @inject.step() -def write_trip_matrices(trips, skim_dict, skim_stack): +def write_trip_matrices(trips, network_los): """ Write trip matrices step. @@ -28,11 +26,18 @@ def write_trip_matrices(trips, skim_dict, skim_stack): """ model_settings = config.read_model_settings('write_trip_matrices.yaml') - trips_df = annotate_trips(trips, skim_dict, skim_stack, model_settings) + trips_df = annotate_trips(trips, network_los, model_settings) if bool(model_settings.get('SAVE_TRIPS_TABLE')): pipeline.replace_table('trips', trips_df) + if 'parking_location' in config.setting('models'): + parking_settings = config.read_model_settings('parking_location_choice.yaml') + parking_taz_col_name = parking_settings['ALT_DEST_COL_NAME'] + if parking_taz_col_name in trips_df: + trips_df.loc[trips_df[parking_taz_col_name] > 0, 'destination'] = trips_df[parking_taz_col_name] + # Also need address the return trip + logger.info('Aggregating trips...') aggregate_trips = trips_df.groupby(['origin', 'destination'], sort=False).sum() @@ -57,7 +62,7 @@ def write_trip_matrices(trips, skim_dict, skim_stack): write_matrices(aggregate_trips, zone_index, orig_index, dest_index, model_settings) -def annotate_trips(trips, skim_dict, skim_stack, model_settings): +def annotate_trips(trips, network_los, model_settings): """ Add columns to local trips table. The annotator has access to the origin/destination skims and everything @@ -71,12 +76,13 @@ def annotate_trips(trips, skim_dict, skim_stack, model_settings): trace_label = 'trip_matrices' + skim_dict = network_los.get_default_skim_dict() + # setup skim keys - assert ('trip_period' not in trips_df) - trips_df['trip_period'] = skim_time_period_label(trips_df.depart) + if 'trip_period' not in trips_df: + trips_df['trip_period'] = network_los.skim_time_period_label(trips_df.depart) od_skim_wrapper = skim_dict.wrap('origin', 'destination') - odt_skim_stack_wrapper = skim_stack.wrap(left_key='origin', right_key='destination', - skim_key='trip_period') + odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key='origin', dest_key='destination', dim3_key='trip_period') skims = { 'od_skims': od_skim_wrapper, "odt_skims": odt_skim_stack_wrapper diff --git a/activitysim/abm/models/trip_mode_choice.py b/activitysim/abm/models/trip_mode_choice.py index cd8b637169..ed8632b938 100644 --- a/activitysim/abm/models/trip_mode_choice.py +++ b/activitysim/abm/models/trip_mode_choice.py @@ -1,26 +1,26 @@ # ActivitySim # See full license in LICENSE.txt. -from builtins import zip -from builtins import range import logging import pandas as pd +import numpy as np from activitysim.core import simulate from activitysim.core import tracing from activitysim.core import config from activitysim.core import inject from activitysim.core import pipeline -from activitysim.core.mem import force_garbage_collect +from activitysim.core import expressions -from .util.expressions import annotate_preprocessors +from activitysim.core.mem import force_garbage_collect from activitysim.core import assign -from activitysim.core.util import assign_in_place +from activitysim.core import los -from .util.expressions import skim_time_period_label +from activitysim.core.util import assign_in_place +from activitysim.core.pathbuilder import TransitVirtualPathBuilder from .util.mode import mode_choice_simulate logger = logging.getLogger(__name__) @@ -30,7 +30,7 @@ def trip_mode_choice( trips, tours_merged, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id): """ Trip mode choice - compute trip_mode (same values as for tour_mode) for each trip. @@ -73,15 +73,24 @@ def trip_mode_choice( # setup skim keys assert ('trip_period' not in trips_merged) - trips_merged['trip_period'] = skim_time_period_label(trips_merged.depart) + trips_merged['trip_period'] = network_los.skim_time_period_label(trips_merged.depart) orig_col = 'origin' dest_col = 'destination' - odt_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col, right_key=dest_col, - skim_key='trip_period') - dot_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col, right_key=orig_col, - skim_key='trip_period') + constants = {} + constants.update(config.get_model_constants(model_settings)) + constants.update({ + 'ORIGIN': orig_col, + 'DESTINATION': dest_col + }) + + skim_dict = network_los.get_default_skim_dict() + + odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col, dest_key=dest_col, + dim3_key='trip_period') + dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col, dest_key=orig_col, + dim3_key='trip_period') od_skim_wrapper = skim_dict.wrap('origin', 'destination') skims = { @@ -90,11 +99,21 @@ def trip_mode_choice( "od_skims": od_skim_wrapper, } - constants = config.get_model_constants(model_settings) - constants.update({ - 'ORIGIN': orig_col, - 'DESTINATION': dest_col - }) + if network_los.zone_system == los.THREE_ZONE: + # fixme - is this a lightweight object? + tvpb = network_los.tvpb + + tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=orig_col, dest_key=dest_col, + tod_key='trip_period', segment_key='demographic_segment', + cache_choices=True, + trace_label=trace_label, tag='tvpb_logsum_odt') + skims.update({ + 'tvpb_logsum_odt': tvpb_logsum_odt, + # 'tvpb_logsum_dot': tvpb_logsum_dot + }) + + # TVPB constants can appear in expressions + constants.update(network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS')) choices_list = [] for primary_purpose, trips_segment in trips_merged.groupby('primary_purpose'): @@ -107,11 +126,15 @@ def trip_mode_choice( # name index so tracing knows how to slice assert trips_segment.index.name == 'trip_id' + if network_los.zone_system == los.THREE_ZONE: + tvpb_logsum_odt.extend_trace_label(primary_purpose) + # tvpb_logsum_dot.extend_trace_label(primary_purpose) + locals_dict = assign.evaluate_constants(omnibus_coefficients[primary_purpose], constants=constants) locals_dict.update(constants) - annotate_preprocessors( + expressions.annotate_preprocessors( trips_segment, locals_dict, skims, model_settings, segment_trace_label) @@ -150,11 +173,27 @@ def trip_mode_choice( # FIXME - force garbage collection force_garbage_collect() - choices = pd.concat(choices_list) + choices_df = pd.concat(choices_list) + + # add cached tvpb_logsum tap choices for modes specified in tvpb_mode_path_types + if network_los.zone_system == los.THREE_ZONE: + + tvpb_mode_path_types = model_settings.get('tvpb_mode_path_types') + for mode, path_type in tvpb_mode_path_types.items(): + + skim_cache = tvpb_logsum_odt.cache[path_type] + + print(f"mode {mode} path_type {path_type}") + + for c in skim_cache: + dest_col = c + if dest_col not in choices_df: + choices_df[dest_col] = np.nan + choices_df[dest_col].where(choices_df[mode_column_name] != mode, skim_cache[c], inplace=True) - # keep mode_choice and (optionally) logsum columns + # update trips table with choices (and otionally logssums) trips_df = trips.to_frame() - assign_in_place(trips_df, choices) + assign_in_place(trips_df, choices_df) tracing.print_summary('tour_modes', trips_merged.tour_mode, value_counts=True) diff --git a/activitysim/abm/models/trip_purpose.py b/activitysim/abm/models/trip_purpose.py index edb9c79948..cc6026c249 100644 --- a/activitysim/abm/models/trip_purpose.py +++ b/activitysim/abm/models/trip_purpose.py @@ -11,41 +11,34 @@ from activitysim.core import tracing from activitysim.core import chunk from activitysim.core import pipeline - -from .util import expressions +from activitysim.core import expressions logger = logging.getLogger(__name__) +PROBS_JOIN_COLUMNS = ['primary_purpose', 'outbound', 'person_type'] + + def trip_purpose_probs(): f = config.config_file_path('trip_purpose_probs.csv') df = pd.read_csv(f, comment='#') return df -def trip_purpose_rpc(chunk_size, choosers, spec, trace_label): +def trip_purpose_calc_row_size(choosers, spec, trace_label): """ rows_per_chunk calculator for trip_purpose """ - num_choosers = len(choosers.index) - - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + sizer = chunk.RowSizeEstimator(trace_label) chooser_row_size = len(choosers.columns) + spec_columns = spec.shape[1] - len(PROBS_JOIN_COLUMNS) - # extra columns from spec - extra_columns = spec.shape[1] - - row_size = chooser_row_size + extra_columns - - # logger.debug("%s #chunk_calc choosers %s", trace_label, choosers.shape) - # logger.debug("%s #chunk_calc spec %s", trace_label, spec.shape) - # logger.debug("%s #chunk_calc extra_columns %s", trace_label, extra_columns) + sizer.add_elements(chooser_row_size + spec_columns, 'choosers') - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + row_size = sizer.get_hwm() + return row_size def choose_intermediate_trip_purpose(trips, probs_spec, trace_hh_id, trace_label): @@ -58,21 +51,19 @@ def choose_intermediate_trip_purpose(trips, probs_spec, trace_hh_id, trace_label purpose: pandas.Series of purpose (str) indexed by trip_id """ - probs_join_cols = ['primary_purpose', 'outbound', 'person_type'] - non_purpose_cols = probs_join_cols + ['depart_range_start', 'depart_range_end'] + non_purpose_cols = PROBS_JOIN_COLUMNS + ['depart_range_start', 'depart_range_end'] purpose_cols = [c for c in probs_spec.columns if c not in non_purpose_cols] num_trips = len(trips.index) have_trace_targets = trace_hh_id and tracing.has_trace_targets(trips) - # probs shold sum to 1 across rows + # probs should sum to 1 across rows sum_probs = probs_spec[purpose_cols].sum(axis=1) probs_spec.loc[:, purpose_cols] = probs_spec.loc[:, purpose_cols].div(sum_probs, axis=0) # left join trips to probs (there may be multiple rows per trip for multiple depart ranges) - choosers = pd.merge(trips.reset_index(), probs_spec, on=probs_join_cols, + choosers = pd.merge(trips.reset_index(), probs_spec, on=PROBS_JOIN_COLUMNS, how='left').set_index('trip_id') - chunk.log_df(trace_label, 'choosers', choosers) # select the matching depart range (this should result on in exactly one chooser row per trip) @@ -166,17 +157,10 @@ def run_trip_purpose( locals_dict=locals_dict, trace_label=trace_label) - rows_per_chunk, effective_chunk_size = \ - trip_purpose_rpc(chunk_size, trips_df, probs_spec, trace_label=trace_label) - - for i, num_chunks, trips_chunk in chunk.chunked_choosers(trips_df, rows_per_chunk): - - logger.info("Running chunk %s of %s size %d", i, num_chunks, len(trips_chunk)) + row_size = chunk_size and trip_purpose_calc_row_size(trips_df, probs_spec, trace_label) - chunk_trace_label = tracing.extend_trace_label(trace_label, 'chunk_%s' % i) \ - if num_chunks > 1 else trace_label - - chunk.log_open(chunk_trace_label, chunk_size, effective_chunk_size) + for i, trips_chunk, chunk_trace_label in \ + chunk.adaptive_chunked_choosers(trips_df, chunk_size, row_size, trace_label): choices = choose_intermediate_trip_purpose( trips_chunk, @@ -184,8 +168,6 @@ def run_trip_purpose( trace_hh_id, trace_label=chunk_trace_label) - chunk.log_close(chunk_trace_label) - result_list.append(choices) if len(result_list) > 1: diff --git a/activitysim/abm/models/trip_scheduling.py b/activitysim/abm/models/trip_scheduling.py index 85ad3b7423..c762ed8eec 100644 --- a/activitysim/abm/models/trip_scheduling.py +++ b/activitysim/abm/models/trip_scheduling.py @@ -14,8 +14,6 @@ from activitysim.core import chunk from activitysim.core import pipeline -from activitysim.core.util import assign_in_place -from .util import expressions from activitysim.core.util import reindex from activitysim.abm.models.util.trip import failed_trip_cohorts @@ -42,6 +40,8 @@ FAILFIX_DROP_AND_CLEANUP = 'drop_and_cleanup' FAILFIX_DEFAULT = FAILFIX_CHOOSE_MOST_INITIAL +PROBS_JOIN_COLUMNS = ['primary_purpose', 'outbound', 'tour_hour', 'trip_num'] + def set_tour_hour(trips, tours): """ @@ -204,11 +204,10 @@ def schedule_nth_trips( depart_alt_base = model_settings.get('DEPART_ALT_BASE') - probs_join_cols = ['primary_purpose', 'outbound', 'tour_hour', 'trip_num'] - probs_cols = [c for c in probs_spec.columns if c not in probs_join_cols] + probs_cols = [c for c in probs_spec.columns if c not in PROBS_JOIN_COLUMNS] # left join trips to probs (there may be multiple rows per trip for multiple depart ranges) - choosers = pd.merge(trips.reset_index(), probs_spec, on=probs_join_cols, + choosers = pd.merge(trips.reset_index(), probs_spec, on=PROBS_JOIN_COLUMNS, how='left').set_index('trip_id') chunk.log_df(trace_label, "choosers", choosers) @@ -221,7 +220,6 @@ def schedule_nth_trips( # zero out probs outside earliest-latest window chooser_probs = clip_probs(trips, choosers[probs_cols], model_settings) - chunk.log_df(trace_label, "chooser_probs", chooser_probs) if first_trip_in_leg: @@ -230,13 +228,12 @@ def schedule_nth_trips( # probs should sum to 1 with residual probs resulting in choice of 'fail' chooser_probs['fail'] = 1 - chooser_probs.sum(axis=1).clip(0, 1) + chunk.log_df(trace_label, "chooser_probs", chooser_probs) if trace_hh_id and tracing.has_trace_targets(trips): tracing.trace_df(chooser_probs, '%s.chooser_probs' % trace_label) - choices, rands = logit.make_choices( - chooser_probs, - trace_label=trace_label, trace_choosers=choosers) + choices, rands = logit.make_choices(chooser_probs, trace_label=trace_label, trace_choosers=choosers) chunk.log_df(trace_label, "choices", choices) chunk.log_df(trace_label, "rands", rands) @@ -304,6 +301,8 @@ def schedule_trips_in_leg( # logger.debug("%s scheduling %s trips" % (trace_label, trips.shape[0])) + assert len(trips) > 0 + assert (trips.outbound == outbound).all() # initial trip of leg and all atwork trips get tour_hour @@ -336,18 +335,15 @@ def schedule_trips_in_leg( nth_trace_label = tracing.extend_trace_label(trace_label, 'num_%s' % i) - chunk.log_open(nth_trace_label, chunk_size=0, effective_chunk_size=0) - - choices = schedule_nth_trips( - nth_trips, - probs_spec, - model_settings, - first_trip_in_leg=first_trip_in_leg, - report_failed_trips=last_iteration, - trace_hh_id=trace_hh_id, - trace_label=nth_trace_label) - - chunk.log_close(nth_trace_label) + with chunk.chunk_log(nth_trace_label): + choices = schedule_nth_trips( + nth_trips, + probs_spec, + model_settings, + first_trip_in_leg=first_trip_in_leg, + report_failed_trips=last_iteration, + trace_hh_id=trace_hh_id, + trace_label=nth_trace_label) # if outbound, this trip's depart constrains next trip's earliest depart option # if inbound, we are handling in reverse order, so it constrains latest depart instead @@ -378,33 +374,45 @@ def schedule_trips_in_leg( return choices -def trip_scheduling_rpc(chunk_size, choosers, spec, trace_label): +def trip_scheduling_calc_row_size(trips, spec, trace_label): + + sizer = chunk.RowSizeEstimator(trace_label) # NOTE we chunk chunk_id - num_choosers = choosers['chunk_id'].max() + 1 + # scale row_size by average number of chooser rows per chunk_id + num_choosers = trips['chunk_id'].max() + 1 + rows_per_chunk_id = len(trips) / num_choosers - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + # only non-initial trips require scheduling, segment handing first such trip in tour will use most space + outbound_chooser = (trips.trip_num == 2) & trips.outbound & (trips.primary_purpose != 'atwork') + inbound_chooser = (trips.trip_num == trips.trip_count-1) & ~trips.outbound & (trips.primary_purpose != 'atwork') - # extra columns from spec - extra_columns = spec.shape[1] + # furthermore, inbound and outbound are scheduled independently + if outbound_chooser.sum() > inbound_chooser.sum(): + is_chooser = outbound_chooser + logger.debug(f"{trace_label} {is_chooser.sum()} outbound_choosers of {len(trips)} require scheduling") + else: + is_chooser = inbound_chooser + logger.debug(f"{trace_label} {is_chooser.sum()} inbound_choosers of {len(trips)} require scheduling") - chooser_row_size = choosers.shape[1] + extra_columns + chooser_fraction = is_chooser.sum()/len(trips) + logger.debug(f"{trace_label} chooser_fraction {chooser_fraction *100}%") - # scale row_size by average number of chooser rows per chunk_id - rows_per_chunk_id = choosers.shape[0] / num_choosers - row_size = (rows_per_chunk_id * chooser_row_size) + chooser_row_size = len(trips.columns) + len(spec.columns) - len(PROBS_JOIN_COLUMNS) + sizer.add_elements(chooser_fraction * chooser_row_size, 'choosers') + + # might be clipped to fewer but this is worst case + chooser_probs_row_size = len(spec.columns) - len(PROBS_JOIN_COLUMNS) + sizer.add_elements(chooser_fraction * chooser_probs_row_size, 'chooser_probs') - # print "num_choosers", num_choosers - # print "choosers.shape", choosers.shape - # print "rows_per_chunk_id", rows_per_chunk_id - # print "chooser_row_size", chooser_row_size - # print "(rows_per_chunk_id * chooser_row_size)", (rows_per_chunk_id * chooser_row_size) - # print "row_size", row_size - # #bug + sizer.add_elements(chooser_fraction, 'choices') + sizer.add_elements(chooser_fraction, 'rands') + sizer.add_elements(chooser_fraction, 'failed') - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + row_size = sizer.get_hwm() + row_size = row_size * rows_per_chunk_id + + return row_size def run_trip_scheduling( @@ -419,45 +427,44 @@ def run_trip_scheduling( set_tour_hour(trips, tours) - rows_per_chunk, effective_chunk_size = \ - trip_scheduling_rpc(chunk_size, trips, probs_spec, trace_label) - - result_list = [] - for i, num_chunks, trips_chunk in chunk.chunked_choosers_by_chunk_id(trips, rows_per_chunk): + row_size = chunk_size and trip_scheduling_calc_row_size(trips, probs_spec, trace_label) - if num_chunks > 1: - chunk_trace_label = tracing.extend_trace_label(trace_label, 'chunk_%s' % i) - logger.info("%s of %s size %d" % (chunk_trace_label, num_chunks, len(trips_chunk))) - else: - chunk_trace_label = trace_label - - leg_trace_label = tracing.extend_trace_label(chunk_trace_label, 'outbound') - chunk.log_open(leg_trace_label, chunk_size, effective_chunk_size) - choices = \ - schedule_trips_in_leg( - outbound=True, - trips=trips_chunk[trips_chunk.outbound], - probs_spec=probs_spec, - model_settings=model_settings, - last_iteration=last_iteration, - trace_hh_id=trace_hh_id, - trace_label=leg_trace_label) - result_list.append(choices) - chunk.log_close(leg_trace_label) + # only non-initial trips require scheduling, segment handing first such trip in tour will use most space + # is_outbound_chooser = (trips.trip_num > 1) & trips.outbound & (trips.primary_purpose != 'atwork') + # is_inbound_chooser = (trips.trip_num < trips.trip_count) & ~trips.outbound & (trips.primary_purpose != 'atwork') + # num_choosers = (is_inbound_chooser | is_outbound_chooser).sum() - leg_trace_label = tracing.extend_trace_label(chunk_trace_label, 'inbound') - chunk.log_open(leg_trace_label, chunk_size, effective_chunk_size) - choices = \ - schedule_trips_in_leg( - outbound=False, - trips=trips_chunk[~trips_chunk.outbound], - probs_spec=probs_spec, - model_settings=model_settings, - last_iteration=last_iteration, - trace_hh_id=trace_hh_id, - trace_label=leg_trace_label) - result_list.append(choices) - chunk.log_close(leg_trace_label) + result_list = [] + for i, trips_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers_by_chunk_id(trips, chunk_size, row_size, trace_label): + + if trips_chunk.outbound.any(): + leg_trace_label = tracing.extend_trace_label(chunk_trace_label, 'outbound') + with chunk.chunk_log(leg_trace_label): + choices = \ + schedule_trips_in_leg( + outbound=True, + trips=trips_chunk[trips_chunk.outbound], + probs_spec=probs_spec, + model_settings=model_settings, + last_iteration=last_iteration, + trace_hh_id=trace_hh_id, + trace_label=leg_trace_label) + result_list.append(choices) + + if (~trips_chunk.outbound).any(): + leg_trace_label = tracing.extend_trace_label(chunk_trace_label, 'inbound') + with chunk.chunk_log(leg_trace_label): + choices = \ + schedule_trips_in_leg( + outbound=False, + trips=trips_chunk[~trips_chunk.outbound], + probs_spec=probs_spec, + model_settings=model_settings, + last_iteration=last_iteration, + trace_hh_id=trace_hh_id, + trace_label=leg_trace_label) + result_list.append(choices) choices = pd.concat(result_list) @@ -530,8 +537,7 @@ def trip_scheduling( tours = tours.to_frame() # add tour-based chunk_id so we can chunk all trips in tour together - trips_df['chunk_id'] = \ - reindex(pd.Series(list(range(tours.shape[0])), tours.index), trips_df.tour_id) + trips_df['chunk_id'] = reindex(pd.Series(list(range(len(tours))), tours.index), trips_df.tour_id) max_iterations = model_settings.get('MAX_ITERATIONS', 1) assert max_iterations > 0 diff --git a/activitysim/abm/models/trip_scheduling_choice.py b/activitysim/abm/models/trip_scheduling_choice.py new file mode 100644 index 0000000000..70156726a0 --- /dev/null +++ b/activitysim/abm/models/trip_scheduling_choice.py @@ -0,0 +1,368 @@ +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import chunk +from activitysim.core import config +from activitysim.core import expressions +from activitysim.core import inject +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import tracing + +from activitysim.abm.models.util.trip import generate_alternative_sizes, get_time_windows +from activitysim.core.interaction_sample_simulate import _interaction_sample_simulate +from activitysim.core.mem import force_garbage_collect + + +logger = logging.getLogger(__name__) + +TOUR_DURATION_COLUMN = 'duration' +NUM_ALTERNATIVES = 'num_alts' +MAIN_LEG_DURATION = 'main_leg_duration' +IB_DURATION = 'inbound_duration' +OB_DURATION = 'outbound_duration' +NUM_OB_STOPS = 'num_outbound_stops' +NUM_IB_STOPS = 'num_inbound_stops' +HAS_OB_STOPS = 'has_outbound_stops' +HAS_IB_STOPS = 'has_inbound_stops' +LAST_OB_STOP = 'last_outbound_stop' +FIRST_IB_STOP = 'last_inbound_stop' + +SCHEDULE_ID = 'schedule_id' + +OUTBOUND_FLAG = 'outbound' + +TEMP_COLS = [NUM_OB_STOPS, LAST_OB_STOP, + NUM_IB_STOPS, FIRST_IB_STOP, + NUM_ALTERNATIVES + ] + + +def generate_schedule_alternatives(tours): + """ + For a set of tours, build out the potential schedule alternatives + for the main leg, outbound leg, and inbound leg. This process handles + the change in three steps. + + Definitions: + - Main Leg: The time from last outbound stop to the first inbound stop. + If the tour does not include any intermediate stops this + will represent the full tour duration. + - Outbound Leg: The time from the tour origin to the last outbound stop + - Inbound Leg: The time from the first inbound stop to the tour origin + + 1. For tours with no intermediate stops, it simple asserts a main leg + duration equal to the tour duration. + + 2. For tours with an intermediate stop on one of the legs, calculate + all possible time combinations that are allowed in the duration + + 3. For tours with an intermediate stop on both legs, calculate + all possible time combinations that are allowed in the tour + duration + + :param tours: pd.Dataframe: Must include a field for tour duration + and boolean fields indicating intermediate inbound or outbound + stops. + :return: pd.Dataframe: Potential time duration windows. + """ + assert set([NUM_IB_STOPS, NUM_OB_STOPS, TOUR_DURATION_COLUMN]).issubset(tours.columns) + + stop_pattern = tours[HAS_OB_STOPS].astype(int) + tours[HAS_IB_STOPS].astype(int) + + no_stops = no_stops_patterns(tours[stop_pattern == 0]) + one_way = stop_one_way_only_patterns(tours[stop_pattern == 1]) + two_way = stop_two_way_only_patterns(tours[stop_pattern > 1]) + + schedules = pd.concat([no_stops, one_way, two_way], sort=True) + schedules[SCHEDULE_ID] = np.arange(1, schedules.shape[0] + 1) + + return schedules + + +def no_stops_patterns(tours): + """ + Asserts the tours with no intermediate stops have a main leg duration equal + to the tour duration and set inbound and outbound windows equal to zero. + :param tours: pd.Dataframe: Tours with no intermediate stops. + :return: pd.Dataframe: Main leg duration, outbound leg duration, and inbound leg duration + """ + alternatives = tours[[TOUR_DURATION_COLUMN]].rename(columns={TOUR_DURATION_COLUMN: MAIN_LEG_DURATION}) + alternatives[[IB_DURATION, OB_DURATION]] = 0 + return alternatives.astype(int) + + +def stop_one_way_only_patterns(tours, travel_duration_col=TOUR_DURATION_COLUMN): + """ + Calculates potential time windows for tours with a single leg with intermediate + stops. It calculates all possibilities for the main leg and one tour leg to sum to + the tour duration. The other leg is asserted with a duration of zero. + :param tours: pd.Dataframe: Tours with no intermediate stops. + :return: pd.Dataframe: Main leg duration, outbound leg duration, and inbound leg duration + The return dataframe is indexed to the tour input index + """ + if tours.empty: + return None + + assert travel_duration_col in tours.columns + + indexes, patterns, pattern_sizes = get_pattern_index_and_arrays(tours.index, tours[travel_duration_col], + one_way=True) + direction = np.repeat(tours[HAS_OB_STOPS], pattern_sizes) + + inbound = np.where(direction == 0, patterns[:, 1], 0) + outbound = np.where(direction == 1, patterns[:, 1], 0) + + patterns = pd.DataFrame(index=indexes, data=np.column_stack((patterns[:, 0], outbound, inbound)), + columns=[MAIN_LEG_DURATION, OB_DURATION, IB_DURATION]) + patterns.index.name = tours.index.name + + return patterns + + +def stop_two_way_only_patterns(tours, travel_duration_col=TOUR_DURATION_COLUMN): + """ + Calculates potential time windows for tours with intermediate stops on both + legs. It calculates all possibilities for the main leg and both tour legs to + sum to the tour duration. + :param tours: pd.Dataframe: Tours with no intermediate stops. + :return: pd.Dataframe: Main leg duration, outbound leg duration, and inbound leg duration + The return dataframe is indexed to the tour input index + """ + if tours.empty: + return None + + assert travel_duration_col in tours.columns + + indexes, patterns, _ = get_pattern_index_and_arrays(tours.index, tours[travel_duration_col], one_way=False) + + patterns = pd.DataFrame(index=indexes, data=patterns, + columns=[MAIN_LEG_DURATION, OB_DURATION, IB_DURATION]) + patterns.index.name = tours.index.name + + return patterns + + +def trip_schedule_calc_row_size(choosers, trace_label): + """ + rows_per_chunk calculator for trip_scheduler + """ + + sizer = chunk.RowSizeEstimator(trace_label) + + chooser_row_size = len(choosers.columns) + spec_columns = 3 + + sizer.add_elements(chooser_row_size + spec_columns, 'choosers') + + row_size = sizer.get_hwm() + return row_size + + +def get_pattern_index_and_arrays(tour_indexes, durations, one_way=True): + """ + A helper method to quickly calculate all of the potential time windows + for a given set of tour indexes and durations. + :param tour_indexes: List of tour indexes + :param durations: List of tour durations + :param one_way: If True, calculate windows for only one tour leg. If False, + calculate tour windows for both legs + :return: np.array: Tour indexes repeated for valid pattern + np.array: array with a column for main tour leg, outbound leg, and inbound leg + np.array: array with the number of patterns for each tour + """ + max_columns = 2 if one_way else 3 + max_duration = np.max(durations) + time_windows = get_time_windows(max_duration, max_columns) + + patterns = [] + pattern_sizes = [] + + for duration in durations: + possible_windows = time_windows[:max_columns, np.where(time_windows.sum(axis=0) == duration)[0]] + possible_windows = np.unique(possible_windows, axis=1).transpose() + patterns.append(possible_windows) + pattern_sizes.append(possible_windows.shape[0]) + + indexes = np.repeat(tour_indexes, pattern_sizes) + + patterns = np.concatenate(patterns) + # If we've done everything right, the indexes + # calculated above should be the same length as + # the pattern options + assert patterns.shape[0] == len(indexes) + + return indexes, patterns, pattern_sizes + + +def get_spec_for_segment(model_settings, spec_name, segment): + """ + Read in the model spec + :param model_settings: model settings file + :param spec_name: name of the key in the settings file + :param segment: which segment of the spec file do you want to read + :return: array of utility equations + """ + + omnibus_spec = simulate.read_model_spec(file_name=model_settings[spec_name]) + + spec = omnibus_spec[[segment]] + + # might as well ignore any spec rows with 0 utility + spec = spec[spec.iloc[:, 0] != 0] + assert spec.shape[0] > 0 + + return spec + + +def run_trip_scheduling_choice(spec, tours, skims, locals_dict, + chunk_size, trace_hh_id, trace_label): + + NUM_TOUR_LEGS = 3 + trace_label = tracing.extend_trace_label(trace_label, 'interaction_sample_simulate') + + # FIXME: The duration, start, and end should be ints well before we get here... + tours[TOUR_DURATION_COLUMN] = tours[TOUR_DURATION_COLUMN].astype(np.int8) + + # Setup boolean columns to make it easier to identify + # intermediate stops later in the model. + tours[HAS_OB_STOPS] = tours[NUM_OB_STOPS] >= 1 + tours[HAS_IB_STOPS] = tours[NUM_IB_STOPS] >= 1 + + # Calculate a matrix with the appropriate alternative sizes + # based on the total tour duration. This is used to calculate + # chunk sizes. + max_duration = tours[TOUR_DURATION_COLUMN].max() + alt_sizes = generate_alternative_sizes(max_duration, NUM_TOUR_LEGS) + + # Assert the number of tour leg schedule alternatives for each tour + tours[NUM_ALTERNATIVES] = 1 + tours.loc[tours[HAS_OB_STOPS] != tours[HAS_IB_STOPS], NUM_ALTERNATIVES] = tours[TOUR_DURATION_COLUMN] + 1 + tours.loc[tours[HAS_OB_STOPS] & tours[HAS_IB_STOPS], NUM_ALTERNATIVES] = \ + tours.apply(lambda x: alt_sizes[1, x.duration], axis=1) + + # If no intermediate stops on the tour, then then main leg duration + # equals the tour duration and the intermediate durations are zero + tours.loc[~tours[HAS_OB_STOPS] & ~tours[HAS_IB_STOPS], MAIN_LEG_DURATION] = tours[TOUR_DURATION_COLUMN] + tours.loc[~tours[HAS_OB_STOPS] & ~tours[HAS_IB_STOPS], [IB_DURATION, OB_DURATION]] = 0 + + # We only need to determine schedules for tours with intermediate stops + indirect_tours = tours.loc[tours[HAS_OB_STOPS] | tours[HAS_IB_STOPS]] + + # Crudely calculate a chunk size + # 5=number of columns in the alternatives (3 leg times + index) + tour_row_size = (4 + len(tours.columns)) * indirect_tours[NUM_ALTERNATIVES].mean() + + # rpc, effective_chunk_size = chunk.rows_per_chunk(chunk_size, tour_row_size, indirect_tours.shape[0], trace_label) + row_size = chunk_size and trip_schedule_calc_row_size(indirect_tours, trace_label) + # Iterate through the chunks + result_list = [] + for i, choosers, chunk_trace_label in \ + chunk.adaptive_chunked_choosers(indirect_tours, chunk_size, row_size, trace_label): + + # Sort the choosers and get the schedule alternatives + choosers = choosers.sort_index() + schedules = generate_schedule_alternatives(choosers).sort_index() + + # Assuming we did the max_alt_size calculation correctly, + # we should get the same sizes here. + assert choosers[NUM_ALTERNATIVES].sum() == schedules.shape[0] + + # Run the simulation + choices = _interaction_sample_simulate( + choosers=choosers, + alternatives=schedules, + spec=spec, + choice_column=SCHEDULE_ID, + allow_zero_probs=True, zero_prob_choice_val=-999, + want_logsums=False, + skims=skims, + locals_d=locals_dict, + trace_label=chunk_trace_label, + trace_choice_name='trip_schedule_stage_1', + estimator=None + ) + + assert len(choices.index) == len(choosers.index) + + choices = schedules[schedules[SCHEDULE_ID].isin(choices)].drop(columns='tour_id') + + result_list.append(choices) + + force_garbage_collect() + + # FIXME: this will require 2X RAM + # if necessary, could append to hdf5 store on disk: + # http://pandas.pydata.org/pandas-docs/stable/io.html#id2 + if len(result_list) > 1: + choices = pd.concat(result_list) + + assert len(choices.index) == len(indirect_tours.index) + + # The choices here are only the indirect tours, so the durations + # need to be updated on the main tour dataframe. + tours.update(choices[[MAIN_LEG_DURATION, OB_DURATION, IB_DURATION]]) + + # Cleanup data types and drop temporary columns + tours[[MAIN_LEG_DURATION, OB_DURATION, IB_DURATION]] = \ + tours[[MAIN_LEG_DURATION, OB_DURATION, IB_DURATION]].astype(np.int8) + tours = tours.drop(columns=TEMP_COLS) + + return tours + + +@inject.step() +def trip_scheduling_choice( + trips, + tours, + skim_dict, + chunk_size, + trace_hh_id): + + trace_label = 'trip_scheduling_choice' + model_settings = config.read_model_settings('trip_scheduling_choice.yaml') + spec = get_spec_for_segment(model_settings, 'SPECIFICATION', 'stage_one') + + trips_df = trips.to_frame() + tours_df = tours.to_frame() + + outbound_trips = trips_df[trips_df[OUTBOUND_FLAG]] + inbound_trips = trips_df[~trips_df[OUTBOUND_FLAG]] + + last_outbound_trip = trips_df.loc[outbound_trips.groupby('tour_id')['trip_num'].idxmax()] + first_inbound_trip = trips_df.loc[inbound_trips.groupby('tour_id')['trip_num'].idxmin()] + + tours_df[NUM_OB_STOPS] = outbound_trips.groupby('tour_id').size().reindex(tours.index) - 1 + tours_df[NUM_IB_STOPS] = inbound_trips.groupby('tour_id').size().reindex(tours.index) - 1 + tours_df[LAST_OB_STOP] = last_outbound_trip[['tour_id', 'origin']].set_index('tour_id').reindex(tours.index) + tours_df[FIRST_IB_STOP] = first_inbound_trip[['tour_id', 'destination']].set_index('tour_id').reindex(tours.index) + + preprocessor_settings = model_settings.get('PREPROCESSOR', None) + + if preprocessor_settings: + # hack: preprocessor adds origin column in place if it does not exist already + od_skim_stack_wrapper = skim_dict.wrap('origin', 'destination') + do_skim_stack_wrapper = skim_dict.wrap('destination', 'origin') + obib_skim_stack_wrapper = skim_dict.wrap(LAST_OB_STOP, FIRST_IB_STOP) + + skims = [od_skim_stack_wrapper, do_skim_stack_wrapper, obib_skim_stack_wrapper] + + locals_dict = { + "od_skims": od_skim_stack_wrapper, + "do_skims": do_skim_stack_wrapper, + "obib_skims": obib_skim_stack_wrapper + } + + simulate.set_skim_wrapper_targets(tours_df, skims) + + expressions.assign_columns( + df=tours_df, + model_settings=preprocessor_settings, + locals_dict=locals_dict, + trace_label=trace_label) + + tours_df = run_trip_scheduling_choice(spec, tours_df, skims, locals_dict, chunk_size, trace_hh_id, trace_label) + + pipeline.replace_table("tours", tours_df) diff --git a/activitysim/abm/models/util/cdap.py b/activitysim/abm/models/util/cdap.py index c783c53e5f..c130242d89 100644 --- a/activitysim/abm/models/util/cdap.py +++ b/activitysim/abm/models/util/cdap.py @@ -35,9 +35,6 @@ MAX_INTERACTION_CARDINALITY = 3 -WORKER_PTYPES = [1, 2] -CHILD_PTYPES = [6, 7, 8] - def set_hh_index(df): @@ -61,7 +58,7 @@ def add_pn(col, pnum): raise RuntimeError("add_pn col not list or str") -def assign_cdap_rank(persons, trace_hh_id=None, trace_label=None): +def assign_cdap_rank(persons, person_type_map, trace_hh_id=None, trace_label=None): """ Assign an integer index, cdap_rank, to each household member. (Starting with 1, not 0) @@ -109,7 +106,7 @@ def assign_cdap_rank(persons, trace_hh_id=None, trace_label=None): # choose up to 2 workers, preferring full over part, older over younger workers = \ - persons.loc[persons[_ptype_].isin(WORKER_PTYPES), [_hh_id_, _ptype_]]\ + persons.loc[persons[_ptype_].isin(person_type_map['WORKER']), [_hh_id_, _ptype_]]\ .sort_values(by=[_hh_id_, _ptype_], ascending=[True, True])\ .groupby(_hh_id_).head(2) # tag the selected workers @@ -118,7 +115,7 @@ def assign_cdap_rank(persons, trace_hh_id=None, trace_label=None): # choose up to 3, preferring youngest children = \ - persons.loc[persons[_ptype_].isin(CHILD_PTYPES), [_hh_id_, _ptype_, _age_]]\ + persons.loc[persons[_ptype_].isin(person_type_map['CHILD']), [_hh_id_, _ptype_, _age_]]\ .sort_values(by=[_hh_id_, _ptype_], ascending=[True, True])\ .groupby(_hh_id_).head(3) # tag the selected children @@ -795,6 +792,7 @@ def extra_hh_member_choices(persons, cdap_fixed_relative_proportions, locals_d, def _run_cdap( persons, + person_type_map, cdap_indiv_spec, interaction_coefficients, cdap_fixed_relative_proportions, @@ -815,7 +813,8 @@ def _run_cdap( # assign integer cdap_rank to each household member # persons with cdap_rank 1..MAX_HHSIZE will be have their activities chose by CDAP model # extra household members, will have activities assigned by in fixed proportions - assign_cdap_rank(persons, trace_hh_id, trace_label) + assign_cdap_rank(persons, person_type_map, trace_hh_id, trace_label) + chunk.log_df(trace_label, 'persons', persons) # Calculate CDAP utilities for each individual, ignoring interactions # ind_utils has index of 'person_id' and a column for each alternative @@ -823,6 +822,7 @@ def _run_cdap( indiv_utils = individual_utilities(persons[persons.cdap_rank <= MAX_HHSIZE], cdap_indiv_spec, locals_d, trace_hh_id, trace_label) + chunk.log_df(trace_label, 'indiv_utils', indiv_utils) # compute interaction utilities, probabilities, and hh activity pattern choices # for each size household separately in turn up to MAX_HHSIZE @@ -836,9 +836,11 @@ def _run_cdap( hh_choices_list.append(choices) del indiv_utils + chunk.log_df(trace_label, 'indiv_utils', None) # concat all the household choices into a single series indexed on _hh_index_ hh_activity_choices = pd.concat(hh_choices_list) + chunk.log_df(trace_label, 'hh_activity_choices', hh_activity_choices) # unpack the household activity choice list into choices for each (non-extra) household member # resulting series contains one activity per individual hh member, indexed on _persons_index_ @@ -858,6 +860,7 @@ def _run_cdap( person_choices = pd.concat([cdap_person_choices, extra_person_choices]) persons['cdap_activity'] = person_choices + chunk.log_df(trace_label, 'persons', persons) # if DUMP: # tracing.trace_df(hh_activity_choices, '%s.DUMP.hh_activity_choices' % trace_label, @@ -865,33 +868,41 @@ def _run_cdap( # tracing.trace_df(cdap_results, '%s.DUMP.cdap_results' % trace_label, # transpose=False, slicer='NONE') - chunk.log_df(trace_label, 'persons', persons) + result = persons[['cdap_rank', 'cdap_activity']] + + del persons + chunk.log_df(trace_label, 'persons', None) + + return result - return persons[['cdap_rank', 'cdap_activity']] +def cdap_calc_row_size(choosers, cdap_indiv_spec, trace_label): -def calc_rows_per_chunk(chunk_size, choosers, trace_label=None): + sizer = chunk.RowSizeEstimator(trace_label) # NOTE we chunk chunk_id num_choosers = choosers['chunk_id'].max() + 1 + rows_per_chunk_id = len(choosers) / num_choosers - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + chooser_row_size = len(choosers.columns) - chooser_row_size = choosers.shape[1] + sizer.add_elements(chooser_row_size, 'persons') + sizer.add_elements(len(cdap_indiv_spec), 'indiv_utils') + sizer.add_elements(1, 'hh_activity_choices') + sizer.add_elements(1, 'cdap_rank') + sizer.add_elements(1, 'cdap_activity') - # scale row_size by average number of chooser rows per chunk_id - rows_per_chunk_id = choosers.shape[0] / float(num_choosers) - row_size = int(rows_per_chunk_id * chooser_row_size) + row_size = sizer.get_hwm() - # logger.debug("%s #chunk_calc choosers %s" % (trace_label, choosers.shape)) + # scale row_size by average number of chooser rows per chunk_id + row_size = row_size * rows_per_chunk_id - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + return row_size def run_cdap( persons, + person_type_map, cdap_indiv_spec, cdap_interaction_coefficients, cdap_fixed_relative_proportions, @@ -935,29 +946,22 @@ def run_cdap( trace_label = tracing.extend_trace_label(trace_label, 'cdap') - rows_per_chunk, effective_chunk_size = \ - calc_rows_per_chunk(chunk_size, persons, trace_label=trace_label) + row_size = chunk_size and cdap_calc_row_size(persons, cdap_indiv_spec, trace_label) result_list = [] # segment by person type and pick the right spec for each person type - for i, num_chunks, persons_chunk in chunk.chunked_choosers_by_chunk_id(persons, rows_per_chunk): - - logger.info("Running chunk %s of %s with %d persons" % (i, num_chunks, len(persons_chunk))) - - chunk_trace_label = tracing.extend_trace_label(trace_label, 'chunk_%s' % i) - - chunk.log_open(chunk_trace_label, chunk_size, effective_chunk_size) + for i, persons_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers_by_chunk_id(persons, chunk_size, row_size, trace_label): cdap_results = \ _run_cdap(persons_chunk, + person_type_map, cdap_indiv_spec, cdap_interaction_coefficients, cdap_fixed_relative_proportions, locals_d, trace_hh_id, chunk_trace_label) - chunk.log_close(chunk_trace_label) - result_list.append(cdap_results) # FIXME: this will require 2X RAM diff --git a/activitysim/abm/models/util/logsums.py b/activitysim/abm/models/util/logsums.py index ecdc01ff06..cb28aaa960 100644 --- a/activitysim/abm/models/util/logsums.py +++ b/activitysim/abm/models/util/logsums.py @@ -5,12 +5,10 @@ from activitysim.core import simulate from activitysim.core import tracing from activitysim.core import config +from activitysim.core import los +from activitysim.core import expressions -from activitysim.core.assign import evaluate_constants - - -from . import expressions - +from activitysim.core.pathbuilder import TransitVirtualPathBuilder logger = logging.getLogger(__name__) @@ -19,7 +17,7 @@ def filter_chooser_columns(choosers, logsum_settings, model_settings): chooser_columns = logsum_settings.get('LOGSUM_CHOOSER_COLUMNS', []) - if 'CHOOSER_ORIG_COL_NAME' in model_settings: + if 'CHOOSER_ORIG_COL_NAME' in model_settings and model_settings['CHOOSER_ORIG_COL_NAME'] not in chooser_columns: chooser_columns.append(model_settings['CHOOSER_ORIG_COL_NAME']) missing_columns = [c for c in chooser_columns if c not in choosers] @@ -36,7 +34,7 @@ def filter_chooser_columns(choosers, logsum_settings, model_settings): def compute_logsums(choosers, tour_purpose, logsum_settings, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_label): """ @@ -46,8 +44,7 @@ def compute_logsums(choosers, tour_purpose logsum_settings model_settings - skim_dict - skim_stack + network_los chunk_size trace_hh_id trace_label @@ -59,6 +56,7 @@ def compute_logsums(choosers, """ trace_label = tracing.extend_trace_label(trace_label, 'compute_logsums') + logger.debug("Running compute_logsums with %d choosers" % choosers.shape[0]) # compute_logsums needs to know name of dest column in interaction_sample orig_col_name = model_settings['CHOOSER_ORIG_COL_NAME'] @@ -66,32 +64,37 @@ def compute_logsums(choosers, # FIXME - are we ok with altering choosers (so caller doesn't have to set these)? assert ('in_period' not in choosers) and ('out_period' not in choosers) - choosers['in_period'] = expressions.skim_time_period_label(model_settings['IN_PERIOD']) - choosers['out_period'] = expressions.skim_time_period_label(model_settings['OUT_PERIOD']) + choosers['in_period'] = network_los.skim_time_period_label(model_settings['IN_PERIOD']) + choosers['out_period'] = network_los.skim_time_period_label(model_settings['OUT_PERIOD']) assert ('duration' not in choosers) choosers['duration'] = model_settings['IN_PERIOD'] - model_settings['OUT_PERIOD'] logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC']) coefficients = simulate.get_segment_coefficients(logsum_settings, tour_purpose) + logsum_spec = simulate.eval_coefficients(logsum_spec, coefficients, estimator=None) nest_spec = config.get_logit_model_settings(logsum_settings) nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients) - constants = config.get_model_constants(logsum_settings) - - logger.debug("Running compute_logsums with %d choosers" % choosers.shape[0]) + locals_dict = {} + # model_constants can appear in expressions + locals_dict.update(config.get_model_constants(logsum_settings)) + # constrained coefficients can appear in expressions + locals_dict.update(coefficients) # setup skim keys - odt_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name, - skim_key='out_period') - dot_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name, - skim_key='in_period') - odr_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name, - skim_key='in_period') - dor_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name, - skim_key='out_period') + skim_dict = network_los.get_default_skim_dict() + + odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='out_period') + dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='in_period') + odr_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='in_period') + dor_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='out_period') od_skim_stack_wrapper = skim_dict.wrap(orig_col_name, dest_col_name) skims = { @@ -104,12 +107,26 @@ def compute_logsums(choosers, 'dest_col_name': dest_col_name } - locals_dict = {} - locals_dict.update(constants) - locals_dict.update(skims) + if network_los.zone_system == los.THREE_ZONE: + # fixme - is this a lightweight object? + tvpb = network_los.tvpb - # constrained coefficients can appear in expressions - locals_dict.update(coefficients) + tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=orig_col_name, dest_key=dest_col_name, + tod_key='out_period', segment_key='demographic_segment', + trace_label=trace_label, tag='tvpb_logsum_odt') + tvpb_logsum_dot = tvpb.wrap_logsum(orig_key=dest_col_name, dest_key=orig_col_name, + tod_key='in_period', segment_key='demographic_segment', + trace_label=trace_label, tag='tvpb_logsum_dot') + + skims.update({ + 'tvpb_logsum_odt': tvpb_logsum_odt, + 'tvpb_logsum_dot': tvpb_logsum_dot + }) + + # TVPB constants can appear in expressions + locals_dict.update(network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS')) + + locals_dict.update(skims) # - run preprocessor to annotate choosers # allow specification of alternate preprocessor for nontour choosers @@ -133,7 +150,6 @@ def compute_logsums(choosers, skims=skims, locals_d=locals_dict, chunk_size=chunk_size, - trace_label=trace_label, - alt_col_name=dest_col_name) + trace_label=trace_label) return logsums diff --git a/activitysim/abm/models/util/mode.py b/activitysim/abm/models/util/mode.py index a89a7b9762..dbb3aed2f4 100644 --- a/activitysim/abm/models/util/mode.py +++ b/activitysim/abm/models/util/mode.py @@ -4,10 +4,8 @@ from activitysim.core import simulate from activitysim.core import config - -from . import expressions -from . import estimation - +from activitysim.core import expressions +from activitysim.core import tracing """ At this time, these utilities are mostly for transforming the mode choice @@ -23,8 +21,29 @@ def mode_choice_simulate( logsum_column_name, trace_label, trace_choice_name, + trace_column_names=None, estimator=None): + """ + common method for both tour_mode_choice and trip_mode_choice + + Parameters + ---------- + choosers + spec + nest_spec + skims + locals_d + chunk_size + mode_column_name + logsum_column_name + trace_label + trace_choice_name + estimator + + Returns + ------- + """ want_logsums = logsum_column_name is not None choices = simulate.simple_simulate( @@ -37,7 +56,8 @@ def mode_choice_simulate( want_logsums=want_logsums, trace_label=trace_label, trace_choice_name=trace_choice_name, - estimator=estimator) + estimator=estimator, + trace_column_names=trace_column_names) # for consistency, always return dataframe, whether or not logsums were requested if isinstance(choices, pd.Series): @@ -59,6 +79,7 @@ def run_tour_mode_choice_simulate( tour_purpose, model_settings, mode_column_name, logsum_column_name, + network_los, skims, constants, estimator, @@ -88,13 +109,18 @@ def run_tour_mode_choice_simulate( assert ('in_period' not in choosers) and ('out_period' not in choosers) in_time = skims['in_time_col_name'] out_time = skims['out_time_col_name'] - choosers['in_period'] = expressions.skim_time_period_label(choosers[in_time]) - choosers['out_period'] = expressions.skim_time_period_label(choosers[out_time]) + choosers['in_period'] = network_los.skim_time_period_label(choosers[in_time]) + choosers['out_period'] = network_los.skim_time_period_label(choosers[out_time]) expressions.annotate_preprocessors( choosers, locals_dict, skims, model_settings, trace_label) + trace_column_names = choosers.index.name + assert trace_column_names == 'tour_id' + if trace_column_names not in choosers: + choosers[trace_column_names] = choosers.index + if estimator: # write choosers after annotation estimator.write_choosers(choosers) @@ -110,6 +136,7 @@ def run_tour_mode_choice_simulate( logsum_column_name=logsum_column_name, trace_label=trace_label, trace_choice_name=trace_choice_name, + trace_column_names=trace_column_names, estimator=estimator) return choices diff --git a/activitysim/abm/models/util/test/configs/cdap.yaml b/activitysim/abm/models/util/test/configs/cdap.yaml new file mode 100644 index 0000000000..f20d2979a4 --- /dev/null +++ b/activitysim/abm/models/util/test/configs/cdap.yaml @@ -0,0 +1,8 @@ +PERSON_TYPE_MAP: + WORKER: + - 1 + - 2 + CHILD: + - 6 + - 7 + - 8 diff --git a/activitysim/abm/models/util/test/test_cdap.py b/activitysim/abm/models/util/test/test_cdap.py index 12bda06ffc..4f90973bfb 100644 --- a/activitysim/abm/models/util/test/test_cdap.py +++ b/activitysim/abm/models/util/test/test_cdap.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os.path +import yaml import pandas as pd import pandas.testing as pdt @@ -10,6 +11,8 @@ from .. import cdap from activitysim.core import simulate +from activitysim.core import inject +from activitysim.core import config @pytest.fixture(scope='module') @@ -17,11 +20,6 @@ def data_dir(): return os.path.join(os.path.dirname(__file__), 'data') -@pytest.fixture(scope='module') -def configs_dir(): - return os.path.join(os.path.dirname(__file__), 'configs') - - @pytest.fixture(scope='module') def people(data_dir): return pd.read_csv( @@ -29,29 +27,33 @@ def people(data_dir): index_col='id') -@pytest.fixture(scope='module') -def cdap_indiv_and_hhsize1(configs_dir): - return simulate.read_model_spec(file_name='cdap_indiv_and_hhsize1.csv', spec_dir=configs_dir) +def teardown_function(func): + inject.clear_cache() + inject.reinject_decorated_tables() @pytest.fixture(scope='module') -def cdap_interaction_coefficients(configs_dir): - f = os.path.join(configs_dir, 'cdap_interaction_coefficients.csv') - coefficients = pd.read_csv(f, comment='#') - coefficients = cdap.preprocess_interaction_coefficients(coefficients) - return coefficients +def model_settings(configs_dir): + yml_file = os.path.join(configs_dir, 'cdap.yaml') + with open(yml_file) as f: + model_settings = yaml.load(f, Loader=yaml.loader.SafeLoader) + return model_settings @pytest.fixture(scope='module') -def individual_utils( - people, cdap_indiv_and_hhsize1): - return cdap.individual_utilities(people, cdap_indiv_and_hhsize1, locals_d=None) +def configs_dir(): + return os.path.join(os.path.dirname(__file__), 'configs') + +def setup_function(): + configs_dir = os.path.join(os.path.dirname(__file__), 'configs') + inject.add_injectable("configs_dir", configs_dir) -def test_bad_coefficients(configs_dir): - f = os.path.join(configs_dir, 'cdap_interaction_coefficients.csv') - coefficients = pd.read_csv(f, comment='#') +def test_bad_coefficients(): + + coefficients = pd.read_csv(config.config_file_path('cdap_interaction_coefficients.csv'), comment='#') + coefficients = cdap.preprocess_interaction_coefficients(coefficients) coefficients.loc[2, 'activity'] = 'AA' @@ -60,9 +62,11 @@ def test_bad_coefficients(configs_dir): assert "Expect only M, N, or H" in str(excinfo.value) -def test_assign_cdap_rank(people): +def test_assign_cdap_rank(people, model_settings): - cdap.assign_cdap_rank(people) + person_type_map = model_settings.get('PERSON_TYPE_MAP', {}) + + cdap.assign_cdap_rank(people, person_type_map) expected = pd.Series( [1, 1, 1, 2, 2, 1, 3, 1, 2, 1, 3, 2, 1, 3, 2, 4, 1, 3, 4, 2], @@ -72,10 +76,14 @@ def test_assign_cdap_rank(people): pdt.assert_series_equal(people['cdap_rank'], expected, check_dtype=False, check_names=False) -def test_individual_utilities(people, cdap_indiv_and_hhsize1): +def test_individual_utilities(people, model_settings): + + cdap_indiv_and_hhsize1 = simulate.read_model_spec(file_name='cdap_indiv_and_hhsize1.csv') - cdap.assign_cdap_rank(people) + person_type_map = model_settings.get('PERSON_TYPE_MAP', {}) + cdap.assign_cdap_rank(people, person_type_map) individual_utils = cdap.individual_utilities(people, cdap_indiv_and_hhsize1, locals_d=None) + individual_utils = individual_utils[['M', 'N', 'H']] expected = pd.DataFrame([ @@ -105,16 +113,21 @@ def test_individual_utilities(people, cdap_indiv_and_hhsize1): individual_utils, expected, check_dtype=False, check_names=False) -def test_build_cdap_spec_hhsize2(people, cdap_indiv_and_hhsize1, cdap_interaction_coefficients): +def test_build_cdap_spec_hhsize2(people, model_settings): hhsize = 2 + cdap_indiv_and_hhsize1 = simulate.read_model_spec(file_name='cdap_indiv_and_hhsize1.csv') + + interaction_coefficients = pd.read_csv(config.config_file_path('cdap_interaction_coefficients.csv'), comment='#') + interaction_coefficients = cdap.preprocess_interaction_coefficients(interaction_coefficients) - cdap.assign_cdap_rank(people) + person_type_map = model_settings.get('PERSON_TYPE_MAP', {}) + cdap.assign_cdap_rank(people, person_type_map) indiv_utils = cdap.individual_utilities(people, cdap_indiv_and_hhsize1, locals_d=None) choosers = cdap.hh_choosers(indiv_utils, hhsize=hhsize) - spec = cdap.build_cdap_spec(cdap_interaction_coefficients, hhsize=hhsize, cache=False) + spec = cdap.build_cdap_spec(interaction_coefficients, hhsize=hhsize, cache=False) vars = simulate.eval_variables(spec.index, choosers) diff --git a/activitysim/abm/models/util/test/test_mandatory_tour_frequency.py b/activitysim/abm/models/util/test/test_mandatory_tour_frequency.py index 8fbe70470a..74aba531bc 100644 --- a/activitysim/abm/models/util/test/test_mandatory_tour_frequency.py +++ b/activitysim/abm/models/util/test/test_mandatory_tour_frequency.py @@ -21,9 +21,9 @@ def test_mtf(): persons = pd.DataFrame({ "is_worker": [True, True, False, False], "mandatory_tour_frequency": ["work1", "work_and_school", "work_and_school", "school2"], - "school_taz": [1, 2, 3, 4], - "workplace_taz": [10, 20, 30, 40], - "home_taz": [100, 200, 300, 400], + "school_zone_id": [1, 2, 3, 4], + "workplace_zone_id": [10, 20, 30, 40], + "home_zone_id": [100, 200, 300, 400], "household_id": [1, 2, 2, 4] }, index=[10, 20, 30, 40]) diff --git a/activitysim/abm/models/util/test/test_non_mandatory_tour_frequency.py b/activitysim/abm/models/util/test/test_non_mandatory_tour_frequency.py index 7f5702b776..a3574b6744 100644 --- a/activitysim/abm/models/util/test/test_non_mandatory_tour_frequency.py +++ b/activitysim/abm/models/util/test/test_non_mandatory_tour_frequency.py @@ -15,7 +15,7 @@ def test_nmtf(): { 'non_mandatory_tour_frequency': [0, 3, 2, 1], 'household_id': [1, 1, 2, 4], - 'home_taz': [100, 100, 200, 400] + 'home_zone_id': [100, 100, 200, 400] }, index=[0, 1, 2, 3] ) diff --git a/activitysim/abm/models/util/tour_destination.py b/activitysim/abm/models/util/tour_destination.py index ba686c8597..bb7a57a3be 100644 --- a/activitysim/abm/models/util/tour_destination.py +++ b/activitysim/abm/models/util/tour_destination.py @@ -62,7 +62,7 @@ def run_destination_sample( tours, persons_merged, model_settings, - skim_dict, + network_los, destination_size_terms, estimator, chunk_size, trace_label): @@ -86,14 +86,15 @@ def run_destination_sample( logger.info("Estimation mode for %s using unsampled alternatives short_circuit_choices" % (trace_label,)) sample_size = 0 - # create wrapper with keys for this lookup - in this case there is a workplace_taz - # in the choosers and a TAZ in the alternatives which get merged during interaction + # create wrapper with keys for this lookup - in this case there is a workplace_zone_id + # in the choosers and a zone_id in the alternatives which get merged during interaction # (logit.interaction_dataset suffixes duplicate chooser column with '_chooser') # the skims will be available under the name "skims" for any @ expressions origin_col_name = model_settings['CHOOSER_ORIG_COL_NAME'] - if origin_col_name == 'TAZ': - origin_col_name = 'TAZ_chooser' - skims = skim_dict.wrap(origin_col_name, 'TAZ') + dest_col_name = 'zone_id' + + skim_dict = network_los.get_default_skim_dict() + skims = skim_dict.wrap(origin_col_name, dest_col_name) locals_d = { 'skims': skims @@ -126,12 +127,12 @@ def run_destination_logsums( persons_merged, destination_sample, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id, trace_label): """ add logsum column to existing tour_destination_sample table - logsum is calculated by running the mode_choice model for each sample (person, dest_taz) pair + logsum is calculated by running the mode_choice model for each sample (person, dest_zone_id) pair in destination_sample, and computing the logsum of all the utilities """ @@ -156,7 +157,7 @@ def run_destination_logsums( choosers, tour_purpose, logsum_settings, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_label) @@ -172,7 +173,7 @@ def run_destination_simulate( destination_sample, want_logsums, model_settings, - skim_dict, + network_los, destination_size_terms, estimator, chunk_size, trace_label): @@ -208,9 +209,10 @@ def run_destination_simulate( logger.info("Running tour_destination_simulate with %d persons", len(choosers)) - # create wrapper with keys for this lookup - in this case there is a TAZ in the choosers - # and a TAZ in the alternatives which get merged during interaction + # create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers + # and a zone_id in the alternatives which get merged during interaction # the skims will be available under the name "skims" for any @ expressions + skim_dict = network_los.get_default_skim_dict() skims = skim_dict.wrap(origin_col_name, alt_dest_col_name) locals_d = { @@ -248,8 +250,7 @@ def run_tour_destination( want_logsums, want_sample_table, model_settings, - skim_dict, - skim_stack, + network_los, estimator, chunk_size, trace_hh_id, trace_label): @@ -284,7 +285,7 @@ def run_tour_destination( choosers, persons_merged, model_settings, - skim_dict, + network_los, segment_destination_size_terms, estimator, chunk_size=chunk_size, @@ -298,7 +299,7 @@ def run_tour_destination( persons_merged, location_sample_df, model_settings, - skim_dict, skim_stack, + network_los, chunk_size=chunk_size, trace_hh_id=trace_hh_id, trace_label=tracing.extend_trace_label(trace_label, 'logsums.%s' % segment_name)) @@ -313,7 +314,7 @@ def run_tour_destination( destination_sample=location_sample_df, want_logsums=want_logsums, model_settings=model_settings, - skim_dict=skim_dict, + network_los=network_los, destination_size_terms=segment_destination_size_terms, estimator=estimator, chunk_size=chunk_size, diff --git a/activitysim/abm/models/util/tour_frequency.py b/activitysim/abm/models/util/tour_frequency.py index 25cb3b8cdb..5de8a12145 100644 --- a/activitysim/abm/models/util/tour_frequency.py +++ b/activitysim/abm/models/util/tour_frequency.py @@ -6,7 +6,6 @@ import pandas as pd from activitysim.core.util import reindex -from activitysim.abm.tables import constants logger = logging.getLogger(__name__) @@ -314,7 +313,7 @@ def process_mandatory_tours(persons, mandatory_tour_frequency_alts): """ person_columns = ['mandatory_tour_frequency', 'is_worker', - 'school_taz', 'workplace_taz', 'home_taz', 'household_id'] + 'school_zone_id', 'workplace_zone_id', 'home_zone_id', 'household_id'] assert not persons.mandatory_tour_frequency.isnull().any() tours = process_tours(persons.mandatory_tour_frequency.dropna(), @@ -332,12 +331,11 @@ def process_mandatory_tours(persons, mandatory_tour_frequency_alts): tours.tour_num = tours.tour_num.where(~work_and_school_and_student, 3 - tours.tour_num) - # work tours destination is workplace_taz, school tours destination is school_taz + # work tours destination is workplace_zone_id, school tours destination is school_zone_id tours['destination'] = \ - tours_merged.workplace_taz.where((tours_merged.tour_type == 'work'), - tours_merged.school_taz) + tours_merged.workplace_zone_id.where((tours_merged.tour_type == 'work'), tours_merged.school_zone_id) - tours['origin'] = tours_merged.home_taz + tours['origin'] = tours_merged.home_zone_id tours['household_id'] = tours_merged.household_id @@ -392,7 +390,7 @@ def process_non_mandatory_tours(persons, tour_counts): tours = create_tours(tour_counts, tour_category='non_mandatory') tours['household_id'] = reindex(persons.household_id, tours.person_id) - tours['origin'] = reindex(persons.home_taz, tours.person_id) + tours['origin'] = reindex(persons.home_zone_id, tours.person_id) # assign stable (predictable) tour_id set_tour_index(tours) @@ -514,7 +512,7 @@ def process_joint_tours(joint_tour_frequency, joint_tour_frequency_alts, point_p A DataFrame which has as a unique index with joint_tour_frequency values and frequency counts for the tours to be generated for that choice point_persons : pandas DataFrame - table with columns for (at least) person_ids and home_taz indexed by household_id + table with columns for (at least) person_ids and home_zone_id indexed by household_id Returns ------- @@ -538,7 +536,7 @@ def process_joint_tours(joint_tour_frequency, joint_tour_frequency_alts, point_p # - assign a temp point person to tour so we can create stable index tours['person_id'] = reindex(point_persons.person_id, tours.household_id) - tours['origin'] = reindex(point_persons.home_taz, tours.household_id) + tours['origin'] = reindex(point_persons.home_zone_id, tours.household_id) # assign stable (predictable) tour_id set_tour_index(tours, is_joint=True) diff --git a/activitysim/abm/models/util/trip.py b/activitysim/abm/models/util/trip.py index 1208a1c300..f7f97151ef 100644 --- a/activitysim/abm/models/util/trip.py +++ b/activitysim/abm/models/util/trip.py @@ -2,6 +2,8 @@ # See full license in LICENSE.txt. import logging +import numpy as np + from activitysim.core.util import assign_in_place @@ -76,3 +78,47 @@ def cleanup_failed_trips(trips): del trips['failed'] return trips + + +def generate_alternative_sizes(max_duration, max_trips): + """ + Builds a lookup Numpy array pattern sizes based on the + number of trips in the leg and the duration available + to the leg. + :param max_duration: + :param max_trips: + :return: + """ + def np_shift(xs, n, fill_zero=True): + if n >= 0: + shift_array = np.concatenate((np.full(n, np.nan), xs[:-n])) + else: + shift_array = np.concatenate((xs[-n:], np.full(-n, np.nan))) + return np.nan_to_num(shift_array, np.nan).astype(np.int) if fill_zero else shift_array + + levels = np.empty([max_trips, max_duration + max_trips]) + levels[0] = np.arange(1, max_duration + max_trips + 1) + + for level in np.arange(1, max_trips): + levels[level] = np_shift(np.cumsum(np_shift(levels[level - 1], 1)), -1, fill_zero=False) + + return levels[:, :max_duration+1].astype(int) + + +def get_time_windows(residual, level): + """ + + :param residual: + :param level: + :return: + """ + ranges = [] + + for a in np.arange(residual + 1): + if level > 1: + windows = get_time_windows(residual - a, level - 1) + width_dim = len(windows.shape) - 1 + ranges.append(np.vstack([np.repeat(a, windows.shape[width_dim]), windows])) + else: + return np.arange(residual + 1) + return np.concatenate(ranges, axis=1) diff --git a/activitysim/abm/models/util/vectorize_tour_scheduling.py b/activitysim/abm/models/util/vectorize_tour_scheduling.py index bd0eee021e..d8056d6026 100644 --- a/activitysim/abm/models/util/vectorize_tour_scheduling.py +++ b/activitysim/abm/models/util/vectorize_tour_scheduling.py @@ -13,50 +13,42 @@ from activitysim.core import chunk from activitysim.core import simulate -from activitysim.core import assign from activitysim.core import logit +from activitysim.core import los from activitysim.core import timetable as tt from activitysim.core.util import reindex +from activitysim.core import expressions + +from activitysim.core.pathbuilder import TransitVirtualPathBuilder -from . import expressions -from . import mode logger = logging.getLogger(__name__) TDD_CHOICE_COLUMN = 'tdd' +USE_BRUTE_FORCE_TO_COMPUTE_LOGSUMS = False -def _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_label): - """ - compute logsums for tours using skims for alt_tdd out_period and in_period - """ - - trace_label = tracing.extend_trace_label(trace_label, 'logsums') - - logsum_settings = config.read_model_settings(model_settings['LOGSUM_SETTINGS']) +def skims_for_logsums(tour_purpose, model_settings, trace_label): - choosers = alt_tdd.join(tours_merged, how='left', rsuffix='_chooser') - logger.info("%s compute_logsums for %d choosers%s alts" % - (trace_label, choosers.shape[0], alt_tdd.shape[0])) + assert 'LOGSUM_SETTINGS' in model_settings - # - setup skims + network_los = inject.get_injectable('network_los') - skim_dict = inject.get_injectable('skim_dict') - skim_stack = inject.get_injectable('skim_stack') + skim_dict = network_los.get_default_skim_dict() - orig_col_name = 'TAZ' + orig_col_name = 'home_zone_id' dest_col_name = model_settings.get('DESTINATION_FOR_TOUR_PURPOSE').get(tour_purpose) - odt_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name, - skim_key='out_period') - dot_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name, - skim_key='in_period') - odr_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name, - skim_key='in_period') - dor_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name, - skim_key='out_period') + odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='out_period') + dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='in_period') + odr_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='in_period') + dor_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='out_period') od_skim_stack_wrapper = skim_dict.wrap(orig_col_name, dest_col_name) skims = { @@ -69,13 +61,53 @@ def _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_ 'dest_col_name': dest_col_name, } + if network_los.zone_system == los.THREE_ZONE: + # fixme - is this a lightweight object? + tvpb = network_los.tvpb + + tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=orig_col_name, dest_key=dest_col_name, + tod_key='out_period', segment_key='demographic_segment', + trace_label=trace_label, tag='tvpb_logsum_odt') + tvpb_logsum_dot = tvpb.wrap_logsum(orig_key=dest_col_name, dest_key=orig_col_name, + tod_key='in_period', segment_key='demographic_segment', + trace_label=trace_label, tag='tvpb_logsum_dot') + + skims.update({ + 'tvpb_logsum_odt': tvpb_logsum_odt, + 'tvpb_logsum_dot': tvpb_logsum_dot + }) + + return skims + + +def _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, network_los, skims, trace_label): + """ + compute logsums for tours using skims for alt_tdd out_period and in_period + """ + + trace_label = tracing.extend_trace_label(trace_label, 'logsums') + + logsum_settings = config.read_model_settings(model_settings['LOGSUM_SETTINGS']) + + choosers = alt_tdd.join(tours_merged, how='left', rsuffix='_chooser') + logger.info("%s compute_logsums for %d choosers%s alts" % + (trace_label, choosers.shape[0], alt_tdd.shape[0])) + # - locals_dict constants = config.get_model_constants(logsum_settings) - locals_dict = {} locals_dict.update(constants) + + if network_los.zone_system == los.THREE_ZONE: + # TVPB constants can appear in expressions + locals_dict.update(network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS')) + locals_dict.update(skims) + # constrained coefficients can appear in expressions + coefficients = simulate.get_segment_coefficients(logsum_settings, tour_purpose) + locals_dict.update(coefficients) + # - run preprocessor to annotate choosers # allow specification of alternate preprocessor for nontour choosers preprocessor = model_settings.get('LOGSUM_PREPROCESSOR', 'preprocessor') @@ -92,17 +124,12 @@ def _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_ trace_label=trace_label) # - compute logsums - - coefficients = simulate.get_segment_coefficients(logsum_settings, tour_purpose) logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC']) logsum_spec = simulate.eval_coefficients(logsum_spec, coefficients, estimator=None) nest_spec = config.get_logit_model_settings(logsum_settings) nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients) - # constrained coefficients can appear in expressions - locals_dict.update(coefficients) - logsums = simulate.simple_simulate_logsums( choosers, logsum_spec, @@ -115,7 +142,7 @@ def _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_ return logsums -def compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_label): +def compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, skims, trace_label): """ Compute logsums for the tour alt_tdds, which will differ based on their different start, stop times of day, which translate to different odt_skim out_period and in_periods. @@ -127,17 +154,19 @@ def compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_l For efficiency, rather compute a lot of redundant logsums, we compute logsums for the unique (out-period, in-period) pairs and then join them back to the alt_tdds. """ + + network_los = inject.get_injectable('network_los') + # - in_period and out_period assert 'out_period' not in alt_tdd assert 'in_period' not in alt_tdd - alt_tdd['out_period'] = expressions.skim_time_period_label(alt_tdd['start']) - alt_tdd['in_period'] = expressions.skim_time_period_label(alt_tdd['end']) + alt_tdd['out_period'] = network_los.skim_time_period_label(alt_tdd['start']) + alt_tdd['in_period'] = network_los.skim_time_period_label(alt_tdd['end']) alt_tdd['duration'] = alt_tdd['end'] - alt_tdd['start'] - USE_BRUTE_FORCE = False - if USE_BRUTE_FORCE: + if USE_BRUTE_FORCE_TO_COMPUTE_LOGSUMS: # compute logsums for all the tour alt_tdds (inefficient) - logsums = _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_label) + logsums = _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, network_los, skims, trace_label) return logsums # - get list of unique (tour_id, out_period, in_period, duration) in alt_tdd_periods @@ -148,7 +177,11 @@ def compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_l # - compute logsums for the alt_tdd_periods alt_tdd_periods['logsums'] = \ - _compute_logsums(alt_tdd_periods, tours_merged, tour_purpose, model_settings, trace_label) + _compute_logsums(alt_tdd_periods, tours_merged, tour_purpose, model_settings, network_los, skims, trace_label) + + logger.debug(f"{trace_label} compute_logsums " + f"alt_tdd_periods reduced number of rows by {round(100*len(alt_tdd_periods)/len(alt_tdd), 2)}% " + f" compared to alt_tdd len when USE_BRUTE_FORCE_TO_COMPUTE_LOGSUMS") # - join the alt_tdd_period logsums to alt_tdd to get logsums for alt_tdd logsums = pd.merge( @@ -244,7 +277,9 @@ def tdd_interaction_dataset(tours, alts, timetable, choice_column, window_id_col # slice out all non-available tours available = timetable.tour_available(alt_tdd[window_id_col], alt_tdd[choice_column]) + logger.debug(f"tdd_interaction_dataset keeping {available.sum()} of ({len(available)}) available alt_tdds") assert available.any() + alt_tdd = alt_tdd[available] # FIXME - don't need this any more after slicing @@ -256,7 +291,7 @@ def tdd_interaction_dataset(tours, alts, timetable, choice_column, window_id_col def _schedule_tours( tours, persons_merged, alts, spec, logsum_tour_purpose, - model_settings, + model_settings, skims, timetable, window_id_col, previous_tour, tour_owner_id_col, estimator, @@ -329,12 +364,13 @@ def _schedule_tours( choice_column = TDD_CHOICE_COLUMN alt_tdd = tdd_interaction_dataset(tours, alts, timetable, choice_column, window_id_col, tour_trace_label) + print(f"tours {tours.shape} alts {alts.shape}") chunk.log_df(tour_trace_label, "alt_tdd", alt_tdd) # - add logsums if logsum_tour_purpose: logsums = \ - compute_logsums(alt_tdd, tours, logsum_tour_purpose, model_settings, tour_trace_label) + compute_logsums(alt_tdd, tours, logsum_tour_purpose, model_settings, skims, tour_trace_label) else: logsums = 0 alt_tdd['mode_choice_logsum'] = logsums @@ -353,6 +389,15 @@ def _schedule_tours( if constants is not None: locals_d.update(constants) + preprocessor_settings = model_settings.get('ALTS_PREPROCESSOR', None) + + if preprocessor_settings and preprocessor_settings.get(logsum_tour_purpose): + expressions.assign_columns( + df=alt_tdd, + model_settings=preprocessor_settings.get(logsum_tour_purpose), + locals_dict=locals_d, + trace_label=tour_trace_label) + if estimator: # write choosers after annotation estimator.write_choosers(tours) @@ -381,25 +426,41 @@ def _schedule_tours( return choices -def calc_rows_per_chunk(chunk_size, tours, persons_merged, alternatives, model_settings, trace_label=None): +def tour_scheduling_calc_row_size(tours, persons_merged, alternatives, skims, spec, model_settings, trace_label): + + # this will no be consistent across mandatory tours (highest), non_mandatory tours, and atwork subtours (lowest) + TIMETABLE_AVAILABILITY_REDUCTION_FACTOR = 1 + # this appears to be more stable + LOGSUM_DUPLICATE_REDUCTION_FACTOR = 0.5 + + sizer = chunk.RowSizeEstimator(trace_label) + + # chooser is tours merged with persons_merged + chooser_row_size = len(tours.columns) + len(persons_merged.columns) - num_choosers = len(tours.index) + # e.g. start, end, duration, + alt_row_size = alternatives.shape[1] + 1 - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + # non-available alternatives will be sliced out so this is a over-estimate + # for atwork subtours this may be a gross over-estimate, but that is presumably ok since we are adaptive + sample_size = len(alternatives) * TIMETABLE_AVAILABILITY_REDUCTION_FACTOR - chooser_row_size = tours.shape[1] - sample_size = alternatives.shape[0] + sizer.add_elements(chooser_row_size, 'tours') # tours_merged with persons - # persons_merged columns plus 2 previous tour columns - extra_chooser_columns = persons_merged.shape[1] + 2 + # alt_tdd tdd_interaction_dataset is cross join of choosers with alternatives + sizer.add_elements((chooser_row_size + alt_row_size) * sample_size, 'interaction_df') - # one column per alternative plus skim and join columns - alt_row_size = alternatives.shape[1] + 2 + # eval_interaction_utilities is parsimonious and doesn't create a separate column for each partial utility + sizer.add_elements(sample_size, 'interaction_utilities') # <- this is probably always the HWM + sizer.drop_elements('interaction_df') + + sizer.drop_elements('interaction_utilities') + + sizer.add_elements(alt_row_size, 'utilities_df') + sizer.add_elements(alt_row_size, 'probs') - logsum_columns = 0 if 'LOGSUM_SETTINGS' in model_settings: + logsum_settings = config.read_model_settings(model_settings['LOGSUM_SETTINGS']) logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC']) logsum_nest_spec = config.get_logit_model_settings(logsum_settings) @@ -416,15 +477,24 @@ def calc_rows_per_chunk(chunk_size, tours, persons_merged, alternatives, model_s nest_count = logit.count_nests(logsum_nest_spec) logsum_columns = logsum_spec.shape[0] + (2 * logsum_spec.shape[1]) + (2 * nest_count) - 1 - row_size = (chooser_row_size + extra_chooser_columns + alt_row_size + logsum_columns) * sample_size + if USE_BRUTE_FORCE_TO_COMPUTE_LOGSUMS: + sizer.add_elements(logsum_columns * sample_size, 'logsum_columns') + else: + # if USE_BRUTE_FORCE_TO_COMPUTE_LOGSUMS is false compute_logsums prunes alt_tdd + # to only compute logsums for unique (tour_id, out_period, in_period, duration) in alt_tdd + # which cuts the number of alts by roughly 50% (44% for 100 hh mtctm1 test dataset) + # grep the log for USE_BRUTE_FORCE_TO_COMPUTE_LOGSUMS to check actual % savings + duplicate_sample_reduction = 0.5 + sizer.add_elements(logsum_columns * sample_size * LOGSUM_DUPLICATE_REDUCTION_FACTOR, 'logsum_columns') - logger.debug("%s #chunk_calc choosers %s" % (trace_label, tours.shape)) - logger.debug("%s #chunk_calc extra_chooser_columns %s" % (trace_label, extra_chooser_columns)) - logger.debug("%s #chunk_calc alternatives %s" % (trace_label, alternatives.shape)) - logger.debug("%s #chunk_calc alt_row_size %s" % (trace_label, alt_row_size)) - logger.debug("%s #chunk_calc logsum_columns %s" % (trace_label, logsum_columns)) + row_size = sizer.get_hwm() - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + if simulate.tvpb_skims(skims): + # DISABLE_TVPB_OVERHEAD + logger.debug("disable calc_row_size for THREE_ZONE with tap skims") + return 0 + + return row_size def schedule_tours( @@ -457,30 +527,27 @@ def schedule_tours( else: assert not tours[timetable_window_id_col].duplicated().any() - rows_per_chunk, effective_chunk_size = \ - calc_rows_per_chunk(chunk_size, tours, persons_merged, alts, - model_settings=model_settings, trace_label=tour_trace_label) - - result_list = [] - for i, num_chunks, chooser_chunk \ - in chunk.chunked_choosers(tours, rows_per_chunk): + if 'LOGSUM_SETTINGS' in model_settings: + # we need skims to calculate tvpb skim overhead in 3_ZONE systems for use by calc_rows_per_chunk + skims = skims_for_logsums(logsum_tour_purpose, model_settings, tour_trace_label) + else: + skims = None - logger.info("Running chunk %s of %s size %d" % (i, num_chunks, len(chooser_chunk))) + row_size = chunk_size and \ + tour_scheduling_calc_row_size(tours, persons_merged, alts, skims, spec, model_settings, tour_trace_label) - chunk_trace_label = tracing.extend_trace_label(tour_trace_label, 'chunk_%s' % i) \ - if num_chunks > 1 else tour_trace_label + result_list = [] + for i, chooser_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers(tours, chunk_size, row_size, tour_trace_label): - chunk.log_open(chunk_trace_label, chunk_size, effective_chunk_size) choices = _schedule_tours(chooser_chunk, persons_merged, alts, spec, logsum_tour_purpose, - model_settings, + model_settings, skims, timetable, timetable_window_id_col, previous_tour, tour_owner_id_col, estimator, tour_trace_label=chunk_trace_label) - chunk.log_close(chunk_trace_label) - result_list.append(choices) mem.force_garbage_collect() diff --git a/activitysim/abm/tables/__init__.py b/activitysim/abm/tables/__init__.py index c2664ceb6e..a6917d30d3 100644 --- a/activitysim/abm/tables/__init__.py +++ b/activitysim/abm/tables/__init__.py @@ -10,5 +10,4 @@ from . import time_windows from . import shadow_pricing -from . import constants from . import table_dict diff --git a/activitysim/abm/tables/constants.py b/activitysim/abm/tables/constants.py deleted file mode 100644 index 773ac2a4ad..0000000000 --- a/activitysim/abm/tables/constants.py +++ /dev/null @@ -1,62 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. -HHT_NONE = 0 -HHT_FAMILY_MARRIED = 1 -HHT_FAMILY_MALE = 2 -HHT_FAMILY_FEMALE = 3 -HHT_NONFAMILY_MALE_ALONE = 4 -HHT_NONFAMILY_MALE_NOTALONE = 5 -HHT_NONFAMILY_FEMALE_ALONE = 6 -HHT_NONFAMILY_FEMALE_NOTALONE = 7 - -# convenience for expression files -HHT_NONFAMILY = [4, 5, 6, 7] -HHT_FAMILY = [1, 2, 3] - -PSTUDENT_GRADE_OR_HIGH = 1 -PSTUDENT_UNIVERSITY = 2 -PSTUDENT_NOT = 3 - -GRADE_SCHOOL_MAX_AGE = 14 -GRADE_SCHOOL_MIN_AGE = 5 - -SCHOOL_SEGMENT_NONE = 0 -SCHOOL_SEGMENT_GRADE = 1 -SCHOOL_SEGMENT_HIGH = 2 -SCHOOL_SEGMENT_UNIV = 3 - -INCOME_SEGMENT_LOW = 1 -INCOME_SEGMENT_MED = 2 -INCOME_SEGMENT_HIGH = 3 -INCOME_SEGMENT_VERYHIGH = 4 - -PEMPLOY_FULL = 1 -PEMPLOY_PART = 2 -PEMPLOY_NOT = 3 -PEMPLOY_CHILD = 4 - -PTYPE_FULL = 1 -PTYPE_PART = 2 -PTYPE_UNIVERSITY = 3 -PTYPE_NONWORK = 4 -PTYPE_RETIRED = 5 -PTYPE_DRIVING = 6 -PTYPE_SCHOOL = 7 -PTYPE_PRESCHOOL = 8 - - -# these appear as column headers in non_mandatory_tour_frequency.csv -PTYPE_NAME = { - PTYPE_FULL: 'PTYPE_FULL', - PTYPE_PART: 'PTYPE_PART', - PTYPE_UNIVERSITY: 'PTYPE_UNIVERSITY', - PTYPE_NONWORK: 'PTYPE_NONWORK', - PTYPE_RETIRED: 'PTYPE_RETIRED', - PTYPE_DRIVING: 'PTYPE_DRIVING', - PTYPE_SCHOOL: 'PTYPE_SCHOOL', - PTYPE_PRESCHOOL: 'PTYPE_PRESCHOOL' -} - -CDAP_ACTIVITY_MANDATORY = 'M' -CDAP_ACTIVITY_NONMANDATORY = 'N' -CDAP_ACTIVITY_HOME = 'H' diff --git a/activitysim/abm/tables/households.py b/activitysim/abm/tables/households.py index 6bfc308aa3..5d52ec004d 100644 --- a/activitysim/abm/tables/households.py +++ b/activitysim/abm/tables/households.py @@ -5,11 +5,11 @@ import logging import pandas as pd -import numpy as np from activitysim.core import tracing from activitysim.core import pipeline from activitysim.core import inject +from activitysim.core import mem from activitysim.core.input import read_input_table @@ -92,17 +92,13 @@ def households(households_sample_size, override_hh_ids, trace_hh_id): logger.info("loaded households %s" % (df.shape,)) - # FIXME - pathological knowledge of name of chunk_id column used by chunked_choosers_by_chunk_id - assert 'chunk_id' not in df.columns - df['chunk_id'] = pd.Series(list(range(len(df))), df.index) - # replace table function with dataframe inject.add_table('households', df) pipeline.get_rn_generator().add_channel('households', df) + tracing.register_traceable_table('households', df) if trace_hh_id: - tracing.register_traceable_table('households', df) tracing.trace_df(df, "raw.households", warn_if_empty=True) return df @@ -111,12 +107,11 @@ def households(households_sample_size, override_hh_ids, trace_hh_id): # this is a common merge so might as well define it once here and use it @inject.table() def households_merged(households, land_use, accessibility): - return inject.merge_tables(households.name, tables=[ - households, land_use, accessibility]) + return inject.merge_tables(households.name, tables=[households, land_use, accessibility]) inject.broadcast('households', 'persons', cast_index=True, onto_on='household_id') # this would be accessibility around the household location - be careful with # this one as accessibility at some other location can also matter -inject.broadcast('accessibility', 'households', cast_index=True, onto_on='TAZ') +inject.broadcast('accessibility', 'households', cast_index=True, onto_on='home_zone_id') diff --git a/activitysim/abm/tables/landuse.py b/activitysim/abm/tables/landuse.py index ba019f22d1..361b293031 100644 --- a/activitysim/abm/tables/landuse.py +++ b/activitysim/abm/tables/landuse.py @@ -21,4 +21,4 @@ def land_use(): return df -inject.broadcast('land_use', 'households', cast_index=True, onto_on='TAZ') +inject.broadcast('land_use', 'households', cast_index=True, onto_on='home_zone_id') diff --git a/activitysim/abm/tables/persons.py b/activitysim/abm/tables/persons.py index 1949d459e3..a1930e461b 100644 --- a/activitysim/abm/tables/persons.py +++ b/activitysim/abm/tables/persons.py @@ -2,9 +2,12 @@ # See full license in LICENSE.txt. import logging +import pandas as pd + from activitysim.core import pipeline from activitysim.core import inject from activitysim.core import tracing +from activitysim.core import mem from activitysim.core.input import read_input_table @@ -16,7 +19,7 @@ def read_raw_persons(households): df = read_input_table("persons") if inject.get_injectable('households_sliced', False): - # keep all persons in the sampled households + # keep only persons in the sampled households df = df[df.household_id.isin(households.index)] return df @@ -34,15 +37,32 @@ def persons(households, trace_hh_id): pipeline.get_rn_generator().add_channel('persons', df) + tracing.register_traceable_table('persons', df) if trace_hh_id: - tracing.register_traceable_table('persons', df) tracing.trace_df(df, "raw.persons", warn_if_empty=True) + print(f"{len(df.household_id.unique())} unique household_ids in persons") + print(f"{len(households.index.unique())} unique household_ids in households") + assert not households.index.duplicated().any() + assert not df.index.duplicated().any() + + persons_without_households = ~df.household_id.isin(households.index) + if persons_without_households.any(): + logger.error(f"{persons_without_households.sum()} persons out of {len(persons)} without households\n" + f"{pd.Series({'person_id': persons_without_households.index.values})}") + raise RuntimeError(f"{persons_without_households.sum()} persons with bad household_id") + + households_without_persons = df.groupby('household_id').size().reindex(households.index).isnull() + if households_without_persons.any(): + logger.error(f"{households_without_persons.sum()} households out of {len(households.index)} without persons\n" + f"{pd.Series({'household_id': households_without_persons.index.values})}") + raise RuntimeError(f"{households_without_persons.sum()} households with no persons") + return df # another common merge for persons @inject.table() def persons_merged(persons, households, land_use, accessibility): - return inject.merge_tables(persons.name, tables=[ - persons, households, land_use, accessibility]) + + return inject.merge_tables(persons.name, tables=[persons, households, land_use, accessibility]) diff --git a/activitysim/abm/tables/shadow_pricing.py b/activitysim/abm/tables/shadow_pricing.py index 700f7c4e54..95c5bf60ac 100644 --- a/activitysim/abm/tables/shadow_pricing.py +++ b/activitysim/abm/tables/shadow_pricing.py @@ -249,6 +249,7 @@ def wait(tally, target): first_in = self.shared_data[TALLY_CHECKIN] == 0 # add local data from df to shared data buffer # final column is used for tallys, hence the negative index + # Ellipsis expands : to fill available dims so [..., 0:-1] is the whole array except for the tallys self.shared_data[..., 0:-1] += local_modeled_size.values self.shared_data[TALLY_CHECKIN] += 1 @@ -364,7 +365,7 @@ def check_fit(self, iteration): total_fails = (rel_diff > 0).values.sum() # FIXME - should not count zones where desired_size < threshold? (could calc in init) - max_fail = (fail_threshold / 100.0) * np.prod(desired_size.shape) + max_fail = (fail_threshold / 100.0) * util.iprod(desired_size.shape) converged = (total_fails <= max_fail) @@ -585,7 +586,7 @@ def get_shadow_pricing_info(): shadow_settings = config.read_model_settings('shadow_pricing.yaml') # shadow_pricing_models is dict of {: } - shadow_pricing_models = shadow_settings['shadow_pricing_models'] + shadow_pricing_models = shadow_settings.get('shadow_pricing_models', {}) blocks = OrderedDict() for model_selector in shadow_pricing_models: @@ -638,11 +639,11 @@ def buffers_for_shadow_pricing(shadow_pricing_info): data_buffers = {} for block_key, block_shape in block_shapes.items(): - # buffer_size must be int (or p2.7 long), not np.int64 - buffer_size = int(np.prod(block_shape, dtype=np.int64)) + # buffer_size must be int, not np.int64 + buffer_size = util.iprod(block_shape) csz = buffer_size * np.dtype(dtype).itemsize - logger.info("allocating shared buffer %s %s buffer_size %s bytes %s (%s)" % + logger.info("allocating shared shadow pricing buffer %s %s buffer_size %s bytes %s (%s)" % (block_key, buffer_size, block_shape, csz, util.GB(csz))) if np.issubdtype(dtype, np.int64): @@ -772,16 +773,16 @@ def add_size_tables(): use_shadow_pricing = bool(config.setting('use_shadow_pricing')) shadow_settings = config.read_model_settings('shadow_pricing.yaml') - shadow_pricing_models = shadow_settings['shadow_pricing_models'] - - # probably ought not scale if not shadow_pricing (breaks partial sample replicability) - # but this allows compatability with existing CTRAMP behavior... - scale_size_table = shadow_settings.get('SCALE_SIZE_TABLE', False) + shadow_pricing_models = shadow_settings.get('shadow_pricing_models') if shadow_pricing_models is None: logger.warning('shadow_pricing_models list not found in shadow_pricing settings') return + # probably ought not scale if not shadow_pricing (breaks partial sample replicability) + # but this allows compatability with existing CTRAMP behavior... + scale_size_table = shadow_settings.get('SCALE_SIZE_TABLE', False) + # shadow_pricing_models is dict of {: } # since these are scaled to model size, they have to be created while single-process diff --git a/activitysim/abm/tables/size_terms.py b/activitysim/abm/tables/size_terms.py index 904ac9ed1c..447081d611 100644 --- a/activitysim/abm/tables/size_terms.py +++ b/activitysim/abm/tables/size_terms.py @@ -73,7 +73,7 @@ def tour_destination_size_terms(land_use, size_terms, model_selector): and for model_selector 'trip', columns will be eatout, escort, othdiscr, ... work_low work_med work_high work_veryhigh - TAZ ... + zone_id ... 1 1267.00000 522.000 1108.000 1540.0000 ... 2 1991.00000 824.500 1759.000 2420.0000 ... ... @@ -87,8 +87,7 @@ def tour_destination_size_terms(land_use, size_terms, model_selector): df = pd.DataFrame({key: size_term(land_use, row) for key, row in size_terms.iterrows()}, index=land_use.index) - # df.index.name = 'TAZ' - assert land_use.index.name == 'TAZ' + assert land_use.index.name == 'zone_id' df.index.name = land_use.index.name if not (df.dtypes == 'float64').all(): diff --git a/activitysim/abm/tables/skims.py b/activitysim/abm/tables/skims.py index 47cd643d0a..05323a00a5 100644 --- a/activitysim/abm/tables/skims.py +++ b/activitysim/abm/tables/skims.py @@ -1,25 +1,14 @@ # ActivitySim # See full license in LICENSE.txt. -from builtins import range -from builtins import int -import sys -import os import logging -import multiprocessing -from collections import OrderedDict -from functools import reduce -from operator import mul +from activitysim.core import los +from activitysim.core import inject -import numpy as np -import openmatrix as omx -from activitysim.core import skim -from activitysim.core import inject -from activitysim.core import util -from activitysim.core import config -from activitysim.core import tracing +from activitysim.core.pathbuilder import TransitVirtualPathBuilder + logger = logging.getLogger(__name__) @@ -28,355 +17,25 @@ """ -def get_skim_info(omx_file_path, tags_to_load=None): - - # this is sys.maxint for p2.7 but no limit for p3 - # windows sys.maxint = 2147483647 - MAX_BLOCK_BYTES = sys.maxint - 1 if sys.version_info < (3,) else sys.maxsize - 1 - - # Note: we load all skims except those with key2 not in tags_to_load - # Note: we require all skims to be of same dtype so they can share buffer - is that ok? - # fixme is it ok to require skims be all the same type? if so, is this the right choice? - skim_dtype = np.float32 - omx_name = os.path.splitext(os.path.basename(omx_file_path))[0] - - with omx.open_file(omx_file_path) as omx_file: - # omx_shape = tuple(map(int, tuple(omx_file.shape()))) # sometimes omx shape are floats! - - # fixme call to omx_file.shape() failing in windows p3.5 - omx_shape = omx_file.shape() - omx_shape = (int(omx_shape[0]), int(omx_shape[1])) # sometimes omx shape are floats! - - omx_skim_names = omx_file.listMatrices() - - offset_map = None - offset_map_name = None - for m in omx_file.listMappings(): - if offset_map is None: - offset_map_name = m - offset_map = omx_file.mapentries(offset_map_name) - assert len(offset_map) == omx_shape[0] - - logger.debug(f"get_skim_info omx_name {omx_name} using offset_map {m}") - else: - # don't really expect more than one, but ok if they are all the same - assert((offset_map == omx_file.mapentries(m).all())), "Multiple different mappings in omx file" - - # - omx_keys dict maps skim key to omx_key - # DISTWALK: DISTWALK - # ('DRV_COM_WLK_BOARDS', 'AM'): DRV_COM_WLK_BOARDS__AM, ... - omx_keys = OrderedDict() - for skim_name in omx_skim_names: - key1, sep, key2 = skim_name.partition('__') - - # - ignore composite tags not in tags_to_load - if tags_to_load and sep and key2 not in tags_to_load: - continue - - skim_key = (key1, key2) if sep else key1 - omx_keys[skim_key] = skim_name - - num_skims = len(omx_keys) - - # - key1_subkeys dict maps key1 to dict of subkeys with that key1 - # DIST: {'DIST': 0} - # DRV_COM_WLK_BOARDS: {'MD': 1, 'AM': 0, 'PM': 2}, ... - key1_subkeys = OrderedDict() - for skim_key, omx_key in omx_keys.items(): - if isinstance(skim_key, tuple): - key1, key2 = skim_key - else: - key1 = key2 = skim_key - key2_dict = key1_subkeys.setdefault(key1, {}) - key2_dict[key2] = len(key2_dict) - - # - blocks dict maps block name to blocksize (number of subkey skims in block) - # skims_0: 198, - # skims_1: 198, ... - # - key1_block_offsets dict maps key1 to (block, offset) of first skim with that key1 - # DISTWALK: (0, 2), - # DRV_COM_WLK_BOARDS: (0, 3), ... - - if MAX_BLOCK_BYTES: - max_block_items = MAX_BLOCK_BYTES // np.dtype(skim_dtype).itemsize - max_skims_per_block = max_block_items // multiply_large_numbers(omx_shape) - else: - max_skims_per_block = num_skims - - def block_name(block): - return "skim_%s_%s" % (omx_name, block) - - key1_block_offsets = OrderedDict() - blocks = OrderedDict() - block = offset = 0 - for key1, v in key1_subkeys.items(): - num_subkeys = len(v) - if offset + num_subkeys > max_skims_per_block: # next block - blocks[block_name(block)] = offset - block += 1 - offset = 0 - key1_block_offsets[key1] = (block, offset) - offset += num_subkeys - blocks[block_name(block)] = offset # last block - - # - block_offsets dict maps skim_key to (block, offset) of omx matrix - # DIST: (0, 0), - # ('DRV_COM_WLK_BOARDS', 'AM'): (0, 3), - # ('DRV_COM_WLK_BOARDS', 'MD') (0, 4), ... - block_offsets = OrderedDict() - for skim_key in omx_keys: - - if isinstance(skim_key, tuple): - key1, key2 = skim_key - else: - key1 = key2 = skim_key - - block, key1_offset = key1_block_offsets[key1] - - key2_relative_offset = key1_subkeys.get(key1).get(key2) - - block_offsets[skim_key] = (block, key1_offset + key2_relative_offset) - - logger.debug("get_skim_info from %s" % (omx_file_path, )) - logger.debug("get_skim_info skim_dtype %s omx_shape %s num_skims %s num_blocks %s" % - (skim_dtype, omx_shape, num_skims, len(blocks))) - - skim_info = { - 'omx_name': omx_name, - 'omx_shape': omx_shape, - 'num_skims': num_skims, - 'dtype': skim_dtype, - 'offset_map_name': offset_map_name, - 'offset_map': offset_map, - 'omx_keys': omx_keys, - 'key1_block_offsets': key1_block_offsets, - 'block_offsets': block_offsets, - 'blocks': blocks, - } - - return skim_info - - -def buffers_for_skims(skim_info, shared=False): - - skim_dtype = skim_info['dtype'] - omx_shape = skim_info['omx_shape'] - blocks = skim_info['blocks'] - - skim_buffers = {} - for block_name, block_size in blocks.items(): - - # buffer_size must be int, not np.int64 - buffer_size = int(multiply_large_numbers(omx_shape) * block_size) - - itemsize = np.dtype(skim_dtype).itemsize - csz = buffer_size * itemsize - logger.info("allocating shared buffer %s for %s skims (skim size: %s * %s bytes = %s) total size: %s (%s)" % - (block_name, block_size, omx_shape, itemsize, buffer_size, csz, util.GB(csz))) - - if shared: - if np.issubdtype(skim_dtype, np.float64): - typecode = 'd' - elif np.issubdtype(skim_dtype, np.float32): - typecode = 'f' - else: - raise RuntimeError("buffers_for_skims unrecognized dtype %s" % skim_dtype) - - buffer = multiprocessing.RawArray(typecode, buffer_size) - else: - buffer = np.zeros(buffer_size, dtype=skim_dtype) - - skim_buffers[block_name] = buffer - - return skim_buffers - - -def skim_data_from_buffers(skim_buffers, skim_info): - - assert type(skim_buffers) == dict - - omx_shape = skim_info['omx_shape'] - skim_dtype = skim_info['dtype'] - blocks = skim_info['blocks'] - - skim_data = [] - for block_name, block_size in blocks.items(): - skims_shape = omx_shape + (block_size,) - block_buffer = skim_buffers[block_name] - assert len(block_buffer) == int(multiply_large_numbers(skims_shape)) - block_data = np.frombuffer(block_buffer, dtype=skim_dtype).reshape(skims_shape) - skim_data.append(block_data) - - return skim_data - - -def default_skim_cache_dir(): - return inject.get_injectable('output_dir') - - -def build_skim_cache_file_name(omx_name, block): - return f"cached_{omx_name}_{block}.mmap" - - -def read_skim_cache(skim_info, skim_data): - """ - read cached memmapped skim data from canonically named cache file(s) in output directory into skim_data - """ - - skim_cache_dir = config.setting('skim_cache_dir', default_skim_cache_dir()) - logger.info(f"load_skims reading skims data from cache directory {skim_cache_dir}") - - omx_name = skim_info['omx_name'] - dtype = np.dtype(skim_info['dtype']) - - blocks = skim_info['blocks'] - block = 0 - for block_name, block_size in blocks.items(): - skim_cache_file_name = build_skim_cache_file_name(omx_name, block) - skim_cache_path = os.path.join(skim_cache_dir, skim_cache_file_name) - - assert os.path.isfile(skim_cache_path), \ - "read_skim_cache could not find skim_cache_path: %s" % (skim_cache_path, ) - - block_data = skim_data[block] - - logger.info(f"load_skims reading block_name {block_name} {block_data.shape} from {skim_cache_file_name}") - - data = np.memmap(skim_cache_path, shape=block_data.shape, dtype=dtype, mode='r') - assert data.shape == block_data.shape - - block_data[::] = data[::] - - block += 1 - - -def write_skim_cache(skim_info, skim_data): - """ - write skim data from skim_data to canonically named cache file(s) in output directory - """ - - skim_cache_dir = config.setting('skim_cache_dir', default_skim_cache_dir()) - logger.info(f"load_skims writing skims data to cache directory {skim_cache_dir}") - - omx_name = skim_info['omx_name'] - dtype = np.dtype(skim_info['dtype']) - - blocks = skim_info['blocks'] - block = 0 - for block_name, block_size in blocks.items(): - skim_cache_file_name = build_skim_cache_file_name(omx_name, block) - skim_cache_path = os.path.join(skim_cache_dir, skim_cache_file_name) - - block_data = skim_data[block] - - logger.info(f"load_skims writing block_name {block_name} {block_data.shape} to {skim_cache_file_name}") - - data = np.memmap(skim_cache_path, shape=block_data.shape, dtype=dtype, mode='w+') - data[::] = block_data - - block += 1 - - -def read_skims_from_omx(skim_info, skim_data, omx_file_path): - """ - read skims from omx file into skim_data - """ - - block_offsets = skim_info['block_offsets'] - omx_keys = skim_info['omx_keys'] - - # read skims into skim_data - with omx.open_file(omx_file_path) as omx_file: - for skim_key, omx_key in omx_keys.items(): - - omx_data = omx_file[omx_key] - assert np.issubdtype(omx_data.dtype, np.floating) - - block, offset = block_offsets[skim_key] - block_data = skim_data[block] - - logger.debug("load_skims load omx_key %s skim_key %s to block %s offset %s" % - (omx_key, skim_key, block, offset)) - - # this will trigger omx readslice to read and copy data to skim_data's buffer - a = block_data[:, :, offset] - a[:] = omx_data[:] - - logger.info("load_skims loaded skims from %s" % (omx_file_path, )) - - -def load_skims(omx_file_path, skim_info, skim_buffers): - - read_cache = config.setting('read_skim_cache') - write_cache = config.setting('write_skim_cache') - assert not (read_cache and write_cache), \ - "read_skim_cache and write_skim_cache are both True in settings file. I am assuming this is a mistake" - - skim_data = skim_data_from_buffers(skim_buffers, skim_info) - - t0 = tracing.print_elapsed_time() - - if read_cache: - read_skim_cache(skim_info, skim_data) - t0 = tracing.print_elapsed_time("read_skim_cache", t0) - else: - read_skims_from_omx(skim_info, skim_data, omx_file_path) - t0 = tracing.print_elapsed_time("read_skims_from_omx", t0) - - if write_cache: - write_skim_cache(skim_info, skim_data) - t0 = tracing.print_elapsed_time("write_skim_cache", t0) - - @inject.injectable(cache=True) -def skim_dict(settings): - - omx_file_path = config.data_file_path(settings["skims_file"]) - tags_to_load = settings['skim_time_periods']['labels'] - - logger.info("loading skim_dict from %s" % (omx_file_path, )) - - # select the skims to load - skim_info = get_skim_info(omx_file_path, tags_to_load) - - logger.debug("omx_shape %s skim_dtype %s" % (skim_info['omx_shape'], skim_info['dtype'])) +def network_los_preload(): - skim_buffers = inject.get_injectable('data_buffers', None) - if skim_buffers: - logger.info('Using existing skim_buffers for skims') - else: - skim_buffers = buffers_for_skims(skim_info, shared=False) - load_skims(omx_file_path, skim_info, skim_buffers) + # when multiprocessing with shared data mp_tasks has to call network_los methods + # allocate_shared_skim_buffers() and load_shared_data() BEFORE network_los.load_data() + logger.debug("loading network_los_without_data_loaded injectable") + nw_los = los.Network_LOS() - skim_data = skim_data_from_buffers(skim_buffers, skim_info) + return nw_los - block_names = list(skim_info['blocks'].keys()) - for i in range(len(skim_data)): - block_name = block_names[i] - block_data = skim_data[i] - logger.info("block_name %s bytes %s (%s)" % - (block_name, block_data.nbytes, util.GB(block_data.nbytes))) - - # create skim dict - skim_dict = skim.SkimDict(skim_data, skim_info) - - offset_map = skim_info['offset_map'] - if offset_map is not None: - skim_dict.offset_mapper.set_offset_list(offset_map) - logger.debug(f"using offset map {skim_info['offset_map_name']}from omx file: {offset_map}") - else: - # assume this is a one-based skim map - skim_dict.offset_mapper.set_offset_int(-1) - - return skim_dict +@inject.injectable(cache=True) +def network_los(network_los_preload): -def multiply_large_numbers(list_of_numbers): - return reduce(mul, list_of_numbers) + logger.debug("loading network_los injectable") + network_los_preload.load_data() + return network_los_preload @inject.injectable(cache=True) -def skim_stack(skim_dict): - - logger.debug("loading skim_stack injectable") - return skim.SkimStack(skim_dict) +def skim_dict(network_los): + return network_los.get_default_skim_dict() diff --git a/activitysim/abm/tables/tours.py b/activitysim/abm/tables/tours.py index 0e36eeeace..3982015885 100644 --- a/activitysim/abm/tables/tours.py +++ b/activitysim/abm/tables/tours.py @@ -9,8 +9,7 @@ @inject.table() def tours_merged(tours, persons_merged): - return inject.merge_tables(tours.name, tables=[ - tours, persons_merged]) + return inject.merge_tables(tours.name, tables=[tours, persons_merged]) inject.broadcast('persons_merged', 'tours', cast_index=True, onto_on='person_id') diff --git a/activitysim/abm/test/configs_test_pipeline/school_location.yaml b/activitysim/abm/test/configs_test_pipeline/school_location.yaml new file mode 100644 index 0000000000..a5841d869b --- /dev/null +++ b/activitysim/abm/test/configs_test_pipeline/school_location.yaml @@ -0,0 +1,4 @@ +inherit_settings: True + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: school_location_logsum diff --git a/activitysim/abm/test/configs_test_pipeline/workplace_location.yaml b/activitysim/abm/test/configs_test_pipeline/workplace_location.yaml new file mode 100644 index 0000000000..6be70bb6cf --- /dev/null +++ b/activitysim/abm/test/configs_test_pipeline/workplace_location.yaml @@ -0,0 +1,7 @@ +inherit_settings: True + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample diff --git a/activitysim/abm/test/data/land_use.csv b/activitysim/abm/test/data/land_use.csv index 2572f5bea5..b27dde6e32 100644 --- a/activitysim/abm/test/data/land_use.csv +++ b/activitysim/abm/test/data/land_use.csv @@ -1,4 +1,4 @@ -ZONE,DISTRICT,SD,COUNTY,TOTHH,HHPOP,TOTPOP,EMPRES,SFDU,MFDU,HHINCQ1,HHINCQ2,HHINCQ3,HHINCQ4,TOTACRE,RESACRE,CIACRE,SHPOP62P,TOTEMP,AGE0004,AGE0519,AGE2044,AGE4564,AGE65P,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL,ZERO,hhlds,sftaz,gqpop +TAZ,DISTRICT,SD,COUNTY,TOTHH,HHPOP,TOTPOP,EMPRES,SFDU,MFDU,HHINCQ1,HHINCQ2,HHINCQ3,HHINCQ4,TOTACRE,RESACRE,CIACRE,SHPOP62P,TOTEMP,AGE0004,AGE0519,AGE2044,AGE4564,AGE65P,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL,ZERO,hhlds,sftaz,gqpop 1,1,1,1,46,74,82,37,1,60,15,13,9,9,20.3,1.0,15.0,0.23800000000000002,27318,3,7,31,27,14,224,21927,2137,2254,18,758,284.01965,932.83514,0,0.0,0.0,0.0,3,5.89564,0,46,1,8 2,1,1,1,134,214,240,107,5,147,57,32,24,21,31.1,1.0,24.79297,0.23800000000000002,42078,8,19,89,81,43,453,33422,4399,2948,56,800,269.6431,885.61682,0,0.0,0.0,0.0,1,5.84871,0,134,2,26 3,1,1,1,267,427,476,214,9,285,101,86,40,40,14.7,1.0,2.31799,0.23800000000000002,2445,16,38,177,160,85,93,1159,950,211,0,32,218.08298,716.27252,0,0.0,0.0,0.0,1,5.53231,0,267,3,49 diff --git a/activitysim/abm/test/run_mp.py b/activitysim/abm/test/run_mp.py index 4113807739..e777ac435d 100644 --- a/activitysim/abm/test/run_mp.py +++ b/activitysim/abm/test/run_mp.py @@ -9,7 +9,7 @@ from activitysim.core import inject from activitysim.core import mp_tasks -from test_pipeline import example_path, setup_dirs +from test_pipeline import setup_dirs # set the max households for all tests (this is to limit memory use on travis) HOUSEHOLDS_SAMPLE_SIZE = 100 diff --git a/activitysim/abm/test/run_multi_zone_mp.py b/activitysim/abm/test/run_multi_zone_mp.py new file mode 100644 index 0000000000..ddbe85916e --- /dev/null +++ b/activitysim/abm/test/run_multi_zone_mp.py @@ -0,0 +1,50 @@ +# ActivitySim +# See full license in LICENSE.txt. +import os + +import pandas as pd +import pandas.testing as pdt + +from activitysim.core import pipeline +from activitysim.core import inject +from activitysim.core import mp_tasks + +from test_multi_zone import example_path +from test_multi_zone import mtc_example_path +from test_multi_zone import setup_dirs +from test_multi_zone import regress_3_zone + +# set the max households for all tests (this is to limit memory use on travis) +HOUSEHOLDS_SAMPLE_SIZE = 25 + +# household with WALK_TRANSIT tours and trips +HH_ID_3_ZONE = 2848373 + + +def test_mp_run(): + + configs_dir = [example_path('configs_3_zone'), mtc_example_path('configs')] + data_dir = example_path('data_3') + + setup_dirs(configs_dir, data_dir) + inject.add_injectable('settings_file_name', 'settings_mp.yaml') + inject.add_injectable('households_sample_size', HOUSEHOLDS_SAMPLE_SIZE) + inject.add_injectable('trace_hh_id', HH_ID_3_ZONE) + + run_list = mp_tasks.get_run_list() + mp_tasks.print_run_list(run_list) + + # do this after config.handle_standard_args, as command line args may override injectables + injectables = ['data_dir', 'configs_dir', 'output_dir', 'settings_file_name', + 'households_sample_size', 'trace_hh_id'] + injectables = {k: inject.get_injectable(k) for k in injectables} + + mp_tasks.run_multiprocess(run_list, injectables) + pipeline.open_pipeline('_') + regress_3_zone() + pipeline.close_pipeline() + + +if __name__ == '__main__': + + test_mp_run() diff --git a/activitysim/abm/test/test_expressions.py b/activitysim/abm/test/test_expressions.py deleted file mode 100644 index e1c7a80616..0000000000 --- a/activitysim/abm/test/test_expressions.py +++ /dev/null @@ -1,96 +0,0 @@ -import os - -import pandas as pd -import pytest -import yaml - -from activitysim.core import inject -from activitysim.abm.models.util import expressions - - -@pytest.fixture(scope="session") -def config_path(): - return os.path.join(os.path.dirname(__file__), 'configs_test_misc') - - -def test_30_minute_windows(config_path): - with open(os.path.join(config_path, 'settings_30_min.yaml')) as f: - settings = yaml.load(f, Loader=yaml.SafeLoader) - - inject.add_injectable("settings", settings) - - assert expressions.skim_time_period_label(1) == 'EA' - assert expressions.skim_time_period_label(16) == 'AM' - assert expressions.skim_time_period_label(24) == 'MD' - assert expressions.skim_time_period_label(36) == 'PM' - assert expressions.skim_time_period_label(46) == 'EV' - - pd.testing.assert_series_equal( - expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])), - pd.Series(['EA', 'AM', 'MD', 'PM', 'EV'])) - - -def test_60_minute_windows(config_path): - with open(os.path.join(config_path, 'settings_60_min.yaml')) as f: - settings = yaml.load(f, Loader=yaml.SafeLoader) - - inject.add_injectable("settings", settings) - - assert expressions.skim_time_period_label(1) == 'EA' - assert expressions.skim_time_period_label(8) == 'AM' - assert expressions.skim_time_period_label(12) == 'MD' - assert expressions.skim_time_period_label(18) == 'PM' - assert expressions.skim_time_period_label(23) == 'EV' - - pd.testing.assert_series_equal( - expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])), - pd.Series(['EA', 'AM', 'MD', 'PM', 'EV'])) - - -def test_1_week_time_window(): - settings = { - 'skim_time_periods': { - 'time_window': 10080, # One Week - 'period_minutes': 1440, # One Day - 'periods': [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7 - ], - 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday', - 'Thursday', 'Friday', 'Saturday'] - } - } - - inject.add_injectable("settings", settings) - - assert expressions.skim_time_period_label(1) == 'Sunday' - assert expressions.skim_time_period_label(2) == 'Monday' - assert expressions.skim_time_period_label(3) == 'Tuesday' - assert expressions.skim_time_period_label(4) == 'Wednesday' - assert expressions.skim_time_period_label(5) == 'Thursday' - assert expressions.skim_time_period_label(6) == 'Friday' - assert expressions.skim_time_period_label(7) == 'Saturday' - - weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7])) - - pd.testing.assert_series_equal(weekly_series, - pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday', - 'Thursday', 'Friday', 'Saturday'])) - - -def test_future_warning(config_path): - with open(os.path.join(config_path, 'settings_60_min.yaml')) as f: - settings = yaml.load(f, Loader=yaml.SafeLoader) - - settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods') - - inject.add_injectable("settings", settings) - - with pytest.warns(FutureWarning) as warning_test: - expressions.skim_time_period_label(1) diff --git a/activitysim/abm/test/test_mp_pipeline.py b/activitysim/abm/test/test_mp_pipeline.py index 799264cf8e..cc5d76af9f 100644 --- a/activitysim/abm/test/test_mp_pipeline.py +++ b/activitysim/abm/test/test_mp_pipeline.py @@ -3,6 +3,13 @@ import os import subprocess +from activitysim.core import inject + + +def teardown_function(func): + inject.clear_cache() + inject.reinject_decorated_tables() + def test_mp_run(): diff --git a/activitysim/abm/test/test_multi_zone.py b/activitysim/abm/test/test_multi_zone.py new file mode 100644 index 0000000000..6051e300ff --- /dev/null +++ b/activitysim/abm/test/test_multi_zone.py @@ -0,0 +1,209 @@ +# ActivitySim +# See full license in LICENSE.txt. +import os +import logging +import pkg_resources + +import openmatrix as omx +import numpy as np +import numpy.testing as npt + +import pandas as pd +import pandas.testing as pdt +import pytest +import yaml + +from activitysim.core import random +from activitysim.core import tracing +from activitysim.core import pipeline +from activitysim.core import inject +from activitysim.core import config + +HOUSEHOLDS_SAMPLE_SIZE = 50 +EXPECT_2_ZONE_TOUR_COUNT = 120 + +# 3-zone is currently big and slow - so set this way low +HOUSEHOLDS_SAMPLE_SIZE_3_ZONE = 5 +EXPECT_3_ZONE_TOUR_COUNT = 13 + + +# household with mandatory, non mandatory, atwork_subtours, and joint tours +HH_ID = 257341 + +# household with WALK_TRANSIT tours and trips +HH_ID_3_ZONE = 2848373 + +# [ 257341 1234246 1402915 1511245 1931827 1931908 2307195 2366390 2408855 +# 2518594 2549865 982981 1594365 1057690 1234121 2098971] + + +def example_path(dirname): + resource = os.path.join('examples', 'example_multiple_zone', dirname) + return pkg_resources.resource_filename('activitysim', resource) + + +def mtc_example_path(dirname): + resource = os.path.join('examples', 'example_mtc', dirname) + return pkg_resources.resource_filename('activitysim', resource) + + +def setup_dirs(configs_dir, data_dir): + + print(f"configs_dir: {configs_dir}") + inject.add_injectable('configs_dir', configs_dir) + + output_dir = os.path.join(os.path.dirname(__file__), 'output') + inject.add_injectable('output_dir', output_dir) + + print(f"data_dir: {data_dir}") + inject.add_injectable('data_dir', data_dir) + + inject.clear_cache() + + tracing.config_logger() + + tracing.delete_output_files('csv') + tracing.delete_output_files('txt') + tracing.delete_output_files('yaml') + tracing.delete_output_files('omx') + + +def teardown_function(func): + inject.clear_cache() + inject.reinject_decorated_tables() + + +def close_handlers(): + + loggers = logging.Logger.manager.loggerDict + for name in loggers: + logger = logging.getLogger(name) + logger.handlers = [] + logger.propagate = True + logger.setLevel(logging.NOTSET) + + +def inject_settings(**kwargs): + + for k in kwargs: + if k == "two_zone": + if kwargs[k]: + settings = config.read_settings_file('settings.yaml', mandatory=True) + else: + settings = config.read_settings_file('settings_static.yaml', mandatory=True) + settings[k] = kwargs[k] + + inject.add_injectable("settings", settings) + + return settings + + +def full_run(configs_dir, data_dir, + resume_after=None, chunk_size=0, + households_sample_size=HOUSEHOLDS_SAMPLE_SIZE, + trace_hh_id=None, trace_od=None, check_for_variability=None, two_zone=True): + + setup_dirs(configs_dir, data_dir) + + settings = inject_settings( + two_zone=two_zone, + households_sample_size=households_sample_size, + chunk_size=chunk_size, + trace_hh_id=trace_hh_id, + trace_od=trace_od, + check_for_variability=check_for_variability, + use_shadow_pricing=False) # shadow pricing breaks replicability when sample_size varies + + MODELS = settings['models'] + + pipeline.run(models=MODELS, resume_after=resume_after) + + tours = pipeline.get_table('tours') + tour_count = len(tours.index) + + return tour_count + + +def get_trace_csv(file_name): + + file_name = config.output_file_path(file_name) + df = pd.read_csv(file_name) + + # label value_1 value_2 value_3 value_4 + # 0 tour_id 38 201 39 40 + # 1 mode DRIVE_LOC DRIVE_COM DRIVE_LOC DRIVE_LOC + # 2 person_id 1888694 1888695 1888695 1888696 + # 3 tour_type work othmaint work school + # 4 tour_num 1 1 1 1 + + # transpose df and rename columns + labels = df.label.values + df = df.transpose()[1:] + df.columns = labels + + return df + + +def regress_2_zone(): + pass + + +def regress_3_zone(): + + tours_df = pipeline.get_table('tours') + assert len(tours_df[tours_df.tour_mode == 'WALK_TRANSIT']) > 0 + + # should cache atap and btap for transit modes only + for c in ['od_atap', 'od_btap', 'do_atap', 'do_btap']: + # tour_mode_choice sets non-transit taps to 0 + assert not (tours_df[tours_df.tour_mode.isin(['WALK_TRANSIT', 'DRIVE_TRANSIT'])][c] == 0).any() + baddies = ~tours_df.tour_mode.isin(['WALK_TRANSIT', 'DRIVE_TRANSIT']) & (tours_df[c] != 0) + if baddies.any(): + print(tours_df[baddies][['tour_type', 'tour_mode', 'od_atap', 'od_btap', 'do_atap', 'do_btap']]) + assert False + + +def test_full_run_2_zone(): + + tour_count = full_run(configs_dir=[example_path('configs_2_zone'), mtc_example_path('configs')], + data_dir=example_path('data_2'), + trace_hh_id=HH_ID, check_for_variability=True, + households_sample_size=HOUSEHOLDS_SAMPLE_SIZE, two_zone=True) + + print("tour_count", tour_count) + + assert(tour_count == EXPECT_2_ZONE_TOUR_COUNT), \ + "EXPECT_2_ZONE_TOUR_COUNT %s but got tour_count %s" % (EXPECT_2_ZONE_TOUR_COUNT, tour_count) + + regress_2_zone() + + pipeline.close_pipeline() + + +def test_full_run_3_zone(): + + tour_count = full_run(configs_dir=[example_path('configs_3_zone'), mtc_example_path('configs')], + data_dir=example_path('data_3'), + trace_hh_id=HH_ID_3_ZONE, check_for_variability=True, + households_sample_size=HOUSEHOLDS_SAMPLE_SIZE_3_ZONE, two_zone=False) + + print("tour_count", tour_count) + + assert(tour_count == EXPECT_3_ZONE_TOUR_COUNT), \ + "EXPECT_3_ZONE_TOUR_COUNT %s but got tour_count %s" % (EXPECT_3_ZONE_TOUR_COUNT, tour_count) + + regress_3_zone() + + pipeline.close_pipeline() + + +if __name__ == "__main__": + + from activitysim import abm # register injectables + print("running test_full_run_2_zone") + test_full_run_2_zone() + + print("running test_full_run_3_zone") + test_full_run_3_zone() + + # teardown_function(None) diff --git a/activitysim/abm/test/test_multi_zone_mp.py b/activitysim/abm/test/test_multi_zone_mp.py new file mode 100644 index 0000000000..74a93890ab --- /dev/null +++ b/activitysim/abm/test/test_multi_zone_mp.py @@ -0,0 +1,23 @@ +# ActivitySim +# See full license in LICENSE.txt. +import os +import subprocess + +from activitysim.core import inject + + +def teardown_function(func): + inject.clear_cache() + inject.reinject_decorated_tables() + + +def test_mp_run(): + + file_path = os.path.join(os.path.dirname(__file__), 'run_multi_zone_mp.py') + + subprocess.check_call(['coverage', 'run', file_path]) + + +if __name__ == '__main__': + + test_mp_run() diff --git a/activitysim/abm/test/test_pipeline.py b/activitysim/abm/test/test_pipeline.py index a69449aa9c..738b40eb05 100644 --- a/activitysim/abm/test/test_pipeline.py +++ b/activitysim/abm/test/test_pipeline.py @@ -20,8 +20,8 @@ from activitysim.core import config # set the max households for all tests (this is to limit memory use on travis) -HOUSEHOLDS_SAMPLE_SIZE = 100 -HOUSEHOLDS_SAMPLE_RATE = 0.02 # HOUSEHOLDS_SAMPLE_RATE / 5000 households +HOUSEHOLDS_SAMPLE_SIZE = 50 +HOUSEHOLDS_SAMPLE_RATE = 0.01 # HOUSEHOLDS_SAMPLE_RATE / 5000 households # household with mandatory, non mandatory, atwork_subtours, and joint tours HH_ID = 257341 @@ -40,6 +40,8 @@ def example_path(dirname): def setup_dirs(ancillary_configs_dir=None, data_dir=None): + # ancillary_configs_dir is used by run_mp to test multiprocess + test_pipeline_configs_dir = os.path.join(os.path.dirname(__file__), 'configs_test_pipeline') example_configs_dir = example_path('configs') configs_dir = [test_pipeline_configs_dir, example_configs_dir] @@ -114,8 +116,8 @@ def regress_mini_auto(): # regression test: these are among the middle households in households table # should be the same results as in run_mp (multiprocessing) test case - hh_ids = [932147, 982875, 983048, 1024353] - choices = [1, 1, 1, 0] + hh_ids = [1099626, 1173905, 1196298, 1286259] + choices = [1, 1, 0, 0] expected_choice = pd.Series(choices, index=pd.Index(hh_ids, name="household_id"), name='auto_ownership') @@ -128,14 +130,14 @@ def regress_mini_auto(): """ auto_choice - household_id - 932147 1 - 982875 1 - 983048 1 - 1024353 0 + household_id + 1099626 1 + 1173905 1 + 1196298 0 + 1286259 0 Name: auto_ownership, dtype: int64 """ - pdt.assert_series_equal(auto_choice, expected_choice) + pdt.assert_series_equal(auto_choice, expected_choice, check_dtype=False) def regress_mini_mtf(): @@ -143,27 +145,25 @@ def regress_mini_mtf(): mtf_choice = pipeline.get_table("persons").sort_index().mandatory_tour_frequency # these choices are for pure regression - their appropriateness has not been checked - per_ids = [2566698, 2877284, 2877287] - choices = ['work1', 'work_and_school', 'school1'] + per_ids = [2566701, 2566702, 3061895] + choices = ['school1', 'school1', 'work1'] expected_choice = pd.Series(choices, index=pd.Index(per_ids, name='person_id'), name='mandatory_tour_frequency') mtf_choice = mtf_choice[mtf_choice != ''] # drop null (empty string) choices offset = len(mtf_choice) // 2 # choose something midway as hh_id ordered by hh size - print("mtf_choice\n%s" % mtf_choice.head(offset).tail(5)) + print("mtf_choice\n%s" % mtf_choice.head(offset).tail(3)) """ mtf_choice - person_id - 2458502 school1 - 2458503 school1 - 2566698 work1 - 2877284 work_and_school - 2877287 school1 + person_id + 2566701 school1 + 2566702 school1 + 3061895 work1 Name: mandatory_tour_frequency, dtype: object """ - pdt.assert_series_equal(mtf_choice.reindex(per_ids), expected_choice) + pdt.assert_series_equal(mtf_choice.reindex(per_ids), expected_choice, check_dtype=False) def regress_mini_location_choice_logsums(): @@ -171,11 +171,11 @@ def regress_mini_location_choice_logsums(): persons = pipeline.get_table("persons") # DEST_CHOICE_LOGSUM_COLUMN_NAME is specified in school_location.yaml and should be assigned - assert 'school_taz_logsum' in persons - assert not persons.school_taz_logsum.isnull().all() + assert 'school_location_logsum' in persons + assert not persons.school_location_logsum.isnull().all() # DEST_CHOICE_LOGSUM_COLUMN_NAME is NOT specified in workplace_location.yaml - assert 'workplace_taz_logsum' not in persons + assert 'workplace_location_logsum' not in persons def test_mini_pipeline_run(): @@ -309,6 +309,7 @@ def full_run(resume_after=None, chunk_size=0, trace_od=trace_od, testing_fail_trip_destination=False, check_for_variability=check_for_variability, + want_dest_choice_sample_tables=False, use_shadow_pricing=False) # shadow pricing breaks replicability when sample_size varies # FIXME should enable testing_fail_trip_destination? @@ -343,7 +344,7 @@ def get_trace_csv(file_name): return df -EXPECT_TOUR_COUNT = 205 +EXPECT_TOUR_COUNT = 120 def regress_tour_modes(tours_df): @@ -518,7 +519,7 @@ def test_full_run4_stability(): return tour_count = full_run(trace_hh_id=HH_ID, - households_sample_size=HOUSEHOLDS_SAMPLE_SIZE+10) + households_sample_size=HOUSEHOLDS_SAMPLE_SIZE-10) regress() @@ -527,13 +528,16 @@ def test_full_run4_stability(): def test_full_run5_singleton(): - # should wrk with only one hh + # should work with only one hh + # run with minimum chunk size to drive potential chunking errors in models + # where choosers has multiple rows that all have to be included in the same chunk if SKIP_FULL_RUN: return tour_count = full_run(trace_hh_id=HH_ID, - households_sample_size=1) + households_sample_size=1, + chunk_size=1) regress() diff --git a/activitysim/abm/test/test_skims.py b/activitysim/abm/test/test_skims.py deleted file mode 100644 index deb4f059c1..0000000000 --- a/activitysim/abm/test/test_skims.py +++ /dev/null @@ -1,67 +0,0 @@ -from collections import OrderedDict - - -import numpy as np -import pytest - -from activitysim.abm.tables import skims - - -@pytest.fixture(scope="session") -def matrix_dimension(): - return 5922 - - -@pytest.fixture(scope="session") -def num_of_matrices(): - return 845 - - -@pytest.fixture(scope="session") -def skim_info(num_of_matrices, matrix_dimension): - time_periods = ['EA', 'AM', 'MD', 'PM', 'NT'] - - omx_keys = OrderedDict() - omx_key1_block_offsets = OrderedDict() - omx_block_offsets = OrderedDict() - omx_blocks = OrderedDict() - omx_blocks['skim_arc_skims_0'] = num_of_matrices - - for i in range(0, num_of_matrices + 1): - key1_name = 'm{}'.format(i // len(time_periods) + 1) - time_period = time_periods[i % len(time_periods)] - - omx_keys[(key1_name, time_period)] = '{}__{}'.format(key1_name, time_period) - omx_block_offsets[(key1_name, time_period)] = (0, i) - - if 0 == i % len(time_periods): - omx_key1_block_offsets[key1_name] = (0, i) - - skim_info = { - 'omx_name': 'arc_skims', - 'omx_shape': (matrix_dimension, matrix_dimension), - 'num_skims': num_of_matrices, - 'dtype': np.float32, - 'omx_keys': omx_keys, - 'key1_block_offsets': omx_key1_block_offsets, - 'block_offsets': omx_block_offsets, - 'blocks': omx_blocks - } - - return skim_info - - -def test_multiply_large_numbers(skim_info, num_of_matrices, matrix_dimension): - omx_shape = skim_info['omx_shape'] - blocks = skim_info['blocks'] - - for block_name, block_size in blocks.items(): - # If overflow, this number will go negative - assert int(skims.multiply_large_numbers(omx_shape) * block_size) == \ - num_of_matrices * matrix_dimension ** 2 - - -def test_multiple_large_floats(): - calculated_value = skims.multiply_large_numbers([6205.1, 5423.2, 932.4, 15.4]) - actual_value = 483200518316.9472 - assert abs(calculated_value - actual_value) < 0.0001 diff --git a/activitysim/abm/test/test_trip_departure_choice.py b/activitysim/abm/test/test_trip_departure_choice.py new file mode 100644 index 0000000000..a3f55e682c --- /dev/null +++ b/activitysim/abm/test/test_trip_departure_choice.py @@ -0,0 +1,99 @@ + +import numpy as np +import pandas as pd +import pytest + +import activitysim.abm.models.trip_departure_choice as tdc +from activitysim.abm.models.util.trip import get_time_windows +from activitysim.core import los +from .test_pipeline import setup_dirs + + +@pytest.fixture(scope='module') +def trips(): + outbound_array = [True, True, False, False, False, True, True, False, False, True] + + trips = pd.DataFrame(data={'tour_id': [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], + 'trip_duration': [2, 2, 7, 7, 7, 12, 12, 4, 4, 5], + 'inbound_duration': [0, 0, 7, 7, 7, 0, 0, 4, 4, 5], + 'main_leg_duration': [4, 4, 2, 2, 2, 2, 2, 1, 1, 2], + 'outbound_duration': [2, 2, 0, 0, 0, 12, 12, 0, 0, 5], + 'trip_count': [2, 2, 3, 3, 3, 2, 2, 2, 2, 1], + 'trip_num': [1, 2, 1, 2, 3, 1, 2, 1, 2, 1], + 'outbound': outbound_array, + 'chunk_id': [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], + 'is_work': [True, True, False, False, False, False, False, False, False, True], + 'is_school': [False, False, False, False, False, False, False, True, True, False], + 'is_eatout': [False, False, True, True, True, True, True, False, False, False], + 'start': [8, 8, 18, 18, 18, 18, 18, 24, 24, 19], + 'end': [14, 14, 39, 39, 39, 39, 39, 29, 29, 26], + 'origin': [3, 5, 15, 12, 24, 8, 17, 8, 9, 6], + 'destination': [5, 9, 12, 24, 20, 17, 18, 9, 11, 14], + }, index=range(10)) + + trips.index.name = 'trip_id' + return trips + + +@pytest.fixture(scope='module') +def settings(): + return {"skims_file": "skims.omx", + "skim_time_periods": { + "labels": ['EA', 'AM', 'MD', 'PM', 'NT']} + } + + +@pytest.fixture(scope='module') +def model_spec(): + index = ["@(df['stop_time_duration'] * df['is_work'].astype(int)).astype(int)", + "@(df['stop_time_duration'] * df['is_school'].astype(int)).astype(int)", + "@(df['stop_time_duration'] * df['is_eatout'].astype(int)).astype(int)"] + + values = {'inbound': [0.933020, 0.370260, 0.994840], + 'outbound': [0.933020, 0.370260, 0.994840] + } + + return pd.DataFrame(index=index, data=values) + + +def test_build_patterns(trips): + time_windows = get_time_windows(48, 3) + patterns = tdc.build_patterns(trips, time_windows) + patterns = patterns.sort_values(['tour_id', 'outbound', 'trip_num']) + + assert patterns.shape[0] == 34 + assert patterns.shape[1] == 6 + assert patterns.index.name == tdc.TOUR_LEG_ID + + output_columns = [tdc.TOUR_ID, tdc.PATTERN_ID, tdc.TRIP_NUM, + tdc.STOP_TIME_DURATION, tdc.TOUR_ID, tdc.OUTBOUND] + + assert set(output_columns).issubset(patterns.columns) + + +def test_get_tour_legs(trips): + tour_legs = tdc.get_tour_legs(trips) + assert tour_legs.index.name == tdc.TOUR_LEG_ID + assert np.unique(tour_legs[tdc.TOUR_ID].values).shape[0] == np.unique(trips[tdc.TOUR_ID].values).shape[0] + + +def test_generate_alternative(trips): + alts = tdc.generate_alternatives(trips, tdc.STOP_TIME_DURATION) + assert alts.shape[0] == 67 + assert alts.shape[1] == 1 + + assert alts.index.name == tdc.TRIP_ID + assert alts.columns[0] == tdc.STOP_TIME_DURATION + + pd.testing.assert_series_equal(trips.groupby(trips.index)['trip_duration'].max(), + alts.groupby(alts.index)[tdc.STOP_TIME_DURATION].max(), + check_names=False) + + +def test_apply_stage_two_model(model_spec, trips): + setup_dirs() + departures = tdc.apply_stage_two_model(model_spec, trips, 0, 'TEST Trip Departure') + assert len(departures) == len(trips) + pd.testing.assert_index_equal(departures.index, trips.index) + + departures = pd.concat([trips, departures], axis=1) diff --git a/activitysim/abm/test/test_trip_scheduling_choice.py b/activitysim/abm/test/test_trip_scheduling_choice.py new file mode 100644 index 0000000000..e8797b9668 --- /dev/null +++ b/activitysim/abm/test/test_trip_scheduling_choice.py @@ -0,0 +1,161 @@ + +import numpy as np +import pandas as pd +import pytest + +from activitysim.abm.models import trip_scheduling_choice as tsc +from activitysim.abm.tables.skims import skim_dict +from activitysim.core import los +from .test_pipeline import setup_dirs + + +@pytest.fixture(scope='module') +def tours(): + tours = pd.DataFrame(data={'duration': [2, 44, 32, 12, 11, 16], + 'num_outbound_stops': [2, 4, 0, 0, 1, 3], + 'num_inbound_stops': [1, 0, 0, 2, 1, 2], + 'tour_type': ['othdisc'] * 2 + ['eatout'] * 4, + 'origin': [3, 10, 15, 23, 5, 8], + 'destination': [5, 9, 12, 24, 20, 17], + tsc.LAST_OB_STOP: [1, 3, 0, 0, 12, 14], + tsc.FIRST_IB_STOP: [2, 0, 0, 4, 6, 20], + }, index=range(6)) + + tours.index.name = 'tour_id' + + tours[tsc.HAS_OB_STOPS] = tours[tsc.NUM_OB_STOPS] >= 1 + tours[tsc.HAS_IB_STOPS] = tours[tsc.NUM_IB_STOPS] >= 1 + + return tours + + +@pytest.fixture(scope='module') +def settings(): + return {"skims_file": "skims.omx", + "skim_time_periods": { + "labels": ['MD']} + } + + +@pytest.fixture(scope='module') +def model_spec(): + index = ["@(df['main_leg_duration']>df['duration']).astype(int)", + "@(df['main_leg_duration'] == 0)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 1)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 2)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 3)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 4)&(df['tour_type']=='othdiscr')", + "@df['tour_type']=='othdiscr'", + "@df['tour_type']=='eatout'", + "@df['tour_type']=='eatout'" + ] + + values = [-999, -6.5884, -5.0326, -2.0526, -1.0313, -0.46489, 0.060382, -0.7508, 0.53247] + + return pd.DataFrame(index=index, data=values, columns=['stage_one']) + + +@pytest.fixture(scope='module') +def skims(settings): + setup_dirs() + nw_los = los.Network_LOS() + nw_los.load_data() + skim_d = skim_dict(nw_los) + + od_skim_stack_wrapper = skim_d.wrap('origin', 'destination') + do_skim_stack_wrapper = skim_d.wrap('destination', 'origin') + obib_skim_stack_wrapper = skim_d.wrap(tsc.LAST_OB_STOP, tsc.FIRST_IB_STOP) + + skims = [od_skim_stack_wrapper, do_skim_stack_wrapper, obib_skim_stack_wrapper] + + return skims + + +@pytest.fixture(scope='module') +def locals_dict(skims): + return { + "od_skims": skims[0], + "do_skims": skims[1], + "obib_skims": skims[2] + } + + +def test_generate_schedule_alternatives(tours): + windows = tsc.generate_schedule_alternatives(tours) + assert windows.shape[0] == 296 + assert windows.shape[1] == 4 + + output_columns = [tsc.SCHEDULE_ID, tsc.MAIN_LEG_DURATION, + tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + +def test_no_stops_patterns(tours): + no_stops = tours[(tours['num_outbound_stops'] == 0) & (tours['num_inbound_stops'] == 0)].copy() + windows = tsc.no_stops_patterns(no_stops) + + assert windows.shape[0] == 1 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, + tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + pd.testing.assert_series_equal(windows[tsc.MAIN_LEG_DURATION], no_stops['duration'], + check_names=False, check_dtype=False) + assert windows[windows[tsc.IB_DURATION] > 0].empty + assert windows[windows[tsc.OB_DURATION] > 0].empty + + +def test_one_way_stop_patterns(tours): + one_way_stops = tours[((tours['num_outbound_stops'] > 0).astype(int) + + (tours['num_inbound_stops'] > 0).astype(int)) == 1].copy() + windows = tsc.stop_one_way_only_patterns(one_way_stops) + + assert windows.shape[0] == 58 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, + tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + inbound_options = windows[(windows[tsc.IB_DURATION] > 0)] + outbound_options = windows[windows[tsc.OB_DURATION] > 0] + assert np.unique(inbound_options.index).shape[0] == 1 + assert np.unique(outbound_options.index).shape[0] == 1 + + +def test_two_way_stop_patterns(tours): + two_way_stops = tours[((tours['num_outbound_stops'] > 0).astype(int) + + (tours['num_inbound_stops'] > 0).astype(int)) == 2].copy() + windows = tsc.stop_two_way_only_patterns(two_way_stops) + + assert windows.shape[0] == 237 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, + tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + +def test_run_trip_scheduling_choice(model_spec, tours, skims, locals_dict): + """ + Test run the model. + """ + + out_tours = tsc.run_trip_scheduling_choice(model_spec, tours, skims, locals_dict, + 2, None, "PyTest Trip Scheduling") + + assert len(tours) == len(out_tours) + pd.testing.assert_index_equal(tours.sort_index().index, out_tours.sort_index().index) + + output_columns = [tsc.MAIN_LEG_DURATION, + tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(out_tours.columns) + + assert len(out_tours[out_tours[output_columns].sum(axis=1) == out_tours[tsc.TOUR_DURATION_COLUMN]]) == len(tours) diff --git a/activitysim/abm/test/test_trip_utils.py b/activitysim/abm/test/test_trip_utils.py new file mode 100644 index 0000000000..bf908986ea --- /dev/null +++ b/activitysim/abm/test/test_trip_utils.py @@ -0,0 +1,25 @@ +import numpy as np +import pandas as pd +import pytest + +from activitysim.abm.models.util.trip import get_time_windows + + +@pytest.mark.parametrize("duration, levels, expected", + [(24, 3, 2925), (24, 2, 325), (24, 1, 25), + (48, 3, 20825), (48, 2, 1225), (48, 1, 49)]) +def test_get_time_windows(duration, levels, expected): + time_windows = get_time_windows(duration, levels) + + if levels == 1: + assert time_windows.ndim == 1 + assert len(time_windows) == expected + assert np.sum(time_windows <= duration) == expected + else: + assert len(time_windows) == levels + assert len(time_windows[0]) == expected + total_duration = np.sum(time_windows, axis=0) + assert np.sum(total_duration <= duration) == expected + + df = pd.DataFrame(np.transpose(time_windows)) + assert len(df) == len(df.drop_duplicates()) diff --git a/activitysim/cli/run.py b/activitysim/cli/run.py index a0ffe8c20c..ea5e8a1754 100644 --- a/activitysim/cli/run.py +++ b/activitysim/cli/run.py @@ -14,7 +14,7 @@ logger = logging.getLogger(__name__) -INJECTABLES = ['data_dir', 'configs_dir', 'output_dir'] +INJECTABLES = ['data_dir', 'configs_dir', 'output_dir', 'settings_file_name'] def add_run_args(parser, multiprocess=True): @@ -46,6 +46,10 @@ def add_run_args(parser, multiprocess=True): type=str, metavar='FILE', help='pipeline file name') + parser.add_argument('-s', '--settings_file', + type=str, + metavar='FILE', + help='settings file name') if multiprocess: parser.add_argument('-m', '--multiprocess', @@ -65,8 +69,7 @@ def validate_injectable(name): "containing 'configs', 'data', and 'output' folders " 'or all three of --config, --data, and --output') - if isinstance(dir_paths, str): - dir_paths = [dir_paths] + dir_paths = [dir_paths] if isinstance(dir_paths, str) else dir_paths for dir_path in dir_paths: if not os.path.exists(dir_path): @@ -88,6 +91,9 @@ def inject_arg(name, value): # 'configs', 'data', and 'output' folders by default os.chdir(args.working_dir) + if args.settings_file: + inject_arg('settings_file_name', args.settings_file) + if args.config: inject_arg('configs_dir', args.config) @@ -176,6 +182,8 @@ def run(args): # cleanup if not resuming if not resume_after: cleanup_output_files() + elif config.setting('cleanup_trace_files_on_resume', False): + tracing.delete_trace_files() if config.setting('multiprocess', False): logger.info('run multiprocess simulation') diff --git a/activitysim/cli/test/test_cli.py b/activitysim/cli/test/test_cli.py index 917de632bd..b96044a3a0 100644 --- a/activitysim/cli/test/test_cli.py +++ b/activitysim/cli/test/test_cli.py @@ -45,7 +45,9 @@ def test_create_copy(): assert 'copying configs ...' in str(cp.stdout) assert 'copying configs_mp ...' in str(cp.stdout) assert 'copying output ...' in str(cp.stdout) - assert str(target) in str(cp.stdout) + + # replace slashes on windows + assert str(target).replace("\\\\", "\\") in str(cp.stdout).replace("\\\\", "\\") assert os.path.exists(target) for folder in ['configs', 'configs_mp', 'data', 'output']: diff --git a/activitysim/core/assign.py b/activitysim/core/assign.py index 6293b8cf8b..435d7780d7 100644 --- a/activitysim/core/assign.py +++ b/activitysim/core/assign.py @@ -11,7 +11,9 @@ from activitysim.core import util from activitysim.core import config +from activitysim.core import expressions from activitysim.core import pipeline +from activitysim.core import inject logger = logging.getLogger(__name__) @@ -65,7 +67,7 @@ def evaluate_constants(expressions, constants): return d -def read_assignment_spec(fname, +def read_assignment_spec(file_name, description_name="Description", target_name="Target", expression_name="Expression"): @@ -81,7 +83,7 @@ def read_assignment_spec(fname, Parameters ---------- - fname : str + file_name : str Name of a CSV spec file. description_name : str, optional Name of the column in `fname` that contains the component description. @@ -96,7 +98,12 @@ def read_assignment_spec(fname, dataframe with three columns: ['description' 'target' 'expression'] """ - cfg = pd.read_csv(fname, comment='#') + try: + cfg = pd.read_csv(file_name, comment='#') + except Exception as e: + logger.error(f"Error reading spec file: {file_name}") + logger.error(str(e)) + raise e # drop null expressions # cfg = cfg.dropna(subset=[expression_name]) @@ -141,11 +148,15 @@ def local_utilities(): 'pd': pd, 'np': np, 'reindex': util.reindex, + 'reindex_i': util.reindex_i, 'setting': config.setting, 'other_than': util.other_than, + 'skim_time_period_label': expressions.skim_time_period_label, 'rng': pipeline.get_rn_generator(), } + utility_dict.update(config.get_global_constants()) + return utility_dict @@ -210,6 +221,7 @@ def to_series(x): trace_assigned_locals = trace_results = None if trace_rows is not None: # convert to numpy array so we can slice ndarrays as well as series + trace_rows = np.asanyarray(trace_rows) if trace_rows.any(): trace_results = OrderedDict() @@ -270,9 +282,13 @@ def to_series(x): np.seterr(**save_err) np.seterrcall(saved_handler) + # except Exception as err: + # logger.error("assign_variables error: %s: %s", type(err).__name__, str(err)) + # logger.error("assign_variables expression: %s = %s", str(target), str(expression)) + # raise err + except Exception as err: - logger.error("assign_variables error: %s: %s", type(err).__name__, str(err)) - logger.error("assign_variables expression: %s = %s", str(target), str(expression)) + logger.exception(f"assign_variables - {type(err).__name__} ({str(err)}) evaluating: {str(expression)}") raise err if not is_temp(target): @@ -293,6 +309,8 @@ def to_series(x): # add df columns to trace_results trace_results = pd.concat([df[trace_rows], trace_results], axis=1) + assert variables, "No non-temp variables were assigned." + # we stored result in dict - convert to df variables = util.df_from_dict(variables, index=df.index) diff --git a/activitysim/core/chunk.py b/activitysim/core/chunk.py index e726b0ef62..1114eb267f 100644 --- a/activitysim/core/chunk.py +++ b/activitysim/core/chunk.py @@ -2,14 +2,17 @@ # See full license in LICENSE.txt. from builtins import input +import math import logging from collections import OrderedDict +from contextlib import contextmanager import numpy as np import pandas as pd from . import util from . import mem +from . import tracing logger = logging.getLogger(__name__) @@ -19,10 +22,20 @@ # array of chunk_size active CHUNK_LOG CHUNK_SIZE = [] -EFFECTIVE_CHUNK_SIZE = [] HWM = [{}] +INITIAL_ROWS_PER_CHUNK = 10 +MAX_ROWSIZE_ERROR = 0.5 # estimated_row_size percentage error warning threshold +INTERACTIVE_TRACE_CHUNKING = False +INTERACTIVE_TRACE_CHUNK_WARNING = False + +CHUNK_RSS = False +BYTES_PER_ELEMENT = 8 + +# chunk size being a bit opaque, it may be helpful to know the chunk size of a small sample run to titrate chunk_size +CHUNK_HISTORY = True # always log chunk history even if chunk_size == 0 + def GB(bytes): # symbols = ('', 'K', 'M', 'G', 'T') @@ -45,29 +58,81 @@ def commas(x): return "%d%s" % (x, result) -def log_open(trace_label, chunk_size, effective_chunk_size): +class RowSizeEstimator(object): + """ + Utility for estimating row_size + """ + def __init__(self, trace_label): + self.row_size = 0 + self.hwm = 0 # element count at high water mark + self.hwm_tag = None # tag that drove hwm (with ref count) + self.trace_label = trace_label + self.elements = {} + self.tag_count = {} + + def add_elements(self, elements, tag): + self.elements[tag] = elements + self.tag_count[tag] = self.tag_count.setdefault(tag, 0) + 1 # number of times tag has been seen + self.row_size += elements + logger.debug(f"{self.trace_label} #chunk_calc {tag} {elements} ({self.row_size})") + input("add_elements>") if INTERACTIVE_TRACE_CHUNKING else None + + if self.row_size > self.hwm: + self.hwm = self.row_size + # tag that drove hwm (with ref count) + self.hwm_tag = f'{tag}_{self.tag_count[tag]}' if self.tag_count[tag] > 1 else tag + + def drop_elements(self, tag): + self.row_size -= self.elements[tag] + self.elements[tag] = 0 + logger.debug(f"{self.trace_label} #chunk_calc {tag} ({self.row_size})") + + def get_hwm(self): + logger.debug(f"{self.trace_label} #chunk_calc hwm {self.hwm} after {self.hwm_tag}") + input("get_hwm>") if INTERACTIVE_TRACE_CHUNKING else None + return self.hwm + + +def get_high_water_mark(tag='elements'): + + # should always have at least the base chunker + assert len(HWM) > 0 + + hwm = HWM[-1] + + # hwm might be empty if there were no calls to log_df + mark = hwm.get(tag).get('mark') if hwm else 0 + + return mark + + +@contextmanager +def chunk_log(trace_label, chunk_size=0): + log_open(trace_label, chunk_size) + try: + yield + finally: + log_close(trace_label) + + +def log_open(trace_label, chunk_size=0): # nested chunkers should be unchunked if len(CHUNK_LOG) > 0: assert chunk_size == 0 assert trace_label not in CHUNK_LOG - logger.debug("log_open chunker %s chunk_size %s effective_chunk_size %s" % - (trace_label, commas(chunk_size), commas(effective_chunk_size))) - CHUNK_LOG[trace_label] = OrderedDict() CHUNK_SIZE.append(chunk_size) - EFFECTIVE_CHUNK_SIZE.append(effective_chunk_size) HWM.append({}) def log_close(trace_label): + # they should be closing the last log opened (LIFO) assert CHUNK_LOG and next(reversed(CHUNK_LOG)) == trace_label - logger.debug("log_close %s" % trace_label) - # if we are closing base level chunker if len(CHUNK_LOG) == 1: log_write_hwm() @@ -75,29 +140,40 @@ def log_close(trace_label): label, _ = CHUNK_LOG.popitem(last=True) assert label == trace_label CHUNK_SIZE.pop() - EFFECTIVE_CHUNK_SIZE.pop() - HWM.pop() def log_df(trace_label, table_name, df): + """ + Parameters + ---------- + trace_label : + serves as a label for this nesting level of logging + table_name : str + name to use logging df, and which will be used in any subsequent calls reporting activity on the table + df: numpy.ndarray, pandas.Series, pandas.DataFrame, or None + table to log (or None if df was deleted) + """ if df is None: # FIXME force_garbage_collect on delete? mem.force_garbage_collect() - cur_chunker = next(reversed(CHUNK_LOG)) + try: + cur_chunker = next(reversed(CHUNK_LOG)) + except StopIteration: + logger.warning(f"log_df called without current chunker. Did you forget to call log_open?") + return if df is None: + # remove table from log CHUNK_LOG.get(cur_chunker).pop(table_name) op = 'del' - - logger.debug("log_df del %s : %s " % (table_name, trace_label)) - + logger.debug(f"log_df {table_name} " + f"elements: {0} : {trace_label}") else: - shape = df.shape - elements = np.prod(shape, dtype=np.int64) + elements = util.iprod(df.shape) op = 'add' if isinstance(df, pd.Series): @@ -113,27 +189,31 @@ def log_df(trace_label, table_name, df): CHUNK_LOG.get(cur_chunker)[table_name] = (elements, bytes) # log this df - logger.debug("log_df add %s elements: %s bytes: %s shape: %s : %s " % - (table_name, commas(elements), GB(bytes), shape, trace_label)) + logger.debug(f"log_df {table_name} " + f"elements: {commas(elements)} " + f"bytes: {GB(bytes)} " + f"shape: {df.shape} : {trace_label}") - total_elements, total_bytes = _chunk_totals() # new chunk totals - cur_mem = mem.get_memory_info() hwm_trace_label = "%s.%s.%s" % (trace_label, op, table_name) - - logger.debug("total_elements: %s, total_bytes: %s cur_mem: %s: %s " % - (total_elements, GB(total_bytes), GB(cur_mem), hwm_trace_label)) - mem.trace_memory_info(hwm_trace_label) + total_elements, total_bytes = _chunk_totals() # new chunk totals + cur_rss = mem.get_rss() + # - check high_water_marks + info = f"elements: {commas(total_elements)} " \ + f"bytes: {GB(total_bytes)} " \ + f"rss: {GB(cur_rss)} " \ + f"chunk_size: {commas(CHUNK_SIZE[0])}" - info = "elements: %s bytes: %s mem: %s chunk_size: %s effective_chunk_size: %s" % \ - (commas(total_elements), GB(total_bytes), GB(cur_mem), - commas(CHUNK_SIZE[0]), commas(EFFECTIVE_CHUNK_SIZE[0])) + if INTERACTIVE_TRACE_CHUNKING: + print(f"table_name {table_name} {df.shape if df is not None else 0}") + print(f"table_name {table_name} {info}") + input("log_df>") - check_hwm('elements', total_elements, info, hwm_trace_label) - check_hwm('bytes', total_bytes, info, hwm_trace_label) - check_hwm('mem', cur_mem, info, hwm_trace_label) + check_for_hwm('elements', total_elements, info, hwm_trace_label) + check_for_hwm('bytes', total_bytes, info, hwm_trace_label) + check_for_hwm('rss', cur_rss, info, hwm_trace_label) def _chunk_totals(): @@ -150,7 +230,7 @@ def _chunk_totals(): return total_elements, total_bytes -def check_hwm(tag, value, info, trace_label): +def check_for_hwm(tag, value, info, trace_label): for d in HWM: @@ -170,7 +250,7 @@ def log_write_hwm(): logger.debug("#chunk_hwm high_water_mark %s: %s (%s) in %s" % (tag, hwm['mark'], hwm['info'], hwm['trace_label']), ) - # - elements shouldn't exceed chunk_size or effective_chunk_size of base chunker + # - elements shouldn't exceed chunk_size of base chunker def check_chunk_size(hwm, chunk_size, label, max_leeway): elements = hwm['mark'] if chunk_size and max_leeway and elements > chunk_size * max_leeway: # too high @@ -183,47 +263,155 @@ def check_chunk_size(hwm, chunk_size, label, max_leeway): if len(HWM) > 1 and HWM[1]: assert 'elements' in HWM[1] # expect an 'elements' hwm dict for base chunker hwm = HWM[1].get('elements') - check_chunk_size(hwm, EFFECTIVE_CHUNK_SIZE[0], 'effective_chunk_size', max_leeway=1.1) check_chunk_size(hwm, CHUNK_SIZE[0], 'chunk_size', max_leeway=1) -def rows_per_chunk(chunk_size, row_size, num_choosers, trace_label): +def write_history(caller, history, trace_label): - if chunk_size > 0: - # closest number of chooser rows to achieve chunk_size without exceeding - max_rpc = int(chunk_size / float(row_size)) - else: - max_rpc = num_choosers + observed_size = history.observed_chunk_size.sum() + number_of_rows = history.rows_per_chunk.sum() + observed_row_size = math.ceil(observed_size / number_of_rows) # FIXME - rpc = int(np.clip(max_rpc, 1, num_choosers)) + num_chunks = len(history) - # chunks = int(ceil(num_choosers / float(rpc))) - effective_chunk_size = row_size * rpc - num_chunks = (num_choosers // rpc) + (num_choosers % rpc > 0) + logger.info(f"#chunk_history {caller} {trace_label} " + f"number_of_rows: {number_of_rows} " + f"observed_row_size: {observed_row_size} " + f"num_chunks: {num_chunks}") - logger.debug(f"#chunk_calc num_chunks: {num_chunks}, rows_per_chunk: {rpc}, " - f"max_rpc: {max_rpc}, " - f"effective_chunk_size: {effective_chunk_size}, num_choosers: {num_choosers} : {trace_label}") + logger.debug(f"#chunk_history {caller} {trace_label}\n{history}") - return rpc, effective_chunk_size + initial_row_size = history.row_size.values[0] + if initial_row_size > 0: + # if they provided an initial estimated row size, then report error -def chunked_choosers(choosers, rows_per_chunk): + error = (initial_row_size - observed_row_size) / observed_row_size + percent_error = round(error * 100, 1) - assert choosers.shape[0] > 0 + logger.info(f"#chunk_history {caller} {trace_label} " + f"initial_row_size: {initial_row_size} " + f"observed_row_size: {observed_row_size} " + f"percent_error: {percent_error}%") + + if abs(error) > MAX_ROWSIZE_ERROR: + + logger.warning(f"#chunk_history MAX_ROWSIZE_ERROR " + f"initial_row_size {initial_row_size} " + f"observed_row_size {observed_row_size} " + f"percent_error: {percent_error}% in {trace_label}") + + if INTERACTIVE_TRACE_CHUNK_WARNING: + # for debugging adaptive chunking internals + print(history) + input(f"{trace_label} type any key to continue") + + +def adaptive_chunked_choosers(choosers, chunk_size, row_size, trace_label): + + # generator to iterate over choosers - # generator to iterate over choosers in chunk_size chunks num_choosers = len(choosers.index) - num_chunks = (num_choosers // rows_per_chunk) + (num_choosers % rows_per_chunk > 0) + assert num_choosers > 0 + assert chunk_size >= 0 + assert row_size >= 0 + + logger.info(f"Running adaptive_chunked_choosers with chunk_size {chunk_size} and {num_choosers} choosers") + + # FIXME do we care if it is an int? + row_size = math.ceil(row_size) + + # #CHUNK_RSS + mem.force_garbage_collect() + initial_rss = mem.get_rss() + logger.debug(f"#CHUNK_RSS initial_rss: {initial_rss}") + + if chunk_size == 0: + assert row_size == 0 # we ignore this but make sure caller realizes that + rows_per_chunk = num_choosers + estimated_number_of_chunks = 1 + else: + assert len(HWM) == 1, f"len(HWM): {len(HWM)}" + if row_size == 0: + rows_per_chunk = min(num_choosers, INITIAL_ROWS_PER_CHUNK) # FIXME parameterize + estimated_number_of_chunks = None + else: + max_rows_per_chunk = np.maximum(int(chunk_size / row_size), 1) + logger.debug(f"#chunk_calc chunk: max rows_per_chunk {max_rows_per_chunk} based on row_size {row_size}") + rows_per_chunk = np.clip(int(chunk_size / row_size), 1, num_choosers) + estimated_number_of_chunks = math.ceil(num_choosers / rows_per_chunk) + + logger.debug(f"#chunk_calc chunk: initial rows_per_chunk {rows_per_chunk} based on row_size {row_size}") + + history = {} i = offset = 0 while offset < num_choosers: - yield i+1, num_chunks, choosers.iloc[offset: offset+rows_per_chunk] - offset += rows_per_chunk + + assert offset + rows_per_chunk <= num_choosers + + chunk_trace_label = \ + tracing.extend_trace_label(trace_label, f'chunk_{i + 1}') if chunk_size > 0 else trace_label + + # grab the next chunk based on current rows_per_chunk + chooser_chunk = choosers.iloc[offset: offset + rows_per_chunk] + + logger.info(f"Running chunk {i+1} of {estimated_number_of_chunks or '?'} " + f"with {len(chooser_chunk)} of {num_choosers} choosers") + + with chunk_log(trace_label, chunk_size): + + yield i+1, chooser_chunk, chunk_trace_label + + # get number of elements allocated during this chunk from the high water mark dict + observed_chunk_size = get_high_water_mark() + + # #CHUNK_RSS + observed_rss_size = (get_high_water_mark('rss') - initial_rss) + observed_rss_size = math.ceil(observed_rss_size / BYTES_PER_ELEMENT) + logger.debug(f"#CHUNK_RSS chunk {i+1} observed_chunk_size: {observed_chunk_size} " + f"observed_rss_size {observed_rss_size}") + observed_rss_size = max(observed_rss_size, 0) + if CHUNK_RSS: + observed_chunk_size = observed_rss_size + i += 1 + offset += rows_per_chunk + rows_remaining = num_choosers - offset + + if CHUNK_HISTORY or chunk_size > 0: + + history.setdefault('chunk', []).append(i) + history.setdefault('row_size', []).append(row_size) + history.setdefault('rows_per_chunk', []).append(rows_per_chunk) + history.setdefault('observed_chunk_size', []).append(observed_chunk_size) + # revise predicted row_size based on observed_chunk_size + row_size = math.ceil(observed_chunk_size / rows_per_chunk) -def chunked_choosers_and_alts(choosers, alternatives, rows_per_chunk): + # closest number of chooser rows to achieve chunk_size without exceeding it + if row_size == 0: + # they don't appear to have used any memory; increase cautiously in case small sample size was to blame + if rows_per_chunk > INITIAL_ROWS_PER_CHUNK * 100: + rows_per_chunk = rows_remaining + else: + rows_per_chunk = 10 * rows_per_chunk + else: + rows_per_chunk = int(chunk_size / row_size) + rows_per_chunk = np.clip(rows_per_chunk, 1, rows_remaining) + + estimated_number_of_chunks = i + math.ceil(rows_remaining / rows_per_chunk) if rows_remaining else i + + history.setdefault('new_row_size', []).append(row_size) + history.setdefault('new_rows_per_chunk', []).append(rows_per_chunk) + history.setdefault('estimated_number_of_chunks', []).append(estimated_number_of_chunks) + + if history: + history = pd.DataFrame.from_dict(history) + write_history('adaptive_chunked_choosers', history, trace_label) + + +def adaptive_chunked_choosers_and_alts(choosers, alternatives, chunk_size, row_size, trace_label): """ generator to iterate over choosers and alternatives in chunk_size chunks @@ -261,61 +449,229 @@ def chunked_choosers_and_alts(choosers, alternatives, rows_per_chunk): # logger.warning('sorting choosers because not monotonic increasing') # choosers = choosers.sort_index() + num_choosers = len(choosers.index) + num_alternatives = len(alternatives.index) + + assert num_choosers > 0 + # alternatives index should match choosers (except with duplicate repeating alt rows) assert choosers.index.equals(alternatives.index[~alternatives.index.duplicated(keep='first')]) last_repeat = alternatives.index != np.roll(alternatives.index, -1) - assert (choosers.shape[0] == 1) or choosers.index.equals(alternatives.index[last_repeat]) - assert choosers.shape[0] > 0 + assert (num_choosers == 1) or choosers.index.equals(alternatives.index[last_repeat]) assert 'pick_count' in alternatives.columns or choosers.index.name == alternatives.index.name + assert choosers.index.name == alternatives.index.name - num_choosers = len(choosers.index) - num_chunks = (num_choosers // rows_per_chunk) + (num_choosers % rows_per_chunk > 0) + logger.info(f"Running adaptive_chunked_choosers_and_alts with chunk_size {chunk_size} " + f"and {num_choosers} choosers and {num_alternatives} alternatives") - assert choosers.index.name == alternatives.index.name + # FIXME do we care if it is an int? + row_size = math.ceil(row_size) + + # #CHUNK_RSS + mem.force_garbage_collect() + initial_rss = mem.get_rss() + logger.debug(f"#CHUNK_RSS initial_rss: {initial_rss}") + + if chunk_size == 0: + assert row_size == 0 # we ignore this but make sure caller realizes that + rows_per_chunk = num_choosers + estimated_number_of_chunks = 1 + row_size = 0 + else: + assert len(HWM) == 1 + if row_size == 0: + rows_per_chunk = min(num_choosers, INITIAL_ROWS_PER_CHUNK) # FIXME parameterize + estimated_number_of_chunks = None + else: + max_rows_per_chunk = np.maximum(int(chunk_size / row_size), 1) + logger.debug(f"#chunk_calc chunk: max rows_per_chunk {max_rows_per_chunk} based on row_size {row_size}") + + rows_per_chunk = np.clip(int(chunk_size / row_size), 1, num_choosers) + estimated_number_of_chunks = math.ceil(num_choosers / rows_per_chunk) + logger.debug(f"#chunk_calc chunk: initial rows_per_chunk {rows_per_chunk} based on row_size {row_size}") + + assert (rows_per_chunk > 0) and (rows_per_chunk <= num_choosers) # alt chunks boundaries are where index changes alt_ids = alternatives.index.values - alt_chunk_end = np.where(alt_ids[:-1] != alt_ids[1:])[0] + 1 - alt_chunk_end = np.append([0], alt_chunk_end) # including the first... - alt_chunk_end = alt_chunk_end[rows_per_chunk::rows_per_chunk] - - # add index to end of array to capture any final partial chunk - alt_chunk_end = np.append(alt_chunk_end, [len(alternatives.index)]) + alt_chunk_ends = np.where(alt_ids[:-1] != alt_ids[1:])[0] + 1 + alt_chunk_ends = np.append([0], alt_chunk_ends) # including the first to simplify indexing + alt_chunk_ends = np.append(alt_chunk_ends, [len(alternatives.index)]) # end of final chunk + history = {} i = offset = alt_offset = 0 while offset < num_choosers: + assert offset + rows_per_chunk <= num_choosers - alt_end = alt_chunk_end[i] + chunk_trace_label = tracing.extend_trace_label(trace_label, f'chunk_{i + 1}') if chunk_size > 0 else trace_label chooser_chunk = choosers[offset: offset + rows_per_chunk] + + alt_end = alt_chunk_ends[offset + rows_per_chunk] alternative_chunk = alternatives[alt_offset: alt_end] assert len(chooser_chunk.index) == len(np.unique(alternative_chunk.index.values)) + assert (chooser_chunk.index == np.unique(alternative_chunk.index.values)).all() + + logger.info(f"Running chunk {i+1} of {estimated_number_of_chunks or '?'} " + f"with {len(chooser_chunk)} of {num_choosers} choosers") - yield i+1, num_chunks, chooser_chunk, alternative_chunk + with chunk_log(trace_label, chunk_size): + + yield i+1, chooser_chunk, alternative_chunk, chunk_trace_label + + # get number of elements allocated during this chunk from the high water mark dict + observed_chunk_size = get_high_water_mark() + + # #CHUNK_RSS + observed_rss_size = (get_high_water_mark('rss') - initial_rss) + observed_rss_size = math.ceil(observed_rss_size / BYTES_PER_ELEMENT) + logger.debug(f"#CHUNK_RSS observed_chunk_size: {observed_chunk_size} observed_rss_size {observed_rss_size}") + observed_rss_size = max(observed_rss_size, 0) + if CHUNK_RSS: + observed_chunk_size = observed_rss_size + + alt_offset = alt_end i += 1 offset += rows_per_chunk - alt_offset = alt_end + rows_remaining = num_choosers - offset + + if CHUNK_HISTORY or chunk_size > 0: + + history.setdefault('chunk', []).append(i) + history.setdefault('row_size', []).append(row_size) + history.setdefault('rows_per_chunk', []).append(rows_per_chunk) + history.setdefault('observed_chunk_size', []).append(observed_chunk_size) + + # revise predicted row_size based on observed_chunk_size + row_size = math.ceil(observed_chunk_size / rows_per_chunk) + + # closest number of chooser rows to achieve chunk_size without exceeding it + if row_size == 0: + # they don't appear to have used any memory; increase cautiously in case small sample size was to blame + if rows_per_chunk > INITIAL_ROWS_PER_CHUNK * 100: + rows_per_chunk = rows_remaining + else: + rows_per_chunk = 10 * rows_per_chunk + else: + rows_per_chunk = int(chunk_size / row_size) + rows_per_chunk = np.clip(rows_per_chunk, 1, rows_remaining) + + estimated_number_of_chunks = i + math.ceil(rows_remaining / rows_per_chunk) if rows_remaining else i + history.setdefault('new_row_size', []).append(row_size) + history.setdefault('new_rows_per_chunk', []).append(rows_per_chunk) + history.setdefault('estimated_number_of_chunks', []).append(estimated_number_of_chunks) -def chunked_choosers_by_chunk_id(choosers, rows_per_chunk): + if history: + history = pd.DataFrame.from_dict(history) + write_history('adaptive_chunked_choosers_and_alts', history, trace_label) + + +def adaptive_chunked_choosers_by_chunk_id(choosers, chunk_size, row_size, trace_label): # generator to iterate over choosers in chunk_size chunks # like chunked_choosers but based on chunk_id field rather than dataframe length # (the presumption is that choosers has multiple rows with the same chunk_id that # all have to be included in the same chunk) # FIXME - we pathologically know name of chunk_id col in households table - assert choosers.shape[0] > 0 - num_choosers = choosers['chunk_id'].max() + 1 - num_chunks = (num_choosers // rows_per_chunk) + (num_choosers % rows_per_chunk > 0) + assert num_choosers > 0 + + # FIXME do we care if it is an int? + row_size = math.ceil(row_size) + + # #CHUNK_RSS + mem.force_garbage_collect() + initial_rss = mem.get_rss() + logger.debug(f"#CHUNK_RSS initial_rss: {initial_rss}") + + if chunk_size == 0: + assert row_size == 0 # we ignore this but make sure caller realizes that + rows_per_chunk = num_choosers + estimated_number_of_chunks = 1 + else: + assert len(HWM) == 1 + + if row_size == 0: + rows_per_chunk = min(num_choosers, INITIAL_ROWS_PER_CHUNK) # FIXME parameterize + estimated_number_of_chunks = None + logger.debug(f"#chunk_calc chunk: initial rows_per_chunk {rows_per_chunk} " + f"based on INITIAL_ROWS_PER_CHUNK {INITIAL_ROWS_PER_CHUNK}") + else: + max_rpc = np.maximum(int(chunk_size / row_size), 1) + logger.debug(f"#chunk_calc chunk: max rows_per_chunk {max_rpc} based on row_size {row_size}") + + rows_per_chunk = np.clip(int(chunk_size / row_size), 1, num_choosers) + estimated_number_of_chunks = math.ceil(num_choosers / rows_per_chunk) + + logger.debug(f"#chunk_calc chunk: initial rows_per_chunk {rows_per_chunk} based on row_size {row_size}") + + history = {} i = offset = 0 while offset < num_choosers: + + assert offset + rows_per_chunk <= num_choosers + + chunk_trace_label = \ + tracing.extend_trace_label(trace_label, f'chunk_{i + 1}') if chunk_size > 0 else trace_label + chooser_chunk = choosers[choosers['chunk_id'].between(offset, offset + rows_per_chunk - 1)] - yield i+1, num_chunks, chooser_chunk - offset += rows_per_chunk + + logger.info(f"Running chunk {i+1} of {estimated_number_of_chunks or '?'} " + f"with {rows_per_chunk} of {num_choosers} choosers") + + with chunk_log(trace_label, chunk_size): + + yield i+1, chooser_chunk, chunk_trace_label + + # get number of elements allocated during this chunk from the high water mark dict + observed_chunk_size = get_high_water_mark() + + # #CHUNK_RSS + observed_rss_size = (get_high_water_mark('rss') - initial_rss) + observed_rss_size = math.ceil(observed_rss_size / BYTES_PER_ELEMENT) + logger.debug(f"#CHUNK_RSS observed_chunk_size: {observed_chunk_size} observed_rss_size {observed_rss_size}") + observed_rss_size = max(observed_rss_size, 0) + if CHUNK_RSS: + observed_chunk_size = observed_rss_size + i += 1 + offset += rows_per_chunk + rows_remaining = num_choosers - offset + + if CHUNK_HISTORY or chunk_size > 0: + + history.setdefault('chunk', []).append(i) + history.setdefault('row_size', []).append(row_size) + history.setdefault('rows_per_chunk', []).append(rows_per_chunk) + history.setdefault('observed_chunk_size', []).append(observed_chunk_size) + + # revise predicted row_size based on observed_chunk_size + row_size = math.ceil(observed_chunk_size / rows_per_chunk) + + if row_size > 0: + # closest number of chooser rows to achieve chunk_size without exceeding it + rows_per_chunk = int(chunk_size / row_size) + if row_size == 0: + # they don't appear to have used any memory; increase cautiously in case small sample size was to blame + if rows_per_chunk > INITIAL_ROWS_PER_CHUNK * 100: + rows_per_chunk = rows_remaining + else: + rows_per_chunk = 10 * rows_per_chunk + + rows_per_chunk = np.clip(rows_per_chunk, 1, rows_remaining) + + estimated_number_of_chunks = i + math.ceil(rows_remaining / rows_per_chunk) if rows_remaining else i + + history.setdefault('new_row_size', []).append(row_size) + history.setdefault('new_rows_per_chunk', []).append(rows_per_chunk) + history.setdefault('estimated_number_of_chunks', []).append(estimated_number_of_chunks) + + if history: + history = pd.DataFrame.from_dict(history) + write_history('adaptive_chunked_choosers_by_chunk_id', history, trace_label) diff --git a/activitysim/core/config.py b/activitysim/core/config.py index 7d647c393a..c41e2b257b 100644 --- a/activitysim/core/config.py +++ b/activitysim/core/config.py @@ -63,14 +63,18 @@ def rng_base_seed(): @inject.injectable(cache=True) -def settings(): - settings_dict = read_settings_file('settings.yaml', mandatory=True) +def settings_file_name(): + return 'settings.yaml' + + +@inject.injectable(cache=True) +def settings(settings_file_name): + settings_dict = read_settings_file(settings_file_name, mandatory=True) return settings_dict def setting(key, default=None): - return inject.get_injectable('settings').get(key, default) @@ -80,6 +84,18 @@ def override_setting(key, value): inject.add_injectable('settings', new_settings) +def get_global_constants(): + """ + Read global constants from settings file + + Returns + ------- + constants : dict + dictionary of constants to add to locals for use by expressions in model spec + """ + return read_settings_file('constants.yaml', mandatory=False) + + def read_model_settings(file_name, mandatory=False): """ @@ -159,15 +175,11 @@ def build_output_file_path(file_name, use_prefix=None): def cascading_input_file_path(file_name, dir_list_injectable_name, mandatory=True): - dir_list = inject.get_injectable(dir_list_injectable_name) - - if isinstance(dir_list, str): - dir_list = [dir_list] - - assert isinstance(dir_list, list) + dir_paths = inject.get_injectable(dir_list_injectable_name) + dir_paths = [dir_paths] if isinstance(dir_paths, str) else dir_paths file_path = None - for dir in dir_list: + for dir in dir_paths: p = os.path.join(dir, file_name) if os.path.isfile(p): file_path = p @@ -175,7 +187,7 @@ def cascading_input_file_path(file_name, dir_list_injectable_name, mandatory=Tru if mandatory and not file_path: raise RuntimeError("file_path %s: file '%s' not in %s" % - (dir_list_injectable_name, file_name, dir_list)) + (dir_list_injectable_name, file_name, dir_paths)) return file_path @@ -246,47 +258,139 @@ def pipeline_file_path(file_name): return build_output_file_path(file_name, use_prefix=prefix) -def read_settings_file(file_name, mandatory=True): +class SettingsFileNotFound(Exception): + def __init__(self, file_name, configs_dir): + self.file_name = file_name + self.configs_dir = configs_dir + + def __str__(self): + return repr(f"Settings file '{self.file_name}' not found in {self.configs_dir}") + + +def read_settings_file(file_name, mandatory=True, include_stack=[], configs_dir_list=None): + """ + + look for first occurence of yaml file named in directories in configs_dir list, + read settings from yaml file and return as dict. + + Settings file may contain directives that affect which file settings are returned: + + inherit_settings: boolean + backfill settings in the current file with values from the next settings file in configs_dir list + include_settings: string + read settings from specified include_file in place of the current file settings + (to avoid confusion, this directive must appea ALONE in fiel, without any additional settings or directives.) + + Parameters + ---------- + file_name + mandatory: booelan + if true, raise SettingsFileNotFound exception if no settings file, otherwise return empty dict + include_stack: boolean + only used for recursive calls to provide list of files included so far to detect cycles + + Returns: dict + settings from speciified settings file/s + ------- + + """ def backfill_settings(settings, backfill): new_settings = backfill.copy() new_settings.update(settings) return new_settings - configs_dir = inject.get_injectable('configs_dir') - - if isinstance(configs_dir, str): - configs_dir = [configs_dir] - - assert isinstance(configs_dir, list) + if configs_dir_list is None: + configs_dir_list = inject.get_injectable('configs_dir') + configs_dir_list = [configs_dir_list] if isinstance(configs_dir_list, str) else configs_dir_list + assert isinstance(configs_dir_list, list) + assert len(configs_dir_list) == len(set(configs_dir_list)), \ + f"repeating file names not allowed in config_dir list: {configs_dir_list}" + inheriting = False settings = {} - for dir in configs_dir: + source_file_paths = include_stack.copy() + for dir in configs_dir_list: file_path = os.path.join(dir, file_name) if os.path.exists(file_path): - if settings: - logger.debug("read settings for %s from %s" % (file_name, file_path)) + if inheriting: + # we must be inheriting + logger.debug("inheriting additional settings for %s from %s" % (file_name, file_path)) + inheriting = True + + assert file_path not in source_file_paths, \ + f"read_settings_file - recursion in reading 'file_path' after loading: {source_file_paths}" with open(file_path) as f: + s = yaml.load(f, Loader=yaml.SafeLoader) if s is None: s = {} settings = backfill_settings(settings, s) - settings['source_file_paths'] = settings.get('source_file_path', []) + [file_path] + # maintain a list of files we read from to improve error message when an expected setting is not found + source_file_paths += [file_path] + + include_file_name = s.get('include_settings', False) + if include_file_name: + # FIXME - prevent users from creating borgesian garden of branching paths? + # There is a lot of opportunity for confusion if this feature were over-used + # Maybe we insist that a file with an include directive is the 'end of the road' + # essentially the current settings firle is an alias for the included file + if len(s) > 1: + logger.error(f"'include_settings' must appear alone in settings file.") + additional_settings = list(set(s.keys()).difference({'include_settings'})) + logger.error(f"Unexpected additional settings: {additional_settings}") + raise RuntimeError(f"'include_settings' must appear alone in settings file.") + + logger.debug("including settings for %s from %s" % (file_name, include_file_name)) - if s.get('inherit_settings', False): - logger.debug("inherit_settings flag set for %s in %s" % (file_name, file_path)) - continue - else: + # recursive call to read included file INSTEAD of the file with include_settings sepcified + s, source_file_paths = \ + read_settings_file(include_file_name, mandatory=True, include_stack=source_file_paths) + + # FIXME backfill with the included file + settings = backfill_settings(settings, s) + + # we are done as soon as we read one file successfully + # unless if inherit_settings is set to true in this file + + if not s.get('inherit_settings', False): break + # if inheriting, continue and backfill settings from the next existing settings file configs_dir_list + + inherit_settings = s.get('inherit_settings') + if isinstance(inherit_settings, str): + inherit_file_name = inherit_settings + assert os.path.join(dir, inherit_file_name) not in source_file_paths, \ + f"circular inheritance of {inherit_file_name}: {source_file_paths}: " + # make a recursive call to switch inheritance chain to specified file + configs_dir_list = None + + logger.debug("inheriting additional settings for %s from %s" % (file_name, inherit_file_name)) + s, source_file_paths = \ + read_settings_file(inherit_file_name, mandatory=True, + include_stack=source_file_paths, + configs_dir_list=configs_dir_list) + + # backfill with the inherited file + settings = backfill_settings(settings, s) + break # break the current inheritance chain (not as bad luck as breaking a chain-letter chain?...) + + if len(source_file_paths) > 0: + settings['source_file_paths'] = source_file_paths + if mandatory and not settings: - raise RuntimeError("read_settings_file: no settings file '%s' in %s" % - (file_name, configs_dir)) + raise SettingsFileNotFound(file_name, configs_dir_list) + + if include_stack: + # if we were called recursively, return an updated list of source_file_paths + return settings, source_file_paths - return settings + else: + return settings def base_settings_file_path(file_name): @@ -307,11 +411,7 @@ def base_settings_file_path(file_name): file_name = '%s.yaml' % (file_name, ) configs_dir = inject.get_injectable('configs_dir') - - if isinstance(configs_dir, str): - configs_dir = [configs_dir] - - assert isinstance(configs_dir, list) + configs_dir = [configs_dir] if isinstance(configs_dir, str) else configs_dir for dir in configs_dir: file_path = os.path.join(dir, file_name) diff --git a/activitysim/abm/models/util/expressions.py b/activitysim/core/expressions.py similarity index 84% rename from activitysim/abm/models/util/expressions.py rename to activitysim/core/expressions.py index ef37c8dff2..83c042c03f 100644 --- a/activitysim/abm/models/util/expressions.py +++ b/activitysim/core/expressions.py @@ -1,61 +1,22 @@ # ActivitySim # See full license in LICENSE.txt. -import os import logging -import warnings import numpy as np import pandas as pd -from activitysim.abm.tables import constants - from activitysim.core import tracing from activitysim.core import config from activitysim.core import assign from activitysim.core import inject from activitysim.core import simulate -from activitysim.core.util import other_than from activitysim.core.util import assign_in_place -from activitysim.core import util logger = logging.getLogger(__name__) -def reindex_i(series1, series2, dtype=np.int8): - """ - version of reindex that replaces missing na values and converts to int - helpful in expression files that compute counts (e.g. num_work_tours) - """ - return util.reindex(series1, series2).fillna(0).astype(dtype) - - -def local_utilities(): - """ - Dict of useful modules and functions to provides as locals for use in eval of expressions - - Returns - ------- - utility_dict : dict - name, entity pairs of locals - """ - - utility_dict = { - 'pd': pd, - 'np': np, - 'constants': constants, - 'reindex': util.reindex, - 'reindex_i': reindex_i, - 'setting': config.setting, - 'skim_time_period_label': skim_time_period_label, - 'other_than': other_than, - 'skim_dict': inject.get_injectable('skim_dict', None) - } - - return utility_dict - - def compute_columns(df, model_settings, locals_dict={}, trace_label=None): """ Evaluate expressions_spec in context of df, with optional additional pipeline tables in locals @@ -95,7 +56,7 @@ def compute_columns(df, model_settings, locals_dict={}, trace_label=None): df_name = model_settings.get('DF') helper_table_names = model_settings.get('TABLES', []) - expressions_spec_name = model_settings.get('SPEC', model_settings_name) + expressions_spec_name = model_settings.get('SPEC', None) assert expressions_spec_name is not None, \ "Expected to find 'SPEC' in %s" % model_settings_name @@ -104,6 +65,7 @@ def compute_columns(df, model_settings, locals_dict={}, trace_label=None): if not expressions_spec_name.endswith(".csv"): expressions_spec_name = '%s.csv' % expressions_spec_name + logger.debug(f"{trace_label} compute_columns using expression spec file {expressions_spec_name}") expressions_spec = assign.read_assignment_spec(config.config_file_path(expressions_spec_name)) assert expressions_spec.shape[0] > 0, \ @@ -118,10 +80,16 @@ def compute_columns(df, model_settings, locals_dict={}, trace_label=None): # be nice and also give it to them as df? tables['df'] = df - _locals_dict = local_utilities() + _locals_dict = assign.local_utilities() _locals_dict.update(locals_dict) _locals_dict.update(tables) + # FIXME a number of asim model preprocessors want skim_dict - should they request it in model_settings.TABLES? + _locals_dict.update({ + # 'los': inject.get_injectable('network_los', None), + 'skim_dict': inject.get_injectable('skim_dict', None), + }) + results, trace_results, trace_assigned_locals \ = assign.assign_variables(expressions_spec, df, @@ -205,7 +173,7 @@ def skim_time_period_label(time_period): return skim_time_periods['labels'][bin] return pd.cut(time_period, skim_time_periods[period_label], - labels=skim_time_periods['labels'], right=True).astype(str) + labels=skim_time_periods['labels'], ordered=False).astype(str) def annotate_preprocessors( @@ -223,7 +191,6 @@ def annotate_preprocessors( simulate.set_skim_wrapper_targets(tours_df, skims) - annotations = None for model_settings in preprocessor_settings: results = compute_columns( diff --git a/activitysim/core/inject.py b/activitysim/core/inject.py index bc4afa294b..eb2934cc03 100644 --- a/activitysim/core/inject.py +++ b/activitysim/core/inject.py @@ -86,6 +86,7 @@ def add_table(table_name, table, replace=False): logger.warning("inject add_table replacing existing table %s" % table_name) assert False + # FIXME - should add table.copy() instead, so it can't be modified behind our back? return orca.add_table(table_name, table, cache=False) diff --git a/activitysim/core/input.py b/activitysim/core/input.py index e345396945..7958ef679c 100644 --- a/activitysim/core/input.py +++ b/activitysim/core/input.py @@ -10,8 +10,9 @@ from activitysim.core import ( inject, config, - util + util, ) +from activitysim.core import mem logger = logging.getLogger(__name__) @@ -19,7 +20,7 @@ def read_input_table(tablename): """Reads input table name and returns cleaned DataFrame. - Uses settings found in input_table_list in settings.yaml + Uses settings found in input_table_list in global settings file Parameters ---------- @@ -38,7 +39,7 @@ def read_input_table(tablename): table_info = info assert table_info is not None, \ - 'could not find info for for tablename %s in settings.yaml' % tablename + f"could not find info for for tablename {tablename} in settings file" return read_from_table_info(table_info) @@ -87,7 +88,7 @@ def read_from_table_info(table_info): df = _read_input_file(data_file_path, h5_tablename=h5_tablename) - logger.debug('raw %s table columns: %s' % (tablename, df.columns.values)) + # logger.debug('raw %s table columns: %s' % (tablename, df.columns.values)) logger.debug('raw %s table size: %s' % (tablename, util.df_size(df))) if create_input_store: @@ -113,7 +114,7 @@ def read_from_table_info(table_info): # rename columns first, so keep_columns can be a stable list of expected/required columns if rename_columns: - logger.info("renaming columns: %s" % rename_columns) + logger.debug("renaming columns: %s" % rename_columns) df.rename(columns=rename_columns, inplace=True) # set index @@ -122,13 +123,26 @@ def read_from_table_info(table_info): assert not df.duplicated(index_col).any() df.set_index(index_col, inplace=True) else: - df.index.names = [index_col] + # FIXME not sure we want to do this. More likely they omitted index col than that they want to name it? + # df.index.names = [index_col] + logger.error(f"index_col '{index_col}' specified in configs but not in {tablename} table!") + logger.error(f"{tablename} columns are: {list(df.columns)}") + raise RuntimeError(f"index_col '{index_col}' not in {tablename} table!") - logger.info("keeping columns: %s" % keep_columns) if keep_columns: - logger.info("keeping columns: %s" % keep_columns) + logger.debug("keeping columns: %s" % keep_columns) + if not set(keep_columns).issubset(set(df.columns)): + logger.error(f"Required columns missing from {tablename} table: " + f"{list(set(keep_columns).difference(set(df.columns)))}") + logger.error(f"{tablename} table has columns: {list(df.columns)}") + raise RuntimeError(f"Required columns missing from {tablename} table") + df = df[keep_columns] + if df.columns.duplicated().any(): + duplicate_column_names = df.columns[df.columns.duplicated(keep=False)].unique().to_list() + assert not df.columns.duplicated().any(), f"duplicate columns names in {tablename}: {duplicate_column_names}" + logger.debug('%s table columns: %s' % (tablename, df.columns.values)) logger.debug('%s table size: %s' % (tablename, util.df_size(df))) logger.info('%s index name: %s' % (tablename, df.index.name)) @@ -157,6 +171,7 @@ def _read_csv_with_fallback_encoding(filepath): but try alternate Windows-compatible cp1252 if unicode fails """ + try: logger.info('Reading CSV file %s' % filepath) return pd.read_csv(filepath, comment='#') diff --git a/activitysim/core/interaction_sample.py b/activitysim/core/interaction_sample.py index 639e9bf770..1af1158a6d 100644 --- a/activitysim/core/interaction_sample.py +++ b/activitysim/core/interaction_sample.py @@ -113,7 +113,7 @@ def make_sample_choices( choices_array[i] = np.take(alts, positions + offsets) choice_probs_array[i] = np.take(alt_probs_array, positions + offsets) - # explode to one row per chooser.index, alt_TAZ + # explode to one row per chooser.index, alt_zone_id choices_df = pd.DataFrame( {alt_col_name: choices_array.flatten(order='F'), 'rand': rands.flatten(order='F'), @@ -185,8 +185,9 @@ def _interaction_sample( """ have_trace_targets = tracing.has_trace_targets(choosers) + num_choosers = len(choosers.index) - assert len(choosers.index) > 0 + assert num_choosers > 0 if have_trace_targets: tracing.trace_df(choosers, tracing.extend_trace_label(trace_label, 'choosers')) @@ -235,6 +236,8 @@ def _interaction_sample( = eval_interaction_utilities(spec, interaction_df, locals_d, trace_label, trace_rows) chunk.log_df(trace_label, 'interaction_utilities', interaction_utilities) + # ########### HWM ############ + del interaction_df chunk.log_df(trace_label, 'interaction_df', None) @@ -259,7 +262,7 @@ def _interaction_sample( chunk.log_df(trace_label, 'interaction_utilities', None) if have_trace_targets: - tracing.trace_df(utilities, tracing.extend_trace_label(trace_label, 'utilities'), + tracing.trace_df(utilities, tracing.extend_trace_label(trace_label, 'utils'), column_labels=['alternative', 'utility']) tracing.dump_df(DUMP, utilities, trace_label, 'utilities') @@ -345,37 +348,33 @@ def _interaction_sample( return choices_df -def calc_rows_per_chunk(chunk_size, choosers, alternatives, trace_label): - - num_choosers = choosers.shape[0] +def interaction_sample_calc_row_size(choosers, alternatives, trace_label): - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + sizer = chunk.RowSizeEstimator(trace_label) # all columns from choosers - chooser_row_size = choosers.shape[1] + chooser_row_size = len(choosers.columns) + sample_size = len(alternatives) - # interaction_df has one column per alternative plus a skim column and a join column - alt_row_size = alternatives.shape[1] + 2 + # interaction_df has one column per alternative plus a skim column + alt_row_size = len(alternatives.columns) + 1 + sizer.add_elements((chooser_row_size + alt_row_size) * sample_size, 'interaction_df') # interaction_utilities - alt_row_size += 1 + sizer.add_elements(sample_size, 'interaction_utilities') + sizer.drop_elements('interaction_df') - # interaction_df includes all alternatives and is only afterwards sampled - row_size = (chooser_row_size + alt_row_size) * alternatives.shape[0] + # show is over once we delete interaction_df - # utilities and probs have one row per chooser and one column per alternative row - row_size += 2 * alternatives.shape[0] + sizer.add_elements(chooser_row_size, 'utilities') + sizer.drop_elements('interaction_utilities') - logger.debug("%s #chunk_calc choosers %s" % (trace_label, choosers.shape)) - logger.debug("%s #chunk_calc alternatives %s" % (trace_label, alternatives.shape)) + sizer.add_elements(chooser_row_size, 'probs') + sizer.drop_elements('utilities') - logger.debug("%s #chunk_calc chooser_row_size %s" % (trace_label, chooser_row_size)) - logger.debug("%s #chunk_calc alt_row_size %s" % (trace_label, alt_row_size)) - logger.debug("%s #chunk_calc row_size %s" % (trace_label, row_size)) + row_size = sizer.get_hwm() - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + return row_size def interaction_sample( @@ -449,26 +448,17 @@ def interaction_sample( sample_size = min(sample_size, len(alternatives.index)) - rows_per_chunk, effective_chunk_size = \ - calc_rows_per_chunk(chunk_size, choosers, alternatives, trace_label) + row_size = chunk_size and interaction_sample_calc_row_size(choosers, alternatives, trace_label) result_list = [] - for i, num_chunks, chooser_chunk in chunk.chunked_choosers(choosers, rows_per_chunk): - - logger.info("Running chunk %s of %s size %d" % (i, num_chunks, len(chooser_chunk))) - - chunk_trace_label = tracing.extend_trace_label(trace_label, 'chunk_%s' % i) \ - if num_chunks > 1 else trace_label - - chunk.log_open(chunk_trace_label, chunk_size, effective_chunk_size) + for i, chooser_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers(choosers, chunk_size, row_size, trace_label): choices = _interaction_sample(chooser_chunk, alternatives, spec, sample_size, alt_col_name, allow_zero_probs, skims, locals_d, chunk_trace_label) - chunk.log_close(chunk_trace_label) - if choices.shape[0] > 0: # might not be any if allow_zero_probs result_list.append(choices) diff --git a/activitysim/core/interaction_sample_simulate.py b/activitysim/core/interaction_sample_simulate.py index b38f3062d1..64a15a6594 100644 --- a/activitysim/core/interaction_sample_simulate.py +++ b/activitysim/core/interaction_sample_simulate.py @@ -2,16 +2,12 @@ # See full license in LICENSE.txt. import logging -import gc - import numpy as np import pandas as pd from . import logit from . import tracing from . import chunk -from . import util -from . import mem from .simulate import set_skim_wrapper_targets from activitysim.core.mem import force_garbage_collect @@ -180,6 +176,7 @@ def _interaction_sample_simulate( # insert the zero-prob utilities to pad each alternative set to same size padded_utilities = np.insert(interaction_utilities.utility.values, inserts, -999) + chunk.log_df(trace_label, 'padded_utilities', padded_utilities) del inserts del interaction_utilities @@ -187,7 +184,6 @@ def _interaction_sample_simulate( # reshape to array with one row per chooser, one column per alternative padded_utilities = padded_utilities.reshape(-1, max_sample_count) - chunk.log_df(trace_label, 'padded_utilities', padded_utilities) # convert to a dataframe with one row per chooser and one column per alternative utilities_df = pd.DataFrame( @@ -272,33 +268,26 @@ def _interaction_sample_simulate( return choices -def calc_rows_per_chunk(chunk_size, choosers, alt_sample, spec, trace_label=None): +def interaction_sample_simulate_calc_row_size(choosers, alt_sample, spec, trace_label): # It is hard to estimate the size of the utilities_df since it conflates duplicate picks. # Currently we ignore it, but maybe we should chunk based on worst case? - num_choosers = len(choosers.index) - - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + sizer = chunk.RowSizeEstimator(trace_label) + num_choosers = len(choosers.index) chooser_row_size = len(choosers.columns) - # one column per alternative plus skims and interaction_utilities - alt_row_size = alt_sample.shape[1] + 2 + # one column per alternative plus skims, interaction_utilities, probs + alt_row_size = alt_sample.shape[1] + 3 # average sample size sample_size = alt_sample.shape[0] / float(num_choosers) - row_size = (chooser_row_size + alt_row_size) * sample_size + # interaction_df + sizer.add_elements((chooser_row_size + alt_row_size) * sample_size, 'interaction_df') - # logger.debug("%s #chunk_calc spec %s" % (trace_label, spec.shape)) - # logger.debug("%s #chunk_calc chooser_row_size %s" % (trace_label, chooser_row_size)) - # logger.debug("%s #chunk_calc sample_size %s" % (trace_label, sample_size)) - # logger.debug("%s #chunk_calc alt_row_size %s" % (trace_label, alt_row_size)) - # logger.debug("%s #chunk_calc alt_sample %s" % (trace_label, alt_sample.shape)) - - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + row_size = sizer.get_hwm() + return row_size def interaction_sample_simulate( @@ -366,19 +355,12 @@ def interaction_sample_simulate( trace_label = tracing.extend_trace_label(trace_label, 'interaction_sample_simulate') - rows_per_chunk, effective_chunk_size = \ - calc_rows_per_chunk(chunk_size, choosers, alternatives, spec=spec, trace_label=trace_label) + row_size = chunk_size and interaction_sample_simulate_calc_row_size(choosers, alternatives, spec, trace_label) result_list = [] - for i, num_chunks, chooser_chunk, alternative_chunk \ - in chunk.chunked_choosers_and_alts(choosers, alternatives, rows_per_chunk): - - logger.info("Running chunk %s of %s size %d" % (i, num_chunks, len(chooser_chunk))) - - chunk_trace_label = tracing.extend_trace_label(trace_label, 'chunk_%s' % i) \ - if num_chunks > 1 else trace_label - - chunk.log_open(chunk_trace_label, chunk_size, effective_chunk_size) + for i, chooser_chunk, alternative_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers_and_alts(choosers, alternatives, + chunk_size, row_size, trace_label): choices = _interaction_sample_simulate( chooser_chunk, alternative_chunk, spec, choice_column, @@ -387,8 +369,6 @@ def interaction_sample_simulate( chunk_trace_label, trace_choice_name, estimator) - chunk.log_close(chunk_trace_label) - result_list.append(choices) force_garbage_collect() diff --git a/activitysim/core/interaction_simulate.py b/activitysim/core/interaction_simulate.py index 938f6867fc..4ed13da024 100644 --- a/activitysim/core/interaction_simulate.py +++ b/activitysim/core/interaction_simulate.py @@ -63,7 +63,7 @@ def eval_interaction_utilities(spec, df, locals_d, trace_label, trace_rows, esti Will have the index of `df` and a single column of utilities """ - trace_label = tracing.extend_trace_label(trace_label, "eval_interaction_utilities") + trace_label = tracing.extend_trace_label(trace_label, "eval_interaction_utils") logger.info("Running eval_interaction_utilities on %s rows" % df.shape[0]) assert(len(spec.columns) == 1) @@ -75,6 +75,8 @@ def eval_interaction_utilities(spec, df, locals_d, trace_label, trace_rows, esti def to_series(x): if np.isscalar(x): return pd.Series([x] * len(df), index=df.index) + if isinstance(x, np.ndarray): + return pd.Series(x, index=df.index) return x if trace_rows is not None and trace_rows.any(): @@ -171,7 +173,7 @@ def to_series(x): trace_eval_results[k] = v[trace_rows] * coefficient except Exception as err: - logger.exception("Variable evaluation failed for: %s" % str(expr)) + logger.exception(f"{trace_label} - {type(err).__name__} ({str(err)}) evaluating: {str(expr)}") raise err # mem.trace_memory_info("eval_interaction_utilities: %s" % expr) @@ -273,6 +275,7 @@ def _interaction_simulate( # if using skims, copy index into the dataframe, so it will be # available as the "destination" for the skims dereference below if skims is not None: + alternatives = alternatives.copy() alternatives[alternatives.index.name] = alternatives.index # cross join choosers and alternatives (cartesian product) @@ -304,12 +307,18 @@ def _interaction_simulate( = eval_interaction_utilities(spec, interaction_df, locals_d, trace_label, trace_rows, estimator) chunk.log_df(trace_label, 'interaction_utilities', interaction_utilities) + print(f"interaction_df {interaction_df.shape}") + print(f"interaction_utilities {interaction_utilities.shape}") + + del interaction_df + chunk.log_df(trace_label, 'interaction_df', None) + if have_trace_targets: tracing.trace_interaction_eval_results(trace_eval_results, trace_ids, tracing.extend_trace_label(trace_label, 'eval')) tracing.trace_df(interaction_utilities[trace_rows], - tracing.extend_trace_label(trace_label, 'interaction_utilities'), + tracing.extend_trace_label(trace_label, 'interaction_utils'), slicer='NONE', transpose=False) # reshape utilities (one utility column and one row per row in model_design) @@ -320,7 +329,7 @@ def _interaction_simulate( chunk.log_df(trace_label, 'utilities', utilities) if have_trace_targets: - tracing.trace_df(utilities, tracing.extend_trace_label(trace_label, 'utilities'), + tracing.trace_df(utilities, tracing.extend_trace_label(trace_label, 'utils'), column_labels=['alternative', 'utility']) tracing.dump_df(DUMP, utilities, trace_label, 'utilities') @@ -330,6 +339,9 @@ def _interaction_simulate( probs = logit.utils_to_probs(utilities, trace_label=trace_label, trace_choosers=choosers) chunk.log_df(trace_label, 'probs', probs) + del utilities + chunk.log_df(trace_label, 'utilities', None) + if have_trace_targets: tracing.trace_df(probs, tracing.extend_trace_label(trace_label, 'probs'), column_labels=['alternative', 'probability']) @@ -363,32 +375,40 @@ def _interaction_simulate( return choices -def calc_rows_per_chunk(chunk_size, choosers, alternatives, sample_size, skims, trace_label=None): - - num_choosers = len(choosers.index) +def interaction_simulate_calc_row_size(choosers, alternatives, sample_size, skims, trace_label): - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + sizer = chunk.RowSizeEstimator(trace_label) + sample_size = sample_size or len(alternatives) chooser_row_size = len(choosers.columns) - - # alternative columns plus join column - alt_row_size = alternatives.shape[1] + 1 - + # alternative columns plus join column and (possibly) skim destination + alt_row_size = alternatives.shape[1] + 1 + int(skims is not None) if skims is not None: alt_row_size += 1 - sample_size = sample_size or alternatives.shape[0] - row_size = (chooser_row_size + alt_row_size) * sample_size + logger.debug(f"{trace_label} #chunk_calc chooser_row_size {chooser_row_size}") + logger.debug(f"{trace_label} #chunk_calc alt_row_size {alt_row_size}") + logger.debug(f"{trace_label} #chunk_calc sample_size {sample_size}") + + # interaction_df + sizer.add_elements((chooser_row_size + alt_row_size) * sample_size, 'interaction_df') - # logger.debug("%s #chunk_calc choosers %s" % (trace_label, choosers.shape)) - # logger.debug("%s #chunk_calc alternatives %s" % (trace_label, alternatives.shape)) - # logger.debug("%s #chunk_calc chooser_row_size %s" % (trace_label, chooser_row_size)) - # logger.debug("%s #chunk_calc sample_size %s" % (trace_label, sample_size)) - # logger.debug("%s #chunk_calc alt_row_size %s" % (trace_label, alt_row_size)) + # interaction_df is almost certainly the HWM - if so, no need to worry about the crumbs... - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + # interaction_utilities utilities probs + sizer.add_elements(0, 'interaction_utilities') + sizer.add_elements(0, 'utilities') + sizer.add_elements(0, 'probs') + + sizer.drop_elements('utilities') + + sizer.add_elements(0, 'positions') + sizer.add_elements(0, 'rands') + sizer.add_elements(0, 'choices') + + row_size = sizer.get_hwm() + + return row_size def interaction_simulate( @@ -450,20 +470,12 @@ def interaction_simulate( assert len(choosers) > 0 - rows_per_chunk, effective_chunk_size = \ - calc_rows_per_chunk(chunk_size, choosers, alternatives=alternatives, - sample_size=sample_size, skims=skims, - trace_label=trace_label) + row_size = chunk_size and \ + interaction_simulate_calc_row_size(choosers, alternatives, sample_size, skims, trace_label) result_list = [] - for i, num_chunks, chooser_chunk in chunk.chunked_choosers(choosers, rows_per_chunk): - - logger.info("Running chunk %s of %s size %d" % (i, num_chunks, len(chooser_chunk))) - - chunk_trace_label = tracing.extend_trace_label(trace_label, 'chunk_%s' % i) \ - if num_chunks > 1 else trace_label - - chunk.log_open(chunk_trace_label, chunk_size, effective_chunk_size) + for i, chooser_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers(choosers, chunk_size, row_size, trace_label): choices = _interaction_simulate(chooser_chunk, alternatives, spec, skims, locals_d, sample_size, @@ -471,8 +483,6 @@ def interaction_simulate( trace_choice_name, estimator) - chunk.log_close(chunk_trace_label) - result_list.append(choices) force_garbage_collect() diff --git a/activitysim/core/logit.py b/activitysim/core/logit.py index 53e965ab9f..a94cdead5d 100644 --- a/activitysim/core/logit.py +++ b/activitysim/core/logit.py @@ -181,7 +181,7 @@ def utils_to_probs(utils, trace_label=None, exponentiated=False, allow_zero_prob return probs -def make_choices(probs, trace_label=None, trace_choosers=None): +def make_choices(probs, trace_label=None, trace_choosers=None, allow_bad_probs=False): """ Make choices for each chooser from among a set of alternatives. @@ -216,7 +216,7 @@ def make_choices(probs, trace_label=None, trace_choosers=None): probs.sum(axis=1).sub(np.ones(len(probs.index))).abs() \ > BAD_PROB_THRESHOLD * np.ones(len(probs.index)) - if bad_probs.any(): + if bad_probs.any() and not allow_bad_probs: report_bad_choices(bad_probs, probs, trace_label=tracing.extend_trace_label(trace_label, 'bad_probs'), @@ -243,8 +243,7 @@ def interaction_dataset(choosers, alternatives, sample_size=None, alt_index_id=N Combine choosers and alternatives into one table for the purposes of creating interaction variables and/or sampling alternatives. - Any duplicate column names in alternatives table will be renamed with an '_r' suffix. - (e.g. TAZ field in alternatives will appear as TAZ_r so that it can be targeted in a skim) + Any duplicate column names in choosers table will be renamed with an '_chooser' suffix. Parameters ---------- @@ -457,8 +456,9 @@ def count_nests(nest_spec): def count_each_nest(spec, count): if isinstance(spec, dict): - return count + sum([count_each_nest(alt, count) for alt in spec['alternatives']]) + return count + 1 + sum([count_each_nest(alt, count) for alt in spec['alternatives']]) else: + assert isinstance(spec, str) return 1 return count_each_nest(nest_spec, 0) if nest_spec is not None else 0 diff --git a/activitysim/core/los.py b/activitysim/core/los.py new file mode 100644 index 0000000000..d9183e2084 --- /dev/null +++ b/activitysim/core/los.py @@ -0,0 +1,599 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import os +import logging +import warnings + +import numpy as np +import pandas as pd + +from activitysim.core import skim_dictionary +from activitysim.core import inject +from activitysim.core import util +from activitysim.core import config +from activitysim.core import pathbuilder +from activitysim.core import mem +from activitysim.core import tracing + +from activitysim.core.skim_dict_factory import NumpyArraySkimFactory +from activitysim.core.skim_dict_factory import MemMapSkimFactory + +skim_factories = { + 'NumpyArraySkimFactory': NumpyArraySkimFactory, + 'MemMapSkimFactory': MemMapSkimFactory, +} + +logger = logging.getLogger(__name__) + +LOS_SETTINGS_FILE_NAME = 'network_los.yaml' + +ONE_ZONE = 1 +TWO_ZONE = 2 +THREE_ZONE = 3 + +DEFAULT_SETTINGS = { + 'rebuild_tvpb_cache': True, + 'zone_system': ONE_ZONE, + 'skim_dict_factory': 'NumpyArraySkimFactory' +} + +TRACE_TRIMMED_MAZ_TO_TAP_TABLES = True + + +class Network_LOS(object): + """ + singleton object to manage skims and skim-related tables + + | los_settings_file_name: str # e.g. 'network_los.yaml' + | skim_dtype_name:str # e.g. 'float32' + | + | dict_factory_name: str # e.g. 'NumpyArraySkimFactory' + | zone_system: str # str (ONE_ZONE, TWO_ZONE, or THREE_ZONE) + | skim_time_periods = None # list of str e.g. ['AM', 'MD', 'PM'] + | + | skims_info: dict # dict of SkimInfo keyed by skim_tag + | skim_buffers: dict # when multiprocessing, dict of multiprocessing.Array buffers keyed by skim_tag + | skim_dicts: dice # dict of SkimDict keyed by skim_tag + | + | # TWO_ZONE and THREE_ZONE + | maz_taz_df: pandas.DataFrame # DataFrame with two columns, MAZ and TAZ, mapping MAZ to containing TAZ + | maz_to_maz_df: pandas.DataFrame # maz_to_maz attributes for MazSkimDict sparse skims + | # indexed by synthetic omaz/dmaz index for faster get_mazpairs lookup) + | maz_ceiling: int # max maz_id + 1 (to compute synthetic omaz/dmaz index by get_mazpairs) + | max_blend_distance: dict # dict of int maz_to_maz max_blend_distance values keyed by skim_tag + | + | # THREE_ZONE only + | tap_df: pandas.DataFrame # taps data frame + | tap_lines_df: pandas.DataFrame # if specified in settings, list of transit lines served, indexed by TAP + | # used to prune maz_to_tap_dfs to drop more distant TAPS with redundant service + | # since a TAP can serve multiple lines, tap_lines_df TAP index is not unique + | maz_to_tap_dfs: dict # dict of maz_to_tap DataFrames indexed by access mode (e.g. 'walk', 'drive') + | # maz_to_tap dfs have OMAZ and DMAZ columns plus additional attribute columns + | tap_tap_uid: TapTapUidCalculator + """ + + def __init__(self, los_settings_file_name=LOS_SETTINGS_FILE_NAME): + + # Note: we require all skims to be of same dtype so they can share buffer - is that ok? + # fixme is it ok to require skims be all the same type? if so, is this the right choice? + self.skim_dtype_name = 'float32' + self.zone_system = None + self.skim_time_periods = None + self.skims_info = {} + self.skim_dicts = {} + + # TWO_ZONE and THREE_ZONE + self.maz_taz_df = None + self.maz_to_maz_df = None + self.maz_ceiling = None + self.max_blend_distance = {} + + # THREE_ZONE only + self.tap_lines_df = None + self.maz_to_tap_dfs = {} + self.tvpb = None + + self.los_settings_file_name = los_settings_file_name + self.load_settings() + + # dependency injection of skim factory (of type specified in skim_dict_factory setting) + skim_dict_factory_name = self.setting('skim_dict_factory') + assert skim_dict_factory_name in skim_factories, \ + f"Unrecognized skim_dict_factory setting '{skim_dict_factory_name}" + self.skim_dict_factory = skim_factories[skim_dict_factory_name](network_los=self) + logger.info(f"Network_LOS using skim_dict_factory: {type(self.skim_dict_factory).__name__}") + + # load SkimInfo for all skims for this zone_system (TAZ for ONE_ZONE and TWO_ZONE, TAZ and MAZ for THREE_ZONE) + self.load_skim_info() + + @property + def rebuild_tvpb_cache(self): + # setting as property here so others don't need to know default + assert self.zone_system == THREE_ZONE, f"Should not even be asking about rebuild_tvpb_cache if not THREE_ZONE" + return self.setting('rebuild_tvpb_cache') + + def setting(self, keys, default=''): + + # if they dont specify a default, check the default defaults + default = DEFAULT_SETTINGS.get(keys, '') if default == '' else default + + # get setting value for single key or dot-delimited key path (e.g. 'maz_to_maz.tables') + key_list = keys.split('.') + s = self.los_settings + for key in key_list[:-1]: + s = s.get(key) + assert isinstance(s, dict), f"expected key '{key}' not found in '{keys}' in {self.los_settings_file_name}" + key = key_list[-1] # last key + if default == '': + assert key in s, f"Expected setting {keys} not found in in {LOS_SETTINGS_FILE_NAME}" + return s.get(key, default) + + def load_settings(self): + """ + Read setting file and initialize object variables (see class docstring for list of object variables) + """ + + try: + self.los_settings = config.read_settings_file(self.los_settings_file_name, mandatory=True) + except config.SettingsFileNotFound as e: + + print(f"los_settings_file_name {self.los_settings_file_name} not found - trying global settings") + print(f"skims_file: {config.setting('skims_file')}") + print(f"skim_time_periods: {config.setting('skim_time_periods')}") + print(f"source_file_paths: {config.setting('source_file_paths')}") + print(f"inject.get_injectable('configs_dir') {inject.get_injectable('configs_dir')}") + + # look for legacy 'skims_file' setting in global settings file + if config.setting('skims_file'): + + warnings.warn("Support for 'skims_file' setting in global settings file will be removed." + "Use 'taz_skims' in network_los.yaml config file instead.", FutureWarning) + + # in which case, we also expect to find skim_time_periods in settings file + skim_time_periods = config.setting('skim_time_periods') + assert skim_time_periods is not None, "'skim_time_periods' setting not found." + warnings.warn("Support for 'skim_time_periods' setting in global settings file will be removed." + "Put 'skim_time_periods' in network_los.yaml config file instead.", FutureWarning) + + self.los_settings = { + 'taz_skims': config.setting('skims_file'), + 'zone_system': ONE_ZONE, + 'skim_time_periods': skim_time_periods + } + + else: + raise e + + # validate skim_time_periods + self.skim_time_periods = self.setting('skim_time_periods') + if 'hours' in self.skim_time_periods: + self.skim_time_periods['periods'] = self.skim_time_periods.pop('hours') + warnings.warn('support for `skim_time_periods` key `hours` will be removed in ' + 'future verions. Use `periods` instead', + FutureWarning) + assert 'periods' in self.skim_time_periods, "'periods' key not found in network_los.skim_time_periods" + assert 'labels' in self.skim_time_periods, "'labels' key not found in network_los.skim_time_periods" + + self.zone_system = self.setting('zone_system') + assert self.zone_system in [ONE_ZONE, TWO_ZONE, THREE_ZONE], \ + f"Network_LOS: unrecognized zone_system: {self.zone_system}" + + if self.zone_system in [TWO_ZONE, THREE_ZONE]: + # maz_to_maz_settings + self.max_blend_distance = self.setting('maz_to_maz.max_blend_distance', default={}) + if isinstance(self.max_blend_distance, int): + self.max_blend_distance = {'DEFAULT': self.max_blend_distance} + self.blend_distance_skim_name = self.setting('maz_to_maz.blend_distance_skim_name', default=None) + + # validate skim_time_periods + self.skim_time_periods = self.setting('skim_time_periods') + assert {'periods', 'labels'}.issubset(set(self.skim_time_periods.keys())) + + def load_skim_info(self): + """ + read skim info from omx files into SkimInfo, and store in self.skims_info dict keyed by skim_tag + + ONE_ZONE and TWO_ZONE systems have only TAZ skims + THREE_ZONE systems have both TAZ and TAP skims + """ + assert self.skim_dict_factory is not None + # load taz skim_info + self.skims_info['taz'] = self.skim_dict_factory.load_skim_info('taz') + + if self.zone_system == THREE_ZONE: + # load tap skim_info + self.skims_info['tap'] = self.skim_dict_factory.load_skim_info('tap') + + if self.zone_system == THREE_ZONE: + # load this here rather than in load_data as it is required during multiprocessing to size TVPBCache + self.tap_df = pd.read_csv(config.data_file_path(self.setting('tap'), mandatory=True)) + self.tvpb = pathbuilder.TransitVirtualPathBuilder(self) # dependent on self.tap_df + + def load_data(self): + """ + Load tables and skims from files specified in network_los settigns + """ + + # load maz tables + if self.zone_system in [TWO_ZONE, THREE_ZONE]: + + # maz + file_name = self.setting('maz') + self.maz_taz_df = pd.read_csv(config.data_file_path(file_name, mandatory=True)) + self.maz_taz_df = self.maz_taz_df[['MAZ', 'TAZ']].sort_values(by='MAZ') # only fields we need + + self.maz_ceiling = self.maz_taz_df.MAZ.max() + 1 + + # maz_to_maz_df + maz_to_maz_tables = self.setting('maz_to_maz.tables') + maz_to_maz_tables = [maz_to_maz_tables] if isinstance(maz_to_maz_tables, str) else maz_to_maz_tables + for file_name in maz_to_maz_tables: + + df = pd.read_csv(config.data_file_path(file_name, mandatory=True)) + + df['i'] = df.OMAZ * self.maz_ceiling + df.DMAZ + df.set_index('i', drop=True, inplace=True, verify_integrity=True) + logger.debug(f"loading maz_to_maz table {file_name} with {len(df)} rows") + + # FIXME - don't really need these columns, but if we do want them, + # we would need to merge them in since files may have different numbers of rows + df.drop(columns=['OMAZ', 'DMAZ'], inplace=True) + + # besides, we only want data columns so we can coerce to same type as skims + df = df.astype(np.dtype(self.skim_dtype_name)) + + if self.maz_to_maz_df is None: + self.maz_to_maz_df = df + else: + self.maz_to_maz_df = pd.concat([self.maz_to_maz_df, df], axis=1) + + # load tap tables + if self.zone_system == THREE_ZONE: + + # tap_df should already have been loaded by load_skim_info because, + # during multiprocessing, it is required by TapTapUidCalculator to size TVPBCache + # self.tap_df = pd.read_csv(config.data_file_path(self.setting('tap'), mandatory=True)) + assert self.tap_df is not None + + # maz_to_tap_dfs - different sized sparse arrays with different columns, so we keep them seperate + for mode, maz_to_tap_settings in self.setting('maz_to_tap').items(): + + assert 'table' in maz_to_tap_settings, \ + f"Expected setting maz_to_tap.{mode}.table not found in in {LOS_SETTINGS_FILE_NAME}" + + file_name = maz_to_tap_settings['table'] + df = pd.read_csv(config.data_file_path(file_name, mandatory=True)) + + # trim tap set + # if provided, use tap_line_distance_col together with tap_lines table to trim the near tap set + # to only include the nearest tap to origin when more than one tap serves the same line + distance_col = maz_to_tap_settings.get('tap_line_distance_col') + if distance_col: + + if self.tap_lines_df is None: + # load tap_lines on demand (required if they specify tap_line_distance_col) + tap_lines_file_name = self.setting('tap_lines', ) + self.tap_lines_df = pd.read_csv(config.data_file_path(tap_lines_file_name, mandatory=True)) + + # csv file has one row per TAP with space-delimited list of lines served by that TAP + # TAP LINES + # 6020 GG_024b_SB GG_068_RT GG_228_WB GG_023X_RT + # stack to create dataframe with one column 'line' indexed by TAP with one row per line served + # TAP line + # 6020 GG_024b_SB + # 6020 GG_068_RT + # 6020 GG_228_WB + self.tap_lines_df = \ + self.tap_lines_df.set_index('TAP').LINES.str.split(expand=True)\ + .stack().droplevel(1).to_frame('line') + + old_len = len(df) + + # NOTE - merge will remove unused taps (not appearing in tap_lines) + df = pd.merge(df, self.tap_lines_df, left_on='TAP', right_index=True) + + # find nearest TAP to MAz that serves line + df = df.sort_values(by=distance_col).drop_duplicates(subset=['MAZ', 'line']) + + # we don't need to remember which lines are served by which TAPs + df = df.drop(columns='line').drop_duplicates(subset=['MAZ', 'TAP']).sort_values(['MAZ', 'TAP']) + + logger.debug(f"trimmed maz_to_tap table {file_name} from {old_len} to {len(df)} rows") + logger.debug(f"maz_to_tap table {file_name} max {distance_col} {df[distance_col].max()}") + + max_dist = maz_to_tap_settings.get('max_dist', None) + if max_dist: + old_len = len(df) + df = df[df[distance_col] <= max_dist] + logger.debug(f"trimmed maz_to_tap table {file_name} from {old_len} to {len(df)} rows " + f"based on max_dist {max_dist}") + + if TRACE_TRIMMED_MAZ_TO_TAP_TABLES: + tracing.write_csv(df, file_name=f"trimmed_{maz_to_tap_settings['table']}", transpose=False) + + df.set_index(['MAZ', 'TAP'], drop=True, inplace=True, verify_integrity=True) + logger.debug(f"loaded maz_to_tap table {file_name} with {len(df)} rows") + + assert mode not in self.maz_to_tap_dfs + self.maz_to_tap_dfs[mode] = df + + mem.trace_memory_info('#MEM network_los.load_data before create_skim_dicts') + + # create taz skim dict + assert 'taz' not in self.skim_dicts + self.skim_dicts['taz'] = self.create_skim_dict('taz') + # make sure skim has all tap_ids + # FIXME - weird that there is no list of tazs? + + # create MazSkimDict facade + if self.zone_system in [TWO_ZONE, THREE_ZONE]: + # create MazSkimDict facade skim_dict + # (must have already loaded dependencies: taz skim_dict, maz_to_maz_df, and maz_taz_df) + assert 'maz' not in self.skim_dicts + self.skim_dicts['maz'] = self.create_skim_dict('maz') + # make sure skim has all maz_ids + assert set(self.maz_taz_df['MAZ'].values).issubset(set(self.skim_dicts['maz'].zone_ids)) + + # create tap skim dict + if self.zone_system == THREE_ZONE: + assert 'tap' not in self.skim_dicts + self.skim_dicts['tap'] = self.create_skim_dict('tap') + # make sure skim has all tap_ids + assert set(self.tap_df['TAP'].values).issubset(set(self.skim_dicts['tap'].zone_ids)) + + mem.trace_memory_info("network_los.load_data after create_skim_dicts") + + def create_skim_dict(self, skim_tag): + """ + Create a new SkimDict of type specified by skim_tag (e.g. 'taz', 'maz' or 'tap') + + Parameters + ---------- + skim_tag: str + + Returns + ------- + SkimDict or subclass (e.g. MazSkimDict) + """ + assert skim_tag not in self.skim_dicts # avoid inadvertently creating multiple copies + + if skim_tag == 'maz': + # MazSkimDict gets a reference to self here, because it has dependencies on self.load_data + # (e.g. maz_to_maz_df, maz_taz_df...) We pass in taz_skim_dict as a parameter + # to hilight the fact that we do not want two copies of its (very large) data array in memory + assert 'taz' in self.skim_dicts, \ + f"create_skim_dict 'maz': backing taz skim_dict not in skim_dicts" + taz_skim_dict = self.skim_dicts['taz'] + skim_dict = skim_dictionary.MazSkimDict('maz', self, taz_skim_dict) + else: + skim_info = self.skims_info[skim_tag] + skim_data = self.skim_dict_factory.get_skim_data(skim_tag, skim_info) + skim_dict = skim_dictionary.SkimDict(skim_tag, skim_info, skim_data) + + logger.debug(f"create_skim_dict {skim_tag} omx_shape {skim_dict.omx_shape}") + + return skim_dict + + def get_cache_dir(self): + """ + return path of cache directory in output_dir (creating it, if need be) + + cache directory is used to store + skim memmaps created by skim+dict_factories + tvpb tap_tap table cache + + Returns + ------- + str path + """ + cache_dir = self.setting('cache_dir', default=None) + if cache_dir is None: + cache_dir = self.setting('cache_dir', os.path.join(inject.get_injectable('output_dir'), 'cache')) + + if not os.path.isdir(cache_dir): + os.mkdir(cache_dir) + assert os.path.isdir(cache_dir) + + return cache_dir + + def omx_file_names(self, skim_tag): + """ + Return list of omx file names from network_los settings file for the specified skim_tag (e.g. 'taz') + + Parameters + ---------- + skim_tag: str (e.g. 'taz') + + Returns + ------- + list of str + """ + file_names = self.setting(f'{skim_tag}_skims') + file_names = [file_names] if isinstance(file_names, str) else file_names + return file_names + + def multiprocess(self): + """ + return True if this is a multiprocessing run (even if it is a main or single-process subprocess) + + Returns + ------- + bool + """ + is_multiprocess = config.setting('multiprocess', False) + return is_multiprocess + + def load_shared_data(self, shared_data_buffers): + """ + Load omx skim data into shared_data buffers + Only called when multiprocessing - BEFORE any models are run or any call to load_data() + + Parameters + ---------- + shared_data_buffers: dict of multiprocessing.RawArray keyed by skim_tag + """ + + assert self.multiprocess() + # assert self.skim_dict_factory.supports_shared_data_for_multiprocessing + + if self.skim_dict_factory.supports_shared_data_for_multiprocessing: + for skim_tag in self.skims_info.keys(): + assert skim_tag in shared_data_buffers, f"load_shared_data expected allocated shared_data_buffers" + self.skim_dict_factory.load_skims_to_buffer(self.skims_info[skim_tag], shared_data_buffers[skim_tag]) + + if self.zone_system == THREE_ZONE: + assert self.tvpb is not None + + if self.rebuild_tvpb_cache and not config.setting('resume_after', None): + # delete old cache at start of new run so that stale cache is not loaded by load_data_to_buffer + # when singleprocess, this call is made (later in program flow) in the initialize_los step + self.tvpb.tap_cache.cleanup() + + self.tvpb.tap_cache.load_data_to_buffer(shared_data_buffers[self.tvpb.tap_cache.cache_tag]) + + def allocate_shared_skim_buffers(self): + """ + Allocate multiprocessing.RawArray shared data buffers sized to hold data for the omx skims. + Only called when multiprocessing - BEFORE load_data() + + Returns dict of allocated buffers so they can be added to mp_tasks can add them to dict of data + to be shared with subprocesses. + + Note: we are only allocating storage, but not loading any skim data into it + + Returns + ------- + dict of multiprocessing.RawArray keyed by skim_tag + """ + + assert self.multiprocess() + assert not self.skim_dicts, f"allocate_shared_skim_buffers must be called BEFORE, not after, load_data" + + skim_buffers = {} + + if self.skim_dict_factory.supports_shared_data_for_multiprocessing: + for skim_tag in self.skims_info.keys(): + skim_buffers[skim_tag] = \ + self.skim_dict_factory.allocate_skim_buffer(self.skims_info[skim_tag], shared=True) + + if self.zone_system == THREE_ZONE: + assert self.tvpb is not None + skim_buffers[self.tvpb.tap_cache.cache_tag] = \ + self.tvpb.tap_cache.allocate_data_buffer(shared=True) + + return skim_buffers + + def get_skim_dict(self, skim_tag): + """ + Get SkimDict for the specified skim_tag (e.g. 'taz', 'maz', or 'tap') + + Returns + ------- + SkimDict or subclass (e.g. MazSkimDict) + """ + + assert skim_tag in self.skim_dicts, \ + f"network_los.get_skim_dict: skim tag '{skim_tag}' not in skim_dicts" + + return self.skim_dicts[skim_tag] + + def get_default_skim_dict(self): + """ + Get the default (non-transit) skim dict for the (1, 2, or 3) zone_system + + Returns + ------- + TAZ SkimDict for ONE_ZONE, MazSkimDict for TWO_ZONE and THREE_ZONE + """ + if self.zone_system == ONE_ZONE: + return self.get_skim_dict('taz') + else: + return self.get_skim_dict('maz') + + def get_mazpairs(self, omaz, dmaz, attribute): + """ + look up attribute values of maz od pairs in sparse maz_to_maz df + + Parameters + ---------- + omaz: array-like list of omaz zone_ids + dmaz: array-like list of omaz zone_ids + attribute: str name of attribute column in maz_to_maz_df + + Returns + ------- + Numpy.ndarray: list of attribute values for od pairs + """ + + # # this is slower + # s = pd.merge(pd.DataFrame({'OMAZ': omaz, 'DMAZ': dmaz}), + # self.maz_to_maz_df, + # how="left")[attribute] + + # synthetic index method i : omaz_dmaz + i = np.asanyarray(omaz) * self.maz_ceiling + np.asanyarray(dmaz) + s = util.quick_loc_df(i, self.maz_to_maz_df, attribute) + + # FIXME - no point in returning series? + return np.asanyarray(s) + + def get_tappairs3d(self, otap, dtap, dim3, key): + """ + TAP skim lookup + + FIXME - why do we provide this for taps, but use skim wrappers for TAZ? + + Parameters + ---------- + otap: pandas.Series + origin (boarding tap) zone_ids + dtap: pandas.Series + dest (aligting tap) zone_ids + dim3: pandas.Series or str + dim3 (e.g. tod) str + key + skim key (e.g. 'IWAIT_SET1') + + Returns + ------- + Numpy.ndarray: list of tap skim values for odt tuples + """ + + s = self.get_skim_dict('tap').lookup_3d(otap, dtap, dim3, key) + return s + + def skim_time_period_label(self, time_period): + """ + convert time period times to skim time period labels (e.g. 9 -> 'AM') + + Parameters + ---------- + time_period : pandas Series + + Returns + ------- + pandas Series + string time period labels + """ + + assert self.skim_time_periods is not None, "'skim_time_periods' setting not found." + + # Default to 60 minute time periods + period_minutes = self.skim_time_periods.get('period_minutes', 60) + + # Default to a day + model_time_window_min = self.skim_time_periods.get('time_window', 1440) + + # Check to make sure the intervals result in no remainder time through 24 hour day + assert 0 == model_time_window_min % period_minutes + total_periods = model_time_window_min / period_minutes + + # FIXME - eventually test and use np version always? + if np.isscalar(time_period): + bin = np.digitize([time_period % total_periods], + self.skim_time_periods['periods'], right=True)[0] - 1 + return self.skim_time_periods['labels'][bin] + + return pd.cut(time_period, self.skim_time_periods['periods'], + labels=self.skim_time_periods['labels'], ordered=False).astype(str) diff --git a/activitysim/core/mem.py b/activitysim/core/mem.py index ea3c50d4dd..61150f4a44 100644 --- a/activitysim/core/mem.py +++ b/activitysim/core/mem.py @@ -19,11 +19,17 @@ def force_garbage_collect(): + was_disabled = not gc.isenabled() + if was_disabled: + gc.enable() gc.collect() + if was_disabled: + gc.disable() def GB(bytes): - return (bytes / (1024 * 1024 * 1024.0)) + gb = (bytes / (1024 * 1024 * 1024.0)) + return round(gb, 2) def init_trace(tick_len=None, file_name="mem.csv", write_header=False): @@ -38,7 +44,7 @@ def init_trace(tick_len=None, file_name="mem.csv", write_header=False): logger.info("init_trace file_name %s" % file_name) # - check for optional process name prefix - MEM['prefix'] = inject.get_injectable('log_file_prefix', '') + MEM['prefix'] = inject.get_injectable('log_file_prefix', 'main') if write_header: with config.open_log_file(file_name, 'w') as log_file: @@ -81,6 +87,8 @@ def trace_memory_info(event=''): if (t - last_tick < tick_len) and not event: return + force_garbage_collect() + vmi = psutil.virtual_memory() MEM['tick'] = t @@ -98,8 +106,8 @@ def trace_memory_info(event=''): trace_hwm('rss', GB(rss), timestamp, event) trace_hwm('used', GB(vmi.used), timestamp, event) - # logger.debug("memory_info: rss: %s available: %s percent: %s" - # % (GB(mi.rss), GB(vmi.available), GB(vmi.percent))) + if event: + logger.info(f"trace_memory_info {event} rss: {GB(rss)}GB used: {GB(vmi.used)} GB percent: {vmi.percent}%") with config.open_log_file(MEM['file_name'], 'a') as output_file: @@ -113,7 +121,7 @@ def trace_memory_info(event=''): event), file=output_file) -def get_memory_info(): +def get_rss(): mi = psutil.Process().memory_info() diff --git a/activitysim/core/mp_tasks.py b/activitysim/core/mp_tasks.py index 40d28989be..d79de3a176 100644 --- a/activitysim/core/mp_tasks.py +++ b/activitysim/core/mp_tasks.py @@ -20,13 +20,13 @@ from activitysim.core import chunk from activitysim.core import mem +from activitysim.core import los from activitysim.core.config import setting # activitysim.abm imported for its side-effects (dependency injection) from activitysim import abm -from activitysim.abm.tables import skims from activitysim.abm.tables import shadow_pricing @@ -34,9 +34,6 @@ LAST_CHECKPOINT = '_' -# TEST_SPAWN = 'mp_households' -TEST_SPAWN = False - """ mp_tasks - activitysim multiprocessing overview @@ -452,12 +449,12 @@ def build_slice_rules(slice_info, pipeline_tables): slice_rules[table_name] = rule for table_name in slice_rules: - debug(f"table_name: {slice_rules[table_name]}") + debug(f"table_name: {table_name} slice_rules: {slice_rules[table_name]}") return slice_rules -def apportion_pipeline(sub_proc_names, slice_info): +def apportion_pipeline(sub_proc_names, step_info): """ apportion pipeline for multiprocessing step @@ -470,14 +467,17 @@ def apportion_pipeline(sub_proc_names, slice_info): ---------- sub_proc_names : list of str names of the sub processes to apportion - slice_info : dict - slice_info from multiprocess_steps + step_info : dict + step_info from multiprocess_steps for step we are apportioning pipeline tables for Returns ------- creates apportioned pipeline files for each sub job """ + slice_info = step_info.get('slice', None) + multiprocess_step_name = step_info.get('name', None) + pipeline_file_name = inject.get_injectable('pipeline_file_name') # get last checkpoint from first job pipeline @@ -539,6 +539,13 @@ def apportion_pipeline(sub_proc_names, slice_info): df = tables[table_name] + if rule['slice_by'] is not None and num_sub_procs > len(df): + + # almost certainly a configuration error + raise RuntimeError(f"apportion_pipeline: multiprocess step {multiprocess_step_name} " + f"slice table {table_name} has fewer rows {df.shape} " + f"than num_processes ({num_sub_procs}).") + if rule['slice_by'] == 'primary': # slice primary apportion table by num_sub_procs strides # this hopefully yields a more random distribution @@ -627,7 +634,8 @@ def coalesce_pipelines(sub_proc_names, slice_info): for table_name, hdf5_key in omnibus_keys.items(): omnibus_tables[table_name].append(pipeline_store[hdf5_key]) - pipeline.open_pipeline() + # open pipeline, preserving existing checkpoints (so resume_after will work for prior steps) + pipeline.open_pipeline('_') # - add mirrored tables to pipeline for table_name in mirrored_tables: @@ -779,11 +787,6 @@ def mp_run_simulation(locutor, queue, injectables, step_info, resume_after, **kw setup_injectables_and_logging(injectables, locutor=locutor) - if TEST_SPAWN and step_info['name'] == TEST_SPAWN: - time.sleep(30) - info(f"work up after TEST_SPAWN sleep - returning without doing anything") - return - try: mem.init_trace(setting('mem_tick')) @@ -803,7 +806,7 @@ def mp_run_simulation(locutor, queue, injectables, step_info, resume_after, **kw raise e -def mp_apportion_pipeline(injectables, sub_proc_names, slice_info): +def mp_apportion_pipeline(injectables, sub_proc_names, step_info): """ mp entry point for apportion_pipeline @@ -813,14 +816,14 @@ def mp_apportion_pipeline(injectables, sub_proc_names, slice_info): injectables from parent sub_proc_names : list of str names of the sub processes to apportion - slice_info : dict - slice_info from multiprocess_steps + step_info : dict + step_info for multiprocess_step we are apportioning """ setup_injectables_and_logging(injectables) try: - apportion_pipeline(sub_proc_names, slice_info) + apportion_pipeline(sub_proc_names, step_info) except Exception as e: exception(f"{type(e).__name__} exception caught in mp_apportion_pipeline: {str(e)}") raise e @@ -843,16 +846,13 @@ def mp_setup_skims(injectables, **kwargs): setup_injectables_and_logging(injectables) + info("mp_setup_skims") + try: shared_data_buffer = kwargs - omx_file_path = config.data_file_path(setting('skims_file')) - tags_to_load = setting('skim_time_periods')['labels'] - skim_info = skims.get_skim_info(omx_file_path, tags_to_load) - if TEST_SPAWN: - warning("mp_setup_skims TEST_SPAWN {TEST_SPAWN} skipping skims.load_skims") - else: - skims.load_skims(omx_file_path, skim_info, shared_data_buffer) + network_los_preload = inject.get_injectable('network_los_preload') + network_los_preload.load_shared_data(shared_data_buffer) except Exception as e: exception(f"{type(e).__name__} exception caught in mp_setup_skims: {str(e)}") @@ -891,27 +891,25 @@ def allocate_shared_skim_buffers(): """ This is called by the main process to allocate shared memory buffer to share with subprocs + Note: Buffers must be allocated BEFORE network_los.load_data + Returns ------- - skim_buffers : dict {: } + skim_buffers : dict {: } """ info("allocate_shared_skim_buffer") - omx_file_path = config.data_file_path(setting('skims_file')) - tags_to_load = setting('skim_time_periods')['labels'] - - # select the skims to load - skim_info = skims.get_skim_info(omx_file_path, tags_to_load) - skim_buffers = skims.buffers_for_skims(skim_info, shared=True) + network_los = inject.get_injectable('network_los_preload') + skim_buffers = network_los.allocate_shared_skim_buffers() return skim_buffers def allocate_shared_shadow_pricing_buffers(): """ - This is called by the main process and allocate memory buffer to share with subprocs + This is called by the main process to allocate memory buffer to share with subprocs Returns ------- @@ -921,7 +919,6 @@ def allocate_shared_shadow_pricing_buffers(): info("allocate_shared_shadow_pricing_buffers") shadow_pricing_info = shadow_pricing.get_shadow_pricing_info() - shadow_pricing_buffers = shadow_pricing.buffers_for_shadow_pricing(shadow_pricing_info) return shadow_pricing_buffers @@ -1059,11 +1056,11 @@ def idle(seconds): step_info=step_info, resume_after=resume_after) - debug(f"create_process {process_name} target={mp_run_simulation}") - for k in args: - debug(f"create_process {process_name} arg {k}={args[k]}") - for k in shared_data_buffers: - debug(f"create_process {process_name} shared_data_buffers {k}={shared_data_buffers[k]}") + # debug(f"create_process {process_name} target={mp_run_simulation}") + # for k in args: + # debug(f"create_process {process_name} arg {k}={args[k]}") + # for k in shared_data_buffers: + # debug(f"create_process {process_name} shared_data_buffers {k}={shared_data_buffers[k]}") p = multiprocessing.Process(target=mp_run_simulation, name=process_name, args=(spokesman, q, injectables, step_info, resume_after,), @@ -1128,7 +1125,7 @@ def run_sub_task(p): ---------- p : multiprocessing.Process """ - info(f"running sub_process {p.name}") + info(f"#run_model running sub_process {p.name}") mem.trace_memory_info("%s.start" % p.name) @@ -1142,10 +1139,10 @@ def run_sub_task(p): # no need to join explicitly since multiprocessing.active_children joins completed procs # p.join() - t0 = tracing.print_elapsed_time('sub_process %s' % p.name, t0) + t0 = tracing.print_elapsed_time('#run_model sub_process %s' % p.name, t0) # info(f'{p.name}.exitcode = {p.exitcode}') - mem.trace_memory_info("%s.completed" % p.name) + mem.trace_memory_info(f"#run_model {p.name} completed") if p.exitcode: error(f"Process {p.name} returned exitcode {p.exitcode}") @@ -1266,7 +1263,7 @@ def find_breadcrumb(crumb, default=None): run_sub_task( multiprocessing.Process( target=mp_apportion_pipeline, name='%s_apportion' % step_name, - args=(injectables, sub_proc_names, slice_info)) + args=(injectables, sub_proc_names, step_info)) ) drop_breadcrumb(step_name, 'apportion') @@ -1430,7 +1427,7 @@ def get_run_list(): multiprocess = inject.get_injectable('multiprocess', False) or setting('multiprocess', False) # default settings that can be overridden by settings in individual steps - global_chunk_size = setting('chunk_size', 0) + global_chunk_size = setting('chunk_size', 0) or 0 default_mp_processes = setting('num_processes', 0) or int(1 + multiprocessing.cpu_count() / 2.0) if multiprocess and multiprocessing.cpu_count() == 1: diff --git a/activitysim/core/pathbuilder.py b/activitysim/core/pathbuilder.py new file mode 100644 index 0000000000..6b9662d49e --- /dev/null +++ b/activitysim/core/pathbuilder.py @@ -0,0 +1,890 @@ +# ActivitySim +# See full license in LICENSE.txt. +from builtins import range + +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import tracing +from activitysim.core import inject +from activitysim.core import config +from activitysim.core import chunk +from activitysim.core import logit +from activitysim.core import simulate +from activitysim.core import los +from activitysim.core import pathbuilder_cache + +from activitysim.core.util import reindex + +from activitysim.core import expressions +from activitysim.core import assign + +from activitysim.core.pathbuilder_cache import memo + +logger = logging.getLogger(__name__) + +TIMING = True +TRACE_CHUNK = True +ERR_CHECK = True +TRACE_COMPLEXITY = False # diagnostic: log the omaz,dmaz pairs with the greatest number of virtual tap-tap paths + +UNAVAILABLE = -999 + +# used as base file name for cached files and as shared buffer tag +CACHE_TAG = 'tap_tap_utilities' + + +def compute_utilities(network_los, model_settings, choosers, model_constants, + trace_label, trace=False, trace_column_names=None): + """ + Compute utilities + """ + with chunk.chunk_log(f'tvpb compute_utilities'): + trace_label = tracing.extend_trace_label(trace_label, 'compute_utils') + + logger.debug(f"{trace_label} Running compute_utilities with {choosers.shape[0]} choosers") + + locals_dict = {'np': np, 'los': network_los} + locals_dict.update(model_constants) + + # we don't grok coefficients, but allow them to use constants in spec alt columns + spec = simulate.read_model_spec(file_name=model_settings['SPEC']) + for c in spec.columns: + if c != simulate.SPEC_LABEL_NAME: + spec[c] = spec[c].map(lambda s: model_constants.get(s, s)).astype(float) + + with chunk.chunk_log(f'compute_utilities'): + + # - run preprocessor to annotate choosers + preprocessor_settings = model_settings.get('PREPROCESSOR') + if preprocessor_settings: + + # don't want to alter caller's dataframe + choosers = choosers.copy() + + expressions.assign_columns( + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_dict, + trace_label=trace_label) + + utilities = simulate.eval_utilities( + spec, + choosers, + locals_d=locals_dict, + trace_all_rows=trace, + trace_label=trace_label, + trace_column_names=trace_column_names) + + return utilities + + +class TransitVirtualPathBuilder(object): + """ + Transit virtual path builder for three zone systems + """ + def __init__(self, network_los): + + self.network_los = network_los + + self.uid_calculator = pathbuilder_cache.TapTapUidCalculator(network_los) + + # note: pathbuilder_cache is lightweight until opened + self.tap_cache = pathbuilder_cache.TVPBCache(self.network_los, self.uid_calculator, CACHE_TAG) + + assert network_los.zone_system == los.THREE_ZONE, \ + f"TransitVirtualPathBuilder: network_los zone_system not THREE_ZONE" + + def trace_df(self, df, trace_label, extension): + assert len(df) > 0 + tracing.trace_df(df, label=tracing.extend_trace_label(trace_label, extension), slicer='NONE', transpose=False) + + def trace_maz_tap(self, maz_od_df, access_mode, egress_mode): + + def maz_tap_stats(mode, name): + maz_tap_df = self.network_los.maz_to_tap_dfs[mode].reset_index() + logger.debug(f"TVPB access_maz_tap {maz_tap_df.shape}") + MAZ_count = len(maz_tap_df.MAZ.unique()) + TAP_count = len(maz_tap_df.TAP.unique()) + MAZ_PER_TAP = MAZ_count / TAP_count + logger.debug(f"TVPB maz_tap_stats {name} {mode} MAZ {MAZ_count} TAP {TAP_count} ratio {MAZ_PER_TAP}") + + logger.debug(f"TVPB maz_od_df {maz_od_df.shape}") + + maz_tap_stats(access_mode, 'access') + maz_tap_stats(egress_mode, 'egress') + + def units_for_recipe(self, recipe): + units = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.units') + assert units in ['utility', 'time'], \ + f"unrecognized units: {units} for {recipe}. Expected either 'time' or 'utility'." + return units + + def compute_maz_tap_utilities(self, recipe, maz_od_df, chooser_attributes, leg, mode, trace_label, trace): + + trace_label = tracing.extend_trace_label(trace_label, f'maz_tap_utils.{leg}') + + maz_tap_settings = \ + self.network_los.setting(f'TVPB_SETTINGS.{recipe}.maz_tap_settings.{mode}') + chooser_columns = maz_tap_settings['CHOOSER_COLUMNS'] + attribute_columns = list(chooser_attributes.columns) if chooser_attributes is not None else [] + model_constants = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.CONSTANTS') + + if leg == 'access': + maz_col = 'omaz' + tap_col = 'btap' + else: + maz_col = 'dmaz' + tap_col = 'atap' + + # maz_to_tap access/egress utilities + # deduped utilities_df - one row per chooser for each boarding tap (btap) accessible from omaz + utilities_df = self.network_los.maz_to_tap_dfs[mode] + + utilities_df = utilities_df[chooser_columns]. \ + reset_index(drop=False). \ + rename(columns={'MAZ': maz_col, 'TAP': tap_col}) + utilities_df = pd.merge( + maz_od_df[['idx', maz_col]].drop_duplicates(), + utilities_df, + on=maz_col, how='inner') + # add any supplemental chooser attributes (e.g. demographic_segment, tod) + for c in attribute_columns: + utilities_df[c] = reindex(chooser_attributes[c], utilities_df['idx']) + + chunk.log_df(trace_label, "utilities_df", utilities_df) + + if self.units_for_recipe(recipe) == 'utility': + + utilities_df[leg] = compute_utilities( + self.network_los, + maz_tap_settings, + utilities_df, + model_constants=model_constants, + trace_label=trace_label, trace=trace, + trace_column_names=['idx', maz_col, tap_col] if trace else None) + + chunk.log_df(trace_label, "utilities_df", utilities_df) # annotated + + else: + + assignment_spec = assign.read_assignment_spec(file_name=config.config_file_path(maz_tap_settings['SPEC'])) + + results, _, _ = assign.assign_variables(assignment_spec, utilities_df, model_constants) + assert len(results.columns == 1) + utilities_df[leg] = results + + chunk.log_df(trace_label, "utilities_df", utilities_df) + + if trace: + self.trace_df(utilities_df, trace_label, 'utilities_df') + + # drop utility computation columns ('tod', 'demographic_segment' and maz_to_tap_df time/distance columns) + utilities_df.drop(columns=attribute_columns + chooser_columns, inplace=True) + + return utilities_df + + def all_transit_paths(self, access_df, egress_df, chooser_attributes, trace_label, trace): + + trace_label = tracing.extend_trace_label(trace_label, 'all_transit_paths') + + # deduped transit_df has one row per chooser for each boarding (btap) and alighting (atap) pair + transit_df = pd.merge( + access_df[['idx', 'btap']], + egress_df[['idx', 'atap']], + on='idx').drop_duplicates() + + # don't want transit trips that start and stop in same tap + transit_df = transit_df[transit_df.atap != transit_df.btap] + + for c in list(chooser_attributes.columns): + transit_df[c] = reindex(chooser_attributes[c], transit_df['idx']) + + transit_df = transit_df.reset_index(drop=True) + + if trace: + self.trace_df(transit_df, trace_label, 'all_transit_df') + + return transit_df + + def compute_tap_tap_utilities(self, recipe, access_df, egress_df, chooser_attributes, path_info, + trace_label, trace): + """ + create transit_df and compute utilities for all atap-btap pairs between omaz in access and dmaz in egress_df + compute the utilities using the tap_tap utilitiy expressions file specified in tap_tap_settings + + transit_df contains all possible access omaz/btap to egress dmaz/atap transit path pairs for each chooser + + trace should be True as we don't encourage/support dynamic utility computation except when tracing + (precompute being fairly fast) + + Parameters + ---------- + recipe: str + 'recipe' key in network_los.yaml TVPB_SETTINGS e.g. tour_mode_choice + access_df: pandas.DataFrame + dataframe with 'idx' and 'omaz' columns + egress_df: pandas.DataFrame + dataframe with 'idx' and 'dmaz' columns + chooser_attributes: dict + path_info + trace_label: str + trace: boolean + + Returns + ------- + transit_df: pandas.dataframe + """ + + assert trace + + trace_label = tracing.extend_trace_label(trace_label, 'compute_tap_tap_utils') + + model_constants = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.CONSTANTS') + tap_tap_settings = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.tap_tap_settings') + + with memo("#TVPB CACHE compute_tap_tap_utilities all_transit_paths"): + transit_df = self.all_transit_paths(access_df, egress_df, chooser_attributes, trace_label, trace) + # note: transit_df index is arbitrary + chunk.log_df(trace_label, "transit_df", transit_df) + + # FIXME some expressions may want to know access mode - + locals_dict = path_info.copy() + locals_dict.update(model_constants) + + # columns needed for compute_utilities + chooser_columns = ['btap', 'atap'] + list(chooser_attributes.columns) + + # deduplicate transit_df to unique_transit_df + with memo("#TVPB compute_tap_tap_utilities deduplicate transit_df"): + + attribute_segments = \ + self.network_los.setting('TVPB_SETTINGS.tour_mode_choice.tap_tap_settings.attribute_segments') + scalar_attributes = {k: locals_dict[k] for k in attribute_segments.keys() if k not in transit_df} + + transit_df['uid'] = self.uid_calculator.get_unique_ids(transit_df, scalar_attributes) + + unique_transit_df = transit_df.loc[~transit_df.uid.duplicated(), chooser_columns + ['uid']] + logger.debug(f"#TVPB CACHE deduped transit_df from {len(transit_df)} to {len(unique_transit_df)}") + + unique_transit_df.set_index('uid', inplace=True) + chunk.log_df(trace_label, "unique_transit_df", unique_transit_df) + + transit_df = transit_df[['idx', 'btap', 'atap', 'uid']] # don't need chooser columns + chunk.log_df(trace_label, "transit_df", transit_df) + + logger.debug(f"#TVPB CACHE compute_tap_tap_utilities dedupe transit_df " + f"from {len(transit_df)} to {len(unique_transit_df)} rows") + + num_unique_transit_rows = len(unique_transit_df) # errcheck + logger.debug(f"#TVPB CACHE compute_tap_tap_utilities compute_utilities for {len(unique_transit_df)} rows") + + with memo("#TVPB compute_tap_tap_utilities compute_utilities"): + unique_utilities_df = compute_utilities( + self.network_los, + tap_tap_settings, + choosers=unique_transit_df, + model_constants=locals_dict, + trace_label=trace_label, + trace=trace, + trace_column_names=chooser_columns if trace else None + ) + chunk.log_df(trace_label, "unique_utilities_df", unique_utilities_df) + chunk.log_df(trace_label, "unique_transit_df", unique_transit_df) # annotated + + if trace: + # combine unique_transit_df with unique_utilities_df for legibility + omnibus_df = pd.merge(unique_transit_df, unique_utilities_df, + left_index=True, right_index=True, how='left') + self.trace_df(omnibus_df, trace_label, 'unique_utilities_df') + chunk.log_df(trace_label, "omnibus_df", omnibus_df) + del omnibus_df + chunk.log_df(trace_label, "omnibus_df", None) + + assert num_unique_transit_rows == len(unique_utilities_df) # errcheck + + # redupe unique_transit_df back into transit_df + with memo("#TVPB compute_tap_tap_utilities redupe transit_df"): + + # idx = transit_df.index + transit_df = pd.merge(transit_df, unique_utilities_df, left_on='uid', right_index=True) + del transit_df['uid'] + # transit_df.index = idx + # note: left merge on columns does not preserve index, + # but transit_df index is arbitrary so no need to restore + + chunk.log_df(trace_label, "transit_df", transit_df) + + for c in unique_utilities_df: + assert ERR_CHECK and not transit_df[c].isnull().any() + + if len(unique_transit_df) > 0: + # if all rows were cached, then unique_utilities_df is just a ref to cache + del unique_utilities_df + chunk.log_df(trace_label, "unique_utilities_df", None) + + chunk.log_df(trace_label, "transit_df", None) + + if trace: + self.trace_df(transit_df, trace_label, 'transit_df') + + return transit_df + + def lookup_tap_tap_utilities(self, recipe, maz_od_df, access_df, egress_df, chooser_attributes, + path_info, trace_label): + """ + create transit_df and compute utilities for all atap-btap pairs between omaz in access and dmaz in egress_df + look up the utilities in the precomputed tap_cache data (which is indexed by uid_calculator unique_ids) + (unique_id can used as a zero-based index into the data array) + + transit_df contains all possible access omaz/btap to egress dmaz/atap transit path pairs for each chooser + + Parameters + ---------- + recipe + maz_od_df + access_df + egress_df + chooser_attributes + path_info + trace_label + + Returns + ------- + + """ + + trace_label = tracing.extend_trace_label(trace_label, 'lookup_tap_tap_utils') + + with memo("#TVPB CACHE lookup_tap_tap_utilities all_transit_paths"): + transit_df = self.all_transit_paths(access_df, egress_df, chooser_attributes, trace_label, trace=False) + # note: transit_df index is arbitrary + chunk.log_df(trace_label, "transit_df", transit_df) + + if TRACE_COMPLEXITY: + # diagnostic: log the omaz,dmaz pairs with the greatest number of virtual tap-tap paths + num_paths = transit_df.groupby(['idx']).size().to_frame('n') + num_paths = pd.merge(maz_od_df, num_paths, left_on='idx', right_index=True) + num_paths = num_paths[['omaz', 'dmaz', 'n']].drop_duplicates(subset=['omaz', 'dmaz']) + num_paths = num_paths.sort_values('n', ascending=False).reset_index(drop=True) + logger.debug(f"num_paths\n{num_paths.head(10)}") + + # FIXME some expressions may want to know access mode - + locals_dict = path_info.copy() + + # add uid column to transit_df + with memo("#TVPB lookup_tap_tap_utilities assign uid"): + attribute_segments = \ + self.network_los.setting('TVPB_SETTINGS.tour_mode_choice.tap_tap_settings.attribute_segments') + scalar_attributes = {k: locals_dict[k] for k in attribute_segments.keys() if k not in transit_df} + + transit_df.index = self.uid_calculator.get_unique_ids(transit_df, scalar_attributes) + transit_df = transit_df[['idx', 'btap', 'atap']] # just needed chooser_columns for uid calculation + chunk.log_df(trace_label, "transit_df add uid index", transit_df) + + with memo("#TVPB lookup_tap_tap_utilities reindex transit_df"): + utilities = self.tap_cache.data + i = 0 + for column_name in self.uid_calculator.set_names: + transit_df[column_name] = utilities[transit_df.index.values, i] + i += 1 + + for c in self.uid_calculator.set_names: + assert ERR_CHECK and not transit_df[c].isnull().any() + + chunk.log_df(trace_label, "transit_df", None) + + return transit_df + + def compute_tap_tap_time(self, recipe, access_df, egress_df, chooser_attributes, trace_label, trace): + + trace_label = tracing.extend_trace_label(trace_label, 'compute_tap_tap_time') + + model_constants = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.CONSTANTS') + tap_tap_settings = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.tap_tap_settings') + + with memo("#TVPB CACHE compute_tap_tap_utilities all_transit_paths"): + transit_df = self.all_transit_paths(access_df, egress_df, chooser_attributes, trace_label, trace) + # note: transit_df index is arbitrary + chunk.log_df(trace_label, "transit_df", transit_df) + + locals_d = {'los': self.network_los} + locals_d.update(model_constants) + + assignment_spec = assign.read_assignment_spec(file_name=config.config_file_path(tap_tap_settings['SPEC'])) + + results, _, _ = assign.assign_variables(assignment_spec, transit_df, locals_d) + assert len(results.columns == 1) + transit_df['transit'] = results + + # filter out unavailable btap_atap pairs + logger.debug(f"{(transit_df['transit'] <= 0).sum()} unavailable tap_tap pairs out of {len(transit_df)}") + transit_df = transit_df[transit_df.transit > 0] + + transit_df.drop(columns=chooser_attributes.columns, inplace=True) + + chunk.log_df(trace_label, "transit_df", None) + + if trace: + self.trace_df(transit_df, trace_label, 'transit_df') + + return transit_df + + def compute_tap_tap(self, recipe, maz_od_df, access_df, egress_df, chooser_attributes, path_info, + trace_label, trace): + + if self.units_for_recipe(recipe) == 'utility': + + if not self.tap_cache.is_open: + with memo("#TVPB compute_tap_tap tap_cache.open"): + self.tap_cache.open() + + if trace: + result = \ + self.compute_tap_tap_utilities(recipe, access_df, egress_df, chooser_attributes, + path_info, trace_label, trace) + else: + result = \ + self.lookup_tap_tap_utilities(recipe, maz_od_df, access_df, egress_df, chooser_attributes, + path_info, trace_label) + return result + else: + assert self.units_for_recipe(recipe) == 'time' + result = self.compute_tap_tap_time(recipe, access_df, egress_df, chooser_attributes, trace_label, trace) + + return result + + def best_paths(self, recipe, path_type, maz_od_df, access_df, egress_df, transit_df, trace_label, trace=False): + + trace_label = tracing.extend_trace_label(trace_label, 'best_paths') + + path_settings = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.path_types.{path_type}') + max_paths_per_tap_set = path_settings.get('max_paths_per_tap_set', 1) + max_paths_across_tap_sets = path_settings.get('max_paths_across_tap_sets', 1) + + units = self.units_for_recipe(recipe) + smaller_is_better = (units in ['time']) + + maz_od_df['seq'] = maz_od_df.index + # maz_od_df has one row per chooser + # inner join to add rows for each access, egress, and transit segment combination + path_df = maz_od_df. \ + merge(access_df, on=['idx', 'omaz'], how='inner'). \ + merge(egress_df, on=['idx', 'dmaz'], how='inner'). \ + merge(transit_df, on=['idx', 'atap', 'btap'], how='inner') + + chunk.log_df(trace_label, "path_df", path_df) + + # transit sets are the transit_df non-join columns + transit_sets = [c for c in transit_df.columns if c not in ['idx', 'atap', 'btap']] + + if trace: + # be nice and show both tap_tap set utility and total_set = access + set + egress + for c in transit_sets: + path_df[f'total_{c}'] = path_df[c] + path_df['access'] + path_df['egress'] + self.trace_df(path_df, trace_label, 'best_paths.full') + for c in transit_sets: + del path_df[f'total_{c}'] + + for c in transit_sets: + path_df[c] = path_df[c] + path_df['access'] + path_df['egress'] + path_df.drop(columns=['access', 'egress'], inplace=True) + + # choose best paths by tap set + best_paths_list = [] + for c in transit_sets: + keep = path_df.index.isin( + path_df[['seq', c]].sort_values(by=c, ascending=smaller_is_better). + groupby(['seq']).head(max_paths_per_tap_set).index + ) + + best_paths_for_set = path_df[keep] + best_paths_for_set['path_set'] = c # remember the path set + best_paths_for_set[units] = path_df[keep][c] + best_paths_for_set.drop(columns=transit_sets, inplace=True) + best_paths_list.append(best_paths_for_set) + + path_df = pd.concat(best_paths_list).sort_values(by=['seq', units], ascending=[True, smaller_is_better]) + + # choose best paths overall by seq + path_df = path_df.sort_values(by=['seq', units], ascending=[True, smaller_is_better]) + path_df = path_df[path_df.index.isin(path_df.groupby(['seq']).head(max_paths_across_tap_sets).index)] + + if trace: + self.trace_df(path_df, trace_label, 'best_paths') + + return path_df + + def build_virtual_path(self, recipe, path_type, orig, dest, tod, demographic_segment, + want_choices, trace_label, + filter_targets=None, trace=False, override_choices=None): + + trace_label = tracing.extend_trace_label(trace_label, 'build_virtual_path') + + # Tracing is implemented as a seperate, second call that operates ONLY on filter_targets + assert not (trace and filter_targets is None) + if filter_targets is not None: + assert filter_targets.any() + + # slice orig and dest + orig = orig[filter_targets] + dest = dest[filter_targets] + assert len(orig) > 0 + assert len(dest) > 0 + + # slice tod and demographic_segment if not scalar + if not isinstance(tod, str): + tod = tod[filter_targets] + if demographic_segment is not None: + demographic_segment = demographic_segment[filter_targets] + assert len(demographic_segment) > 0 + + # slice choices + # (requires actual choices from the previous call lest rands change on second call) + assert want_choices == (override_choices is not None) + if want_choices: + override_choices = override_choices[filter_targets] + + units = self.units_for_recipe(recipe) + assert units == 'utility' or not want_choices, "'want_choices' only supported supported if units is utility" + + access_mode = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.path_types.{path_type}.access') + egress_mode = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.path_types.{path_type}.egress') + path_types_settings = self.network_los.setting(f'TVPB_SETTINGS.{recipe}.path_types.{path_type}') + paths_nest_nesting_coefficient = path_types_settings.get('paths_nest_nesting_coefficient', 1) + + # maz od pairs requested + with memo("#TVPB build_virtual_path maz_od_df"): + maz_od_df = pd.DataFrame({ + 'idx': orig.index.values, + 'omaz': orig.values, + 'dmaz': dest.values, + 'seq': range(len(orig)) + }) + chunk.log_df(trace_label, "maz_od_df", maz_od_df) + self.trace_maz_tap(maz_od_df, access_mode, egress_mode) + + # for location choice, there will be multiple alt dest rows per chooser and duplicate orig.index values + # but tod and demographic_segment should be the same for all chooser rows (unique orig index values) + # knowing this allows us to eliminate redundant computations (e.g. utilities of maz_tap pairs) + duplicated = orig.index.duplicated(keep='first') + chooser_attributes = pd.DataFrame(index=orig.index[~duplicated]) + chooser_attributes['tod'] = tod if isinstance(tod, str) else tod.loc[~duplicated] + if demographic_segment is not None: + chooser_attributes['demographic_segment'] = demographic_segment.loc[~duplicated] + + with memo("#TVPB build_virtual_path access_df"): + with chunk.chunk_log(f'#TVPB.access.{access_mode}'): + access_df = self.compute_maz_tap_utilities( + recipe, + maz_od_df, + chooser_attributes, + leg='access', + mode=access_mode, + trace_label=trace_label, trace=trace) + chunk.log_df(trace_label, "access_df", access_df) + + with memo("#TVPB build_virtual_path egress_df"): + with chunk.chunk_log(f'#TVPB.compute_maz_tap_utilities.egress.{egress_mode}'): + egress_df = self.compute_maz_tap_utilities( + recipe, + maz_od_df, + chooser_attributes, + leg='egress', + mode=egress_mode, + trace_label=trace_label, trace=trace) + chunk.log_df(trace_label, "egress_df", egress_df) + + # path_info for use by expressions (e.g. penalty for drive access if no parking at access tap) + with memo("#TVPB build_virtual_path compute_tap_tap"): + with chunk.chunk_log(f'#TVPB.compute_tap_tap'): + path_info = {'path_type': path_type, 'access_mode': access_mode, 'egress_mode': egress_mode} + transit_df = self.compute_tap_tap( + recipe, + maz_od_df, + access_df, + egress_df, + chooser_attributes, + path_info=path_info, + trace_label=trace_label, trace=trace) + chunk.log_df(trace_label, "transit_df", transit_df) + + with memo("#TVPB build_virtual_path best_paths"): + with chunk.chunk_log(f'#TVPB.best_paths'): + path_df = self.best_paths( + recipe, path_type, + maz_od_df, access_df, egress_df, transit_df, + trace_label, trace) + chunk.log_df(trace_label, "path_df", path_df) + + # now that we have created path_df, we are done with the dataframes for the separate legs + del access_df + chunk.log_df(trace_label, "access_df", None) + del egress_df + chunk.log_df(trace_label, "egress_df", None) + del transit_df + chunk.log_df(trace_label, "transit_df", None) + + if units == 'utility': + + # logsums + with memo("#TVPB build_virtual_path logsums"): + # one row per seq with utilities in columns + # path_num 0-based to aligh with logit.make_choices 0-based choice indexes + path_df['path_num'] = path_df.groupby('seq').cumcount() + chunk.log_df(trace_label, "path_df", path_df) + + utilities_df = path_df[['seq', 'path_num', units]].set_index(['seq', 'path_num']).unstack() + utilities_df.columns = utilities_df.columns.droplevel() # for legibility + + # add rows missing because no access or egress availability + utilities_df = pd.concat([pd.DataFrame(index=maz_od_df.seq), utilities_df], axis=1) + utilities_df = utilities_df.fillna(UNAVAILABLE) # set utilities for missing paths to UNAVAILABLE + + chunk.log_df(trace_label, "utilities_df", utilities_df) + + logsums = np.maximum(np.log(np.nansum(np.exp(utilities_df.values/paths_nest_nesting_coefficient), + axis=1)), UNAVAILABLE) + + if want_choices: + + # orig index to identify appropriate random number channel to use making choices + utilities_df.index = orig.index + + with memo("#TVPB build_virtual_path make_choices"): + + probs = logit.utils_to_probs(utilities_df, allow_zero_probs=True, trace_label=trace_label) + chunk.log_df(trace_label, "probs", probs) + + if trace: + choices = override_choices + + utilities_df['choices'] = choices + self.trace_df(utilities_df, trace_label, 'utilities_df') + + probs['choices'] = choices + self.trace_df(probs, trace_label, 'probs') + else: + + choices, rands = logit.make_choices(probs, allow_bad_probs=True, trace_label=trace_label) + + chunk.log_df(trace_label, "rands", rands) + del rands + chunk.log_df(trace_label, "rands", None) + + del probs + chunk.log_df(trace_label, "probs", None) + + # we need to get path_set, btap, atap from path_df row with same seq and path_num + # drop seq join column, but keep path_num of choice to override_choices when tracing + columns_to_cache = ['btap', 'atap', 'path_set', 'path_num'] + logsum_df = \ + pd.merge(pd.DataFrame({'seq': range(len(orig)), 'path_num': choices.values}), + path_df[['seq'] + columns_to_cache], + on=['seq', 'path_num'], how='left')\ + .drop(columns=['seq'])\ + .set_index(orig.index) + + logsum_df['logsum'] = logsums + + else: + + assert len(logsums) == len(orig) + logsum_df = pd.DataFrame({'logsum': logsums}, index=orig.index) + + chunk.log_df(trace_label, "logsum_df", logsum_df) + + del utilities_df + chunk.log_df(trace_label, "utilities_df", None) + + if trace: + self.trace_df(logsum_df, trace_label, 'logsum_df') + + chunk.log_df(trace_label, "logsum_df", logsum_df) + results = logsum_df + + else: + assert units == 'time' + + # return a series + results = pd.Series(path_df[units].values, index=path_df['idx']) + + # zero-fill rows for O-D pairs where no best path exists because there was no tap-tap transit availability + results = reindex(results, maz_od_df.idx).fillna(0.0) + + chunk.log_df(trace_label, "results", results) + + assert len(results) == len(orig) + + del path_df + chunk.log_df(trace_label, "path_df", None) + + # diagnostic + # maz_od_df['DIST'] = self.network_los.get_default_skim_dict().get('DIST').get(maz_od_df.omaz, maz_od_df.dmaz) + # maz_od_df[units] = results.logsum if units == 'utility' else results.values + # print(f"maz_od_df\n{maz_od_df}") + + return results + + def get_tvpb_logsum(self, path_type, orig, dest, tod, demographic_segment, want_choices, trace_label=None): + + # assume they have given us a more specific name (since there may be more than one active wrapper) + trace_label = trace_label or 'get_tvpb_logsum' + trace_label = tracing.extend_trace_label(trace_label, path_type) + + recipe = 'tour_mode_choice' + + with chunk.chunk_log(trace_label): + + logsum_df = \ + self.build_virtual_path(recipe, path_type, orig, dest, tod, demographic_segment, + want_choices=want_choices, trace_label=trace_label) + + trace_hh_id = inject.get_injectable("trace_hh_id", None) + if trace_hh_id: + filter_targets = tracing.trace_targets(orig) + # choices from preceding run (because random numbers) + override_choices = logsum_df['path_num'] if want_choices else None + if filter_targets.any(): + self.build_virtual_path(recipe, path_type, orig, dest, tod, demographic_segment, + want_choices=want_choices, override_choices=override_choices, + trace_label=trace_label, filter_targets=filter_targets, trace=True) + + return logsum_df + + def get_tvpb_best_transit_time(self, orig, dest, tod): + + # FIXME lots of pathological knowledge here as we are only called by accessibility directly from expressions + + trace_label = tracing.extend_trace_label('accessibility.tvpb_best_time', tod) + recipe = 'accessibility' + path_type = 'WTW' + + with chunk.chunk_log(trace_label): + result = \ + self.build_virtual_path(recipe, path_type, orig, dest, tod, + demographic_segment=None, want_choices=False, + trace_label=trace_label) + + trace_od = inject.get_injectable("trace_od", None) + if trace_od: + filter_targets = (orig == trace_od[0]) & (dest == trace_od[1]) + if filter_targets.any(): + self.build_virtual_path(recipe, path_type, orig, dest, tod, + demographic_segment=None, want_choices=False, + trace_label=trace_label, filter_targets=filter_targets, trace=True) + + return result + + def wrap_logsum(self, orig_key, dest_key, tod_key, segment_key, + cache_choices=False, trace_label=None, tag=None): + + return TransitVirtualPathLogsumWrapper(self, orig_key, dest_key, tod_key, segment_key, + cache_choices, trace_label, tag) + + +class TransitVirtualPathLogsumWrapper(object): + """ + Transit virtual path builder logsum wrapper for three zone systems + """ + def __init__(self, pathbuilder, orig_key, dest_key, tod_key, segment_key, + cache_choices, trace_label, tag): + + self.tvpb = pathbuilder + assert hasattr(pathbuilder, 'get_tvpb_logsum') + + self.orig_key = orig_key + self.dest_key = dest_key + self.tod_key = tod_key + self.segment_key = segment_key + self.df = None + + self.cache_choices = cache_choices + self.cache = {} if cache_choices else None + + self.base_trace_label = tracing.extend_trace_label(trace_label, tag) or f'tvpb_logsum.{tag}' + self.trace_label = self.base_trace_label + self.tag = tag + + self.chunk_overhead = None + + assert isinstance(orig_key, str) + assert isinstance(dest_key, str) + assert isinstance(tod_key, str) + assert isinstance(segment_key, str) + + def set_df(self, df): + """ + Set the dataframe + + Parameters + ---------- + df : DataFrame + The dataframe which contains the origin and destination ids + + Returns + ------- + self (to facilitiate chaining) + """ + self.df = df + return self + + def extend_trace_label(self, extension=None): + if extension: + self.trace_label = tracing.extend_trace_label(self.base_trace_label, extension) + else: + self.trace_label = self.base_trace_label + + def __getitem__(self, path_type): + """ + Get an available skim object + + Parameters + ---------- + key : hashable + The key (identifier) for this skim object + + Returns + ------- + skim: Skim + The skim object + """ + + assert self.df is not None, "Call set_df first" + assert(self.orig_key in self.df), \ + f"TransitVirtualPathLogsumWrapper: orig_key '{self.orig_key}' not in df" + assert(self.dest_key in self.df), \ + f"TransitVirtualPathLogsumWrapper: dest_key '{self.dest_key}' not in df" + assert(self.tod_key in self.df), \ + f"TransitVirtualPathLogsumWrapper: tod_key '{self.tod_key}' not in df" + assert(self.segment_key in self.df), \ + f"TransitVirtualPathLogsumWrapper: segment_key '{self.segment_key}' not in df" + + orig = self.df[self.orig_key].astype('int') + dest = self.df[self.dest_key].astype('int') + tod = self.df[self.tod_key] + segment = self.df[self.segment_key] + + logsum_df = \ + self.tvpb.get_tvpb_logsum(path_type, orig, dest, tod, segment, + want_choices=self.cache_choices, + trace_label=self.trace_label) + + if self.cache_choices: + + # not tested on duplicate index because not currently needed + # caching strategy does not require unique indexes but care would need to be taken to maintain alignment + assert not orig.index.duplicated().any() + + # we only need to cache taps and path_set + choices_df = logsum_df[['atap', 'btap', 'path_set']] + + if path_type in self.cache: + assert len(self.cache.get(path_type).index.intersection(logsum_df.index)) == 0 + choices_df = pd.concat([self.cache.get(path_type), choices_df]) + + self.cache[path_type] = choices_df + + return logsum_df.logsum diff --git a/activitysim/core/pathbuilder_cache.py b/activitysim/core/pathbuilder_cache.py new file mode 100644 index 0000000000..0f2a55b9f3 --- /dev/null +++ b/activitysim/core/pathbuilder_cache.py @@ -0,0 +1,431 @@ +# ActivitySim +# See full license in LICENSE.txt. +from builtins import range + +import logging +import os +import itertools +import multiprocessing +import gc as _gc +import psutil +import time + +from contextlib import contextmanager + +import numpy as np +import pandas as pd + +from activitysim.core import util +from activitysim.core import config +from activitysim.core import inject +from activitysim.core import simulate +from activitysim.core import tracing + +logger = logging.getLogger(__name__) + +RAWARRAY = False +DTYPE_NAME = 'float32' +RESCALE = 1000 + +DYNAMIC = 'dynamic' +STATIC = 'static' +TRACE = 'trace' + +MEMO_STACK = [] + + +@contextmanager +def memo(tag, console=False, disable_gc=True): + t0 = time.time() + + MEMO_STACK.append(tag) + + gc_was_enabled = _gc.isenabled() + if gc_was_enabled: + _gc.collect() + if disable_gc: + _gc.disable() + + previous_mem = psutil.Process(os.getpid()).memory_info().rss + try: + yield + finally: + elapsed_time = time.time() - t0 + + current_mem = (psutil.Process(os.getpid()).memory_info().rss) + marginal_mem = current_mem - previous_mem + mem_str = f"net {tracing.si_units(marginal_mem)} ({str(marginal_mem)}) total {tracing.si_units(current_mem)}" + + if gc_was_enabled and disable_gc: + _gc.enable() + if _gc.isenabled(): + _gc.collect() + + if console: + print(f"MEMO {tag} Time: {tracing.si_units(elapsed_time, kind='s')} Memory: {mem_str} ") + else: + logger.debug(f"MEM {tag} {mem_str} in {tracing.si_units(elapsed_time, kind='s')}") + + MEMO_STACK.pop() + + +class TVPBCache(object): + """ + Transit virtual path builder cache for three zone systems + """ + def __init__(self, network_los, uid_calculator, cache_tag): + + # lightweight until opened + + self.cache_tag = cache_tag + + self.network_los = network_los + self.uid_calculator = uid_calculator + + self.is_open = False + self.is_changed = False + self._data = None + + @property + def cache_path(self): + file_type = 'mmap' + return os.path.join(self.network_los.get_cache_dir(), f'{self.cache_tag}.{file_type}') + + @property + def csv_trace_path(self): + file_type = 'csv' + return os.path.join(self.network_los.get_cache_dir(), f'{self.cache_tag}.{file_type}') + + def cleanup(self): + """ + Called prior to + """ + if os.path.isfile(self.cache_path): + logger.debug(f"deleting cache {self.cache_path}") + os.unlink(self.cache_path) + + def write_static_cache(self, data): + + assert not self.is_open + assert self._data is None + assert not self.is_changed + + data = data.reshape(self.uid_calculator.fully_populated_shape) + + logger.debug(f"#TVPB CACHE write_static_cache df {data.shape}") + + mm_data = np.memmap(self.cache_path, + shape=data.shape, + dtype=DTYPE_NAME, + mode='w+') + np.copyto(mm_data, data) + mm_data._mmap.close() + del mm_data + + logger.debug(f"#TVPB CACHE write_static_cache wrote static cache table " + f"({data.shape}) to {self.cache_path}") + + def open(self): + """ + open STATIC cache and populate with cached data + + if multiprocessing + always STATIC cache with data fully_populated preloaded shared data buffer + """ + # MMAP only supported for fully_populated_uids (STATIC) + # otherwise we would have to store uid index as float, which has roundoff issues for float32 + + assert not self.is_open, f"TVPBCache open called but already open" + self.is_open = True + + if self.network_los.multiprocess(): + # multiprocessing usex preloaded fully_populated shared data buffer + with memo("TVPBCache.open get_data_and_lock_from_buffers"): + data, _ = self.get_data_and_lock_from_buffers() + logger.info(f"TVBPCache.open {self.cache_tag} STATIC cache using existing data_buffers") + elif os.path.isfile(self.cache_path): + # single process ought have created a precomputed fully_populated STATIC file + data = np.memmap(self.cache_path, dtype=DTYPE_NAME, mode='r') + logger.info(f"TVBPCache.open {self.cache_tag} read fully_populated data array from mmap file") + else: + raise RuntimeError(f"Pathbuilder cache not found. Did you forget to run initialize tvpb?" + f"Expected cache file: {self.cache_path}") + + # create no-copy pandas DataFrame from numpy wrapped RawArray or Memmap buffer + column_names = self.uid_calculator.set_names + with memo("TVPBCache.open data.reshape"): + data = data.reshape((-1, len(column_names))) # reshape so there is one column per set + + # data should be fully_populated and in canonical order - so we can assign canonical uid index + with memo("TVPBCache.open uid_calculator.fully_populated_uids"): + fully_populated_uids = self.uid_calculator.fully_populated_uids + + # check fully_populated, but we have to take order on faith (internal error if it is not) + assert data.shape[0] == len(fully_populated_uids) + + self._data = data + logger.debug(f"TVBPCache.open initialized STATIC cache table") + + def close(self, trace=False): + """ + write any changes, free data, and mark as closed + """ + + assert self.is_open, f"TVPBCache close called but not open" + + self.is_open = False + self._data = None + self.cache_type = None + + @property + def data(self): + assert self._data is not None + return self._data + + def allocate_data_buffer(self, shared=False): + """ + allocate fully_populated_shape data buffer for cached data + + if shared, return a multiprocessing.Array that can be shared across subprocesses + if not shared, return a numpy ndarrray + + Parameters + ---------- + shared: boolean + + Returns + ------- + multiprocessing.Array or numpy ndarray sized to hole fully_populated utility array + """ + + assert not self.is_open + assert shared == self.network_los.multiprocess() + + dtype_name = DTYPE_NAME + dtype = np.dtype(DTYPE_NAME) + + # multiprocessing.Array argument buffer_size must be int, not np.int64 + shape = self.uid_calculator.fully_populated_shape + buffer_size = util.iprod(self.uid_calculator.fully_populated_shape) + + csz = buffer_size * dtype.itemsize + logger.info(f"TVPBCache.allocate_data_buffer allocating data buffer " + f"shape {shape} buffer_size {buffer_size} total size: {csz} ({tracing.si_units(csz)})") + + if shared: + if dtype_name == 'float64': + typecode = 'd' + elif dtype_name == 'float32': + typecode = 'f' + else: + raise RuntimeError("allocate_data_buffer unrecognized dtype %s" % dtype_name) + + if RAWARRAY: + with memo("TVPBCache.allocate_data_buffer allocate RawArray"): + buffer = multiprocessing.RawArray(typecode, buffer_size) + logger.info(f"TVPBCache.allocate_data_buffer allocated shared multiprocessing.RawArray as buffer") + else: + with memo("TVPBCache.allocate_data_buffer allocate Array"): + buffer = multiprocessing.Array(typecode, buffer_size) + logger.info(f"TVPBCache.allocate_data_buffer allocated shared multiprocessing.Array as buffer") + + else: + buffer = np.empty(buffer_size, dtype=dtype) + np.copyto(buffer, np.nan) # fill with np.nan + + logger.info(f"TVPBCache.allocate_data_buffer allocating non-shared numpy array as buffer") + + return buffer + + def load_data_to_buffer(self, data_buffer): + # 1) we are called before initialize_los, there is a saved cache, and it will be honored + # 2) we are called before initialize_los and there is no saved cache yet + # 3) we are resuming after initialize_los and so there must be a saved cache + + assert not self.is_open + + # wrap multiprocessing.Array (or RawArray) as a numpy array + with memo("TVPBCache.load_data_to_buffer frombuffer"): + if RAWARRAY: + np_wrapped_data_buffer = np.ctypeslib.as_array(data_buffer) + else: + np_wrapped_data_buffer = np.ctypeslib.as_array(data_buffer.get_obj()) + + if os.path.isfile(self.cache_path): + with memo("TVPBCache.load_data_to_buffer copy memmap"): + data = np.memmap(self.cache_path, dtype=DTYPE_NAME, mode='r') + np.copyto(np_wrapped_data_buffer, data) + data._mmap.close() + del data + logger.debug(f"TVPBCache.load_data_to_buffer loaded data from {self.cache_path}") + else: + np.copyto(np_wrapped_data_buffer, np.nan) + logger.debug(f"TVPBCache.load_data_to_buffer - saved cache file not found.") + + def get_data_and_lock_from_buffers(self): + """ + return shared data buffer previously allocated by allocate_data_buffer and injected mp_tasks.run_simulation + Returns either multiprocessing.Array and lock or multiprocessing.RawArray and None according to RAWARRAY + """ + data_buffers = inject.get_injectable('data_buffers', None) + assert self.cache_tag in data_buffers # internal error + logger.debug(f"TVPBCache.get_data_and_lock_from_buffers") + data_buffer = data_buffers[self.cache_tag] + if RAWARRAY: + data = np.ctypeslib.as_array(data_buffer) + lock = None + else: + data = np.ctypeslib.as_array(data_buffer.get_obj()) + lock = data_buffer.get_lock() + + return data, lock + + +class TapTapUidCalculator(object): + """ + Transit virtual path builder TAP to TAP unique ID calculator for three zone systems + """ + def __init__(self, network_los): + + self.network_los = network_los + + # ensure that tap_df has been loaded + # (during multiprocessing we are initialized before network_los.load_data is called) + assert network_los.tap_df is not None + self.tap_ids = network_los.tap_df['TAP'].values + + self.segmentation = \ + network_los.setting('TVPB_SETTINGS.tour_mode_choice.tap_tap_settings.attribute_segments') + + # e.g. [(0, 'AM', 'walk'), (0, 'AM', 'walk')...]) for attributes demographic_segment, tod, and access_mode + self.attribute_combination_tuples = list(itertools.product(*list(self.segmentation.values()))) + + # ordinalizers - for mapping attribute values to canonical ordinal values for uid computation + # (pandas series of ordinal position with attribute value index (e.g. map tod value 'AM' to 0, 'MD' to 1,...) + # FIXME dict might be faster than Series.map() and Series.at[]? + self.ordinalizers = {} + for k, v in self.segmentation.items(): + self.ordinalizers[k] = pd.Series(range(len(v)), index=v) + # orig/dest go last so all rows in same 'skim' end up with adjacent uids + self.ordinalizers['btap'] = pd.Series(range(len(self.tap_ids)), index=self.tap_ids) + self.ordinalizers['atap'] = self.ordinalizers['btap'] + + # for k,v in self.ordinalizers.items(): + # print(f"\ordinalizer {k}\n{v}") + + spec_name = self.network_los.setting(f'TVPB_SETTINGS.tour_mode_choice.tap_tap_settings.SPEC') + self.set_names = list(simulate.read_model_spec(file_name=spec_name).columns) + + @property + def fully_populated_shape(self): + # (num_combinations * num_orig_zones * num_dest_zones, num_sets) + num_combinations = len(self.attribute_combination_tuples) + num_orig_zones = num_dest_zones = len(self.tap_ids) + num_rows = num_combinations * num_orig_zones * num_dest_zones + num_sets = len(self.set_names) + return (num_rows, num_sets) + + @property + def skim_shape(self): + # (num_combinations, num_od_rows, num_sets) + num_combinations = len(self.attribute_combination_tuples) + num_orig_zones = num_dest_zones = len(self.tap_ids) + num_od_rows = num_orig_zones * num_dest_zones + num_sets = len(self.set_names) + return (num_combinations, num_od_rows, num_sets) + + @property + def fully_populated_uids(self): + num_combinations = len(self.attribute_combination_tuples) + num_orig_zones = num_dest_zones = len(self.tap_ids) + return np.arange(num_combinations * num_orig_zones * num_dest_zones) + + def get_unique_ids(self, df, scalar_attributes): + """ + compute canonical unique_id for each row in df + btap and atap will be in dataframe, but the other attributes may be either df columns or scalar_attributes + + Parameters + ---------- + df: pandas DataFrame + with btap, atap, and optionally additional attribute columns + scalar_attributes: dict + dict of scalar attributes e.g. {'tod': 'AM', 'demographic_segment': 0} + Returns + ------- + ndarray of integer uids + """ + uid = np.zeros(len(df), dtype=int) + + # need to know cardinality and integer representation of each tap/attribute + for name, ordinalizer in self.ordinalizers.items(): + + cardinality = ordinalizer.max() + 1 + + if name in df: + # if there is a column, use it + uid = uid * cardinality + np.asanyarray(df[name].map(ordinalizer)) + else: + # otherwise it should be in scalar_attributes + assert name in scalar_attributes, f"attribute '{name}' not found in df.columns or scalar_attributes." + uid = uid * cardinality + ordinalizer.at[scalar_attributes[name]] + + return uid + + def get_od_dataframe(self, scalar_attributes): + """ + return tap-tap od dataframe with unique_id index for 'skim_offset' for scalar_attributes + + i.e. a dataframe which may be used to compute utilities, together with scalar or column attributes + + Parameters + ---------- + scalar_attributes: dict of scalar attribute name:value pairs + + Returns + ------- + pandas.Dataframe + """ + + # create OD dataframe in ROW_MAJOR_LAYOUT + num_taps = len(self.tap_ids) + od_choosers_df = pd.DataFrame( + data={ + 'btap': np.repeat(self.tap_ids, num_taps), + 'atap': np.tile(self.tap_ids, num_taps) + } + ) + od_choosers_df.index = self.get_unique_ids(od_choosers_df, scalar_attributes) + assert not od_choosers_df.index.duplicated().any() + + return od_choosers_df + + def get_skim_offset(self, scalar_attributes): + # return ordinal position of this set of attributes in the list of attribute_combination_tuples + offset = 0 + for name, ordinalizer in self.ordinalizers.items(): + cardinality = ordinalizer.max() + 1 + if name in scalar_attributes: + offset = offset * cardinality + ordinalizer.at[scalar_attributes[name]] + return offset + + def each_scalar_attribute_combination(self): + # iterate through attribute_combination_tuples, yielding dict of scalar attribute name:value pairs + + # attribute names as list of strings + attribute_names = list(self.segmentation.keys()) + for attribute_value_tuple in self.attribute_combination_tuples: + + # attribute_value_tuple is an tuple of attribute values - e.g. (0, 'AM', 'walk') + # build dict of attribute name:value pairs - e.g. {'demographic_segment': 0, 'tod': 'AM', }) + scalar_attributes = {name: value for name, value in zip(attribute_names, attribute_value_tuple)} + + yield scalar_attributes + + def scalar_attribute_combinations(self): + attribute_names = list(self.segmentation.keys()) + attribute_tuples = self.attribute_combination_tuples + x = [list(t) for t in attribute_tuples] + df = pd.DataFrame(data=x, columns=attribute_names) + df.index.name = 'offset' + return df diff --git a/activitysim/core/pipeline.py b/activitysim/core/pipeline.py index 2958604aef..7982295d6b 100644 --- a/activitysim/core/pipeline.py +++ b/activitysim/core/pipeline.py @@ -17,6 +17,7 @@ from . import tracing from . import mem + from . import util from .tracing import print_elapsed_time @@ -389,6 +390,7 @@ def load_checkpoint(checkpoint_name): # register for tracing in order that tracing.register_traceable_table wants us to register them traceable_tables = inject.get_injectable('traceable_tables', []) + for table_name in traceable_tables: if table_name in loaded_tables: tracing.register_traceable_table(table_name, loaded_tables[table_name]) @@ -462,15 +464,15 @@ def run_model(model_name): inject.set_step_args(args) t0 = print_elapsed_time() + logger.info(f"#run_model running step {step_name}") orca.run([step_name]) - t0 = print_elapsed_time("run_model step '%s'" % model_name, t0, debug=True) + t0 = print_elapsed_time("#run_model completed step '%s'" % model_name, t0, debug=True) inject.set_step_args(None) _PIPELINE.rng().end_step(model_name) if checkpoint: add_checkpoint(model_name) - t0 = print_elapsed_time("run_model add_checkpoint '%s'" % model_name, t0, debug=True) else: logger.info("##### skipping %s checkpoint for %s" % (step_name, model_name)) @@ -578,15 +580,22 @@ def run(models, resume_after=None): if resume_after in models: models = models[models.index(resume_after) + 1:] + mem.init_trace(config.setting('mem_tick'), write_header=True) + mem.trace_memory_info('#MEM pipeline.run before preload_injectables') + # preload any bulky injectables (e.g. skims) not in pipeline if orca.is_injectable('preload_injectables'): orca.get_injectable('preload_injectables') t0 = print_elapsed_time('preload_injectables', t0) + mem.trace_memory_info('#MEM pipeline.run before run_models') + t0 = print_elapsed_time() for model in models: - run_model(model) + mem.trace_memory_info(f"pipeline.run after {model}") + + mem.trace_memory_info('#MEM pipeline.run after run_models') t0 = print_elapsed_time("run_model (%s models)" % len(models), t0) diff --git a/activitysim/core/simulate.py b/activitysim/core/simulate.py index 9e8af50df0..63bef24c2f 100644 --- a/activitysim/core/simulate.py +++ b/activitysim/core/simulate.py @@ -10,7 +10,6 @@ import numpy as np import pandas as pd -from .skim import SkimDictWrapper, SkimStackWrapper from . import logit from . import tracing from . import pipeline @@ -19,6 +18,8 @@ from . import assign from . import chunk +from . import pathbuilder + logger = logging.getLogger(__name__) SPEC_DESCRIPTION_NAME = 'Description' @@ -61,7 +62,7 @@ def read_model_alts(file_name, set_index=None): return df -def read_model_spec(file_name, spec_dir=None): +def read_model_spec(file_name): """ Read a CSV model specification into a Pandas DataFrame or Series. @@ -79,9 +80,7 @@ def read_model_spec(file_name, spec_dir=None): model_settings : dict name of spec_file is in model_settings['SPEC'] and file is relative to configs file_name : str - file_name id spec file in configs folder (or in spec_dir is specified) - spec_dir : str - directory in which to fine spec file if not in configs + file_name id spec file in configs folder description_name : str, optional Name of the column in `fname` that contains the component description. @@ -99,13 +98,14 @@ def read_model_spec(file_name, spec_dir=None): if not file_name.lower().endswith('.csv'): file_name = '%s.csv' % (file_name,) - if spec_dir is not None: - # FIXME sadly, this is only used in test_cdap to read cdap_indiv_and_hhsize1 - file_path = os.path.join(spec_dir, file_name) - else: - file_path = config.config_file_path(file_name) + file_path = config.config_file_path(file_name) - spec = pd.read_csv(file_path, comment='#') + try: + spec = pd.read_csv(file_path, comment='#') + except Exception as err: + logger.error(f"read_model_spec error reading {file_path}") + logger.error(f"read_model_spec error {type(err).__name__}: {str(err)}") + raise(err) spec = spec.dropna(subset=[SPEC_EXPRESSION_NAME]) @@ -294,7 +294,8 @@ def eval_coefficients(spec, coefficients, estimator): def eval_utilities(spec, choosers, locals_d=None, trace_label=None, - have_trace_targets=False, estimator=None, alt_col_name=None): + have_trace_targets=False, trace_all_rows=False, + estimator=None, trace_column_names=None): """ Parameters @@ -307,10 +308,13 @@ def eval_utilities(spec, choosers, locals_d=None, trace_label=None, locals_d : Dict or None This is a dictionary of local variables that will be the environment for an evaluation of an expression that begins with @ - trace_label - have_trace_targets + trace_label: str + have_trace_targets: boolean - choosers has targets to trace + trace_all_rows: boolean - trace all chooser rows, bypassing tracing.trace_targets estimator : called to report intermediate table results (used for estimation) + trace_column_names: str or list of str + chooser columns to include when tracing expression_values Returns ------- @@ -319,33 +323,19 @@ def eval_utilities(spec, choosers, locals_d=None, trace_label=None, # fixme - restore tracing and _check_for_variability - t0 = tracing.print_elapsed_time() - - # if False: #fixme SLOWER - # expression_values = eval_variables(spec.index, choosers, locals_d) - # # chunk.log_df(trace_label, 'expression_values', expression_values) - # # if trace_label and tracing.has_trace_targets(choosers): - # # tracing.trace_df(expression_values, '%s.expression_values' % trace_label, - # # column_labels=['expression', None]) - # # if config.setting('check_for_variability'): - # # _check_for_variability(expression_values, trace_label) - # utilities = compute_utilities(expression_values, spec) - # - # # chunk.log_df(trace_label, 'expression_values', None) - # t0 = tracing.print_elapsed_time(" eval_utilities SLOWER", t0) - # - # return utilities - - # - eval spec expressions + trace_label = tracing.extend_trace_label(trace_label, 'eval_utils') # avoid altering caller's passed-in locals_d parameter (they may be looping) locals_dict = assign.local_utilities() + if locals_d is not None: locals_dict.update(locals_d) globals_dict = {} locals_dict['df'] = choosers + # - eval spec expressions + if isinstance(spec.index, pd.MultiIndex): # spec MultiIndex with expression and label exprs = spec.index.get_level_values(SPEC_EXPRESSION_NAME) @@ -353,14 +343,18 @@ def eval_utilities(spec, choosers, locals_d=None, trace_label=None, exprs = spec.index expression_values = np.empty((spec.shape[0], choosers.shape[0])) + chunk.log_df(trace_label, "expression_values", expression_values) + for i, expr in enumerate(exprs): try: + # logger.debug(f"{trace_label} expr {expr}") if expr.startswith('@'): expression_values[i] = eval(expr[1:], globals_dict, locals_dict) else: expression_values[i] = choosers.eval(expr) + except Exception as err: - logger.exception("Variable evaluation failed for: %s" % str(expr)) + logger.exception(f"{trace_label} - {type(err).__name__} ({str(err)}) evaluating: {str(expr)}") raise err if estimator: @@ -374,13 +368,18 @@ def eval_utilities(spec, choosers, locals_d=None, trace_label=None, # - compute_utilities utilities = np.dot(expression_values.transpose(), spec.astype(np.float64).values) utilities = pd.DataFrame(data=utilities, index=choosers.index, columns=spec.columns) + chunk.log_df(trace_label, "utilities", utilities) - t0 = tracing.print_elapsed_time(" eval_utilities", t0) + if trace_all_rows or have_trace_targets: - if have_trace_targets: + if trace_all_rows: + trace_targets = pd.Series(True, index=choosers.index) + else: + trace_targets = tracing.trace_targets(choosers) + + assert trace_targets.any() # since they claimed to have targets... # get int offsets of the trace_targets (offsets of bool=True values) - trace_targets = tracing.trace_targets(choosers) offsets = np.nonzero(list(trace_targets))[0] # get array of expression_values @@ -389,29 +388,32 @@ def eval_utilities(spec, choosers, locals_d=None, trace_label=None, data = expression_values[:, offsets] # index is utility expressions - index = spec.index + index = spec.index.get_level_values(SPEC_LABEL_NAME) if isinstance(spec.index, pd.MultiIndex) else spec.index - trace_df = pd.DataFrame(data=data, index=index) + expression_values_df = pd.DataFrame(data=data, index=index) - if alt_col_name is not None: - trace_df.columns = choosers[alt_col_name][trace_targets].values + if trace_column_names is not None: + if isinstance(trace_column_names, str): + trace_column_names = [trace_column_names] + expression_values_df.columns = pd.MultiIndex.from_frame(choosers.loc[trace_targets, trace_column_names]) - tracing.trace_df(trace_df, '%s.expression_values' % trace_label, + tracing.trace_df(expression_values_df, tracing.extend_trace_label(trace_label, 'expression_values'), slicer=None, transpose=False) - # excruciating level of detail for debugging problems with coefficients - # for id in trace_df.columns: - # df = spec.copy() - # for c in df.columns: - # df[c] = df[c] * trace_df[id] - # - # row_sums = df.sum(axis=1) - # tracing.trace_df(row_sums, '%s.%s.utility_row_sums' % (trace_label, id), - # slicer=None, transpose=False) - # - # df.insert(0, id, trace_df[id]) - # tracing.trace_df(df, '%s.%s.expression_values' % (trace_label, id), - # slicer=None, transpose=False) + if len(spec.columns) > 1: + + for c in spec.columns: + name = f'expression_value_{c}' + + tracing.trace_df(expression_values_df.multiply(spec[c].values, axis=0), + tracing.extend_trace_label(trace_label, name), + slicer=None, transpose=False) + + del expression_values + chunk.log_df(trace_label, "expression_values", None) + + # no longer our problem - but our caller should re-log this... + chunk.log_df(trace_label, "utilities", None) return utilities @@ -488,9 +490,9 @@ def to_array(x): # read model spec should ensure uniqueness, otherwise we should uniquify assert expr not in values values[expr] = expr_values - except Exception as err: - logger.exception("Variable evaluation failed for: %s" % str(expr)) + except Exception as err: + logger.exception(f"Variable evaluation failed {type(err).__name__} ({str(err)}) evaluating: {str(expr)}") raise err values = util.df_from_dict(values, index=df.index) @@ -524,7 +526,7 @@ def compute_utilities(expression_values, spec): def set_skim_wrapper_targets(df, skims): """ - Add the dataframe to the SkimDictWrapper object so that it can be dereferenced + Add the dataframe to the SkimWrapper object so that it can be dereferenced using the parameters of the skims object. Parameters @@ -532,7 +534,7 @@ def set_skim_wrapper_targets(df, skims): df : pandas.DataFrame Table to which to add skim data as new columns. `df` is modified in-place. - skims : SkimDictWrapper or SkimStackWrapper object, or a list or dict of skims + skims : SkimWrapper or Skim3dWrapper object, or a list or dict of skims The skims object is used to contain multiple matrices of origin-destination impedances. Make sure to also add it to the locals_d below in order to access it in expressions. The *only* job @@ -542,19 +544,16 @@ def set_skim_wrapper_targets(df, skims): the skims object is intended to be used. """ - if isinstance(skims, list): - for skim in skims: - assert isinstance(skim, SkimDictWrapper) or isinstance(skim, SkimStackWrapper) + skims = skims if isinstance(skims, list) \ + else skims.values() if isinstance(skims, dict) \ + else [skims] + + # assume any object in skims can be treated as a skim + for skim in skims: + try: skim.set_df(df) - elif isinstance(skims, dict): - # it it is a dict, then check for known types, ignore anything we don't recognize as a skim - # (this allows putting skim column names in same dict as skims for use in locals_dicts) - for skim in skims.values(): - if isinstance(skim, SkimDictWrapper) or isinstance(skim, SkimStackWrapper): - skim.set_df(df) - else: - assert isinstance(skims, SkimDictWrapper) or isinstance(skims, SkimStackWrapper) - skims.set_df(df) + except AttributeError: + pass def _check_for_variability(expression_values, trace_label): @@ -715,7 +714,8 @@ def compute_base_probabilities(nested_probabilities, nests, spec): def eval_mnl(choosers, spec, locals_d, custom_chooser, estimator, - want_logsums=False, trace_label=None, trace_choice_name=None): + want_logsums=False, trace_label=None, + trace_choice_name=None, trace_column_names=None): """ Run a simulation for when the model spec does not involve alternative specific data, e.g. there are no interactions with alternative @@ -748,6 +748,8 @@ def eval_mnl(choosers, spec, locals_d, custom_chooser, estimator, when household tracing enabled. No tracing occurs if label is empty or None. trace_choice_name: str This is the column label to be used in trace file csv dump of choices + trace_column_names: str or list of str + chooser columns to include when tracing expression_values Returns ------- @@ -767,7 +769,7 @@ def eval_mnl(choosers, spec, locals_d, custom_chooser, estimator, utilities = eval_utilities(spec, choosers, locals_d, trace_label=trace_label, have_trace_targets=have_trace_targets, - estimator=estimator) + estimator=estimator, trace_column_names=trace_column_names) chunk.log_df(trace_label, "utilities", utilities) if have_trace_targets: @@ -804,7 +806,8 @@ def eval_mnl(choosers, spec, locals_d, custom_chooser, estimator, def eval_nl(choosers, spec, nest_spec, locals_d, custom_chooser, estimator, - want_logsums=False, trace_label=None, trace_choice_name=None): + want_logsums=False, trace_label=None, + trace_choice_name=None, trace_column_names=None): """ Run a nested-logit simulation for when the model spec does not involve alternative specific data, e.g. there are no interactions with alternative @@ -832,6 +835,8 @@ def eval_nl(choosers, spec, nest_spec, locals_d, custom_chooser, estimator, when household tracing enabled. No tracing occurs if label is empty or None. trace_choice_name: str This is the column label to be used in trace file csv dump of choices + trace_column_names: str or list of str + chooser columns to include when tracing expression_values Returns ------- @@ -851,7 +856,7 @@ def eval_nl(choosers, spec, nest_spec, locals_d, custom_chooser, estimator, raw_utilities = eval_utilities(spec, choosers, locals_d, trace_label=trace_label, have_trace_targets=have_trace_targets, - estimator=estimator) + estimator=estimator, trace_column_names=trace_column_names) chunk.log_df(trace_label, "raw_utilities", raw_utilities) if have_trace_targets: @@ -939,7 +944,7 @@ def _simple_simulate(choosers, spec, nest_spec, skims=None, locals_d=None, custom_chooser=None, want_logsums=False, estimator=None, - trace_label=None, trace_choice_name=None, + trace_label=None, trace_choice_name=None, trace_column_names=None, ): """ Run an MNL or NL simulation for when the model spec does not involve alternative @@ -976,6 +981,8 @@ def _simple_simulate(choosers, spec, nest_spec, skims=None, locals_d=None, when household tracing enabled. No tracing occurs if label is empty or None. trace_choice_name: str This is the column label to be used in trace file csv dump of choices + trace_column_names: str or list of str + chooser columns to include when tracing expression_values Returns ------- @@ -991,57 +998,78 @@ def _simple_simulate(choosers, spec, nest_spec, skims=None, locals_d=None, choices = eval_mnl(choosers, spec, locals_d, custom_chooser, want_logsums=want_logsums, estimator=estimator, - trace_label=trace_label, trace_choice_name=trace_choice_name) + trace_label=trace_label, + trace_choice_name=trace_choice_name, trace_column_names=trace_column_names) else: choices = eval_nl(choosers, spec, nest_spec, locals_d, custom_chooser, want_logsums=want_logsums, estimator=estimator, - trace_label=trace_label, trace_choice_name=trace_choice_name) + trace_label=trace_label, + trace_choice_name=trace_choice_name, trace_column_names=trace_column_names) return choices -def simple_simulate_rpc(chunk_size, choosers, spec, nest_spec, trace_label): +def tvpb_skims(skims): + + def list_of_skims(skims): + return \ + skims if isinstance(skims, list) \ + else skims.values() if isinstance(skims, dict) \ + else [skims] if skims is not None \ + else [] + + return [skim for skim in list_of_skims(skims) if isinstance(skim, pathbuilder.TransitVirtualPathLogsumWrapper)] + + +def simple_simulate_calc_row_size(choosers, spec, nest_spec, skims=None, trace_label=None): """ rows_per_chunk calculator for simple_simulate """ - num_choosers = len(choosers.index) + trace_label = tracing.extend_trace_label(trace_label, 'simple_simulate_calc_row_size') - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + sizer = chunk.RowSizeEstimator(trace_label) - chooser_row_size = len(choosers.columns) + # if there are skims, and zone_system is THREE_ZONE, and there are any + # then we want to estimate the per-row overhead tvpb skims + # (do this first to facilitate tracing of rowsize estimation below) + if tvpb_skims(skims): + # DISABLE_TVPB_OVERHEAD + logger.debug("disable calc_row_size for THREE_ZONE with tap skims") + return 0 - if nest_spec is None: - # expression_values for each spec row - # utilities and probs for each alt - extra_columns = spec.shape[0] + (2 * spec.shape[1]) - else: - # expression_values for each spec row - # raw_utilities and base_probabilities) for each alt - # nested_exp_utilities, nested_probabilities for each nest - # less 1 as nested_probabilities lacks root - nest_count = logit.count_nests(nest_spec) - extra_columns = spec.shape[0] + (2 * spec.shape[1]) + (2 * nest_count) - 1 + # expression_values for each spec row + sizer.add_elements(spec.shape[0], 'expression_values') + + # raw utilities and probs for each alt + sizer.add_elements(spec.shape[1], 'utilities') - # logger.debug("%s #chunk_calc nest_count %s" % (trace_label, nest_count)) + # del expression_values when done with them + sizer.drop_elements('expression_values') - row_size = chooser_row_size + extra_columns + # probs for each alt + sizer.add_elements(spec.shape[1], 'probs') - # logger.debug("%s #chunk_calc choosers %s" % (trace_label, choosers.shape)) - # logger.debug("%s #chunk_calc spec %s" % (trace_label, spec.shape)) - # logger.debug("%s #chunk_calc extra_columns %s" % (trace_label, extra_columns)) + if nest_spec is not None: + nest_size = logit.count_nests(nest_spec) + # nested_exp_utilities for each nest + sizer.add_elements(nest_size, 'nested_exp_utilities') + # nested_probabilities less 1 since it lacks root + sizer.add_elements(nest_size - 1, 'nested_probabilities') + + logger.debug(f"{trace_label} #chunk_calc row_size hwm after {sizer.hwm_tag} {sizer.hwm}") - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + row_size = sizer.get_hwm() + + return row_size def simple_simulate(choosers, spec, nest_spec, skims=None, locals_d=None, chunk_size=0, custom_chooser=None, want_logsums=False, estimator=None, - trace_label=None, trace_choice_name=None): + trace_label=None, trace_choice_name=None, trace_column_names=None): """ Run an MNL or NL simulation for when the model spec does not involve alternative specific data, e.g. there are no interactions with alternative @@ -1052,19 +1080,12 @@ def simple_simulate(choosers, spec, nest_spec, skims=None, locals_d=None, assert len(choosers) > 0 - rows_per_chunk, effective_chunk_size = \ - simple_simulate_rpc(chunk_size, choosers, spec, nest_spec, trace_label) + row_size = chunk_size and simple_simulate_calc_row_size(choosers, spec, nest_spec, skims, trace_label) result_list = [] # segment by person type and pick the right spec for each person type - for i, num_chunks, chooser_chunk in chunk.chunked_choosers(choosers, rows_per_chunk): - - logger.info("Running chunk %s of %s size %d" % (i, num_chunks, len(chooser_chunk))) - - chunk_trace_label = tracing.extend_trace_label(trace_label, 'chunk_%s' % i) \ - if num_chunks > 1 else trace_label - - chunk.log_open(chunk_trace_label, chunk_size, effective_chunk_size) + for i, chooser_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers(choosers, chunk_size, row_size, trace_label): choices = _simple_simulate( chooser_chunk, spec, nest_spec, @@ -1074,9 +1095,8 @@ def simple_simulate(choosers, spec, nest_spec, skims=None, locals_d=None, want_logsums=want_logsums, estimator=estimator, trace_label=chunk_trace_label, - trace_choice_name=trace_choice_name) - - chunk.log_close(chunk_trace_label) + trace_choice_name=trace_choice_name, + trace_column_names=trace_column_names) result_list.append(choices) @@ -1088,6 +1108,44 @@ def simple_simulate(choosers, spec, nest_spec, skims=None, locals_d=None, return choices +def simple_simulate_by_chunk_id(choosers, spec, nest_spec, + skims=None, locals_d=None, + chunk_size=0, custom_chooser=None, + want_logsums=False, + estimator=None, + trace_label=None, + trace_choice_name=None): + """ + chunk_by_chunk_id wrapper for simple_simulate + """ + row_size = chunk_size and simple_simulate_calc_row_size(choosers, spec, nest_spec, trace_label=trace_label) + + # NOTE we chunk chunk_id so we have to scale row_size by average number of chooser rows per chunk_id + num_choosers = choosers['chunk_id'].max() + 1 + rows_per_chunk_id = len(choosers) / num_choosers + row_size = row_size * rows_per_chunk_id + + result_list = [] + for i, chooser_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers_by_chunk_id(choosers, chunk_size, row_size, trace_label): + + choices = _simple_simulate( + chooser_chunk, spec, nest_spec, + skims=skims, + locals_d=locals_d, + custom_chooser=custom_chooser, + want_logsums=want_logsums, + estimator=estimator, + trace_label=chunk_trace_label, + trace_choice_name=trace_choice_name) + + result_list.append(choices) + + choices = pd.concat(result_list) + + return choices + + def eval_mnl_logsums(choosers, spec, locals_d, trace_label=None): """ like eval_nl except return logsums instead of making choices @@ -1130,7 +1188,7 @@ def eval_mnl_logsums(choosers, spec, locals_d, trace_label=None): return logsums -def eval_nl_logsums(choosers, spec, nest_spec, locals_d, trace_label=None, alt_col_name=None): +def eval_nl_logsums(choosers, spec, nest_spec, locals_d, trace_label=None): """ like eval_nl except return logsums instead of making choices @@ -1150,8 +1208,7 @@ def eval_nl_logsums(choosers, spec, nest_spec, locals_d, trace_label=None, alt_c tracing.trace_df(choosers, '%s.choosers' % trace_label) raw_utilities = eval_utilities(spec, choosers, locals_d, - trace_label=trace_label, have_trace_targets=have_trace_targets, - alt_col_name=alt_col_name) + trace_label=trace_label, have_trace_targets=have_trace_targets) chunk.log_df(trace_label, "raw_utilities", raw_utilities) if have_trace_targets: @@ -1185,7 +1242,7 @@ def eval_nl_logsums(choosers, spec, nest_spec, locals_d, trace_label=None, alt_c def _simple_simulate_logsums(choosers, spec, nest_spec, - skims=None, locals_d=None, trace_label=None, alt_col_name=None): + skims=None, locals_d=None, trace_label=None): """ like simple_simulate except return logsums instead of making choices @@ -1200,50 +1257,53 @@ def _simple_simulate_logsums(choosers, spec, nest_spec, if nest_spec is None: logsums = eval_mnl_logsums(choosers, spec, locals_d, - trace_label=trace_label, alt_col_name=alt_col_name) + trace_label=trace_label) else: logsums = eval_nl_logsums(choosers, spec, nest_spec, locals_d, - trace_label=trace_label, alt_col_name=alt_col_name) + trace_label=trace_label) return logsums -def simple_simulate_logsums_rpc(chunk_size, choosers, spec, nest_spec, trace_label): +def simple_simulate_logsums_calc_row_size(choosers, spec, nest_spec, skims, trace_label): """ calculate rows_per_chunk for simple_simulate_logsums """ - num_choosers = len(choosers.index) + # if there are skims, and zone_system is THREE_ZONE, and there are any + # then we want to estimate the per-row overhead tvpb skims + # (do this first to facilitate tracing of rowsize estimation below) + if tvpb_skims(skims): + # DISABLE_TVPB_OVERHEAD + # skim_oh, skim_tag = estimate_tvpb_skims_overhead(choosers, skims, trace_label) + logger.debug("disable calc_row_size for THREE_ZONE with tap skims") + return 0 - # if not chunking, then return num_choosers - # if chunk_size == 0: - # return num_choosers, 0 + sizer = chunk.RowSizeEstimator(trace_label) - chooser_row_size = len(choosers.columns) + # expression_values for each spec row + sizer.add_elements(spec.shape[0], 'expression_values') + + # expression_values for each spec row + sizer.add_elements(spec.shape[1], 'utilities') + + # del expression_values when done with them + sizer.drop_elements('expression_values') if nest_spec is None: - # expression_values for each spec row - # utilities for each alt - extra_columns = spec.shape[0] + spec.shape[1] logger.warning("simple_simulate_logsums_rpc rows_per_chunk not validated for mnl" " so chunk sizing might be a bit off") else: - # expression_values for each spec row - # raw_utilities for each alt - # nested_exp_utilities for each nest - extra_columns = spec.shape[0] + spec.shape[1] + logit.count_nests(nest_spec) - - row_size = chooser_row_size + extra_columns + sizer.add_elements(logit.count_nests(nest_spec), 'nested_exp_utilities') - # logger.debug("%s #chunk_calc chooser_row_size %s" % (trace_label, chooser_row_size)) - # logger.debug("%s #chunk_calc extra_columns %s" % (trace_label, extra_columns)) + row_size = sizer.get_hwm() - return chunk.rows_per_chunk(chunk_size, row_size, num_choosers, trace_label) + return row_size def simple_simulate_logsums(choosers, spec, nest_spec, skims=None, locals_d=None, chunk_size=0, - trace_label=None, alt_col_name=None): + trace_label=None): """ like simple_simulate except return logsums instead of making choices @@ -1257,26 +1317,17 @@ def simple_simulate_logsums(choosers, spec, nest_spec, assert len(choosers) > 0 - rows_per_chunk, effective_chunk_size = \ - simple_simulate_logsums_rpc(chunk_size, choosers, spec, nest_spec, trace_label) + row_size = chunk_size and simple_simulate_logsums_calc_row_size(choosers, spec, nest_spec, skims, trace_label) result_list = [] # segment by person type and pick the right spec for each person type - for i, num_chunks, chooser_chunk in chunk.chunked_choosers(choosers, rows_per_chunk): - - logger.info("Running chunk %s of %s size %d" % (i, num_chunks, len(chooser_chunk))) - - chunk_trace_label = tracing.extend_trace_label(trace_label, 'chunk_%s' % i) \ - if num_chunks > 1 else trace_label - - chunk.log_open(chunk_trace_label, chunk_size, effective_chunk_size) + for i, chooser_chunk, chunk_trace_label \ + in chunk.adaptive_chunked_choosers(choosers, chunk_size, row_size, trace_label): logsums = _simple_simulate_logsums( chooser_chunk, spec, nest_spec, skims, locals_d, - chunk_trace_label, alt_col_name) - - chunk.log_close(chunk_trace_label) + chunk_trace_label) result_list.append(logsums) diff --git a/activitysim/core/skim.py b/activitysim/core/skim.py deleted file mode 100644 index 5c4a56c541..0000000000 --- a/activitysim/core/skim.py +++ /dev/null @@ -1,562 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. - -from builtins import range -from builtins import object - -import logging - -from collections import OrderedDict - -import numpy as np -import pandas as pd - -from activitysim.core.util import quick_loc_series - - -logger = logging.getLogger(__name__) - - -class OffsetMapper(object): - """ - Utility to map skim zone ids to ordinal offsets (e.g. numpy array indices) - - Can map either by a fixed offset (e.g. -1 to map 1-based to 0-based) - or by an explicit mapping of zone id to offset (slower but more flexible) - """ - - def __init__(self, offset_int=None): - self.offset_series = None - self.offset_int = offset_int - - def set_offset_list(self, offset_list): - """ - Specify the zone ids corresponding to the offsets (ordinal positions) - - set_offset_list([10, 20, 30, 40]) - map([30, 20, 40]) - returns offsets [2, 1, 3] - - Parameters - ---------- - offset_list : list of int - """ - assert isinstance(offset_list, list) - assert self.offset_int is None - - # - for performance, check if this is a simple int-based series - first_offset = offset_list[0] - if (offset_list == list(range(first_offset, len(offset_list)+first_offset))): - offset_int = -1 * first_offset - # print "set_offset_list substituting offset_int of %s" % offset_int - self.set_offset_int(offset_int) - return - - if self.offset_series is None: - self.offset_series = pd.Series(data=list(range(len(offset_list))), index=offset_list) - else: - # make sure it offsets are the same - assert (offset_list == self.offset_series.index).all() - - def set_offset_int(self, offset_int): - """ - specify fixed offset (e.g. -1 to map 1-based to 0-based) - - Parameters - ---------- - offset_int : int - """ - - # should be some kind of integer - assert int(offset_int) == offset_int - assert self.offset_series is None - - if self.offset_int is None: - self.offset_int = int(offset_int) - else: - # make sure it is the same - assert offset_int == self.offset_int - - def map(self, zone_ids): - """ - map zone_ids to offsets - - Parameters - ---------- - zone_ids - - Returns - ------- - offsets : numpy array of int - """ - - NOT_IN_SKIM = -1 - - if self.offset_series is not None: - assert(self.offset_int is None) - assert isinstance(self.offset_series, pd.Series) - offsets = np.asanyarray(quick_loc_series(zone_ids, self.offset_series).fillna(NOT_IN_SKIM).astype(int)) - - elif self.offset_int: - assert (self.offset_series is None) - offsets = zone_ids + self.offset_int - else: - offsets = zone_ids - - return offsets - - -class SkimWrapper(object): - """ - Container for skim arrays. - - Parameters - ---------- - data : 2D array - offset : int, optional - An optional offset that will be added to origin/destination - values to turn them into array indices. - For example, if zone IDs are 1-based, an offset of -1 - would turn them into 0-based array indices. - - """ - def __init__(self, data, offset_mapper=None): - - self.data = data - self.offset_mapper = offset_mapper if offset_mapper is not None else OffsetMapper() - - def get(self, orig, dest): - """ - Get impedence values for a set of origin, destination pairs. - - Parameters - ---------- - orig : 1D array - dest : 1D array - - Returns - ------- - values : 1D array - - """ - - # fixme - remove? - assert not (np.isnan(orig) | np.isnan(dest)).any() - - # only working with numpy in here - orig = np.asanyarray(orig).astype(int) - dest = np.asanyarray(dest).astype(int) - - mapped_orig = self.offset_mapper.map(orig) - mapped_dest = self.offset_mapper.map(dest) - result = self.data[mapped_orig, mapped_dest] - - # FIXME - should return nan if not in skim (negative indices wrap around) - # NOT_IN_SKIM = np.nan - # in_skim = \ - # (mapped_orig <0) & (mapped_orig < self.data.shape[0]) & \ - # (mapped_dest <0) & (mapped_dest < self.data.shape[0]) - # result = np.where(in_skim, result, NOT_IN_SKIM) - - return result - - -class SkimDict(object): - """ - A SkimDict object is a wrapper around a dict of multiple skim objects, - where each object is identified by a key. It operates like a - dictionary - i.e. use brackets to add and get skim objects. - - Note that keys are either strings or tuples of two strings (to support stacking of skims.) - """ - - def __init__(self, skim_data, skim_info): - - self.skim_info = skim_info - self.skim_data = skim_data - - self.offset_mapper = OffsetMapper() - self.usage = set() - - def touch(self, key): - - self.usage.add(key) - - def get(self, key): - """ - Get an available wrapped skim object (not the lookup) - - Parameters - ---------- - key : hashable - The key (identifier) for this skim object - - Returns - ------- - skim: Skim - The skim object - """ - - block, offset = self.skim_info['block_offsets'].get(key) - block_data = self.skim_data[block] - - self.touch(key) - - data = block_data[:, :, offset] - - return SkimWrapper(data, self.offset_mapper) - - def wrap(self, left_key, right_key): - """ - return a SkimDictWrapper for self - """ - return SkimDictWrapper(self, left_key, right_key) - - -class SkimDictWrapper(object): - """ - A SkimDictWrapper object is an access wrapper around a SkimDict of multiple skim objects, - where each object is identified by a key. It operates like a - dictionary - i.e. use brackets to add and get skim objects - but also - has information on how to lookup against the skim objects. - Specifically, this object has a dataframe, a left_key and right_key. - It is assumed that left_key and right_key identify columns in df. The - parameter df is usually set by the simulation itself as it's a result of - interacting choosers and alternatives. - - When the user calls skims[key], key is an identifier for which skim - to use, and the object automatically looks up impedances of that skim - using the specified left_key column in df as the origin and - the right_key column in df as the destination. In this way, the user - does not do the O-D lookup by hand and only specifies which skim to use - for this lookup. This is the only purpose of this object: to - abstract away the O-D lookup and use skims by specifying which skim - to use in the expressions. - - Note that keys are either strings or tuples of two strings (to support stacking of skims.) - """ - - def __init__(self, skim_dict, left_key, right_key): - self.skim_dict = skim_dict - self.left_key = left_key - self.right_key = right_key - self.df = None - - def set_df(self, df): - """ - Set the dataframe - - Parameters - ---------- - df : DataFrame - The dataframe which contains the origin and destination ids - - Returns - ------- - Nothing - """ - self.df = df - - def lookup(self, key, reverse=False): - """ - Generally not called by the user - use __getitem__ instead - - Parameters - ---------- - key : hashable - The key (identifier) for this skim object - - od : bool (optional) - od=True means lookup standard origin-destination skim value - od=False means lookup destination-origin skim value - - Returns - ------- - impedances: pd.Series - A Series of impedances which are elements of the Skim object and - with the same index as df - """ - - # The skim object to perform the lookup - # using df[left_key] as the origin and df[right_key] as the destination - skim = self.skim_dict.get(key) - - # assert self.df is not None, "Call set_df first" - # origins = self.df[self.left_key].astype('int') - # destinations = self.df[self.right_key].astype('int') - # if self.offset: - # origins = origins + self.offset - # destinations = destinations + self.offset - - assert self.df is not None, "Call set_df first" - - if reverse: - s = skim.get(self.df[self.right_key], self.df[self.left_key]) - else: - s = skim.get(self.df[self.left_key], self.df[self.right_key]) - - return pd.Series(s, index=self.df.index) - - def reverse(self, key): - """ - return skim value in reverse (d-o) direction - """ - return self.lookup(key, reverse=True) - - def max(self, key): - """ - return max skim value in either o-d or d-o direction - """ - - skim = self.skim_dict.get(key) - - assert self.df is not None, "Call set_df first" - - s = np.maximum( - skim.get(self.df[self.right_key], self.df[self.left_key]), - skim.get(self.df[self.left_key], self.df[self.right_key]) - ) - - return pd.Series(s, index=self.df.index) - - def __getitem__(self, key): - """ - Get the (df implicit) lookup for an available skim object - - Parameters - ---------- - key : hashable - The key (identifier) for the skim object - - Returns - ------- - impedances: pd.Series - A Series of impedances which are elements of the Skim object and - with the same index as df - """ - return self.lookup(key) - - -class SkimStack(object): - - def __init__(self, skim_dict): - - self.offset_mapper = skim_dict.offset_mapper - self.skim_dict = skim_dict - - # - key1_blocks dict maps key1 to block number - # DISTWALK: 0, - # DRV_COM_WLK_BOARDS: 0, ... - key1_block_offsets = skim_dict.skim_info['key1_block_offsets'] - self.key1_blocks = {k: v[0] for k, v in key1_block_offsets.items()} - - # - skim_dim3 dict maps key1 to dict of key2 absolute offsets into block - # DRV_COM_WLK_BOARDS: {'MD': 4, 'AM': 3, 'PM': 5}, ... - block_offsets = skim_dict.skim_info['block_offsets'] - skim_dim3 = OrderedDict() - for skim_key in block_offsets: - - if not isinstance(skim_key, tuple): - continue - - key1, key2 = skim_key - block, offset = block_offsets[skim_key] - - assert block == self.key1_blocks[key1] - - skim_dim3.setdefault(key1, OrderedDict())[key2] = offset - - self.skim_dim3 = skim_dim3 - - logger.info("SkimStack.__init__ loaded %s keys with %s total skims" - % (len(self.skim_dim3), - sum([len(d) for d in self.skim_dim3.values()]))) - - self.usage = set() - - def touch(self, key): - self.usage.add(key) - - def lookup(self, orig, dest, dim3, key): - - orig = self.offset_mapper.map(orig) - dest = self.offset_mapper.map(dest) - - assert key in self.key1_blocks, "SkimStack key %s missing" % key - assert key in self.skim_dim3, "SkimStack key %s missing" % key - - block = self.key1_blocks[key] - stacked_skim_data = self.skim_dict.skim_data[block] - skim_keys_to_indexes = self.skim_dim3[key] - - self.touch(key) - - # skim_indexes = dim3.map(skim_keys_to_indexes).astype('int') - # this should be faster than map - skim_indexes = np.vectorize(skim_keys_to_indexes.get)(dim3) - - return stacked_skim_data[orig, dest, skim_indexes] - - def wrap(self, left_key, right_key, skim_key): - """ - return a SkimStackWrapper for self - """ - return SkimStackWrapper(stack=self, - left_key=left_key, right_key=right_key, skim_key=skim_key) - - -class SkimStackWrapper(object): - """ - A SkimStackWrapper object wraps a SkimStack object to add an additional wrinkle of - lookup functionality. Upon init the separate skims objects are - processed into a 3D matrix so that lookup of the different skims can - be performed quickly for each row in the dataframe. In this very - particular formulation, the keys are assumed to be tuples with two - elements - the second element of which will be taken from the - different rows in the dataframe. The first element can then be - dereferenced like an array. This is useful, for instance, to have a - certain skim vary by time of day - the skims are set with keys of - ('SOV', 'AM"), ('SOV', 'PM') etc. The time of day is then taken to - be different for every row in the tours table, and the 'SOV' portion - of the key can be used in __getitem__. - - To be more explicit, the input is a dictionary of Skims objects, each of - which contains a 2D matrix. These are stacked into a 3D matrix with a - mapping of keys to indexes which is applied using pandas .map to a third - column in the object dataframe. The three columns - left_key and - right_key from the Skims object and skim_key from this one, are then used to - dereference the 3D matrix. The tricky part comes in defining the key which - matches the 3rd dimension of the matrix, and the key which is passed into - __getitem__ below (i.e. the one used in the specs). By convention, - every key in the Skims object that is passed in MUST be a tuple with 2 - items. The second item in the tuple maps to the items in the dataframe - referred to by the skim_key column and the first item in the tuple is - then available to pass directly to __getitem__. - - The sum conclusion of this is that in the specs, you can say something - like out_skim['SOV'] and it will automatically dereference the 3D matrix - using origin, destination, and time of day. - - Parameters - ---------- - skims: Skims - This is the Skims object to wrap - skim_key : str - This identifies the column in the dataframe which is used to - select among Skim object using the SECOND item in each tuple (see - above for a more complete description) - """ - - def __init__(self, stack, left_key, right_key, skim_key): - - self.stack = stack - - self.left_key = left_key - self.right_key = right_key - self.skim_key = skim_key - self.df = None - - def set_df(self, df): - """ - Set the dataframe - - Parameters - ---------- - df : DataFrame - The dataframe which contains the origin and destination ids - - Returns - ------- - Nothing - """ - self.df = df - - def __getitem__(self, key): - """ - Get an available skim object - - Parameters - ---------- - key : hashable - The key (identifier) for this skim object - - Returns - ------- - skim: Skim - The skim object - """ - - assert self.df is not None, "Call set_df first" - orig = self.df[self.left_key].astype('int') - dest = self.df[self.right_key].astype('int') - dim3 = self.df[self.skim_key] - - skim_values = self.stack.lookup(orig, dest, dim3, key) - - return pd.Series(skim_values, self.df.index) - - -class DataFrameMatrix(object): - """ - Utility class to allow a pandas dataframe to be treated like a 2-D array, - indexed by rowid, colname - - For use in vectorized expressions where the desired values depend on both a row column selector - e.g. size_terms.get(df.dest_taz, df.purpose) - - :: - - df = pd.DataFrame({'a': [1,2,3,4,5], 'b': [10,20,30,40,50]}, index=[100,101,102,103,104]) - - dfm = DataFrameMatrix(df) - - dfm.get(row_ids=[100,100,103], col_ids=['a', 'b', 'a']) - - returns [1, 10, 4] - - """ - - def __init__(self, df): - """ - - Parameters - ---------- - df - pandas dataframe of uniform type - """ - - self.df = df - self.data = df.values - - self.offset_mapper = OffsetMapper() - self.offset_mapper.set_offset_list(list(df.index)) - - self.cols_to_indexes = {k: v for v, k in enumerate(df.columns)} - - def get(self, row_ids, col_ids): - """ - - Parameters - ---------- - row_ids - list of row_ids (df index values) - col_ids - list of column names, one per row_id, - specifying column from which the value for that row should be retrieved - - Returns - ------- - - series with one row per row_id, with the value from the column specified in col_ids - - """ - # col_indexes = segments.map(self.cols_to_indexes).astype('int') - # this should be faster than map - col_indexes = np.vectorize(self.cols_to_indexes.get)(col_ids) - - row_indexes = self.offset_mapper.map(np.asanyarray(row_ids)) - - result = self.data[row_indexes, col_indexes] - - # FIXME - if ids (or col_ids?) is a series, return series with same index? - if isinstance(row_ids, pd.Series): - result = pd.Series(result, index=row_ids.index) - - return result diff --git a/activitysim/core/skim_dict_factory.py b/activitysim/core/skim_dict_factory.py new file mode 100644 index 0000000000..9403d3537c --- /dev/null +++ b/activitysim/core/skim_dict_factory.py @@ -0,0 +1,567 @@ +# ActivitySim +# See full license in LICENSE.txt. +# from builtins import int + +import os +import multiprocessing +import logging +import numpy as np +import openmatrix as omx +from abc import ABC, abstractmethod + +from activitysim.core import util +from activitysim.core import config +from activitysim.core import inject +from activitysim.core import tracing +from activitysim.core import skim_dictionary + +logger = logging.getLogger(__name__) + + +class SkimData(object): + """ + A facade for 3D skim data exposing numpy indexing and shape + The primary purpose is to document and police the api used to access skim data + Subclasses using a different backing store to perform additional/alternative + only need to implement the methods exposed here. + + For instance, to open/close memmapped files just in time, or to access backing data via an alternate api + """ + def __init__(self, skim_data): + """ + skim_data is an np.ndarray or anything that implements the methods/properties of this class + + Parameters + ---------- + skim_data : np.ndarray or quack-alike + """ + self._skim_data = skim_data + + def __getitem__(self, indexes): + if len(indexes) != 3: + raise ValueError(f'number of indexes ({len(indexes)}) should be 3') + return self._skim_data[indexes] + + @property + def shape(self): + """ + Returns + ------- + list-like shape tuple as returned by numpy.shape + """ + return self._skim_data.shape + + +class SkimInfo(object): + def __init__(self, skim_tag, network_los): + """ + + skim_tag: str (e.g. 'TAZ') + dtype_name: str (e.g. 'float32') + omx_manifest: dict dict mapping { omx_key: omx_file_name } + omx_shape: 2D tuple shape of omx matrix: (, ) + num_skims: int total number of individual skim matrices in omx files + skim_data_shape: 3D tuple (num_skims, omx_shape[0], omx_shape[1]) if ROW_MAJOR_LAYOUT + offset_map: dict or None 1D ndarray as returned by omx_file.mapentries, if omx file has mappings + offset_map_name: str name of offset_map in omx_filecorresponding to offset_map, if there was one + omx_keys: dict dict mapping skim key (str or tuple) to skim key in omx file + {DISTWALK: DISTWALK, + ('DRV_COM_WLK_BOARDS', 'AM'): DRV_COM_WLK_BOARDS__AM, ...} + base_keys: list of str e.g. 'BIKEDIST' or 'SOVTOLL_VTOLL' (base key of 3d skim) + block_offsets: dict dict mapping skim key tuple to offset + + Parameters + ---------- + skim_tag + """ + + self.network_los = network_los + self.skim_tag = skim_tag + self.dtype_name = network_los.skim_dtype_name + + self.omx_manifest = None + self.omx_shape = None + self.num_skims = None + self.skim_data_shape = None + self.offset_map_name = None + self.offset_map = None + self.omx_keys = None + self.base_keys = None + self.block_offsets = None + + self.load_skim_info(skim_tag) + + def load_skim_info(self, skim_tag): + """ + Read omx files for skim (e.g. 'TAZ') and build skim_info dict + + Parameters + ---------- + skim_tag: str + + """ + + self.omx_file_names = self.network_los.omx_file_names(skim_tag) + + # ignore any 3D skims not in skim_time_periods + # specifically, load all skims except those with key2 not in dim3_tags_to_load + skim_time_periods = self.network_los.skim_time_periods + dim3_tags_to_load = skim_time_periods and skim_time_periods['labels'] + + self.omx_manifest = {} # dict mapping { omx_key: skim_name } + + for omx_file_name in self.omx_file_names: + + omx_file_path = config.data_file_path(omx_file_name) + + # logger.debug(f"load_skim_info {skim_tag} reading {omx_file_path}") + + with omx.open_file(omx_file_path) as omx_file: + + # fixme call to omx_file.shape() failing in windows p3.5 + if self.omx_shape is None: + self.omx_shape = tuple(int(i) for i in omx_file.shape()) # sometimes omx shape are floats! + else: + assert (self.omx_shape == tuple(int(i) for i in omx_file.shape())) + + for skim_name in omx_file.listMatrices(): + assert skim_name not in self.omx_manifest, \ + f"duplicate skim '{skim_name}' found in {self.omx_manifest[skim_name]} and {omx_file}" + self.omx_manifest[skim_name] = omx_file_name + + for m in omx_file.listMappings(): + if self.offset_map is None: + self.offset_map_name = m + self.offset_map = omx_file.mapentries(self.offset_map_name) + assert len(self.offset_map) == self.omx_shape[0] + else: + # don't really expect more than one, but ok if they are all the same + if not (self.offset_map == omx_file.mapentries(m)): + raise RuntimeError(f"Multiple mappings in omx file: {self.offset_map_name} != {m}") + + # - omx_keys dict maps skim key to omx_key + # DISTWALK: DISTWALK + # ('DRV_COM_WLK_BOARDS', 'AM'): DRV_COM_WLK_BOARDS__AM, ... + self.omx_keys = dict() + for skim_name in self.omx_manifest.keys(): + key1, sep, key2 = skim_name.partition('__') + + # - ignore composite tags not in dim3_tags_to_load + if dim3_tags_to_load and sep and key2 not in dim3_tags_to_load: + continue + + skim_key = (key1, key2) if sep else key1 + + self.omx_keys[skim_key] = skim_name + + self.num_skims = len(self.omx_keys) + + # - key1_subkeys dict maps key1 to dict of subkeys with that key1 + # DIST: {'DIST': 0} + # DRV_COM_WLK_BOARDS: {'MD': 1, 'AM': 0, 'PM': 2}, ... + key1_subkeys = dict() + for skim_key, omx_key in self.omx_keys.items(): + if isinstance(skim_key, tuple): + key1, key2 = skim_key + else: + key1 = key2 = skim_key + key2_dict = key1_subkeys.setdefault(key1, {}) + key2_dict[key2] = len(key2_dict) + + key1_block_offsets = dict() + offset = 0 + for key1, v in key1_subkeys.items(): + num_subkeys = len(v) + key1_block_offsets[key1] = offset + offset += num_subkeys + + # - block_offsets dict maps skim_key to offset of omx matrix + # DIST: 0, + # ('DRV_COM_WLK_BOARDS', 'AM'): 3, + # ('DRV_COM_WLK_BOARDS', 'MD') 4, ... + self.block_offsets = dict() + for skim_key in self.omx_keys: + + if isinstance(skim_key, tuple): + key1, key2 = skim_key + else: + key1 = key2 = skim_key + + key1_offset = key1_block_offsets[key1] + key2_relative_offset = key1_subkeys.get(key1).get(key2) + self.block_offsets[skim_key] = key1_offset + key2_relative_offset + + if skim_dictionary.ROW_MAJOR_LAYOUT: + self.skim_data_shape = (self.num_skims, self.omx_shape[0], self.omx_shape[1]) + else: + self.skim_data_shape = self.omx_shape + (self.num_skims,) + + # list of base keys (keys + self.base_keys = tuple(k for k in key1_block_offsets.keys()) + + def print(self): + print(f"SkimInfo for {self.skim_tag}") + print(f"omx_shape {self.omx_shape}") + print(f"num_skims {self.num_skims}") + print(f"skim_data_shape {self.skim_data_shape}") + print(f"offset_map_name {self.offset_map_name}") + # print(f"omx_manifest {self.omx_manifest}") + # print(f"offset_map {self.offset_map}") + # print(f"omx_keys {self.omx_keys}") + # print(f"base_keys {self.base_keys}") + # print(f"block_offsets {self.block_offsets}") + + +class AbstractSkimFactory(ABC): + """ + Provide access to skim data from store. + + load_skim_info(skim_tag: str): dict + Read omx files for skim (e.g. 'TAZ') and build skim_info dict + + get_skim_data(skim_tag: str, skim_info: dict): SkimData + Read skim data from backing store and return it as a 3D ndarray quack-alike SkimData object + + allocate_skim_buffer(skim_info, shared: bool): 1D array buffer sized for 3D SkimData + Allocate a ram skim buffer (ndarray or multiprocessing.Array) to use as frombuffer for SkimData + + """ + + def __init__(self, network_los): + self.network_los = network_los + + @property + def supports_shared_data_for_multiprocessing(self): + """ + Does subclass support shareable data for multiprocessing + + Returns + ------- + boolean + """ + return False + + def allocate_skim_buffer(self, skim_info, shared=False): + """ + For multiprocessing + """ + assert False, "Not supported" + + def _skim_data_from_buffer(self, skim_info, skim_buffer): + assert False, "Not supported" + + def _memmap_skim_data_path(self, skim_tag): + return os.path.join(self.network_los.get_cache_dir(), f"cached_{skim_tag}.mmap") + + def load_skim_info(self, skim_tag): + return SkimInfo(skim_tag, self.network_los) + + def _read_skims_from_omx(self, skim_info, skim_data): + """ + read skims from omx file into skim_data + """ + + skim_tag = skim_info.skim_tag + omx_keys = skim_info.omx_keys + omx_manifest = skim_info.omx_manifest # dict mapping { omx_key: skim_name } + + for omx_file_name in skim_info.omx_file_names: + + omx_file_path = config.data_file_path(omx_file_name) + num_skims_loaded = 0 + + logger.info(f"_read_skims_from_omx {omx_file_path}") + + # read skims into skim_data + with omx.open_file(omx_file_path) as omx_file: + for skim_key, omx_key in omx_keys.items(): + + if omx_manifest[omx_key] == omx_file_name: + + offset = skim_info.block_offsets[skim_key] + logger.debug(f"_read_skims_from_omx file {omx_file_name} omx_key {omx_key} " + f"skim_key {skim_key} to offset {offset}") + + if skim_dictionary.ROW_MAJOR_LAYOUT: + a = skim_data[offset, :, :] + else: + a = skim_data[:, :, offset] + + # this will trigger omx readslice to read and copy data to skim_data's buffer + omx_data = omx_file[omx_key] + a[:] = omx_data[:] + + num_skims_loaded += 1 + + logger.info(f"_read_skims_from_omx loaded {num_skims_loaded} skims from {omx_file_name}") + + def _open_existing_readonly_memmap_skim_cache(self, skim_info): + """ + read cached memmapped skim data from canonically named cache file(s) in output directory into skim_data + return True if it was there and we read it, return False if not found + """ + + dtype = np.dtype(skim_info.dtype_name) + + skim_cache_path = self._memmap_skim_data_path(skim_info.skim_tag) + + if not os.path.isfile(skim_cache_path): + logger.warning(f"read_skim_cache file not found: {skim_cache_path}") + return None + + logger.info(f"reading skim cache {skim_info.skim_tag} {skim_info.skim_data_shape} from {skim_cache_path}") + + data = np.memmap(skim_cache_path, shape=skim_info.skim_data_shape, dtype=dtype, mode='r') + + return data + + def _create_empty_writable_memmap_skim_cache(self, skim_info): + """ + write skim data from skim_data to canonically named cache file(s) in output directory + """ + + dtype = np.dtype(skim_info.dtype_name) + + skim_cache_path = self._memmap_skim_data_path(skim_info.skim_tag) + + logger.info(f"writing skim cache {skim_info.skim_tag} {skim_info.skim_data_shape} to {skim_cache_path}") + + data = np.memmap(skim_cache_path, shape=skim_info.skim_data_shape, dtype=dtype, mode='w+') + + return data + + def copy_omx_to_mmap_file(self, skim_info): + + skim_data = self._create_empty_writable_memmap_skim_cache(skim_info) + self._read_skims_from_omx(skim_info, skim_data) + skim_data._mmap.close() + del skim_data + + +class NumpyArraySkimFactory(AbstractSkimFactory): + + def __init__(self, network_los): + super().__init__(network_los) + + @property + def supports_shared_data_for_multiprocessing(self): + return True + + def allocate_skim_buffer(self, skim_info, shared=False): + """ + Allocate a ram skim buffer to use as frombuffer for SkimData + If shared is True, return a shareable multiprocessing.RawArray, otherwise a numpy.ndarray + + Parameters + ---------- + skim_info: dict + shared: boolean + + Returns + ------- + multiprocessing.RawArray or numpy.ndarray + """ + + assert shared == self.network_los.multiprocess(), \ + f"NumpyArraySkimFactory.allocate_skim_buffer shared {shared} multiprocess {not shared}" + + dtype_name = skim_info.dtype_name + dtype = np.dtype(dtype_name) + + # multiprocessing.RawArray argument buffer_size must be int, not np.int64 + buffer_size = util.iprod(skim_info.skim_data_shape) + + csz = buffer_size * dtype.itemsize + logger.info(f"allocate_skim_buffer shared {shared} {skim_info.skim_tag} shape {skim_info.skim_data_shape} " + f"total size: {csz} ({tracing.si_units(csz)})") + + if shared: + if dtype_name == 'float64': + typecode = 'd' + elif dtype_name == 'float32': + typecode = 'f' + else: + raise RuntimeError("allocate_skim_buffer unrecognized dtype %s" % dtype_name) + + buffer = multiprocessing.RawArray(typecode, buffer_size) + else: + buffer = np.zeros(buffer_size, dtype=dtype) + + return buffer + + def _skim_data_from_buffer(self, skim_info, skim_buffer): + """ + return a numpy ndarray using skim_buffer as backing store + + Parameters + ---------- + skim_info + skim_buffer + + Returns + ------- + + """ + + dtype = np.dtype(skim_info.dtype_name) + assert len(skim_buffer) == util.iprod(skim_info.skim_data_shape) + skim_data = np.frombuffer(skim_buffer, dtype=dtype).reshape(skim_info.skim_data_shape) + return skim_data + + def load_skims_to_buffer(self, skim_info, skim_buffer): + """ + Load skims from disk store (omx or cache) into ram skim buffer (multiprocessing.RawArray or numpy.ndarray) + + Parameters + ---------- + skim_info: doct + skim_buffer: 1D buffer sized to hold all skims (multiprocessing.RawArray or numpy.ndarray) + """ + + read_cache = self.network_los.setting('read_skim_cache', False) + write_cache = self.network_los.setting('write_skim_cache', False) + + skim_data = self._skim_data_from_buffer(skim_info, skim_buffer) + assert skim_data.shape == skim_info.skim_data_shape + + if read_cache: + # returns None if cache file not found + cache_data = self._open_existing_readonly_memmap_skim_cache(skim_info) + + # copy memmapped cache to RAM numpy ndarray + if cache_data is not None: + assert cache_data.shape == skim_data.shape + np.copyto(skim_data, cache_data) + cache_data._mmap.close() + del cache_data + return + + # read omx skims into skim_buffer (np array) + self._read_skims_from_omx(skim_info, skim_data) + + if write_cache: + cache_data = self._create_empty_writable_memmap_skim_cache(skim_info) + np.copyto(cache_data, skim_data) + cache_data._mmap.close() + del cache_data + + # bug - do we need to close it? + + logger.info(f"load_skims_to_buffer {skim_info.skim_tag} shape {skim_data.shape}") + + def get_skim_data(self, skim_tag, skim_info): + """ + Read skim data from backing store and return it as a 3D ndarray quack-alike SkimData object + + Parameters + ---------- + skim_tag: str + skim_info: string + + Returns + ------- + SkimData + """ + + data_buffers = inject.get_injectable('data_buffers', None) + if data_buffers: + # we assume any existing skim buffers will already have skim data loaded into them + logger.info(f"get_skim_data {skim_tag} using existing shared skim_buffers for skims") + skim_buffer = data_buffers[skim_tag] + else: + skim_buffer = self.allocate_skim_buffer(skim_info, shared=False) + self.load_skims_to_buffer(skim_info, skim_buffer) + + skim_data = SkimData(self._skim_data_from_buffer(skim_info, skim_buffer)) + + logger.info(f"get_skim_data {skim_tag} {type(skim_data).__name__} shape {skim_data.shape}") + + return skim_data + + +class JitMemMapSkimData(SkimData): + """ + SkimData subclass for just-in-time memmap. + + Since opening a memmap is fast, open the memmap read the data on demand and immediately close it. + This essentially eliminates RAM usage, but it means we are loading the data every time we access the skim, + which may be significantly slower, depending on patterns of usage. + """ + + def __init__(self, skim_cache_path, skim_info): + super().__init__(skim_info) + self.skim_cache_path = skim_cache_path + self.dtype = np.dtype(skim_info.dtype_name) + self._shape = skim_info.skim_data_shape + + def __getitem__(self, indexes): + assert len(indexes) == 3, f'number of indexes ({len(indexes)}) should be 3' + # open memmap + data = np.memmap(self.skim_cache_path, shape=self._shape, dtype=self.dtype, mode='r') + # dereference skim values + result = data[indexes] + # closing memmap's underlying mmap frees data read into (not really needed as we are exiting scope) + data._mmap.close() + return result + + @property + def shape(self): + return self._shape + + +class MemMapSkimFactory(AbstractSkimFactory): + """ + The numpy.memmap docs states: The memmap object can be used anywhere an ndarray is accepted. + You might think that since memmap duck-types ndarray, we could simply wrap it in a SkimData object. + + But, as the numpy.memmap docs also say: "Memory-mapped files are used for accessing + small segments of large files on disk, without reading the entire file into memory." + + The words "small segments" are not accidental, because, as you gradually access all the parts + of the memmapped array, memory usage increases as all the memory is loaded into RAM. + + Under this scenario, the MemMapSkimFactory operates as a just-in-time loader, with no net savings + in RAM footprint (other than potentially avoiding loading any unused skims). + + Alternatively, since opening a memmap is fast, you could just open the memmap read the data on demand, + and immediately close it. This essentially eliminates RAM usage, but it means you are loading the data + every time you access the skim, which, depending on you patterns of usage, may or may not be acceptable. + + """ + + def __init__(self, network_los): + super().__init__(network_los) + + def get_skim_data(self, skim_tag, skim_info): + """ + Read skim data from backing store and return it as a 3D ndarray quack-alike SkimData object + (either a JitMemMapSkimData or a memmap backed SkimData object) + + Parameters + ---------- + skim_tag: str + skim_info: string + + Returns + ------- + SkimData or subclass + """ + + # don't expect legacy shared memory buffers + assert not inject.get_injectable('data_buffers', {}).get(skim_tag) + + skim_cache_path = self._memmap_skim_data_path(skim_tag) + if not os.path.isfile(skim_cache_path): + self.copy_omx_to_mmap_file(skim_info) + + JIT = True # FIXME - this should be a network_los setting, along with selection of the factory? + if JIT: + skim_data = JitMemMapSkimData(skim_cache_path, skim_info) + else: + # WARNING: memmap gobbles ram up to skim size - see note above + skim_data = self._open_existing_readonly_memmap_skim_cache(skim_info) + skim_data = SkimData(skim_data) + + logger.info(f"get_skim_data {skim_tag} {type(skim_data).__name__} shape {skim_data.shape}") + + return skim_data diff --git a/activitysim/core/skim_dictionary.py b/activitysim/core/skim_dictionary.py new file mode 100644 index 0000000000..2fb4b9c14e --- /dev/null +++ b/activitysim/core/skim_dictionary.py @@ -0,0 +1,780 @@ +# ActivitySim +# See full license in LICENSE.txt. + +from builtins import range +from builtins import object + +import logging + +import numpy as np +import pandas as pd + +from activitysim.core.util import quick_loc_series + +logger = logging.getLogger(__name__) + +NOT_IN_SKIM_ZONE_ID = -1 +NOT_IN_SKIM_NAN = np.nan + +ROW_MAJOR_LAYOUT = True + + +class OffsetMapper(object): + """ + Utility to map skim zone ids to ordinal offsets (e.g. numpy array indices) + + Can map either by a fixed offset (e.g. -1 to map 1-based to 0-based) + or by an explicit mapping of zone id to offset (slower but more flexible) + + Internally, there are two representations: + + offset_int: + int offset which when added to zone_id yields skim array index (e.g. -1 to map 1-based zones to 0-based index) + offset_series: + pandas series with zone_id index and skim array offset values. Ordinarily, index is just range(0, omx_size) + if series has duplicate offset values, this can map multiple zone_ids to a single skim array index + (e.g. can map maz zone_ids to corresponding taz skim offset) + """ + + def __init__(self, offset_int=None, offset_list=None, offset_series=None): + + self.offset_int = self.offset_series = None + + assert (offset_int is not None) + (offset_list is not None) + (offset_series is not None) <= 1 + + if offset_int is not None: + self.set_offset_int(offset_int) + elif offset_list is not None: + self.set_offset_list(offset_list) + elif offset_series is not None: + self.set_offset_series(offset_series) + + def print_offset(self, message=''): + assert (self.offset_int is not None) or (self.offset_series is not None) + + if self.offset_int is not None: + print(f"{message} offset_int: {self.offset_int}") + elif self.offset_series is not None: + print(f"{message} offset_series:\n {self.offset_series}") + else: + print(f"{message} offset: None") + + def set_offset_series(self, offset_series): + """ + Parameters + ---------- + offset_series: pandas.Series + series with zone_id index and skim array offset values (can map many zone_ids to skim array index) + """ + assert isinstance(offset_series, pd.Series) + self.offset_series = offset_series + self.offset_int = None + + def set_offset_list(self, offset_list): + """ + Convenience method to set offset_series using an integer list the same size as target skim dimension + with implicit skim index mapping (e.g. an omx mapping as returned by omx_file.mapentries) + + Parameters + ---------- + offset_list : list of int + """ + assert isinstance(offset_list, list) + + # - for performance, check if this is a simple range that can ber represented by an int offset + first_offset = offset_list[0] + if (offset_list == list(range(first_offset, len(offset_list)+first_offset))): + offset_int = -1 * first_offset + self.set_offset_int(offset_int) + else: + offset_series = pd.Series(data=list(range(len(offset_list))), index=offset_list) + self.set_offset_series(offset_series) + + def set_offset_int(self, offset_int): + """ + specify int offset which when added to zone_id yields skim array index (e.g. -1 to map 1-based to 0-based) + + Parameters + ---------- + offset_int : int + """ + # should be some duck subtype of integer (but might be, say, numpy.int64) + assert int(offset_int) == offset_int + + self.offset_int = int(offset_int) + self.offset_series = None + + def map(self, zone_ids): + """ + map zone_ids to skim indexes + + Parameters + ---------- + zone_ids + + Returns + ------- + offsets : numpy array of int + """ + + if self.offset_series is not None: + assert(self.offset_int is None) + assert isinstance(self.offset_series, pd.Series) + # FIXME - faster to use series.map if zone_ids is a series? + offsets = quick_loc_series(zone_ids, self.offset_series).fillna(NOT_IN_SKIM_ZONE_ID).astype(int) + + elif self.offset_int: + assert (self.offset_series is None) + offsets = zone_ids + self.offset_int + else: + offsets = zone_ids + + return offsets + + +class SkimDict(object): + """ + A SkimDict object is a wrapper around a dict of multiple skim objects, + where each object is identified by a key. + + Note that keys are either strings or tuples of two strings (to support stacking of skims.) + """ + + def __init__(self, skim_tag, skim_info, skim_data): + + logger.info(f"SkimDict init {skim_tag}") + + self.skim_tag = skim_tag + self.skim_info = skim_info + self.usage = set() # track keys of skims looked up + + self.offset_mapper = self._offset_mapper() # (in function so subclass can override) + + self.omx_shape = skim_info.omx_shape + self.skim_data = skim_data + self.dtype = np.dtype(skim_info.dtype_name) # so we can coerce if we have missing values + + # - skim_dim3 dict maps key1 to dict of key2 absolute offsets into block + # DRV_COM_WLK_BOARDS: {'MD': 4, 'AM': 3, 'PM': 5}, ... + self.skim_dim3 = {} + + for skim_key, offset in skim_info.block_offsets.items(): + if isinstance(skim_key, tuple): + key1, key2 = skim_key + self.skim_dim3.setdefault(key1, {})[key2] = offset + logger.info(f"SkimDict.build_3d_skim_block_offset_table registered {len(self.skim_dim3)} 3d keys") + + def _offset_mapper(self): + """ + Return an OffsetMapper to set self.offset_mapper for use with skims + This allows subclasses (e.g. MazSkimDict) to 'tweak' the parent offset mapper. + + Returns + ------- + OffsetMapper + """ + offset_mapper = OffsetMapper() + if self.skim_info.offset_map is not None: + offset_mapper.set_offset_list(offset_list=self.skim_info.offset_map) + else: + # assume this is a one-based skim map + offset_mapper.set_offset_int(-1) + + return offset_mapper + + @property + def zone_ids(self): + """ + Return list of zone_ids we grok in skim index order + + Returns + ------- + ndarray of int domain zone_ids + """ + + if self.offset_mapper.offset_series is not None: + ids = self.offset_mapper.offset_series.index.values + else: + ids = np.array(range(self.omx_shape[0])) - self.offset_mapper.offset_int + return ids + + def get_skim_usage(self): + """ + return set of keys of skims looked up. e.g. {'DIST', 'SOV'} + + Returns + ------- + set: + """ + return self.usage + + def _lookup(self, orig, dest, block_offsets): + """ + Return list of skim values of skims(s) at orig/dest for the skim(s) at block_offset in skim_data + + Supplying a single int block_offset makes the lookup 2-D + Supplying a list of block_offsets (same length as orig and dest lists) allows 3D lookup + + Parameters + ---------- + orig: list of orig zone_ids + dest: list of dest zone_ids + block_offsets: int or list of dim3 blockoffsets for the od pairs + + Returns + ------- + Numpy.ndarray: list of skim values for od pairs + """ + + # fixme - remove? + assert not (np.isnan(orig) | np.isnan(dest)).any() + + # only working with numpy in here + orig = np.asanyarray(orig).astype(int) + dest = np.asanyarray(dest).astype(int) + + mapped_orig = self.offset_mapper.map(orig) + mapped_dest = self.offset_mapper.map(dest) + if ROW_MAJOR_LAYOUT: + result = self.skim_data[block_offsets, mapped_orig, mapped_dest] + else: + result = self.skim_data[mapped_orig, mapped_dest, block_offsets] + + # FIXME - should return nan if not in skim (negative indices wrap around) + in_skim = (mapped_orig >= 0) & (mapped_orig < self.omx_shape[0]) & \ + (mapped_dest >= 0) & (mapped_dest < self.omx_shape[1]) + + # check for bad indexes (other than NOT_IN_SKIM_ZONE_ID) + assert (in_skim | (orig == NOT_IN_SKIM_ZONE_ID) | (dest == NOT_IN_SKIM_ZONE_ID)).all(), \ + f"{(~in_skim).sum()} od pairs not in skim" + + if not in_skim.all(): + result = np.where(in_skim, result, NOT_IN_SKIM_NAN).astype(self.dtype) + + return result + + def lookup(self, orig, dest, key): + """ + Return list of skim values of skims(s) at orig/dest in skim with the specified key (e.g. 'DIST') + + Parameters + ---------- + orig: list of orig zone_ids + dest: list of dest zone_ids + key: str + + Returns + ------- + Numpy.ndarray: list of skim values for od pairs + """ + + self.usage.add(key) + + block_offset = self.skim_info.block_offsets.get(key) + assert block_offset is not None, f"SkimDict lookup key '{key}' not in skims" + + try: + result = self._lookup(orig, dest, block_offset) + except Exception as err: + logger.error("SkimDict lookup error: %s: %s", type(err).__name__, str(err)) + logger.error(f"key {key}") + logger.error(f"orig max {orig.max()} min {orig.min()}") + logger.error(f"dest max {dest.max()} min {dest.min()}") + raise err + + return result + + def lookup_3d(self, orig, dest, dim3, key): + """ + 3D lookup of skim values of skims(s) at orig/dest for stacked skims indexed by dim3 selector + + The idea is that skims may be stacked in groups with a base key and a dim3 key (usually a time of day key) + + On import (from omx) skims stacks are represented by base and dim3 keys seperated by a double_underscore + + e.g. DRV_COM_WLK_BOARDS__AM indicates base skim key DRV_COM_WLK_BOARDS with a time of day (dim3) of 'AM' + + Since all the skimsa re stored in a single contiguous 3D array, we can use the dim3 key as a third index + and thus rapidly get skim values for a list of (orig, dest, tod) tuples using index arrays ('fancy indexing') + + Parameters + ---------- + orig: list of orig zone_ids + dest: list of dest zone_ids + block_offsets: list with one dim3 key for each orig/dest pair + + Returns + ------- + Numpy.ndarray: list of skim values + """ + + self.usage.add(key) # should we keep usage stats by (key, dim3)? + + assert key in self.skim_dim3, f"3d skim key {key} not in skims." + + # map dim3 to block_offsets + skim_keys_to_indexes = self.skim_dim3[key] + + # skim_indexes = dim3.map(skim_keys_to_indexes).astype('int') + try: + block_offsets = np.vectorize(skim_keys_to_indexes.get)(dim3) # this should be faster than map + result = self._lookup(orig, dest, block_offsets) + except Exception as err: + logger.error("SkimDict lookup_3d error: %s: %s", type(err).__name__, str(err)) + logger.error(f"key {key}") + logger.error(f"orig max {orig.max()} min {orig.min()}") + logger.error(f"dest max {dest.max()} min {dest.min()}") + logger.error(f"skim_keys_to_indexes: {skim_keys_to_indexes}") + logger.error(f"dim3 {np.unique(dim3)}") + logger.error(f"dim3 block_offsets {np.unique(block_offsets)}") + raise err + + return result + + def wrap(self, orig_key, dest_key): + """ + return a SkimWrapper for self + """ + return SkimWrapper(self, orig_key, dest_key) + + def wrap_3d(self, orig_key, dest_key, dim3_key): + """ + return a SkimWrapper for self + """ + return Skim3dWrapper(self, orig_key, dest_key, dim3_key) + + +class SkimWrapper(object): + + """ + A SkimWrapper object is an access wrapper around a SkimDict of multiple skim objects, + where each object is identified by a key. + + This is just a way to simplify expression files by hiding the and orig, dest arguments + when the orig and dest vectors are in a dataframe with known column names (specified at init time) + The dataframe is identified by set_df because it may not be available (e.g. due to chunking) + at the time the SkimWrapper is instantiated. + + When the user calls skims[key], key is an identifier for which skim + to use, and the object automatically looks up impedances of that skim + using the specified orig_key column in df as the origin and + the dest_key column in df as the destination. In this way, the user + does not do the O-D lookup by hand and only specifies which skim to use + for this lookup. This is the only purpose of this object: to + abstract away the O-D lookup and use skims by specifying which skim + to use in the expressions. + + Note that keys are either strings or tuples of two strings (to support stacking of skims.) + """ + + def __init__(self, skim_dict, orig_key, dest_key): + """ + + Parameters + ---------- + skim_dict: SkimDict + + orig_key: str + name of column in dataframe to use as implicit orig for lookups + dest_key: str + name of column in dataframe to use as implicit dest for lookups + """ + self.skim_dict = skim_dict + self.orig_key = orig_key + self.dest_key = dest_key + self.df = None + + def set_df(self, df): + """ + Set the dataframe + + Parameters + ---------- + df : DataFrame + The dataframe which contains the origin and destination ids + + Returns + ------- + self (to facilitiate chaining) + """ + assert self.orig_key in df + assert self.dest_key in df + self.df = df + return self + + def lookup(self, key, reverse=False): + """ + Generally not called by the user - use __getitem__ instead + + Parameters + ---------- + key : hashable + The key (identifier) for this skim object + + od : bool (optional) + od=True means lookup standard origin-destination skim value + od=False means lookup destination-origin skim value + + Returns + ------- + impedances: pd.Series + A Series of impedances which are elements of the Skim object and + with the same index as df + """ + + assert self.df is not None, "Call set_df first" + + if reverse: + s = self.skim_dict.lookup(self.df[self.dest_key], self.df[self.orig_key], key) + else: + s = self.skim_dict.lookup(self.df[self.orig_key], self.df[self.dest_key], key) + + return pd.Series(s, index=self.df.index) + + def reverse(self, key): + """ + return skim value in reverse (d-o) direction + """ + return self.lookup(key, reverse=True) + + def max(self, key): + """ + return max skim value in either o-d or d-o direction + """ + assert self.df is not None, "Call set_df first" + + s = np.maximum( + self.skim_dict.lookup(self.df[self.dest_key], self.df[self.orig_key], key), + self.skim_dict.lookup(self.df[self.orig_key], self.df[self.dest_key], key) + ) + + return pd.Series(s, index=self.df.index) + + def __getitem__(self, key): + """ + Get the lookup for an available skim object (df and orig/dest and column names implicit) + + Parameters + ---------- + key : hashable + The key (identifier) for the skim object + + Returns + ------- + impedances: pd.Series with the same index as df + A Series of impedances values from the single Skim with specified key, indexed byt orig/dest pair + """ + + return self.lookup(key) + + +class Skim3dWrapper(object): + """ + + This works the same as a SkimWrapper above, except the third dim3 is also supplied, + and a 3D lookup is performed using orig, dest, and dim3. + + Parameters + ---------- + skims: Skims + This is the Skims object to wrap + dim3_key : str + This identifies the column in the dataframe which is used to + select among Skim object using the SECOND item in each tuple (see + above for a more complete description) + """ + + def __init__(self, skim_dict, orig_key, dest_key, dim3_key): + """ + + Parameters + ---------- + skim_dict: SkimDict + + orig_key: str + name of column of zone_ids in dataframe to use as implicit orig for lookups + dest_key: str + name of column of zone_ids in dataframe to use as implicit dest for lookups + dim3_key: str + name of column of dim3 keys in dataframe to use as implicit third dim3 key for 3D lookups + e.g. string column with time_of_day keys (such as 'AM', 'MD', 'PM', etc.) + """ + self.skim_dict = skim_dict + + self.orig_key = orig_key + self.dest_key = dest_key + self.dim3_key = dim3_key + self.df = None + + def set_df(self, df): + """ + Set the dataframe + + Parameters + ---------- + df : DataFrame + The dataframe which contains the orig, dest, and dim3 values + + Returns + ------- + self (to facilitiate chaining) + """ + assert self.orig_key in df + assert self.dest_key in df + assert self.dim3_key in df + self.df = df + return self + + def __getitem__(self, key): + """ + Get the lookup for an available skim object (df and orig/dest/dim3 and column names implicit) + + Parameters + ---------- + key : hashable + The key (identifier) for this skim object + + Returns + ------- + impedances: pd.Series with the same index as df + A Series of impedances values from the set of skims with specified base key, indexed by orig/dest/dim3 + """ + assert self.df is not None, "Call set_df first" + orig = self.df[self.orig_key].astype('int') + dest = self.df[self.dest_key].astype('int') + dim3 = self.df[self.dim3_key] + + skim_values = self.skim_dict.lookup_3d(orig, dest, dim3, key) + + return pd.Series(skim_values, self.df.index) + + +class MazSkimDict(SkimDict): + """ + MazSkimDict provides a facade that allows skim-like lookup by maz orig,dest zone_id + when there are often too many maz zones to create maz skims. + + Dependencies: network_los.load_data must have already loaded: taz skim_dict, maz_to_maz_df, and maz_taz_df + + It performs lookups from a sparse list of maz-maz od pairs on selected attributes (e.g. WALKDIST) + where accuracy for nearby od pairs is critical. And is backed by a fallback taz skim dict + to return values of for more distant pairs (or for skims that are not attributes in the maz-maz table.) + """ + + def __init__(self, skim_tag, network_los, taz_skim_dict): + """ + we need network_los because we have dependencies on network_los.load_data (e.g. maz_to_maz_df, maz_taz_df, + and the fallback taz skim_dict) + + We require taz_skim_dict as an explicit parameter to emphasize that we are piggybacking on taz_skim_dict's + preexisting skim_data and skim_info, rather than instantiating duplicate copies thereof. + + Note, however, that we override _offset_mapper (called by super.__init__) to create our own + custom self.offset_mapper that maps directly from MAZ zone_ids to TAZ skim array indexes + + Parameters + ---------- + skim_tag: str + network_los: Network_LOS + taz_skim_dict: SkimDict + """ + + self.network_los = network_los + + super().__init__(skim_tag, taz_skim_dict.skim_info, taz_skim_dict.skim_data) + assert self.offset_mapper is not None # should have been set with _init_offset_mapper + + self.dtype = np.dtype(self.skim_info.dtype_name) + self.base_keys = taz_skim_dict.skim_info.base_keys + self.sparse_keys = list(set(network_los.maz_to_maz_df.columns) - {'OMAZ', 'DMAZ'}) + self.sparse_key_usage = set() + + def _offset_mapper(self): + """ + return an OffsetMapper to map maz zone_ids to taz skim indexes + Specifically, an offset_series with MAZ zone_id index and TAZ skim array offset values + + This is called by super().__init__ AFTER + + Returns + ------- + OffsetMapper + """ + + # start with a series with MAZ zone_id index and TAZ zone id values + maz_to_taz = self.network_los.maz_taz_df[['MAZ', 'TAZ']].set_index('MAZ').sort_values(by='TAZ').TAZ + + # use taz offset_mapper to create series mapping directly from MAZ to TAZ skim index + taz_offset_mapper = super()._offset_mapper() + maz_to_skim_offset = taz_offset_mapper.map(maz_to_taz) + + offset_mapper = OffsetMapper(offset_series=maz_to_skim_offset) + + return offset_mapper + + def get_skim_usage(self): + return self.sparse_key_usage.union(self.usage) + + def sparse_lookup(self, orig, dest, key): + """ + Get impedence values for a set of origin, destination pairs. + + Parameters + ---------- + orig : 1D array + dest : 1D array + + Returns + ------- + values : numpy 1D array + """ + + self.sparse_key_usage.add(key) + + max_blend_distance = self.network_los.max_blend_distance.get(key, 0) + + if max_blend_distance == 0: + blend_distance_skim_name = None + else: + blend_distance_skim_name = self.network_los.blend_distance_skim_name + + # fixme - remove? + assert not (np.isnan(orig) | np.isnan(dest)).any() + + # we want values from mazpairs, where we have them + values = self.network_los.get_mazpairs(orig, dest, key) + + is_nan = np.isnan(values) + + if max_blend_distance > 0: + + # print(f"{is_nan.sum()} nans out of {len(is_nan)} for key '{self.key}") + # print(f"blend_distance_skim_name {self.blend_distance_skim_name}") + + backstop_values = super().lookup(orig, dest, key) + + # get distance skim if a different key was specified by blend_distance_skim_name + if (blend_distance_skim_name != key): + distance = self.network_los.get_mazpairs(orig, dest, blend_distance_skim_name) + else: + distance = values + + # for distances less than max_blend_distance, we blend maz-maz and skim backstop values + # shorter distances have less fractional backstop, and more maz-maz + # beyond max_blend_distance, just use the skim values + backstop_fractions = np.minimum(distance / max_blend_distance, 1) + + values = np.where(is_nan, + backstop_values, + backstop_fractions * backstop_values + (1 - backstop_fractions) * values) + + elif is_nan.any(): + + # print(f"{is_nan.sum()} nans out of {len(is_nan)} for key '{self.key}") + + if key in self.base_keys: + # replace nan values using simple backstop without blending + backstop_values = super().lookup(orig, dest, key) + values = np.where(is_nan, backstop_values, values) + else: + # FIXME - if no backstop skim, then return 0 (which conventionally means "not available") + values = np.where(is_nan, 0, values) + + # want to return same type as backstop skim + values = values.astype(self.dtype) + + return values + + def lookup(self, orig, dest, key): + """ + Return list of skim values of skims(s) at orig/dest in skim with the specified key (e.g. 'DIST') + + Look up in sparse table (backed by taz skims) if key is a sparse_key, otherwise look up in taz skims + For taz skim lookups, the offset_mapper will convert maz zone_ids directly to taz skim indexes. + + Parameters + ---------- + orig: list of orig zone_ids + dest: list of dest zone_ids + key: str + + Returns + ------- + Numpy.ndarray: list of skim values for od pairs + """ + + if key in self.sparse_keys: + # logger.debug(f"MazSkimDict using SparseSkimDict for key '{key}'") + values = self.sparse_lookup(orig, dest, key) + else: + values = super().lookup(orig, dest, key) + + return values + + +class DataFrameMatrix(object): + """ + Utility class to allow a pandas dataframe to be treated like a 2-D array, + indexed by rowid, colname + + For use in vectorized expressions where the desired values depend on both a row column selector + e.g. size_terms.get(df.dest_taz, df.purpose) + + :: + + df = pd.DataFrame({'a': [1,2,3,4,5], 'b': [10,20,30,40,50]}, index=[100,101,102,103,104]) + + dfm = DataFrameMatrix(df) + + dfm.get(row_ids=[100,100,103], col_ids=['a', 'b', 'a']) + + returns [1, 10, 4] + + """ + + def __init__(self, df): + """ + + Parameters + ---------- + df - pandas dataframe of uniform type + """ + + self.df = df + self.data = df.values + + self.offset_mapper = OffsetMapper() + self.offset_mapper.set_offset_list(list(df.index)) + + self.cols_to_indexes = {k: v for v, k in enumerate(df.columns)} + + def get(self, row_ids, col_ids): + """ + + Parameters + ---------- + row_ids - list of row_ids (df index values) + col_ids - list of column names, one per row_id, + specifying column from which the value for that row should be retrieved + + Returns + ------- + + series with one row per row_id, with the value from the column specified in col_ids + + """ + # col_indexes = segments.map(self.cols_to_indexes).astype('int') + # this should be faster than map + col_indexes = np.vectorize(self.cols_to_indexes.get)(col_ids) + + row_indexes = self.offset_mapper.map(np.asanyarray(row_ids)) + + assert (row_indexes >= 0).all(), f"{row_indexes}" + + result = self.data[row_indexes, col_indexes] + + # FIXME - if ids (or col_ids?) is a series, return series with same index? + if isinstance(row_ids, pd.Series): + result = pd.Series(result, index=row_ids.index) + + return result diff --git a/activitysim/core/steps/output.py b/activitysim/core/steps/output.py index ebda9f4127..f940a976bd 100644 --- a/activitysim/core/steps/output.py +++ b/activitysim/core/steps/output.py @@ -4,8 +4,6 @@ import sys import pandas as pd -from collections import OrderedDict - from activitysim.core import pipeline from activitysim.core import inject from activitysim.core import config @@ -21,6 +19,8 @@ def track_skim_usage(output_dir): FIXME - have not yet implemented a facility to avoid loading of unused skims + FIXME - if resume_after, this will only reflect skims used after resume + Parameters ---------- output_dir: str @@ -30,42 +30,18 @@ def track_skim_usage(output_dir): pd.options.display.max_rows = 100 skim_dict = inject.get_injectable('skim_dict') - skim_stack = inject.get_injectable('skim_stack', None) mode = 'wb' if sys.version_info < (3,) else 'w' with open(config.output_file_path('skim_usage.txt'), mode) as output_file: print("\n### skim_dict usage", file=output_file) - for key in skim_dict.usage: + for key in skim_dict.get_skim_usage(): print(key, file=output_file) - if skim_stack is None: - - unused_keys = {k for k in skim_dict.skim_info['omx_keys']} - \ - {k for k in skim_dict.usage} - - print("\n### unused skim keys", file=output_file) - for key in unused_keys: - print(key, file=output_file) - - else: - - print("\n### skim_stack usage", file=output_file) - for key in skim_stack.usage: - print(key, file=output_file) - - unused = {k for k in skim_dict.skim_info['omx_keys'] if not isinstance(k, tuple)} - \ - {k for k in skim_dict.usage if not isinstance(k, tuple)} - print("\n### unused skim str keys", file=output_file) - for key in unused: - print(key, file=output_file) + unused = set(k for k in skim_dict.skim_info.base_keys) - set(k for k in skim_dict.get_skim_usage()) - unused = {k[0] for k in skim_dict.skim_info['omx_keys'] if isinstance(k, tuple)} - \ - {k[0] for k in skim_dict.usage if isinstance(k, tuple)} - \ - {k for k in skim_stack.usage} - print("\n### unused skim dim3 keys", file=output_file) - for key in unused: - print(key, file=output_file) + for key in unused: + print(key, file=output_file) def write_data_dictionary(output_dir): @@ -227,16 +203,16 @@ def write_tables(output_dir): tables = output_tables_settings.get('tables') prefix = output_tables_settings.get('prefix', 'final_') h5_store = output_tables_settings.get('h5_store', False) - - if action not in ['include', 'skip']: - raise "expected %s action '%s' to be either 'include' or 'skip'" % \ - (output_tables_settings_name, action) + sort = output_tables_settings.get('sort', False) checkpointed_tables = pipeline.checkpointed_tables() if action == 'include': output_tables_list = tables elif action == 'skip': output_tables_list = [t for t in checkpointed_tables if t not in tables] + else: + raise "expected %s action '%s' to be either 'include' or 'skip'" % \ + (output_tables_settings_name, action) for table_name in output_tables_list: @@ -248,6 +224,23 @@ def write_tables(output_dir): continue df = pipeline.get_table(table_name) + if sort: + traceable_table_indexes = inject.get_injectable('traceable_table_indexes', {}) + + if df.index.name in traceable_table_indexes: + df = df.sort_index() + logger.debug(f"write_tables sorting {table_name} on index {df.index.name}") + else: + # find all registered columns we can use to sort this table + # (they are ordered appropriately in traceable_table_indexes) + sort_columns = [c for c in traceable_table_indexes if c in df.columns] + if len(sort_columns) > 0: + df = df.sort_values(by=sort_columns) + logger.debug(f"write_tables sorting {table_name} on columns {sort_columns}") + else: + logger.debug(f"write_tables couldn't find a column or index to sort {table_name}" + f" in traceable_table_indexes: {traceable_table_indexes}") + if h5_store: file_path = config.output_file_path('%soutput_tables.h5' % prefix) df.to_hdf(file_path, key=table_name, mode='a', format='fixed') diff --git a/activitysim/core/test/data/assignment_spec.csv b/activitysim/core/test/configs/assignment_spec.csv similarity index 100% rename from activitysim/core/test/data/assignment_spec.csv rename to activitysim/core/test/configs/assignment_spec.csv diff --git a/activitysim/core/test/data/assignment_spec_alias_df.csv b/activitysim/core/test/configs/assignment_spec_alias_df.csv similarity index 100% rename from activitysim/core/test/data/assignment_spec_alias_df.csv rename to activitysim/core/test/configs/assignment_spec_alias_df.csv diff --git a/activitysim/core/test/data/assignment_spec_failing.csv b/activitysim/core/test/configs/assignment_spec_failing.csv similarity index 100% rename from activitysim/core/test/data/assignment_spec_failing.csv rename to activitysim/core/test/configs/assignment_spec_failing.csv diff --git a/activitysim/core/test/data/sample_spec.csv b/activitysim/core/test/configs/sample_spec.csv similarity index 100% rename from activitysim/core/test/data/sample_spec.csv rename to activitysim/core/test/configs/sample_spec.csv diff --git a/activitysim/core/test/extensions/steps.py b/activitysim/core/test/extensions/steps.py index b6ca0a70a0..b9e598b93c 100644 --- a/activitysim/core/test/extensions/steps.py +++ b/activitysim/core/test/extensions/steps.py @@ -57,10 +57,9 @@ def step_forget_tab(): @inject.step() def create_households(trace_hh_id): - df = pd.DataFrame({'household_id': [1, 2, 3], 'TAZ': {100, 100, 101}}) + df = pd.DataFrame({'household_id': [1, 2, 3], 'home_zone_id': {100, 100, 101}}) inject.add_table('households', df) pipeline.get_rn_generator().add_channel('households', df) - if trace_hh_id: - tracing.register_traceable_table('households', df) + tracing.register_traceable_table('households', df) diff --git a/activitysim/core/test/los/configs_1z/network_los.yaml b/activitysim/core/test/los/configs_1z/network_los.yaml new file mode 100644 index 0000000000..04ac40a78f --- /dev/null +++ b/activitysim/core/test/los/configs_1z/network_los.yaml @@ -0,0 +1,11 @@ + +zone_system: 1 + +taz_skims: z1_taz_skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] + diff --git a/activitysim/core/test/los/configs_1z/settings.yaml b/activitysim/core/test/los/configs_1z/settings.yaml new file mode 100644 index 0000000000..6e94895e71 --- /dev/null +++ b/activitysim/core/test/los/configs_1z/settings.yaml @@ -0,0 +1,2 @@ + +multiprocess: False diff --git a/activitysim/core/test/los/configs_2z/network_los.yaml b/activitysim/core/test/los/configs_2z/network_los.yaml new file mode 100644 index 0000000000..d16aa5f9c9 --- /dev/null +++ b/activitysim/core/test/los/configs_2z/network_los.yaml @@ -0,0 +1,27 @@ +zone_system: 2 + +taz_skims: z2_taz_skims.omx + +maz: maz.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + DIST: 5 + # blend distance of 0 means no blending + DISTBIKE: 0 + DISTWALK: 1 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + blend_distance_skim_name: DIST + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] + diff --git a/activitysim/core/test/los/configs_2z/settings.yaml b/activitysim/core/test/los/configs_2z/settings.yaml new file mode 100644 index 0000000000..6e94895e71 --- /dev/null +++ b/activitysim/core/test/los/configs_2z/settings.yaml @@ -0,0 +1,2 @@ + +multiprocess: False diff --git a/activitysim/core/test/los/configs_3z/network_los.yaml b/activitysim/core/test/los/configs_3z/network_los.yaml new file mode 100644 index 0000000000..8925d6ec35 --- /dev/null +++ b/activitysim/core/test/los/configs_3z/network_los.yaml @@ -0,0 +1,61 @@ +zone_system: 3 + +taz_skims: z3_taz_skims.omx + +# we require that skims for all tap_tap sets have unique names +# and can therefor share a single skim_dict without name collision +# e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM +tap_skims: z3_tap_skims.omx + +maz: maz.csv + +tap: tap.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + DIST: 5 + # blend distance of 0 means no blending + DISTBIKE: 0 + DISTWALK: 1 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + blend_distance_skim_name: DIST + +maz_to_tap: + walk: + table: maz_to_tap_walk.csv + drive: + table: maz_to_tap_drive.csv + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + + +# transit virtual path builder settings +TVPB_SETTINGS: + tour_mode_choice: + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + demographic_segment: *demographic_segments + tod: *skim_time_period_labels + access_mode: ['drive', 'walk'] + attributes_as_columns: + - demographic_segment + - tod diff --git a/activitysim/core/test/los/configs_3z/settings.yaml b/activitysim/core/test/los/configs_3z/settings.yaml new file mode 100644 index 0000000000..6e94895e71 --- /dev/null +++ b/activitysim/core/test/los/configs_3z/settings.yaml @@ -0,0 +1,2 @@ + +multiprocess: False diff --git a/activitysim/core/test/los/configs_3z/tvpb_utility_tap_tap.csv b/activitysim/core/test/los/configs_3z/tvpb_utility_tap_tap.csv new file mode 100644 index 0000000000..0d74c09c8f --- /dev/null +++ b/activitysim/core/test/los/configs_3z/tvpb_utility_tap_tap.csv @@ -0,0 +1,3 @@ +Label,Description,Expression,fastest,cheapest,shortest +# fastest,,,,, +util_transit_available_fastest,transit_available,@~df.transit_available_fastest * C_UNAVAILABLE,1,, diff --git a/activitysim/abm/test/configs_test_misc/settings_60_min.yaml b/activitysim/core/test/los/configs_legacy_settings/settings.yaml similarity index 82% rename from activitysim/abm/test/configs_test_misc/settings_60_min.yaml rename to activitysim/core/test/los/configs_legacy_settings/settings.yaml index 40d1e00fb7..7bec041d32 100644 --- a/activitysim/abm/test/configs_test_misc/settings_60_min.yaml +++ b/activitysim/core/test/los/configs_legacy_settings/settings.yaml @@ -1,6 +1,7 @@ +skims_file: z1_taz_skims.omx + skim_time_periods: - time_window: 1440 period_minutes: 60 periods: - 0 @@ -14,4 +15,4 @@ skim_time_periods: - AM - MD - PM - - EV \ No newline at end of file + - EV diff --git a/activitysim/core/test/los/configs_test_misc/settings_1_week.yaml b/activitysim/core/test/los/configs_test_misc/settings_1_week.yaml new file mode 100644 index 0000000000..8b2c573bdd --- /dev/null +++ b/activitysim/core/test/los/configs_test_misc/settings_1_week.yaml @@ -0,0 +1,17 @@ + +zone_system: 1 + +taz_skims: z1_taz_skims.omx + +skim_time_periods: + time_window: 10080 # One Week + period_minutes: 1440 # One Day + periods: [0, 1, 2, 3, 4, 5, 6, 7] + labels: + - Sunday + - Monday + - Tuesday + - Wednesday + - Thursday + - Friday + - Saturday diff --git a/activitysim/abm/test/configs_test_misc/settings_30_min.yaml b/activitysim/core/test/los/configs_test_misc/settings_30_min.yaml similarity index 55% rename from activitysim/abm/test/configs_test_misc/settings_30_min.yaml rename to activitysim/core/test/los/configs_test_misc/settings_30_min.yaml index a030877892..47e8529e8c 100644 --- a/activitysim/abm/test/configs_test_misc/settings_30_min.yaml +++ b/activitysim/core/test/los/configs_test_misc/settings_30_min.yaml @@ -1,17 +1,15 @@ +zone_system: 1 + +taz_skims: z1_taz_skims.omx + skim_time_periods: time_window: 1440 period_minutes: 30 - periods: - - 0 - - 12 - - 22 - - 32 - - 40 - - 48 + periods: [0, 12, 22, 32, 40, 48] labels: - EA - AM - MD - PM - - EV \ No newline at end of file + - EV diff --git a/activitysim/core/test/los/configs_test_misc/settings_60_min.yaml b/activitysim/core/test/los/configs_test_misc/settings_60_min.yaml new file mode 100644 index 0000000000..a9ca5a0816 --- /dev/null +++ b/activitysim/core/test/los/configs_test_misc/settings_60_min.yaml @@ -0,0 +1,9 @@ +zone_system: 1 + +taz_skims: z1_taz_skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/core/test/los/configs_test_misc/settings_legacy_hours_key.yaml b/activitysim/core/test/los/configs_test_misc/settings_legacy_hours_key.yaml new file mode 100644 index 0000000000..c553a617e7 --- /dev/null +++ b/activitysim/core/test/los/configs_test_misc/settings_legacy_hours_key.yaml @@ -0,0 +1,9 @@ +zone_system: 1 + +taz_skims: z1_taz_skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + hours: [0, 6, 11, 16, 20, 24] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/core/test/los/data/maz.csv b/activitysim/core/test/los/data/maz.csv new file mode 100644 index 0000000000..32415af5a1 --- /dev/null +++ b/activitysim/core/test/los/data/maz.csv @@ -0,0 +1,26 @@ +MAZ,TAZ +1000,2 +2000,2 +3000,2 +4000,2 +5000,5 +6000,6 +7000,7 +8000,8 +9000,9 +10000,10 +11000,11 +12000,12 +13000,14 +14000,14 +15000,14 +16000,16 +17000,17 +18000,18 +19000,19 +20000,20 +21000,21 +22000,22 +23000,23 +24000,24 +25000,25 diff --git a/activitysim/core/test/los/data/maz_to_maz_bike.csv b/activitysim/core/test/los/data/maz_to_maz_bike.csv new file mode 100644 index 0000000000..f8d32bf022 --- /dev/null +++ b/activitysim/core/test/los/data/maz_to_maz_bike.csv @@ -0,0 +1,626 @@ +OMAZ,DMAZ,DIST,DISTBIKE +1000,1000,0.12,0.12 +1000,2000,0.24,0.24 +1000,3000,0.44,0.44 +1000,4000,0.41,0.41 +1000,5000,0.68,0.68 +1000,6000,0.97,0.97 +1000,7000,0.98,0.98 +1000,8000,1.15,1.15 +1000,9000,1.56,1.56 +1000,10000,1.59,1.59 +1000,11000,1.13,1.13 +1000,12000,0.66,0.66 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+25000,90025,0.13,2.6 diff --git a/activitysim/core/test/los/data/tap.csv b/activitysim/core/test/los/data/tap.csv new file mode 100644 index 0000000000..7f7e8b9203 --- /dev/null +++ b/activitysim/core/test/los/data/tap.csv @@ -0,0 +1,21 @@ +TAP,MAZ +90002,2000 +90005,5000 +90006,6000 +90007,7000 +90008,8000 +90009,9000 +90010,10000 +90011,11000 +90012,12000 +90014,14000 +90016,16000 +90017,17000 +90018,18000 +90019,19000 +90020,20000 +90021,21000 +90022,22000 +90023,23000 +90024,24000 +90025,25000 diff --git a/activitysim/core/test/los/data/z1_taz_skims.omx b/activitysim/core/test/los/data/z1_taz_skims.omx new file mode 100644 index 0000000000..18c6dc4408 Binary files /dev/null and b/activitysim/core/test/los/data/z1_taz_skims.omx differ diff --git a/activitysim/core/test/los/data/z2_taz_skims.omx b/activitysim/core/test/los/data/z2_taz_skims.omx new file mode 100644 index 0000000000..8b5e67f8a4 Binary files /dev/null and b/activitysim/core/test/los/data/z2_taz_skims.omx differ diff --git a/activitysim/core/test/los/data/z3_tap_skims.omx b/activitysim/core/test/los/data/z3_tap_skims.omx new file mode 100644 index 0000000000..d77a3c0f97 Binary files /dev/null and b/activitysim/core/test/los/data/z3_tap_skims.omx differ diff --git a/activitysim/core/test/los/data/z3_taz_skims.omx b/activitysim/core/test/los/data/z3_taz_skims.omx new file mode 100644 index 0000000000..0b2b7c2215 Binary files /dev/null and b/activitysim/core/test/los/data/z3_taz_skims.omx differ diff --git a/activitysim/core/test/test_assign.py b/activitysim/core/test/test_assign.py index de5f949f7e..1efd4201f5 100644 --- a/activitysim/core/test/test_assign.py +++ b/activitysim/core/test/test_assign.py @@ -8,11 +8,17 @@ import pandas as pd import pytest +from .. import config from .. import assign from .. import tracing from .. import inject +def setup_function(): + configs_dir = os.path.join(os.path.dirname(__file__), 'configs') + inject.add_injectable("configs_dir", configs_dir) + + def close_handlers(): loggers = logging.Logger.manager.loggerDict @@ -33,11 +39,6 @@ def data_dir(): return os.path.join(os.path.dirname(__file__), 'data') -@pytest.fixture(scope='module') -def configs_dir(): - return os.path.join(os.path.dirname(__file__), 'configs') - - @pytest.fixture(scope='module') def spec_name(data_dir): return os.path.join(data_dir, 'assignment_spec.csv') @@ -53,18 +54,17 @@ def data(data_name): return pd.read_csv(data_name) -def test_read_model_spec(spec_name): - - spec = assign.read_assignment_spec(spec_name) +def test_read_model_spec(): + spec = assign.read_assignment_spec(config.config_file_path('assignment_spec.csv')) assert len(spec) == 8 assert list(spec.columns) == ['description', 'target', 'expression'] -def test_assign_variables(capsys, spec_name, data): +def test_assign_variables(capsys, data): - spec = assign.read_assignment_spec(spec_name) + spec = assign.read_assignment_spec(config.config_file_path('assignment_spec.csv')) locals_d = {'CONSTANT': 7, '_shadow': 99} @@ -111,10 +111,7 @@ def test_assign_variables(capsys, spec_name, data): def test_assign_variables_aliased(capsys, data): - spec_name = \ - os.path.join(os.path.dirname(__file__), 'data', 'assignment_spec_alias_df.csv') - - spec = assign.read_assignment_spec(spec_name) + spec = assign.read_assignment_spec(config.config_file_path('assignment_spec_alias_df.csv')) locals_d = {'CONSTANT': 7, '_shadow': 99} @@ -157,10 +154,7 @@ def test_assign_variables_failing(capsys, data): tracing.config_logger(basic=True) - spec_name = \ - os.path.join(os.path.dirname(__file__), 'data', 'assignment_spec_failing.csv') - - spec = assign.read_assignment_spec(spec_name) + spec = assign.read_assignment_spec(config.config_file_path('assignment_spec_failing.csv')) locals_d = { 'CONSTANT': 7, diff --git a/activitysim/core/test/test_input.py b/activitysim/core/test/test_input.py index 3b58684ef8..fa94590246 100644 --- a/activitysim/core/test/test_input.py +++ b/activitysim/core/test/test_input.py @@ -15,7 +15,7 @@ def seed_households(): return pd.DataFrame({ 'HHID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - 'TAZ': [8, 8, 8, 8, 12, 12, 15, 16, 16, 18], + 'home_zone_id': [8, 8, 8, 8, 12, 12, 15, 16, 16, 18], }) @@ -59,7 +59,7 @@ def test_csv_reader(seed_households, data_dir): - tablename: households filename: households.csv index_col: household_id - column_map: + rename_columns: HHID: household_id """ @@ -83,7 +83,7 @@ def test_hdf_reader1(seed_households, data_dir): - tablename: households filename: households.h5 index_col: household_id - column_map: + rename_columns: HHID: household_id """ @@ -108,7 +108,7 @@ def test_hdf_reader2(seed_households, data_dir): h5_tablename: seed_households filename: households.h5 index_col: household_id - column_map: + rename_columns: HHID: household_id """ @@ -132,7 +132,7 @@ def test_hdf_reader3(seed_households, data_dir): input_table_list: - tablename: households index_col: household_id - column_map: + rename_columns: HHID: household_id """ @@ -155,7 +155,7 @@ def test_missing_filename(seed_households, data_dir): input_table_list: - tablename: households index_col: household_id - column_map: + rename_columns: HHID: household_id """ @@ -176,7 +176,7 @@ def test_create_input_store(seed_households, data_dir): h5_tablename: seed_households filename: households.csv index_col: household_id - column_map: + rename_columns: HHID: household_id """ diff --git a/activitysim/core/test/test_logit.py b/activitysim/core/test/test_logit.py index d97bf579e5..15db8a5ae0 100644 --- a/activitysim/core/test/test_logit.py +++ b/activitysim/core/test/test_logit.py @@ -14,6 +14,16 @@ from .. import inject +def setup_function(): + configs_dir = os.path.join(os.path.dirname(__file__), 'configs') + inject.add_injectable("configs_dir", configs_dir) + + +def teardown_function(func): + inject.clear_cache() + inject.reinject_decorated_tables() + + @pytest.fixture(scope='module') def data_dir(): return os.path.join(os.path.dirname(__file__), 'data') diff --git a/activitysim/core/test/test_los.py b/activitysim/core/test/test_los.py new file mode 100644 index 0000000000..08d8c97910 --- /dev/null +++ b/activitysim/core/test/test_los.py @@ -0,0 +1,216 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import os + +import numpy as np +import pandas as pd +import numpy.testing as npt +import pandas.testing as pdt +import pytest + +from activitysim.core import orca + +from .. import inject +from .. import los + + +def teardown_function(func): + inject.clear_cache() + inject.reinject_decorated_tables() + + +def add_canonical_dirs(configs_dir_name): + + configs_dir = os.path.join(os.path.dirname(__file__), f'los/{configs_dir_name}') + inject.add_injectable("configs_dir", configs_dir) + + data_dir = os.path.join(os.path.dirname(__file__), f'los/data') + inject.add_injectable("data_dir", data_dir) + + output_dir = os.path.join(os.path.dirname(__file__), f'output') + inject.add_injectable("output_dir", output_dir) + + +def test_legacy_configs(): + + add_canonical_dirs('configs_legacy_settings') + + with pytest.warns(FutureWarning): + network_los = los.Network_LOS() + + assert network_los.setting('zone_system') == los.ONE_ZONE + + assert 'z1_taz_skims.omx' in network_los.omx_file_names('taz') + + +def test_one_zone(): + + add_canonical_dirs('configs_1z') + + network_los = los.Network_LOS() + + assert network_los.setting('zone_system') == los.ONE_ZONE + + assert 'z1_taz_skims.omx' in network_los.omx_file_names('taz') + + network_los.load_data() + + # OMAZ, DMAZ, DIST, DISTBIKE + # 23000,21000,1.89,1.89 + # 23000,22000,0.89,0.89 + # 23000,23000,0.19,0.19 + + od_df = pd.DataFrame({ + 'orig': [5, 23, 23, 23], + 'dest': [7, 20, 21, 22] + }) + + skim_dict = network_los.get_default_skim_dict() + + # skims should be the same as maz_to_maz distances in test data where 1 MAZ per TAZ + # OMAZ, DMAZ, DIST, DISTBIKE + # 1000, 2000, 0.24, 0.24 + # 23000,20000,2.55,2.55 + # 23000,21000,1.9,1.9 + # 23000,22000,0.62,0.62 + skims = skim_dict.wrap('orig', 'dest') + skims.set_df(od_df) + pdt.assert_series_equal(skims['DIST'], pd.Series([0.4, 2.55, 1.9, 0.62]).astype(np.float32)) + + # OMAZ, DMAZ, DIST, DISTBIKE + # 2000, 1000, 0.37, 0.37 + # 20000,23000,2.45,2.45 + # 21000,23000,1.89,1.89 + # 22000,23000,0.89,0.89 + + skims = skim_dict.wrap('dest', 'orig') + skims.set_df(od_df) + pdt.assert_series_equal(skims['DIST'], pd.Series([0.46, 2.45, 1.89, 0.89]).astype(np.float32)) + + +def test_two_zone(): + + add_canonical_dirs('configs_2z') + + network_los = los.Network_LOS() + + assert network_los.setting('zone_system') == los.TWO_ZONE + + assert 'z2_taz_skims.omx' in network_los.omx_file_names('taz') + + assert network_los.blend_distance_skim_name == 'DIST' + + network_los.load_data() + + skim_dict = network_los.get_default_skim_dict() + + # skims should be the same as maz_to_maz distances when no blending + od_df = pd.DataFrame({ + 'orig': [1000, 2000, 23000, 23000, 23000], + 'dest': [2000, 2000, 20000, 21000, 22000] + }) + # compare to distances from maz_to_maz table + dist = pd.Series(network_los.get_mazpairs(od_df.orig, od_df.dest, 'DIST')).astype(np.float32) + # make sure we got the right values + pdt.assert_series_equal(dist, pd.Series([0.24, 0.14, 2.55, 1.9, 0.62]).astype(np.float32)) + + skims = skim_dict.wrap('orig', 'dest') + skims.set_df(od_df) + # assert no blending for DISTBIKE + assert network_los.max_blend_distance.get('DISTBIKE', 0) == 0 + + skim_dist = skims['DISTBIKE'] + + print(type(skims), type(skim_dist.iloc[0])) + print(type(dist.iloc[0])) + pdt.assert_series_equal(skim_dist, dist) + + # but should be different where maz-maz distance differs from skim backstop and blending desired + # blending enabled for DIST + assert network_los.max_blend_distance.get('DIST') > 0 + with pytest.raises(AssertionError) as excinfo: + pdt.assert_series_equal(skims['DIST'], dist) + + +def test_three_zone(): + + add_canonical_dirs('configs_3z') + + network_los = los.Network_LOS() + + assert network_los.setting('zone_system') == los.THREE_ZONE + + assert 'z3_taz_skims.omx' in network_los.omx_file_names('taz') + + assert network_los.blend_distance_skim_name == 'DIST' + + network_los.load_data() + + od_df = pd.DataFrame({ + 'orig': [1000, 2000, 23000, 23000, 23000], + 'dest': [2000, 2000, 20000, 21000, 22000] + }) + + dist = network_los.get_mazpairs(od_df.orig, od_df.dest, 'DIST').astype(np.float32) + np.testing.assert_almost_equal(dist, [0.24, 0.14, 2.55, 1.9, 0.62]) + + +def test_30_minute_windows(): + + add_canonical_dirs('configs_test_misc') + network_los = los.Network_LOS(los_settings_file_name='settings_30_min.yaml') + + assert network_los.skim_time_period_label(1) == 'EA' + assert network_los.skim_time_period_label(16) == 'AM' + assert network_los.skim_time_period_label(24) == 'MD' + assert network_los.skim_time_period_label(36) == 'PM' + assert network_los.skim_time_period_label(46) == 'EV' + + pd.testing.assert_series_equal( + network_los.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])), + pd.Series(['EA', 'AM', 'MD', 'PM', 'EV'])) + + +def test_60_minute_windows(): + + add_canonical_dirs('configs_test_misc') + network_los = los.Network_LOS(los_settings_file_name='settings_60_min.yaml') + + assert network_los.skim_time_period_label(1) == 'EA' + assert network_los.skim_time_period_label(8) == 'AM' + assert network_los.skim_time_period_label(12) == 'MD' + assert network_los.skim_time_period_label(18) == 'PM' + assert network_los.skim_time_period_label(23) == 'EV' + + pd.testing.assert_series_equal( + network_los.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])), + pd.Series(['EA', 'AM', 'MD', 'PM', 'EV'])) + + +def test_1_week_time_window(): + + add_canonical_dirs('configs_test_misc') + network_los = los.Network_LOS(los_settings_file_name='settings_1_week.yaml') + + assert network_los.skim_time_period_label(1) == 'Sunday' + assert network_los.skim_time_period_label(2) == 'Monday' + assert network_los.skim_time_period_label(3) == 'Tuesday' + assert network_los.skim_time_period_label(4) == 'Wednesday' + assert network_los.skim_time_period_label(5) == 'Thursday' + assert network_los.skim_time_period_label(6) == 'Friday' + assert network_los.skim_time_period_label(7) == 'Saturday' + + weekly_series = network_los.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7])) + + pd.testing.assert_series_equal(weekly_series, + pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday', + 'Thursday', 'Friday', 'Saturday'])) + + +def test_skim_time_periods_future_warning(): + + add_canonical_dirs('configs_test_misc') + + with pytest.warns(FutureWarning) as warning_test: + network_los = los.Network_LOS(los_settings_file_name='settings_legacy_hours_key.yaml') diff --git a/activitysim/core/test/test_simulate.py b/activitysim/core/test/test_simulate.py index f637e78078..3fbee00db1 100644 --- a/activitysim/core/test/test_simulate.py +++ b/activitysim/core/test/test_simulate.py @@ -26,9 +26,7 @@ def spec_name(data_dir): @pytest.fixture(scope='module') def spec(data_dir, spec_name): - return simulate.read_model_spec( - file_name=spec_name, - spec_dir=data_dir) + return simulate.read_model_spec(file_name=spec_name) @pytest.fixture(scope='module') @@ -36,11 +34,17 @@ def data(data_dir): return pd.read_csv(os.path.join(data_dir, 'data.csv')) -def test_read_model_spec(data_dir, spec_name): +def setup_function(): + configs_dir = os.path.join(os.path.dirname(__file__), 'configs') + inject.add_injectable("configs_dir", configs_dir) - spec = simulate.read_model_spec( - file_name=spec_name, - spec_dir=data_dir) + output_dir = os.path.join(os.path.dirname(__file__), f'output') + inject.add_injectable("output_dir", output_dir) + + +def test_read_model_spec(spec_name): + + spec = simulate.read_model_spec(file_name=spec_name) assert len(spec) == 4 assert spec.index.name == 'Expression' diff --git a/activitysim/core/test/test_skim.py b/activitysim/core/test/test_skim.py index 7d594f908d..0605250378 100644 --- a/activitysim/core/test/test_skim.py +++ b/activitysim/core/test/test_skim.py @@ -7,7 +7,7 @@ import pandas.testing as pdt import pytest -from .. import skim +from .. import skim_dictionary @pytest.fixture @@ -15,75 +15,28 @@ def data(): return np.arange(100, dtype='int').reshape((10, 10)) -def test_basic(data): - sk = skim.SkimWrapper(data) - - orig = [5, 9, 1] - dest = [2, 9, 6] - - npt.assert_array_equal( - sk.get(orig, dest), - [52, 99, 16]) - - -def test_offset_int(data): - sk = skim.SkimWrapper(data, skim.OffsetMapper(-1)) - - orig = [6, 10, 2] - dest = [3, 10, 7] - - npt.assert_array_equal( - sk.get(orig, dest), - [52, 99, 16]) - - -def test_offset_list(data): - - offset_mapper = skim.OffsetMapper() - offset_mapper.set_offset_list([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) - - # should have figured out it could use an int offset instead of list - assert offset_mapper.offset_int == -1 - - offset_mapper = skim.OffsetMapper() - offset_mapper.set_offset_list([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) - - sk = skim.SkimWrapper(data, offset_mapper) - - orig = [60, 100, 20] - dest = [30, 100, 70] - - npt.assert_array_equal( - sk.get(orig, dest), - [52, 99, 16]) - - -# fixme - nan support disabled in skim.py (not sure we need it?) -# def test_skim_nans(data): -# sk = skim.SkimWrapper(data) -# -# orig = [5, np.nan, 1, 2] -# dest = [np.nan, 9, 6, 4] -# -# npt.assert_array_equal( -# sk.get(orig, dest), -# [np.nan, np.nan, 16, 24]) +class FakeSkimInfo(object): + def __init__(self): + self.offset_map = None def test_skims(data): - skims_shape = data.shape + (2,) - - skim_data = np.zeros(skims_shape, dtype=data.dtype) - skim_data[:, :, 0] = data - skim_data[:, :, 1] = data*10 - - skim_info = { - 'block_offsets': {'AM': (0, 0), 'PM': (0, 1)} - } - - skim_dict = skim.SkimDict([skim_data], skim_info) - + # ROW_MAJOR_LAYOUT + omx_shape = (10, 10) + num_skims = 2 + skim_data_shape = (num_skims, ) + omx_shape + skim_data = np.zeros(skim_data_shape, dtype=int) + skim_data[0, :, :] = data + skim_data[1, :, :] = data*10 + + skim_info = FakeSkimInfo() + skim_info.block_offsets = {'AM': 0, 'PM': 1} + skim_info.omx_shape = omx_shape + skim_info.dtype_name = 'int' + + skim_dict = skim_dictionary.SkimDict('taz', skim_info, skim_data) + skim_dict.offset_mapper.set_offset_int(0) # default is -1 skims = skim_dict.wrap("taz_l", "taz_r") df = pd.DataFrame({ @@ -94,7 +47,7 @@ def test_skims(data): skims.set_df(df) pdt.assert_series_equal( - skims["AM"], + skims['AM'], pd.Series( [12, 93, 47], index=[0, 1, 2] @@ -102,7 +55,7 @@ def test_skims(data): ) pdt.assert_series_equal( - skims["PM"], + skims['PM'], pd.Series( [120, 930, 470], index=[0, 1, 2] @@ -112,26 +65,28 @@ def test_skims(data): def test_3dskims(data): - skims_shape = data.shape + (2,) - - skim_data = np.zeros(skims_shape, dtype=int) - skim_data[:, :, 0] = data - skim_data[:, :, 1] = data*10 - - skim_info = { - 'block_offsets': {('SOV', 'AM'): (0, 0), ('SOV', 'PM'): (0, 1)}, - 'key1_block_offsets': {'SOV': (0, 0)} - } - skim_dict = skim.SkimDict([skim_data], skim_info) + # ROW_MAJOR_LAYOUT + omx_shape = (10, 10) + num_skims = 2 + skim_data_shape = (num_skims, ) + omx_shape + skim_data = np.zeros(skim_data_shape, dtype=int) + skim_data[0, :, :] = data + skim_data[1, :, :] = data*10 - stack = skim.SkimStack(skim_dict) + skim_info = FakeSkimInfo() + skim_info.block_offsets = {('SOV', 'AM'): 0, ('SOV', 'PM'): 1} + skim_info.omx_shape = omx_shape + skim_info.dtype_name = 'int' + skim_info.key1_block_offsets = {'SOV': 0} - skims3d = stack.wrap(left_key="taz_l", right_key="taz_r", skim_key="period") + skim_dict = skim_dictionary.SkimDict('taz', skim_info, skim_data) + skim_dict.offset_mapper.set_offset_int(0) # default is -1 + skims3d = skim_dict.wrap_3d(orig_key="taz_l", dest_key="taz_r", dim3_key="period") df = pd.DataFrame({ "taz_l": [1, 9, 4], "taz_r": [2, 3, 7], - "period": ["AM", "PM", "AM"] + "period": ['AM', 'PM', 'AM'] }) skims3d.set_df(df) diff --git a/activitysim/core/test/test_timetable.py b/activitysim/core/test/test_timetable.py index 5069cfdf3f..236aecbdd2 100644 --- a/activitysim/core/test/test_timetable.py +++ b/activitysim/core/test/test_timetable.py @@ -183,4 +183,4 @@ def test_basic(persons, tdd_alts): starts = pd.Series([9, 6, 9, 5]) ends = pd.Series([10, 10, 10, 9]) periods_available = timetable.remaining_periods_available(person_ids, starts, ends) - pdt.assert_series_equal(periods_available, pd.Series([6, 3, 4, 3])) + pdt.assert_series_equal(periods_available, pd.Series([6, 3, 4, 3]), check_dtype=False) diff --git a/activitysim/core/test/test_tracing.py b/activitysim/core/test/test_tracing.py index 34414641c5..5b614bbabf 100644 --- a/activitysim/core/test/test_tracing.py +++ b/activitysim/core/test/test_tracing.py @@ -8,6 +8,7 @@ from .. import tracing from .. import inject +from .. import orca def close_handlers(): @@ -20,6 +21,11 @@ def close_handlers(): logger.setLevel(logging.NOTSET) +def teardown_function(func): + inject.clear_cache() + inject.reinject_decorated_tables() + + def add_canonical_dirs(): inject.clear_cache() @@ -126,6 +132,7 @@ def test_register_tours(capsys): # in case another test injected this inject.add_injectable("trace_tours", []) + inject.add_injectable("trace_hh_id", 3) # need this or register_traceable_table is a nop tours_df = pd.DataFrame({'zort': ['a', 'b', 'c']}, index=[10, 11, 12]) tours_df.index.name = 'tour_id' @@ -133,8 +140,6 @@ def test_register_tours(capsys): tracing.register_traceable_table('tours', tours_df) out, err = capsys.readouterr() - # print out # don't consume output - assert "can't find a registered table to slice table 'tours' index name 'tour_id'" in out inject.add_injectable("trace_hh_id", 3) diff --git a/activitysim/core/timetable.py b/activitysim/core/timetable.py index 4990e6f51f..2993b2609a 100644 --- a/activitysim/core/timetable.py +++ b/activitysim/core/timetable.py @@ -9,10 +9,7 @@ import numpy as np import pandas as pd -from activitysim.core import config from activitysim.core import pipeline -from activitysim.core import tracing -from activitysim.core import util logger = logging.getLogger(__name__) diff --git a/activitysim/core/tracing.py b/activitysim/core/tracing.py index 620023ada7..84209fe4d8 100644 --- a/activitysim/core/tracing.py +++ b/activitysim/core/tracing.py @@ -9,7 +9,6 @@ import logging.config import sys import time - import yaml import numpy as np @@ -28,6 +27,24 @@ logger = logging.getLogger(__name__) +# nano micro milli kilo mega giga tera peta exa zeta yotta +tiers = ['n', 'µ', 'm', '', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y'] + + +def si_units(x, kind='B', f="{}{:.3g} {}{}"): + tier = 3 + shift = 1024 if kind == 'B' else 1000 + sign = '-' if x < 0 else '' + x = abs(x) + if x > 0: + while x > shift and tier < len(tiers): + x /= shift + tier += 1 + while x < 1 and tier >= 0: + x *= shift + tier -= 1 + return f.format(sign, x, tiers[tier], kind) + def extend_trace_label(trace_label, extension): if trace_label: @@ -68,7 +85,8 @@ def delete_output_files(file_type, ignore=None, subdir=None): output_dir = inject.get_injectable('output_dir') - directories = ['', 'log', 'trace'] + subdir = [subdir] if subdir else None + directories = subdir or ['', 'log', 'trace'] for subdir in directories: @@ -97,7 +115,7 @@ def delete_output_files(file_type, ignore=None, subdir=None): print(e) -def delete_csv_files(): +def delete_trace_files(): """ Delete CSV files in output_dir @@ -105,7 +123,10 @@ def delete_csv_files(): ------- Nothing """ - delete_output_files(CSV_FILE_TYPE) + delete_output_files(CSV_FILE_TYPE, subdir='trace') + + active_log_files = [h.baseFilename for h in logger.root.handlers if isinstance(h, logging.FileHandler)] + delete_output_files('log', ignore=active_log_files) def config_logger(basic=False): @@ -120,20 +141,42 @@ def config_logger(basic=False): """ # look for conf file in configs_dir - log_config_file = None - if not basic: + if basic: + log_config_file = None + else: log_config_file = config.config_file_path(LOGGING_CONF_FILE_NAME, mandatory=False) if log_config_file: - with open(log_config_file) as f: - # FIXME need alternative to yaml.UnsafeLoader? - config_dict = yaml.load(f, Loader=yaml.UnsafeLoader) + try: + with open(log_config_file) as f: + config_dict = yaml.load(f, Loader=yaml.UnsafeLoader) + except Exception as e: + print(f"Unable to read logging config file {log_config_file}") + raise e + + try: config_dict = config_dict['logging'] config_dict.setdefault('version', 1) logging.config.dictConfig(config_dict) + except Exception as e: + print(f"Unable to config logging as specified in {log_config_file}") + raise e + else: logging.basicConfig(level=logging.INFO, stream=sys.stdout) + # if log_config_file: + # with open(log_config_file) as f: + # #bug + # print("############################################# opening", log_config_file) + # # FIXME need alternative to yaml.UnsafeLoader? + # config_dict = yaml.load(f, Loader=yaml.UnsafeLoader) + # config_dict = config_dict['logging'] + # config_dict.setdefault('version', 1) + # logging.config.dictConfig(config_dict) + # else: + # logging.basicConfig(level=logging.INFO, stream=sys.stdout) + logger = logging.getLogger(ASIM_LOGGER) if log_config_file: @@ -188,12 +231,9 @@ def register_traceable_table(table_name, df): Nothing """ - trace_hh_id = inject.get_injectable("trace_hh_id", None) - - new_traced_ids = [] + # add index name to traceable_table_indexes - if trace_hh_id is None: - return + logger.debug(f"register_traceable_table {table_name}") traceable_tables = inject.get_injectable('traceable_tables', []) if table_name not in traceable_tables: @@ -205,14 +245,27 @@ def register_traceable_table(table_name, df): logger.error("Can't register table '%s' without index name" % table_name) return - traceable_table_ids = inject.get_injectable('traceable_table_ids') - traceable_table_indexes = inject.get_injectable('traceable_table_indexes') + traceable_table_ids = inject.get_injectable('traceable_table_ids', {}) + traceable_table_indexes = inject.get_injectable('traceable_table_indexes', {}) if idx_name in traceable_table_indexes and traceable_table_indexes[idx_name] != table_name: logger.error("table '%s' index name '%s' already registered for table '%s'" % (table_name, idx_name, traceable_table_indexes[idx_name])) return + # update traceable_table_indexes with this traceable_table's idx_name + if idx_name not in traceable_table_indexes: + traceable_table_indexes[idx_name] = table_name + logger.debug("adding table %s.%s to traceable_table_indexes" % (table_name, idx_name)) + inject.add_injectable('traceable_table_indexes', traceable_table_indexes) + + # add any new indexes associated with trace_hh_id to traceable_table_ids + + trace_hh_id = inject.get_injectable("trace_hh_id", None) + if trace_hh_id is None: + return + + new_traced_ids = [] if table_name == 'households': if trace_hh_id not in df.index: logger.warning("trace_hh_id %s not in dataframe" % trace_hh_id) @@ -224,6 +277,7 @@ def register_traceable_table(table_name, df): # find first already registered ref_col we can use to slice this table ref_col = next((c for c in traceable_table_indexes if c in df.columns), None) + if ref_col is None: logger.error("can't find a registered table to slice table '%s' index name '%s'" " in traceable_table_indexes: %s" % @@ -242,12 +296,6 @@ def register_traceable_table(table_name, df): logger.warning("register %s: no rows with %s in %s." % (table_name, ref_col, ref_col_traced_ids)) - # update traceable_table_indexes with this traceable_table's idx_name - if idx_name not in traceable_table_indexes: - traceable_table_indexes[idx_name] = table_name - print("adding table %s.%s to traceable_table_indexes" % (table_name, idx_name)) - inject.add_injectable('traceable_table_indexes', traceable_table_indexes) - # update the list of trace_ids for this table prior_traced_ids = traceable_table_ids.get(table_name, []) @@ -344,6 +392,10 @@ def write_csv(df, file_name, index_label=None, columns=None, column_labels=None, file_path = config.trace_file_path(file_name) + if os.name == 'nt': + abs_path = os.path.abspath(file_path) + assert len(abs_path) <= 255, f"Path length ({len(abs_path)}) exceeds maximum length for windows: {abs_path}" + if os.path.isfile(file_path): logger.debug("write_csv file exists %s %s" % (type(df).__name__, file_name)) @@ -397,7 +449,7 @@ def slice_ids(df, ids, column=None): return df -def get_trace_target(df, slicer): +def get_trace_target(df, slicer, column=None): """ get target ids and column or index to identify target trace rows in df @@ -419,7 +471,6 @@ def get_trace_target(df, slicer): """ target_ids = None # id or ids to slice by (e.g. hh_id or person_ids or tour_ids) - column = None # column name to slice on or None to slice on index # special do-not-slice code for dumping entire df if slicer == 'NONE': @@ -447,15 +498,15 @@ def get_trace_target(df, slicer): # maps 'person_id' to 'persons', etc table_name = traceable_table_indexes[slicer] target_ids = traceable_table_ids.get(table_name, []) - elif slicer == 'TAZ': + elif slicer == 'zone_id': target_ids = inject.get_injectable('trace_od', []) return target_ids, column -def trace_targets(df, slicer=None): +def trace_targets(df, slicer=None, column=None): - target_ids, column = get_trace_target(df, slicer) + target_ids, column = get_trace_target(df, slicer, column) if target_ids is None: targets = None @@ -470,9 +521,9 @@ def trace_targets(df, slicer=None): return targets -def has_trace_targets(df, slicer=None): +def has_trace_targets(df, slicer=None, column=None): - target_ids, column = get_trace_target(df, slicer) + target_ids, column = get_trace_target(df, slicer, column) if target_ids is None: found = False diff --git a/activitysim/core/util.py b/activitysim/core/util.py index f04f9f8d67..d07ed82cba 100644 --- a/activitysim/core/util.py +++ b/activitysim/core/util.py @@ -2,7 +2,6 @@ # See full license in LICENSE.txt. from builtins import zip - import logging from operator import itemgetter @@ -12,8 +11,6 @@ from zbox import toolz as tz -from . import mem - logger = logging.getLogger(__name__) @@ -32,6 +29,26 @@ def df_size(df): return "%s %s" % (df.shape, GB(bytes)) +def iprod(ints): + """ + Return the product of hte ints in the list or tuple as an unlimited precision python int + + Specifically intended to compute arrray/buffer size for skims where np.proc might overflow for default dtypes. + (Narrowing rules for np.prod are different on Windows and linux) + an alternative to the unwieldy: int(np.prod(ints, dtype=np.int64)) + + Parameters + ---------- + ints: list or tuple of ints or int wannabees + + Returns + ------- + returns python int + """ + assert len(ints) > 0 + return int(np.prod(ints, dtype=np.int64)) + + def left_merge_on_index_and_col(left_df, right_df, join_col, target_col): """ like pandas left merge, but join on both index and a specified join_col @@ -120,6 +137,14 @@ def reindex(series1, series2): # return pd.Series(series1.loc[series2.values].values, index=series2.index) +def reindex_i(series1, series2, dtype=np.int8): + """ + version of reindex that replaces missing na values and converts to int + helpful in expression files that compute counts (e.g. num_work_tours) + """ + return reindex(series1, series2).fillna(0).astype(dtype) + + def other_than(groups, bools): """ Construct a Series that has booleans indicating the presence of diff --git a/activitysim/examples/.gitignore b/activitysim/examples/.gitignore index dfb616e6fd..a4c1c1bfe4 100644 --- a/activitysim/examples/.gitignore +++ b/activitysim/examples/.gitignore @@ -1 +1,2 @@ -example_data_sf/ \ No newline at end of file +example_data_sf/ +scratch/ diff --git a/activitysim/examples/example_estimation/.gitignore b/activitysim/examples/example_estimation/.gitignore index ec718a572d..84ae1e4c2d 100644 --- a/activitysim/examples/example_estimation/.gitignore +++ b/activitysim/examples/example_estimation/.gitignore @@ -3,3 +3,4 @@ configs_build*/ #data_*/ output_*/ simulation.py +override_configs/ diff --git a/activitysim/examples/example_estimation/configs/settings.yaml b/activitysim/examples/example_estimation/configs/settings.yaml index cd7ef3ed59..42a2a642c8 100644 --- a/activitysim/examples/example_estimation/configs/settings.yaml +++ b/activitysim/examples/example_estimation/configs/settings.yaml @@ -11,9 +11,8 @@ input_table_list: index_col: person_id - tablename: land_use filename: land_use.csv - index_col: TAZ + index_col: zone_id rename_columns: - ZONE: TAZ COUNTY: county_id keep_columns: - DISTRICT @@ -47,6 +46,7 @@ rng_base_seed: 0 use_shadow_pricing: False # turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) want_dest_choice_sample_tables: False # number of households to simulate diff --git a/activitysim/examples/example_estimation/data_full/land_use.csv b/activitysim/examples/example_estimation/data_full/land_use.csv index a72550b9f2..87f003fd87 100644 --- a/activitysim/examples/example_estimation/data_full/land_use.csv +++ b/activitysim/examples/example_estimation/data_full/land_use.csv @@ -1,4 +1,4 @@ -TAZ,DISTRICT,SD,county_id,TOTHH,TOTPOP,TOTACRE,RESACRE,CIACRE,TOTEMP,AGE0519,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL +zone_id,DISTRICT,SD,county_id,TOTHH,TOTPOP,TOTACRE,RESACRE,CIACRE,TOTEMP,AGE0519,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL 1,1,1,1,46,82,20.3,1.0,15.0,27318,7,224,21927,2137,2254,18,758,284.01965,932.83514,0,0.0,0.0,0.0,3,5.89564 2,1,1,1,134,240,31.1,1.0,24.79297,42078,19,453,33422,4399,2948,56,800,269.6431,885.61682,0,0.0,0.0,0.0,1,5.84871 3,1,1,1,267,476,14.7,1.0,2.31799,2445,38,93,1159,950,211,0,32,218.08298,716.27252,0,0.0,0.0,0.0,1,5.53231 diff --git a/activitysim/examples/example_estimation/data_full/survey_data/override_households.csv b/activitysim/examples/example_estimation/data_full/survey_data/override_households.csv index a7bc22be08..3459001719 100644 --- a/activitysim/examples/example_estimation/data_full/survey_data/override_households.csv +++ b/activitysim/examples/example_estimation/data_full/survey_data/override_households.csv @@ -1,4 +1,4 @@ -household_id,TAZ,income,hhsize,HHT,auto_ownership,num_workers,joint_tour_frequency +household_id,home_zone_id,income,hhsize,HHT,auto_ownership,num_workers,joint_tour_frequency 2223759,16,144100,2,1,0,2,1_Eat 2200560,1230,197000,2,1,3,2,0_tours 1508345,1309,87000,2,1,2,1,0_tours diff --git a/activitysim/examples/example_estimation/data_full/survey_data/override_persons.csv b/activitysim/examples/example_estimation/data_full/survey_data/override_persons.csv index a7ae615b12..bc9a3c9bc7 100644 --- a/activitysim/examples/example_estimation/data_full/survey_data/override_persons.csv +++ b/activitysim/examples/example_estimation/data_full/survey_data/override_persons.csv @@ -1,4 +1,4 @@ -person_id,household_id,age,PNUM,sex,pemploy,pstudent,ptype,school_taz,workplace_taz,free_parking_at_work,cdap_activity,mandatory_tour_frequency,_escort,_shopping,_othmaint,_othdiscr,_eatout,_social,non_mandatory_tour_frequency +person_id,household_id,age,PNUM,sex,pemploy,pstudent,ptype,school_zone_id,workplace_zone_id,free_parking_at_work,cdap_activity,mandatory_tour_frequency,_escort,_shopping,_othmaint,_othdiscr,_eatout,_social,non_mandatory_tour_frequency 5385,5385,64,1,1,3,3,4,-1,-1,False,H,,0,0,0,0,0,0,0 6972,6972,60,1,1,3,3,4,-1,-1,False,N,,1,0,0,0,1,0,36 9510,9510,57,1,2,3,2,3,557,-1,False,N,,0,0,0,1,0,0,1 diff --git a/activitysim/examples/example_estimation/data_sf/land_use.csv b/activitysim/examples/example_estimation/data_sf/land_use.csv index 50c73317ea..6741b7e733 100644 --- a/activitysim/examples/example_estimation/data_sf/land_use.csv +++ b/activitysim/examples/example_estimation/data_sf/land_use.csv @@ -1,4 +1,4 @@ -TAZ,DISTRICT,SD,county_id,TOTHH,TOTPOP,TOTACRE,RESACRE,CIACRE,TOTEMP,AGE0519,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL +zone_id,DISTRICT,SD,county_id,TOTHH,TOTPOP,TOTACRE,RESACRE,CIACRE,TOTEMP,AGE0519,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL 1,1,1,1,46,82,20.3,1.0,15.0,27318,7,224,21927,2137,2254,18,758,284.01965,932.83514,0,0.0,0.0,0.0,3,5.89564 2,1,1,1,134,240,31.1,1.0,24.79297,42078,19,453,33422,4399,2948,56,800,269.6431,885.61682,0,0.0,0.0,0.0,1,5.84871 3,1,1,1,267,476,14.7,1.0,2.31799,2445,38,93,1159,950,211,0,32,218.08298,716.27252,0,0.0,0.0,0.0,1,5.53231 diff --git a/activitysim/examples/example_estimation/data_sf/survey_data/override_households.csv b/activitysim/examples/example_estimation/data_sf/survey_data/override_households.csv index 364cf3351e..236cb2cbcd 100644 --- a/activitysim/examples/example_estimation/data_sf/survey_data/override_households.csv +++ b/activitysim/examples/example_estimation/data_sf/survey_data/override_households.csv @@ -1,4 +1,4 @@ -household_id,TAZ,income,hhsize,HHT,auto_ownership,num_workers,joint_tour_frequency +household_id,home_zone_id,income,hhsize,HHT,auto_ownership,num_workers,joint_tour_frequency 2223759,16,144100,2,1,0,2,1_Main 990869,134,48000,2,1,2,2,0_tours 125886,113,25900,1,4,1,1,0_tours diff --git a/activitysim/examples/example_estimation/data_sf/survey_data/override_persons.csv b/activitysim/examples/example_estimation/data_sf/survey_data/override_persons.csv index 8b8a486397..b429fdba84 100644 --- a/activitysim/examples/example_estimation/data_sf/survey_data/override_persons.csv +++ b/activitysim/examples/example_estimation/data_sf/survey_data/override_persons.csv @@ -1,4 +1,4 @@ -person_id,household_id,age,PNUM,sex,pemploy,pstudent,ptype,school_taz,workplace_taz,free_parking_at_work,cdap_activity,mandatory_tour_frequency,_escort,_shopping,_othmaint,_othdiscr,_eatout,_social,non_mandatory_tour_frequency +person_id,household_id,age,PNUM,sex,pemploy,pstudent,ptype,school_zone_id,workplace_zone_id,free_parking_at_work,cdap_activity,mandatory_tour_frequency,_escort,_shopping,_othmaint,_othdiscr,_eatout,_social,non_mandatory_tour_frequency 166,166,54,1,2,3,3,4,-1,-1,False,N,,0,0,0,0,1,0,4 197,197,46,1,2,3,3,4,-1,-1,False,N,,0,1,0,0,0,0,16 268,268,46,1,1,3,3,4,-1,-1,False,N,,0,0,1,1,0,0,9 diff --git a/activitysim/examples/example_estimation/data_test/land_use.csv b/activitysim/examples/example_estimation/data_test/land_use.csv index 97cbddaaeb..492ad762b8 100644 --- a/activitysim/examples/example_estimation/data_test/land_use.csv +++ b/activitysim/examples/example_estimation/data_test/land_use.csv @@ -1,4 +1,4 @@ -TAZ,DISTRICT,SD,county_id,TOTHH,TOTPOP,TOTACRE,RESACRE,CIACRE,TOTEMP,AGE0519,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL +zone_id,DISTRICT,SD,county_id,TOTHH,TOTPOP,TOTACRE,RESACRE,CIACRE,TOTEMP,AGE0519,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL 1,1,1,1,46,82,20.3,1.0,15.0,27318,7,224,21927,2137,2254,18,758,284.01965,932.83514,0,0.0,0.0,0.0,3,5.89564 2,1,1,1,134,240,31.1,1.0,24.79297,42078,19,453,33422,4399,2948,56,800,269.6431,885.61682,0,0.0,0.0,0.0,1,5.84871 3,1,1,1,267,476,14.7,1.0,2.31799,2445,38,93,1159,950,211,0,32,218.08298,716.27252,0,0.0,0.0,0.0,1,5.53231 diff --git a/activitysim/examples/example_estimation/data_test/survey_data/override_households.csv b/activitysim/examples/example_estimation/data_test/survey_data/override_households.csv index 05094777fa..9a991cd68e 100644 --- a/activitysim/examples/example_estimation/data_test/survey_data/override_households.csv +++ b/activitysim/examples/example_estimation/data_test/survey_data/override_households.csv @@ -1,4 +1,4 @@ -household_id,TAZ,income,hhsize,HHT,auto_ownership,num_workers,joint_tour_frequency +household_id,home_zone_id,income,hhsize,HHT,auto_ownership,num_workers,joint_tour_frequency 982875,16,30900,2,5,1,2,0_tours 1810015,16,99700,9,2,1,4,0_tours 1099626,20,58160,3,1,1,1,0_tours diff --git a/activitysim/examples/example_estimation/data_test/survey_data/override_persons.csv b/activitysim/examples/example_estimation/data_test/survey_data/override_persons.csv index aff5a19311..2ab85286eb 100644 --- a/activitysim/examples/example_estimation/data_test/survey_data/override_persons.csv +++ b/activitysim/examples/example_estimation/data_test/survey_data/override_persons.csv @@ -1,4 +1,4 @@ -person_id,household_id,age,PNUM,sex,pemploy,pstudent,ptype,school_taz,workplace_taz,free_parking_at_work,cdap_activity,mandatory_tour_frequency,_escort,_shopping,_othmaint,_othdiscr,_eatout,_social,non_mandatory_tour_frequency +person_id,household_id,age,PNUM,sex,pemploy,pstudent,ptype,school_zone_id,workplace_zone_id,free_parking_at_work,cdap_activity,mandatory_tour_frequency,_escort,_shopping,_othmaint,_othdiscr,_eatout,_social,non_mandatory_tour_frequency 25675,25675,27,1,2,3,2,3,13,-1,False,M,school1,0,0,0,0,0,0,0 25678,25678,30,1,2,3,3,4,-1,-1,False,N,,2,0,0,0,0,0,64 25683,25683,23,1,1,3,3,4,-1,-1,False,N,,0,0,1,0,0,0,8 diff --git a/activitysim/examples/example_estimation/data_test/survey_data/override_trips.csv b/activitysim/examples/example_estimation/data_test/survey_data/override_trips.csv new file mode 100644 index 0000000000..fc7d60a64f --- /dev/null +++ b/activitysim/examples/example_estimation/data_test/survey_data/override_trips.csv @@ -0,0 +1,9616 @@ +trip_id,survey_trip_id,person_id,household_id,survey_tour_id,outbound,purpose,destination,origin,depart,trip_mode,tour_id,trip_num +8421649,84216490,25675,25675,10527060,True,social,4,5,18.0,WALK_LOC,1052706,1 +8421650,84216500,25675,25675,10527060,True,univ,13,4,19.0,WALK,1052706,2 +8421653,84216530,25675,25675,10527060,False,shopping,11,13,21.0,WALK_LOC,1052706,1 +8421654,84216540,25675,25675,10527060,False,univ,14,11,21.0,WALK,1052706,2 +8421655,84216550,25675,25675,10527060,False,work,15,14,21.0,WALK_LOC,1052706,3 +8421656,84216560,25675,25675,10527060,False,Home,5,15,21.0,WALK_LOC,1052706,4 +8422457,84224570,25678,25678,10528070,True,escort,5,6,8.0,WALK,1052807,1 +8422461,84224610,25678,25678,10528070,False,Home,6,5,9.0,WALK,1052807,1 +8422465,84224650,25678,25678,10528080,True,escort,7,6,15.0,WALK,1052808,1 +8422469,84224690,25678,25678,10528080,False,Home,6,7,18.0,WALK,1052808,1 +8424249,84242490,25683,25683,10530310,True,othmaint,4,6,8.0,WALK,1053031,1 +8424253,84242530,25683,25683,10530310,False,Home,6,4,21.0,WALK,1053031,1 +8424577,84245770,25684,25684,10530720,True,othmaint,2,6,14.0,TNC_SINGLE,1053072,1 +8424581,84245810,25684,25684,10530720,False,Home,6,2,14.0,TNC_SINGLE,1053072,1 +8426897,84268970,25691,25691,10533620,True,univ,13,6,6.0,WALK_LRF,1053362,1 +8426901,84269010,25691,25691,10533620,False,Home,6,13,6.0,WALK_LRF,1053362,1 +8438001,84380010,25725,25725,10547500,True,othdiscr,4,6,7.0,WALK,1054750,1 +8438005,84380050,25725,25725,10547500,False,Home,6,4,10.0,WALK,1054750,1 +8438065,84380650,25725,25725,10547580,True,shopping,11,6,15.0,WALK,1054758,1 +8438069,84380690,25725,25725,10547580,False,Home,6,11,16.0,WALK,1054758,1 +8440801,84408010,25734,25734,10551000,True,eatout,5,6,11.0,WALK,1055100,1 +8440805,84408050,25734,25734,10551000,False,Home,6,5,15.0,WALK,1055100,1 +8447561,84475610,25754,25754,10559450,True,othmaint,5,6,7.0,WALK,1055945,1 +8447562,84475620,25754,25754,10559450,True,univ,13,5,8.0,WALK,1055945,2 +8447565,84475650,25754,25754,10559450,False,Home,6,13,14.0,WALK,1055945,1 +8447569,84475690,25754,25754,10559460,True,univ,13,6,18.0,WALK,1055946,1 +8447573,84475730,25754,25754,10559460,False,univ,12,13,21.0,WALK,1055946,1 +8447574,84475740,25754,25754,10559460,False,othmaint,5,12,21.0,WALK,1055946,2 +8447575,84475750,25754,25754,10559460,False,othmaint,5,5,21.0,WALK,1055946,3 +8447576,84475760,25754,25754,10559460,False,Home,6,5,21.0,WALK,1055946,4 +8450337,84503370,25763,25763,10562920,True,escort,7,6,12.0,WALK,1056292,1 +8450341,84503410,25763,25763,10562920,False,Home,6,7,12.0,WALK,1056292,1 +8461337,84613370,25796,25796,10576670,True,univ,13,6,5.0,WALK_LRF,1057667,1 +8461341,84613410,25796,25796,10576670,False,Home,6,13,14.0,WALK_LOC,1057667,1 +8476881,84768810,25844,25844,10596100,True,eatout,5,6,15.0,WALK,1059610,1 +8476885,84768850,25844,25844,10596100,False,Home,6,5,16.0,WALK,1059610,1 +8503953,85039530,25926,25926,10629940,True,othmaint,2,7,15.0,WALK,1062994,1 +8503957,85039570,25926,25926,10629940,False,Home,7,2,15.0,WALK,1062994,1 +8503993,85039930,25926,25926,10629990,True,shopping,5,7,11.0,WALK_LOC,1062999,1 +8503997,85039970,25926,25926,10629990,False,shopping,3,5,12.0,WALK_LOC,1062999,1 +8503998,85039980,25926,25926,10629990,False,Home,7,3,12.0,TNC_SINGLE,1062999,2 +8514801,85148010,25959,25959,10643500,True,othmaint,1,7,8.0,WALK_LOC,1064350,1 +8514802,85148020,25959,25959,10643500,True,escort,14,1,9.0,WALK_LOC,1064350,2 +8514803,85148030,25959,25959,10643500,True,univ,12,14,9.0,WALK_LOC,1064350,3 +8514805,85148050,25959,25959,10643500,False,Home,7,12,15.0,WALK,1064350,1 +8515937,85159370,25963,25963,10644920,True,escort,20,7,17.0,TNC_SINGLE,1064492,1 +8515941,85159410,25963,25963,10644920,False,Home,7,20,17.0,TNC_SINGLE,1064492,1 +8516089,85160890,25963,25963,10645110,True,othmaint,22,7,9.0,SHARED3FREE,1064511,1 +8516093,85160930,25963,25963,10645110,False,shopping,5,22,12.0,WALK,1064511,1 +8516094,85160940,25963,25963,10645110,False,Home,7,5,12.0,SHARED3FREE,1064511,2 +8516097,85160970,25963,25963,10645120,True,othmaint,2,7,14.0,WALK,1064512,1 +8516101,85161010,25963,25963,10645120,False,Home,7,2,16.0,WALK,1064512,1 +8523673,85236730,25986,25986,10654590,True,shopping,5,7,16.0,WALK,1065459,1 +8523677,85236770,25986,25986,10654590,False,Home,7,5,16.0,WALK,1065459,1 +8530673,85306730,26008,26008,10663340,True,eatout,11,7,16.0,WALK,1066334,1 +8530677,85306770,26008,26008,10663340,False,Home,7,11,16.0,DRIVEALONEFREE,1066334,1 +8530825,85308250,26008,26008,10663530,True,eatout,7,7,8.0,WALK,1066353,1 +8530826,85308260,26008,26008,10663530,True,shopping,8,7,9.0,WALK,1066353,2 +8530827,85308270,26008,26008,10663530,True,othdiscr,10,8,9.0,WALK,1066353,3 +8530829,85308290,26008,26008,10663530,False,shopping,7,10,10.0,WALK,1066353,1 +8530830,85308300,26008,26008,10663530,False,Home,7,7,10.0,WALK,1066353,2 +8530849,85308490,26008,26008,10663560,True,othmaint,13,7,11.0,WALK,1066356,1 +8530853,85308530,26008,26008,10663560,False,Home,7,13,13.0,WALK,1066356,1 +8542681,85426810,26044,26044,10678350,True,escort,8,7,16.0,WALK,1067835,1 +8542682,85426820,26044,26044,10678350,True,eatout,11,8,16.0,WALK_LOC,1067835,2 +8542683,85426830,26044,26044,10678350,True,univ,12,11,18.0,WALK_LOC,1067835,3 +8542685,85426850,26044,26044,10678350,False,Home,7,12,18.0,WALK_LOC,1067835,1 +8581385,85813850,26162,26162,10726730,True,univ,12,8,8.0,WALK_LOC,1072673,1 +8581389,85813890,26162,26162,10726730,False,Home,8,12,17.0,WALK_LOC,1072673,1 +8586257,85862570,26177,26177,10732820,True,othdiscr,12,8,10.0,WALK,1073282,1 +8586261,85862610,26177,26177,10732820,False,Home,8,12,14.0,WALK,1073282,1 +8586633,85866330,26178,26178,10733290,True,univ,10,8,8.0,WALK,1073329,1 +8586637,85866370,26178,26178,10733290,False,Home,8,10,11.0,WALK,1073329,1 +8586641,85866410,26178,26178,10733300,True,univ,10,8,17.0,DRIVEALONEFREE,1073330,1 +8586645,85866450,26178,26178,10733300,False,eatout,5,10,17.0,DRIVEALONEFREE,1073330,1 +8586646,85866460,26178,26178,10733300,False,Home,8,5,17.0,WALK,1073330,2 +8595113,85951130,26204,26204,10743890,True,othdiscr,16,8,14.0,WALK,1074389,1 +8595117,85951170,26204,26204,10743890,False,Home,8,16,17.0,WALK,1074389,1 +8595137,85951370,26204,26204,10743920,True,othmaint,8,8,12.0,WALK,1074392,1 +8595141,85951410,26204,26204,10743920,False,Home,8,8,14.0,WALK,1074392,1 +8596297,85962970,26208,26208,10745370,True,shopping,7,8,11.0,WALK,1074537,1 +8596298,85962980,26208,26208,10745370,True,escort,13,7,11.0,WALK,1074537,2 +8596301,85963010,26208,26208,10745370,False,Home,8,13,11.0,WALK,1074537,1 +8603337,86033370,26229,26229,10754170,True,othmaint,7,8,6.0,WALK,1075417,1 +8603341,86033410,26229,26229,10754170,False,escort,8,7,22.0,WALK,1075417,1 +8603342,86033420,26229,26229,10754170,False,Home,8,8,22.0,WALK,1075417,2 +8629073,86290730,26308,26308,10786340,True,eatout,15,8,17.0,WALK,1078634,1 +8629077,86290770,26308,26308,10786340,False,Home,8,15,19.0,WALK,1078634,1 +8638737,86387370,26337,26337,10798420,True,othdiscr,9,8,18.0,WALK,1079842,1 +8638741,86387410,26337,26337,10798420,False,Home,8,9,21.0,WALK,1079842,1 +8651617,86516170,26376,26376,10814520,True,social,11,8,12.0,TNC_SINGLE,1081452,1 +8651621,86516210,26376,26376,10814520,False,shopping,8,11,17.0,TNC_SINGLE,1081452,1 +8651622,86516220,26376,26376,10814520,False,Home,8,8,17.0,TNC_SINGLE,1081452,2 +8651625,86516250,26376,26376,10814530,True,social,7,8,18.0,WALK_LOC,1081453,1 +8651629,86516290,26376,26376,10814530,False,Home,8,7,21.0,WALK_LOC,1081453,1 +8665305,86653050,26418,26418,10831630,True,othdiscr,16,8,18.0,WALK,1083163,1 +8665309,86653090,26418,26418,10831630,False,Home,8,16,19.0,WALK,1083163,1 +8665353,86653530,26418,26418,10831690,True,univ,9,8,10.0,WALK,1083169,1 +8665357,86653570,26418,26418,10831690,False,othdiscr,9,9,13.0,WALK,1083169,1 +8665358,86653580,26418,26418,10831690,False,Home,8,9,13.0,WALK,1083169,2 +8684705,86847050,26477,26477,10855880,True,shopping,9,8,7.0,WALK,1085588,1 +8684706,86847060,26477,26477,10855880,True,othdiscr,10,9,8.0,WALK_LOC,1085588,2 +8684707,86847070,26477,26477,10855880,True,univ,13,10,8.0,WALK_LRF,1085588,3 +8684709,86847090,26477,26477,10855880,False,othmaint,9,13,12.0,WALK_LRF,1085588,1 +8684710,86847100,26477,26477,10855880,False,escort,4,9,13.0,WALK_LRF,1085588,2 +8684711,86847110,26477,26477,10855880,False,othmaint,22,4,13.0,WALK_LRF,1085588,3 +8684712,86847120,26477,26477,10855880,False,Home,8,22,13.0,WALK_LRF,1085588,4 +8684745,86847450,26477,26477,10855930,True,social,19,8,17.0,WALK,1085593,1 +8684749,86847490,26477,26477,10855930,False,Home,8,19,23.0,WALK,1085593,1 +8684833,86848330,26478,26478,10856040,True,eatout,13,8,11.0,WALK,1085604,1 +8684837,86848370,26478,26478,10856040,False,Home,8,13,11.0,WALK,1085604,1 +8685009,86850090,26478,26478,10856260,True,othmaint,10,8,12.0,BIKE,1085626,1 +8685013,86850130,26478,26478,10856260,False,Home,8,10,13.0,BIKE,1085626,1 +8700097,87000970,26524,26524,10875120,True,othmaint,11,8,12.0,WALK,1087512,1 +8700101,87001010,26524,26524,10875120,False,Home,8,11,15.0,BIKE,1087512,1 +8721441,87214410,26589,26589,10901800,True,univ,10,8,16.0,WALK,1090180,1 +8721445,87214450,26589,26589,10901800,False,Home,8,10,22.0,WALK,1090180,1 +8735889,87358890,26633,26633,10919860,True,shopping,9,8,10.0,WALK,1091986,1 +8735890,87358900,26633,26633,10919860,True,shopping,17,9,12.0,WALK_LRF,1091986,2 +8735893,87358930,26633,26633,10919860,False,Home,8,17,20.0,WALK_LRF,1091986,1 +8753057,87530570,26686,26686,10941320,True,eatout,5,8,19.0,WALK,1094132,1 +8753061,87530610,26686,26686,10941320,False,Home,8,5,19.0,WALK,1094132,1 +8753233,87532330,26686,26686,10941540,True,othmaint,9,8,12.0,BIKE,1094154,1 +8753237,87532370,26686,26686,10941540,False,Home,8,9,13.0,WALK,1094154,1 +8754913,87549130,26691,26691,10943640,True,shopping,16,8,14.0,WALK,1094364,1 +8754917,87549170,26691,26691,10943640,False,Home,8,16,16.0,WALK,1094364,1 +8757145,87571450,26698,26698,10946430,True,othdiscr,20,8,8.0,WALK_LOC,1094643,1 +8757149,87571490,26698,26698,10946430,False,othmaint,16,20,14.0,WALK_LOC,1094643,1 +8757150,87571500,26698,26698,10946430,False,shopping,5,16,14.0,WALK,1094643,2 +8757151,87571510,26698,26698,10946430,False,Home,8,5,14.0,WALK,1094643,3 +8785049,87850490,26783,26783,10981310,True,othmaint,3,8,8.0,WALK,1098131,1 +8785053,87850530,26783,26783,10981310,False,Home,8,3,12.0,WALK,1098131,1 +8787041,87870410,26789,26789,10983800,True,univ,12,8,8.0,WALK_LOC,1098380,1 +8787045,87870450,26789,26789,10983800,False,Home,8,12,8.0,WALK_LOC,1098380,1 +8798193,87981930,26823,26823,10997740,True,univ,12,8,7.0,WALK_LOC,1099774,1 +8798197,87981970,26823,26823,10997740,False,social,16,12,11.0,WALK_LOC,1099774,1 +8798198,87981980,26823,26823,10997740,False,shopping,13,16,11.0,WALK_LOC,1099774,2 +8798199,87981990,26823,26823,10997740,False,eatout,16,13,11.0,WALK_LOC,1099774,3 +8798200,87982000,26823,26823,10997740,False,Home,8,16,11.0,WALK_LOC,1099774,4 +8801161,88011610,26832,26832,11001450,True,shopping,6,8,16.0,WALK,1100145,1 +8801165,88011650,26832,26832,11001450,False,shopping,11,6,16.0,WALK,1100145,1 +8801166,88011660,26832,26832,11001450,False,Home,8,11,17.0,WALK,1100145,2 +8804097,88040970,26841,26841,11005120,True,univ,12,8,16.0,DRIVEALONEFREE,1100512,1 +8804101,88041010,26841,26841,11005120,False,shopping,19,12,16.0,DRIVEALONEFREE,1100512,1 +8804102,88041020,26841,26841,11005120,False,Home,8,19,16.0,DRIVEALONEFREE,1100512,2 +8805121,88051210,26844,26844,11006400,True,social,7,8,8.0,WALK,1100640,1 +8805125,88051250,26844,26844,11006400,False,othmaint,5,7,12.0,WALK,1100640,1 +8805126,88051260,26844,26844,11006400,False,Home,8,5,12.0,WALK,1100640,2 +8809953,88099530,26859,26859,11012440,True,othdiscr,15,8,9.0,WALK,1101244,1 +8809957,88099570,26859,26859,11012440,False,Home,8,15,14.0,WALK,1101244,1 +8812953,88129530,26868,26868,11016190,True,univ,12,8,20.0,WALK_LOC,1101619,1 +8812957,88129570,26868,26868,11016190,False,Home,8,12,20.0,WALK_LOC,1101619,1 +8823097,88230970,26899,26899,11028870,True,othmaint,4,8,11.0,BIKE,1102887,1 +8823101,88231010,26899,26899,11028870,False,Home,8,4,14.0,BIKE,1102887,1 +8823105,88231050,26899,26899,11028880,True,othmaint,13,8,14.0,WALK,1102888,1 +8823109,88231090,26899,26899,11028880,False,Home,8,13,16.0,WALK,1102888,1 +8826529,88265290,26910,26910,11033160,True,eatout,12,8,9.0,WALK,1103316,1 +8826533,88265330,26910,26910,11033160,False,Home,8,12,11.0,WALK,1103316,1 +8826745,88267450,26910,26910,11033430,True,shopping,16,8,13.0,WALK,1103343,1 +8826749,88267490,26910,26910,11033430,False,Home,8,16,16.0,WALK,1103343,1 +8826753,88267530,26910,26910,11033440,True,shopping,9,8,16.0,BIKE,1103344,1 +8826757,88267570,26910,26910,11033440,False,Home,8,9,17.0,BIKE,1103344,1 +8834097,88340970,26933,26933,11042620,True,escort,6,8,7.0,WALK,1104262,1 +8834101,88341010,26933,26933,11042620,False,Home,8,6,7.0,WALK,1104262,1 +8834289,88342890,26933,26933,11042860,True,shopping,16,8,12.0,WALK,1104286,1 +8834293,88342930,26933,26933,11042860,False,Home,8,16,12.0,WALK,1104286,1 +8835273,88352730,26936,26936,11044090,True,eatout,7,8,10.0,WALK,1104409,1 +8835274,88352740,26936,26936,11044090,True,social,7,7,10.0,WALK,1104409,2 +8835275,88352750,26936,26936,11044090,True,shopping,21,7,11.0,WALK,1104409,3 +8835277,88352770,26936,26936,11044090,False,shopping,5,21,15.0,WALK,1104409,1 +8835278,88352780,26936,26936,11044090,False,Home,8,5,15.0,WALK,1104409,2 +8876257,88762570,27061,27061,11095320,True,univ,12,9,16.0,WALK_LRF,1109532,1 +8876261,88762610,27061,27061,11095320,False,othmaint,5,12,19.0,WALK_LOC,1109532,1 +8876262,88762620,27061,27061,11095320,False,Home,9,5,19.0,WALK_LOC,1109532,2 +8883425,88834250,27083,27083,11104280,True,othdiscr,12,9,10.0,WALK_LRF,1110428,1 +8883429,88834290,27083,27083,11104280,False,Home,9,12,14.0,WALK_LRF,1110428,1 +8899497,88994970,27132,27132,11124370,True,othdiscr,9,9,9.0,WALK,1112437,1 +8899501,88995010,27132,27132,11124370,False,Home,9,9,14.0,WALK,1112437,1 +8909993,89099930,27164,27164,11137490,True,othdiscr,19,9,8.0,WALK,1113749,1 +8909997,89099970,27164,27164,11137490,False,Home,9,19,23.0,BIKE,1113749,1 +8915657,89156570,27181,27181,11144570,True,social,16,9,12.0,WALK_LRF,1114457,1 +8915661,89156610,27181,27181,11144570,False,Home,9,16,14.0,WALK_LRF,1114457,1 +8921497,89214970,27199,27199,11151870,True,othmaint,9,9,11.0,WALK,1115187,1 +8921501,89215010,27199,27199,11151870,False,Home,9,9,12.0,WALK,1115187,1 +8933633,89336330,27236,27236,11167040,True,othmaint,15,9,13.0,WALK_LRF,1116704,1 +8933637,89336370,27236,27236,11167040,False,shopping,16,15,15.0,WALK,1116704,1 +8933638,89336380,27236,27236,11167040,False,Home,9,16,15.0,WALK_LRF,1116704,2 +8941529,89415290,27260,27260,11176910,True,shopping,12,9,8.0,WALK_LRF,1117691,1 +8941530,89415300,27260,27260,11176910,True,univ,14,12,8.0,WALK,1117691,2 +8941533,89415330,27260,27260,11176910,False,Home,9,14,16.0,WALK_LRF,1117691,1 +8945153,89451530,27271,27271,11181440,True,shopping,5,9,10.0,TNC_SINGLE,1118144,1 +8945157,89451570,27271,27271,11181440,False,Home,9,5,15.0,TNC_SINGLE,1118144,1 +8977281,89772810,27369,27369,11221600,True,escort,9,9,12.0,WALK,1122160,1 +8977282,89772820,27369,27369,11221600,True,univ,9,9,13.0,WALK,1122160,2 +8977285,89772850,27369,27369,11221600,False,Home,9,9,20.0,WALK,1122160,1 +8986417,89864170,27397,27397,11233020,True,othdiscr,17,9,13.0,WALK_LRF,1123302,1 +8986421,89864210,27397,27397,11233020,False,Home,9,17,16.0,WALK_LRF,1123302,1 +8992321,89923210,27415,27415,11240400,True,othdiscr,18,9,11.0,WALK,1124040,1 +8992325,89923250,27415,27415,11240400,False,Home,9,18,20.0,WALK,1124040,1 +8992673,89926730,27416,27416,11240840,True,othmaint,16,9,14.0,WALK_LRF,1124084,1 +8992677,89926770,27416,27416,11240840,False,Home,9,16,14.0,WALK_LRF,1124084,1 +9014145,90141450,27482,27482,11267680,True,eatout,11,10,14.0,WALK,1126768,1 +9014149,90141490,27482,27482,11267680,False,Home,10,11,17.0,WALK,1126768,1 +9035465,90354650,27547,27547,11294330,True,eatout,9,10,18.0,WALK,1129433,1 +9035469,90354690,27547,27547,11294330,False,Home,10,9,22.0,WALK,1129433,1 +9036929,90369290,27551,27551,11296160,True,othdiscr,7,10,12.0,WALK,1129616,1 +9036933,90369330,27551,27551,11296160,False,Home,10,7,16.0,WALK,1129616,1 +9042857,90428570,27569,27569,11303570,True,othmaint,9,10,8.0,WALK_LOC,1130357,1 +9042861,90428610,27569,27569,11303570,False,Home,10,9,11.0,TNC_SINGLE,1130357,1 +9069249,90692490,27650,27650,11336560,True,eatout,14,10,15.0,WALK_LRF,1133656,1 +9069253,90692530,27650,27650,11336560,False,Home,10,14,16.0,WALK,1133656,1 +9075393,90753930,27668,27668,11344240,True,social,9,10,8.0,WALK,1134424,1 +9075397,90753970,27668,27668,11344240,False,Home,10,9,16.0,WALK,1134424,1 +9075809,90758090,27670,27670,11344760,True,eatout,5,10,16.0,WALK,1134476,1 +9075813,90758130,27670,27670,11344760,False,Home,10,5,20.0,WALK,1134476,1 +9075961,90759610,27670,27670,11344950,True,othdiscr,6,10,13.0,WALK,1134495,1 +9075962,90759620,27670,27670,11344950,True,shopping,16,6,14.0,WALK_LOC,1134495,2 +9075963,90759630,27670,27670,11344950,True,othdiscr,17,16,14.0,WALK_LRF,1134495,3 +9075965,90759650,27670,27670,11344950,False,shopping,8,17,16.0,WALK_LRF,1134495,1 +9075966,90759660,27670,27670,11344950,False,shopping,8,8,16.0,WALK,1134495,2 +9075967,90759670,27670,27670,11344950,False,Home,10,8,16.0,WALK,1134495,3 +9075985,90759850,27670,27670,11344980,True,othmaint,5,10,11.0,WALK_LOC,1134498,1 +9075986,90759860,27670,27670,11344980,True,othmaint,22,5,11.0,TNC_SINGLE,1134498,2 +9075989,90759890,27670,27670,11344980,False,escort,7,22,12.0,TNC_SINGLE,1134498,1 +9075990,90759900,27670,27670,11344980,False,othmaint,10,7,12.0,WALK_LOC,1134498,2 +9075991,90759910,27670,27670,11344980,False,Home,10,10,12.0,TNC_SINGLE,1134498,3 +9087441,90874410,27705,27705,11359300,True,othdiscr,8,10,18.0,WALK,1135930,1 +9087445,90874450,27705,27705,11359300,False,Home,10,8,21.0,WALK,1135930,1 +9087505,90875050,27705,27705,11359380,True,shopping,12,10,11.0,WALK_LOC,1135938,1 +9087509,90875090,27705,27705,11359380,False,shopping,8,12,16.0,TNC_SHARED,1135938,1 +9087510,90875100,27705,27705,11359380,False,Home,10,8,16.0,WALK_LOC,1135938,2 +9094329,90943290,27726,27726,11367910,True,othdiscr,9,10,8.0,WALK,1136791,1 +9094333,90943330,27726,27726,11367910,False,Home,10,9,10.0,WALK,1136791,1 +9098945,90989450,27740,27740,11373680,True,othmaint,24,10,13.0,WALK_LRF,1137368,1 +9098949,90989490,27740,27740,11373680,False,Home,10,24,17.0,WALK_LRF,1137368,1 +9123849,91238490,27816,27816,11404810,True,othdiscr,17,10,10.0,WALK_LRF,1140481,1 +9123853,91238530,27816,27816,11404810,False,Home,10,17,14.0,WALK_LRF,1140481,1 +9127457,91274570,27827,27827,11409320,True,othdiscr,9,10,14.0,WALK,1140932,1 +9127461,91274610,27827,27827,11409320,False,Home,10,9,16.0,WALK,1140932,1 +9134041,91340410,27847,27847,11417550,True,othmaint,4,10,7.0,WALK,1141755,1 +9134045,91340450,27847,27847,11417550,False,Home,10,4,13.0,WALK,1141755,1 +9134081,91340810,27847,27847,11417600,True,shopping,16,10,15.0,TNC_SINGLE,1141760,1 +9134085,91340850,27847,27847,11417600,False,Home,10,16,15.0,DRIVEALONEFREE,1141760,1 +9144209,91442090,27878,27878,11430260,True,othmaint,9,10,14.0,BIKE,1143026,1 +9144213,91442130,27878,27878,11430260,False,eatout,9,9,16.0,BIKE,1143026,1 +9144214,91442140,27878,27878,11430260,False,Home,10,9,16.0,BIKE,1143026,2 +9192401,91924010,28025,28025,11490500,True,othdiscr,3,10,15.0,WALK_LOC,1149050,1 +9192405,91924050,28025,28025,11490500,False,Home,10,3,22.0,WALK_LRF,1149050,1 +9192465,91924650,28025,28025,11490580,True,shopping,11,10,13.0,WALK,1149058,1 +9192469,91924690,28025,28025,11490580,False,eatout,7,11,14.0,WALK,1149058,1 +9192470,91924700,28025,28025,11490580,False,Home,10,7,14.0,WALK,1149058,2 +9196073,91960730,28036,28036,11495090,True,shopping,16,10,9.0,TNC_SINGLE,1149509,1 +9196077,91960770,28036,28036,11495090,False,Home,10,16,10.0,WALK_LOC,1149509,1 +9196081,91960810,28036,28036,11495100,True,shopping,11,10,14.0,WALK,1149510,1 +9196085,91960850,28036,28036,11495100,False,Home,10,11,15.0,WALK,1149510,1 +9197673,91976730,28041,28041,11497090,True,othmaint,25,10,14.0,WALK,1149709,1 +9197677,91976770,28041,28041,11497090,False,Home,10,25,19.0,WALK,1149709,1 +9197977,91979770,28042,28042,11497470,True,othdiscr,23,10,18.0,WALK_LOC,1149747,1 +9197981,91979810,28042,28042,11497470,False,shopping,22,23,20.0,WALK,1149747,1 +9197982,91979820,28042,28042,11497470,False,Home,10,22,20.0,WALK_LRF,1149747,2 +9198001,91980010,28042,28042,11497500,True,othmaint,15,10,10.0,WALK,1149750,1 +9198005,91980050,28042,28042,11497500,False,Home,10,15,15.0,WALK_LRF,1149750,1 +9228833,92288330,28136,28136,11536040,True,othmaint,5,10,10.0,WALK,1153604,1 +9228837,92288370,28136,28136,11536040,False,Home,10,5,12.0,WALK,1153604,1 +9228841,92288410,28136,28136,11536050,True,othmaint,4,10,14.0,WALK,1153605,1 +9228845,92288450,28136,28136,11536050,False,Home,10,4,18.0,WALK,1153605,1 +9228873,92288730,28136,28136,11536090,True,shopping,19,10,12.0,WALK,1153609,1 +9228877,92288770,28136,28136,11536090,False,Home,10,19,13.0,WALK,1153609,1 +9239809,92398090,28170,28170,11549760,True,eatout,19,10,14.0,WALK,1154976,1 +9239813,92398130,28170,28170,11549760,False,Home,10,19,14.0,WALK,1154976,1 +9282009,92820090,28298,28298,11602510,True,shopping,5,11,9.0,WALK,1160251,1 +9282013,92820130,28298,28298,11602510,False,Home,11,5,13.0,WALK,1160251,1 +9303113,93031130,28363,28363,11628890,True,eatout,11,11,13.0,WALK,1162889,1 +9303117,93031170,28363,28363,11628890,False,Home,11,11,14.0,WALK,1162889,1 +9303289,93032890,28363,28363,11629110,True,othmaint,7,11,12.0,WALK,1162911,1 +9303293,93032930,28363,28363,11629110,False,Home,11,7,12.0,WALK,1162911,1 +9303313,93033130,28363,28363,11629140,True,univ,9,11,15.0,WALK,1162914,1 +9303317,93033170,28363,28363,11629140,False,Home,11,9,20.0,WALK,1162914,1 +9308865,93088650,28380,28380,11636080,True,othmaint,1,11,11.0,WALK,1163608,1 +9308869,93088690,28380,28380,11636080,False,Home,11,1,15.0,WALK_LRF,1163608,1 +9321961,93219610,28420,28420,11652450,True,othdiscr,11,11,8.0,WALK,1165245,1 +9321965,93219650,28420,28420,11652450,False,Home,11,11,17.0,WALK,1165245,1 +9340065,93400650,28475,28475,11675080,True,shopping,15,11,11.0,WALK,1167508,1 +9340069,93400690,28475,28475,11675080,False,Home,11,15,15.0,WALK,1167508,1 +9344657,93446570,28489,28489,11680820,True,othmaint,7,11,8.0,WALK,1168082,1 +9344658,93446580,28489,28489,11680820,True,shopping,5,7,10.0,WALK,1168082,2 +9344661,93446610,28489,28489,11680820,False,Home,11,5,17.0,WALK,1168082,1 +9351545,93515450,28510,28510,11689430,True,othmaint,9,11,11.0,WALK_LOC,1168943,1 +9351546,93515460,28510,28510,11689430,True,shopping,16,9,13.0,WALK_LRF,1168943,2 +9351549,93515490,28510,28510,11689430,False,shopping,25,16,19.0,TNC_SINGLE,1168943,1 +9351550,93515500,28510,28510,11689430,False,Home,11,25,19.0,TNC_SINGLE,1168943,2 +9351553,93515530,28510,28510,11689440,True,shopping,12,11,20.0,WALK,1168944,1 +9351557,93515570,28510,28510,11689440,False,shopping,11,12,20.0,WALK,1168944,1 +9351558,93515580,28510,28510,11689440,False,Home,11,11,20.0,WALK,1168944,2 +9361497,93614970,28541,28541,11701870,True,eatout,12,11,16.0,WALK,1170187,1 +9361501,93615010,28541,28541,11701870,False,Home,11,12,17.0,WALK,1170187,1 +9361673,93616730,28541,28541,11702090,True,othmaint,21,11,8.0,WALK,1170209,1 +9361677,93616770,28541,28541,11702090,False,eatout,7,21,14.0,BIKE,1170209,1 +9361678,93616780,28541,28541,11702090,False,Home,11,7,14.0,BIKE,1170209,2 +9366305,93663050,28555,28555,11707880,True,shopping,16,11,7.0,WALK,1170788,1 +9366309,93663090,28555,28555,11707880,False,Home,11,16,20.0,WALK,1170788,1 +9396921,93969210,28649,28649,11746150,True,eatout,2,13,11.0,WALK,1174615,1 +9396925,93969250,28649,28649,11746150,False,Home,13,2,16.0,WALK,1174615,1 +9397401,93974010,28650,28650,11746750,True,othdiscr,9,13,12.0,WALK_LRF,1174675,1 +9397405,93974050,28650,28650,11746750,False,Home,13,9,15.0,WALK_LRF,1174675,1 +9397465,93974650,28650,28650,11746830,True,shopping,24,13,9.0,WALK,1174683,1 +9397469,93974690,28650,28650,11746830,False,Home,13,24,12.0,WALK,1174683,1 +9411897,94118970,28694,28694,11764870,True,eatout,16,14,10.0,WALK,1176487,1 +9411898,94118980,28694,28694,11764870,True,shopping,2,16,10.0,WALK,1176487,2 +9411901,94119010,28694,28694,11764870,False,eatout,7,2,12.0,WALK,1176487,1 +9411902,94119020,28694,28694,11764870,False,Home,14,7,12.0,WALK,1176487,2 +9432369,94323690,28757,28757,11790460,True,escort,8,16,13.0,DRIVEALONEFREE,1179046,1 +9432373,94323730,28757,28757,11790460,False,escort,17,8,14.0,DRIVEALONEFREE,1179046,1 +9432374,94323740,28757,28757,11790460,False,Home,16,17,14.0,SHARED2FREE,1179046,2 +9432521,94325210,28757,28757,11790650,True,othmaint,16,16,6.0,WALK,1179065,1 +9432525,94325250,28757,28757,11790650,False,Home,16,16,6.0,WALK,1179065,1 +9432585,94325850,28757,28757,11790730,True,social,22,16,14.0,WALK,1179073,1 +9432589,94325890,28757,28757,11790730,False,Home,16,22,16.0,WALK,1179073,1 +9435473,94354730,28766,28766,11794340,True,othmaint,21,16,14.0,TNC_SINGLE,1179434,1 +9435477,94354770,28766,28766,11794340,False,Home,16,21,15.0,DRIVEALONEFREE,1179434,1 +9435513,94355130,28766,28766,11794390,True,shopping,5,16,9.0,WALK,1179439,1 +9435517,94355170,28766,28766,11794390,False,Home,16,5,11.0,WALK,1179439,1 +9464969,94649690,28856,28856,11831210,True,othdiscr,12,16,12.0,WALK,1183121,1 +9464973,94649730,28856,28856,11831210,False,Home,16,12,13.0,WALK,1183121,1 +9483513,94835130,28913,28913,11854390,True,eatout,2,16,14.0,WALK,1185439,1 +9483517,94835170,28913,28913,11854390,False,Home,16,2,17.0,WALK,1185439,1 +9491257,94912570,28936,28936,11864070,True,univ,14,16,14.0,WALK_LOC,1186407,1 +9491261,94912610,28936,28936,11864070,False,eatout,9,14,14.0,WALK_LOC,1186407,1 +9491262,94912620,28936,28936,11864070,False,escort,10,9,15.0,WALK_LOC,1186407,2 +9491263,94912630,28936,28936,11864070,False,Home,16,10,15.0,WALK_LOC,1186407,3 +9492041,94920410,28939,28939,11865050,True,social,10,16,6.0,DRIVEALONEFREE,1186505,1 +9492042,94920420,28939,28939,11865050,True,eatout,22,10,6.0,TNC_SINGLE,1186505,2 +9492045,94920450,28939,28939,11865050,False,eatout,12,22,6.0,DRIVEALONEFREE,1186505,1 +9492046,94920460,28939,28939,11865050,False,Home,16,12,6.0,TNC_SHARED,1186505,2 +9492193,94921930,28939,28939,11865240,True,othdiscr,17,16,15.0,WALK,1186524,1 +9492197,94921970,28939,28939,11865240,False,Home,16,17,17.0,WALK,1186524,1 +9492257,94922570,28939,28939,11865320,True,eatout,5,16,9.0,TNC_SINGLE,1186532,1 +9492258,94922580,28939,28939,11865320,True,shopping,22,5,10.0,TNC_SINGLE,1186532,2 +9492261,94922610,28939,28939,11865320,False,Home,16,22,13.0,TNC_SINGLE,1186532,1 +9492913,94929130,28941,28941,11866140,True,shopping,16,16,9.0,WALK,1186614,1 +9492917,94929170,28941,28941,11866140,False,Home,16,16,16.0,WALK,1186614,1 +9495825,94958250,28950,28950,11869780,True,othmaint,11,16,10.0,WALK,1186978,1 +9495829,94958290,28950,28950,11869780,False,shopping,6,11,15.0,WALK,1186978,1 +9495830,94958300,28950,28950,11869780,False,escort,5,6,15.0,WALK,1186978,2 +9495831,94958310,28950,28950,11869780,False,Home,16,5,15.0,WALK,1186978,3 +9495865,94958650,28950,28950,11869830,True,shopping,5,16,9.0,WALK_LOC,1186983,1 +9495866,94958660,28950,28950,11869830,True,shopping,3,5,10.0,TNC_SINGLE,1186983,2 +9495869,94958690,28950,28950,11869830,False,Home,16,3,10.0,WALK_LOC,1186983,1 +9507785,95077850,28987,28987,11884730,True,shopping,6,16,12.0,WALK_LOC,1188473,1 +9507786,95077860,28987,28987,11884730,True,eatout,7,6,12.0,WALK,1188473,2 +9507789,95077890,28987,28987,11884730,False,eatout,6,7,12.0,WALK_LOC,1188473,1 +9507790,95077900,28987,28987,11884730,False,shopping,5,6,20.0,WALK,1188473,2 +9507791,95077910,28987,28987,11884730,False,Home,16,5,20.0,WALK_LOC,1188473,3 +9513513,95135130,29004,29004,11891890,True,othdiscr,1,16,9.0,WALK,1189189,1 +9513517,95135170,29004,29004,11891890,False,Home,16,1,19.0,WALK,1189189,1 +9520121,95201210,29024,29024,11900150,True,univ,13,16,18.0,WALK,1190015,1 +9520125,95201250,29024,29024,11900150,False,Home,16,13,23.0,WALK,1190015,1 +9551913,95519130,29121,29121,11939890,True,othmaint,7,16,12.0,WALK,1193989,1 +9551917,95519170,29121,29121,11939890,False,shopping,5,7,14.0,WALK,1193989,1 +9551918,95519180,29121,29121,11939890,False,shopping,16,5,17.0,WALK,1193989,2 +9551919,95519190,29121,29121,11939890,False,Home,16,16,17.0,WALK,1193989,3 +9553857,95538570,29127,29127,11942320,True,othdiscr,21,16,9.0,WALK,1194232,1 +9553861,95538610,29127,29127,11942320,False,Home,16,21,17.0,WALK,1194232,1 +9556481,95564810,29135,29135,11945600,True,othdiscr,24,16,9.0,WALK,1194560,1 +9556485,95564850,29135,29135,11945600,False,Home,16,24,14.0,WALK,1194560,1 +9558977,95589770,29143,29143,11948720,True,escort,8,16,7.0,WALK,1194872,1 +9558981,95589810,29143,29143,11948720,False,Home,16,8,7.0,WALK,1194872,1 +9559105,95591050,29143,29143,11948880,True,othdiscr,4,16,14.0,WALK,1194888,1 +9559109,95591090,29143,29143,11948880,False,Home,16,4,14.0,WALK,1194888,1 +9564025,95640250,29158,29158,11955030,True,eatout,7,16,10.0,WALK_LOC,1195503,1 +9564026,95640260,29158,29158,11955030,True,othdiscr,11,7,10.0,WALK_LOC,1195503,2 +9564029,95640290,29158,29158,11955030,False,othmaint,9,11,13.0,WALK_LOC,1195503,1 +9564030,95640300,29158,29158,11955030,False,Home,16,9,13.0,WALK_LOC,1195503,2 +9567041,95670410,29167,29167,11958800,True,shopping,22,16,9.0,WALK,1195880,1 +9567045,95670450,29167,29167,11958800,False,Home,16,22,16.0,WALK,1195880,1 +9581761,95817610,29212,29212,11977200,True,othmaint,14,16,10.0,WALK,1197720,1 +9581765,95817650,29212,29212,11977200,False,Home,16,14,16.0,WALK,1197720,1 +9600457,96004570,29269,29269,12000570,True,othmaint,16,16,13.0,WALK,1200057,1 +9600461,96004610,29269,29269,12000570,False,Home,16,16,15.0,WALK,1200057,1 +9608065,96080650,29292,29292,12010080,True,social,15,16,9.0,WALK,1201008,1 +9608069,96080690,29292,29292,12010080,False,Home,16,15,17.0,WALK,1201008,1 +9608369,96083690,29293,29293,12010460,True,shopping,16,16,8.0,WALK,1201046,1 +9608373,96083730,29293,29293,12010460,False,Home,16,16,16.0,WALK,1201046,1 +9619961,96199610,29329,29329,12024950,True,eatout,13,16,12.0,WALK,1202495,1 +9619965,96199650,29329,29329,12024950,False,Home,16,13,14.0,WALK,1202495,1 +9620769,96207690,29331,29331,12025960,True,othdiscr,17,16,14.0,WALK,1202596,1 +9620773,96207730,29331,29331,12025960,False,Home,16,17,16.0,WALK,1202596,1 +9625713,96257130,29346,29346,12032140,True,othmaint,4,16,10.0,WALK,1203214,1 +9625717,96257170,29346,29346,12032140,False,Home,16,4,11.0,WALK,1203214,1 +9630281,96302810,29360,29360,12037850,True,eatout,7,16,10.0,WALK,1203785,1 +9630282,96302820,29360,29360,12037850,True,othdiscr,20,7,11.0,WALK,1203785,2 +9630285,96302850,29360,29360,12037850,False,Home,16,20,12.0,WALK,1203785,1 +9656897,96568970,29441,29441,12071120,True,univ,13,16,15.0,WALK,1207112,1 +9656901,96569010,29441,29441,12071120,False,Home,16,13,16.0,WALK,1207112,1 +9667081,96670810,29472,29472,12083850,True,shopping,5,16,9.0,WALK,1208385,1 +9667085,96670850,29472,29472,12083850,False,Home,16,5,10.0,WALK,1208385,1 +9667105,96671050,29472,29472,12083880,True,social,13,16,11.0,WALK,1208388,1 +9667109,96671090,29472,29472,12083880,False,Home,16,13,21.0,WALK,1208388,1 +9668745,96687450,29477,29477,12085930,True,social,5,16,8.0,WALK,1208593,1 +9668749,96687490,29477,29477,12085930,False,Home,16,5,22.0,WALK,1208593,1 +9679857,96798570,29511,29511,12099820,True,univ,13,16,8.0,WALK_LOC,1209982,1 +9679861,96798610,29511,29511,12099820,False,Home,16,13,15.0,WALK_LOC,1209982,1 +9686433,96864330,29531,29531,12108040,True,shopping,20,17,10.0,WALK_LRF,1210804,1 +9686437,96864370,29531,29531,12108040,False,Home,17,20,12.0,WALK_LOC,1210804,1 +9689385,96893850,29540,29540,12111730,True,shopping,19,17,14.0,TNC_SINGLE,1211173,1 +9689389,96893890,29540,29540,12111730,False,othdiscr,18,19,14.0,TAXI,1211173,1 +9689390,96893900,29540,29540,12111730,False,Home,17,18,14.0,TNC_SINGLE,1211173,2 +9697545,96975450,29565,29565,12121930,True,othmaint,5,17,7.0,WALK,1212193,1 +9697549,96975490,29565,29565,12121930,False,Home,17,5,15.0,WALK_LRF,1212193,1 +9705417,97054170,29589,29589,12131770,True,othmaint,4,17,16.0,WALK,1213177,1 +9705421,97054210,29589,29589,12131770,False,Home,17,4,17.0,WALK,1213177,1 +9709657,97096570,29602,29602,12137070,True,othdiscr,16,17,21.0,TNC_SHARED,1213707,1 +9709661,97096610,29602,29602,12137070,False,Home,17,16,21.0,WALK,1213707,1 +9709721,97097210,29602,29602,12137150,True,shopping,16,17,11.0,WALK,1213715,1 +9709725,97097250,29602,29602,12137150,False,Home,17,16,13.0,WALK,1213715,1 +9709729,97097290,29602,29602,12137160,True,othdiscr,19,17,17.0,WALK,1213716,1 +9709730,97097300,29602,29602,12137160,True,eatout,19,19,17.0,WALK,1213716,2 +9709731,97097310,29602,29602,12137160,True,shopping,19,19,17.0,SHARED2FREE,1213716,3 +9709733,97097330,29602,29602,12137160,False,Home,17,19,17.0,SHARED2FREE,1213716,1 +9709745,97097450,29602,29602,12137180,True,social,10,17,13.0,SHARED2FREE,1213718,1 +9709749,97097490,29602,29602,12137180,False,Home,17,10,13.0,SHARED2FREE,1213718,1 +9710969,97109690,29606,29606,12138710,True,othdiscr,18,17,15.0,WALK,1213871,1 +9710973,97109730,29606,29606,12138710,False,Home,17,18,17.0,WALK,1213871,1 +9715433,97154330,29620,29620,12144290,True,escort,11,17,8.0,SHARED3FREE,1214429,1 +9715437,97154370,29620,29620,12144290,False,othmaint,4,11,8.0,DRIVEALONEFREE,1214429,1 +9715438,97154380,29620,29620,12144290,False,Home,17,4,8.0,DRIVEALONEFREE,1214429,2 +9717265,97172650,29625,29625,12146580,True,shopping,17,17,15.0,WALK,1214658,1 +9717269,97172690,29625,29625,12146580,False,Home,17,17,15.0,WALK,1214658,1 +9728089,97280890,29658,29658,12160110,True,shopping,5,17,18.0,WALK,1216011,1 +9728093,97280930,29658,29658,12160110,False,social,5,5,19.0,WALK,1216011,1 +9728094,97280940,29658,29658,12160110,False,Home,17,5,19.0,WALK,1216011,2 +9779257,97792570,29814,29814,12224070,True,shopping,16,17,9.0,WALK,1222407,1 +9779261,97792610,29814,29814,12224070,False,Home,17,16,10.0,WALK_LRF,1222407,1 +9782801,97828010,29825,29825,12228500,True,othdiscr,17,17,14.0,WALK,1222850,1 +9782805,97828050,29825,29825,12228500,False,Home,17,17,16.0,WALK,1222850,1 +9782865,97828650,29825,29825,12228580,True,shopping,11,17,10.0,WALK,1222858,1 +9782869,97828690,29825,29825,12228580,False,Home,17,11,13.0,WALK,1222858,1 +9806465,98064650,29897,29897,12258080,True,univ,14,17,12.0,WALK_LRF,1225808,1 +9806469,98064690,29897,29897,12258080,False,Home,17,14,13.0,WALK_LOC,1225808,1 +9810881,98108810,29911,29911,12263600,True,escort,23,17,18.0,SHARED2FREE,1226360,1 +9810885,98108850,29911,29911,12263600,False,Home,17,23,19.0,SHARED2FREE,1226360,1 +9811033,98110330,29911,29911,12263790,True,shopping,16,17,10.0,WALK,1226379,1 +9811034,98110340,29911,29911,12263790,True,othmaint,9,16,11.0,SHARED3FREE,1226379,2 +9811037,98110370,29911,29911,12263790,False,escort,16,9,11.0,SHARED3FREE,1226379,1 +9811038,98110380,29911,29911,12263790,False,social,16,16,11.0,WALK,1226379,2 +9811039,98110390,29911,29911,12263790,False,Home,17,16,11.0,DRIVEALONEFREE,1226379,3 +9811097,98110970,29911,29911,12263870,True,social,16,17,11.0,WALK,1226387,1 +9811101,98111010,29911,29911,12263870,False,Home,17,16,14.0,WALK,1226387,1 +9811105,98111050,29911,29911,12263880,True,escort,2,17,19.0,TNC_SHARED,1226388,1 +9811106,98111060,29911,29911,12263880,True,social,12,2,19.0,DRIVEALONEFREE,1226388,2 +9811109,98111090,29911,29911,12263880,False,shopping,16,12,19.0,DRIVEALONEFREE,1226388,1 +9811110,98111100,29911,29911,12263880,False,shopping,16,16,19.0,WALK,1226388,2 +9811111,98111110,29911,29911,12263880,False,Home,17,16,19.0,DRIVEALONEFREE,1226388,3 +9814025,98140250,29920,29920,12267530,True,shopping,5,17,10.0,WALK_LOC,1226753,1 +9814029,98140290,29920,29920,12267530,False,Home,17,5,10.0,WALK_LRF,1226753,1 +9818641,98186410,29934,29934,12273300,True,social,15,17,10.0,BIKE,1227330,1 +9818645,98186450,29934,29934,12273300,False,Home,17,15,16.0,BIKE,1227330,1 +9820217,98202170,29939,29939,12275270,True,othmaint,4,18,9.0,WALK_LOC,1227527,1 +9820221,98202210,29939,29939,12275270,False,Home,18,4,15.0,WALK_LOC,1227527,1 +9820225,98202250,29939,29939,12275280,True,othmaint,13,18,16.0,DRIVEALONEFREE,1227528,1 +9820229,98202290,29939,29939,12275280,False,Home,18,13,17.0,TNC_SINGLE,1227528,1 +9845777,98457770,30017,30017,12307220,True,shopping,11,21,12.0,WALK,1230722,1 +9845778,98457780,30017,30017,12307220,True,othdiscr,19,11,15.0,WALK,1230722,2 +9845781,98457810,30017,30017,12307220,False,Home,21,19,19.0,WALK,1230722,1 +9856801,98568010,30051,30051,12321000,True,escort,20,24,13.0,DRIVEALONEFREE,1232100,1 +9856805,98568050,30051,30051,12321000,False,eatout,3,20,13.0,SHARED3FREE,1232100,1 +9856806,98568060,30051,30051,12321000,False,Home,24,3,13.0,SHARED3FREE,1232100,2 +9856993,98569930,30051,30051,12321240,True,shopping,5,24,15.0,WALK,1232124,1 +9856997,98569970,30051,30051,12321240,False,Home,24,5,20.0,WALK,1232124,1 +9862681,98626810,30069,30069,12328350,True,eatout,14,25,10.0,WALK,1232835,1 +9862685,98626850,30069,30069,12328350,False,Home,25,14,10.0,WALK,1232835,1 +9862833,98628330,30069,30069,12328540,True,othdiscr,11,25,12.0,WALK_LOC,1232854,1 +9862837,98628370,30069,30069,12328540,False,Home,25,11,15.0,WALK,1232854,1 +9862897,98628970,30069,30069,12328620,True,shopping,16,25,16.0,WALK_LOC,1232862,1 +9862898,98628980,30069,30069,12328620,True,shopping,8,16,17.0,WALK_LOC,1232862,2 +9862901,98629010,30069,30069,12328620,False,Home,25,8,20.0,WALK_LOC,1232862,1 +9863881,98638810,30072,30072,12329850,True,shopping,5,25,11.0,WALK,1232985,1 +9863885,98638850,30072,30072,12329850,False,Home,25,5,13.0,WALK,1232985,1 +35291145,352911450,107594,107594,44113930,True,work,1,1,7.0,WALK,4411393,1 +35291149,352911490,107594,107594,44113930,False,Home,1,1,19.0,WALK,4411393,1 +35302033,353020330,107628,107628,44127540,True,eatout,6,6,12.0,WALK,4412754,1 +35302037,353020370,107628,107628,44127540,False,Home,6,6,12.0,WALK,4412754,1 +35303281,353032810,107631,107631,44129100,True,work,2,6,16.0,WALK,4412910,1 +35303285,353032850,107631,107631,44129100,False,Home,6,2,18.0,WALK,4412910,1 +35306233,353062330,107640,107640,44132790,True,work,2,6,7.0,WALK,4413279,1 +35306237,353062370,107640,107640,44132790,False,Home,6,2,18.0,WALK,4413279,1 +35308529,353085290,107647,107647,44135660,True,work,24,6,8.0,WALK,4413566,1 +35308533,353085330,107647,107647,44135660,False,Home,6,24,18.0,WALK,4413566,1 +35312201,353122010,107659,107659,44140250,True,eatout,5,6,18.0,WALK,4414025,1 +35312205,353122050,107659,107659,44140250,False,Home,6,5,18.0,WALK,4414025,1 +35312441,353124410,107659,107659,44140550,True,social,12,6,19.0,TNC_SHARED,4414055,1 +35312445,353124450,107659,107659,44140550,False,Home,6,12,20.0,TNC_SHARED,4414055,1 +35312465,353124650,107659,107659,44140580,True,work,4,6,6.0,TNC_SINGLE,4414058,1 +35312469,353124690,107659,107659,44140580,False,Home,6,4,15.0,WALK_LOC,4414058,1 +35316121,353161210,107671,107671,44145150,True,atwork,9,10,10.0,WALK,4414515,1 +35316125,353161250,107671,107671,44145150,False,work,9,9,14.0,WALK,4414515,1 +35316126,353161260,107671,107671,44145150,False,work,9,9,14.0,WALK,4414515,2 +35316127,353161270,107671,107671,44145150,False,Work,10,9,14.0,WALK,4414515,3 +35316137,353161370,107671,107671,44145170,True,eatout,7,6,18.0,WALK,4414517,1 +35316141,353161410,107671,107671,44145170,False,Home,6,7,19.0,WALK,4414517,1 +35316401,353164010,107671,107671,44145500,True,othmaint,9,6,5.0,WALK,4414550,1 +35316402,353164020,107671,107671,44145500,True,escort,9,9,6.0,WALK,4414550,2 +35316403,353164030,107671,107671,44145500,True,work,10,9,7.0,WALK,4414550,3 +35316405,353164050,107671,107671,44145500,False,othmaint,9,10,17.0,WALK,4414550,1 +35316406,353164060,107671,107671,44145500,False,social,8,9,17.0,WALK,4414550,2 +35316407,353164070,107671,107671,44145500,False,Home,6,8,17.0,WALK,4414550,3 +35331137,353311370,107716,107716,44163920,True,social,17,6,14.0,WALK_LRF,4416392,1 +35331141,353311410,107716,107716,44163920,False,Home,6,17,14.0,WALK_LOC,4416392,1 +35337393,353373930,107735,107735,44171740,True,othmaint,2,7,9.0,WALK,4417174,1 +35337394,353373940,107735,107735,44171740,True,work,8,2,10.0,WALK_LOC,4417174,2 +35337397,353373970,107735,107735,44171740,False,Home,7,8,17.0,WALK_LOC,4417174,1 +35339689,353396890,107742,107742,44174610,True,work,1,7,6.0,WALK_LRF,4417461,1 +35339693,353396930,107742,107742,44174610,False,Home,7,1,17.0,WALK,4417461,1 +35345329,353453290,107760,107760,44181660,True,eatout,6,7,16.0,WALK,4418166,1 +35345333,353453330,107760,107760,44181660,False,Home,7,6,19.0,WALK,4418166,1 +35345593,353455930,107760,107760,44181990,True,work,11,7,11.0,WALK,4418199,1 +35345597,353455970,107760,107760,44181990,False,Home,7,11,15.0,WALK,4418199,1 +35353465,353534650,107784,107784,44191830,True,work,13,7,8.0,WALK,4419183,1 +35353469,353534690,107784,107784,44191830,False,Home,7,13,20.0,WALK_LOC,4419183,1 +35374345,353743450,107848,107848,44217930,True,othdiscr,5,7,20.0,WALK_LOC,4421793,1 +35374349,353743490,107848,107848,44217930,False,Home,7,5,20.0,WALK,4421793,1 +35374457,353744570,107848,107848,44218070,True,work,7,7,7.0,WALK,4421807,1 +35374461,353744610,107848,107848,44218070,False,Home,7,7,19.0,WALK,4421807,1 +35377321,353773210,107857,107857,44221650,True,othmaint,5,7,15.0,WALK,4422165,1 +35377325,353773250,107857,107857,44221650,False,Home,7,5,15.0,SHARED2FREE,4422165,1 +35377409,353774090,107857,107857,44221760,True,work,5,7,5.0,WALK_LOC,4422176,1 +35377413,353774130,107857,107857,44221760,False,eatout,5,5,15.0,WALK,4422176,1 +35377414,353774140,107857,107857,44221760,False,Home,7,5,15.0,WALK_LOC,4422176,2 +35389545,353895450,107894,107894,44236930,True,work,11,7,7.0,WALK_LOC,4423693,1 +35389549,353895490,107894,107894,44236930,False,Home,7,11,18.0,WALK,4423693,1 +35414489,354144890,107971,107971,44268110,True,atwork,23,15,11.0,WALK,4426811,1 +35414493,354144930,107971,107971,44268110,False,Work,15,23,13.0,WALK,4426811,1 +35414801,354148010,107971,107971,44268500,True,escort,5,7,7.0,WALK,4426850,1 +35414802,354148020,107971,107971,44268500,True,work,15,5,8.0,WALK,4426850,2 +35414805,354148050,107971,107971,44268500,False,othmaint,2,15,22.0,WALK_LOC,4426850,1 +35414806,354148060,107971,107971,44268500,False,Home,7,2,22.0,TNC_SINGLE,4426850,2 +35437673,354376730,108041,108041,44297090,True,othmaint,9,8,12.0,WALK,4429709,1 +35437674,354376740,108041,108041,44297090,True,othmaint,22,9,13.0,WALK_LRF,4429709,2 +35437677,354376770,108041,108041,44297090,False,Home,8,22,16.0,WALK_LRF,4429709,1 +35437737,354377370,108041,108041,44297170,True,social,24,8,18.0,WALK,4429717,1 +35437741,354377410,108041,108041,44297170,False,Home,8,24,21.0,WALK,4429717,1 +35440057,354400570,108048,108048,44300070,True,work,19,8,12.0,WALK,4430007,1 +35440061,354400610,108048,108048,44300070,False,Home,8,19,20.0,WALK,4430007,1 +35441913,354419130,108054,108054,44302390,True,othdiscr,5,8,18.0,WALK,4430239,1 +35441917,354419170,108054,108054,44302390,False,Home,8,5,21.0,WALK,4430239,1 +35441937,354419370,108054,108054,44302420,True,othmaint,5,8,10.0,BIKE,4430242,1 +35441941,354419410,108054,108054,44302420,False,Home,8,5,10.0,WALK,4430242,1 +35442025,354420250,108054,108054,44302530,True,work,4,8,10.0,WALK,4430253,1 +35442029,354420290,108054,108054,44302530,False,Home,8,4,18.0,WALK,4430253,1 +35466625,354666250,108129,108129,44333280,True,work,2,8,12.0,SHARED2FREE,4433328,1 +35466629,354666290,108129,108129,44333280,False,Home,8,2,17.0,WALK_LOC,4433328,1 +35466633,354666330,108129,108129,44333290,True,work,2,8,17.0,WALK_LOC,4433329,1 +35466637,354666370,108129,108129,44333290,False,Home,8,2,17.0,WALK,4433329,1 +35473073,354730730,108149,108149,44341340,True,othdiscr,10,8,18.0,WALK,4434134,1 +35473077,354730770,108149,108149,44341340,False,Home,8,10,18.0,WALK,4434134,1 +35473185,354731850,108149,108149,44341480,True,work,13,8,7.0,WALK_LOC,4434148,1 +35473189,354731890,108149,108149,44341480,False,Home,8,13,17.0,WALK,4434148,1 +35473513,354735130,108150,108150,44341890,True,work,11,8,6.0,WALK,4434189,1 +35473517,354735170,108150,108150,44341890,False,Home,8,11,20.0,WALK,4434189,1 +35493193,354931930,108210,108210,44366490,True,work,4,9,6.0,WALK_LRF,4436649,1 +35493197,354931970,108210,108210,44366490,False,Home,9,4,15.0,WALK_LRF,4436649,1 +35493409,354934090,108211,108211,44366760,True,othdiscr,24,9,17.0,BIKE,4436676,1 +35493413,354934130,108211,108211,44366760,False,Home,9,24,21.0,BIKE,4436676,1 +35493433,354934330,108211,108211,44366790,True,othmaint,12,9,6.0,WALK_LRF,4436679,1 +35493437,354934370,108211,108211,44366790,False,Home,9,12,15.0,WALK_LRF,4436679,1 +35499097,354990970,108228,108228,44373870,True,work,4,9,8.0,WALK,4437387,1 +35499101,354991010,108228,108228,44373870,False,Home,9,4,17.0,WALK_LRF,4437387,1 +35514841,355148410,108276,108276,44393550,True,work,21,9,6.0,WALK_LRF,4439355,1 +35514845,355148450,108276,108276,44393550,False,Home,9,21,21.0,WALK_LOC,4439355,1 +35522713,355227130,108300,108300,44403390,True,escort,18,9,5.0,WALK_LOC,4440339,1 +35522714,355227140,108300,108300,44403390,True,work,22,18,7.0,WALK_LRF,4440339,2 +35522717,355227170,108300,108300,44403390,False,shopping,12,22,16.0,WALK_LOC,4440339,1 +35522718,355227180,108300,108300,44403390,False,Home,9,12,17.0,WALK_LRF,4440339,2 +35528945,355289450,108319,108319,44411180,True,work,9,9,6.0,WALK,4441118,1 +35528949,355289490,108319,108319,44411180,False,Home,9,9,18.0,TNC_SINGLE,4441118,1 +35560169,355601690,108415,108415,44450210,True,escort,7,9,9.0,WALK,4445021,1 +35560170,355601700,108415,108415,44450210,True,eatout,7,7,12.0,WALK,4445021,2 +35560173,355601730,108415,108415,44450210,False,Home,9,7,14.0,WALK,4445021,1 +35608649,356086490,108562,108562,44510810,True,work,16,9,7.0,WALK_LOC,4451081,1 +35608653,356086530,108562,108562,44510810,False,Home,9,16,14.0,WALK_LRF,4451081,1 +35619145,356191450,108594,108594,44523930,True,work,2,9,7.0,TNC_SINGLE,4452393,1 +35619149,356191490,108594,108594,44523930,False,Home,9,2,19.0,TNC_SINGLE,4452393,1 +35625705,356257050,108614,108614,44532130,True,work,4,9,9.0,WALK_LRF,4453213,1 +35625706,356257060,108614,108614,44532130,True,work,17,4,10.0,WALK_LRF,4453213,2 +35625709,356257090,108614,108614,44532130,False,Home,9,17,20.0,WALK_LRF,4453213,1 +35652929,356529290,108697,108697,44566160,True,work,4,10,8.0,WALK_LRF,4456616,1 +35652933,356529330,108697,108697,44566160,False,Home,10,4,17.0,WALK_LRF,4456616,1 +35658505,356585050,108714,108714,44573130,True,work,1,10,10.0,WALK_LRF,4457313,1 +35658509,356585090,108714,108714,44573130,False,eatout,9,1,21.0,WALK_LRF,4457313,1 +35658510,356585100,108714,108714,44573130,False,othmaint,9,9,21.0,WALK,4457313,2 +35658511,356585110,108714,108714,44573130,False,Home,10,9,21.0,WALK,4457313,3 +35660521,356605210,108721,108721,44575650,True,atwork,5,5,13.0,WALK,4457565,1 +35660525,356605250,108721,108721,44575650,False,Work,5,5,13.0,SHARED2FREE,4457565,1 +35660801,356608010,108721,108721,44576000,True,work,5,10,10.0,WALK,4457600,1 +35660805,356608050,108721,108721,44576000,False,Home,10,5,19.0,WALK,4457600,1 +35663097,356630970,108728,108728,44578870,True,escort,5,10,16.0,WALK,4457887,1 +35663098,356630980,108728,108728,44578870,True,work,2,5,17.0,WALK_LOC,4457887,2 +35663101,356631010,108728,108728,44578870,False,escort,11,2,19.0,WALK_LOC,4457887,1 +35663102,356631020,108728,108728,44578870,False,shopping,16,11,19.0,WALK_LOC,4457887,2 +35663103,356631030,108728,108728,44578870,False,eatout,4,16,19.0,WALK,4457887,3 +35663104,356631040,108728,108728,44578870,False,Home,10,4,19.0,WALK_LRF,4457887,4 +35686057,356860570,108798,108798,44607570,True,work,9,10,17.0,TNC_SINGLE,4460757,1 +35686061,356860610,108798,108798,44607570,False,Home,10,9,18.0,WALK,4460757,1 +35704753,357047530,108855,108855,44630940,True,work,1,10,7.0,WALK_LRF,4463094,1 +35704757,357047570,108855,108855,44630940,False,Home,10,1,17.0,WALK_LRF,4463094,1 +35707377,357073770,108863,108863,44634220,True,work,14,10,7.0,WALK_LRF,4463422,1 +35707381,357073810,108863,108863,44634220,False,Home,10,14,19.0,WALK_LRF,4463422,1 +35725089,357250890,108917,108917,44656360,True,escort,9,10,8.0,WALK,4465636,1 +35725090,357250900,108917,108917,44656360,True,work,14,9,8.0,WALK_LRF,4465636,2 +35725093,357250930,108917,108917,44656360,False,Home,10,14,21.0,WALK_LRF,4465636,1 +35728697,357286970,108928,108928,44660870,True,work,15,10,8.0,WALK_LOC,4466087,1 +35728701,357287010,108928,108928,44660870,False,Home,10,15,18.0,WALK_LRF,4466087,1 +35728705,357287050,108928,108928,44660880,True,work,15,10,19.0,WALK,4466088,1 +35728709,357287090,108928,108928,44660880,False,Home,10,15,22.0,WALK,4466088,1 +35747113,357471130,108985,108985,44683890,True,atwork,15,16,12.0,WALK,4468389,1 +35747117,357471170,108985,108985,44683890,False,Work,16,15,14.0,WALK,4468389,1 +35747345,357473450,108985,108985,44684180,True,shopping,19,10,20.0,WALK,4468418,1 +35747349,357473490,108985,108985,44684180,False,Home,10,19,21.0,WALK,4468418,1 +35747393,357473930,108985,108985,44684240,True,work,16,10,8.0,WALK_LOC,4468424,1 +35747397,357473970,108985,108985,44684240,False,shopping,11,16,18.0,WALK_LOC,4468424,1 +35747398,357473980,108985,108985,44684240,False,Home,10,11,20.0,WALK,4468424,2 +35766369,357663690,109043,109043,44707960,True,shopping,17,10,21.0,WALK_LRF,4470796,1 +35766373,357663730,109043,109043,44707960,False,Home,10,17,21.0,WALK_LRF,4470796,1 +35766417,357664170,109043,109043,44708020,True,work,2,10,8.0,TNC_SHARED,4470802,1 +35766421,357664210,109043,109043,44708020,False,eatout,8,2,20.0,WALK,4470802,1 +35766422,357664220,109043,109043,44708020,False,Home,10,8,21.0,WALK_LOC,4470802,2 +35778881,357788810,109081,109081,44723600,True,escort,8,10,8.0,WALK,4472360,1 +35778882,357788820,109081,109081,44723600,True,work,9,8,8.0,WALK_LOC,4472360,2 +35778885,357788850,109081,109081,44723600,False,Home,10,9,18.0,WALK,4472360,1 +35797249,357972490,109137,109137,44746560,True,work,2,10,9.0,WALK_LRF,4474656,1 +35797253,357972530,109137,109137,44746560,False,Home,10,2,18.0,WALK_LRF,4474656,1 +35797257,357972570,109137,109137,44746570,True,work,2,10,18.0,WALK_LRF,4474657,1 +35797261,357972610,109137,109137,44746570,False,Home,10,2,20.0,WALK_LRF,4474657,1 +35828081,358280810,109231,109231,44785100,True,work,7,10,14.0,WALK,4478510,1 +35828082,358280820,109231,109231,44785100,True,work,2,7,15.0,WALK,4478510,2 +35828083,358280830,109231,109231,44785100,True,work,4,2,15.0,WALK,4478510,3 +35828084,358280840,109231,109231,44785100,True,work,2,4,15.0,WALK,4478510,4 +35828085,358280850,109231,109231,44785100,False,shopping,5,2,21.0,WALK,4478510,1 +35828086,358280860,109231,109231,44785100,False,Home,10,5,21.0,WALK,4478510,2 +35830049,358300490,109237,109237,44787560,True,work,7,10,11.0,WALK_LOC,4478756,1 +35830053,358300530,109237,109237,44787560,False,work,9,7,17.0,WALK,4478756,1 +35830054,358300540,109237,109237,44787560,False,Home,10,9,18.0,WALK,4478756,2 +35837593,358375930,109260,109260,44796990,True,work,5,10,11.0,WALK,4479699,1 +35837597,358375970,109260,109260,44796990,False,Home,10,5,15.0,WALK,4479699,1 +35839561,358395610,109266,109266,44799450,True,work,2,10,6.0,WALK_HVY,4479945,1 +35839565,358395650,109266,109266,44799450,False,Home,10,2,13.0,WALK_LRF,4479945,1 +35853993,358539930,109310,109310,44817490,True,work,13,10,7.0,WALK_LRF,4481749,1 +35853994,358539940,109310,109310,44817490,True,work,4,13,8.0,WALK,4481749,2 +35853997,358539970,109310,109310,44817490,False,social,11,4,17.0,WALK,4481749,1 +35853998,358539980,109310,109310,44817490,False,othmaint,7,11,17.0,WALK,4481749,2 +35853999,358539990,109310,109310,44817490,False,othmaint,9,7,20.0,WALK,4481749,3 +35854000,358540000,109310,109310,44817490,False,Home,10,9,21.0,WALK_LOC,4481749,4 +35854649,358546490,109312,109312,44818310,True,work,9,10,8.0,WALK,4481831,1 +35854653,358546530,109312,109312,44818310,False,Home,10,9,16.0,WALK,4481831,1 +35862193,358621930,109335,109335,44827740,True,work,7,10,7.0,WALK,4482774,1 +35862197,358621970,109335,109335,44827740,False,Home,10,7,17.0,WALK,4482774,1 +35874985,358749850,109374,109374,44843730,True,work,9,10,6.0,WALK,4484373,1 +35874989,358749890,109374,109374,44843730,False,Home,10,9,19.0,WALK,4484373,1 +35886465,358864650,109409,109409,44858080,True,work,1,10,12.0,WALK_LRF,4485808,1 +35886469,358864690,109409,109409,44858080,False,eatout,7,1,21.0,WALK_LOC,4485808,1 +35886470,358864700,109409,109409,44858080,False,Home,10,7,21.0,WALK_LRF,4485808,2 +35892369,358923690,109427,109427,44865460,True,work,15,10,7.0,WALK,4486546,1 +35892373,358923730,109427,109427,44865460,False,Home,10,15,18.0,WALK,4486546,1 +35893897,358938970,109432,109432,44867370,True,othdiscr,9,10,21.0,WALK,4486737,1 +35893901,358939010,109432,109432,44867370,False,Home,10,9,21.0,WALK,4486737,1 +35893961,358939610,109432,109432,44867450,True,shopping,12,10,17.0,WALK_LOC,4486745,1 +35893965,358939650,109432,109432,44867450,False,Home,10,12,19.0,WALK_LOC,4486745,1 +35893969,358939690,109432,109432,44867460,True,shopping,5,10,21.0,WALK_LOC,4486746,1 +35893973,358939730,109432,109432,44867460,False,Home,10,5,21.0,WALK_LOC,4486746,1 +35894009,358940090,109432,109432,44867510,True,work,1,10,7.0,WALK,4486751,1 +35894013,358940130,109432,109432,44867510,False,Home,10,1,16.0,WALK,4486751,1 +35904745,359047450,109465,109465,44880930,True,othmaint,12,10,8.0,WALK_LOC,4488093,1 +35904749,359047490,109465,109465,44880930,False,Home,10,12,16.0,WALK_LOC,4488093,1 +35904753,359047530,109465,109465,44880940,True,othmaint,1,10,20.0,WALK,4488094,1 +35904757,359047570,109465,109465,44880940,False,Home,10,1,20.0,WALK,4488094,1 +35910081,359100810,109481,109481,44887600,True,work,12,10,8.0,WALK,4488760,1 +35910085,359100850,109481,109481,44887600,False,Home,10,12,21.0,WALK,4488760,1 +35913081,359130810,109491,109491,44891350,True,atwork,13,11,11.0,WALK,4489135,1 +35913085,359130850,109491,109491,44891350,False,Work,11,13,11.0,WALK,4489135,1 +35913361,359133610,109491,109491,44891700,True,work,11,10,7.0,WALK,4489170,1 +35913365,359133650,109491,109491,44891700,False,Home,10,11,18.0,WALK,4489170,1 +35915281,359152810,109497,109497,44894100,True,shopping,5,10,13.0,BIKE,4489410,1 +35915285,359152850,109497,109497,44894100,False,Home,10,5,14.0,BIKE,4489410,1 +35920529,359205290,109513,109513,44900660,True,shopping,5,10,11.0,DRIVEALONEFREE,4490066,1 +35920533,359205330,109513,109513,44900660,False,escort,16,5,13.0,TNC_SHARED,4490066,1 +35920534,359205340,109513,109513,44900660,False,Home,10,16,13.0,DRIVEALONEFREE,4490066,2 +35920537,359205370,109513,109513,44900670,True,othmaint,9,10,13.0,WALK_LOC,4490067,1 +35920538,359205380,109513,109513,44900670,True,othmaint,13,9,13.0,TNC_SINGLE,4490067,2 +35920539,359205390,109513,109513,44900670,True,shopping,19,13,13.0,WALK_LOC,4490067,3 +35920541,359205410,109513,109513,44900670,False,Home,10,19,13.0,WALK_LOC,4490067,1 +35920577,359205770,109513,109513,44900720,True,work,20,10,14.0,WALK,4490072,1 +35920581,359205810,109513,109513,44900720,False,Home,10,20,21.0,WALK,4490072,1 +35922961,359229610,109521,109521,44903700,True,escort,9,10,15.0,WALK,4490370,1 +35922965,359229650,109521,109521,44903700,False,Home,10,9,16.0,WALK,4490370,1 +35923153,359231530,109521,109521,44903940,True,shopping,12,10,9.0,WALK,4490394,1 +35923157,359231570,109521,109521,44903940,False,Home,10,12,9.0,BIKE,4490394,1 +35925825,359258250,109529,109529,44907280,True,work,24,10,5.0,WALK_LRF,4490728,1 +35925829,359258290,109529,109529,44907280,False,Home,10,24,12.0,WALK,4490728,1 +35925833,359258330,109529,109529,44907290,True,work,24,10,13.0,WALK_LRF,4490729,1 +35925837,359258370,109529,109529,44907290,False,Home,10,24,17.0,WALK_LRF,4490729,1 +35925889,359258890,109530,109530,44907360,True,eatout,19,10,10.0,WALK,4490736,1 +35925893,359258930,109530,109530,44907360,False,Home,10,19,15.0,WALK,4490736,1 +35942553,359425530,109580,109580,44928190,True,othmaint,11,10,8.0,WALK,4492819,1 +35942554,359425540,109580,109580,44928190,True,work,14,11,9.0,WALK,4492819,2 +35942557,359425570,109580,109580,44928190,False,social,7,14,17.0,WALK,4492819,1 +35942558,359425580,109580,109580,44928190,False,Home,10,7,18.0,WALK,4492819,2 +35948017,359480170,109597,109597,44935020,True,othdiscr,20,10,10.0,WALK_LOC,4493502,1 +35948021,359480210,109597,109597,44935020,False,shopping,9,20,14.0,WALK_LOC,4493502,1 +35948022,359480220,109597,109597,44935020,False,Home,10,9,14.0,WALK,4493502,2 +35948345,359483450,109598,109598,44935430,True,othdiscr,22,10,5.0,WALK_LRF,4493543,1 +35948349,359483490,109598,109598,44935430,False,Home,10,22,13.0,WALK_LOC,4493543,1 +35948369,359483690,109598,109598,44935460,True,escort,7,10,15.0,TNC_SINGLE,4493546,1 +35948370,359483700,109598,109598,44935460,True,othmaint,19,7,15.0,TNC_SINGLE,4493546,2 +35948373,359483730,109598,109598,44935460,False,social,9,19,15.0,WALK_LOC,4493546,1 +35948374,359483740,109598,109598,44935460,False,Home,10,9,15.0,TNC_SHARED,4493546,2 +35955345,359553450,109619,109619,44944180,True,work,16,10,8.0,WALK_LOC,4494418,1 +35955349,359553490,109619,109619,44944180,False,Home,10,16,16.0,WALK_LOC,4494418,1 +35968793,359687930,109660,109660,44960990,True,work,13,10,10.0,WALK,4496099,1 +35968797,359687970,109660,109660,44960990,False,Home,10,13,20.0,WALK,4496099,1 +35976073,359760730,109683,109683,44970090,True,eatout,19,10,19.0,WALK,4497009,1 +35976077,359760770,109683,109683,44970090,False,Home,10,19,19.0,WALK,4497009,1 +35976337,359763370,109683,109683,44970420,True,work,1,10,6.0,WALK_LOC,4497042,1 +35976341,359763410,109683,109683,44970420,False,Home,10,1,18.0,WALK_LOC,4497042,1 +35992081,359920810,109731,109731,44990100,True,work,4,10,11.0,WALK_LRF,4499010,1 +35992082,359920820,109731,109731,44990100,True,work,23,4,11.0,WALK,4499010,2 +35992085,359920850,109731,109731,44990100,False,eatout,15,23,17.0,WALK,4499010,1 +35992086,359920860,109731,109731,44990100,False,Home,10,15,17.0,WALK_LRF,4499010,2 +36003561,360035610,109766,109766,45004450,True,work,7,10,8.0,WALK,4500445,1 +36003565,360035650,109766,109766,45004450,False,Home,10,7,17.0,WALK,4500445,1 +36005201,360052010,109771,109771,45006500,True,work,20,10,8.0,WALK,4500650,1 +36005205,360052050,109771,109771,45006500,False,Home,10,20,17.0,WALK_LOC,4500650,1 +36013729,360137290,109797,109797,45017160,True,eatout,24,10,9.0,WALK_LRF,4501716,1 +36013730,360137300,109797,109797,45017160,True,work,4,24,9.0,WALK,4501716,2 +36013733,360137330,109797,109797,45017160,False,Home,10,4,18.0,WALK_LRF,4501716,1 +36032097,360320970,109853,109853,45040120,True,work,1,11,5.0,WALK,4504012,1 +36032101,360321010,109853,109853,45040120,False,Home,11,1,19.0,WALK_LRF,4504012,1 +36071129,360711290,109972,109972,45088910,True,work,14,11,8.0,WALK,4508891,1 +36071133,360711330,109972,109972,45088910,False,Home,11,14,18.0,WALK,4508891,1 +36072441,360724410,109976,109976,45090550,True,work,5,11,7.0,WALK_LOC,4509055,1 +36072445,360724450,109976,109976,45090550,False,Home,11,5,19.0,WALK,4509055,1 +36075721,360757210,109986,109986,45094650,True,work,5,11,8.0,SHARED2FREE,4509465,1 +36075725,360757250,109986,109986,45094650,False,Home,11,5,17.0,WALK,4509465,1 +36089497,360894970,110028,110028,45111870,True,work,8,11,7.0,BIKE,4511187,1 +36089501,360895010,110028,110028,45111870,False,Home,11,8,13.0,BIKE,4511187,1 +36099753,360997530,110060,110060,45124690,True,escort,9,11,7.0,TNC_SINGLE,4512469,1 +36099757,360997570,110060,110060,45124690,False,shopping,1,9,8.0,TNC_SINGLE,4512469,1 +36099758,360997580,110060,110060,45124690,False,Home,11,1,8.0,WALK_LOC,4512469,2 +36101681,361016810,110066,110066,45127100,True,atwork,8,2,11.0,WALK,4512710,1 +36101685,361016850,110066,110066,45127100,False,Work,2,8,11.0,WALK,4512710,1 +36101961,361019610,110066,110066,45127450,True,work,2,11,6.0,DRIVEALONEFREE,4512745,1 +36101965,361019650,110066,110066,45127450,False,Home,11,2,17.0,WALK,4512745,1 +36104257,361042570,110073,110073,45130320,True,work,9,11,7.0,WALK,4513032,1 +36104258,361042580,110073,110073,45130320,True,work,11,9,8.0,WALK,4513032,2 +36104261,361042610,110073,110073,45130320,False,Home,11,11,17.0,WALK,4513032,1 +36149849,361498490,110212,110212,45187310,True,work,16,12,5.0,WALK,4518731,1 +36149853,361498530,110212,110212,45187310,False,Home,12,16,15.0,DRIVEALONEFREE,4518731,1 +36155425,361554250,110229,110229,45194280,True,escort,4,12,11.0,WALK,4519428,1 +36155426,361554260,110229,110229,45194280,True,escort,22,4,11.0,WALK,4519428,2 +36155427,361554270,110229,110229,45194280,True,work,24,22,11.0,WALK,4519428,3 +36155429,361554290,110229,110229,45194280,False,Home,12,24,20.0,WALK,4519428,1 +36164937,361649370,110258,110258,45206170,True,work,12,13,14.0,WALK_LOC,4520617,1 +36164938,361649380,110258,110258,45206170,True,work,2,12,14.0,TNC_SINGLE,4520617,2 +36164941,361649410,110258,110258,45206170,False,Home,13,2,23.0,TNC_SINGLE,4520617,1 +36172217,361722170,110281,110281,45215270,True,eatout,5,14,15.0,WALK,4521527,1 +36172221,361722210,110281,110281,45215270,False,Home,14,5,16.0,WALK,4521527,1 +36172481,361724810,110281,110281,45215600,True,work,13,14,7.0,WALK_LOC,4521560,1 +36172485,361724850,110281,110281,45215600,False,Home,14,13,14.0,WALK,4521560,1 +36179257,361792570,110302,110302,45224070,True,othdiscr,11,14,18.0,WALK,4522407,1 +36179261,361792610,110302,110302,45224070,False,Home,14,11,21.0,WALK,4522407,1 +36179369,361793690,110302,110302,45224210,True,work,13,14,5.0,WALK,4522421,1 +36179373,361793730,110302,110302,45224210,False,Home,14,13,18.0,WALK_LOC,4522421,1 +36184617,361846170,110318,110318,45230770,True,work,13,14,6.0,WALK,4523077,1 +36184621,361846210,110318,110318,45230770,False,Home,14,13,17.0,WALK,4523077,1 +36210201,362102010,110396,110396,45262750,True,work,1,16,6.0,WALK,4526275,1 +36210205,362102050,110396,110396,45262750,False,Home,16,1,18.0,WALK,4526275,1 +36211841,362118410,110401,110401,45264800,True,work,13,16,8.0,TNC_SINGLE,4526480,1 +36211845,362118450,110401,110401,45264800,False,Home,16,13,18.0,WALK,4526480,1 +36211905,362119050,110402,110402,45264880,True,eatout,16,16,12.0,SHARED2FREE,4526488,1 +36211909,362119090,110402,110402,45264880,False,Home,16,16,12.0,WALK,4526488,1 +36212081,362120810,110402,110402,45265100,True,othmaint,2,16,15.0,WALK,4526510,1 +36212085,362120850,110402,110402,45265100,False,Home,16,2,15.0,WALK,4526510,1 +36212497,362124970,110403,110403,45265620,True,work,14,16,7.0,WALK,4526562,1 +36212501,362125010,110403,110403,45265620,False,Home,16,14,16.0,WALK,4526562,1 +36230489,362304890,110458,110458,45288110,True,shopping,2,16,13.0,WALK_LOC,4528811,1 +36230493,362304930,110458,110458,45288110,False,Home,16,2,17.0,WALK_LOC,4528811,1 +36230513,362305130,110458,110458,45288140,True,social,21,16,9.0,WALK,4528814,1 +36230517,362305170,110458,110458,45288140,False,Home,16,21,11.0,WALK,4528814,1 +36230521,362305210,110458,110458,45288150,True,social,22,16,17.0,WALK,4528815,1 +36230525,362305250,110458,110458,45288150,False,Home,16,22,21.0,WALK,4528815,1 +36297777,362977770,110663,110663,45372220,True,work,24,16,8.0,WALK,4537222,1 +36297781,362977810,110663,110663,45372220,False,Home,16,24,17.0,WALK,4537222,1 +36305697,363056970,110688,110688,45382120,True,atwork,5,22,13.0,WALK,4538212,1 +36305701,363057010,110688,110688,45382120,False,Work,22,5,13.0,WALK,4538212,1 +36305977,363059770,110688,110688,45382470,True,othdiscr,16,16,13.0,DRIVEALONEFREE,4538247,1 +36305978,363059780,110688,110688,45382470,True,work,22,16,13.0,DRIVEALONEFREE,4538247,2 +36305981,363059810,110688,110688,45382470,False,Home,16,22,21.0,DRIVEALONEFREE,4538247,1 +36308929,363089290,110697,110697,45386160,True,work,14,16,14.0,WALK,4538616,1 +36308933,363089330,110697,110697,45386160,False,Home,16,14,21.0,WALK,4538616,1 +36313521,363135210,110711,110711,45391900,True,work,13,16,8.0,WALK,4539190,1 +36313525,363135250,110711,110711,45391900,False,Home,16,13,11.0,WALK,4539190,1 +36319753,363197530,110730,110730,45399690,True,work,2,16,7.0,WALK,4539969,1 +36319757,363197570,110730,110730,45399690,False,Home,16,2,17.0,WALK,4539969,1 +36323689,363236890,110742,110742,45404610,True,work,4,16,8.0,WALK,4540461,1 +36323693,363236930,110742,110742,45404610,False,Home,16,4,16.0,WALK,4540461,1 +36348201,363482010,110817,110817,45435250,True,othmaint,5,16,14.0,WALK,4543525,1 +36348205,363482050,110817,110817,45435250,False,Home,16,5,17.0,WALK,4543525,1 +36348241,363482410,110817,110817,45435300,True,shopping,16,16,13.0,DRIVEALONEFREE,4543530,1 +36348245,363482450,110817,110817,45435300,False,Home,16,16,14.0,SHARED3FREE,4543530,1 +36358017,363580170,110847,110847,45447520,True,othdiscr,20,16,10.0,WALK,4544752,1 +36358021,363580210,110847,110847,45447520,False,Home,16,20,12.0,WALK,4544752,1 +36358041,363580410,110847,110847,45447550,True,othmaint,9,16,7.0,WALK_LOC,4544755,1 +36358045,363580450,110847,110847,45447550,False,Home,16,9,10.0,WALK,4544755,1 +36358129,363581290,110847,110847,45447660,True,work,13,16,12.0,WALK,4544766,1 +36358133,363581330,110847,110847,45447660,False,othmaint,5,13,19.0,WALK,4544766,1 +36358134,363581340,110847,110847,45447660,False,Home,16,5,21.0,WALK,4544766,2 +36387977,363879770,110938,110938,45484970,True,work,16,16,6.0,WALK,4548497,1 +36387981,363879810,110938,110938,45484970,False,Home,16,16,15.0,WALK,4548497,1 +36390601,363906010,110946,110946,45488250,True,work,2,16,7.0,WALK,4548825,1 +36390605,363906050,110946,110946,45488250,False,Home,16,2,15.0,WALK,4548825,1 +36391913,363919130,110950,110950,45489890,True,work,11,16,8.0,WALK,4548989,1 +36391917,363919170,110950,110950,45489890,False,Home,16,11,18.0,WALK,4548989,1 +36412641,364126410,111014,111014,45515800,True,eatout,5,16,13.0,WALK,4551580,1 +36412645,364126450,111014,111014,45515800,False,Home,16,5,18.0,WALK,4551580,1 +36412857,364128570,111014,111014,45516070,True,shopping,11,16,10.0,WALK_LOC,4551607,1 +36412861,364128610,111014,111014,45516070,False,Home,16,11,13.0,WALK_LOC,4551607,1 +36415529,364155290,111022,111022,45519410,True,work,11,16,7.0,WALK,4551941,1 +36415533,364155330,111022,111022,45519410,False,Home,16,11,17.0,WALK,4551941,1 +36421433,364214330,111040,111040,45526790,True,work,10,16,7.0,WALK,4552679,1 +36421437,364214370,111040,111040,45526790,False,Home,16,10,17.0,TAXI,4552679,1 +36443737,364437370,111108,111108,45554670,True,work,12,16,8.0,WALK_LOC,4555467,1 +36443741,364437410,111108,111108,45554670,False,othmaint,5,12,18.0,WALK_LOC,4555467,1 +36443742,364437420,111108,111108,45554670,False,Home,16,5,19.0,WALK_LOC,4555467,2 +36461777,364617770,111163,111163,45577220,True,work,5,16,11.0,WALK_LOC,4557722,1 +36461781,364617810,111163,111163,45577220,False,othmaint,4,5,19.0,WALK,4557722,1 +36461782,364617820,111163,111163,45577220,False,Home,16,4,20.0,WALK_LOC,4557722,2 +36477849,364778490,111212,111212,45597310,True,work,12,16,8.0,BIKE,4559731,1 +36477853,364778530,111212,111212,45597310,False,Home,16,12,17.0,BIKE,4559731,1 +36480801,364808010,111221,111221,45601000,True,work,9,16,12.0,WALK_LRF,4560100,1 +36480805,364808050,111221,111221,45601000,False,othdiscr,5,9,18.0,WALK,4560100,1 +36480806,364808060,111221,111221,45601000,False,Home,16,5,21.0,WALK,4560100,2 +36497529,364975290,111272,111272,45621910,True,work,18,16,8.0,WALK_LOC,4562191,1 +36497533,364975330,111272,111272,45621910,False,Home,16,18,18.0,WALK_LOC,4562191,1 +36500697,365006970,111282,111282,45625870,True,othdiscr,4,16,10.0,WALK,4562587,1 +36500701,365007010,111282,111282,45625870,False,shopping,3,4,15.0,WALK_LOC,4562587,1 +36500702,365007020,111282,111282,45625870,False,Home,16,3,15.0,WALK_LOC,4562587,2 +36513273,365132730,111320,111320,45641590,True,work,22,16,7.0,WALK,4564159,1 +36513277,365132770,111320,111320,45641590,False,Home,16,22,22.0,WALK,4564159,1 +36518521,365185210,111336,111336,45648150,True,work,2,16,7.0,WALK_LOC,4564815,1 +36518525,365185250,111336,111336,45648150,False,Home,16,2,16.0,TNC_SINGLE,4564815,1 +36536233,365362330,111390,111390,45670290,True,work,1,16,7.0,WALK,4567029,1 +36536237,365362370,111390,111390,45670290,False,Home,16,1,17.0,WALK,4567029,1 +36540169,365401690,111402,111402,45675210,True,work,2,16,7.0,WALK,4567521,1 +36540173,365401730,111402,111402,45675210,False,Home,16,2,14.0,WALK,4567521,1 +36568049,365680490,111487,111487,45710060,True,work,12,16,6.0,WALK,4571006,1 +36568053,365680530,111487,111487,45710060,False,Home,16,12,14.0,WALK,4571006,1 +36584449,365844490,111537,111537,45730560,True,work,17,16,9.0,WALK,4573056,1 +36584453,365844530,111537,111537,45730560,False,shopping,16,17,13.0,WALK,4573056,1 +36584454,365844540,111537,111537,45730560,False,work,24,16,13.0,WALK,4573056,2 +36584455,365844550,111537,111537,45730560,False,Home,16,24,13.0,WALK,4573056,3 +36609705,366097050,111614,111614,45762130,True,work,1,16,14.0,WALK_LOC,4576213,1 +36609709,366097090,111614,111614,45762130,False,Home,16,1,21.0,WALK,4576213,1 +36613969,366139690,111627,111627,45767460,True,othmaint,11,16,13.0,WALK_LOC,4576746,1 +36613970,366139700,111627,111627,45767460,True,work,16,11,14.0,WALK,4576746,2 +36613973,366139730,111627,111627,45767460,False,shopping,1,16,15.0,WALK,4576746,1 +36613974,366139740,111627,111627,45767460,False,Home,16,1,22.0,TNC_SINGLE,4576746,2 +36624481,366244810,111660,111660,45780600,True,atwork,16,18,12.0,WALK,4578060,1 +36624485,366244850,111660,111660,45780600,False,othmaint,16,16,14.0,WALK,4578060,1 +36624486,366244860,111660,111660,45780600,False,shopping,16,16,14.0,WALK,4578060,2 +36624487,366244870,111660,111660,45780600,False,eatout,7,16,14.0,WALK,4578060,3 +36624488,366244880,111660,111660,45780600,False,Work,18,7,14.0,WALK,4578060,4 +36624745,366247450,111660,111660,45780930,True,shopping,16,16,19.0,BIKE,4578093,1 +36624749,366247490,111660,111660,45780930,False,Home,16,16,19.0,BIKE,4578093,1 +36624793,366247930,111660,111660,45780990,True,work,18,16,6.0,WALK_LRF,4578099,1 +36624797,366247970,111660,111660,45780990,False,Home,16,18,17.0,WALK_LRF,4578099,1 +36637649,366376490,111700,111700,45797060,True,eatout,12,16,16.0,WALK,4579706,1 +36637653,366376530,111700,111700,45797060,False,Home,16,12,20.0,WALK,4579706,1 +36637865,366378650,111700,111700,45797330,True,shopping,14,16,11.0,WALK_LOC,4579733,1 +36637869,366378690,111700,111700,45797330,False,Home,16,14,14.0,WALK_LOC,4579733,1 +36642897,366428970,111716,111716,45803620,True,eatout,16,16,16.0,WALK,4580362,1 +36642901,366429010,111716,111716,45803620,False,Home,16,16,21.0,WALK,4580362,1 +36643049,366430490,111716,111716,45803810,True,othdiscr,16,16,11.0,WALK,4580381,1 +36643053,366430530,111716,111716,45803810,False,Home,16,16,16.0,WALK,4580381,1 +36643161,366431610,111716,111716,45803950,True,work,12,16,7.0,DRIVEALONEFREE,4580395,1 +36643165,366431650,111716,111716,45803950,False,Home,16,12,11.0,DRIVEALONEFREE,4580395,1 +36656937,366569370,111758,111758,45821170,True,work,16,16,7.0,WALK,4582117,1 +36656941,366569410,111758,111758,45821170,False,Home,16,16,16.0,WALK,4582117,1 +36673665,366736650,111809,111809,45842080,True,work,4,16,7.0,WALK,4584208,1 +36673669,366736690,111809,111809,45842080,False,Home,16,4,17.0,WALK,4584208,1 +36680113,366801130,111829,111829,45850140,True,othdiscr,11,16,17.0,DRIVEALONEFREE,4585014,1 +36680117,366801170,111829,111829,45850140,False,Home,16,11,17.0,TNC_SHARED,4585014,1 +36680225,366802250,111829,111829,45850280,True,work,14,16,6.0,WALK,4585028,1 +36680229,366802290,111829,111829,45850280,False,Home,16,14,16.0,WALK,4585028,1 +36684161,366841610,111841,111841,45855200,True,work,11,16,6.0,WALK,4585520,1 +36684165,366841650,111841,111841,45855200,False,Home,16,11,17.0,WALK,4585520,1 +36715321,367153210,111936,111936,45894150,True,work,7,16,7.0,WALK_LOC,4589415,1 +36715325,367153250,111936,111936,45894150,False,Home,16,7,17.0,WALK_LOC,4589415,1 +36721769,367217690,111956,111956,45902210,True,othdiscr,4,16,7.0,WALK,4590221,1 +36721773,367217730,111956,111956,45902210,False,Home,16,4,11.0,WALK,4590221,1 +36721833,367218330,111956,111956,45902290,True,shopping,11,16,12.0,WALK,4590229,1 +36721837,367218370,111956,111956,45902290,False,Home,16,11,13.0,WALK,4590229,1 +36727177,367271770,111973,111973,45908970,True,atwork,12,10,10.0,SHARED2FREE,4590897,1 +36727181,367271810,111973,111973,45908970,False,Work,10,12,10.0,WALK,4590897,1 +36727457,367274570,111973,111973,45909320,True,escort,12,16,8.0,DRIVEALONEFREE,4590932,1 +36727458,367274580,111973,111973,45909320,True,eatout,5,12,8.0,WALK,4590932,2 +36727459,367274590,111973,111973,45909320,True,escort,11,5,8.0,SHARED2FREE,4590932,3 +36727460,367274600,111973,111973,45909320,True,work,10,11,9.0,WALK,4590932,4 +36727461,367274610,111973,111973,45909320,False,Home,16,10,18.0,SHARED2FREE,4590932,1 +36730473,367304730,111983,111983,45913090,True,eatout,2,16,14.0,WALK,4591309,1 +36730477,367304770,111983,111983,45913090,False,Home,16,2,16.0,WALK,4591309,1 +36730625,367306250,111983,111983,45913280,True,othdiscr,16,16,14.0,WALK,4591328,1 +36730629,367306290,111983,111983,45913280,False,Home,16,16,14.0,WALK,4591328,1 +36730689,367306890,111983,111983,45913360,True,shopping,11,16,18.0,WALK,4591336,1 +36730693,367306930,111983,111983,45913360,False,Home,16,11,20.0,WALK,4591336,1 +36733641,367336410,111992,111992,45917050,True,shopping,19,16,10.0,WALK_LOC,4591705,1 +36733645,367336450,111992,111992,45917050,False,Home,16,19,15.0,WALK_LOC,4591705,1 +36740905,367409050,112014,112014,45926130,True,work,14,16,7.0,WALK,4592613,1 +36740909,367409090,112014,112014,45926130,False,Home,16,14,17.0,WALK,4592613,1 +36744401,367444010,112025,112025,45930500,True,othdiscr,25,16,10.0,WALK,4593050,1 +36744405,367444050,112025,112025,45930500,False,Home,16,25,12.0,WALK,4593050,1 +36744425,367444250,112025,112025,45930530,True,othmaint,16,16,15.0,WALK,4593053,1 +36744429,367444290,112025,112025,45930530,False,Home,16,16,16.0,WALK,4593053,1 +36757193,367571930,112064,112064,45946490,True,othdiscr,21,16,12.0,DRIVEALONEFREE,4594649,1 +36757197,367571970,112064,112064,45946490,False,Home,16,21,14.0,TNC_SHARED,4594649,1 +36757257,367572570,112064,112064,45946570,True,shopping,3,16,16.0,WALK,4594657,1 +36757261,367572610,112064,112064,45946570,False,Home,16,3,17.0,WALK,4594657,1 +36762505,367625050,112080,112080,45953130,True,shopping,4,16,21.0,DRIVEALONEFREE,4595313,1 +36762509,367625090,112080,112080,45953130,False,Home,16,4,22.0,DRIVEALONEFREE,4595313,1 +36762553,367625530,112080,112080,45953190,True,work,14,16,6.0,WALK,4595319,1 +36762557,367625570,112080,112080,45953190,False,Home,16,14,16.0,WALK,4595319,1 +36767145,367671450,112094,112094,45958930,True,work,14,16,11.0,WALK,4595893,1 +36767149,367671490,112094,112094,45958930,False,Home,16,14,20.0,WALK,4595893,1 +36777073,367770730,112125,112125,45971340,True,shopping,16,16,7.0,SHARED2FREE,4597134,1 +36777074,367770740,112125,112125,45971340,True,eatout,3,16,7.0,WALK,4597134,2 +36777075,367770750,112125,112125,45971340,True,escort,25,3,7.0,WALK,4597134,3 +36777077,367770770,112125,112125,45971340,False,Home,16,25,7.0,SHARED2FREE,4597134,1 +36777313,367773130,112125,112125,45971640,True,work,16,16,7.0,WALK,4597164,1 +36777317,367773170,112125,112125,45971640,False,Home,16,16,18.0,WALK,4597164,1 +36782561,367825610,112141,112141,45978200,True,work,12,16,10.0,WALK_LOC,4597820,1 +36782565,367825650,112141,112141,45978200,False,Home,16,12,17.0,WALK,4597820,1 +36783217,367832170,112143,112143,45979020,True,work,16,16,13.0,WALK,4597902,1 +36783218,367832180,112143,112143,45979020,True,work,1,16,14.0,WALK_LOC,4597902,2 +36783221,367832210,112143,112143,45979020,False,shopping,13,1,21.0,WALK,4597902,1 +36783222,367832220,112143,112143,45979020,False,Home,16,13,22.0,WALK,4597902,2 +36800321,368003210,112196,112196,46000400,True,atwork,13,19,12.0,WALK,4600040,1 +36800325,368003250,112196,112196,46000400,False,Work,19,13,13.0,WALK,4600040,1 +36800489,368004890,112196,112196,46000610,True,othdiscr,11,16,20.0,DRIVEALONEFREE,4600061,1 +36800493,368004930,112196,112196,46000610,False,eatout,2,11,20.0,SHARED3FREE,4600061,1 +36800494,368004940,112196,112196,46000610,False,shopping,8,2,20.0,WALK,4600061,2 +36800495,368004950,112196,112196,46000610,False,Home,16,8,20.0,WALK,4600061,3 +36800601,368006010,112196,112196,46000750,True,escort,4,16,8.0,WALK,4600075,1 +36800602,368006020,112196,112196,46000750,True,escort,9,4,9.0,WALK,4600075,2 +36800603,368006030,112196,112196,46000750,True,eatout,5,9,9.0,WALK_LOC,4600075,3 +36800604,368006040,112196,112196,46000750,True,work,19,5,10.0,SHARED3FREE,4600075,4 +36800605,368006050,112196,112196,46000750,False,Home,16,19,19.0,WALK_LOC,4600075,1 +36814377,368143770,112238,112238,46017970,True,work,16,16,7.0,WALK,4601797,1 +36814381,368143810,112238,112238,46017970,False,Home,16,16,22.0,WALK,4601797,1 +36835305,368353050,112302,112302,46044130,True,univ,12,17,18.0,WALK,4604413,1 +36835309,368353090,112302,112302,46044130,False,othmaint,21,12,21.0,WALK,4604413,1 +36835310,368353100,112302,112302,46044130,False,Home,17,21,21.0,WALK,4604413,2 +36835369,368353690,112302,112302,46044210,True,work,7,17,14.0,WALK,4604421,1 +36835373,368353730,112302,112302,46044210,False,work,22,7,15.0,DRIVEALONEFREE,4604421,1 +36835374,368353740,112302,112302,46044210,False,escort,17,22,17.0,DRIVEALONEFREE,4604421,2 +36835375,368353750,112302,112302,46044210,False,Home,17,17,17.0,WALK,4604421,3 +36836353,368363530,112305,112305,46045440,True,work,4,17,8.0,WALK_LOC,4604544,1 +36836357,368363570,112305,112305,46045440,False,Home,17,4,17.0,WALK,4604544,1 +36842257,368422570,112323,112323,46052820,True,work,22,17,8.0,WALK_LRF,4605282,1 +36842261,368422610,112323,112323,46052820,False,Home,17,22,18.0,WALK_LRF,4605282,1 +36847833,368478330,112340,112340,46059790,True,work,5,17,8.0,WALK_LOC,4605979,1 +36847837,368478370,112340,112340,46059790,False,Home,17,5,17.0,WALK_LRF,4605979,1 +36849361,368493610,112345,112345,46061700,True,othdiscr,22,17,11.0,WALK,4606170,1 +36849365,368493650,112345,112345,46061700,False,Home,17,22,17.0,WALK,4606170,1 +36850457,368504570,112348,112348,46063070,True,work,2,17,7.0,WALK_LOC,4606307,1 +36850461,368504610,112348,112348,46063070,False,Home,17,2,16.0,WALK_LRF,4606307,1 +36892657,368926570,112477,112477,46115820,True,othdiscr,21,17,18.0,WALK_LOC,4611582,1 +36892661,368926610,112477,112477,46115820,False,Home,17,21,23.0,WALK_LOC,4611582,1 +36892681,368926810,112477,112477,46115850,True,othmaint,5,17,9.0,WALK_LOC,4611585,1 +36892685,368926850,112477,112477,46115850,False,Home,17,5,10.0,WALK,4611585,1 +36892721,368927210,112477,112477,46115900,True,shopping,9,17,12.0,WALK_LRF,4611590,1 +36892722,368927220,112477,112477,46115900,True,shopping,11,9,12.0,WALK_LOC,4611590,2 +36892725,368927250,112477,112477,46115900,False,shopping,8,11,14.0,WALK_LOC,4611590,1 +36892726,368927260,112477,112477,46115900,False,othmaint,5,8,15.0,WALK_LOC,4611590,2 +36892727,368927270,112477,112477,46115900,False,Home,17,5,15.0,WALK_LRF,4611590,3 +36896769,368967690,112490,112490,46120960,True,eatout,16,17,17.0,WALK,4612096,1 +36896773,368967730,112490,112490,46120960,False,Home,17,16,17.0,WALK,4612096,1 +36896945,368969450,112490,112490,46121180,True,othmaint,11,17,20.0,SHARED2FREE,4612118,1 +36896949,368969490,112490,112490,46121180,False,Home,17,11,22.0,TNC_SINGLE,4612118,1 +36897033,368970330,112490,112490,46121290,True,work,12,17,6.0,WALK_LOC,4612129,1 +36897037,368970370,112490,112490,46121290,False,Home,17,12,17.0,WALK_LRF,4612129,1 +36899329,368993290,112497,112497,46124160,True,work,17,17,7.0,WALK,4612416,1 +36899333,368993330,112497,112497,46124160,False,Home,17,17,17.0,WALK,4612416,1 +36900921,369009210,112502,112502,46126150,True,shopping,13,17,10.0,WALK,4612615,1 +36900925,369009250,112502,112502,46126150,False,Home,17,13,11.0,WALK,4612615,1 +36900929,369009290,112502,112502,46126160,True,shopping,17,17,11.0,WALK,4612616,1 +36900933,369009330,112502,112502,46126160,False,Home,17,17,13.0,WALK,4612616,1 +36906545,369065450,112519,112519,46133180,True,work,16,17,7.0,WALK,4613318,1 +36906549,369065490,112519,112519,46133180,False,Home,17,16,14.0,WALK_LRF,4613318,1 +36913761,369137610,112541,112541,46142200,True,work,22,17,6.0,WALK,4614220,1 +36913765,369137650,112541,112541,46142200,False,Home,17,22,16.0,WALK,4614220,1 +36916009,369160090,112548,112548,46145010,True,shopping,16,17,11.0,WALK,4614501,1 +36916013,369160130,112548,112548,46145010,False,Home,17,16,14.0,WALK,4614501,1 +36920209,369202090,112561,112561,46150260,True,othdiscr,9,17,17.0,TNC_SINGLE,4615026,1 +36920213,369202130,112561,112561,46150260,False,Home,17,9,17.0,DRIVEALONEFREE,4615026,1 +36920273,369202730,112561,112561,46150340,True,othmaint,9,17,16.0,WALK_LRF,4615034,1 +36920274,369202740,112561,112561,46150340,True,shopping,16,9,16.0,WALK_LRF,4615034,2 +36920277,369202770,112561,112561,46150340,False,Home,17,16,16.0,WALK_LRF,4615034,1 +36920281,369202810,112561,112561,46150350,True,shopping,16,17,18.0,WALK,4615035,1 +36920285,369202850,112561,112561,46150350,False,Home,17,16,19.0,WALK,4615035,1 +36920321,369203210,112561,112561,46150400,True,work,5,17,6.0,WALK,4615040,1 +36920325,369203250,112561,112561,46150400,False,Home,17,5,15.0,WALK_LRF,4615040,1 +36924913,369249130,112575,112575,46156140,True,work,2,17,11.0,WALK,4615614,1 +36924917,369249170,112575,112575,46156140,False,Home,17,2,17.0,WALK,4615614,1 +36927585,369275850,112584,112584,46159480,True,atwork,16,1,12.0,WALK,4615948,1 +36927589,369275890,112584,112584,46159480,False,shopping,25,16,18.0,WALK,4615948,1 +36927590,369275900,112584,112584,46159480,False,Work,1,25,18.0,WALK,4615948,2 +36927865,369278650,112584,112584,46159830,True,work,14,17,11.0,WALK,4615983,1 +36927866,369278660,112584,112584,46159830,True,eatout,16,14,11.0,WALK,4615983,2 +36927867,369278670,112584,112584,46159830,True,work,1,16,12.0,WALK,4615983,3 +36927869,369278690,112584,112584,46159830,False,othdiscr,17,1,18.0,WALK,4615983,1 +36927870,369278700,112584,112584,46159830,False,Home,17,17,19.0,WALK,4615983,2 +36965633,369656330,112700,112700,46207040,True,atwork,10,15,11.0,SHARED3FREE,4620704,1 +36965637,369656370,112700,112700,46207040,False,Work,15,10,11.0,SHARED3FREE,4620704,1 +36965913,369659130,112700,112700,46207390,True,work,15,17,7.0,WALK_LRF,4620739,1 +36965917,369659170,112700,112700,46207390,False,Home,17,15,21.0,WALK,4620739,1 +36984017,369840170,112756,112756,46230020,True,eatout,18,19,15.0,WALK,4623002,1 +36984021,369840210,112756,112756,46230020,False,Home,19,18,19.0,WALK,4623002,1 +36984233,369842330,112756,112756,46230290,True,shopping,11,19,13.0,WALK,4623029,1 +36984237,369842370,112756,112756,46230290,False,escort,9,11,13.0,WALK,4623029,1 +36984238,369842380,112756,112756,46230290,False,Home,19,9,14.0,WALK_LOC,4623029,2 +36989201,369892010,112771,112771,46236500,True,work,21,19,7.0,DRIVEALONEFREE,4623650,1 +36989205,369892050,112771,112771,46236500,False,Home,19,21,12.0,DRIVEALONEFREE,4623650,1 +36990401,369904010,112775,112775,46238000,True,othdiscr,9,19,7.0,WALK_LOC,4623800,1 +36990405,369904050,112775,112775,46238000,False,Home,19,9,10.0,TNC_SINGLE,4623800,1 +36990513,369905130,112775,112775,46238140,True,othdiscr,7,19,11.0,TNC_SINGLE,4623814,1 +36990514,369905140,112775,112775,46238140,True,work,13,7,12.0,TNC_SINGLE,4623814,2 +36990517,369905170,112775,112775,46238140,False,othmaint,5,13,18.0,TNC_SINGLE,4623814,1 +36990518,369905180,112775,112775,46238140,False,Home,19,5,18.0,WALK_LOC,4623814,2 +37006017,370060170,112823,112823,46257520,True,escort,12,19,15.0,SHARED2FREE,4625752,1 +37006021,370060210,112823,112823,46257520,False,shopping,17,12,16.0,DRIVEALONEFREE,4625752,1 +37006022,370060220,112823,112823,46257520,False,Home,19,17,16.0,DRIVEALONEFREE,4625752,2 +37006209,370062090,112823,112823,46257760,True,shopping,2,19,9.0,WALK_LOC,4625776,1 +37006213,370062130,112823,112823,46257760,False,Home,19,2,13.0,WALK_LOC,4625776,1 +37016425,370164250,112854,112854,46270530,True,work,16,19,9.0,WALK,4627053,1 +37016429,370164290,112854,112854,46270530,False,shopping,11,16,17.0,WALK,4627053,1 +37016430,370164300,112854,112854,46270530,False,Home,19,11,18.0,WALK,4627053,2 +37035121,370351210,112911,112911,46293900,True,work,9,19,17.0,WALK_LOC,4629390,1 +37035125,370351250,112911,112911,46293900,False,othmaint,9,9,22.0,WALK,4629390,1 +37035126,370351260,112911,112911,46293900,False,Home,19,9,22.0,WALK,4629390,2 +37044961,370449610,112941,112941,46306200,True,work,22,20,5.0,WALK_LRF,4630620,1 +37044965,370449650,112941,112941,46306200,False,Home,20,22,14.0,WALK_LRF,4630620,1 +37050777,370507770,112959,112959,46313470,True,othmaint,4,20,15.0,WALK_LRF,4631347,1 +37050781,370507810,112959,112959,46313470,False,Home,20,4,17.0,WALK_LRF,4631347,1 +37050865,370508650,112959,112959,46313580,True,work,21,20,8.0,WALK,4631358,1 +37050869,370508690,112959,112959,46313580,False,Home,20,21,15.0,WALK,4631358,1 +37060377,370603770,112988,112988,46325470,True,work,16,21,9.0,WALK,4632547,1 +37060381,370603810,112988,112988,46325470,False,Home,21,16,18.0,WALK,4632547,1 +37065297,370652970,113003,113003,46331620,True,escort,4,21,6.0,WALK_LOC,4633162,1 +37065298,370652980,113003,113003,46331620,True,escort,3,4,7.0,WALK,4633162,2 +37065299,370652990,113003,113003,46331620,True,work,2,3,7.0,WALK,4633162,3 +37065301,370653010,113003,113003,46331620,False,Home,21,2,17.0,WALK,4633162,1 +37073217,370732170,113028,113028,46341520,True,atwork,2,4,10.0,WALK,4634152,1 +37073221,370732210,113028,113028,46341520,False,shopping,6,2,16.0,WALK,4634152,1 +37073222,370732220,113028,113028,46341520,False,Work,4,6,16.0,WALK,4634152,2 +37073497,370734970,113028,113028,46341870,True,work,4,21,7.0,WALK,4634187,1 +37073501,370735010,113028,113028,46341870,False,Home,21,4,16.0,WALK,4634187,1 +37085633,370856330,113065,113065,46357040,True,work,14,22,12.0,WALK,4635704,1 +37085637,370856370,113065,113065,46357040,False,Home,22,14,21.0,WALK,4635704,1 +37087553,370875530,113071,113071,46359440,True,shopping,16,23,14.0,WALK,4635944,1 +37087557,370875570,113071,113071,46359440,False,eatout,16,16,14.0,WALK,4635944,1 +37087558,370875580,113071,113071,46359440,False,othdiscr,16,16,14.0,WALK,4635944,2 +37087559,370875590,113071,113071,46359440,False,othmaint,25,16,14.0,WALK,4635944,3 +37087560,370875600,113071,113071,46359440,False,Home,23,25,14.0,WALK,4635944,4 +37087577,370875770,113071,113071,46359470,True,social,7,23,19.0,WALK,4635947,1 +37087581,370875810,113071,113071,46359470,False,Home,23,7,20.0,WALK_LRF,4635947,1 +37103673,371036730,113120,113120,46379590,True,work,13,23,6.0,WALK_LRF,4637959,1 +37103677,371036770,113120,113120,46379590,False,Home,23,13,20.0,WALK_LRF,4637959,1 +37115809,371158090,113157,113157,46394760,True,work,7,24,6.0,WALK_LRF,4639476,1 +37115813,371158130,113157,113157,46394760,False,Home,24,7,21.0,WALK,4639476,1 +37116417,371164170,113159,113159,46395520,True,shopping,12,25,13.0,WALK_LOC,4639552,1 +37116421,371164210,113159,113159,46395520,False,Home,25,12,15.0,WALK,4639552,1 +69634337,696343370,212299,200965,87042920,True,shopping,2,6,10.0,WALK,8704292,1 +69634341,696343410,212299,200965,87042920,False,Home,6,2,11.0,WALK,8704292,1 +69634449,696344490,212300,200965,87043060,True,eatout,5,6,20.0,WALK,8704306,1 +69634453,696344530,212300,200965,87043060,False,Home,6,5,21.0,WALK,8704306,1 +69634625,696346250,212300,200965,87043280,True,othmaint,11,6,9.0,WALK,8704328,1 +69634629,696346290,212300,200965,87043280,False,Home,6,11,20.0,BIKE,8704328,1 +69659905,696599050,212377,201004,87074880,True,univ,9,7,21.0,WALK_LRF,8707488,1 +69659909,696599090,212377,201004,87074880,False,Home,7,9,21.0,WALK_LRF,8707488,1 +69660233,696602330,212378,201004,87075290,True,univ,9,7,20.0,WALK_LOC,8707529,1 +69660237,696602370,212378,201004,87075290,False,work,22,9,21.0,WALK_LRF,8707529,1 +69660238,696602380,212378,201004,87075290,False,shopping,4,22,22.0,WALK_LRF,8707529,2 +69660239,696602390,212378,201004,87075290,False,Home,7,4,22.0,WALK_LOC,8707529,3 +69663841,696638410,212389,201010,87079800,True,univ,12,8,18.0,WALK_LOC,8707980,1 +69663845,696638450,212389,201010,87079800,False,othmaint,21,12,19.0,WALK_LOC,8707980,1 +69663846,696638460,212389,201010,87079800,False,Home,8,21,19.0,WALK_LOC,8707980,2 +69664169,696641690,212390,201010,87080210,True,univ,13,8,15.0,WALK_LOC,8708021,1 +69664173,696641730,212390,201010,87080210,False,social,4,13,15.0,WALK_LOC,8708021,1 +69664174,696641740,212390,201010,87080210,False,Home,8,4,16.0,WALK_LRF,8708021,2 +69716321,697163210,212549,201090,87145400,True,univ,13,8,14.0,WALK_LOC,8714540,1 +69716325,697163250,212549,201090,87145400,False,Home,8,13,15.0,WALK_LRF,8714540,1 +69716649,697166490,212550,201090,87145810,True,univ,13,8,7.0,WALK_LOC,8714581,1 +69716653,697166530,212550,201090,87145810,False,othdiscr,9,13,16.0,WALK_LRF,8714581,1 +69716654,697166540,212550,201090,87145810,False,Home,8,9,16.0,WALK_LOC,8714581,2 +69742561,697425610,212629,201130,87178200,True,othmaint,7,8,14.0,WALK_LOC,8717820,1 +69742562,697425620,212629,201130,87178200,True,escort,11,7,15.0,WALK,8717820,2 +69742563,697425630,212629,201130,87178200,True,univ,9,11,16.0,WALK,8717820,3 +69742565,697425650,212629,201130,87178200,False,Home,8,9,17.0,WALK_LOC,8717820,1 +69742889,697428890,212630,201130,87178610,True,univ,12,8,13.0,WALK_LOC,8717861,1 +69742893,697428930,212630,201130,87178610,False,othmaint,7,12,21.0,WALK_LOC,8717861,1 +69742894,697428940,212630,201130,87178610,False,Home,8,7,21.0,WALK,8717861,2 +69760929,697609290,212685,201158,87201160,True,univ,12,8,19.0,WALK,8720116,1 +69760933,697609330,212685,201158,87201160,False,univ,13,12,21.0,WALK,8720116,1 +69760934,697609340,212685,201158,87201160,False,escort,7,13,22.0,WALK,8720116,2 +69760935,697609350,212685,201158,87201160,False,othmaint,7,7,23.0,WALK,8720116,3 +69760936,697609360,212685,201158,87201160,False,Home,8,7,23.0,WALK,8720116,4 +69761273,697612730,212686,201158,87201590,True,social,8,8,19.0,WALK,8720159,1 +69761274,697612740,212686,201158,87201590,True,shopping,11,8,19.0,WALK_LOC,8720159,2 +69761277,697612770,212686,201158,87201590,False,Home,8,11,19.0,WALK,8720159,1 +69779953,697799530,212743,201187,87224940,True,univ,9,9,12.0,WALK,8722494,1 +69779957,697799570,212743,201187,87224940,False,Home,9,9,17.0,WALK,8722494,1 +69780281,697802810,212744,201187,87225350,True,othmaint,16,9,16.0,WALK_LRF,8722535,1 +69780282,697802820,212744,201187,87225350,True,univ,12,16,17.0,WALK_LOC,8722535,2 +69780285,697802850,212744,201187,87225350,False,social,2,12,20.0,WALK,8722535,1 +69780286,697802860,212744,201187,87225350,False,othmaint,1,2,20.0,WALK,8722535,2 +69780287,697802870,212744,201187,87225350,False,Home,9,1,20.0,WALK_LRF,8722535,3 +69789417,697894170,212772,201201,87236770,True,othdiscr,3,9,8.0,WALK_LRF,8723677,1 +69789421,697894210,212772,201201,87236770,False,shopping,8,3,17.0,WALK,8723677,1 +69789422,697894220,212772,201201,87236770,False,Home,9,8,17.0,WALK_LOC,8723677,2 +69803521,698035210,212815,201223,87254400,True,othdiscr,10,10,7.0,BIKE,8725440,1 +69803525,698035250,212815,201223,87254400,False,Home,10,10,11.0,BIKE,8725440,1 +69803873,698038730,212816,201223,87254840,True,othmaint,12,10,14.0,WALK,8725484,1 +69803877,698038770,212816,201223,87254840,False,Home,10,12,15.0,WALK,8725484,1 +69803913,698039130,212816,201223,87254890,True,shopping,18,10,15.0,DRIVEALONEFREE,8725489,1 +69803917,698039170,212816,201223,87254890,False,Home,10,18,16.0,DRIVEALONEFREE,8725489,1 +69832945,698329450,212905,201268,87291180,True,eatout,9,10,10.0,TNC_SINGLE,8729118,1 +69832949,698329490,212905,201268,87291180,False,othmaint,9,9,20.0,WALK,8729118,1 +69832950,698329500,212905,201268,87291180,False,Home,10,9,21.0,WALK,8729118,2 +69873057,698730570,213027,201329,87341320,True,othdiscr,9,13,9.0,WALK_LRF,8734132,1 +69873061,698730610,213027,201329,87341320,False,Home,13,9,17.0,WALK_LRF,8734132,1 +69873257,698732570,213028,201329,87341570,True,escort,24,13,15.0,WALK,8734157,1 +69873261,698732610,213028,201329,87341570,False,Home,13,24,20.0,WALK,8734157,1 +69899385,698993850,213107,201369,87374230,True,social,20,21,11.0,WALK,8737423,1 +69899389,698993890,213107,201369,87374230,False,Home,21,20,13.0,WALK,8737423,1 +69899689,698996890,213108,201369,87374610,True,shopping,18,21,8.0,WALK,8737461,1 +69899693,698996930,213108,201369,87374610,False,Home,21,18,10.0,WALK,8737461,1 +69900017,699000170,213109,201370,87375020,True,shopping,16,21,15.0,WALK,8737502,1 +69900018,699000180,213109,201370,87375020,True,shopping,16,16,15.0,WALK,8737502,2 +69900021,699000210,213109,201370,87375020,False,Home,21,16,22.0,WALK,8737502,1 +69900041,699000410,213109,201370,87375050,True,social,21,21,11.0,WALK,8737505,1 +69900045,699000450,213109,201370,87375050,False,Home,21,21,11.0,WALK,8737505,1 +69906865,699068650,213130,201380,87383580,True,othmaint,17,25,12.0,WALK_LRF,8738358,1 +69906869,699068690,213130,201380,87383580,False,Home,25,17,17.0,WALK_LOC,8738358,1 +86565745,865657450,263919,226775,108207180,True,work,1,6,8.0,WALK,10820718,1 +86565749,865657490,263919,226775,108207180,False,Home,6,1,19.0,WALK,10820718,1 +86566009,865660090,263920,226775,108207510,True,univ,9,6,21.0,WALK_LRF,10820751,1 +86566013,865660130,263920,226775,108207510,False,escort,12,9,21.0,WALK_LRF,10820751,1 +86566014,865660140,263920,226775,108207510,False,othmaint,9,12,21.0,WALK_LRF,10820751,2 +86566015,865660150,263920,226775,108207510,False,othmaint,17,9,21.0,WALK_LRF,10820751,3 +86566016,865660160,263920,226775,108207510,False,Home,6,17,21.0,WALK_LRF,10820751,4 +86572961,865729610,263941,226786,108216200,True,work,2,7,6.0,TNC_SHARED,10821620,1 +86572965,865729650,263941,226786,108216200,False,Home,7,2,17.0,WALK_LOC,10821620,1 +86573225,865732250,263942,226786,108216530,True,univ,9,7,14.0,WALK,10821653,1 +86573229,865732290,263942,226786,108216530,False,escort,6,9,21.0,WALK_LRF,10821653,1 +86573230,865732300,263942,226786,108216530,False,Home,7,6,23.0,WALK_LOC,10821653,2 +86581985,865819850,263969,226800,108227480,True,escort,7,8,8.0,TNC_SHARED,10822748,1 +86581986,865819860,263969,226800,108227480,True,shopping,5,7,8.0,WALK,10822748,2 +86581987,865819870,263969,226800,108227480,True,shopping,5,5,8.0,TAXI,10822748,3 +86581989,865819890,263969,226800,108227480,False,Home,8,5,8.0,TNC_SHARED,10822748,1 +86582473,865824730,263970,226800,108228090,True,othdiscr,13,8,8.0,WALK_LOC,10822809,1 +86582474,865824740,263970,226800,108228090,True,work,1,13,8.0,WALK,10822809,2 +86582477,865824770,263970,226800,108228090,False,Home,8,1,18.0,TNC_SHARED,10822809,1 +86589233,865892330,263991,226811,108236540,True,othmaint,7,12,10.0,WALK,10823654,1 +86589234,865892340,263991,226811,108236540,True,atwork,4,7,10.0,WALK,10823654,2 +86589237,865892370,263991,226811,108236540,False,Work,12,4,11.0,WALK,10823654,1 +86589361,865893610,263991,226811,108236700,True,escort,8,8,8.0,WALK,10823670,1 +86589362,865893620,263991,226811,108236700,True,work,12,8,9.0,WALK_LOC,10823670,2 +86589365,865893650,263991,226811,108236700,False,shopping,2,12,20.0,WALK,10823670,1 +86589366,865893660,263991,226811,108236700,False,Home,8,2,20.0,WALK_LOC,10823670,2 +86596297,865962970,264013,226822,108245370,True,atwork,5,14,11.0,WALK,10824537,1 +86596301,865963010,264013,226822,108245370,False,Work,14,5,11.0,WALK,10824537,1 +86596577,865965770,264013,226822,108245720,True,work,14,9,7.0,WALK_LRF,10824572,1 +86596581,865965810,264013,226822,108245720,False,Home,9,14,17.0,WALK_LRF,10824572,1 +86596817,865968170,264014,226822,108246020,True,othmaint,4,9,9.0,WALK_LRF,10824602,1 +86596821,865968210,264014,226822,108246020,False,Home,9,4,18.0,WALK_LRF,10824602,1 +86603793,866037930,264035,226833,108254740,True,work,15,9,9.0,WALK_LRF,10825474,1 +86603797,866037970,264035,226833,108254740,False,Home,9,15,20.0,WALK_LRF,10825474,1 +86604033,866040330,264036,226833,108255040,True,othmaint,9,9,8.0,WALK,10825504,1 +86604037,866040370,264036,226833,108255040,False,Home,9,9,9.0,WALK,10825504,1 +86604073,866040730,264036,226833,108255090,True,shopping,11,9,14.0,WALK,10825509,1 +86604077,866040770,264036,226833,108255090,False,Home,9,11,17.0,WALK,10825509,1 +86627409,866274090,264107,226869,108284260,True,work,24,9,7.0,WALK_LRF,10828426,1 +86627413,866274130,264107,226869,108284260,False,Home,9,24,18.0,WALK_LRF,10828426,1 +86627625,866276250,264108,226869,108284530,True,othdiscr,19,9,10.0,WALK,10828453,1 +86627629,866276290,264108,226869,108284530,False,Home,9,19,11.0,WALK,10828453,1 +86631065,866310650,264119,226875,108288830,True,atwork,24,2,12.0,WALK,10828883,1 +86631069,866310690,264119,226875,108288830,False,Work,2,24,12.0,WALK,10828883,1 +86631345,866313450,264119,226875,108289180,True,work,2,10,10.0,WALK_LRF,10828918,1 +86631349,866313490,264119,226875,108289180,False,escort,11,2,12.0,WALK,10828918,1 +86631350,866313500,264119,226875,108289180,False,social,5,11,12.0,WALK,10828918,2 +86631351,866313510,264119,226875,108289180,False,eatout,3,5,18.0,WALK,10828918,3 +86631352,866313520,264119,226875,108289180,False,Home,10,3,18.0,WALK_LRF,10828918,4 +86631609,866316090,264120,226875,108289510,True,univ,10,10,7.0,WALK,10828951,1 +86631613,866316130,264120,226875,108289510,False,eatout,9,10,10.0,BIKE,10828951,1 +86631614,866316140,264120,226875,108289510,False,eatout,19,9,11.0,BIKE,10828951,2 +86631615,866316150,264120,226875,108289510,False,escort,11,19,11.0,WALK,10828951,3 +86631616,866316160,264120,226875,108289510,False,Home,10,11,11.0,WALK,10828951,4 +86633689,866336890,264127,226879,108292110,True,atwork,16,2,13.0,WALK,10829211,1 +86633693,866336930,264127,226879,108292110,False,Work,2,16,13.0,WALK,10829211,1 +86633969,866339690,264127,226879,108292460,True,escort,17,10,9.0,WALK_LRF,10829246,1 +86633970,866339700,264127,226879,108292460,True,work,2,17,10.0,WALK,10829246,2 +86633973,866339730,264127,226879,108292460,False,othmaint,5,2,13.0,WALK,10829246,1 +86633974,866339740,264127,226879,108292460,False,Home,10,5,13.0,WALK_LOC,10829246,2 +86634185,866341850,264128,226879,108292730,True,othdiscr,19,10,13.0,WALK,10829273,1 +86634189,866341890,264128,226879,108292730,False,Home,10,19,20.0,WALK,10829273,1 +86641777,866417770,264151,226891,108302220,True,univ,9,10,11.0,WALK,10830222,1 +86641781,866417810,264151,226891,108302220,False,Home,10,9,19.0,WALK_LOC,10830222,1 +86642057,866420570,264152,226891,108302570,True,othdiscr,8,10,18.0,WALK,10830257,1 +86642061,866420610,264152,226891,108302570,False,Home,10,8,19.0,WALK,10830257,1 +86653585,866535850,264187,226909,108316980,True,univ,13,10,16.0,DRIVEALONEFREE,10831698,1 +86653589,866535890,264187,226909,108316980,False,Home,10,13,16.0,DRIVEALONEFREE,10831698,1 +86653977,866539770,264188,226909,108317470,True,work,2,10,7.0,WALK,10831747,1 +86653981,866539810,264188,226909,108317470,False,Home,10,2,14.0,WALK,10831747,1 +86672081,866720810,264244,226937,108340100,True,eatout,8,10,12.0,WALK,10834010,1 +86672085,866720850,264244,226937,108340100,False,Home,10,8,14.0,WALK,10834010,1 +86672105,866721050,264244,226937,108340130,True,escort,10,10,16.0,WALK,10834013,1 +86672109,866721090,264244,226937,108340130,False,Home,10,10,17.0,WALK,10834013,1 +86673657,866736570,264248,226939,108342070,True,othmaint,4,10,7.0,WALK_LRF,10834207,1 +86673658,866736580,264248,226939,108342070,True,eatout,5,4,8.0,WALK,10834207,2 +86673659,866736590,264248,226939,108342070,True,escort,2,5,8.0,WALK,10834207,3 +86673660,866736600,264248,226939,108342070,True,work,22,2,8.0,WALK,10834207,4 +86673661,866736610,264248,226939,108342070,False,Home,10,22,19.0,WALK_LRF,10834207,1 +86680129,866801290,264268,226949,108350160,True,othmaint,7,10,13.0,WALK,10835016,1 +86680133,866801330,264268,226949,108350160,False,Home,10,7,16.0,WALK_LOC,10835016,1 +86715969,867159690,264377,227004,108394960,True,work,8,10,11.0,WALK,10839496,1 +86715973,867159730,264377,227004,108394960,False,Home,10,8,19.0,WALK,10839496,1 +86716057,867160570,264378,227004,108395070,True,escort,9,10,11.0,WALK,10839507,1 +86716061,867160610,264378,227004,108395070,False,Home,10,9,12.0,WALK,10839507,1 +86716249,867162490,264378,227004,108395310,True,shopping,11,10,17.0,WALK,10839531,1 +86716253,867162530,264378,227004,108395310,False,Home,10,11,18.0,WALK,10839531,1 +86727777,867277770,264413,227022,108409720,True,work,9,10,7.0,WALK,10840972,1 +86727781,867277810,264413,227022,108409720,False,Home,10,9,18.0,WALK,10840972,1 +86728041,867280410,264414,227022,108410050,True,univ,9,10,9.0,WALK,10841005,1 +86728045,867280450,264414,227022,108410050,False,Home,10,9,18.0,WALK_LOC,10841005,1 +86759833,867598330,264511,227071,108449790,True,othmaint,1,10,9.0,WALK_HVY,10844979,1 +86759837,867598370,264511,227071,108449790,False,Home,10,1,10.0,WALK_HVY,10844979,1 +86759873,867598730,264511,227071,108449840,True,shopping,4,10,12.0,WALK_LRF,10844984,1 +86759877,867598770,264511,227071,108449840,False,Home,10,4,15.0,WALK_LRF,10844984,1 +86760161,867601610,264512,227071,108450200,True,othmaint,20,10,11.0,WALK,10845020,1 +86760165,867601650,264512,227071,108450200,False,Home,10,20,12.0,WALK_LOC,10845020,1 +86825473,868254730,264711,227171,108531840,True,shopping,16,16,12.0,WALK,10853184,1 +86825477,868254770,264711,227171,108531840,False,Home,16,16,13.0,WALK,10853184,1 +86825849,868258490,264712,227171,108532310,True,work,2,16,9.0,WALK,10853231,1 +86825853,868258530,264712,227171,108532310,False,Home,16,2,15.0,WALK,10853231,1 +86866129,868661290,264835,227233,108582660,True,univ,12,16,8.0,WALK_LOC,10858266,1 +86866133,868661330,264835,227233,108582660,False,social,5,12,15.0,WALK_LOC,10858266,1 +86866134,868661340,264835,227233,108582660,False,Home,16,5,17.0,WALK_LOC,10858266,2 +86866473,868664730,264836,227233,108583090,True,shopping,13,16,8.0,WALK,10858309,1 +86866477,868664770,264836,227233,108583090,False,shopping,16,13,10.0,WALK,10858309,1 +86866478,868664780,264836,227233,108583090,False,Home,16,16,10.0,WALK,10858309,2 +86881889,868818890,264883,227257,108602360,True,shopping,19,19,7.0,WALK,10860236,1 +86881893,868818930,264883,227257,108602360,False,Home,19,19,12.0,WALK,10860236,1 +86882177,868821770,264884,227257,108602720,True,othmaint,7,19,11.0,WALK,10860272,1 +86882181,868821810,264884,227257,108602720,False,Home,19,7,15.0,WALK,10860272,1 +86891777,868917770,264913,227272,108614720,True,work,1,23,5.0,WALK,10861472,1 +86891781,868917810,264913,227272,108614720,False,Home,23,1,10.0,WALK,10861472,1 +86891785,868917850,264913,227272,108614730,True,work,1,23,13.0,WALK,10861473,1 +86891789,868917890,264913,227272,108614730,False,Home,23,1,18.0,WALK,10861473,1 +86891865,868918650,264914,227272,108614830,True,escort,14,23,12.0,SHARED2FREE,10861483,1 +86891866,868918660,264914,227272,108614830,True,escort,17,14,12.0,DRIVEALONEFREE,10861483,2 +86891869,868918690,264914,227272,108614830,False,eatout,5,17,12.0,DRIVEALONEFREE,10861483,1 +86891870,868918700,264914,227272,108614830,False,Home,23,5,12.0,SHARED2FREE,10861483,2 +86891993,868919930,264914,227272,108614990,True,othdiscr,14,23,17.0,WALK_LOC,10861499,1 +86891997,868919970,264914,227272,108614990,False,othdiscr,16,14,19.0,WALK_LOC,10861499,1 +86891998,868919980,264914,227272,108614990,False,shopping,12,16,19.0,WALK_LOC,10861499,2 +86891999,868919990,264914,227272,108614990,False,Home,23,12,19.0,WALK_LOC,10861499,3 +86905113,869051130,264954,227292,108631390,True,othdiscr,6,25,16.0,WALK,10863139,1 +86905117,869051170,264954,227292,108631390,False,Home,25,6,19.0,WALK,10863139,1 +86905121,869051210,264954,227292,108631400,True,othdiscr,13,25,19.0,WALK,10863140,1 +86905125,869051250,264954,227292,108631400,False,Home,25,13,20.0,WALK,10863140,1 +86907761,869077610,264962,227296,108634700,True,othmaint,1,25,14.0,WALK,10863470,1 +86907765,869077650,264962,227296,108634700,False,Home,25,1,19.0,WALK,10863470,1 +86907825,869078250,264962,227296,108634780,True,escort,7,25,7.0,WALK_LOC,10863478,1 +86907826,869078260,264962,227296,108634780,True,social,9,7,9.0,WALK_LOC,10863478,2 +86907829,869078290,264962,227296,108634780,False,Home,25,9,10.0,WALK_LOC,10863478,1 +105943049,1059430490,322997,256314,132428810,True,atwork,9,9,11.0,WALK,13242881,1 +105943053,1059430530,322997,256314,132428810,False,Work,9,9,13.0,WALK,13242881,1 +105943329,1059433290,322997,256314,132429160,True,work,9,7,7.0,WALK,13242916,1 +105943333,1059433330,322997,256314,132429160,False,Home,7,9,14.0,WALK,13242916,1 +105943657,1059436570,322998,256314,132429570,True,work,14,7,7.0,TNC_SINGLE,13242957,1 +105943661,1059436610,322998,256314,132429570,False,Home,7,14,18.0,TNC_SINGLE,13242957,1 +105950785,1059507850,323020,256325,132438480,True,othmaint,6,8,18.0,WALK,13243848,1 +105950789,1059507890,323020,256325,132438480,False,Home,8,6,21.0,WALK,13243848,1 +105950873,1059508730,323020,256325,132438590,True,work,2,8,6.0,WALK,13243859,1 +105950877,1059508770,323020,256325,132438590,False,social,11,2,16.0,WALK,13243859,1 +105950878,1059508780,323020,256325,132438590,False,eatout,5,11,16.0,WALK,13243859,2 +105950879,1059508790,323020,256325,132438590,False,eatout,12,5,17.0,WALK,13243859,3 +105950880,1059508800,323020,256325,132438590,False,Home,8,12,17.0,WALK,13243859,4 +105950881,1059508810,323020,256325,132438600,True,work,2,8,17.0,WALK,13243860,1 +105950885,1059508850,323020,256325,132438600,False,Home,8,2,17.0,WALK,13243860,1 +105952889,1059528890,323027,256329,132441110,True,atwork,2,21,15.0,WALK,13244111,1 +105952893,1059528930,323027,256329,132441110,False,Work,21,2,15.0,WALK,13244111,1 +105952977,1059529770,323027,256329,132441220,True,othdiscr,20,8,16.0,WALK,13244122,1 +105952981,1059529810,323027,256329,132441220,False,Home,8,20,16.0,TNC_SHARED,13244122,1 +105953169,1059531690,323027,256329,132441460,True,work,21,8,6.0,WALK,13244146,1 +105953173,1059531730,323027,256329,132441460,False,Home,8,21,16.0,WALK,13244146,1 +105966289,1059662890,323067,256349,132457860,True,work,2,9,9.0,WALK,13245786,1 +105966293,1059662930,323067,256349,132457860,False,Home,9,2,17.0,WALK,13245786,1 +105966505,1059665050,323068,256349,132458130,True,othdiscr,3,9,8.0,WALK_LRF,13245813,1 +105966509,1059665090,323068,256349,132458130,False,Home,9,3,12.0,WALK_LRF,13245813,1 +105966513,1059665130,323068,256349,132458140,True,othdiscr,15,9,12.0,WALK_LRF,13245814,1 +105966517,1059665170,323068,256349,132458140,False,Home,9,15,13.0,WALK_LRF,13245814,1 +105966617,1059666170,323068,256349,132458270,True,work,16,9,15.0,WALK_LRF,13245827,1 +105966621,1059666210,323068,256349,132458270,False,othdiscr,6,16,21.0,WALK,13245827,1 +105966622,1059666220,323068,256349,132458270,False,Home,9,6,21.0,WALK_LOC,13245827,2 +106003681,1060036810,323181,256406,132504600,True,work,7,10,7.0,WALK_LOC,13250460,1 +106003685,1060036850,323181,256406,132504600,False,Home,10,7,12.0,WALK_LOC,13250460,1 +106004009,1060040090,323182,256406,132505010,True,work,10,10,7.0,WALK,13250501,1 +106004013,1060040130,323182,256406,132505010,False,Home,10,10,13.0,WALK,13250501,1 +106029745,1060297450,323261,256446,132537180,True,escort,5,10,8.0,TNC_SHARED,13253718,1 +106029746,1060297460,323261,256446,132537180,True,othmaint,5,5,8.0,SHARED3FREE,13253718,2 +106029747,1060297470,323261,256446,132537180,True,othmaint,10,5,8.0,TAXI,13253718,3 +106029748,1060297480,323261,256446,132537180,True,othmaint,9,10,8.0,SHARED2FREE,13253718,4 +106029749,1060297490,323261,256446,132537180,False,Home,10,9,8.0,TNC_SHARED,13253718,1 +106029833,1060298330,323261,256446,132537290,True,othmaint,7,10,12.0,BIKE,13253729,1 +106029837,1060298370,323261,256446,132537290,False,Home,10,7,15.0,WALK,13253729,1 +106029921,1060299210,323261,256446,132537400,True,work,4,10,17.0,WALK_LRF,13253740,1 +106029925,1060299250,323261,256446,132537400,False,Home,10,4,20.0,WALK_LRF,13253740,1 +106030249,1060302490,323262,256446,132537810,True,work,4,10,8.0,SHARED3FREE,13253781,1 +106030253,1060302530,323262,256446,132537810,False,eatout,11,4,17.0,WALK,13253781,1 +106030254,1060302540,323262,256446,132537810,False,othmaint,6,11,17.0,WALK,13253781,2 +106030255,1060302550,323262,256446,132537810,False,escort,4,6,17.0,WALK,13253781,3 +106030256,1060302560,323262,256446,132537810,False,Home,10,4,17.0,WALK_LRF,13253781,4 +106056817,1060568170,323343,256487,132571020,True,work,7,10,7.0,WALK,13257102,1 +106056821,1060568210,323343,256487,132571020,False,Home,10,7,17.0,WALK,13257102,1 +106057145,1060571450,323344,256487,132571430,True,work,7,10,7.0,WALK_LOC,13257143,1 +106057149,1060571490,323344,256487,132571430,False,Home,10,7,18.0,WALK_LOC,13257143,1 +106060665,1060606650,323355,256493,132575830,True,othmaint,13,10,7.0,WALK_LRF,13257583,1 +106060669,1060606690,323355,256493,132575830,False,Home,10,13,7.0,WALK_LRF,13257583,1 +106061081,1060610810,323356,256493,132576350,True,work,14,10,12.0,WALK_LRF,13257635,1 +106061085,1060610850,323356,256493,132576350,False,Home,10,14,21.0,WALK_LRF,13257635,1 +106064033,1060640330,323365,256498,132580040,True,work,4,10,7.0,WALK,13258004,1 +106064037,1060640370,323365,256498,132580040,False,Home,10,4,16.0,WALK_LRF,13258004,1 +106064313,1060643130,323366,256498,132580390,True,shopping,21,10,18.0,WALK,13258039,1 +106064317,1060643170,323366,256498,132580390,False,Home,10,21,18.0,WALK,13258039,1 +106064361,1060643610,323366,256498,132580450,True,escort,9,10,7.0,WALK,13258045,1 +106064362,1060643620,323366,256498,132580450,True,social,8,9,7.0,WALK,13258045,2 +106064363,1060643630,323366,256498,132580450,True,work,20,8,8.0,WALK,13258045,3 +106064365,1060643650,323366,256498,132580450,False,Home,10,20,18.0,WALK,13258045,1 +106113889,1061138890,323517,256574,132642360,True,work,13,10,6.0,WALK_LRF,13264236,1 +106113893,1061138930,323517,256574,132642360,False,Home,10,13,16.0,WALK_LRF,13264236,1 +106113937,1061139370,323518,256574,132642420,True,othmaint,4,4,10.0,WALK,13264242,1 +106113938,1061139380,323518,256574,132642420,True,atwork,13,4,10.0,WALK,13264242,2 +106113941,1061139410,323518,256574,132642420,False,shopping,8,13,10.0,WALK,13264242,1 +106113942,1061139420,323518,256574,132642420,False,Work,4,8,10.0,WALK,13264242,2 +106114217,1061142170,323518,256574,132642770,True,work,4,10,6.0,WALK_LRF,13264277,1 +106114221,1061142210,323518,256574,132642770,False,Home,10,4,15.0,WALK_HVY,13264277,1 +106119681,1061196810,323535,256583,132649600,True,othdiscr,9,10,16.0,WALK,13264960,1 +106119685,1061196850,323535,256583,132649600,False,Home,10,9,18.0,WALK_LOC,13264960,1 +106119705,1061197050,323535,256583,132649630,True,othmaint,5,10,16.0,BIKE,13264963,1 +106119709,1061197090,323535,256583,132649630,False,Home,10,5,16.0,BIKE,13264963,1 +106119745,1061197450,323535,256583,132649680,True,shopping,12,10,18.0,WALK,13264968,1 +106119749,1061197490,323535,256583,132649680,False,Home,10,12,19.0,WALK,13264968,1 +106119753,1061197530,323535,256583,132649690,True,shopping,5,10,19.0,WALK_LOC,13264969,1 +106119757,1061197570,323535,256583,132649690,False,Home,10,5,20.0,WALK,13264969,1 +106119793,1061197930,323535,256583,132649740,True,work,16,10,6.0,SHARED2FREE,13264974,1 +106119797,1061197970,323535,256583,132649740,False,Home,10,16,15.0,WALK_LOC,13264974,1 +106119841,1061198410,323536,256583,132649800,True,shopping,8,9,12.0,WALK,13264980,1 +106119842,1061198420,323536,256583,132649800,True,atwork,9,8,12.0,WALK,13264980,2 +106119845,1061198450,323536,256583,132649800,False,Work,9,9,13.0,WALK,13264980,1 +106120121,1061201210,323536,256583,132650150,True,work,9,10,7.0,WALK,13265015,1 +106120125,1061201250,323536,256583,132650150,False,Home,10,9,23.0,WALK,13265015,1 +106170305,1061703050,323689,256660,132712880,True,work,1,10,11.0,WALK_LRF,13271288,1 +106170309,1061703090,323689,256660,132712880,False,Home,10,1,18.0,WALK_LRF,13271288,1 +106192609,1061926090,323757,256694,132740760,True,work,4,13,10.0,WALK,13274076,1 +106192613,1061926130,323757,256694,132740760,False,eatout,16,4,19.0,WALK,13274076,1 +106192614,1061926140,323757,256694,132740760,False,Home,13,16,19.0,WALK,13274076,2 +106199169,1061991690,323777,256704,132748960,True,work,16,16,8.0,WALK,13274896,1 +106199173,1061991730,323777,256704,132748960,False,Home,16,16,17.0,WALK,13274896,1 +106199409,1061994090,323778,256704,132749260,True,othmaint,7,16,7.0,WALK,13274926,1 +106199413,1061994130,323778,256704,132749260,False,Home,16,7,8.0,WALK,13274926,1 +106199497,1061994970,323778,256704,132749370,True,work,11,16,16.0,WALK,13274937,1 +106199501,1061995010,323778,256704,132749370,False,Home,16,11,21.0,BIKE,13274937,1 +106201137,1062011370,323783,256707,132751420,True,work,16,16,7.0,WALK,13275142,1 +106201141,1062011410,323783,256707,132751420,False,Home,16,16,17.0,WALK,13275142,1 +106201417,1062014170,323784,256707,132751770,True,shopping,16,16,10.0,WALK,13275177,1 +106201421,1062014210,323784,256707,132751770,False,Home,16,16,10.0,WALK,13275177,1 +106201465,1062014650,323784,256707,132751830,True,work,16,16,11.0,WALK,13275183,1 +106201469,1062014690,323784,256707,132751830,False,Home,16,16,19.0,WALK,13275183,1 +106233001,1062330010,323881,256756,132791250,True,atwork,2,12,12.0,WALK,13279125,1 +106233005,1062330050,323881,256756,132791250,False,eatout,7,2,12.0,WALK,13279125,1 +106233006,1062330060,323881,256756,132791250,False,Work,12,7,12.0,WALK,13279125,2 +106233169,1062331690,323881,256756,132791460,True,othdiscr,16,16,19.0,WALK,13279146,1 +106233173,1062331730,323881,256756,132791460,False,Home,16,16,20.0,WALK,13279146,1 +106233281,1062332810,323881,256756,132791600,True,work,12,16,6.0,SHARED2FREE,13279160,1 +106233285,1062332850,323881,256756,132791600,False,Home,16,12,18.0,SHARED2FREE,13279160,1 +106233609,1062336090,323882,256756,132792010,True,work,7,16,9.0,WALK_LOC,13279201,1 +106233613,1062336130,323882,256756,132792010,False,Home,16,7,20.0,WALK_LOC,13279201,1 +106235249,1062352490,323887,256759,132794060,True,work,9,16,7.0,WALK_LOC,13279406,1 +106235253,1062352530,323887,256759,132794060,False,Home,16,9,18.0,WALK,13279406,1 +106235577,1062355770,323888,256759,132794470,True,escort,24,16,6.0,WALK,13279447,1 +106235578,1062355780,323888,256759,132794470,True,work,22,24,7.0,WALK,13279447,2 +106235581,1062355810,323888,256759,132794470,False,shopping,16,22,15.0,WALK,13279447,1 +106235582,1062355820,323888,256759,132794470,False,escort,25,16,15.0,WALK_LOC,13279447,2 +106235583,1062355830,323888,256759,132794470,False,Home,16,25,15.0,WALK_LOC,13279447,3 +106236433,1062364330,323891,256761,132795540,True,atwork,4,4,13.0,WALK,13279554,1 +106236437,1062364370,323891,256761,132795540,False,shopping,6,4,14.0,WALK,13279554,1 +106236438,1062364380,323891,256761,132795540,False,Work,4,6,14.0,WALK,13279554,2 +106236561,1062365610,323891,256761,132795700,True,work,4,16,7.0,WALK,13279570,1 +106236565,1062365650,323891,256761,132795700,False,Home,16,4,17.0,WALK,13279570,1 +106236625,1062366250,323892,256761,132795780,True,eatout,16,16,9.0,WALK,13279578,1 +106236629,1062366290,323892,256761,132795780,False,Home,16,16,18.0,WALK,13279578,1 +106271329,1062713290,323997,256814,132839160,True,work,22,16,5.0,WALK,13283916,1 +106271333,1062713330,323997,256814,132839160,False,Home,16,22,21.0,WALK_LOC,13283916,1 +106271657,1062716570,323998,256814,132839570,True,work,18,16,12.0,WALK_LRF,13283957,1 +106271661,1062716610,323998,256814,132839570,False,Home,16,18,16.0,WALK,13283957,1 +106287945,1062879450,324048,256839,132859930,True,othdiscr,22,16,8.0,WALK_LOC,13285993,1 +106287949,1062879490,324048,256839,132859930,False,Home,16,22,15.0,WALK_LOC,13285993,1 +106288601,1062886010,324050,256840,132860750,True,othdiscr,23,16,17.0,WALK,13286075,1 +106288605,1062886050,324050,256840,132860750,False,Home,16,23,21.0,WALK,13286075,1 +106288665,1062886650,324050,256840,132860830,True,shopping,2,16,16.0,BIKE,13286083,1 +106288669,1062886690,324050,256840,132860830,False,Home,16,2,17.0,BIKE,13286083,1 +106288713,1062887130,324050,256840,132860890,True,work,4,16,7.0,WALK,13286089,1 +106288717,1062887170,324050,256840,132860890,False,Home,16,4,16.0,WALK,13286089,1 +106289041,1062890410,324051,256841,132861300,True,work,13,16,6.0,WALK,13286130,1 +106289045,1062890450,324051,256841,132861300,False,Home,16,13,18.0,WALK,13286130,1 +106289089,1062890890,324052,256841,132861360,True,eatout,5,2,10.0,WALK,13286136,1 +106289090,1062890900,324052,256841,132861360,True,escort,7,5,10.0,WALK,13286136,2 +106289091,1062890910,324052,256841,132861360,True,othmaint,7,7,10.0,WALK,13286136,3 +106289092,1062890920,324052,256841,132861360,True,atwork,25,7,10.0,WALK,13286136,4 +106289093,1062890930,324052,256841,132861360,False,Work,2,25,10.0,WALK,13286136,1 +106289369,1062893690,324052,256841,132861710,True,work,2,16,7.0,WALK,13286171,1 +106289373,1062893730,324052,256841,132861710,False,Home,16,2,17.0,WALK,13286171,1 +106313969,1063139690,324127,256879,132892460,True,eatout,13,16,12.0,TNC_SINGLE,13289246,1 +106313970,1063139700,324127,256879,132892460,True,work,4,13,13.0,WALK,13289246,2 +106313973,1063139730,324127,256879,132892460,False,Home,16,4,17.0,WALK,13289246,1 +106314297,1063142970,324128,256879,132892870,True,shopping,16,16,8.0,WALK,13289287,1 +106314298,1063142980,324128,256879,132892870,True,escort,22,16,9.0,TNC_SINGLE,13289287,2 +106314299,1063142990,324128,256879,132892870,True,work,14,22,9.0,TNC_SINGLE,13289287,3 +106314301,1063143010,324128,256879,132892870,False,work,7,14,13.0,TNC_SINGLE,13289287,1 +106314302,1063143020,324128,256879,132892870,False,Home,16,7,13.0,WALK_LOC,13289287,2 +106318449,1063184490,324141,256886,132898060,True,othdiscr,16,16,18.0,WALK,13289806,1 +106318453,1063184530,324141,256886,132898060,False,Home,16,16,19.0,WALK,13289806,1 +106318561,1063185610,324141,256886,132898200,True,escort,24,16,9.0,WALK,13289820,1 +106318562,1063185620,324141,256886,132898200,True,work,10,24,9.0,TNC_SINGLE,13289820,2 +106318565,1063185650,324141,256886,132898200,False,Home,16,10,18.0,TNC_SINGLE,13289820,1 +106318889,1063188890,324142,256886,132898610,True,work,18,16,6.0,WALK,13289861,1 +106318893,1063188930,324142,256886,132898610,False,Home,16,18,17.0,TAXI,13289861,1 +106319105,1063191050,324143,256887,132898880,True,othdiscr,25,16,8.0,WALK,13289888,1 +106319109,1063191090,324143,256887,132898880,False,Home,16,25,15.0,WALK,13289888,1 +106323761,1063237610,324157,256894,132904700,True,shopping,16,16,11.0,WALK,13290470,1 +106323765,1063237650,324157,256894,132904700,False,Home,16,16,11.0,WALK,13290470,1 +106324137,1063241370,324158,256894,132905170,True,work,24,16,5.0,TNC_SHARED,13290517,1 +106324141,1063241410,324158,256894,132905170,False,escort,4,24,15.0,TNC_SHARED,13290517,1 +106324142,1063241420,324158,256894,132905170,False,Home,16,4,16.0,WALK_LOC,13290517,2 +106342177,1063421770,324213,256922,132927720,True,work,16,16,8.0,WALK,13292772,1 +106342181,1063421810,324213,256922,132927720,False,social,12,16,20.0,WALK,13292772,1 +106342182,1063421820,324213,256922,132927720,False,Home,16,12,22.0,WALK,13292772,2 +106342505,1063425050,324214,256922,132928130,True,work,12,16,9.0,WALK_LOC,13292813,1 +106342509,1063425090,324214,256922,132928130,False,escort,14,12,13.0,WALK,13292813,1 +106342510,1063425100,324214,256922,132928130,False,othmaint,13,14,18.0,WALK,13292813,2 +106342511,1063425110,324214,256922,132928130,False,escort,22,13,20.0,WALK_LOC,13292813,3 +106342512,1063425120,324214,256922,132928130,False,Home,16,22,20.0,WALK_LOC,13292813,4 +106350705,1063507050,324239,256935,132938380,True,work,13,16,6.0,WALK,13293838,1 +106350709,1063507090,324239,256935,132938380,False,Home,16,13,17.0,WALK,13293838,1 +106350921,1063509210,324240,256935,132938650,True,othdiscr,12,16,16.0,WALK,13293865,1 +106350925,1063509250,324240,256935,132938650,False,Home,16,12,19.0,WALK,13293865,1 +106364393,1063643930,324281,256956,132955490,True,othmaint,16,16,19.0,WALK,13295549,1 +106364397,1063643970,324281,256956,132955490,False,Home,16,16,22.0,WALK,13295549,1 +106364433,1063644330,324281,256956,132955540,True,shopping,5,16,15.0,WALK,13295554,1 +106364437,1063644370,324281,256956,132955540,False,Home,16,5,19.0,WALK,13295554,1 +106364481,1063644810,324281,256956,132955600,True,work,9,16,7.0,WALK,13295560,1 +106364485,1063644850,324281,256956,132955600,False,Home,16,9,15.0,WALK,13295560,1 +106364697,1063646970,324282,256956,132955870,True,othdiscr,8,16,20.0,WALK_LOC,13295587,1 +106364701,1063647010,324282,256956,132955870,False,Home,16,8,20.0,SHARED2FREE,13295587,1 +106364809,1063648090,324282,256956,132956010,True,work,11,16,7.0,WALK_LOC,13295601,1 +106364813,1063648130,324282,256956,132956010,False,Home,16,11,18.0,WALK,13295601,1 +106389953,1063899530,324359,256995,132987440,True,othdiscr,1,16,14.0,WALK,13298744,1 +106389957,1063899570,324359,256995,132987440,False,Home,16,1,15.0,WALK,13298744,1 +106390393,1063903930,324360,256995,132987990,True,work,9,16,8.0,WALK_LOC,13298799,1 +106390397,1063903970,324360,256995,132987990,False,escort,9,9,18.0,WALK,13298799,1 +106390398,1063903980,324360,256995,132987990,False,escort,16,9,21.0,WALK_LRF,13298799,2 +106390399,1063903990,324360,256995,132987990,False,othmaint,16,16,21.0,WALK,13298799,3 +106390400,1063904000,324360,256995,132987990,False,Home,16,16,21.0,WALK,13298799,4 +106421553,1064215530,324455,257043,133026940,True,escort,5,16,8.0,TNC_SINGLE,13302694,1 +106421554,1064215540,324455,257043,133026940,True,work,21,5,9.0,TNC_SINGLE,13302694,2 +106421557,1064215570,324455,257043,133026940,False,shopping,16,21,17.0,TNC_SINGLE,13302694,1 +106421558,1064215580,324455,257043,133026940,False,Home,16,16,18.0,TNC_SINGLE,13302694,2 +106426145,1064261450,324469,257050,133032680,True,othmaint,1,16,9.0,WALK_LOC,13303268,1 +106426146,1064261460,324469,257050,133032680,True,work,15,1,10.0,WALK,13303268,2 +106426149,1064261490,324469,257050,133032680,False,Home,16,15,16.0,TNC_SHARED,13303268,1 +106426473,1064264730,324470,257050,133033090,True,work,16,16,8.0,WALK,13303309,1 +106426477,1064264770,324470,257050,133033090,False,Home,16,16,18.0,WALK,13303309,1 +106464849,1064648490,324587,257109,133081060,True,work,16,16,6.0,WALK,13308106,1 +106464853,1064648530,324587,257109,133081060,False,Home,16,16,18.0,WALK,13308106,1 +106465177,1064651770,324588,257109,133081470,True,work,17,16,9.0,SHARED2FREE,13308147,1 +106465181,1064651810,324588,257109,133081470,False,escort,17,17,16.0,WALK,13308147,1 +106465182,1064651820,324588,257109,133081470,False,Home,16,17,20.0,WALK,13308147,2 +106485841,1064858410,324651,257141,133107300,True,work,12,16,7.0,WALK,13310730,1 +106485845,1064858450,324651,257141,133107300,False,work,16,12,11.0,WALK,13310730,1 +106485846,1064858460,324651,257141,133107300,False,Home,16,16,18.0,WALK,13310730,2 +106486169,1064861690,324652,257141,133107710,True,work,14,16,7.0,WALK,13310771,1 +106486173,1064861730,324652,257141,133107710,False,Home,16,14,17.0,WALK,13310771,1 +106506177,1065061770,324713,257172,133132720,True,work,16,16,6.0,WALK,13313272,1 +106506181,1065061810,324713,257172,133132720,False,Home,16,16,17.0,WALK,13313272,1 +106506225,1065062250,324714,257172,133132780,True,atwork,13,15,11.0,WALK,13313278,1 +106506229,1065062290,324714,257172,133132780,False,othmaint,12,13,11.0,WALK,13313278,1 +106506230,1065062300,324714,257172,133132780,False,Work,15,12,11.0,WALK,13313278,2 +106506393,1065063930,324714,257172,133132990,True,othdiscr,9,16,18.0,BIKE,13313299,1 +106506397,1065063970,324714,257172,133132990,False,Home,16,9,20.0,BIKE,13313299,1 +106506505,1065065050,324714,257172,133133130,True,work,2,16,8.0,WALK,13313313,1 +106506506,1065065060,324714,257172,133133130,True,work,15,2,8.0,WALK_LOC,13313313,2 +106506509,1065065090,324714,257172,133133130,False,Home,16,15,18.0,WALK_LOC,13313313,1 +106508801,1065088010,324721,257176,133136000,True,work,15,16,6.0,WALK,13313600,1 +106508805,1065088050,324721,257176,133136000,False,Home,16,15,18.0,WALK_LOC,13313600,1 +106509017,1065090170,324722,257176,133136270,True,othdiscr,7,16,21.0,WALK,13313627,1 +106509021,1065090210,324722,257176,133136270,False,Home,16,7,22.0,WALK,13313627,1 +106509129,1065091290,324722,257176,133136410,True,work,12,16,8.0,TNC_SINGLE,13313641,1 +106509133,1065091330,324722,257176,133136410,False,Home,16,12,18.0,WALK,13313641,1 +106520609,1065206090,324757,257194,133150760,True,work,6,16,9.0,WALK,13315076,1 +106520613,1065206130,324757,257194,133150760,False,Home,16,6,18.0,WALK_LOC,13315076,1 +106520937,1065209370,324758,257194,133151170,True,escort,2,16,8.0,WALK,13315117,1 +106520938,1065209380,324758,257194,133151170,True,escort,16,2,9.0,WALK_LOC,13315117,2 +106520939,1065209390,324758,257194,133151170,True,shopping,4,16,9.0,WALK,13315117,3 +106520940,1065209400,324758,257194,133151170,True,work,18,4,12.0,WALK_LOC,13315117,4 +106520941,1065209410,324758,257194,133151170,False,Home,16,18,16.0,WALK_LRF,13315117,1 +106534929,1065349290,324801,257216,133168660,True,shopping,5,16,17.0,WALK,13316866,1 +106534930,1065349300,324801,257216,133168660,True,othdiscr,7,5,19.0,WALK_LOC,13316866,2 +106534933,1065349330,324801,257216,133168660,False,Home,16,7,23.0,WALK_LOC,13316866,1 +106535041,1065350410,324801,257216,133168800,True,work,15,16,9.0,WALK,13316880,1 +106535045,1065350450,324801,257216,133168800,False,Home,16,15,17.0,WALK,13316880,1 +106535369,1065353690,324802,257216,133169210,True,work,8,16,11.0,WALK,13316921,1 +106535373,1065353730,324802,257216,133169210,False,Home,16,8,21.0,WALK,13316921,1 +106541977,1065419770,324823,257227,133177470,True,atwork,17,17,14.0,WALK,13317747,1 +106541981,1065419810,324823,257227,133177470,False,Work,17,17,14.0,WALK,13317747,1 +106542257,1065422570,324823,257227,133177820,True,work,17,16,8.0,WALK,13317782,1 +106542261,1065422610,324823,257227,133177820,False,Home,16,17,18.0,WALK,13317782,1 +106542321,1065423210,324824,257227,133177900,True,eatout,4,16,16.0,WALK,13317790,1 +106542325,1065423250,324824,257227,133177900,False,Home,16,4,19.0,WALK,13317790,1 +106542585,1065425850,324824,257227,133178230,True,work,24,16,7.0,WALK,13317823,1 +106542589,1065425890,324824,257227,133178230,False,Home,16,24,15.0,WALK,13317823,1 +106570377,1065703770,324909,257270,133212970,True,othmaint,7,16,16.0,TNC_SINGLE,13321297,1 +106570381,1065703810,324909,257270,133212970,False,shopping,5,7,16.0,TNC_SHARED,13321297,1 +106570382,1065703820,324909,257270,133212970,False,othmaint,9,5,18.0,TNC_SINGLE,13321297,2 +106570383,1065703830,324909,257270,133212970,False,Home,16,9,19.0,WALK_LOC,13321297,3 +106570465,1065704650,324909,257270,133213080,True,work,4,16,6.0,WALK_LOC,13321308,1 +106570469,1065704690,324909,257270,133213080,False,Home,16,4,15.0,WALK,13321308,1 +106623553,1066235530,325071,257351,133279440,True,shopping,16,16,14.0,WALK,13327944,1 +106623557,1066235570,325071,257351,133279440,False,Home,16,16,16.0,WALK,13327944,1 +106623929,1066239290,325072,257351,133279910,True,work,1,16,7.0,WALK_LOC,13327991,1 +106623933,1066239330,325072,257351,133279910,False,Home,16,1,18.0,WALK_LOC,13327991,1 +106638033,1066380330,325115,257373,133297540,True,work,1,16,7.0,BIKE,13329754,1 +106638037,1066380370,325115,257373,133297540,False,Home,16,1,15.0,BIKE,13329754,1 +106638361,1066383610,325116,257373,133297950,True,work,14,16,10.0,WALK,13329795,1 +106638365,1066383650,325116,257373,133297950,False,Home,16,14,18.0,WALK,13329795,1 +106657057,1066570570,325173,257402,133321320,True,work,9,16,7.0,WALK_LOC,13332132,1 +106657061,1066570610,325173,257402,133321320,False,Home,16,9,16.0,WALK_LRF,13332132,1 +106657385,1066573850,325174,257402,133321730,True,work,15,16,7.0,WALK_LOC,13332173,1 +106657389,1066573890,325174,257402,133321730,False,Home,16,15,18.0,TNC_SINGLE,13332173,1 +106680017,1066800170,325243,257437,133350020,True,work,14,16,8.0,WALK_LOC,13335002,1 +106680021,1066800210,325243,257437,133350020,False,othdiscr,15,14,20.0,WALK_LOC,13335002,1 +106680022,1066800220,325243,257437,133350020,False,Home,16,15,20.0,WALK_LOC,13335002,2 +106680345,1066803450,325244,257437,133350430,True,work,4,16,7.0,SHARED2FREE,13335043,1 +106680349,1066803490,325244,257437,133350430,False,Home,16,4,20.0,WALK_LOC,13335043,1 +106712161,1067121610,325341,257486,133390200,True,work,21,16,12.0,WALK,13339020,1 +106712165,1067121650,325341,257486,133390200,False,Home,16,21,21.0,WALK,13339020,1 +106712209,1067122090,325342,257486,133390260,True,atwork,1,8,13.0,TNC_SINGLE,13339026,1 +106712213,1067122130,325342,257486,133390260,False,Work,8,1,13.0,WALK_LRF,13339026,1 +106712489,1067124890,325342,257486,133390610,True,work,8,16,6.0,WALK,13339061,1 +106712493,1067124930,325342,257486,133390610,False,Home,16,8,17.0,WALK,13339061,1 +106723969,1067239690,325377,257504,133404960,True,work,4,16,6.0,WALK,13340496,1 +106723973,1067239730,325377,257504,133404960,False,Home,16,4,16.0,WALK,13340496,1 +106724297,1067242970,325378,257504,133405370,True,work,14,16,14.0,WALK,13340537,1 +106724301,1067243010,325378,257504,133405370,False,Home,16,14,17.0,WALK,13340537,1 +106725849,1067258490,325383,257507,133407310,True,othmaint,22,16,7.0,WALK,13340731,1 +106725853,1067258530,325383,257507,133407310,False,Home,16,22,15.0,WALK,13340731,1 +106725857,1067258570,325383,257507,133407320,True,othmaint,11,16,18.0,WALK,13340732,1 +106725861,1067258610,325383,257507,133407320,False,Home,16,11,19.0,WALK,13340732,1 +106726201,1067262010,325384,257507,133407750,True,univ,12,16,17.0,WALK,13340775,1 +106726205,1067262050,325384,257507,133407750,False,escort,12,12,17.0,WALK,13340775,1 +106726206,1067262060,325384,257507,133407750,False,Home,16,12,17.0,WALK,13340775,2 +106726265,1067262650,325384,257507,133407830,True,othdiscr,4,16,7.0,WALK,13340783,1 +106726266,1067262660,325384,257507,133407830,True,escort,3,4,7.0,WALK,13340783,2 +106726267,1067262670,325384,257507,133407830,True,othdiscr,25,3,7.0,WALK,13340783,3 +106726268,1067262680,325384,257507,133407830,True,work,1,25,7.0,WALK,13340783,4 +106726269,1067262690,325384,257507,133407830,False,shopping,16,1,10.0,WALK,13340783,1 +106726270,1067262700,325384,257507,133407830,False,work,16,16,10.0,WALK,13340783,2 +106726271,1067262710,325384,257507,133407830,False,eatout,17,16,10.0,WALK,13340783,3 +106726272,1067262720,325384,257507,133407830,False,Home,16,17,10.0,WALK,13340783,4 +106741681,1067416810,325431,257531,133427100,True,shopping,2,16,7.0,WALK_LOC,13342710,1 +106741682,1067416820,325431,257531,133427100,True,work,12,2,8.0,WALK,13342710,2 +106741685,1067416850,325431,257531,133427100,False,eatout,17,12,17.0,WALK_LRF,13342710,1 +106741686,1067416860,325431,257531,133427100,False,shopping,19,17,17.0,TNC_SINGLE,13342710,2 +106741687,1067416870,325431,257531,133427100,False,escort,11,19,17.0,TNC_SHARED,13342710,3 +106741688,1067416880,325431,257531,133427100,False,Home,16,11,17.0,WALK,13342710,4 +106741961,1067419610,325432,257531,133427450,True,shopping,14,16,12.0,WALK_LOC,13342745,1 +106741965,1067419650,325432,257531,133427450,False,Home,16,14,13.0,TNC_SINGLE,13342745,1 +106756769,1067567690,325477,257554,133445960,True,work,16,16,14.0,WALK,13344596,1 +106756773,1067567730,325477,257554,133445960,False,Home,16,16,17.0,WALK,13344596,1 +106757097,1067570970,325478,257554,133446370,True,eatout,16,16,17.0,WALK,13344637,1 +106757098,1067570980,325478,257554,133446370,True,work,2,16,18.0,WALK,13344637,2 +106757101,1067571010,325478,257554,133446370,False,Home,16,2,20.0,WALK,13344637,1 +106760657,1067606570,325489,257560,133450820,True,escort,16,16,15.0,WALK,13345082,1 +106760658,1067606580,325489,257560,133450820,True,shopping,16,16,15.0,WALK,13345082,2 +106760661,1067606610,325489,257560,133450820,False,Home,16,16,15.0,WALK,13345082,1 +106760681,1067606810,325489,257560,133450850,True,social,9,16,14.0,SHARED2FREE,13345085,1 +106760685,1067606850,325489,257560,133450850,False,Home,16,9,14.0,SHARED2FREE,13345085,1 +106760945,1067609450,325490,257560,133451180,True,othmaint,16,16,12.0,WALK,13345118,1 +106760946,1067609460,325490,257560,133451180,True,othmaint,9,16,13.0,WALK_LRF,13345118,2 +106760949,1067609490,325490,257560,133451180,False,Home,16,9,15.0,WALK_LRF,13345118,1 +106781697,1067816970,325553,257592,133477120,True,work,7,16,7.0,WALK,13347712,1 +106781701,1067817010,325553,257592,133477120,False,Home,16,7,17.0,WALK,13347712,1 +106782025,1067820250,325554,257592,133477530,True,work,2,16,7.0,WALK,13347753,1 +106782029,1067820290,325554,257592,133477530,False,Home,16,2,20.0,WALK,13347753,1 +106804593,1068045930,325623,257627,133505740,True,univ,13,16,16.0,TNC_SHARED,13350574,1 +106804597,1068045970,325623,257627,133505740,False,escort,9,13,18.0,TNC_SINGLE,13350574,1 +106804598,1068045980,325623,257627,133505740,False,Home,16,9,19.0,WALK_LRF,13350574,2 +106804657,1068046570,325623,257627,133505820,True,work,17,16,8.0,WALK,13350582,1 +106804661,1068046610,325623,257627,133505820,False,escort,16,17,10.0,WALK,13350582,1 +106804662,1068046620,325623,257627,133505820,False,work,13,16,10.0,WALK,13350582,2 +106804663,1068046630,325623,257627,133505820,False,Home,16,13,10.0,WALK,13350582,3 +106804985,1068049850,325624,257627,133506230,True,work,15,16,6.0,WALK,13350623,1 +106804989,1068049890,325624,257627,133506230,False,Home,16,15,17.0,WALK,13350623,1 +106805289,1068052890,325625,257628,133506610,True,social,3,16,7.0,WALK,13350661,1 +106805293,1068052930,325625,257628,133506610,False,social,2,3,22.0,WALK,13350661,1 +106805294,1068052940,325625,257628,133506610,False,Home,16,2,22.0,WALK,13350661,2 +106846641,1068466410,325751,257691,133558300,True,work,16,17,5.0,WALK,13355830,1 +106846645,1068466450,325751,257691,133558300,False,Home,17,16,18.0,WALK,13355830,1 +106846969,1068469690,325752,257691,133558710,True,work,13,17,8.0,WALK,13355871,1 +106846973,1068469730,325752,257691,133558710,False,Home,17,13,18.0,WALK,13355871,1 +106847297,1068472970,325753,257692,133559120,True,work,7,17,7.0,WALK,13355912,1 +106847301,1068473010,325753,257692,133559120,False,Home,17,7,18.0,WALK,13355912,1 +106847625,1068476250,325754,257692,133559530,True,work,16,17,6.0,WALK,13355953,1 +106847629,1068476290,325754,257692,133559530,False,Home,17,16,16.0,WALK,13355953,1 +106859105,1068591050,325789,257710,133573880,True,work,16,17,8.0,WALK,13357388,1 +106859109,1068591090,325789,257710,133573880,False,Home,17,16,16.0,WALK,13357388,1 +106859433,1068594330,325790,257710,133574290,True,work,12,17,9.0,WALK,13357429,1 +106859437,1068594370,325790,257710,133574290,False,Home,17,12,20.0,WALK,13357429,1 +106908305,1069083050,325939,257785,133635380,True,work,2,19,7.0,WALK_LOC,13363538,1 +106908309,1069083090,325939,257785,133635380,False,Home,19,2,18.0,WALK_LOC,13363538,1 +106908633,1069086330,325940,257785,133635790,True,work,10,19,6.0,WALK,13363579,1 +106908637,1069086370,325940,257785,133635790,False,Home,19,10,19.0,WALK,13363579,1 +106947009,1069470090,326057,257844,133683760,True,work,22,21,7.0,WALK_LOC,13368376,1 +106947013,1069470130,326057,257844,133683760,False,Home,21,22,17.0,WALK_LRF,13368376,1 +106947289,1069472890,326058,257844,133684110,True,shopping,16,21,17.0,WALK,13368411,1 +106947293,1069472930,326058,257844,133684110,False,Home,21,16,18.0,WALK,13368411,1 +106947337,1069473370,326058,257844,133684170,True,work,2,21,6.0,WALK,13368417,1 +106947341,1069473410,326058,257844,133684170,False,Home,21,2,16.0,WALK,13368417,1 +126406529,1264065290,385385,287508,158008160,True,othdiscr,4,6,18.0,WALK,15800816,1 +126406530,1264065300,385385,287508,158008160,True,univ,12,4,19.0,WALK_LOC,15800816,2 +126406533,1264065330,385385,287508,158008160,False,othdiscr,5,12,21.0,WALK_LOC,15800816,1 +126406534,1264065340,385385,287508,158008160,False,Home,6,5,21.0,WALK,15800816,2 +126406657,1264066570,385386,287508,158008320,True,eatout,5,6,10.0,WALK,15800832,1 +126406661,1264066610,385386,287508,158008320,False,Home,6,5,20.0,WALK,15800832,1 +126427521,1264275210,385449,287540,158034400,True,escort,22,7,21.0,WALK_LRF,15803440,1 +126427522,1264275220,385449,287540,158034400,True,univ,9,22,21.0,WALK_LRF,15803440,2 +126427525,1264275250,385449,287540,158034400,False,Home,7,9,21.0,WALK_LRF,15803440,1 +126427849,1264278490,385450,287540,158034810,True,school,8,7,7.0,WALK,15803481,1 +126427853,1264278530,385450,287540,158034810,False,Home,7,8,15.0,WALK,15803481,1 +126450497,1264504970,385519,287575,158063120,True,shopping,16,8,14.0,WALK_LOC,15806312,1 +126450501,1264505010,385519,287575,158063120,False,Home,8,16,14.0,WALK_LOC,15806312,1 +126450809,1264508090,385520,287575,158063510,True,school,3,8,7.0,WALK_LOC,15806351,1 +126450813,1264508130,385520,287575,158063510,False,Home,8,3,18.0,WALK_LOC,15806351,1 +126455089,1264550890,385533,287582,158068860,True,shopping,11,8,11.0,WALK_LOC,15806886,1 +126455093,1264550930,385533,287582,158068860,False,Home,8,11,12.0,WALK,15806886,1 +126467553,1264675530,385571,287601,158084440,True,shopping,11,8,18.0,TNC_SHARED,15808444,1 +126467557,1264675570,385571,287601,158084440,False,Home,8,11,21.0,WALK_LOC,15808444,1 +126467865,1264678650,385572,287601,158084830,True,school,8,8,8.0,WALK,15808483,1 +126467869,1264678690,385572,287601,158084830,False,escort,9,8,13.0,WALK,15808483,1 +126467870,1264678700,385572,287601,158084830,False,eatout,11,9,13.0,WALK,15808483,2 +126467871,1264678710,385572,287601,158084830,False,Home,8,11,13.0,WALK,15808483,3 +126479977,1264799770,385609,287620,158099970,True,othmaint,7,8,14.0,WALK_LOC,15809997,1 +126479978,1264799780,385609,287620,158099970,True,othmaint,9,7,15.0,WALK,15809997,2 +126479981,1264799810,385609,287620,158099970,False,escort,7,9,14.0,WALK_LOC,15809997,1 +126479982,1264799820,385609,287620,158099970,False,Home,8,7,15.0,WALK,15809997,2 +126480281,1264802810,385610,287620,158100350,True,othdiscr,9,8,8.0,WALK,15810035,1 +126480282,1264802820,385610,287620,158100350,True,othdiscr,17,9,9.0,WALK_LRF,15810035,2 +126480285,1264802850,385610,287620,158100350,False,Home,8,17,15.0,WALK_LOC,15810035,1 +126509321,1265093210,385699,287665,158136650,True,eatout,11,8,16.0,SHARED2FREE,15813665,1 +126509325,1265093250,385699,287665,158136650,False,Home,8,11,19.0,SHARED2FREE,15813665,1 +126523297,1265232970,385741,287686,158154120,True,univ,13,8,18.0,WALK,15815412,1 +126523301,1265233010,385741,287686,158154120,False,Home,8,13,18.0,WALK,15815412,1 +126523625,1265236250,385742,287686,158154530,True,escort,7,8,7.0,WALK,15815453,1 +126523626,1265236260,385742,287686,158154530,True,school,9,7,8.0,WALK,15815453,2 +126523629,1265236290,385742,287686,158154530,False,shopping,5,9,23.0,WALK,15815453,1 +126523630,1265236300,385742,287686,158154530,False,Home,8,5,23.0,WALK,15815453,2 +126550825,1265508250,385825,287728,158188530,True,othmaint,8,8,12.0,WALK,15818853,1 +126550829,1265508290,385825,287728,158188530,False,Home,8,8,15.0,WALK,15818853,1 +126551177,1265511770,385826,287728,158188970,True,school,13,8,6.0,WALK_HVY,15818897,1 +126551181,1265511810,385826,287728,158188970,False,social,9,13,14.0,WALK_LRF,15818897,1 +126551182,1265511820,385826,287728,158188970,False,Home,8,9,14.0,WALK_LOC,15818897,2 +126608577,1266085770,386001,287816,158260720,True,escort,22,9,17.0,WALK_LRF,15826072,1 +126608578,1266085780,386001,287816,158260720,True,work,9,22,17.0,WALK_LRF,15826072,2 +126608579,1266085790,386001,287816,158260720,True,univ,12,9,18.0,WALK_LRF,15826072,3 +126608581,1266085810,386001,287816,158260720,False,Home,9,12,23.0,WALK_LRF,15826072,1 +126608905,1266089050,386002,287816,158261130,True,school,10,9,7.0,SHARED2FREE,15826113,1 +126608909,1266089090,386002,287816,158261130,False,Home,9,10,13.0,WALK_LOC,15826113,1 +126610345,1266103450,386007,287819,158262930,True,eatout,8,9,7.0,WALK,15826293,1 +126610349,1266103490,386007,287819,158262930,False,Home,9,8,12.0,WALK,15826293,1 +126610561,1266105610,386007,287819,158263200,True,shopping,16,9,13.0,WALK_LRF,15826320,1 +126610565,1266105650,386007,287819,158263200,False,shopping,16,16,14.0,WALK,15826320,1 +126610566,1266105660,386007,287819,158263200,False,Home,9,16,14.0,WALK_LRF,15826320,2 +126610673,1266106730,386008,287819,158263340,True,eatout,16,9,17.0,WALK_LRF,15826334,1 +126610677,1266106770,386008,287819,158263340,False,Home,9,16,21.0,WALK_LRF,15826334,1 +126610873,1266108730,386008,287819,158263590,True,school,9,9,8.0,WALK,15826359,1 +126610877,1266108770,386008,287819,158263590,False,Home,9,9,13.0,WALK,15826359,1 +126614153,1266141530,386018,287824,158267690,True,school,8,9,7.0,WALK_LOC,15826769,1 +126614157,1266141570,386018,287824,158267690,False,Home,9,8,16.0,WALK_LOC,15826769,1 +126628233,1266282330,386061,287846,158285290,True,othmaint,24,9,8.0,WALK,15828529,1 +126628237,1266282370,386061,287846,158285290,False,Home,9,24,16.0,WALK,15828529,1 +126628585,1266285850,386062,287846,158285730,True,school,13,9,7.0,WALK_LRF,15828573,1 +126628589,1266285890,386062,287846,158285730,False,Home,9,13,15.0,WALK_LRF,15828573,1 +126639425,1266394250,386095,287863,158299280,True,shopping,11,9,6.0,TNC_SINGLE,15829928,1 +126639429,1266394290,386095,287863,158299280,False,Home,9,11,13.0,TNC_SINGLE,15829928,1 +126639753,1266397530,386096,287863,158299690,True,shopping,16,9,9.0,WALK,15829969,1 +126639757,1266397570,386096,287863,158299690,False,Home,9,16,11.0,WALK,15829969,1 +126648569,1266485690,386123,287877,158310710,True,othmaint,7,10,16.0,TNC_SINGLE,15831071,1 +126648573,1266485730,386123,287877,158310710,False,eatout,9,7,19.0,TNC_SINGLE,15831071,1 +126648574,1266485740,386123,287877,158310710,False,Home,10,9,19.0,TNC_SINGLE,15831071,2 +126659041,1266590410,386155,287893,158323800,True,othdiscr,14,10,8.0,WALK,15832380,1 +126659045,1266590450,386155,287893,158323800,False,Home,10,14,12.0,WALK,15832380,1 +126659417,1266594170,386156,287893,158324270,True,escort,9,10,7.0,WALK,15832427,1 +126659418,1266594180,386156,287893,158324270,True,escort,11,9,8.0,WALK,15832427,2 +126659419,1266594190,386156,287893,158324270,True,school,21,11,8.0,WALK,15832427,3 +126659421,1266594210,386156,287893,158324270,False,Home,10,21,21.0,WALK,15832427,1 +126685329,1266853290,386235,287933,158356660,True,othmaint,4,10,8.0,WALK_LRF,15835666,1 +126685330,1266853300,386235,287933,158356660,True,eatout,9,4,9.0,WALK_LRF,15835666,2 +126685331,1266853310,386235,287933,158356660,True,univ,13,9,9.0,WALK_LRF,15835666,3 +126685333,1266853330,386235,287933,158356660,False,Home,10,13,15.0,WALK_LRF,15835666,1 +126685657,1266856570,386236,287933,158357070,True,school,10,10,8.0,WALK,15835707,1 +126685661,1266856610,386236,287933,158357070,False,othmaint,10,10,15.0,WALK,15835707,1 +126685662,1266856620,386236,287933,158357070,False,shopping,7,10,18.0,WALK,15835707,2 +126685663,1266856630,386236,287933,158357070,False,Home,10,7,18.0,WALK,15835707,3 +126701745,1267017450,386285,287958,158377180,True,shopping,5,11,16.0,WALK,15837718,1 +126701749,1267017490,386285,287958,158377180,False,Home,11,5,16.0,WALK,15837718,1 +126702057,1267020570,386286,287958,158377570,True,school,10,11,7.0,WALK_LOC,15837757,1 +126702061,1267020610,386286,287958,158377570,False,Home,11,10,15.0,WALK_LOC,15837757,1 +136971385,1369713850,417595,303613,171214230,True,othmaint,5,2,20.0,DRIVEALONEFREE,17121423,1 +136971389,1369713890,417595,303613,171214230,False,othmaint,9,5,20.0,TNC_SHARED,17121423,1 +136971390,1369713900,417595,303613,171214230,False,Home,2,9,21.0,TAXI,17121423,2 +136971425,1369714250,417595,303613,171214280,True,shopping,4,2,10.0,WALK,17121428,1 +136971429,1369714290,417595,303613,171214280,False,Home,2,4,10.0,WALK,17121428,1 +136971473,1369714730,417595,303613,171214340,True,work,1,2,11.0,WALK,17121434,1 +136971474,1369714740,417595,303613,171214340,True,work,19,1,11.0,DRIVEALONEFREE,17121434,2 +136971477,1369714770,417595,303613,171214340,False,Home,2,19,18.0,DRIVEALONEFREE,17121434,1 +137013409,1370134090,417723,303677,171266760,True,shopping,5,7,14.0,BIKE,17126676,1 +137013413,1370134130,417723,303677,171266760,False,Home,7,5,16.0,BIKE,17126676,1 +137013417,1370134170,417723,303677,171266770,True,shopping,2,7,18.0,WALK,17126677,1 +137013421,1370134210,417723,303677,171266770,False,Home,7,2,20.0,WALK,17126677,1 +137013433,1370134330,417723,303677,171266790,True,social,2,7,20.0,WALK,17126679,1 +137013437,1370134370,417723,303677,171266790,False,Home,7,2,23.0,WALK,17126679,1 +137013721,1370137210,417724,303677,171267150,True,school,7,7,8.0,WALK,17126715,1 +137013725,1370137250,417724,303677,171267150,False,Home,7,7,15.0,WALK,17126715,1 +137018657,1370186570,417739,303685,171273320,True,shopping,5,7,17.0,WALK,17127332,1 +137018661,1370186610,417739,303685,171273320,False,Home,7,5,19.0,WALK,17127332,1 +137018769,1370187690,417740,303685,171273460,True,eatout,6,7,8.0,WALK,17127346,1 +137018773,1370187730,417740,303685,171273460,False,Home,7,6,15.0,WALK,17127346,1 +137067905,1370679050,417889,303760,171334880,True,work,22,8,7.0,DRIVEALONEFREE,17133488,1 +137067909,1370679090,417889,303760,171334880,False,Home,8,22,18.0,DRIVEALONEFREE,17133488,1 +137068169,1370681690,417890,303760,171335210,True,school,9,8,7.0,WALK,17133521,1 +137068173,1370681730,417890,303760,171335210,False,Home,8,9,12.0,WALK,17133521,1 +137086929,1370869290,417947,303789,171358660,True,work,7,9,7.0,WALK_LOC,17135866,1 +137086933,1370869330,417947,303789,171358660,False,Home,9,7,17.0,WALK,17135866,1 +137087193,1370871930,417948,303789,171358990,True,school,10,9,13.0,WALK,17135899,1 +137087197,1370871970,417948,303789,171358990,False,Home,9,10,20.0,WALK_LOC,17135899,1 +137090777,1370907770,417959,303795,171363470,True,othmaint,19,9,10.0,WALK_LOC,17136347,1 +137090781,1370907810,417959,303795,171363470,False,Home,9,19,10.0,WALK_LOC,17136347,1 +137090817,1370908170,417959,303795,171363520,True,shopping,4,9,11.0,WALK,17136352,1 +137090821,1370908210,417959,303795,171363520,False,Home,9,4,17.0,WALK,17136352,1 +137091129,1370911290,417960,303795,171363910,True,school,10,9,7.0,WALK,17136391,1 +137091133,1370911330,417960,303795,171363910,False,Home,9,10,14.0,WALK,17136391,1 +137098625,1370986250,417983,303807,171373280,True,othdiscr,9,9,11.0,WALK,17137328,1 +137098629,1370986290,417983,303807,171373280,False,Home,9,9,16.0,WALK,17137328,1 +137098689,1370986890,417983,303807,171373360,True,shopping,25,9,16.0,WALK,17137336,1 +137098693,1370986930,417983,303807,171373360,False,Home,9,25,18.0,WALK,17137336,1 +137100049,1371000490,417987,303809,171375060,True,work,14,9,7.0,WALK,17137506,1 +137100053,1371000530,417987,303809,171375060,False,Home,9,14,19.0,WALK,17137506,1 +137100313,1371003130,417988,303809,171375390,True,school,9,9,8.0,WALK,17137539,1 +137100317,1371003170,417988,303809,171375390,False,Home,9,9,10.0,WALK,17137539,1 +137113169,1371131690,418027,303829,171391460,True,escort,7,9,7.0,WALK,17139146,1 +137113170,1371131700,418027,303829,171391460,True,work,5,7,8.0,WALK,17139146,2 +137113173,1371131730,418027,303829,171391460,False,shopping,5,5,17.0,WALK,17139146,1 +137113174,1371131740,418027,303829,171391460,False,othmaint,7,5,18.0,WALK,17139146,2 +137113175,1371131750,418027,303829,171391460,False,Home,9,7,18.0,WALK,17139146,3 +137113433,1371134330,418028,303829,171391790,True,school,11,9,7.0,WALK,17139179,1 +137113437,1371134370,418028,303829,171391790,False,Home,9,11,7.0,WALK,17139179,1 +137132849,1371328490,418087,303859,171416060,True,work,14,9,7.0,WALK,17141606,1 +137132853,1371328530,418087,303859,171416060,False,Home,9,14,18.0,WALK,17141606,1 +137133065,1371330650,418088,303859,171416330,True,othdiscr,13,9,16.0,WALK_LRF,17141633,1 +137133069,1371330690,418088,303859,171416330,False,Home,9,13,17.0,WALK_LRF,17141633,1 +137133113,1371331130,418088,303859,171416390,True,school,13,9,8.0,WALK_LRF,17141639,1 +137133117,1371331170,418088,303859,171416390,False,Home,9,13,14.0,WALK_LRF,17141639,1 +137174769,1371747690,418215,303923,171468460,True,univ,10,10,11.0,WALK,17146846,1 +137174773,1371747730,418215,303923,171468460,False,work,10,10,11.0,WALK,17146846,1 +137174774,1371747740,418215,303923,171468460,False,Home,10,10,11.0,WALK,17146846,2 +137174833,1371748330,418215,303923,171468540,True,work,10,10,5.0,WALK,17146854,1 +137174837,1371748370,418215,303923,171468540,False,Home,10,10,10.0,WALK,17146854,1 +137175097,1371750970,418216,303923,171468870,True,school,11,10,7.0,SHARED2FREE,17146887,1 +137175101,1371751010,418216,303923,171468870,False,shopping,9,11,11.0,WALK,17146887,1 +137175102,1371751020,418216,303923,171468870,False,Home,10,9,11.0,SHARED3FREE,17146887,2 +137226001,1372260010,418371,304001,171532500,True,work,1,10,7.0,WALK_LRF,17153250,1 +137226005,1372260050,418371,304001,171532500,False,Home,10,1,17.0,WALK_LRF,17153250,1 +137226217,1372262170,418372,304001,171532770,True,othdiscr,3,10,8.0,WALK,17153277,1 +137226221,1372262210,418372,304001,171532770,False,Home,10,3,19.0,WALK,17153277,1 +137231817,1372318170,418389,304010,171539770,True,othmaint,9,10,8.0,WALK,17153977,1 +137231821,1372318210,418389,304010,171539770,False,Home,10,9,10.0,WALK_LOC,17153977,1 +137232169,1372321690,418390,304010,171540210,True,school,20,10,8.0,WALK_LOC,17154021,1 +137232173,1372321730,418390,304010,171540210,False,Home,10,20,15.0,WALK_LOC,17154021,1 +137232209,1372322090,418390,304010,171540260,True,social,13,10,16.0,WALK_LRF,17154026,1 +137232213,1372322130,418390,304010,171540260,False,Home,10,13,21.0,WALK_LRF,17154026,1 +137248721,1372487210,418441,304036,171560900,True,escort,7,10,7.0,SHARED2FREE,17156090,1 +137248725,1372487250,418441,304036,171560900,False,Home,10,7,7.0,SHARED2FREE,17156090,1 +137248873,1372488730,418441,304036,171561090,True,othmaint,11,10,18.0,WALK,17156109,1 +137248877,1372488770,418441,304036,171561090,False,Home,10,11,20.0,WALK,17156109,1 +137248961,1372489610,418441,304036,171561200,True,work,14,10,7.0,WALK_LRF,17156120,1 +137248965,1372489650,418441,304036,171561200,False,Home,10,14,17.0,WALK_LRF,17156120,1 +137249225,1372492250,418442,304036,171561530,True,school,10,10,8.0,WALK,17156153,1 +137249229,1372492290,418442,304036,171561530,False,Home,10,10,10.0,SHARED3FREE,17156153,1 +137271921,1372719210,418511,304071,171589900,True,work,15,10,7.0,WALK_LOC,17158990,1 +137271925,1372719250,418511,304071,171589900,False,Home,10,15,16.0,WALK_LRF,17158990,1 +137272185,1372721850,418512,304071,171590230,True,school,13,10,10.0,WALK_LRF,17159023,1 +137272189,1372721890,418512,304071,171590230,False,Home,10,13,13.0,WALK_LRF,17159023,1 +137282177,1372821770,418543,304087,171602720,True,escort,7,10,15.0,WALK,17160272,1 +137282181,1372821810,418543,304087,171602720,False,Home,10,7,16.0,WALK,17160272,1 +137282329,1372823290,418543,304087,171602910,True,othmaint,7,10,17.0,BIKE,17160291,1 +137282333,1372823330,418543,304087,171602910,False,Home,10,7,18.0,BIKE,17160291,1 +137282369,1372823690,418543,304087,171602960,True,shopping,19,10,16.0,WALK_LOC,17160296,1 +137282373,1372823730,418543,304087,171602960,False,Home,10,19,16.0,WALK_LOC,17160296,1 +137282417,1372824170,418543,304087,171603020,True,work,9,10,7.0,WALK,17160302,1 +137282421,1372824210,418543,304087,171603020,False,Home,10,9,15.0,WALK,17160302,1 +137282681,1372826810,418544,304087,171603350,True,school,7,10,7.0,WALK,17160335,1 +137282685,1372826850,418544,304087,171603350,False,Home,10,7,21.0,WALK_LRF,17160335,1 +137306033,1373060330,418615,304123,171632540,True,work,19,11,10.0,WALK,17163254,1 +137306037,1373060370,418615,304123,171632540,False,othmaint,10,19,18.0,WALK,17163254,1 +137306038,1373060380,418615,304123,171632540,False,Home,11,10,18.0,WALK,17163254,2 +137306273,1373062730,418616,304123,171632840,True,othmaint,5,11,12.0,WALK,17163284,1 +137306277,1373062770,418616,304123,171632840,False,Home,11,5,14.0,WALK,17163284,1 +137327681,1373276810,418681,304156,171659600,True,work,12,11,7.0,WALK,17165960,1 +137327685,1373276850,418681,304156,171659600,False,Home,11,12,17.0,WALK,17165960,1 +137327745,1373277450,418682,304156,171659680,True,shopping,16,11,8.0,WALK,17165968,1 +137327746,1373277460,418682,304156,171659680,True,eatout,13,16,8.0,WALK,17165968,2 +137327749,1373277490,418682,304156,171659680,False,Home,11,13,15.0,WALK,17165968,1 +137333585,1373335850,418699,304165,171666980,True,work,24,11,7.0,WALK,17166698,1 +137333589,1373335890,418699,304165,171666980,False,shopping,4,24,18.0,WALK,17166698,1 +137333590,1373335900,418699,304165,171666980,False,Home,11,4,18.0,WALK,17166698,2 +137355889,1373558890,418767,304199,171694860,True,work,16,16,6.0,TNC_SINGLE,17169486,1 +137355893,1373558930,418767,304199,171694860,False,Home,16,16,17.0,TNC_SINGLE,17169486,1 +137356153,1373561530,418768,304199,171695190,True,school,17,16,8.0,WALK_LRF,17169519,1 +137356157,1373561570,418768,304199,171695190,False,Home,16,17,11.0,WALK_LRF,17169519,1 +137381865,1373818650,418847,304239,171727330,True,eatout,16,16,12.0,WALK,17172733,1 +137381869,1373818690,418847,304239,171727330,False,Home,16,16,12.0,WALK,17172733,1 +137382017,1373820170,418847,304239,171727520,True,othdiscr,16,16,9.0,WALK,17172752,1 +137382021,1373820210,418847,304239,171727520,False,Home,16,16,10.0,WALK,17172752,1 +137382041,1373820410,418847,304239,171727550,True,othmaint,12,16,12.0,WALK,17172755,1 +137382045,1373820450,418847,304239,171727550,False,Home,16,12,13.0,WALK_LOC,17172755,1 +137382081,1373820810,418847,304239,171727600,True,shopping,16,16,15.0,WALK,17172760,1 +137382085,1373820850,418847,304239,171727600,False,Home,16,16,17.0,WALK,17172760,1 +137382393,1373823930,418848,304239,171727990,True,escort,2,16,8.0,WALK_LOC,17172799,1 +137382394,1373823940,418848,304239,171727990,True,school,8,2,9.0,WALK,17172799,2 +137382397,1373823970,418848,304239,171727990,False,Home,16,8,19.0,WALK_LOC,17172799,1 +137405745,1374057450,418919,304275,171757180,True,work,22,17,11.0,WALK_LOC,17175718,1 +137405746,1374057460,418919,304275,171757180,True,work,16,22,11.0,WALK_LOC,17175718,2 +137405749,1374057490,418919,304275,171757180,False,Home,17,16,21.0,WALK,17175718,1 +137405961,1374059610,418920,304275,171757450,True,othdiscr,10,17,7.0,SHARED3FREE,17175745,1 +137405965,1374059650,418920,304275,171757450,False,eatout,5,10,12.0,SHARED3FREE,17175745,1 +137405966,1374059660,418920,304275,171757450,False,Home,17,5,12.0,SHARED3FREE,17175745,2 +137405985,1374059850,418920,304275,171757480,True,othmaint,16,17,12.0,TNC_SHARED,17175748,1 +137405989,1374059890,418920,304275,171757480,False,Home,17,16,22.0,WALK_LRF,17175748,1 +137429273,1374292730,418991,304311,171786590,True,othmaint,5,19,15.0,WALK_LOC,17178659,1 +137429277,1374292770,418991,304311,171786590,False,shopping,11,5,18.0,WALK,17178659,1 +137429278,1374292780,418991,304311,171786590,False,Home,19,11,18.0,WALK,17178659,2 +137429313,1374293130,418991,304311,171786640,True,shopping,11,19,13.0,WALK,17178664,1 +137429317,1374293170,418991,304311,171786640,False,Home,19,11,13.0,WALK,17178664,1 +137429625,1374296250,418992,304311,171787030,True,school,11,19,11.0,WALK,17178703,1 +137429629,1374296290,418992,304311,171787030,False,Home,19,11,14.0,WALK,17178703,1 +154276993,1542769930,470356,328721,192846240,True,othmaint,16,6,15.0,WALK_LOC,19284624,1 +154276997,1542769970,470356,328721,192846240,False,Home,6,16,22.0,WALK_LOC,19284624,1 +154277297,1542772970,470357,328721,192846620,True,othdiscr,13,6,11.0,WALK_LOC,19284662,1 +154277301,1542773010,470357,328721,192846620,False,Home,6,13,15.0,WALK_LOC,19284662,1 +154277361,1542773610,470357,328721,192846700,True,shopping,18,6,15.0,WALK_LOC,19284670,1 +154277365,1542773650,470357,328721,192846700,False,Home,6,18,15.0,WALK_LOC,19284670,1 +154277673,1542776730,470358,328721,192847090,True,univ,12,6,8.0,WALK,19284709,1 +154277677,1542776770,470358,328721,192847090,False,Home,6,12,12.0,WALK_LOC,19284709,1 +175499009,1754990090,535057,350288,219373760,True,work,15,19,8.0,BIKE,21937376,1 +175499013,1754990130,535057,350288,219373760,False,Home,19,15,15.0,BIKE,21937376,1 +175499337,1754993370,535058,350288,219374170,True,work,9,19,18.0,DRIVEALONEFREE,21937417,1 +175499341,1754993410,535058,350288,219374170,False,social,11,9,18.0,DRIVEALONEFREE,21937417,1 +175499342,1754993420,535058,350288,219374170,False,Home,19,11,19.0,DRIVEALONEFREE,21937417,2 +175499665,1754996650,535059,350288,219374580,True,work,21,19,7.0,WALK,21937458,1 +175499669,1754996690,535059,350288,219374580,False,Home,19,21,13.0,WALK_LOC,21937458,1 +175523609,1755236090,535132,350313,219404510,True,work,16,19,9.0,WALK,21940451,1 +175523613,1755236130,535132,350313,219404510,False,Home,19,16,15.0,WALK,21940451,1 +175523617,1755236170,535132,350313,219404520,True,work,16,19,17.0,WALK,21940452,1 +175523621,1755236210,535132,350313,219404520,False,Home,19,16,18.0,WALK,21940452,1 +175523937,1755239370,535133,350313,219404920,True,work,22,19,8.0,WALK_LOC,21940492,1 +175523941,1755239410,535133,350313,219404920,False,shopping,8,22,10.0,WALK_LRF,21940492,1 +175523942,1755239420,535133,350313,219404920,False,Home,19,8,10.0,WALK,21940492,2 +175523945,1755239450,535133,350313,219404930,True,work,22,19,11.0,WALK_LOC,21940493,1 +175523949,1755239490,535133,350313,219404930,False,eatout,7,22,20.0,WALK_LOC,21940493,1 +175523950,1755239500,535133,350313,219404930,False,Home,19,7,21.0,WALK,21940493,2 +175524265,1755242650,535134,350313,219405330,True,work,22,19,7.0,BIKE,21940533,1 +175524269,1755242690,535134,350313,219405330,False,Home,19,22,10.0,BIKE,21940533,1 +181831985,1818319850,554365,356724,227289980,True,othmaint,7,6,10.0,WALK,22728998,1 +181831986,1818319860,554365,356724,227289980,True,shopping,16,7,10.0,WALK_LOC,22728998,2 +181831989,1818319890,554365,356724,227289980,False,eatout,7,16,16.0,WALK,22728998,1 +181831990,1818319900,554365,356724,227289980,False,Home,6,7,16.0,WALK,22728998,2 +181832249,1818322490,554366,356724,227290310,True,othdiscr,12,6,10.0,WALK,22729031,1 +181832253,1818322530,554366,356724,227290310,False,Home,6,12,13.0,WALK,22729031,1 +181832449,1818324490,554367,356724,227290560,True,escort,9,6,12.0,WALK,22729056,1 +181832453,1818324530,554367,356724,227290560,False,Home,6,9,12.0,WALK,22729056,1 +181865097,1818650970,554466,356757,227331370,True,school,8,8,8.0,WALK,22733137,1 +181865101,1818651010,554466,356757,227331370,False,Home,8,8,16.0,WALK,22733137,1 +181916921,1819169210,554624,356810,227396150,True,univ,13,9,7.0,WALK_LRF,22739615,1 +181916925,1819169250,554624,356810,227396150,False,work,8,13,15.0,WALK_LRF,22739615,1 +181916926,1819169260,554624,356810,227396150,False,Home,9,8,16.0,WALK_LOC,22739615,2 +181917289,1819172890,554625,356810,227396610,True,social,2,9,8.0,WALK,22739661,1 +181917293,1819172930,554625,356810,227396610,False,Home,9,2,18.0,WALK,22739661,1 +181927433,1819274330,554656,356821,227409290,True,shopping,5,17,13.0,WALK,22740929,1 +181927437,1819274370,554656,356821,227409290,False,Home,17,5,19.0,WALK,22740929,1 +181927745,1819277450,554657,356821,227409680,True,school,10,17,7.0,WALK_LRF,22740968,1 +181927749,1819277490,554657,356821,227409680,False,escort,2,10,12.0,WALK_LRF,22740968,1 +181927750,1819277500,554657,356821,227409680,False,social,9,2,12.0,WALK_LRF,22740968,2 +181927751,1819277510,554657,356821,227409680,False,Home,17,9,12.0,WALK_LRF,22740968,3 +181928073,1819280730,554658,356821,227410090,True,school,9,17,10.0,WALK_LRF,22741009,1 +181928077,1819280770,554658,356821,227410090,False,eatout,6,9,18.0,WALK_LOC,22741009,1 +181928078,1819280780,554658,356821,227410090,False,Home,17,6,23.0,WALK_LRF,22741009,2 +195037313,1950373130,594625,370144,243796640,True,work,12,7,5.0,WALK,24379664,1 +195037317,1950373170,594625,370144,243796640,False,shopping,8,12,17.0,WALK,24379664,1 +195037318,1950373180,594625,370144,243796640,False,escort,7,8,17.0,WALK,24379664,2 +195037319,1950373190,594625,370144,243796640,False,eatout,7,7,17.0,WALK,24379664,3 +195037320,1950373200,594625,370144,243796640,False,Home,7,7,17.0,WALK,24379664,4 +195037377,1950373770,594626,370144,243796720,True,eatout,10,7,15.0,WALK,24379672,1 +195037381,1950373810,594626,370144,243796720,False,Home,7,10,17.0,WALK,24379672,1 +195037593,1950375930,594626,370144,243796990,True,shopping,16,7,9.0,WALK,24379699,1 +195037597,1950375970,594626,370144,243796990,False,Home,7,16,12.0,WALK,24379699,1 +195037905,1950379050,594627,370144,243797380,True,school,7,7,8.0,WALK,24379738,1 +195037909,1950379090,594627,370144,243797380,False,Home,7,7,11.0,WALK,24379738,1 +195073961,1950739610,594737,370181,243842450,True,othmaint,7,9,17.0,WALK_LOC,24384245,1 +195073965,1950739650,594737,370181,243842450,False,Home,9,7,19.0,WALK,24384245,1 +195074049,1950740490,594737,370181,243842560,True,work,5,9,8.0,WALK,24384256,1 +195074053,1950740530,594737,370181,243842560,False,Home,9,5,17.0,WALK,24384256,1 +195074313,1950743130,594738,370181,243842890,True,school,10,9,7.0,WALK_LOC,24384289,1 +195074317,1950743170,594738,370181,243842890,False,Home,9,10,18.0,WALK_LOC,24384289,1 +195097337,1950973370,594808,370205,243871670,True,work,14,9,8.0,WALK_LRF,24387167,1 +195097341,1950973410,594808,370205,243871670,False,Home,9,14,17.0,WALK_LRF,24387167,1 +195097601,1950976010,594809,370205,243872000,True,escort,9,9,18.0,WALK,24387200,1 +195097602,1950976020,594809,370205,243872000,True,escort,6,9,18.0,WALK,24387200,2 +195097603,1950976030,594809,370205,243872000,True,univ,9,6,18.0,WALK,24387200,3 +195097605,1950976050,594809,370205,243872000,False,Home,9,9,18.0,WALK,24387200,1 +195097929,1950979290,594810,370205,243872410,True,school,7,9,7.0,WALK_LOC,24387241,1 +195097933,1950979330,594810,370205,243872410,False,Home,9,7,23.0,WALK_LRF,24387241,1 +195153185,1951531850,594979,370262,243941480,True,escort,3,10,8.0,WALK,24394148,1 +195153189,1951531890,594979,370262,243941480,False,Home,10,3,9.0,WALK,24394148,1 +195153193,1951531930,594979,370262,243941490,True,escort,4,10,11.0,DRIVEALONEFREE,24394149,1 +195153197,1951531970,594979,370262,243941490,False,Home,10,4,11.0,DRIVEALONEFREE,24394149,1 +195153201,1951532010,594979,370262,243941500,True,escort,5,10,11.0,WALK,24394150,1 +195153205,1951532050,594979,370262,243941500,False,Home,10,5,12.0,WALK,24394150,1 +195153377,1951533770,594979,370262,243941720,True,shopping,9,10,13.0,WALK,24394172,1 +195153378,1951533780,594979,370262,243941720,True,shopping,10,9,13.0,WALK,24394172,2 +195153381,1951533810,594979,370262,243941720,False,Home,10,10,13.0,WALK,24394172,1 +195153689,1951536890,594980,370262,243942110,True,univ,9,10,16.0,WALK,24394211,1 +195153693,1951536930,594980,370262,243942110,False,work,9,9,16.0,WALK,24394211,1 +195153694,1951536940,594980,370262,243942110,False,Home,10,9,17.0,WALK,24394211,2 +195153705,1951537050,594980,370262,243942130,True,shopping,13,10,13.0,WALK,24394213,1 +195153709,1951537090,594980,370262,243942130,False,shopping,12,13,13.0,WALK,24394213,1 +195153710,1951537100,594980,370262,243942130,False,Home,10,12,13.0,WALK,24394213,2 +195154017,1951540170,594981,370262,243942520,True,school,9,10,7.0,WALK,24394252,1 +195154021,1951540210,594981,370262,243942520,False,Home,10,9,15.0,WALK,24394252,1 +195185897,1951858970,595078,370295,243982370,True,eatout,11,10,8.0,WALK,24398237,1 +195185898,1951858980,595078,370295,243982370,True,work,9,11,10.0,SHARED3FREE,24398237,2 +195185901,1951859010,595078,370295,243982370,False,work,7,9,11.0,WALK,24398237,1 +195185902,1951859020,595078,370295,243982370,False,Home,10,7,15.0,DRIVEALONEFREE,24398237,2 +195186161,1951861610,595079,370295,243982700,True,school,11,10,8.0,SHARED3FREE,24398270,1 +195186165,1951861650,595079,370295,243982700,False,othdiscr,9,11,15.0,SHARED3FREE,24398270,1 +195186166,1951861660,595079,370295,243982700,False,Home,10,9,15.0,SHARED2FREE,24398270,2 +195186489,1951864890,595080,370295,243983110,True,school,10,10,8.0,WALK,24398311,1 +195186493,1951864930,595080,370295,243983110,False,Home,10,10,15.0,WALK,24398311,1 +195238049,1952380490,595237,370348,244047560,True,work,1,10,8.0,WALK,24404756,1 +195238053,1952380530,595237,370348,244047560,False,Home,10,1,17.0,WALK,24404756,1 +195238313,1952383130,595238,370348,244047890,True,school,10,10,8.0,WALK,24404789,1 +195238317,1952383170,595238,370348,244047890,False,eatout,11,10,14.0,WALK,24404789,1 +195238318,1952383180,595238,370348,244047890,False,shopping,11,11,15.0,WALK,24404789,2 +195238319,1952383190,595238,370348,244047890,False,Home,10,11,15.0,WALK,24404789,3 +195238641,1952386410,595239,370348,244048300,True,school,9,10,7.0,WALK,24404830,1 +195238645,1952386450,595239,370348,244048300,False,Home,10,9,14.0,WALK_LOC,24404830,1 +195283225,1952832250,595375,370394,244104030,True,othmaint,9,10,12.0,BIKE,24410403,1 +195283229,1952832290,595375,370394,244104030,False,Home,10,9,15.0,BIKE,24410403,1 +195283377,1952833770,595376,370394,244104220,True,eatout,10,10,16.0,WALK,24410422,1 +195283381,1952833810,595376,370394,244104220,False,Home,10,10,18.0,WALK,24410422,1 +195283641,1952836410,595376,370394,244104550,True,eatout,21,10,8.0,SHARED3FREE,24410455,1 +195283642,1952836420,595376,370394,244104550,True,work,1,21,12.0,WALK,24410455,2 +195283645,1952836450,595376,370394,244104550,False,Home,10,1,16.0,WALK_LRF,24410455,1 +195283857,1952838570,595377,370394,244104820,True,othdiscr,16,10,6.0,WALK,24410482,1 +195283861,1952838610,595377,370394,244104820,False,Home,10,16,17.0,WALK,24410482,1 +195351097,1953510970,595582,370463,244188870,True,othdiscr,14,16,9.0,WALK,24418887,1 +195351101,1953511010,595582,370463,244188870,False,Home,16,14,15.0,WALK,24418887,1 +195351537,1953515370,595583,370463,244189420,True,work,1,16,7.0,WALK_LOC,24418942,1 +195351541,1953515410,595583,370463,244189420,False,Home,16,1,16.0,WALK,24418942,1 +195351801,1953518010,595584,370463,244189750,True,school,16,16,8.0,WALK,24418975,1 +195351805,1953518050,595584,370463,244189750,False,Home,16,16,15.0,WALK,24418975,1 +195385257,1953852570,595686,370497,244231570,True,school,8,21,7.0,WALK_LOC,24423157,1 +195385261,1953852610,595686,370497,244231570,False,Home,21,8,16.0,WALK,24423157,1 +195400409,1954004090,595732,370513,244250510,True,work,15,22,7.0,WALK,24425051,1 +195400413,1954004130,595732,370513,244250510,False,Home,22,15,18.0,WALK,24425051,1 +195400473,1954004730,595733,370513,244250590,True,eatout,12,22,18.0,WALK_LRF,24425059,1 +195400477,1954004770,595733,370513,244250590,False,Home,22,12,20.0,WALK,24425059,1 +195400497,1954004970,595733,370513,244250620,True,escort,11,22,10.0,WALK,24425062,1 +195400501,1954005010,595733,370513,244250620,False,Home,22,11,14.0,WALK,24425062,1 +195401001,1954010010,595734,370513,244251250,True,school,10,22,7.0,WALK_LRF,24425125,1 +195401005,1954010050,595734,370513,244251250,False,Home,22,10,13.0,WALK_LRF,24425125,1 +195405065,1954050650,595747,370518,244256330,True,eatout,2,24,7.0,WALK,24425633,1 +195405069,1954050690,595747,370518,244256330,False,Home,24,2,15.0,WALK,24425633,1 +195405609,1954056090,595748,370518,244257010,True,othmaint,12,24,10.0,WALK,24425701,1 +195405610,1954056100,595748,370518,244257010,True,shopping,4,12,11.0,WALK,24425701,2 +195405613,1954056130,595748,370518,244257010,False,Home,24,4,15.0,WALK,24425701,1 +195405937,1954059370,595749,370518,244257420,True,shopping,11,24,10.0,SHARED2FREE,24425742,1 +195405941,1954059410,595749,370518,244257420,False,Home,24,11,11.0,SHARED2FREE,24425742,1 +211327433,2113274330,644290,386699,264159290,True,work,12,7,12.0,WALK_LOC,26415929,1 +211327437,2113274370,644290,386699,264159290,False,Home,7,12,22.0,WALK_LOC,26415929,1 +211327673,2113276730,644291,386699,264159590,True,othmaint,2,7,19.0,WALK_LOC,26415959,1 +211327677,2113276770,644291,386699,264159590,False,Home,7,2,23.0,TNC_SINGLE,26415959,1 +211327761,2113277610,644291,386699,264159700,True,work,2,7,7.0,WALK,26415970,1 +211327765,2113277650,644291,386699,264159700,False,Home,7,2,17.0,WALK,26415970,1 +211328025,2113280250,644292,386699,264160030,True,school,9,7,13.0,WALK_LOC,26416003,1 +211328029,2113280290,644292,386699,264160030,False,othdiscr,22,9,21.0,WALK_LRF,26416003,1 +211328030,2113280300,644292,386699,264160030,False,Home,7,22,21.0,WALK_LRF,26416003,2 +211376457,2113764570,644440,386749,264220570,True,othmaint,5,9,17.0,WALK,26422057,1 +211376461,2113764610,644440,386749,264220570,False,Home,9,5,17.0,SHARED3FREE,26422057,1 +211376585,2113765850,644440,386749,264220730,True,shopping,17,9,16.0,SHARED2FREE,26422073,1 +211376589,2113765890,644440,386749,264220730,False,Home,9,17,16.0,SHARED2FREE,26422073,1 +211376633,2113766330,644440,386749,264220790,True,work,1,9,5.0,WALK_LRF,26422079,1 +211376637,2113766370,644440,386749,264220790,False,Home,9,1,15.0,WALK_LRF,26422079,1 +211376897,2113768970,644441,386749,264221120,True,school,9,9,8.0,WALK,26422112,1 +211376901,2113769010,644441,386749,264221120,False,Home,9,9,16.0,WALK,26422112,1 +211377177,2113771770,644442,386749,264221470,True,othdiscr,9,9,15.0,WALK,26422147,1 +211377181,2113771810,644442,386749,264221470,False,Home,9,9,16.0,WALK,26422147,1 +211377289,2113772890,644442,386749,264221610,True,work,9,9,6.0,WALK,26422161,1 +211377293,2113772930,644442,386749,264221610,False,Home,9,9,15.0,WALK,26422161,1 +211387457,2113874570,644473,386760,264234320,True,work,13,16,8.0,WALK,26423432,1 +211387461,2113874610,644473,386760,264234320,False,Home,16,13,19.0,WALK,26423432,1 +211387465,2113874650,644473,386760,264234330,True,work,13,16,20.0,WALK,26423433,1 +211387469,2113874690,644473,386760,264234330,False,Home,16,13,23.0,WALK,26423433,1 +211387785,2113877850,644474,386760,264234730,True,work,11,16,6.0,WALK,26423473,1 +211387789,2113877890,644474,386760,264234730,False,Home,16,11,16.0,WALK,26423473,1 +211388049,2113880490,644475,386760,264235060,True,school,16,16,7.0,WALK,26423506,1 +211388053,2113880530,644475,386760,264235060,False,Home,16,16,15.0,WALK,26423506,1 +211388201,2113882010,644476,386761,264235250,True,escort,11,16,5.0,WALK_LOC,26423525,1 +211388205,2113882050,644476,386761,264235250,False,Home,16,11,6.0,WALK_LOC,26423525,1 +211388329,2113883290,644476,386761,264235410,True,othdiscr,16,16,18.0,WALK,26423541,1 +211388333,2113883330,644476,386761,264235410,False,Home,16,16,18.0,WALK,26423541,1 +211388353,2113883530,644476,386761,264235440,True,othmaint,13,16,18.0,WALK,26423544,1 +211388357,2113883570,644476,386761,264235440,False,Home,16,13,19.0,WALK,26423544,1 +211388441,2113884410,644476,386761,264235550,True,work,4,16,7.0,DRIVEALONEFREE,26423555,1 +211388445,2113884450,644476,386761,264235550,False,Home,16,4,17.0,SHARED3FREE,26423555,1 +211388721,2113887210,644477,386761,264235900,True,shopping,16,16,14.0,WALK,26423590,1 +211388725,2113887250,644477,386761,264235900,False,Home,16,16,14.0,WALK,26423590,1 +211389033,2113890330,644478,386761,264236290,True,school,20,16,8.0,WALK_LOC,26423629,1 +211389037,2113890370,644478,386761,264236290,False,shopping,17,20,13.0,WALK,26423629,1 +211389038,2113890380,644478,386761,264236290,False,Home,16,17,23.0,WALK,26423629,2 +211407025,2114070250,644533,386780,264258780,True,othdiscr,8,19,18.0,WALK,26425878,1 +211407029,2114070290,644533,386780,264258780,False,Home,19,8,21.0,WALK,26425878,1 +211407137,2114071370,644533,386780,264258920,True,work,17,19,8.0,WALK,26425892,1 +211407141,2114071410,644533,386780,264258920,False,Home,19,17,17.0,WALK,26425892,1 +211407377,2114073770,644534,386780,264259220,True,othmaint,15,19,15.0,WALK_LOC,26425922,1 +211407381,2114073810,644534,386780,264259220,False,Home,19,15,19.0,TNC_SINGLE,26425922,1 +211409057,2114090570,644539,386782,264261320,True,shopping,11,19,9.0,WALK_LOC,26426132,1 +211409058,2114090580,644539,386782,264261320,True,shopping,13,11,11.0,WALK_LOC,26426132,2 +211409061,2114090610,644539,386782,264261320,False,Home,19,13,13.0,WALK_LOC,26426132,1 +211409433,2114094330,644540,386782,264261790,True,work,7,19,8.0,WALK,26426179,1 +211409437,2114094370,644540,386782,264261790,False,Home,19,7,17.0,WALK,26426179,1 +211409697,2114096970,644541,386782,264262120,True,school,11,19,8.0,WALK,26426212,1 +211409701,2114097010,644541,386782,264262120,False,Home,19,11,13.0,WALK,26426212,1 +211447369,2114473690,644656,386821,264309210,True,othdiscr,15,25,11.0,WALK,26430921,1 +211447373,2114473730,644656,386821,264309210,False,Home,25,15,15.0,WALK,26430921,1 +211447433,2114474330,644656,386821,264309290,True,shopping,2,25,10.0,WALK,26430929,1 +211447437,2114474370,644656,386821,264309290,False,Home,25,2,11.0,WALK,26430929,1 +211447809,2114478090,644657,386821,264309760,True,work,3,25,7.0,WALK,26430976,1 +211447813,2114478130,644657,386821,264309760,False,Home,25,3,18.0,WALK,26430976,1 +211448049,2114480490,644658,386821,264310060,True,othmaint,12,25,10.0,BIKE,26431006,1 +211448053,2114480530,644658,386821,264310060,False,Home,25,12,14.0,BIKE,26431006,1 +277530233,2775302330,846128,431923,346912790,True,school,8,6,8.0,WALK_LOC,34691279,1 +277530237,2775302370,846128,431923,346912790,False,Home,6,8,15.0,WALK_LOC,34691279,1 +277530561,2775305610,846129,431923,346913200,True,school,6,6,8.0,WALK,34691320,1 +277530565,2775305650,846129,431923,346913200,False,Home,6,6,15.0,WALK,34691320,1 +277530889,2775308890,846130,431923,346913610,True,school,9,6,6.0,WALK_HVY,34691361,1 +277530893,2775308930,846130,431923,346913610,False,othmaint,9,9,15.0,WALK,34691361,1 +277530894,2775308940,846130,431923,346913610,False,eatout,2,9,15.0,WALK_HVY,34691361,2 +277530895,2775308950,846130,431923,346913610,False,Home,6,2,15.0,WALK,34691361,3 +277530905,2775309050,846130,431923,346913630,True,othdiscr,6,6,17.0,WALK,34691363,1 +277530906,2775309060,846130,431923,346913630,True,shopping,13,6,17.0,WALK,34691363,2 +277530909,2775309090,846130,431923,346913630,False,Home,6,13,17.0,WALK_LOC,34691363,1 +277530929,2775309290,846130,431923,346913660,True,social,2,6,17.0,TNC_SINGLE,34691366,1 +277530933,2775309330,846130,431923,346913660,False,Home,6,2,18.0,TNC_SHARED,34691366,1 +277537929,2775379290,846152,431929,346922410,True,escort,23,8,7.0,WALK_LOC,34692241,1 +277537933,2775379330,846152,431929,346922410,False,Home,8,23,7.0,TNC_SINGLE,34692241,1 +277538081,2775380810,846152,431929,346922600,True,othmaint,7,8,8.0,WALK,34692260,1 +277538085,2775380850,846152,431929,346922600,False,Home,8,7,15.0,WALK,34692260,1 +277538449,2775384490,846153,431929,346923060,True,shopping,21,8,18.0,DRIVEALONEFREE,34692306,1 +277538453,2775384530,846153,431929,346923060,False,Home,8,21,20.0,DRIVEALONEFREE,34692306,1 +277538761,2775387610,846154,431929,346923450,True,school,9,8,7.0,WALK,34692345,1 +277538765,2775387650,846154,431929,346923450,False,Home,8,9,10.0,WALK,34692345,1 +277539041,2775390410,846155,431929,346923800,True,othdiscr,5,8,20.0,SHARED2FREE,34692380,1 +277539045,2775390450,846155,431929,346923800,False,Home,8,5,22.0,WALK,34692380,1 +277539089,2775390890,846155,431929,346923860,True,school,8,8,8.0,WALK,34692386,1 +277539093,2775390930,846155,431929,346923860,False,Home,8,8,14.0,WALK,34692386,1 +277539369,2775393690,846156,431929,346924210,True,othdiscr,17,8,7.0,WALK_LRF,34692421,1 +277539373,2775393730,846156,431929,346924210,False,Home,8,17,19.0,WALK_LRF,34692421,1 +277575121,2775751210,846265,431952,346968900,True,othdiscr,22,8,15.0,WALK_HVY,34696890,1 +277575125,2775751250,846265,431952,346968900,False,Home,8,22,19.0,WALK_LRF,34696890,1 +277575513,2775755130,846266,431952,346969390,True,shopping,16,8,15.0,WALK,34696939,1 +277575517,2775755170,846266,431952,346969390,False,Home,8,16,16.0,WALK_LOC,34696939,1 +277575825,2775758250,846267,431952,346969780,True,school,13,8,8.0,WALK,34696978,1 +277575829,2775758290,846267,431952,346969780,False,Home,8,13,13.0,WALK_LRF,34696978,1 +277576153,2775761530,846268,431952,346970190,True,escort,10,8,13.0,WALK,34697019,1 +277576154,2775761540,846268,431952,346970190,True,escort,1,10,15.0,WALK_LRF,34697019,2 +277576155,2775761550,846268,431952,346970190,True,othdiscr,10,1,15.0,WALK_LRF,34697019,3 +277576156,2775761560,846268,431952,346970190,True,school,13,10,16.0,WALK_LRF,34697019,4 +277576157,2775761570,846268,431952,346970190,False,Home,8,13,20.0,WALK_LRF,34697019,1 +277667337,2776673370,846546,432008,347084170,True,school,7,9,18.0,WALK_LOC,34708417,1 +277667341,2776673410,846546,432008,347084170,False,Home,9,7,21.0,WALK_LRF,34708417,1 +277667665,2776676650,846547,432008,347084580,True,school,6,9,5.0,WALK,34708458,1 +277667669,2776676690,846547,432008,347084580,False,Home,9,6,19.0,WALK,34708458,1 +277690313,2776903130,846616,432022,347112890,True,shopping,23,9,9.0,WALK_LRF,34711289,1 +277690317,2776903170,846616,432022,347112890,False,Home,9,23,13.0,WALK_LRF,34711289,1 +277690953,2776909530,846618,432022,347113690,True,school,9,9,14.0,WALK,34711369,1 +277690957,2776909570,846618,432022,347113690,False,Home,9,9,21.0,WALK,34711369,1 +277750473,2777504730,846800,432059,347188090,True,escort,7,19,7.0,WALK,34718809,1 +277750477,2777504770,846800,432059,347188090,False,Home,19,7,8.0,WALK,34718809,1 +277750977,2777509770,846801,432059,347188720,True,school,21,19,7.0,BIKE,34718872,1 +277750981,2777509810,846801,432059,347188720,False,Home,19,21,14.0,BIKE,34718872,1 +277751305,2777513050,846802,432059,347189130,True,school,8,19,7.0,WALK,34718913,1 +277751309,2777513090,846802,432059,347189130,False,Home,19,8,10.0,WALK,34718913,1 +277751585,2777515850,846803,432059,347189480,True,othdiscr,12,19,11.0,WALK,34718948,1 +277751589,2777515890,846803,432059,347189480,False,Home,19,12,14.0,WALK,34718948,1 +277753425,2777534250,846809,432061,347191780,True,escort,24,19,8.0,DRIVEALONEFREE,34719178,1 +277753429,2777534290,846809,432061,347191780,False,Home,19,24,8.0,SHARED2FREE,34719178,1 +277753729,2777537290,846810,432061,347192160,True,eatout,11,19,9.0,WALK,34719216,1 +277753733,2777537330,846810,432061,347192160,False,Home,19,11,15.0,WALK,34719216,1 +277754273,2777542730,846811,432061,347192840,True,shopping,16,19,17.0,BIKE,34719284,1 +277754277,2777542770,846811,432061,347192840,False,Home,19,16,20.0,BIKE,34719284,1 +277754625,2777546250,846812,432061,347193280,True,social,5,19,11.0,WALK,34719328,1 +277754629,2777546290,846812,432061,347193280,False,Home,19,5,21.0,WALK,34719328,1 +316561641,3165616410,965126,456554,395702050,True,work,24,9,8.0,WALK_HVY,39570205,1 +316561645,3165616450,965126,456554,395702050,False,Home,9,24,19.0,WALK_LRF,39570205,1 +316561881,3165618810,965127,456554,395702350,True,othmaint,8,9,10.0,WALK,39570235,1 +316561882,3165618820,965127,456554,395702350,True,othmaint,12,8,10.0,WALK,39570235,2 +316561885,3165618850,965127,456554,395702350,False,Home,9,12,10.0,WALK_LRF,39570235,1 +316561921,3165619210,965127,456554,395702400,True,shopping,14,9,10.0,WALK_LRF,39570240,1 +316561925,3165619250,965127,456554,395702400,False,shopping,16,14,20.0,WALK,39570240,1 +316561926,3165619260,965127,456554,395702400,False,shopping,16,16,20.0,WALK,39570240,2 +316561927,3165619270,965127,456554,395702400,False,Home,9,16,20.0,WALK_LRF,39570240,3 +316562561,3165625610,965129,456554,395703200,True,school,8,9,8.0,WALK,39570320,1 +316562565,3165625650,965129,456554,395703200,False,Home,9,8,15.0,SHARED3FREE,39570320,1 +316588209,3165882090,965207,456572,395735260,True,work,9,9,7.0,WALK,39573526,1 +316588213,3165882130,965207,456572,395735260,False,Home,9,9,18.0,WALK,39573526,1 +316588473,3165884730,965208,456572,395735590,True,school,9,9,11.0,WALK,39573559,1 +316588477,3165884770,965208,456572,395735590,False,Home,9,9,16.0,WALK,39573559,1 +316600937,3166009370,965246,456581,395751170,True,escort,9,9,7.0,WALK,39575117,1 +316600938,3166009380,965246,456581,395751170,True,school,13,9,8.0,WALK_LRF,39575117,2 +316600941,3166009410,965246,456581,395751170,False,Home,9,13,17.0,WALK_LRF,39575117,1 +316601265,3166012650,965247,456581,395751580,True,school,9,9,8.0,WALK,39575158,1 +316601269,3166012690,965247,456581,395751580,False,Home,9,9,16.0,WALK,39575158,1 +316601593,3166015930,965248,456581,395751990,True,school,21,9,6.0,WALK_LRF,39575199,1 +316601597,3166015970,965248,456581,395751990,False,othdiscr,22,21,15.0,WALK,39575199,1 +316601598,3166015980,965248,456581,395751990,False,Home,9,22,21.0,WALK_LRF,39575199,2 +316601921,3166019210,965249,456581,395752400,True,school,9,9,8.0,WALK,39575240,1 +316601925,3166019250,965249,456581,395752400,False,Home,9,9,12.0,WALK,39575240,1 +316620353,3166203530,965305,456594,395775440,True,work,22,9,10.0,WALK_LRF,39577544,1 +316620357,3166203570,965305,456594,395775440,False,Home,9,22,17.0,WALK_LRF,39577544,1 +316620361,3166203610,965305,456594,395775450,True,work,22,9,19.0,WALK_LOC,39577545,1 +316620365,3166203650,965305,456594,395775450,False,Home,9,22,19.0,WALK_LOC,39577545,1 +316634457,3166344570,965348,456604,395793070,True,work,9,9,7.0,TNC_SINGLE,39579307,1 +316634461,3166344610,965348,456604,395793070,False,othdiscr,12,9,18.0,TNC_SINGLE,39579307,1 +316634462,3166344620,965348,456604,395793070,False,Home,9,12,19.0,WALK_HVY,39579307,2 +316634737,3166347370,965349,456604,395793420,True,shopping,21,9,17.0,WALK_LOC,39579342,1 +316634741,3166347410,965349,456604,395793420,False,Home,9,21,17.0,WALK_LOC,39579342,1 +316635049,3166350490,965350,456604,395793810,True,school,7,9,9.0,SHARED3FREE,39579381,1 +316635053,3166350530,965350,456604,395793810,False,Home,9,7,18.0,WALK,39579381,1 +316635353,3166353530,965351,456604,395794190,True,othmaint,19,9,18.0,BIKE,39579419,1 +316635357,3166353570,965351,456604,395794190,False,Home,9,19,20.0,WALK,39579419,1 +316635377,3166353770,965351,456604,395794220,True,school,9,9,13.0,WALK,39579422,1 +316635381,3166353810,965351,456604,395794220,False,Home,9,9,16.0,WALK,39579422,1 +316659145,3166591450,965424,456621,395823930,True,escort,9,9,7.0,WALK,39582393,1 +316659149,3166591490,965424,456621,395823930,False,Home,9,9,7.0,WALK,39582393,1 +316659153,3166591530,965424,456621,395823940,True,escort,9,9,17.0,SHARED2FREE,39582394,1 +316659154,3166591540,965424,456621,395823940,True,escort,11,9,17.0,SHARED2FREE,39582394,2 +316659157,3166591570,965424,456621,395823940,False,shopping,13,11,17.0,DRIVEALONEFREE,39582394,1 +316659158,3166591580,965424,456621,395823940,False,Home,9,13,17.0,SHARED2FREE,39582394,2 +316659385,3166593850,965424,456621,395824230,True,work,10,9,7.0,DRIVEALONEFREE,39582423,1 +316659389,3166593890,965424,456621,395824230,False,Home,9,10,17.0,WALK,39582423,1 +316659977,3166599770,965426,456621,395824970,True,school,10,9,8.0,WALK,39582497,1 +316659981,3166599810,965426,456621,395824970,False,Home,9,10,15.0,WALK,39582497,1 +316660305,3166603050,965427,456621,395825380,True,othmaint,9,9,9.0,WALK,39582538,1 +316660306,3166603060,965427,456621,395825380,True,school,9,9,9.0,WALK,39582538,2 +316660309,3166603090,965427,456621,395825380,False,escort,9,9,15.0,WALK,39582538,1 +316660310,3166603100,965427,456621,395825380,False,othmaint,11,9,17.0,WALK,39582538,2 +316660311,3166603110,965427,456621,395825380,False,escort,8,11,17.0,WALK,39582538,3 +316660312,3166603120,965427,456621,395825380,False,Home,9,8,18.0,WALK,39582538,4 +316660633,3166606330,965428,456621,395825790,True,school,9,9,7.0,WALK,39582579,1 +316660637,3166606370,965428,456621,395825790,False,Home,9,9,13.0,WALK,39582579,1 +361596585,3615965850,1102428,484574,451995730,True,othdiscr,11,8,7.0,WALK,45199573,1 +361596589,3615965890,1102428,484574,451995730,False,Home,8,11,14.0,WALK,45199573,1 +361596977,3615969770,1102429,484574,451996220,True,shopping,11,8,10.0,BIKE,45199622,1 +361596981,3615969810,1102429,484574,451996220,False,Home,8,11,15.0,BIKE,45199622,1 +361596985,3615969850,1102429,484574,451996230,True,shopping,2,8,16.0,WALK,45199623,1 +361596989,3615969890,1102429,484574,451996230,False,othmaint,7,2,16.0,WALK,45199623,1 +361596990,3615969900,1102429,484574,451996230,False,shopping,7,7,16.0,WALK,45199623,2 +361596991,3615969910,1102429,484574,451996230,False,social,7,7,16.0,WALK,45199623,3 +361596992,3615969920,1102429,484574,451996230,False,Home,8,7,16.0,WALK,45199623,4 +361597265,3615972650,1102430,484574,451996580,True,othmaint,6,8,12.0,WALK,45199658,1 +361597269,3615972690,1102430,484574,451996580,False,shopping,7,6,12.0,WALK,45199658,1 +361597270,3615972700,1102430,484574,451996580,False,Home,8,7,13.0,WALK,45199658,2 +361629497,3616294970,1102528,484594,452036870,True,work,1,9,8.0,WALK_LRF,45203687,1 +361629501,3616295010,1102528,484594,452036870,False,Home,9,1,17.0,WALK,45203687,1 +361630065,3616300650,1102530,484594,452037580,True,othmaint,12,9,16.0,WALK,45203758,1 +361630069,3616300690,1102530,484594,452037580,False,eatout,7,12,16.0,WALK,45203758,1 +361630070,3616300700,1102530,484594,452037580,False,othmaint,7,7,17.0,WALK,45203758,2 +361630071,3616300710,1102530,484594,452037580,False,Home,9,7,17.0,WALK,45203758,3 +361630809,3616308090,1102532,484594,452038510,True,othmaint,7,9,5.0,WALK,45203851,1 +361630810,3616308100,1102532,484594,452038510,True,work,21,7,7.0,WALK,45203851,2 +361630813,3616308130,1102532,484594,452038510,False,work,5,21,15.0,WALK,45203851,1 +361630814,3616308140,1102532,484594,452038510,False,shopping,5,5,16.0,WALK,45203851,2 +361630815,3616308150,1102532,484594,452038510,False,Home,9,5,16.0,WALK,45203851,3 +361631073,3616310730,1102533,484594,452038840,True,school,10,9,7.0,WALK,45203884,1 +361631077,3616310770,1102533,484594,452038840,False,Home,9,10,15.0,WALK,45203884,1 +361647585,3616475850,1102584,484606,452059480,True,atwork,16,1,10.0,WALK,45205948,1 +361647589,3616475890,1102584,484606,452059480,False,Work,1,16,10.0,WALK,45205948,1 +361647865,3616478650,1102584,484606,452059830,True,work,1,9,5.0,WALK_LRF,45205983,1 +361647869,3616478690,1102584,484606,452059830,False,shopping,5,1,17.0,WALK_LOC,45205983,1 +361647870,3616478700,1102584,484606,452059830,False,shopping,12,5,17.0,WALK,45205983,2 +361647871,3616478710,1102584,484606,452059830,False,Home,9,12,17.0,WALK_LRF,45205983,3 +361649113,3616491130,1102588,484606,452061390,True,school,10,9,7.0,WALK_LOC,45206139,1 +361649117,3616491170,1102588,484606,452061390,False,Home,9,10,15.0,WALK_LOC,45206139,1 +361649593,3616495930,1102590,484606,452061990,True,escort,1,9,10.0,WALK_LRF,45206199,1 +361649597,3616495970,1102590,484606,452061990,False,Home,9,1,10.0,WALK_LRF,45206199,1 +361650097,3616500970,1102591,484606,452062620,True,school,5,9,7.0,WALK,45206262,1 +361650101,3616501010,1102591,484606,452062620,False,Home,9,5,14.0,WALK,45206262,1 +361650577,3616505770,1102593,484606,452063220,True,escort,17,9,14.0,WALK_LRF,45206322,1 +361650581,3616505810,1102593,484606,452063220,False,Home,9,17,17.0,WALK_LRF,45206322,1 +361652457,3616524570,1102598,484608,452065570,True,work,13,9,9.0,WALK,45206557,1 +361652461,3616524610,1102598,484608,452065570,False,Home,9,13,13.0,WALK,45206557,1 +361652785,3616527850,1102599,484608,452065980,True,othmaint,4,9,8.0,BIKE,45206598,1 +361652786,3616527860,1102599,484608,452065980,True,work,9,4,8.0,BIKE,45206598,2 +361652789,3616527890,1102599,484608,452065980,False,Home,9,9,18.0,BIKE,45206598,1 +361652849,3616528490,1102600,484608,452066060,True,eatout,6,9,8.0,WALK,45206606,1 +361652853,3616528530,1102600,484608,452066060,False,Home,9,6,19.0,WALK,45206606,1 +361653377,3616533770,1102601,484608,452066720,True,school,6,9,6.0,WALK_LRF,45206672,1 +361653381,3616533810,1102601,484608,452066720,False,Home,9,6,14.0,WALK_LOC,45206672,1 +361653705,3616537050,1102602,484608,452067130,True,school,6,9,6.0,WALK_LRF,45206713,1 +361653709,3616537090,1102602,484608,452067130,False,Home,9,6,13.0,WALK_LOC,45206713,1 +361658689,3616586890,1102617,484612,452073360,True,work,9,11,8.0,WALK,45207336,1 +361658693,3616586930,1102617,484612,452073360,False,Home,11,9,18.0,WALK,45207336,1 +361659281,3616592810,1102619,484612,452074100,True,school,8,11,7.0,WALK_LOC,45207410,1 +361659285,3616592850,1102619,484612,452074100,False,escort,12,8,11.0,WALK_LOC,45207410,1 +361659286,3616592860,1102619,484612,452074100,False,Home,11,12,12.0,WALK,45207410,2 +361659609,3616596090,1102620,484612,452074510,True,school,8,11,7.0,WALK_LOC,45207451,1 +361659613,3616596130,1102620,484612,452074510,False,Home,11,8,10.0,WALK,45207451,1 +361659617,3616596170,1102620,484612,452074520,True,school,8,11,14.0,WALK,45207452,1 +361659621,3616596210,1102620,484612,452074520,False,Home,11,8,19.0,WALK,45207452,1 +361753809,3617538090,1102907,484670,452192260,True,work,16,17,9.0,WALK,45219226,1 +361753813,3617538130,1102907,484670,452192260,False,othmaint,17,16,17.0,WALK,45219226,1 +361753814,3617538140,1102907,484670,452192260,False,Home,17,17,18.0,WALK,45219226,2 +361753897,3617538970,1102908,484670,452192370,True,escort,25,17,17.0,WALK_LOC,45219237,1 +361753901,3617539010,1102908,484670,452192370,False,Home,17,25,17.0,TNC_SINGLE,45219237,1 +361754025,3617540250,1102908,484670,452192530,True,othdiscr,14,17,18.0,WALK_LOC,45219253,1 +361754029,3617540290,1102908,484670,452192530,False,Home,17,14,21.0,WALK_LRF,45219253,1 +361754137,3617541370,1102908,484670,452192670,True,work,1,17,5.0,WALK,45219267,1 +361754141,3617541410,1102908,484670,452192670,False,Home,17,1,14.0,WALK,45219267,1 +361754401,3617544010,1102909,484670,452193000,True,school,16,17,8.0,WALK_LRF,45219300,1 +361754405,3617544050,1102909,484670,452193000,False,Home,17,16,16.0,WALK_LRF,45219300,1 +361754553,3617545530,1102910,484670,452193190,True,social,7,17,7.0,TNC_SINGLE,45219319,1 +361754554,3617545540,1102910,484670,452193190,True,escort,11,7,7.0,TNC_SINGLE,45219319,2 +361754557,3617545570,1102910,484670,452193190,False,shopping,6,11,7.0,WALK_LOC,45219319,1 +361754558,3617545580,1102910,484670,452193190,False,Home,17,6,7.0,WALK_LRF,45219319,2 +361754729,3617547290,1102910,484670,452193410,True,school,9,17,8.0,WALK_LRF,45219341,1 +361754733,3617547330,1102910,484670,452193410,False,Home,17,9,15.0,WALK_LRF,45219341,1 +361760697,3617606970,1102928,484675,452200870,True,work,15,17,12.0,WALK,45220087,1 +361760701,3617607010,1102928,484675,452200870,False,Home,17,15,22.0,WALK,45220087,1 +361760785,3617607850,1102929,484675,452200980,True,escort,21,17,6.0,TNC_SHARED,45220098,1 +361760786,3617607860,1102929,484675,452200980,True,escort,18,21,7.0,WALK_LOC,45220098,2 +361760789,3617607890,1102929,484675,452200980,False,Home,17,18,7.0,WALK_LOC,45220098,1 +361761025,3617610250,1102929,484675,452201280,True,work,13,17,10.0,WALK,45220128,1 +361761026,3617610260,1102929,484675,452201280,True,work,12,13,14.0,WALK,45220128,2 +361761029,3617610290,1102929,484675,452201280,False,eatout,12,12,19.0,WALK,45220128,1 +361761030,3617610300,1102929,484675,452201280,False,Home,17,12,20.0,WALK,45220128,2 +361761289,3617612890,1102930,484675,452201610,True,school,13,17,13.0,SHARED2FREE,45220161,1 +361761293,3617612930,1102930,484675,452201610,False,Home,17,13,22.0,WALK_LRF,45220161,1 +361761617,3617616170,1102931,484675,452202020,True,school,25,17,8.0,WALK_LOC,45220202,1 +361761621,3617616210,1102931,484675,452202020,False,Home,17,25,16.0,WALK_LRF,45220202,1 +361761897,3617618970,1102932,484675,452202370,True,othdiscr,9,17,18.0,WALK_LRF,45220237,1 +361761901,3617619010,1102932,484675,452202370,False,Home,17,9,22.0,WALK_LRF,45220237,1 +361761905,3617619050,1102932,484675,452202380,True,othdiscr,12,17,22.0,WALK,45220238,1 +361761909,3617619090,1102932,484675,452202380,False,Home,17,12,23.0,WALK,45220238,1 +361761945,3617619450,1102932,484675,452202430,True,school,8,17,8.0,WALK_LRF,45220243,1 +361761949,3617619490,1102932,484675,452202430,False,Home,17,8,15.0,WALK_LRF,45220243,1 +361769225,3617692250,1102954,484680,452211530,True,work,2,21,12.0,WALK,45221153,1 +361769229,3617692290,1102954,484680,452211530,False,Home,21,2,15.0,WALK,45221153,1 +361769505,3617695050,1102955,484680,452211880,True,shopping,5,21,18.0,BIKE,45221188,1 +361769509,3617695090,1102955,484680,452211880,False,othmaint,7,5,18.0,BIKE,45221188,1 +361769510,3617695100,1102955,484680,452211880,False,Home,21,7,19.0,BIKE,45221188,2 +361769817,3617698170,1102956,484680,452212270,True,school,21,21,15.0,WALK,45221227,1 +361769821,3617698210,1102956,484680,452212270,False,Home,21,21,19.0,WALK,45221227,1 +361770145,3617701450,1102957,484680,452212680,True,school,8,21,8.0,SHARED3FREE,45221268,1 +361770149,3617701490,1102957,484680,452212680,False,Home,21,8,15.0,WALK,45221268,1 +361779721,3617797210,1102986,484686,452224650,True,work,4,25,11.0,WALK,45222465,1 +361779725,3617797250,1102986,484686,452224650,False,Home,25,4,20.0,WALK,45222465,1 +361780049,3617800490,1102987,484686,452225060,True,work,21,25,9.0,WALK,45222506,1 +361780053,3617800530,1102987,484686,452225060,False,Home,25,21,19.0,WALK,45222506,1 +361780113,3617801130,1102988,484686,452225140,True,eatout,7,25,17.0,WALK,45222514,1 +361780117,3617801170,1102988,484686,452225140,False,Home,25,7,20.0,WALK,45222514,1 +361780313,3617803130,1102988,484686,452225390,True,school,6,25,7.0,WALK_LOC,45222539,1 +361780317,3617803170,1102988,484686,452225390,False,Home,25,6,15.0,WALK_LOC,45222539,1 +361780641,3617806410,1102989,484686,452225800,True,school,25,25,7.0,WALK,45222580,1 +361780645,3617806450,1102989,484686,452225800,False,Home,25,25,15.0,WALK,45222580,1 +393801001,3938010010,1200612,503499,492251250,True,othmaint,7,5,16.0,SHARED3FREE,49225125,1 +393801002,3938010020,1200612,503499,492251250,True,othmaint,4,7,16.0,SHARED3FREE,49225125,2 +393801003,3938010030,1200612,503499,492251250,True,shopping,22,4,17.0,SHARED3FREE,49225125,3 +393801005,3938010050,1200612,503499,492251250,False,Home,5,22,20.0,DRIVEALONEFREE,49225125,1 +393821665,3938216650,1200675,503562,492277080,True,shopping,11,25,10.0,SHARED3FREE,49227708,1 +393821669,3938216690,1200675,503562,492277080,False,Home,25,11,13.0,WALK,49227708,1 +414031713,4140317130,1262291,565178,517539640,True,shopping,13,2,12.0,WALK,51753964,1 +414031717,4140317170,1262291,565178,517539640,False,Home,2,13,17.0,WALK,51753964,1 +414037881,4140378810,1262310,565197,517547350,True,othdiscr,17,3,19.0,WALK,51754735,1 +414037885,4140378850,1262310,565197,517547350,False,Home,3,17,22.0,WALK,51754735,1 +414046737,4140467370,1262337,565224,517558420,True,othdiscr,22,3,7.0,WALK,51755842,1 +414046741,4140467410,1262337,565224,517558420,False,Home,3,22,18.0,WALK,51755842,1 +414056641,4140566410,1262367,565254,517570800,True,shopping,2,5,9.0,WALK,51757080,1 +414056645,4140566450,1262367,565254,517570800,False,Home,5,2,14.0,WALK,51757080,1 +414056649,4140566490,1262367,565254,517570810,True,social,7,5,16.0,WALK,51757081,1 +414056650,4140566500,1262367,565254,517570810,True,shopping,19,7,16.0,TNC_SHARED,51757081,2 +414056653,4140566530,1262367,565254,517570810,False,Home,5,19,17.0,DRIVEALONEFREE,51757081,1 +414070881,4140708810,1262411,565298,517588600,True,escort,7,5,7.0,WALK,51758860,1 +414070885,4140708850,1262411,565298,517588600,False,social,5,7,8.0,WALK,51758860,1 +414070886,4140708860,1262411,565298,517588600,False,escort,12,5,8.0,WALK,51758860,2 +414070887,4140708870,1262411,565298,517588600,False,Home,5,12,8.0,WALK,51758860,3 +414081201,4140812010,1262442,565329,517601500,True,othmaint,9,5,12.0,WALK,51760150,1 +414081205,4140812050,1262442,565329,517601500,False,Home,5,9,16.0,WALK,51760150,1 +414103897,4141038970,1262511,565398,517629870,True,social,9,6,14.0,WALK_LOC,51762987,1 +414103901,4141039010,1262511,565398,517629870,False,Home,6,9,15.0,WALK_LOC,51762987,1 +414109385,4141093850,1262528,565415,517636730,True,othmaint,6,6,10.0,WALK,51763673,1 +414109386,4141093860,1262528,565415,517636730,True,othdiscr,2,6,11.0,WALK,51763673,2 +414109389,4141093890,1262528,565415,517636730,False,Home,6,2,18.0,WALK,51763673,1 +414143889,4141438890,1262633,565520,517679860,True,shopping,19,6,14.0,SHARED2FREE,51767986,1 +414143893,4141438930,1262633,565520,517679860,False,shopping,5,19,14.0,SHARED2FREE,51767986,1 +414143894,4141438940,1262633,565520,517679860,False,shopping,5,5,14.0,WALK,51767986,2 +414143895,4141438950,1262633,565520,517679860,False,shopping,11,5,14.0,WALK,51767986,3 +414143896,4141438960,1262633,565520,517679860,False,Home,6,11,14.0,SHARED2FREE,51767986,4 +414169825,4141698250,1262712,565599,517712280,True,social,7,6,12.0,WALK,51771228,1 +414169829,4141698290,1262712,565599,517712280,False,Home,6,7,13.0,WALK,51771228,1 +414183865,4141838650,1262755,565642,517729830,True,othmaint,2,7,5.0,WALK,51772983,1 +414183869,4141838690,1262755,565642,517729830,False,Home,7,2,13.0,WALK,51772983,1 +414183873,4141838730,1262755,565642,517729840,True,othmaint,16,7,15.0,WALK_LOC,51772984,1 +414183877,4141838770,1262755,565642,517729840,False,shopping,12,16,16.0,WALK,51772984,1 +414183878,4141838780,1262755,565642,517729840,False,escort,11,12,17.0,WALK_LOC,51772984,2 +414183879,4141838790,1262755,565642,517729840,False,othmaint,5,11,17.0,WALK,51772984,3 +414183880,4141838800,1262755,565642,517729840,False,Home,7,5,17.0,WALK,51772984,4 +414192105,4141921050,1262780,565667,517740130,True,shopping,11,7,10.0,WALK,51774013,1 +414192109,4141921090,1262780,565667,517740130,False,shopping,9,11,12.0,WALK,51774013,1 +414192110,4141921100,1262780,565667,517740130,False,Home,7,9,14.0,WALK,51774013,2 +414208137,4142081370,1262829,565716,517760170,True,othmaint,9,7,10.0,WALK,51776017,1 +414208141,4142081410,1262829,565716,517760170,False,Home,7,9,13.0,WALK,51776017,1 +414235073,4142350730,1262911,565798,517793840,True,shopping,16,7,9.0,WALK_LOC,51779384,1 +414235077,4142350770,1262911,565798,517793840,False,Home,7,16,17.0,TNC_SHARED,51779384,1 +414298641,4142986410,1263105,565992,517873300,True,othdiscr,5,7,16.0,DRIVEALONEFREE,51787330,1 +414298645,4142986450,1263105,565992,517873300,False,Home,7,5,16.0,DRIVEALONEFREE,51787330,1 +414314889,4143148890,1263155,566042,517893610,True,eatout,8,7,7.0,WALK,51789361,1 +414314893,4143148930,1263155,566042,517893610,False,Home,7,8,16.0,WALK,51789361,1 +414323241,4143232410,1263180,566067,517904050,True,othdiscr,9,7,9.0,WALK,51790405,1 +414323245,4143232450,1263180,566067,517904050,False,Home,7,9,17.0,WALK,51790405,1 +414342657,4143426570,1263239,566126,517928320,True,shopping,7,7,13.0,WALK,51792832,1 +414342661,4143426610,1263239,566126,517928320,False,Home,7,7,14.0,WALK,51792832,1 +414342681,4143426810,1263239,566126,517928350,True,social,7,7,16.0,WALK,51792835,1 +414342685,4143426850,1263239,566126,517928350,False,Home,7,7,16.0,WALK,51792835,1 +414348017,4143480170,1263256,566143,517935020,True,eatout,5,7,14.0,WALK,51793502,1 +414348021,4143480210,1263256,566143,517935020,False,Home,7,5,15.0,WALK,51793502,1 +414348169,4143481690,1263256,566143,517935210,True,othdiscr,5,7,15.0,BIKE,51793521,1 +414348173,4143481730,1263256,566143,517935210,False,Home,7,5,21.0,WALK,51793521,1 +414393169,4143931690,1263393,566280,517991460,True,shopping,22,8,16.0,WALK_LRF,51799146,1 +414393173,4143931730,1263393,566280,517991460,False,Home,8,22,19.0,WALK_LRF,51799146,1 +414395449,4143954490,1263400,566287,517994310,True,univ,14,8,18.0,WALK_LOC,51799431,1 +414395453,4143954530,1263400,566287,517994310,False,escort,2,14,20.0,WALK_LOC,51799431,1 +414395454,4143954540,1263400,566287,517994310,False,Home,8,2,21.0,WALK_LRF,51799431,2 +414427609,4144276090,1263498,566385,518034510,True,shopping,5,8,11.0,WALK,51803451,1 +414427613,4144276130,1263498,566385,518034510,False,Home,8,5,13.0,WALK,51803451,1 +414428857,4144288570,1263502,566389,518036070,True,othdiscr,5,8,13.0,WALK,51803607,1 +414428861,4144288610,1263502,566389,518036070,False,Home,8,5,19.0,WALK,51803607,1 +414438545,4144385450,1263532,566419,518048180,True,eatout,13,8,12.0,DRIVEALONEFREE,51804818,1 +414438549,4144385490,1263532,566419,518048180,False,Home,8,13,12.0,SHARED2FREE,51804818,1 +414463145,4144631450,1263607,566494,518078930,True,eatout,9,8,11.0,WALK,51807893,1 +414463149,4144631490,1263607,566494,518078930,False,Home,8,9,19.0,WALK,51807893,1 +414477137,4144771370,1263649,566536,518096420,True,shopping,11,8,16.0,WALK,51809642,1 +414477141,4144771410,1263649,566536,518096420,False,Home,8,11,16.0,WALK,51809642,1 +414508737,4145087370,1263746,566633,518135920,True,eatout,22,8,7.0,WALK,51813592,1 +414508741,4145087410,1263746,566633,518135920,False,Home,8,22,14.0,WALK,51813592,1 +414515209,4145152090,1263765,566652,518144010,True,social,9,8,8.0,WALK,51814401,1 +414515213,4145152130,1263765,566652,518144010,False,Home,8,9,22.0,WALK,51814401,1 +414520065,4145200650,1263780,566667,518150080,True,othmaint,22,8,8.0,WALK,51815008,1 +414520069,4145200690,1263780,566667,518150080,False,Home,8,22,19.0,WALK_LRF,51815008,1 +414537489,4145374890,1263833,566720,518171860,True,shopping,9,8,11.0,WALK_LOC,51817186,1 +414537490,4145374900,1263833,566720,518171860,True,shopping,11,9,12.0,WALK_LOC,51817186,2 +414537493,4145374930,1263833,566720,518171860,False,Home,8,11,13.0,TNC_SINGLE,51817186,1 +414562857,4145628570,1263911,566798,518203570,True,eatout,22,8,14.0,WALK,51820357,1 +414562861,4145628610,1263911,566798,518203570,False,Home,8,22,20.0,WALK,51820357,1 +414573921,4145739210,1263944,566831,518217400,True,social,24,8,10.0,WALK,51821740,1 +414573925,4145739250,1263944,566831,518217400,False,Home,8,24,13.0,WALK,51821740,1 +414574993,4145749930,1263948,566835,518218740,True,eatout,5,8,9.0,WALK,51821874,1 +414574997,4145749970,1263948,566835,518218740,False,Home,8,5,16.0,WALK,51821874,1 +414582689,4145826890,1263971,566858,518228360,True,othdiscr,15,8,8.0,WALK,51822836,1 +414582693,4145826930,1263971,566858,518228360,False,Home,8,15,12.0,WALK,51822836,1 +414588265,4145882650,1263988,566875,518235330,True,othdiscr,5,8,12.0,WALK,51823533,1 +414588269,4145882690,1263988,566875,518235330,False,Home,8,5,18.0,WALK,51823533,1 +414624193,4146241930,1264098,566985,518280240,True,eatout,11,9,15.0,WALK,51828024,1 +414624197,4146241970,1264098,566985,518280240,False,Home,9,11,17.0,WALK,51828024,1 +414624369,4146243690,1264098,566985,518280460,True,othmaint,9,9,11.0,WALK,51828046,1 +414624373,4146243730,1264098,566985,518280460,False,Home,9,9,14.0,WALK,51828046,1 +414624409,4146244090,1264098,566985,518280510,True,shopping,5,9,15.0,TNC_SINGLE,51828051,1 +414624413,4146244130,1264098,566985,518280510,False,shopping,6,5,15.0,DRIVEALONEFREE,51828051,1 +414624414,4146244140,1264098,566985,518280510,False,Home,9,6,15.0,DRIVEALONEFREE,51828051,2 +414630641,4146306410,1264117,567004,518288300,True,shopping,8,9,9.0,WALK,51828830,1 +414630645,4146306450,1264117,567004,518288300,False,Home,9,8,12.0,WALK,51828830,1 +414633553,4146335530,1264126,567013,518291940,True,othdiscr,5,9,10.0,WALK,51829194,1 +414633554,4146335540,1264126,567013,518291940,True,othmaint,24,5,11.0,WALK,51829194,2 +414633557,4146335570,1264126,567013,518291940,False,Home,9,24,15.0,WALK_LRF,51829194,1 +414633617,4146336170,1264126,567013,518292020,True,social,11,9,18.0,WALK,51829202,1 +414633621,4146336210,1264126,567013,518292020,False,Home,9,11,21.0,WALK,51829202,1 +414635169,4146351690,1264131,567018,518293960,True,othdiscr,9,9,12.0,WALK,51829396,1 +414635173,4146351730,1264131,567018,518293960,False,Home,9,9,22.0,WALK,51829396,1 +414637529,4146375290,1264138,567025,518296910,True,shopping,3,9,15.0,WALK_LRF,51829691,1 +414637530,4146375300,1264138,567025,518296910,True,shopping,13,3,15.0,WALK_LOC,51829691,2 +414637533,4146375330,1264138,567025,518296910,False,shopping,16,13,15.0,WALK_LOC,51829691,1 +414637534,4146375340,1264138,567025,518296910,False,Home,9,16,16.0,WALK_LRF,51829691,2 +414638449,4146384490,1264141,567028,518298060,True,othdiscr,7,9,7.0,WALK_LOC,51829806,1 +414638453,4146384530,1264141,567028,518298060,False,Home,9,7,12.0,WALK_LOC,51829806,1 +414657865,4146578650,1264200,567087,518322330,True,shopping,14,9,16.0,WALK_LRF,51832233,1 +414657869,4146578690,1264200,567087,518322330,False,Home,9,14,18.0,WALK_LRF,51832233,1 +414665697,4146656970,1264224,567111,518332120,True,othmaint,4,9,8.0,WALK,51833212,1 +414665701,4146657010,1264224,567111,518332120,False,Home,9,4,11.0,WALK,51833212,1 +414667049,4146670490,1264228,567115,518333810,True,shopping,16,9,13.0,WALK_LRF,51833381,1 +414667053,4146670530,1264228,567115,518333810,False,Home,9,16,15.0,WALK_LRF,51833381,1 +414667057,4146670570,1264228,567115,518333820,True,shopping,16,9,17.0,SHARED3FREE,51833382,1 +414667061,4146670610,1264228,567115,518333820,False,eatout,16,16,17.0,SHARED3FREE,51833382,1 +414667062,4146670620,1264228,567115,518333820,False,Home,9,16,17.0,SHARED3FREE,51833382,2 +414686401,4146864010,1264287,567174,518358000,True,othmaint,22,9,9.0,WALK_LRF,51835800,1 +414686402,4146864020,1264287,567174,518358000,True,shopping,4,22,10.0,WALK_LRF,51835800,2 +414686405,4146864050,1264287,567174,518358000,False,Home,9,4,15.0,WALK_LRF,51835800,1 +414686409,4146864090,1264287,567174,518358010,True,shopping,5,9,15.0,WALK,51835801,1 +414686413,4146864130,1264287,567174,518358010,False,Home,9,5,16.0,WALK,51835801,1 +414704769,4147047690,1264343,567230,518380960,True,shopping,5,9,14.0,WALK,51838096,1 +414704773,4147047730,1264343,567230,518380960,False,Home,9,5,15.0,WALK_LOC,51838096,1 +414704777,4147047770,1264343,567230,518380970,True,shopping,16,9,15.0,WALK_LRF,51838097,1 +414704781,4147047810,1264343,567230,518380970,False,Home,9,16,15.0,WALK_LRF,51838097,1 +414731361,4147313610,1264424,567311,518414200,True,social,9,9,15.0,WALK,51841420,1 +414731365,4147313650,1264424,567311,518414200,False,Home,9,9,16.0,WALK,51841420,1 +414756377,4147563770,1264501,567388,518445470,True,eatout,21,9,16.0,WALK,51844547,1 +414756381,4147563810,1264501,567388,518445470,False,Home,9,21,16.0,WALK,51844547,1 +414756577,4147565770,1264501,567388,518445720,True,escort,9,9,17.0,WALK,51844572,1 +414756578,4147565780,1264501,567388,518445720,True,univ,9,9,17.0,WALK,51844572,2 +414756581,4147565810,1264501,567388,518445720,False,Home,9,9,17.0,WALK,51844572,1 +414756593,4147565930,1264501,567388,518445740,True,shopping,11,9,13.0,WALK,51844574,1 +414756597,4147565970,1264501,567388,518445740,False,Home,9,11,14.0,WALK,51844574,1 +414788217,4147882170,1264598,567485,518485270,True,escort,16,9,9.0,WALK,51848527,1 +414788221,4147882210,1264598,567485,518485270,False,Home,9,16,10.0,WALK,51848527,1 +414788369,4147883690,1264598,567485,518485460,True,othmaint,4,9,11.0,WALK,51848546,1 +414788373,4147883730,1264598,567485,518485460,False,eatout,7,4,13.0,WALK,51848546,1 +414788374,4147883740,1264598,567485,518485460,False,Home,9,7,13.0,WALK,51848546,2 +414788409,4147884090,1264598,567485,518485510,True,shopping,1,9,15.0,WALK_LRF,51848551,1 +414788413,4147884130,1264598,567485,518485510,False,Home,9,1,17.0,WALK_LRF,51848551,1 +414788417,4147884170,1264598,567485,518485520,True,shopping,16,9,18.0,SHARED2FREE,51848552,1 +414788421,4147884210,1264598,567485,518485520,False,Home,9,16,20.0,SHARED2FREE,51848552,1 +414797201,4147972010,1264625,567512,518496500,True,eatout,9,9,8.0,WALK,51849650,1 +414797202,4147972020,1264625,567512,518496500,True,othdiscr,9,9,9.0,WALK,51849650,2 +414797205,4147972050,1264625,567512,518496500,False,Home,9,9,13.0,WALK,51849650,1 +414807761,4148077610,1264657,567544,518509700,True,shopping,19,9,9.0,WALK,51850970,1 +414807765,4148077650,1264657,567544,518509700,False,Home,9,19,10.0,WALK,51850970,1 +414819833,4148198330,1264694,567581,518524790,True,othdiscr,25,10,12.0,WALK_LOC,51852479,1 +414819837,4148198370,1264694,567581,518524790,False,Home,10,25,14.0,WALK_LOC,51852479,1 +414828561,4148285610,1264721,567608,518535700,True,escort,8,10,8.0,DRIVEALONEFREE,51853570,1 +414828565,4148285650,1264721,567608,518535700,False,Home,10,8,8.0,DRIVEALONEFREE,51853570,1 +414851321,4148513210,1264790,567677,518564150,True,othdiscr,9,10,14.0,WALK,51856415,1 +414851325,4148513250,1264790,567677,518564150,False,Home,10,9,18.0,WALK,51856415,1 +414851385,4148513850,1264790,567677,518564230,True,shopping,11,10,11.0,WALK,51856423,1 +414851389,4148513890,1264790,567677,518564230,False,shopping,5,11,11.0,WALK,51856423,1 +414851390,4148513900,1264790,567677,518564230,False,shopping,8,5,11.0,WALK,51856423,2 +414851391,4148513910,1264790,567677,518564230,False,Home,10,8,11.0,WALK,51856423,3 +414902489,4149024890,1264946,567833,518628110,True,othdiscr,10,10,10.0,WALK,51862811,1 +414902493,4149024930,1264946,567833,518628110,False,Home,10,10,17.0,WALK,51862811,1 +414945305,4149453050,1265077,567964,518681630,True,eatout,16,10,16.0,SHARED3FREE,51868163,1 +414945309,4149453090,1265077,567964,518681630,False,Home,10,16,16.0,SHARED3FREE,51868163,1 +414945457,4149454570,1265077,567964,518681820,True,othdiscr,4,10,16.0,WALK,51868182,1 +414945461,4149454610,1265077,567964,518681820,False,Home,10,4,20.0,WALK,51868182,1 +414945481,4149454810,1265077,567964,518681850,True,othmaint,2,10,7.0,WALK,51868185,1 +414945485,4149454850,1265077,567964,518681850,False,Home,10,2,16.0,WALK,51868185,1 +414989585,4149895850,1265212,568099,518736980,True,eatout,12,11,11.0,WALK,51873698,1 +414989589,4149895890,1265212,568099,518736980,False,Home,11,12,14.0,WALK,51873698,1 +415001281,4150012810,1265247,568134,518751600,True,shopping,17,11,15.0,DRIVEALONEFREE,51875160,1 +415001285,4150012850,1265247,568134,518751600,False,Home,11,17,15.0,DRIVEALONEFREE,51875160,1 +415018297,4150182970,1265299,568186,518772870,True,othmaint,1,11,20.0,WALK_LRF,51877287,1 +415018301,4150183010,1265299,568186,518772870,False,Home,11,1,22.0,WALK_LRF,51877287,1 +415034673,4150346730,1265349,568236,518793340,True,othdiscr,2,11,9.0,TNC_SINGLE,51879334,1 +415034677,4150346770,1265349,568236,518793340,False,Home,11,2,11.0,TNC_SINGLE,51879334,1 +415034697,4150346970,1265349,568236,518793370,True,othmaint,7,11,12.0,WALK,51879337,1 +415034698,4150346980,1265349,568236,518793370,True,othmaint,9,7,15.0,WALK_LOC,51879337,2 +415034701,4150347010,1265349,568236,518793370,False,social,9,9,13.0,WALK,51879337,1 +415034702,4150347020,1265349,568236,518793370,False,Home,11,9,15.0,WALK_LOC,51879337,2 +415036313,4150363130,1265354,568241,518795390,True,othdiscr,9,11,14.0,WALK,51879539,1 +415036317,4150363170,1265354,568241,518795390,False,Home,11,9,16.0,WALK,51879539,1 +415036377,4150363770,1265354,568241,518795470,True,shopping,11,11,8.0,WALK,51879547,1 +415036381,4150363810,1265354,568241,518795470,False,othdiscr,7,11,10.0,WALK,51879547,1 +415036382,4150363820,1265354,568241,518795470,False,Home,11,7,10.0,WALK,51879547,2 +415037953,4150379530,1265359,568246,518797440,True,othdiscr,21,11,8.0,WALK,51879744,1 +415037957,4150379570,1265359,568246,518797440,False,Home,11,21,14.0,WALK,51879744,1 +415057633,4150576330,1265419,568306,518822040,True,othdiscr,10,11,9.0,TNC_SINGLE,51882204,1 +415057637,4150576370,1265419,568306,518822040,False,Home,11,10,23.0,WALK_LOC,51882204,1 +415066489,4150664890,1265446,568333,518833110,True,othdiscr,2,12,10.0,WALK,51883311,1 +415066493,4150664930,1265446,568333,518833110,False,Home,12,2,16.0,WALK,51883311,1 +415069833,4150698330,1265456,568343,518837290,True,shopping,4,12,10.0,WALK,51883729,1 +415069837,4150698370,1265456,568343,518837290,False,shopping,6,4,20.0,WALK,51883729,1 +415069838,4150698380,1265456,568343,518837290,False,Home,12,6,20.0,WALK,51883729,2 +415097057,4150970570,1265539,568426,518871320,True,shopping,16,15,14.0,WALK,51887132,1 +415097061,4150970610,1265539,568426,518871320,False,Home,15,16,14.0,WALK,51887132,1 +415110529,4151105290,1265580,568467,518888160,True,social,12,16,9.0,WALK_LOC,51888816,1 +415110533,4151105330,1265580,568467,518888160,False,Home,16,12,17.0,WALK_LOC,51888816,1 +415110537,4151105370,1265580,568467,518888170,True,social,16,16,19.0,TNC_SINGLE,51888817,1 +415110541,4151105410,1265580,568467,518888170,False,Home,16,16,19.0,TNC_SINGLE,51888817,1 +415117065,4151170650,1265600,568487,518896330,True,shopping,23,16,20.0,WALK_LRF,51889633,1 +415117069,4151170690,1265600,568487,518896330,False,Home,16,23,20.0,WALK_LRF,51889633,1 +415120785,4151207850,1265612,568499,518900980,True,eatout,16,16,12.0,WALK,51890098,1 +415120789,4151207890,1265612,568499,518900980,False,shopping,16,16,22.0,WALK,51890098,1 +415120790,4151207900,1265612,568499,518900980,False,Home,16,16,22.0,WALK,51890098,2 +415121329,4151213290,1265613,568500,518901660,True,shopping,5,16,10.0,WALK,51890166,1 +415121333,4151213330,1265613,568500,518901660,False,Home,16,5,10.0,WALK_LOC,51890166,1 +415121337,4151213370,1265613,568500,518901670,True,shopping,5,16,18.0,WALK,51890167,1 +415121341,4151213410,1265613,568500,518901670,False,Home,16,5,18.0,WALK,51890167,1 +415121353,4151213530,1265613,568500,518901690,True,social,14,16,15.0,WALK,51890169,1 +415121357,4151213570,1265613,568500,518901690,False,Home,16,14,15.0,WALK,51890169,1 +415154089,4151540890,1265713,568600,518942610,True,othmaint,15,16,10.0,WALK_LOC,51894261,1 +415154093,4151540930,1265713,568600,518942610,False,othmaint,3,15,12.0,WALK,51894261,1 +415154094,4151540940,1265713,568600,518942610,False,shopping,25,3,13.0,WALK,51894261,2 +415154095,4151540950,1265713,568600,518942610,False,othmaint,4,25,14.0,WALK_LOC,51894261,3 +415154096,4151540960,1265713,568600,518942610,False,Home,16,4,14.0,WALK_LOC,51894261,4 +415167929,4151679290,1265755,568642,518959910,True,social,9,16,9.0,TNC_SHARED,51895991,1 +415167933,4151679330,1265755,568642,518959910,False,Home,16,9,10.0,TNC_SINGLE,51895991,1 +415178073,4151780730,1265786,568673,518972590,True,shopping,21,16,11.0,WALK_LOC,51897259,1 +415178077,4151780770,1265786,568673,518972590,False,othmaint,11,21,14.0,WALK,51897259,1 +415178078,4151780780,1265786,568673,518972590,False,Home,16,11,14.0,WALK_LOC,51897259,2 +415182953,4151829530,1265801,568688,518978690,True,othmaint,16,16,16.0,TNC_SINGLE,51897869,1 +415182957,4151829570,1265801,568688,518978690,False,Home,16,16,20.0,TNC_SINGLE,51897869,1 +415192641,4151926410,1265831,568718,518990800,True,escort,16,16,8.0,WALK,51899080,1 +415192645,4151926450,1265831,568718,518990800,False,shopping,17,16,12.0,WALK,51899080,1 +415192646,4151926460,1265831,568718,518990800,False,Home,16,17,12.0,WALK,51899080,2 +415194785,4151947850,1265837,568724,518993480,True,escort,15,16,20.0,WALK,51899348,1 +415194786,4151947860,1265837,568724,518993480,True,escort,14,15,20.0,WALK,51899348,2 +415194787,4151947870,1265837,568724,518993480,True,univ,14,14,20.0,WALK,51899348,3 +415194789,4151947890,1265837,568724,518993480,False,shopping,16,14,23.0,WALK,51899348,1 +415194790,4151947900,1265837,568724,518993480,False,escort,16,16,23.0,WALK,51899348,2 +415194791,4151947910,1265837,568724,518993480,False,othmaint,17,16,23.0,WALK,51899348,3 +415194792,4151947920,1265837,568724,518993480,False,Home,16,17,23.0,WALK,51899348,4 +415203113,4152031130,1265863,568750,519003890,True,eatout,13,17,9.0,WALK,51900389,1 +415203117,4152031170,1265863,568750,519003890,False,Home,17,13,17.0,WALK,51900389,1 +415214745,4152147450,1265898,568785,519018430,True,othdiscr,20,17,9.0,WALK_LRF,51901843,1 +415214749,4152147490,1265898,568785,519018430,False,Home,17,20,20.0,WALK_LOC,51901843,1 +415223993,4152239930,1265926,568813,519029990,True,shopping,16,17,12.0,WALK,51902999,1 +415223997,4152239970,1265926,568813,519029990,False,Home,17,16,14.0,WALK,51902999,1 +415241049,4152410490,1265978,568865,519051310,True,shopping,5,17,13.0,WALK,51905131,1 +415241053,4152410530,1265978,568865,519051310,False,Home,17,5,13.0,WALK_LRF,51905131,1 +415257121,4152571210,1266027,568914,519071400,True,eatout,16,17,11.0,WALK,51907140,1 +415257122,4152571220,1266027,568914,519071400,True,shopping,16,16,12.0,WALK,51907140,2 +415257125,4152571250,1266027,568914,519071400,False,Home,17,16,16.0,WALK,51907140,1 +415263641,4152636410,1266047,568934,519079550,True,othmaint,16,18,13.0,DRIVEALONEFREE,51907955,1 +415263645,4152636450,1266047,568934,519079550,False,Home,18,16,13.0,DRIVEALONEFREE,51907955,1 +415270353,4152703530,1266068,568955,519087940,True,eatout,5,18,6.0,WALK,51908794,1 +415270357,4152703570,1266068,568955,519087940,False,Home,18,5,15.0,WALK,51908794,1 +415270681,4152706810,1266069,568956,519088350,True,eatout,20,18,15.0,WALK,51908835,1 +415270685,4152706850,1266069,568956,519088350,False,Home,18,20,23.0,WALK,51908835,1 +415272561,4152725610,1266074,568961,519090700,True,othmaint,12,18,12.0,WALK_LOC,51909070,1 +415272562,4152725620,1266074,568961,519090700,True,social,16,12,13.0,WALK_LOC,51909070,2 +415272565,4152725650,1266074,568961,519090700,False,shopping,4,16,14.0,WALK_LOC,51909070,1 +415272566,4152725660,1266074,568961,519090700,False,Home,18,4,15.0,WALK,51909070,2 +415274617,4152746170,1266081,568968,519093270,True,eatout,16,18,9.0,WALK,51909327,1 +415274621,4152746210,1266081,568968,519093270,False,Home,18,16,13.0,WALK,51909327,1 +415280873,4152808730,1266100,568987,519101090,True,escort,16,18,15.0,WALK_LOC,51910109,1 +415280877,4152808770,1266100,568987,519101090,False,Home,18,16,16.0,TNC_SINGLE,51910109,1 +415284609,4152846090,1266111,568998,519105760,True,othdiscr,10,18,11.0,WALK,51910576,1 +415284613,4152846130,1266111,568998,519105760,False,Home,18,10,19.0,WALK,51910576,1 +415301209,4153012090,1266162,569049,519126510,True,escort,8,20,8.0,WALK,51912651,1 +415301213,4153012130,1266162,569049,519126510,False,Home,20,8,11.0,WALK,51912651,1 +415301425,4153014250,1266162,569049,519126780,True,social,20,20,15.0,WALK,51912678,1 +415301429,4153014290,1266162,569049,519126780,False,Home,20,20,15.0,WALK,51912678,1 +415305625,4153056250,1266175,569062,519132030,True,othmaint,12,20,8.0,WALK,51913203,1 +415305629,4153056290,1266175,569062,519132030,False,Home,20,12,16.0,WALK,51913203,1 +415313537,4153135370,1266199,569086,519141920,True,shopping,12,20,15.0,WALK_LOC,51914192,1 +415313538,4153135380,1266199,569086,519141920,True,shopping,15,12,16.0,WALK_LOC,51914192,2 +415313541,4153135410,1266199,569086,519141920,False,shopping,7,15,17.0,WALK_LOC,51914192,1 +415313542,4153135420,1266199,569086,519141920,False,Home,20,7,17.0,WALK_LOC,51914192,2 +415341377,4153413770,1266284,569171,519176720,True,othmaint,11,20,8.0,TNC_SINGLE,51917672,1 +415341381,4153413810,1266284,569171,519176720,False,Home,20,11,9.0,WALK_LOC,51917672,1 +415352569,4153525690,1266318,569205,519190710,True,shopping,24,20,10.0,WALK,51919071,1 +415352573,4153525730,1266318,569205,519190710,False,Home,20,24,14.0,WALK,51919071,1 +415358145,4153581450,1266335,569222,519197680,True,shopping,19,20,12.0,WALK,51919768,1 +415358149,4153581490,1266335,569222,519197680,False,Home,20,19,17.0,WALK,51919768,1 +415360769,4153607690,1266343,569230,519200960,True,shopping,22,20,11.0,SHARED2FREE,51920096,1 +415360773,4153607730,1266343,569230,519200960,False,Home,20,22,13.0,SHARED2FREE,51920096,1 +415365625,4153656250,1266358,569245,519207030,True,othdiscr,8,20,17.0,WALK,51920703,1 +415365629,4153656290,1266358,569245,519207030,False,Home,20,8,18.0,WALK,51920703,1 +415365689,4153656890,1266358,569245,519207110,True,othmaint,10,20,13.0,DRIVEALONEFREE,51920711,1 +415365690,4153656900,1266358,569245,519207110,True,shopping,16,10,13.0,DRIVEALONEFREE,51920711,2 +415365693,4153656930,1266358,569245,519207110,False,eatout,9,16,13.0,TNC_SINGLE,51920711,1 +415365694,4153656940,1266358,569245,519207110,False,shopping,16,9,13.0,DRIVEALONEFREE,51920711,2 +415365695,4153656950,1266358,569245,519207110,False,Home,20,16,13.0,DRIVEALONEFREE,51920711,3 +415366017,4153660170,1266359,569246,519207520,True,shopping,17,20,9.0,WALK,51920752,1 +415366021,4153660210,1266359,569246,519207520,False,Home,20,17,15.0,WALK,51920752,1 +415374809,4153748090,1266386,569273,519218510,True,eatout,8,20,12.0,TNC_SINGLE,51921851,1 +415374810,4153748100,1266386,569273,519218510,True,othdiscr,10,8,13.0,WALK_LOC,51921851,2 +415374813,4153748130,1266386,569273,519218510,False,shopping,5,10,15.0,WALK_LOC,51921851,1 +415374814,4153748140,1266386,569273,519218510,False,social,7,5,15.0,TNC_SINGLE,51921851,2 +415374815,4153748150,1266386,569273,519218510,False,Home,20,7,15.0,WALK_LOC,51921851,3 +415381761,4153817610,1266407,569294,519227200,True,shopping,5,20,16.0,DRIVEALONEFREE,51922720,1 +415381765,4153817650,1266407,569294,519227200,False,Home,20,5,16.0,DRIVEALONEFREE,51922720,1 +415385961,4153859610,1266420,569307,519232450,True,othdiscr,9,20,9.0,WALK,51923245,1 +415385965,4153859650,1266420,569307,519232450,False,Home,20,9,12.0,WALK,51923245,1 +415392849,4153928490,1266441,569328,519241060,True,othmaint,9,20,10.0,WALK,51924106,1 +415392850,4153928500,1266441,569328,519241060,True,othdiscr,9,9,11.0,WALK,51924106,2 +415392853,4153928530,1266441,569328,519241060,False,shopping,11,9,20.0,WALK,51924106,1 +415392854,4153928540,1266441,569328,519241060,False,Home,20,11,20.0,WALK,51924106,2 +415397313,4153973130,1266455,569342,519246640,True,escort,11,20,8.0,WALK,51924664,1 +415397317,4153973170,1266455,569342,519246640,False,Home,20,11,10.0,WALK,51924664,1 +415397321,4153973210,1266455,569342,519246650,True,escort,2,20,14.0,SHARED3FREE,51924665,1 +415397325,4153973250,1266455,569342,519246650,False,shopping,17,2,15.0,DRIVEALONEFREE,51924665,1 +415397326,4153973260,1266455,569342,519246650,False,Home,20,17,15.0,SHARED2FREE,51924665,2 +415397505,4153975050,1266455,569342,519246880,True,shopping,6,20,16.0,WALK,51924688,1 +415397509,4153975090,1266455,569342,519246880,False,Home,20,6,23.0,WALK,51924688,1 +415398489,4153984890,1266458,569345,519248110,True,shopping,5,20,10.0,WALK_LOC,51924811,1 +415398493,4153984930,1266458,569345,519248110,False,Home,20,5,15.0,WALK_LOC,51924811,1 +415444737,4154447370,1266599,569486,519305920,True,shopping,5,20,15.0,WALK,51930592,1 +415444741,4154447410,1266599,569486,519305920,False,Home,20,5,16.0,WALK,51930592,1 +415450641,4154506410,1266617,569504,519313300,True,shopping,16,20,12.0,WALK,51931330,1 +415450645,4154506450,1266617,569504,519313300,False,Home,20,16,13.0,WALK,51931330,1 +415459609,4154596090,1266645,569532,519324510,True,eatout,21,20,11.0,WALK,51932451,1 +415459613,4154596130,1266645,569532,519324510,False,Home,20,21,14.0,WALK,51932451,1 +415487641,4154876410,1266730,569617,519359550,True,othdiscr,3,21,9.0,BIKE,51935955,1 +415487645,4154876450,1266730,569617,519359550,False,Home,21,3,11.0,BIKE,51935955,1 +415487665,4154876650,1266730,569617,519359580,True,othmaint,5,21,14.0,WALK,51935958,1 +415487669,4154876690,1266730,569617,519359580,False,Home,21,5,17.0,WALK,51935958,1 +415487705,4154877050,1266730,569617,519359630,True,shopping,11,21,13.0,TAXI,51935963,1 +415487709,4154877090,1266730,569617,519359630,False,Home,21,11,13.0,DRIVEALONEFREE,51935963,1 +415501945,4155019450,1266774,569661,519377430,True,escort,24,21,8.0,WALK,51937743,1 +415501949,4155019490,1266774,569661,519377430,False,Home,21,24,8.0,WALK,51937743,1 +415502425,4155024250,1266775,569662,519378030,True,othmaint,19,21,9.0,WALK,51937803,1 +415502429,4155024290,1266775,569662,519378030,False,escort,11,19,17.0,WALK,51937803,1 +415502430,4155024300,1266775,569662,519378030,False,eatout,7,11,21.0,WALK,51937803,2 +415502431,4155024310,1266775,569662,519378030,False,Home,21,7,21.0,WALK,51937803,3 +415506401,4155064010,1266787,569674,519383000,True,shopping,11,21,14.0,WALK,51938300,1 +415506405,4155064050,1266787,569674,519383000,False,shopping,11,11,15.0,WALK,51938300,1 +415506406,4155064060,1266787,569674,519383000,False,Home,21,11,17.0,WALK,51938300,2 +415535593,4155355930,1266876,569763,519419490,True,shopping,18,21,13.0,WALK_LOC,51941949,1 +415535597,4155355970,1266876,569763,519419490,False,Home,21,18,20.0,WALK_LOC,51941949,1 +415546769,4155467690,1266910,569797,519433460,True,social,10,21,14.0,WALK,51943346,1 +415546773,4155467730,1266910,569797,519433460,False,Home,21,10,21.0,WALK,51943346,1 +415556913,4155569130,1266941,569828,519446140,True,shopping,5,21,13.0,WALK,51944614,1 +415556917,4155569170,1266941,569828,519446140,False,Home,21,5,14.0,WALK,51944614,1 +415558841,4155588410,1266947,569834,519448550,True,othmaint,13,21,10.0,WALK,51944855,1 +415558845,4155588450,1266947,569834,519448550,False,Home,21,13,11.0,WALK,51944855,1 +415559169,4155591690,1266948,569835,519448960,True,othmaint,8,21,8.0,BIKE,51944896,1 +415559173,4155591730,1266948,569835,519448960,False,Home,21,8,17.0,BIKE,51944896,1 +415564113,4155641130,1266963,569850,519455140,True,escort,14,21,8.0,WALK_LOC,51945514,1 +415564114,4155641140,1266963,569850,519455140,True,escort,5,14,8.0,TAXI,51945514,2 +415564115,4155641150,1266963,569850,519455140,True,univ,9,5,9.0,WALK,51945514,3 +415564117,4155641170,1266963,569850,519455140,False,escort,11,9,14.0,WALK,51945514,1 +415564118,4155641180,1266963,569850,519455140,False,social,5,11,15.0,WALK_LOC,51945514,2 +415564119,4155641190,1266963,569850,519455140,False,Home,21,5,16.0,WALK_LOC,51945514,3 +415595401,4155954010,1267059,569946,519494250,True,eatout,5,21,14.0,WALK,51949425,1 +415595405,4155954050,1267059,569946,519494250,False,Home,21,5,14.0,WALK,51949425,1 +415606113,4156061130,1267091,569978,519507640,True,shopping,13,21,12.0,TNC_SINGLE,51950764,1 +415606117,4156061170,1267091,569978,519507640,False,shopping,16,13,14.0,TNC_SHARED,51950764,1 +415606118,4156061180,1267091,569978,519507640,False,Home,21,16,15.0,TNC_SINGLE,51950764,2 +415608737,4156087370,1267099,569986,519510920,True,shopping,11,21,8.0,WALK,51951092,1 +415608741,4156087410,1267099,569986,519510920,False,shopping,8,11,12.0,WALK,51951092,1 +415608742,4156087420,1267099,569986,519510920,False,othmaint,7,8,12.0,WALK,51951092,2 +415608743,4156087430,1267099,569986,519510920,False,Home,21,7,12.0,WALK,51951092,3 +415621185,4156211850,1267137,570024,519526480,True,univ,12,21,14.0,WALK_LOC,51952648,1 +415621189,4156211890,1267137,570024,519526480,False,Home,21,12,17.0,WALK_LOC,51952648,1 +415635745,4156357450,1267182,570069,519544680,True,eatout,2,21,10.0,WALK,51954468,1 +415635749,4156357490,1267182,570069,519544680,False,Home,21,2,11.0,WALK,51954468,1 +415635897,4156358970,1267182,570069,519544870,True,othdiscr,16,21,12.0,WALK,51954487,1 +415635901,4156359010,1267182,570069,519544870,False,Home,21,16,14.0,WALK,51954487,1 +415643313,4156433130,1267205,570092,519554140,True,escort,20,21,8.0,WALK,51955414,1 +415643317,4156433170,1267205,570092,519554140,False,Home,21,20,9.0,WALK,51955414,1 +415643441,4156434410,1267205,570092,519554300,True,othdiscr,20,21,11.0,WALK_LOC,51955430,1 +415643445,4156434450,1267205,570092,519554300,False,othmaint,9,20,16.0,WALK_LOC,51955430,1 +415643446,4156434460,1267205,570092,519554300,False,Home,21,9,16.0,WALK,51955430,2 +415643529,4156435290,1267205,570092,519554410,True,social,17,21,17.0,WALK_LRF,51955441,1 +415643533,4156435330,1267205,570092,519554410,False,Home,21,17,18.0,WALK_LOC,51955441,1 +415645473,4156454730,1267211,570098,519556840,True,shopping,25,21,13.0,WALK_LOC,51955684,1 +415645477,4156454770,1267211,570098,519556840,False,shopping,7,25,13.0,WALK_LOC,51955684,1 +415645478,4156454780,1267211,570098,519556840,False,Home,21,7,13.0,WALK_LOC,51955684,2 +415670385,4156703850,1267287,570174,519587980,True,univ,12,21,8.0,WALK_LOC,51958798,1 +415670389,4156703890,1267287,570174,519587980,False,Home,21,12,8.0,WALK_LOC,51958798,1 +415696577,4156965770,1267367,570254,519620720,True,othdiscr,14,21,10.0,WALK,51962072,1 +415696581,4156965810,1267367,570254,519620720,False,Home,21,14,14.0,WALK,51962072,1 +415706113,4157061130,1267396,570283,519632640,True,othmaint,15,21,10.0,TNC_SINGLE,51963264,1 +415706117,4157061170,1267396,570283,519632640,False,Home,21,15,10.0,TNC_SINGLE,51963264,1 +415711073,4157110730,1267411,570298,519638840,True,shopping,11,21,10.0,WALK,51963884,1 +415711077,4157110770,1267411,570298,519638840,False,social,7,11,10.0,WALK_LOC,51963884,1 +415711078,4157110780,1267411,570298,519638840,False,Home,21,7,10.0,WALK,51963884,2 +415712321,4157123210,1267415,570302,519640400,True,othdiscr,7,21,12.0,WALK,51964040,1 +415712325,4157123250,1267415,570302,519640400,False,Home,21,7,17.0,WALK,51964040,1 +415733377,4157333770,1267479,570366,519666720,True,shopping,11,21,10.0,DRIVEALONEFREE,51966672,1 +415733381,4157333810,1267479,570366,519666720,False,Home,21,11,10.0,DRIVEALONEFREE,51966672,1 +415750065,4157500650,1267530,570417,519687580,True,othmaint,5,21,16.0,WALK,51968758,1 +415750069,4157500690,1267530,570417,519687580,False,Home,21,5,18.0,WALK,51968758,1 +415750105,4157501050,1267530,570417,519687630,True,shopping,19,21,7.0,WALK,51968763,1 +415750109,4157501090,1267530,570417,519687630,False,Home,21,19,13.0,WALK,51968763,1 +415752073,4157520730,1267536,570423,519690090,True,shopping,21,21,11.0,BIKE,51969009,1 +415752077,4157520770,1267536,570423,519690090,False,Home,21,21,14.0,BIKE,51969009,1 +415757345,4157573450,1267552,570439,519696680,True,social,11,21,9.0,WALK,51969668,1 +415757349,4157573490,1267552,570439,519696680,False,Home,21,11,18.0,WALK,51969668,1 +415757913,4157579130,1267554,570441,519697390,True,othdiscr,3,21,18.0,WALK_LOC,51969739,1 +415757917,4157579170,1267554,570441,519697390,False,Home,21,3,20.0,WALK_LOC,51969739,1 +415782513,4157825130,1267629,570516,519728140,True,othdiscr,15,21,18.0,WALK,51972814,1 +415782517,4157825170,1267629,570516,519728140,False,Home,21,15,23.0,WALK,51972814,1 +415782577,4157825770,1267629,570516,519728220,True,shopping,24,21,14.0,WALK,51972822,1 +415782581,4157825810,1267629,570516,519728220,False,shopping,25,24,18.0,WALK,51972822,1 +415782582,4157825820,1267629,570516,519728220,False,Home,21,25,18.0,WALK,51972822,2 +415783825,4157838250,1267633,570520,519729780,True,othdiscr,6,21,14.0,WALK,51972978,1 +415783829,4157838290,1267633,570520,519729780,False,Home,21,6,17.0,WALK,51972978,1 +415783849,4157838490,1267633,570520,519729810,True,othmaint,11,21,11.0,WALK,51972981,1 +415783853,4157838530,1267633,570520,519729810,False,Home,21,11,12.0,WALK_LOC,51972981,1 +415784857,4157848570,1267636,570523,519731070,True,univ,12,21,18.0,WALK,51973107,1 +415784861,4157848610,1267636,570523,519731070,False,Home,21,12,21.0,WALK_LOC,51973107,1 +415814681,4158146810,1267727,570614,519768350,True,othmaint,7,22,12.0,BIKE,51976835,1 +415814682,4158146820,1267727,570614,519768350,True,othmaint,6,7,12.0,BIKE,51976835,2 +415814685,4158146850,1267727,570614,519768350,False,Home,22,6,13.0,BIKE,51976835,1 +415816689,4158166890,1267733,570620,519770860,True,shopping,16,22,10.0,WALK,51977086,1 +415816693,4158166930,1267733,570620,519770860,False,Home,22,16,16.0,WALK,51977086,1 +415823249,4158232490,1267753,570640,519779060,True,shopping,25,22,9.0,WALK,51977906,1 +415823253,4158232530,1267753,570640,519779060,False,othmaint,5,25,9.0,WALK,51977906,1 +415823254,4158232540,1267753,570640,519779060,False,eatout,1,5,10.0,WALK,51977906,2 +415823255,4158232550,1267753,570640,519779060,False,Home,22,1,10.0,WALK,51977906,3 +415830249,4158302490,1267775,570662,519787810,True,eatout,2,22,15.0,WALK,51978781,1 +415830253,4158302530,1267775,570662,519787810,False,Home,22,2,16.0,WALK,51978781,1 +415838297,4158382970,1267799,570686,519797870,True,othmaint,5,23,11.0,BIKE,51979787,1 +415838301,4158383010,1267799,570686,519797870,False,eatout,8,5,16.0,BIKE,51979787,1 +415838302,4158383020,1267799,570686,519797870,False,Home,23,8,17.0,BIKE,51979787,2 +415838305,4158383050,1267799,570686,519797880,True,othmaint,9,23,18.0,WALK_LOC,51979788,1 +415838309,4158383090,1267799,570686,519797880,False,Home,23,9,19.0,WALK_LOC,51979788,1 +415840961,4158409610,1267807,570694,519801200,True,shopping,5,23,12.0,WALK,51980120,1 +415840965,4158409650,1267807,570694,519801200,False,Home,23,5,14.0,WALK,51980120,1 +415841313,4158413130,1267808,570695,519801640,True,shopping,4,23,13.0,WALK,51980164,1 +415841314,4158413140,1267808,570695,519801640,True,social,24,4,13.0,WALK,51980164,2 +415841317,4158413170,1267808,570695,519801640,False,Home,23,24,15.0,WALK,51980164,1 +415845865,4158458650,1267822,570709,519807330,True,univ,12,23,15.0,DRIVEALONEFREE,51980733,1 +415845869,4158458690,1267822,570709,519807330,False,Home,23,12,15.0,DRIVEALONEFREE,51980733,1 +415846977,4158469770,1267826,570713,519808720,True,eatout,12,23,9.0,WALK,51980872,1 +415846981,4158469810,1267826,570713,519808720,False,Home,23,12,12.0,WALK,51980872,1 +415847129,4158471290,1267826,570713,519808910,True,othdiscr,7,23,12.0,WALK_LRF,51980891,1 +415847133,4158471330,1267826,570713,519808910,False,Home,23,7,20.0,WALK_LRF,51980891,1 +415848289,4158482890,1267830,570717,519810360,True,eatout,11,23,12.0,WALK_LRF,51981036,1 +415848293,4158482930,1267830,570717,519810360,False,shopping,4,11,19.0,WALK,51981036,1 +415848294,4158482940,1267830,570717,519810360,False,Home,23,4,19.0,WALK,51981036,2 +415855745,4158557450,1267852,570739,519819680,True,social,6,24,7.0,WALK,51981968,1 +415855749,4158557490,1267852,570739,519819680,False,Home,24,6,19.0,WALK,51981968,1 +415856009,4158560090,1267853,570740,519820010,True,othmaint,6,24,13.0,TNC_SINGLE,51982001,1 +415856013,4158560130,1267853,570740,519820010,False,Home,24,6,13.0,DRIVEALONEFREE,51982001,1 +415856049,4158560490,1267853,570740,519820060,True,shopping,4,24,10.0,TNC_SINGLE,51982006,1 +415856053,4158560530,1267853,570740,519820060,False,Home,24,4,13.0,TNC_SINGLE,51982006,1 +415861913,4158619130,1267871,570758,519827390,True,othmaint,23,24,8.0,TAXI,51982739,1 +415861917,4158619170,1267871,570758,519827390,False,Home,24,23,14.0,WALK,51982739,1 +415862609,4158626090,1267873,570760,519828260,True,shopping,23,24,12.0,WALK,51982826,1 +415862613,4158626130,1267873,570760,519828260,False,eatout,25,23,13.0,WALK,51982826,1 +415862614,4158626140,1267873,570760,519828260,False,Home,24,25,13.0,WALK,51982826,2 +415864273,4158642730,1267878,570765,519830340,True,social,17,24,6.0,WALK,51983034,1 +415864277,4158642770,1267878,570765,519830340,False,Home,24,17,13.0,WALK,51983034,1 +415865233,4158652330,1267881,570768,519831540,True,shopping,16,24,11.0,WALK,51983154,1 +415865237,4158652370,1267881,570768,519831540,False,Home,24,16,16.0,WALK,51983154,1 +415866833,4158668330,1267886,570773,519833540,True,othmaint,13,24,11.0,WALK,51983354,1 +415866837,4158668370,1267886,570773,519833540,False,Home,24,13,12.0,WALK,51983354,1 +415871249,4158712490,1267900,570787,519839060,True,eatout,16,24,15.0,WALK,51983906,1 +415871253,4158712530,1267900,570787,519839060,False,Home,24,16,17.0,WALK,51983906,1 +415875729,4158757290,1267913,570800,519844660,True,othdiscr,9,24,13.0,WALK_LRF,51984466,1 +415875730,4158757300,1267913,570800,519844660,True,shopping,11,9,13.0,TNC_SHARED,51984466,2 +415875733,4158757330,1267913,570800,519844660,False,eatout,9,11,14.0,WALK_LOC,51984466,1 +415875734,4158757340,1267913,570800,519844660,False,shopping,12,9,15.0,WALK_LRF,51984466,2 +415875735,4158757350,1267913,570800,519844660,False,Home,24,12,15.0,WALK_LOC,51984466,3 +415878969,4158789690,1267923,570810,519848710,True,othmaint,24,24,9.0,WALK,51984871,1 +415878973,4158789730,1267923,570810,519848710,False,Home,24,24,15.0,WALK,51984871,1 +415883561,4158835610,1267937,570824,519854450,True,othmaint,19,24,7.0,WALK_LOC,51985445,1 +415883565,4158835650,1267937,570824,519854450,False,Home,24,19,13.0,WALK_LOC,51985445,1 +415907529,4159075290,1268010,570897,519884410,True,univ,13,25,16.0,DRIVEALONEFREE,51988441,1 +415907533,4159075330,1268010,570897,519884410,False,Home,25,13,17.0,DRIVEALONEFREE,51988441,1 +415931753,4159317530,1268084,570971,519914690,True,othdiscr,22,25,17.0,WALK,51991469,1 +415931757,4159317570,1268084,570971,519914690,False,Home,25,22,19.0,WALK,51991469,1 +415939977,4159399770,1268109,570996,519924970,True,othmaint,14,25,14.0,WALK,51992497,1 +415939981,4159399810,1268109,570996,519924970,False,othmaint,5,14,16.0,WALK,51992497,1 +415939982,4159399820,1268109,570996,519924970,False,Home,25,5,16.0,WALK_LOC,51992497,2 +415940017,4159400170,1268109,570996,519925020,True,shopping,10,25,13.0,BIKE,51992502,1 +415940021,4159400210,1268109,570996,519925020,False,Home,25,10,13.0,BIKE,51992502,1 +415956745,4159567450,1268160,571047,519945930,True,shopping,2,25,12.0,WALK,51994593,1 +415956749,4159567490,1268160,571047,519945930,False,Home,25,2,13.0,WALK,51994593,1 +415968905,4159689050,1268197,571084,519961130,True,social,15,25,16.0,WALK_LOC,51996113,1 +415968909,4159689090,1268197,571084,519961130,False,Home,25,15,17.0,WALK_LOC,51996113,1 +444603657,4446036570,1355498,658385,555754570,True,work,2,2,7.0,WALK,55575457,1 +444603661,4446036610,1355498,658385,555754570,False,Home,2,2,14.0,WALK,55575457,1 +444630113,4446301130,1355579,658466,555787640,True,othdiscr,21,7,7.0,WALK,55578764,1 +444630117,4446301170,1355579,658466,555787640,False,Home,7,21,11.0,WALK,55578764,1 +444630121,4446301210,1355579,658466,555787650,True,othdiscr,2,7,16.0,WALK,55578765,1 +444630125,4446301250,1355579,658466,555787650,False,Home,7,2,18.0,WALK,55578765,1 +444630225,4446302250,1355579,658466,555787780,True,work,9,7,11.0,WALK,55578778,1 +444630229,4446302290,1355579,658466,555787780,False,Home,7,9,16.0,WALK,55578778,1 +444666849,4446668490,1355691,658578,555833560,True,othdiscr,9,9,17.0,WALK,55583356,1 +444666853,4446668530,1355691,658578,555833560,False,Home,9,9,21.0,WALK,55583356,1 +444666857,4446668570,1355691,658578,555833570,True,othdiscr,24,9,21.0,SHARED2FREE,55583357,1 +444666861,4446668610,1355691,658578,555833570,False,Home,9,24,21.0,DRIVEALONEFREE,55583357,1 +444666961,4446669610,1355691,658578,555833700,True,work,24,9,7.0,WALK_LRF,55583370,1 +444666965,4446669650,1355691,658578,555833700,False,Home,9,24,17.0,WALK_LRF,55583370,1 +444679377,4446793770,1355729,658616,555849220,True,shopping,11,10,6.0,WALK,55584922,1 +444679381,4446793810,1355729,658616,555849220,False,Home,10,11,14.0,WALK,55584922,1 +444679425,4446794250,1355729,658616,555849280,True,work,9,10,14.0,WALK,55584928,1 +444679429,4446794290,1355729,658616,555849280,False,escort,8,9,17.0,WALK,55584928,1 +444679430,4446794300,1355729,658616,555849280,False,Home,10,8,18.0,WALK,55584928,2 +444683689,4446836890,1355742,658629,555854610,True,work,2,10,6.0,WALK_LRF,55585461,1 +444683693,4446836930,1355742,658629,555854610,False,Home,10,2,16.0,WALK_HVY,55585461,1 +444698185,4446981850,1355787,658674,555872730,True,eatout,7,11,20.0,WALK,55587273,1 +444698189,4446981890,1355787,658674,555872730,False,Home,11,7,20.0,WALK,55587273,1 +444698449,4446984490,1355787,658674,555873060,True,work,13,11,5.0,WALK,55587306,1 +444698453,4446984530,1355787,658674,555873060,False,eatout,16,13,16.0,WALK_LOC,55587306,1 +444698454,4446984540,1355787,658674,555873060,False,othmaint,7,16,16.0,WALK,55587306,2 +444698455,4446984550,1355787,658674,555873060,False,eatout,9,7,16.0,WALK_LOC,55587306,3 +444698456,4446984560,1355787,658674,555873060,False,Home,11,9,17.0,WALK,55587306,4 +444698457,4446984570,1355787,658674,555873070,True,escort,14,11,18.0,DRIVEALONEFREE,55587307,1 +444698458,4446984580,1355787,658674,555873070,True,work,13,14,18.0,DRIVEALONEFREE,55587307,2 +444698461,4446984610,1355787,658674,555873070,False,escort,5,13,18.0,DRIVEALONEFREE,55587307,1 +444698462,4446984620,1355787,658674,555873070,False,Home,11,5,18.0,DRIVEALONEFREE,55587307,2 +444739689,4447396890,1355913,658800,555924610,True,othmaint,9,17,12.0,WALK_LRF,55592461,1 +444739693,4447396930,1355913,658800,555924610,False,eatout,16,9,13.0,WALK_LRF,55592461,1 +444739694,4447396940,1355913,658800,555924610,False,Home,17,16,14.0,WALK_LOC,55592461,2 +444748305,4447483050,1355939,658826,555935380,True,work,24,19,8.0,TNC_SINGLE,55593538,1 +444748309,4447483090,1355939,658826,555935380,False,Home,19,24,17.0,TNC_SINGLE,55593538,1 +444764049,4447640490,1355987,658874,555955060,True,work,5,20,6.0,TNC_SINGLE,55595506,1 +444764053,4447640530,1355987,658874,555955060,False,Home,20,5,17.0,WALK_LOC,55595506,1 +444770281,4447702810,1356006,658893,555962850,True,work,2,20,12.0,WALK_LOC,55596285,1 +444770285,4447702850,1356006,658893,555962850,False,Home,20,2,19.0,WALK_LOC,55596285,1 +444785369,4447853690,1356052,658939,555981710,True,work,19,20,6.0,WALK,55598171,1 +444785373,4447853730,1356052,658939,555981710,False,Home,20,19,20.0,WALK,55598171,1 +444793569,4447935690,1356077,658964,555991960,True,work,2,20,9.0,SHARED3FREE,55599196,1 +444793573,4447935730,1356077,658964,555991960,False,eatout,13,2,16.0,WALK,55599196,1 +444793574,4447935740,1356077,658964,555991960,False,Home,20,13,17.0,WALK,55599196,2 +444812593,4448125930,1356135,659022,556015740,True,work,23,21,8.0,WALK_LOC,55601574,1 +444812597,4448125970,1356135,659022,556015740,False,Home,21,23,18.0,WALK_LRF,55601574,1 +444813465,4448134650,1356138,659025,556016830,True,othdiscr,16,21,16.0,WALK,55601683,1 +444813469,4448134690,1356138,659025,556016830,False,Home,21,16,18.0,WALK,55601683,1 +444853217,4448532170,1356259,659146,556066520,True,shopping,17,21,9.0,WALK,55606652,1 +444853221,4448532210,1356259,659146,556066520,False,Home,21,17,12.0,WALK,55606652,1 +444853265,4448532650,1356259,659146,556066580,True,work,12,21,18.0,WALK,55606658,1 +444853269,4448532690,1356259,659146,556066580,False,Home,21,12,23.0,WALK,55606658,1 +444868073,4448680730,1356305,659192,556085090,True,atwork,16,7,10.0,WALK,55608509,1 +444868077,4448680770,1356305,659192,556085090,False,Work,7,16,12.0,WALK,55608509,1 +444868353,4448683530,1356305,659192,556085440,True,work,7,21,7.0,WALK,55608544,1 +444868357,4448683570,1356305,659192,556085440,False,Home,21,7,20.0,WALK,55608544,1 +444872049,4448720490,1356317,659204,556090060,True,escort,9,21,11.0,SHARED3FREE,55609006,1 +444872053,4448720530,1356317,659204,556090060,False,escort,10,9,12.0,WALK,55609006,1 +444872054,4448720540,1356317,659204,556090060,False,Home,21,10,12.0,DRIVEALONEFREE,55609006,2 +444872177,4448721770,1356317,659204,556090220,True,escort,8,21,14.0,BIKE,55609022,1 +444872178,4448721780,1356317,659204,556090220,True,othdiscr,20,8,15.0,BIKE,55609022,2 +444872181,4448721810,1356317,659204,556090220,False,Home,21,20,20.0,BIKE,55609022,1 +444872617,4448726170,1356318,659205,556090770,True,work,15,21,7.0,WALK,55609077,1 +444872621,4448726210,1356318,659205,556090770,False,Home,21,15,17.0,WALK,55609077,1 +444873929,4448739290,1356322,659209,556092410,True,eatout,1,21,8.0,WALK,55609241,1 +444873930,4448739300,1356322,659209,556092410,True,work,24,1,9.0,WALK,55609241,2 +444873933,4448739330,1356322,659209,556092410,False,work,4,24,17.0,WALK,55609241,1 +444873934,4448739340,1356322,659209,556092410,False,Home,21,4,18.0,WALK,55609241,2 +444873993,4448739930,1356323,659210,556092490,True,eatout,16,21,15.0,WALK,55609249,1 +444873997,4448739970,1356323,659210,556092490,False,Home,21,16,16.0,WALK,55609249,1 +444874145,4448741450,1356323,659210,556092680,True,othdiscr,6,21,12.0,WALK,55609268,1 +444874149,4448741490,1356323,659210,556092680,False,Home,21,6,14.0,WALK,55609268,1 +444874233,4448742330,1356323,659210,556092790,True,social,9,21,16.0,WALK,55609279,1 +444874237,4448742370,1356323,659210,556092790,False,Home,21,9,21.0,WALK,55609279,1 +444875241,4448752410,1356326,659213,556094050,True,work,2,21,14.0,DRIVEALONEFREE,55609405,1 +444875245,4448752450,1356326,659213,556094050,False,othmaint,5,2,17.0,WALK,55609405,1 +444875246,4448752460,1356326,659213,556094050,False,social,11,5,19.0,WALK,55609405,2 +444875247,4448752470,1356326,659213,556094050,False,eatout,12,11,19.0,WALK,55609405,3 +444875248,4448752480,1356326,659213,556094050,False,Home,21,12,19.0,WALK,55609405,4 +444887001,4448870010,1356362,659249,556108750,True,shopping,11,21,12.0,WALK,55610875,1 +444887005,4448870050,1356362,659249,556108750,False,Home,21,11,14.0,WALK,55610875,1 +444887009,4448870090,1356362,659249,556108760,True,shopping,14,21,17.0,TNC_SINGLE,55610876,1 +444887013,4448870130,1356362,659249,556108760,False,Home,21,14,20.0,WALK_LOC,55610876,1 +444896233,4448962330,1356390,659277,556120290,True,work,10,21,5.0,WALK_LRF,55612029,1 +444896237,4448962370,1356390,659277,556120290,False,Home,21,10,15.0,WALK_LOC,55612029,1 +444913617,4449136170,1356443,659330,556142020,True,work,18,21,7.0,WALK,55614202,1 +444913621,4449136210,1356443,659330,556142020,False,Home,21,18,18.0,WALK,55614202,1 +444923129,4449231290,1356472,659359,556153910,True,work,16,21,8.0,WALK,55615391,1 +444923130,4449231300,1356472,659359,556153910,True,work,18,16,8.0,WALK,55615391,2 +444923133,4449231330,1356472,659359,556153910,False,Home,21,18,23.0,WALK,55615391,1 +444928617,4449286170,1356489,659376,556160770,True,othmaint,19,21,10.0,WALK,55616077,1 +444928621,4449286210,1356489,659376,556160770,False,Home,21,19,12.0,WALK,55616077,1 +444928985,4449289850,1356490,659377,556161230,True,shopping,5,21,13.0,WALK,55616123,1 +444928989,4449289890,1356490,659377,556161230,False,Home,21,5,14.0,WALK,55616123,1 +444928993,4449289930,1356490,659377,556161240,True,escort,16,21,15.0,WALK,55616124,1 +444928994,4449289940,1356490,659377,556161240,True,shopping,12,16,15.0,WALK,55616124,2 +444928997,4449289970,1356490,659377,556161240,False,othmaint,5,12,17.0,WALK,55616124,1 +444928998,4449289980,1356490,659377,556161240,False,escort,7,5,17.0,WALK,55616124,2 +444928999,4449289990,1356490,659377,556161240,False,Home,21,7,17.0,WALK_LOC,55616124,3 +444930673,4449306730,1356495,659382,556163340,True,work,9,21,7.0,WALK,55616334,1 +444930677,4449306770,1356495,659382,556163340,False,Home,21,9,18.0,WALK,55616334,1 +444930737,4449307370,1356496,659383,556163420,True,eatout,22,21,10.0,WALK,55616342,1 +444930741,4449307410,1356496,659383,556163420,False,Home,21,22,15.0,WALK,55616342,1 +444938873,4449388730,1356520,659407,556173590,True,work,5,22,7.0,BIKE,55617359,1 +444938877,4449388770,1356520,659407,556173590,False,Home,22,5,14.0,BIKE,55617359,1 +444939073,4449390730,1356521,659408,556173840,True,atwork,21,17,10.0,WALK,55617384,1 +444939077,4449390770,1356521,659408,556173840,False,Work,17,21,12.0,WALK,55617384,1 +444939201,4449392010,1356521,659408,556174000,True,work,17,22,7.0,WALK_LRF,55617400,1 +444939205,4449392050,1356521,659408,556174000,False,Home,22,17,16.0,WALK_LRF,55617400,1 +444942153,4449421530,1356530,659417,556177690,True,work,2,22,7.0,WALK,55617769,1 +444942154,4449421540,1356530,659417,556177690,True,work,4,2,7.0,WALK,55617769,2 +444942157,4449421570,1356530,659417,556177690,False,shopping,11,4,22.0,WALK_LRF,55617769,1 +444942158,4449421580,1356530,659417,556177690,False,Home,22,11,22.0,WALK_LRF,55617769,2 +444944689,4449446890,1356538,659425,556180860,True,othmaint,3,22,8.0,WALK,55618086,1 +444944693,4449446930,1356538,659425,556180860,False,Home,22,3,10.0,WALK,55618086,1 +444944777,4449447770,1356538,659425,556180970,True,work,2,22,12.0,BIKE,55618097,1 +444944781,4449447810,1356538,659425,556180970,False,Home,22,2,17.0,BIKE,55618097,1 +444949913,4449499130,1356554,659441,556187390,True,othmaint,9,22,8.0,WALK_LRF,55618739,1 +444949914,4449499140,1356554,659441,556187390,True,escort,22,9,9.0,WALK_LRF,55618739,2 +444949915,4449499150,1356554,659441,556187390,True,othdiscr,12,22,10.0,WALK_LRF,55618739,3 +444949917,4449499170,1356554,659441,556187390,False,Home,22,12,10.0,WALK_LOC,55618739,1 +444949977,4449499770,1356554,659441,556187470,True,shopping,15,22,12.0,WALK,55618747,1 +444949981,4449499810,1356554,659441,556187470,False,Home,22,15,12.0,WALK,55618747,1 +444961505,4449615050,1356589,659476,556201880,True,work,7,25,7.0,WALK_LOC,55620188,1 +444961509,4449615090,1356589,659476,556201880,False,Home,25,7,15.0,WALK_LOC,55620188,1 +444965441,4449654410,1356601,659488,556206800,True,work,16,25,9.0,WALK,55620680,1 +444965445,4449654450,1356601,659488,556206800,False,work,4,16,17.0,WALK,55620680,1 +444965446,4449654460,1356601,659488,556206800,False,Home,25,4,18.0,WALK,55620680,2 +467005569,4670055690,1423797,701683,583756960,True,shopping,19,3,11.0,SHARED2FREE,58375696,1 +467005573,4670055730,1423797,701683,583756960,False,shopping,11,19,13.0,SHARED2FREE,58375696,1 +467005574,4670055740,1423797,701683,583756960,False,Home,3,11,13.0,SHARED2FREE,58375696,2 +467017361,4670173610,1423833,701701,583771700,True,othmaint,5,5,9.0,WALK,58377170,1 +467017365,4670173650,1423833,701701,583771700,False,Home,5,5,14.0,WALK,58377170,1 +467033913,4670339130,1423883,701726,583792390,True,social,2,5,11.0,WALK,58379239,1 +467033917,4670339170,1423883,701726,583792390,False,Home,5,2,23.0,WALK,58379239,1 +467034177,4670341770,1423884,701726,583792720,True,othmaint,5,5,7.0,WALK,58379272,1 +467034181,4670341810,1423884,701726,583792720,False,Home,5,5,13.0,WALK,58379272,1 +467039161,4670391610,1423899,701734,583798950,True,social,2,6,13.0,WALK_LOC,58379895,1 +467039165,4670391650,1423899,701734,583798950,False,shopping,16,2,16.0,WALK,58379895,1 +467039166,4670391660,1423899,701734,583798950,False,Home,6,16,16.0,WALK_LOC,58379895,2 +467077169,4670771690,1424015,701792,583846460,True,univ,13,7,9.0,WALK_LOC,58384646,1 +467077173,4670771730,1424015,701792,583846460,False,Home,7,13,16.0,WALK,58384646,1 +467077513,4670775130,1424016,701792,583846890,True,othmaint,10,7,13.0,WALK_LOC,58384689,1 +467077514,4670775140,1424016,701792,583846890,True,shopping,13,10,13.0,WALK_LRF,58384689,2 +467077517,4670775170,1424016,701792,583846890,False,Home,7,13,13.0,TNC_SINGLE,58384689,1 +467085017,4670850170,1424039,701804,583856270,True,othmaint,22,7,7.0,WALK_LOC,58385627,1 +467085021,4670850210,1424039,701804,583856270,False,shopping,16,22,9.0,WALK,58385627,1 +467085022,4670850220,1424039,701804,583856270,False,othmaint,8,16,9.0,WALK_LOC,58385627,2 +467085023,4670850230,1424039,701804,583856270,False,Home,7,8,9.0,WALK,58385627,3 +467085385,4670853850,1424040,701804,583856730,True,shopping,16,7,15.0,WALK_LOC,58385673,1 +467085389,4670853890,1424040,701804,583856730,False,Home,7,16,18.0,TNC_SINGLE,58385673,1 +467111321,4671113210,1424119,701844,583889150,True,social,23,8,9.0,WALK_LOC,58388915,1 +467111325,4671113250,1424119,701844,583889150,False,Home,8,23,14.0,SHARED2FREE,58388915,1 +467111409,4671114090,1424120,701844,583889260,True,eatout,6,8,17.0,WALK,58388926,1 +467111413,4671114130,1424120,701844,583889260,False,Home,8,6,19.0,WALK,58388926,1 +467111561,4671115610,1424120,701844,583889450,True,othdiscr,9,8,8.0,WALK_LOC,58388945,1 +467111565,4671115650,1424120,701844,583889450,False,social,10,9,10.0,WALK_LOC,58388945,1 +467111566,4671115660,1424120,701844,583889450,False,Home,8,10,10.0,WALK_LOC,58388945,2 +467124881,4671248810,1424161,701865,583906100,True,escort,25,8,8.0,SHARED3FREE,58390610,1 +467124885,4671248850,1424161,701865,583906100,False,Home,8,25,8.0,SHARED3FREE,58390610,1 +467125033,4671250330,1424161,701865,583906290,True,othmaint,16,8,8.0,TNC_SHARED,58390629,1 +467125037,4671250370,1424161,701865,583906290,False,Home,8,16,10.0,TNC_SINGLE,58390629,1 +467125097,4671250970,1424161,701865,583906370,True,social,5,8,12.0,WALK,58390637,1 +467125101,4671251010,1424161,701865,583906370,False,Home,8,5,17.0,WALK,58390637,1 +467125185,4671251850,1424162,701865,583906480,True,eatout,16,8,11.0,WALK,58390648,1 +467125189,4671251890,1424162,701865,583906480,False,Home,8,16,13.0,WALK,58390648,1 +467125209,4671252090,1424162,701865,583906510,True,escort,18,8,7.0,TNC_SINGLE,58390651,1 +467125213,4671252130,1424162,701865,583906510,False,Home,8,18,8.0,TNC_SINGLE,58390651,1 +467125401,4671254010,1424162,701865,583906750,True,shopping,18,8,14.0,TNC_SINGLE,58390675,1 +467125405,4671254050,1424162,701865,583906750,False,othdiscr,7,18,14.0,WALK_LOC,58390675,1 +467125406,4671254060,1424162,701865,583906750,False,shopping,16,7,15.0,WALK_LOC,58390675,2 +467125407,4671254070,1424162,701865,583906750,False,eatout,7,16,15.0,TNC_SINGLE,58390675,3 +467125408,4671254080,1424162,701865,583906750,False,Home,8,7,15.0,WALK_LOC,58390675,4 +467125425,4671254250,1424162,701865,583906780,True,social,4,8,16.0,WALK,58390678,1 +467125429,4671254290,1424162,701865,583906780,False,Home,8,4,18.0,WALK,58390678,1 +467149673,4671496730,1424236,701902,583937090,True,shopping,10,8,14.0,WALK_LOC,58393709,1 +467149677,4671496770,1424236,701902,583937090,False,Home,8,10,14.0,WALK_LOC,58393709,1 +467169617,4671696170,1424297,701933,583962020,True,othdiscr,21,8,10.0,WALK,58396202,1 +467169621,4671696210,1424297,701933,583962020,False,Home,8,21,22.0,WALK,58396202,1 +467169945,4671699450,1424298,701933,583962430,True,othdiscr,5,8,12.0,WALK,58396243,1 +467169949,4671699490,1424298,701933,583962430,False,Home,8,5,16.0,WALK,58396243,1 +467207185,4672071850,1424412,701990,584008980,True,eatout,19,8,10.0,WALK,58400898,1 +467207189,4672071890,1424412,701990,584008980,False,Home,8,19,15.0,WALK,58400898,1 +467207337,4672073370,1424412,701990,584009170,True,othdiscr,7,8,16.0,WALK,58400917,1 +467207341,4672073410,1424412,701990,584009170,False,Home,8,7,19.0,WALK,58400917,1 +467207401,4672074010,1424412,701990,584009250,True,shopping,13,8,15.0,BIKE,58400925,1 +467207405,4672074050,1424412,701990,584009250,False,Home,8,13,16.0,BIKE,58400925,1 +467229377,4672293770,1424479,702024,584036720,True,shopping,13,8,11.0,WALK_LOC,58403672,1 +467229381,4672293810,1424479,702024,584036720,False,Home,8,13,11.0,WALK_LRF,58403672,1 +467229665,4672296650,1424480,702024,584037080,True,othmaint,11,8,9.0,WALK,58403708,1 +467229669,4672296690,1424480,702024,584037080,False,Home,8,11,17.0,WALK,58403708,1 +467235721,4672357210,1424499,702034,584044650,True,eatout,6,8,17.0,WALK,58404465,1 +467235725,4672357250,1424499,702034,584044650,False,Home,8,6,21.0,WALK,58404465,1 +467235921,4672359210,1424499,702034,584044900,True,escort,7,8,8.0,WALK,58404490,1 +467235922,4672359220,1424499,702034,584044900,True,univ,12,7,8.0,TAXI,58404490,2 +467235925,4672359250,1424499,702034,584044900,False,shopping,5,12,14.0,WALK_LOC,58404490,1 +467235926,4672359260,1424499,702034,584044900,False,Home,8,5,15.0,WALK,58404490,2 +467236265,4672362650,1424500,702034,584045330,True,shopping,16,8,10.0,WALK_LOC,58404533,1 +467236266,4672362660,1424500,702034,584045330,True,shopping,18,16,11.0,WALK_LRF,58404533,2 +467236269,4672362690,1424500,702034,584045330,False,Home,8,18,23.0,WALK_LRF,58404533,1 +467236273,4672362730,1424500,702034,584045340,True,shopping,21,8,23.0,WALK_LOC,58404534,1 +467236277,4672362770,1424500,702034,584045340,False,Home,8,21,23.0,WALK,58404534,1 +467236529,4672365290,1424501,702035,584045660,True,othdiscr,6,8,17.0,WALK,58404566,1 +467236533,4672365330,1424501,702035,584045660,False,Home,8,6,23.0,WALK,58404566,1 +467236617,4672366170,1424501,702035,584045770,True,social,9,8,13.0,WALK,58404577,1 +467236621,4672366210,1424501,702035,584045770,False,Home,8,9,16.0,WALK,58404577,1 +467236921,4672369210,1424502,702035,584046150,True,shopping,13,8,12.0,WALK,58404615,1 +467236925,4672369250,1424502,702035,584046150,False,othmaint,9,13,21.0,WALK_LRF,58404615,1 +467236926,4672369260,1424502,702035,584046150,False,Home,8,9,21.0,WALK_LOC,58404615,2 +467269417,4672694170,1424601,702085,584086770,True,social,5,8,13.0,WALK,58408677,1 +467269421,4672694210,1424601,702085,584086770,False,Home,8,5,13.0,WALK,58408677,1 +467314593,4673145930,1424739,702154,584143240,True,othdiscr,13,8,9.0,BIKE,58414324,1 +467314597,4673145970,1424739,702154,584143240,False,eatout,25,13,18.0,BIKE,58414324,1 +467314598,4673145980,1424739,702154,584143240,False,Home,8,25,18.0,BIKE,58414324,2 +467316625,4673166250,1424745,702157,584145780,True,shopping,12,8,8.0,WALK,58414578,1 +467316626,4673166260,1424745,702157,584145780,True,shopping,16,12,9.0,DRIVEALONEFREE,58414578,2 +467316629,4673166290,1424745,702157,584145780,False,Home,8,16,16.0,SHARED2FREE,58414578,1 +467316889,4673168890,1424746,702157,584146110,True,othdiscr,7,8,10.0,WALK_LOC,58414611,1 +467316893,4673168930,1424746,702157,584146110,False,eatout,9,7,14.0,WALK_LOC,58414611,1 +467316894,4673168940,1424746,702157,584146110,False,Home,8,9,14.0,WALK_LOC,58414611,2 +467353033,4673530330,1424856,702212,584191290,True,shopping,21,9,10.0,WALK,58419129,1 +467353037,4673530370,1424856,702212,584191290,False,Home,9,21,10.0,WALK,58419129,1 +467353041,4673530410,1424856,702212,584191300,True,shopping,11,9,11.0,WALK,58419130,1 +467353045,4673530450,1424856,702212,584191300,False,shopping,7,11,15.0,WALK,58419130,1 +467353046,4673530460,1424856,702212,584191300,False,othdiscr,7,7,15.0,WALK,58419130,2 +467353047,4673530470,1424856,702212,584191300,False,Home,9,7,15.0,WALK,58419130,3 +467364953,4673649530,1424893,702231,584206190,True,eatout,16,9,9.0,WALK_LOC,58420619,1 +467364957,4673649570,1424893,702231,584206190,False,othdiscr,7,16,13.0,WALK,58420619,1 +467364958,4673649580,1424893,702231,584206190,False,social,7,7,13.0,WALK,58420619,2 +467364959,4673649590,1424893,702231,584206190,False,Home,9,7,13.0,WALK,58420619,3 +467365193,4673651930,1424893,702231,584206490,True,social,5,9,14.0,WALK,58420649,1 +467365197,4673651970,1424893,702231,584206490,False,Home,9,5,14.0,WALK,58420649,1 +467365457,4673654570,1424894,702231,584206820,True,othmaint,9,9,8.0,WALK,58420682,1 +467365461,4673654610,1424894,702231,584206820,False,Home,9,9,14.0,WALK,58420682,1 +467399281,4673992810,1424997,702283,584249100,True,shopping,16,9,11.0,WALK_LOC,58424910,1 +467399285,4673992850,1424997,702283,584249100,False,Home,9,16,11.0,WALK_LOC,58424910,1 +467477937,4674779370,1425237,702403,584347420,True,othdiscr,14,10,13.0,WALK_LRF,58434742,1 +467477941,4674779410,1425237,702403,584347420,False,Home,10,14,16.0,WALK_LRF,58434742,1 +467478329,4674783290,1425238,702403,584347910,True,shopping,11,10,12.0,TNC_SINGLE,58434791,1 +467478333,4674783330,1425238,702403,584347910,False,eatout,9,11,12.0,TNC_SINGLE,58434791,1 +467478334,4674783340,1425238,702403,584347910,False,Home,10,9,12.0,TNC_SHARED,58434791,2 +467482593,4674825930,1425251,702410,584353240,True,shopping,19,10,10.0,WALK,58435324,1 +467482597,4674825970,1425251,702410,584353240,False,Home,10,19,12.0,WALK,58435324,1 +467482881,4674828810,1425252,702410,584353600,True,othmaint,12,10,7.0,WALK,58435360,1 +467482885,4674828850,1425252,702410,584353600,False,shopping,5,12,10.0,WALK,58435360,1 +467482886,4674828860,1425252,702410,584353600,False,shopping,5,5,10.0,WALK,58435360,2 +467482887,4674828870,1425252,702410,584353600,False,Home,10,5,10.0,WALK,58435360,3 +467528145,4675281450,1425390,702479,584410180,True,othmaint,7,11,8.0,WALK,58441018,1 +467528149,4675281490,1425390,702479,584410180,False,Home,11,7,17.0,WALK,58441018,1 +467533761,4675337610,1425407,702488,584417200,True,shopping,14,11,13.0,BIKE,58441720,1 +467533765,4675337650,1425407,702488,584417200,False,Home,11,14,17.0,BIKE,58441720,1 +467534049,4675340490,1425408,702488,584417560,True,shopping,2,11,12.0,TNC_SINGLE,58441756,1 +467534050,4675340500,1425408,702488,584417560,True,othmaint,2,2,13.0,TNC_SINGLE,58441756,2 +467534053,4675340530,1425408,702488,584417560,False,shopping,5,2,15.0,TNC_SINGLE,58441756,1 +467534054,4675340540,1425408,702488,584417560,False,shopping,4,5,15.0,TNC_SINGLE,58441756,2 +467534055,4675340550,1425408,702488,584417560,False,shopping,6,4,15.0,TNC_SINGLE,58441756,3 +467534056,4675340560,1425408,702488,584417560,False,Home,11,6,15.0,WALK_LOC,58441756,4 +467573665,4675736650,1425529,702549,584467080,True,shopping,15,11,10.0,WALK,58446708,1 +467573669,4675736690,1425529,702549,584467080,False,Home,11,15,16.0,WALK,58446708,1 +467574393,4675743930,1425531,702550,584467990,True,othmaint,11,12,8.0,WALK,58446799,1 +467574397,4675743970,1425531,702550,584467990,False,Home,12,11,12.0,WALK,58446799,1 +467574569,4675745690,1425532,702550,584468210,True,escort,10,12,7.0,WALK,58446821,1 +467574573,4675745730,1425532,702550,584468210,False,Home,12,10,7.0,WALK,58446821,1 +467614889,4676148890,1425655,702612,584518610,True,eatout,17,16,10.0,WALK,58451861,1 +467614893,4676148930,1425655,702612,584518610,False,Home,16,17,13.0,WALK,58451861,1 +467615369,4676153690,1425656,702612,584519210,True,othdiscr,12,16,9.0,WALK_LOC,58451921,1 +467615373,4676153730,1425656,702612,584519210,False,Home,16,12,17.0,WALK_LOC,58451921,1 +467675481,4676754810,1425839,702704,584594350,True,social,18,20,12.0,WALK,58459435,1 +467675485,4676754850,1425839,702704,584594350,False,Home,20,18,18.0,WALK,58459435,1 +467675745,4676757450,1425840,702704,584594680,True,othmaint,12,20,10.0,WALK,58459468,1 +467675749,4676757490,1425840,702704,584594680,False,Home,20,12,11.0,WALK,58459468,1 +467675785,4676757850,1425840,702704,584594730,True,shopping,11,20,16.0,WALK,58459473,1 +467675789,4676757890,1425840,702704,584594730,False,Home,20,11,16.0,WALK,58459473,1 +467675793,4676757930,1425840,702704,584594740,True,shopping,21,20,17.0,DRIVEALONEFREE,58459474,1 +467675797,4676757970,1425840,702704,584594740,False,Home,20,21,18.0,TNC_SINGLE,58459474,1 +467675809,4676758090,1425840,702704,584594760,True,social,9,20,14.0,WALK,58459476,1 +467675813,4676758130,1425840,702704,584594760,False,Home,20,9,16.0,WALK,58459476,1 +467675897,4676758970,1425841,702705,584594870,True,eatout,2,20,15.0,WALK,58459487,1 +467675901,4676759010,1425841,702705,584594870,False,Home,20,2,21.0,WALK,58459487,1 +467676073,4676760730,1425841,702705,584595090,True,othmaint,10,20,9.0,WALK,58459509,1 +467676077,4676760770,1425841,702705,584595090,False,Home,20,10,11.0,WALK,58459509,1 +467676441,4676764410,1425842,702705,584595550,True,shopping,9,20,10.0,WALK,58459555,1 +467676445,4676764450,1425842,702705,584595550,False,Home,20,9,16.0,WALK,58459555,1 +467688905,4676889050,1425880,702724,584611130,True,shopping,15,20,8.0,WALK_LRF,58461113,1 +467688909,4676889090,1425880,702724,584611130,False,shopping,9,15,12.0,WALK_LRF,58461113,1 +467688910,4676889100,1425880,702724,584611130,False,shopping,4,9,14.0,WALK_LRF,58461113,2 +467688911,4676889110,1425880,702724,584611130,False,Home,20,4,14.0,WALK_LRF,58461113,3 +467688913,4676889130,1425880,702724,584611140,True,othmaint,22,20,17.0,WALK_LRF,58461114,1 +467688914,4676889140,1425880,702724,584611140,True,shopping,11,22,17.0,WALK_LRF,58461114,2 +467688917,4676889170,1425880,702724,584611140,False,Home,20,11,17.0,TNC_SINGLE,58461114,1 +467712481,4677124810,1425952,702760,584640600,True,othmaint,5,20,9.0,WALK,58464060,1 +467712485,4677124850,1425952,702760,584640600,False,eatout,16,5,10.0,WALK_LOC,58464060,1 +467712486,4677124860,1425952,702760,584640600,False,othmaint,7,16,10.0,WALK,58464060,2 +467712487,4677124870,1425952,702760,584640600,False,Home,20,7,10.0,WALK,58464060,3 +467715329,4677153290,1425961,702765,584644160,True,othdiscr,9,20,11.0,TAXI,58464416,1 +467715333,4677153330,1425961,702765,584644160,False,Home,20,9,14.0,TNC_SHARED,58464416,1 +467736929,4677369290,1426027,702798,584671160,True,othmaint,12,20,10.0,SHARED2FREE,58467116,1 +467736930,4677369300,1426027,702798,584671160,True,escort,13,12,10.0,SHARED2FREE,58467116,2 +467736933,4677369330,1426027,702798,584671160,False,Home,20,13,10.0,SHARED2FREE,58467116,1 +467737449,4677374490,1426028,702798,584671810,True,shopping,5,20,11.0,SHARED2FREE,58467181,1 +467737450,4677374500,1426028,702798,584671810,True,shopping,16,5,11.0,SHARED2FREE,58467181,2 +467737453,4677374530,1426028,702798,584671810,False,escort,10,16,12.0,SHARED2FREE,58467181,1 +467737454,4677374540,1426028,702798,584671810,False,Home,20,10,13.0,SHARED2FREE,58467181,2 +467797473,4677974730,1426211,702890,584746840,True,shopping,15,21,12.0,WALK_LOC,58474684,1 +467797477,4677974770,1426211,702890,584746840,False,shopping,11,15,12.0,WALK_LOC,58474684,1 +467797478,4677974780,1426211,702890,584746840,False,shopping,5,11,13.0,WALK_LOC,58474684,2 +467797479,4677974790,1426211,702890,584746840,False,Home,21,5,13.0,WALK_LOC,58474684,3 +467797609,4677976090,1426212,702890,584747010,True,escort,9,21,8.0,WALK,58474701,1 +467797613,4677976130,1426212,702890,584747010,False,Home,21,9,13.0,WALK,58474701,1 +467828241,4678282410,1426305,702937,584785300,True,othdiscr,21,21,17.0,WALK,58478530,1 +467828245,4678282450,1426305,702937,584785300,False,Home,21,21,23.0,WALK,58478530,1 +467828305,4678283050,1426305,702937,584785380,True,shopping,5,21,15.0,WALK_LOC,58478538,1 +467828309,4678283090,1426305,702937,584785380,False,shopping,25,5,15.0,TNC_SINGLE,58478538,1 +467828310,4678283100,1426305,702937,584785380,False,Home,21,25,15.0,TNC_SINGLE,58478538,2 +467828569,4678285690,1426306,702937,584785710,True,othdiscr,9,21,15.0,WALK_LOC,58478571,1 +467828573,4678285730,1426306,702937,584785710,False,Home,21,9,17.0,WALK_LOC,58478571,1 +467857825,4678578250,1426395,702982,584822280,True,eatout,7,21,10.0,WALK,58482228,1 +467857826,4678578260,1426395,702982,584822280,True,shopping,2,7,11.0,WALK,58482228,2 +467857829,4678578290,1426395,702982,584822280,False,Home,21,2,12.0,WALK,58482228,1 +467857961,4678579610,1426396,702982,584822450,True,escort,10,21,14.0,SHARED2FREE,58482245,1 +467857965,4678579650,1426396,702982,584822450,False,Home,21,10,14.0,DRIVEALONEFREE,58482245,1 +467858089,4678580890,1426396,702982,584822610,True,othdiscr,16,21,14.0,WALK,58482261,1 +467858093,4678580930,1426396,702982,584822610,False,Home,21,16,17.0,WALK,58482261,1 +467896337,4678963370,1426513,703041,584870420,True,escort,11,21,11.0,WALK,58487042,1 +467896341,4678963410,1426513,703041,584870420,False,Home,21,11,17.0,WALK,58487042,1 +467896465,4678964650,1426513,703041,584870580,True,othdiscr,16,21,18.0,WALK,58487058,1 +467896469,4678964690,1426513,703041,584870580,False,Home,21,16,23.0,WALK,58487058,1 +467896857,4678968570,1426514,703041,584871070,True,shopping,5,21,8.0,WALK,58487107,1 +467896861,4678968610,1426514,703041,584871070,False,Home,21,5,14.0,WALK,58487107,1 +467896865,4678968650,1426514,703041,584871080,True,othmaint,16,21,15.0,TNC_SINGLE,58487108,1 +467896866,4678968660,1426514,703041,584871080,True,shopping,19,16,17.0,TNC_SINGLE,58487108,2 +467896869,4678968690,1426514,703041,584871080,False,Home,21,19,17.0,TNC_SINGLE,58487108,1 +467907537,4679075370,1426547,703058,584884420,True,othdiscr,16,21,9.0,WALK,58488442,1 +467907541,4679075410,1426547,703058,584884420,False,Home,21,16,17.0,TNC_SHARED,58488442,1 +467914241,4679142410,1426567,703068,584892800,True,shopping,11,21,8.0,WALK_LOC,58489280,1 +467914245,4679142450,1426567,703068,584892800,False,Home,21,11,12.0,WALK_LOC,58489280,1 +467914265,4679142650,1426567,703068,584892830,True,social,9,21,13.0,WALK,58489283,1 +467914269,4679142690,1426567,703068,584892830,False,Home,21,9,22.0,WALK,58489283,1 +467919473,4679194730,1426583,703076,584899340,True,escort,14,21,18.0,DRIVEALONEFREE,58489934,1 +467919474,4679194740,1426583,703076,584899340,True,escort,10,14,18.0,DRIVEALONEFREE,58489934,2 +467919475,4679194750,1426583,703076,584899340,True,univ,12,10,18.0,DRIVEALONEFREE,58489934,3 +467919477,4679194770,1426583,703076,584899340,False,othmaint,11,12,18.0,WALK,58489934,1 +467919478,4679194780,1426583,703076,584899340,False,Home,21,11,18.0,DRIVEALONEFREE,58489934,2 +467919753,4679197530,1426584,703076,584899690,True,othdiscr,17,21,12.0,WALK_LOC,58489969,1 +467919757,4679197570,1426584,703076,584899690,False,Home,21,17,16.0,WALK_LOC,58489969,1 +467919801,4679198010,1426584,703076,584899750,True,univ,12,21,18.0,TNC_SHARED,58489975,1 +467919805,4679198050,1426584,703076,584899750,False,Home,21,12,18.0,WALK_LOC,58489975,1 +467937225,4679372250,1426637,703103,584921530,True,social,16,21,12.0,WALK,58492153,1 +467937229,4679372290,1426637,703103,584921530,False,Home,21,16,13.0,WALK,58492153,1 +467937513,4679375130,1426638,703103,584921890,True,univ,12,21,8.0,WALK,58492189,1 +467937517,4679375170,1426638,703103,584921890,False,shopping,18,12,15.0,WALK_LOC,58492189,1 +467937518,4679375180,1426638,703103,584921890,False,othmaint,23,18,15.0,WALK_LOC,58492189,2 +467937519,4679375190,1426638,703103,584921890,False,social,22,23,15.0,WALK,58492189,3 +467937520,4679375200,1426638,703103,584921890,False,Home,21,22,15.0,WALK_LOC,58492189,4 +467940921,4679409210,1426649,703109,584926150,True,eatout,4,21,12.0,WALK,58492615,1 +467940925,4679409250,1426649,703109,584926150,False,Home,21,4,15.0,WALK,58492615,1 +467941137,4679411370,1426649,703109,584926420,True,shopping,5,21,18.0,DRIVEALONEFREE,58492642,1 +467941141,4679411410,1426649,703109,584926420,False,social,22,5,19.0,WALK,58492642,1 +467941142,4679411420,1426649,703109,584926420,False,Home,21,22,19.0,TNC_SHARED,58492642,2 +467946825,4679468250,1426667,703118,584933530,True,eatout,8,21,11.0,WALK,58493353,1 +467946829,4679468290,1426667,703118,584933530,False,Home,21,8,12.0,WALK,58493353,1 +467947329,4679473290,1426668,703118,584934160,True,othmaint,9,21,8.0,WALK_LOC,58493416,1 +467947333,4679473330,1426668,703118,584934160,False,Home,21,9,11.0,WALK,58493416,1 +467947337,4679473370,1426668,703118,584934170,True,othmaint,17,21,13.0,WALK,58493417,1 +467947341,4679473410,1426668,703118,584934170,False,eatout,4,17,15.0,WALK,58493417,1 +467947342,4679473420,1426668,703118,584934170,False,Home,21,4,15.0,WALK,58493417,2 +467958129,4679581290,1426701,703135,584947660,True,eatout,7,21,10.0,WALK,58494766,1 +467958130,4679581300,1426701,703135,584947660,True,othdiscr,9,7,10.0,WALK,58494766,2 +467958133,4679581330,1426701,703135,584947660,False,Home,21,9,12.0,WALK,58494766,1 +467958481,4679584810,1426702,703135,584948100,True,othmaint,13,21,9.0,BIKE,58494810,1 +467958485,4679584850,1426702,703135,584948100,False,Home,21,13,13.0,BIKE,58494810,1 +467974553,4679745530,1426751,703160,584968190,True,othmaint,14,21,10.0,WALK_LOC,58496819,1 +467974557,4679745570,1426751,703160,584968190,False,Home,21,14,10.0,WALK,58496819,1 +467974881,4679748810,1426752,703160,584968600,True,othmaint,9,21,7.0,WALK_LOC,58496860,1 +467974885,4679748850,1426752,703160,584968600,False,Home,21,9,11.0,WALK,58496860,1 +468006081,4680060810,1426847,703208,585007600,True,shopping,5,22,10.0,WALK,58500760,1 +468006085,4680060850,1426847,703208,585007600,False,Home,22,5,14.0,WALK,58500760,1 +468006369,4680063690,1426848,703208,585007960,True,othmaint,21,22,7.0,WALK,58500796,1 +468006373,4680063730,1426848,703208,585007960,False,shopping,16,21,13.0,WALK,58500796,1 +468006374,4680063740,1426848,703208,585007960,False,shopping,5,16,13.0,WALK,58500796,2 +468006375,4680063750,1426848,703208,585007960,False,Home,22,5,13.0,WALK,58500796,3 +468037441,4680374410,1426943,703256,585046800,True,othmaint,12,24,10.0,TNC_SINGLE,58504680,1 +468037445,4680374450,1426943,703256,585046800,False,Home,24,12,17.0,TNC_SINGLE,58504680,1 +468106945,4681069450,1427155,703362,585133680,True,eatout,25,25,10.0,WALK,58513368,1 +468106949,4681069490,1427155,703362,585133680,False,othmaint,2,25,13.0,WALK,58513368,1 +468106950,4681069500,1427155,703362,585133680,False,eatout,7,2,13.0,WALK,58513368,2 +468106951,4681069510,1427155,703362,585133680,False,Home,25,7,13.0,WALK,58513368,3 +468107433,4681074330,1427156,703362,585134290,True,shopping,16,25,15.0,WALK,58513429,1 +468107437,4681074370,1427156,703362,585134290,False,shopping,25,16,19.0,WALK_LOC,58513429,1 +468107438,4681074380,1427156,703362,585134290,False,Home,25,25,19.0,WALK,58513429,2 +468109753,4681097530,1427163,703366,585137190,True,social,9,25,12.0,BIKE,58513719,1 +468109757,4681097570,1427163,703366,585137190,False,Home,25,9,22.0,BIKE,58513719,1 +468110057,4681100570,1427164,703366,585137570,True,shopping,5,25,8.0,WALK_LOC,58513757,1 +468110061,4681100610,1427164,703366,585137570,False,Home,25,5,15.0,WALK,58513757,1 +468119569,4681195690,1427193,703381,585149460,True,shopping,14,25,13.0,WALK,58514946,1 +468119573,4681195730,1427193,703381,585149460,False,Home,25,14,19.0,WALK,58514946,1 +468119857,4681198570,1427194,703381,585149820,True,othmaint,14,25,7.0,WALK,58514982,1 +468119861,4681198610,1427194,703381,585149820,False,Home,25,14,13.0,WALK,58514982,1 +468119921,4681199210,1427194,703381,585149900,True,eatout,16,25,16.0,WALK,58514990,1 +468119922,4681199220,1427194,703381,585149900,True,social,2,16,19.0,WALK,58514990,2 +468119925,4681199250,1427194,703381,585149900,False,social,2,2,22.0,WALK,58514990,1 +468119926,4681199260,1427194,703381,585149900,False,shopping,4,2,22.0,WALK,58514990,2 +468119927,4681199270,1427194,703381,585149900,False,social,7,4,22.0,WALK,58514990,3 +468119928,4681199280,1427194,703381,585149900,False,Home,25,7,22.0,WALK,58514990,4 +478088193,4780881930,1457585,718577,597610240,True,work,10,21,6.0,WALK,59761024,1 +478088197,4780881970,1457585,718577,597610240,False,Home,21,10,15.0,WALK,59761024,1 +478088473,4780884730,1457586,718577,597610590,True,shopping,19,21,12.0,WALK,59761059,1 +478088477,4780884770,1457586,718577,597610590,False,Home,21,19,17.0,WALK,59761059,1 +478088481,4780884810,1457586,718577,597610600,True,escort,8,21,17.0,WALK,59761060,1 +478088482,4780884820,1457586,718577,597610600,True,shopping,5,8,17.0,DRIVEALONEFREE,59761060,2 +478088485,4780884850,1457586,718577,597610600,False,Home,21,5,17.0,DRIVEALONEFREE,59761060,1 +478088849,4780888490,1457587,718578,597611060,True,work,2,21,7.0,WALK,59761106,1 +478088853,4780888530,1457587,718578,597611060,False,Home,21,2,18.0,WALK,59761106,1 +478088913,4780889130,1457588,718578,597611140,True,eatout,11,21,9.0,WALK,59761114,1 +478088917,4780889170,1457588,718578,597611140,False,Home,21,11,14.0,WALK,59761114,1 +484173857,4841738570,1476139,727854,605217320,True,shopping,5,7,14.0,TNC_SINGLE,60521732,1 +484173861,4841738610,1476139,727854,605217320,False,Home,7,5,17.0,WALK_LOC,60521732,1 +484173905,4841739050,1476139,727854,605217380,True,work,22,7,7.0,WALK_LOC,60521738,1 +484173909,4841739090,1476139,727854,605217380,False,Home,7,22,14.0,WALK_LOC,60521738,1 +484174185,4841741850,1476140,727854,605217730,True,shopping,5,7,12.0,WALK,60521773,1 +484174189,4841741890,1476140,727854,605217730,False,Home,7,5,14.0,WALK,60521773,1 +484206377,4842063770,1476238,727903,605257970,True,work,20,9,6.0,WALK,60525797,1 +484206381,4842063810,1476238,727903,605257970,False,Home,9,20,21.0,WALK,60525797,1 +484219497,4842194970,1476278,727923,605274370,True,work,16,9,8.0,WALK_LOC,60527437,1 +484219501,4842195010,1476278,727923,605274370,False,Home,9,16,17.0,WALK_LRF,60527437,1 +484298521,4842985210,1476519,728044,605373150,True,othdiscr,22,10,9.0,WALK_LRF,60537315,1 +484298522,4842985220,1476519,728044,605373150,True,social,4,22,9.0,WALK_LRF,60537315,2 +484298525,4842985250,1476519,728044,605373150,False,Home,10,4,19.0,WALK,60537315,1 +484315993,4843159930,1476573,728071,605394990,True,eatout,11,11,18.0,WALK,60539499,1 +484315997,4843159970,1476573,728071,605394990,False,Home,11,11,18.0,WALK,60539499,1 +484316257,4843162570,1476573,728071,605395320,True,work,14,11,6.0,WALK,60539532,1 +484316261,4843162610,1476573,728071,605395320,False,Home,11,14,15.0,WALK_LOC,60539532,1 +484316537,4843165370,1476574,728071,605395670,True,shopping,11,11,15.0,WALK,60539567,1 +484316541,4843165410,1476574,728071,605395670,False,Home,11,11,17.0,WALK,60539567,1 +484316561,4843165610,1476574,728071,605395700,True,social,5,11,5.0,WALK,60539570,1 +484316565,4843165650,1476574,728071,605395700,False,Home,11,5,10.0,WALK,60539570,1 +484350369,4843503690,1476677,728123,605437960,True,work,13,11,9.0,WALK,60543796,1 +484350373,4843503730,1476677,728123,605437960,False,shopping,4,13,14.0,WALK,60543796,1 +484350374,4843503740,1476677,728123,605437960,False,escort,11,4,15.0,WALK,60543796,2 +484350375,4843503750,1476677,728123,605437960,False,escort,11,11,15.0,WALK,60543796,3 +484350376,4843503760,1476677,728123,605437960,False,Home,11,11,15.0,WALK,60543796,4 +484350649,4843506490,1476678,728123,605438310,True,escort,7,11,8.0,WALK,60543831,1 +484350650,4843506500,1476678,728123,605438310,True,shopping,11,7,9.0,WALK,60543831,2 +484350653,4843506530,1476678,728123,605438310,False,Home,11,11,13.0,WALK,60543831,1 +484397337,4843973370,1476821,728195,605496670,True,eatout,16,16,15.0,WALK,60549667,1 +484397341,4843973410,1476821,728195,605496670,False,Home,16,16,18.0,WALK,60549667,1 +484397513,4843975130,1476821,728195,605496890,True,othmaint,23,16,11.0,WALK,60549689,1 +484397517,4843975170,1476821,728195,605496890,False,Home,16,23,14.0,WALK_LRF,60549689,1 +484409145,4844091450,1476857,728213,605511430,True,eatout,14,16,15.0,WALK,60551143,1 +484409149,4844091490,1476857,728213,605511430,False,escort,5,14,20.0,WALK,60551143,1 +484409150,4844091500,1476857,728213,605511430,False,Home,16,5,20.0,WALK,60551143,2 +484409385,4844093850,1476857,728213,605511730,True,social,22,16,13.0,WALK,60551173,1 +484409389,4844093890,1476857,728213,605511730,False,Home,16,22,13.0,WALK,60551173,1 +484430289,4844302890,1476921,728245,605537860,True,othdiscr,9,17,11.0,BIKE,60553786,1 +484430293,4844302930,1476921,728245,605537860,False,Home,17,9,18.0,BIKE,60553786,1 +484430681,4844306810,1476922,728245,605538350,True,eatout,16,17,14.0,WALK,60553835,1 +484430682,4844306820,1476922,728245,605538350,True,shopping,16,16,14.0,WALK,60553835,2 +484430685,4844306850,1476922,728245,605538350,False,Home,17,16,15.0,WALK,60553835,1 +484452617,4844526170,1476989,728279,605565770,True,othmaint,14,20,10.0,WALK_LOC,60556577,1 +484452621,4844526210,1476989,728279,605565770,False,Home,20,14,13.0,WALK_LOC,60556577,1 +484452921,4844529210,1476990,728279,605566150,True,othdiscr,9,20,18.0,WALK,60556615,1 +484452925,4844529250,1476990,728279,605566150,False,Home,20,9,18.0,WALK,60556615,1 +484476233,4844762330,1477061,728315,605595290,True,othmaint,5,20,12.0,BIKE,60559529,1 +484476237,4844762370,1477061,728315,605595290,False,Home,20,5,13.0,BIKE,60559529,1 +484476321,4844763210,1477061,728315,605595400,True,work,23,20,14.0,BIKE,60559540,1 +484476325,4844763250,1477061,728315,605595400,False,Home,20,23,17.0,BIKE,60559540,1 +484476601,4844766010,1477062,728315,605595750,True,shopping,5,20,8.0,WALK,60559575,1 +484476605,4844766050,1477062,728315,605595750,False,Home,20,5,17.0,WALK,60559575,1 +484476929,4844769290,1477063,728316,605596160,True,shopping,5,20,15.0,WALK_LOC,60559616,1 +484476933,4844769330,1477063,728316,605596160,False,Home,20,5,15.0,WALK,60559616,1 +484477025,4844770250,1477064,728316,605596280,True,atwork,11,16,13.0,WALK,60559628,1 +484477029,4844770290,1477064,728316,605596280,False,Work,16,11,13.0,WALK,60559628,1 +484477305,4844773050,1477064,728316,605596630,True,work,16,20,12.0,WALK,60559663,1 +484477309,4844773090,1477064,728316,605596630,False,Home,20,16,22.0,WALK,60559663,1 +484533785,4845337850,1477237,728403,605667230,True,eatout,5,21,14.0,WALK,60566723,1 +484533789,4845337890,1477237,728403,605667230,False,Home,21,5,15.0,WALK,60566723,1 +484534001,4845340010,1477237,728403,605667500,True,shopping,18,21,9.0,TNC_SINGLE,60566750,1 +484534005,4845340050,1477237,728403,605667500,False,Home,21,18,10.0,TNC_SHARED,60566750,1 +484534265,4845342650,1477238,728403,605667830,True,othdiscr,1,21,11.0,WALK,60566783,1 +484534269,4845342690,1477238,728403,605667830,False,othmaint,7,1,11.0,WALK,60566783,1 +484534270,4845342700,1477238,728403,605667830,False,Home,21,7,11.0,WALK,60566783,2 +484546401,4845464010,1477275,728422,605683000,True,othdiscr,21,21,14.0,WALK,60568300,1 +484546405,4845464050,1477275,728422,605683000,False,Home,21,21,16.0,WALK,60568300,1 +484560897,4845608970,1477319,728444,605701120,True,othmaint,10,21,12.0,DRIVEALONEFREE,60570112,1 +484560898,4845608980,1477319,728444,605701120,True,shopping,19,10,14.0,TNC_SINGLE,60570112,2 +484560901,4845609010,1477319,728444,605701120,False,shopping,24,19,21.0,DRIVEALONEFREE,60570112,1 +484560902,4845609020,1477319,728444,605701120,False,Home,21,24,21.0,TNC_SINGLE,60570112,2 +484560905,4845609050,1477319,728444,605701130,True,shopping,16,21,21.0,SHARED3FREE,60570113,1 +484560909,4845609090,1477319,728444,605701130,False,shopping,13,16,21.0,SHARED3FREE,60570113,1 +484560910,4845609100,1477319,728444,605701130,False,othdiscr,16,13,21.0,SHARED3FREE,60570113,2 +484560911,4845609110,1477319,728444,605701130,False,Home,21,16,21.0,SHARED3FREE,60570113,3 +484561273,4845612730,1477320,728444,605701590,True,work,22,21,8.0,DRIVEALONEFREE,60570159,1 +484561277,4845612770,1477320,728444,605701590,False,Home,21,22,15.0,SHARED2FREE,60570159,1 +484561281,4845612810,1477320,728444,605701600,True,othmaint,9,21,15.0,WALK,60570160,1 +484561282,4845612820,1477320,728444,605701600,True,work,22,9,16.0,WALK_LOC,60570160,2 +484561285,4845612850,1477320,728444,605701600,False,shopping,11,22,17.0,WALK_LRF,60570160,1 +484561286,4845612860,1477320,728444,605701600,False,othmaint,8,11,17.0,WALK,60570160,2 +484561287,4845612870,1477320,728444,605701600,False,Home,21,8,17.0,WALK,60570160,3 +484576929,4845769290,1477368,728468,605721160,True,othmaint,8,21,8.0,WALK,60572116,1 +484576933,4845769330,1477368,728468,605721160,False,Home,21,8,9.0,WALK,60572116,1 +484577017,4845770170,1477368,728468,605721270,True,work,2,21,9.0,WALK,60572127,1 +484577021,4845770210,1477368,728468,605721270,False,Home,21,2,19.0,WALK,60572127,1 +484611433,4846114330,1477473,728521,605764290,True,social,5,21,18.0,WALK,60576429,1 +484611437,4846114370,1477473,728521,605764290,False,Home,21,5,21.0,WALK,60576429,1 +484611457,4846114570,1477473,728521,605764320,True,work,5,21,6.0,WALK,60576432,1 +484611461,4846114610,1477473,728521,605764320,False,Home,21,5,17.0,WALK,60576432,1 +484621249,4846212490,1477503,728536,605776560,True,shopping,20,23,13.0,SHARED2FREE,60577656,1 +484621253,4846212530,1477503,728536,605776560,False,othmaint,16,20,13.0,SHARED2FREE,60577656,1 +484621254,4846212540,1477503,728536,605776560,False,Home,23,16,13.0,SHARED2FREE,60577656,2 +501577081,5015770810,1529198,747627,626971350,True,othmaint,4,8,14.0,TNC_SHARED,62697135,1 +501577085,5015770850,1529198,747627,626971350,False,eatout,6,4,18.0,WALK,62697135,1 +501577086,5015770860,1529198,747627,626971350,False,Home,8,6,18.0,TNC_SHARED,62697135,2 +501577849,5015778490,1529200,747627,626972310,True,school,9,8,8.0,WALK_LOC,62697231,1 +501577853,5015778530,1529200,747627,626972310,False,Home,8,9,13.0,WALK_LOC,62697231,1 +501580953,5015809530,1529210,747630,626976190,True,escort,7,8,8.0,WALK,62697619,1 +501580954,5015809540,1529210,747630,626976190,True,escort,2,7,8.0,WALK,62697619,2 +501580957,5015809570,1529210,747630,626976190,False,eatout,5,2,13.0,WALK,62697619,1 +501580958,5015809580,1529210,747630,626976190,False,Home,8,5,13.0,WALK,62697619,2 +501580961,5015809610,1529210,747630,626976200,True,escort,2,8,14.0,WALK,62697620,1 +501580965,5015809650,1529210,747630,626976200,False,social,6,2,14.0,WALK,62697620,1 +501580966,5015809660,1529210,747630,626976200,False,othdiscr,6,6,14.0,WALK,62697620,2 +501580967,5015809670,1529210,747630,626976200,False,Home,8,6,14.0,WALK,62697620,3 +501581081,5015810810,1529210,747630,626976350,True,othdiscr,9,8,15.0,WALK,62697635,1 +501581085,5015810850,1529210,747630,626976350,False,Home,8,9,21.0,WALK,62697635,1 +501581785,5015817850,1529212,747630,626977230,True,school,21,8,12.0,WALK,62697723,1 +501581789,5015817890,1529212,747630,626977230,False,Home,8,21,18.0,WALK,62697723,1 +515832417,5158324170,1572659,763879,644790520,True,othmaint,25,6,7.0,WALK,64479052,1 +515832418,5158324180,1572659,763879,644790520,True,escort,25,25,12.0,WALK,64479052,2 +515832419,5158324190,1572659,763879,644790520,True,shopping,24,25,17.0,WALK,64479052,3 +515832421,5158324210,1572659,763879,644790520,False,shopping,25,24,18.0,WALK,64479052,1 +515832422,5158324220,1572659,763879,644790520,False,escort,25,25,20.0,WALK,64479052,2 +515832423,5158324230,1572659,763879,644790520,False,Home,6,25,20.0,WALK,64479052,3 +515838761,5158387610,1572679,763899,644798450,True,eatout,16,6,18.0,WALK,64479845,1 +515838765,5158387650,1572679,763899,644798450,False,Home,6,16,21.0,WALK,64479845,1 +515838977,5158389770,1572679,763899,644798720,True,shopping,15,6,7.0,WALK,64479872,1 +515838981,5158389810,1572679,763899,644798720,False,Home,6,15,15.0,WALK,64479872,1 +515843833,5158438330,1572694,763914,644804790,True,othdiscr,8,6,11.0,WALK,64480479,1 +515843837,5158438370,1572694,763914,644804790,False,Home,6,8,11.0,WALK,64480479,1 +515843881,5158438810,1572694,763914,644804850,True,univ,13,6,8.0,WALK,64480485,1 +515843885,5158438850,1572694,763914,644804850,False,eatout,2,13,11.0,WALK,64480485,1 +515843886,5158438860,1572694,763914,644804850,False,Home,6,2,11.0,WALK,64480485,2 +515843889,5158438890,1572694,763914,644804860,True,univ,13,6,17.0,DRIVEALONEFREE,64480486,1 +515843893,5158438930,1572694,763914,644804860,False,Home,6,13,17.0,DRIVEALONEFREE,64480486,1 +515856953,5158569530,1572734,763954,644821190,True,othdiscr,10,7,8.0,WALK,64482119,1 +515856957,5158569570,1572734,763954,644821190,False,Home,7,10,9.0,WALK,64482119,1 +515859097,5158590970,1572741,763961,644823870,True,eatout,7,7,8.0,WALK,64482387,1 +515859101,5158591010,1572741,763961,644823870,False,Home,7,7,15.0,WALK,64482387,1 +515859337,5158593370,1572741,763961,644824170,True,social,4,7,18.0,WALK,64482417,1 +515859341,5158593410,1572741,763961,644824170,False,Home,7,4,21.0,WALK,64482417,1 +515859953,5158599530,1572743,763963,644824940,True,univ,12,7,6.0,WALK_LOC,64482494,1 +515859957,5158599570,1572743,763963,644824940,False,othdiscr,8,12,13.0,WALK_LOC,64482494,1 +515859958,5158599580,1572743,763963,644824940,False,Home,7,8,13.0,WALK,64482494,2 +515867777,5158677770,1572767,763987,644834720,True,othdiscr,6,7,16.0,WALK,64483472,1 +515867781,5158677810,1572767,763987,644834720,False,Home,7,6,21.0,WALK,64483472,1 +515867785,5158677850,1572767,763987,644834730,True,othdiscr,7,7,21.0,WALK,64483473,1 +515867789,5158677890,1572767,763987,644834730,False,Home,7,7,21.0,WALK,64483473,1 +515867801,5158678010,1572767,763987,644834750,True,eatout,8,7,9.0,WALK,64483475,1 +515867802,5158678020,1572767,763987,644834750,True,othmaint,7,8,12.0,WALK,64483475,2 +515867805,5158678050,1572767,763987,644834750,False,escort,6,7,13.0,WALK,64483475,1 +515867806,5158678060,1572767,763987,644834750,False,social,14,6,14.0,WALK,64483475,2 +515867807,5158678070,1572767,763987,644834750,False,shopping,7,14,14.0,WALK,64483475,3 +515867808,5158678080,1572767,763987,644834750,False,Home,7,7,14.0,WALK,64483475,4 +515867841,5158678410,1572767,763987,644834800,True,shopping,5,7,8.0,WALK,64483480,1 +515867845,5158678450,1572767,763987,644834800,False,Home,7,5,9.0,WALK,64483480,1 +515869921,5158699210,1572774,763994,644837400,True,eatout,12,8,9.0,WALK,64483740,1 +515869925,5158699250,1572774,763994,644837400,False,Home,8,12,16.0,WALK,64483740,1 +515887505,5158875050,1572827,764047,644859380,True,univ,12,8,7.0,WALK_LOC,64485938,1 +515887509,5158875090,1572827,764047,644859380,False,Home,8,12,15.0,WALK_LOC,64485938,1 +515903073,5159030730,1572875,764095,644878840,True,escort,14,8,7.0,DRIVEALONEFREE,64487884,1 +515903077,5159030770,1572875,764095,644878840,False,Home,8,14,7.0,SHARED3FREE,64487884,1 +515903201,5159032010,1572875,764095,644879000,True,othdiscr,7,8,18.0,WALK,64487900,1 +515903205,5159032050,1572875,764095,644879000,False,Home,8,7,19.0,WALK_LOC,64487900,1 +515903249,5159032490,1572875,764095,644879060,True,univ,12,8,9.0,WALK,64487906,1 +515903253,5159032530,1572875,764095,644879060,False,escort,5,12,15.0,WALK,64487906,1 +515903254,5159032540,1572875,764095,644879060,False,Home,8,5,15.0,WALK,64487906,2 +515903265,5159032650,1572875,764095,644879080,True,shopping,14,8,17.0,TNC_SHARED,64487908,1 +515903269,5159032690,1572875,764095,644879080,False,Home,8,14,17.0,DRIVEALONEFREE,64487908,1 +515910137,5159101370,1572896,764116,644887670,True,work,14,9,13.0,WALK_LRF,64488767,1 +515910138,5159101380,1572896,764116,644887670,True,escort,9,14,14.0,WALK_LRF,64488767,2 +515910139,5159101390,1572896,764116,644887670,True,work,22,9,15.0,WALK_LRF,64488767,3 +515910140,5159101400,1572896,764116,644887670,True,univ,12,22,15.0,WALK_LRF,64488767,4 +515910141,5159101410,1572896,764116,644887670,False,Home,9,12,16.0,WALK_LRF,64488767,1 +515920585,5159205850,1572928,764148,644900730,True,othdiscr,11,9,8.0,WALK,64490073,1 +515920589,5159205890,1572928,764148,644900730,False,escort,7,11,15.0,WALK,64490073,1 +515920590,5159205900,1572928,764148,644900730,False,Home,9,7,15.0,WALK,64490073,2 +515921265,5159212650,1572930,764150,644901580,True,othmaint,9,9,8.0,WALK,64490158,1 +515921266,5159212660,1572930,764150,644901580,True,othmaint,9,9,8.0,WALK,64490158,2 +515921269,5159212690,1572930,764150,644901580,False,Home,9,9,11.0,WALK,64490158,1 +515921305,5159213050,1572930,764150,644901630,True,shopping,7,9,11.0,WALK,64490163,1 +515921309,5159213090,1572930,764150,644901630,False,Home,9,7,16.0,WALK,64490163,1 +515979361,5159793610,1573107,764327,644974200,True,shopping,16,17,11.0,WALK,64497420,1 +515979365,5159793650,1573107,764327,644974200,False,Home,17,16,14.0,WALK,64497420,1 +515979385,5159793850,1573107,764327,644974230,True,social,6,17,18.0,WALK_LOC,64497423,1 +515979389,5159793890,1573107,764327,644974230,False,escort,12,6,18.0,WALK_LOC,64497423,1 +515979390,5159793900,1573107,764327,644974230,False,Home,17,12,18.0,WALK_LOC,64497423,2 +518349585,5183495850,1580334,771554,647936980,True,atwork,13,1,13.0,SHARED3FREE,64793698,1 +518349589,5183495890,1580334,771554,647936980,False,Work,1,13,13.0,SHARED3FREE,64793698,1 +518349865,5183498650,1580334,771554,647937330,True,work,1,5,9.0,WALK,64793733,1 +518349869,5183498690,1580334,771554,647937330,False,Home,5,1,17.0,WALK,64793733,1 +534858761,5348587610,1630666,821886,668573450,True,eatout,14,4,9.0,WALK,66857345,1 +534858762,5348587620,1630666,821886,668573450,True,work,7,14,10.0,WALK,66857345,2 +534858763,5348587630,1630666,821886,668573450,True,work,5,7,10.0,WALK,66857345,3 +534858764,5348587640,1630666,821886,668573450,True,work,6,5,14.0,WALK,66857345,4 +534858765,5348587650,1630666,821886,668573450,False,escort,9,6,17.0,WALK,66857345,1 +534858766,5348587660,1630666,821886,668573450,False,work,9,9,17.0,WALK,66857345,2 +534858767,5348587670,1630666,821886,668573450,False,work,7,9,17.0,WALK,66857345,3 +534858768,5348587680,1630666,821886,668573450,False,Home,4,7,18.0,WALK,66857345,4 +534880737,5348807370,1630733,821953,668600920,True,work,21,6,8.0,WALK,66860092,1 +534880741,5348807410,1630733,821953,668600920,False,Home,6,21,18.0,WALK,66860092,1 +534895497,5348954970,1630778,821998,668619370,True,work,9,6,7.0,TNC_SINGLE,66861937,1 +534895501,5348955010,1630778,821998,668619370,False,Home,6,9,15.0,WALK,66861937,1 +534901401,5349014010,1630796,822016,668626750,True,work,5,6,7.0,WALK,66862675,1 +534901405,5349014050,1630796,822016,668626750,False,Home,6,5,20.0,WALK,66862675,1 +534905009,5349050090,1630807,822027,668631260,True,work,9,6,7.0,TNC_SINGLE,66863126,1 +534905013,5349050130,1630807,822027,668631260,False,Home,6,9,17.0,WALK_LOC,66863126,1 +534914193,5349141930,1630835,822055,668642740,True,work,23,6,6.0,WALK_LOC,66864274,1 +534914197,5349141970,1630835,822055,668642740,False,Home,6,23,15.0,WALK,66864274,1 +534971593,5349715930,1631010,822230,668714490,True,work,16,7,7.0,WALK_LOC,66871449,1 +534971597,5349715970,1631010,822230,668714490,False,Home,7,16,17.0,TNC_SINGLE,66871449,1 +534980121,5349801210,1631036,822256,668725150,True,shopping,13,7,5.0,DRIVEALONEFREE,66872515,1 +534980122,5349801220,1631036,822256,668725150,True,work,16,13,5.0,DRIVEALONEFREE,66872515,2 +534980125,5349801250,1631036,822256,668725150,False,shopping,15,16,15.0,WALK,66872515,1 +534980126,5349801260,1631036,822256,668725150,False,Home,7,15,16.0,SHARED3FREE,66872515,2 +534983729,5349837290,1631047,822267,668729660,True,work,5,7,8.0,TNC_SINGLE,66872966,1 +534983733,5349837330,1631047,822267,668729660,False,shopping,16,5,17.0,TNC_SINGLE,66872966,1 +534983734,5349837340,1631047,822267,668729660,False,Home,7,16,17.0,WALK_LOC,66872966,2 +534987401,5349874010,1631059,822279,668734250,True,eatout,8,7,8.0,WALK,66873425,1 +534987405,5349874050,1631059,822279,668734250,False,Home,7,8,11.0,WALK,66873425,1 +534987617,5349876170,1631059,822279,668734520,True,shopping,3,7,11.0,WALK,66873452,1 +534987621,5349876210,1631059,822279,668734520,False,Home,7,3,15.0,WALK,66873452,1 +535000457,5350004570,1631098,822318,668750570,True,work,9,7,7.0,WALK_LOC,66875057,1 +535000461,5350004610,1631098,822318,668750570,False,Home,7,9,21.0,WALK_LOC,66875057,1 +535025713,5350257130,1631175,822395,668782140,True,eatout,13,7,8.0,DRIVEALONEFREE,66878214,1 +535025714,5350257140,1631175,822395,668782140,True,work,19,13,8.0,DRIVEALONEFREE,66878214,2 +535025717,5350257170,1631175,822395,668782140,False,Home,7,19,21.0,DRIVEALONEFREE,66878214,1 +535029321,5350293210,1631186,822406,668786650,True,work,14,7,7.0,WALK,66878665,1 +535029325,5350293250,1631186,822406,668786650,False,Home,7,14,16.0,WALK_LOC,66878665,1 +535029977,5350299770,1631188,822408,668787470,True,work,15,7,8.0,WALK,66878747,1 +535029981,5350299810,1631188,822408,668787470,False,Home,7,15,19.0,WALK,66878747,1 +535036209,5350362090,1631207,822427,668795260,True,othdiscr,9,7,10.0,WALK,66879526,1 +535036210,5350362100,1631207,822427,668795260,True,work,11,9,10.0,WALK,66879526,2 +535036213,5350362130,1631207,822427,668795260,False,Home,7,11,19.0,WALK,66879526,1 +535053265,5350532650,1631259,822479,668816580,True,work,14,7,9.0,WALK_LOC,66881658,1 +535053269,5350532690,1631259,822479,668816580,False,shopping,5,14,9.0,WALK,66881658,1 +535053270,5350532700,1631259,822479,668816580,False,Home,7,5,16.0,WALK,66881658,2 +535065729,5350657290,1631297,822517,668832160,True,work,24,7,7.0,DRIVEALONEFREE,66883216,1 +535065733,5350657330,1631297,822517,668832160,False,Home,7,24,11.0,DRIVEALONEFREE,66883216,1 +535087377,5350873770,1631363,822583,668859220,True,work,2,8,7.0,WALK,66885922,1 +535087381,5350873810,1631363,822583,668859220,False,Home,8,2,17.0,WALK,66885922,1 +535090001,5350900010,1631371,822591,668862500,True,work,1,8,6.0,WALK_LRF,66886250,1 +535090005,5350900050,1631371,822591,668862500,False,Home,8,1,16.0,WALK_LRF,66886250,1 +535125337,5351253370,1631479,822699,668906670,True,othmaint,16,9,6.0,WALK_LRF,66890667,1 +535125341,5351253410,1631479,822699,668906670,False,Home,9,16,6.0,WALK_LOC,66890667,1 +535125425,5351254250,1631479,822699,668906780,True,work,1,9,8.0,WALK_LRF,66890678,1 +535125429,5351254290,1631479,822699,668906780,False,eatout,5,1,16.0,WALK,66890678,1 +535125430,5351254300,1631479,822699,668906780,False,Home,9,5,17.0,WALK_LOC,66890678,2 +535149697,5351496970,1631553,822773,668937120,True,work,2,9,5.0,WALK_LOC,66893712,1 +535149701,5351497010,1631553,822773,668937120,False,Home,9,2,10.0,WALK_LOC,66893712,1 +535149705,5351497050,1631553,822773,668937130,True,work,2,9,12.0,WALK_LRF,66893713,1 +535149709,5351497090,1631553,822773,668937130,False,Home,9,2,15.0,WALK_LRF,66893713,1 +535161505,5351615050,1631589,822809,668951880,True,work,1,9,9.0,WALK_LRF,66895188,1 +535161509,5351615090,1631589,822809,668951880,False,othmaint,10,1,19.0,WALK_LRF,66895188,1 +535161510,5351615100,1631589,822809,668951880,False,shopping,13,10,19.0,WALK,66895188,2 +535161511,5351615110,1631589,822809,668951880,False,Home,9,13,19.0,WALK_LRF,66895188,3 +535161881,5351618810,1631591,822811,668952350,True,atwork,11,9,12.0,WALK,66895235,1 +535161885,5351618850,1631591,822811,668952350,False,Work,9,11,14.0,WALK,66895235,1 +535162161,5351621610,1631591,822811,668952700,True,work,9,9,7.0,WALK,66895270,1 +535162165,5351621650,1631591,822811,668952700,False,Home,9,9,17.0,WALK,66895270,1 +535166753,5351667530,1631605,822825,668958440,True,othmaint,3,9,8.0,WALK_LRF,66895844,1 +535166754,5351667540,1631605,822825,668958440,True,work,14,3,8.0,WALK_LOC,66895844,2 +535166757,5351667570,1631605,822825,668958440,False,Home,9,14,21.0,WALK_LOC,66895844,1 +535170409,5351704090,1631617,822837,668963010,True,atwork,2,4,13.0,WALK,66896301,1 +535170413,5351704130,1631617,822837,668963010,False,Work,4,2,13.0,WALK,66896301,1 +535170689,5351706890,1631617,822837,668963360,True,work,4,9,10.0,WALK_LOC,66896336,1 +535170693,5351706930,1631617,822837,668963360,False,othmaint,16,4,18.0,WALK,66896336,1 +535170694,5351706940,1631617,822837,668963360,False,Home,9,16,20.0,WALK_LRF,66896336,2 +535187137,5351871370,1631668,822888,668983920,True,eatout,12,25,11.0,WALK,66898392,1 +535187138,5351871380,1631668,822888,668983920,True,atwork,12,12,11.0,WALK,66898392,2 +535187141,5351871410,1631668,822888,668983920,False,Work,25,12,11.0,WALK,66898392,1 +535187417,5351874170,1631668,822888,668984270,True,work,25,9,7.0,WALK_LOC,66898427,1 +535187421,5351874210,1631668,822888,668984270,False,Home,9,25,18.0,WALK_LOC,66898427,1 +535190089,5351900890,1631677,822897,668987610,True,atwork,16,1,10.0,WALK_LOC,66898761,1 +535190093,5351900930,1631677,822897,668987610,False,Work,1,16,13.0,TNC_SINGLE,66898761,1 +535190369,5351903690,1631677,822897,668987960,True,work,1,9,9.0,WALK_LRF,66898796,1 +535190373,5351903730,1631677,822897,668987960,False,Home,9,1,17.0,WALK_LRF,66898796,1 +535212297,5352122970,1631744,822964,669015370,True,shopping,3,9,18.0,WALK,66901537,1 +535212301,5352123010,1631744,822964,669015370,False,Home,9,3,19.0,WALK,66901537,1 +535212345,5352123450,1631744,822964,669015430,True,work,7,9,9.0,WALK,66901543,1 +535212349,5352123490,1631744,822964,669015430,False,Home,9,7,18.0,WALK,66901543,1 +535225857,5352258570,1631786,823006,669032320,True,eatout,13,9,20.0,SHARED3FREE,66903232,1 +535225861,5352258610,1631786,823006,669032320,False,Home,9,13,20.0,SHARED3FREE,66903232,1 +535225993,5352259930,1631786,823006,669032490,True,atwork,4,22,11.0,BIKE,66903249,1 +535225997,5352259970,1631786,823006,669032490,False,Work,22,4,11.0,BIKE,66903249,1 +535226073,5352260730,1631786,823006,669032590,True,shopping,13,9,21.0,TNC_SHARED,66903259,1 +535226077,5352260770,1631786,823006,669032590,False,Home,9,13,22.0,TNC_SHARED,66903259,1 +535226121,5352261210,1631786,823006,669032650,True,work,22,9,7.0,BIKE,66903265,1 +535226125,5352261250,1631786,823006,669032650,False,Home,9,22,20.0,BIKE,66903265,1 +535226777,5352267770,1631788,823008,669033470,True,escort,10,9,5.0,WALK,66903347,1 +535226778,5352267780,1631788,823008,669033470,True,escort,5,10,6.0,TNC_SINGLE,66903347,2 +535226779,5352267790,1631788,823008,669033470,True,work,9,5,12.0,TNC_SINGLE,66903347,3 +535226781,5352267810,1631788,823008,669033470,False,othmaint,2,9,15.0,WALK_HVY,66903347,1 +535226782,5352267820,1631788,823008,669033470,False,shopping,9,2,15.0,TNC_SINGLE,66903347,2 +535226783,5352267830,1631788,823008,669033470,False,eatout,12,9,15.0,WALK_LOC,66903347,3 +535226784,5352267840,1631788,823008,669033470,False,Home,9,12,15.0,WALK_LRF,66903347,4 +535238257,5352382570,1631823,823043,669047820,True,work,12,9,5.0,WALK,66904782,1 +535238261,5352382610,1631823,823043,669047820,False,shopping,11,12,15.0,WALK_LOC,66904782,1 +535238262,5352382620,1631823,823043,669047820,False,Home,9,11,15.0,TNC_SINGLE,66904782,2 +535241913,5352419130,1631835,823055,669052390,True,atwork,15,14,14.0,WALK,66905239,1 +535241917,5352419170,1631835,823055,669052390,False,eatout,7,15,14.0,WALK,66905239,1 +535241918,5352419180,1631835,823055,669052390,False,eatout,5,7,14.0,WALK,66905239,2 +535241919,5352419190,1631835,823055,669052390,False,Work,14,5,14.0,WALK,66905239,3 +535242081,5352420810,1631835,823055,669052600,True,othdiscr,9,9,16.0,WALK,66905260,1 +535242085,5352420850,1631835,823055,669052600,False,Home,9,9,18.0,WALK,66905260,1 +535242089,5352420890,1631835,823055,669052610,True,othdiscr,21,9,21.0,WALK,66905261,1 +535242093,5352420930,1631835,823055,669052610,False,Home,9,21,23.0,WALK,66905261,1 +535242097,5352420970,1631835,823055,669052620,True,othdiscr,1,9,23.0,WALK_LRF,66905262,1 +535242101,5352421010,1631835,823055,669052620,False,Home,9,1,23.0,WALK_LRF,66905262,1 +535242193,5352421930,1631835,823055,669052740,True,work,14,9,6.0,WALK_LRF,66905274,1 +535242197,5352421970,1631835,823055,669052740,False,Home,9,14,15.0,WALK_LRF,66905274,1 +535251097,5352510970,1631863,823083,669063870,True,atwork,7,22,11.0,WALK,66906387,1 +535251101,5352511010,1631863,823083,669063870,False,shopping,25,7,13.0,WALK,66906387,1 +535251102,5352511020,1631863,823083,669063870,False,Work,22,25,13.0,WALK,66906387,2 +535251377,5352513770,1631863,823083,669064220,True,work,22,9,8.0,WALK_HVY,66906422,1 +535251381,5352513810,1631863,823083,669064220,False,Home,9,22,17.0,TNC_SINGLE,66906422,1 +535262905,5352629050,1631899,823119,669078630,True,atwork,21,13,11.0,WALK,66907863,1 +535262909,5352629090,1631899,823119,669078630,False,work,7,21,11.0,WALK,66907863,1 +535262910,5352629100,1631899,823119,669078630,False,Work,13,7,11.0,WALK,66907863,2 +535263185,5352631850,1631899,823119,669078980,True,work,13,9,9.0,WALK,66907898,1 +535263189,5352631890,1631899,823119,669078980,False,Home,9,13,21.0,WALK,66907898,1 +535263841,5352638410,1631901,823121,669079800,True,work,4,9,8.0,WALK,66907980,1 +535263845,5352638450,1631901,823121,669079800,False,Home,9,4,18.0,WALK,66907980,1 +535264217,5352642170,1631903,823123,669080270,True,work,9,5,10.0,WALK,66908027,1 +535264218,5352642180,1631903,823123,669080270,True,othmaint,9,9,10.0,WALK,66908027,2 +535264219,5352642190,1631903,823123,669080270,True,atwork,19,9,10.0,WALK,66908027,3 +535264221,5352642210,1631903,823123,669080270,False,Work,5,19,10.0,WALK,66908027,1 +535264497,5352644970,1631903,823123,669080620,True,work,5,9,6.0,WALK_LRF,66908062,1 +535264501,5352645010,1631903,823123,669080620,False,work,22,5,22.0,WALK,66908062,1 +535264502,5352645020,1631903,823123,669080620,False,Home,9,22,22.0,WALK_LRF,66908062,2 +535283537,5352835370,1631962,823182,669104420,True,atwork,22,2,12.0,WALK,66910442,1 +535283541,5352835410,1631962,823182,669104420,False,work,4,22,14.0,WALK,66910442,1 +535283542,5352835420,1631962,823182,669104420,False,Work,2,4,14.0,WALK,66910442,2 +535283849,5352838490,1631962,823182,669104810,True,work,2,9,6.0,WALK,66910481,1 +535283853,5352838530,1631962,823182,669104810,False,Home,9,2,21.0,WALK,66910481,1 +535315337,5353153370,1632058,823278,669144170,True,work,2,10,8.0,WALK_LOC,66914417,1 +535315341,5353153410,1632058,823278,669144170,False,othmaint,20,2,16.0,WALK_LRF,66914417,1 +535315342,5353153420,1632058,823278,669144170,False,shopping,22,20,16.0,WALK_LRF,66914417,2 +535315343,5353153430,1632058,823278,669144170,False,escort,9,22,16.0,WALK_LRF,66914417,3 +535315344,5353153440,1632058,823278,669144170,False,Home,10,9,16.0,WALK_LOC,66914417,4 +535339497,5353394970,1632132,823352,669174370,True,othdiscr,9,11,10.0,WALK_LOC,66917437,1 +535339501,5353395010,1632132,823352,669174370,False,Home,11,9,11.0,TNC_SINGLE,66917437,1 +535339609,5353396090,1632132,823352,669174510,True,work,4,11,14.0,WALK,66917451,1 +535339613,5353396130,1632132,823352,669174510,False,Home,11,4,21.0,WALK_LRF,66917451,1 +535339849,5353398490,1632133,823353,669174810,True,othmaint,7,11,21.0,WALK,66917481,1 +535339850,5353398500,1632133,823353,669174810,True,othmaint,6,7,21.0,DRIVEALONEFREE,66917481,2 +535339853,5353398530,1632133,823353,669174810,False,othmaint,7,6,21.0,WALK,66917481,1 +535339854,5353398540,1632133,823353,669174810,False,escort,10,7,21.0,DRIVEALONEFREE,66917481,2 +535339855,5353398550,1632133,823353,669174810,False,Home,11,10,21.0,DRIVEALONEFREE,66917481,3 +535339937,5353399370,1632133,823353,669174920,True,work,9,11,7.0,DRIVEALONEFREE,66917492,1 +535339941,5353399410,1632133,823353,669174920,False,Home,11,9,18.0,DRIVEALONEFREE,66917492,1 +535341249,5353412490,1632137,823357,669176560,True,work,13,11,9.0,WALK_LOC,66917656,1 +535341253,5353412530,1632137,823357,669176560,False,escort,13,13,16.0,WALK,66917656,1 +535341254,5353412540,1632137,823357,669176560,False,Home,11,13,16.0,TNC_SINGLE,66917656,2 +535351089,5353510890,1632167,823387,669188860,True,work,9,11,6.0,WALK,66918886,1 +535351090,5353510900,1632167,823387,669188860,True,work,9,9,7.0,WALK,66918886,2 +535351091,5353510910,1632167,823387,669188860,True,work,5,9,7.0,WALK,66918886,3 +535351092,5353510920,1632167,823387,669188860,True,work,11,5,7.0,WALK,66918886,4 +535351093,5353510930,1632167,823387,669188860,False,Home,11,11,17.0,WALK,66918886,1 +535351097,5353510970,1632167,823387,669188870,True,work,11,11,18.0,WALK,66918887,1 +535351101,5353511010,1632167,823387,669188870,False,Home,11,11,21.0,WALK,66918887,1 +535354761,5353547610,1632179,823399,669193450,True,eatout,11,11,21.0,WALK,66919345,1 +535354765,5353547650,1632179,823399,669193450,False,Home,11,11,21.0,WALK,66919345,1 +535354937,5353549370,1632179,823399,669193670,True,othmaint,5,11,9.0,WALK,66919367,1 +535354941,5353549410,1632179,823399,669193670,False,Home,11,5,15.0,WALK,66919367,1 +535354977,5353549770,1632179,823399,669193720,True,shopping,7,11,17.0,WALK,66919372,1 +535354978,5353549780,1632179,823399,669193720,True,shopping,11,7,17.0,WALK,66919372,2 +535354981,5353549810,1632179,823399,669193720,False,Home,11,11,18.0,WALK,66919372,1 +535370985,5353709850,1632228,823448,669213730,True,othdiscr,9,11,20.0,WALK,66921373,1 +535370989,5353709890,1632228,823448,669213730,False,Home,11,9,21.0,WALK,66921373,1 +535371097,5353710970,1632228,823448,669213870,True,work,5,11,6.0,WALK,66921387,1 +535371101,5353711010,1632228,823448,669213870,False,Home,11,5,17.0,WALK_LOC,66921387,1 +535377657,5353776570,1632248,823468,669222070,True,work,9,11,7.0,DRIVEALONEFREE,66922207,1 +535377661,5353776610,1632248,823468,669222070,False,Home,11,9,18.0,DRIVEALONEFREE,66922207,1 +535388201,5353882010,1632281,823501,669235250,True,work,13,13,13.0,WALK,66923525,1 +535388202,5353882020,1632281,823501,669235250,True,atwork,8,13,13.0,WALK,66923525,2 +535388205,5353882050,1632281,823501,669235250,False,Work,13,8,14.0,WALK,66923525,1 +535388481,5353884810,1632281,823501,669235600,True,work,13,12,7.0,WALK,66923560,1 +535388485,5353884850,1632281,823501,669235600,False,Home,12,13,19.0,WALK,66923560,1 +535394105,5353941050,1632299,823519,669242630,True,work,2,2,11.0,WALK,66924263,1 +535394106,5353941060,1632299,823519,669242630,True,atwork,12,2,11.0,WALK,66924263,2 +535394109,5353941090,1632299,823519,669242630,False,Work,2,12,13.0,WALK,66924263,1 +535394385,5353943850,1632299,823519,669242980,True,work,2,12,8.0,WALK,66924298,1 +535394389,5353943890,1632299,823519,669242980,False,work,12,2,23.0,WALK,66924298,1 +535394390,5353943900,1632299,823519,669242980,False,Home,12,12,23.0,WALK,66924298,2 +535412425,5354124250,1632354,823574,669265530,True,work,24,12,8.0,WALK_LOC,66926553,1 +535412429,5354124290,1632354,823574,669265530,False,Home,12,24,17.0,WALK,66926553,1 +535414393,5354143930,1632360,823580,669267990,True,work,11,12,7.0,WALK,66926799,1 +535414397,5354143970,1632360,823580,669267990,False,Home,12,11,17.0,WALK,66926799,1 +535416249,5354162490,1632366,823586,669270310,True,othdiscr,5,12,6.0,WALK,66927031,1 +535416253,5354162530,1632366,823586,669270310,False,Home,12,5,6.0,WALK,66927031,1 +535416257,5354162570,1632366,823586,669270320,True,othdiscr,13,12,19.0,WALK,66927032,1 +535416261,5354162610,1632366,823586,669270320,False,Home,12,13,19.0,WALK,66927032,1 +535416313,5354163130,1632366,823586,669270390,True,shopping,13,12,20.0,WALK,66927039,1 +535416317,5354163170,1632366,823586,669270390,False,shopping,16,13,20.0,WALK,66927039,1 +535416318,5354163180,1632366,823586,669270390,False,Home,12,16,21.0,WALK,66927039,2 +535416361,5354163610,1632366,823586,669270450,True,escort,25,12,7.0,WALK,66927045,1 +535416362,5354163620,1632366,823586,669270450,True,work,24,25,8.0,WALK_LOC,66927045,2 +535416365,5354163650,1632366,823586,669270450,False,othdiscr,8,24,17.0,WALK_LOC,66927045,1 +535416366,5354163660,1632366,823586,669270450,False,shopping,5,8,19.0,WALK_LOC,66927045,2 +535416367,5354163670,1632366,823586,669270450,False,Home,12,5,19.0,TNC_SINGLE,66927045,3 +535431777,5354317770,1632413,823633,669289720,True,shopping,19,14,9.0,TNC_SINGLE,66928972,1 +535431778,5354317780,1632413,823633,669289720,True,work,11,19,9.0,WALK,66928972,2 +535431781,5354317810,1632413,823633,669289720,False,work,2,11,16.0,TNC_SINGLE,66928972,1 +535431782,5354317820,1632413,823633,669289720,False,Home,14,2,19.0,TNC_SINGLE,66928972,2 +535432433,5354324330,1632415,823635,669290540,True,work,4,14,6.0,WALK_LOC,66929054,1 +535432437,5354324370,1632415,823635,669290540,False,Home,14,4,15.0,WALK_LOC,66929054,1 +535471465,5354714650,1632534,823754,669339330,True,work,16,16,7.0,DRIVEALONEFREE,66933933,1 +535471469,5354714690,1632534,823754,669339330,False,Home,16,16,20.0,WALK,66933933,1 +535496065,5354960650,1632609,823829,669370080,True,work,17,17,5.0,WALK,66937008,1 +535496069,5354960690,1632609,823829,669370080,False,Home,17,17,17.0,WALK,66937008,1 +535513449,5355134490,1632662,823882,669391810,True,work,2,17,7.0,WALK_LOC,66939181,1 +535513453,5355134530,1632662,823882,669391810,False,Home,17,2,18.0,WALK_LRF,66939181,1 +535523289,5355232890,1632692,823912,669404110,True,work,2,17,7.0,WALK,66940411,1 +535523293,5355232930,1632692,823912,669404110,False,Home,17,2,18.0,WALK_LRF,66940411,1 +535530505,5355305050,1632714,823934,669413130,True,work,16,17,7.0,WALK,66941313,1 +535530509,5355305090,1632714,823934,669413130,False,Home,17,16,15.0,WALK,66941313,1 +535538705,5355387050,1632739,823959,669423380,True,work,17,17,7.0,WALK,66942338,1 +535538709,5355387090,1632739,823959,669423380,False,Home,17,17,18.0,TNC_SINGLE,66942338,1 +535546857,5355468570,1632764,823984,669433570,True,shopping,25,17,18.0,WALK_LOC,66943357,1 +535546861,5355468610,1632764,823984,669433570,False,Home,17,25,19.0,WALK_LRF,66943357,1 +535546905,5355469050,1632764,823984,669433630,True,work,14,17,7.0,WALK_LRF,66943363,1 +535546909,5355469090,1632764,823984,669433630,False,Home,17,14,16.0,WALK_LOC,66943363,1 +535598449,5355984490,1632922,824142,669498060,True,atwork,9,9,11.0,WALK,66949806,1 +535598453,5355984530,1632922,824142,669498060,False,Work,9,9,11.0,WALK,66949806,1 +535598729,5355987290,1632922,824142,669498410,True,work,9,18,7.0,WALK,66949841,1 +535598733,5355987330,1632922,824142,669498410,False,Home,18,9,18.0,WALK,66949841,1 +535599777,5355997770,1632926,824146,669499720,True,eatout,7,18,18.0,WALK,66949972,1 +535599781,5355997810,1632926,824146,669499720,False,Home,18,7,21.0,WALK,66949972,1 +535599929,5355999290,1632926,824146,669499910,True,othdiscr,7,18,8.0,WALK,66949991,1 +535599933,5355999330,1632926,824146,669499910,False,Home,18,7,15.0,WALK,66949991,1 +535604305,5356043050,1632939,824159,669505380,True,work,6,18,6.0,DRIVEALONEFREE,66950538,1 +535604309,5356043090,1632939,824159,669505380,False,eatout,11,6,13.0,DRIVEALONEFREE,66950538,1 +535604310,5356043100,1632939,824159,669505380,False,Home,18,11,13.0,WALK,66950538,2 +535613881,5356138810,1632969,824189,669517350,True,shopping,11,18,18.0,WALK,66951735,1 +535613882,5356138820,1632969,824189,669517350,True,eatout,9,11,19.0,WALK,66951735,2 +535613885,5356138850,1632969,824189,669517350,False,shopping,11,9,21.0,WALK,66951735,1 +535613886,5356138860,1632969,824189,669517350,False,othdiscr,8,11,21.0,WALK,66951735,2 +535613887,5356138870,1632969,824189,669517350,False,Home,18,8,21.0,WALK,66951735,3 +535614033,5356140330,1632969,824189,669517540,True,othdiscr,9,18,12.0,WALK_LOC,66951754,1 +535614037,5356140370,1632969,824189,669517540,False,Home,18,9,16.0,WALK_LOC,66951754,1 +535620049,5356200490,1632987,824207,669525060,True,work,4,18,15.0,WALK_LOC,66952506,1 +535620053,5356200530,1632987,824207,669525060,False,Home,18,4,22.0,WALK,66952506,1 +535636121,5356361210,1633036,824256,669545150,True,work,2,19,7.0,TNC_SINGLE,66954515,1 +535636125,5356361250,1633036,824256,669545150,False,othdiscr,6,2,17.0,TNC_SINGLE,66954515,1 +535636126,5356361260,1633036,824256,669545150,False,Home,19,6,18.0,TNC_SINGLE,66954515,2 +535641369,5356413690,1633052,824272,669551710,True,work,11,19,6.0,WALK,66955171,1 +535641373,5356413730,1633052,824272,669551710,False,Home,19,11,16.0,WALK,66955171,1 +535642353,5356423530,1633055,824275,669552940,True,othdiscr,7,19,11.0,WALK_LOC,66955294,1 +535642354,5356423540,1633055,824275,669552940,True,work,4,7,13.0,WALK_LOC,66955294,2 +535642357,5356423570,1633055,824275,669552940,False,shopping,20,4,16.0,WALK_LRF,66955294,1 +535642358,5356423580,1633055,824275,669552940,False,Home,19,20,17.0,WALK_LOC,66955294,2 +535646945,5356469450,1633069,824289,669558680,True,escort,9,19,5.0,WALK_LOC,66955868,1 +535646946,5356469460,1633069,824289,669558680,True,work,10,9,16.0,WALK_LOC,66955868,2 +535646949,5356469490,1633069,824289,669558680,False,Home,19,10,16.0,WALK,66955868,1 +535647273,5356472730,1633070,824290,669559090,True,work,5,19,8.0,WALK,66955909,1 +535647277,5356472770,1633070,824290,669559090,False,Home,19,5,18.0,WALK,66955909,1 +535656369,5356563690,1633098,824318,669570460,True,othmaint,24,20,12.0,SHARED2FREE,66957046,1 +535656373,5356563730,1633098,824318,669570460,False,Home,20,24,13.0,SHARED2FREE,66957046,1 +535656457,5356564570,1633098,824318,669570570,True,work,4,20,7.0,WALK_LRF,66957057,1 +535656461,5356564610,1633098,824318,669570570,False,Home,20,4,10.0,WALK_LRF,66957057,1 +535663393,5356633930,1633120,824340,669579240,True,atwork,2,14,12.0,WALK,66957924,1 +535663397,5356633970,1633120,824340,669579240,False,eatout,16,2,13.0,WALK,66957924,1 +535663398,5356633980,1633120,824340,669579240,False,Work,14,16,13.0,WALK,66957924,2 +535663673,5356636730,1633120,824340,669579590,True,work,14,20,9.0,WALK_LOC,66957959,1 +535663677,5356636770,1633120,824340,669579590,False,shopping,11,14,21.0,TNC_SHARED,66957959,1 +535663678,5356636780,1633120,824340,669579590,False,Home,20,11,22.0,WALK,66957959,2 +535669577,5356695770,1633138,824358,669586970,True,work,9,20,8.0,DRIVEALONEFREE,66958697,1 +535669581,5356695810,1633138,824358,669586970,False,work,10,9,17.0,WALK,66958697,1 +535669582,5356695820,1633138,824358,669586970,False,Home,20,10,17.0,DRIVEALONEFREE,66958697,2 +535672921,5356729210,1633149,824369,669591150,True,eatout,13,21,8.0,WALK,66959115,1 +535672925,5356729250,1633149,824369,669591150,False,Home,21,13,14.0,WALK,66959115,1 +535681273,5356812730,1633174,824394,669601590,True,othdiscr,20,21,7.0,DRIVEALONEFREE,66960159,1 +535681277,5356812770,1633174,824394,669601590,False,Home,21,20,7.0,DRIVEALONEFREE,66960159,1 +535681337,5356813370,1633174,824394,669601670,True,shopping,16,21,18.0,WALK,66960167,1 +535681341,5356813410,1633174,824394,669601670,False,Home,21,16,21.0,WALK,66960167,1 +535681385,5356813850,1633174,824394,669601730,True,escort,8,21,8.0,WALK,66960173,1 +535681386,5356813860,1633174,824394,669601730,True,work,12,8,8.0,BIKE,66960173,2 +535681389,5356813890,1633174,824394,669601730,False,Home,21,12,17.0,BIKE,66960173,1 +535690897,5356908970,1633203,824423,669613620,True,work,4,22,6.0,WALK,66961362,1 +535690901,5356909010,1633203,824423,669613620,False,Home,22,4,19.0,WALK,66961362,1 +535694417,5356944170,1633214,824434,669618020,True,othmaint,21,22,16.0,TNC_SINGLE,66961802,1 +535694421,5356944210,1633214,824434,669618020,False,Home,22,21,19.0,TNC_SINGLE,66961802,1 +535694457,5356944570,1633214,824434,669618070,True,shopping,19,22,12.0,WALK_LOC,66961807,1 +535694461,5356944610,1633214,824434,669618070,False,Home,22,19,14.0,TNC_SHARED,66961807,1 +535694465,5356944650,1633214,824434,669618080,True,othmaint,16,22,14.0,WALK_LOC,66961808,1 +535694466,5356944660,1633214,824434,669618080,True,shopping,18,16,14.0,WALK_LRF,66961808,2 +535694469,5356944690,1633214,824434,669618080,False,Home,22,18,15.0,WALK_LRF,66961808,1 +535714561,5357145610,1633276,824496,669643200,True,atwork,1,2,10.0,WALK,66964320,1 +535714565,5357145650,1633276,824496,669643200,False,eatout,3,1,10.0,WALK,66964320,1 +535714566,5357145660,1633276,824496,669643200,False,Work,2,3,10.0,WALK,66964320,2 +535714753,5357147530,1633276,824496,669643440,True,othmaint,23,23,7.0,WALK,66964344,1 +535714757,5357147570,1633276,824496,669643440,False,Home,23,23,9.0,WALK,66964344,1 +535714841,5357148410,1633276,824496,669643550,True,work,2,23,9.0,WALK,66964355,1 +535714845,5357148450,1633276,824496,669643550,False,Home,23,2,18.0,WALK,66964355,1 +535720793,5357207930,1633295,824515,669650990,True,atwork,13,21,10.0,WALK,66965099,1 +535720797,5357207970,1633295,824515,669650990,False,Work,21,13,11.0,SHARED3FREE,66965099,1 +535721073,5357210730,1633295,824515,669651340,True,work,21,24,5.0,WALK_LOC,66965134,1 +535721077,5357210770,1633295,824515,669651340,False,Home,24,21,16.0,WALK_LOC,66965134,1 +535728553,5357285530,1633318,824538,669660690,True,univ,13,24,17.0,DRIVEALONEFREE,66966069,1 +535728557,5357285570,1633318,824538,669660690,False,Home,24,13,18.0,DRIVEALONEFREE,66966069,1 +535728617,5357286170,1633318,824538,669660770,True,work,13,24,7.0,BIKE,66966077,1 +535728621,5357286210,1633318,824538,669660770,False,Home,24,13,13.0,BIKE,66966077,1 +535735177,5357351770,1633338,824558,669668970,True,work,2,25,7.0,DRIVEALONEFREE,66966897,1 +535735181,5357351810,1633338,824558,669668970,False,eatout,2,2,11.0,DRIVEALONEFREE,66966897,1 +535735182,5357351820,1633338,824558,669668970,False,Home,25,2,16.0,DRIVEALONEFREE,66966897,2 +535747641,5357476410,1633376,824596,669684550,True,work,22,25,7.0,WALK,66968455,1 +535747645,5357476450,1633376,824596,669684550,False,Home,25,22,18.0,WALK_LOC,66968455,1 +564983897,5649838970,1722511,906270,706229870,True,social,11,8,11.0,WALK,70622987,1 +564983901,5649839010,1722511,906270,706229870,False,Home,8,11,16.0,WALK,70622987,1 +564983905,5649839050,1722511,906270,706229880,True,social,16,8,17.0,WALK,70622988,1 +564983909,5649839090,1722511,906270,706229880,False,Home,8,16,19.0,WALK,70622988,1 +564984137,5649841370,1722512,906270,706230170,True,othdiscr,20,8,8.0,WALK,70623017,1 +564984141,5649841410,1722512,906270,706230170,False,Home,8,20,16.0,WALK,70623017,1 +565004825,5650048250,1722575,906302,706256030,True,othmaint,11,8,10.0,WALK,70625603,1 +565004829,5650048290,1722575,906302,706256030,False,Home,8,11,18.0,WALK,70625603,1 +565005001,5650050010,1722576,906302,706256250,True,escort,21,8,8.0,WALK,70625625,1 +565005005,5650050050,1722576,906302,706256250,False,othmaint,8,21,8.0,WALK,70625625,1 +565005006,5650050060,1722576,906302,706256250,False,Home,8,8,9.0,WALK,70625625,2 +565027169,5650271690,1722643,906336,706283960,True,shopping,12,9,12.0,WALK,70628396,1 +565027173,5650271730,1722643,906336,706283960,False,Home,9,12,20.0,WALK,70628396,1 +565027281,5650272810,1722644,906336,706284100,True,eatout,21,9,20.0,WALK,70628410,1 +565027285,5650272850,1722644,906336,706284100,False,Home,9,21,21.0,WALK,70628410,1 +565027497,5650274970,1722644,906336,706284370,True,shopping,11,9,8.0,WALK,70628437,1 +565027501,5650275010,1722644,906336,706284370,False,othmaint,9,11,17.0,WALK,70628437,1 +565027502,5650275020,1722644,906336,706284370,False,Home,9,9,18.0,WALK,70628437,2 +581938897,5819388970,1774203,932116,727423620,True,work,2,6,6.0,WALK,72742362,1 +581938898,5819388980,1774203,932116,727423620,True,work,13,2,8.0,WALK_LOC,72742362,2 +581938901,5819389010,1774203,932116,727423620,False,work,24,13,15.0,WALK_LOC,72742362,1 +581938902,5819389020,1774203,932116,727423620,False,Home,6,24,15.0,WALK_LOC,72742362,2 +581959169,5819591690,1774265,932147,727448960,True,escort,7,7,15.0,WALK,72744896,1 +581959170,5819591700,1774265,932147,727448960,True,eatout,5,7,15.0,WALK,72744896,2 +581959171,5819591710,1774265,932147,727448960,True,escort,8,5,16.0,WALK,72744896,3 +581959172,5819591720,1774265,932147,727448960,True,univ,12,8,16.0,WALK,72744896,4 +581959173,5819591730,1774265,932147,727448960,False,othdiscr,9,12,18.0,WALK,72744896,1 +581959174,5819591740,1774265,932147,727448960,False,shopping,5,9,23.0,WALK,72744896,2 +581959175,5819591750,1774265,932147,727448960,False,Home,7,5,23.0,WALK,72744896,3 +581959561,5819595610,1774266,932147,727449450,True,work,14,7,10.0,WALK_LOC,72744945,1 +581959565,5819595650,1774266,932147,727449450,False,Home,7,14,20.0,WALK_LOC,72744945,1 +581960545,5819605450,1774269,932149,727450680,True,work,24,7,7.0,WALK_LOC,72745068,1 +581960549,5819605490,1774269,932149,727450680,False,Home,7,24,17.0,WALK,72745068,1 +581960825,5819608250,1774270,932149,727451030,True,shopping,5,7,16.0,TNC_SINGLE,72745103,1 +581960829,5819608290,1774270,932149,727451030,False,shopping,2,5,17.0,TAXI,72745103,1 +581960830,5819608300,1774270,932149,727451030,False,Home,7,2,17.0,DRIVEALONEFREE,72745103,2 +581999905,5819999050,1774389,932209,727499880,True,work,23,8,14.0,WALK_LOC,72749988,1 +581999909,5819999090,1774389,932209,727499880,False,shopping,23,23,17.0,WALK,72749988,1 +581999910,5819999100,1774389,932209,727499880,False,Home,8,23,20.0,WALK_LOC,72749988,2 +582000169,5820001690,1774390,932209,727500210,True,univ,13,8,17.0,WALK,72750021,1 +582000173,5820001730,1774390,932209,727500210,False,Home,8,13,17.0,WALK_LRF,72750021,1 +582009089,5820090890,1774417,932223,727511360,True,work,10,8,6.0,WALK,72751136,1 +582009093,5820090930,1774417,932223,727511360,False,Home,8,10,16.0,WALK,72751136,1 +582009329,5820093290,1774418,932223,727511660,True,othmaint,17,8,9.0,WALK_LRF,72751166,1 +582009333,5820093330,1774418,932223,727511660,False,Home,8,17,13.0,WALK_LRF,72751166,1 +582009745,5820097450,1774419,932224,727512180,True,work,5,8,6.0,WALK,72751218,1 +582009749,5820097490,1774419,932224,727512180,False,Home,8,5,16.0,WALK,72751218,1 +582010025,5820100250,1774420,932224,727512530,True,escort,3,8,12.0,BIKE,72751253,1 +582010026,5820100260,1774420,932224,727512530,True,shopping,2,3,13.0,BIKE,72751253,2 +582010027,5820100270,1774420,932224,727512530,True,shopping,19,2,13.0,BIKE,72751253,3 +582010029,5820100290,1774420,932224,727512530,False,Home,8,19,15.0,BIKE,72751253,1 +582031769,5820317690,1774487,932258,727539710,True,atwork,9,1,11.0,WALK,72753971,1 +582031773,5820317730,1774487,932258,727539710,False,Work,1,9,11.0,WALK,72753971,1 +582032049,5820320490,1774487,932258,727540060,True,work,1,9,6.0,WALK_LRF,72754006,1 +582032053,5820320530,1774487,932258,727540060,False,Home,9,1,15.0,WALK_LRF,72754006,1 +582032137,5820321370,1774488,932258,727540170,True,escort,7,9,12.0,SHARED2FREE,72754017,1 +582032141,5820321410,1774488,932258,727540170,False,Home,9,7,14.0,WALK,72754017,1 +582032145,5820321450,1774488,932258,727540180,True,escort,10,9,15.0,DRIVEALONEFREE,72754018,1 +582032149,5820321490,1774488,932258,727540180,False,Home,9,10,16.0,SHARED2FREE,72754018,1 +582032289,5820322890,1774488,932258,727540360,True,othmaint,10,9,7.0,WALK,72754036,1 +582032293,5820322930,1774488,932258,727540360,False,Home,9,10,12.0,WALK,72754036,1 +582032313,5820323130,1774488,932258,727540390,True,univ,9,9,17.0,WALK,72754039,1 +582032317,5820323170,1774488,932258,727540390,False,shopping,8,9,21.0,WALK,72754039,1 +582032318,5820323180,1774488,932258,727540390,False,Home,9,8,22.0,WALK,72754039,2 +582048385,5820483850,1774537,932283,727560480,True,univ,9,9,8.0,WALK,72756048,1 +582048389,5820483890,1774537,932283,727560480,False,Home,9,9,13.0,WALK,72756048,1 +582054305,5820543050,1774555,932292,727567880,True,shopping,11,9,14.0,WALK,72756788,1 +582054309,5820543090,1774555,932292,727567880,False,Home,9,11,15.0,DRIVEALONEFREE,72756788,1 +582054633,5820546330,1774556,932292,727568290,True,shopping,11,9,10.0,TNC_SINGLE,72756829,1 +582054637,5820546370,1774556,932292,727568290,False,Home,9,11,13.0,WALK,72756829,1 +582081249,5820812490,1774637,932333,727601560,True,work,23,10,7.0,WALK_LOC,72760156,1 +582081253,5820812530,1774637,932333,727601560,False,Home,10,23,15.0,WALK_LRF,72760156,1 +582081529,5820815290,1774638,932333,727601910,True,othdiscr,7,10,11.0,WALK,72760191,1 +582081530,5820815300,1774638,932333,727601910,True,shopping,24,7,12.0,WALK,72760191,2 +582081533,5820815330,1774638,932333,727601910,False,eatout,6,24,14.0,WALK_LOC,72760191,1 +582081534,5820815340,1774638,932333,727601910,False,Home,10,6,15.0,WALK,72760191,2 +582081553,5820815530,1774638,932333,727601940,True,social,8,10,18.0,WALK,72760194,1 +582081557,5820815570,1774638,932333,727601940,False,Home,10,8,18.0,WALK,72760194,1 +582099617,5820996170,1774693,932361,727624520,True,work,9,11,7.0,WALK,72762452,1 +582099621,5820996210,1774693,932361,727624520,False,Home,11,9,20.0,WALK,72762452,1 +582099833,5820998330,1774694,932361,727624790,True,othdiscr,24,11,10.0,WALK,72762479,1 +582099837,5820998370,1774694,932361,727624790,False,Home,11,24,16.0,WALK,72762479,1 +582125577,5821255770,1774773,932401,727656970,True,atwork,16,5,9.0,SHARED3FREE,72765697,1 +582125581,5821255810,1774773,932401,727656970,False,Work,5,16,9.0,SHARED3FREE,72765697,1 +582125809,5821258090,1774773,932401,727657260,True,shopping,16,13,18.0,SHARED2FREE,72765726,1 +582125813,5821258130,1774773,932401,727657260,False,Home,13,16,18.0,SHARED2FREE,72765726,1 +582125857,5821258570,1774773,932401,727657320,True,work,5,13,8.0,DRIVEALONEFREE,72765732,1 +582125861,5821258610,1774773,932401,727657320,False,shopping,5,5,17.0,WALK,72765732,1 +582125862,5821258620,1774773,932401,727657320,False,Home,13,5,17.0,WALK,72765732,2 +582159689,5821596890,1774877,932453,727699610,True,eatout,5,12,10.0,WALK,72769961,1 +582159690,5821596900,1774877,932453,727699610,True,atwork,1,5,10.0,WALK,72769961,2 +582159693,5821596930,1774877,932453,727699610,False,Work,12,1,10.0,WALK,72769961,1 +582159969,5821599690,1774877,932453,727699960,True,escort,5,24,8.0,WALK_LOC,72769996,1 +582159970,5821599700,1774877,932453,727699960,True,work,12,5,8.0,WALK_LOC,72769996,2 +582159973,5821599730,1774877,932453,727699960,False,Home,24,12,17.0,WALK,72769996,1 +582160233,5821602330,1774878,932453,727700290,True,univ,13,24,9.0,WALK_LOC,72770029,1 +582160237,5821602370,1774878,932453,727700290,False,Home,24,13,16.0,WALK_LOC,72770029,1 +582160577,5821605770,1774879,932454,727700720,True,shopping,12,24,15.0,WALK,72770072,1 +582160581,5821605810,1774879,932454,727700720,False,Home,24,12,15.0,WALK,72770072,1 +582160585,5821605850,1774879,932454,727700730,True,shopping,11,24,18.0,WALK_LOC,72770073,1 +582160589,5821605890,1774879,932454,727700730,False,Home,24,11,18.0,WALK_LOC,72770073,1 +582160953,5821609530,1774880,932454,727701190,True,work,12,24,7.0,WALK,72770119,1 +582160957,5821609570,1774880,932454,727701190,False,Home,24,12,18.0,WALK,72770119,1 +614930185,6149301850,1874787,982408,768662730,True,eatout,8,6,15.0,WALK,76866273,1 +614930189,6149301890,1874787,982408,768662730,False,Home,6,8,16.0,WALK,76866273,1 +614930401,6149304010,1874787,982408,768663000,True,escort,5,6,17.0,WALK_LOC,76866300,1 +614930402,6149304020,1874787,982408,768663000,True,shopping,7,5,17.0,WALK,76866300,2 +614930405,6149304050,1874787,982408,768663000,False,Home,6,7,17.0,WALK,76866300,1 +614930497,6149304970,1874788,982408,768663120,True,atwork,5,21,13.0,WALK,76866312,1 +614930501,6149305010,1874788,982408,768663120,False,Work,21,5,13.0,WALK,76866312,1 +614930777,6149307770,1874788,982408,768663470,True,work,21,6,7.0,WALK,76866347,1 +614930781,6149307810,1874788,982408,768663470,False,Home,6,21,23.0,WALK,76866347,1 +614935041,6149350410,1874801,982415,768668800,True,work,5,6,8.0,WALK,76866880,1 +614935045,6149350450,1874801,982415,768668800,False,Home,6,5,18.0,WALK,76866880,1 +614935369,6149353690,1874802,982415,768669210,True,work,16,6,8.0,WALK_LOC,76866921,1 +614935373,6149353730,1874802,982415,768669210,False,Home,6,16,16.0,WALK_LOC,76866921,1 +614942257,6149422570,1874823,982426,768677820,True,work,2,6,13.0,WALK_LOC,76867782,1 +614942261,6149422610,1874823,982426,768677820,False,Home,6,2,20.0,TNC_SINGLE,76867782,1 +614942585,6149425850,1874824,982426,768678230,True,work,23,6,8.0,BIKE,76867823,1 +614942589,6149425890,1874824,982426,768678230,False,Home,6,23,16.0,BIKE,76867823,1 +614954721,6149547210,1874861,982445,768693400,True,work,9,7,11.0,WALK,76869340,1 +614954725,6149547250,1874861,982445,768693400,False,Home,7,9,21.0,WALK,76869340,1 +614954737,6149547370,1874862,982445,768693420,True,atwork,15,12,12.0,WALK,76869342,1 +614954741,6149547410,1874862,982445,768693420,False,Work,12,15,12.0,WALK,76869342,1 +614955049,6149550490,1874862,982445,768693810,True,work,12,7,8.0,WALK,76869381,1 +614955053,6149550530,1874862,982445,768693810,False,Home,7,12,18.0,WALK,76869381,1 +614971057,6149710570,1874911,982470,768713820,True,othmaint,22,7,21.0,WALK_LRF,76871382,1 +614971058,6149710580,1874911,982470,768713820,True,univ,13,22,21.0,WALK_LRF,76871382,2 +614971061,6149710610,1874911,982470,768713820,False,Home,7,13,21.0,WALK_LOC,76871382,1 +614971121,6149711210,1874911,982470,768713900,True,work,2,7,7.0,WALK_LOC,76871390,1 +614971125,6149711250,1874911,982470,768713900,False,Home,7,2,14.0,WALK,76871390,1 +614971449,6149714490,1874912,982470,768714310,True,work,9,7,6.0,WALK,76871431,1 +614971453,6149714530,1874912,982470,768714310,False,Home,7,9,15.0,WALK,76871431,1 +614975057,6149750570,1874923,982476,768718820,True,work,9,7,9.0,WALK_LOC,76871882,1 +614975061,6149750610,1874923,982476,768718820,False,Home,7,9,18.0,WALK,76871882,1 +614975385,6149753850,1874924,982476,768719230,True,work,16,7,7.0,SHARED3FREE,76871923,1 +614975389,6149753890,1874924,982476,768719230,False,Home,7,16,20.0,WALK_LOC,76871923,1 +615009825,6150098250,1875029,982529,768762280,True,work,2,8,17.0,WALK,76876228,1 +615009829,6150098290,1875029,982529,768762280,False,Home,8,2,21.0,WALK,76876228,1 +615010065,6150100650,1875030,982529,768762580,True,othmaint,21,8,11.0,WALK,76876258,1 +615010069,6150100690,1875030,982529,768762580,False,Home,8,21,14.0,WALK,76876258,1 +615032737,6150327370,1875099,982564,768790920,True,shopping,18,8,8.0,TNC_SINGLE,76879092,1 +615032741,6150327410,1875099,982564,768790920,False,Home,8,18,8.0,TNC_SINGLE,76879092,1 +615032745,6150327450,1875099,982564,768790930,True,shopping,8,8,20.0,TNC_SINGLE,76879093,1 +615032749,6150327490,1875099,982564,768790930,False,Home,8,8,21.0,TNC_SINGLE,76879093,1 +615032785,6150327850,1875099,982564,768790980,True,work,19,8,8.0,TNC_SINGLE,76879098,1 +615032789,6150327890,1875099,982564,768790980,False,escort,12,19,19.0,TNC_SINGLE,76879098,1 +615032790,6150327900,1875099,982564,768790980,False,othmaint,7,12,19.0,TNC_SINGLE,76879098,2 +615032791,6150327910,1875099,982564,768790980,False,Home,8,7,19.0,WALK_LOC,76879098,3 +615032833,6150328330,1875100,982564,768791040,True,atwork,7,4,8.0,WALK_LOC,76879104,1 +615032837,6150328370,1875100,982564,768791040,False,Work,4,7,9.0,TNC_SINGLE,76879104,1 +615033113,6150331130,1875100,982564,768791390,True,work,4,8,6.0,WALK,76879139,1 +615033117,6150331170,1875100,982564,768791390,False,Home,8,4,18.0,TNC_SINGLE,76879139,1 +615038409,6150384090,1875117,982573,768798010,True,atwork,16,18,12.0,WALK,76879801,1 +615038413,6150384130,1875117,982573,768798010,False,eatout,16,16,12.0,WALK,76879801,1 +615038414,6150384140,1875117,982573,768798010,False,Work,18,16,12.0,WALK,76879801,2 +615038497,6150384970,1875117,982573,768798120,True,othdiscr,25,8,8.0,TNC_SINGLE,76879812,1 +615038501,6150385010,1875117,982573,768798120,False,Home,8,25,8.0,TNC_SINGLE,76879812,1 +615038601,6150386010,1875117,982573,768798250,True,othmaint,21,8,18.0,WALK,76879825,1 +615038605,6150386050,1875117,982573,768798250,False,Home,8,21,21.0,WALK_LOC,76879825,1 +615038689,6150386890,1875117,982573,768798360,True,escort,9,8,8.0,TNC_SINGLE,76879836,1 +615038690,6150386900,1875117,982573,768798360,True,work,18,9,9.0,TNC_SINGLE,76879836,2 +615038693,6150386930,1875117,982573,768798360,False,shopping,19,18,17.0,WALK,76879836,1 +615038694,6150386940,1875117,982573,768798360,False,Home,8,19,17.0,WALK_LOC,76879836,2 +615038993,6150389930,1875118,982573,768798740,True,othmaint,7,8,11.0,WALK,76879874,1 +615038994,6150389940,1875118,982573,768798740,True,social,11,7,12.0,WALK,76879874,2 +615038997,6150389970,1875118,982573,768798740,False,Home,8,11,20.0,WALK,76879874,1 +615043281,6150432810,1875131,982580,768804100,True,work,2,9,6.0,WALK_LOC,76880410,1 +615043285,6150432850,1875131,982580,768804100,False,Home,9,2,13.0,WALK_LRF,76880410,1 +615043609,6150436090,1875132,982580,768804510,True,work,8,9,7.0,WALK,76880451,1 +615043613,6150436130,1875132,982580,768804510,False,Home,9,8,18.0,WALK,76880451,1 +615056945,6150569450,1875173,982601,768821180,True,othdiscr,9,9,10.0,WALK,76882118,1 +615056949,6150569490,1875173,982601,768821180,False,Home,9,9,13.0,WALK,76882118,1 +615057057,6150570570,1875173,982601,768821320,True,work,16,9,14.0,BIKE,76882132,1 +615057061,6150570610,1875173,982601,768821320,False,Home,9,16,18.0,BIKE,76882132,1 +615057297,6150572970,1875174,982601,768821620,True,othmaint,2,9,19.0,WALK_LRF,76882162,1 +615057301,6150573010,1875174,982601,768821620,False,Home,9,2,20.0,WALK_LRF,76882162,1 +615057385,6150573850,1875174,982601,768821730,True,work,12,9,8.0,WALK_LRF,76882173,1 +615057389,6150573890,1875174,982601,768821730,False,Home,9,12,18.0,WALK_LRF,76882173,1 +615083297,6150832970,1875253,982641,768854120,True,work,2,10,8.0,WALK_LRF,76885412,1 +615083301,6150833010,1875253,982641,768854120,False,Home,10,2,18.0,WALK_LRF,76885412,1 +615083625,6150836250,1875254,982641,768854530,True,work,5,10,10.0,WALK,76885453,1 +615083629,6150836290,1875254,982641,768854530,False,Home,10,5,21.0,WALK,76885453,1 +615109913,6151099130,1875335,982682,768887390,True,atwork,9,9,13.0,WALK,76888739,1 +615109917,6151099170,1875335,982682,768887390,False,Work,9,9,13.0,WALK,76888739,1 +615109929,6151099290,1875335,982682,768887410,True,eatout,13,11,7.0,WALK,76888741,1 +615109933,6151099330,1875335,982682,768887410,False,Home,11,13,10.0,WALK,76888741,1 +615110193,6151101930,1875335,982682,768887740,True,work,9,11,10.0,WALK,76888774,1 +615110197,6151101970,1875335,982682,768887740,False,Home,11,9,19.0,WALK,76888774,1 +615110409,6151104090,1875336,982682,768888010,True,othdiscr,13,11,8.0,WALK_LOC,76888801,1 +615110413,6151104130,1875336,982682,768888010,False,Home,11,13,14.0,WALK_LOC,76888801,1 +615165185,6151651850,1875503,982766,768956480,True,othdiscr,14,15,11.0,WALK,76895648,1 +615165189,6151651890,1875503,982766,768956480,False,Home,15,14,13.0,WALK,76895648,1 +615165625,6151656250,1875504,982766,768957030,True,work,2,15,18.0,WALK_LOC,76895703,1 +615165626,6151656260,1875504,982766,768957030,True,work,2,2,19.0,WALK,76895703,2 +615165627,6151656270,1875504,982766,768957030,True,work,16,2,19.0,WALK,76895703,3 +615165629,6151656290,1875504,982766,768957030,False,Home,15,16,20.0,WALK,76895703,1 +615169233,6151692330,1875515,982772,768961540,True,work,16,16,7.0,WALK,76896154,1 +615169237,6151692370,1875515,982772,768961540,False,Home,16,16,19.0,WALK,76896154,1 +615169561,6151695610,1875516,982772,768961950,True,work,12,16,5.0,WALK,76896195,1 +615169565,6151695650,1875516,982772,768961950,False,Home,16,12,18.0,WALK_LOC,76896195,1 +615204001,6152040010,1875621,982825,769005000,True,escort,16,16,5.0,WALK,76900500,1 +615204002,6152040020,1875621,982825,769005000,True,work,2,16,6.0,WALK,76900500,2 +615204005,6152040050,1875621,982825,769005000,False,Home,16,2,21.0,WALK,76900500,1 +615204281,6152042810,1875622,982825,769005350,True,shopping,13,16,20.0,TNC_SINGLE,76900535,1 +615204285,6152042850,1875622,982825,769005350,False,Home,16,13,20.0,WALK,76900535,1 +615204329,6152043290,1875622,982825,769005410,True,work,21,16,9.0,WALK_LOC,76900541,1 +615204333,6152043330,1875622,982825,769005410,False,Home,16,21,20.0,WALK_LOC,76900541,1 +615236801,6152368010,1875721,982875,769046000,True,work,10,16,8.0,WALK_LOC,76904600,1 +615236805,6152368050,1875721,982875,769046000,False,Home,16,10,17.0,WALK_LOC,76904600,1 +615236865,6152368650,1875722,982875,769046080,True,escort,3,16,10.0,WALK,76904608,1 +615236866,6152368660,1875722,982875,769046080,True,eatout,14,3,13.0,WALK,76904608,2 +615236869,6152368690,1875722,982875,769046080,False,Home,16,14,17.0,WALK,76904608,1 +615245985,6152459850,1875749,982889,769057480,True,work,13,16,8.0,WALK_LOC,76905748,1 +615245989,6152459890,1875749,982889,769057480,False,othmaint,25,13,19.0,WALK_LOC,76905748,1 +615245990,6152459900,1875749,982889,769057480,False,Home,16,25,19.0,WALK,76905748,2 +615246073,6152460730,1875750,982889,769057590,True,escort,16,16,6.0,WALK,76905759,1 +615246077,6152460770,1875750,982889,769057590,False,Home,16,16,7.0,WALK,76905759,1 +615246201,6152462010,1875750,982889,769057750,True,othdiscr,20,16,18.0,WALK_LOC,76905775,1 +615246205,6152462050,1875750,982889,769057750,False,Home,16,20,18.0,SHARED3FREE,76905775,1 +615246313,6152463130,1875750,982889,769057890,True,work,1,16,8.0,WALK,76905789,1 +615246317,6152463170,1875750,982889,769057890,False,eatout,5,1,15.0,WALK,76905789,1 +615246318,6152463180,1875750,982889,769057890,False,Home,16,5,15.0,WALK,76905789,2 +615250577,6152505770,1875763,982896,769063220,True,work,2,16,13.0,WALK,76906322,1 +615250581,6152505810,1875763,982896,769063220,False,Home,16,2,18.0,WALK,76906322,1 +615250857,6152508570,1875764,982896,769063570,True,shopping,19,16,10.0,WALK_LOC,76906357,1 +615250861,6152508610,1875764,982896,769063570,False,Home,16,19,10.0,WALK_LOC,76906357,1 +615255121,6152551210,1875777,982903,769068900,True,shopping,5,16,8.0,WALK,76906890,1 +615255125,6152551250,1875777,982903,769068900,False,Home,16,5,15.0,WALK,76906890,1 +615255473,6152554730,1875778,982903,769069340,True,social,12,16,18.0,WALK,76906934,1 +615255477,6152554770,1875778,982903,769069340,False,Home,16,12,22.0,WALK,76906934,1 +615255497,6152554970,1875778,982903,769069370,True,work,9,16,7.0,WALK_LOC,76906937,1 +615255501,6152555010,1875778,982903,769069370,False,Home,16,9,17.0,WALK_LRF,76906937,1 +615263417,6152634170,1875803,982916,769079270,True,atwork,2,16,13.0,WALK,76907927,1 +615263421,6152634210,1875803,982916,769079270,False,eatout,4,2,14.0,WALK,76907927,1 +615263422,6152634220,1875803,982916,769079270,False,Work,16,4,14.0,WALK,76907927,2 +615263697,6152636970,1875803,982916,769079620,True,work,16,16,12.0,WALK,76907962,1 +615263701,6152637010,1875803,982916,769079620,False,Home,16,16,21.0,WALK,76907962,1 +615264025,6152640250,1875804,982916,769080030,True,work,15,16,7.0,WALK,76908003,1 +615264029,6152640290,1875804,982916,769080030,False,Home,16,15,14.0,BIKE,76908003,1 +615265425,6152654250,1875809,982919,769081780,True,escort,13,16,7.0,WALK_LOC,76908178,1 +615265429,6152654290,1875809,982919,769081780,False,Home,16,13,7.0,WALK_LOC,76908178,1 +615265433,6152654330,1875809,982919,769081790,True,escort,5,16,13.0,SHARED2FREE,76908179,1 +615265437,6152654370,1875809,982919,769081790,False,Home,16,5,14.0,SHARED2FREE,76908179,1 +615265993,6152659930,1875810,982919,769082490,True,work,2,16,7.0,WALK,76908249,1 +615265997,6152659970,1875810,982919,769082490,False,Home,16,2,20.0,WALK,76908249,1 +615266977,6152669770,1875813,982921,769083720,True,escort,12,16,15.0,WALK_LOC,76908372,1 +615266978,6152669780,1875813,982921,769083720,True,work,1,12,16.0,WALK,76908372,2 +615266981,6152669810,1875813,982921,769083720,False,social,10,1,16.0,TNC_SINGLE,76908372,1 +615266982,6152669820,1875813,982921,769083720,False,escort,22,10,16.0,WALK_LRF,76908372,2 +615266983,6152669830,1875813,982921,769083720,False,escort,21,22,17.0,TNC_SINGLE,76908372,3 +615266984,6152669840,1875813,982921,769083720,False,Home,16,21,17.0,TNC_SINGLE,76908372,4 +615267305,6152673050,1875814,982921,769084130,True,shopping,16,16,8.0,WALK,76908413,1 +615267306,6152673060,1875814,982921,769084130,True,work,13,16,9.0,TNC_SINGLE,76908413,2 +615267309,6152673090,1875814,982921,769084130,False,eatout,5,13,17.0,TNC_SINGLE,76908413,1 +615267310,6152673100,1875814,982921,769084130,False,social,2,5,17.0,WALK,76908413,2 +615267311,6152673110,1875814,982921,769084130,False,Home,16,2,18.0,WALK,76908413,3 +615288625,6152886250,1875879,982954,769110780,True,work,12,16,8.0,SHARED2FREE,76911078,1 +615288629,6152886290,1875879,982954,769110780,False,Home,16,12,18.0,WALK_LOC,76911078,1 +615288953,6152889530,1875880,982954,769111190,True,work,5,16,9.0,WALK,76911119,1 +615288957,6152889570,1875880,982954,769111190,False,Home,16,5,17.0,WALK,76911119,1 +615295841,6152958410,1875901,982965,769119800,True,work,1,17,8.0,WALK,76911980,1 +615295845,6152958450,1875901,982965,769119800,False,shopping,15,1,18.0,WALK,76911980,1 +615295846,6152958460,1875901,982965,769119800,False,Home,17,15,19.0,WALK,76911980,2 +615295929,6152959290,1875902,982965,769119910,True,escort,11,17,10.0,WALK_LRF,76911991,1 +615295933,6152959330,1875902,982965,769119910,False,Home,17,11,10.0,WALK_LRF,76911991,1 +615295937,6152959370,1875902,982965,769119920,True,escort,6,17,16.0,WALK,76911992,1 +615295941,6152959410,1875902,982965,769119920,False,Home,17,6,17.0,WALK,76911992,1 +615296057,6152960570,1875902,982965,769120070,True,shopping,16,17,11.0,WALK,76912007,1 +615296058,6152960580,1875902,982965,769120070,True,escort,12,16,14.0,WALK,76912007,2 +615296059,6152960590,1875902,982965,769120070,True,othdiscr,5,12,15.0,WALK,76912007,3 +615296061,6152960610,1875902,982965,769120070,False,Home,17,5,16.0,WALK,76912007,1 +615309353,6153093530,1875943,982986,769136690,True,eatout,1,17,14.0,WALK,76913669,1 +615309357,6153093570,1875943,982986,769136690,False,shopping,8,1,14.0,WALK_LRF,76913669,1 +615309358,6153093580,1875943,982986,769136690,False,Home,17,8,14.0,WALK_LRF,76913669,2 +615309529,6153095290,1875943,982986,769136910,True,othmaint,2,17,15.0,TAXI,76913691,1 +615309530,6153095300,1875943,982986,769136910,True,othmaint,12,2,15.0,WALK,76913691,2 +615309533,6153095330,1875943,982986,769136910,False,Home,17,12,15.0,WALK_LRF,76913691,1 +615309569,6153095690,1875943,982986,769136960,True,shopping,19,17,12.0,WALK,76913696,1 +615309573,6153095730,1875943,982986,769136960,False,Home,17,19,12.0,WALK,76913696,1 +615309665,6153096650,1875944,982986,769137080,True,atwork,7,13,12.0,WALK,76913708,1 +615309669,6153096690,1875944,982986,769137080,False,shopping,6,7,13.0,WALK,76913708,1 +615309670,6153096700,1875944,982986,769137080,False,eatout,3,6,13.0,WALK,76913708,2 +615309671,6153096710,1875944,982986,769137080,False,Work,13,3,13.0,WALK,76913708,3 +615309945,6153099450,1875944,982986,769137430,True,work,13,17,6.0,WALK_LOC,76913743,1 +615309949,6153099490,1875944,982986,769137430,False,Home,17,13,20.0,WALK,76913743,1 +615350289,6153502890,1876067,983048,769187860,True,work,2,17,8.0,WALK,76918786,1 +615350293,6153502930,1876067,983048,769187860,False,Home,17,2,18.0,WALK,76918786,1 +615350553,6153505530,1876068,983048,769188190,True,univ,9,17,16.0,WALK_LRF,76918819,1 +615350557,6153505570,1876068,983048,769188190,False,escort,16,9,16.0,WALK_LRF,76918819,1 +615350558,6153505580,1876068,983048,769188190,False,Home,17,16,17.0,WALK_LRF,76918819,2 +615350617,6153506170,1876068,983048,769188270,True,work,22,17,7.0,WALK,76918827,1 +615350621,6153506210,1876068,983048,769188270,False,Home,17,22,14.0,WALK,76918827,1 +615411361,6154113610,1876254,983141,769264200,True,eatout,9,19,21.0,WALK,76926420,1 +615411365,6154113650,1876254,983141,769264200,False,Home,19,9,23.0,WALK,76926420,1 +615420857,6154208570,1876283,983156,769276070,True,atwork,16,16,11.0,WALK,76927607,1 +615420861,6154208610,1876283,983156,769276070,False,Work,16,16,11.0,WALK,76927607,1 +615421113,6154211130,1876283,983156,769276390,True,social,21,20,7.0,SHARED3FREE,76927639,1 +615421117,6154211170,1876283,983156,769276390,False,Home,20,21,7.0,SHARED3FREE,76927639,1 +615421137,6154211370,1876283,983156,769276420,True,work,16,20,8.0,WALK,76927642,1 +615421141,6154211410,1876283,983156,769276420,False,Home,20,16,19.0,WALK,76927642,1 +615421225,6154212250,1876284,983156,769276530,True,escort,7,20,7.0,SHARED3FREE,76927653,1 +615421229,6154212290,1876284,983156,769276530,False,shopping,8,7,7.0,WALK,76927653,1 +615421230,6154212300,1876284,983156,769276530,False,Home,20,8,7.0,DRIVEALONEFREE,76927653,2 +615421465,6154214650,1876284,983156,769276830,True,work,5,20,7.0,WALK,76927683,1 +615421469,6154214690,1876284,983156,769276830,False,Home,20,5,18.0,WALK,76927683,1 +615426105,6154261050,1876299,983164,769282630,True,atwork,11,17,12.0,WALK,76928263,1 +615426109,6154261090,1876299,983164,769282630,False,Work,17,11,13.0,WALK,76928263,1 +615426385,6154263850,1876299,983164,769282980,True,work,17,20,7.0,WALK,76928298,1 +615426389,6154263890,1876299,983164,769282980,False,Home,20,17,15.0,WALK,76928298,1 +615426601,6154266010,1876300,983164,769283250,True,othdiscr,7,20,17.0,WALK,76928325,1 +615426605,6154266050,1876300,983164,769283250,False,Home,20,7,22.0,WALK,76928325,1 +615426713,6154267130,1876300,983164,769283390,True,work,18,20,6.0,WALK_LOC,76928339,1 +615426717,6154267170,1876300,983164,769283390,False,Home,20,18,16.0,WALK_LOC,76928339,1 +615426721,6154267210,1876300,983164,769283400,True,work,18,20,16.0,WALK,76928340,1 +615426725,6154267250,1876300,983164,769283400,False,Home,20,18,17.0,WALK,76928340,1 +615433601,6154336010,1876321,983175,769292000,True,work,14,21,7.0,WALK,76929200,1 +615433605,6154336050,1876321,983175,769292000,False,Home,21,14,18.0,WALK,76929200,1 +615433841,6154338410,1876322,983175,769292300,True,othmaint,5,21,8.0,WALK,76929230,1 +615433845,6154338450,1876322,983175,769292300,False,Home,21,5,9.0,WALK,76929230,1 +615433929,6154339290,1876322,983175,769292410,True,work,5,21,9.0,WALK,76929241,1 +615433933,6154339330,1876322,983175,769292410,False,Home,21,5,18.0,SHARED2FREE,76929241,1 +615457217,6154572170,1876393,983211,769321520,True,work,20,21,8.0,WALK,76932152,1 +615457221,6154572210,1876393,983211,769321520,False,Home,21,20,11.0,WALK,76932152,1 +615457545,6154575450,1876394,983211,769321930,True,work,7,21,6.0,WALK,76932193,1 +615457549,6154575490,1876394,983211,769321930,False,Home,21,7,16.0,WALK,76932193,1 +615457873,6154578730,1876395,983212,769322340,True,work,18,21,8.0,WALK,76932234,1 +615457877,6154578770,1876395,983212,769322340,False,Home,21,18,22.0,WALK,76932234,1 +615458089,6154580890,1876396,983212,769322610,True,othdiscr,17,21,14.0,WALK,76932261,1 +615458093,6154580930,1876396,983212,769322610,False,social,17,17,14.0,WALK,76932261,1 +615458094,6154580940,1876396,983212,769322610,False,escort,16,17,14.0,WALK,76932261,2 +615458095,6154580950,1876396,983212,769322610,False,othmaint,5,16,14.0,WALK,76932261,3 +615458096,6154580960,1876396,983212,769322610,False,Home,21,5,14.0,WALK,76932261,4 +615458201,6154582010,1876396,983212,769322750,True,escort,12,21,8.0,WALK_LOC,76932275,1 +615458202,6154582020,1876396,983212,769322750,True,work,4,12,9.0,WALK,76932275,2 +615458205,6154582050,1876396,983212,769322750,False,escort,5,4,12.0,WALK,76932275,1 +615458206,6154582060,1876396,983212,769322750,False,Home,21,5,12.0,WALK,76932275,2 +615461497,6154614970,1876407,983218,769326870,True,atwork,12,8,9.0,WALK,76932687,1 +615461501,6154615010,1876407,983218,769326870,False,shopping,5,12,11.0,WALK,76932687,1 +615461502,6154615020,1876407,983218,769326870,False,work,9,5,11.0,WALK,76932687,2 +615461503,6154615030,1876407,983218,769326870,False,Work,8,9,11.0,WALK,76932687,3 +615461809,6154618090,1876407,983218,769327260,True,work,8,21,8.0,BIKE,76932726,1 +615461813,6154618130,1876407,983218,769327260,False,shopping,21,8,20.0,BIKE,76932726,1 +615461814,6154618140,1876407,983218,769327260,False,Home,21,21,21.0,BIKE,76932726,2 +615461825,6154618250,1876408,983218,769327280,True,atwork,16,5,12.0,WALK,76932728,1 +615461829,6154618290,1876408,983218,769327280,False,Work,5,16,13.0,WALK,76932728,1 +615461873,6154618730,1876408,983218,769327340,True,shopping,16,21,18.0,WALK,76932734,1 +615461874,6154618740,1876408,983218,769327340,True,eatout,14,16,19.0,WALK,76932734,2 +615461877,6154618770,1876408,983218,769327340,False,Home,21,14,21.0,WALK,76932734,1 +615462137,6154621370,1876408,983218,769327670,True,work,5,21,5.0,WALK,76932767,1 +615462141,6154621410,1876408,983218,769327670,False,social,4,5,17.0,WALK_LOC,76932767,1 +615462142,6154621420,1876408,983218,769327670,False,Home,21,4,18.0,WALK,76932767,2 +615467057,6154670570,1876423,983226,769333820,True,work,4,21,8.0,WALK,76933382,1 +615467061,6154670610,1876423,983226,769333820,False,Home,21,4,17.0,WALK,76933382,1 +615467361,6154673610,1876424,983226,769334200,True,social,17,21,9.0,DRIVEALONEFREE,76933420,1 +615467362,6154673620,1876424,983226,769334200,True,social,10,17,11.0,SHARED3FREE,76933420,2 +615467365,6154673650,1876424,983226,769334200,False,Home,21,10,17.0,DRIVEALONEFREE,76933420,1 +615472961,6154729610,1876441,983235,769341200,True,work,15,22,8.0,WALK,76934120,1 +615472962,6154729620,1876441,983235,769341200,True,work,15,15,10.0,WALK,76934120,2 +615472965,6154729650,1876441,983235,769341200,False,work,1,15,17.0,WALK,76934120,1 +615472966,6154729660,1876441,983235,769341200,False,Home,22,1,18.0,WALK,76934120,2 +615473177,6154731770,1876442,983235,769341470,True,othdiscr,10,22,16.0,WALK_LRF,76934147,1 +615473181,6154731810,1876442,983235,769341470,False,Home,22,10,17.0,WALK_LRF,76934147,1 +615473201,6154732010,1876442,983235,769341500,True,othmaint,17,22,12.0,WALK,76934150,1 +615473205,6154732050,1876442,983235,769341500,False,Home,22,17,15.0,WALK_LRF,76934150,1 +642393073,6423930730,1958515,1024272,802991340,True,shopping,18,7,10.0,WALK_LOC,80299134,1 +642393077,6423930770,1958515,1024272,802991340,False,Home,7,18,16.0,WALK_LOC,80299134,1 +642412145,6424121450,1958573,1024301,803015180,True,othdiscr,12,8,11.0,WALK,80301518,1 +642412149,6424121490,1958573,1024301,803015180,False,Home,8,12,20.0,WALK,80301518,1 +642412345,6424123450,1958574,1024301,803015430,True,escort,5,8,15.0,WALK,80301543,1 +642412349,6424123490,1958574,1024301,803015430,False,Home,8,5,16.0,SHARED3FREE,80301543,1 +642429073,6424290730,1958625,1024327,803036340,True,escort,11,8,13.0,WALK,80303634,1 +642429077,6424290770,1958625,1024327,803036340,False,Home,8,11,13.0,WALK,80303634,1 +642429201,6424292010,1958625,1024327,803036500,True,othdiscr,6,8,15.0,WALK,80303650,1 +642429205,6424292050,1958625,1024327,803036500,False,Home,8,6,17.0,WALK,80303650,1 +642429265,6424292650,1958625,1024327,803036580,True,shopping,11,8,9.0,WALK,80303658,1 +642429269,6424292690,1958625,1024327,803036580,False,Home,8,11,10.0,WALK,80303658,1 +642429577,6424295770,1958626,1024327,803036970,True,school,9,8,8.0,WALK,80303697,1 +642429581,6424295810,1958626,1024327,803036970,False,Home,8,9,16.0,WALK,80303697,1 +642429857,6424298570,1958627,1024328,803037320,True,othdiscr,9,8,12.0,WALK_LOC,80303732,1 +642429861,6424298610,1958627,1024328,803037320,False,Home,8,9,14.0,WALK_LOC,80303732,1 +642430233,6424302330,1958628,1024328,803037790,True,escort,8,8,8.0,WALK,80303779,1 +642430234,6424302340,1958628,1024328,803037790,True,social,8,8,8.0,WALK,80303779,2 +642430235,6424302350,1958628,1024328,803037790,True,school,8,8,12.0,WALK,80303779,3 +642430237,6424302370,1958628,1024328,803037790,False,Home,8,8,14.0,WALK,80303779,1 +642432481,6424324810,1958635,1024332,803040600,True,othdiscr,10,8,9.0,WALK,80304060,1 +642432485,6424324850,1958635,1024332,803040600,False,Home,8,10,13.0,WALK,80304060,1 +642432857,6424328570,1958636,1024332,803041070,True,school,13,8,8.0,WALK_LOC,80304107,1 +642432861,6424328610,1958636,1024332,803041070,False,othmaint,9,13,17.0,WALK_LRF,80304107,1 +642432862,6424328620,1958636,1024332,803041070,False,Home,8,9,17.0,WALK,80304107,2 +642438449,6424384490,1958653,1024341,803048060,True,shopping,11,9,15.0,WALK,80304806,1 +642438453,6424384530,1958653,1024341,803048060,False,Home,9,11,17.0,WALK,80304806,1 +642438761,6424387610,1958654,1024341,803048450,True,escort,4,9,10.0,WALK_LRF,80304845,1 +642438762,6424387620,1958654,1024341,803048450,True,school,13,4,12.0,WALK_LOC,80304845,2 +642438765,6424387650,1958654,1024341,803048450,False,escort,8,13,15.0,WALK_LRF,80304845,1 +642438766,6424387660,1958654,1024341,803048450,False,social,22,8,19.0,WALK_LRF,80304845,2 +642438767,6424387670,1958654,1024341,803048450,False,othmaint,4,22,19.0,WALK_LRF,80304845,3 +642438768,6424387680,1958654,1024341,803048450,False,Home,9,4,19.0,WALK_LRF,80304845,4 +642446345,6424463450,1958677,1024353,803057930,True,social,14,9,10.0,WALK_LRF,80305793,1 +642446349,6424463490,1958677,1024353,803057930,False,Home,9,14,11.0,WALK_LRF,80305793,1 +642446633,6424466330,1958678,1024353,803058290,True,school,25,9,6.0,WALK_LRF,80305829,1 +642446637,6424466370,1958678,1024353,803058290,False,Home,9,25,15.0,WALK_LOC,80305829,1 +643014353,6430143530,1960409,1025219,803767940,True,othdiscr,9,5,18.0,WALK,80376794,1 +643014357,6430143570,1960409,1025219,803767940,False,Home,5,9,18.0,WALK,80376794,1 +643014417,6430144170,1960409,1025219,803768020,True,shopping,14,5,6.0,WALK,80376802,1 +643014421,6430144210,1960409,1025219,803768020,False,Home,5,14,6.0,WALK,80376802,1 +643014425,6430144250,1960409,1025219,803768030,True,othmaint,7,5,19.0,WALK_LOC,80376803,1 +643014426,6430144260,1960409,1025219,803768030,True,shopping,6,7,19.0,TNC_SINGLE,80376803,2 +643014429,6430144290,1960409,1025219,803768030,False,Home,5,6,19.0,TNC_SHARED,80376803,1 +643014465,6430144650,1960409,1025219,803768080,True,work,23,5,7.0,WALK_LRF,80376808,1 +643014469,6430144690,1960409,1025219,803768080,False,eatout,13,23,17.0,TNC_SHARED,80376808,1 +643014470,6430144700,1960409,1025219,803768080,False,Home,5,13,18.0,TNC_SINGLE,80376808,2 +643014729,6430147290,1960410,1025219,803768410,True,school,6,5,8.0,WALK,80376841,1 +643014733,6430147330,1960410,1025219,803768410,False,Home,5,6,14.0,WALK_LOC,80376841,1 +649296209,6492962090,1979561,1034795,811620260,True,othdiscr,16,7,18.0,WALK,81162026,1 +649296213,6492962130,1979561,1034795,811620260,False,Home,7,16,18.0,WALK,81162026,1 +649296321,6492963210,1979561,1034795,811620400,True,work,6,7,7.0,TNC_SHARED,81162040,1 +649296325,6492963250,1979561,1034795,811620400,False,Home,7,6,16.0,WALK,81162040,1 +649296585,6492965850,1979562,1034795,811620730,True,school,8,7,8.0,WALK_LOC,81162073,1 +649296589,6492965890,1979562,1034795,811620730,False,Home,7,8,14.0,WALK_LOC,81162073,1 +649300801,6493008010,1979575,1034802,811626000,True,othdiscr,22,7,15.0,WALK_LOC,81162600,1 +649300805,6493008050,1979575,1034802,811626000,False,escort,9,22,18.0,WALK_LOC,81162600,1 +649300806,6493008060,1979575,1034802,811626000,False,Home,7,9,18.0,WALK_LOC,81162600,2 +649300913,6493009130,1979575,1034802,811626140,True,work,5,7,7.0,WALK,81162614,1 +649300917,6493009170,1979575,1034802,811626140,False,Home,7,5,15.0,WALK,81162614,1 +649301129,6493011290,1979576,1034802,811626410,True,othdiscr,12,7,7.0,WALK,81162641,1 +649301133,6493011330,1979576,1034802,811626410,False,Home,7,12,10.0,WALK,81162641,1 +649312457,6493124570,1979611,1034820,811640570,True,eatout,7,9,8.0,WALK,81164057,1 +649312461,6493124610,1979611,1034820,811640570,False,Home,9,7,8.0,WALK,81164057,1 +649312721,6493127210,1979611,1034820,811640900,True,work,9,9,9.0,WALK,81164090,1 +649312722,6493127220,1979611,1034820,811640900,True,othmaint,9,9,9.0,WALK,81164090,2 +649312723,6493127230,1979611,1034820,811640900,True,work,11,9,10.0,WALK,81164090,3 +649312725,6493127250,1979611,1034820,811640900,False,Home,9,11,22.0,WALK,81164090,1 +649312985,6493129850,1979612,1034820,811641230,True,school,9,9,8.0,WALK,81164123,1 +649312989,6493129890,1979612,1034820,811641230,False,Home,9,9,16.0,WALK,81164123,1 +649313265,6493132650,1979613,1034821,811641580,True,othdiscr,17,9,18.0,WALK_LRF,81164158,1 +649313269,6493132690,1979613,1034821,811641580,False,Home,9,17,18.0,WALK_LRF,81164158,1 +649313377,6493133770,1979613,1034821,811641720,True,eatout,20,9,5.0,TNC_SHARED,81164172,1 +649313378,6493133780,1979613,1034821,811641720,True,work,9,20,6.0,WALK,81164172,2 +649313381,6493133810,1979613,1034821,811641720,False,Home,9,9,18.0,TNC_SHARED,81164172,1 +649321905,6493219050,1979639,1034834,811652380,True,work,5,9,9.0,WALK,81165238,1 +649321909,6493219090,1979639,1034834,811652380,False,work,9,5,20.0,WALK,81165238,1 +649321910,6493219100,1979639,1034834,811652380,False,Home,9,9,20.0,WALK,81165238,2 +649344209,6493442090,1979707,1034868,811680260,True,work,9,20,7.0,WALK,81168026,1 +649344213,6493442130,1979707,1034868,811680260,False,Home,20,9,17.0,WALK,81168026,1 +649344449,6493444490,1979708,1034868,811680560,True,othmaint,5,20,17.0,SHARED2FREE,81168056,1 +649344453,6493444530,1979708,1034868,811680560,False,escort,6,5,17.0,SHARED2FREE,81168056,1 +649344454,6493444540,1979708,1034868,811680560,False,Home,20,6,18.0,SHARED2FREE,81168056,2 +649344473,6493444730,1979708,1034868,811680590,True,school,20,20,7.0,WALK,81168059,1 +649344477,6493444770,1979708,1034868,811680590,False,shopping,11,20,16.0,WALK,81168059,1 +649344478,6493444780,1979708,1034868,811680590,False,escort,11,11,17.0,WALK,81168059,2 +649344479,6493444790,1979708,1034868,811680590,False,Home,20,11,17.0,WALK,81168059,3 +649348801,6493488010,1979721,1034875,811686000,True,escort,7,21,6.0,WALK,81168600,1 +649348802,6493488020,1979721,1034875,811686000,True,work,9,7,8.0,WALK_LOC,81168600,2 +649348805,6493488050,1979721,1034875,811686000,False,eatout,8,9,16.0,WALK_LOC,81168600,1 +649348806,6493488060,1979721,1034875,811686000,False,Home,21,8,16.0,WALK,81168600,2 +649349065,6493490650,1979722,1034875,811686330,True,school,8,21,8.0,WALK,81168633,1 +649349069,6493490690,1979722,1034875,811686330,False,Home,21,8,11.0,WALK,81168633,1 +649352081,6493520810,1979731,1034880,811690100,True,work,9,24,7.0,SHARED2FREE,81169010,1 +649352085,6493520850,1979731,1034880,811690100,False,Home,24,9,18.0,WALK_HVY,81169010,1 +649352345,6493523450,1979732,1034880,811690430,True,school,8,24,8.0,SHARED3FREE,81169043,1 +649352349,6493523490,1979732,1034880,811690430,False,escort,5,8,14.0,WALK_LOC,81169043,1 +649352350,6493523500,1979732,1034880,811690430,False,Home,24,5,17.0,WALK_LOC,81169043,2 +666869337,6668693370,2033138,1057653,833586670,True,escort,2,10,13.0,WALK,83358667,1 +666869341,6668693410,2033138,1057653,833586670,False,escort,7,2,13.0,WALK,83358667,1 +666869342,6668693420,2033138,1057653,833586670,False,Home,10,7,13.0,WALK,83358667,2 +679341777,6793417770,2071163,1070328,849177220,True,work,23,11,7.0,SHARED2FREE,84917722,1 +679341781,6793417810,2071163,1070328,849177220,False,Home,11,23,10.0,WALK_LRF,84917722,1 +679341785,6793417850,2071163,1070328,849177230,True,work,23,11,12.0,WALK_LRF,84917723,1 +679341789,6793417890,2071163,1070328,849177230,False,Home,11,23,17.0,WALK_LRF,84917723,1 +679342369,6793423690,2071165,1070328,849177960,True,univ,12,11,8.0,WALK_LOC,84917796,1 +679342373,6793423730,2071165,1070328,849177960,False,Home,11,12,9.0,WALK_LOC,84917796,1 +690443945,6904439450,2105012,1081611,863054930,True,atwork,3,1,12.0,WALK,86305493,1 +690443949,6904439490,2105012,1081611,863054930,False,eatout,3,3,13.0,WALK,86305493,1 +690443950,6904439500,2105012,1081611,863054930,False,Work,1,3,13.0,WALK,86305493,2 +690444249,6904442490,2105012,1081611,863055310,True,work,1,2,7.0,WALK,86305531,1 +690444253,6904442530,2105012,1081611,863055310,False,Home,2,1,9.0,DRIVEALONEFREE,86305531,1 +690444257,6904442570,2105012,1081611,863055320,True,work,1,2,11.0,WALK,86305532,1 +690444261,6904442610,2105012,1081611,863055320,False,Home,2,1,18.0,WALK,86305532,1 +690444577,6904445770,2105013,1081611,863055720,True,work,15,2,6.0,WALK_LOC,86305572,1 +690444581,6904445810,2105013,1081611,863055720,False,Home,2,15,17.0,WALK_LOC,86305572,1 +690459993,6904599930,2105060,1081627,863074990,True,work,10,11,11.0,WALK,86307499,1 +690459997,6904599970,2105060,1081627,863074990,False,Home,11,10,17.0,WALK,86307499,1 +690460321,6904603210,2105061,1081627,863075400,True,work,4,11,6.0,WALK,86307540,1 +690460325,6904603250,2105061,1081627,863075400,False,Home,11,4,15.0,WALK,86307540,1 +690460649,6904606490,2105062,1081627,863075810,True,work,12,11,11.0,WALK_LOC,86307581,1 +690460653,6904606530,2105062,1081627,863075810,False,Home,11,12,22.0,WALK,86307581,1 +690461961,6904619610,2105066,1081629,863077450,True,work,13,11,7.0,TNC_SHARED,86307745,1 +690461965,6904619650,2105066,1081629,863077450,False,Home,11,13,22.0,TNC_SINGLE,86307745,1 +690462177,6904621770,2105067,1081629,863077720,True,othdiscr,24,11,8.0,WALK,86307772,1 +690462181,6904621810,2105067,1081629,863077720,False,Home,11,24,14.0,WALK,86307772,1 +690462617,6904626170,2105068,1081629,863078270,True,work,3,11,7.0,WALK,86307827,1 +690462621,6904626210,2105068,1081629,863078270,False,Home,11,3,18.0,WALK,86307827,1 +690462785,6904627850,2105070,1081630,863078480,True,shopping,7,11,8.0,WALK,86307848,1 +690462789,6904627890,2105070,1081630,863078480,False,Home,11,7,8.0,WALK,86307848,1 +690462945,6904629450,2105069,1081630,863078680,True,work,1,11,5.0,WALK_HVY,86307868,1 +690462949,6904629490,2105069,1081630,863078680,False,Home,11,1,9.0,TNC_SINGLE,86307868,1 +690463273,6904632730,2105070,1081630,863079090,True,work,10,11,8.0,WALK,86307909,1 +690463277,6904632770,2105070,1081630,863079090,False,shopping,11,10,13.0,WALK_LOC,86307909,1 +690463278,6904632780,2105070,1081630,863079090,False,Home,11,11,13.0,WALK,86307909,2 +690463601,6904636010,2105071,1081630,863079500,True,work,16,11,9.0,WALK_LOC,86307950,1 +690463605,6904636050,2105071,1081630,863079500,False,Home,11,16,16.0,WALK_LOC,86307950,1 +695855153,6958551530,2121509,1087110,869818940,True,othdiscr,4,8,11.0,WALK,86981894,1 +695855157,6958551570,2121509,1087110,869818940,False,Home,8,4,15.0,WALK,86981894,1 +695855529,6958555290,2121510,1087110,869819410,True,school,16,8,8.0,WALK_LOC,86981941,1 +695855533,6958555330,2121510,1087110,869819410,False,othdiscr,7,16,17.0,WALK_LOC,86981941,1 +695855534,6958555340,2121510,1087110,869819410,False,Home,8,7,18.0,WALK_LOC,86981941,2 +695855873,6958558730,2121511,1087110,869819840,True,shopping,25,8,11.0,WALK,86981984,1 +695855877,6958558770,2121511,1087110,869819840,False,Home,8,25,12.0,BIKE,86981984,1 +695860097,6958600970,2121524,1087115,869825120,True,othmaint,9,8,14.0,WALK,86982512,1 +695860101,6958601010,2121524,1087115,869825120,False,Home,8,9,16.0,WALK,86982512,1 +695860105,6958601050,2121524,1087115,869825130,True,othmaint,1,8,20.0,WALK_LRF,86982513,1 +695860109,6958601090,2121524,1087115,869825130,False,Home,8,1,22.0,WALK_LRF,86982513,1 +695860137,6958601370,2121524,1087115,869825170,True,shopping,17,8,12.0,SHARED2FREE,86982517,1 +695860141,6958601410,2121524,1087115,869825170,False,shopping,5,17,12.0,SHARED2FREE,86982517,1 +695860142,6958601420,2121524,1087115,869825170,False,Home,8,5,12.0,WALK,86982517,2 +695860273,6958602730,2121525,1087115,869825340,True,escort,16,8,11.0,WALK_LOC,86982534,1 +695860277,6958602770,2121525,1087115,869825340,False,Home,8,16,12.0,TNC_SHARED,86982534,1 +695860401,6958604010,2121525,1087115,869825500,True,othdiscr,2,8,13.0,WALK,86982550,1 +695860405,6958604050,2121525,1087115,869825500,False,Home,8,2,16.0,WALK,86982550,1 +695860777,6958607770,2121526,1087115,869825970,True,school,9,8,7.0,WALK,86982597,1 +695860781,6958607810,2121526,1087115,869825970,False,Home,8,9,15.0,WALK,86982597,1 +708087089,7080870890,2158802,1099541,885108860,True,atwork,8,2,14.0,WALK,88510886,1 +708087093,7080870930,2158802,1099541,885108860,False,Work,2,8,14.0,WALK,88510886,1 +708087281,7080872810,2158802,1099541,885109100,True,othmaint,5,9,17.0,DRIVEALONEFREE,88510910,1 +708087285,7080872850,2158802,1099541,885109100,False,Home,9,5,17.0,TNC_SHARED,88510910,1 +708087369,7080873690,2158802,1099541,885109210,True,work,2,9,7.0,TNC_SHARED,88510921,1 +708087373,7080873730,2158802,1099541,885109210,False,Home,9,2,17.0,WALK_LRF,88510921,1 +708087585,7080875850,2158803,1099541,885109480,True,othdiscr,9,9,17.0,WALK,88510948,1 +708087589,7080875890,2158803,1099541,885109480,False,Home,9,9,21.0,WALK,88510948,1 +708087633,7080876330,2158803,1099541,885109540,True,school,10,9,12.0,WALK,88510954,1 +708087637,7080876370,2158803,1099541,885109540,False,Home,9,10,16.0,WALK_LOC,88510954,1 +708087961,7080879610,2158804,1099541,885109950,True,school,9,9,8.0,WALK,88510995,1 +708087965,7080879650,2158804,1099541,885109950,False,Home,9,9,13.0,WALK,88510995,1 +708087969,7080879690,2158804,1099541,885109960,True,school,9,9,14.0,WALK,88510996,1 +708087973,7080879730,2158804,1099541,885109960,False,Home,9,9,17.0,WALK,88510996,1 +708121809,7081218090,2158907,1099576,885152260,True,work,9,10,7.0,WALK_LOC,88515226,1 +708121813,7081218130,2158907,1099576,885152260,False,Home,10,9,17.0,WALK,88515226,1 +708122073,7081220730,2158908,1099576,885152590,True,escort,5,10,8.0,WALK_LOC,88515259,1 +708122074,7081220740,2158908,1099576,885152590,True,escort,6,5,8.0,WALK_LOC,88515259,2 +708122075,7081220750,2158908,1099576,885152590,True,school,8,6,9.0,WALK_LOC,88515259,3 +708122077,7081220770,2158908,1099576,885152590,False,Home,10,8,17.0,WALK_LOC,88515259,1 +708122401,7081224010,2158909,1099576,885153000,True,school,21,10,8.0,WALK_LOC,88515300,1 +708122405,7081224050,2158909,1099576,885153000,False,Home,10,21,15.0,WALK_LOC,88515300,1 +708171009,7081710090,2159057,1099626,885213760,True,work,2,20,7.0,WALK_LOC,88521376,1 +708171013,7081710130,2159057,1099626,885213760,False,shopping,16,2,18.0,WALK,88521376,1 +708171014,7081710140,2159057,1099626,885213760,False,Home,20,16,18.0,WALK_LOC,88521376,2 +708171273,7081712730,2159058,1099626,885214090,True,univ,9,20,16.0,WALK_LOC,88521409,1 +708171277,7081712770,2159058,1099626,885214090,False,Home,20,9,16.0,WALK_LOC,88521409,1 +708171601,7081716010,2159059,1099626,885214500,True,school,20,20,8.0,WALK,88521450,1 +708171605,7081716050,2159059,1099626,885214500,False,Home,20,20,13.0,WALK,88521450,1 +708253665,7082536650,2159309,1099710,885317080,True,work,24,25,8.0,WALK,88531708,1 +708253666,7082536660,2159309,1099710,885317080,True,work,4,24,9.0,WALK,88531708,2 +708253669,7082536690,2159309,1099710,885317080,False,shopping,16,4,16.0,WALK,88531708,1 +708253670,7082536700,2159309,1099710,885317080,False,shopping,5,16,16.0,WALK_LOC,88531708,2 +708253671,7082536710,2159309,1099710,885317080,False,work,5,5,17.0,WALK,88531708,3 +708253672,7082536720,2159309,1099710,885317080,False,Home,25,5,17.0,WALK,88531708,4 +708253945,7082539450,2159310,1099710,885317430,True,shopping,12,25,10.0,WALK,88531743,1 +708253949,7082539490,2159310,1099710,885317430,False,Home,25,12,12.0,WALK,88531743,1 +708254081,7082540810,2159311,1099710,885317600,True,escort,10,25,13.0,SHARED3FREE,88531760,1 +708254085,7082540850,2159311,1099710,885317600,False,Home,25,10,15.0,SHARED3FREE,88531760,1 +728000577,7280005770,2219513,1119778,910000720,True,work,2,3,8.0,WALK,91000072,1 +728000581,7280005810,2219513,1119778,910000720,False,shopping,5,2,17.0,WALK,91000072,1 +728000582,7280005820,2219513,1119778,910000720,False,Home,3,5,20.0,WALK,91000072,2 +728000905,7280009050,2219514,1119778,910001130,True,work,24,3,6.0,WALK,91000113,1 +728000909,7280009090,2219514,1119778,910001130,False,Home,3,24,18.0,WALK,91000113,1 +728001169,7280011690,2219515,1119778,910001460,True,school,13,3,8.0,WALK,91000146,1 +728001173,7280011730,2219515,1119778,910001460,False,Home,3,13,15.0,WALK,91000146,1 +728016321,7280163210,2219561,1119794,910020400,True,work,5,7,6.0,WALK,91002040,1 +728016325,7280163250,2219561,1119794,910020400,False,Home,7,5,16.0,WALK,91002040,1 +728016369,7280163690,2219562,1119794,910020460,True,atwork,15,13,12.0,WALK,91002046,1 +728016373,7280163730,2219562,1119794,910020460,False,Work,13,15,13.0,WALK,91002046,1 +728016649,7280166490,2219562,1119794,910020810,True,work,13,7,6.0,WALK_LRF,91002081,1 +728016653,7280166530,2219562,1119794,910020810,False,othmaint,9,13,17.0,WALK,91002081,1 +728016654,7280166540,2219562,1119794,910020810,False,othdiscr,6,9,18.0,WALK_LOC,91002081,2 +728016655,7280166550,2219562,1119794,910020810,False,othmaint,3,6,18.0,WALK,91002081,3 +728016656,7280166560,2219562,1119794,910020810,False,Home,7,3,18.0,WALK_LOC,91002081,4 +728016913,7280169130,2219563,1119794,910021140,True,school,11,7,9.0,WALK_LOC,91002114,1 +728016917,7280169170,2219563,1119794,910021140,False,Home,7,11,16.0,WALK_LOC,91002114,1 +728025177,7280251770,2219588,1119803,910031470,True,work,7,7,6.0,WALK,91003147,1 +728025181,7280251810,2219588,1119803,910031470,False,Home,7,7,17.0,WALK,91003147,1 +728025505,7280255050,2219589,1119803,910031880,True,work,17,7,7.0,WALK_LOC,91003188,1 +728025509,7280255090,2219589,1119803,910031880,False,Home,7,17,10.0,WALK,91003188,1 +728025769,7280257690,2219590,1119803,910032210,True,escort,5,7,14.0,WALK,91003221,1 +728025770,7280257700,2219590,1119803,910032210,True,school,7,5,14.0,WALK,91003221,2 +728025773,7280257730,2219590,1119803,910032210,False,eatout,7,7,14.0,WALK,91003221,1 +728025774,7280257740,2219590,1119803,910032210,False,Home,7,7,23.0,WALK,91003221,2 +728033049,7280330490,2219612,1119811,910041310,True,shopping,2,7,8.0,WALK,91004131,1 +728033050,7280330500,2219612,1119811,910041310,True,work,17,2,9.0,WALK,91004131,2 +728033053,7280330530,2219612,1119811,910041310,False,Home,7,17,21.0,WALK,91004131,1 +728033377,7280333770,2219613,1119811,910041720,True,work,9,7,9.0,WALK,91004172,1 +728033378,7280333780,2219613,1119811,910041720,True,work,11,9,10.0,WALK,91004172,2 +728033381,7280333810,2219613,1119811,910041720,False,escort,6,11,18.0,WALK,91004172,1 +728033382,7280333820,2219613,1119811,910041720,False,Home,7,6,18.0,WALK,91004172,2 +728033641,7280336410,2219614,1119811,910042050,True,school,8,7,7.0,SHARED2FREE,91004205,1 +728033645,7280336450,2219614,1119811,910042050,False,escort,7,8,16.0,WALK,91004205,1 +728033646,7280336460,2219614,1119811,910042050,False,Home,7,7,17.0,WALK,91004205,2 +728088153,7280881530,2219780,1119867,910110190,True,work,1,9,7.0,WALK_LRF,91011019,1 +728088157,7280881570,2219780,1119867,910110190,False,Home,9,1,19.0,WALK_HVY,91011019,1 +728088201,7280882010,2219781,1119867,910110250,True,atwork,4,1,14.0,WALK,91011025,1 +728088205,7280882050,2219781,1119867,910110250,False,Work,1,4,15.0,WALK,91011025,1 +728088481,7280884810,2219781,1119867,910110600,True,work,1,9,6.0,WALK_LRF,91011060,1 +728088485,7280884850,2219781,1119867,910110600,False,Home,9,1,17.0,WALK_LRF,91011060,1 +728114721,7281147210,2219861,1119894,910143400,True,work,11,11,9.0,WALK,91014340,1 +728114725,7281147250,2219861,1119894,910143400,False,shopping,5,11,20.0,WALK,91014340,1 +728114726,7281147260,2219861,1119894,910143400,False,Home,11,5,21.0,WALK,91014340,2 +728115049,7281150490,2219862,1119894,910143810,True,escort,12,11,7.0,WALK_LOC,91014381,1 +728115050,7281150500,2219862,1119894,910143810,True,eatout,16,12,8.0,WALK_LOC,91014381,2 +728115051,7281150510,2219862,1119894,910143810,True,work,22,16,8.0,WALK_LOC,91014381,3 +728115053,7281150530,2219862,1119894,910143810,False,escort,6,22,18.0,TAXI,91014381,1 +728115054,7281150540,2219862,1119894,910143810,False,shopping,2,6,18.0,WALK,91014381,2 +728115055,7281150550,2219862,1119894,910143810,False,shopping,12,2,19.0,WALK_LOC,91014381,3 +728115056,7281150560,2219862,1119894,910143810,False,Home,11,12,20.0,WALK,91014381,4 +728115313,7281153130,2219863,1119894,910144140,True,school,9,11,8.0,WALK,91014414,1 +728115317,7281153170,2219863,1119894,910144140,False,Home,11,9,10.0,WALK,91014414,1 +728159001,7281590010,2219996,1119939,910198750,True,work,1,16,7.0,WALK,91019875,1 +728159005,7281590050,2219996,1119939,910198750,False,Home,16,1,18.0,WALK,91019875,1 +728159049,7281590490,2219997,1119939,910198810,True,atwork,11,2,13.0,WALK,91019881,1 +728159053,7281590530,2219997,1119939,910198810,False,Work,2,11,13.0,WALK,91019881,1 +728159329,7281593290,2219997,1119939,910199160,True,work,2,16,7.0,SHARED2FREE,91019916,1 +728159333,7281593330,2219997,1119939,910199160,False,Home,16,2,18.0,WALK,91019916,1 +728159569,7281595690,2219998,1119939,910199460,True,othmaint,9,16,15.0,WALK_HVY,91019946,1 +728159573,7281595730,2219998,1119939,910199460,False,Home,16,9,17.0,TNC_SINGLE,91019946,1 +728159593,7281595930,2219998,1119939,910199490,True,school,8,16,7.0,WALK_LOC,91019949,1 +728159597,7281595970,2219998,1119939,910199490,False,Home,16,8,13.0,WALK_LOC,91019949,1 +728162937,7281629370,2220008,1119943,910203670,True,work,3,16,8.0,WALK,91020367,1 +728162941,7281629410,2220008,1119943,910203670,False,Home,16,3,18.0,WALK,91020367,1 +728163265,7281632650,2220009,1119943,910204080,True,work,12,16,7.0,WALK,91020408,1 +728163269,7281632690,2220009,1119943,910204080,False,Home,16,12,11.0,WALK,91020408,1 +728163273,7281632730,2220009,1119943,910204090,True,work,12,16,14.0,WALK,91020409,1 +728163277,7281632770,2220009,1119943,910204090,False,Home,16,12,16.0,WALK,91020409,1 +728163481,7281634810,2220010,1119943,910204350,True,othdiscr,21,16,16.0,SHARED3FREE,91020435,1 +728163485,7281634850,2220010,1119943,910204350,False,Home,16,21,21.0,SHARED3FREE,91020435,1 +728163529,7281635290,2220010,1119943,910204410,True,school,16,16,6.0,WALK,91020441,1 +728163533,7281635330,2220010,1119943,910204410,False,Home,16,16,14.0,WALK,91020441,1 +728184537,7281845370,2220074,1119965,910230670,True,shopping,16,16,17.0,WALK,91023067,1 +728184541,7281845410,2220074,1119965,910230670,False,Home,16,16,19.0,WALK,91023067,1 +728184585,7281845850,2220074,1119965,910230730,True,work,13,16,6.0,WALK_LOC,91023073,1 +728184589,7281845890,2220074,1119965,910230730,False,Home,16,13,16.0,WALK_LOC,91023073,1 +728184865,7281848650,2220075,1119965,910231080,True,shopping,16,16,11.0,TNC_SINGLE,91023108,1 +728184869,7281848690,2220075,1119965,910231080,False,Home,16,16,13.0,TNC_SINGLE,91023108,1 +728184873,7281848730,2220075,1119965,910231090,True,shopping,5,16,21.0,WALK_LOC,91023109,1 +728184877,7281848770,2220075,1119965,910231090,False,Home,16,5,21.0,WALK_LOC,91023109,1 +728184913,7281849130,2220075,1119965,910231140,True,work,14,16,13.0,WALK,91023114,1 +728184917,7281849170,2220075,1119965,910231140,False,Home,16,14,20.0,WALK_LOC,91023114,1 +728184921,7281849210,2220075,1119965,910231150,True,work,14,16,20.0,TNC_SINGLE,91023115,1 +728184925,7281849250,2220075,1119965,910231150,False,Home,16,14,20.0,WALK,91023115,1 +728185129,7281851290,2220076,1119965,910231410,True,othdiscr,12,16,13.0,WALK,91023141,1 +728185133,7281851330,2220076,1119965,910231410,False,Home,16,12,16.0,WALK,91023141,1 +728185177,7281851770,2220076,1119965,910231470,True,school,13,16,9.0,WALK_LOC,91023147,1 +728185181,7281851810,2220076,1119965,910231470,False,Home,16,13,13.0,WALK_LOC,91023147,1 +728221737,7282217370,2220188,1120003,910277170,True,escort,13,16,15.0,TNC_SINGLE,91027717,1 +728221741,7282217410,2220188,1120003,910277170,False,Home,16,13,18.0,TNC_SINGLE,91027717,1 +728221977,7282219770,2220188,1120003,910277470,True,work,14,16,5.0,WALK,91027747,1 +728221981,7282219810,2220188,1120003,910277470,False,Home,16,14,11.0,WALK,91027747,1 +728222305,7282223050,2220189,1120003,910277880,True,work,14,16,13.0,WALK,91027788,1 +728222309,7282223090,2220189,1120003,910277880,False,Home,16,14,18.0,WALK,91027788,1 +728222569,7282225690,2220190,1120003,910278210,True,school,18,16,7.0,WALK_LRF,91027821,1 +728222573,7282225730,2220190,1120003,910278210,False,Home,16,18,16.0,WALK_LRF,91027821,1 +728258145,7282581450,2220299,1120040,910322680,True,escort,22,19,18.0,WALK_LRF,91032268,1 +728258149,7282581490,2220299,1120040,910322680,False,Home,19,22,21.0,WALK_LRF,91032268,1 +728258337,7282583370,2220299,1120040,910322920,True,shopping,16,19,11.0,SHARED2FREE,91032292,1 +728258338,7282583380,2220299,1120040,910322920,True,social,11,16,11.0,SHARED2FREE,91032292,2 +728258339,7282583390,2220299,1120040,910322920,True,shopping,16,11,12.0,SHARED2FREE,91032292,3 +728258341,7282583410,2220299,1120040,910322920,False,eatout,11,16,14.0,SHARED2FREE,91032292,1 +728258342,7282583420,2220299,1120040,910322920,False,othmaint,10,11,14.0,SHARED2FREE,91032292,2 +728258343,7282583430,2220299,1120040,910322920,False,Home,19,10,14.0,DRIVEALONEFREE,91032292,3 +728258713,7282587130,2220300,1120040,910323390,True,work,11,19,7.0,WALK,91032339,1 +728258717,7282587170,2220300,1120040,910323390,False,Home,19,11,16.0,WALK,91032339,1 +728258977,7282589770,2220301,1120040,910323720,True,school,9,19,7.0,WALK,91032372,1 +728258981,7282589810,2220301,1120040,910323720,False,Home,19,9,15.0,WALK,91032372,1 +728268929,7282689290,2220332,1120051,910336160,True,atwork,25,5,15.0,WALK,91033616,1 +728268933,7282689330,2220332,1120051,910336160,False,Work,5,25,15.0,WALK,91033616,1 +728268969,7282689690,2220332,1120051,910336210,True,escort,9,20,15.0,WALK_LOC,91033621,1 +728268973,7282689730,2220332,1120051,910336210,False,Home,20,9,15.0,TNC_SINGLE,91033621,1 +728269209,7282692090,2220332,1120051,910336510,True,work,5,20,6.0,WALK,91033651,1 +728269213,7282692130,2220332,1120051,910336510,False,Home,20,5,15.0,WALK,91033651,1 +728269537,7282695370,2220333,1120051,910336920,True,work,4,20,8.0,WALK_LRF,91033692,1 +728269538,7282695380,2220333,1120051,910336920,True,othmaint,16,4,9.0,WALK_LOC,91033692,2 +728269539,7282695390,2220333,1120051,910336920,True,work,2,16,9.0,WALK,91033692,3 +728269541,7282695410,2220333,1120051,910336920,False,othdiscr,8,2,17.0,WALK_LOC,91033692,1 +728269542,7282695420,2220333,1120051,910336920,False,eatout,9,8,17.0,WALK,91033692,2 +728269543,7282695430,2220333,1120051,910336920,False,escort,8,9,17.0,WALK_LOC,91033692,3 +728269544,7282695440,2220333,1120051,910336920,False,Home,20,8,21.0,WALK_LOC,91033692,4 +728269801,7282698010,2220334,1120051,910337250,True,school,8,20,7.0,WALK_LOC,91033725,1 +728269805,7282698050,2220334,1120051,910337250,False,Home,20,8,15.0,WALK,91033725,1 +728311521,7283115210,2220461,1120094,910389400,True,work,4,25,7.0,WALK,91038940,1 +728311525,7283115250,2220461,1120094,910389400,False,Home,25,4,17.0,WALK,91038940,1 +728311849,7283118490,2220462,1120094,910389810,True,othmaint,4,25,5.0,WALK,91038981,1 +728311850,7283118500,2220462,1120094,910389810,True,work,13,4,6.0,WALK,91038981,2 +728311853,7283118530,2220462,1120094,910389810,False,Home,25,13,15.0,WALK_LOC,91038981,1 +728312113,7283121130,2220463,1120094,910390140,True,school,22,25,6.0,WALK_LRF,91039014,1 +728312117,7283121170,2220463,1120094,910390140,False,Home,25,22,6.0,WALK_LRF,91039014,1 +739441545,7394415450,2254394,1131405,924301930,True,work,13,7,7.0,WALK,92430193,1 +739441549,7394415490,2254394,1131405,924301930,False,Home,7,13,20.0,WALK,92430193,1 +739441809,7394418090,2254395,1131405,924302260,True,school,13,7,5.0,WALK_LRF,92430226,1 +739441813,7394418130,2254395,1131405,924302260,False,Home,7,13,23.0,WALK_LOC,92430226,1 +739442153,7394421530,2254396,1131405,924302690,True,shopping,15,7,20.0,WALK,92430269,1 +739442157,7394421570,2254396,1131405,924302690,False,Home,7,15,20.0,WALK,92430269,1 +739442201,7394422010,2254396,1131405,924302750,True,work,17,7,8.0,WALK,92430275,1 +739442205,7394422050,2254396,1131405,924302750,False,Home,7,17,18.0,WALK,92430275,1 +739453113,7394531130,2254430,1131417,924316390,True,escort,2,7,15.0,WALK_LOC,92431639,1 +739453117,7394531170,2254430,1131417,924316390,False,escort,6,2,18.0,TNC_SINGLE,92431639,1 +739453118,7394531180,2254430,1131417,924316390,False,othmaint,16,6,18.0,WALK_LOC,92431639,2 +739453119,7394531190,2254430,1131417,924316390,False,eatout,17,16,18.0,WALK_LRF,92431639,3 +739453120,7394531200,2254430,1131417,924316390,False,Home,7,17,18.0,WALK_LOC,92431639,4 +739453241,7394532410,2254430,1131417,924316550,True,othdiscr,10,7,8.0,WALK,92431655,1 +739453245,7394532450,2254430,1131417,924316550,False,Home,7,10,14.0,WALK,92431655,1 +739453945,7394539450,2254432,1131417,924317430,True,school,13,7,6.0,WALK_LRF,92431743,1 +739453949,7394539490,2254432,1131417,924317430,False,Home,7,13,10.0,WALK_LOC,92431743,1 +739453985,7394539850,2254432,1131417,924317480,True,othdiscr,8,7,11.0,WALK,92431748,1 +739453986,7394539860,2254432,1131417,924317480,True,social,6,8,11.0,WALK,92431748,2 +739453989,7394539890,2254432,1131417,924317480,False,Home,7,6,21.0,WALK,92431748,1 +772521937,7725219370,2355249,1152868,965652420,True,shopping,11,10,18.0,DRIVEALONEFREE,96565242,1 +772521941,7725219410,2355249,1152868,965652420,False,Home,10,11,20.0,TNC_SHARED,96565242,1 +772522313,7725223130,2355250,1152868,965652890,True,work,13,10,8.0,WALK_LRF,96565289,1 +772522317,7725223170,2355250,1152868,965652890,False,shopping,14,13,16.0,WALK,96565289,1 +772522318,7725223180,2355250,1152868,965652890,False,othdiscr,9,14,17.0,WALK_LRF,96565289,2 +772522319,7725223190,2355250,1152868,965652890,False,Home,10,9,17.0,TAXI,96565289,3 +772522401,7725224010,2355251,1152868,965653000,True,escort,10,10,12.0,WALK,96565300,1 +772522405,7725224050,2355251,1152868,965653000,False,Home,10,10,13.0,WALK,96565300,1 +772522905,7725229050,2355252,1152868,965653630,True,univ,9,10,15.0,WALK,96565363,1 +772522909,7725229090,2355252,1152868,965653630,False,othmaint,9,9,15.0,WALK,96565363,1 +772522910,7725229100,2355252,1152868,965653630,False,Home,10,9,15.0,WALK,96565363,2 +772523233,7725232330,2355253,1152868,965654040,True,school,10,10,5.0,WALK,96565404,1 +772523237,7725232370,2355253,1152868,965654040,False,Home,10,10,15.0,WALK,96565404,1 +772523561,7725235610,2355254,1152868,965654450,True,school,6,10,6.0,WALK_HVY,96565445,1 +772523565,7725235650,2355254,1152868,965654450,False,Home,10,6,18.0,WALK_LOC,96565445,1 +772523577,7725235770,2355254,1152868,965654470,True,shopping,11,10,21.0,WALK,96565447,1 +772523581,7725235810,2355254,1152868,965654470,False,Home,10,11,22.0,WALK,96565447,1 +772523889,7725238890,2355255,1152868,965654860,True,school,19,10,7.0,SHARED2FREE,96565486,1 +772523893,7725238930,2355255,1152868,965654860,False,Home,10,19,17.0,SHARED2FREE,96565486,1 +772524001,7725240010,2355256,1152868,965655000,True,atwork,2,2,11.0,WALK,96565500,1 +772524005,7725240050,2355256,1152868,965655000,False,eatout,4,2,11.0,WALK,96565500,1 +772524006,7725240060,2355256,1152868,965655000,False,Work,2,4,11.0,WALK,96565500,2 +772524281,7725242810,2355256,1152868,965655350,True,work,2,10,7.0,WALK,96565535,1 +772524285,7725242850,2355256,1152868,965655350,False,Home,10,2,20.0,WALK_LRF,96565535,1 +772542889,7725428890,2355313,1152879,965678610,True,othmaint,18,20,17.0,TNC_SINGLE,96567861,1 +772542893,7725428930,2355313,1152879,965678610,False,Home,20,18,17.0,TNC_SINGLE,96567861,1 +772542977,7725429770,2355313,1152879,965678720,True,work,5,20,8.0,WALK,96567872,1 +772542981,7725429810,2355313,1152879,965678720,False,Home,20,5,16.0,WALK,96567872,1 +772543193,7725431930,2355314,1152879,965678990,True,othdiscr,9,20,20.0,WALK,96567899,1 +772543197,7725431970,2355314,1152879,965678990,False,Home,20,9,22.0,WALK,96567899,1 +772543305,7725433050,2355314,1152879,965679130,True,work,10,20,7.0,WALK,96567913,1 +772543309,7725433090,2355314,1152879,965679130,False,Home,20,10,18.0,WALK,96567913,1 +772543585,7725435850,2355315,1152879,965679480,True,shopping,5,20,10.0,WALK,96567948,1 +772543589,7725435890,2355315,1152879,965679480,False,Home,20,5,11.0,WALK,96567948,1 +772543913,7725439130,2355316,1152879,965679890,True,shopping,11,20,18.0,WALK,96567989,1 +772543917,7725439170,2355316,1152879,965679890,False,shopping,10,11,20.0,WALK,96567989,1 +772543918,7725439180,2355316,1152879,965679890,False,Home,20,10,22.0,WALK,96567989,2 +806380921,8063809210,2458478,1173900,1007976150,True,othmaint,17,8,11.0,WALK_LRF,100797615,1 +806380925,8063809250,2458478,1173900,1007976150,False,Home,8,17,13.0,WALK_LRF,100797615,1 +806382017,8063820170,2458481,1173900,1007977520,True,school,8,8,8.0,WALK,100797752,1 +806382021,8063820210,2458481,1173900,1007977520,False,Home,8,8,16.0,WALK,100797752,1 +806388153,8063881530,2458502,1173905,1007985190,True,shopping,5,8,8.0,SHARED2FREE,100798519,1 +806388157,8063881570,2458502,1173905,1007985190,False,Home,8,5,8.0,SHARED2FREE,100798519,1 +806388225,8063882250,2458500,1173905,1007985280,True,othmaint,7,8,12.0,WALK,100798528,1 +806388229,8063882290,2458500,1173905,1007985280,False,Home,8,7,20.0,WALK,100798528,1 +806388401,8063884010,2458501,1173905,1007985500,True,escort,16,8,15.0,WALK_LOC,100798550,1 +806388405,8063884050,2458501,1173905,1007985500,False,Home,8,16,16.0,WALK_LOC,100798550,1 +806388905,8063889050,2458502,1173905,1007986130,True,social,5,8,8.0,WALK,100798613,1 +806388906,8063889060,2458502,1173905,1007986130,True,school,8,5,8.0,WALK,100798613,2 +806388909,8063889090,2458502,1173905,1007986130,False,Home,8,8,18.0,WALK,100798613,1 +806389233,8063892330,2458503,1173905,1007986540,True,school,8,8,8.0,WALK,100798654,1 +806389237,8063892370,2458503,1173905,1007986540,False,Home,8,8,14.0,WALK,100798654,1 +841865537,8418655370,2566663,1196291,1052331920,True,escort,8,21,8.0,WALK_LOC,105233192,1 +841865541,8418655410,2566663,1196291,1052331920,False,shopping,11,8,8.0,TNC_SINGLE,105233192,1 +841865542,8418655420,2566663,1196291,1052331920,False,Home,21,11,8.0,TNC_SHARED,105233192,2 +841865545,8418655450,2566663,1196291,1052331930,True,escort,21,21,12.0,TNC_SINGLE,105233193,1 +841865549,8418655490,2566663,1196291,1052331930,False,Home,21,21,13.0,TNC_SINGLE,105233193,1 +841866369,8418663690,2566665,1196291,1052332960,True,school,9,21,7.0,WALK_LOC,105233296,1 +841866373,8418663730,2566665,1196291,1052332960,False,othdiscr,16,9,17.0,WALK_LOC,105233296,1 +841866374,8418663740,2566665,1196291,1052332960,False,Home,21,16,17.0,WALK_LOC,105233296,2 +841866697,8418666970,2566666,1196291,1052333370,True,school,21,21,8.0,WALK,105233337,1 +841866701,8418667010,2566666,1196291,1052333370,False,Home,21,21,15.0,WALK,105233337,1 +841867025,8418670250,2566667,1196291,1052333780,True,school,9,21,8.0,WALK,105233378,1 +841867029,8418670290,2566667,1196291,1052333780,False,Home,21,9,14.0,WALK,105233378,1 +841867177,8418671770,2566668,1196291,1052333970,True,escort,10,21,6.0,DRIVEALONEFREE,105233397,1 +841867181,8418671810,2566668,1196291,1052333970,False,Home,21,10,6.0,SHARED3FREE,105233397,1 +841867417,8418674170,2566668,1196291,1052334270,True,work,16,21,6.0,WALK,105233427,1 +841867421,8418674210,2566668,1196291,1052334270,False,Home,21,16,21.0,WALK,105233427,1 +841877257,8418772570,2566698,1196298,1052346570,True,work,1,25,6.0,WALK_LOC,105234657,1 +841877261,8418772610,2566698,1196298,1052346570,False,Home,25,1,17.0,WALK_LOC,105234657,1 +841877849,8418778490,2566700,1196298,1052347310,True,school,25,25,7.0,WALK,105234731,1 +841877853,8418778530,2566700,1196298,1052347310,False,Home,25,25,15.0,WALK,105234731,1 +841878177,8418781770,2566701,1196298,1052347720,True,school,3,25,8.0,WALK,105234772,1 +841878181,8418781810,2566701,1196298,1052347720,False,Home,25,3,13.0,WALK,105234772,1 +841878505,8418785050,2566702,1196298,1052348130,True,school,6,25,12.0,WALK_LOC,105234813,1 +841878509,8418785090,2566702,1196298,1052348130,False,shopping,13,6,20.0,WALK_LOC,105234813,1 +841878510,8418785100,2566702,1196298,1052348130,False,Home,25,13,20.0,WALK_LOC,105234813,2 +841893217,8418932170,2566747,1196308,1052366520,True,othdiscr,6,25,11.0,WALK,105236652,1 +841893221,8418932210,2566747,1196308,1052366520,False,Home,25,6,16.0,WALK,105236652,1 +841893593,8418935930,2566748,1196308,1052366990,True,school,25,25,7.0,WALK,105236699,1 +841893597,8418935970,2566748,1196308,1052366990,False,Home,25,25,15.0,WALK,105236699,1 +841898529,8418985290,2566763,1196312,1052373160,True,shopping,16,25,12.0,WALK,105237316,1 +841898533,8418985330,2566763,1196312,1052373160,False,Home,25,16,19.0,WALK_LOC,105237316,1 +841909289,8419092890,2566796,1196319,1052386610,True,othdiscr,14,25,14.0,WALK,105238661,1 +841909293,8419092930,2566796,1196319,1052386610,False,Home,25,14,17.0,WALK,105238661,1 +841909465,8419094650,2566797,1196319,1052386830,True,eatout,6,25,10.0,WALK,105238683,1 +841909469,8419094690,2566797,1196319,1052386830,False,Home,25,6,14.0,WALK,105238683,1 +841909777,8419097770,2566798,1196319,1052387220,True,atwork,13,14,10.0,WALK_LOC,105238722,1 +841909781,8419097810,2566798,1196319,1052387220,False,Work,14,13,10.0,WALK,105238722,1 +841909945,8419099450,2566798,1196319,1052387430,True,othdiscr,12,25,18.0,WALK_LOC,105238743,1 +841909949,8419099490,2566798,1196319,1052387430,False,eatout,7,12,20.0,WALK,105238743,1 +841909950,8419099500,2566798,1196319,1052387430,False,Home,25,7,20.0,WALK_LOC,105238743,2 +841910057,8419100570,2566798,1196319,1052387570,True,work,14,25,7.0,SHARED2FREE,105238757,1 +841910061,8419100610,2566798,1196319,1052387570,False,Home,25,14,17.0,WALK,105238757,1 +841910321,8419103210,2566799,1196319,1052387900,True,school,25,25,8.0,WALK,105238790,1 +841910325,8419103250,2566799,1196319,1052387900,False,Home,25,25,18.0,WALK,105238790,1 +857896777,8578967770,2615538,1206644,1072370970,True,work,10,25,8.0,WALK_LOC,107237097,1 +857896781,8578967810,2615538,1206644,1072370970,False,Home,25,10,18.0,WALK_LOC,107237097,1 +857897105,8578971050,2615539,1206644,1072371380,True,work,4,25,8.0,WALK,107237138,1 +857897109,8578971090,2615539,1206644,1072371380,False,Home,25,4,18.0,TNC_SHARED,107237138,1 +857897369,8578973690,2615540,1206644,1072371710,True,univ,12,25,7.0,WALK_LOC,107237171,1 +857897373,8578973730,2615540,1206644,1072371710,False,Home,25,12,14.0,WALK_LOC,107237171,1 +857897697,8578976970,2615541,1206644,1072372120,True,school,13,25,7.0,WALK_LOC,107237212,1 +857897701,8578977010,2615541,1206644,1072372120,False,Home,25,13,15.0,WALK_LOC,107237212,1 +900829073,9008290730,2746430,1234020,1126036340,True,atwork,5,4,12.0,WALK,112603634,1 +900829077,9008290770,2746430,1234020,1126036340,False,Work,4,5,15.0,WALK,112603634,1 +900829241,9008292410,2746430,1234020,1126036550,True,othdiscr,2,10,20.0,WALK_LRF,112603655,1 +900829245,9008292450,2746430,1234020,1126036550,False,Home,10,2,21.0,TNC_SINGLE,112603655,1 +900829353,9008293530,2746430,1234020,1126036690,True,othdiscr,7,10,5.0,WALK,112603669,1 +900829354,9008293540,2746430,1234020,1126036690,True,work,4,7,7.0,WALK_LOC,112603669,2 +900829357,9008293570,2746430,1234020,1126036690,False,Home,10,4,17.0,WALK_HVY,112603669,1 +900829681,9008296810,2746431,1234020,1126037100,True,shopping,5,10,7.0,WALK,112603710,1 +900829682,9008296820,2746431,1234020,1126037100,True,work,14,5,8.0,TNC_SINGLE,112603710,2 +900829685,9008296850,2746431,1234020,1126037100,False,Home,10,14,17.0,TNC_SINGLE,112603710,1 +900829745,9008297450,2746432,1234020,1126037180,True,eatout,12,10,18.0,WALK,112603718,1 +900829749,9008297490,2746432,1234020,1126037180,False,Home,10,12,22.0,WALK,112603718,1 +900829945,9008299450,2746432,1234020,1126037430,True,school,9,10,8.0,WALK_LOC,112603743,1 +900829949,9008299490,2746432,1234020,1126037430,False,Home,10,9,14.0,WALK_LOC,112603743,1 +900830273,9008302730,2746433,1234020,1126037840,True,escort,8,10,8.0,WALK_LOC,112603784,1 +900830274,9008302740,2746433,1234020,1126037840,True,school,9,8,8.0,WALK_LOC,112603784,2 +900830277,9008302770,2746433,1234020,1126037840,False,social,20,9,17.0,WALK_LOC,112603784,1 +900830278,9008302780,2746433,1234020,1126037840,False,Home,10,20,18.0,WALK_LOC,112603784,2 +900830289,9008302890,2746433,1234020,1126037860,True,shopping,14,10,19.0,WALK_LOC,112603786,1 +900830293,9008302930,2746433,1234020,1126037860,False,Home,10,14,20.0,WALK_LRF,112603786,1 +900851377,9008513770,2746498,1234034,1126064220,True,atwork,9,9,13.0,WALK,112606422,1 +900851381,9008513810,2746498,1234034,1126064220,False,Work,9,9,14.0,WALK,112606422,1 +900851657,9008516570,2746498,1234034,1126064570,True,work,9,10,5.0,SHARED2FREE,112606457,1 +900851661,9008516610,2746498,1234034,1126064570,False,social,9,9,21.0,WALK,112606457,1 +900851662,9008516620,2746498,1234034,1126064570,False,Home,10,9,22.0,WALK,112606457,2 +900851985,9008519850,2746499,1234034,1126064980,True,work,12,10,11.0,WALK,112606498,1 +900851986,9008519860,2746499,1234034,1126064980,True,work,2,12,12.0,WALK,112606498,2 +900851989,9008519890,2746499,1234034,1126064980,False,shopping,7,2,20.0,WALK,112606498,1 +900851990,9008519900,2746499,1234034,1126064980,False,Home,10,7,21.0,WALK,112606498,2 +900852249,9008522490,2746500,1234034,1126065310,True,school,20,10,7.0,WALK_LOC,112606531,1 +900852253,9008522530,2746500,1234034,1126065310,False,Home,10,20,15.0,WALK_LOC,112606531,1 +900852529,9008525290,2746501,1234034,1126065660,True,othdiscr,11,10,9.0,WALK,112606566,1 +900852533,9008525330,2746501,1234034,1126065660,False,Home,10,11,11.0,WALK,112606566,1 +900852577,9008525770,2746501,1234034,1126065720,True,school,10,10,11.0,SHARED2FREE,112606572,1 +900852581,9008525810,2746501,1234034,1126065720,False,Home,10,10,18.0,WALK,112606572,1 +900966521,9009665210,2746849,1234104,1126208150,True,eatout,9,10,15.0,WALK,112620815,1 +900966525,9009665250,2746849,1234104,1126208150,False,Home,10,9,20.0,WALK,112620815,1 +900967113,9009671130,2746850,1234104,1126208890,True,work,16,10,5.0,WALK_LRF,112620889,1 +900967117,9009671170,2746850,1234104,1126208890,False,othmaint,22,16,15.0,WALK,112620889,1 +900967118,9009671180,2746850,1234104,1126208890,False,Home,10,22,15.0,WALK_HVY,112620889,2 +900967705,9009677050,2746852,1234104,1126209630,True,escort,5,10,8.0,WALK_LOC,112620963,1 +900967706,9009677060,2746852,1234104,1126209630,True,school,9,5,8.0,WALK,112620963,2 +900967709,9009677090,2746852,1234104,1126209630,False,Home,10,9,18.0,WALK_LOC,112620963,1 +900982137,9009821370,2746896,1234114,1126227670,True,school,9,11,7.0,SHARED3FREE,112622767,1 +900982141,9009821410,2746896,1234114,1126227670,False,Home,11,9,14.0,WALK,112622767,1 +901053049,9010530490,2747112,1234158,1126316310,True,work,14,11,9.0,WALK_LOC,112631631,1 +901053053,9010530530,2747112,1234158,1126316310,False,Home,11,14,16.0,WALK_LOC,112631631,1 +901053353,9010533530,2747113,1234158,1126316690,True,social,11,11,12.0,WALK,112631669,1 +901053357,9010533570,2747113,1234158,1126316690,False,Home,11,11,19.0,WALK,112631669,1 +901053641,9010536410,2747114,1234158,1126317050,True,univ,10,11,16.0,WALK,112631705,1 +901053645,9010536450,2747114,1234158,1126317050,False,Home,11,10,16.0,WALK_LOC,112631705,1 +901053705,9010537050,2747114,1234158,1126317130,True,work,1,11,5.0,WALK,112631713,1 +901053709,9010537090,2747114,1234158,1126317130,False,Home,11,1,10.0,WALK,112631713,1 +901053793,9010537930,2747115,1234158,1126317240,True,escort,11,11,10.0,DRIVEALONEFREE,112631724,1 +901053797,9010537970,2747115,1234158,1126317240,False,shopping,8,11,11.0,SHARED2FREE,112631724,1 +901053798,9010537980,2747115,1234158,1126317240,False,Home,11,8,12.0,DRIVEALONEFREE,112631724,2 +901053969,9010539690,2747115,1234158,1126317460,True,school,13,11,6.0,WALK_LOC,112631746,1 +901053973,9010539730,2747115,1234158,1126317460,False,Home,11,13,6.0,SHARED2FREE,112631746,1 +901054297,9010542970,2747116,1234158,1126317870,True,school,13,11,8.0,WALK_LOC,112631787,1 +901054301,9010543010,2747116,1234158,1126317870,False,Home,11,13,18.0,WALK,112631787,1 +901054625,9010546250,2747117,1234158,1126318280,True,school,13,11,7.0,WALK_LOC,112631828,1 +901054629,9010546290,2747117,1234158,1126318280,False,Home,11,13,13.0,SHARED3FREE,112631828,1 +901054953,9010549530,2747118,1234158,1126318690,True,school,10,11,7.0,WALK,112631869,1 +901054957,9010549570,2747118,1234158,1126318690,False,Home,11,10,14.0,WALK,112631869,1 +901076273,9010762730,2747183,1234171,1126345340,True,school,18,18,7.0,WALK,112634534,1 +901076277,9010762770,2747183,1234171,1126345340,False,Home,18,18,11.0,WALK,112634534,1 +901095361,9010953610,2747241,1234184,1126369200,True,work,22,20,10.0,WALK_LRF,112636920,1 +901095365,9010953650,2747241,1234184,1126369200,False,work,9,22,18.0,WALK_LRF,112636920,1 +901095366,9010953660,2747241,1234184,1126369200,False,othmaint,13,9,19.0,WALK_LRF,112636920,2 +901095367,9010953670,2747241,1234184,1126369200,False,Home,20,13,19.0,WALK,112636920,3 +901095641,9010956410,2747242,1234184,1126369550,True,shopping,12,20,11.0,WALK,112636955,1 +901095645,9010956450,2747242,1234184,1126369550,False,Home,20,12,15.0,WALK,112636955,1 +901096017,9010960170,2747243,1234184,1126370020,True,work,12,20,5.0,WALK,112637002,1 +901096021,9010960210,2747243,1234184,1126370020,False,Home,20,12,15.0,WALK,112637002,1 +901096081,9010960810,2747244,1234184,1126370100,True,eatout,9,20,18.0,WALK,112637010,1 +901096085,9010960850,2747244,1234184,1126370100,False,Home,20,9,21.0,WALK,112637010,1 +901096609,9010966090,2747245,1234184,1126370760,True,school,10,20,10.0,WALK,112637076,1 +901096613,9010966130,2747245,1234184,1126370760,False,Home,20,10,14.0,WALK,112637076,1 +901096937,9010969370,2747246,1234184,1126371170,True,school,20,20,8.0,WALK,112637117,1 +901096941,9010969410,2747246,1234184,1126371170,False,Home,20,20,15.0,WALK,112637117,1 +943687097,9436870970,2877094,1259325,1179608870,True,shopping,16,9,13.0,BIKE,117960887,1 +943687101,9436871010,2877094,1259325,1179608870,False,Home,9,16,13.0,BIKE,117960887,1 +943687105,9436871050,2877094,1259325,1179608880,True,shopping,22,9,17.0,WALK_LRF,117960888,1 +943687109,9436871090,2877094,1259325,1179608880,False,Home,9,22,18.0,WALK_LOC,117960888,1 +943687145,9436871450,2877094,1259325,1179608930,True,work,9,9,19.0,WALK,117960893,1 +943687149,9436871490,2877094,1259325,1179608930,False,othdiscr,9,9,19.0,WALK,117960893,1 +943687150,9436871500,2877094,1259325,1179608930,False,shopping,11,9,21.0,WALK,117960893,2 +943687151,9436871510,2877094,1259325,1179608930,False,Home,9,11,22.0,WALK,117960893,3 +943687473,9436874730,2877095,1259325,1179609340,True,escort,11,9,9.0,WALK,117960934,1 +943687474,9436874740,2877095,1259325,1179609340,True,othmaint,21,11,9.0,WALK,117960934,2 +943687475,9436874750,2877095,1259325,1179609340,True,work,19,21,9.0,WALK,117960934,3 +943687477,9436874770,2877095,1259325,1179609340,False,social,9,19,21.0,WALK,117960934,1 +943687478,9436874780,2877095,1259325,1179609340,False,othmaint,9,9,22.0,WALK,117960934,2 +943687479,9436874790,2877095,1259325,1179609340,False,social,8,9,22.0,WALK,117960934,3 +943687480,9436874800,2877095,1259325,1179609340,False,Home,9,8,22.0,WALK,117960934,4 +943687737,9436877370,2877096,1259325,1179609670,True,school,16,9,7.0,WALK_LOC,117960967,1 +943687741,9436877410,2877096,1259325,1179609670,False,Home,9,16,15.0,WALK_LRF,117960967,1 +943688129,9436881290,2877097,1259325,1179610160,True,work,9,9,6.0,WALK,117961016,1 +943688133,9436881330,2877097,1259325,1179610160,False,Home,9,9,20.0,WALK,117961016,1 +943749401,9437494010,2877284,1259353,1179686750,True,univ,10,10,15.0,WALK,117968675,1 +943749405,9437494050,2877284,1259353,1179686750,False,Home,10,10,17.0,WALK,117968675,1 +943749465,9437494650,2877284,1259353,1179686830,True,work,13,10,7.0,WALK_LRF,117968683,1 +943749469,9437494690,2877284,1259353,1179686830,False,eatout,9,13,11.0,WALK_LRF,117968683,1 +943749470,9437494700,2877284,1259353,1179686830,False,work,9,9,11.0,WALK,117968683,2 +943749471,9437494710,2877284,1259353,1179686830,False,Home,10,9,11.0,WALK,117968683,3 +943750385,9437503850,2877287,1259353,1179687980,True,school,10,10,10.0,WALK,117968798,1 +943750389,9437503890,2877287,1259353,1179687980,False,Home,10,10,15.0,WALK,117968798,1 +943811457,9438114570,2877473,1259382,1179764320,True,work,13,16,7.0,WALK,117976432,1 +943811461,9438114610,2877473,1259382,1179764320,False,Home,16,13,16.0,TNC_SINGLE,117976432,1 +943812377,9438123770,2877476,1259382,1179765470,True,school,10,16,8.0,WALK_LOC,117976547,1 +943812381,9438123810,2877476,1259382,1179765470,False,Home,16,10,21.0,WALK_LRF,117976547,1 +943825777,9438257770,2877517,1259389,1179782220,True,othdiscr,18,17,15.0,WALK,117978222,1 +943825781,9438257810,2877517,1259389,1179782220,False,Home,17,18,15.0,WALK,117978222,1 +943825889,9438258890,2877517,1259389,1179782360,True,work,13,17,15.0,WALK,117978236,1 +943825893,9438258930,2877517,1259389,1179782360,False,Home,17,13,21.0,WALK,117978236,1 +943825937,9438259370,2877518,1259389,1179782420,True,atwork,12,16,12.0,WALK,117978242,1 +943825941,9438259410,2877518,1259389,1179782420,False,shopping,16,12,14.0,WALK,117978242,1 +943825942,9438259420,2877518,1259389,1179782420,False,Work,16,16,14.0,WALK,117978242,2 +943826217,9438262170,2877518,1259389,1179782770,True,escort,16,17,11.0,WALK,117978277,1 +943826218,9438262180,2877518,1259389,1179782770,True,work,16,16,11.0,WALK,117978277,2 +943826221,9438262210,2877518,1259389,1179782770,False,othdiscr,17,16,15.0,WALK,117978277,1 +943826222,9438262220,2877518,1259389,1179782770,False,Home,17,17,20.0,WALK,117978277,2 +943826761,9438267610,2877520,1259389,1179783450,True,othdiscr,9,17,17.0,SHARED3FREE,117978345,1 +943826765,9438267650,2877520,1259389,1179783450,False,Home,17,9,19.0,WALK_LRF,117978345,1 +943826809,9438268090,2877520,1259389,1179783510,True,school,20,17,8.0,WALK_LRF,117978351,1 +943826813,9438268130,2877520,1259389,1179783510,False,Home,17,20,15.0,WALK_LOC,117978351,1 +943827249,9438272490,2877522,1259390,1179784060,True,atwork,5,13,14.0,WALK,117978406,1 +943827253,9438272530,2877522,1259390,1179784060,False,Work,13,5,14.0,WALK,117978406,1 +943827481,9438274810,2877522,1259390,1179784350,True,shopping,5,17,20.0,WALK,117978435,1 +943827485,9438274850,2877522,1259390,1179784350,False,Home,17,5,20.0,WALK,117978435,1 +943827529,9438275290,2877522,1259390,1179784410,True,work,13,17,8.0,WALK,117978441,1 +943827533,9438275330,2877522,1259390,1179784410,False,Home,17,13,20.0,WALK,117978441,1 +943827809,9438278090,2877523,1259390,1179784760,True,shopping,19,17,17.0,WALK_LOC,117978476,1 +943827813,9438278130,2877523,1259390,1179784760,False,eatout,4,19,17.0,WALK_LOC,117978476,1 +943827814,9438278140,2877523,1259390,1179784760,False,eatout,9,4,18.0,TNC_SHARED,117978476,2 +943827815,9438278150,2877523,1259390,1179784760,False,othmaint,12,9,18.0,WALK_LRF,117978476,3 +943827816,9438278160,2877523,1259390,1179784760,False,Home,17,12,18.0,WALK_LRF,117978476,4 +943827857,9438278570,2877523,1259390,1179784820,True,work,24,17,9.0,WALK_LOC,117978482,1 +943827861,9438278610,2877523,1259390,1179784820,False,Home,17,24,16.0,WALK_LOC,117978482,1 +943828185,9438281850,2877524,1259390,1179785230,True,work,17,17,6.0,WALK,117978523,1 +943828189,9438281890,2877524,1259390,1179785230,False,Home,17,17,15.0,WALK,117978523,1 +943828449,9438284490,2877525,1259390,1179785560,True,school,9,17,8.0,WALK_LRF,117978556,1 +943828453,9438284530,2877525,1259390,1179785560,False,Home,17,9,15.0,WALK_LRF,117978556,1 +943828777,9438287770,2877526,1259390,1179785970,True,school,18,17,7.0,WALK_LRF,117978597,1 +943828781,9438287810,2877526,1259390,1179785970,False,Home,17,18,14.0,WALK_LRF,117978597,1 +943855409,9438554090,2877607,1259403,1179819260,True,work,5,17,8.0,WALK,117981926,1 +943855413,9438554130,2877607,1259403,1179819260,False,shopping,12,5,20.0,WALK,117981926,1 +943855414,9438554140,2877607,1259403,1179819260,False,Home,17,12,20.0,WALK,117981926,2 +943855673,9438556730,2877608,1259403,1179819590,True,school,10,17,7.0,WALK_LRF,117981959,1 +943855677,9438556770,2877608,1259403,1179819590,False,Home,17,10,15.0,WALK_LRF,117981959,1 +943856001,9438560010,2877609,1259403,1179820000,True,school,18,17,8.0,WALK_LRF,117982000,1 +943856005,9438560050,2877609,1259403,1179820000,False,Home,17,18,13.0,WALK_LRF,117982000,1 +943856329,9438563290,2877610,1259403,1179820410,True,school,9,17,8.0,WALK_LRF,117982041,1 +943856333,9438563330,2877610,1259403,1179820410,False,Home,17,9,16.0,WALK_LRF,117982041,1 +943856809,9438568090,2877612,1259403,1179821010,True,escort,16,17,16.0,TNC_SHARED,117982101,1 +943856813,9438568130,2877612,1259403,1179821010,False,Home,17,16,17.0,WALK_LRF,117982101,1 +943856817,9438568170,2877612,1259403,1179821020,True,escort,17,17,18.0,TNC_SHARED,117982102,1 +943856821,9438568210,2877612,1259403,1179821020,False,Home,17,17,19.0,TNC_SHARED,117982102,1 +943856961,9438569610,2877612,1259403,1179821200,True,othmaint,9,17,18.0,DRIVEALONEFREE,117982120,1 +943856965,9438569650,2877612,1259403,1179821200,False,Home,17,9,18.0,DRIVEALONEFREE,117982120,1 +943857049,9438570490,2877612,1259403,1179821310,True,work,24,17,5.0,WALK,117982131,1 +943857053,9438570530,2877612,1259403,1179821310,False,Home,17,24,16.0,WALK,117982131,1 +943857065,9438570650,2877613,1259403,1179821330,True,atwork,25,13,14.0,WALK,117982133,1 +943857069,9438570690,2877613,1259403,1179821330,False,Work,13,25,14.0,WALK,117982133,1 +943857377,9438573770,2877613,1259403,1179821720,True,work,13,17,14.0,WALK,117982172,1 +943857381,9438573810,2877613,1259403,1179821720,False,Home,17,13,23.0,WALK,117982172,1 +943857425,9438574250,2877614,1259403,1179821780,True,atwork,4,5,14.0,WALK,117982178,1 +943857429,9438574290,2877614,1259403,1179821780,False,Work,5,4,14.0,WALK,117982178,1 +943857705,9438577050,2877614,1259403,1179822130,True,othdiscr,12,17,7.0,WALK,117982213,1 +943857706,9438577060,2877614,1259403,1179822130,True,work,5,12,8.0,WALK,117982213,2 +943857709,9438577090,2877614,1259403,1179822130,False,Home,17,5,21.0,WALK_LRF,117982213,1 +943910249,9439102490,2877775,1259428,1179887810,True,eatout,17,17,17.0,WALK,117988781,1 +943910253,9439102530,2877775,1259428,1179887810,False,Home,17,17,17.0,WALK,117988781,1 +943910273,9439102730,2877775,1259428,1179887840,True,escort,12,17,14.0,TNC_SINGLE,117988784,1 +943910277,9439102770,2877775,1259428,1179887840,False,Home,17,12,14.0,TNC_SINGLE,117988784,1 +943910513,9439105130,2877775,1259428,1179888140,True,work,22,17,7.0,WALK_LRF,117988814,1 +943910517,9439105170,2877775,1259428,1179888140,False,Home,17,22,13.0,WALK,117988814,1 +943910577,9439105770,2877776,1259428,1179888220,True,eatout,4,17,17.0,WALK_LRF,117988822,1 +943910581,9439105810,2877776,1259428,1179888220,False,Home,17,4,20.0,WALK_LRF,117988822,1 +943910777,9439107770,2877776,1259428,1179888470,True,school,10,17,7.0,WALK_LRF,117988847,1 +943910781,9439107810,2877776,1259428,1179888470,False,Home,17,10,14.0,WALK_LRF,117988847,1 +943911169,9439111690,2877777,1259428,1179888960,True,work,11,17,12.0,WALK_LRF,117988896,1 +943911173,9439111730,2877777,1259428,1179888960,False,othdiscr,14,11,22.0,WALK,117988896,1 +943911174,9439111740,2877777,1259428,1179888960,False,Home,17,14,22.0,WALK_LOC,117988896,2 +943911433,9439114330,2877778,1259428,1179889290,True,school,18,17,8.0,WALK_LRF,117988929,1 +943911437,9439114370,2877778,1259428,1179889290,False,Home,17,18,17.0,WALK_LRF,117988929,1 +943911737,9439117370,2877779,1259428,1179889670,True,othmaint,21,17,7.0,TNC_SHARED,117988967,1 +943911741,9439117410,2877779,1259428,1179889670,False,Home,17,21,14.0,TNC_SINGLE,117988967,1 +943912153,9439121530,2877780,1259428,1179890190,True,work,2,17,9.0,WALK_LOC,117989019,1 +943912157,9439121570,2877780,1259428,1179890190,False,Home,17,2,18.0,WALK,117989019,1 +943912481,9439124810,2877781,1259428,1179890600,True,work,12,17,12.0,WALK,117989060,1 +943912485,9439124850,2877781,1259428,1179890600,False,Home,17,12,20.0,WALK,117989060,1 +943931505,9439315050,2877839,1259438,1179914380,True,work,4,25,7.0,TAXI,117991438,1 +943931509,9439315090,2877839,1259438,1179914380,False,Home,25,4,11.0,WALK,117991438,1 +943932097,9439320970,2877841,1259438,1179915120,True,univ,12,25,8.0,WALK_LOC,117991512,1 +943932101,9439321010,2877841,1259438,1179915120,False,Home,25,12,8.0,WALK_LOC,117991512,1 +943932105,9439321050,2877841,1259438,1179915130,True,univ,12,25,13.0,WALK_LOC,117991513,1 +943932109,9439321090,2877841,1259438,1179915130,False,Home,25,12,17.0,WALK_LOC,117991513,1 +943932489,9439324890,2877842,1259438,1179915610,True,work,14,25,12.0,WALK_LOC,117991561,1 +943932493,9439324930,2877842,1259438,1179915610,False,Home,25,14,18.0,WALK,117991561,1 +943932753,9439327530,2877843,1259438,1179915940,True,univ,13,25,18.0,WALK_LOC,117991594,1 +943932757,9439327570,2877843,1259438,1179915940,False,Home,25,13,19.0,WALK_LOC,117991594,1 +943932817,9439328170,2877843,1259438,1179916020,True,work,1,25,8.0,WALK_LOC,117991602,1 +943932821,9439328210,2877843,1259438,1179916020,False,work,1,1,12.0,TNC_SINGLE,117991602,1 +943932822,9439328220,2877843,1259438,1179916020,False,work,14,1,12.0,WALK,117991602,2 +943932823,9439328230,2877843,1259438,1179916020,False,Home,25,14,12.0,WALK_LOC,117991602,3 +943933081,9439330810,2877844,1259438,1179916350,True,school,25,25,8.0,WALK,117991635,1 +943933085,9439330850,2877844,1259438,1179916350,False,Home,25,25,13.0,WALK,117991635,1 +963058409,9630584090,2936153,1285862,1203823010,True,othmaint,13,3,12.0,BIKE,120382301,1 +963058413,9630584130,2936153,1285862,1203823010,False,Home,3,13,15.0,WALK,120382301,1 +963058417,9630584170,2936153,1285862,1203823020,True,othmaint,10,3,17.0,WALK_LRF,120382302,1 +963058421,9630584210,2936153,1285862,1203823020,False,Home,3,10,18.0,WALK_LOC,120382302,1 +963058449,9630584490,2936153,1285862,1203823060,True,shopping,11,3,12.0,WALK_LOC,120382306,1 +963058453,9630584530,2936153,1285862,1203823060,False,Home,3,11,12.0,WALK_LOC,120382306,1 +963063961,9630639610,2936170,1285879,1203829950,True,othdiscr,14,5,12.0,WALK,120382995,1 +963063965,9630639650,2936170,1285879,1203829950,False,Home,5,14,17.0,WALK,120382995,1 +963075793,9630757930,2936206,1285915,1203844740,True,othmaint,7,6,11.0,WALK,120384474,1 +963075797,9630757970,2936206,1285915,1203844740,False,Home,6,7,20.0,WALK,120384474,1 +963087753,9630877530,2936243,1285952,1203859690,True,eatout,16,6,10.0,DRIVEALONEFREE,120385969,1 +963087757,9630877570,2936243,1285952,1203859690,False,Home,6,16,12.0,SHARED2FREE,120385969,1 +963087777,9630877770,2936243,1285952,1203859720,True,escort,25,6,13.0,SHARED3FREE,120385972,1 +963087781,9630877810,2936243,1285952,1203859720,False,Home,6,25,13.0,SHARED2FREE,120385972,1 +963087905,9630879050,2936243,1285952,1203859880,True,social,12,6,15.0,WALK,120385988,1 +963087906,9630879060,2936243,1285952,1203859880,True,othdiscr,5,12,16.0,SHARED2FREE,120385988,2 +963087909,9630879090,2936243,1285952,1203859880,False,eatout,6,5,16.0,DRIVEALONEFREE,120385988,1 +963087910,9630879100,2936243,1285952,1203859880,False,Home,6,6,16.0,WALK,120385988,2 +963087929,9630879290,2936243,1285952,1203859910,True,othmaint,7,6,14.0,WALK,120385991,1 +963087933,9630879330,2936243,1285952,1203859910,False,Home,6,7,15.0,WALK,120385991,1 +963093481,9630934810,2936260,1285969,1203866850,True,othdiscr,9,6,16.0,WALK,120386685,1 +963093485,9630934850,2936260,1285969,1203866850,False,Home,6,9,16.0,WALK,120386685,1 +963093545,9630935450,2936260,1285969,1203866930,True,shopping,16,6,10.0,WALK,120386693,1 +963093549,9630935490,2936260,1285969,1203866930,False,Home,6,16,14.0,WALK,120386693,1 +963097921,9630979210,2936274,1285983,1203872400,True,eatout,6,6,16.0,WALK,120387240,1 +963097925,9630979250,2936274,1285983,1203872400,False,Home,6,6,16.0,WALK,120387240,1 +963098073,9630980730,2936274,1285983,1203872590,True,othdiscr,10,6,13.0,SHARED2FREE,120387259,1 +963098077,9630980770,2936274,1285983,1203872590,False,Home,6,10,16.0,WALK,120387259,1 +963098137,9630981370,2936274,1285983,1203872670,True,othmaint,8,6,13.0,TNC_SINGLE,120387267,1 +963098138,9630981380,2936274,1285983,1203872670,True,shopping,16,8,13.0,TNC_SINGLE,120387267,2 +963098141,9630981410,2936274,1285983,1203872670,False,eatout,6,16,13.0,DRIVEALONEFREE,120387267,1 +963098142,9630981420,2936274,1285983,1203872670,False,shopping,6,6,13.0,TNC_SHARED,120387267,2 +963098143,9630981430,2936274,1285983,1203872670,False,Home,6,6,13.0,TNC_SHARED,120387267,3 +963107673,9631076730,2936303,1286012,1203884590,True,social,9,6,11.0,WALK,120388459,1 +963107677,9631076770,2936303,1286012,1203884590,False,Home,6,9,22.0,WALK,120388459,1 +963143993,9631439930,2936414,1286123,1203929990,True,othdiscr,8,7,16.0,WALK,120392999,1 +963143997,9631439970,2936414,1286123,1203929990,False,Home,7,8,17.0,WALK,120392999,1 +963178457,9631784570,2936519,1286228,1203973070,True,othmaint,20,7,8.0,WALK,120397307,1 +963178461,9631784610,2936519,1286228,1203973070,False,Home,7,20,13.0,WALK,120397307,1 +963187137,9631871370,2936546,1286255,1203983920,True,eatout,9,8,11.0,WALK,120398392,1 +963187141,9631871410,2936546,1286255,1203983920,False,Home,8,9,14.0,WALK,120398392,1 +963188601,9631886010,2936550,1286259,1203985750,True,othdiscr,6,8,14.0,WALK,120398575,1 +963188605,9631886050,2936550,1286259,1203985750,False,Home,8,6,17.0,WALK,120398575,1 +963188665,9631886650,2936550,1286259,1203985830,True,shopping,11,8,12.0,WALK,120398583,1 +963188669,9631886690,2936550,1286259,1203985830,False,Home,8,11,13.0,WALK,120398583,1 +963196865,9631968650,2936575,1286284,1203996080,True,shopping,1,8,8.0,WALK,120399608,1 +963196869,9631968690,2936575,1286284,1203996080,False,Home,8,1,14.0,WALK,120399608,1 +963221073,9632210730,2936649,1286358,1204026340,True,othdiscr,1,9,11.0,WALK_LOC,120402634,1 +963221077,9632210770,2936649,1286358,1204026340,False,Home,9,1,16.0,WALK_LRF,120402634,1 +963252953,9632529530,2936746,1286455,1204066190,True,shopping,18,9,11.0,WALK,120406619,1 +963252957,9632529570,2936746,1286455,1204066190,False,Home,9,18,14.0,WALK,120406619,1 +963256521,9632565210,2936757,1286466,1204070650,True,othmaint,9,9,10.0,TNC_SINGLE,120407065,1 +963256525,9632565250,2936757,1286466,1204070650,False,Home,9,9,10.0,DRIVEALONEFREE,120407065,1 +963256561,9632565610,2936757,1286466,1204070700,True,othmaint,8,9,15.0,WALK,120407070,1 +963256562,9632565620,2936757,1286466,1204070700,True,shopping,7,8,15.0,DRIVEALONEFREE,120407070,2 +963256563,9632565630,2936757,1286466,1204070700,True,shopping,10,7,17.0,TNC_SHARED,120407070,3 +963256565,9632565650,2936757,1286466,1204070700,False,shopping,13,10,17.0,DRIVEALONEFREE,120407070,1 +963256566,9632565660,2936757,1286466,1204070700,False,shopping,18,13,17.0,TNC_SINGLE,120407070,2 +963256567,9632565670,2936757,1286466,1204070700,False,Home,9,18,17.0,DRIVEALONEFREE,120407070,3 +963275521,9632755210,2936815,1286524,1204094400,True,othdiscr,6,10,9.0,WALK_LOC,120409440,1 +963275525,9632755250,2936815,1286524,1204094400,False,Home,10,6,17.0,WALK_LOC,120409440,1 +963285425,9632854250,2936845,1286554,1204106780,True,othmaint,8,11,13.0,WALK_LOC,120410678,1 +963285426,9632854260,2936845,1286554,1204106780,True,othmaint,6,8,13.0,WALK,120410678,2 +963285427,9632854270,2936845,1286554,1204106780,True,shopping,16,6,14.0,WALK_LOC,120410678,3 +963285429,9632854290,2936845,1286554,1204106780,False,Home,11,16,15.0,WALK_LOC,120410678,1 +963285433,9632854330,2936845,1286554,1204106790,True,shopping,16,11,16.0,TNC_SINGLE,120410679,1 +963285437,9632854370,2936845,1286554,1204106790,False,social,24,16,17.0,WALK_LOC,120410679,1 +963285438,9632854380,2936845,1286554,1204106790,False,shopping,7,24,17.0,TNC_SINGLE,120410679,2 +963285439,9632854390,2936845,1286554,1204106790,False,Home,11,7,17.0,TNC_SINGLE,120410679,3 +963286193,9632861930,2936848,1286557,1204107740,True,eatout,9,11,14.0,WALK,120410774,1 +963286197,9632861970,2936848,1286557,1204107740,False,Home,11,9,15.0,TNC_SHARED,120410774,1 +963286345,9632863450,2936848,1286557,1204107930,True,escort,10,11,15.0,DRIVEALONEFREE,120410793,1 +963286346,9632863460,2936848,1286557,1204107930,True,othdiscr,11,10,16.0,DRIVEALONEFREE,120410793,2 +963286349,9632863490,2936848,1286557,1204107930,False,Home,11,11,16.0,DRIVEALONEFREE,120410793,1 +963294281,9632942810,2936872,1286581,1204117850,True,shopping,16,14,17.0,WALK,120411785,1 +963294285,9632942850,2936872,1286581,1204117850,False,Home,14,16,17.0,WALK,120411785,1 +963307401,9633074010,2936912,1286621,1204134250,True,othmaint,17,17,9.0,WALK,120413425,1 +963307402,9633074020,2936912,1286621,1204134250,True,othmaint,1,17,10.0,DRIVEALONEFREE,120413425,2 +963307403,9633074030,2936912,1286621,1204134250,True,othdiscr,19,1,10.0,SHARED2FREE,120413425,3 +963307404,9633074040,2936912,1286621,1204134250,True,shopping,12,19,10.0,DRIVEALONEFREE,120413425,4 +963307405,9633074050,2936912,1286621,1204134250,False,Home,17,12,10.0,WALK,120413425,1 +963311993,9633119930,2936926,1286635,1204139990,True,shopping,21,20,15.0,WALK,120413999,1 +963311997,9633119970,2936926,1286635,1204139990,False,Home,20,21,16.0,WALK,120413999,1 +963321441,9633214410,2936955,1286664,1204151800,True,othdiscr,15,22,9.0,WALK,120415180,1 +963321445,9633214450,2936955,1286664,1204151800,False,Home,22,15,18.0,WALK,120415180,1 +963357697,9633576970,2937066,1286775,1204197120,True,eatout,16,23,14.0,WALK,120419712,1 +963357701,9633577010,2937066,1286775,1204197120,False,Home,23,16,17.0,WALK,120419712,1 +963357913,9633579130,2937066,1286775,1204197390,True,shopping,11,23,12.0,SHARED2FREE,120419739,1 +963357917,9633579170,2937066,1286775,1204197390,False,shopping,16,11,12.0,DRIVEALONEFREE,120419739,1 +963357918,9633579180,2937066,1286775,1204197390,False,Home,23,16,12.0,SHARED2FREE,120419739,2 +963371297,9633712970,2937107,1286816,1204214120,True,othdiscr,5,25,11.0,WALK,120421412,1 +963371301,9633713010,2937107,1286816,1204214120,False,Home,25,5,14.0,WALK,120421412,1 +969922769,9699227690,2957081,1306790,1212403460,True,othdiscr,6,3,18.0,WALK,121240346,1 +969922773,9699227730,2957081,1306790,1212403460,False,Home,3,6,20.0,WALK,121240346,1 +969922881,9699228810,2957081,1306790,1212403600,True,work,22,3,6.0,WALK,121240360,1 +969922885,9699228850,2957081,1306790,1212403600,False,Home,3,22,15.0,WALK,121240360,1 +969937641,9699376410,2957126,1306835,1212422050,True,work,2,5,6.0,WALK_LOC,121242205,1 +969937642,9699376420,2957126,1306835,1212422050,True,work,21,2,7.0,WALK,121242205,2 +969937645,9699376450,2957126,1306835,1212422050,False,Home,5,21,17.0,WALK_LOC,121242205,1 +969949777,9699497770,2957163,1306872,1212437220,True,work,9,6,18.0,WALK,121243722,1 +969949781,9699497810,2957163,1306872,1212437220,False,Home,6,9,20.0,WALK,121243722,1 +969951697,9699516970,2957169,1306878,1212439620,True,shopping,2,7,18.0,WALK,121243962,1 +969951701,9699517010,2957169,1306878,1212439620,False,Home,7,2,20.0,WALK,121243962,1 +969951745,9699517450,2957169,1306878,1212439680,True,escort,7,7,6.0,WALK,121243968,1 +969951746,9699517460,2957169,1306878,1212439680,True,work,4,7,8.0,WALK,121243968,2 +969951749,9699517490,2957169,1306878,1212439680,False,Home,7,4,16.0,WALK,121243968,1 +969957321,9699573210,2957186,1306895,1212446650,True,work,2,7,7.0,WALK,121244665,1 +969957325,9699573250,2957186,1306895,1212446650,False,Home,7,2,19.0,WALK,121244665,1 +969965537,9699655370,2957212,1306921,1212456920,True,atwork,9,9,12.0,WALK,121245692,1 +969965541,9699655410,2957212,1306921,1212456920,False,shopping,11,9,15.0,WALK,121245692,1 +969965542,9699655420,2957212,1306921,1212456920,False,Work,9,11,15.0,WALK,121245692,2 +969965849,9699658490,2957212,1306921,1212457310,True,work,9,7,8.0,WALK,121245731,1 +969965853,9699658530,2957212,1306921,1212457310,False,Home,7,9,17.0,TNC_SINGLE,121245731,1 +969970113,9699701130,2957225,1306934,1212462640,True,eatout,6,7,5.0,WALK,121246264,1 +969970114,9699701140,2957225,1306934,1212462640,True,work,1,6,7.0,WALK,121246264,2 +969970117,9699701170,2957225,1306934,1212462640,False,othmaint,8,1,17.0,WALK_LRF,121246264,1 +969970118,9699701180,2957225,1306934,1212462640,False,othmaint,2,8,17.0,WALK,121246264,2 +969970119,9699701190,2957225,1306934,1212462640,False,work,2,2,18.0,WALK,121246264,3 +969970120,9699701200,2957225,1306934,1212462640,False,Home,7,2,18.0,WALK_LOC,121246264,4 +970006193,9700061930,2957335,1307044,1212507740,True,work,13,11,6.0,DRIVEALONEFREE,121250774,1 +970006197,9700061970,2957335,1307044,1212507740,False,Home,11,13,18.0,DRIVEALONEFREE,121250774,1 +970010985,9700109850,2957350,1307059,1212513730,True,atwork,25,2,13.0,WALK,121251373,1 +970010989,9700109890,2957350,1307059,1212513730,False,Work,2,25,13.0,WALK,121251373,1 +970011113,9700111130,2957350,1307059,1212513890,True,work,2,17,8.0,WALK,121251389,1 +970011117,9700111170,2957350,1307059,1212513890,False,Home,17,2,18.0,WALK,121251389,1 +970013409,9700134090,2957357,1307066,1212516760,True,work,5,17,7.0,BIKE,121251676,1 +970013413,9700134130,2957357,1307066,1212516760,False,Home,17,5,19.0,BIKE,121251676,1 +970013785,9700137850,2957359,1307068,1212517230,True,atwork,5,12,11.0,WALK,121251723,1 +970013789,9700137890,2957359,1307068,1212517230,False,Work,12,5,13.0,WALK,121251723,1 +970014065,9700140650,2957359,1307068,1212517580,True,work,12,17,7.0,WALK,121251758,1 +970014069,9700140690,2957359,1307068,1212517580,False,Home,17,12,17.0,WALK_LRF,121251758,1 +970028873,9700288730,2957405,1307114,1212536090,True,atwork,11,14,9.0,WALK,121253609,1 +970028877,9700288770,2957405,1307114,1212536090,False,Work,14,11,9.0,WALK,121253609,1 +970029153,9700291530,2957405,1307114,1212536440,True,escort,7,20,8.0,TNC_SINGLE,121253644,1 +970029154,9700291540,2957405,1307114,1212536440,True,work,14,7,8.0,WALK,121253644,2 +970029157,9700291570,2957405,1307114,1212536440,False,social,3,14,18.0,TNC_SINGLE,121253644,1 +970029158,9700291580,2957405,1307114,1212536440,False,Home,20,3,19.0,WALK_LRF,121253644,2 +970032105,9700321050,2957414,1307123,1212540130,True,work,4,20,7.0,WALK_LRF,121254013,1 +970032109,9700321090,2957414,1307123,1212540130,False,Home,20,4,18.0,WALK_LRF,121254013,1 +970054953,9700549530,2957484,1307193,1212568690,True,othdiscr,14,21,10.0,WALK_LOC,121256869,1 +970054957,9700549570,2957484,1307193,1212568690,False,Home,21,14,11.0,WALK_LOC,121256869,1 +970068729,9700687290,2957526,1307235,1212585910,True,othdiscr,17,21,11.0,WALK,121258591,1 +970068733,9700687330,2957526,1307235,1212585910,False,othmaint,16,17,17.0,WALK_LOC,121258591,1 +970068734,9700687340,2957526,1307235,1212585910,False,othdiscr,7,16,17.0,WALK_LOC,121258591,2 +970068735,9700687350,2957526,1307235,1212585910,False,othdiscr,11,7,17.0,WALK_LOC,121258591,3 +970068736,9700687360,2957526,1307235,1212585910,False,Home,21,11,17.0,WALK_LOC,121258591,4 +970070153,9700701530,2957530,1307239,1212587690,True,escort,9,21,8.0,WALK_LOC,121258769,1 +970070154,9700701540,2957530,1307239,1212587690,True,work,4,9,8.0,WALK_LRF,121258769,2 +970070157,9700701570,2957530,1307239,1212587690,False,Home,21,4,19.0,WALK_LRF,121258769,1 +970098689,9700986890,2957617,1307326,1212623360,True,work,4,23,12.0,WALK,121262336,1 +970098693,9700986930,2957617,1307326,1212623360,False,Home,23,4,17.0,WALK,121262336,1 +970102185,9701021850,2957628,1307337,1212627730,True,othdiscr,23,23,10.0,WALK,121262773,1 +970102189,9701021890,2957628,1307337,1212627730,False,Home,23,23,13.0,WALK,121262773,1 +970102297,9701022970,2957628,1307337,1212627870,True,work,24,23,14.0,WALK,121262787,1 +970102301,9701023010,2957628,1307337,1212627870,False,Home,23,24,21.0,WALK,121262787,1 +970103673,9701036730,2957633,1307342,1212629590,True,eatout,4,23,12.0,WALK,121262959,1 +970103677,9701036770,2957633,1307342,1212629590,False,Home,23,4,13.0,WALK,121262959,1 +970103889,9701038890,2957633,1307342,1212629860,True,shopping,20,23,14.0,DRIVEALONEFREE,121262986,1 +970103893,9701038930,2957633,1307342,1212629860,False,shopping,13,20,14.0,TNC_SINGLE,121262986,1 +970103894,9701038940,2957633,1307342,1212629860,False,Home,23,13,14.0,TNC_SINGLE,121262986,2 +970103897,9701038970,2957633,1307342,1212629870,True,othmaint,2,23,14.0,WALK,121262987,1 +970103898,9701038980,2957633,1307342,1212629870,True,shopping,16,2,14.0,WALK,121262987,2 +970103901,9701039010,2957633,1307342,1212629870,False,othmaint,2,16,15.0,WALK,121262987,1 +970103902,9701039020,2957633,1307342,1212629870,False,escort,2,2,16.0,WALK,121262987,2 +970103903,9701039030,2957633,1307342,1212629870,False,shopping,25,2,16.0,WALK,121262987,3 +970103904,9701039040,2957633,1307342,1212629870,False,Home,23,25,16.0,WALK,121262987,4 +970103937,9701039370,2957633,1307342,1212629920,True,work,14,23,7.0,DRIVEALONEFREE,121262992,1 +970103941,9701039410,2957633,1307342,1212629920,False,Home,23,14,10.0,DRIVEALONEFREE,121262992,1 +970110121,9701101210,2957652,1307361,1212637650,True,shopping,5,23,5.0,TNC_SINGLE,121263765,1 +970110125,9701101250,2957652,1307361,1212637650,False,Home,23,5,5.0,TNC_SINGLE,121263765,1 +970110129,9701101290,2957652,1307361,1212637660,True,shopping,12,23,17.0,TNC_SINGLE,121263766,1 +970110130,9701101300,2957652,1307361,1212637660,True,shopping,5,12,17.0,DRIVEALONEFREE,121263766,2 +970110133,9701101330,2957652,1307361,1212637660,False,Home,23,5,17.0,DRIVEALONEFREE,121263766,1 +970110169,9701101690,2957652,1307361,1212637710,True,work,2,23,7.0,DRIVEALONEFREE,121263771,1 +970110173,9701101730,2957652,1307361,1212637710,False,Home,23,2,15.0,DRIVEALONEFREE,121263771,1 +970119681,9701196810,2957681,1307390,1212649600,True,work,7,25,8.0,BIKE,121264960,1 +970119685,9701196850,2957681,1307390,1212649600,False,Home,25,7,17.0,BIKE,121264960,1 +970120993,9701209930,2957685,1307394,1212651240,True,work,15,25,11.0,WALK,121265124,1 +970120997,9701209970,2957685,1307394,1212651240,False,Home,25,15,18.0,WALK_LOC,121265124,1 +970123945,9701239450,2957694,1307403,1212654930,True,work,2,25,8.0,WALK,121265493,1 +970123949,9701239490,2957694,1307403,1212654930,False,Home,25,2,18.0,WALK,121265493,1 +989462777,9894627770,3016654,1340847,1236828470,True,shopping,17,8,11.0,DRIVEALONEFREE,123682847,1 +989462781,9894627810,3016654,1340847,1236828470,False,shopping,6,17,12.0,TNC_SINGLE,123682847,1 +989462782,9894627820,3016654,1340847,1236828470,False,shopping,10,6,12.0,DRIVEALONEFREE,123682847,2 +989462783,9894627830,3016654,1340847,1236828470,False,shopping,11,10,12.0,DRIVEALONEFREE,123682847,3 +989462784,9894627840,3016654,1340847,1236828470,False,Home,8,11,12.0,TNC_SINGLE,123682847,4 +989462785,9894627850,3016654,1340847,1236828480,True,shopping,20,8,16.0,TNC_SINGLE,123682848,1 +989462789,9894627890,3016654,1340847,1236828480,False,othmaint,13,20,16.0,DRIVEALONEFREE,123682848,1 +989462790,9894627900,3016654,1340847,1236828480,False,othdiscr,5,13,16.0,DRIVEALONEFREE,123682848,2 +989462791,9894627910,3016654,1340847,1236828480,False,Home,8,5,16.0,TAXI,123682848,3 +989463041,9894630410,3016655,1340847,1236828800,True,othdiscr,9,8,11.0,TNC_SINGLE,123682880,1 +989463045,9894630450,3016655,1340847,1236828800,False,eatout,20,9,14.0,TAXI,123682880,1 +989463046,9894630460,3016655,1340847,1236828800,False,Home,8,20,14.0,TNC_SINGLE,123682880,2 +989497353,9894973530,3016760,1340900,1236871690,True,escort,16,10,9.0,WALK,123687169,1 +989497357,9894973570,3016760,1340900,1236871690,False,Home,10,16,14.0,WALK,123687169,1 +989497505,9894975050,3016760,1340900,1236871880,True,othmaint,4,10,14.0,WALK_LOC,123687188,1 +989497509,9894975090,3016760,1340900,1236871880,False,Home,10,4,15.0,WALK_HVY,123687188,1 +989497657,9894976570,3016761,1340900,1236872070,True,eatout,9,10,10.0,WALK,123687207,1 +989497661,9894976610,3016761,1340900,1236872070,False,Home,10,9,13.0,WALK,123687207,1 +989497833,9894978330,3016761,1340900,1236872290,True,othmaint,9,10,13.0,WALK,123687229,1 +989497837,9894978370,3016761,1340900,1236872290,False,Home,10,9,16.0,WALK,123687229,1 +989521121,9895211210,3016832,1340936,1236901400,True,othmaint,6,22,6.0,WALK,123690140,1 +989521125,9895211250,3016832,1340936,1236901400,False,Home,22,6,10.0,WALK,123690140,1 +989521129,9895211290,3016832,1340936,1236901410,True,othmaint,17,22,12.0,WALK,123690141,1 +989521133,9895211330,3016832,1340936,1236901410,False,Home,22,17,12.0,WALK,123690141,1 +989521137,9895211370,3016832,1340936,1236901420,True,othmaint,6,22,15.0,SHARED3FREE,123690142,1 +989521141,9895211410,3016832,1340936,1236901420,False,Home,22,6,15.0,DRIVEALONEFREE,123690142,1 +989521297,9895212970,3016833,1340936,1236901620,True,escort,5,22,14.0,TNC_SINGLE,123690162,1 +989521301,9895213010,3016833,1340936,1236901620,False,shopping,5,5,14.0,TNC_SINGLE,123690162,1 +989521302,9895213020,3016833,1340936,1236901620,False,Home,22,5,15.0,WALK_LOC,123690162,2 +989521489,9895214890,3016833,1340936,1236901860,True,shopping,13,22,12.0,TNC_SHARED,123690186,1 +989521490,9895214900,3016833,1340936,1236901860,True,shopping,16,13,13.0,TNC_SINGLE,123690186,2 +989521493,9895214930,3016833,1340936,1236901860,False,Home,22,16,13.0,TAXI,123690186,1 +1004039689,10040396890,3061096,1363068,1255049610,True,othdiscr,18,3,7.0,WALK_LOC,125504961,1 +1004039693,10040396930,3061096,1363068,1255049610,False,Home,3,18,17.0,WALK_LOC,125504961,1 +1004040129,10040401290,3061097,1363068,1255050160,True,work,2,3,8.0,WALK,125505016,1 +1004040133,10040401330,3061097,1363068,1255050160,False,shopping,5,2,18.0,WALK,125505016,1 +1004040134,10040401340,3061097,1363068,1255050160,False,Home,3,5,19.0,WALK_LOC,125505016,2 +1004064489,10040644890,3061172,1363106,1255080610,True,escort,22,7,8.0,WALK,125508061,1 +1004064493,10040644930,3061172,1363106,1255080610,False,Home,7,22,8.0,WALK,125508061,1 +1004064969,10040649690,3061173,1363106,1255081210,True,othmaint,11,7,7.0,WALK,125508121,1 +1004064973,10040649730,3061173,1363106,1255081210,False,Home,7,11,7.0,WALK,125508121,1 +1004065057,10040650570,3061173,1363106,1255081320,True,work,12,7,8.0,BIKE,125508132,1 +1004065061,10040650610,3061173,1363106,1255081320,False,Home,7,12,17.0,BIKE,125508132,1 +1004089417,10040894170,3061248,1363144,1255111770,True,escort,3,8,9.0,WALK,125511177,1 +1004089421,10040894210,3061248,1363144,1255111770,False,Home,8,3,9.0,WALK,125511177,1 +1004089873,10040898730,3061249,1363144,1255112340,True,othdiscr,12,8,12.0,WALK,125511234,1 +1004089877,10040898770,3061249,1363144,1255112340,False,Home,8,12,17.0,WALK,125511234,1 +1004113753,10041137530,3061322,1363181,1255142190,True,othmaint,4,10,8.0,TNC_SINGLE,125514219,1 +1004113757,10041137570,3061322,1363181,1255142190,False,Home,10,4,8.0,TNC_SINGLE,125514219,1 +1004113977,10041139770,3061323,1363181,1255142470,True,atwork,19,2,13.0,SHARED2FREE,125514247,1 +1004113981,10041139810,3061323,1363181,1255142470,False,Work,2,19,13.0,SHARED2FREE,125514247,1 +1004114257,10041142570,3061323,1363181,1255142820,True,escort,8,10,8.0,WALK,125514282,1 +1004114258,10041142580,3061323,1363181,1255142820,True,work,2,8,9.0,WALK,125514282,2 +1004114261,10041142610,3061323,1363181,1255142820,False,Home,10,2,17.0,WALK_LRF,125514282,1 +1004119881,10041198810,3061341,1363190,1255149850,True,atwork,3,1,12.0,WALK,125514985,1 +1004119885,10041198850,3061341,1363190,1255149850,False,Work,1,3,13.0,WALK,125514985,1 +1004120161,10041201610,3061341,1363190,1255150200,True,work,1,10,7.0,WALK,125515020,1 +1004120165,10041201650,3061341,1363190,1255150200,False,Home,10,1,21.0,WALK_LRF,125515020,1 +1004147713,10041477130,3061425,1363232,1255184640,True,work,21,10,7.0,WALK_LOC,125518464,1 +1004147717,10041477170,3061425,1363232,1255184640,False,Home,10,21,16.0,WALK,125518464,1 +1004159737,10041597370,3061462,1363251,1255199670,True,othdiscr,20,11,18.0,WALK_LOC,125519967,1 +1004159741,10041597410,3061462,1363251,1255199670,False,Home,11,20,21.0,WALK_LOC,125519967,1 +1004159849,10041598490,3061462,1363251,1255199810,True,work,10,11,13.0,WALK,125519981,1 +1004159853,10041598530,3061462,1363251,1255199810,False,Home,11,10,16.0,WALK,125519981,1 +1004159913,10041599130,3061463,1363251,1255199890,True,eatout,14,11,17.0,WALK,125519989,1 +1004159917,10041599170,3061463,1363251,1255199890,False,shopping,5,14,20.0,WALK,125519989,1 +1004159918,10041599180,3061463,1363251,1255199890,False,Home,11,5,20.0,WALK,125519989,2 +1004160153,10041601530,3061463,1363251,1255200190,True,social,16,11,7.0,WALK,125520019,1 +1004160157,10041601570,3061463,1363251,1255200190,False,Home,11,16,11.0,WALK,125520019,1 +1004166409,10041664090,3061482,1363261,1255208010,True,work,9,11,6.0,WALK_LOC,125520801,1 +1004166413,10041664130,3061482,1363261,1255208010,False,Home,11,9,15.0,WALK,125520801,1 +1004246161,10042461610,3061726,1363383,1255307700,True,atwork,16,12,12.0,WALK,125530770,1 +1004246165,10042461650,3061726,1363383,1255307700,False,Work,12,16,14.0,WALK,125530770,1 +1004246441,10042464410,3061726,1363383,1255308050,True,work,12,20,7.0,WALK_LOC,125530805,1 +1004246445,10042464450,3061726,1363383,1255308050,False,Home,20,12,17.0,WALK_LOC,125530805,1 +1004254593,10042545930,3061751,1363395,1255318240,True,shopping,5,20,11.0,WALK,125531824,1 +1004254597,10042545970,3061751,1363395,1255318240,False,Home,20,5,13.0,WALK,125531824,1 +1004301497,10043014970,3061894,1363467,1255376870,True,shopping,20,24,12.0,TNC_SHARED,125537687,1 +1004301501,10043015010,3061894,1363467,1255376870,False,Home,24,20,13.0,DRIVEALONEFREE,125537687,1 +1004301761,10043017610,3061895,1363467,1255377200,True,othdiscr,9,24,17.0,WALK_HVY,125537720,1 +1004301765,10043017650,3061895,1363467,1255377200,False,Home,24,9,19.0,WALK_HVY,125537720,1 +1004301785,10043017850,3061895,1363467,1255377230,True,othmaint,7,24,15.0,WALK,125537723,1 +1004301789,10043017890,3061895,1363467,1255377230,False,Home,24,7,16.0,WALK,125537723,1 +1004301873,10043018730,3061895,1363467,1255377340,True,work,25,24,6.0,WALK,125537734,1 +1004301877,10043018770,3061895,1363467,1255377340,False,Home,24,25,13.0,WALK,125537734,1 +1008051809,10080518090,3073328,1369184,1260064760,True,othmaint,9,22,11.0,WALK_HVY,126006476,1 +1008051813,10080518130,3073328,1369184,1260064760,False,Home,22,9,15.0,WALK_LRF,126006476,1 +1008051897,10080518970,3073328,1369184,1260064870,True,work,4,22,15.0,BIKE,126006487,1 +1008051901,10080519010,3073328,1369184,1260064870,False,Home,22,4,22.0,BIKE,126006487,1 +1008052225,10080522250,3073329,1369184,1260065280,True,work,11,22,7.0,WALK_LRF,126006528,1 +1008052229,10080522290,3073329,1369184,1260065280,False,Home,22,11,21.0,WALK_LRF,126006528,1 +1008053209,10080532090,3073332,1369186,1260066510,True,work,12,22,7.0,WALK,126006651,1 +1008053213,10080532130,3073332,1369186,1260066510,False,Home,22,12,14.0,WALK_LOC,126006651,1 +1008053273,10080532730,3073333,1369186,1260066590,True,eatout,3,22,16.0,WALK,126006659,1 +1008053277,10080532770,3073333,1369186,1260066590,False,Home,22,3,16.0,WALK,126006659,1 +1008053449,10080534490,3073333,1369186,1260066810,True,othmaint,22,22,16.0,TNC_SINGLE,126006681,1 +1008053453,10080534530,3073333,1369186,1260066810,False,Home,22,22,17.0,TNC_SINGLE,126006681,1 +1008053489,10080534890,3073333,1369186,1260066860,True,shopping,7,22,15.0,DRIVEALONEFREE,126006686,1 +1008053493,10080534930,3073333,1369186,1260066860,False,Home,22,7,15.0,DRIVEALONEFREE,126006686,1 +1008053497,10080534970,3073333,1369186,1260066870,True,shopping,2,22,17.0,WALK,126006687,1 +1008053501,10080535010,3073333,1369186,1260066870,False,Home,22,2,19.0,WALK,126006687,1 +1008053537,10080535370,3073333,1369186,1260066920,True,escort,3,22,7.0,WALK_LOC,126006692,1 +1008053538,10080535380,3073333,1369186,1260066920,True,work,14,3,7.0,WALK_LOC,126006692,2 +1008053541,10080535410,3073333,1369186,1260066920,False,escort,2,14,14.0,WALK,126006692,1 +1008053542,10080535420,3073333,1369186,1260066920,False,Home,22,2,15.0,WALK_LOC,126006692,2 +1011081065,10110810650,3082564,1373802,1263851330,True,escort,17,16,17.0,WALK,126385133,1 +1011081069,10110810690,3082564,1373802,1263851330,False,Home,16,17,17.0,WALK,126385133,1 +1011081073,10110810730,3082564,1373802,1263851340,True,escort,16,16,17.0,TNC_SHARED,126385134,1 +1011081077,10110810770,3082564,1373802,1263851340,False,Home,16,16,18.0,TNC_SINGLE,126385134,1 +1011081633,10110816330,3082565,1373802,1263852040,True,work,18,16,7.0,WALK,126385204,1 +1011081637,10110816370,3082565,1373802,1263852040,False,Home,16,18,18.0,WALK,126385204,1 +1011109513,10111095130,3082650,1373845,1263886890,True,work,2,21,6.0,WALK,126388689,1 +1011109517,10111095170,3082650,1373845,1263886890,False,Home,21,2,19.0,WALK,126388689,1 +1011109561,10111095610,3082651,1373845,1263886950,True,atwork,22,5,13.0,TNC_SINGLE,126388695,1 +1011109565,10111095650,3082651,1373845,1263886950,False,Work,5,22,13.0,TNC_SINGLE,126388695,1 +1011109577,10111095770,3082651,1373845,1263886970,True,eatout,1,21,21.0,DRIVEALONEFREE,126388697,1 +1011109581,10111095810,3082651,1373845,1263886970,False,Home,21,1,21.0,SHARED2FREE,126388697,1 +1011109793,10111097930,3082651,1373845,1263887240,True,shopping,16,21,18.0,WALK,126388724,1 +1011109797,10111097970,3082651,1373845,1263887240,False,Home,21,16,20.0,WALK,126388724,1 +1011109841,10111098410,3082651,1373845,1263887300,True,work,5,21,7.0,WALK,126388730,1 +1011109845,10111098450,3082651,1373845,1263887300,False,Home,21,5,18.0,WALK,126388730,1 +1011158057,10111580570,3082798,1373919,1263947570,True,work,11,25,6.0,WALK,126394757,1 +1011158061,10111580610,3082798,1373919,1263947570,False,Home,25,11,17.0,WALK,126394757,1 +1011158385,10111583850,3082799,1373919,1263947980,True,work,25,25,8.0,WALK,126394798,1 +1011158389,10111583890,3082799,1373919,1263947980,False,Home,25,25,21.0,WALK,126394798,1 +1020944745,10209447450,3112636,1384875,1276180930,True,othmaint,13,10,10.0,SHARED2FREE,127618093,1 +1020944749,10209447490,3112636,1384875,1276180930,False,othmaint,4,13,12.0,SHARED2FREE,127618093,1 +1020944750,10209447500,3112636,1384875,1276180930,False,Home,10,4,13.0,TNC_SINGLE,127618093,2 +1020944873,10209448730,3112636,1384875,1276181090,True,shopping,1,10,15.0,WALK_LRF,127618109,1 +1020944877,10209448770,3112636,1384875,1276181090,False,othmaint,7,1,16.0,WALK_LOC,127618109,1 +1020944878,10209448780,3112636,1384875,1276181090,False,Home,10,7,16.0,WALK_LOC,127618109,2 +1020945313,10209453130,3112638,1384875,1276181640,True,eatout,11,10,10.0,WALK,127618164,1 +1020945317,10209453170,3112638,1384875,1276181640,False,Home,10,11,13.0,WALK,127618164,1 +1020959305,10209593050,3112680,1384889,1276199130,True,shopping,14,11,20.0,WALK_LOC,127619913,1 +1020959309,10209593090,3112680,1384889,1276199130,False,Home,11,14,22.0,TNC_SINGLE,127619913,1 +1020959353,10209593530,3112680,1384889,1276199190,True,escort,13,11,10.0,BIKE,127619919,1 +1020959354,10209593540,3112680,1384889,1276199190,True,work,16,13,10.0,BIKE,127619919,2 +1020959357,10209593570,3112680,1384889,1276199190,False,Home,11,16,20.0,BIKE,127619919,1 +1020978753,10209787530,3112740,1384909,1276223440,True,atwork,13,14,10.0,WALK,127622344,1 +1020978757,10209787570,3112740,1384909,1276223440,False,othmaint,14,13,10.0,WALK,127622344,1 +1020978758,10209787580,3112740,1384909,1276223440,False,Work,14,14,10.0,WALK,127622344,2 +1020978945,10209789450,3112740,1384909,1276223680,True,othmaint,16,21,14.0,BIKE,127622368,1 +1020978949,10209789490,3112740,1384909,1276223680,False,Home,21,16,17.0,BIKE,127622368,1 +1020979033,10209790330,3112740,1384909,1276223790,True,work,14,21,7.0,WALK,127622379,1 +1020979037,10209790370,3112740,1384909,1276223790,False,Home,21,14,13.0,WALK_LOC,127622379,1 +1025529329,10255293290,3126613,1389534,1281911660,True,shopping,11,16,12.0,WALK_LOC,128191166,1 +1025529333,10255293330,3126613,1389534,1281911660,False,Home,16,11,13.0,TNC_SINGLE,128191166,1 +1025529337,10255293370,3126613,1389534,1281911670,True,shopping,16,16,18.0,WALK,128191167,1 +1025529341,10255293410,3126613,1389534,1281911670,False,Home,16,16,18.0,WALK,128191167,1 +1025529705,10255297050,3126614,1389534,1281912130,True,work,9,16,8.0,WALK_LOC,128191213,1 +1025529709,10255297090,3126614,1389534,1281912130,False,Home,16,9,18.0,WALK_LRF,128191213,1 +1029292673,10292926730,3138091,1392610,1286615840,True,othmaint,22,25,19.0,WALK_LOC,128661584,1 +1029292677,10292926770,3138091,1392610,1286615840,False,Home,25,22,21.0,WALK,128661584,1 +1029292801,10292928010,3138087,1392610,1286616000,True,shopping,5,25,13.0,WALK,128661600,1 +1029292805,10292928050,3138087,1392610,1286616000,False,eatout,6,5,16.0,BIKE,128661600,1 +1029292806,10292928060,3138087,1392610,1286616000,False,Home,25,6,16.0,BIKE,128661600,2 +1029294097,10292940970,3138091,1392610,1286617620,True,school,18,25,7.0,WALK_LOC,128661762,1 +1029294101,10292941010,3138091,1392610,1286617620,False,Home,25,18,16.0,WALK_LOC,128661762,1 +1029294425,10292944250,3138092,1392610,1286618030,True,school,24,25,8.0,WALK,128661803,1 +1029294429,10292944290,3138092,1392610,1286618030,False,Home,25,24,19.0,WALK_LOC,128661803,1 +1029294705,10292947050,3138093,1392610,1286618380,True,othdiscr,10,25,21.0,WALK_LRF,128661838,1 +1029294709,10292947090,3138093,1392610,1286618380,False,Home,25,10,21.0,WALK_LRF,128661838,1 +1029294729,10292947290,3138093,1392610,1286618410,True,othmaint,22,25,10.0,WALK_LOC,128661841,1 +1029294733,10292947330,3138093,1392610,1286618410,False,Home,25,22,16.0,WALK_LOC,128661841,1 +1029295409,10292954090,3138095,1392610,1286619260,True,univ,13,25,13.0,WALK_LOC,128661926,1 +1029295413,10292954130,3138095,1392610,1286619260,False,Home,25,13,13.0,WALK_LOC,128661926,1 +1041329465,10413294650,3174784,1400319,1301661830,True,work,2,10,8.0,WALK,130166183,1 +1041329469,10413294690,3174784,1400319,1301661830,False,Home,10,2,19.0,WALK,130166183,1 +1041385601,10413856010,3174956,1400342,1301732000,True,atwork,9,9,13.0,WALK,130173200,1 +1041385605,10413856050,3174956,1400342,1301732000,False,Work,9,9,13.0,WALK,130173200,1 +1041385881,10413858810,3174956,1400342,1301732350,True,work,9,10,8.0,WALK,130173235,1 +1041385885,10413858850,3174956,1400342,1301732350,False,Home,10,9,21.0,WALK,130173235,1 +1041386209,10413862090,3174957,1400342,1301732760,True,work,22,10,7.0,WALK_LRF,130173276,1 +1041386213,10413862130,3174957,1400342,1301732760,False,Home,10,22,16.0,WALK_LRF,130173276,1 +1041386273,10413862730,3174958,1400342,1301732840,True,eatout,8,10,15.0,WALK,130173284,1 +1041386277,10413862770,3174958,1400342,1301732840,False,Home,10,8,17.0,WALK,130173284,1 +1041386777,10413867770,3174959,1400342,1301733470,True,othmaint,6,10,13.0,SHARED2FREE,130173347,1 +1041386781,10413867810,3174959,1400342,1301733470,False,Home,10,6,14.0,DRIVEALONEFREE,130173347,1 +1041386785,10413867850,3174959,1400342,1301733480,True,othmaint,7,10,17.0,WALK,130173348,1 +1041386789,10413867890,3174959,1400342,1301733480,False,Home,10,7,17.0,WALK,130173348,1 +1041386841,10413868410,3174959,1400342,1301733550,True,social,1,10,17.0,WALK_LRF,130173355,1 +1041386845,10413868450,3174959,1400342,1301733550,False,Home,10,1,19.0,WALK_LRF,130173355,1 +1041386953,10413869530,3174960,1400342,1301733690,True,escort,13,10,14.0,WALK,130173369,1 +1041386957,10413869570,3174960,1400342,1301733690,False,Home,10,13,23.0,WALK,130173369,1 +1041387145,10413871450,3174960,1400342,1301733930,True,shopping,11,10,12.0,WALK,130173393,1 +1041387149,10413871490,3174960,1400342,1301733930,False,Home,10,11,14.0,WALK,130173393,1 +1041387409,10413874090,3174961,1400342,1301734260,True,othdiscr,9,10,14.0,BIKE,130173426,1 +1041387413,10413874130,3174961,1400342,1301734260,False,Home,10,9,16.0,BIKE,130173426,1 +1041387785,10413877850,3174962,1400342,1301734730,True,school,10,10,8.0,WALK,130173473,1 +1041387789,10413877890,3174962,1400342,1301734730,False,Home,10,10,13.0,WALK,130173473,1 +1041387937,10413879370,3174963,1400342,1301734920,True,escort,5,10,16.0,SHARED2FREE,130173492,1 +1041387941,10413879410,3174963,1400342,1301734920,False,Home,10,5,17.0,WALK,130173492,1 +1041388113,10413881130,3174963,1400342,1301735140,True,school,20,10,8.0,WALK,130173514,1 +1041388117,10413881170,3174963,1400342,1301735140,False,Home,10,20,15.0,WALK,130173514,1 +1041388129,10413881290,3174963,1400342,1301735160,True,othdiscr,11,10,17.0,WALK,130173516,1 +1041388130,10413881300,3174963,1400342,1301735160,True,shopping,16,11,18.0,WALK,130173516,2 +1041388133,10413881330,3174963,1400342,1301735160,False,Home,10,16,21.0,WALK,130173516,1 +1041419553,10414195530,3175059,1400360,1301774440,True,othdiscr,24,11,13.0,WALK,130177444,1 +1041419557,10414195570,3175059,1400360,1301774440,False,Home,11,24,17.0,WALK,130177444,1 +1041419993,10414199930,3175060,1400360,1301774990,True,work,25,11,6.0,WALK,130177499,1 +1041419997,10414199970,3175060,1400360,1301774990,False,Home,11,25,20.0,WALK,130177499,1 +1041420585,10414205850,3175062,1400360,1301775730,True,school,21,11,8.0,WALK,130177573,1 +1041420589,10414205890,3175062,1400360,1301775730,False,othdiscr,8,21,17.0,WALK_LOC,130177573,1 +1041420590,10414205900,3175062,1400360,1301775730,False,escort,7,8,17.0,WALK,130177573,2 +1041420591,10414205910,3175062,1400360,1301775730,False,Home,11,7,17.0,WALK_LOC,130177573,3 +1041420713,10414207130,3175063,1400360,1301775890,True,eatout,16,11,9.0,WALK,130177589,1 +1041420717,10414207170,3175063,1400360,1301775890,False,Home,11,16,18.0,WALK,130177589,1 +1041420721,10414207210,3175063,1400360,1301775900,True,eatout,16,11,18.0,WALK,130177590,1 +1041420725,10414207250,3175063,1400360,1301775900,False,Home,11,16,18.0,WALK,130177590,1 +1041472561,10414725610,3175221,1400387,1301840700,True,escort,12,21,10.0,TNC_SINGLE,130184070,1 +1041472565,10414725650,3175221,1400387,1301840700,False,Home,21,12,17.0,WALK_LOC,130184070,1 +1041473081,10414730810,3175222,1400387,1301841350,True,shopping,5,21,7.0,WALK,130184135,1 +1041473085,10414730850,3175222,1400387,1301841350,False,Home,21,5,12.0,WALK,130184135,1 +1041473105,10414731050,3175222,1400387,1301841380,True,social,11,21,14.0,WALK,130184138,1 +1041473109,10414731090,3175222,1400387,1301841380,False,Home,21,11,17.0,WALK,130184138,1 +1041473113,10414731130,3175222,1400387,1301841390,True,social,5,21,20.0,WALK,130184139,1 +1041473117,10414731170,3175222,1400387,1301841390,False,Home,21,5,20.0,WALK,130184139,1 +1041473457,10414734570,3175223,1400387,1301841820,True,work,5,21,7.0,WALK,130184182,1 +1041473461,10414734610,3175223,1400387,1301841820,False,Home,21,5,16.0,WALK,130184182,1 +1041473673,10414736730,3175224,1400387,1301842090,True,eatout,10,21,8.0,WALK_LOC,130184209,1 +1041473674,10414736740,3175224,1400387,1301842090,True,othdiscr,9,10,8.0,WALK_LOC,130184209,2 +1041473677,10414736770,3175224,1400387,1301842090,False,escort,7,9,22.0,WALK_LOC,130184209,1 +1041473678,10414736780,3175224,1400387,1301842090,False,Home,21,7,22.0,WALK_LOC,130184209,2 +1041473873,10414738730,3175225,1400387,1301842340,True,escort,16,21,8.0,WALK,130184234,1 +1041473877,10414738770,3175225,1400387,1301842340,False,Home,21,16,9.0,WALK,130184234,1 +1045757945,10457579450,3188286,1402915,1307197430,True,othmaint,16,10,15.0,TNC_SHARED,130719743,1 +1045757949,10457579490,3188286,1402915,1307197430,False,eatout,13,16,16.0,WALK,130719743,1 +1045757950,10457579500,3188286,1402915,1307197430,False,Home,10,13,18.0,WALK,130719743,2 +1045757977,10457579770,3188286,1402915,1307197470,True,social,16,10,15.0,TNC_SHARED,130719747,1 +1045757981,10457579810,3188286,1402915,1307197470,False,Home,10,16,15.0,WALK_LOC,130719747,1 +1045759345,10457593450,3188290,1402915,1307199180,True,othmaint,14,10,18.0,WALK,130719918,1 +1045759349,10457593490,3188290,1402915,1307199180,False,Home,10,14,20.0,WALK,130719918,1 +1045759433,10457594330,3188290,1402915,1307199290,True,work,9,10,11.0,WALK,130719929,1 +1045759437,10457594370,3188290,1402915,1307199290,False,Home,10,9,15.0,WALK,130719929,1 +1045760025,10457600250,3188292,1402915,1307200030,True,school,11,10,7.0,WALK,130720003,1 +1045760029,10457600290,3188292,1402915,1307200030,False,Home,10,11,15.0,WALK,130720003,1 +1045760633,10457606330,3188294,1402915,1307200790,True,othdiscr,4,10,11.0,WALK_LRF,130720079,1 +1045760637,10457606370,3188294,1402915,1307200790,False,Home,10,4,13.0,WALK_LRF,130720079,1 +1045761009,10457610090,3188295,1402915,1307201260,True,school,17,10,7.0,WALK_LRF,130720126,1 +1045761013,10457610130,3188295,1402915,1307201260,False,Home,10,17,15.0,WALK_LRF,130720126,1 +1045795841,10457958410,3188401,1402932,1307244800,True,work,14,17,5.0,WALK,130724480,1 +1045795845,10457958450,3188401,1402932,1307244800,False,Home,17,14,16.0,WALK,130724480,1 +1045796169,10457961690,3188402,1402932,1307245210,True,work,14,17,14.0,WALK,130724521,1 +1045796173,10457961730,3188402,1402932,1307245210,False,Home,17,14,16.0,WALK,130724521,1 +1045796497,10457964970,3188403,1402932,1307245620,True,work,5,17,6.0,WALK_LRF,130724562,1 +1045796501,10457965010,3188403,1402932,1307245620,False,Home,17,5,18.0,WALK_LOC,130724562,1 +1045796825,10457968250,3188404,1402932,1307246030,True,work,4,17,8.0,WALK,130724603,1 +1045796829,10457968290,3188404,1402932,1307246030,False,escort,14,4,18.0,WALK,130724603,1 +1045796830,10457968300,3188404,1402932,1307246030,False,escort,16,14,18.0,WALK,130724603,2 +1045796831,10457968310,3188404,1402932,1307246030,False,othmaint,16,16,19.0,WALK,130724603,3 +1045796832,10457968320,3188404,1402932,1307246030,False,Home,17,16,19.0,WALK,130724603,4 +1045797025,10457970250,3188405,1402932,1307246280,True,atwork,16,11,11.0,WALK,130724628,1 +1045797029,10457970290,3188405,1402932,1307246280,False,eatout,5,16,14.0,WALK,130724628,1 +1045797030,10457970300,3188405,1402932,1307246280,False,othmaint,7,5,14.0,WALK,130724628,2 +1045797031,10457970310,3188405,1402932,1307246280,False,othmaint,9,7,14.0,WALK,130724628,3 +1045797032,10457970320,3188405,1402932,1307246280,False,Work,11,9,14.0,WALK,130724628,4 +1045797153,10457971530,3188405,1402932,1307246440,True,work,11,17,7.0,WALK,130724644,1 +1045797157,10457971570,3188405,1402932,1307246440,False,Home,17,11,17.0,SHARED2FREE,130724644,1 +1045798465,10457984650,3188409,1402933,1307248080,True,work,11,17,5.0,WALK,130724808,1 +1045798469,10457984690,3188409,1402933,1307248080,False,shopping,13,11,23.0,WALK,130724808,1 +1045798470,10457984700,3188409,1402933,1307248080,False,Home,17,13,23.0,WALK,130724808,2 +1045822217,10458222170,3188483,1402945,1307277770,True,othdiscr,24,25,9.0,WALK,130727777,1 +1045822221,10458222210,3188483,1402945,1307277770,False,Home,25,24,9.0,TNC_SINGLE,130727777,1 +1045822409,10458224090,3188482,1402945,1307278010,True,work,6,25,8.0,WALK,130727801,1 +1045822410,10458224100,3188482,1402945,1307278010,True,work,24,6,9.0,WALK,130727801,2 +1045822413,10458224130,3188482,1402945,1307278010,False,Home,25,24,17.0,WALK,130727801,1 +1045822737,10458227370,3188483,1402945,1307278420,True,work,2,25,9.0,WALK,130727842,1 +1045822741,10458227410,3188483,1402945,1307278420,False,Home,25,2,17.0,WALK_LOC,130727842,1 +1045823001,10458230010,3188484,1402945,1307278750,True,univ,12,25,16.0,WALK,130727875,1 +1045823005,10458230050,3188484,1402945,1307278750,False,shopping,5,12,17.0,WALK,130727875,1 +1045823006,10458230060,3188484,1402945,1307278750,False,othmaint,7,5,17.0,WALK,130727875,2 +1045823007,10458230070,3188484,1402945,1307278750,False,Home,25,7,17.0,WALK,130727875,3 +1045823065,10458230650,3188484,1402945,1307278830,True,work,2,25,9.0,WALK,130727883,1 +1045823069,10458230690,3188484,1402945,1307278830,False,Home,25,2,12.0,WALK_LOC,130727883,1 +1045823393,10458233930,3188485,1402945,1307279240,True,escort,24,25,10.0,BIKE,130727924,1 +1045823394,10458233940,3188485,1402945,1307279240,True,work,2,24,12.0,BIKE,130727924,2 +1045823397,10458233970,3188485,1402945,1307279240,False,eatout,5,2,23.0,BIKE,130727924,1 +1045823398,10458233980,3188485,1402945,1307279240,False,Home,25,5,23.0,BIKE,130727924,2 +1047989721,10479897210,3195090,1406850,1309987150,True,othdiscr,7,3,10.0,WALK,130998715,1 +1047989725,10479897250,3195090,1406850,1309987150,False,Home,3,7,16.0,WALK,130998715,1 +1047995345,10479953450,3195107,1406867,1309994180,True,univ,12,5,7.0,WALK,130999418,1 +1047995349,10479953490,3195107,1406867,1309994180,False,Home,5,12,7.0,WALK,130999418,1 +1047995353,10479953530,3195107,1406867,1309994190,True,shopping,16,5,11.0,DRIVEALONEFREE,130999419,1 +1047995354,10479953540,3195107,1406867,1309994190,True,othmaint,5,16,11.0,DRIVEALONEFREE,130999419,2 +1047995355,10479953550,3195107,1406867,1309994190,True,univ,12,5,11.0,DRIVEALONEFREE,130999419,3 +1047995357,10479953570,3195107,1406867,1309994190,False,Home,5,12,11.0,DRIVEALONEFREE,130999419,1 +1047996937,10479969370,3195112,1406872,1309996170,True,othdiscr,16,5,10.0,WALK_LOC,130999617,1 +1047996941,10479969410,3195112,1406872,1309996170,False,Home,5,16,13.0,WALK,130999617,1 +1048009073,10480090730,3195149,1406909,1310011340,True,othdiscr,11,6,12.0,WALK,131001134,1 +1048009077,10480090770,3195149,1406909,1310011340,False,Home,6,11,21.0,WALK,131001134,1 +1048034657,10480346570,3195227,1406987,1310043320,True,othdiscr,9,8,11.0,WALK,131004332,1 +1048034661,10480346610,3195227,1406987,1310043320,False,Home,8,9,14.0,WALK,131004332,1 +1048054057,10480540570,3195286,1407046,1310067570,True,univ,9,9,8.0,WALK,131006757,1 +1048054061,10480540610,3195286,1407046,1310067570,False,Home,9,9,10.0,WALK,131006757,1 +1048055697,10480556970,3195291,1407051,1310069620,True,univ,10,9,18.0,WALK,131006962,1 +1048055701,10480557010,3195291,1407051,1310069620,False,eatout,9,10,19.0,WALK_LOC,131006962,1 +1048055702,10480557020,3195291,1407051,1310069620,False,Home,9,9,19.0,WALK,131006962,2 +1048062209,10480622090,3195311,1407071,1310077760,True,othdiscr,22,9,16.0,TNC_SINGLE,131007776,1 +1048062213,10480622130,3195311,1407071,1310077760,False,shopping,6,22,18.0,TNC_SINGLE,131007776,1 +1048062214,10480622140,3195311,1407071,1310077760,False,othdiscr,8,6,18.0,TNC_SINGLE,131007776,2 +1048062215,10480622150,3195311,1407071,1310077760,False,Home,9,8,18.0,WALK_LOC,131007776,3 +1048062233,10480622330,3195311,1407071,1310077790,True,othmaint,2,9,18.0,WALK_LRF,131007779,1 +1048062237,10480622370,3195311,1407071,1310077790,False,Home,9,2,20.0,WALK_LRF,131007779,1 +1048062257,10480622570,3195311,1407071,1310077820,True,shopping,5,9,8.0,WALK_LOC,131007782,1 +1048062258,10480622580,3195311,1407071,1310077820,True,univ,10,5,8.0,WALK_LOC,131007782,2 +1048062261,10480622610,3195311,1407071,1310077820,False,Home,9,10,11.0,WALK_LOC,131007782,1 +1048062273,10480622730,3195311,1407071,1310077840,True,shopping,5,9,12.0,TNC_SINGLE,131007784,1 +1048062277,10480622770,3195311,1407071,1310077840,False,eatout,7,5,14.0,TNC_SINGLE,131007784,1 +1048062278,10480622780,3195311,1407071,1310077840,False,shopping,11,7,15.0,WALK_LOC,131007784,2 +1048062279,10480622790,3195311,1407071,1310077840,False,shopping,21,11,15.0,TNC_SINGLE,131007784,3 +1048062280,10480622800,3195311,1407071,1310077840,False,Home,9,21,15.0,WALK_LOC,131007784,4 +1048069425,10480694250,3195333,1407093,1310086780,True,othdiscr,16,17,12.0,SHARED2FREE,131008678,1 +1048069429,10480694290,3195333,1407093,1310086780,False,Home,17,16,13.0,WALK,131008678,1 +1048069449,10480694490,3195333,1407093,1310086810,True,social,13,17,14.0,SHARED2FREE,131008681,1 +1048069450,10480694500,3195333,1407093,1310086810,True,othmaint,2,13,15.0,DRIVEALONEFREE,131008681,2 +1048069453,10480694530,3195333,1407093,1310086810,False,Home,17,2,16.0,DRIVEALONEFREE,131008681,1 +1048070785,10480707850,3195337,1407097,1310088480,True,univ,12,17,13.0,WALK_LOC,131008848,1 +1048070789,10480707890,3195337,1407097,1310088480,False,shopping,4,12,16.0,WALK_LOC,131008848,1 +1048070790,10480707900,3195337,1407097,1310088480,False,Home,17,4,17.0,WALK_LRF,131008848,2 +1048070801,10480708010,3195337,1407097,1310088500,True,shopping,21,17,11.0,SHARED3FREE,131008850,1 +1048070805,10480708050,3195337,1407097,1310088500,False,shopping,5,21,13.0,DRIVEALONEFREE,131008850,1 +1048070806,10480708060,3195337,1407097,1310088500,False,shopping,7,5,13.0,WALK,131008850,2 +1048070807,10480708070,3195337,1407097,1310088500,False,shopping,11,7,13.0,SHARED3FREE,131008850,3 +1048070808,10480708080,3195337,1407097,1310088500,False,Home,17,11,13.0,SHARED3FREE,131008850,4 +1048075529,10480755290,3195352,1407112,1310094410,True,escort,8,18,13.0,TNC_SINGLE,131009441,1 +1048075533,10480755330,3195352,1407112,1310094410,False,shopping,7,8,13.0,TNC_SINGLE,131009441,1 +1048075534,10480755340,3195352,1407112,1310094410,False,shopping,9,7,13.0,WALK_LOC,131009441,2 +1048075535,10480755350,3195352,1407112,1310094410,False,Home,18,9,13.0,TNC_SINGLE,131009441,3 +1048078633,10480786330,3195361,1407121,1310098290,True,othmaint,10,18,11.0,WALK,131009829,1 +1048078637,10480786370,3195361,1407121,1310098290,False,Home,18,10,14.0,DRIVEALONEFREE,131009829,1 +1048097633,10480976330,3195419,1407179,1310122040,True,othdiscr,10,19,9.0,WALK,131012204,1 +1048097637,10480976370,3195419,1407179,1310122040,False,Home,19,10,11.0,WALK,131012204,1 +1060119649,10601196490,3232072,1443832,1325149560,True,atwork,4,16,16.0,WALK,132514956,1 +1060119653,10601196530,3232072,1443832,1325149560,False,Work,16,4,16.0,WALK,132514956,1 +1060119841,10601198410,3232072,1443832,1325149800,True,othmaint,15,3,6.0,WALK,132514980,1 +1060119845,10601198450,3232072,1443832,1325149800,False,Home,3,15,6.0,WALK,132514980,1 +1060119929,10601199290,3232072,1443832,1325149910,True,work,16,3,7.0,DRIVEALONEFREE,132514991,1 +1060119933,10601199330,3232072,1443832,1325149910,False,Home,3,16,16.0,SHARED2FREE,132514991,1 +1060121897,10601218970,3232078,1443838,1325152370,True,work,5,4,19.0,WALK,132515237,1 +1060121901,10601219010,3232078,1443838,1325152370,False,Home,4,5,22.0,WALK,132515237,1 +1060131737,10601317370,3232108,1443868,1325164670,True,work,16,6,7.0,WALK,132516467,1 +1060131741,10601317410,3232108,1443868,1325164670,False,Home,6,16,17.0,WALK,132516467,1 +1060140657,10601406570,3232136,1443896,1325175820,True,social,7,6,16.0,WALK,132517582,1 +1060140658,10601406580,3232136,1443896,1325175820,True,eatout,22,7,18.0,WALK_LOC,132517582,2 +1060140661,10601406610,3232136,1443896,1325175820,False,shopping,5,22,17.0,WALK_LOC,132517582,1 +1060140662,10601406620,3232136,1443896,1325175820,False,Home,6,5,18.0,WALK,132517582,2 +1060140833,10601408330,3232136,1443896,1325176040,True,escort,12,6,16.0,WALK_LOC,132517604,1 +1060140834,10601408340,3232136,1443896,1325176040,True,escort,8,12,16.0,WALK_LOC,132517604,2 +1060140835,10601408350,3232136,1443896,1325176040,True,othmaint,9,8,16.0,WALK_LOC,132517604,3 +1060140837,10601408370,3232136,1443896,1325176040,False,shopping,7,9,16.0,WALK_LOC,132517604,1 +1060140838,10601408380,3232136,1443896,1325176040,False,Home,6,7,16.0,WALK,132517604,2 +1060140921,10601409210,3232136,1443896,1325176150,True,work,17,6,7.0,WALK_LOC,132517615,1 +1060140925,10601409250,3232136,1443896,1325176150,False,Home,6,17,16.0,WALK_LOC,132517615,1 +1060153713,10601537130,3232175,1443935,1325192140,True,work,7,6,7.0,WALK,132519214,1 +1060153717,10601537170,3232175,1443935,1325192140,False,Home,6,7,18.0,WALK,132519214,1 +1060171097,10601710970,3232228,1443988,1325213870,True,work,2,7,6.0,WALK_LOC,132521387,1 +1060171101,10601711010,3232228,1443988,1325213870,False,Home,7,2,17.0,WALK_LOC,132521387,1 +1060174377,10601743770,3232238,1443998,1325217970,True,work,22,7,9.0,WALK_LOC,132521797,1 +1060174381,10601743810,3232238,1443998,1325217970,False,shopping,5,22,17.0,TNC_SHARED,132521797,1 +1060174382,10601743820,3232238,1443998,1325217970,False,Home,7,5,17.0,WALK,132521797,2 +1060187561,10601875610,3232279,1444039,1325234450,True,eatout,5,7,15.0,WALK,132523445,1 +1060187565,10601875650,3232279,1444039,1325234450,False,Home,7,5,18.0,WALK,132523445,1 +1060195369,10601953690,3232302,1444062,1325244210,True,work,16,7,8.0,WALK,132524421,1 +1060195373,10601953730,3232302,1444062,1325244210,False,Home,7,16,20.0,WALK,132524421,1 +1060200833,10602008330,3232319,1444079,1325251040,True,othdiscr,2,7,18.0,WALK,132525104,1 +1060200837,10602008370,3232319,1444079,1325251040,False,Home,7,2,22.0,WALK,132525104,1 +1060200897,10602008970,3232319,1444079,1325251120,True,shopping,4,7,17.0,DRIVEALONEFREE,132525112,1 +1060200898,10602008980,3232319,1444079,1325251120,True,shopping,5,4,17.0,SHARED3FREE,132525112,2 +1060200901,10602009010,3232319,1444079,1325251120,False,Home,7,5,17.0,SHARED3FREE,132525112,1 +1060200945,10602009450,3232319,1444079,1325251180,True,work,9,7,7.0,WALK,132525118,1 +1060200949,10602009490,3232319,1444079,1325251180,False,Home,7,9,16.0,WALK,132525118,1 +1060205209,10602052090,3232332,1444092,1325256510,True,work,2,7,7.0,TNC_SINGLE,132525651,1 +1060205213,10602052130,3232332,1444092,1325256510,False,Home,7,2,18.0,TNC_SHARED,132525651,1 +1060225545,10602255450,3232394,1444154,1325281930,True,work,4,8,10.0,WALK,132528193,1 +1060225549,10602255490,3232394,1444154,1325281930,False,Home,8,4,21.0,WALK,132528193,1 +1060254145,10602541450,3232482,1444242,1325317680,True,eatout,2,9,21.0,WALK_LRF,132531768,1 +1060254149,10602541490,3232482,1444242,1325317680,False,Home,9,2,22.0,WALK_LRF,132531768,1 +1060254385,10602543850,3232482,1444242,1325317980,True,social,2,9,20.0,DRIVEALONEFREE,132531798,1 +1060254389,10602543890,3232482,1444242,1325317980,False,Home,9,2,20.0,DRIVEALONEFREE,132531798,1 +1060254409,10602544090,3232482,1444242,1325318010,True,work,9,9,8.0,BIKE,132531801,1 +1060254413,10602544130,3232482,1444242,1325318010,False,Home,9,9,19.0,TAXI,132531801,1 +1060255393,10602553930,3232485,1444245,1325319240,True,work,8,9,7.0,WALK,132531924,1 +1060255397,10602553970,3232485,1444245,1325319240,False,Home,9,8,19.0,WALK,132531924,1 +1060257249,10602572490,3232491,1444251,1325321560,True,shopping,6,9,11.0,WALK_LOC,132532156,1 +1060257250,10602572500,3232491,1444251,1325321560,True,othdiscr,2,6,14.0,WALK_LOC,132532156,2 +1060257253,10602572530,3232491,1444251,1325321560,False,Home,9,2,15.0,WALK_LRF,132532156,1 +1060257257,10602572570,3232491,1444251,1325321570,True,othdiscr,8,9,17.0,WALK_LOC,132532157,1 +1060257261,10602572610,3232491,1444251,1325321570,False,Home,9,8,19.0,TNC_SINGLE,132532157,1 +1060259329,10602593290,3232497,1444257,1325324160,True,work,10,9,7.0,WALK,132532416,1 +1060259333,10602593330,3232497,1444257,1325324160,False,Home,9,10,17.0,WALK,132532416,1 +1060269057,10602690570,3232527,1444287,1325336320,True,othdiscr,11,9,17.0,WALK,132533632,1 +1060269061,10602690610,3232527,1444287,1325336320,False,Home,9,11,19.0,WALK,132533632,1 +1060269169,10602691690,3232527,1444287,1325336460,True,work,2,9,6.0,WALK_LRF,132533646,1 +1060269173,10602691730,3232527,1444287,1325336460,False,Home,9,2,16.0,WALK_LRF,132533646,1 +1060284145,10602841450,3232573,1444333,1325355180,True,othdiscr,5,9,12.0,BIKE,132535518,1 +1060284149,10602841490,3232573,1444333,1325355180,False,Home,9,5,15.0,BIKE,132535518,1 +1060284257,10602842570,3232573,1444333,1325355320,True,work,17,9,18.0,BIKE,132535532,1 +1060284261,10602842610,3232573,1444333,1325355320,False,Home,9,17,20.0,BIKE,132535532,1 +1060290489,10602904890,3232592,1444352,1325363110,True,work,20,9,9.0,WALK,132536311,1 +1060290493,10602904930,3232592,1444352,1325363110,False,Home,9,20,19.0,WALK,132536311,1 +1060291345,10602913450,3232595,1444355,1325364180,True,atwork,4,7,14.0,WALK,132536418,1 +1060291349,10602913490,3232595,1444355,1325364180,False,Work,7,4,14.0,WALK,132536418,1 +1060291361,10602913610,3232595,1444355,1325364200,True,othdiscr,12,9,10.0,WALK_LRF,132536420,1 +1060291365,10602913650,3232595,1444355,1325364200,False,Home,9,12,12.0,WALK_LRF,132536420,1 +1060291473,10602914730,3232595,1444355,1325364340,True,work,7,9,14.0,WALK,132536434,1 +1060291477,10602914770,3232595,1444355,1325364340,False,eatout,5,7,19.0,WALK,132536434,1 +1060291478,10602914780,3232595,1444355,1325364340,False,Home,9,5,20.0,WALK,132536434,2 +1060302625,10603026250,3232629,1444389,1325378280,True,work,14,9,8.0,WALK,132537828,1 +1060302629,10603026290,3232629,1444389,1325378280,False,Home,9,14,18.0,WALK,132537828,1 +1060316793,10603167930,3232673,1444433,1325395990,True,eatout,14,9,21.0,WALK_LRF,132539599,1 +1060316797,10603167970,3232673,1444433,1325395990,False,Home,9,14,22.0,WALK_LRF,132539599,1 +1060316945,10603169450,3232673,1444433,1325396180,True,othdiscr,13,9,9.0,WALK_LRF,132539618,1 +1060316949,10603169490,3232673,1444433,1325396180,False,Home,9,13,10.0,WALK_LRF,132539618,1 +1060317057,10603170570,3232673,1444433,1325396320,True,work,9,9,11.0,WALK,132539632,1 +1060317061,10603170610,3232673,1444433,1325396320,False,Home,9,9,19.0,WALK,132539632,1 +1060353137,10603531370,3232783,1444543,1325441420,True,work,9,10,6.0,WALK,132544142,1 +1060353141,10603531410,3232783,1444543,1325441420,False,Home,10,9,17.0,WALK,132544142,1 +1060353185,10603531850,3232784,1444544,1325441480,True,eatout,8,13,11.0,WALK,132544148,1 +1060353186,10603531860,3232784,1444544,1325441480,True,shopping,5,8,11.0,WALK,132544148,2 +1060353187,10603531870,3232784,1444544,1325441480,True,atwork,2,5,11.0,WALK,132544148,3 +1060353189,10603531890,3232784,1444544,1325441480,False,Work,13,2,13.0,WALK,132544148,1 +1060353465,10603534650,3232784,1444544,1325441830,True,work,13,10,9.0,WALK_LRF,132544183,1 +1060353469,10603534690,3232784,1444544,1325441830,False,othmaint,16,13,19.0,WALK_LOC,132544183,1 +1060353470,10603534700,3232784,1444544,1325441830,False,Home,10,16,19.0,WALK_LOC,132544183,2 +1060359745,10603597450,3232804,1444564,1325449680,True,atwork,17,14,11.0,WALK,132544968,1 +1060359749,10603597490,3232804,1444564,1325449680,False,othmaint,12,17,14.0,WALK,132544968,1 +1060359750,10603597500,3232804,1444564,1325449680,False,Work,14,12,14.0,WALK,132544968,2 +1060359913,10603599130,3232804,1444564,1325449890,True,othdiscr,9,11,17.0,WALK,132544989,1 +1060359917,10603599170,3232804,1444564,1325449890,False,Home,11,9,20.0,WALK,132544989,1 +1060360025,10603600250,3232804,1444564,1325450030,True,work,14,11,7.0,DRIVEALONEFREE,132545003,1 +1060360029,10603600290,3232804,1444564,1325450030,False,Home,11,14,17.0,DRIVEALONEFREE,132545003,1 +1060405665,10604056650,3232944,1444704,1325507080,True,atwork,9,9,14.0,WALK,132550708,1 +1060405669,10604056690,3232944,1444704,1325507080,False,Work,9,9,14.0,WALK,132550708,1 +1060405833,10604058330,3232944,1444704,1325507290,True,othdiscr,14,14,5.0,WALK,132550729,1 +1060405837,10604058370,3232944,1444704,1325507290,False,Home,14,14,6.0,WALK,132550729,1 +1060405945,10604059450,3232944,1444704,1325507430,True,work,9,14,7.0,DRIVEALONEFREE,132550743,1 +1060405949,10604059490,3232944,1444704,1325507430,False,Home,14,9,18.0,DRIVEALONEFREE,132550743,1 +1060421689,10604216890,3232992,1444752,1325527110,True,work,14,16,7.0,WALK,132552711,1 +1060421693,10604216930,3232992,1444752,1325527110,False,Home,16,14,13.0,WALK,132552711,1 +1060433825,10604338250,3233029,1444789,1325542280,True,shopping,24,16,8.0,WALK,132554228,1 +1060433826,10604338260,3233029,1444789,1325542280,True,othmaint,1,24,8.0,WALK,132554228,2 +1060433827,10604338270,3233029,1444789,1325542280,True,work,14,1,12.0,WALK,132554228,3 +1060433829,10604338290,3233029,1444789,1325542280,False,shopping,5,14,18.0,WALK,132554228,1 +1060433830,10604338300,3233029,1444789,1325542280,False,Home,16,5,18.0,WALK_LOC,132554228,2 +1060436449,10604364490,3233037,1444797,1325545560,True,work,2,16,7.0,WALK,132554556,1 +1060436453,10604364530,3233037,1444797,1325545560,False,Home,16,2,18.0,WALK_LOC,132554556,1 +1060440105,10604401050,3233049,1444809,1325550130,True,atwork,15,14,8.0,WALK,132555013,1 +1060440109,10604401090,3233049,1444809,1325550130,False,Work,14,15,8.0,WALK,132555013,1 +1060440385,10604403850,3233049,1444809,1325550480,True,work,14,16,7.0,TNC_SINGLE,132555048,1 +1060440389,10604403890,3233049,1444809,1325550480,False,Home,16,14,19.0,TNC_SINGLE,132555048,1 +1060450881,10604508810,3233081,1444841,1325563600,True,escort,6,17,7.0,DRIVEALONEFREE,132556360,1 +1060450882,10604508820,3233081,1444841,1325563600,True,work,25,6,8.0,WALK,132556360,2 +1060450885,10604508850,3233081,1444841,1325563600,False,eatout,11,25,16.0,DRIVEALONEFREE,132556360,1 +1060450886,10604508860,3233081,1444841,1325563600,False,Home,17,11,17.0,DRIVEALONEFREE,132556360,2 +1060456849,10604568490,3233100,1444860,1325571060,True,eatout,17,17,18.0,WALK,132557106,1 +1060456853,10604568530,3233100,1444860,1325571060,False,Home,17,17,22.0,WALK,132557106,1 +1060456873,10604568730,3233100,1444860,1325571090,True,escort,18,17,17.0,DRIVEALONEFREE,132557109,1 +1060456877,10604568770,3233100,1444860,1325571090,False,Home,17,18,17.0,SHARED3FREE,132557109,1 +1060457113,10604571130,3233100,1444860,1325571390,True,eatout,5,17,8.0,WALK,132557139,1 +1060457114,10604571140,3233100,1444860,1325571390,True,work,6,5,9.0,WALK,132557139,2 +1060457117,10604571170,3233100,1444860,1325571390,False,shopping,4,6,13.0,WALK,132557139,1 +1060457118,10604571180,3233100,1444860,1325571390,False,Home,17,4,17.0,WALK,132557139,2 +1060461113,10604611130,3233113,1444873,1325576390,True,eatout,13,17,18.0,WALK,132557639,1 +1060461117,10604611170,3233113,1444873,1325576390,False,Home,17,13,19.0,WALK,132557639,1 +1060461265,10604612650,3233113,1444873,1325576580,True,othdiscr,17,17,15.0,WALK,132557658,1 +1060461269,10604612690,3233113,1444873,1325576580,False,Home,17,17,17.0,WALK,132557658,1 +1060461377,10604613770,3233113,1444873,1325576720,True,work,22,17,7.0,WALK,132557672,1 +1060461381,10604613810,3233113,1444873,1325576720,False,Home,17,22,11.0,WALK,132557672,1 +1060461385,10604613850,3233113,1444873,1325576730,True,work,22,17,11.0,WALK,132557673,1 +1060461389,10604613890,3233113,1444873,1325576730,False,othmaint,12,22,13.0,WALK,132557673,1 +1060461390,10604613900,3233113,1444873,1325576730,False,Home,17,12,14.0,WALK,132557673,2 +1060467937,10604679370,3233133,1444893,1325584920,True,work,15,17,7.0,WALK_LOC,132558492,1 +1060467941,10604679410,3233133,1444893,1325584920,False,Home,17,15,19.0,TNC_SINGLE,132558492,1 +1060467985,10604679850,3233134,1444894,1325584980,True,atwork,12,1,13.0,SHARED2FREE,132558498,1 +1060467989,10604679890,3233134,1444894,1325584980,False,eatout,15,12,13.0,SHARED2FREE,132558498,1 +1060467990,10604679900,3233134,1444894,1325584980,False,Work,1,15,13.0,WALK,132558498,2 +1060468265,10604682650,3233134,1444894,1325585330,True,escort,5,17,5.0,TNC_SINGLE,132558533,1 +1060468266,10604682660,3233134,1444894,1325585330,True,work,1,5,5.0,SHARED3FREE,132558533,2 +1060468269,10604682690,3233134,1444894,1325585330,False,Home,17,1,19.0,WALK_LRF,132558533,1 +1060487177,10604871770,3233192,1444952,1325608970,True,othdiscr,24,17,9.0,SHARED3FREE,132560897,1 +1060487181,10604871810,3233192,1444952,1325608970,False,Home,17,24,23.0,WALK_LRF,132560897,1 +1060489913,10604899130,3233200,1444960,1325612390,True,work,2,17,8.0,SHARED3FREE,132561239,1 +1060489917,10604899170,3233200,1444960,1325612390,False,Home,17,2,18.0,WALK_LRF,132561239,1 +1060516369,10605163690,3233281,1445041,1325645460,True,othdiscr,9,17,11.0,WALK_LRF,132564546,1 +1060516373,10605163730,3233281,1445041,1325645460,False,Home,17,9,11.0,SHARED3FREE,132564546,1 +1060516809,10605168090,3233282,1445042,1325646010,True,work,22,17,7.0,WALK_LRF,132564601,1 +1060516813,10605168130,3233282,1445042,1325646010,False,shopping,4,22,20.0,WALK_LRF,132564601,1 +1060516814,10605168140,3233282,1445042,1325646010,False,Home,17,4,20.0,WALK,132564601,2 +1060521313,10605213130,3233296,1445056,1325651640,True,othmaint,9,17,18.0,DRIVEALONEFREE,132565164,1 +1060521317,10605213170,3233296,1445056,1325651640,False,shopping,16,9,18.0,WALK,132565164,1 +1060521318,10605213180,3233296,1445056,1325651640,False,escort,12,16,19.0,WALK,132565164,2 +1060521319,10605213190,3233296,1445056,1325651640,False,social,2,12,19.0,SHARED3FREE,132565164,3 +1060521320,10605213200,3233296,1445056,1325651640,False,Home,17,2,19.0,SHARED3FREE,132565164,4 +1060521401,10605214010,3233296,1445056,1325651750,True,work,13,17,7.0,WALK,132565175,1 +1060521405,10605214050,3233296,1445056,1325651750,False,Home,17,13,18.0,WALK,132565175,1 +1060523369,10605233690,3233302,1445062,1325654210,True,work,12,17,7.0,WALK,132565421,1 +1060523373,10605233730,3233302,1445062,1325654210,False,Home,17,12,18.0,WALK,132565421,1 +1060524745,10605247450,3233307,1445067,1325655930,True,eatout,2,17,18.0,WALK,132565593,1 +1060524749,10605247490,3233307,1445067,1325655930,False,Home,17,2,20.0,WALK,132565593,1 +1060524961,10605249610,3233307,1445067,1325656200,True,shopping,11,17,11.0,WALK_LOC,132565620,1 +1060524965,10605249650,3233307,1445067,1325656200,False,Home,17,11,18.0,WALK_LOC,132565620,1 +1060535177,10605351770,3233338,1445098,1325668970,True,work,4,17,8.0,DRIVEALONEFREE,132566897,1 +1060535181,10605351810,3233338,1445098,1325668970,False,Home,17,4,11.0,DRIVEALONEFREE,132566897,1 +1060535185,10605351850,3233338,1445098,1325668980,True,work,4,17,13.0,WALK_LOC,132566898,1 +1060535189,10605351890,3233338,1445098,1325668980,False,Home,17,4,17.0,WALK,132566898,1 +1060546657,10605466570,3233373,1445133,1325683320,True,work,12,17,11.0,WALK_LOC,132568332,1 +1060546661,10605466610,3233373,1445133,1325683320,False,Home,17,12,18.0,WALK,132568332,1 +1060572617,10605726170,3233453,1445213,1325715770,True,eatout,5,2,11.0,WALK,132571577,1 +1060572618,10605726180,3233453,1445213,1325715770,True,eatout,7,5,11.0,WALK,132571577,2 +1060572619,10605726190,3233453,1445213,1325715770,True,atwork,4,7,11.0,WALK,132571577,3 +1060572621,10605726210,3233453,1445213,1325715770,False,Work,2,4,14.0,WALK,132571577,1 +1060572897,10605728970,3233453,1445213,1325716120,True,work,2,17,6.0,WALK_LRF,132571612,1 +1060572901,10605729010,3233453,1445213,1325716120,False,Home,17,2,18.0,WALK_LRF,132571612,1 +1060575585,10605755850,3233462,1445222,1325719480,True,eatout,5,17,13.0,WALK,132571948,1 +1060575589,10605755890,3233462,1445222,1325719480,False,Home,17,5,13.0,WALK,132571948,1 +1060575737,10605757370,3233462,1445222,1325719670,True,othdiscr,14,17,19.0,WALK,132571967,1 +1060575741,10605757410,3233462,1445222,1325719670,False,Home,17,14,19.0,WALK_LOC,132571967,1 +1060575801,10605758010,3233462,1445222,1325719750,True,shopping,16,17,15.0,WALK,132571975,1 +1060575805,10605758050,3233462,1445222,1325719750,False,Home,17,16,18.0,WALK_LRF,132571975,1 +1060575809,10605758090,3233462,1445222,1325719760,True,shopping,13,17,20.0,WALK_LOC,132571976,1 +1060575813,10605758130,3233462,1445222,1325719760,False,Home,17,13,23.0,WALK_LRF,132571976,1 +1060575849,10605758490,3233462,1445222,1325719810,True,work,22,17,8.0,TNC_SINGLE,132571981,1 +1060575853,10605758530,3233462,1445222,1325719810,False,Home,17,22,13.0,TNC_SINGLE,132571981,1 +1060581097,10605810970,3233478,1445238,1325726370,True,work,4,17,6.0,WALK,132572637,1 +1060581101,10605811010,3233478,1445238,1325726370,False,Home,17,4,16.0,WALK,132572637,1 +1060590281,10605902810,3233506,1445266,1325737850,True,work,16,18,7.0,WALK_LRF,132573785,1 +1060590285,10605902850,3233506,1445266,1325737850,False,Home,18,16,18.0,WALK_LOC,132573785,1 +1060601481,10606014810,3233541,1445301,1325751850,True,atwork,5,9,10.0,WALK,132575185,1 +1060601485,10606014850,3233541,1445301,1325751850,False,eatout,7,5,10.0,WALK,132575185,1 +1060601486,10606014860,3233541,1445301,1325751850,False,work,9,7,10.0,WALK,132575185,2 +1060601487,10606014870,3233541,1445301,1325751850,False,eatout,9,9,10.0,WALK,132575185,3 +1060601488,10606014880,3233541,1445301,1325751850,False,Work,9,9,10.0,WALK,132575185,4 +1060601649,10606016490,3233541,1445301,1325752060,True,othdiscr,21,18,18.0,WALK,132575206,1 +1060601653,10606016530,3233541,1445301,1325752060,False,Home,18,21,23.0,WALK,132575206,1 +1060601761,10606017610,3233541,1445301,1325752200,True,work,9,18,7.0,WALK,132575220,1 +1060601765,10606017650,3233541,1445301,1325752200,False,Home,18,9,17.0,WALK,132575220,1 +1060603401,10606034010,3233546,1445306,1325754250,True,work,9,18,8.0,WALK_LOC,132575425,1 +1060603405,10606034050,3233546,1445306,1325754250,False,Home,18,9,13.0,TNC_SHARED,132575425,1 +1060609961,10606099610,3233566,1445326,1325762450,True,work,13,18,6.0,DRIVEALONEFREE,132576245,1 +1060609965,10606099650,3233566,1445326,1325762450,False,Home,18,13,17.0,DRIVEALONEFREE,132576245,1 +1060612305,10606123050,3233574,1445334,1325765380,True,atwork,18,19,13.0,SHARED3FREE,132576538,1 +1060612309,10606123090,3233574,1445334,1325765380,False,Work,19,18,13.0,WALK,132576538,1 +1060612585,10606125850,3233574,1445334,1325765730,True,work,19,18,8.0,WALK,132576573,1 +1060612589,10606125890,3233574,1445334,1325765730,False,Home,18,19,15.0,WALK,132576573,1 +1060613129,10606131290,3233576,1445336,1325766410,True,othdiscr,23,18,16.0,WALK_LRF,132576641,1 +1060613133,10606131330,3233576,1445336,1325766410,False,Home,18,23,18.0,WALK_LRF,132576641,1 +1060613241,10606132410,3233576,1445336,1325766550,True,work,12,18,8.0,TNC_SINGLE,132576655,1 +1060613242,10606132420,3233576,1445336,1325766550,True,work,19,12,8.0,WALK_LOC,132576655,2 +1060613245,10606132450,3233576,1445336,1325766550,False,Home,18,19,13.0,WALK,132576655,1 +1060615097,10606150970,3233582,1445342,1325768870,True,othdiscr,19,19,7.0,WALK,132576887,1 +1060615101,10606151010,3233582,1445342,1325768870,False,Home,19,19,10.0,WALK,132576887,1 +1060615209,10606152090,3233582,1445342,1325769010,True,work,4,19,11.0,WALK_LOC,132576901,1 +1060615213,10606152130,3233582,1445342,1325769010,False,Home,19,4,17.0,WALK_LOC,132576901,1 +1060618489,10606184890,3233592,1445352,1325773110,True,escort,5,19,13.0,DRIVEALONEFREE,132577311,1 +1060618490,10606184900,3233592,1445352,1325773110,True,work,1,5,13.0,DRIVEALONEFREE,132577311,2 +1060618493,10606184930,3233592,1445352,1325773110,False,othmaint,5,1,18.0,SHARED2FREE,132577311,1 +1060618494,10606184940,3233592,1445352,1325773110,False,Home,19,5,21.0,SHARED2FREE,132577311,2 +1060640793,10606407930,3233660,1445420,1325800990,True,work,21,21,10.0,WALK,132580099,1 +1060640797,10606407970,3233660,1445420,1325800990,False,Home,21,21,21.0,WALK,132580099,1 +1060643745,10606437450,3233669,1445429,1325804680,True,work,14,21,8.0,WALK,132580468,1 +1060643749,10606437490,3233669,1445429,1325804680,False,Home,21,14,17.0,WALK,132580468,1 +1060644049,10606440490,3233670,1445430,1325805060,True,othmaint,9,21,9.0,WALK,132580506,1 +1060644050,10606440500,3233670,1445430,1325805060,True,social,4,9,10.0,WALK_LRF,132580506,2 +1060644053,10606440530,3233670,1445430,1325805060,False,Home,21,4,17.0,WALK_LRF,132580506,1 +1060644057,10606440570,3233670,1445430,1325805070,True,social,6,21,18.0,SHARED2FREE,132580507,1 +1060644061,10606440610,3233670,1445430,1325805070,False,Home,21,6,20.0,SHARED2FREE,132580507,1 +1060645385,10606453850,3233674,1445434,1325806730,True,work,5,21,7.0,WALK,132580673,1 +1060645389,10606453890,3233674,1445434,1325806730,False,Home,21,5,18.0,WALK,132580673,1 +1060664473,10606644730,3233733,1445493,1325830590,True,othdiscr,2,22,16.0,WALK,132583059,1 +1060664474,10606644740,3233733,1445493,1325830590,True,eatout,7,2,16.0,SHARED2FREE,132583059,2 +1060664477,10606644770,3233733,1445493,1325830590,False,Home,22,7,16.0,SHARED2FREE,132583059,1 +1060664649,10606646490,3233733,1445493,1325830810,True,escort,8,22,9.0,TAXI,132583081,1 +1060664650,10606646500,3233733,1445493,1325830810,True,othmaint,9,8,9.0,TNC_SHARED,132583081,2 +1060664653,10606646530,3233733,1445493,1325830810,False,othdiscr,10,9,14.0,TNC_SINGLE,132583081,1 +1060664654,10606646540,3233733,1445493,1325830810,False,Home,22,10,14.0,DRIVEALONEFREE,132583081,2 +1060664657,10606646570,3233733,1445493,1325830820,True,othmaint,2,22,14.0,WALK,132583082,1 +1060664661,10606646610,3233733,1445493,1325830820,False,Home,22,2,16.0,WALK,132583082,1 +1060664737,10606647370,3233733,1445493,1325830920,True,othmaint,4,22,16.0,WALK,132583092,1 +1060664738,10606647380,3233733,1445493,1325830920,True,work,7,4,17.0,WALK_LOC,132583092,2 +1060664741,10606647410,3233733,1445493,1325830920,False,shopping,9,7,18.0,WALK_LOC,132583092,1 +1060664742,10606647420,3233733,1445493,1325830920,False,shopping,4,9,23.0,WALK_LRF,132583092,2 +1060664743,10606647430,3233733,1445493,1325830920,False,Home,22,4,23.0,WALK,132583092,3 +1060689009,10606890090,3233807,1445567,1325861260,True,work,24,24,11.0,WALK,132586126,1 +1060689013,10606890130,3233807,1445567,1325861260,False,Home,24,24,17.0,WALK,132586126,1 +1075792993,10757929930,3279856,1486878,1344741240,True,othmaint,13,6,11.0,WALK_LOC,134474124,1 +1075792997,10757929970,3279856,1486878,1344741240,False,Home,6,13,11.0,WALK_LOC,134474124,1 +1075793361,10757933610,3279857,1486878,1344741700,True,shopping,11,6,8.0,WALK,134474170,1 +1075793365,10757933650,3279857,1486878,1344741700,False,Home,6,11,20.0,WALK,134474170,1 +1075806089,10758060890,3279896,1486898,1344757610,True,othdiscr,22,8,11.0,WALK_LRF,134475761,1 +1075806093,10758060930,3279896,1486898,1344757610,False,othdiscr,10,22,15.0,WALK_LRF,134475761,1 +1075806094,10758060940,3279896,1486898,1344757610,False,othdiscr,9,10,15.0,WALK_LOC,134475761,2 +1075806095,10758060950,3279896,1486898,1344757610,False,Home,8,9,15.0,WALK_LOC,134475761,3 +1075806481,10758064810,3279897,1486898,1344758100,True,shopping,5,8,13.0,WALK,134475810,1 +1075806485,10758064850,3279897,1486898,1344758100,False,Home,8,5,16.0,WALK,134475810,1 +1075810705,10758107050,3279910,1486905,1344763380,True,othmaint,7,8,13.0,TNC_SINGLE,134476338,1 +1075810709,10758107090,3279910,1486905,1344763380,False,Home,8,7,15.0,WALK_LOC,134476338,1 +1075811073,10758110730,3279911,1486905,1344763840,True,shopping,21,8,8.0,WALK_LOC,134476384,1 +1075811077,10758110770,3279911,1486905,1344763840,False,Home,8,21,16.0,WALK_LOC,134476384,1 +1075825201,10758252010,3279954,1486927,1344781500,True,social,16,25,15.0,TNC_SINGLE,134478150,1 +1075825205,10758252050,3279954,1486927,1344781500,False,Home,25,16,18.0,TNC_SINGLE,134478150,1 +1075825465,10758254650,3279955,1486927,1344781830,True,othmaint,2,25,11.0,WALK_LOC,134478183,1 +1075825469,10758254690,3279955,1486927,1344781830,False,Home,25,2,21.0,WALK_LOC,134478183,1 +1091730553,10917305530,3328446,1511173,1364663190,True,shopping,11,8,12.0,WALK_LOC,136466319,1 +1091730557,10917305570,3328446,1511173,1364663190,False,Home,8,11,16.0,TNC_SHARED,136466319,1 +1091730929,10917309290,3328447,1511173,1364663660,True,work,23,8,6.0,SHARED2FREE,136466366,1 +1091730933,10917309330,3328447,1511173,1364663660,False,Home,8,23,15.0,SHARED2FREE,136466366,1 +1091733225,10917332250,3328454,1511177,1364666530,True,work,23,8,8.0,WALK_LOC,136466653,1 +1091733229,10917332290,3328454,1511177,1364666530,False,Home,8,23,17.0,WALK_LRF,136466653,1 +1091747609,10917476090,3328498,1511199,1364684510,True,shopping,8,8,10.0,TNC_SINGLE,136468451,1 +1091747613,10917476130,3328498,1511199,1364684510,False,Home,8,8,13.0,TNC_SINGLE,136468451,1 +1091747705,10917477050,3328499,1511199,1364684630,True,atwork,2,16,11.0,WALK,136468463,1 +1091747709,10917477090,3328499,1511199,1364684630,False,Work,16,2,13.0,WALK,136468463,1 +1091747985,10917479850,3328499,1511199,1364684980,True,work,16,8,6.0,SHARED2FREE,136468498,1 +1091747989,10917479890,3328499,1511199,1364684980,False,Home,8,16,22.0,WALK_LOC,136468498,1 +1091748857,10917488570,3328502,1511201,1364686070,True,othdiscr,16,8,9.0,TNC_SINGLE,136468607,1 +1091748861,10917488610,3328502,1511201,1364686070,False,Home,8,16,10.0,TNC_SINGLE,136468607,1 +1091748921,10917489210,3328502,1511201,1364686150,True,shopping,1,8,13.0,DRIVEALONEFREE,136468615,1 +1091748925,10917489250,3328502,1511201,1364686150,False,social,16,1,13.0,WALK,136468615,1 +1091748926,10917489260,3328502,1511201,1364686150,False,Home,8,16,13.0,DRIVEALONEFREE,136468615,2 +1091749017,10917490170,3328503,1511201,1364686270,True,atwork,19,19,10.0,WALK,136468627,1 +1091749021,10917490210,3328503,1511201,1364686270,False,Work,19,19,13.0,WALK,136468627,1 +1091749297,10917492970,3328503,1511201,1364686620,True,work,19,8,6.0,DRIVEALONEFREE,136468662,1 +1091749301,10917493010,3328503,1511201,1364686620,False,Home,8,19,16.0,DRIVEALONEFREE,136468662,1 +1091770617,10917706170,3328568,1511234,1364713270,True,work,1,8,6.0,WALK_LRF,136471327,1 +1091770621,10917706210,3328568,1511234,1364713270,False,Home,8,1,17.0,WALK,136471327,1 +1091770681,10917706810,3328569,1511234,1364713350,True,eatout,13,8,14.0,WALK,136471335,1 +1091770685,10917706850,3328569,1511234,1364713350,False,Home,8,13,16.0,WALK,136471335,1 +1091770897,10917708970,3328569,1511234,1364713620,True,eatout,9,8,10.0,WALK_LOC,136471362,1 +1091770898,10917708980,3328569,1511234,1364713620,True,shopping,4,9,10.0,WALK_LRF,136471362,2 +1091770901,10917709010,3328569,1511234,1364713620,False,Home,8,4,10.0,WALK_LRF,136471362,1 +1091777553,10917775530,3328590,1511245,1364721940,True,atwork,8,15,10.0,WALK,136472194,1 +1091777557,10917775570,3328590,1511245,1364721940,False,Work,15,8,13.0,WALK,136472194,1 +1091777625,10917776250,3328590,1511245,1364722030,True,eatout,9,9,18.0,SHARED2FREE,136472203,1 +1091777629,10917776290,3328590,1511245,1364722030,False,Home,9,9,21.0,WALK,136472203,1 +1091777785,10917777850,3328590,1511245,1364722230,True,shopping,4,9,16.0,BIKE,136472223,1 +1091777789,10917777890,3328590,1511245,1364722230,False,Home,9,4,16.0,BIKE,136472223,1 +1091777833,10917778330,3328590,1511245,1364722290,True,work,15,9,6.0,WALK,136472229,1 +1091777837,10917778370,3328590,1511245,1364722290,False,Home,9,15,16.0,WALK,136472229,1 +1091784961,10917849610,3328612,1511256,1364731200,True,othmaint,13,9,7.0,WALK_LRF,136473120,1 +1091784965,10917849650,3328612,1511256,1364731200,False,Home,9,13,10.0,WALK_HVY,136473120,1 +1091785377,10917853770,3328613,1511256,1364731720,True,work,1,9,6.0,WALK,136473172,1 +1091785381,10917853810,3328613,1511256,1364731720,False,Home,9,1,18.0,WALK,136473172,1 +1091794889,10917948890,3328642,1511271,1364743610,True,escort,8,9,8.0,WALK,136474361,1 +1091794890,10917948900,3328642,1511271,1364743610,True,work,18,8,9.0,WALK,136474361,2 +1091794893,10917948930,3328642,1511271,1364743610,False,Home,9,18,18.0,WALK,136474361,1 +1091813801,10918138010,3328700,1511300,1364767250,True,othdiscr,19,11,18.0,TNC_SINGLE,136476725,1 +1091813805,10918138050,3328700,1511300,1364767250,False,Home,11,19,18.0,TNC_SINGLE,136476725,1 +1091814241,10918142410,3328701,1511300,1364767800,True,escort,5,11,8.0,WALK_LOC,136476780,1 +1091814242,10918142420,3328701,1511300,1364767800,True,work,2,5,8.0,WALK_LOC,136476780,2 +1091814245,10918142450,3328701,1511300,1364767800,False,Home,11,2,17.0,TNC_SINGLE,136476780,1 +1091820425,10918204250,3328720,1511310,1364775530,True,shopping,5,11,16.0,WALK,136477553,1 +1091820429,10918204290,3328720,1511310,1364775530,False,Home,11,5,17.0,WALK,136477553,1 +1091820521,10918205210,3328721,1511310,1364775650,True,atwork,16,10,14.0,WALK,136477565,1 +1091820525,10918205250,3328721,1511310,1364775650,False,Work,10,16,14.0,WALK,136477565,1 +1091820801,10918208010,3328721,1511310,1364776000,True,escort,9,11,9.0,WALK,136477600,1 +1091820802,10918208020,3328721,1511310,1364776000,True,work,10,9,10.0,WALK,136477600,2 +1091820805,10918208050,3328721,1511310,1364776000,False,work,11,10,17.0,WALK,136477600,1 +1091820806,10918208060,3328721,1511310,1364776000,False,escort,8,11,18.0,WALK,136477600,2 +1091820807,10918208070,3328721,1511310,1364776000,False,work,8,8,18.0,WALK,136477600,3 +1091820808,10918208080,3328721,1511310,1364776000,False,Home,11,8,18.0,WALK,136477600,4 +1091823665,10918236650,3328730,1511315,1364779580,True,othmaint,2,11,14.0,BIKE,136477958,1 +1091823669,10918236690,3328730,1511315,1364779580,False,Home,11,2,15.0,BIKE,136477958,1 +1091823841,10918238410,3328731,1511315,1364779800,True,escort,13,11,7.0,WALK,136477980,1 +1091823845,10918238450,3328731,1511315,1364779800,False,Home,11,13,10.0,WALK,136477980,1 +1091824033,10918240330,3328731,1511315,1364780040,True,shopping,5,11,12.0,WALK,136478004,1 +1091824034,10918240340,3328731,1511315,1364780040,True,shopping,16,5,13.0,WALK,136478004,2 +1091824037,10918240370,3328731,1511315,1364780040,False,Home,11,16,13.0,WALK,136478004,1 +1091831561,10918315610,3328754,1511327,1364789450,True,othmaint,5,11,8.0,WALK,136478945,1 +1091831562,10918315620,3328754,1511327,1364789450,True,univ,10,5,9.0,WALK_LOC,136478945,2 +1091831565,10918315650,3328754,1511327,1364789450,False,Home,11,10,12.0,WALK_LOC,136478945,1 +1091831953,10918319530,3328755,1511327,1364789940,True,work,1,11,7.0,WALK,136478994,1 +1091831957,10918319570,3328755,1511327,1364789940,False,Home,11,1,15.0,WALK,136478994,1 +1091860209,10918602090,3328842,1511371,1364825260,True,othmaint,5,5,12.0,WALK,136482526,1 +1091860210,10918602100,3328842,1511371,1364825260,True,atwork,16,5,12.0,WALK,136482526,2 +1091860213,10918602130,3328842,1511371,1364825260,False,Work,5,16,13.0,WALK,136482526,1 +1091860489,10918604890,3328842,1511371,1364825610,True,work,5,21,6.0,WALK,136482561,1 +1091860493,10918604930,3328842,1511371,1364825610,False,Home,21,5,16.0,WALK_LOC,136482561,1 +1091860705,10918607050,3328843,1511371,1364825880,True,othdiscr,6,21,11.0,WALK,136482588,1 +1091860709,10918607090,3328843,1511371,1364825880,False,Home,21,6,13.0,WALK,136482588,1 +1091860769,10918607690,3328843,1511371,1364825960,True,shopping,8,21,13.0,WALK,136482596,1 +1091860773,10918607730,3328843,1511371,1364825960,False,eatout,6,8,19.0,WALK,136482596,1 +1091860774,10918607740,3328843,1511371,1364825960,False,Home,21,6,19.0,WALK,136482596,2 +1091872209,10918722090,3328878,1511389,1364840260,True,othdiscr,14,23,9.0,WALK,136484026,1 +1091872210,10918722100,3328878,1511389,1364840260,True,othmaint,6,14,9.0,WALK_LOC,136484026,2 +1091872213,10918722130,3328878,1511389,1364840260,False,shopping,25,6,9.0,WALK,136484026,1 +1091872214,10918722140,3328878,1511389,1364840260,False,escort,1,25,9.0,WALK,136484026,2 +1091872215,10918722150,3328878,1511389,1364840260,False,Home,23,1,9.0,WALK,136484026,3 +1091872577,10918725770,3328879,1511389,1364840720,True,othmaint,2,23,8.0,WALK,136484072,1 +1091872578,10918725780,3328879,1511389,1364840720,True,shopping,24,2,15.0,WALK,136484072,2 +1091872581,10918725810,3328879,1511389,1364840720,False,shopping,25,24,20.0,WALK,136484072,1 +1091872582,10918725820,3328879,1511389,1364840720,False,othmaint,2,25,20.0,WALK,136484072,2 +1091872583,10918725830,3328879,1511389,1364840720,False,Home,23,2,21.0,WALK,136484072,3 +1091888697,10918886970,3328928,1511414,1364860870,True,work,24,25,13.0,WALK,136486087,1 +1091888701,10918887010,3328928,1511414,1364860870,False,Home,25,24,22.0,WALK,136486087,1 +1091888977,10918889770,3328929,1511414,1364861220,True,shopping,11,25,8.0,WALK,136486122,1 +1091888981,10918889810,3328929,1511414,1364861220,False,Home,25,11,10.0,WALK,136486122,1 +1091889305,10918893050,3328930,1511415,1364861630,True,shopping,13,25,13.0,WALK,136486163,1 +1091889309,10918893090,3328930,1511415,1364861630,False,Home,25,13,13.0,WALK,136486163,1 +1091889633,10918896330,3328931,1511415,1364862040,True,shopping,12,25,17.0,WALK_LOC,136486204,1 +1091889637,10918896370,3328931,1511415,1364862040,False,othmaint,9,12,18.0,TNC_SINGLE,136486204,1 +1091889638,10918896380,3328931,1511415,1364862040,False,shopping,8,9,18.0,WALK_LOC,136486204,2 +1091889639,10918896390,3328931,1511415,1364862040,False,Home,25,8,18.0,TNC_SINGLE,136486204,3 +1091889681,10918896810,3328931,1511415,1364862100,True,work,24,25,8.0,WALK,136486210,1 +1091889685,10918896850,3328931,1511415,1364862100,False,Home,25,24,15.0,WALK,136486210,1 +1146245233,11462452330,3494650,1594275,1432806540,True,atwork,15,16,10.0,WALK,143280654,1 +1146245237,11462452370,3494650,1594275,1432806540,False,Work,16,15,10.0,WALK,143280654,1 +1146245513,11462455130,3494650,1594275,1432806890,True,work,16,5,8.0,WALK,143280689,1 +1146245517,11462455170,3494650,1594275,1432806890,False,Home,5,16,17.0,WALK,143280689,1 +1146245841,11462458410,3494651,1594275,1432807300,True,work,22,5,6.0,WALK_LOC,143280730,1 +1146245845,11462458450,3494651,1594275,1432807300,False,Home,5,22,21.0,WALK_LRF,143280730,1 +1146246169,11462461690,3494652,1594276,1432807710,True,work,4,6,7.0,BIKE,143280771,1 +1146246173,11462461730,3494652,1594276,1432807710,False,Home,6,4,18.0,BIKE,143280771,1 +1146246497,11462464970,3494653,1594276,1432808120,True,work,7,6,7.0,WALK,143280812,1 +1146246501,11462465010,3494653,1594276,1432808120,False,Home,6,7,16.0,WALK,143280812,1 +1146269081,11462690810,3494722,1594311,1432836350,True,shopping,9,6,10.0,WALK,143283635,1 +1146269085,11462690850,3494722,1594311,1432836350,False,Home,6,9,21.0,WALK_LRF,143283635,1 +1146269345,11462693450,3494723,1594311,1432836680,True,othdiscr,14,6,10.0,WALK,143283668,1 +1146269349,11462693490,3494723,1594311,1432836680,False,Home,6,14,10.0,WALK,143283668,1 +1146269369,11462693690,3494723,1594311,1432836710,True,othmaint,6,6,12.0,TNC_SHARED,143283671,1 +1146269373,11462693730,3494723,1594311,1432836710,False,escort,9,6,13.0,WALK_LOC,143283671,1 +1146269374,11462693740,3494723,1594311,1432836710,False,eatout,7,9,13.0,TNC_SINGLE,143283671,2 +1146269375,11462693750,3494723,1594311,1432836710,False,escort,8,7,14.0,WALK_LOC,143283671,3 +1146269376,11462693760,3494723,1594311,1432836710,False,Home,6,8,14.0,TNC_SINGLE,143283671,4 +1146297337,11462973370,3494808,1594354,1432871670,True,work,2,7,7.0,WALK,143287167,1 +1146297341,11462973410,3494808,1594354,1432871670,False,Home,7,2,17.0,WALK,143287167,1 +1146297385,11462973850,3494809,1594354,1432871730,True,atwork,14,4,10.0,WALK,143287173,1 +1146297389,11462973890,3494809,1594354,1432871730,False,Work,4,14,10.0,WALK,143287173,1 +1146297401,11462974010,3494809,1594354,1432871750,True,eatout,2,7,19.0,WALK,143287175,1 +1146297405,11462974050,3494809,1594354,1432871750,False,Home,7,2,20.0,WALK,143287175,1 +1146297665,11462976650,3494809,1594354,1432872080,True,work,4,7,7.0,WALK,143287208,1 +1146297669,11462976690,3494809,1594354,1432872080,False,Home,7,4,17.0,WALK,143287208,1 +1146328169,11463281690,3494902,1594401,1432910210,True,work,2,7,11.0,WALK,143291021,1 +1146328173,11463281730,3494902,1594401,1432910210,False,Home,7,2,15.0,WALK,143291021,1 +1146328473,11463284730,3494903,1594401,1432910590,True,othdiscr,10,7,17.0,TNC_SINGLE,143291059,1 +1146328474,11463284740,3494903,1594401,1432910590,True,social,14,10,18.0,WALK_LRF,143291059,2 +1146328477,11463284770,3494903,1594401,1432910590,False,Home,7,14,19.0,WALK_LOC,143291059,1 +1146328497,11463284970,3494903,1594401,1432910620,True,work,14,7,8.0,WALK,143291062,1 +1146328498,11463284980,3494903,1594401,1432910620,True,eatout,16,14,8.0,WALK,143291062,2 +1146328499,11463284990,3494903,1594401,1432910620,True,work,4,16,9.0,WALK,143291062,3 +1146328501,11463285010,3494903,1594401,1432910620,False,escort,5,4,13.0,WALK_LOC,143291062,1 +1146328502,11463285020,3494903,1594401,1432910620,False,shopping,8,5,14.0,WALK,143291062,2 +1146328503,11463285030,3494903,1594401,1432910620,False,Home,7,8,14.0,WALK,143291062,3 +1146347193,11463471930,3494960,1594430,1432933990,True,work,13,7,7.0,WALK,143293399,1 +1146347197,11463471970,3494960,1594430,1432933990,False,shopping,21,13,18.0,WALK,143293399,1 +1146347198,11463471980,3494960,1594430,1432933990,False,Home,7,21,18.0,TNC_SINGLE,143293399,2 +1146347409,11463474090,3494961,1594430,1432934260,True,othdiscr,10,7,16.0,WALK_LOC,143293426,1 +1146347413,11463474130,3494961,1594430,1432934260,False,Home,7,10,16.0,SHARED3FREE,143293426,1 +1146347473,11463474730,3494961,1594430,1432934340,True,shopping,5,7,18.0,WALK,143293434,1 +1146347477,11463474770,3494961,1594430,1432934340,False,Home,7,5,21.0,WALK,143293434,1 +1146347521,11463475210,3494961,1594430,1432934400,True,work,23,7,7.0,WALK,143293440,1 +1146347525,11463475250,3494961,1594430,1432934400,False,Home,7,23,16.0,WALK,143293440,1 +1146360033,11463600330,3495000,1594450,1432950040,True,atwork,5,5,12.0,WALK,143295004,1 +1146360037,11463600370,3495000,1594450,1432950040,False,eatout,5,5,18.0,WALK,143295004,1 +1146360038,11463600380,3495000,1594450,1432950040,False,Work,5,5,18.0,WALK,143295004,2 +1146360313,11463603130,3495000,1594450,1432950390,True,work,5,7,8.0,WALK,143295039,1 +1146360317,11463603170,3495000,1594450,1432950390,False,Home,7,5,18.0,WALK,143295039,1 +1146360377,11463603770,3495001,1594450,1432950470,True,eatout,13,7,9.0,WALK,143295047,1 +1146360381,11463603810,3495001,1594450,1432950470,False,Home,7,13,9.0,WALK,143295047,1 +1146360641,11463606410,3495001,1594450,1432950800,True,work,2,7,10.0,WALK,143295080,1 +1146360645,11463606450,3495001,1594450,1432950800,False,Home,7,2,19.0,WALK,143295080,1 +1146384961,11463849610,3495076,1594488,1432981200,True,escort,2,4,11.0,WALK,143298120,1 +1146384962,11463849620,3495076,1594488,1432981200,True,atwork,22,2,11.0,WALK,143298120,2 +1146384965,11463849650,3495076,1594488,1432981200,False,Work,4,22,11.0,WALK,143298120,1 +1146385241,11463852410,3495076,1594488,1432981550,True,escort,8,9,6.0,WALK,143298155,1 +1146385242,11463852420,3495076,1594488,1432981550,True,work,4,8,7.0,WALK,143298155,2 +1146385245,11463852450,3495076,1594488,1432981550,False,Home,9,4,18.0,TNC_SINGLE,143298155,1 +1146385305,11463853050,3495077,1594488,1432981630,True,eatout,9,9,20.0,WALK,143298163,1 +1146385309,11463853090,3495077,1594488,1432981630,False,Home,9,9,20.0,WALK,143298163,1 +1146385521,11463855210,3495077,1594488,1432981900,True,shopping,4,9,18.0,WALK,143298190,1 +1146385525,11463855250,3495077,1594488,1432981900,False,Home,9,4,19.0,WALK_LOC,143298190,1 +1146385569,11463855690,3495077,1594488,1432981960,True,work,14,9,8.0,WALK_LRF,143298196,1 +1146385570,11463855700,3495077,1594488,1432981960,True,work,2,14,8.0,WALK,143298196,2 +1146385573,11463855730,3495077,1594488,1432981960,False,Home,9,2,18.0,WALK_LRF,143298196,1 +1146386553,11463865530,3495080,1594490,1432983190,True,work,8,9,6.0,WALK,143298319,1 +1146386557,11463865570,3495080,1594490,1432983190,False,Home,9,8,11.0,WALK,143298319,1 +1146386561,11463865610,3495080,1594490,1432983200,True,work,8,9,13.0,BIKE,143298320,1 +1146386565,11463865650,3495080,1594490,1432983200,False,Home,9,8,17.0,BIKE,143298320,1 +1146386881,11463868810,3495081,1594490,1432983600,True,work,2,9,7.0,DRIVEALONEFREE,143298360,1 +1146386885,11463868850,3495081,1594490,1432983600,False,Home,9,2,19.0,SHARED2FREE,143298360,1 +1146433017,11464330170,3495222,1594561,1433041270,True,othdiscr,17,9,17.0,WALK_LRF,143304127,1 +1146433021,11464330210,3495222,1594561,1433041270,False,Home,9,17,17.0,WALK_LRF,143304127,1 +1146433457,11464334570,3495223,1594561,1433041820,True,work,6,9,8.0,SHARED2FREE,143304182,1 +1146433461,11464334610,3495223,1594561,1433041820,False,Home,9,6,15.0,WALK_LOC,143304182,1 +1146460681,11464606810,3495306,1594603,1433075850,True,shopping,11,10,10.0,WALK_LOC,143307585,1 +1146460682,11464606820,3495306,1594603,1433075850,True,work,7,11,11.0,WALK,143307585,2 +1146460685,11464606850,3495306,1594603,1433075850,False,Home,10,7,21.0,WALK_LRF,143307585,1 +1146461009,11464610090,3495307,1594603,1433076260,True,work,9,10,10.0,WALK_LOC,143307626,1 +1146461010,11464610100,3495307,1594603,1433076260,True,eatout,7,9,10.0,WALK,143307626,2 +1146461011,11464610110,3495307,1594603,1433076260,True,work,9,7,12.0,WALK,143307626,3 +1146461013,11464610130,3495307,1594603,1433076260,False,Home,10,9,21.0,TNC_SINGLE,143307626,1 +1146472377,11464723770,3495342,1594621,1433090470,True,othdiscr,15,10,16.0,WALK_LRF,143309047,1 +1146472381,11464723810,3495342,1594621,1433090470,False,Home,10,15,22.0,WALK_LRF,143309047,1 +1146472489,11464724890,3495342,1594621,1433090610,True,work,11,10,7.0,WALK_LOC,143309061,1 +1146472493,11464724930,3495342,1594621,1433090610,False,Home,10,11,16.0,WALK,143309061,1 +1146472537,11464725370,3495343,1594621,1433090670,True,othmaint,6,9,14.0,WALK,143309067,1 +1146472538,11464725380,3495343,1594621,1433090670,True,shopping,16,6,14.0,WALK,143309067,2 +1146472539,11464725390,3495343,1594621,1433090670,True,escort,16,16,14.0,WALK,143309067,3 +1146472540,11464725400,3495343,1594621,1433090670,True,atwork,16,16,14.0,WALK,143309067,4 +1146472541,11464725410,3495343,1594621,1433090670,False,Work,9,16,14.0,WALK,143309067,1 +1146472817,11464728170,3495343,1594621,1433091020,True,work,9,10,13.0,WALK,143309102,1 +1146472821,11464728210,3495343,1594621,1433091020,False,Home,10,9,17.0,WALK,143309102,1 +1146474041,11464740410,3495347,1594623,1433092550,True,othmaint,7,10,8.0,BIKE,143309255,1 +1146474042,11464740420,3495347,1594623,1433092550,True,eatout,25,7,8.0,BIKE,143309255,2 +1146474043,11464740430,3495347,1594623,1433092550,True,othmaint,12,25,8.0,BIKE,143309255,3 +1146474045,11464740450,3495347,1594623,1433092550,False,Home,10,12,8.0,BIKE,143309255,1 +1146474129,11464741290,3495347,1594623,1433092660,True,work,2,10,8.0,WALK_LRF,143309266,1 +1146474133,11464741330,3495347,1594623,1433092660,False,Home,10,2,18.0,WALK_LRF,143309266,1 +1146475769,11464757690,3495352,1594626,1433094710,True,work,12,10,7.0,WALK,143309471,1 +1146475773,11464757730,3495352,1594626,1433094710,False,Home,10,12,16.0,WALK,143309471,1 +1146476097,11464760970,3495353,1594626,1433095120,True,work,22,10,7.0,WALK,143309512,1 +1146476101,11464761010,3495353,1594626,1433095120,False,Home,10,22,16.0,WALK,143309512,1 +1146489545,11464895450,3495394,1594647,1433111930,True,work,13,10,9.0,WALK_LRF,143311193,1 +1146489549,11464895490,3495394,1594647,1433111930,False,Home,10,13,12.0,WALK_HVY,143311193,1 +1146489553,11464895530,3495394,1594647,1433111940,True,work,13,10,12.0,WALK,143311194,1 +1146489557,11464895570,3495394,1594647,1433111940,False,Home,10,13,15.0,WALK_LRF,143311194,1 +1146489825,11464898250,3495395,1594647,1433112280,True,shopping,19,10,10.0,TNC_SINGLE,143311228,1 +1146489829,11464898290,3495395,1594647,1433112280,False,shopping,16,19,12.0,TNC_SINGLE,143311228,1 +1146489830,11464898300,3495395,1594647,1433112280,False,Home,10,16,14.0,TNC_SINGLE,143311228,2 +1146489873,11464898730,3495395,1594647,1433112340,True,work,21,10,8.0,DRIVEALONEFREE,143311234,1 +1146489877,11464898770,3495395,1594647,1433112340,False,Home,10,21,10.0,SHARED2FREE,143311234,1 +1146498025,11464980250,3495420,1594660,1433122530,True,escort,6,10,18.0,WALK_LOC,143312253,1 +1146498026,11464980260,3495420,1594660,1433122530,True,shopping,16,6,18.0,TNC_SINGLE,143312253,2 +1146498029,11464980290,3495420,1594660,1433122530,False,Home,10,16,20.0,WALK_LOC,143312253,1 +1146498073,11464980730,3495420,1594660,1433122590,True,work,9,10,8.0,WALK,143312259,1 +1146498077,11464980770,3495420,1594660,1433122590,False,Home,10,9,17.0,WALK,143312259,1 +1146498121,11464981210,3495421,1594660,1433122650,True,atwork,11,4,14.0,WALK,143312265,1 +1146498125,11464981250,3495421,1594660,1433122650,False,Work,4,11,14.0,WALK,143312265,1 +1146498401,11464984010,3495421,1594660,1433123000,True,work,4,10,8.0,WALK,143312300,1 +1146498405,11464984050,3495421,1594660,1433123000,False,Home,10,4,17.0,WALK_LRF,143312300,1 +1146535465,11465354650,3495534,1594717,1433169330,True,work,18,10,14.0,WALK,143316933,1 +1146535469,11465354690,3495534,1594717,1433169330,False,Home,10,18,17.0,WALK,143316933,1 +1146535473,11465354730,3495534,1594717,1433169340,True,othmaint,11,10,19.0,WALK,143316934,1 +1146535474,11465354740,3495534,1594717,1433169340,True,work,8,11,19.0,WALK,143316934,2 +1146535475,11465354750,3495534,1594717,1433169340,True,eatout,21,8,19.0,WALK,143316934,3 +1146535476,11465354760,3495534,1594717,1433169340,True,work,18,21,19.0,WALK,143316934,4 +1146535477,11465354770,3495534,1594717,1433169340,False,Home,10,18,19.0,WALK,143316934,1 +1146535553,11465355530,3495535,1594717,1433169440,True,escort,10,10,8.0,WALK,143316944,1 +1146535557,11465355570,3495535,1594717,1433169440,False,Home,10,10,11.0,WALK,143316944,1 +1146535561,11465355610,3495535,1594717,1433169450,True,escort,11,10,13.0,TNC_SINGLE,143316945,1 +1146535565,11465355650,3495535,1594717,1433169450,False,Home,10,11,13.0,TNC_SINGLE,143316945,1 +1146535681,11465356810,3495535,1594717,1433169600,True,othdiscr,7,10,17.0,WALK,143316960,1 +1146535685,11465356850,3495535,1594717,1433169600,False,Home,10,7,19.0,WALK,143316960,1 +1146535745,11465357450,3495535,1594717,1433169680,True,othmaint,9,10,16.0,WALK_LOC,143316968,1 +1146535746,11465357460,3495535,1594717,1433169680,True,shopping,16,9,16.0,WALK_LOC,143316968,2 +1146535749,11465357490,3495535,1594717,1433169680,False,shopping,5,16,17.0,WALK_LOC,143316968,1 +1146535750,11465357500,3495535,1594717,1433169680,False,Home,10,5,17.0,WALK_LOC,143316968,2 +1146547273,11465472730,3495570,1594735,1433184090,True,work,3,11,6.0,WALK_LOC,143318409,1 +1146547277,11465472770,3495570,1594735,1433184090,False,Home,11,3,10.0,WALK,143318409,1 +1146547281,11465472810,3495570,1594735,1433184100,True,shopping,5,11,11.0,WALK,143318410,1 +1146547282,11465472820,3495570,1594735,1433184100,True,work,3,5,12.0,TNC_SINGLE,143318410,2 +1146547285,11465472850,3495570,1594735,1433184100,False,Home,11,3,14.0,TNC_SINGLE,143318410,1 +1146547601,11465476010,3495571,1594735,1433184500,True,work,1,11,8.0,WALK,143318450,1 +1146547605,11465476050,3495571,1594735,1433184500,False,Home,11,1,15.0,WALK_LOC,143318450,1 +1146572745,11465727450,3495648,1594774,1433215930,True,othdiscr,16,11,16.0,WALK,143321593,1 +1146572749,11465727490,3495648,1594774,1433215930,False,Home,11,16,18.0,WALK,143321593,1 +1146572857,11465728570,3495648,1594774,1433216070,True,work,9,11,6.0,WALK,143321607,1 +1146572861,11465728610,3495648,1594774,1433216070,False,Home,11,9,10.0,WALK,143321607,1 +1146572865,11465728650,3495648,1594774,1433216080,True,work,9,11,13.0,WALK,143321608,1 +1146572869,11465728690,3495648,1594774,1433216080,False,work,9,9,14.0,WALK,143321608,1 +1146572870,11465728700,3495648,1594774,1433216080,False,Home,11,9,14.0,WALK,143321608,2 +1146584009,11465840090,3495682,1594791,1433230010,True,work,1,11,9.0,WALK,143323001,1 +1146584013,11465840130,3495682,1594791,1433230010,False,Home,11,1,20.0,WALK,143323001,1 +1146584337,11465843370,3495683,1594791,1433230420,True,work,7,11,7.0,WALK_LOC,143323042,1 +1146584341,11465843410,3495683,1594791,1433230420,False,Home,11,7,20.0,WALK_LOC,143323042,1 +1146585977,11465859770,3495688,1594794,1433232470,True,work,12,11,8.0,WALK,143323247,1 +1146585981,11465859810,3495688,1594794,1433232470,False,Home,11,12,18.0,WALK,143323247,1 +1146586257,11465862570,3495689,1594794,1433232820,True,shopping,19,11,20.0,DRIVEALONEFREE,143323282,1 +1146586261,11465862610,3495689,1594794,1433232820,False,Home,11,19,20.0,DRIVEALONEFREE,143323282,1 +1146586305,11465863050,3495689,1594794,1433232880,True,work,23,11,8.0,WALK_LOC,143323288,1 +1146586309,11465863090,3495689,1594794,1433232880,False,Home,11,23,18.0,WALK_LOC,143323288,1 +1146589257,11465892570,3495698,1594799,1433236570,True,work,2,11,7.0,SHARED2FREE,143323657,1 +1146589261,11465892610,3495698,1594799,1433236570,False,Home,11,2,18.0,WALK,143323657,1 +1146589585,11465895850,3495699,1594799,1433236980,True,work,12,11,7.0,WALK,143323698,1 +1146589589,11465895890,3495699,1594799,1433236980,False,Home,11,12,10.0,WALK,143323698,1 +1146602113,11466021130,3495738,1594819,1433252640,True,eatout,10,11,12.0,WALK,143325264,1 +1146602117,11466021170,3495738,1594819,1433252640,False,Home,11,10,12.0,WALK,143325264,1 +1146606201,11466062010,3495750,1594825,1433257750,True,othdiscr,9,11,10.0,WALK,143325775,1 +1146606205,11466062050,3495750,1594825,1433257750,False,Home,11,9,21.0,WALK,143325775,1 +1146606641,11466066410,3495751,1594825,1433258300,True,work,1,11,9.0,WALK_LOC,143325830,1 +1146606645,11466066450,3495751,1594825,1433258300,False,Home,11,1,19.0,WALK,143325830,1 +1146620745,11466207450,3495794,1594847,1433275930,True,work,4,11,8.0,WALK,143327593,1 +1146620749,11466207490,3495794,1594847,1433275930,False,Home,11,4,17.0,WALK,143327593,1 +1146621073,11466210730,3495795,1594847,1433276340,True,work,17,11,6.0,WALK,143327634,1 +1146621077,11466210770,3495795,1594847,1433276340,False,Home,11,17,19.0,WALK,143327634,1 +1146626385,11466263850,3495812,1594856,1433282980,True,eatout,13,11,7.0,WALK,143328298,1 +1146626389,11466263890,3495812,1594856,1433282980,False,Home,11,13,14.0,WALK,143328298,1 +1146626977,11466269770,3495813,1594856,1433283720,True,work,9,11,8.0,WALK,143328372,1 +1146626981,11466269810,3495813,1594856,1433283720,False,Home,11,9,18.0,TAXI,143328372,1 +1146658681,11466586810,3495910,1594905,1433323350,True,othdiscr,16,12,12.0,WALK,143332335,1 +1146658685,11466586850,3495910,1594905,1433323350,False,Home,12,16,15.0,WALK,143332335,1 +1146658793,11466587930,3495910,1594905,1433323490,True,work,2,12,17.0,WALK,143332349,1 +1146658797,11466587970,3495910,1594905,1433323490,False,Home,12,2,20.0,WALK,143332349,1 +1146659033,11466590330,3495911,1594905,1433323790,True,othmaint,13,12,17.0,TNC_SINGLE,143332379,1 +1146659037,11466590370,3495911,1594905,1433323790,False,Home,12,13,21.0,TNC_SINGLE,143332379,1 +1146659073,11466590730,3495911,1594905,1433323840,True,shopping,4,12,17.0,WALK,143332384,1 +1146659077,11466590770,3495911,1594905,1433323840,False,Home,12,4,17.0,WALK,143332384,1 +1146659121,11466591210,3495911,1594905,1433323900,True,work,2,12,8.0,WALK,143332390,1 +1146659125,11466591250,3495911,1594905,1433323900,False,eatout,1,2,16.0,WALK,143332390,1 +1146659126,11466591260,3495911,1594905,1433323900,False,Home,12,1,17.0,WALK,143332390,2 +1146693281,11466932810,3496016,1594958,1433366600,True,atwork,13,22,12.0,WALK,143336660,1 +1146693285,11466932850,3496016,1594958,1433366600,False,Work,22,13,13.0,WALK,143336660,1 +1146693561,11466935610,3496016,1594958,1433366950,True,work,22,15,7.0,TNC_SINGLE,143336695,1 +1146693565,11466935650,3496016,1594958,1433366950,False,Home,15,22,17.0,WALK_LRF,143336695,1 +1146693889,11466938890,3496017,1594958,1433367360,True,work,15,15,10.0,WALK,143336736,1 +1146693893,11466938930,3496017,1594958,1433367360,False,Home,15,15,20.0,WALK,143336736,1 +1146697497,11466974970,3496028,1594964,1433371870,True,work,22,15,7.0,WALK,143337187,1 +1146697501,11466975010,3496028,1594964,1433371870,False,Home,15,22,12.0,WALK,143337187,1 +1146697505,11466975050,3496028,1594964,1433371880,True,othmaint,24,15,13.0,SHARED3FREE,143337188,1 +1146697506,11466975060,3496028,1594964,1433371880,True,work,22,24,13.0,SHARED3FREE,143337188,2 +1146697509,11466975090,3496028,1594964,1433371880,False,work,22,22,17.0,SHARED3FREE,143337188,1 +1146697510,11466975100,3496028,1594964,1433371880,False,Home,15,22,18.0,WALK,143337188,2 +1146697713,11466977130,3496029,1594964,1433372140,True,othdiscr,15,15,10.0,WALK,143337214,1 +1146697717,11466977170,3496029,1594964,1433372140,False,Home,15,15,18.0,WALK,143337214,1 +1146701433,11467014330,3496040,1594970,1433376790,True,work,15,16,7.0,WALK,143337679,1 +1146701437,11467014370,3496040,1594970,1433376790,False,Home,16,15,20.0,WALK,143337679,1 +1146701481,11467014810,3496041,1594970,1433376850,True,eatout,7,1,9.0,WALK,143337685,1 +1146701482,11467014820,3496041,1594970,1433376850,True,atwork,1,7,9.0,WALK,143337685,2 +1146701485,11467014850,3496041,1594970,1433376850,False,Work,1,1,9.0,WALK,143337685,1 +1146701649,11467016490,3496041,1594970,1433377060,True,othdiscr,13,16,18.0,WALK,143337706,1 +1146701653,11467016530,3496041,1594970,1433377060,False,Home,16,13,20.0,WALK,143337706,1 +1146701761,11467017610,3496041,1594970,1433377200,True,work,1,16,8.0,WALK,143337720,1 +1146701765,11467017650,3496041,1594970,1433377200,False,Home,16,1,18.0,WALK,143337720,1 +1146713897,11467138970,3496078,1594989,1433392370,True,escort,2,16,8.0,WALK_LOC,143339237,1 +1146713898,11467138980,3496078,1594989,1433392370,True,work,4,2,8.0,WALK,143339237,2 +1146713901,11467139010,3496078,1594989,1433392370,False,shopping,23,4,10.0,TNC_SINGLE,143339237,1 +1146713902,11467139020,3496078,1594989,1433392370,False,Home,16,23,16.0,TNC_SINGLE,143339237,2 +1146713945,11467139450,3496079,1594989,1433392430,True,atwork,11,21,10.0,WALK,143339243,1 +1146713949,11467139490,3496079,1594989,1433392430,False,Work,21,11,10.0,WALK,143339243,1 +1146714225,11467142250,3496079,1594989,1433392780,True,work,21,16,8.0,TNC_SINGLE,143339278,1 +1146714229,11467142290,3496079,1594989,1433392780,False,othmaint,20,21,18.0,TNC_SINGLE,143339278,1 +1146714230,11467142300,3496079,1594989,1433392780,False,Home,16,20,19.0,WALK_LOC,143339278,2 +1146717553,11467175530,3496090,1594995,1433396940,True,atwork,12,2,14.0,WALK,143339694,1 +1146717557,11467175570,3496090,1594995,1433396940,False,Work,2,12,14.0,WALK,143339694,1 +1146717833,11467178330,3496090,1594995,1433397290,True,work,2,16,6.0,WALK,143339729,1 +1146717837,11467178370,3496090,1594995,1433397290,False,Home,16,2,17.0,WALK,143339729,1 +1146717897,11467178970,3496091,1594995,1433397370,True,eatout,17,16,12.0,WALK,143339737,1 +1146717901,11467179010,3496091,1594995,1433397370,False,Home,16,17,13.0,WALK,143339737,1 +1146718113,11467181130,3496091,1594995,1433397640,True,shopping,10,16,11.0,WALK_LOC,143339764,1 +1146718117,11467181170,3496091,1594995,1433397640,False,Home,16,10,11.0,TNC_SINGLE,143339764,1 +1146748665,11467486650,3496184,1595042,1433435830,True,work,23,16,8.0,WALK,143343583,1 +1146748669,11467486690,3496184,1595042,1433435830,False,Home,16,23,18.0,WALK,143343583,1 +1146748993,11467489930,3496185,1595042,1433436240,True,work,4,16,7.0,BIKE,143343624,1 +1146748997,11467489970,3496185,1595042,1433436240,False,Home,16,4,15.0,BIKE,143343624,1 +1146759161,11467591610,3496216,1595058,1433448950,True,work,1,16,7.0,WALK,143344895,1 +1146759165,11467591650,3496216,1595058,1433448950,False,Home,16,1,21.0,WALK,143344895,1 +1146759489,11467594890,3496217,1595058,1433449360,True,work,4,16,8.0,WALK,143344936,1 +1146759493,11467594930,3496217,1595058,1433449360,False,Home,16,4,18.0,WALK,143344936,1 +1146788401,11467884010,3496306,1595103,1433485500,True,shopping,16,16,11.0,WALK,143348550,1 +1146788402,11467884020,3496306,1595103,1433485500,True,atwork,16,16,11.0,WALK,143348550,2 +1146788405,11467884050,3496306,1595103,1433485500,False,Work,16,16,13.0,WALK,143348550,1 +1146788681,11467886810,3496306,1595103,1433485850,True,work,16,17,7.0,WALK,143348585,1 +1146788685,11467886850,3496306,1595103,1433485850,False,Home,17,16,21.0,WALK,143348585,1 +1146788769,11467887690,3496307,1595103,1433485960,True,escort,22,17,7.0,SHARED2FREE,143348596,1 +1146788773,11467887730,3496307,1595103,1433485960,False,Home,17,22,7.0,DRIVEALONEFREE,143348596,1 +1146792617,11467926170,3496318,1595109,1433490770,True,work,14,17,16.0,WALK_LOC,143349077,1 +1146792621,11467926210,3496318,1595109,1433490770,False,Home,17,14,20.0,WALK_LOC,143349077,1 +1146792665,11467926650,3496319,1595109,1433490830,True,atwork,15,14,12.0,WALK,143349083,1 +1146792669,11467926690,3496319,1595109,1433490830,False,eatout,16,15,14.0,WALK,143349083,1 +1146792670,11467926700,3496319,1595109,1433490830,False,escort,2,16,14.0,WALK,143349083,2 +1146792671,11467926710,3496319,1595109,1433490830,False,Work,14,2,14.0,WALK,143349083,3 +1146792945,11467929450,3496319,1595109,1433491180,True,work,14,17,8.0,WALK_LOC,143349118,1 +1146792949,11467929490,3496319,1595109,1433491180,False,escort,2,14,17.0,WALK,143349118,1 +1146792950,11467929500,3496319,1595109,1433491180,False,Home,17,2,18.0,WALK,143349118,2 +1146793649,11467936490,3496322,1595111,1433492060,True,atwork,25,22,11.0,WALK,143349206,1 +1146793653,11467936530,3496322,1595111,1433492060,False,eatout,2,25,14.0,WALK,143349206,1 +1146793654,11467936540,3496322,1595111,1433492060,False,Work,22,2,14.0,WALK,143349206,2 +1146793929,11467939290,3496322,1595111,1433492410,True,work,22,17,9.0,TNC_SINGLE,143349241,1 +1146793933,11467939330,3496322,1595111,1433492410,False,shopping,5,22,16.0,TNC_SINGLE,143349241,1 +1146793934,11467939340,3496322,1595111,1433492410,False,Home,17,5,17.0,TNC_SINGLE,143349241,2 +1146794257,11467942570,3496323,1595111,1433492820,True,work,10,17,5.0,WALK_LRF,143349282,1 +1146794261,11467942610,3496323,1595111,1433492820,False,Home,17,10,16.0,WALK_LOC,143349282,1 +1146807393,11468073930,3496364,1595132,1433509240,True,atwork,3,1,13.0,WALK,143350924,1 +1146807397,11468073970,3496364,1595132,1433509240,False,Work,1,3,14.0,WALK,143350924,1 +1146807657,11468076570,3496364,1595132,1433509570,True,shopping,12,17,10.0,WALK,143350957,1 +1146807661,11468076610,3496364,1595132,1433509570,False,shopping,16,12,11.0,WALK,143350957,1 +1146807662,11468076620,3496364,1595132,1433509570,False,shopping,16,16,11.0,WALK,143350957,2 +1146807663,11468076630,3496364,1595132,1433509570,False,Home,17,16,11.0,WALK,143350957,3 +1146807665,11468076650,3496364,1595132,1433509580,True,shopping,16,17,11.0,TNC_SINGLE,143350958,1 +1146807669,11468076690,3496364,1595132,1433509580,False,Home,17,16,13.0,TNC_SINGLE,143350958,1 +1146807705,11468077050,3496364,1595132,1433509630,True,work,1,17,13.0,WALK_LOC,143350963,1 +1146807709,11468077090,3496364,1595132,1433509630,False,othdiscr,18,1,17.0,WALK_LRF,143350963,1 +1146807710,11468077100,3496364,1595132,1433509630,False,Home,17,18,20.0,WALK_LOC,143350963,2 +1146808033,11468080330,3496365,1595132,1433510040,True,escort,5,17,8.0,WALK,143351004,1 +1146808034,11468080340,3496365,1595132,1433510040,True,work,14,5,9.0,WALK,143351004,2 +1146808037,11468080370,3496365,1595132,1433510040,False,escort,4,14,19.0,WALK,143351004,1 +1146808038,11468080380,3496365,1595132,1433510040,False,eatout,2,4,19.0,WALK,143351004,2 +1146808039,11468080390,3496365,1595132,1433510040,False,escort,16,2,19.0,WALK,143351004,3 +1146808040,11468080400,3496365,1595132,1433510040,False,Home,17,16,19.0,WALK,143351004,4 +1146826073,11468260730,3496420,1595160,1433532590,True,work,14,17,8.0,WALK_LRF,143353259,1 +1146826077,11468260770,3496420,1595160,1433532590,False,othdiscr,8,14,17.0,WALK_LOC,143353259,1 +1146826078,11468260780,3496420,1595160,1433532590,False,Home,17,8,18.0,WALK_LRF,143353259,2 +1146826081,11468260810,3496420,1595160,1433532600,True,work,14,17,18.0,WALK,143353260,1 +1146826085,11468260850,3496420,1595160,1433532600,False,Home,17,14,21.0,WALK,143353260,1 +1146826401,11468264010,3496421,1595160,1433533000,True,work,18,17,9.0,DRIVEALONEFREE,143353300,1 +1146826405,11468264050,3496421,1595160,1433533000,False,shopping,16,18,16.0,DRIVEALONEFREE,143353300,1 +1146826406,11468264060,3496421,1595160,1433533000,False,work,2,16,19.0,DRIVEALONEFREE,143353300,2 +1146826407,11468264070,3496421,1595160,1433533000,False,othmaint,1,2,19.0,DRIVEALONEFREE,143353300,3 +1146826408,11468264080,3496421,1595160,1433533000,False,Home,17,1,19.0,DRIVEALONEFREE,143353300,4 +1146860561,11468605610,3496526,1595213,1433575700,True,atwork,10,18,11.0,WALK,143357570,1 +1146860565,11468605650,3496526,1595213,1433575700,False,work,5,10,16.0,WALK,143357570,1 +1146860566,11468605660,3496526,1595213,1433575700,False,Work,18,5,16.0,WALK,143357570,2 +1146860841,11468608410,3496526,1595213,1433576050,True,work,18,17,8.0,WALK,143357605,1 +1146860845,11468608450,3496526,1595213,1433576050,False,escort,17,18,16.0,WALK,143357605,1 +1146860846,11468608460,3496526,1595213,1433576050,False,Home,17,17,19.0,WALK,143357605,2 +1146867401,11468674010,3496546,1595223,1433584250,True,work,22,17,7.0,TNC_SINGLE,143358425,1 +1146867405,11468674050,3496546,1595223,1433584250,False,escort,8,22,14.0,WALK_LOC,143358425,1 +1146867406,11468674060,3496546,1595223,1433584250,False,escort,4,8,19.0,WALK,143358425,2 +1146867407,11468674070,3496546,1595223,1433584250,False,Home,17,4,19.0,WALK_LRF,143358425,3 +1146867465,11468674650,3496547,1595223,1433584330,True,eatout,20,17,18.0,WALK_LRF,143358433,1 +1146867469,11468674690,3496547,1595223,1433584330,False,Home,17,20,18.0,WALK_LOC,143358433,1 +1146867729,11468677290,3496547,1595223,1433584660,True,work,13,17,9.0,WALK,143358466,1 +1146867733,11468677330,3496547,1595223,1433584660,False,Home,17,13,18.0,WALK,143358466,1 +1146914353,11469143530,3496690,1595295,1433642940,True,atwork,11,20,15.0,WALK,143364294,1 +1146914357,11469143570,3496690,1595295,1433642940,False,Work,20,11,15.0,WALK,143364294,1 +1146914633,11469146330,3496690,1595295,1433643290,True,work,20,20,6.0,WALK,143364329,1 +1146914637,11469146370,3496690,1595295,1433643290,False,Home,20,20,17.0,WALK,143364329,1 +1146916601,11469166010,3496696,1595298,1433645750,True,work,22,20,8.0,WALK_LRF,143364575,1 +1146916605,11469166050,3496696,1595298,1433645750,False,Home,20,22,18.0,WALK_LRF,143364575,1 +1146916665,11469166650,3496697,1595298,1433645830,True,eatout,11,20,8.0,WALK,143364583,1 +1146916669,11469166690,3496697,1595298,1433645830,False,Home,20,11,13.0,WALK,143364583,1 +1146917913,11469179130,3496700,1595300,1433647390,True,work,9,20,7.0,DRIVEALONEFREE,143364739,1 +1146917917,11469179170,3496700,1595300,1433647390,False,Home,20,9,16.0,WALK,143364739,1 +1146918241,11469182410,3496701,1595300,1433647800,True,escort,10,20,8.0,WALK_LOC,143364780,1 +1146918242,11469182420,3496701,1595300,1433647800,True,work,4,10,8.0,WALK_LRF,143364780,2 +1146918245,11469182450,3496701,1595300,1433647800,False,social,16,4,14.0,WALK_LOC,143364780,1 +1146918246,11469182460,3496701,1595300,1433647800,False,Home,20,16,16.0,WALK_LOC,143364780,2 +1146944809,11469448090,3496782,1595341,1433681010,True,work,17,21,8.0,WALK,143368101,1 +1146944813,11469448130,3496782,1595341,1433681010,False,Home,21,17,18.0,WALK,143368101,1 +1146945025,11469450250,3496783,1595341,1433681280,True,othdiscr,16,21,11.0,WALK,143368128,1 +1146945029,11469450290,3496783,1595341,1433681280,False,Home,21,16,18.0,WALK,143368128,1 +1179519753,11795197530,3596096,1644998,1474399690,True,shopping,11,6,12.0,BIKE,147439969,1 +1179519757,11795197570,3596096,1644998,1474399690,False,shopping,11,11,12.0,BIKE,147439969,1 +1179519758,11795197580,3596096,1644998,1474399690,False,Home,6,11,13.0,WALK,147439969,2 +1179520065,11795200650,3596097,1644998,1474400080,True,school,8,6,8.0,WALK,147440008,1 +1179520069,11795200690,3596097,1644998,1474400080,False,Home,6,8,18.0,WALK,147440008,1 +1179530841,11795308410,3596130,1645015,1474413550,True,othdiscr,16,7,14.0,WALK,147441355,1 +1179530845,11795308450,3596130,1645015,1474413550,False,Home,7,16,17.0,WALK,147441355,1 +1179531217,11795312170,3596131,1645015,1474414020,True,school,7,7,14.0,WALK,147441402,1 +1179531221,11795312210,3596131,1645015,1474414020,False,Home,7,7,16.0,WALK,147441402,1 +1179567641,11795676410,3596242,1645071,1474459550,True,shopping,5,8,15.0,WALK_LOC,147445955,1 +1179567645,11795676450,3596242,1645071,1474459550,False,Home,8,5,15.0,WALK_LOC,147445955,1 +1179567953,11795679530,3596243,1645071,1474459940,True,school,8,8,6.0,WALK,147445994,1 +1179567957,11795679570,3596243,1645071,1474459940,False,Home,8,8,13.0,WALK,147445994,1 +1179607441,11796074410,3596364,1645132,1474509300,True,eatout,5,9,14.0,WALK,147450930,1 +1179607445,11796074450,3596364,1645132,1474509300,False,Home,9,5,14.0,WALK,147450930,1 +1179607657,11796076570,3596364,1645132,1474509570,True,shopping,22,9,19.0,WALK_LRF,147450957,1 +1179607661,11796076610,3596364,1645132,1474509570,False,shopping,8,22,20.0,WALK_LRF,147450957,1 +1179607662,11796076620,3596364,1645132,1474509570,False,eatout,2,8,20.0,WALK_LOC,147450957,2 +1179607663,11796076630,3596364,1645132,1474509570,False,Home,9,2,20.0,WALK_LRF,147450957,3 +1179608009,11796080090,3596365,1645132,1474510010,True,social,9,9,7.0,TNC_SINGLE,147451001,1 +1179608013,11796080130,3596365,1645132,1474510010,False,Home,9,9,18.0,TNC_SINGLE,147451001,1 +1179624713,11796247130,3596416,1645158,1474530890,True,shopping,11,9,9.0,WALK,147453089,1 +1179624717,11796247170,3596416,1645158,1474530890,False,Home,9,11,13.0,WALK,147453089,1 +1179640921,11796409210,3596466,1645183,1474551150,True,escort,19,11,8.0,WALK,147455115,1 +1179640925,11796409250,3596466,1645183,1474551150,False,Home,11,19,10.0,WALK,147455115,1 +1179640929,11796409290,3596466,1645183,1474551160,True,escort,5,11,15.0,WALK,147455116,1 +1179640933,11796409330,3596466,1645183,1474551160,False,Home,11,5,16.0,WALK,147455116,1 +1179641113,11796411130,3596466,1645183,1474551390,True,shopping,11,11,16.0,WALK,147455139,1 +1179641117,11796411170,3596466,1645183,1474551390,False,Home,11,11,16.0,WALK,147455139,1 +1179641249,11796412490,3596467,1645183,1474551560,True,escort,12,11,14.0,WALK,147455156,1 +1179641253,11796412530,3596467,1645183,1474551560,False,Home,11,12,14.0,WALK,147455156,1 +1179643697,11796436970,3596474,1645187,1474554620,True,escort,9,11,12.0,WALK_LOC,147455462,1 +1179643698,11796436980,3596474,1645187,1474554620,True,othmaint,5,9,14.0,TNC_SINGLE,147455462,2 +1179643701,11796437010,3596474,1645187,1474554620,False,eatout,22,5,17.0,WALK_LOC,147455462,1 +1179643702,11796437020,3596474,1645187,1474554620,False,Home,11,22,17.0,TNC_SINGLE,147455462,2 +1179644001,11796440010,3596475,1645187,1474555000,True,othdiscr,17,11,10.0,WALK_LOC,147455500,1 +1179644005,11796440050,3596475,1645187,1474555000,False,shopping,5,17,21.0,WALK,147455500,1 +1179644006,11796440060,3596475,1645187,1474555000,False,Home,11,5,21.0,WALK,147455500,2 +1183490521,11834905210,3608202,1651051,1479363150,True,shopping,22,7,15.0,WALK_LOC,147936315,1 +1183490525,11834905250,3608202,1651051,1479363150,False,Home,7,22,19.0,TNC_SINGLE,147936315,1 +1183490833,11834908330,3608203,1651051,1479363540,True,school,13,7,5.0,WALK_HVY,147936354,1 +1183490837,11834908370,3608203,1651051,1479363540,False,Home,7,13,10.0,WALK_LOC,147936354,1 +1183497081,11834970810,3608222,1651061,1479371350,True,shopping,11,7,11.0,WALK_LOC,147937135,1 +1183497085,11834970850,3608222,1651061,1479371350,False,Home,7,11,12.0,WALK_LOC,147937135,1 +1183497393,11834973930,3608223,1651061,1479371740,True,school,9,7,7.0,WALK,147937174,1 +1183497397,11834973970,3608223,1651061,1479371740,False,shopping,6,9,15.0,WALK_LOC,147937174,1 +1183497398,11834973980,3608223,1651061,1479371740,False,Home,7,6,23.0,WALK_LOC,147937174,2 +1183522601,11835226010,3608300,1651100,1479403250,True,othdiscr,21,7,18.0,WALK,147940325,1 +1183522605,11835226050,3608300,1651100,1479403250,False,Home,7,21,18.0,WALK,147940325,1 +1183522665,11835226650,3608300,1651100,1479403330,True,shopping,13,7,14.0,WALK,147940333,1 +1183522669,11835226690,3608300,1651100,1479403330,False,Home,7,13,16.0,WALK,147940333,1 +1183522977,11835229770,3608301,1651100,1479403720,True,school,13,7,6.0,WALK_LRF,147940372,1 +1183522981,11835229810,3608301,1651100,1479403720,False,Home,7,13,14.0,WALK_LOC,147940372,1 +1183546329,11835463290,3608372,1651136,1479432910,True,work,20,8,8.0,WALK,147943291,1 +1183546333,11835463330,3608372,1651136,1479432910,False,shopping,21,20,8.0,WALK,147943291,1 +1183546334,11835463340,3608372,1651136,1479432910,False,work,10,21,18.0,WALK,147943291,2 +1183546335,11835463350,3608372,1651136,1479432910,False,Home,8,10,18.0,WALK,147943291,3 +1183546545,11835465450,3608373,1651136,1479433180,True,escort,4,8,10.0,WALK,147943318,1 +1183546546,11835465460,3608373,1651136,1479433180,True,othdiscr,16,4,10.0,SHARED3FREE,147943318,2 +1183546549,11835465490,3608373,1651136,1479433180,False,Home,8,16,15.0,SHARED3FREE,147943318,1 +1183546593,11835465930,3608373,1651136,1479433240,True,school,8,8,16.0,WALK,147943324,1 +1183546597,11835465970,3608373,1651136,1479433240,False,Home,8,8,16.0,WALK,147943324,1 +1183550001,11835500010,3608384,1651142,1479437500,True,eatout,5,8,9.0,WALK,147943750,1 +1183550005,11835500050,3608384,1651142,1479437500,False,Home,8,5,11.0,WALK,147943750,1 +1183550265,11835502650,3608384,1651142,1479437830,True,escort,5,8,11.0,WALK,147943783,1 +1183550266,11835502660,3608384,1651142,1479437830,True,work,4,5,11.0,WALK,147943783,2 +1183550269,11835502690,3608384,1651142,1479437830,False,shopping,5,4,16.0,WALK_LOC,147943783,1 +1183550270,11835502700,3608384,1651142,1479437830,False,othdiscr,11,5,21.0,TNC_SINGLE,147943783,2 +1183550271,11835502710,3608384,1651142,1479437830,False,Home,8,11,21.0,TNC_SINGLE,147943783,3 +1183550529,11835505290,3608385,1651142,1479438160,True,eatout,25,8,5.0,WALK_LOC,147943816,1 +1183550530,11835505300,3608385,1651142,1479438160,True,school,6,25,7.0,WALK,147943816,2 +1183550533,11835505330,3608385,1651142,1479438160,False,Home,8,6,13.0,WALK_LOC,147943816,1 +1183562449,11835624490,3608422,1651161,1479453060,True,atwork,18,10,13.0,WALK,147945306,1 +1183562453,11835624530,3608422,1651161,1479453060,False,Work,10,18,13.0,WALK,147945306,1 +1183562681,11835626810,3608422,1651161,1479453350,True,shopping,5,8,9.0,WALK_LOC,147945335,1 +1183562685,11835626850,3608422,1651161,1479453350,False,Home,8,5,11.0,WALK_LOC,147945335,1 +1183562729,11835627290,3608422,1651161,1479453410,True,work,5,8,13.0,WALK,147945341,1 +1183562730,11835627300,3608422,1651161,1479453410,True,work,10,5,13.0,WALK,147945341,2 +1183562733,11835627330,3608422,1651161,1479453410,False,Home,8,10,20.0,WALK,147945341,1 +1183562993,11835629930,3608423,1651161,1479453740,True,school,13,8,7.0,WALK_LOC,147945374,1 +1183562997,11835629970,3608423,1651161,1479453740,False,Home,8,13,14.0,WALK_LRF,147945374,1 +1183563009,11835630090,3608423,1651161,1479453760,True,eatout,7,8,15.0,WALK_LOC,147945376,1 +1183563010,11835630100,3608423,1651161,1479453760,True,shopping,5,7,16.0,WALK_LOC,147945376,2 +1183563013,11835630130,3608423,1651161,1479453760,False,shopping,7,5,23.0,WALK_LOC,147945376,1 +1183563014,11835630140,3608423,1651161,1479453760,False,Home,8,7,23.0,TNC_SINGLE,147945376,2 +1183613897,11836138970,3608578,1651239,1479517370,True,work,13,9,8.0,WALK_LRF,147951737,1 +1183613901,11836139010,3608578,1651239,1479517370,False,Home,9,13,19.0,WALK_LRF,147951737,1 +1183614161,11836141610,3608579,1651239,1479517700,True,school,9,9,7.0,WALK,147951770,1 +1183614165,11836141650,3608579,1651239,1479517700,False,Home,9,9,15.0,WALK,147951770,1 +1183629553,11836295530,3608626,1651263,1479536940,True,othmaint,9,9,10.0,TNC_SINGLE,147953694,1 +1183629557,11836295570,3608626,1651263,1479536940,False,shopping,16,9,13.0,WALK_LRF,147953694,1 +1183629558,11836295580,3608626,1651263,1479536940,False,othmaint,10,16,13.0,TNC_SHARED,147953694,2 +1183629559,11836295590,3608626,1651263,1479536940,False,Home,9,10,13.0,WALK_LOC,147953694,3 +1183629561,11836295610,3608626,1651263,1479536950,True,othmaint,2,9,13.0,WALK_LRF,147953695,1 +1183629565,11836295650,3608626,1651263,1479536950,False,Home,9,2,16.0,WALK_LOC,147953695,1 +1183629905,11836299050,3608627,1651263,1479537380,True,school,11,9,7.0,WALK_LOC,147953738,1 +1183629909,11836299090,3608627,1651263,1479537380,False,shopping,21,11,17.0,WALK,147953738,1 +1183629910,11836299100,3608627,1651263,1479537380,False,Home,9,21,17.0,WALK_LOC,147953738,2 +1183678185,11836781850,3608774,1651337,1479597730,True,work,10,11,7.0,WALK,147959773,1 +1183678189,11836781890,3608774,1651337,1479597730,False,Home,11,10,17.0,WALK,147959773,1 +1183678449,11836784490,3608775,1651337,1479598060,True,school,11,11,7.0,WALK,147959806,1 +1183678453,11836784530,3608775,1651337,1479598060,False,Home,11,11,15.0,WALK,147959806,1 +1183682497,11836824970,3608788,1651344,1479603120,True,atwork,25,14,13.0,WALK,147960312,1 +1183682501,11836825010,3608788,1651344,1479603120,False,Work,14,25,13.0,WALK,147960312,1 +1183682777,11836827770,3608788,1651344,1479603470,True,work,14,16,7.0,WALK,147960347,1 +1183682781,11836827810,3608788,1651344,1479603470,False,Home,16,14,13.0,WALK,147960347,1 +1183682785,11836827850,3608788,1651344,1479603480,True,work,14,16,17.0,WALK,147960348,1 +1183682789,11836827890,3608788,1651344,1479603480,False,Home,16,14,21.0,WALK,147960348,1 +1183682993,11836829930,3608789,1651344,1479603740,True,othdiscr,12,16,12.0,WALK,147960374,1 +1183682997,11836829970,3608789,1651344,1479603740,False,Home,16,12,18.0,WALK,147960374,1 +1183692617,11836926170,3608818,1651359,1479615770,True,eatout,23,18,15.0,WALK_LRF,147961577,1 +1183692618,11836926180,3608818,1651359,1479615770,True,work,15,23,15.0,WALK,147961577,2 +1183692621,11836926210,3608818,1651359,1479615770,False,Home,18,15,18.0,WALK_LRF,147961577,1 +1183692881,11836928810,3608819,1651359,1479616100,True,school,17,18,8.0,WALK_LRF,147961610,1 +1183692885,11836928850,3608819,1651359,1479616100,False,othmaint,1,17,17.0,WALK_LRF,147961610,1 +1183692886,11836928860,3608819,1651359,1479616100,False,Home,18,1,17.0,WALK_LRF,147961610,2 +1183701209,11837012090,3608845,1651372,1479626510,True,eatout,18,20,13.0,WALK,147962651,1 +1183701213,11837012130,3608845,1651372,1479626510,False,Home,20,18,20.0,WALK,147962651,1 +1183701409,11837014090,3608845,1651372,1479626760,True,school,25,20,6.0,WALK_LRF,147962676,1 +1183701413,11837014130,3608845,1651372,1479626760,False,Home,20,25,13.0,WALK_LOC,147962676,1 +1183713673,11837136730,3608883,1651391,1479642090,True,eatout,9,21,16.0,WALK,147964209,1 +1183713677,11837136770,3608883,1651391,1479642090,False,Home,21,9,18.0,WALK,147964209,1 +1183713873,11837138730,3608883,1651391,1479642340,True,school,18,21,7.0,TNC_SHARED,147964234,1 +1183713877,11837138770,3608883,1651391,1479642340,False,othdiscr,15,18,15.0,WALK_LOC,147964234,1 +1183713878,11837138780,3608883,1651391,1479642340,False,Home,21,15,15.0,WALK_LOC,147964234,2 +1183723449,11837234490,3608912,1651406,1479654310,True,work,3,21,14.0,TNC_SINGLE,147965431,1 +1183723450,11837234500,3608912,1651406,1479654310,True,work,6,3,15.0,WALK,147965431,2 +1183723453,11837234530,3608912,1651406,1479654310,False,escort,7,6,17.0,WALK,147965431,1 +1183723454,11837234540,3608912,1651406,1479654310,False,Home,21,7,18.0,WALK_LOC,147965431,2 +1183723713,11837237130,3608913,1651406,1479654640,True,school,11,21,6.0,WALK,147965464,1 +1183723717,11837237170,3608913,1651406,1479654640,False,social,12,11,14.0,WALK_LOC,147965464,1 +1183723718,11837237180,3608913,1651406,1479654640,False,Home,21,12,15.0,WALK_LOC,147965464,2 +1183724481,11837244810,3608916,1651408,1479655600,True,atwork,14,14,11.0,WALK,147965560,1 +1183724485,11837244850,3608916,1651408,1479655600,False,Work,14,14,13.0,WALK,147965560,1 +1183724761,11837247610,3608916,1651408,1479655950,True,work,14,21,8.0,TNC_SINGLE,147965595,1 +1183724762,11837247620,3608916,1651408,1479655950,True,work,13,14,8.0,TNC_SINGLE,147965595,2 +1183724763,11837247630,3608916,1651408,1479655950,True,work,14,13,9.0,WALK_LOC,147965595,3 +1183724765,11837247650,3608916,1651408,1479655950,False,Home,21,14,18.0,WALK_LOC,147965595,1 +1183725025,11837250250,3608917,1651408,1479656280,True,school,11,21,8.0,WALK_LOC,147965628,1 +1183725029,11837250290,3608917,1651408,1479656280,False,Home,21,11,13.0,WALK_LOC,147965628,1 +1183727385,11837273850,3608924,1651412,1479659230,True,work,7,21,8.0,WALK,147965923,1 +1183727389,11837273890,3608924,1651412,1479659230,False,Home,21,7,12.0,WALK,147965923,1 +1183727649,11837276490,3608925,1651412,1479659560,True,school,10,21,6.0,WALK_LRF,147965956,1 +1183727653,11837276530,3608925,1651412,1479659560,False,othdiscr,22,10,15.0,WALK_LRF,147965956,1 +1183727654,11837276540,3608925,1651412,1479659560,False,Home,21,22,15.0,WALK_LOC,147965956,2 +1183737881,11837378810,3608956,1651428,1479672350,True,work,2,25,8.0,WALK,147967235,1 +1183737885,11837378850,3608956,1651428,1479672350,False,shopping,25,2,16.0,WALK,147967235,1 +1183737886,11837378860,3608956,1651428,1479672350,False,shopping,5,25,17.0,WALK,147967235,2 +1183737887,11837378870,3608956,1651428,1479672350,False,work,4,5,18.0,WALK,147967235,3 +1183737888,11837378880,3608956,1651428,1479672350,False,Home,25,4,18.0,WALK,147967235,4 +1183738145,11837381450,3608957,1651428,1479672680,True,school,9,25,7.0,WALK_LOC,147967268,1 +1183738149,11837381490,3608957,1651428,1479672680,False,eatout,10,9,17.0,WALK_LOC,147967268,1 +1183738150,11837381500,3608957,1651428,1479672680,False,Home,25,10,20.0,WALK_LOC,147967268,2 +1183738257,11837382570,3608958,1651429,1479672820,True,escort,5,4,8.0,WALK,147967282,1 +1183738258,11837382580,3608958,1651429,1479672820,True,atwork,4,5,8.0,WALK,147967282,2 +1183738261,11837382610,3608958,1651429,1479672820,False,Work,4,4,10.0,WALK,147967282,1 +1183738537,11837385370,3608958,1651429,1479673170,True,work,4,25,7.0,WALK_LOC,147967317,1 +1183738541,11837385410,3608958,1651429,1479673170,False,work,5,4,17.0,WALK_LOC,147967317,1 +1183738542,11837385420,3608958,1651429,1479673170,False,Home,25,5,17.0,WALK_LOC,147967317,2 +1183738801,11837388010,3608959,1651429,1479673500,True,school,25,25,11.0,WALK,147967350,1 +1183738805,11837388050,3608959,1651429,1479673500,False,Home,25,25,20.0,WALK,147967350,1 +1183739193,11837391930,3608960,1651430,1479673990,True,work,5,25,7.0,WALK,147967399,1 +1183739197,11837391970,3608960,1651430,1479673990,False,Home,25,5,18.0,WALK_LOC,147967399,1 +1183739457,11837394570,3608961,1651430,1479674320,True,school,25,25,7.0,WALK,147967432,1 +1183739461,11837394610,3608961,1651430,1479674320,False,Home,25,25,13.0,WALK,147967432,1 +1210300633,12103006330,3689940,1680413,1512875790,True,work,5,10,8.0,WALK,151287579,1 +1210300637,12103006370,3689940,1680413,1512875790,False,Home,10,5,20.0,WALK_LOC,151287579,1 +1210300873,12103008730,3689941,1680413,1512876090,True,othmaint,22,10,10.0,TNC_SINGLE,151287609,1 +1210300877,12103008770,3689941,1680413,1512876090,False,eatout,16,22,10.0,TNC_SINGLE,151287609,1 +1210300878,12103008780,3689941,1680413,1512876090,False,Home,10,16,10.0,TNC_SINGLE,151287609,2 +1210301177,12103011770,3689942,1680413,1512876470,True,othdiscr,9,10,16.0,TNC_SINGLE,151287647,1 +1210301181,12103011810,3689942,1680413,1512876470,False,Home,10,9,18.0,WALK_LOC,151287647,1 +1210301289,12103012890,3689942,1680413,1512876610,True,work,22,10,7.0,WALK_LRF,151287661,1 +1210301293,12103012930,3689942,1680413,1512876610,False,Home,10,22,15.0,WALK_LRF,151287661,1 +1210332825,12103328250,3690039,1680446,1512916030,True,atwork,11,4,10.0,WALK,151291603,1 +1210332829,12103328290,3690039,1680446,1512916030,False,othmaint,4,11,10.0,WALK,151291603,1 +1210332830,12103328300,3690039,1680446,1512916030,False,Work,4,4,10.0,WALK,151291603,2 +1210333105,12103331050,3690039,1680446,1512916380,True,work,4,10,9.0,WALK,151291638,1 +1210333109,12103331090,3690039,1680446,1512916380,False,Home,10,4,20.0,WALK,151291638,1 +1210333345,12103333450,3690040,1680446,1512916680,True,othmaint,13,10,8.0,WALK_LRF,151291668,1 +1210333349,12103333490,3690040,1680446,1512916680,False,Home,10,13,15.0,WALK_LRF,151291668,1 +1210333369,12103333690,3690040,1680446,1512916710,True,univ,9,10,16.0,WALK_LOC,151291671,1 +1210333373,12103333730,3690040,1680446,1512916710,False,Home,10,9,16.0,WALK_LOC,151291671,1 +1210333761,12103337610,3690041,1680446,1512917200,True,work,9,10,7.0,WALK,151291720,1 +1210333765,12103337650,3690041,1680446,1512917200,False,Home,10,9,17.0,WALK,151291720,1 +1229739273,12297392730,3749205,1700168,1537174090,True,atwork,22,14,12.0,TNC_SINGLE,153717409,1 +1229739277,12297392770,3749205,1700168,1537174090,False,Work,14,22,12.0,WALK,153717409,1 +1229739553,12297395530,3749205,1700168,1537174440,True,work,14,12,7.0,WALK,153717444,1 +1229739557,12297395570,3749205,1700168,1537174440,False,Home,12,14,18.0,WALK,153717444,1 +1229739881,12297398810,3749206,1700168,1537174850,True,work,2,12,8.0,WALK,153717485,1 +1229739885,12297398850,3749206,1700168,1537174850,False,Home,12,2,18.0,WALK,153717485,1 +1229739929,12297399290,3749207,1700168,1537174910,True,atwork,5,12,10.0,WALK,153717491,1 +1229739933,12297399330,3749207,1700168,1537174910,False,work,7,5,13.0,WALK,153717491,1 +1229739934,12297399340,3749207,1700168,1537174910,False,Work,12,7,13.0,WALK,153717491,2 +1229740209,12297402090,3749207,1700168,1537175260,True,work,12,12,8.0,WALK,153717526,1 +1229740213,12297402130,3749207,1700168,1537175260,False,Home,12,12,18.0,WALK,153717526,1 +1229765809,12297658090,3749286,1700195,1537207260,True,atwork,6,21,13.0,WALK,153720726,1 +1229765813,12297658130,3749286,1700195,1537207260,False,Work,21,6,14.0,WALK,153720726,1 +1229766121,12297661210,3749286,1700195,1537207650,True,work,21,18,7.0,WALK,153720765,1 +1229766125,12297661250,3749286,1700195,1537207650,False,Home,18,21,20.0,WALK,153720765,1 +1229766449,12297664490,3749287,1700195,1537208060,True,work,12,18,8.0,WALK,153720806,1 +1229766453,12297664530,3749287,1700195,1537208060,False,Home,18,12,17.0,WALK,153720806,1 +1229766777,12297667770,3749288,1700195,1537208470,True,work,5,18,8.0,WALK,153720847,1 +1229766781,12297667810,3749288,1700195,1537208470,False,Home,18,5,17.0,WALK,153720847,1 +1229786505,12297865050,3749349,1700216,1537233130,True,atwork,21,15,10.0,WALK,153723313,1 +1229786509,12297865090,3749349,1700216,1537233130,False,Work,15,21,10.0,TNC_SINGLE,153723313,1 +1229786785,12297867850,3749349,1700216,1537233480,True,work,15,21,7.0,TNC_SINGLE,153723348,1 +1229786789,12297867890,3749349,1700216,1537233480,False,Home,21,15,18.0,TNC_SINGLE,153723348,1 +1237441273,12374412730,3772686,1707995,1546801590,True,shopping,21,21,20.0,TNC_SHARED,154680159,1 +1237441277,12374412770,3772686,1707995,1546801590,False,Home,21,21,21.0,TNC_SINGLE,154680159,1 +1237441321,12374413210,3772686,1707995,1546801650,True,work,12,21,9.0,WALK,154680165,1 +1237441325,12374413250,3772686,1707995,1546801650,False,escort,11,12,11.0,WALK,154680165,1 +1237441326,12374413260,3772686,1707995,1546801650,False,Home,21,11,18.0,WALK,154680165,2 +1237441585,12374415850,3772687,1707995,1546801980,True,school,20,21,7.0,WALK,154680198,1 +1237441589,12374415890,3772687,1707995,1546801980,False,Home,21,20,15.0,WALK,154680198,1 +1237441913,12374419130,3772688,1707995,1546802390,True,school,9,21,6.0,WALK_HVY,154680239,1 +1237441917,12374419170,3772688,1707995,1546802390,False,Home,21,9,12.0,WALK_LOC,154680239,1 +1275963937,12759639370,3890133,1747144,1594954920,True,work,11,1,7.0,WALK,159495492,1 +1275963941,12759639410,3890133,1747144,1594954920,False,Home,1,11,17.0,WALK,159495492,1 +1275964177,12759641770,3890134,1747144,1594955220,True,othmaint,6,1,8.0,WALK,159495522,1 +1275964181,12759641810,3890134,1747144,1594955220,False,Home,1,6,13.0,WALK,159495522,1 +1275964529,12759645290,3890135,1747144,1594955660,True,school,8,1,7.0,WALK_LRF,159495566,1 +1275964533,12759645330,3890135,1747144,1594955660,False,othmaint,23,8,17.0,WALK_LRF,159495566,1 +1275964534,12759645340,3890135,1747144,1594955660,False,Home,1,23,17.0,WALK_LOC,159495566,2 +1275964537,12759645370,3890135,1747144,1594955670,True,school,8,1,18.0,WALK_LRF,159495567,1 +1275964541,12759645410,3890135,1747144,1594955670,False,Home,1,8,18.0,WALK_LRF,159495567,1 +1275965905,12759659050,3890139,1747146,1594957380,True,work,16,3,5.0,WALK_LOC,159495738,1 +1275965909,12759659090,3890139,1747146,1594957380,False,escort,5,16,15.0,WALK_LOC,159495738,1 +1275965910,12759659100,3890139,1747146,1594957380,False,eatout,13,5,15.0,WALK,159495738,2 +1275965911,12759659110,3890139,1747146,1594957380,False,Home,3,13,19.0,WALK,159495738,3 +1275965953,12759659530,3890140,1747146,1594957440,True,atwork,21,4,11.0,WALK,159495744,1 +1275965957,12759659570,3890140,1747146,1594957440,False,eatout,4,21,12.0,WALK,159495744,1 +1275965958,12759659580,3890140,1747146,1594957440,False,Work,4,4,12.0,WALK,159495744,2 +1275966121,12759661210,3890140,1747146,1594957650,True,othdiscr,11,3,20.0,WALK_LOC,159495765,1 +1275966125,12759661250,3890140,1747146,1594957650,False,Home,3,11,21.0,WALK_LOC,159495765,1 +1275966129,12759661290,3890140,1747146,1594957660,True,othdiscr,11,3,21.0,WALK,159495766,1 +1275966133,12759661330,3890140,1747146,1594957660,False,Home,3,11,23.0,WALK,159495766,1 +1275966137,12759661370,3890140,1747146,1594957670,True,shopping,7,3,23.0,WALK,159495767,1 +1275966138,12759661380,3890140,1747146,1594957670,True,othdiscr,17,7,23.0,WALK_LRF,159495767,2 +1275966141,12759661410,3890140,1747146,1594957670,False,Home,3,17,23.0,WALK,159495767,1 +1275966233,12759662330,3890140,1747146,1594957790,True,work,4,3,6.0,WALK,159495779,1 +1275966237,12759662370,3890140,1747146,1594957790,False,Home,3,4,19.0,WALK,159495779,1 +1275966497,12759664970,3890141,1747146,1594958120,True,school,9,3,7.0,WALK_LRF,159495812,1 +1275966501,12759665010,3890141,1747146,1594958120,False,Home,3,9,15.0,WALK_LRF,159495812,1 +1275982193,12759821930,3890189,1747162,1594977740,True,shopping,5,6,9.0,WALK,159497774,1 +1275982194,12759821940,3890189,1747162,1594977740,True,othdiscr,12,5,9.0,WALK,159497774,2 +1275982197,12759821970,3890189,1747162,1594977740,False,Home,6,12,22.0,WALK,159497774,1 +1275999233,12759992330,3890241,1747180,1594999040,True,eatout,5,2,11.0,WALK,159499904,1 +1275999234,12759992340,3890241,1747180,1594999040,True,atwork,1,5,11.0,WALK,159499904,2 +1275999237,12759992370,3890241,1747180,1594999040,False,Work,2,1,14.0,WALK,159499904,1 +1275999361,12759993610,3890241,1747180,1594999200,True,work,2,7,7.0,TNC_SINGLE,159499920,1 +1275999365,12759993650,3890241,1747180,1594999200,False,work,24,2,17.0,TNC_SINGLE,159499920,1 +1275999366,12759993660,3890241,1747180,1594999200,False,eatout,6,24,17.0,TNC_SINGLE,159499920,2 +1275999367,12759993670,3890241,1747180,1594999200,False,social,8,6,17.0,TNC_SINGLE,159499920,3 +1275999368,12759993680,3890241,1747180,1594999200,False,Home,7,8,19.0,WALK_LOC,159499920,4 +1275999577,12759995770,3890242,1747180,1594999470,True,othdiscr,5,7,12.0,WALK,159499947,1 +1275999581,12759995810,3890242,1747180,1594999470,False,Home,7,5,20.0,WALK,159499947,1 +1276001049,12760010490,3890247,1747182,1595001310,True,atwork,7,6,11.0,WALK,159500131,1 +1276001053,12760010530,3890247,1747182,1595001310,False,Work,6,7,12.0,WALK,159500131,1 +1276001329,12760013290,3890247,1747182,1595001660,True,work,6,7,6.0,WALK,159500166,1 +1276001333,12760013330,3890247,1747182,1595001660,False,Home,7,6,16.0,WALK,159500166,1 +1276001657,12760016570,3890248,1747182,1595002070,True,work,2,7,7.0,WALK,159500207,1 +1276001661,12760016610,3890248,1747182,1595002070,False,Home,7,2,17.0,WALK,159500207,1 +1276001921,12760019210,3890249,1747182,1595002400,True,school,6,7,8.0,WALK,159500240,1 +1276001925,12760019250,3890249,1747182,1595002400,False,Home,7,6,13.0,WALK,159500240,1 +1276044625,12760446250,3890379,1747226,1595055780,True,work,7,9,7.0,WALK,159505578,1 +1276044629,12760446290,3890379,1747226,1595055780,False,Home,9,7,21.0,WALK_LOC,159505578,1 +1276044713,12760447130,3890380,1747226,1595055890,True,escort,8,9,17.0,TNC_SINGLE,159505589,1 +1276044717,12760447170,3890380,1747226,1595055890,False,Home,9,8,17.0,TNC_SINGLE,159505589,1 +1276044953,12760449530,3890380,1747226,1595056190,True,work,2,9,7.0,WALK_HVY,159505619,1 +1276044957,12760449570,3890380,1747226,1595056190,False,Home,9,2,15.0,WALK_HVY,159505619,1 +1276045217,12760452170,3890381,1747226,1595056520,True,school,8,9,7.0,WALK,159505652,1 +1276045221,12760452210,3890381,1747226,1595056520,False,Home,9,8,16.0,WALK,159505652,1 +1276099641,12760996410,3890547,1747282,1595124550,True,othmaint,7,10,21.0,WALK_LOC,159512455,1 +1276099645,12760996450,3890547,1747282,1595124550,False,Home,10,7,22.0,WALK_LRF,159512455,1 +1276099729,12760997290,3890547,1747282,1595124660,True,escort,9,10,8.0,WALK_LOC,159512466,1 +1276099730,12760997300,3890547,1747282,1595124660,True,work,23,9,8.0,WALK_LOC,159512466,2 +1276099733,12760997330,3890547,1747282,1595124660,False,othmaint,9,23,18.0,WALK_LRF,159512466,1 +1276099734,12760997340,3890547,1747282,1595124660,False,Home,10,9,19.0,WALK,159512466,2 +1276100057,12761000570,3890548,1747282,1595125070,True,work,10,10,7.0,WALK,159512507,1 +1276100061,12761000610,3890548,1747282,1595125070,False,Home,10,10,17.0,WALK,159512507,1 +1276100273,12761002730,3890549,1747282,1595125340,True,othdiscr,8,10,10.0,WALK,159512534,1 +1276100277,12761002770,3890549,1747282,1595125340,False,Home,10,8,10.0,WALK,159512534,1 +1276100321,12761003210,3890549,1747282,1595125400,True,school,21,10,12.0,WALK,159512540,1 +1276100325,12761003250,3890549,1747282,1595125400,False,Home,10,21,20.0,WALK_LOC,159512540,1 +1276102401,12761024010,3890556,1747285,1595128000,True,atwork,5,24,11.0,WALK,159512800,1 +1276102405,12761024050,3890556,1747285,1595128000,False,Work,24,5,13.0,WALK,159512800,1 +1276102681,12761026810,3890556,1747285,1595128350,True,shopping,11,10,6.0,TNC_SINGLE,159512835,1 +1276102682,12761026820,3890556,1747285,1595128350,True,work,22,11,7.0,WALK_LOC,159512835,2 +1276102683,12761026830,3890556,1747285,1595128350,True,work,24,22,7.0,TNC_SINGLE,159512835,3 +1276102685,12761026850,3890556,1747285,1595128350,False,Home,10,24,17.0,WALK_HVY,159512835,1 +1276103009,12761030090,3890557,1747285,1595128760,True,work,9,10,9.0,WALK_LOC,159512876,1 +1276103013,12761030130,3890557,1747285,1595128760,False,Home,10,9,18.0,TNC_SINGLE,159512876,1 +1276120113,12761201130,3890610,1747303,1595150140,True,atwork,1,12,9.0,WALK_LOC,159515014,1 +1276120117,12761201170,3890610,1747303,1595150140,False,Work,12,1,9.0,TNC_SINGLE,159515014,1 +1276120393,12761203930,3890610,1747303,1595150490,True,work,12,10,5.0,WALK_LRF,159515049,1 +1276120397,12761203970,3890610,1747303,1595150490,False,Home,10,12,20.0,WALK_LOC,159515049,1 +1276120721,12761207210,3890611,1747303,1595150900,True,work,4,10,13.0,WALK_LOC,159515090,1 +1276120722,12761207220,3890611,1747303,1595150900,True,work,14,4,13.0,WALK_LOC,159515090,2 +1276120725,12761207250,3890611,1747303,1595150900,False,Home,10,14,16.0,TNC_SINGLE,159515090,1 +1276120985,12761209850,3890612,1747303,1595151230,True,school,11,10,11.0,WALK_LOC,159515123,1 +1276120989,12761209890,3890612,1747303,1595151230,False,Home,10,11,19.0,WALK_LOC,159515123,1 +1276144993,12761449930,3890685,1747328,1595181240,True,work,16,10,16.0,DRIVEALONEFREE,159518124,1 +1276144997,12761449970,3890685,1747328,1595181240,False,social,16,16,16.0,DRIVEALONEFREE,159518124,1 +1276144998,12761449980,3890685,1747328,1595181240,False,Home,10,16,16.0,WALK,159518124,2 +1276145321,12761453210,3890686,1747328,1595181650,True,work,15,10,7.0,WALK_LRF,159518165,1 +1276145325,12761453250,3890686,1747328,1595181650,False,othmaint,7,15,17.0,WALK_LOC,159518165,1 +1276145326,12761453260,3890686,1747328,1595181650,False,othmaint,9,7,20.0,WALK,159518165,2 +1276145327,12761453270,3890686,1747328,1595181650,False,Home,10,9,20.0,WALK,159518165,3 +1276145585,12761455850,3890687,1747328,1595181980,True,school,10,10,8.0,WALK,159518198,1 +1276145589,12761455890,3890687,1747328,1595181980,False,Home,10,10,14.0,WALK,159518198,1 +1276155817,12761558170,3890718,1747339,1595194770,True,work,22,10,13.0,WALK_LRF,159519477,1 +1276155821,12761558210,3890718,1747339,1595194770,False,othdiscr,9,22,16.0,WALK,159519477,1 +1276155822,12761558220,3890718,1747339,1595194770,False,othmaint,4,9,16.0,WALK_LOC,159519477,2 +1276155823,12761558230,3890718,1747339,1595194770,False,escort,9,4,19.0,TAXI,159519477,3 +1276155824,12761558240,3890718,1747339,1595194770,False,Home,10,9,20.0,WALK,159519477,4 +1276156097,12761560970,3890719,1747339,1595195120,True,shopping,2,10,18.0,WALK_LRF,159519512,1 +1276156101,12761561010,3890719,1747339,1595195120,False,Home,10,2,21.0,WALK_LRF,159519512,1 +1276156145,12761561450,3890719,1747339,1595195180,True,work,22,10,6.0,WALK_LRF,159519518,1 +1276156149,12761561490,3890719,1747339,1595195180,False,Home,10,22,16.0,WALK_LRF,159519518,1 +1276156409,12761564090,3890720,1747339,1595195510,True,school,10,10,8.0,WALK,159519551,1 +1276156413,12761564130,3890720,1747339,1595195510,False,Home,10,10,14.0,WALK,159519551,1 +1276186145,12761861450,3890811,1747370,1595232680,True,social,5,10,7.0,WALK,159523268,1 +1276186146,12761861460,3890811,1747370,1595232680,True,othmaint,12,5,7.0,SHARED2FREE,159523268,2 +1276186149,12761861490,3890811,1747370,1595232680,False,Home,10,12,7.0,SHARED2FREE,159523268,1 +1276186321,12761863210,3890811,1747370,1595232900,True,work,11,10,7.0,WALK,159523290,1 +1276186325,12761863250,3890811,1747370,1595232900,False,Home,10,11,21.0,WALK,159523290,1 +1276186585,12761865850,3890812,1747370,1595233230,True,school,10,10,7.0,WALK,159523323,1 +1276186589,12761865890,3890812,1747370,1595233230,False,Home,10,10,14.0,WALK,159523323,1 +1276186697,12761866970,3890813,1747370,1595233370,True,escort,8,21,14.0,WALK,159523337,1 +1276186698,12761866980,3890813,1747370,1595233370,True,atwork,7,8,14.0,WALK,159523337,2 +1276186701,12761867010,3890813,1747370,1595233370,False,Work,21,7,14.0,WALK,159523337,1 +1276186977,12761869770,3890813,1747370,1595233720,True,work,21,10,7.0,WALK_LOC,159523372,1 +1276186981,12761869810,3890813,1747370,1595233720,False,Home,10,21,22.0,WALK,159523372,1 +1276221745,12762217450,3890919,1747406,1595277180,True,work,7,11,8.0,WALK,159527718,1 +1276221749,12762217490,3890919,1747406,1595277180,False,Home,11,7,19.0,WALK,159527718,1 +1276221793,12762217930,3890920,1747406,1595277240,True,atwork,5,18,10.0,WALK,159527724,1 +1276221797,12762217970,3890920,1747406,1595277240,False,Work,18,5,11.0,WALK,159527724,1 +1276222073,12762220730,3890920,1747406,1595277590,True,work,18,11,7.0,WALK,159527759,1 +1276222077,12762220770,3890920,1747406,1595277590,False,eatout,8,18,17.0,WALK_LOC,159527759,1 +1276222078,12762220780,3890920,1747406,1595277590,False,Home,11,8,17.0,WALK_LOC,159527759,2 +1276222337,12762223370,3890921,1747406,1595277920,True,school,16,11,7.0,WALK,159527792,1 +1276222341,12762223410,3890921,1747406,1595277920,False,Home,11,16,11.0,WALK,159527792,1 +1276229617,12762296170,3890943,1747414,1595287020,True,work,12,11,8.0,WALK,159528702,1 +1276229621,12762296210,3890943,1747414,1595287020,False,Home,11,12,22.0,WALK,159528702,1 +1276229705,12762297050,3890944,1747414,1595287130,True,escort,8,11,6.0,WALK,159528713,1 +1276229709,12762297090,3890944,1747414,1595287130,False,Home,11,8,6.0,WALK,159528713,1 +1276230209,12762302090,3890945,1747414,1595287760,True,school,9,11,12.0,TAXI,159528776,1 +1276230213,12762302130,3890945,1747414,1595287760,False,escort,12,9,17.0,WALK,159528776,1 +1276230214,12762302140,3890945,1747414,1595287760,False,Home,11,12,17.0,SHARED3FREE,159528776,2 +1276235521,12762355210,3890961,1747420,1595294400,True,work,12,11,7.0,DRIVEALONEFREE,159529440,1 +1276235525,12762355250,3890961,1747420,1595294400,False,Home,11,12,18.0,DRIVEALONEFREE,159529440,1 +1276235849,12762358490,3890962,1747420,1595294810,True,work,1,11,8.0,WALK,159529481,1 +1276235853,12762358530,3890962,1747420,1595294810,False,Home,11,1,18.0,WALK_LRF,159529481,1 +1276236113,12762361130,3890963,1747420,1595295140,True,escort,11,11,8.0,WALK,159529514,1 +1276236114,12762361140,3890963,1747420,1595295140,True,school,8,11,8.0,WALK,159529514,2 +1276236117,12762361170,3890963,1747420,1595295140,False,Home,11,8,17.0,WALK,159529514,1 +1276281769,12762817690,3891102,1747467,1595352210,True,work,11,16,7.0,WALK_LOC,159535221,1 +1276281773,12762817730,3891102,1747467,1595352210,False,Home,16,11,16.0,WALK_LOC,159535221,1 +1276282049,12762820490,3891103,1747467,1595352560,True,shopping,16,16,13.0,WALK,159535256,1 +1276282053,12762820530,3891103,1747467,1595352560,False,Home,16,16,16.0,WALK,159535256,1 +1276282313,12762823130,3891104,1747467,1595352890,True,othdiscr,2,16,14.0,WALK,159535289,1 +1276282317,12762823170,3891104,1747467,1595352890,False,Home,16,2,15.0,WALK,159535289,1 +1276282321,12762823210,3891104,1747467,1595352900,True,othdiscr,19,16,16.0,WALK_LOC,159535290,1 +1276282325,12762823250,3891104,1747467,1595352900,False,Home,16,19,21.0,WALK_LOC,159535290,1 +1276282361,12762823610,3891104,1747467,1595352950,True,school,17,16,7.0,WALK_LRF,159535295,1 +1276282365,12762823650,3891104,1747467,1595352950,False,Home,16,17,14.0,WALK_LRF,159535295,1 +1276292313,12762923130,3891135,1747478,1595365390,True,atwork,2,23,11.0,WALK,159536539,1 +1276292317,12762923170,3891135,1747478,1595365390,False,othmaint,3,2,13.0,WALK,159536539,1 +1276292318,12762923180,3891135,1747478,1595365390,False,Work,23,3,13.0,WALK,159536539,2 +1276292329,12762923290,3891135,1747478,1595365410,True,othmaint,14,16,15.0,WALK,159536541,1 +1276292330,12762923300,3891135,1747478,1595365410,True,eatout,13,14,18.0,WALK,159536541,2 +1276292333,12762923330,3891135,1747478,1595365410,False,Home,16,13,21.0,WALK,159536541,1 +1276292593,12762925930,3891135,1747478,1595365740,True,work,23,16,5.0,WALK,159536574,1 +1276292597,12762925970,3891135,1747478,1595365740,False,eatout,1,23,13.0,WALK,159536574,1 +1276292598,12762925980,3891135,1747478,1595365740,False,Home,16,1,13.0,WALK,159536574,2 +1276292857,12762928570,3891136,1747478,1595366070,True,school,13,16,8.0,WALK,159536607,1 +1276292861,12762928610,3891136,1747478,1595366070,False,othmaint,13,13,18.0,WALK,159536607,1 +1276292862,12762928620,3891136,1747478,1595366070,False,Home,16,13,18.0,WALK,159536607,2 +1276293185,12762931850,3891137,1747478,1595366480,True,school,11,16,8.0,SHARED2FREE,159536648,1 +1276293189,12762931890,3891137,1747478,1595366480,False,Home,16,11,16.0,SHARED2FREE,159536648,1 +1276315225,12763152250,3891204,1747501,1595394030,True,social,8,17,9.0,DRIVEALONEFREE,159539403,1 +1276315226,12763152260,3891204,1747501,1595394030,True,work,2,8,11.0,DRIVEALONEFREE,159539403,2 +1276315229,12763152290,3891204,1747501,1595394030,False,Home,17,2,20.0,DRIVEALONEFREE,159539403,1 +1276315617,12763156170,3891206,1747501,1595394520,True,eatout,19,17,18.0,SHARED2FREE,159539452,1 +1276315621,12763156210,3891206,1747501,1595394520,False,Home,17,19,21.0,SHARED2FREE,159539452,1 +1276315817,12763158170,3891206,1747501,1595394770,True,school,25,17,7.0,WALK,159539477,1 +1276315821,12763158210,3891206,1747501,1595394770,False,Home,17,25,15.0,SHARED2FREE,159539477,1 +1276373281,12763732810,3891381,1747560,1595466600,True,work,10,25,6.0,WALK_LRF,159546660,1 +1276373285,12763732850,3891381,1747560,1595466600,False,Home,25,10,16.0,WALK_LOC,159546660,1 +1276373609,12763736090,3891382,1747560,1595467010,True,work,1,25,7.0,WALK_LOC,159546701,1 +1276373613,12763736130,3891382,1747560,1595467010,False,Home,25,1,16.0,WALK,159546701,1 +1276373873,12763738730,3891383,1747560,1595467340,True,school,25,25,8.0,WALK,159546734,1 +1276373877,12763738770,3891383,1747560,1595467340,False,Home,25,25,12.0,WALK,159546734,1 +1276384809,12763848090,3891417,1747572,1595481010,True,atwork,13,22,13.0,WALK,159548101,1 +1276384813,12763848130,3891417,1747572,1595481010,False,Work,22,13,13.0,WALK,159548101,1 +1276384913,12763849130,3891417,1747572,1595481140,True,othmaint,17,25,14.0,SHARED3FREE,159548114,1 +1276384914,12763849140,3891417,1747572,1595481140,True,othmaint,19,17,15.0,TNC_SHARED,159548114,2 +1276384917,12763849170,3891417,1747572,1595481140,False,shopping,19,19,16.0,SHARED2FREE,159548114,1 +1276384918,12763849180,3891417,1747572,1595481140,False,othmaint,18,19,16.0,SHARED2FREE,159548114,2 +1276384919,12763849190,3891417,1747572,1595481140,False,eatout,11,18,16.0,TNC_SHARED,159548114,3 +1276384920,12763849200,3891417,1747572,1595481140,False,Home,25,11,16.0,SHARED3FREE,159548114,4 +1276385089,12763850890,3891417,1747572,1595481360,True,work,22,25,6.0,WALK,159548136,1 +1276385093,12763850930,3891417,1747572,1595481360,False,Home,25,22,14.0,WALK,159548136,1 +1276385353,12763853530,3891418,1747572,1595481690,True,school,25,25,8.0,WALK,159548169,1 +1276385357,12763853570,3891418,1747572,1595481690,False,Home,25,25,14.0,WALK,159548169,1 +1276385745,12763857450,3891419,1747572,1595482180,True,work,5,25,5.0,WALK,159548218,1 +1276385749,12763857490,3891419,1747572,1595482180,False,othmaint,7,5,20.0,WALK,159548218,1 +1276385750,12763857500,3891419,1747572,1595482180,False,Home,25,7,21.0,WALK,159548218,2 +1368108865,13681088650,4171063,1809932,1710136080,True,othdiscr,9,9,10.0,WALK,171013608,1 +1368108869,13681088690,4171063,1809932,1710136080,False,Home,9,9,16.0,WALK,171013608,1 +1368109305,13681093050,4171064,1809932,1710136630,True,work,15,9,6.0,TNC_SINGLE,171013663,1 +1368109309,13681093090,4171064,1809932,1710136630,False,Home,9,15,17.0,TNC_SINGLE,171013663,1 +1368109681,13681096810,4171066,1809932,1710137100,True,atwork,5,6,10.0,WALK,171013710,1 +1368109685,13681096850,4171066,1809932,1710137100,False,shopping,5,5,10.0,WALK,171013710,1 +1368109686,13681096860,4171066,1809932,1710137100,False,Work,6,5,10.0,WALK,171013710,2 +1368109961,13681099610,4171066,1809932,1710137450,True,work,6,9,8.0,WALK,171013745,1 +1368109965,13681099650,4171066,1809932,1710137450,False,Home,9,6,17.0,WALK,171013745,1 +1368110225,13681102250,4171067,1809932,1710137780,True,shopping,12,9,12.0,WALK_LRF,171013778,1 +1368110226,13681102260,4171067,1809932,1710137780,True,univ,13,12,13.0,WALK,171013778,2 +1368110229,13681102290,4171067,1809932,1710137780,False,Home,9,13,16.0,WALK_LRF,171013778,1 +1368110617,13681106170,4171068,1809932,1710138270,True,work,11,9,5.0,WALK_LOC,171013827,1 +1368110621,13681106210,4171068,1809932,1710138270,False,Home,9,11,18.0,WALK,171013827,1 +1368110897,13681108970,4171069,1809932,1710138620,True,shopping,5,9,18.0,WALK,171013862,1 +1368110901,13681109010,4171069,1809932,1710138620,False,Home,9,5,19.0,WALK,171013862,1 +1368110945,13681109450,4171069,1809932,1710138680,True,escort,24,9,5.0,WALK_LRF,171013868,1 +1368110946,13681109460,4171069,1809932,1710138680,True,work,5,24,5.0,WALK,171013868,2 +1368110949,13681109490,4171069,1809932,1710138680,False,shopping,6,5,18.0,WALK,171013868,1 +1368110950,13681109500,4171069,1809932,1710138680,False,Home,9,6,18.0,WALK_LOC,171013868,2 +1368111161,13681111610,4171070,1809932,1710138950,True,othdiscr,20,9,10.0,WALK,171013895,1 +1368111165,13681111650,4171070,1809932,1710138950,False,Home,9,20,10.0,WALK_LOC,171013895,1 +1368111537,13681115370,4171071,1809932,1710139420,True,school,13,9,7.0,WALK_LRF,171013942,1 +1368111541,13681115410,4171071,1809932,1710139420,False,Home,9,13,11.0,WALK_LRF,171013942,1 +1368154305,13681543050,4171202,1809952,1710192880,True,eatout,5,11,14.0,WALK,171019288,1 +1368154309,13681543090,4171202,1809952,1710192880,False,Home,11,5,14.0,WALK,171019288,1 +1368154785,13681547850,4171203,1809952,1710193480,True,othdiscr,22,11,17.0,WALK_LRF,171019348,1 +1368154789,13681547890,4171203,1809952,1710193480,False,Home,11,22,19.0,WALK_LRF,171019348,1 +1368154897,13681548970,4171203,1809952,1710193620,True,work,24,11,7.0,WALK,171019362,1 +1368154901,13681549010,4171203,1809952,1710193620,False,Home,11,24,17.0,WALK,171019362,1 +1368155097,13681550970,4171204,1809952,1710193870,True,atwork,7,5,14.0,WALK,171019387,1 +1368155101,13681551010,4171204,1809952,1710193870,False,Work,5,7,14.0,WALK,171019387,1 +1368155177,13681551770,4171204,1809952,1710193970,True,shopping,16,11,6.0,TNC_SHARED,171019397,1 +1368155181,13681551810,4171204,1809952,1710193970,False,Home,11,16,6.0,DRIVEALONEFREE,171019397,1 +1368155225,13681552250,4171204,1809952,1710194030,True,work,5,11,7.0,WALK,171019403,1 +1368155229,13681552290,4171204,1809952,1710194030,False,Home,11,5,20.0,WALK,171019403,1 +1368155553,13681555530,4171205,1809952,1710194440,True,escort,11,11,8.0,WALK,171019444,1 +1368155554,13681555540,4171205,1809952,1710194440,True,work,10,11,9.0,WALK,171019444,2 +1368155557,13681555570,4171205,1809952,1710194440,False,othmaint,11,10,17.0,WALK,171019444,1 +1368155558,13681555580,4171205,1809952,1710194440,False,eatout,7,11,17.0,WALK,171019444,2 +1368155559,13681555590,4171205,1809952,1710194440,False,Home,11,7,18.0,WALK,171019444,3 +1368289969,13682899690,4171615,1810015,1710362460,True,univ,12,16,13.0,WALK,171036246,1 +1368289973,13682899730,4171615,1810015,1710362460,False,Home,16,12,17.0,WALK,171036246,1 +1368290273,13682902730,4171616,1810015,1710362840,True,othmaint,3,16,9.0,WALK,171036284,1 +1368290277,13682902770,4171616,1810015,1710362840,False,Home,16,3,11.0,WALK,171036284,1 +1368290689,13682906890,4171617,1810015,1710363360,True,social,12,16,9.0,WALK,171036336,1 +1368290690,13682906900,4171617,1810015,1710363360,True,work,15,12,12.0,WALK,171036336,2 +1368290693,13682906930,4171617,1810015,1710363360,False,Home,16,15,18.0,WALK,171036336,1 +1368291297,13682912970,4171619,1810015,1710364120,True,shopping,1,16,13.0,WALK,171036412,1 +1368291301,13682913010,4171619,1810015,1710364120,False,shopping,16,1,14.0,WALK,171036412,1 +1368291302,13682913020,4171619,1810015,1710364120,False,Home,16,16,15.0,WALK,171036412,2 +1368291609,13682916090,4171620,1810015,1710364510,True,school,8,16,7.0,WALK_LOC,171036451,1 +1368291613,13682916130,4171620,1810015,1710364510,False,Home,16,8,15.0,WALK_LOC,171036451,1 +1368292281,13682922810,4171622,1810015,1710365350,True,shopping,19,16,9.0,WALK,171036535,1 +1368292285,13682922850,4171622,1810015,1710365350,False,Home,16,19,15.0,WALK,171036535,1 +1368292657,13682926570,4171623,1810015,1710365820,True,shopping,16,16,9.0,WALK,171036582,1 +1368292658,13682926580,4171623,1810015,1710365820,True,work,21,16,9.0,WALK,171036582,2 +1368292661,13682926610,4171623,1810015,1710365820,False,Home,16,21,13.0,WALK,171036582,1 +1387047257,13870472570,4228802,1821121,1733809070,True,othdiscr,24,9,13.0,WALK_LRF,173380907,1 +1387047261,13870472610,4228802,1821121,1733809070,False,othmaint,5,24,17.0,TNC_SINGLE,173380907,1 +1387047262,13870472620,4228802,1821121,1733809070,False,Home,9,5,17.0,TNC_SINGLE,173380907,2 +1387047305,13870473050,4228802,1821121,1733809130,True,univ,13,9,20.0,WALK_LRF,173380913,1 +1387047309,13870473090,4228802,1821121,1733809130,False,othmaint,7,13,20.0,WALK_LOC,173380913,1 +1387047310,13870473100,4228802,1821121,1733809130,False,othmaint,12,7,20.0,WALK_LOC,173380913,2 +1387047311,13870473110,4228802,1821121,1733809130,False,Home,9,12,20.0,WALK_LRF,173380913,3 +1387047633,13870476330,4228803,1821121,1733809540,True,school,8,9,9.0,WALK,173380954,1 +1387047637,13870476370,4228803,1821121,1733809540,False,Home,9,8,17.0,WALK,173380954,1 +1387047977,13870479770,4228804,1821121,1733809970,True,shopping,8,9,11.0,WALK,173380997,1 +1387047981,13870479810,4228804,1821121,1733809970,False,Home,9,8,15.0,WALK,173380997,1 +1387047985,13870479850,4228804,1821121,1733809980,True,shopping,11,9,18.0,SHARED3FREE,173380998,1 +1387047989,13870479890,4228804,1821121,1733809980,False,shopping,12,11,19.0,SHARED3FREE,173380998,1 +1387047990,13870479900,4228804,1821121,1733809980,False,Home,9,12,19.0,DRIVEALONEFREE,173380998,2 +1387051441,13870514410,4228816,1821124,1733814300,True,eatout,10,9,6.0,WALK,173381430,1 +1387051442,13870514420,4228816,1821124,1733814300,True,othdiscr,20,10,7.0,SHARED3FREE,173381430,2 +1387051445,13870514450,4228816,1821124,1733814300,False,Home,9,20,7.0,SHARED3FREE,173381430,1 +1387051897,13870518970,4228816,1821124,1733814870,True,school,16,9,8.0,WALK_LOC,173381487,1 +1387051901,13870519010,4228816,1821124,1733814870,False,Home,9,16,16.0,WALK_LRF,173381487,1 +1387052225,13870522250,4228817,1821124,1733815280,True,school,8,9,7.0,WALK,173381528,1 +1387052229,13870522290,4228817,1821124,1733815280,False,Home,9,8,14.0,WALK,173381528,1 +1387052553,13870525530,4228818,1821124,1733815690,True,school,9,9,8.0,WALK,173381569,1 +1387052557,13870525570,4228818,1821124,1733815690,False,Home,9,9,13.0,WALK,173381569,1 +1387066281,13870662810,4228860,1821133,1733832850,True,othdiscr,20,11,8.0,WALK,173383285,1 +1387066285,13870662850,4228860,1821133,1733832850,False,Home,11,20,10.0,WALK,173383285,1 +1387066305,13870663050,4228860,1821133,1733832880,True,othmaint,7,11,11.0,WALK,173383288,1 +1387066309,13870663090,4228860,1821133,1733832880,False,Home,11,7,20.0,WALK,173383288,1 +1387066657,13870666570,4228861,1821133,1733833320,True,school,20,11,7.0,BIKE,173383332,1 +1387066661,13870666610,4228861,1821133,1733833320,False,Home,11,20,14.0,WALK,173383332,1 +1387067313,13870673130,4228863,1821133,1733834140,True,escort,8,11,8.0,WALK_LOC,173383414,1 +1387067314,13870673140,4228863,1821133,1733834140,True,school,7,8,9.0,WALK,173383414,2 +1387067317,13870673170,4228863,1821133,1733834140,False,eatout,14,7,17.0,WALK_LOC,173383414,1 +1387067318,13870673180,4228863,1821133,1733834140,False,Home,11,14,17.0,WALK,173383414,2 +1387067465,13870674650,4228864,1821133,1733834330,True,escort,6,11,17.0,SHARED3FREE,173383433,1 +1387067469,13870674690,4228864,1821133,1733834330,False,Home,11,6,18.0,WALK,173383433,1 +1387067617,13870676170,4228864,1821133,1733834520,True,othmaint,5,11,20.0,DRIVEALONEFREE,173383452,1 +1387067621,13870676210,4228864,1821133,1733834520,False,Home,11,5,20.0,TAXI,173383452,1 +1387067641,13870676410,4228864,1821133,1733834550,True,univ,9,11,8.0,WALK,173383455,1 +1387067645,13870676450,4228864,1821133,1733834550,False,social,10,9,14.0,WALK,173383455,1 +1387067646,13870676460,4228864,1821133,1733834550,False,Home,11,10,15.0,WALK,173383455,2 +1387067657,13870676570,4228864,1821133,1733834570,True,shopping,5,11,16.0,WALK,173383457,1 +1387067661,13870676610,4228864,1821133,1733834570,False,Home,11,5,16.0,WALK,173383457,1 +1387095409,13870954090,4228949,1821151,1733869260,True,othmaint,3,22,10.0,TNC_SHARED,173386926,1 +1387095413,13870954130,4228949,1821151,1733869260,False,Home,22,3,13.0,TNC_SINGLE,173386926,1 +1387095537,13870955370,4228949,1821151,1733869420,True,shopping,16,22,18.0,WALK,173386942,1 +1387095541,13870955410,4228949,1821151,1733869420,False,Home,22,16,18.0,WALK,173386942,1 +1387095865,13870958650,4228950,1821151,1733869830,True,shopping,5,22,17.0,TNC_SINGLE,173386983,1 +1387095869,13870958690,4228950,1821151,1733869830,False,Home,22,5,17.0,TNC_SHARED,173386983,1 +1387096785,13870967850,4228953,1821151,1733870980,True,othdiscr,18,22,14.0,TNC_SINGLE,173387098,1 +1387096789,13870967890,4228953,1821151,1733870980,False,Home,22,18,15.0,TNC_SINGLE,173387098,1 +1387097313,13870973130,4228955,1821152,1733871640,True,shopping,16,22,15.0,SHARED2FREE,173387164,1 +1387097314,13870973140,4228955,1821152,1733871640,True,escort,12,16,15.0,SHARED2FREE,173387164,2 +1387097317,13870973170,4228955,1821152,1733871640,False,Home,22,12,15.0,DRIVEALONEFREE,173387164,1 +1387097641,13870976410,4228956,1821152,1733872050,True,escort,3,22,8.0,WALK,173387205,1 +1387097645,13870976450,4228956,1821152,1733872050,False,Home,22,3,9.0,WALK,173387205,1 +1387097769,13870977690,4228956,1821152,1733872210,True,othdiscr,11,22,14.0,WALK,173387221,1 +1387097773,13870977730,4228956,1821152,1733872210,False,Home,22,11,19.0,WALK,173387221,1 +1387097969,13870979690,4228957,1821152,1733872460,True,escort,16,22,10.0,SHARED3FREE,173387246,1 +1387097973,13870979730,4228957,1821152,1733872460,False,Home,22,16,10.0,SHARED3FREE,173387246,1 +1387098489,13870984890,4228958,1821152,1733873110,True,shopping,16,22,13.0,WALK,173387311,1 +1387098493,13870984930,4228958,1821152,1733873110,False,Home,22,16,13.0,WALK,173387311,1 +1419657457,14196574570,4328223,1842600,1774571820,True,work,13,10,8.0,WALK_LRF,177457182,1 +1419657461,14196574610,4328223,1842600,1774571820,False,Home,10,13,16.0,WALK_LRF,177457182,1 +1419657873,14196578730,4328225,1842600,1774572340,True,escort,9,10,5.0,WALK_LOC,177457234,1 +1419657877,14196578770,4328225,1842600,1774572340,False,Home,10,9,6.0,TNC_SINGLE,177457234,1 +1419657881,14196578810,4328225,1842600,1774572350,True,escort,5,10,8.0,WALK,177457235,1 +1419657885,14196578850,4328225,1842600,1774572350,False,Home,10,5,11.0,WALK,177457235,1 +1419658377,14196583770,4328226,1842600,1774572970,True,school,9,10,7.0,WALK_LOC,177457297,1 +1419658381,14196583810,4328226,1842600,1774572970,False,social,11,9,16.0,WALK,177457297,1 +1419658382,14196583820,4328226,1842600,1774572970,False,Home,10,11,17.0,WALK_LOC,177457297,2 +1419658529,14196585290,4328227,1842600,1774573160,True,escort,16,10,11.0,TNC_SINGLE,177457316,1 +1419658533,14196585330,4328227,1842600,1774573160,False,Home,10,16,12.0,TNC_SINGLE,177457316,1 +1419658537,14196585370,4328227,1842600,1774573170,True,escort,11,10,17.0,TNC_SINGLE,177457317,1 +1419658541,14196585410,4328227,1842600,1774573170,False,othmaint,1,11,17.0,WALK_LRF,177457317,1 +1419658542,14196585420,4328227,1842600,1774573170,False,Home,10,1,17.0,TNC_SINGLE,177457317,2 +1419659097,14196590970,4328228,1842601,1774573870,True,work,24,10,9.0,WALK_LRF,177457387,1 +1419659101,14196591010,4328228,1842601,1774573870,False,work,1,24,16.0,WALK,177457387,1 +1419659102,14196591020,4328228,1842601,1774573870,False,eatout,9,1,19.0,WALK_LOC,177457387,2 +1419659103,14196591030,4328228,1842601,1774573870,False,Home,10,9,19.0,WALK_LOC,177457387,3 +1419659361,14196593610,4328229,1842601,1774574200,True,school,8,10,8.0,WALK_LOC,177457420,1 +1419659365,14196593650,4328229,1842601,1774574200,False,Home,10,8,13.0,WALK_LOC,177457420,1 +1419659689,14196596890,4328230,1842601,1774574610,True,school,10,10,7.0,WALK,177457461,1 +1419659693,14196596930,4328230,1842601,1774574610,False,Home,10,10,15.0,WALK,177457461,1 +1419660017,14196600170,4328231,1842601,1774575020,True,univ,9,10,18.0,WALK_LOC,177457502,1 +1419660021,14196600210,4328231,1842601,1774575020,False,othmaint,9,9,21.0,WALK,177457502,1 +1419660022,14196600220,4328231,1842601,1774575020,False,Home,10,9,21.0,WALK_LOC,177457502,2 +1500781697,15007816970,4575553,1896787,1875977120,True,work,4,3,13.0,WALK,187597712,1 +1500781701,15007817010,4575553,1896787,1875977120,False,shopping,5,4,18.0,WALK,187597712,1 +1500781702,15007817020,4575553,1896787,1875977120,False,Home,3,5,23.0,WALK,187597712,2 +1500781913,15007819130,4575554,1896787,1875977390,True,othdiscr,10,3,16.0,WALK_LRF,187597739,1 +1500781917,15007819170,4575554,1896787,1875977390,False,Home,3,10,22.0,WALK_LRF,187597739,1 +1500782025,15007820250,4575554,1896787,1875977530,True,work,1,3,6.0,WALK,187597753,1 +1500782029,15007820290,4575554,1896787,1875977530,False,Home,3,1,15.0,WALK,187597753,1 +1500782289,15007822890,4575555,1896787,1875977860,True,school,9,3,8.0,WALK_LRF,187597786,1 +1500782293,15007822930,4575555,1896787,1875977860,False,Home,3,9,16.0,WALK_LRF,187597786,1 +1500782617,15007826170,4575556,1896787,1875978270,True,school,10,3,8.0,WALK_LRF,187597827,1 +1500782621,15007826210,4575556,1896787,1875978270,False,othmaint,24,10,17.0,WALK_LRF,187597827,1 +1500782622,15007826220,4575556,1896787,1875978270,False,Home,3,24,17.0,WALK,187597827,2 +1500898201,15008982010,4575909,1896857,1876122750,True,eatout,2,24,16.0,TAXI,187612275,1 +1500898205,15008982050,4575909,1896857,1876122750,False,social,5,2,20.0,WALK,187612275,1 +1500898206,15008982060,4575909,1896857,1876122750,False,Home,24,5,20.0,WALK,187612275,2 +1500898681,15008986810,4575910,1896857,1876123350,True,othdiscr,2,24,8.0,WALK,187612335,1 +1500898685,15008986850,4575910,1896857,1876123350,False,Home,24,2,10.0,WALK,187612335,1 +1500898857,15008988570,4575911,1896857,1876123570,True,eatout,4,24,21.0,WALK,187612357,1 +1500898861,15008988610,4575911,1896857,1876123570,False,Home,24,4,22.0,WALK,187612357,1 +1500899009,15008990090,4575911,1896857,1876123760,True,othdiscr,15,24,10.0,WALK,187612376,1 +1500899013,15008990130,4575911,1896857,1876123760,False,Home,24,15,21.0,WALK,187612376,1 +1517029505,15170295050,4625089,1907359,1896286880,True,work,20,11,8.0,WALK,189628688,1 +1517029509,15170295090,4625089,1907359,1896286880,False,social,12,20,12.0,WALK,189628688,1 +1517029510,15170295100,4625089,1907359,1896286880,False,Home,11,12,12.0,WALK,189628688,2 +1517029513,15170295130,4625089,1907359,1896286890,True,work,20,11,12.0,DRIVEALONEFREE,189628689,1 +1517029517,15170295170,4625089,1907359,1896286890,False,Home,11,20,17.0,DRIVEALONEFREE,189628689,1 +1517029593,15170295930,4625090,1907359,1896286990,True,escort,23,11,8.0,SHARED2FREE,189628699,1 +1517029597,15170295970,4625090,1907359,1896286990,False,Home,11,23,8.0,SHARED2FREE,189628699,1 +1517029601,15170296010,4625090,1907359,1896287000,True,escort,4,11,16.0,WALK_LOC,189628700,1 +1517029605,15170296050,4625090,1907359,1896287000,False,Home,11,4,17.0,WALK_LRF,189628700,1 +1517029609,15170296090,4625090,1907359,1896287010,True,escort,16,11,17.0,WALK_LOC,189628701,1 +1517029613,15170296130,4625090,1907359,1896287010,False,Home,11,16,17.0,TNC_SINGLE,189628701,1 +1517029785,15170297850,4625090,1907359,1896287230,True,shopping,13,11,9.0,DRIVEALONEFREE,189628723,1 +1517029789,15170297890,4625090,1907359,1896287230,False,Home,11,13,10.0,DRIVEALONEFREE,189628723,1 +1517030161,15170301610,4625091,1907359,1896287700,True,work,12,11,11.0,WALK,189628770,1 +1517030165,15170301650,4625091,1907359,1896287700,False,Home,11,12,16.0,WALK,189628770,1 +1517030425,15170304250,4625092,1907359,1896288030,True,school,9,11,7.0,WALK,189628803,1 +1517030429,15170304290,4625092,1907359,1896288030,False,Home,11,9,15.0,WALK,189628803,1 +1517030753,15170307530,4625093,1907359,1896288440,True,school,10,11,7.0,SHARED3FREE,189628844,1 +1517030757,15170307570,4625093,1907359,1896288440,False,Home,11,10,14.0,WALK_LOC,189628844,1 +1517030865,15170308650,4625094,1907359,1896288580,True,atwork,9,15,13.0,SHARED2FREE,189628858,1 +1517030869,15170308690,4625094,1907359,1896288580,False,social,7,9,13.0,WALK,189628858,1 +1517030870,15170308700,4625094,1907359,1896288580,False,Work,15,7,13.0,SHARED2FREE,189628858,2 +1517031145,15170311450,4625094,1907359,1896288930,True,work,15,11,6.0,WALK,189628893,1 +1517031149,15170311490,4625094,1907359,1896288930,False,Home,11,15,19.0,WALK_LOC,189628893,1 +1561922537,15619225370,4761958,1931827,1952403170,True,work,14,8,7.0,WALK,195240317,1 +1561922541,15619225410,4761958,1931827,1952403170,False,Home,8,14,20.0,WALK,195240317,1 +1561922865,15619228650,4761959,1931827,1952403580,True,work,1,8,11.0,WALK,195240358,1 +1561922869,15619228690,4761959,1931827,1952403580,False,Home,8,1,18.0,WALK_LRF,195240358,1 +1561923129,15619231290,4761960,1931827,1952403910,True,school,6,8,7.0,WALK,195240391,1 +1561923133,15619231330,4761960,1931827,1952403910,False,Home,8,6,17.0,WALK,195240391,1 +1561923457,15619234570,4761961,1931827,1952404320,True,school,13,8,8.0,WALK_LOC,195240432,1 +1561923461,15619234610,4761961,1931827,1952404320,False,Home,8,13,16.0,WALK_LRF,195240432,1 +1561924481,15619244810,4761964,1931827,1952405600,True,social,5,8,11.0,WALK_LOC,195240560,1 +1561924485,15619244850,4761964,1931827,1952405600,False,Home,8,5,21.0,WALK,195240560,1 +1561924569,15619245690,4761965,1931827,1952405710,True,eatout,7,8,10.0,WALK,195240571,1 +1561924573,15619245730,4761965,1931827,1952405710,False,Home,8,7,13.0,WALK,195240571,1 +1561925377,15619253770,4761967,1931827,1952406720,True,othdiscr,12,8,8.0,WALK,195240672,1 +1561925381,15619253810,4761967,1931827,1952406720,False,Home,8,12,15.0,WALK,195240672,1 +1561925905,15619259050,4761969,1931827,1952407380,True,escort,16,8,18.0,WALK,195240738,1 +1561925909,15619259090,4761969,1931827,1952407380,False,Home,8,16,19.0,WALK,195240738,1 +1561926081,15619260810,4761969,1931827,1952407600,True,school,9,8,7.0,WALK_LOC,195240760,1 +1561926085,15619260850,4761969,1931827,1952407600,False,Home,8,9,15.0,WALK_LOC,195240760,1 +1562026137,15620261370,4762274,1931860,1952532670,True,shopping,4,10,10.0,WALK,195253267,1 +1562026141,15620261410,4762274,1931860,1952532670,False,Home,10,4,10.0,WALK,195253267,1 +1562027169,15620271690,4762277,1931860,1952533960,True,work,14,10,9.0,WALK_LRF,195253396,1 +1562027173,15620271730,4762277,1931860,1952533960,False,Home,10,14,20.0,WALK_LRF,195253396,1 +1562028481,15620284810,4762281,1931860,1952535600,True,work,1,10,8.0,WALK_LRF,195253560,1 +1562028485,15620284850,4762281,1931860,1952535600,False,Home,10,1,15.0,WALK,195253560,1 +1562028809,15620288090,4762282,1931860,1952536010,True,work,24,10,8.0,WALK_LRF,195253601,1 +1562028813,15620288130,4762282,1931860,1952536010,False,Home,10,24,17.0,WALK_LRF,195253601,1 +1562200745,15622007450,4762807,1931915,1952750930,True,eatout,5,16,11.0,WALK,195275093,1 +1562200749,15622007490,4762807,1931915,1952750930,False,Home,16,5,14.0,WALK,195275093,1 +1562201009,15622010090,4762807,1931915,1952751260,True,work,8,16,14.0,WALK,195275126,1 +1562201013,15622010130,4762807,1931915,1952751260,False,Home,16,8,19.0,WALK,195275126,1 +1562201337,15622013370,4762808,1931915,1952751670,True,work,12,16,5.0,WALK,195275167,1 +1562201341,15622013410,4762808,1931915,1952751670,False,Home,16,12,16.0,WALK,195275167,1 +1562201601,15622016010,4762809,1931915,1952752000,True,school,13,16,8.0,WALK,195275200,1 +1562201605,15622016050,4762809,1931915,1952752000,False,Home,16,13,16.0,WALK,195275200,1 +1562213801,15622138010,4762846,1931920,1952767250,True,work,9,16,7.0,WALK_LOC,195276725,1 +1562213805,15622138050,4762846,1931920,1952767250,False,Home,16,9,18.0,WALK_LRF,195276725,1 +1562214017,15622140170,4762847,1931920,1952767520,True,othdiscr,14,16,8.0,WALK,195276752,1 +1562214021,15622140210,4762847,1931920,1952767520,False,Home,16,14,10.0,WALK,195276752,1 +1562214393,15622143930,4762848,1931920,1952767990,True,school,9,16,7.0,WALK_LOC,195276799,1 +1562214397,15622143970,4762848,1931920,1952767990,False,Home,16,9,17.0,WALK_LRF,195276799,1 +1562214721,15622147210,4762849,1931920,1952768400,True,school,5,16,7.0,WALK_LOC,195276840,1 +1562214725,15622147250,4762849,1931920,1952768400,False,Home,16,5,16.0,WALK_LOC,195276840,1 +1562214873,15622148730,4762850,1931920,1952768590,True,escort,5,16,16.0,WALK,195276859,1 +1562214877,15622148770,4762850,1931920,1952768590,False,Home,16,5,20.0,WALK,195276859,1 +1562215049,15622150490,4762850,1931920,1952768810,True,school,8,16,8.0,WALK,195276881,1 +1562215053,15622150530,4762850,1931920,1952768810,False,Home,16,8,15.0,WALK,195276881,1 +1562215529,15622155290,4762852,1931920,1952769410,True,escort,13,16,9.0,SHARED2FREE,195276941,1 +1562215533,15622155330,4762852,1931920,1952769410,False,Home,16,13,9.0,SHARED2FREE,195276941,1 +1562215537,15622155370,4762852,1931920,1952769420,True,escort,9,16,13.0,TNC_SINGLE,195276942,1 +1562215541,15622155410,4762852,1931920,1952769420,False,Home,16,9,13.0,TNC_SINGLE,195276942,1 +1562215657,15622156570,4762852,1931920,1952769570,True,othdiscr,24,16,16.0,WALK,195276957,1 +1562215661,15622156610,4762852,1931920,1952769570,False,eatout,2,24,18.0,WALK,195276957,1 +1562215662,15622156620,4762852,1931920,1952769570,False,Home,16,2,18.0,WALK,195276957,2 +1562216097,15622160970,4762853,1931920,1952770120,True,work,2,16,9.0,WALK,195277012,1 +1562216101,15622161010,4762853,1931920,1952770120,False,Home,16,2,18.0,WALK_LOC,195277012,1 +1562216425,15622164250,4762854,1931920,1952770530,True,work,9,16,8.0,BIKE,195277053,1 +1562216429,15622164290,4762854,1931920,1952770530,False,Home,16,9,15.0,BIKE,195277053,1 +1562216665,15622166650,4762855,1931920,1952770830,True,othmaint,20,16,18.0,DRIVEALONEFREE,195277083,1 +1562216669,15622166690,4762855,1931920,1952770830,False,Home,16,20,18.0,TNC_SINGLE,195277083,1 +1562216753,15622167530,4762855,1931920,1952770940,True,work,5,16,7.0,WALK,195277094,1 +1562216757,15622167570,4762855,1931920,1952770940,False,Home,16,5,16.0,WALK,195277094,1 +1562216841,15622168410,4762856,1931920,1952771050,True,escort,16,16,8.0,TNC_SINGLE,195277105,1 +1562216845,15622168450,4762856,1931920,1952771050,False,Home,16,16,9.0,TNC_SINGLE,195277105,1 +1562216849,15622168490,4762856,1931920,1952771060,True,escort,5,16,13.0,WALK_LOC,195277106,1 +1562216853,15622168530,4762856,1931920,1952771060,False,Home,16,5,14.0,TNC_SINGLE,195277106,1 +1562217033,15622170330,4762856,1931920,1952771290,True,shopping,16,16,9.0,WALK,195277129,1 +1562217037,15622170370,4762856,1931920,1952771290,False,Home,16,16,12.0,WALK,195277129,1 +1562219705,15622197050,4762864,1931922,1952774630,True,work,1,16,8.0,WALK,195277463,1 +1562219709,15622197090,4762864,1931922,1952774630,False,Home,16,1,14.0,WALK,195277463,1 +1562219713,15622197130,4762864,1931922,1952774640,True,escort,16,16,17.0,WALK,195277464,1 +1562219714,15622197140,4762864,1931922,1952774640,True,work,1,16,17.0,WALK,195277464,2 +1562219717,15622197170,4762864,1931922,1952774640,False,Home,16,1,20.0,WALK,195277464,1 +1562220033,15622200330,4762865,1931922,1952775040,True,work,22,16,12.0,WALK,195277504,1 +1562220037,15622200370,4762865,1931922,1952775040,False,Home,16,22,19.0,WALK,195277504,1 +1562220297,15622202970,4762866,1931922,1952775370,True,school,16,16,8.0,WALK,195277537,1 +1562220301,15622203010,4762866,1931922,1952775370,False,Home,16,16,16.0,WALK,195277537,1 +1562220625,15622206250,4762867,1931922,1952775780,True,school,13,16,11.0,WALK,195277578,1 +1562220629,15622206290,4762867,1931922,1952775780,False,Home,16,13,21.0,WALK,195277578,1 +1562220953,15622209530,4762868,1931922,1952776190,True,school,18,16,8.0,WALK,195277619,1 +1562220957,15622209570,4762868,1931922,1952776190,False,Home,16,18,15.0,WALK_LRF,195277619,1 +1562221889,15622218890,4762871,1931922,1952777360,True,othdiscr,8,16,7.0,WALK,195277736,1 +1562221893,15622218930,4762871,1931922,1952777360,False,Home,16,8,10.0,WALK,195277736,1 +1562222049,15622220490,4762872,1931922,1952777560,True,atwork,12,4,16.0,BIKE,195277756,1 +1562222053,15622220530,4762872,1931922,1952777560,False,eatout,7,12,16.0,BIKE,195277756,1 +1562222054,15622220540,4762872,1931922,1952777560,False,Work,4,7,16.0,BIKE,195277756,2 +1562222329,15622223290,4762872,1931922,1952777910,True,work,4,16,7.0,BIKE,195277791,1 +1562222333,15622223330,4762872,1931922,1952777910,False,Home,16,4,18.0,WALK,195277791,1 +1562222609,15622226090,4762873,1931922,1952778260,True,shopping,16,16,13.0,WALK,195277826,1 +1562222613,15622226130,4762873,1931922,1952778260,False,Home,16,16,13.0,WALK,195277826,1 +1562298337,15622983370,4763104,1931947,1952872920,True,othmaint,21,16,13.0,DRIVEALONEFREE,195287292,1 +1562298341,15622983410,4763104,1931947,1952872920,False,Home,16,21,15.0,TNC_SHARED,195287292,1 +1562300393,15623003930,4763110,1931947,1952875490,True,work,11,16,7.0,WALK,195287549,1 +1562300397,15623003970,4763110,1931947,1952875490,False,Home,16,11,19.0,WALK_LOC,195287549,1 +1562300721,15623007210,4763111,1931947,1952875900,True,work,23,16,7.0,WALK,195287590,1 +1562300725,15623007250,4763111,1931947,1952875900,False,Home,16,23,19.0,WALK,195287590,1 +1562300769,15623007690,4763112,1931947,1952875960,True,atwork,13,4,10.0,WALK,195287596,1 +1562300773,15623007730,4763112,1931947,1952875960,False,Work,4,13,10.0,WALK,195287596,1 +1562301049,15623010490,4763112,1931947,1952876310,True,work,4,16,7.0,WALK_LOC,195287631,1 +1562301053,15623010530,4763112,1931947,1952876310,False,Home,16,4,16.0,WALK,195287631,1 +1562342793,15623427930,4763240,1931961,1952928490,True,escort,1,17,14.0,WALK_LOC,195292849,1 +1562342797,15623427970,4763240,1931961,1952928490,False,Home,17,1,14.0,TNC_SINGLE,195292849,1 +1562343689,15623436890,4763242,1931961,1952929610,True,work,4,17,8.0,WALK,195292961,1 +1562343693,15623436930,4763242,1931961,1952929610,False,Home,17,4,17.0,WALK,195292961,1 +1562344017,15623440170,4763243,1931961,1952930020,True,work,22,17,7.0,WALK_LRF,195293002,1 +1562344021,15623440210,4763243,1931961,1952930020,False,Home,17,22,17.0,WALK_LRF,195293002,1 +1562344281,15623442810,4763244,1931961,1952930350,True,school,13,17,6.0,WALK_LRF,195293035,1 +1562344285,15623442850,4763244,1931961,1952930350,False,Home,17,13,10.0,WALK,195293035,1 +1562344673,15623446730,4763245,1931961,1952930840,True,work,4,17,9.0,WALK,195293084,1 +1562344674,15623446740,4763245,1931961,1952930840,True,work,10,4,10.0,WALK_LRF,195293084,2 +1562344677,15623446770,4763245,1931961,1952930840,False,escort,4,10,17.0,WALK_LRF,195293084,1 +1562344678,15623446780,4763245,1931961,1952930840,False,escort,16,4,17.0,WALK,195293084,2 +1562344679,15623446790,4763245,1931961,1952930840,False,othdiscr,23,16,17.0,WALK_LOC,195293084,3 +1562344680,15623446800,4763245,1931961,1952930840,False,Home,17,23,18.0,WALK_LRF,195293084,4 +1562344937,15623449370,4763246,1931961,1952931170,True,school,8,17,7.0,WALK_LRF,195293117,1 +1562344941,15623449410,4763246,1931961,1952931170,False,Home,17,8,17.0,WALK_LRF,195293117,1 +1562345329,15623453290,4763247,1931961,1952931660,True,escort,12,17,8.0,WALK,195293166,1 +1562345330,15623453300,4763247,1931961,1952931660,True,work,15,12,8.0,WALK,195293166,2 +1562345333,15623453330,4763247,1931961,1952931660,False,social,5,15,17.0,WALK,195293166,1 +1562345334,15623453340,4763247,1931961,1952931660,False,shopping,5,5,17.0,WALK,195293166,2 +1562345335,15623453350,4763247,1931961,1952931660,False,Home,17,5,17.0,WALK,195293166,3 +1562345593,15623455930,4763248,1931961,1952931990,True,school,17,17,7.0,WALK,195293199,1 +1562345597,15623455970,4763248,1931961,1952931990,False,Home,17,17,15.0,WALK,195293199,1 +1562345705,15623457050,4763249,1931961,1952932130,True,atwork,14,14,12.0,WALK,195293213,1 +1562345709,15623457090,4763249,1931961,1952932130,False,escort,7,14,15.0,WALK,195293213,1 +1562345710,15623457100,4763249,1931961,1952932130,False,work,1,7,15.0,WALK,195293213,2 +1562345711,15623457110,4763249,1931961,1952932130,False,Work,14,1,15.0,WALK,195293213,3 +1562345985,15623459850,4763249,1931961,1952932480,True,work,14,17,9.0,SHARED2FREE,195293248,1 +1562345989,15623459890,4763249,1931961,1952932480,False,Home,17,14,18.0,SHARED3FREE,195293248,1 +1562346249,15623462490,4763250,1931961,1952932810,True,school,10,17,17.0,WALK_LRF,195293281,1 +1562346253,15623462530,4763250,1931961,1952932810,False,Home,17,10,23.0,WALK_LRF,195293281,1 +1562346361,15623463610,4763251,1931961,1952932950,True,atwork,5,13,11.0,WALK,195293295,1 +1562346365,15623463650,4763251,1931961,1952932950,False,Work,13,5,11.0,WALK,195293295,1 +1562346641,15623466410,4763251,1931961,1952933300,True,work,13,17,7.0,SHARED2FREE,195293330,1 +1562346645,15623466450,4763251,1931961,1952933300,False,Home,17,13,17.0,WALK,195293330,1 +1562346969,15623469690,4763252,1931962,1952933710,True,work,14,17,5.0,WALK,195293371,1 +1562346973,15623469730,4763252,1931962,1952933710,False,Home,17,14,20.0,WALK,195293371,1 +1562347057,15623470570,4763253,1931962,1952933820,True,escort,5,17,7.0,WALK_LOC,195293382,1 +1562347061,15623470610,4763253,1931962,1952933820,False,Home,17,5,8.0,WALK_LRF,195293382,1 +1562347065,15623470650,4763253,1931962,1952933830,True,escort,24,17,8.0,DRIVEALONEFREE,195293383,1 +1562347069,15623470690,4763253,1931962,1952933830,False,Home,17,24,9.0,DRIVEALONEFREE,195293383,1 +1562347497,15623474970,4763254,1931962,1952934370,True,atwork,10,9,12.0,WALK,195293437,1 +1562347501,15623475010,4763254,1931962,1952934370,False,Work,9,10,13.0,WALK,195293437,1 +1562347625,15623476250,4763254,1931962,1952934530,True,work,9,17,11.0,WALK_LRF,195293453,1 +1562347629,15623476290,4763254,1931962,1952934530,False,Home,17,9,18.0,WALK_LRF,195293453,1 +1562347689,15623476890,4763255,1931962,1952934610,True,eatout,12,17,17.0,WALK,195293461,1 +1562347693,15623476930,4763255,1931962,1952934610,False,Home,17,12,19.0,WALK,195293461,1 +1562347889,15623478890,4763255,1931962,1952934860,True,school,11,17,7.0,WALK,195293486,1 +1562347893,15623478930,4763255,1931962,1952934860,False,Home,17,11,15.0,WALK,195293486,1 +1562348281,15623482810,4763256,1931962,1952935350,True,work,21,17,14.0,WALK,195293535,1 +1562348285,15623482850,4763256,1931962,1952935350,False,Home,17,21,20.0,WALK,195293535,1 +1562348481,15623484810,4763257,1931962,1952935600,True,atwork,16,16,8.0,WALK,195293560,1 +1562348485,15623484850,4763257,1931962,1952935600,False,Work,16,16,8.0,WALK,195293560,1 +1562348609,15623486090,4763257,1931962,1952935760,True,work,16,17,8.0,WALK,195293576,1 +1562348613,15623486130,4763257,1931962,1952935760,False,Home,17,16,17.0,WALK,195293576,1 +1562349201,15623492010,4763259,1931962,1952936500,True,school,18,17,7.0,WALK_LRF,195293650,1 +1562349205,15623492050,4763259,1931962,1952936500,False,Home,17,18,14.0,WALK_LRF,195293650,1 +1562349313,15623493130,4763260,1931962,1952936640,True,work,13,2,11.0,WALK,195293664,1 +1562349314,15623493140,4763260,1931962,1952936640,True,atwork,22,13,11.0,WALK,195293664,2 +1562349317,15623493170,4763260,1931962,1952936640,False,Work,2,22,11.0,WALK,195293664,1 +1562349593,15623495930,4763260,1931962,1952936990,True,work,2,17,7.0,WALK,195293699,1 +1562349597,15623495970,4763260,1931962,1952936990,False,Home,17,2,15.0,WALK,195293699,1 +1562396825,15623968250,4763404,1931978,1952996030,True,shopping,16,17,13.0,WALK_LRF,195299603,1 +1562396826,15623968260,4763404,1931978,1952996030,True,work,2,16,14.0,WALK,195299603,2 +1562396829,15623968290,4763404,1931978,1952996030,False,Home,17,2,22.0,WALK_LRF,195299603,1 +1562397417,15623974170,4763406,1931978,1952996770,True,work,14,17,18.0,WALK,195299677,1 +1562397418,15623974180,4763406,1931978,1952996770,True,escort,16,14,19.0,WALK,195299677,2 +1562397419,15623974190,4763406,1931978,1952996770,True,univ,14,16,19.0,WALK,195299677,3 +1562397421,15623974210,4763406,1931978,1952996770,False,Home,17,14,21.0,WALK,195299677,1 +1562397481,15623974810,4763406,1931978,1952996850,True,work,23,17,13.0,WALK,195299685,1 +1562397485,15623974850,4763406,1931978,1952996850,False,Home,17,23,17.0,WALK,195299685,1 +1562397745,15623977450,4763407,1931978,1952997180,True,school,18,17,8.0,WALK_LRF,195299718,1 +1562397749,15623977490,4763407,1931978,1952997180,False,Home,17,18,15.0,WALK_LRF,195299718,1 +1562397761,15623977610,4763407,1931978,1952997200,True,shopping,14,17,15.0,WALK,195299720,1 +1562397765,15623977650,4763407,1931978,1952997200,False,Home,17,14,20.0,WALK,195299720,1 +1562398137,15623981370,4763408,1931978,1952997670,True,escort,16,17,10.0,WALK_LRF,195299767,1 +1562398138,15623981380,4763408,1931978,1952997670,True,othmaint,2,16,10.0,WALK,195299767,2 +1562398139,15623981390,4763408,1931978,1952997670,True,eatout,12,2,10.0,SHARED2FREE,195299767,3 +1562398140,15623981400,4763408,1931978,1952997670,True,work,16,12,13.0,WALK,195299767,4 +1562398141,15623981410,4763408,1931978,1952997670,False,escort,11,16,17.0,WALK,195299767,1 +1562398142,15623981420,4763408,1931978,1952997670,False,eatout,11,11,17.0,WALK,195299767,2 +1562398143,15623981430,4763408,1931978,1952997670,False,eatout,10,11,18.0,WALK,195299767,3 +1562398144,15623981440,4763408,1931978,1952997670,False,Home,17,10,19.0,WALK_LRF,195299767,4 +1562398185,15623981850,4763409,1931978,1952997730,True,escort,5,7,10.0,WALK,195299773,1 +1562398186,15623981860,4763409,1931978,1952997730,True,atwork,10,5,10.0,WALK,195299773,2 +1562398189,15623981890,4763409,1931978,1952997730,False,Work,7,10,13.0,WALK,195299773,1 +1562398465,15623984650,4763409,1931978,1952998080,True,work,7,17,7.0,WALK,195299808,1 +1562398469,15623984690,4763409,1931978,1952998080,False,Home,17,7,20.0,WALK_LOC,195299808,1 +1562398793,15623987930,4763410,1931978,1952998490,True,escort,12,17,8.0,WALK,195299849,1 +1562398794,15623987940,4763410,1931978,1952998490,True,work,12,12,8.0,WALK,195299849,2 +1562398797,15623987970,4763410,1931978,1952998490,False,Home,17,12,23.0,WALK,195299849,1 +1562399449,15623994490,4763412,1931978,1952999310,True,work,4,17,9.0,WALK,195299931,1 +1562399453,15623994530,4763412,1931978,1952999310,False,Home,17,4,20.0,WALK,195299931,1 +1562408145,15624081450,4763439,1931982,1953010180,True,shopping,11,17,18.0,TNC_SINGLE,195301018,1 +1562408146,15624081460,4763439,1931982,1953010180,True,shopping,18,11,18.0,TNC_SINGLE,195301018,2 +1562408149,15624081490,4763439,1931982,1953010180,False,Home,17,18,21.0,TNC_SINGLE,195301018,1 +1562408353,15624083530,4763440,1931982,1953010440,True,atwork,7,21,9.0,WALK,195301044,1 +1562408357,15624083570,4763440,1931982,1953010440,False,shopping,9,7,9.0,WALK,195301044,1 +1562408358,15624083580,4763440,1931982,1953010440,False,Work,21,9,9.0,WALK,195301044,2 +1562408633,15624086330,4763440,1931982,1953010790,True,work,21,17,7.0,WALK,195301079,1 +1562408637,15624086370,4763440,1931982,1953010790,False,Home,17,21,12.0,WALK,195301079,1 +1562409225,15624092250,4763442,1931982,1953011530,True,school,8,17,8.0,WALK_LRF,195301153,1 +1562409229,15624092290,4763442,1931982,1953011530,False,Home,17,8,16.0,WALK_LRF,195301153,1 +1562409553,15624095530,4763443,1931982,1953011940,True,school,13,17,6.0,WALK_LRF,195301194,1 +1562409557,15624095570,4763443,1931982,1953011940,False,Home,17,13,12.0,WALK_LOC,195301194,1 +1562409881,15624098810,4763444,1931982,1953012350,True,school,17,17,8.0,WALK,195301235,1 +1562409885,15624098850,4763444,1931982,1953012350,False,Home,17,17,15.0,WALK,195301235,1 +1562409993,15624099930,4763445,1931982,1953012490,True,othmaint,9,5,10.0,WALK,195301249,1 +1562409994,15624099940,4763445,1931982,1953012490,True,atwork,8,9,10.0,WALK,195301249,2 +1562409997,15624099970,4763445,1931982,1953012490,False,Work,5,8,11.0,WALK,195301249,1 +1562410273,15624102730,4763445,1931982,1953012840,True,work,5,17,5.0,WALK,195301284,1 +1562410277,15624102770,4763445,1931982,1953012840,False,Home,17,5,16.0,WALK,195301284,1 +1562414273,15624142730,4763458,1931984,1953017840,True,eatout,16,17,15.0,WALK,195301784,1 +1562414277,15624142770,4763458,1931984,1953017840,False,Home,17,16,20.0,WALK,195301784,1 +1562414449,15624144490,4763458,1931984,1953018060,True,othmaint,13,17,10.0,WALK,195301806,1 +1562414453,15624144530,4763458,1931984,1953018060,False,Home,17,13,15.0,WALK,195301806,1 +1562416457,15624164570,4763464,1931984,1953020570,True,shopping,16,17,15.0,WALK,195302057,1 +1562416461,15624164610,4763464,1931984,1953020570,False,Home,17,16,17.0,WALK,195302057,1 +1562416833,15624168330,4763465,1931984,1953021040,True,work,9,17,7.0,SHARED2FREE,195302104,1 +1562416837,15624168370,4763465,1931984,1953021040,False,Home,17,9,17.0,WALK_LRF,195302104,1 +1562417161,15624171610,4763466,1931984,1953021450,True,work,8,17,13.0,WALK_LOC,195302145,1 +1562417165,15624171650,4763466,1931984,1953021450,False,Home,17,8,22.0,WALK_LRF,195302145,1 +1562435481,15624354810,4763522,1931991,1953044350,True,shopping,19,20,19.0,WALK,195304435,1 +1562435485,15624354850,4763522,1931991,1953044350,False,Home,20,19,20.0,WALK,195304435,1 +1562435769,15624357690,4763523,1931991,1953044710,True,othmaint,14,20,18.0,WALK_LOC,195304471,1 +1562435773,15624357730,4763523,1931991,1953044710,False,Home,20,14,19.0,WALK_LOC,195304471,1 +1562435857,15624358570,4763523,1931991,1953044820,True,work,5,20,9.0,WALK,195304482,1 +1562435861,15624358610,4763523,1931991,1953044820,False,Home,20,5,17.0,WALK_LOC,195304482,1 +1562436185,15624361850,4763524,1931991,1953045230,True,work,9,20,6.0,WALK,195304523,1 +1562436189,15624361890,4763524,1931991,1953045230,False,Home,20,9,13.0,WALK,195304523,1 +1562436193,15624361930,4763524,1931991,1953045240,True,work,9,20,15.0,SHARED2FREE,195304524,1 +1562436197,15624361970,4763524,1931991,1953045240,False,Home,20,9,17.0,WALK,195304524,1 +1562436513,15624365130,4763525,1931991,1953045640,True,work,1,20,7.0,WALK_HVY,195304564,1 +1562436517,15624365170,4763525,1931991,1953045640,False,Home,20,1,16.0,WALK,195304564,1 +1562436521,15624365210,4763525,1931991,1953045650,True,work,4,20,16.0,DRIVEALONEFREE,195304565,1 +1562436522,15624365220,4763525,1931991,1953045650,True,work,14,4,17.0,WALK,195304565,2 +1562436523,15624365230,4763525,1931991,1953045650,True,work,1,14,17.0,DRIVEALONEFREE,195304565,3 +1562436525,15624365250,4763525,1931991,1953045650,False,Home,20,1,18.0,SHARED3FREE,195304565,1 +1562436729,15624367290,4763526,1931991,1953045910,True,othdiscr,5,20,18.0,WALK_LOC,195304591,1 +1562436733,15624367330,4763526,1931991,1953045910,False,Home,20,5,20.0,WALK,195304591,1 +1562436777,15624367770,4763526,1931991,1953045970,True,school,13,20,6.0,WALK_LRF,195304597,1 +1562436781,15624367810,4763526,1931991,1953045970,False,Home,20,13,16.0,WALK_LOC,195304597,1 +1562437169,15624371690,4763527,1931991,1953046460,True,work,6,20,7.0,SHARED2FREE,195304646,1 +1562437173,15624371730,4763527,1931991,1953046460,False,Home,20,6,17.0,WALK,195304646,1 +1562437433,15624374330,4763528,1931991,1953046790,True,school,20,20,11.0,WALK,195304679,1 +1562437437,15624374370,4763528,1931991,1953046790,False,Home,20,20,17.0,WALK,195304679,1 +1562437825,15624378250,4763529,1931991,1953047280,True,work,9,20,7.0,WALK,195304728,1 +1562437829,15624378290,4763529,1931991,1953047280,False,Home,20,9,18.0,WALK,195304728,1 +1562438089,15624380890,4763530,1931991,1953047610,True,school,7,20,7.0,WALK,195304761,1 +1562438093,15624380930,4763530,1931991,1953047610,False,Home,20,7,13.0,WALK,195304761,1 +1562438433,15624384330,4763531,1931991,1953048040,True,shopping,12,20,14.0,WALK,195304804,1 +1562438437,15624384370,4763531,1931991,1953048040,False,Home,20,12,15.0,WALK,195304804,1 +1562438825,15624388250,4763533,1931991,1953048530,True,atwork,11,10,7.0,WALK,195304853,1 +1562438829,15624388290,4763533,1931991,1953048530,False,Work,10,11,7.0,WALK,195304853,1 +1562439137,15624391370,4763533,1931991,1953048920,True,work,10,20,7.0,WALK,195304892,1 +1562439141,15624391410,4763533,1931991,1953048920,False,Home,20,10,10.0,WALK,195304892,1 +1562439145,15624391450,4763533,1931991,1953048930,True,work,10,20,13.0,WALK,195304893,1 +1562439149,15624391490,4763533,1931991,1953048930,False,Home,20,10,20.0,WALK,195304893,1 +1562468377,15624683770,4763623,1932002,1953085470,True,atwork,24,2,10.0,WALK,195308547,1 +1562468381,15624683810,4763623,1932002,1953085470,False,eatout,5,24,13.0,WALK,195308547,1 +1562468382,15624683820,4763623,1932002,1953085470,False,shopping,8,5,13.0,WALK,195308547,2 +1562468383,15624683830,4763623,1932002,1953085470,False,Work,2,8,13.0,WALK,195308547,3 +1562468657,15624686570,4763623,1932002,1953085820,True,work,2,21,7.0,DRIVEALONEFREE,195308582,1 +1562468661,15624686610,4763623,1932002,1953085820,False,Home,21,2,18.0,WALK,195308582,1 +1562468985,15624689850,4763624,1932002,1953086230,True,work,19,21,7.0,WALK_LOC,195308623,1 +1562468989,15624689890,4763624,1932002,1953086230,False,Home,21,19,17.0,WALK,195308623,1 +1562469577,15624695770,4763626,1932002,1953086970,True,school,8,21,8.0,WALK,195308697,1 +1562469581,15624695810,4763626,1932002,1953086970,False,Home,21,8,15.0,WALK,195308697,1 +1562469905,15624699050,4763627,1932002,1953087380,True,school,13,21,13.0,WALK,195308738,1 +1562469909,15624699090,4763627,1932002,1953087380,False,Home,21,13,21.0,WALK,195308738,1 +1562470625,15624706250,4763629,1932002,1953088280,True,work,10,21,8.0,WALK,195308828,1 +1562470629,15624706290,4763629,1932002,1953088280,False,Home,21,10,18.0,WALK,195308828,1 +1562489977,15624899770,4763688,1932009,1953112470,True,work,2,21,9.0,WALK,195311247,1 +1562489981,15624899810,4763688,1932009,1953112470,False,Home,21,2,16.0,WALK,195311247,1 +1562490257,15624902570,4763689,1932009,1953112820,True,shopping,25,21,12.0,WALK_LOC,195311282,1 +1562490261,15624902610,4763689,1932009,1953112820,False,Home,21,25,15.0,WALK_LOC,195311282,1 +1562490569,15624905690,4763690,1932009,1953113210,True,school,17,21,9.0,WALK_LRF,195311321,1 +1562490573,15624905730,4763690,1932009,1953113210,False,Home,21,17,15.0,WALK_LOC,195311321,1 +1562490897,15624908970,4763691,1932009,1953113620,True,school,25,21,10.0,WALK,195311362,1 +1562490901,15624909010,4763691,1932009,1953113620,False,Home,21,25,15.0,WALK,195311362,1 +1562491225,15624912250,4763692,1932009,1953114030,True,school,7,21,8.0,WALK_LOC,195311403,1 +1562491229,15624912290,4763692,1932009,1953114030,False,Home,21,7,12.0,WALK_LOC,195311403,1 +1562491705,15624917050,4763694,1932009,1953114630,True,escort,6,21,7.0,WALK_LOC,195311463,1 +1562491709,15624917090,4763694,1932009,1953114630,False,Home,21,6,13.0,WALK_LOC,195311463,1 +1562491713,15624917130,4763694,1932009,1953114640,True,escort,21,21,15.0,TNC_SHARED,195311464,1 +1562491717,15624917170,4763694,1932009,1953114640,False,Home,21,21,15.0,TNC_SINGLE,195311464,1 +1562492009,15624920090,4763695,1932009,1953115010,True,eatout,5,21,12.0,WALK,195311501,1 +1562492013,15624920130,4763695,1932009,1953115010,False,Home,21,5,13.0,WALK,195311501,1 +1562492601,15624926010,4763696,1932009,1953115750,True,work,23,21,9.0,WALK_LRF,195311575,1 +1562492605,15624926050,4763696,1932009,1953115750,False,Home,21,23,12.0,WALK_LRF,195311575,1 +1562492609,15624926090,4763696,1932009,1953115760,True,escort,9,21,17.0,DRIVEALONEFREE,195311576,1 +1562492610,15624926100,4763696,1932009,1953115760,True,work,23,9,17.0,DRIVEALONEFREE,195311576,2 +1562492613,15624926130,4763696,1932009,1953115760,False,othmaint,4,23,21.0,DRIVEALONEFREE,195311576,1 +1562492614,15624926140,4763696,1932009,1953115760,False,Home,21,4,21.0,SHARED2FREE,195311576,2 +1579966097,15799660970,4816969,1945964,1974957620,True,othmaint,9,6,13.0,WALK_LOC,197495762,1 +1579966098,15799660980,4816969,1945964,1974957620,True,shopping,5,9,14.0,TNC_SHARED,197495762,2 +1579966101,15799661010,4816969,1945964,1974957620,False,Home,6,5,16.0,WALK_LOC,197495762,1 +1579968857,15799688570,4816978,1945973,1974961070,True,escort,7,6,10.0,TNC_SINGLE,197496107,1 +1579968861,15799688610,4816978,1945973,1974961070,False,shopping,5,7,10.0,WALK_LOC,197496107,1 +1579968862,15799688620,4816978,1945973,1974961070,False,Home,6,5,10.0,WALK_LOC,197496107,2 +1579969377,15799693770,4816979,1945974,1974961720,True,shopping,13,6,12.0,WALK,197496172,1 +1579969381,15799693810,4816979,1945974,1974961720,False,Home,6,13,14.0,WALK_LOC,197496172,1 +1579999841,15799998410,4817072,1946067,1974999800,True,othmaint,12,23,13.0,DRIVEALONEFREE,197499980,1 +1579999842,15799998420,4817072,1946067,1974999800,True,othmaint,5,12,14.0,TNC_SHARED,197499980,2 +1579999845,15799998450,4817072,1946067,1974999800,False,Home,23,5,14.0,DRIVEALONEFREE,197499980,1 +1582193025,15821930250,4823759,1952754,1977741280,True,othdiscr,5,7,10.0,WALK,197774128,1 +1582193026,15821930260,4823759,1952754,1977741280,True,social,16,5,11.0,WALK,197774128,2 +1582193027,15821930270,4823759,1952754,1977741280,True,escort,16,16,11.0,WALK,197774128,3 +1582193029,15821930290,4823759,1952754,1977741280,False,Home,7,16,20.0,WALK,197774128,1 +1582193033,15821930330,4823759,1952754,1977741290,True,escort,7,7,21.0,WALK,197774129,1 +1582193037,15821930370,4823759,1952754,1977741290,False,Home,7,7,21.0,WALK,197774129,1 +1582199057,15821990570,4823777,1952772,1977748820,True,othdiscr,9,7,13.0,TNC_SINGLE,197774882,1 +1582199061,15821990610,4823777,1952772,1977748820,False,Home,7,9,16.0,TNC_SINGLE,197774882,1 +1582199081,15821990810,4823777,1952772,1977748850,True,othmaint,5,7,8.0,WALK,197774885,1 +1582199085,15821990850,4823777,1952772,1977748850,False,Home,7,5,10.0,WALK,197774885,1 +1582204745,15822047450,4823794,1952789,1977755930,True,work,12,12,7.0,WALK,197775593,1 +1582204749,15822047490,4823794,1952789,1977755930,False,Home,12,12,16.0,TNC_SINGLE,197775593,1 +1582205641,15822056410,4823797,1952792,1977757050,True,othmaint,7,14,9.0,WALK,197775705,1 +1582205645,15822056450,4823797,1952792,1977757050,False,Home,14,7,10.0,WALK,197775705,1 +1582205729,15822057290,4823797,1952792,1977757160,True,work,12,14,11.0,BIKE,197775716,1 +1582205733,15822057330,4823797,1952792,1977757160,False,Home,14,12,17.0,BIKE,197775716,1 +1582227113,15822271130,4823863,1952858,1977783890,True,eatout,9,20,12.0,SHARED3FREE,197778389,1 +1582227117,15822271170,4823863,1952858,1977783890,False,Home,20,9,21.0,SHARED3FREE,197778389,1 +1582228249,15822282490,4823866,1952861,1977785310,True,othdiscr,24,20,15.0,DRIVEALONEFREE,197778531,1 +1582228253,15822282530,4823866,1952861,1977785310,False,Home,20,24,17.0,SHARED2FREE,197778531,1 +1582228361,15822283610,4823866,1952861,1977785450,True,work,20,20,5.0,SHARED2FREE,197778545,1 +1582228365,15822283650,4823866,1952861,1977785450,False,shopping,19,20,11.0,SHARED2FREE,197778545,1 +1582228366,15822283660,4823866,1952861,1977785450,False,Home,20,19,11.0,DRIVEALONEFREE,197778545,2 +1582231689,15822316890,4823877,1952872,1977789610,True,atwork,25,13,11.0,WALK,197778961,1 +1582231693,15822316930,4823877,1952872,1977789610,False,Work,13,25,11.0,WALK,197778961,1 +1582231969,15822319690,4823877,1952872,1977789960,True,work,13,20,8.0,WALK_LOC,197778996,1 +1582231973,15822319730,4823877,1952872,1977789960,False,Home,20,13,20.0,WALK_LOC,197778996,1 +1582240825,15822408250,4823904,1952899,1977801030,True,work,8,21,7.0,WALK,197780103,1 +1582240829,15822408290,4823904,1952899,1977801030,False,Home,21,8,17.0,WALK,197780103,1 +1582241217,15822412170,4823906,1952901,1977801520,True,eatout,11,21,15.0,WALK,197780152,1 +1582241221,15822412210,4823906,1952901,1977801520,False,Home,21,11,20.0,WALK,197780152,1 +1582249729,15822497290,4823932,1952927,1977812160,True,atwork,17,2,18.0,WALK,197781216,1 +1582249733,15822497330,4823932,1952927,1977812160,False,work,14,17,18.0,WALK,197781216,1 +1582249734,15822497340,4823932,1952927,1977812160,False,eatout,25,14,18.0,WALK,197781216,2 +1582249735,15822497350,4823932,1952927,1977812160,False,eatout,7,25,18.0,WALK,197781216,3 +1582249736,15822497360,4823932,1952927,1977812160,False,Work,2,7,18.0,WALK,197781216,4 +1582250009,15822500090,4823932,1952927,1977812510,True,work,2,22,7.0,TNC_SINGLE,197781251,1 +1582250013,15822500130,4823932,1952927,1977812510,False,Home,22,2,19.0,WALK,197781251,1 +1591190697,15911906970,4851191,1967890,1988988370,True,eatout,4,6,16.0,WALK,198898837,1 +1591190701,15911907010,4851191,1967890,1988988370,False,Home,6,4,17.0,WALK,198898837,1 +1591190913,15911909130,4851191,1967890,1988988640,True,social,7,6,17.0,WALK,198898864,1 +1591190914,15911909140,4851191,1967890,1988988640,True,shopping,5,7,17.0,WALK,198898864,2 +1591190917,15911909170,4851191,1967890,1988988640,False,Home,6,5,20.0,WALK,198898864,1 +1591194457,15911944570,4851202,1967896,1988993070,True,othdiscr,18,7,15.0,WALK,198899307,1 +1591194461,15911944610,4851202,1967896,1988993070,False,Home,7,18,19.0,WALK,198899307,1 +1591194481,15911944810,4851202,1967896,1988993100,True,othmaint,16,7,12.0,WALK_LOC,198899310,1 +1591194485,15911944850,4851202,1967896,1988993100,False,Home,7,16,14.0,WALK_LOC,198899310,1 +1591194521,15911945210,4851202,1967896,1988993150,True,shopping,11,7,8.0,WALK,198899315,1 +1591194525,15911945250,4851202,1967896,1988993150,False,Home,7,11,10.0,WALK,198899315,1 +1591223913,15912239130,4851292,1967941,1989029890,True,othmaint,19,8,14.0,TNC_SHARED,198902989,1 +1591223917,15912239170,4851292,1967941,1989029890,False,Home,8,19,14.0,SHARED3FREE,198902989,1 +1591224025,15912240250,4851292,1967941,1989030030,True,work,10,8,8.0,WALK_LOC,198903003,1 +1591224026,15912240260,4851292,1967941,1989030030,True,univ,9,10,9.0,WALK_LOC,198903003,2 +1591224029,15912240290,4851292,1967941,1989030030,False,work,7,9,14.0,WALK_LOC,198903003,1 +1591224030,15912240300,4851292,1967941,1989030030,False,shopping,5,7,14.0,WALK,198903003,2 +1591224031,15912240310,4851292,1967941,1989030030,False,escort,10,5,14.0,WALK_LOC,198903003,3 +1591224032,15912240320,4851292,1967941,1989030030,False,Home,8,10,14.0,WALK_LOC,198903003,4 +1591224305,15912243050,4851293,1967941,1989030380,True,othdiscr,11,8,12.0,WALK_LOC,198903038,1 +1591224309,15912243090,4851293,1967941,1989030380,False,Home,8,11,13.0,WALK_LOC,198903038,1 +1591224329,15912243290,4851293,1967941,1989030410,True,othmaint,9,8,14.0,WALK,198903041,1 +1591224333,15912243330,4851293,1967941,1989030410,False,Home,8,9,16.0,WALK,198903041,1 +1591224353,15912243530,4851293,1967941,1989030440,True,univ,13,8,18.0,WALK_LOC,198903044,1 +1591224357,15912243570,4851293,1967941,1989030440,False,work,1,13,18.0,WALK_LOC,198903044,1 +1591224358,15912243580,4851293,1967941,1989030440,False,Home,8,1,18.0,WALK_LRF,198903044,2 +1591224369,15912243690,4851293,1967941,1989030460,True,shopping,1,8,17.0,DRIVEALONEFREE,198903046,1 +1591224373,15912243730,4851293,1967941,1989030460,False,Home,8,1,17.0,DRIVEALONEFREE,198903046,1 +1591268433,15912684330,4851428,1968009,1989085540,True,eatout,12,22,8.0,WALK,198908554,1 +1591268437,15912684370,4851428,1968009,1989085540,False,Home,22,12,13.0,WALK,198908554,1 +1591268649,15912686490,4851428,1968009,1989085810,True,shopping,5,22,14.0,WALK,198908581,1 +1591268653,15912686530,4851428,1968009,1989085810,False,Home,22,5,16.0,WALK,198908581,1 +1591269001,15912690010,4851429,1968009,1989086250,True,social,1,22,16.0,WALK,198908625,1 +1591269005,15912690050,4851429,1968009,1989086250,False,Home,22,1,21.0,WALK,198908625,1 +1591305321,15913053210,4851540,1968065,1989131650,True,othdiscr,25,25,13.0,WALK,198913165,1 +1591305325,15913053250,4851540,1968065,1989131650,False,Home,25,25,15.0,WALK,198913165,1 +1591305649,15913056490,4851541,1968065,1989132060,True,othdiscr,13,25,9.0,WALK,198913206,1 +1591305653,15913056530,4851541,1968065,1989132060,False,Home,25,13,11.0,WALK,198913206,1 +1591305657,15913056570,4851541,1968065,1989132070,True,social,23,25,11.0,WALK,198913207,1 +1591305658,15913056580,4851541,1968065,1989132070,True,othdiscr,25,23,11.0,WALK,198913207,2 +1591305661,15913056610,4851541,1968065,1989132070,False,Home,25,25,11.0,TNC_SHARED,198913207,1 +1591305665,15913056650,4851541,1968065,1989132080,True,othdiscr,2,25,14.0,WALK,198913208,1 +1591305669,15913056690,4851541,1968065,1989132080,False,Home,25,2,17.0,WALK,198913208,1 +1592785913,15927859130,4856054,1970322,1990982390,True,othdiscr,22,20,15.0,WALK_LRF,199098239,1 +1592785917,15927859170,4856054,1970322,1990982390,False,Home,20,22,20.0,WALK_HVY,199098239,1 +1592786073,15927860730,4856055,1970322,1990982590,True,atwork,25,12,10.0,WALK,199098259,1 +1592786077,15927860770,4856055,1970322,1990982590,False,Work,12,25,10.0,WALK,199098259,1 +1592786089,15927860890,4856055,1970322,1990982610,True,eatout,12,20,18.0,SHARED3FREE,199098261,1 +1592786093,15927860930,4856055,1970322,1990982610,False,Home,20,12,19.0,SHARED3FREE,199098261,1 +1592786353,15927863530,4856055,1970322,1990982940,True,work,12,20,8.0,SHARED3FREE,199098294,1 +1592786357,15927863570,4856055,1970322,1990982940,False,Home,20,12,18.0,SHARED2FREE,199098294,1 +1592789697,15927896970,4856066,1970328,1990987120,True,eatout,16,21,11.0,WALK,199098712,1 +1592789701,15927897010,4856066,1970328,1990987120,False,Home,21,16,19.0,WALK,199098712,1 +1592790009,15927900090,4856067,1970328,1990987510,True,atwork,7,4,11.0,SHARED3FREE,199098751,1 +1592790013,15927900130,4856067,1970328,1990987510,False,Work,4,7,11.0,SHARED3FREE,199098751,1 +1592790025,15927900250,4856067,1970328,1990987530,True,eatout,12,21,15.0,TNC_SINGLE,199098753,1 +1592790029,15927900290,4856067,1970328,1990987530,False,Home,21,12,15.0,DRIVEALONEFREE,199098753,1 +1592790241,15927902410,4856067,1970328,1990987800,True,shopping,25,21,16.0,SHARED2FREE,199098780,1 +1592790242,15927902420,4856067,1970328,1990987800,True,shopping,7,25,16.0,WALK,199098780,2 +1592790245,15927902450,4856067,1970328,1990987800,False,eatout,9,7,16.0,SHARED2FREE,199098780,1 +1592790246,15927902460,4856067,1970328,1990987800,False,shopping,11,9,16.0,DRIVEALONEFREE,199098780,2 +1592790247,15927902470,4856067,1970328,1990987800,False,shopping,11,11,16.0,SHARED2FREE,199098780,3 +1592790248,15927902480,4856067,1970328,1990987800,False,Home,21,11,16.0,SHARED2FREE,199098780,4 +1592790289,15927902890,4856067,1970328,1990987860,True,work,4,21,6.0,WALK,199098786,1 +1592790293,15927902930,4856067,1970328,1990987860,False,Home,21,4,15.0,WALK,199098786,1 +1592798225,15927982250,4856092,1970341,1990997780,True,eatout,5,22,9.0,WALK,199099778,1 +1592798229,15927982290,4856092,1970341,1990997780,False,Home,22,5,16.0,WALK,199099778,1 +1592798233,15927982330,4856092,1970341,1990997790,True,shopping,8,22,17.0,WALK_LRF,199099779,1 +1592798234,15927982340,4856092,1970341,1990997790,True,eatout,4,8,18.0,WALK,199099779,2 +1592798237,15927982370,4856092,1970341,1990997790,False,shopping,5,4,20.0,WALK,199099779,1 +1592798238,15927982380,4856092,1970341,1990997790,False,Home,22,5,20.0,WALK,199099779,2 +1592798553,15927985530,4856093,1970341,1990998190,True,eatout,21,22,20.0,WALK,199099819,1 +1592798557,15927985570,4856093,1970341,1990998190,False,Home,22,21,22.0,WALK,199099819,1 +1592798769,15927987690,4856093,1970341,1990998460,True,shopping,18,22,13.0,SHARED2FREE,199099846,1 +1592798773,15927987730,4856093,1970341,1990998460,False,Home,22,18,17.0,DRIVEALONEFREE,199099846,1 +1601200585,16012005850,4881709,1983149,2001500730,True,othmaint,7,2,13.0,WALK,200150073,1 +1601200586,16012005860,4881709,1983149,2001500730,True,atwork,25,7,13.0,WALK,200150073,2 +1601200589,16012005890,4881709,1983149,2001500730,False,Work,2,25,13.0,WALK,200150073,1 +1601200865,16012008650,4881709,1983149,2001501080,True,work,2,7,8.0,WALK,200150108,1 +1601200869,16012008690,4881709,1983149,2001501080,False,Home,7,2,17.0,WALK_LOC,200150108,1 +1601222137,16012221370,4881774,1983182,2001527670,True,shopping,5,8,10.0,TNC_SINGLE,200152767,1 +1601222141,16012221410,4881774,1983182,2001527670,False,Home,8,5,10.0,TNC_SINGLE,200152767,1 +1601222145,16012221450,4881774,1983182,2001527680,True,shopping,2,8,13.0,TNC_SINGLE,200152768,1 +1601222149,16012221490,4881774,1983182,2001527680,False,shopping,11,2,17.0,TNC_SINGLE,200152768,1 +1601222150,16012221500,4881774,1983182,2001527680,False,Home,8,11,17.0,TNC_SINGLE,200152768,2 +1601222465,16012224650,4881775,1983182,2001528080,True,shopping,16,8,18.0,WALK,200152808,1 +1601222469,16012224690,4881775,1983182,2001528080,False,othmaint,7,16,19.0,WALK,200152808,1 +1601222470,16012224700,4881775,1983182,2001528080,False,Home,8,7,19.0,WALK,200152808,2 +1601223449,16012234490,4881778,1983184,2001529310,True,othdiscr,9,12,18.0,WALK_LRF,200152931,1 +1601223450,16012234500,4881778,1983184,2001529310,True,shopping,11,9,18.0,WALK_LOC,200152931,2 +1601223453,16012234530,4881778,1983184,2001529310,False,Home,12,11,18.0,WALK_LOC,200152931,1 +1601223561,16012235610,4881779,1983184,2001529450,True,eatout,12,12,7.0,WALK,200152945,1 +1601223565,16012235650,4881779,1983184,2001529450,False,Home,12,12,15.0,WALK,200152945,1 +1601233969,16012339690,4881810,1983200,2001542460,True,escort,7,20,10.0,WALK_LOC,200154246,1 +1601233970,16012339700,4881810,1983200,2001542460,True,social,12,7,12.0,TNC_SINGLE,200154246,2 +1601233973,16012339730,4881810,1983200,2001542460,False,Home,20,12,17.0,TNC_SINGLE,200154246,1 +1601233977,16012339770,4881810,1983200,2001542470,True,social,6,20,17.0,WALK_LOC,200154247,1 +1601233981,16012339810,4881810,1983200,2001542470,False,social,13,6,17.0,WALK,200154247,1 +1601233982,16012339820,4881810,1983200,2001542470,False,Home,20,13,17.0,WALK_LOC,200154247,2 +1601234057,16012340570,4881811,1983200,2001542570,True,eatout,8,20,12.0,WALK,200154257,1 +1601234061,16012340610,4881811,1983200,2001542570,False,Home,20,8,13.0,WALK,200154257,1 +1601234321,16012343210,4881811,1983200,2001542900,True,work,2,20,13.0,TNC_SINGLE,200154290,1 +1601234325,16012343250,4881811,1983200,2001542900,False,work,4,2,15.0,WALK_LOC,200154290,1 +1601234326,16012343260,4881811,1983200,2001542900,False,shopping,5,4,16.0,TNC_SINGLE,200154290,2 +1601234327,16012343270,4881811,1983200,2001542900,False,Home,20,5,16.0,WALK,200154290,3 +1601264825,16012648250,4881904,1983247,2001581030,True,work,16,22,10.0,TNC_SINGLE,200158103,1 +1601264826,16012648260,4881904,1983247,2001581030,True,escort,5,16,11.0,TNC_SINGLE,200158103,2 +1601264827,16012648270,4881904,1983247,2001581030,True,work,2,5,12.0,WALK_LOC,200158103,3 +1601264828,16012648280,4881904,1983247,2001581030,True,work,22,2,15.0,WALK_LOC,200158103,4 +1601264829,16012648290,4881904,1983247,2001581030,False,escort,16,22,17.0,TNC_SINGLE,200158103,1 +1601264830,16012648300,4881904,1983247,2001581030,False,escort,16,16,17.0,TNC_SINGLE,200158103,2 +1601264831,16012648310,4881904,1983247,2001581030,False,Home,22,16,17.0,TNC_SINGLE,200158103,3 +1601265217,16012652170,4881906,1983248,2001581520,True,eatout,11,22,10.0,DRIVEALONEFREE,200158152,1 +1601265221,16012652210,4881906,1983248,2001581520,False,Home,22,11,14.0,SHARED3FREE,200158152,1 +1601265241,16012652410,4881906,1983248,2001581550,True,escort,10,22,8.0,SHARED3FREE,200158155,1 +1601265245,16012652450,4881906,1983248,2001581550,False,othmaint,10,10,9.0,SHARED3FREE,200158155,1 +1601265246,16012652460,4881906,1983248,2001581550,False,escort,21,10,9.0,SHARED3FREE,200158155,2 +1601265247,16012652470,4881906,1983248,2001581550,False,Home,22,21,9.0,SHARED3FREE,200158155,3 +1601265545,16012655450,4881907,1983248,2001581930,True,eatout,25,22,12.0,TNC_SINGLE,200158193,1 +1601265549,16012655490,4881907,1983248,2001581930,False,Home,22,25,12.0,WALK_LOC,200158193,1 +1601265697,16012656970,4881907,1983248,2001582120,True,othdiscr,18,22,14.0,WALK_LRF,200158212,1 +1601265701,16012657010,4881907,1983248,2001582120,False,shopping,16,18,15.0,WALK_LRF,200158212,1 +1601265702,16012657020,4881907,1983248,2001582120,False,Home,22,16,15.0,WALK,200158212,2 +1601265761,16012657610,4881907,1983248,2001582200,True,shopping,5,22,17.0,WALK,200158220,1 +1601265765,16012657650,4881907,1983248,2001582200,False,Home,22,5,22.0,WALK,200158220,1 +1601298017,16012980170,4882006,1983298,2001622520,True,eatout,16,24,7.0,WALK,200162252,1 +1601298021,16012980210,4882006,1983298,2001622520,False,Home,24,16,17.0,WALK,200162252,1 +1601298329,16012983290,4882007,1983298,2001622910,True,atwork,5,10,10.0,SHARED2FREE,200162291,1 +1601298333,16012983330,4882007,1983298,2001622910,False,Work,10,5,13.0,WALK,200162291,1 +1601298609,16012986090,4882007,1983298,2001623260,True,shopping,7,24,8.0,WALK_LOC,200162326,1 +1601298610,16012986100,4882007,1983298,2001623260,True,work,10,7,8.0,WALK,200162326,2 +1601298613,16012986130,4882007,1983298,2001623260,False,othmaint,12,10,22.0,WALK,200162326,1 +1601298614,16012986140,4882007,1983298,2001623260,False,Home,24,12,22.0,WALK,200162326,2 +1601299505,16012995050,4882010,1983300,2001624380,True,othmaint,9,25,11.0,BIKE,200162438,1 +1601299509,16012995090,4882010,1983300,2001624380,False,Home,25,9,11.0,BIKE,200162438,1 +1610697993,16106979930,4910664,1997089,2013372490,True,othdiscr,1,9,10.0,TNC_SINGLE,201337249,1 +1610697997,16106979970,4910664,1997089,2013372490,False,eatout,14,1,13.0,TNC_SHARED,201337249,1 +1610697998,16106979980,4910664,1997089,2013372490,False,Home,9,14,13.0,WALK_LOC,201337249,2 +1610698369,16106983690,4910665,1997089,2013372960,True,school,13,9,8.0,WALK_LRF,201337296,1 +1610698373,16106983730,4910665,1997089,2013372960,False,Home,9,13,18.0,WALK_LRF,201337296,1 +1610698697,16106986970,4910666,1997089,2013373370,True,univ,9,9,6.0,WALK,201337337,1 +1610698701,16106987010,4910666,1997089,2013373370,False,othmaint,9,9,13.0,WALK,201337337,1 +1610698702,16106987020,4910666,1997089,2013373370,False,Home,9,9,13.0,WALK,201337337,2 +1616285169,16162851690,4927698,2002767,2020356460,True,othmaint,11,20,8.0,TNC_SHARED,202035646,1 +1616285173,16162851730,4927698,2002767,2020356460,False,Home,20,11,13.0,TNC_SINGLE,202035646,1 +1616285177,16162851770,4927698,2002767,2020356470,True,othmaint,4,20,14.0,WALK,202035647,1 +1616285181,16162851810,4927698,2002767,2020356470,False,Home,20,4,16.0,WALK,202035647,1 +1616285913,16162859130,4927700,2002767,2020357390,True,work,14,20,7.0,DRIVEALONEFREE,202035739,1 +1616285917,16162859170,4927700,2002767,2020357390,False,Home,20,14,17.0,DRIVEALONEFREE,202035739,1 +1623789481,16237894810,4950577,2010076,2029736850,True,othmaint,6,8,7.0,BIKE,202973685,1 +1623789485,16237894850,4950577,2010076,2029736850,False,Home,8,6,13.0,BIKE,202973685,1 +1623789489,16237894890,4950577,2010076,2029736860,True,othmaint,1,8,17.0,WALK,202973686,1 +1623789493,16237894930,4950577,2010076,2029736860,False,Home,8,1,19.0,WALK_LOC,202973686,1 +1623789833,16237898330,4950578,2010076,2029737290,True,school,6,8,7.0,WALK,202973729,1 +1623789837,16237898370,4950578,2010076,2029737290,False,Home,8,6,12.0,WALK_LOC,202973729,1 +1623798313,16237983130,4950604,2010083,2029747890,True,othdiscr,7,8,8.0,WALK,202974789,1 +1623798317,16237983170,4950604,2010083,2029747890,False,Home,8,7,20.0,WALK,202974789,1 +1623798993,16237989930,4950606,2010083,2029748740,True,othmaint,10,8,10.0,TNC_SHARED,202974874,1 +1623798997,16237989970,4950606,2010083,2029748740,False,Home,8,10,11.0,WALK_LOC,202974874,1 +1649585081,16495850810,5029222,2025165,2061981350,True,shopping,16,16,15.0,WALK,206198135,1 +1649585085,16495850850,5029222,2025165,2061981350,False,Home,16,16,20.0,WALK,206198135,1 +1649585457,16495854570,5029223,2025165,2061981820,True,work,1,16,6.0,WALK,206198182,1 +1649585461,16495854610,5029223,2025165,2061981820,False,Home,16,1,12.0,WALK_LOC,206198182,1 +1649585785,16495857850,5029224,2025165,2061982230,True,work,2,16,9.0,WALK,206198223,1 +1649585789,16495857890,5029224,2025165,2061982230,False,Home,16,2,18.0,WALK,206198223,1 +1649586113,16495861130,5029225,2025165,2061982640,True,work,1,16,17.0,WALK,206198264,1 +1649586117,16495861170,5029225,2025165,2061982640,False,Home,16,1,20.0,WALK,206198264,1 +1649586377,16495863770,5029226,2025165,2061982970,True,school,16,16,12.0,WALK,206198297,1 +1649586381,16495863810,5029226,2025165,2061982970,False,Home,16,16,16.0,WALK,206198297,1 +1649586705,16495867050,5029227,2025165,2061983380,True,univ,12,16,8.0,SHARED2FREE,206198338,1 +1649586709,16495867090,5029227,2025165,2061983380,False,Home,16,12,14.0,WALK_LOC,206198338,1 +1649634281,16496342810,5029372,2025189,2062042850,True,shopping,3,16,8.0,WALK,206204285,1 +1649634285,16496342850,5029372,2025189,2062042850,False,Home,16,3,16.0,WALK,206204285,1 +1649634985,16496349850,5029374,2025189,2062043730,True,escort,4,16,8.0,WALK,206204373,1 +1649634986,16496349860,5029374,2025189,2062043730,True,work,2,4,9.0,WALK,206204373,2 +1649634989,16496349890,5029374,2025189,2062043730,False,eatout,16,2,13.0,WALK,206204373,1 +1649634990,16496349900,5029374,2025189,2062043730,False,Home,16,16,16.0,WALK,206204373,2 +1649635313,16496353130,5029375,2025189,2062044140,True,work,2,16,7.0,WALK,206204414,1 +1649635317,16496353170,5029375,2025189,2062044140,False,Home,16,2,16.0,WALK,206204414,1 +1649635577,16496355770,5029376,2025189,2062044470,True,school,8,16,8.0,WALK_LOC,206204447,1 +1649635581,16496355810,5029376,2025189,2062044470,False,Home,16,8,16.0,WALK_LOC,206204447,1 +1649635905,16496359050,5029377,2025189,2062044880,True,univ,14,16,12.0,WALK_LOC,206204488,1 +1649635909,16496359090,5029377,2025189,2062044880,False,social,13,14,17.0,WALK_LOC,206204488,1 +1649635910,16496359100,5029377,2025189,2062044880,False,escort,5,13,17.0,WALK_LOC,206204488,2 +1649635911,16496359110,5029377,2025189,2062044880,False,othmaint,2,5,17.0,WALK_LOC,206204488,3 +1649635912,16496359120,5029377,2025189,2062044880,False,Home,16,2,17.0,WALK,206204488,4 +1649687185,16496871850,5029534,2025217,2062108980,True,social,12,2,11.0,WALK,206210898,1 +1649687186,16496871860,5029534,2025217,2062108980,True,escort,7,12,11.0,WALK,206210898,2 +1649687187,16496871870,5029534,2025217,2062108980,True,atwork,11,7,11.0,WALK,206210898,3 +1649687189,16496871890,5029534,2025217,2062108980,False,Work,2,11,11.0,WALK,206210898,1 +1649687465,16496874650,5029534,2025217,2062109330,True,work,2,16,7.0,WALK,206210933,1 +1649687469,16496874690,5029534,2025217,2062109330,False,Home,16,2,20.0,WALK,206210933,1 +1649688121,16496881210,5029536,2025217,2062110150,True,work,16,16,6.0,WALK,206211015,1 +1649688125,16496881250,5029536,2025217,2062110150,False,Home,16,16,17.0,WALK,206211015,1 +1649688337,16496883370,5029537,2025217,2062110420,True,othdiscr,11,16,10.0,WALK,206211042,1 +1649688341,16496883410,5029537,2025217,2062110420,False,Home,16,11,19.0,WALK,206211042,1 +1649688713,16496887130,5029538,2025217,2062110890,True,school,17,16,7.0,WALK_LRF,206211089,1 +1649688717,16496887170,5029538,2025217,2062110890,False,Home,16,17,15.0,WALK_LRF,206211089,1 +1649688993,16496889930,5029539,2025217,2062111240,True,othdiscr,2,16,13.0,WALK,206211124,1 +1649688997,16496889970,5029539,2025217,2062111240,False,Home,16,2,15.0,WALK,206211124,1 +1649689369,16496893690,5029540,2025217,2062111710,True,univ,13,16,17.0,TNC_SHARED,206211171,1 +1649689373,16496893730,5029540,2025217,2062111710,False,Home,16,13,17.0,TNC_SINGLE,206211171,1 +1649689521,16496895210,5029541,2025217,2062111900,True,escort,2,16,7.0,WALK_LOC,206211190,1 +1649689525,16496895250,5029541,2025217,2062111900,False,Home,16,2,7.0,TNC_SHARED,206211190,1 +1649689529,16496895290,5029541,2025217,2062111910,True,escort,2,16,12.0,WALK,206211191,1 +1649689533,16496895330,5029541,2025217,2062111910,False,Home,16,2,13.0,WALK,206211191,1 +1649689649,16496896490,5029541,2025217,2062112060,True,othdiscr,7,16,9.0,WALK_LOC,206211206,1 +1649689653,16496896530,5029541,2025217,2062112060,False,Home,16,7,10.0,WALK_LOC,206211206,1 +1649713049,16497130490,5029612,2025228,2062141310,True,work,2,16,11.0,WALK,206214131,1 +1649713053,16497130530,5029612,2025228,2062141310,False,Home,16,2,11.0,WALK,206214131,1 +1649713265,16497132650,5029613,2025228,2062141580,True,othdiscr,20,16,18.0,WALK_LOC,206214158,1 +1649713269,16497132690,5029613,2025228,2062141580,False,Home,16,20,21.0,WALK_LRF,206214158,1 +1649713377,16497133770,5029613,2025228,2062141720,True,escort,5,16,5.0,TNC_SINGLE,206214172,1 +1649713378,16497133780,5029613,2025228,2062141720,True,work,12,5,6.0,TNC_SINGLE,206214172,2 +1649713381,16497133810,5029613,2025228,2062141720,False,eatout,9,12,17.0,WALK_LRF,206214172,1 +1649713382,16497133820,5029613,2025228,2062141720,False,Home,16,9,17.0,TNC_SINGLE,206214172,2 +1649713425,16497134250,5029614,2025228,2062141780,True,othmaint,16,4,11.0,WALK,206214178,1 +1649713426,16497134260,5029614,2025228,2062141780,True,atwork,24,16,11.0,WALK,206214178,2 +1649713429,16497134290,5029614,2025228,2062141780,False,Work,4,24,11.0,WALK,206214178,1 +1649713705,16497137050,5029614,2025228,2062142130,True,work,4,16,8.0,TNC_SINGLE,206214213,1 +1649713709,16497137090,5029614,2025228,2062142130,False,Home,16,4,18.0,WALK_LOC,206214213,1 +1649713753,16497137530,5029615,2025228,2062142190,True,atwork,16,5,10.0,WALK,206214219,1 +1649713757,16497137570,5029615,2025228,2062142190,False,Work,5,16,10.0,WALK,206214219,1 +1649714033,16497140330,5029615,2025228,2062142540,True,work,5,16,5.0,TNC_SINGLE,206214254,1 +1649714037,16497140370,5029615,2025228,2062142540,False,Home,16,5,14.0,TNC_SINGLE,206214254,1 +1649714041,16497140410,5029615,2025228,2062142550,True,work,5,16,15.0,WALK,206214255,1 +1649714045,16497140450,5029615,2025228,2062142550,False,Home,16,5,18.0,WALK,206214255,1 +1649714297,16497142970,5029616,2025228,2062142870,True,school,13,16,8.0,WALK_LOC,206214287,1 +1649714301,16497143010,5029616,2025228,2062142870,False,Home,16,13,13.0,WALK_LOC,206214287,1 +1649721249,16497212490,5029637,2025233,2062151560,True,work,5,16,8.0,WALK,206215156,1 +1649721253,16497212530,5029637,2025233,2062151560,False,Home,16,5,17.0,WALK,206215156,1 +1649721297,16497212970,5029638,2025233,2062151620,True,shopping,25,2,9.0,WALK,206215162,1 +1649721298,16497212980,5029638,2025233,2062151620,True,atwork,3,25,9.0,WALK,206215162,2 +1649721301,16497213010,5029638,2025233,2062151620,False,Work,2,3,11.0,WALK,206215162,1 +1649721577,16497215770,5029638,2025233,2062151970,True,work,2,16,7.0,WALK,206215197,1 +1649721581,16497215810,5029638,2025233,2062151970,False,Home,16,2,19.0,WALK,206215197,1 +1649721905,16497219050,5029639,2025233,2062152380,True,escort,14,16,8.0,WALK,206215238,1 +1649721906,16497219060,5029639,2025233,2062152380,True,work,4,14,9.0,WALK,206215238,2 +1649721909,16497219090,5029639,2025233,2062152380,False,Home,16,4,17.0,WALK,206215238,1 +1649722169,16497221690,5029640,2025233,2062152710,True,school,10,16,16.0,WALK_LOC,206215271,1 +1649722173,16497221730,5029640,2025233,2062152710,False,Home,16,10,23.0,WALK_LRF,206215271,1 +1649777993,16497779930,5029810,2025265,2062222490,True,work,4,17,17.0,WALK,206222249,1 +1649777997,16497779970,5029810,2025265,2062222490,False,Home,17,4,18.0,WALK,206222249,1 +1649778257,16497782570,5029811,2025265,2062222820,True,school,11,17,7.0,WALK_LRF,206222282,1 +1649778261,16497782610,5029811,2025265,2062222820,False,Home,17,11,10.0,WALK_LRF,206222282,1 +1649778273,16497782730,5029811,2025265,2062222840,True,shopping,19,17,12.0,WALK,206222284,1 +1649778277,16497782770,5029811,2025265,2062222840,False,Home,17,19,15.0,WALK,206222284,1 +1649778601,16497786010,5029812,2025265,2062223250,True,shopping,11,17,8.0,WALK,206222325,1 +1649778605,16497786050,5029812,2025265,2062223250,False,Home,17,11,12.0,WALK,206222325,1 +1649778609,16497786090,5029812,2025265,2062223260,True,shopping,4,17,13.0,WALK,206222326,1 +1649778613,16497786130,5029812,2025265,2062223260,False,Home,17,4,13.0,WALK,206222326,1 +1649778625,16497786250,5029812,2025265,2062223280,True,othdiscr,9,17,14.0,SHARED2FREE,206222328,1 +1649778626,16497786260,5029812,2025265,2062223280,True,othdiscr,9,9,15.0,WALK,206222328,2 +1649778627,16497786270,5029812,2025265,2062223280,True,social,5,9,15.0,SHARED2FREE,206222328,3 +1649778629,16497786290,5029812,2025265,2062223280,False,Home,17,5,19.0,SHARED2FREE,206222328,1 +1649778697,16497786970,5029813,2025265,2062223370,True,atwork,22,5,15.0,SHARED3FREE,206222337,1 +1649778701,16497787010,5029813,2025265,2062223370,False,Work,5,22,15.0,SHARED3FREE,206222337,1 +1649778977,16497789770,5029813,2025265,2062223720,True,work,5,17,8.0,TAXI,206222372,1 +1649778981,16497789810,5029813,2025265,2062223720,False,Home,17,5,15.0,WALK_LRF,206222372,1 +1649779193,16497791930,5029814,2025265,2062223990,True,othdiscr,2,17,14.0,WALK,206222399,1 +1649779197,16497791970,5029814,2025265,2062223990,False,Home,17,2,18.0,WALK_LRF,206222399,1 +1649779241,16497792410,5029814,2025265,2062224050,True,school,9,17,8.0,WALK_LRF,206222405,1 +1649779245,16497792450,5029814,2025265,2062224050,False,Home,17,9,14.0,WALK_LRF,206222405,1 +1649779569,16497795690,5029815,2025265,2062224460,True,school,18,17,7.0,WALK_LRF,206222446,1 +1649779573,16497795730,5029815,2025265,2062224460,False,Home,17,18,13.0,WALK_LRF,206222446,1 +1649779961,16497799610,5029816,2025265,2062224950,True,work,12,17,7.0,SHARED2FREE,206222495,1 +1649779965,16497799650,5029816,2025265,2062224950,False,Home,17,12,18.0,WALK,206222495,1 +1649785033,16497850330,5029832,2025269,2062231290,True,othmaint,9,17,7.0,WALK_LRF,206223129,1 +1649785037,16497850370,5029832,2025269,2062231290,False,eatout,4,9,7.0,WALK,206223129,1 +1649785038,16497850380,5029832,2025269,2062231290,False,Home,17,4,7.0,WALK,206223129,2 +1649786457,16497864570,5029836,2025269,2062233070,True,school,10,17,8.0,WALK_LRF,206223307,1 +1649786461,16497864610,5029836,2025269,2062233070,False,Home,17,10,13.0,WALK_LRF,206223307,1 +1649786801,16497868010,5029837,2025269,2062233500,True,shopping,16,17,15.0,WALK,206223350,1 +1649786805,16497868050,5029837,2025269,2062233500,False,Home,17,16,17.0,WALK,206223350,1 +1649786825,16497868250,5029837,2025269,2062233530,True,social,4,17,17.0,WALK,206223353,1 +1649786829,16497868290,5029837,2025269,2062233530,False,Home,17,4,19.0,WALK,206223353,1 +1649787177,16497871770,5029838,2025269,2062233970,True,work,23,17,7.0,WALK_LRF,206223397,1 +1649787181,16497871810,5029838,2025269,2062233970,False,Home,17,23,17.0,WALK,206223397,1 +1649787441,16497874410,5029839,2025269,2062234300,True,school,11,17,16.0,WALK_LRF,206223430,1 +1649787445,16497874450,5029839,2025269,2062234300,False,Home,17,11,23.0,WALK_LRF,206223430,1 +1649787769,16497877690,5029840,2025269,2062234710,True,school,21,17,8.0,WALK,206223471,1 +1649787773,16497877730,5029840,2025269,2062234710,False,Home,17,21,17.0,WALK,206223471,1 +1649787849,16497878490,5029841,2025269,2062234810,True,eatout,6,22,11.0,WALK,206223481,1 +1649787850,16497878500,5029841,2025269,2062234810,True,shopping,5,6,11.0,WALK,206223481,2 +1649787851,16497878510,5029841,2025269,2062234810,True,atwork,8,5,11.0,WALK,206223481,3 +1649787853,16497878530,5029841,2025269,2062234810,False,Work,22,8,17.0,WALK,206223481,1 +1649788161,16497881610,5029841,2025269,2062235200,True,work,22,17,6.0,WALK,206223520,1 +1649788165,16497881650,5029841,2025269,2062235200,False,Home,17,22,19.0,WALK,206223520,1 +1652036993,16520369930,5036698,2027742,2065046240,True,eatout,12,15,16.0,WALK,206504624,1 +1652036997,16520369970,5036698,2027742,2065046240,False,Home,15,12,21.0,WALK,206504624,1 +1652037017,16520370170,5036698,2027742,2065046270,True,escort,11,15,14.0,SHARED3FREE,206504627,1 +1652037018,16520370180,5036698,2027742,2065046270,True,escort,16,11,14.0,SHARED3FREE,206504627,2 +1652037021,16520370210,5036698,2027742,2065046270,False,othmaint,16,16,14.0,SHARED3FREE,206504627,1 +1652037022,16520370220,5036698,2027742,2065046270,False,Home,15,16,15.0,WALK,206504627,2 +1652037145,16520371450,5036698,2027742,2065046430,True,othdiscr,17,15,10.0,WALK,206504643,1 +1652037149,16520371490,5036698,2027742,2065046430,False,othmaint,16,17,11.0,WALK,206504643,1 +1652037150,16520371500,5036698,2027742,2065046430,False,shopping,16,16,11.0,WALK,206504643,2 +1652037151,16520371510,5036698,2027742,2065046430,False,eatout,16,16,11.0,WALK,206504643,3 +1652037152,16520371520,5036698,2027742,2065046430,False,Home,15,16,11.0,WALK,206504643,4 +1652045737,16520457370,5036724,2027768,2065057170,True,shopping,2,24,8.0,WALK,206505717,1 +1652045741,16520457410,5036724,2027768,2065057170,False,Home,24,2,17.0,WALK,206505717,1 +1658748793,16587487930,5057160,2048204,2073435990,True,work,23,5,7.0,TNC_SINGLE,207343599,1 +1658748797,16587487970,5057160,2048204,2073435990,False,Home,5,23,15.0,WALK_LRF,207343599,1 +1658753433,16587534330,5057175,2048219,2073441790,True,atwork,9,23,12.0,SHARED2FREE,207344179,1 +1658753437,16587534370,5057175,2048219,2073441790,False,Work,23,9,14.0,SHARED2FREE,207344179,1 +1658753449,16587534490,5057175,2048219,2073441810,True,eatout,22,6,18.0,WALK,207344181,1 +1658753453,16587534530,5057175,2048219,2073441810,False,Home,6,22,19.0,WALK,207344181,1 +1658753713,16587537130,5057175,2048219,2073442140,True,work,23,6,5.0,WALK_LOC,207344214,1 +1658753717,16587537170,5057175,2048219,2073442140,False,Home,6,23,16.0,WALK_LOC,207344214,1 +1658754041,16587540410,5057176,2048220,2073442550,True,work,2,6,7.0,TNC_SHARED,207344255,1 +1658754042,16587540420,5057176,2048220,2073442550,True,work,17,2,9.0,WALK_LOC,207344255,2 +1658754045,16587540450,5057176,2048220,2073442550,False,social,12,17,17.0,TNC_SINGLE,207344255,1 +1658754046,16587540460,5057176,2048220,2073442550,False,Home,6,12,17.0,TNC_SINGLE,207344255,2 +1658758257,16587582570,5057189,2048233,2073447820,True,shopping,11,6,11.0,WALK_LOC,207344782,1 +1658758261,16587582610,5057189,2048233,2073447820,False,othdiscr,9,11,12.0,WALK_LOC,207344782,1 +1658758262,16587582620,5057189,2048233,2073447820,False,social,8,9,12.0,WALK_LOC,207344782,2 +1658758263,16587582630,5057189,2048233,2073447820,False,Home,6,8,12.0,WALK_LOC,207344782,3 +1658777569,16587775690,5057248,2048292,2073471960,True,othmaint,2,6,14.0,WALK_LOC,207347196,1 +1658777573,16587775730,5057248,2048292,2073471960,False,shopping,25,2,14.0,WALK,207347196,1 +1658777574,16587775740,5057248,2048292,2073471960,False,Home,6,25,14.0,WALK,207347196,2 +1658777657,16587776570,5057248,2048292,2073472070,True,work,14,6,7.0,TNC_SHARED,207347207,1 +1658777661,16587776610,5057248,2048292,2073472070,False,Home,6,14,12.0,WALK_LOC,207347207,1 +1658789793,16587897930,5057285,2048329,2073487240,True,work,7,6,8.0,WALK,207348724,1 +1658789797,16587897970,5057285,2048329,2073487240,False,Home,6,7,18.0,WALK,207348724,1 +1658800289,16588002890,5057317,2048361,2073500360,True,work,2,6,6.0,TNC_SINGLE,207350036,1 +1658800293,16588002930,5057317,2048361,2073500360,False,Home,6,2,17.0,WALK,207350036,1 +1658802169,16588021690,5057323,2048367,2073502710,True,othmaint,9,7,18.0,WALK,207350271,1 +1658802173,16588021730,5057323,2048367,2073502710,False,Home,7,9,18.0,WALK,207350271,1 +1658802177,16588021770,5057323,2048367,2073502720,True,othmaint,24,7,18.0,WALK,207350272,1 +1658802181,16588021810,5057323,2048367,2073502720,False,Home,7,24,20.0,WALK,207350272,1 +1658802209,16588022090,5057323,2048367,2073502760,True,shopping,15,7,16.0,SHARED2FREE,207350276,1 +1658802213,16588022130,5057323,2048367,2073502760,False,Home,7,15,16.0,DRIVEALONEFREE,207350276,1 +1658802257,16588022570,5057323,2048367,2073502820,True,work,5,7,6.0,WALK,207350282,1 +1658802261,16588022610,5057323,2048367,2073502820,False,Home,7,5,15.0,WALK,207350282,1 +1658806913,16588069130,5057338,2048382,2073508640,True,eatout,10,7,20.0,WALK,207350864,1 +1658806917,16588069170,5057338,2048382,2073508640,False,Home,7,10,20.0,WALK,207350864,1 +1658807089,16588070890,5057338,2048382,2073508860,True,othmaint,7,7,18.0,WALK,207350886,1 +1658807093,16588070930,5057338,2048382,2073508860,False,Home,7,7,18.0,WALK,207350886,1 +1658807129,16588071290,5057338,2048382,2073508910,True,shopping,25,7,21.0,WALK,207350891,1 +1658807133,16588071330,5057338,2048382,2073508910,False,Home,7,25,21.0,WALK,207350891,1 +1658807153,16588071530,5057338,2048382,2073508940,True,social,6,7,18.0,WALK_LOC,207350894,1 +1658807157,16588071570,5057338,2048382,2073508940,False,Home,7,6,19.0,TNC_SINGLE,207350894,1 +1658807177,16588071770,5057338,2048382,2073508970,True,work,2,7,6.0,WALK,207350897,1 +1658807181,16588071810,5057338,2048382,2073508970,False,Home,7,2,17.0,WALK,207350897,1 +1658813801,16588138010,5057359,2048403,2073517250,True,eatout,7,7,18.0,TNC_SINGLE,207351725,1 +1658813805,16588138050,5057359,2048403,2073517250,False,Home,7,7,20.0,TNC_SINGLE,207351725,1 +1658814065,16588140650,5057359,2048403,2073517580,True,work,14,7,7.0,WALK,207351758,1 +1658814069,16588140690,5057359,2048403,2073517580,False,Home,7,14,17.0,WALK_LOC,207351758,1 +1658819265,16588192650,5057375,2048419,2073524080,True,shopping,21,7,18.0,TNC_SINGLE,207352408,1 +1658819269,16588192690,5057375,2048419,2073524080,False,Home,7,21,23.0,TNC_SINGLE,207352408,1 +1658819313,16588193130,5057375,2048419,2073524140,True,work,14,7,8.0,WALK,207352414,1 +1658819317,16588193170,5057375,2048419,2073524140,False,Home,7,14,15.0,WALK,207352414,1 +1658829153,16588291530,5057405,2048449,2073536440,True,othdiscr,14,7,10.0,TNC_SINGLE,207353644,1 +1658829154,16588291540,5057405,2048449,2073536440,True,work,1,14,10.0,TNC_SINGLE,207353644,2 +1658829157,16588291570,5057405,2048449,2073536440,False,eatout,12,1,18.0,WALK,207353644,1 +1658829158,16588291580,5057405,2048449,2073536440,False,Home,7,12,19.0,TNC_SINGLE,207353644,2 +1658833633,16588336330,5057419,2048463,2073542040,True,othdiscr,10,7,11.0,WALK,207354204,1 +1658833637,16588336370,5057419,2048463,2073542040,False,Home,7,10,14.0,WALK,207354204,1 +1658835665,16588356650,5057425,2048469,2073544580,True,shopping,19,7,12.0,SHARED3FREE,207354458,1 +1658835669,16588356690,5057425,2048469,2073544580,False,Home,7,19,13.0,WALK,207354458,1 +1658839649,16588396490,5057437,2048481,2073549560,True,work,22,7,8.0,SHARED2FREE,207354956,1 +1658839653,16588396530,5057437,2048481,2073549560,False,Home,7,22,18.0,WALK_LOC,207354956,1 +1658851673,16588516730,5057474,2048518,2073564590,True,othdiscr,17,7,15.0,WALK_LOC,207356459,1 +1658851677,16588516770,5057474,2048518,2073564590,False,Home,7,17,19.0,WALK_LOC,207356459,1 +1658873649,16588736490,5057541,2048585,2073592060,True,othdiscr,4,7,20.0,WALK,207359206,1 +1658873653,16588736530,5057541,2048585,2073592060,False,Home,7,4,23.0,WALK,207359206,1 +1658873761,16588737610,5057541,2048585,2073592200,True,eatout,5,7,9.0,WALK_LOC,207359220,1 +1658873762,16588737620,5057541,2048585,2073592200,True,shopping,16,5,10.0,WALK,207359220,2 +1658873763,16588737630,5057541,2048585,2073592200,True,work,22,16,10.0,WALK,207359220,3 +1658873765,16588737650,5057541,2048585,2073592200,False,shopping,11,22,18.0,WALK_LOC,207359220,1 +1658873766,16588737660,5057541,2048585,2073592200,False,othmaint,5,11,18.0,WALK_LOC,207359220,2 +1658873767,16588737670,5057541,2048585,2073592200,False,work,8,5,19.0,WALK,207359220,3 +1658873768,16588737680,5057541,2048585,2073592200,False,Home,7,8,19.0,WALK_LOC,207359220,4 +1658900001,16589000010,5057621,2048665,2073625000,True,work,9,10,6.0,WALK,207362500,1 +1658900005,16589000050,5057621,2048665,2073625000,False,Home,10,9,18.0,WALK,207362500,1 +1658916617,16589166170,5057672,2048716,2073645770,True,othdiscr,1,11,15.0,WALK_LRF,207364577,1 +1658916621,16589166210,5057672,2048716,2073645770,False,Home,11,1,17.0,WALK_LRF,207364577,1 +1658916705,16589167050,5057672,2048716,2073645880,True,social,10,11,14.0,TNC_SINGLE,207364588,1 +1658916709,16589167090,5057672,2048716,2073645880,False,Home,11,10,14.0,TNC_SINGLE,207364588,1 +1658922193,16589221930,5057689,2048733,2073652740,True,othdiscr,10,11,14.0,WALK,207365274,1 +1658922197,16589221970,5057689,2048733,2073652740,False,escort,9,10,15.0,WALK_LOC,207365274,1 +1658922198,16589221980,5057689,2048733,2073652740,False,Home,11,9,15.0,WALK_LOC,207365274,2 +1658922353,16589223530,5057690,2048734,2073652940,True,atwork,17,23,11.0,WALK,207365294,1 +1658922357,16589223570,5057690,2048734,2073652940,False,Work,23,17,12.0,WALK,207365294,1 +1658922369,16589223690,5057690,2048734,2073652960,True,eatout,6,11,16.0,TNC_SINGLE,207365296,1 +1658922373,16589223730,5057690,2048734,2073652960,False,Home,11,6,16.0,DRIVEALONEFREE,207365296,1 +1658922393,16589223930,5057690,2048734,2073652990,True,escort,25,11,7.0,WALK,207365299,1 +1658922397,16589223970,5057690,2048734,2073652990,False,Home,11,25,7.0,WALK,207365299,1 +1658922633,16589226330,5057690,2048734,2073653290,True,work,23,11,7.0,DRIVEALONEFREE,207365329,1 +1658922637,16589226370,5057690,2048734,2073653290,False,Home,11,23,16.0,DRIVEALONEFREE,207365329,1 +1658938329,16589383290,5057738,2048782,2073672910,True,shopping,16,11,20.0,DRIVEALONEFREE,207367291,1 +1658938333,16589383330,5057738,2048782,2073672910,False,shopping,10,16,20.0,TNC_SINGLE,207367291,1 +1658938334,16589383340,5057738,2048782,2073672910,False,shopping,16,10,20.0,TNC_SHARED,207367291,2 +1658938335,16589383350,5057738,2048782,2073672910,False,shopping,16,16,20.0,WALK,207367291,3 +1658938336,16589383360,5057738,2048782,2073672910,False,Home,11,16,20.0,TNC_SINGLE,207367291,4 +1658938377,16589383770,5057738,2048782,2073672970,True,work,7,11,8.0,WALK,207367297,1 +1658938381,16589383810,5057738,2048782,2073672970,False,Home,11,7,17.0,WALK,207367297,1 +1658943953,16589439530,5057755,2048799,2073679940,True,work,5,11,7.0,WALK,207367994,1 +1658943957,16589439570,5057755,2048799,2073679940,False,Home,11,5,17.0,WALK_LOC,207367994,1 +1658947233,16589472330,5057765,2048809,2073684040,True,othmaint,11,11,9.0,WALK,207368404,1 +1658947234,16589472340,5057765,2048809,2073684040,True,work,9,11,10.0,DRIVEALONEFREE,207368404,2 +1658947237,16589472370,5057765,2048809,2073684040,False,othdiscr,9,9,18.0,DRIVEALONEFREE,207368404,1 +1658947238,16589472380,5057765,2048809,2073684040,False,Home,11,9,19.0,DRIVEALONEFREE,207368404,2 +1658948497,16589484970,5057769,2048813,2073685620,True,shopping,16,11,9.0,WALK_LOC,207368562,1 +1658948501,16589485010,5057769,2048813,2073685620,False,Home,11,16,10.0,TNC_SINGLE,207368562,1 +1658948545,16589485450,5057769,2048813,2073685680,True,shopping,11,11,11.0,TNC_SINGLE,207368568,1 +1658948546,16589485460,5057769,2048813,2073685680,True,work,12,11,12.0,TNC_SINGLE,207368568,2 +1658948549,16589485490,5057769,2048813,2073685680,False,Home,11,12,19.0,WALK_LOC,207368568,1 +1658951873,16589518730,5057780,2048824,2073689840,True,atwork,17,13,10.0,WALK,207368984,1 +1658951877,16589518770,5057780,2048824,2073689840,False,othmaint,16,17,15.0,WALK,207368984,1 +1658951878,16589518780,5057780,2048824,2073689840,False,Work,13,16,15.0,WALK,207368984,2 +1658952153,16589521530,5057780,2048824,2073690190,True,work,13,11,7.0,TNC_SINGLE,207369019,1 +1658952157,16589521570,5057780,2048824,2073690190,False,Home,11,13,17.0,WALK,207369019,1 +1658953185,16589531850,5057784,2048828,2073691480,True,work,5,21,10.0,WALK,207369148,1 +1658953186,16589531860,5057784,2048828,2073691480,True,atwork,7,5,10.0,WALK,207369148,2 +1658953189,16589531890,5057784,2048828,2073691480,False,Work,21,7,11.0,WALK,207369148,1 +1658953465,16589534650,5057784,2048828,2073691830,True,shopping,11,11,8.0,WALK,207369183,1 +1658953466,16589534660,5057784,2048828,2073691830,True,escort,11,11,8.0,WALK,207369183,2 +1658953467,16589534670,5057784,2048828,2073691830,True,work,21,11,10.0,WALK,207369183,3 +1658953469,16589534690,5057784,2048828,2073691830,False,Home,11,21,21.0,WALK,207369183,1 +1658958433,16589584330,5057800,2048844,2073698040,True,atwork,5,21,14.0,WALK,207369804,1 +1658958437,16589584370,5057800,2048844,2073698040,False,Work,21,5,14.0,WALK,207369804,1 +1658958713,16589587130,5057800,2048844,2073698390,True,work,21,11,8.0,WALK,207369839,1 +1658958717,16589587170,5057800,2048844,2073698390,False,Home,11,21,19.0,WALK,207369839,1 +1658963681,16589636810,5057816,2048860,2073704600,True,atwork,5,13,15.0,WALK,207370460,1 +1658963685,16589636850,5057816,2048860,2073704600,False,Work,13,5,15.0,WALK,207370460,1 +1658963697,16589636970,5057816,2048860,2073704620,True,eatout,2,11,8.0,WALK,207370462,1 +1658963701,16589637010,5057816,2048860,2073704620,False,Home,11,2,8.0,WALK,207370462,1 +1658963849,16589638490,5057816,2048860,2073704810,True,othdiscr,8,11,5.0,WALK,207370481,1 +1658963853,16589638530,5057816,2048860,2073704810,False,Home,11,8,6.0,WALK,207370481,1 +1658963961,16589639610,5057816,2048860,2073704950,True,work,13,11,8.0,WALK,207370495,1 +1658963965,16589639650,5057816,2048860,2073704950,False,Home,11,13,22.0,WALK,207370495,1 +1658977129,16589771290,5057857,2048901,2073721410,True,atwork,16,1,18.0,TNC_SINGLE,207372141,1 +1658977133,16589771330,5057857,2048901,2073721410,False,Work,1,16,18.0,TNC_SINGLE,207372141,1 +1658977409,16589774090,5057857,2048901,2073721760,True,work,1,11,6.0,WALK_HVY,207372176,1 +1658977413,16589774130,5057857,2048901,2073721760,False,Home,11,1,19.0,WALK,207372176,1 +1658978393,16589783930,5057860,2048904,2073722990,True,work,18,11,6.0,WALK_LOC,207372299,1 +1658978397,16589783970,5057860,2048904,2073722990,False,Home,11,18,16.0,TNC_SHARED,207372299,1 +1658996105,16589961050,5057914,2048958,2073745130,True,work,20,14,15.0,WALK_LOC,207374513,1 +1658996109,16589961090,5057914,2048958,2073745130,False,shopping,11,20,18.0,WALK_LOC,207374513,1 +1658996110,16589961100,5057914,2048958,2073745130,False,Home,14,11,18.0,WALK,207374513,2 +1658997481,16589974810,5057919,2048963,2073746850,True,eatout,5,14,18.0,WALK,207374685,1 +1658997485,16589974850,5057919,2048963,2073746850,False,Home,14,5,19.0,WALK,207374685,1 +1658997505,16589975050,5057919,2048963,2073746880,True,escort,5,14,17.0,WALK,207374688,1 +1658997509,16589975090,5057919,2048963,2073746880,False,Home,14,5,18.0,WALK,207374688,1 +1658997633,16589976330,5057919,2048963,2073747040,True,othdiscr,15,14,12.0,WALK,207374704,1 +1658997637,16589976370,5057919,2048963,2073747040,False,Home,14,15,15.0,WALK,207374704,1 +1658997697,16589976970,5057919,2048963,2073747120,True,shopping,11,14,8.0,WALK,207374712,1 +1658997701,16589977010,5057919,2048963,2073747120,False,Home,14,11,11.0,WALK,207374712,1 +1658997705,16589977050,5057919,2048963,2073747130,True,shopping,10,14,15.0,WALK,207374713,1 +1658997709,16589977090,5057919,2048963,2073747130,False,Home,14,10,16.0,WALK,207374713,1 +1658998401,16589984010,5057921,2048965,2073748000,True,work,16,14,9.0,WALK,207374800,1 +1658998405,16589984050,5057921,2048965,2073748000,False,Home,14,16,19.0,WALK,207374800,1 +1659002057,16590020570,5057933,2048977,2073752570,True,atwork,25,6,9.0,WALK,207375257,1 +1659002061,16590020610,5057933,2048977,2073752570,False,Work,6,25,10.0,WALK,207375257,1 +1659002289,16590022890,5057933,2048977,2073752860,True,shopping,8,14,17.0,WALK_LOC,207375286,1 +1659002293,16590022930,5057933,2048977,2073752860,False,Home,14,8,20.0,WALK,207375286,1 +1659002337,16590023370,5057933,2048977,2073752920,True,work,6,14,6.0,WALK,207375292,1 +1659002341,16590023410,5057933,2048977,2073752920,False,Home,14,6,16.0,WALK,207375292,1 +1659008289,16590082890,5057952,2048996,2073760360,True,atwork,5,13,10.0,WALK,207376036,1 +1659008293,16590082930,5057952,2048996,2073760360,False,Work,13,5,10.0,WALK,207376036,1 +1659008569,16590085690,5057952,2048996,2073760710,True,work,13,14,7.0,WALK,207376071,1 +1659008573,16590085730,5057952,2048996,2073760710,False,Home,14,13,17.0,WALK,207376071,1 +1659012553,16590125530,5057965,2049009,2073765690,True,atwork,13,1,10.0,WALK_LOC,207376569,1 +1659012557,16590125570,5057965,2049009,2073765690,False,Work,1,13,12.0,WALK_LOC,207376569,1 +1659012833,16590128330,5057965,2049009,2073766040,True,work,1,15,8.0,WALK_LOC,207376604,1 +1659012837,16590128370,5057965,2049009,2073766040,False,Home,15,1,18.0,WALK,207376604,1 +1659015785,16590157850,5057974,2049018,2073769730,True,work,18,15,8.0,WALK_LRF,207376973,1 +1659015789,16590157890,5057974,2049018,2073769730,False,Home,15,18,18.0,WALK_LRF,207376973,1 +1659016441,16590164410,5057976,2049020,2073770550,True,work,5,15,5.0,WALK,207377055,1 +1659016445,16590164450,5057976,2049020,2073770550,False,Home,15,5,13.0,WALK,207377055,1 +1659021049,16590210490,5057991,2049035,2073776310,True,atwork,16,12,12.0,WALK,207377631,1 +1659021053,16590210530,5057991,2049035,2073776310,False,Work,12,16,13.0,WALK,207377631,1 +1659021361,16590213610,5057991,2049035,2073776700,True,escort,16,15,8.0,DRIVEALONEFREE,207377670,1 +1659021362,16590213620,5057991,2049035,2073776700,True,work,12,16,9.0,DRIVEALONEFREE,207377670,2 +1659021365,16590213650,5057991,2049035,2073776700,False,othmaint,24,12,13.0,DRIVEALONEFREE,207377670,1 +1659021366,16590213660,5057991,2049035,2073776700,False,Home,15,24,13.0,WALK,207377670,2 +1659034529,16590345290,5058032,2049076,2073793160,True,atwork,16,12,14.0,WALK,207379316,1 +1659034533,16590345330,5058032,2049076,2073793160,False,Work,12,16,14.0,WALK,207379316,1 +1659034809,16590348090,5058032,2049076,2073793510,True,work,16,16,7.0,WALK,207379351,1 +1659034810,16590348100,5058032,2049076,2073793510,True,work,12,16,9.0,WALK,207379351,2 +1659034813,16590348130,5058032,2049076,2073793510,False,Home,16,12,16.0,WALK_LOC,207379351,1 +1659061705,16590617050,5058114,2049158,2073827130,True,eatout,17,17,14.0,WALK,207382713,1 +1659061706,16590617060,5058114,2049158,2073827130,True,work,12,17,14.0,WALK_LOC,207382713,2 +1659061709,16590617090,5058114,2049158,2073827130,False,othmaint,9,12,16.0,WALK_LRF,207382713,1 +1659061710,16590617100,5058114,2049158,2073827130,False,escort,6,9,16.0,WALK,207382713,2 +1659061711,16590617110,5058114,2049158,2073827130,False,Home,17,6,17.0,WALK_LRF,207382713,3 +1659063721,16590637210,5058121,2049165,2073829650,True,atwork,21,4,14.0,WALK,207382965,1 +1659063725,16590637250,5058121,2049165,2073829650,False,Work,4,21,15.0,WALK,207382965,1 +1659064001,16590640010,5058121,2049165,2073830000,True,work,4,17,7.0,WALK,207383000,1 +1659064005,16590640050,5058121,2049165,2073830000,False,Home,17,4,18.0,WALK,207383000,1 +1659073073,16590730730,5058149,2049193,2073841340,True,othdiscr,22,17,18.0,WALK,207384134,1 +1659073077,16590730770,5058149,2049193,2073841340,False,Home,17,22,21.0,WALK,207384134,1 +1659073185,16590731850,5058149,2049193,2073841480,True,work,23,17,9.0,TNC_SINGLE,207384148,1 +1659073189,16590731890,5058149,2049193,2073841480,False,Home,17,23,18.0,TNC_SINGLE,207384148,1 +1659076137,16590761370,5058158,2049202,2073845170,True,work,14,17,5.0,WALK_LRF,207384517,1 +1659076141,16590761410,5058158,2049202,2073845170,False,Home,17,14,9.0,WALK,207384517,1 +1659076145,16590761450,5058158,2049202,2073845180,True,work,14,17,9.0,WALK,207384518,1 +1659076149,16590761490,5058158,2049202,2073845180,False,Home,17,14,14.0,WALK,207384518,1 +1659094505,16590945050,5058214,2049258,2073868130,True,work,13,17,12.0,WALK,207386813,1 +1659094509,16590945090,5058214,2049258,2073868130,False,shopping,11,13,18.0,WALK,207386813,1 +1659094510,16590945100,5058214,2049258,2073868130,False,othmaint,17,11,21.0,DRIVEALONEFREE,207386813,2 +1659094511,16590945110,5058214,2049258,2073868130,False,othmaint,2,17,21.0,SHARED2FREE,207386813,3 +1659094512,16590945120,5058214,2049258,2073868130,False,Home,17,2,21.0,DRIVEALONEFREE,207386813,4 +1659109641,16591096410,5058261,2049305,2073887050,True,atwork,19,18,8.0,WALK,207388705,1 +1659109645,16591096450,5058261,2049305,2073887050,False,othmaint,10,19,10.0,WALK,207388705,1 +1659109646,16591096460,5058261,2049305,2073887050,False,Work,18,10,10.0,WALK,207388705,2 +1659109921,16591099210,5058261,2049305,2073887400,True,work,18,17,8.0,WALK,207388740,1 +1659109925,16591099250,5058261,2049305,2073887400,False,Home,17,18,19.0,WALK_LOC,207388740,1 +1659115825,16591158250,5058279,2049323,2073894780,True,work,15,17,8.0,WALK,207389478,1 +1659115829,16591158290,5058279,2049323,2073894780,False,work,9,15,17.0,TNC_SINGLE,207389478,1 +1659115830,16591158300,5058279,2049323,2073894780,False,Home,17,9,17.0,TNC_SINGLE,207389478,2 +1659127681,16591276810,5058316,2049360,2073909600,True,atwork,9,4,12.0,WALK,207390960,1 +1659127685,16591276850,5058316,2049360,2073909600,False,Work,4,9,13.0,WALK,207390960,1 +1659127961,16591279610,5058316,2049360,2073909950,True,escort,5,18,8.0,WALK_LOC,207390995,1 +1659127962,16591279620,5058316,2049360,2073909950,True,work,4,5,9.0,WALK,207390995,2 +1659127965,16591279650,5058316,2049360,2073909950,False,Home,18,4,18.0,WALK_LRF,207390995,1 +1659134537,16591345370,5058337,2049381,2073918170,True,shopping,17,18,11.0,WALK,207391817,1 +1659134538,16591345380,5058337,2049381,2073918170,True,atwork,17,17,11.0,WALK,207391817,2 +1659134541,16591345410,5058337,2049381,2073918170,False,Work,18,17,11.0,WALK,207391817,1 +1659134553,16591345530,5058337,2049381,2073918190,True,atwork,22,18,11.0,WALK,207391819,1 +1659134557,16591345570,5058337,2049381,2073918190,False,eatout,5,22,16.0,WALK,207391819,1 +1659134558,16591345580,5058337,2049381,2073918190,False,Work,18,5,16.0,WALK,207391819,2 +1659134737,16591347370,5058337,2049381,2073918420,True,othdiscr,7,18,21.0,TNC_SHARED,207391842,1 +1659134741,16591347410,5058337,2049381,2073918420,False,Home,18,7,21.0,DRIVEALONEFREE,207391842,1 +1659134849,16591348490,5058337,2049381,2073918560,True,work,18,18,8.0,WALK,207391856,1 +1659134853,16591348530,5058337,2049381,2073918560,False,Home,18,18,20.0,WALK,207391856,1 +1659143377,16591433770,5058363,2049407,2073929220,True,work,2,18,12.0,WALK_LOC,207392922,1 +1659143381,16591433810,5058363,2049407,2073929220,False,Home,18,2,14.0,WALK_LOC,207392922,1 +1659143385,16591433850,5058363,2049407,2073929230,True,work,2,18,15.0,WALK_LOC,207392923,1 +1659143389,16591433890,5058363,2049407,2073929230,False,othmaint,5,2,17.0,WALK,207392923,1 +1659143390,16591433900,5058363,2049407,2073929230,False,Home,18,5,18.0,WALK_LOC,207392923,2 +1659155841,16591558410,5058401,2049445,2073944800,True,work,9,18,7.0,WALK_LOC,207394480,1 +1659155845,16591558450,5058401,2049445,2073944800,False,Home,18,9,17.0,WALK_LOC,207394480,1 +1659172289,16591722890,5058452,2049496,2073965360,True,atwork,1,15,8.0,WALK,207396536,1 +1659172293,16591722930,5058452,2049496,2073965360,False,othmaint,2,1,8.0,WALK,207396536,1 +1659172294,16591722940,5058452,2049496,2073965360,False,Work,15,2,8.0,WALK,207396536,2 +1659172569,16591725690,5058452,2049496,2073965710,True,work,15,19,7.0,WALK_LOC,207396571,1 +1659172573,16591725730,5058452,2049496,2073965710,False,Home,19,15,20.0,WALK_LOC,207396571,1 +1659179849,16591798490,5058475,2049519,2073974810,True,eatout,11,20,11.0,WALK,207397481,1 +1659179853,16591798530,5058475,2049519,2073974810,False,Home,20,11,11.0,WALK,207397481,1 +1659189361,16591893610,5058504,2049548,2073986700,True,eatout,5,20,13.0,WALK,207398670,1 +1659189365,16591893650,5058504,2049548,2073986700,False,Home,20,5,18.0,WALK,207398670,1 +1659189537,16591895370,5058504,2049548,2073986920,True,othmaint,5,20,9.0,WALK_LOC,207398692,1 +1659189541,16591895410,5058504,2049548,2073986920,False,Home,20,5,11.0,WALK_LRF,207398692,1 +1659195857,16591958570,5058523,2049567,2073994820,True,work,19,21,8.0,WALK,207399482,1 +1659195861,16591958610,5058523,2049567,2073994820,False,shopping,11,19,15.0,WALK,207399482,1 +1659195862,16591958620,5058523,2049567,2073994820,False,shopping,5,11,18.0,WALK,207399482,2 +1659195863,16591958630,5058523,2049567,2073994820,False,Home,21,5,18.0,WALK,207399482,3 +1659215209,16592152090,5058582,2049626,2074019010,True,work,7,21,8.0,WALK,207401901,1 +1659215213,16592152130,5058582,2049626,2074019010,False,work,8,7,14.0,WALK,207401901,1 +1659215214,16592152140,5058582,2049626,2074019010,False,Home,21,8,14.0,DRIVEALONEFREE,207401901,2 +1659216849,16592168490,5058587,2049631,2074021060,True,work,2,21,5.0,WALK,207402106,1 +1659216853,16592168530,5058587,2049631,2074021060,False,Home,21,2,15.0,WALK,207402106,1 +1659218537,16592185370,5058593,2049637,2074023170,True,atwork,14,14,9.0,WALK,207402317,1 +1659218541,16592185410,5058593,2049637,2074023170,False,shopping,5,14,9.0,WALK,207402317,1 +1659218542,16592185420,5058593,2049637,2074023170,False,Work,14,5,9.0,WALK,207402317,2 +1659218817,16592188170,5058593,2049637,2074023520,True,work,14,21,9.0,WALK,207402352,1 +1659218821,16592188210,5058593,2049637,2074023520,False,Home,21,14,16.0,WALK,207402352,1 +1659219145,16592191450,5058594,2049638,2074023930,True,escort,9,21,8.0,DRIVEALONEFREE,207402393,1 +1659219146,16592191460,5058594,2049638,2074023930,True,work,13,9,8.0,DRIVEALONEFREE,207402393,2 +1659219149,16592191490,5058594,2049638,2074023930,False,Home,21,13,17.0,WALK,207402393,1 +1659225905,16592259050,5058615,2049659,2074032380,True,atwork,16,4,12.0,DRIVEALONEFREE,207403238,1 +1659225909,16592259090,5058615,2049659,2074032380,False,Work,4,16,13.0,DRIVEALONEFREE,207403238,1 +1659226033,16592260330,5058615,2049659,2074032540,True,escort,2,22,7.0,DRIVEALONEFREE,207403254,1 +1659226034,16592260340,5058615,2049659,2074032540,True,work,4,2,8.0,DRIVEALONEFREE,207403254,2 +1659226037,16592260370,5058615,2049659,2074032540,False,work,14,4,13.0,DRIVEALONEFREE,207403254,1 +1659226038,16592260380,5058615,2049659,2074032540,False,othdiscr,9,14,18.0,DRIVEALONEFREE,207403254,2 +1659226039,16592260390,5058615,2049659,2074032540,False,work,12,9,19.0,DRIVEALONEFREE,207403254,3 +1659226040,16592260400,5058615,2049659,2074032540,False,Home,22,12,19.0,DRIVEALONEFREE,207403254,4 +1659230297,16592302970,5058628,2049672,2074037870,True,work,2,22,7.0,WALK,207403787,1 +1659230301,16592303010,5058628,2049672,2074037870,False,Home,22,2,18.0,WALK,207403787,1 +1659247401,16592474010,5058681,2049725,2074059250,True,atwork,7,5,10.0,WALK,207405925,1 +1659247405,16592474050,5058681,2049725,2074059250,False,Work,5,7,10.0,WALK,207405925,1 +1659247681,16592476810,5058681,2049725,2074059600,True,work,5,23,7.0,WALK_LRF,207405960,1 +1659247685,16592476850,5058681,2049725,2074059600,False,Home,23,5,18.0,WALK_LRF,207405960,1 +1659250353,16592503530,5058690,2049734,2074062940,True,atwork,17,22,13.0,WALK,207406294,1 +1659250357,16592503570,5058690,2049734,2074062940,False,eatout,5,17,13.0,WALK,207406294,1 +1659250358,16592503580,5058690,2049734,2074062940,False,Work,22,5,13.0,WALK,207406294,2 +1659250633,16592506330,5058690,2049734,2074063290,True,work,22,24,6.0,WALK,207406329,1 +1659250637,16592506370,5058690,2049734,2074063290,False,Home,24,22,15.0,WALK,207406329,1 +1659253633,16592536330,5058700,2049744,2074067040,True,atwork,5,1,12.0,TNC_SINGLE,207406704,1 +1659253637,16592536370,5058700,2049744,2074067040,False,Work,1,5,13.0,WALK_LOC,207406704,1 +1659253865,16592538650,5058700,2049744,2074067330,True,shopping,21,24,18.0,DRIVEALONEFREE,207406733,1 +1659253869,16592538690,5058700,2049744,2074067330,False,escort,22,21,18.0,TNC_SHARED,207406733,1 +1659253870,16592538700,5058700,2049744,2074067330,False,Home,24,22,18.0,DRIVEALONEFREE,207406733,2 +1659253913,16592539130,5058700,2049744,2074067390,True,work,1,24,6.0,WALK_LOC,207406739,1 +1659253917,16592539170,5058700,2049744,2074067390,False,Home,24,1,18.0,WALK,207406739,1 +1685502985,16855029850,5138728,2098510,2106878730,True,othdiscr,17,5,21.0,WALK,210687873,1 +1685502989,16855029890,5138728,2098510,2106878730,False,Home,5,17,23.0,WALK,210687873,1 +1685503009,16855030090,5138728,2098510,2106878760,True,othmaint,7,5,18.0,WALK,210687876,1 +1685503013,16855030130,5138728,2098510,2106878760,False,Home,5,7,20.0,WALK,210687876,1 +1685503097,16855030970,5138728,2098510,2106878870,True,work,16,5,6.0,DRIVE_LOC,210687887,1 +1685503101,16855031010,5138728,2098510,2106878870,False,Home,5,16,18.0,DRIVE_LOC,210687887,1 +1685503313,16855033130,5138729,2098510,2106879140,True,othdiscr,9,5,7.0,WALK,210687914,1 +1685503317,16855033170,5138729,2098510,2106879140,False,Home,5,9,9.0,WALK,210687914,1 +1685503377,16855033770,5138729,2098510,2106879220,True,escort,7,5,11.0,WALK_LOC,210687922,1 +1685503378,16855033780,5138729,2098510,2106879220,True,shopping,11,7,12.0,WALK_LOC,210687922,2 +1685503381,16855033810,5138729,2098510,2106879220,False,escort,8,11,19.0,TNC_SINGLE,210687922,1 +1685503382,16855033820,5138729,2098510,2106879220,False,othmaint,7,8,19.0,TNC_SINGLE,210687922,2 +1685503383,16855033830,5138729,2098510,2106879220,False,shopping,16,7,19.0,TNC_SINGLE,210687922,3 +1685503384,16855033840,5138729,2098510,2106879220,False,Home,5,16,19.0,WALK_LOC,210687922,4 +1685503401,16855034010,5138729,2098510,2106879250,True,social,5,5,11.0,WALK,210687925,1 +1685503405,16855034050,5138729,2098510,2106879250,False,Home,5,5,11.0,WALK,210687925,1 +1685553609,16855536090,5138882,2098587,2106942010,True,work,11,9,6.0,WALK,210694201,1 +1685553613,16855536130,5138882,2098587,2106942010,False,Home,9,11,17.0,WALK,210694201,1 +1685578537,16855785370,5138958,2098625,2106973170,True,work,15,9,8.0,WALK_LOC,210697317,1 +1685578541,16855785410,5138958,2098625,2106973170,False,Home,9,15,18.0,WALK_LRF,210697317,1 +1685578817,16855788170,5138959,2098625,2106973520,True,shopping,5,9,15.0,BIKE,210697352,1 +1685578821,16855788210,5138959,2098625,2106973520,False,Home,9,5,18.0,BIKE,210697352,1 +1685605153,16856051530,5139040,2098666,2107006440,True,atwork,7,20,10.0,SHARED2FREE,210700644,1 +1685605157,16856051570,5139040,2098666,2107006440,False,shopping,5,7,14.0,SHARED2FREE,210700644,1 +1685605158,16856051580,5139040,2098666,2107006440,False,Work,20,5,14.0,SHARED2FREE,210700644,2 +1685605433,16856054330,5139040,2098666,2107006790,True,work,20,9,8.0,WALK,210700679,1 +1685605437,16856054370,5139040,2098666,2107006790,False,Home,9,20,20.0,WALK,210700679,1 +1685605713,16856057130,5139041,2098666,2107007140,True,shopping,11,9,13.0,TNC_SINGLE,210700714,1 +1685605717,16856057170,5139041,2098666,2107007140,False,Home,9,11,16.0,WALK_LOC,210700714,1 +1685632329,16856323290,5139122,2098707,2107040410,True,work,2,9,13.0,WALK_LRF,210704041,1 +1685632330,16856323300,5139122,2098707,2107040410,True,shopping,5,2,14.0,WALK,210704041,2 +1685632331,16856323310,5139122,2098707,2107040410,True,work,2,5,14.0,WALK_LOC,210704041,3 +1685632333,16856323330,5139122,2098707,2107040410,False,othmaint,7,2,17.0,WALK_LOC,210704041,1 +1685632334,16856323340,5139122,2098707,2107040410,False,shopping,5,7,18.0,WALK,210704041,2 +1685632335,16856323350,5139122,2098707,2107040410,False,escort,8,5,18.0,WALK,210704041,3 +1685632336,16856323360,5139122,2098707,2107040410,False,Home,9,8,18.0,WALK,210704041,4 +1685632545,16856325450,5139123,2098707,2107040680,True,othdiscr,11,9,12.0,WALK,210704068,1 +1685632549,16856325490,5139123,2098707,2107040680,False,Home,9,11,15.0,WALK,210704068,1 +1685780457,16857804570,5139574,2098933,2107225570,True,atwork,21,13,11.0,SHARED2FREE,210722557,1 +1685780461,16857804610,5139574,2098933,2107225570,False,Work,13,21,12.0,SHARED3FREE,210722557,1 +1685780585,16857805850,5139574,2098933,2107225730,True,work,13,21,8.0,WALK,210722573,1 +1685780589,16857805890,5139574,2098933,2107225730,False,shopping,11,13,17.0,WALK,210722573,1 +1685780590,16857805900,5139574,2098933,2107225730,False,Home,21,11,17.0,WALK,210722573,2 +1685780865,16857808650,5139575,2098933,2107226080,True,shopping,24,21,12.0,WALK,210722608,1 +1685780869,16857808690,5139575,2098933,2107226080,False,escort,5,24,14.0,WALK,210722608,1 +1685780870,16857808700,5139575,2098933,2107226080,False,Home,21,5,14.0,WALK,210722608,2 +1690202353,16902023530,5153055,2105673,2112752940,True,work,2,21,7.0,WALK,211275294,1 +1690202357,16902023570,5153055,2105673,2112752940,False,Home,21,2,20.0,WALK,211275294,1 +1690203057,16902030570,5153058,2105675,2112753820,True,atwork,19,24,10.0,WALK_LOC,211275382,1 +1690203061,16902030610,5153058,2105675,2112753820,False,eatout,8,19,10.0,TNC_SINGLE,211275382,1 +1690203062,16902030620,5153058,2105675,2112753820,False,Work,24,8,10.0,TNC_SINGLE,211275382,2 +1690203337,16902033370,5153058,2105675,2112754170,True,othmaint,11,21,8.0,WALK_LOC,211275417,1 +1690203338,16902033380,5153058,2105675,2112754170,True,escort,13,11,9.0,WALK,211275417,2 +1690203339,16902033390,5153058,2105675,2112754170,True,work,24,13,9.0,WALK,211275417,3 +1690203341,16902033410,5153058,2105675,2112754170,False,Home,21,24,17.0,WALK_LOC,211275417,1 +1690203385,16902033850,5153059,2105675,2112754230,True,atwork,13,13,12.0,WALK,211275423,1 +1690203389,16902033890,5153059,2105675,2112754230,False,work,7,13,14.0,WALK,211275423,1 +1690203390,16902033900,5153059,2105675,2112754230,False,Work,13,7,14.0,WALK,211275423,2 +1690203665,16902036650,5153059,2105675,2112754580,True,escort,22,21,8.0,WALK_LOC,211275458,1 +1690203666,16902036660,5153059,2105675,2112754580,True,shopping,16,22,9.0,WALK_LOC,211275458,2 +1690203667,16902036670,5153059,2105675,2112754580,True,work,13,16,9.0,WALK,211275458,3 +1690203669,16902036690,5153059,2105675,2112754580,False,Home,21,13,19.0,WALK,211275458,1 +1766790681,17667906810,5386556,2222424,2208488350,True,work,2,6,8.0,WALK,220848835,1 +1766790685,17667906850,5386556,2222424,2208488350,False,othmaint,4,2,16.0,WALK,220848835,1 +1766790686,17667906860,5386556,2222424,2208488350,False,escort,11,4,16.0,WALK,220848835,2 +1766790687,17667906870,5386556,2222424,2208488350,False,shopping,5,11,17.0,WALK,220848835,3 +1766790688,17667906880,5386556,2222424,2208488350,False,Home,6,5,17.0,WALK,220848835,4 +1766791009,17667910090,5386557,2222424,2208488760,True,othmaint,5,6,8.0,WALK,220848876,1 +1766791010,17667910100,5386557,2222424,2208488760,True,work,1,5,8.0,WALK,220848876,2 +1766791013,17667910130,5386557,2222424,2208488760,False,Home,6,1,16.0,WALK,220848876,1 +1766817313,17668173130,5386638,2222465,2208521640,True,eatout,4,6,10.0,WALK,220852164,1 +1766817317,17668173170,5386638,2222465,2208521640,False,Home,6,4,10.0,WALK,220852164,1 +1766817465,17668174650,5386638,2222465,2208521830,True,othdiscr,7,6,14.0,WALK,220852183,1 +1766817469,17668174690,5386638,2222465,2208521830,False,Home,6,7,17.0,WALK,220852183,1 +1766817793,17668177930,5386639,2222465,2208522240,True,othdiscr,12,6,9.0,WALK,220852224,1 +1766817797,17668177970,5386639,2222465,2208522240,False,Home,6,12,10.0,WALK,220852224,1 +1766817817,17668178170,5386639,2222465,2208522270,True,othmaint,9,6,11.0,WALK_LOC,220852227,1 +1766817821,17668178210,5386639,2222465,2208522270,False,Home,6,9,12.0,WALK_LOC,220852227,1 +1766817857,17668178570,5386639,2222465,2208522320,True,shopping,19,6,14.0,WALK_LOC,220852232,1 +1766817861,17668178610,5386639,2222465,2208522320,False,Home,6,19,18.0,WALK_LOC,220852232,1 +1766872681,17668726810,5386806,2222549,2208590850,True,work,6,7,11.0,WALK,220859085,1 +1766872685,17668726850,5386806,2222549,2208590850,False,Home,7,6,20.0,WALK,220859085,1 +1766872897,17668728970,5386807,2222549,2208591120,True,othdiscr,12,7,8.0,WALK,220859112,1 +1766872901,17668729010,5386807,2222549,2208591120,False,Home,7,12,11.0,WALK,220859112,1 +1766872921,17668729210,5386807,2222549,2208591150,True,othmaint,3,7,11.0,WALK,220859115,1 +1766872925,17668729250,5386807,2222549,2208591150,False,Home,7,3,20.0,WALK_LOC,220859115,1 +1766880929,17668809290,5386832,2222562,2208601160,True,atwork,14,16,10.0,WALK,220860116,1 +1766880933,17668809330,5386832,2222562,2208601160,False,work,16,14,10.0,WALK,220860116,1 +1766880934,17668809340,5386832,2222562,2208601160,False,Work,16,16,10.0,WALK,220860116,2 +1766881209,17668812090,5386832,2222562,2208601510,True,work,16,7,7.0,WALK_LOC,220860151,1 +1766881213,17668812130,5386832,2222562,2208601510,False,Home,7,16,12.0,TNC_SINGLE,220860151,1 +1766881273,17668812730,5386833,2222562,2208601590,True,eatout,2,7,10.0,WALK,220860159,1 +1766881277,17668812770,5386833,2222562,2208601590,False,Home,7,2,12.0,WALK,220860159,1 +1766881449,17668814490,5386833,2222562,2208601810,True,othmaint,5,7,13.0,WALK,220860181,1 +1766881453,17668814530,5386833,2222562,2208601810,False,Home,7,5,13.0,WALK,220860181,1 +1766903249,17669032490,5386900,2222596,2208629060,True,eatout,5,7,12.0,WALK,220862906,1 +1766903253,17669032530,5386900,2222596,2208629060,False,Home,7,5,19.0,WALK,220862906,1 +1766903465,17669034650,5386900,2222596,2208629330,True,shopping,6,7,10.0,BIKE,220862933,1 +1766903469,17669034690,5386900,2222596,2208629330,False,Home,7,6,11.0,BIKE,220862933,1 +1766903841,17669038410,5386901,2222596,2208629800,True,othmaint,7,7,21.0,WALK,220862980,1 +1766903842,17669038420,5386901,2222596,2208629800,True,escort,7,7,21.0,WALK,220862980,2 +1766903843,17669038430,5386901,2222596,2208629800,True,work,7,7,21.0,WALK,220862980,3 +1766903844,17669038440,5386901,2222596,2208629800,True,work,9,7,21.0,WALK_LRF,220862980,4 +1766903845,17669038450,5386901,2222596,2208629800,False,Home,7,9,21.0,WALK_LRF,220862980,1 +1766907841,17669078410,5386914,2222603,2208634800,True,eatout,8,7,20.0,WALK,220863480,1 +1766907845,17669078450,5386914,2222603,2208634800,False,Home,7,8,21.0,WALK,220863480,1 +1766907993,17669079930,5386914,2222603,2208634990,True,othdiscr,11,7,15.0,WALK,220863499,1 +1766907997,17669079970,5386914,2222603,2208634990,False,Home,7,11,20.0,WALK,220863499,1 +1766908001,17669080010,5386914,2222603,2208635000,True,othdiscr,22,7,21.0,WALK_LRF,220863500,1 +1766908005,17669080050,5386914,2222603,2208635000,False,social,9,22,21.0,WALK_LRF,220863500,1 +1766908006,17669080060,5386914,2222603,2208635000,False,Home,7,9,21.0,WALK_LRF,220863500,2 +1766908017,17669080170,5386914,2222603,2208635020,True,othmaint,7,7,10.0,WALK,220863502,1 +1766908021,17669080210,5386914,2222603,2208635020,False,Home,7,7,14.0,WALK,220863502,1 +1766908433,17669084330,5386915,2222603,2208635540,True,work,2,7,8.0,WALK,220863554,1 +1766908437,17669084370,5386915,2222603,2208635540,False,Home,7,2,18.0,WALK,220863554,1 +1766911713,17669117130,5386925,2222608,2208639640,True,work,7,7,11.0,WALK,220863964,1 +1766911717,17669117170,5386925,2222608,2208639640,False,Home,7,7,23.0,TNC_SINGLE,220863964,1 +1766917289,17669172890,5386942,2222617,2208646610,True,work,19,7,7.0,TNC_SINGLE,220864661,1 +1766917293,17669172930,5386942,2222617,2208646610,False,Home,7,19,21.0,TNC_SINGLE,220864661,1 +1766917617,17669176170,5386943,2222617,2208647020,True,work,22,7,8.0,SHARED2FREE,220864702,1 +1766917621,17669176210,5386943,2222617,2208647020,False,social,7,22,12.0,WALK_LOC,220864702,1 +1766917622,17669176220,5386943,2222617,2208647020,False,shopping,25,7,16.0,WALK,220864702,2 +1766917623,17669176230,5386943,2222617,2208647020,False,Home,7,25,21.0,WALK,220864702,3 +1766956369,17669563690,5387062,2222677,2208695460,True,shopping,3,4,10.0,WALK,220869546,1 +1766956370,17669563700,5387062,2222677,2208695460,True,atwork,24,3,10.0,WALK,220869546,2 +1766956373,17669563730,5387062,2222677,2208695460,False,Work,4,24,10.0,WALK,220869546,1 +1766956649,17669566490,5387062,2222677,2208695810,True,work,4,7,9.0,WALK,220869581,1 +1766956653,17669566530,5387062,2222677,2208695810,False,social,7,4,17.0,WALK,220869581,1 +1766956654,17669566540,5387062,2222677,2208695810,False,Home,7,7,18.0,WALK,220869581,2 +1766956697,17669566970,5387063,2222677,2208695870,True,atwork,5,13,11.0,WALK,220869587,1 +1766956701,17669567010,5387063,2222677,2208695870,False,Work,13,5,13.0,WALK,220869587,1 +1766956977,17669569770,5387063,2222677,2208696220,True,work,13,7,5.0,WALK_LRF,220869622,1 +1766956981,17669569810,5387063,2222677,2208696220,False,Home,7,13,17.0,WALK_LOC,220869622,1 +1766963865,17669638650,5387084,2222688,2208704830,True,work,13,7,7.0,WALK,220870483,1 +1766963869,17669638690,5387084,2222688,2208704830,False,Home,7,13,20.0,WALK,220870483,1 +1766964193,17669641930,5387085,2222688,2208705240,True,work,7,7,7.0,WALK,220870524,1 +1766964197,17669641970,5387085,2222688,2208705240,False,Home,7,7,16.0,WALK,220870524,1 +1766972769,17669727690,5387112,2222702,2208715960,True,othmaint,24,24,10.0,WALK,220871596,1 +1766972770,17669727700,5387112,2222702,2208715960,True,atwork,2,24,10.0,WALK,220871596,2 +1766972773,17669727730,5387112,2222702,2208715960,False,Work,24,2,10.0,WALK,220871596,1 +1766973049,17669730490,5387112,2222702,2208716310,True,work,24,7,7.0,WALK,220871631,1 +1766973053,17669730530,5387112,2222702,2208716310,False,Home,7,24,16.0,WALK_LOC,220871631,1 +1766973377,17669733770,5387113,2222702,2208716720,True,work,24,7,6.0,WALK_LOC,220871672,1 +1766973381,17669733810,5387113,2222702,2208716720,False,Home,7,24,14.0,TNC_SINGLE,220871672,1 +1766973705,17669737050,5387114,2222703,2208717130,True,work,10,7,8.0,WALK_LOC,220871713,1 +1766973709,17669737090,5387114,2222703,2208717130,False,Home,7,10,17.0,WALK_LOC,220871713,1 +1766973985,17669739850,5387115,2222703,2208717480,True,shopping,18,7,12.0,TNC_SINGLE,220871748,1 +1766973986,17669739860,5387115,2222703,2208717480,True,shopping,11,18,14.0,TNC_SINGLE,220871748,2 +1766973989,17669739890,5387115,2222703,2208717480,False,othmaint,11,11,20.0,TNC_SINGLE,220871748,1 +1766973990,17669739900,5387115,2222703,2208717480,False,Home,7,11,20.0,TNC_SINGLE,220871748,2 +1766983265,17669832650,5387144,2222718,2208729080,True,atwork,16,14,11.0,WALK,220872908,1 +1766983269,17669832690,5387144,2222718,2208729080,False,Work,14,16,11.0,WALK,220872908,1 +1766983545,17669835450,5387144,2222718,2208729430,True,escort,6,9,8.0,WALK_LOC,220872943,1 +1766983546,17669835460,5387144,2222718,2208729430,True,work,14,6,9.0,WALK_LOC,220872943,2 +1766983549,17669835490,5387144,2222718,2208729430,False,shopping,16,14,18.0,WALK,220872943,1 +1766983550,17669835500,5387144,2222718,2208729430,False,Home,9,16,18.0,WALK_LRF,220872943,2 +1766983809,17669838090,5387145,2222718,2208729760,True,univ,9,9,21.0,WALK,220872976,1 +1766983813,17669838130,5387145,2222718,2208729760,False,eatout,9,9,21.0,WALK,220872976,1 +1766983814,17669838140,5387145,2222718,2208729760,False,Home,9,9,21.0,WALK,220872976,2 +1766983873,17669838730,5387145,2222718,2208729840,True,work,15,9,7.0,WALK_LRF,220872984,1 +1766983877,17669838770,5387145,2222718,2208729840,False,work,9,15,14.0,WALK_LRF,220872984,1 +1766983878,17669838780,5387145,2222718,2208729840,False,Home,9,9,14.0,WALK,220872984,2 +1766986561,17669865610,5387154,2222723,2208733200,True,eatout,8,9,17.0,WALK,220873320,1 +1766986565,17669865650,5387154,2222723,2208733200,False,Home,9,8,20.0,WALK,220873320,1 +1766986825,17669868250,5387154,2222723,2208733530,True,work,22,9,7.0,SHARED2FREE,220873353,1 +1766986829,17669868290,5387154,2222723,2208733530,False,Home,9,22,16.0,SHARED3FREE,220873353,1 +1766986873,17669868730,5387155,2222723,2208733590,True,atwork,4,2,8.0,WALK,220873359,1 +1766986877,17669868770,5387155,2222723,2208733590,False,work,7,4,8.0,WALK,220873359,1 +1766986878,17669868780,5387155,2222723,2208733590,False,Work,2,7,8.0,WALK,220873359,2 +1766987153,17669871530,5387155,2222723,2208733940,True,escort,7,9,5.0,WALK_LRF,220873394,1 +1766987154,17669871540,5387155,2222723,2208733940,True,eatout,9,7,6.0,WALK_LOC,220873394,2 +1766987155,17669871550,5387155,2222723,2208733940,True,work,2,9,6.0,WALK_LRF,220873394,3 +1766987157,17669871570,5387155,2222723,2208733940,False,Home,9,2,16.0,WALK_LRF,220873394,1 +1766997977,17669979770,5387188,2222740,2208747470,True,work,17,9,7.0,BIKE,220874747,1 +1766997981,17669979810,5387188,2222740,2208747470,False,Home,9,17,12.0,BIKE,220874747,1 +1766997985,17669979850,5387188,2222740,2208747480,True,work,17,9,13.0,WALK_LRF,220874748,1 +1766997989,17669979890,5387188,2222740,2208747480,False,Home,9,17,18.0,WALK_LRF,220874748,1 +1766998025,17669980250,5387189,2222740,2208747530,True,atwork,13,13,13.0,WALK,220874753,1 +1766998029,17669980290,5387189,2222740,2208747530,False,Work,13,13,13.0,WALK,220874753,1 +1766998305,17669983050,5387189,2222740,2208747880,True,work,13,9,8.0,WALK,220874788,1 +1766998309,17669983090,5387189,2222740,2208747880,False,Home,9,13,18.0,WALK,220874788,1 +1767003833,17670038330,5387206,2222749,2208754790,True,shopping,21,9,18.0,WALK,220875479,1 +1767003837,17670038370,5387206,2222749,2208754790,False,Home,9,21,20.0,WALK_LOC,220875479,1 +1767003881,17670038810,5387206,2222749,2208754850,True,work,4,9,5.0,TNC_SINGLE,220875485,1 +1767003885,17670038850,5387206,2222749,2208754850,False,escort,8,4,17.0,WALK,220875485,1 +1767003886,17670038860,5387206,2222749,2208754850,False,Home,9,8,18.0,WALK,220875485,2 +1767004209,17670042090,5387207,2222749,2208755260,True,work,11,9,7.0,TNC_SINGLE,220875526,1 +1767004213,17670042130,5387207,2222749,2208755260,False,Home,9,11,16.0,TNC_SINGLE,220875526,1 +1767013721,17670137210,5387236,2222764,2208767150,True,work,1,9,7.0,WALK_LRF,220876715,1 +1767013725,17670137250,5387236,2222764,2208767150,False,shopping,8,1,16.0,WALK_LRF,220876715,1 +1767013726,17670137260,5387236,2222764,2208767150,False,Home,9,8,16.0,WALK_LOC,220876715,2 +1767013785,17670137850,5387237,2222764,2208767230,True,eatout,18,9,7.0,WALK,220876723,1 +1767013789,17670137890,5387237,2222764,2208767230,False,Home,9,18,10.0,WALK,220876723,1 +1767015425,17670154250,5387242,2222767,2208769280,True,eatout,10,9,21.0,WALK,220876928,1 +1767015429,17670154290,5387242,2222767,2208769280,False,Home,9,10,21.0,WALK,220876928,1 +1767015689,17670156890,5387242,2222767,2208769610,True,work,4,9,8.0,WALK,220876961,1 +1767015693,17670156930,5387242,2222767,2208769610,False,Home,9,4,20.0,WALK,220876961,1 +1767016017,17670160170,5387243,2222767,2208770020,True,work,5,9,11.0,WALK,220877002,1 +1767016021,17670160210,5387243,2222767,2208770020,False,Home,9,5,15.0,WALK,220877002,1 +1767031345,17670313450,5387290,2222791,2208789180,True,othmaint,24,9,8.0,TNC_SINGLE,220878918,1 +1767031349,17670313490,5387290,2222791,2208789180,False,Home,9,24,11.0,WALK_LOC,220878918,1 +1767031433,17670314330,5387290,2222791,2208789290,True,work,10,9,17.0,WALK_LOC,220878929,1 +1767031437,17670314370,5387290,2222791,2208789290,False,Home,9,10,23.0,WALK,220878929,1 +1767031761,17670317610,5387291,2222791,2208789700,True,work,2,9,9.0,WALK_HVY,220878970,1 +1767031765,17670317650,5387291,2222791,2208789700,False,escort,25,2,18.0,TNC_SINGLE,220878970,1 +1767031766,17670317660,5387291,2222791,2208789700,False,Home,9,25,19.0,WALK_LOC,220878970,2 +1767046897,17670468970,5387338,2222815,2208808620,True,atwork,19,1,13.0,SHARED2FREE,220880862,1 +1767046901,17670469010,5387338,2222815,2208808620,False,Work,1,19,13.0,SHARED2FREE,220880862,1 +1767047177,17670471770,5387338,2222815,2208808970,True,work,1,9,6.0,WALK_HVY,220880897,1 +1767047181,17670471810,5387338,2222815,2208808970,False,Home,9,1,16.0,WALK_LRF,220880897,1 +1767047225,17670472250,5387339,2222815,2208809030,True,atwork,7,5,12.0,WALK,220880903,1 +1767047229,17670472290,5387339,2222815,2208809030,False,Work,5,7,13.0,WALK,220880903,1 +1767047393,17670473930,5387339,2222815,2208809240,True,othdiscr,7,9,16.0,SHARED2FREE,220880924,1 +1767047397,17670473970,5387339,2222815,2208809240,False,escort,9,7,16.0,WALK,220880924,1 +1767047398,17670473980,5387339,2222815,2208809240,False,Home,9,9,16.0,WALK,220880924,2 +1767047505,17670475050,5387339,2222815,2208809380,True,work,5,9,6.0,WALK,220880938,1 +1767047509,17670475090,5387339,2222815,2208809380,False,Home,9,5,15.0,WALK,220880938,1 +1767057673,17670576730,5387370,2222831,2208822090,True,work,22,9,7.0,WALK_LRF,220882209,1 +1767057677,17670576770,5387370,2222831,2208822090,False,Home,9,22,18.0,WALK_LRF,220882209,1 +1767057721,17670577210,5387371,2222831,2208822150,True,atwork,16,19,10.0,TNC_SINGLE,220882215,1 +1767057725,17670577250,5387371,2222831,2208822150,False,Work,19,16,12.0,TNC_SINGLE,220882215,1 +1767057737,17670577370,5387371,2222831,2208822170,True,eatout,11,9,6.0,WALK,220882217,1 +1767057741,17670577410,5387371,2222831,2208822170,False,Home,9,11,6.0,WALK,220882217,1 +1767058001,17670580010,5387371,2222831,2208822500,True,work,19,9,7.0,WALK_LOC,220882250,1 +1767058005,17670580050,5387371,2222831,2208822500,False,Home,9,19,20.0,WALK_LOC,220882250,1 +1767066745,17670667450,5387398,2222845,2208833430,True,othdiscr,4,9,15.0,WALK,220883343,1 +1767066749,17670667490,5387398,2222845,2208833430,False,Home,9,4,17.0,WALK,220883343,1 +1767066857,17670668570,5387398,2222845,2208833570,True,work,10,9,6.0,WALK,220883357,1 +1767066861,17670668610,5387398,2222845,2208833570,False,Home,9,10,15.0,WALK,220883357,1 +1767066873,17670668730,5387399,2222845,2208833590,True,shopping,5,10,12.0,WALK,220883359,1 +1767066874,17670668740,5387399,2222845,2208833590,True,atwork,16,5,12.0,WALK,220883359,2 +1767066877,17670668770,5387399,2222845,2208833590,False,Work,10,16,13.0,WALK,220883359,1 +1767067185,17670671850,5387399,2222845,2208833980,True,work,10,9,6.0,WALK,220883398,1 +1767067189,17670671890,5387399,2222845,2208833980,False,Home,9,10,21.0,WALK,220883398,1 +1767091785,17670917850,5387474,2222883,2208864730,True,work,24,9,8.0,WALK_LRF,220886473,1 +1767091789,17670917890,5387474,2222883,2208864730,False,shopping,5,24,21.0,WALK_LOC,220886473,1 +1767091790,17670917900,5387474,2222883,2208864730,False,Home,9,5,21.0,WALK,220886473,2 +1767092113,17670921130,5387475,2222883,2208865140,True,work,14,9,7.0,WALK_LRF,220886514,1 +1767092117,17670921170,5387475,2222883,2208865140,False,Home,9,14,18.0,WALK_LRF,220886514,1 +1767092817,17670928170,5387478,2222885,2208866020,True,eatout,6,2,10.0,WALK,220886602,1 +1767092818,17670928180,5387478,2222885,2208866020,True,atwork,1,6,10.0,WALK,220886602,2 +1767092821,17670928210,5387478,2222885,2208866020,False,Work,2,1,10.0,WALK,220886602,1 +1767093097,17670930970,5387478,2222885,2208866370,True,work,2,9,6.0,WALK,220886637,1 +1767093101,17670931010,5387478,2222885,2208866370,False,Home,9,2,20.0,WALK,220886637,1 +1767093113,17670931130,5387479,2222885,2208866390,True,atwork,16,19,15.0,WALK,220886639,1 +1767093117,17670931170,5387479,2222885,2208866390,False,work,12,16,15.0,WALK,220886639,1 +1767093118,17670931180,5387479,2222885,2208866390,False,Work,19,12,15.0,WALK,220886639,2 +1767093313,17670933130,5387479,2222885,2208866640,True,othdiscr,16,9,12.0,WALK_LRF,220886664,1 +1767093317,17670933170,5387479,2222885,2208866640,False,Home,9,16,14.0,WALK_LRF,220886664,1 +1767093425,17670934250,5387479,2222885,2208866780,True,work,19,9,14.0,WALK,220886678,1 +1767093429,17670934290,5387479,2222885,2208866780,False,othmaint,9,19,16.0,WALK,220886678,1 +1767093430,17670934300,5387479,2222885,2208866780,False,othmaint,9,9,17.0,WALK,220886678,2 +1767093431,17670934310,5387479,2222885,2208866780,False,social,9,9,17.0,WALK,220886678,3 +1767093432,17670934320,5387479,2222885,2208866780,False,Home,9,9,17.0,WALK,220886678,4 +1767124961,17671249610,5387576,2222934,2208906200,True,atwork,5,4,10.0,WALK,220890620,1 +1767124965,17671249650,5387576,2222934,2208906200,False,eatout,7,5,10.0,WALK,220890620,1 +1767124966,17671249660,5387576,2222934,2208906200,False,work,7,7,10.0,WALK,220890620,2 +1767124967,17671249670,5387576,2222934,2208906200,False,Work,4,7,10.0,WALK,220890620,3 +1767125241,17671252410,5387576,2222934,2208906550,True,work,4,9,10.0,WALK_LRF,220890655,1 +1767125245,17671252450,5387576,2222934,2208906550,False,Home,9,4,20.0,WALK_LRF,220890655,1 +1767125569,17671255690,5387577,2222934,2208906960,True,work,24,9,7.0,WALK_LOC,220890696,1 +1767125573,17671255730,5387577,2222934,2208906960,False,Home,9,24,17.0,WALK_LRF,220890696,1 +1767134425,17671344250,5387604,2222948,2208918030,True,work,6,9,7.0,TNC_SINGLE,220891803,1 +1767134429,17671344290,5387604,2222948,2208918030,False,Home,9,6,17.0,TNC_SINGLE,220891803,1 +1767134705,17671347050,5387605,2222948,2208918380,True,shopping,13,9,9.0,WALK_LRF,220891838,1 +1767134709,17671347090,5387605,2222948,2208918380,False,shopping,5,13,10.0,TNC_SINGLE,220891838,1 +1767134710,17671347100,5387605,2222948,2208918380,False,Home,9,5,10.0,TNC_SHARED,220891838,2 +1767182857,17671828570,5387752,2223022,2208978570,True,othdiscr,2,9,12.0,WALK_LRF,220897857,1 +1767182861,17671828610,5387752,2223022,2208978570,False,Home,9,2,16.0,WALK_LRF,220897857,1 +1767182865,17671828650,5387752,2223022,2208978580,True,othdiscr,15,9,17.0,WALK_HVY,220897858,1 +1767182869,17671828690,5387752,2223022,2208978580,False,Home,9,15,22.0,TNC_SINGLE,220897858,1 +1767182945,17671829450,5387752,2223022,2208978680,True,social,1,9,16.0,WALK,220897868,1 +1767182949,17671829490,5387752,2223022,2208978680,False,Home,9,1,17.0,WALK_LRF,220897868,1 +1767182985,17671829850,5387753,2223022,2208978730,True,atwork,16,16,14.0,WALK,220897873,1 +1767182989,17671829890,5387753,2223022,2208978730,False,Work,16,16,14.0,WALK,220897873,1 +1767183297,17671832970,5387753,2223022,2208979120,True,work,16,9,14.0,TNC_SINGLE,220897912,1 +1767183301,17671833010,5387753,2223022,2208979120,False,Home,9,16,17.0,TNC_SINGLE,220897912,1 +1767183313,17671833130,5387754,2223023,2208979140,True,atwork,7,21,12.0,WALK,220897914,1 +1767183317,17671833170,5387754,2223023,2208979140,False,Work,21,7,14.0,WALK,220897914,1 +1767183537,17671835370,5387754,2223023,2208979420,True,othmaint,16,9,5.0,WALK_LRF,220897942,1 +1767183541,17671835410,5387754,2223023,2208979420,False,escort,24,16,7.0,WALK_LOC,220897942,1 +1767183542,17671835420,5387754,2223023,2208979420,False,Home,9,24,7.0,WALK_LRF,220897942,2 +1767183625,17671836250,5387754,2223023,2208979530,True,work,21,9,8.0,WALK,220897953,1 +1767183629,17671836290,5387754,2223023,2208979530,False,Home,9,21,18.0,WALK,220897953,1 +1767183633,17671836330,5387754,2223023,2208979540,True,work,21,9,18.0,TNC_SINGLE,220897954,1 +1767183637,17671836370,5387754,2223023,2208979540,False,escort,8,21,18.0,WALK_LOC,220897954,1 +1767183638,17671836380,5387754,2223023,2208979540,False,Home,9,8,19.0,TNC_SINGLE,220897954,2 +1767183905,17671839050,5387755,2223023,2208979880,True,shopping,11,9,20.0,SHARED3FREE,220897988,1 +1767183909,17671839090,5387755,2223023,2208979880,False,Home,9,11,20.0,WALK,220897988,1 +1767183953,17671839530,5387755,2223023,2208979940,True,work,11,9,6.0,WALK,220897994,1 +1767183957,17671839570,5387755,2223023,2208979940,False,Home,9,11,16.0,WALK,220897994,1 +1767186249,17671862490,5387762,2223027,2208982810,True,work,4,9,7.0,WALK_HVY,220898281,1 +1767186253,17671862530,5387762,2223027,2208982810,False,Home,9,4,17.0,WALK_LRF,220898281,1 +1767186577,17671865770,5387763,2223027,2208983220,True,escort,10,9,8.0,WALK,220898322,1 +1767186578,17671865780,5387763,2223027,2208983220,True,work,14,10,8.0,WALK_LRF,220898322,2 +1767186581,17671865810,5387763,2223027,2208983220,False,othmaint,13,14,17.0,WALK,220898322,1 +1767186582,17671865820,5387763,2223027,2208983220,False,Home,9,13,17.0,WALK_LRF,220898322,2 +1767211713,17672117130,5387840,2223066,2209014640,True,atwork,4,5,18.0,WALK,220901464,1 +1767211717,17672117170,5387840,2223066,2209014640,False,Work,5,4,18.0,WALK,220901464,1 +1767211833,17672118330,5387840,2223066,2209014790,True,work,5,9,6.0,WALK,220901479,1 +1767211837,17672118370,5387840,2223066,2209014790,False,Home,9,5,16.0,WALK,220901479,1 +1767211841,17672118410,5387840,2223066,2209014800,True,work,5,9,16.0,WALK,220901480,1 +1767211845,17672118450,5387840,2223066,2209014800,False,othdiscr,9,5,18.0,WALK_LOC,220901480,1 +1767211846,17672118460,5387840,2223066,2209014800,False,shopping,11,9,20.0,WALK_LOC,220901480,2 +1767211847,17672118470,5387840,2223066,2209014800,False,univ,9,11,20.0,WALK_LOC,220901480,3 +1767211848,17672118480,5387840,2223066,2209014800,False,Home,9,9,21.0,WALK,220901480,4 +1767211881,17672118810,5387841,2223066,2209014850,True,atwork,2,22,10.0,WALK,220901485,1 +1767211885,17672118850,5387841,2223066,2209014850,False,work,2,2,12.0,WALK,220901485,1 +1767211886,17672118860,5387841,2223066,2209014850,False,Work,22,2,12.0,WALK,220901485,2 +1767212161,17672121610,5387841,2223066,2209015200,True,work,22,9,7.0,WALK_LRF,220901520,1 +1767212165,17672121650,5387841,2223066,2209015200,False,Home,9,22,21.0,WALK_LRF,220901520,1 +1767281745,17672817450,5388054,2223173,2209102180,True,atwork,15,13,10.0,WALK,220910218,1 +1767281749,17672817490,5388054,2223173,2209102180,False,eatout,16,15,13.0,WALK,220910218,1 +1767281750,17672817500,5388054,2223173,2209102180,False,Work,13,16,13.0,WALK,220910218,2 +1767282025,17672820250,5388054,2223173,2209102530,True,shopping,12,10,9.0,WALK_LOC,220910253,1 +1767282026,17672820260,5388054,2223173,2209102530,True,work,13,12,10.0,WALK_LOC,220910253,2 +1767282029,17672820290,5388054,2223173,2209102530,False,shopping,5,13,17.0,WALK_LOC,220910253,1 +1767282030,17672820300,5388054,2223173,2209102530,False,othmaint,13,5,18.0,WALK_LOC,220910253,2 +1767282031,17672820310,5388054,2223173,2209102530,False,Home,10,13,18.0,WALK_LRF,220910253,3 +1767282041,17672820410,5388055,2223173,2209102550,True,work,22,23,12.0,WALK,220910255,1 +1767282042,17672820420,5388055,2223173,2209102550,True,atwork,22,22,12.0,WALK,220910255,2 +1767282045,17672820450,5388055,2223173,2209102550,False,Work,23,22,14.0,WALK,220910255,1 +1767282353,17672823530,5388055,2223173,2209102940,True,eatout,16,10,9.0,WALK_LOC,220910294,1 +1767282354,17672823540,5388055,2223173,2209102940,True,work,23,16,10.0,WALK,220910294,2 +1767282357,17672823570,5388055,2223173,2209102940,False,othdiscr,16,23,16.0,TNC_SINGLE,220910294,1 +1767282358,17672823580,5388055,2223173,2209102940,False,Home,10,16,18.0,WALK_LOC,220910294,2 +1767287273,17672872730,5388070,2223181,2209109090,True,work,5,10,13.0,WALK,220910909,1 +1767287277,17672872770,5388070,2223181,2209109090,False,Home,10,5,19.0,WALK,220910909,1 +1767287601,17672876010,5388071,2223181,2209109500,True,work,1,10,8.0,WALK_LRF,220910950,1 +1767287605,17672876050,5388071,2223181,2209109500,False,othmaint,12,1,19.0,WALK,220910950,1 +1767287606,17672876060,5388071,2223181,2209109500,False,shopping,13,12,19.0,WALK_LOC,220910950,2 +1767287607,17672876070,5388071,2223181,2209109500,False,othmaint,9,13,19.0,WALK_LRF,220910950,3 +1767287608,17672876080,5388071,2223181,2209109500,False,Home,10,9,19.0,WALK,220910950,4 +1767309577,17673095770,5388138,2223215,2209136970,True,work,23,10,8.0,WALK_LOC,220913697,1 +1767309581,17673095810,5388138,2223215,2209136970,False,Home,10,23,18.0,TNC_SINGLE,220913697,1 +1767309857,17673098570,5388139,2223215,2209137320,True,shopping,13,10,13.0,WALK_LRF,220913732,1 +1767309861,17673098610,5388139,2223215,2209137320,False,Home,10,13,13.0,WALK_LRF,220913732,1 +1767335401,17673354010,5388217,2223254,2209169250,True,othmaint,20,10,8.0,WALK,220916925,1 +1767335405,17673354050,5388217,2223254,2209169250,False,Home,10,20,8.0,WALK,220916925,1 +1767335489,17673354890,5388217,2223254,2209169360,True,work,19,10,8.0,WALK,220916936,1 +1767335493,17673354930,5388217,2223254,2209169360,False,Home,10,19,17.0,WALK,220916936,1 +1767337785,17673377850,5388224,2223258,2209172230,True,work,10,10,7.0,TNC_SINGLE,220917223,1 +1767337789,17673377890,5388224,2223258,2209172230,False,eatout,11,10,17.0,TNC_SINGLE,220917223,1 +1767337790,17673377900,5388224,2223258,2209172230,False,Home,10,11,18.0,TNC_SINGLE,220917223,2 +1767338113,17673381130,5388225,2223258,2209172640,True,work,5,10,7.0,TNC_SINGLE,220917264,1 +1767338117,17673381170,5388225,2223258,2209172640,False,Home,10,5,18.0,WALK,220917264,1 +1767344721,17673447210,5388246,2223269,2209180900,True,atwork,13,13,11.0,WALK,220918090,1 +1767344725,17673447250,5388246,2223269,2209180900,False,Work,13,13,11.0,WALK,220918090,1 +1767345001,17673450010,5388246,2223269,2209181250,True,work,13,10,8.0,WALK_LRF,220918125,1 +1767345005,17673450050,5388246,2223269,2209181250,False,Home,10,13,18.0,WALK_LRF,220918125,1 +1767345329,17673453290,5388247,2223269,2209181660,True,work,1,10,8.0,WALK_LRF,220918166,1 +1767345333,17673453330,5388247,2223269,2209181660,False,Home,10,1,19.0,WALK_LRF,220918166,1 +1767345985,17673459850,5388249,2223270,2209182480,True,work,10,10,7.0,WALK,220918248,1 +1767345989,17673459890,5388249,2223270,2209182480,False,Home,10,10,15.0,WALK,220918248,1 +1767353249,17673532490,5388272,2223282,2209191560,True,atwork,20,9,16.0,WALK,220919156,1 +1767353253,17673532530,5388272,2223282,2209191560,False,Work,9,20,16.0,SHARED3FREE,220919156,1 +1767353265,17673532650,5388272,2223282,2209191580,True,eatout,12,11,17.0,WALK,220919158,1 +1767353269,17673532690,5388272,2223282,2209191580,False,Home,11,12,19.0,WALK,220919158,1 +1767353529,17673535290,5388272,2223282,2209191910,True,work,9,11,8.0,WALK_LOC,220919191,1 +1767353533,17673535330,5388272,2223282,2209191910,False,Home,11,9,17.0,TNC_SINGLE,220919191,1 +1767353857,17673538570,5388273,2223282,2209192320,True,work,14,11,7.0,WALK,220919232,1 +1767353861,17673538610,5388273,2223282,2209192320,False,Home,11,14,17.0,TNC_SINGLE,220919232,1 +1767381081,17673810810,5388356,2223324,2209226350,True,work,13,11,8.0,WALK,220922635,1 +1767381085,17673810850,5388356,2223324,2209226350,False,Home,11,13,19.0,WALK,220922635,1 +1767381425,17673814250,5388358,2223325,2209226780,True,atwork,14,11,12.0,WALK,220922678,1 +1767381429,17673814290,5388358,2223325,2209226780,False,Work,11,14,14.0,WALK,220922678,1 +1767381737,17673817370,5388358,2223325,2209227170,True,escort,13,11,8.0,TNC_SINGLE,220922717,1 +1767381738,17673817380,5388358,2223325,2209227170,True,work,11,13,10.0,WALK,220922717,2 +1767381741,17673817410,5388358,2223325,2209227170,False,eatout,2,11,18.0,WALK,220922717,1 +1767381742,17673817420,5388358,2223325,2209227170,False,Home,11,2,19.0,WALK_LOC,220922717,2 +1767382065,17673820650,5388359,2223325,2209227580,True,work,2,11,14.0,WALK,220922758,1 +1767382069,17673820690,5388359,2223325,2209227580,False,Home,11,2,18.0,WALK,220922758,1 +1767389609,17673896090,5388382,2223337,2209237010,True,escort,11,11,5.0,SHARED3FREE,220923701,1 +1767389610,17673896100,5388382,2223337,2209237010,True,work,12,11,5.0,SHARED3FREE,220923701,2 +1767389613,17673896130,5388382,2223337,2209237010,False,escort,20,12,16.0,SHARED3FREE,220923701,1 +1767389614,17673896140,5388382,2223337,2209237010,False,Home,11,20,16.0,WALK,220923701,2 +1767389657,17673896570,5388383,2223337,2209237070,True,atwork,16,21,12.0,WALK,220923707,1 +1767389661,17673896610,5388383,2223337,2209237070,False,Work,21,16,14.0,WALK,220923707,1 +1767389937,17673899370,5388383,2223337,2209237420,True,work,21,11,7.0,WALK,220923742,1 +1767389941,17673899410,5388383,2223337,2209237420,False,Home,11,21,20.0,WALK,220923742,1 +1767427657,17674276570,5388498,2223395,2209284570,True,escort,5,11,7.0,WALK,220928457,1 +1767427658,17674276580,5388498,2223395,2209284570,True,work,4,5,8.0,WALK_LOC,220928457,2 +1767427661,17674276610,5388498,2223395,2209284570,False,othmaint,5,4,15.0,WALK,220928457,1 +1767427662,17674276620,5388498,2223395,2209284570,False,Home,11,5,19.0,WALK,220928457,2 +1767427873,17674278730,5388499,2223395,2209284840,True,othdiscr,10,11,11.0,WALK,220928484,1 +1767427877,17674278770,5388499,2223395,2209284840,False,Home,11,10,13.0,WALK,220928484,1 +1767427985,17674279850,5388499,2223395,2209284980,True,escort,21,11,13.0,SHARED2FREE,220928498,1 +1767427986,17674279860,5388499,2223395,2209284980,True,work,16,21,13.0,WALK,220928498,2 +1767427989,17674279890,5388499,2223395,2209284980,False,Home,11,16,20.0,WALK_LOC,220928498,1 +1767478825,17674788250,5388654,2223473,2209348530,True,escort,21,12,7.0,WALK,220934853,1 +1767478826,17674788260,5388654,2223473,2209348530,True,work,24,21,8.0,SHARED2FREE,220934853,2 +1767478829,17674788290,5388654,2223473,2209348530,False,eatout,25,24,19.0,DRIVEALONEFREE,220934853,1 +1767478830,17674788300,5388654,2223473,2209348530,False,shopping,13,25,20.0,DRIVEALONEFREE,220934853,2 +1767478831,17674788310,5388654,2223473,2209348530,False,Home,12,13,20.0,DRIVEALONEFREE,220934853,3 +1767479153,17674791530,5388655,2223473,2209348940,True,work,1,12,18.0,TNC_SINGLE,220934894,1 +1767479157,17674791570,5388655,2223473,2209348940,False,Home,12,1,21.0,WALK_LRF,220934894,1 +1767485337,17674853370,5388674,2223483,2209356670,True,shopping,13,12,14.0,WALK,220935667,1 +1767485341,17674853410,5388674,2223483,2209356670,False,Home,12,13,16.0,WALK,220935667,1 +1767485625,17674856250,5388675,2223483,2209357030,True,othmaint,13,12,6.0,WALK_LOC,220935703,1 +1767485629,17674856290,5388675,2223483,2209357030,False,Home,12,13,6.0,TNC_SINGLE,220935703,1 +1767485633,17674856330,5388675,2223483,2209357040,True,othmaint,11,12,16.0,WALK,220935704,1 +1767485637,17674856370,5388675,2223483,2209357040,False,Home,12,11,16.0,WALK,220935704,1 +1767485665,17674856650,5388675,2223483,2209357080,True,shopping,16,12,18.0,SHARED3FREE,220935708,1 +1767485669,17674856690,5388675,2223483,2209357080,False,Home,12,16,19.0,SHARED3FREE,220935708,1 +1767485713,17674857130,5388675,2223483,2209357140,True,work,16,12,7.0,WALK_LOC,220935714,1 +1767485717,17674857170,5388675,2223483,2209357140,False,Home,12,16,16.0,WALK_LOC,220935714,1 +1767514249,17675142490,5388762,2223527,2209392810,True,work,3,14,8.0,WALK,220939281,1 +1767514253,17675142530,5388762,2223527,2209392810,False,Home,14,3,19.0,WALK,220939281,1 +1767514449,17675144490,5388763,2223527,2209393060,True,atwork,11,19,13.0,WALK,220939306,1 +1767514453,17675144530,5388763,2223527,2209393060,False,eatout,9,11,13.0,WALK,220939306,1 +1767514454,17675144540,5388763,2223527,2209393060,False,Work,19,9,13.0,WALK,220939306,2 +1767514577,17675145770,5388763,2223527,2209393220,True,escort,5,14,8.0,WALK,220939322,1 +1767514578,17675145780,5388763,2223527,2209393220,True,work,19,5,9.0,WALK_LRF,220939322,2 +1767514581,17675145810,5388763,2223527,2209393220,False,othmaint,9,19,17.0,WALK,220939322,1 +1767514582,17675145820,5388763,2223527,2209393220,False,Home,14,9,17.0,WALK_LRF,220939322,2 +1767515953,17675159530,5388768,2223530,2209394940,True,eatout,11,14,14.0,WALK,220939494,1 +1767515957,17675159570,5388768,2223530,2209394940,False,Home,14,11,18.0,WALK,220939494,1 +1767516233,17675162330,5388769,2223530,2209395290,True,atwork,2,2,11.0,WALK,220939529,1 +1767516237,17675162370,5388769,2223530,2209395290,False,Work,2,2,11.0,WALK,220939529,1 +1767516265,17675162650,5388769,2223530,2209395330,True,atwork,2,2,12.0,WALK,220939533,1 +1767516269,17675162690,5388769,2223530,2209395330,False,shopping,16,2,18.0,WALK,220939533,1 +1767516270,17675162700,5388769,2223530,2209395330,False,Work,2,16,18.0,WALK,220939533,2 +1767516433,17675164330,5388769,2223530,2209395540,True,othdiscr,12,14,6.0,WALK,220939554,1 +1767516437,17675164370,5388769,2223530,2209395540,False,Home,14,12,6.0,WALK,220939554,1 +1767516497,17675164970,5388769,2223530,2209395620,True,shopping,2,14,21.0,WALK_LOC,220939562,1 +1767516501,17675165010,5388769,2223530,2209395620,False,Home,14,2,22.0,TNC_SHARED,220939562,1 +1767516545,17675165450,5388769,2223530,2209395680,True,work,2,14,7.0,WALK,220939568,1 +1767516549,17675165490,5388769,2223530,2209395680,False,shopping,5,2,18.0,WALK,220939568,1 +1767516550,17675165500,5388769,2223530,2209395680,False,Home,14,5,21.0,WALK,220939568,2 +1767518577,17675185770,5388776,2223534,2209398220,True,eatout,22,14,12.0,BIKE,220939822,1 +1767518581,17675185810,5388776,2223534,2209398220,False,Home,14,22,13.0,BIKE,220939822,1 +1767519057,17675190570,5388777,2223534,2209398820,True,othdiscr,8,14,18.0,WALK,220939882,1 +1767519061,17675190610,5388777,2223534,2209398820,False,Home,14,8,19.0,WALK,220939882,1 +1767519169,17675191690,5388777,2223534,2209398960,True,work,24,14,6.0,WALK,220939896,1 +1767519173,17675191730,5388777,2223534,2209398960,False,Home,14,24,17.0,WALK,220939896,1 +1767524745,17675247450,5388794,2223543,2209405930,True,work,12,14,7.0,WALK_LOC,220940593,1 +1767524749,17675247490,5388794,2223543,2209405930,False,Home,14,12,17.0,TAXI,220940593,1 +1767525073,17675250730,5388795,2223543,2209406340,True,work,2,14,5.0,WALK,220940634,1 +1767525077,17675250770,5388795,2223543,2209406340,False,Home,14,2,11.0,WALK,220940634,1 +1767525081,17675250810,5388795,2223543,2209406350,True,work,2,14,13.0,WALK,220940635,1 +1767525085,17675250850,5388795,2223543,2209406350,False,Home,14,2,16.0,WALK,220940635,1 +1767532569,17675325690,5388818,2223555,2209415710,True,shopping,2,15,16.0,WALK,220941571,1 +1767532573,17675325730,5388818,2223555,2209415710,False,Home,15,2,16.0,WALK,220941571,1 +1767532577,17675325770,5388818,2223555,2209415720,True,shopping,13,15,16.0,WALK,220941572,1 +1767532581,17675325810,5388818,2223555,2209415720,False,Home,15,13,18.0,WALK,220941572,1 +1767532617,17675326170,5388818,2223555,2209415770,True,work,19,15,8.0,WALK,220941577,1 +1767532621,17675326210,5388818,2223555,2209415770,False,Home,15,19,16.0,WALK,220941577,1 +1767532705,17675327050,5388819,2223555,2209415880,True,escort,9,15,6.0,WALK,220941588,1 +1767532709,17675327090,5388819,2223555,2209415880,False,Home,15,9,7.0,WALK,220941588,1 +1767532833,17675328330,5388819,2223555,2209416040,True,othdiscr,9,15,12.0,WALK_LRF,220941604,1 +1767532837,17675328370,5388819,2223555,2209416040,False,Home,15,9,14.0,WALK_LRF,220941604,1 +1767532921,17675329210,5388819,2223555,2209416150,True,social,2,15,16.0,WALK,220941615,1 +1767532925,17675329250,5388819,2223555,2209416150,False,Home,15,2,23.0,WALK,220941615,1 +1767613305,17676133050,5389064,2223678,2209516630,True,work,21,16,6.0,WALK_LOC,220951663,1 +1767613309,17676133090,5389064,2223678,2209516630,False,Home,16,21,17.0,WALK_LOC,220951663,1 +1767613633,17676136330,5389065,2223678,2209517040,True,work,2,16,5.0,WALK,220951704,1 +1767613637,17676136370,5389065,2223678,2209517040,False,Home,16,2,18.0,WALK,220951704,1 +1767666161,17676661610,5389226,2223759,2209582700,True,atwork,2,1,13.0,WALK,220958270,1 +1767666165,17676661650,5389226,2223759,2209582700,False,Work,1,2,13.0,WALK,220958270,1 +1767666233,17676662330,5389226,2223759,2209582790,True,eatout,13,16,20.0,TNC_SHARED,220958279,1 +1767666237,17676662370,5389226,2223759,2209582790,False,Home,16,13,20.0,TNC_SHARED,220958279,1 +1767666441,17676664410,5389226,2223759,2209583050,True,work,1,16,9.0,WALK,220958305,1 +1767666445,17676664450,5389226,2223759,2209583050,False,Home,16,1,18.0,WALK,220958305,1 +1767666769,17676667690,5389227,2223759,2209583460,True,work,23,16,6.0,WALK,220958346,1 +1767666773,17676667730,5389227,2223759,2209583460,False,Home,16,23,20.0,WALK,220958346,1 +1767676657,17676766570,5389258,2223775,2209595820,True,atwork,4,21,12.0,TNC_SINGLE,220959582,1 +1767676661,17676766610,5389258,2223775,2209595820,False,Work,21,4,13.0,TNC_SINGLE,220959582,1 +1767676937,17676769370,5389258,2223775,2209596170,True,othdiscr,21,16,8.0,WALK,220959617,1 +1767676938,17676769380,5389258,2223775,2209596170,True,work,21,21,8.0,TNC_SINGLE,220959617,2 +1767676941,17676769410,5389258,2223775,2209596170,False,shopping,5,21,13.0,WALK_LOC,220959617,1 +1767676942,17676769420,5389258,2223775,2209596170,False,eatout,9,5,23.0,TNC_SINGLE,220959617,2 +1767676943,17676769430,5389258,2223775,2209596170,False,Home,16,9,23.0,TNC_SINGLE,220959617,3 +1767677001,17676770010,5389259,2223775,2209596250,True,eatout,16,16,10.0,WALK,220959625,1 +1767677005,17676770050,5389259,2223775,2209596250,False,Home,16,16,12.0,WALK,220959625,1 +1767677153,17676771530,5389259,2223775,2209596440,True,eatout,9,16,9.0,SHARED2FREE,220959644,1 +1767677154,17676771540,5389259,2223775,2209596440,True,eatout,7,9,9.0,WALK,220959644,2 +1767677155,17676771550,5389259,2223775,2209596440,True,othdiscr,11,7,10.0,SHARED2FREE,220959644,3 +1767677157,17676771570,5389259,2223775,2209596440,False,Home,16,11,10.0,SHARED2FREE,220959644,1 +1767677217,17676772170,5389259,2223775,2209596520,True,shopping,22,16,12.0,WALK_LOC,220959652,1 +1767677221,17676772210,5389259,2223775,2209596520,False,Home,16,22,15.0,WALK_LOC,220959652,1 +1767677265,17676772650,5389259,2223775,2209596580,True,work,4,16,16.0,WALK,220959658,1 +1767677269,17676772690,5389259,2223775,2209596580,False,Home,16,4,20.0,WALK,220959658,1 +1767704489,17677044890,5389342,2223817,2209630610,True,work,15,16,8.0,TNC_SHARED,220963061,1 +1767704493,17677044930,5389342,2223817,2209630610,False,Home,16,15,17.0,WALK_LOC,220963061,1 +1767704537,17677045370,5389343,2223817,2209630670,True,shopping,7,4,10.0,WALK,220963067,1 +1767704538,17677045380,5389343,2223817,2209630670,True,atwork,2,7,10.0,WALK,220963067,2 +1767704541,17677045410,5389343,2223817,2209630670,False,Work,4,2,10.0,WALK,220963067,1 +1767704769,17677047690,5389343,2223817,2209630960,True,eatout,11,16,19.0,TNC_SHARED,220963096,1 +1767704770,17677047700,5389343,2223817,2209630960,True,shopping,5,11,20.0,WALK_LOC,220963096,2 +1767704771,17677047710,5389343,2223817,2209630960,True,shopping,16,5,20.0,TNC_SINGLE,220963096,3 +1767704773,17677047730,5389343,2223817,2209630960,False,Home,16,16,20.0,TNC_SINGLE,220963096,1 +1767704817,17677048170,5389343,2223817,2209631020,True,othdiscr,12,16,7.0,WALK,220963102,1 +1767704818,17677048180,5389343,2223817,2209631020,True,work,4,12,8.0,TNC_SINGLE,220963102,2 +1767704821,17677048210,5389343,2223817,2209631020,False,Home,16,4,18.0,TNC_SINGLE,220963102,1 +1767712361,17677123610,5389366,2223829,2209640450,True,work,2,16,10.0,WALK,220964045,1 +1767712362,17677123620,5389366,2223829,2209640450,True,work,14,2,11.0,WALK,220964045,2 +1767712365,17677123650,5389366,2223829,2209640450,False,shopping,5,14,17.0,WALK,220964045,1 +1767712366,17677123660,5389366,2223829,2209640450,False,Home,16,5,20.0,WALK,220964045,2 +1767712689,17677126890,5389367,2223829,2209640860,True,work,14,16,7.0,WALK,220964086,1 +1767712693,17677126930,5389367,2223829,2209640860,False,Home,16,14,17.0,WALK,220964086,1 +1767718921,17677189210,5389386,2223839,2209648650,True,work,17,16,6.0,WALK,220964865,1 +1767718925,17677189250,5389386,2223839,2209648650,False,Home,16,17,16.0,WALK,220964865,1 +1767718985,17677189850,5389387,2223839,2209648730,True,eatout,4,16,6.0,WALK,220964873,1 +1767718989,17677189890,5389387,2223839,2209648730,False,Home,16,4,6.0,WALK,220964873,1 +1767719137,17677191370,5389387,2223839,2209648920,True,othdiscr,9,16,7.0,WALK,220964892,1 +1767719141,17677191410,5389387,2223839,2209648920,False,Home,16,9,7.0,WALK,220964892,1 +1767719249,17677192490,5389387,2223839,2209649060,True,work,25,16,9.0,WALK_LOC,220964906,1 +1767719253,17677192530,5389387,2223839,2209649060,False,Home,16,25,21.0,WALK_LOC,220964906,1 +1767746473,17677464730,5389470,2223881,2209683090,True,work,4,16,7.0,WALK_LOC,220968309,1 +1767746477,17677464770,5389470,2223881,2209683090,False,Home,16,4,17.0,WALK,220968309,1 +1767746801,17677468010,5389471,2223881,2209683500,True,work,5,16,9.0,WALK,220968350,1 +1767746805,17677468050,5389471,2223881,2209683500,False,Home,16,5,17.0,WALK,220968350,1 +1767779273,17677792730,5389570,2223931,2209724090,True,work,4,16,7.0,WALK_LOC,220972409,1 +1767779277,17677792770,5389570,2223931,2209724090,False,Home,16,4,17.0,WALK,220972409,1 +1767779321,17677793210,5389571,2223931,2209724150,True,atwork,9,1,12.0,WALK_HVY,220972415,1 +1767779325,17677793250,5389571,2223931,2209724150,False,Work,1,9,12.0,WALK_LRF,220972415,1 +1767779601,17677796010,5389571,2223931,2209724500,True,work,1,16,7.0,WALK_LOC,220972450,1 +1767779605,17677796050,5389571,2223931,2209724500,False,Home,16,1,12.0,WALK,220972450,1 +1767779609,17677796090,5389571,2223931,2209724510,True,work,1,16,13.0,SHARED2FREE,220972451,1 +1767779613,17677796130,5389571,2223931,2209724510,False,Home,16,1,17.0,WALK_LOC,220972451,1 +1767781633,17677816330,5389578,2223935,2209727040,True,eatout,16,16,9.0,WALK,220972704,1 +1767781637,17677816370,5389578,2223935,2209727040,False,Home,16,16,13.0,WALK,220972704,1 +1767782097,17677820970,5389579,2223935,2209727620,True,atwork,18,18,14.0,WALK,220972762,1 +1767782101,17677821010,5389579,2223935,2209727620,False,Work,18,18,14.0,WALK,220972762,1 +1767782225,17677822250,5389579,2223935,2209727780,True,work,18,16,7.0,WALK_LOC,220972778,1 +1767782229,17677822290,5389579,2223935,2209727780,False,Home,16,18,16.0,WALK_LRF,220972778,1 +1767796985,17677969850,5389624,2223958,2209746230,True,work,24,16,8.0,BIKE,220974623,1 +1767796989,17677969890,5389624,2223958,2209746230,False,Home,16,24,22.0,BIKE,220974623,1 +1767797313,17677973130,5389625,2223958,2209746640,True,escort,24,16,12.0,WALK,220974664,1 +1767797314,17677973140,5389625,2223958,2209746640,True,work,19,24,14.0,WALK_LOC,220974664,2 +1767797317,17677973170,5389625,2223958,2209746640,False,escort,16,19,16.0,WALK_LOC,220974664,1 +1767797318,17677973180,5389625,2223958,2209746640,False,Home,16,16,21.0,WALK,220974664,2 +1767816553,17678165530,5389684,2223988,2209770690,True,othdiscr,23,17,17.0,WALK_LRF,220977069,1 +1767816557,17678165570,5389684,2223988,2209770690,False,Home,17,23,18.0,SHARED3FREE,220977069,1 +1767816993,17678169930,5389685,2223988,2209771240,True,work,19,17,10.0,TNC_SINGLE,220977124,1 +1767816997,17678169970,5389685,2223988,2209771240,False,Home,17,19,20.0,TNC_SINGLE,220977124,1 +1767818353,17678183530,5389690,2223991,2209772940,True,atwork,19,19,12.0,WALK,220977294,1 +1767818357,17678183570,5389690,2223991,2209772940,False,Work,19,19,13.0,WALK,220977294,1 +1767818633,17678186330,5389690,2223991,2209773290,True,work,19,17,7.0,WALK,220977329,1 +1767818637,17678186370,5389690,2223991,2209773290,False,Home,17,19,22.0,WALK,220977329,1 +1767818913,17678189130,5389691,2223991,2209773640,True,shopping,11,17,8.0,WALK,220977364,1 +1767818917,17678189170,5389691,2223991,2209773640,False,Home,17,11,13.0,WALK,220977364,1 +1767876361,17678763610,5389866,2224079,2209845450,True,work,14,17,8.0,WALK,220984545,1 +1767876365,17678763650,5389866,2224079,2209845450,False,Home,17,14,18.0,WALK,220984545,1 +1767880841,17678808410,5389880,2224086,2209851050,True,othdiscr,18,17,12.0,WALK,220985105,1 +1767880845,17678808450,5389880,2224086,2209851050,False,Home,17,18,12.0,WALK,220985105,1 +1767881281,17678812810,5389881,2224086,2209851600,True,escort,5,17,8.0,WALK_LOC,220985160,1 +1767881282,17678812820,5389881,2224086,2209851600,True,work,2,5,9.0,WALK,220985160,2 +1767881285,17678812850,5389881,2224086,2209851600,False,Home,17,2,17.0,WALK,220985160,1 +1767903257,17679032570,5389948,2224120,2209879070,True,work,2,17,7.0,WALK,220987907,1 +1767903261,17679032610,5389948,2224120,2209879070,False,Home,17,2,19.0,WALK,220987907,1 +1767903585,17679035850,5389949,2224120,2209879480,True,work,2,17,6.0,WALK,220987948,1 +1767903589,17679035890,5389949,2224120,2209879480,False,Home,17,2,13.0,WALK,220987948,1 +1767903593,17679035930,5389949,2224120,2209879490,True,work,2,17,16.0,WALK,220987949,1 +1767903597,17679035970,5389949,2224120,2209879490,False,Home,17,2,20.0,WALK_LRF,220987949,1 +1767932665,17679326650,5390038,2224165,2209915830,True,othdiscr,16,17,20.0,WALK,220991583,1 +1767932669,17679326690,5390038,2224165,2209915830,False,shopping,13,16,23.0,TNC_SHARED,220991583,1 +1767932670,17679326700,5390038,2224165,2209915830,False,Home,17,13,23.0,WALK_LOC,220991583,2 +1767932777,17679327770,5390038,2224165,2209915970,True,work,1,17,7.0,WALK,220991597,1 +1767932781,17679327810,5390038,2224165,2209915970,False,Home,17,1,19.0,WALK,220991597,1 +1767933105,17679331050,5390039,2224165,2209916380,True,work,2,17,9.0,WALK,220991638,1 +1767933109,17679331090,5390039,2224165,2209916380,False,Home,17,2,18.0,WALK,220991638,1 +1767933321,17679333210,5390040,2224166,2209916650,True,othdiscr,16,17,11.0,WALK,220991665,1 +1767933325,17679333250,5390040,2224166,2209916650,False,Home,17,16,15.0,WALK,220991665,1 +1767933433,17679334330,5390040,2224166,2209916790,True,escort,16,17,7.0,WALK_LRF,220991679,1 +1767933434,17679334340,5390040,2224166,2209916790,True,work,12,16,7.0,WALK_LOC,220991679,2 +1767933435,17679334350,5390040,2224166,2209916790,True,work,12,12,7.0,TNC_SINGLE,220991679,3 +1767933437,17679334370,5390040,2224166,2209916790,False,work,2,12,10.0,WALK,220991679,1 +1767933438,17679334380,5390040,2224166,2209916790,False,othmaint,2,2,10.0,TNC_SINGLE,220991679,2 +1767933439,17679334390,5390040,2224166,2209916790,False,Home,17,2,10.0,TNC_SINGLE,220991679,3 +1767933761,17679337610,5390041,2224166,2209917200,True,work,9,17,7.0,WALK_LOC,220991720,1 +1767933765,17679337650,5390041,2224166,2209917200,False,Home,17,9,18.0,TNC_SINGLE,220991720,1 +1767947209,17679472090,5390082,2224187,2209934010,True,work,13,17,7.0,WALK_LOC,220993401,1 +1767947213,17679472130,5390082,2224187,2209934010,False,Home,17,13,18.0,WALK_LOC,220993401,1 +1767947537,17679475370,5390083,2224187,2209934420,True,work,2,17,9.0,WALK,220993442,1 +1767947541,17679475410,5390083,2224187,2209934420,False,Home,17,2,22.0,WALK,220993442,1 +1767960329,17679603290,5390122,2224207,2209950410,True,eatout,16,17,10.0,TNC_SINGLE,220995041,1 +1767960330,17679603300,5390122,2224207,2209950410,True,work,14,16,10.0,WALK,220995041,2 +1767960333,17679603330,5390122,2224207,2209950410,False,Home,17,14,19.0,TNC_SINGLE,220995041,1 +1767960657,17679606570,5390123,2224207,2209950820,True,work,23,17,7.0,TNC_SINGLE,220995082,1 +1767960661,17679606610,5390123,2224207,2209950820,False,Home,17,23,21.0,TNC_SINGLE,220995082,1 +1767992193,17679921930,5390220,2224256,2209990240,True,atwork,5,15,12.0,WALK,220999024,1 +1767992197,17679921970,5390220,2224256,2209990240,False,Work,15,5,14.0,WALK,220999024,1 +1767992473,17679924730,5390220,2224256,2209990590,True,work,15,17,7.0,WALK,220999059,1 +1767992477,17679924770,5390220,2224256,2209990590,False,Home,17,15,16.0,WALK,220999059,1 +1767999033,17679990330,5390240,2224266,2209998790,True,work,22,17,5.0,WALK,220999879,1 +1767999037,17679990370,5390240,2224266,2209998790,False,work,10,22,17.0,WALK_LRF,220999879,1 +1767999038,17679990380,5390240,2224266,2209998790,False,shopping,21,10,17.0,WALK,220999879,2 +1767999039,17679990390,5390240,2224266,2209998790,False,eatout,16,21,17.0,WALK,220999879,3 +1767999040,17679990400,5390240,2224266,2209998790,False,Home,17,16,18.0,WALK,220999879,4 +1767999361,17679993610,5390241,2224266,2209999200,True,work,11,17,9.0,WALK_LRF,220999920,1 +1767999362,17679993620,5390241,2224266,2209999200,True,work,5,11,9.0,WALK_LOC,220999920,2 +1767999365,17679993650,5390241,2224266,2209999200,False,social,5,5,17.0,WALK,220999920,1 +1767999366,17679993660,5390241,2224266,2209999200,False,shopping,7,5,17.0,WALK_LOC,220999920,2 +1767999367,17679993670,5390241,2224266,2209999200,False,Home,17,7,18.0,WALK_LOC,220999920,3 +1768001545,17680015450,5390248,2224270,2210001930,True,othdiscr,20,17,18.0,WALK_LRF,221000193,1 +1768001549,17680015490,5390248,2224270,2210001930,False,Home,17,20,21.0,WALK_LRF,221000193,1 +1768001657,17680016570,5390248,2224270,2210002070,True,work,19,17,8.0,WALK,221000207,1 +1768001661,17680016610,5390248,2224270,2210002070,False,Home,17,19,18.0,WALK_LOC,221000207,1 +1768001705,17680017050,5390249,2224270,2210002130,True,atwork,1,9,12.0,WALK,221000213,1 +1768001709,17680017090,5390249,2224270,2210002130,False,Work,9,1,13.0,WALK,221000213,1 +1768001721,17680017210,5390249,2224270,2210002150,True,eatout,11,17,15.0,SHARED2FREE,221000215,1 +1768001725,17680017250,5390249,2224270,2210002150,False,Home,17,11,17.0,SHARED2FREE,221000215,1 +1768001985,17680019850,5390249,2224270,2210002480,True,work,9,17,6.0,BIKE,221000248,1 +1768001989,17680019890,5390249,2224270,2210002480,False,Home,17,9,15.0,BIKE,221000248,1 +1768005313,17680053130,5390260,2224276,2210006640,True,atwork,24,2,11.0,WALK,221000664,1 +1768005317,17680053170,5390260,2224276,2210006640,False,eatout,25,24,14.0,WALK,221000664,1 +1768005318,17680053180,5390260,2224276,2210006640,False,othmaint,7,25,14.0,WALK,221000664,2 +1768005319,17680053190,5390260,2224276,2210006640,False,Work,2,7,14.0,WALK,221000664,3 +1768005569,17680055690,5390260,2224276,2210006960,True,social,12,17,19.0,WALK,221000696,1 +1768005573,17680055730,5390260,2224276,2210006960,False,Home,17,12,23.0,WALK,221000696,1 +1768005593,17680055930,5390260,2224276,2210006990,True,work,2,17,8.0,WALK,221000699,1 +1768005597,17680055970,5390260,2224276,2210006990,False,Home,17,2,18.0,WALK,221000699,1 +1768005921,17680059210,5390261,2224276,2210007400,True,work,18,17,8.0,WALK,221000740,1 +1768005925,17680059250,5390261,2224276,2210007400,False,Home,17,18,18.0,WALK,221000740,1 +1768010841,17680108410,5390276,2224284,2210013550,True,work,2,17,14.0,WALK,221001355,1 +1768010845,17680108450,5390276,2224284,2210013550,False,othmaint,16,2,19.0,WALK,221001355,1 +1768010846,17680108460,5390276,2224284,2210013550,False,shopping,16,16,19.0,WALK,221001355,2 +1768010847,17680108470,5390276,2224284,2210013550,False,work,2,16,20.0,WALK,221001355,3 +1768010848,17680108480,5390276,2224284,2210013550,False,Home,17,2,20.0,WALK,221001355,4 +1768011169,17680111690,5390277,2224284,2210013960,True,work,5,17,7.0,WALK,221001396,1 +1768011173,17680111730,5390277,2224284,2210013960,False,Home,17,5,18.0,WALK,221001396,1 +1768014649,17680146490,5390288,2224290,2210018310,True,atwork,5,4,11.0,WALK,221001831,1 +1768014653,17680146530,5390288,2224290,2210018310,False,Work,4,5,11.0,WALK,221001831,1 +1768014777,17680147770,5390288,2224290,2210018470,True,work,4,17,7.0,SHARED2FREE,221001847,1 +1768014781,17680147810,5390288,2224290,2210018470,False,Home,17,4,17.0,SHARED2FREE,221001847,1 +1768015105,17680151050,5390289,2224290,2210018880,True,work,4,17,7.0,WALK_LOC,221001888,1 +1768015109,17680151090,5390289,2224290,2210018880,False,Home,17,4,17.0,WALK_LRF,221001888,1 +1768021057,17680210570,5390308,2224300,2210026320,True,work,12,4,14.0,WALK,221002632,1 +1768021058,17680210580,5390308,2224300,2210026320,True,atwork,12,12,14.0,WALK,221002632,2 +1768021061,17680210610,5390308,2224300,2210026320,False,Work,4,12,14.0,WALK,221002632,1 +1768021337,17680213370,5390308,2224300,2210026670,True,work,4,17,14.0,SHARED2FREE,221002667,1 +1768021341,17680213410,5390308,2224300,2210026670,False,Home,17,4,18.0,WALK,221002667,1 +1768044689,17680446890,5390380,2224336,2210055860,True,eatout,5,17,18.0,WALK,221005586,1 +1768044693,17680446930,5390380,2224336,2210055860,False,Home,17,5,18.0,WALK,221005586,1 +1768044745,17680447450,5390380,2224336,2210055930,True,eatout,18,17,18.0,TNC_SHARED,221005593,1 +1768044749,17680447490,5390380,2224336,2210055930,False,Home,17,18,21.0,SHARED2FREE,221005593,1 +1768044953,17680449530,5390380,2224336,2210056190,True,work,14,17,5.0,WALK,221005619,1 +1768044957,17680449570,5390380,2224336,2210056190,False,Home,17,14,17.0,WALK,221005619,1 +1768045281,17680452810,5390381,2224336,2210056600,True,work,1,17,8.0,WALK,221005660,1 +1768045285,17680452850,5390381,2224336,2210056600,False,othmaint,16,1,17.0,WALK_LOC,221005660,1 +1768045286,17680452860,5390381,2224336,2210056600,False,Home,17,16,18.0,WALK,221005660,2 +1768098089,17680980890,5390542,2224417,2210122610,True,work,15,17,6.0,WALK,221012261,1 +1768098093,17680980930,5390542,2224417,2210122610,False,Home,17,15,16.0,WALK,221012261,1 +1768098137,17680981370,5390543,2224417,2210122670,True,othmaint,24,14,10.0,WALK,221012267,1 +1768098138,17680981380,5390543,2224417,2210122670,True,atwork,1,24,10.0,WALK,221012267,2 +1768098141,17680981410,5390543,2224417,2210122670,False,Work,14,1,13.0,WALK,221012267,1 +1768098417,17680984170,5390543,2224417,2210123020,True,work,14,17,8.0,TNC_SINGLE,221012302,1 +1768098421,17680984210,5390543,2224417,2210123020,False,escort,5,14,17.0,WALK_LOC,221012302,1 +1768098422,17680984220,5390543,2224417,2210123020,False,Home,17,5,18.0,WALK_LOC,221012302,2 +1768159561,17681595610,5390730,2224511,2210199450,True,othdiscr,24,17,19.0,TNC_SINGLE,221019945,1 +1768159565,17681595650,5390730,2224511,2210199450,False,Home,17,24,21.0,TNC_SINGLE,221019945,1 +1768159753,17681597530,5390730,2224511,2210199690,True,work,24,17,6.0,WALK,221019969,1 +1768159757,17681597570,5390730,2224511,2210199690,False,Home,17,24,17.0,WALK,221019969,1 +1768159769,17681597690,5390731,2224511,2210199710,True,atwork,24,14,12.0,WALK,221019971,1 +1768159773,17681597730,5390731,2224511,2210199710,False,Work,14,24,13.0,WALK,221019971,1 +1768160081,17681600810,5390731,2224511,2210200100,True,work,14,17,8.0,WALK,221020010,1 +1768160085,17681600850,5390731,2224511,2210200100,False,Home,17,14,19.0,WALK,221020010,1 +1768184417,17681844170,5390806,2224549,2210230520,True,eatout,12,17,8.0,WALK,221023052,1 +1768184421,17681844210,5390806,2224549,2210230520,False,Home,17,12,10.0,WALK,221023052,1 +1768184569,17681845690,5390806,2224549,2210230710,True,othdiscr,16,17,12.0,WALK,221023071,1 +1768184573,17681845730,5390806,2224549,2210230710,False,Home,17,16,12.0,WALK_LOC,221023071,1 +1768184593,17681845930,5390806,2224549,2210230740,True,othmaint,22,17,12.0,DRIVEALONEFREE,221023074,1 +1768184597,17681845970,5390806,2224549,2210230740,False,Home,17,22,15.0,SHARED2FREE,221023074,1 +1768184601,17681846010,5390806,2224549,2210230750,True,othmaint,5,17,15.0,TNC_SINGLE,221023075,1 +1768184605,17681846050,5390806,2224549,2210230750,False,Home,17,5,15.0,TNC_SINGLE,221023075,1 +1768184681,17681846810,5390806,2224549,2210230850,True,work,11,17,16.0,WALK_LRF,221023085,1 +1768184685,17681846850,5390806,2224549,2210230850,False,Home,17,11,21.0,WALK_LOC,221023085,1 +1768185009,17681850090,5390807,2224549,2210231260,True,work,16,17,8.0,WALK,221023126,1 +1768185013,17681850130,5390807,2224549,2210231260,False,Home,17,16,18.0,WALK,221023126,1 +1768206985,17682069850,5390874,2224583,2210258730,True,work,12,17,8.0,SHARED2FREE,221025873,1 +1768206986,17682069860,5390874,2224583,2210258730,True,work,5,12,9.0,WALK,221025873,2 +1768206989,17682069890,5390874,2224583,2210258730,False,Home,17,5,20.0,WALK,221025873,1 +1768207313,17682073130,5390875,2224583,2210259140,True,work,2,17,7.0,WALK,221025914,1 +1768207317,17682073170,5390875,2224583,2210259140,False,Home,17,2,17.0,WALK,221025914,1 +1768236897,17682368970,5390966,2224629,2210296120,True,eatout,6,17,21.0,WALK,221029612,1 +1768236901,17682369010,5390966,2224629,2210296120,False,Home,17,6,21.0,WALK,221029612,1 +1768237161,17682371610,5390966,2224629,2210296450,True,work,16,17,8.0,WALK,221029645,1 +1768237165,17682371650,5390966,2224629,2210296450,False,shopping,16,16,17.0,WALK,221029645,1 +1768237166,17682371660,5390966,2224629,2210296450,False,work,9,16,18.0,WALK_LRF,221029645,2 +1768237167,17682371670,5390966,2224629,2210296450,False,Home,17,9,19.0,WALK_LOC,221029645,3 +1768237489,17682374890,5390967,2224629,2210296860,True,work,17,17,6.0,WALK,221029686,1 +1768237493,17682374930,5390967,2224629,2210296860,False,Home,17,17,16.0,WALK,221029686,1 +1768253561,17682535610,5391016,2224654,2210316950,True,work,18,17,8.0,WALK_LRF,221031695,1 +1768253565,17682535650,5391016,2224654,2210316950,False,Home,17,18,16.0,WALK_LOC,221031695,1 +1768253889,17682538890,5391017,2224654,2210317360,True,work,6,17,7.0,WALK_LOC,221031736,1 +1768253893,17682538930,5391017,2224654,2210317360,False,Home,17,6,20.0,WALK_LOC,221031736,1 +1768256561,17682565610,5391026,2224659,2210320700,True,shopping,16,16,14.0,WALK,221032070,1 +1768256562,17682565620,5391026,2224659,2210320700,True,atwork,2,16,14.0,WALK,221032070,2 +1768256565,17682565650,5391026,2224659,2210320700,False,Work,16,2,14.0,WALK,221032070,1 +1768256841,17682568410,5391026,2224659,2210321050,True,work,16,17,11.0,WALK_LOC,221032105,1 +1768256845,17682568450,5391026,2224659,2210321050,False,work,17,16,21.0,WALK,221032105,1 +1768256846,17682568460,5391026,2224659,2210321050,False,Home,17,17,23.0,WALK,221032105,2 +1768256889,17682568890,5391027,2224659,2210321110,True,atwork,14,2,9.0,WALK,221032111,1 +1768256893,17682568930,5391027,2224659,2210321110,False,eatout,7,14,15.0,WALK,221032111,1 +1768256894,17682568940,5391027,2224659,2210321110,False,Work,2,7,15.0,WALK,221032111,2 +1768257169,17682571690,5391027,2224659,2210321460,True,work,2,17,6.0,TNC_SINGLE,221032146,1 +1768257173,17682571730,5391027,2224659,2210321460,False,shopping,5,2,15.0,WALK_LOC,221032146,1 +1768257174,17682571740,5391027,2224659,2210321460,False,Home,17,5,18.0,TNC_SINGLE,221032146,2 +1768265745,17682657450,5391054,2224673,2210332180,True,atwork,10,10,18.0,WALK,221033218,1 +1768265749,17682657490,5391054,2224673,2210332180,False,Work,10,10,18.0,WALK,221033218,1 +1768266025,17682660250,5391054,2224673,2210332530,True,othdiscr,17,17,8.0,WALK,221033253,1 +1768266026,17682660260,5391054,2224673,2210332530,True,work,1,17,9.0,WALK_LOC,221033253,2 +1768266027,17682660270,5391054,2224673,2210332530,True,work,10,1,9.0,WALK_LRF,221033253,3 +1768266029,17682660290,5391054,2224673,2210332530,False,eatout,2,10,18.0,WALK_LRF,221033253,1 +1768266030,17682660300,5391054,2224673,2210332530,False,escort,5,2,18.0,WALK,221033253,2 +1768266031,17682660310,5391054,2224673,2210332530,False,Home,17,5,18.0,WALK_LRF,221033253,3 +1768285049,17682850490,5391112,2224702,2210356310,True,work,18,17,6.0,SHARED3FREE,221035631,1 +1768285053,17682850530,5391112,2224702,2210356310,False,Home,17,18,17.0,DRIVEALONEFREE,221035631,1 +1768285097,17682850970,5391113,2224702,2210356370,True,atwork,22,12,10.0,WALK,221035637,1 +1768285101,17682851010,5391113,2224702,2210356370,False,Work,12,22,13.0,WALK,221035637,1 +1768285377,17682853770,5391113,2224702,2210356720,True,work,12,17,7.0,WALK,221035672,1 +1768285381,17682853810,5391113,2224702,2210356720,False,Home,17,12,16.0,WALK,221035672,1 +1768292265,17682922650,5391134,2224713,2210365330,True,work,14,17,6.0,WALK,221036533,1 +1768292269,17682922690,5391134,2224713,2210365330,False,Home,17,14,14.0,WALK,221036533,1 +1768292313,17682923130,5391135,2224713,2210365390,True,atwork,8,12,12.0,WALK,221036539,1 +1768292317,17682923170,5391135,2224713,2210365390,False,eatout,13,8,13.0,WALK,221036539,1 +1768292318,17682923180,5391135,2224713,2210365390,False,Work,12,13,13.0,WALK,221036539,2 +1768292593,17682925930,5391135,2224713,2210365740,True,work,12,17,8.0,WALK_LOC,221036574,1 +1768292597,17682925970,5391135,2224713,2210365740,False,Home,17,12,21.0,WALK_LRF,221036574,1 +1768310633,17683106330,5391190,2224741,2210388290,True,work,20,18,7.0,DRIVEALONEFREE,221038829,1 +1768310637,17683106370,5391190,2224741,2210388290,False,Home,18,20,20.0,WALK,221038829,1 +1768310961,17683109610,5391191,2224741,2210388700,True,work,19,18,7.0,WALK,221038870,1 +1768310965,17683109650,5391191,2224741,2210388700,False,Home,18,19,18.0,WALK,221038870,1 +1768396985,17683969850,5391454,2224873,2210496230,True,escort,17,19,8.0,WALK,221049623,1 +1768396989,17683969890,5391454,2224873,2210496230,False,Home,19,17,8.0,WALK,221049623,1 +1768396993,17683969930,5391454,2224873,2210496240,True,escort,7,19,8.0,TNC_SINGLE,221049624,1 +1768396997,17683969970,5391454,2224873,2210496240,False,othmaint,25,7,8.0,TNC_SINGLE,221049624,1 +1768396998,17683969980,5391454,2224873,2210496240,False,Home,19,25,8.0,TNC_SINGLE,221049624,2 +1768397225,17683972250,5391454,2224873,2210496530,True,othdiscr,11,19,8.0,WALK_LOC,221049653,1 +1768397226,17683972260,5391454,2224873,2210496530,True,work,20,11,9.0,WALK_LOC,221049653,2 +1768397229,17683972290,5391454,2224873,2210496530,False,work,20,20,17.0,WALK,221049653,1 +1768397230,17683972300,5391454,2224873,2210496530,False,othmaint,17,20,19.0,WALK_LOC,221049653,2 +1768397231,17683972310,5391454,2224873,2210496530,False,Home,19,17,21.0,WALK_LOC,221049653,3 +1768397441,17683974410,5391455,2224873,2210496800,True,othdiscr,20,19,18.0,TNC_SINGLE,221049680,1 +1768397445,17683974450,5391455,2224873,2210496800,False,eatout,9,20,20.0,DRIVEALONEFREE,221049680,1 +1768397446,17683974460,5391455,2224873,2210496800,False,escort,8,9,20.0,TNC_SINGLE,221049680,2 +1768397447,17683974470,5391455,2224873,2210496800,False,Home,19,8,20.0,DRIVEALONEFREE,221049680,3 +1768397465,17683974650,5391455,2224873,2210496830,True,othmaint,3,19,9.0,WALK_LOC,221049683,1 +1768397469,17683974690,5391455,2224873,2210496830,False,Home,19,3,17.0,WALK_LOC,221049683,1 +1768397505,17683975050,5391455,2224873,2210496880,True,shopping,19,19,17.0,TNC_SINGLE,221049688,1 +1768397509,17683975090,5391455,2224873,2210496880,False,Home,19,19,17.0,TNC_SINGLE,221049688,1 +1768398345,17683983450,5391458,2224875,2210497930,True,othdiscr,7,19,8.0,SHARED2FREE,221049793,1 +1768398349,17683983490,5391458,2224875,2210497930,False,social,10,7,8.0,SHARED2FREE,221049793,1 +1768398350,17683983500,5391458,2224875,2210497930,False,Home,19,10,8.0,SHARED3FREE,221049793,2 +1768398537,17683985370,5391458,2224875,2210498170,True,shopping,16,19,10.0,DRIVEALONEFREE,221049817,1 +1768398538,17683985380,5391458,2224875,2210498170,True,work,1,16,10.0,WALK,221049817,2 +1768398541,17683985410,5391458,2224875,2210498170,False,Home,19,1,10.0,DRIVEALONEFREE,221049817,1 +1768398545,17683985450,5391458,2224875,2210498180,True,work,1,19,15.0,WALK_LRF,221049818,1 +1768398549,17683985490,5391458,2224875,2210498180,False,Home,19,1,21.0,WALK_LRF,221049818,1 +1768398865,17683988650,5391459,2224875,2210498580,True,work,14,19,8.0,WALK,221049858,1 +1768398869,17683988690,5391459,2224875,2210498580,False,Home,19,14,21.0,WALK,221049858,1 +1768411377,17684113770,5391498,2224895,2210514220,True,atwork,5,4,14.0,WALK,221051422,1 +1768411381,17684113810,5391498,2224895,2210514220,False,Work,4,5,14.0,WALK,221051422,1 +1768411513,17684115130,5391498,2224895,2210514390,True,othmaint,5,19,18.0,SHARED3FREE,221051439,1 +1768411514,17684115140,5391498,2224895,2210514390,True,social,15,5,19.0,TNC_SHARED,221051439,2 +1768411517,17684115170,5391498,2224895,2210514390,False,Home,19,15,21.0,SHARED2FREE,221051439,1 +1768411657,17684116570,5391498,2224895,2210514570,True,shopping,16,19,7.0,WALK,221051457,1 +1768411658,17684116580,5391498,2224895,2210514570,True,work,4,16,8.0,SHARED3FREE,221051457,2 +1768411661,17684116610,5391498,2224895,2210514570,False,escort,11,4,15.0,SHARED3FREE,221051457,1 +1768411662,17684116620,5391498,2224895,2210514570,False,Home,19,11,16.0,WALK,221051457,2 +1768411985,17684119850,5391499,2224895,2210514980,True,work,12,19,7.0,DRIVEALONEFREE,221051498,1 +1768411989,17684119890,5391499,2224895,2210514980,False,Home,19,12,17.0,WALK,221051498,1 +1768446425,17684464250,5391604,2224948,2210558030,True,work,1,19,8.0,WALK_LRF,221055803,1 +1768446429,17684464290,5391604,2224948,2210558030,False,Home,19,1,18.0,WALK_LRF,221055803,1 +1768446473,17684464730,5391605,2224948,2210558090,True,atwork,2,14,12.0,WALK,221055809,1 +1768446477,17684464770,5391605,2224948,2210558090,False,Work,14,2,13.0,WALK,221055809,1 +1768446753,17684467530,5391605,2224948,2210558440,True,work,14,19,7.0,WALK_LOC,221055844,1 +1768446757,17684467570,5391605,2224948,2210558440,False,Home,19,14,18.0,WALK_LOC,221055844,1 +1768460201,17684602010,5391646,2224969,2210575250,True,work,24,20,8.0,WALK_LOC,221057525,1 +1768460205,17684602050,5391646,2224969,2210575250,False,Home,20,24,16.0,WALK_LOC,221057525,1 +1768460529,17684605290,5391647,2224969,2210575660,True,work,19,20,8.0,BIKE,221057566,1 +1768460533,17684605330,5391647,2224969,2210575660,False,Home,20,19,20.0,BIKE,221057566,1 +1768476321,17684763210,5391696,2224994,2210595400,True,shopping,25,2,10.0,WALK,221059540,1 +1768476322,17684763220,5391696,2224994,2210595400,True,atwork,1,25,10.0,WALK,221059540,2 +1768476325,17684763250,5391696,2224994,2210595400,False,Work,2,1,14.0,WALK,221059540,1 +1768476601,17684766010,5391696,2224994,2210595750,True,work,2,21,7.0,WALK,221059575,1 +1768476605,17684766050,5391696,2224994,2210595750,False,Home,21,2,16.0,WALK,221059575,1 +1768476665,17684766650,5391697,2224994,2210595830,True,eatout,4,21,8.0,WALK,221059583,1 +1768476669,17684766690,5391697,2224994,2210595830,False,shopping,11,4,14.0,WALK,221059583,1 +1768476670,17684766700,5391697,2224994,2210595830,False,Home,21,11,14.0,WALK,221059583,2 +1768476977,17684769770,5391698,2224995,2210596220,True,atwork,17,7,12.0,SHARED2FREE,221059622,1 +1768476981,17684769810,5391698,2224995,2210596220,False,Work,7,17,13.0,SHARED3FREE,221059622,1 +1768477257,17684772570,5391698,2224995,2210596570,True,work,7,21,8.0,WALK,221059657,1 +1768477261,17684772610,5391698,2224995,2210596570,False,Home,21,7,17.0,WALK,221059657,1 +1768477305,17684773050,5391699,2224995,2210596630,True,atwork,5,9,13.0,WALK,221059663,1 +1768477309,17684773090,5391699,2224995,2210596630,False,eatout,7,5,13.0,WALK,221059663,1 +1768477310,17684773100,5391699,2224995,2210596630,False,Work,9,7,13.0,WALK,221059663,2 +1768477585,17684775850,5391699,2224995,2210596980,True,work,9,21,8.0,WALK,221059698,1 +1768477589,17684775890,5391699,2224995,2210596980,False,Home,21,9,19.0,WALK,221059698,1 +1768480537,17684805370,5391708,2225000,2210600670,True,work,20,21,8.0,WALK,221060067,1 +1768480541,17684805410,5391708,2225000,2210600670,False,Home,21,20,21.0,WALK,221060067,1 +1768480865,17684808650,5391709,2225000,2210601080,True,escort,9,21,8.0,WALK,221060108,1 +1768480866,17684808660,5391709,2225000,2210601080,True,work,2,9,9.0,WALK_LRF,221060108,2 +1768480869,17684808690,5391709,2225000,2210601080,False,Home,21,2,15.0,WALK,221060108,1 +1768485129,17684851290,5391722,2225007,2210606410,True,work,2,21,17.0,TNC_SINGLE,221060641,1 +1768485130,17684851300,5391722,2225007,2210606410,True,work,2,2,18.0,TNC_SINGLE,221060641,2 +1768485133,17684851330,5391722,2225007,2210606410,False,Home,21,2,19.0,WALK,221060641,1 +1768485457,17684854570,5391723,2225007,2210606820,True,work,1,21,7.0,TNC_SINGLE,221060682,1 +1768485461,17684854610,5391723,2225007,2210606820,False,Home,21,1,20.0,WALK_LRF,221060682,1 +1768495625,17684956250,5391754,2225023,2210619530,True,work,2,21,7.0,TNC_SINGLE,221061953,1 +1768495629,17684956290,5391754,2225023,2210619530,False,Home,21,2,16.0,TNC_SINGLE,221061953,1 +1768499281,17684992810,5391766,2225029,2210624100,True,atwork,22,2,13.0,WALK,221062410,1 +1768499285,17684992850,5391766,2225029,2210624100,False,Work,2,22,13.0,WALK,221062410,1 +1768499513,17684995130,5391766,2225029,2210624390,True,shopping,5,21,17.0,WALK_LOC,221062439,1 +1768499514,17684995140,5391766,2225029,2210624390,True,shopping,16,5,17.0,WALK_LOC,221062439,2 +1768499517,17684995170,5391766,2225029,2210624390,False,Home,21,16,17.0,WALK_LOC,221062439,1 +1768499561,17684995610,5391766,2225029,2210624450,True,work,2,21,7.0,WALK,221062445,1 +1768499565,17684995650,5391766,2225029,2210624450,False,Home,21,2,17.0,WALK,221062445,1 +1768499609,17684996090,5391767,2225029,2210624510,True,atwork,7,13,10.0,WALK,221062451,1 +1768499613,17684996130,5391767,2225029,2210624510,False,eatout,6,7,10.0,WALK,221062451,1 +1768499614,17684996140,5391767,2225029,2210624510,False,shopping,7,6,10.0,WALK,221062451,2 +1768499615,17684996150,5391767,2225029,2210624510,False,Work,13,7,10.0,WALK,221062451,3 +1768499889,17684998890,5391767,2225029,2210624860,True,work,13,21,8.0,WALK,221062486,1 +1768499893,17684998930,5391767,2225029,2210624860,False,eatout,5,13,16.0,WALK,221062486,1 +1768499894,17684998940,5391767,2225029,2210624860,False,Home,21,5,16.0,WALK_LOC,221062486,2 +1768508089,17685080890,5391792,2225042,2210635110,True,work,14,21,9.0,WALK,221063511,1 +1768508093,17685080930,5391792,2225042,2210635110,False,Home,21,14,17.0,WALK_LOC,221063511,1 +1768508137,17685081370,5391793,2225042,2210635170,True,atwork,11,24,13.0,SHARED3FREE,221063517,1 +1768508141,17685081410,5391793,2225042,2210635170,False,Work,24,11,13.0,SHARED3FREE,221063517,1 +1768508369,17685083690,5391793,2225042,2210635460,True,shopping,25,21,17.0,DRIVEALONEFREE,221063546,1 +1768508373,17685083730,5391793,2225042,2210635460,False,Home,21,25,20.0,SHARED2FREE,221063546,1 +1768508417,17685084170,5391793,2225042,2210635520,True,work,24,21,6.0,WALK_LOC,221063552,1 +1768508421,17685084210,5391793,2225042,2210635520,False,Home,21,24,15.0,WALK_LOC,221063552,1 +1768521097,17685210970,5391832,2225062,2210651370,True,othdiscr,21,21,18.0,WALK,221065137,1 +1768521101,17685211010,5391832,2225062,2210651370,False,Home,21,21,21.0,WALK,221065137,1 +1768521209,17685212090,5391832,2225062,2210651510,True,work,16,21,6.0,WALK,221065151,1 +1768521213,17685212130,5391832,2225062,2210651510,False,Home,21,16,18.0,WALK,221065151,1 +1768521425,17685214250,5391833,2225062,2210651780,True,othdiscr,17,21,18.0,WALK_LOC,221065178,1 +1768521429,17685214290,5391833,2225062,2210651780,False,Home,21,17,18.0,WALK_LOC,221065178,1 +1768521537,17685215370,5391833,2225062,2210651920,True,work,14,21,7.0,WALK_LOC,221065192,1 +1768521541,17685215410,5391833,2225062,2210651920,False,Home,21,14,13.0,WALK,221065192,1 +1768567673,17685676730,5391974,2225133,2210709590,True,othmaint,9,22,10.0,WALK_LRF,221070959,1 +1768567674,17685676740,5391974,2225133,2210709590,True,othdiscr,10,9,11.0,WALK_LOC,221070959,2 +1768567677,17685676770,5391974,2225133,2210709590,False,Home,22,10,13.0,WALK_LRF,221070959,1 +1768567737,17685677370,5391974,2225133,2210709670,True,shopping,1,22,13.0,TNC_SINGLE,221070967,1 +1768567741,17685677410,5391974,2225133,2210709670,False,Home,22,1,14.0,TNC_SINGLE,221070967,1 +1768567801,17685678010,5391975,2225133,2210709750,True,atwork,5,23,11.0,WALK,221070975,1 +1768567805,17685678050,5391975,2225133,2210709750,False,Work,23,5,13.0,WALK,221070975,1 +1768568113,17685681130,5391975,2225133,2210710140,True,work,23,22,7.0,WALK,221071014,1 +1768568117,17685681170,5391975,2225133,2210710140,False,Home,22,23,17.0,WALK,221071014,1 +1768581561,17685815610,5392016,2225154,2210726950,True,work,15,22,8.0,TNC_SHARED,221072695,1 +1768581565,17685815650,5392016,2225154,2210726950,False,Home,22,15,19.0,WALK,221072695,1 +1768581889,17685818890,5392017,2225154,2210727360,True,work,22,22,8.0,TNC_SHARED,221072736,1 +1768581893,17685818930,5392017,2225154,2210727360,False,Home,22,22,17.0,WALK,221072736,1 +1768595993,17685959930,5392060,2225176,2210744990,True,work,4,22,8.0,WALK,221074499,1 +1768595997,17685959970,5392060,2225176,2210744990,False,Home,22,4,20.0,WALK_LRF,221074499,1 +1768596001,17685960010,5392060,2225176,2210745000,True,work,4,22,22.0,WALK,221074500,1 +1768596005,17685960050,5392060,2225176,2210745000,False,Home,22,4,23.0,WALK,221074500,1 +1768596209,17685962090,5392061,2225176,2210745260,True,othdiscr,21,22,17.0,TNC_SINGLE,221074526,1 +1768596213,17685962130,5392061,2225176,2210745260,False,Home,22,21,19.0,DRIVEALONEFREE,221074526,1 +1768596321,17685963210,5392061,2225176,2210745400,True,othmaint,24,22,10.0,WALK,221074540,1 +1768596322,17685963220,5392061,2225176,2210745400,True,work,11,24,12.0,WALK_LOC,221074540,2 +1768596325,17685963250,5392061,2225176,2210745400,False,escort,3,11,17.0,WALK,221074540,1 +1768596326,17685963260,5392061,2225176,2210745400,False,Home,22,3,17.0,WALK_LOC,221074540,2 +1768636385,17686363850,5392184,2225238,2210795480,True,atwork,7,8,12.0,WALK,221079548,1 +1768636389,17686363890,5392184,2225238,2210795480,False,Work,8,7,12.0,WALK,221079548,1 +1768636665,17686366650,5392184,2225238,2210795830,True,work,8,23,8.0,WALK_LOC,221079583,1 +1768636669,17686366690,5392184,2225238,2210795830,False,Home,23,8,17.0,WALK_LRF,221079583,1 +1768636881,17686368810,5392185,2225238,2210796100,True,othdiscr,10,23,16.0,WALK_LRF,221079610,1 +1768636885,17686368850,5392185,2225238,2210796100,False,Home,23,10,20.0,WALK_LRF,221079610,1 +1768636993,17686369930,5392185,2225238,2210796240,True,work,16,23,7.0,WALK,221079624,1 +1768636997,17686369970,5392185,2225238,2210796240,False,Home,23,16,16.0,WALK,221079624,1 +1831548753,18315487530,5583990,2307187,2289435940,True,atwork,13,24,14.0,WALK,228943594,1 +1831548757,18315487570,5583990,2307187,2289435940,False,Work,24,13,14.0,WALK,228943594,1 +1831549033,18315490330,5583990,2307187,2289436290,True,work,14,16,9.0,WALK_LOC,228943629,1 +1831549034,18315490340,5583990,2307187,2289436290,True,work,24,14,10.0,WALK,228943629,2 +1831549037,18315490370,5583990,2307187,2289436290,False,Home,16,24,16.0,WALK,228943629,1 +1831549361,18315493610,5583991,2307187,2289436700,True,work,20,16,7.0,WALK,228943670,1 +1831549365,18315493650,5583991,2307187,2289436700,False,Home,16,20,18.0,WALK,228943670,1 +1831549577,18315495770,5583992,2307187,2289436970,True,othdiscr,4,16,9.0,WALK,228943697,1 +1831549581,18315495810,5583992,2307187,2289436970,False,Home,16,4,12.0,WALK,228943697,1 +1834643761,18346437610,5593426,2310332,2293304700,True,atwork,15,16,10.0,WALK,229330470,1 +1834643765,18346437650,5593426,2310332,2293304700,False,Work,16,15,10.0,WALK,229330470,1 +1834644041,18346440410,5593426,2310332,2293305050,True,work,16,23,8.0,TNC_SINGLE,229330505,1 +1834644045,18346440450,5593426,2310332,2293305050,False,Home,23,16,18.0,TNC_SINGLE,229330505,1 +1868570785,18685707850,5696862,2344811,2335713480,True,eatout,5,11,18.0,WALK,233571348,1 +1868570789,18685707890,5696862,2344811,2335713480,False,Home,11,5,21.0,WALK,233571348,1 +1868570937,18685709370,5696862,2344811,2335713670,True,othdiscr,9,11,14.0,WALK,233571367,1 +1868570941,18685709410,5696862,2344811,2335713670,False,Home,11,9,18.0,WALK,233571367,1 +1868571001,18685710010,5696862,2344811,2335713750,True,shopping,10,11,9.0,WALK,233571375,1 +1868571005,18685710050,5696862,2344811,2335713750,False,Home,11,10,13.0,WALK,233571375,1 +1868571377,18685713770,5696863,2344811,2335714220,True,work,1,11,8.0,WALK,233571422,1 +1868571381,18685713810,5696863,2344811,2335714220,False,Home,11,1,19.0,WALK,233571422,1 +1868628825,18686288250,5697039,2344870,2335786030,True,atwork,14,14,12.0,WALK,233578603,1 +1868628829,18686288290,5697039,2344870,2335786030,False,Work,14,14,13.0,WALK,233578603,1 +1868629105,18686291050,5697039,2344870,2335786380,True,work,14,16,8.0,WALK,233578638,1 +1868629109,18686291090,5697039,2344870,2335786380,False,work,14,14,23.0,TNC_SINGLE,233578638,1 +1868629110,18686291100,5697039,2344870,2335786380,False,Home,16,14,23.0,WALK,233578638,2 +1868629433,18686294330,5697040,2344870,2335786790,True,work,2,16,6.0,WALK,233578679,1 +1868629437,18686294370,5697040,2344870,2335786790,False,Home,16,2,15.0,WALK,233578679,1 +1868629761,18686297610,5697041,2344870,2335787200,True,work,2,16,7.0,WALK_LOC,233578720,1 +1868629765,18686297650,5697041,2344870,2335787200,False,Home,16,2,18.0,WALK_LOC,233578720,1 +1868642601,18686426010,5697081,2344884,2335803250,True,atwork,22,22,12.0,WALK,233580325,1 +1868642605,18686426050,5697081,2344884,2335803250,False,Work,22,22,13.0,WALK,233580325,1 +1868642769,18686427690,5697081,2344884,2335803460,True,othdiscr,12,16,11.0,WALK,233580346,1 +1868642773,18686427730,5697081,2344884,2335803460,False,Home,16,12,11.0,WALK,233580346,1 +1868642881,18686428810,5697081,2344884,2335803600,True,work,2,16,12.0,WALK,233580360,1 +1868642882,18686428820,5697081,2344884,2335803600,True,work,22,2,12.0,DRIVEALONEFREE,233580360,2 +1868642885,18686428850,5697081,2344884,2335803600,False,othmaint,18,22,18.0,SHARED3FREE,233580360,1 +1868642886,18686428860,5697081,2344884,2335803600,False,Home,16,18,23.0,SHARED3FREE,233580360,2 +1868643209,18686432090,5697082,2344884,2335804010,True,shopping,16,16,9.0,WALK,233580401,1 +1868643210,18686432100,5697082,2344884,2335804010,True,work,1,16,9.0,WALK,233580401,2 +1868643213,18686432130,5697082,2344884,2335804010,False,Home,16,1,21.0,WALK,233580401,1 +1868643537,18686435370,5697083,2344884,2335804420,True,work,11,16,8.0,WALK_LOC,233580442,1 +1868643541,18686435410,5697083,2344884,2335804420,False,Home,16,11,18.0,WALK_LOC,233580442,1 +1868660313,18686603130,5697135,2344902,2335825390,True,atwork,11,20,10.0,WALK,233582539,1 +1868660317,18686603170,5697135,2344902,2335825390,False,Work,20,11,10.0,WALK,233582539,1 +1868660593,18686605930,5697135,2344902,2335825740,True,work,20,16,6.0,WALK,233582574,1 +1868660597,18686605970,5697135,2344902,2335825740,False,Home,16,20,17.0,WALK_LOC,233582574,1 +1868660921,18686609210,5697136,2344902,2335826150,True,work,18,16,11.0,WALK,233582615,1 +1868660925,18686609250,5697136,2344902,2335826150,False,othmaint,17,18,22.0,WALK,233582615,1 +1868660926,18686609260,5697136,2344902,2335826150,False,Home,16,17,22.0,WALK_LRF,233582615,2 +1868661249,18686612490,5697137,2344902,2335826560,True,work,1,16,7.0,WALK_LOC,233582656,1 +1868661253,18686612530,5697137,2344902,2335826560,False,Home,16,1,13.0,WALK,233582656,1 +1868719521,18687195210,5697315,2344962,2335899400,True,othdiscr,25,16,9.0,TNC_SINGLE,233589940,1 +1868719525,18687195250,5697315,2344962,2335899400,False,Home,16,25,12.0,TNC_SINGLE,233589940,1 +1868719697,18687196970,5697316,2344962,2335899620,True,eatout,14,16,10.0,WALK,233589962,1 +1868719701,18687197010,5697316,2344962,2335899620,False,Home,16,14,16.0,WALK,233589962,1 +1868720177,18687201770,5697317,2344962,2335900220,True,othdiscr,16,16,7.0,WALK,233590022,1 +1868720181,18687201810,5697317,2344962,2335900220,False,Home,16,16,12.0,WALK,233590022,1 +1868727505,18687275050,5697339,2344970,2335909380,True,work,4,16,8.0,WALK,233590938,1 +1868727509,18687275090,5697339,2344970,2335909380,False,Home,16,4,19.0,WALK,233590938,1 +1868727833,18687278330,5697340,2344970,2335909790,True,work,2,16,7.0,WALK,233590979,1 +1868727837,18687278370,5697340,2344970,2335909790,False,Home,16,2,17.0,WALK,233590979,1 +1868728113,18687281130,5697341,2344970,2335910140,True,shopping,11,16,15.0,WALK,233591014,1 +1868728117,18687281170,5697341,2344970,2335910140,False,Home,16,11,15.0,WALK,233591014,1 +1868750137,18687501370,5697408,2344993,2335937670,True,work,16,16,6.0,WALK,233593767,1 +1868750141,18687501410,5697408,2344993,2335937670,False,Home,16,16,14.0,WALK,233593767,1 +1868750465,18687504650,5697409,2344993,2335938080,True,work,13,16,11.0,WALK,233593808,1 +1868750466,18687504660,5697409,2344993,2335938080,True,work,21,13,12.0,DRIVEALONEFREE,233593808,2 +1868750469,18687504690,5697409,2344993,2335938080,False,Home,16,21,21.0,WALK,233593808,1 +1868750793,18687507930,5697410,2344993,2335938490,True,work,22,16,14.0,WALK,233593849,1 +1868750797,18687507970,5697410,2344993,2335938490,False,Home,16,22,18.0,WALK,233593849,1 +1868760961,18687609610,5697441,2345004,2335951200,True,work,2,16,5.0,WALK,233595120,1 +1868760965,18687609650,5697441,2345004,2335951200,False,Home,16,2,16.0,WALK,233595120,1 +1868761289,18687612890,5697442,2345004,2335951610,True,work,23,16,9.0,BIKE,233595161,1 +1868761293,18687612930,5697442,2345004,2335951610,False,escort,7,23,20.0,BIKE,233595161,1 +1868761294,18687612940,5697442,2345004,2335951610,False,Home,16,7,21.0,BIKE,233595161,2 +1868761617,18687616170,5697443,2345004,2335952020,True,work,14,16,8.0,WALK_LOC,233595202,1 +1868761621,18687616210,5697443,2345004,2335952020,False,othmaint,22,14,14.0,WALK,233595202,1 +1868761622,18687616220,5697443,2345004,2335952020,False,eatout,12,22,15.0,WALK,233595202,2 +1868761623,18687616230,5697443,2345004,2335952020,False,shopping,7,12,15.0,WALK,233595202,3 +1868761624,18687616240,5697443,2345004,2335952020,False,Home,16,7,15.0,WALK,233595202,4 +1868791185,18687911850,5697534,2345035,2335988980,True,atwork,11,2,14.0,WALK,233598898,1 +1868791189,18687911890,5697534,2345035,2335988980,False,Work,2,11,14.0,WALK,233598898,1 +1868791465,18687914650,5697534,2345035,2335989330,True,work,2,17,7.0,TNC_SINGLE,233598933,1 +1868791469,18687914690,5697534,2345035,2335989330,False,Home,17,2,15.0,TNC_SHARED,233598933,1 +1868791681,18687916810,5697535,2345035,2335989600,True,othdiscr,5,17,7.0,WALK_LOC,233598960,1 +1868791685,18687916850,5697535,2345035,2335989600,False,Home,17,5,12.0,WALK_LRF,233598960,1 +1868791745,18687917450,5697535,2345035,2335989680,True,shopping,16,17,12.0,WALK,233598968,1 +1868791749,18687917490,5697535,2345035,2335989680,False,Home,17,16,17.0,WALK_LRF,233598968,1 +1868791753,18687917530,5697535,2345035,2335989690,True,shopping,5,17,17.0,TNC_SINGLE,233598969,1 +1868791757,18687917570,5697535,2345035,2335989690,False,Home,17,5,18.0,TNC_SINGLE,233598969,1 +1868792121,18687921210,5697536,2345035,2335990150,True,work,14,17,10.0,WALK,233599015,1 +1868792125,18687921250,5697536,2345035,2335990150,False,Home,17,14,20.0,WALK,233599015,1 +1868815081,18688150810,5697606,2345059,2336018850,True,work,16,17,8.0,WALK,233601885,1 +1868815085,18688150850,5697606,2345059,2336018850,False,Home,17,16,15.0,WALK,233601885,1 +1868815129,18688151290,5697607,2345059,2336018910,True,atwork,13,20,10.0,WALK,233601891,1 +1868815133,18688151330,5697607,2345059,2336018910,False,Work,20,13,10.0,WALK,233601891,1 +1868815409,18688154090,5697607,2345059,2336019260,True,work,20,17,6.0,WALK,233601926,1 +1868815413,18688154130,5697607,2345059,2336019260,False,Home,17,20,17.0,WALK,233601926,1 +1868815457,18688154570,5697608,2345059,2336019320,True,atwork,21,17,12.0,WALK,233601932,1 +1868815461,18688154610,5697608,2345059,2336019320,False,Work,17,21,12.0,WALK,233601932,1 +1868815737,18688157370,5697608,2345059,2336019670,True,escort,16,17,9.0,TNC_SINGLE,233601967,1 +1868815738,18688157380,5697608,2345059,2336019670,True,othdiscr,17,16,9.0,WALK,233601967,2 +1868815739,18688157390,5697608,2345059,2336019670,True,work,17,17,10.0,WALK,233601967,3 +1868815741,18688157410,5697608,2345059,2336019670,False,Home,17,17,19.0,TNC_SHARED,233601967,1 +1868843617,18688436170,5697693,2345088,2336054520,True,work,6,17,10.0,TNC_SINGLE,233605452,1 +1868843621,18688436210,5697693,2345088,2336054520,False,Home,17,6,18.0,WALK_LOC,233605452,1 +1868843945,18688439450,5697694,2345088,2336054930,True,work,4,17,7.0,WALK,233605493,1 +1868843949,18688439490,5697694,2345088,2336054930,False,Home,17,4,21.0,WALK_LRF,233605493,1 +1868844273,18688442730,5697695,2345088,2336055340,True,work,19,17,8.0,WALK,233605534,1 +1868844277,18688442770,5697695,2345088,2336055340,False,work,2,19,17.0,TNC_SINGLE,233605534,1 +1868844278,18688442780,5697695,2345088,2336055340,False,othdiscr,5,2,17.0,WALK,233605534,2 +1868844279,18688442790,5697695,2345088,2336055340,False,Home,17,5,18.0,WALK_LOC,233605534,3 +1868907297,18689072970,5697888,2345153,2336134120,True,atwork,12,2,13.0,WALK,233613412,1 +1868907301,18689073010,5697888,2345153,2336134120,False,eatout,3,12,13.0,WALK,233613412,1 +1868907302,18689073020,5697888,2345153,2336134120,False,Work,2,3,13.0,WALK,233613412,2 +1868907465,18689074650,5697888,2345153,2336134330,True,othdiscr,17,17,8.0,WALK,233613433,1 +1868907469,18689074690,5697888,2345153,2336134330,False,Home,17,17,8.0,WALK,233613433,1 +1868907577,18689075770,5697888,2345153,2336134470,True,work,2,17,9.0,WALK,233613447,1 +1868907581,18689075810,5697888,2345153,2336134470,False,Home,17,2,20.0,WALK,233613447,1 +1868907905,18689079050,5697889,2345153,2336134880,True,eatout,25,17,13.0,WALK_LOC,233613488,1 +1868907906,18689079060,5697889,2345153,2336134880,True,work,2,25,13.0,WALK,233613488,2 +1868907909,18689079090,5697889,2345153,2336134880,False,Home,17,2,23.0,WALK_LRF,233613488,1 +1868908233,18689082330,5697890,2345153,2336135290,True,work,14,17,8.0,WALK,233613529,1 +1868908237,18689082370,5697890,2345153,2336135290,False,social,12,14,18.0,WALK,233613529,1 +1868908238,18689082380,5697890,2345153,2336135290,False,Home,17,12,19.0,WALK,233613529,2 +1868914465,18689144650,5697909,2345160,2336143080,True,work,13,17,8.0,WALK,233614308,1 +1868914469,18689144690,5697909,2345160,2336143080,False,Home,17,13,17.0,WALK,233614308,1 +1868914513,18689145130,5697910,2345160,2336143140,True,atwork,12,22,15.0,WALK,233614314,1 +1868914517,18689145170,5697910,2345160,2336143140,False,work,16,12,15.0,WALK,233614314,1 +1868914518,18689145180,5697910,2345160,2336143140,False,eatout,23,16,15.0,WALK,233614314,2 +1868914519,18689145190,5697910,2345160,2336143140,False,Work,22,23,15.0,WALK,233614314,3 +1868914793,18689147930,5697910,2345160,2336143490,True,work,22,17,5.0,TNC_SINGLE,233614349,1 +1868914797,18689147970,5697910,2345160,2336143490,False,work,24,22,21.0,TNC_SINGLE,233614349,1 +1868914798,18689147980,5697910,2345160,2336143490,False,Home,17,24,21.0,TNC_SINGLE,233614349,2 +1868915121,18689151210,5697911,2345160,2336143900,True,work,5,17,7.0,WALK,233614390,1 +1868915125,18689151250,5697911,2345160,2336143900,False,Home,17,5,13.0,WALK_LRF,233614390,1 +1868915129,18689151290,5697911,2345160,2336143910,True,work,5,17,15.0,WALK_LOC,233614391,1 +1868915133,18689151330,5697911,2345160,2336143910,False,Home,17,5,18.0,WALK_LRF,233614391,1 +1868973505,18689735050,5698089,2345220,2336216880,True,work,5,23,8.0,WALK,233621688,1 +1868973509,18689735090,5698089,2345220,2336216880,False,Home,23,5,18.0,WALK,233621688,1 +1868973833,18689738330,5698090,2345220,2336217290,True,work,2,23,7.0,WALK,233621729,1 +1868973837,18689738370,5698090,2345220,2336217290,False,Home,23,2,17.0,WALK,233621729,1 +1889746617,18897466170,5761422,2366331,2362183270,True,othdiscr,15,8,12.0,WALK_LOC,236218327,1 +1889746621,18897466210,5761422,2366331,2362183270,False,Home,8,15,19.0,WALK_LOC,236218327,1 +1889747057,18897470570,5761423,2366331,2362183820,True,eatout,5,8,8.0,WALK,236218382,1 +1889747058,18897470580,5761423,2366331,2362183820,True,work,13,5,10.0,WALK,236218382,2 +1889747061,18897470610,5761423,2366331,2362183820,False,work,4,13,21.0,WALK,236218382,1 +1889747062,18897470620,5761423,2366331,2362183820,False,Home,8,4,21.0,WALK,236218382,2 +1889747337,18897473370,5761424,2366331,2362184170,True,shopping,5,8,12.0,WALK,236218417,1 +1889747338,18897473380,5761424,2366331,2362184170,True,othmaint,7,5,12.0,WALK,236218417,2 +1889747339,18897473390,5761424,2366331,2362184170,True,othmaint,7,7,13.0,WALK,236218417,3 +1889747340,18897473400,5761424,2366331,2362184170,True,shopping,11,7,13.0,WALK,236218417,4 +1889747341,18897473410,5761424,2366331,2362184170,False,shopping,5,11,21.0,WALK,236218417,1 +1889747342,18897473420,5761424,2366331,2362184170,False,Home,8,5,21.0,WALK,236218417,2 +1889783089,18897830890,5761533,2366368,2362228860,True,shopping,11,8,10.0,WALK,236222886,1 +1889783093,18897830930,5761533,2366368,2362228860,False,othmaint,7,11,20.0,BIKE,236222886,1 +1889783094,18897830940,5761533,2366368,2362228860,False,escort,6,7,20.0,BIKE,236222886,2 +1889783095,18897830950,5761533,2366368,2362228860,False,Home,8,6,20.0,BIKE,236222886,3 +1889783729,18897837290,5761535,2366368,2362229660,True,school,9,8,7.0,WALK,236222966,1 +1889783733,18897837330,5761535,2366368,2362229660,False,Home,8,9,15.0,WALK,236222966,1 +1889784121,18897841210,5761536,2366369,2362230150,True,work,12,8,8.0,WALK,236223015,1 +1889784122,18897841220,5761536,2366369,2362230150,True,work,5,12,9.0,WALK,236223015,2 +1889784125,18897841250,5761536,2366369,2362230150,False,othdiscr,9,5,21.0,WALK,236223015,1 +1889784126,18897841260,5761536,2366369,2362230150,False,Home,8,9,21.0,WALK,236223015,2 +1889784209,18897842090,5761537,2366369,2362230260,True,escort,3,8,8.0,TNC_SINGLE,236223026,1 +1889784213,18897842130,5761537,2366369,2362230260,False,shopping,25,3,9.0,WALK_LOC,236223026,1 +1889784214,18897842140,5761537,2366369,2362230260,False,Home,8,25,9.0,WALK_LOC,236223026,2 +1889839929,18898399290,5761707,2366426,2362299910,True,atwork,21,4,10.0,WALK,236229991,1 +1889839933,18898399330,5761707,2366426,2362299910,False,Work,4,21,10.0,WALK,236229991,1 +1889840161,18898401610,5761707,2366426,2362300200,True,shopping,11,21,18.0,WALK,236230020,1 +1889840165,18898401650,5761707,2366426,2362300200,False,shopping,11,11,18.0,TAXI,236230020,1 +1889840166,18898401660,5761707,2366426,2362300200,False,Home,21,11,18.0,TNC_SHARED,236230020,2 +1889840209,18898402090,5761707,2366426,2362300260,True,work,4,21,8.0,WALK,236230026,1 +1889840213,18898402130,5761707,2366426,2362300260,False,Home,21,4,17.0,WALK_LRF,236230026,1 +1889840425,18898404250,5761708,2366426,2362300530,True,othdiscr,14,21,10.0,WALK,236230053,1 +1889840429,18898404290,5761708,2366426,2362300530,False,Home,21,14,13.0,WALK,236230053,1 +1889840489,18898404890,5761708,2366426,2362300610,True,shopping,19,21,18.0,DRIVEALONEFREE,236230061,1 +1889840493,18898404930,5761708,2366426,2362300610,False,Home,21,19,21.0,TNC_SHARED,236230061,1 +1889840801,18898408010,5761709,2366426,2362301000,True,school,6,21,9.0,WALK_LOC,236230100,1 +1889840805,18898408050,5761709,2366426,2362301000,False,Home,21,6,14.0,WALK_LOC,236230100,1 +1889870601,18898706010,5761800,2366457,2362338250,True,othdiscr,16,21,10.0,WALK,236233825,1 +1889870605,18898706050,5761800,2366457,2362338250,False,Home,21,16,12.0,WALK_LOC,236233825,1 +1889870665,18898706650,5761800,2366457,2362338330,True,shopping,25,21,15.0,WALK_LOC,236233833,1 +1889870669,18898706690,5761800,2366457,2362338330,False,Home,21,25,15.0,WALK_LOC,236233833,1 +1889870929,18898709290,5761801,2366457,2362338660,True,othdiscr,4,21,8.0,WALK_LOC,236233866,1 +1889870933,18898709330,5761801,2366457,2362338660,False,shopping,22,4,19.0,WALK_LRF,236233866,1 +1889870934,18898709340,5761801,2366457,2362338660,False,Home,21,22,19.0,WALK_LRF,236233866,2 +1889871321,18898713210,5761802,2366457,2362339150,True,shopping,25,21,9.0,WALK_LOC,236233915,1 +1889871325,18898713250,5761802,2366457,2362339150,False,Home,21,25,17.0,WALK_LOC,236233915,1 +1931558857,19315588570,5888898,2408823,2414448570,True,work,22,16,7.0,WALK,241444857,1 +1931558861,19315588610,5888898,2408823,2414448570,False,Home,16,22,22.0,WALK,241444857,1 +1931559185,19315591850,5888899,2408823,2414448980,True,escort,16,16,8.0,WALK,241444898,1 +1931559186,19315591860,5888899,2408823,2414448980,True,work,4,16,10.0,WALK,241444898,2 +1931559189,19315591890,5888899,2408823,2414448980,False,work,2,4,16.0,WALK,241444898,1 +1931559190,19315591900,5888899,2408823,2414448980,False,escort,14,2,16.0,WALK,241444898,2 +1931559191,19315591910,5888899,2408823,2414448980,False,Home,16,14,16.0,WALK,241444898,3 +1931559449,19315594490,5888900,2408823,2414449310,True,school,16,16,8.0,WALK,241444931,1 +1931559453,19315594530,5888900,2408823,2414449310,False,Home,16,16,10.0,WALK,241444931,1 +1931566449,19315664490,5888922,2408831,2414458060,True,atwork,18,20,12.0,WALK,241445806,1 +1931566453,19315664530,5888922,2408831,2414458060,False,Work,20,18,12.0,WALK,241445806,1 +1931566729,19315667290,5888922,2408831,2414458410,True,work,20,16,7.0,WALK,241445841,1 +1931566733,19315667330,5888922,2408831,2414458410,False,Home,16,20,18.0,WALK,241445841,1 +1931567321,19315673210,5888924,2408831,2414459150,True,school,16,16,7.0,WALK,241445915,1 +1931567325,19315673250,5888924,2408831,2414459150,False,Home,16,16,23.0,WALK,241445915,1 +1931567665,19315676650,5888925,2408832,2414459580,True,shopping,18,16,9.0,TNC_SINGLE,241445958,1 +1931567669,19315676690,5888925,2408832,2414459580,False,Home,16,18,10.0,WALK_LRF,241445958,1 +1931568305,19315683050,5888927,2408832,2414460380,True,eatout,11,16,7.0,WALK_LOC,241446038,1 +1931568306,19315683060,5888927,2408832,2414460380,True,school,8,11,8.0,WALK_LOC,241446038,2 +1931568309,19315683090,5888927,2408832,2414460380,False,Home,16,8,21.0,WALK_LOC,241446038,1 +1931574601,19315746010,5888946,2408839,2414468250,True,work,8,16,7.0,BIKE,241446825,1 +1931574605,19315746050,5888946,2408839,2414468250,False,Home,16,8,18.0,BIKE,241446825,1 +1931574929,19315749290,5888947,2408839,2414468660,True,work,4,16,7.0,WALK,241446866,1 +1931574933,19315749330,5888947,2408839,2414468660,False,Home,16,4,20.0,WALK,241446866,1 +1931575193,19315751930,5888948,2408839,2414468990,True,school,9,16,8.0,WALK_LOC,241446899,1 +1931575197,19315751970,5888948,2408839,2414468990,False,Home,16,9,18.0,WALK_LRF,241446899,1 +1931580505,19315805050,5888964,2408845,2414475630,True,work,13,16,7.0,WALK,241447563,1 +1931580509,19315805090,5888964,2408845,2414475630,False,Home,16,13,17.0,WALK,241447563,1 +1931580593,19315805930,5888965,2408845,2414475740,True,escort,7,16,7.0,WALK_LOC,241447574,1 +1931580597,19315805970,5888965,2408845,2414475740,False,Home,16,7,9.0,WALK_LOC,241447574,1 +1931580601,19315806010,5888965,2408845,2414475750,True,escort,15,16,11.0,SHARED2FREE,241447575,1 +1931580605,19315806050,5888965,2408845,2414475750,False,escort,18,15,11.0,SHARED2FREE,241447575,1 +1931580606,19315806060,5888965,2408845,2414475750,False,othdiscr,25,18,11.0,SHARED2FREE,241447575,2 +1931580607,19315806070,5888965,2408845,2414475750,False,escort,5,25,11.0,SHARED2FREE,241447575,3 +1931580608,19315806080,5888965,2408845,2414475750,False,Home,16,5,11.0,SHARED2FREE,241447575,4 +1931580809,19315808090,5888965,2408845,2414476010,True,social,20,16,13.0,WALK,241447601,1 +1931580813,19315808130,5888965,2408845,2414476010,False,Home,16,20,20.0,WALK,241447601,1 +1931581049,19315810490,5888966,2408845,2414476310,True,othdiscr,9,16,21.0,WALK_LRF,241447631,1 +1931581053,19315810530,5888966,2408845,2414476310,False,shopping,16,9,22.0,WALK_LRF,241447631,1 +1931581054,19315810540,5888966,2408845,2414476310,False,othdiscr,10,16,22.0,WALK_LRF,241447631,2 +1931581055,19315810550,5888966,2408845,2414476310,False,Home,16,10,22.0,WALK_LRF,241447631,3 +1931581097,19315810970,5888966,2408845,2414476370,True,eatout,2,16,7.0,WALK_LOC,241447637,1 +1931581098,19315810980,5888966,2408845,2414476370,True,school,9,2,8.0,WALK_LRF,241447637,2 +1931581101,19315811010,5888966,2408845,2414476370,False,Home,16,9,21.0,WALK_LRF,241447637,1 +1931621721,19316217210,5889090,2408887,2414527150,True,othdiscr,4,17,11.0,WALK,241452715,1 +1931621725,19316217250,5889090,2408887,2414527150,False,Home,17,4,19.0,WALK,241452715,1 +1931622161,19316221610,5889091,2408887,2414527700,True,work,2,17,10.0,WALK,241452770,1 +1931622165,19316221650,5889091,2408887,2414527700,False,Home,17,2,20.0,WALK_LOC,241452770,1 +1931622425,19316224250,5889092,2408887,2414528030,True,school,6,17,7.0,WALK_LOC,241452803,1 +1931622429,19316224290,5889092,2408887,2414528030,False,Home,17,6,11.0,WALK_LRF,241452803,1 +1931638281,19316382810,5889141,2408904,2414547850,True,atwork,4,1,11.0,WALK,241454785,1 +1931638285,19316382850,5889141,2408904,2414547850,False,eatout,6,4,11.0,WALK,241454785,1 +1931638286,19316382860,5889141,2408904,2414547850,False,eatout,7,6,11.0,WALK,241454785,2 +1931638287,19316382870,5889141,2408904,2414547850,False,Work,1,7,11.0,WALK,241454785,3 +1931638561,19316385610,5889141,2408904,2414548200,True,work,1,18,7.0,WALK,241454820,1 +1931638565,19316385650,5889141,2408904,2414548200,False,Home,18,1,18.0,WALK,241454820,1 +1931638889,19316388890,5889142,2408904,2414548610,True,work,9,18,8.0,WALK,241454861,1 +1931638893,19316388930,5889142,2408904,2414548610,False,shopping,11,9,16.0,WALK,241454861,1 +1931638894,19316388940,5889142,2408904,2414548610,False,shopping,5,11,16.0,WALK_LOC,241454861,2 +1931638895,19316388950,5889142,2408904,2414548610,False,Home,18,5,16.0,WALK,241454861,3 +1931639153,19316391530,5889143,2408904,2414548940,True,school,9,18,6.0,WALK,241454894,1 +1931639157,19316391570,5889143,2408904,2414548940,False,Home,18,9,14.0,WALK,241454894,1 +1931639545,19316395450,5889144,2408905,2414549430,True,escort,9,18,7.0,DRIVEALONEFREE,241454943,1 +1931639546,19316395460,5889144,2408905,2414549430,True,escort,16,9,8.0,DRIVEALONEFREE,241454943,2 +1931639547,19316395470,5889144,2408905,2414549430,True,work,16,16,8.0,DRIVEALONEFREE,241454943,3 +1931639549,19316395490,5889144,2408905,2414549430,False,othdiscr,16,16,17.0,DRIVEALONEFREE,241454943,1 +1931639550,19316395500,5889144,2408905,2414549430,False,Home,18,16,17.0,DRIVEALONEFREE,241454943,2 +1931639873,19316398730,5889145,2408905,2414549840,True,eatout,9,18,9.0,DRIVEALONEFREE,241454984,1 +1931639874,19316398740,5889145,2408905,2414549840,True,othmaint,13,9,10.0,DRIVEALONEFREE,241454984,2 +1931639875,19316398750,5889145,2408905,2414549840,True,othdiscr,14,13,10.0,DRIVEALONEFREE,241454984,3 +1931639876,19316398760,5889145,2408905,2414549840,True,work,16,14,12.0,WALK,241454984,4 +1931639877,19316398770,5889145,2408905,2414549840,False,Home,18,16,22.0,DRIVEALONEFREE,241454984,1 +1931640137,19316401370,5889146,2408905,2414550170,True,school,8,18,11.0,WALK,241455017,1 +1931640141,19316401410,5889146,2408905,2414550170,False,Home,18,8,18.0,WALK,241455017,1 +2086467113,20864671130,6361180,2506726,2608083890,True,escort,10,16,13.0,WALK_LOC,260808389,1 +2086467117,20864671170,6361180,2506726,2608083890,False,othdiscr,5,10,14.0,TNC_SINGLE,260808389,1 +2086467118,20864671180,6361180,2506726,2608083890,False,Home,16,5,14.0,TNC_SINGLE,260808389,2 +2086467681,20864676810,6361181,2506726,2608084600,True,work,23,16,7.0,WALK,260808460,1 +2086467685,20864676850,6361181,2506726,2608084600,False,Home,16,23,18.0,WALK,260808460,1 +2086467769,20864677690,6361182,2506726,2608084710,True,escort,19,16,11.0,TNC_SINGLE,260808471,1 +2086467773,20864677730,6361182,2506726,2608084710,False,Home,16,19,12.0,TNC_SINGLE,260808471,1 +2086467921,20864679210,6361182,2506726,2608084900,True,othmaint,16,16,13.0,TNC_SINGLE,260808490,1 +2086467925,20864679250,6361182,2506726,2608084900,False,Home,16,16,16.0,TAXI,260808490,1 +2086467961,20864679610,6361182,2506726,2608084950,True,shopping,1,16,16.0,TNC_SHARED,260808495,1 +2086467965,20864679650,6361182,2506726,2608084950,False,Home,16,1,16.0,TNC_SINGLE,260808495,1 +2086468337,20864683370,6361183,2506726,2608085420,True,work,11,16,7.0,WALK,260808542,1 +2086468341,20864683410,6361183,2506726,2608085420,False,Home,16,11,16.0,WALK,260808542,1 +2107104641,21071046410,6424099,2518598,2633880800,True,social,24,22,12.0,WALK,263388080,1 +2107104645,21071046450,6424099,2518598,2633880800,False,social,24,24,15.0,WALK,263388080,1 +2107104646,21071046460,6424099,2518598,2633880800,False,Home,22,24,16.0,TNC_SHARED,263388080,2 +2107104785,21071047850,6424099,2518598,2633880980,True,work,2,22,5.0,WALK,263388098,1 +2107104789,21071047890,6424099,2518598,2633880980,False,Home,22,2,5.0,WALK,263388098,1 +2107104793,21071047930,6424099,2518598,2633880990,True,work,2,22,6.0,WALK,263388099,1 +2107104797,21071047970,6424099,2518598,2633880990,False,Home,22,2,6.0,WALK,263388099,1 +2107104873,21071048730,6424100,2518598,2633881090,True,escort,5,22,8.0,WALK,263388109,1 +2107104877,21071048770,6424100,2518598,2633881090,False,Home,22,5,11.0,WALK,263388109,1 +2107104881,21071048810,6424100,2518598,2633881100,True,escort,5,22,17.0,DRIVEALONEFREE,263388110,1 +2107104885,21071048850,6424100,2518598,2633881100,False,Home,22,5,17.0,DRIVEALONEFREE,263388110,1 +2107105001,21071050010,6424100,2518598,2633881250,True,othdiscr,9,22,17.0,WALK_LRF,263388125,1 +2107105005,21071050050,6424100,2518598,2633881250,False,Home,22,9,20.0,WALK_LRF,263388125,1 +2107105505,21071055050,6424102,2518598,2633881880,True,eatout,24,22,8.0,WALK,263388188,1 +2107105509,21071055090,6424102,2518598,2633881880,False,Home,22,24,10.0,WALK,263388188,1 +2153537257,21535372570,6565667,2549865,2691921570,True,othdiscr,8,18,15.0,SHARED3FREE,269192157,1 +2153537261,21535372610,6565667,2549865,2691921570,False,eatout,5,8,15.0,WALK,269192157,1 +2153537262,21535372620,6565667,2549865,2691921570,False,Home,18,5,15.0,SHARED2FREE,269192157,2 +2153537537,21535375370,6565663,2549865,2691921920,True,escort,12,18,7.0,DRIVEALONEFREE,269192192,1 +2153537541,21535375410,6565663,2549865,2691921920,False,Home,18,12,7.0,DRIVEALONEFREE,269192192,1 +2153537777,21535377770,6565663,2549865,2691922220,True,work,21,18,7.0,DRIVEALONEFREE,269192222,1 +2153537781,21535377810,6565663,2549865,2691922220,False,eatout,11,21,17.0,DRIVEALONEFREE,269192222,1 +2153537782,21535377820,6565663,2549865,2691922220,False,Home,18,11,18.0,DRIVEALONEFREE,269192222,2 +2153538041,21535380410,6565664,2549865,2691922550,True,school,13,18,7.0,WALK,269192255,1 +2153538045,21535380450,6565664,2549865,2691922550,False,Home,18,13,12.0,WALK_LOC,269192255,1 +2153538369,21535383690,6565665,2549865,2691922960,True,school,9,18,8.0,WALK,269192296,1 +2153538373,21535383730,6565665,2549865,2691922960,False,Home,18,9,16.0,WALK,269192296,1 +2153538497,21535384970,6565666,2549865,2691923120,True,eatout,21,18,14.0,SHARED2FREE,269192312,1 +2153538501,21535385010,6565666,2549865,2691923120,False,Home,18,21,15.0,WALK,269192312,1 +2153538697,21535386970,6565666,2549865,2691923370,True,school,7,18,17.0,WALK,269192337,1 +2153538701,21535387010,6565666,2549865,2691923370,False,Home,18,7,18.0,WALK,269192337,1 +2153539025,21535390250,6565667,2549865,2691923780,True,school,25,18,7.0,WALK_LOC,269192378,1 +2153539029,21535390290,6565667,2549865,2691923780,False,Home,18,25,15.0,WALK_LOC,269192378,1 +2153539353,21535393530,6565668,2549865,2691924190,True,school,16,18,8.0,WALK_LRF,269192419,1 +2153539357,21535393570,6565668,2549865,2691924190,False,Home,18,16,15.0,WALK_LRF,269192419,1 +2153539681,21535396810,6565669,2549865,2691924600,True,school,18,18,7.0,WALK,269192460,1 +2153539685,21535396850,6565669,2549865,2691924600,False,Home,18,18,13.0,WALK,269192460,1 +2258627097,22586270970,6886058,2621752,2823283870,True,escort,10,16,18.0,TNC_SINGLE,282328387,1 +2258627101,22586271010,6886058,2621752,2823283870,False,Home,16,10,18.0,TNC_SINGLE,282328387,1 +2258627337,22586273370,6886058,2621752,2823284170,True,work,16,16,8.0,WALK,282328417,1 +2258627341,22586273410,6886058,2621752,2823284170,False,Home,16,16,18.0,WALK,282328417,1 +2258627729,22586277290,6886060,2621752,2823284660,True,eatout,11,16,19.0,SHARED2FREE,282328466,1 +2258627733,22586277330,6886060,2621752,2823284660,False,Home,16,11,20.0,WALK,282328466,1 +2258627929,22586279290,6886060,2621752,2823284910,True,school,21,16,7.0,WALK_LOC,282328491,1 +2258627933,22586279330,6886060,2621752,2823284910,False,Home,16,21,16.0,WALK_LOC,282328491,1 +2258628257,22586282570,6886061,2621752,2823285320,True,school,16,16,11.0,WALK,282328532,1 +2258628261,22586282610,6886061,2621752,2823285320,False,Home,16,16,20.0,WALK,282328532,1 +2258628265,22586282650,6886061,2621752,2823285330,True,eatout,2,16,20.0,WALK,282328533,1 +2258628266,22586282660,6886061,2621752,2823285330,True,school,16,2,20.0,WALK,282328533,2 +2258628269,22586282690,6886061,2621752,2823285330,False,Home,16,16,21.0,WALK,282328533,1 +2258673305,22586733050,6886199,2621785,2823341630,True,atwork,25,24,13.0,WALK,282334163,1 +2258673309,22586733090,6886199,2621785,2823341630,False,Work,24,25,13.0,WALK,282334163,1 +2258673585,22586735850,6886199,2621785,2823341980,True,work,24,25,7.0,WALK,282334198,1 +2258673589,22586735890,6886199,2621785,2823341980,False,Home,25,24,22.0,WALK,282334198,1 +2258673673,22586736730,6886200,2621785,2823342090,True,escort,18,25,7.0,TNC_SINGLE,282334209,1 +2258673677,22586736770,6886200,2621785,2823342090,False,Home,25,18,8.0,TNC_SHARED,282334209,1 +2258673801,22586738010,6886200,2621785,2823342250,True,othdiscr,16,25,21.0,WALK_LOC,282334225,1 +2258673805,22586738050,6886200,2621785,2823342250,False,Home,25,16,21.0,WALK_LOC,282334225,1 +2258673825,22586738250,6886200,2621785,2823342280,True,othmaint,9,25,9.0,WALK,282334228,1 +2258673829,22586738290,6886200,2621785,2823342280,False,Home,25,9,9.0,WALK,282334228,1 +2258673865,22586738650,6886200,2621785,2823342330,True,shopping,16,25,21.0,TNC_SHARED,282334233,1 +2258673869,22586738690,6886200,2621785,2823342330,False,Home,25,16,21.0,TNC_SINGLE,282334233,1 +2258673913,22586739130,6886200,2621785,2823342390,True,work,5,25,9.0,WALK,282334239,1 +2258673917,22586739170,6886200,2621785,2823342390,False,othmaint,24,5,17.0,WALK,282334239,1 +2258673918,22586739180,6886200,2621785,2823342390,False,eatout,23,24,18.0,WALK,282334239,2 +2258673919,22586739190,6886200,2621785,2823342390,False,Home,25,23,18.0,WALK,282334239,3 +2258674177,22586741770,6886201,2621785,2823342720,True,school,9,25,7.0,WALK,282334272,1 +2258674181,22586741810,6886201,2621785,2823342720,False,Home,25,9,13.0,WALK,282334272,1 +2258674505,22586745050,6886202,2621785,2823343130,True,school,25,25,8.0,WALK,282334313,1 +2258674509,22586745090,6886202,2621785,2823343130,False,Home,25,25,19.0,WALK,282334313,1 +2258674833,22586748330,6886203,2621785,2823343540,True,school,25,25,8.0,WALK,282334354,1 +2258674837,22586748370,6886203,2621785,2823343540,False,Home,25,25,18.0,WALK,282334354,1 +2357979937,23579799370,7188963,2677133,2947474920,True,escort,9,9,8.0,TNC_SINGLE,294747492,1 +2357979941,23579799410,7188963,2677133,2947474920,False,Home,9,9,9.0,TNC_SHARED,294747492,1 +2357979945,23579799450,7188963,2677133,2947474930,True,escort,14,9,16.0,DRIVEALONEFREE,294747493,1 +2357979949,23579799490,7188963,2677133,2947474930,False,Home,9,14,16.0,SHARED2FREE,294747493,1 +2357980505,23579805050,7188964,2677133,2947475630,True,escort,5,9,13.0,WALK_LOC,294747563,1 +2357980506,23579805060,7188964,2677133,2947475630,True,eatout,2,5,13.0,WALK,294747563,2 +2357980507,23579805070,7188964,2677133,2947475630,True,othmaint,4,2,14.0,WALK,294747563,3 +2357980508,23579805080,7188964,2677133,2947475630,True,work,23,4,17.0,WALK_LRF,294747563,4 +2357980509,23579805090,7188964,2677133,2947475630,False,othmaint,7,23,22.0,SHARED3FREE,294747563,1 +2357980510,23579805100,7188964,2677133,2947475630,False,Home,9,7,22.0,WALK_LOC,294747563,2 +2357980833,23579808330,7188965,2677133,2947476040,True,work,13,9,7.0,WALK,294747604,1 +2357980837,23579808370,7188965,2677133,2947476040,False,Home,9,13,14.0,WALK,294747604,1 +2357980841,23579808410,7188965,2677133,2947476050,True,work,13,9,14.0,WALK,294747605,1 +2357980845,23579808450,7188965,2677133,2947476050,False,Home,9,13,14.0,WALK,294747605,1 +2357980881,23579808810,7188966,2677133,2947476100,True,atwork,8,6,10.0,WALK,294747610,1 +2357980885,23579808850,7188966,2677133,2947476100,False,Work,6,8,10.0,WALK,294747610,1 +2357981161,23579811610,7188966,2677133,2947476450,True,work,6,9,7.0,WALK,294747645,1 +2357981165,23579811650,7188966,2677133,2947476450,False,Home,9,6,18.0,WALK,294747645,1 +2357981425,23579814250,7188967,2677133,2947476780,True,school,10,9,7.0,WALK,294747678,1 +2357981429,23579814290,7188967,2677133,2947476780,False,Home,9,10,20.0,WALK,294747678,1 +2357981553,23579815530,7188968,2677133,2947476940,True,eatout,5,9,15.0,WALK,294747694,1 +2357981557,23579815570,7188968,2677133,2947476940,False,Home,9,5,18.0,WALK,294747694,1 +2357981753,23579817530,7188968,2677133,2947477190,True,school,10,9,6.0,WALK,294747719,1 +2357981757,23579817570,7188968,2677133,2947477190,False,Home,9,10,15.0,WALK,294747719,1 +2357982145,23579821450,7188969,2677133,2947477680,True,work,7,9,6.0,WALK,294747768,1 +2357982149,23579821490,7188969,2677133,2947477680,False,Home,9,7,15.0,WALK,294747768,1 +2358106737,23581067370,7189349,2677180,2947633420,True,shopping,13,10,17.0,WALK,294763342,1 +2358106741,23581067410,7189349,2677180,2947633420,False,Home,10,13,18.0,WALK,294763342,1 +2358107001,23581070010,7189350,2677180,2947633750,True,escort,9,10,12.0,WALK,294763375,1 +2358107002,23581070020,7189350,2677180,2947633750,True,othmaint,8,9,13.0,WALK,294763375,2 +2358107003,23581070030,7189350,2677180,2947633750,True,othdiscr,21,8,13.0,WALK,294763375,3 +2358107005,23581070050,7189350,2677180,2947633750,False,social,7,21,19.0,WALK,294763375,1 +2358107006,23581070060,7189350,2677180,2947633750,False,othdiscr,8,7,19.0,WALK,294763375,2 +2358107007,23581070070,7189350,2677180,2947633750,False,Home,10,8,19.0,WALK,294763375,3 +2358107009,23581070090,7189350,2677180,2947633760,True,othdiscr,16,10,19.0,SHARED3FREE,294763376,1 +2358107013,23581070130,7189350,2677180,2947633760,False,Home,10,16,20.0,SHARED3FREE,294763376,1 +2358107049,23581070490,7189350,2677180,2947633810,True,school,9,10,7.0,WALK,294763381,1 +2358107053,23581070530,7189350,2677180,2947633810,False,Home,10,9,10.0,WALK,294763381,1 +2358107201,23581072010,7189351,2677180,2947634000,True,escort,10,10,8.0,TNC_SINGLE,294763400,1 +2358107205,23581072050,7189351,2677180,2947634000,False,Home,10,10,12.0,TNC_SHARED,294763400,1 +2358107721,23581077210,7189352,2677180,2947634650,True,shopping,5,10,17.0,WALK_LOC,294763465,1 +2358107725,23581077250,7189352,2677180,2947634650,False,shopping,20,5,17.0,TNC_SINGLE,294763465,1 +2358107726,23581077260,7189352,2677180,2947634650,False,Home,10,20,18.0,TNC_SINGLE,294763465,2 +2358107769,23581077690,7189352,2677180,2947634710,True,work,17,10,8.0,SHARED3FREE,294763471,1 +2358107773,23581077730,7189352,2677180,2947634710,False,Home,10,17,16.0,SHARED3FREE,294763471,1 +2358107985,23581079850,7189353,2677180,2947634980,True,othdiscr,9,10,17.0,WALK,294763498,1 +2358107989,23581079890,7189353,2677180,2947634980,False,Home,10,9,21.0,WALK,294763498,1 +2358108377,23581083770,7189354,2677180,2947635470,True,shopping,19,10,15.0,WALK,294763547,1 +2358108381,23581083810,7189354,2677180,2947635470,False,Home,10,19,19.0,WALK,294763547,1 +2358108753,23581087530,7189355,2677180,2947635940,True,work,16,10,7.0,WALK,294763594,1 +2358108757,23581087570,7189355,2677180,2947635940,False,Home,10,16,18.0,WALK,294763594,1 +2358108841,23581088410,7189356,2677180,2947636050,True,escort,25,10,8.0,SHARED2FREE,294763605,1 +2358108845,23581088450,7189356,2677180,2947636050,False,Home,10,25,11.0,DRIVEALONEFREE,294763605,1 +2358230769,23582307690,7189727,2677226,2947788460,True,work,17,17,5.0,WALK,294778846,1 +2358230773,23582307730,7189727,2677226,2947788460,False,Home,17,17,18.0,WALK,294778846,1 +2358230985,23582309850,7189728,2677226,2947788730,True,othdiscr,8,17,8.0,WALK_LRF,294778873,1 +2358230986,23582309860,7189728,2677226,2947788730,True,othdiscr,22,8,9.0,WALK_LRF,294778873,2 +2358230989,23582309890,7189728,2677226,2947788730,False,Home,17,22,9.0,WALK_LRF,294778873,1 +2358231033,23582310330,7189728,2677226,2947788790,True,school,13,17,9.0,WALK_LOC,294778879,1 +2358231037,23582310370,7189728,2677226,2947788790,False,Home,17,13,22.0,WALK_LRF,294778879,1 +2358231361,23582313610,7189729,2677226,2947789200,True,school,25,17,8.0,WALK_LOC,294778920,1 +2358231365,23582313650,7189729,2677226,2947789200,False,Home,17,25,10.0,WALK_LRF,294778920,1 +2358231953,23582319530,7189731,2677226,2947789940,True,atwork,9,13,10.0,WALK,294778994,1 +2358231957,23582319570,7189731,2677226,2947789940,False,eatout,7,9,17.0,WALK,294778994,1 +2358231958,23582319580,7189731,2677226,2947789940,False,Work,13,7,17.0,WALK,294778994,2 +2358232081,23582320810,7189731,2677226,2947790100,True,work,13,17,7.0,WALK_LOC,294779010,1 +2358232085,23582320850,7189731,2677226,2947790100,False,Home,17,13,17.0,WALK,294779010,1 +2358232673,23582326730,7189733,2677226,2947790840,True,school,13,17,7.0,WALK,294779084,1 +2358232677,23582326770,7189733,2677226,2947790840,False,Home,17,13,23.0,WALK_LRF,294779084,1 +2358233393,23582333930,7189735,2677226,2947791740,True,work,2,17,8.0,WALK,294779174,1 +2358233397,23582333970,7189735,2677226,2947791740,False,Home,17,2,19.0,WALK_LRF,294779174,1 +2358233721,23582337210,7189736,2677226,2947792150,True,work,1,17,8.0,WALK,294779215,1 +2358233725,23582337250,7189736,2677226,2947792150,False,Home,17,1,17.0,WALK,294779215,1 +2358233937,23582339370,7189737,2677226,2947792420,True,othdiscr,9,17,7.0,WALK_LOC,294779242,1 +2358233941,23582339410,7189737,2677226,2947792420,False,Home,17,9,19.0,WALK_LRF,294779242,1 +2358234313,23582343130,7189738,2677226,2947792890,True,school,13,17,6.0,WALK_LRF,294779289,1 +2358234317,23582343170,7189738,2677226,2947792890,False,Home,17,13,15.0,WALK_LOC,294779289,1 +2371727225,23717272250,7230875,2683536,2964659030,True,othmaint,5,25,6.0,WALK,296465903,1 +2371727229,23717272290,7230875,2683536,2964659030,False,Home,25,5,13.0,WALK,296465903,1 +2372886681,23728866810,7234410,2687071,2966108350,True,othdiscr,10,4,17.0,TNC_SINGLE,296610835,1 +2372886685,23728866850,7234410,2687071,2966108350,False,Home,4,10,19.0,TNC_SINGLE,296610835,1 +2372886729,23728867290,7234410,2687071,2966108410,True,escort,5,4,11.0,WALK_LOC,296610841,1 +2372886730,23728867300,7234410,2687071,2966108410,True,univ,12,5,12.0,WALK_LOC,296610841,2 +2372886733,23728867330,7234410,2687071,2966108410,False,shopping,13,12,11.0,WALK_LOC,296610841,1 +2372886734,23728867340,7234410,2687071,2966108410,False,Home,4,13,12.0,WALK_LOC,296610841,2 +2372886745,23728867450,7234410,2687071,2966108430,True,shopping,19,4,14.0,TNC_SINGLE,296610843,1 +2372886749,23728867490,7234410,2687071,2966108430,False,Home,4,19,14.0,TNC_SHARED,296610843,1 +2372889657,23728896570,7234419,2687080,2966112070,True,othmaint,8,5,14.0,WALK,296611207,1 +2372889661,23728896610,7234419,2687080,2966112070,False,Home,5,8,15.0,WALK,296611207,1 +2372898665,23728986650,7234447,2687108,2966123330,True,eatout,9,8,12.0,WALK,296612333,1 +2372898669,23728986690,7234447,2687108,2966123330,False,Home,8,9,14.0,WALK,296612333,1 +2372933585,23729335850,7234553,2687214,2966166980,True,othdiscr,17,8,14.0,WALK,296616698,1 +2372933589,23729335890,7234553,2687214,2966166980,False,Home,8,17,19.0,WALK,296616698,1 +2372939553,23729395530,7234571,2687232,2966174440,True,othdiscr,10,8,9.0,TNC_SINGLE,296617444,1 +2372939554,23729395540,7234571,2687232,2966174440,True,othdiscr,4,10,10.0,TNC_SINGLE,296617444,2 +2372939555,23729395550,7234571,2687232,2966174440,True,shopping,11,4,10.0,TNC_SINGLE,296617444,3 +2372939557,23729395570,7234571,2687232,2966174440,False,Home,8,11,12.0,WALK_LOC,296617444,1 +2372971961,23729719610,7234670,2687331,2966214950,True,othdiscr,20,8,14.0,WALK,296621495,1 +2372971965,23729719650,7234670,2687331,2966214950,False,Home,8,20,14.0,WALK,296621495,1 +2372971985,23729719850,7234670,2687331,2966214980,True,othmaint,5,8,15.0,WALK,296621498,1 +2372971989,23729719890,7234670,2687331,2966214980,False,Home,8,5,22.0,WALK,296621498,1 +2372980665,23729806650,7234697,2687358,2966225830,True,eatout,12,20,15.0,WALK,296622583,1 +2372980669,23729806690,7234697,2687358,2966225830,False,Home,20,12,17.0,WALK,296622583,1 +2372988425,23729884250,7234720,2687381,2966235530,True,shopping,11,20,17.0,DRIVEALONEFREE,296623553,1 +2372988429,23729884290,7234720,2687381,2966235530,False,Home,20,11,18.0,TAXI,296623553,1 +2373007081,23730070810,7234777,2687438,2966258850,True,othmaint,25,25,13.0,WALK,296625885,1 +2373007085,23730070850,7234777,2687438,2966258850,False,shopping,22,25,17.0,WALK,296625885,1 +2373007086,23730070860,7234777,2687438,2966258850,False,Home,25,22,17.0,WALK,296625885,2 +2374992793,23749927930,7240831,2693492,2968740990,True,othmaint,22,5,15.0,WALK,296874099,1 +2374992797,23749927970,7240831,2693492,2968740990,False,Home,5,22,16.0,WALK,296874099,1 +2374992881,23749928810,7240831,2693492,2968741100,True,work,1,5,6.0,WALK,296874110,1 +2374992885,23749928850,7240831,2693492,2968741100,False,Home,5,1,14.0,WALK,296874110,1 +2374992889,23749928890,7240831,2693492,2968741110,True,work,1,5,16.0,WALK,296874111,1 +2374992893,23749928930,7240831,2693492,2968741110,False,escort,17,1,17.0,WALK,296874111,1 +2374992894,23749928940,7240831,2693492,2968741110,False,Home,5,17,18.0,WALK,296874111,2 +2374995065,23749950650,7240838,2693499,2968743830,True,othdiscr,12,5,18.0,WALK,296874383,1 +2374995069,23749950690,7240838,2693499,2968743830,False,Home,5,12,18.0,WALK,296874383,1 +2374995089,23749950890,7240838,2693499,2968743860,True,othmaint,5,5,17.0,TNC_SINGLE,296874386,1 +2374995093,23749950930,7240838,2693499,2968743860,False,Home,5,5,18.0,TNC_SINGLE,296874386,1 +2374995177,23749951770,7240838,2693499,2968743970,True,work,2,5,7.0,WALK,296874397,1 +2374995181,23749951810,7240838,2693499,2968743970,False,Home,5,2,17.0,WALK,296874397,1 +2375040721,23750407210,7240977,2693638,2968800900,True,shopping,5,20,18.0,WALK,296880090,1 +2375040725,23750407250,7240977,2693638,2968800900,False,Home,20,5,19.0,WALK,296880090,1 +2375040769,23750407690,7240977,2693638,2968800960,True,work,22,20,6.0,WALK_LRF,296880096,1 +2375040773,23750407730,7240977,2693638,2968800960,False,Home,20,22,18.0,WALK_LRF,296880096,1 +2375041769,23750417690,7240981,2693642,2968802210,True,atwork,5,23,11.0,WALK,296880221,1 +2375041773,23750417730,7240981,2693642,2968802210,False,shopping,25,5,11.0,WALK,296880221,1 +2375041774,23750417740,7240981,2693642,2968802210,False,Work,23,25,11.0,WALK,296880221,2 +2375041801,23750418010,7240981,2693642,2968802250,True,atwork,25,23,12.0,WALK,296880225,1 +2375041805,23750418050,7240981,2693642,2968802250,False,shopping,25,25,15.0,WALK,296880225,1 +2375041806,23750418060,7240981,2693642,2968802250,False,Work,23,25,15.0,WALK,296880225,2 +2375041969,23750419690,7240981,2693642,2968802460,True,othdiscr,5,20,21.0,WALK_LOC,296880246,1 +2375041973,23750419730,7240981,2693642,2968802460,False,Home,20,5,21.0,WALK_LOC,296880246,1 +2375042081,23750420810,7240981,2693642,2968802600,True,work,23,20,7.0,WALK_LRF,296880260,1 +2375042085,23750420850,7240981,2693642,2968802600,False,Home,20,23,19.0,WALK_LRF,296880260,1 +2375054281,23750542810,7241019,2693680,2968817850,True,eatout,5,22,12.0,DRIVEALONEFREE,296881785,1 +2375054285,23750542850,7241019,2693680,2968817850,False,Home,22,5,14.0,TNC_SINGLE,296881785,1 +2375054305,23750543050,7241019,2693680,2968817880,True,escort,16,22,15.0,DRIVEALONEFREE,296881788,1 +2375054309,23750543090,7241019,2693680,2968817880,False,eatout,16,16,16.0,DRIVEALONEFREE,296881788,1 +2375054310,23750543100,7241019,2693680,2968817880,False,Home,22,16,16.0,SHARED3FREE,296881788,2 +2375054433,23750544330,7241019,2693680,2968818040,True,othdiscr,9,22,7.0,DRIVEALONEFREE,296881804,1 +2375054437,23750544370,7241019,2693680,2968818040,False,Home,22,9,12.0,TNC_SHARED,296881804,1 +2381109009,23811090090,7259478,2704242,2976386260,True,othmaint,5,7,7.0,WALK,297638626,1 +2381109013,23811090130,7259478,2704242,2976386260,False,Home,7,5,12.0,WALK,297638626,1 +2381109313,23811093130,7259479,2704242,2976386640,True,othdiscr,12,7,13.0,WALK,297638664,1 +2381109317,23811093170,7259479,2704242,2976386640,False,Home,7,12,16.0,WALK,297638664,1 +2381110033,23811100330,7259481,2704243,2976387540,True,shopping,16,7,15.0,TNC_SHARED,297638754,1 +2381110037,23811100370,7259481,2704243,2976387540,False,othmaint,7,16,15.0,WALK_LOC,297638754,1 +2381110038,23811100380,7259481,2704243,2976387540,False,shopping,8,7,15.0,WALK,297638754,2 +2381110039,23811100390,7259481,2704243,2976387540,False,Home,7,8,15.0,WALK_LOC,297638754,3 +2381116265,23811162650,7259500,2704253,2976395330,True,shopping,20,7,8.0,DRIVEALONEFREE,297639533,1 +2381116269,23811162690,7259500,2704253,2976395330,False,shopping,11,20,19.0,SHARED3FREE,297639533,1 +2381116270,23811162700,7259500,2704253,2976395330,False,Home,7,11,19.0,SHARED3FREE,297639533,2 +2381121297,23811212970,7259516,2704261,2976401620,True,eatout,5,7,15.0,WALK,297640162,1 +2381121301,23811213010,7259516,2704261,2976401620,False,Home,7,5,17.0,WALK,297640162,1 +2381121473,23811214730,7259516,2704261,2976401840,True,othmaint,5,7,15.0,BIKE,297640184,1 +2381121477,23811214770,7259516,2704261,2976401840,False,Home,7,5,15.0,BIKE,297640184,1 +2381121537,23811215370,7259516,2704261,2976401920,True,social,5,7,15.0,WALK_LOC,297640192,1 +2381121541,23811215410,7259516,2704261,2976401920,False,social,8,5,15.0,WALK,297640192,1 +2381121542,23811215420,7259516,2704261,2976401920,False,Home,7,8,15.0,WALK_LOC,297640192,2 +2381121841,23811218410,7259517,2704261,2976402300,True,shopping,5,7,12.0,WALK_LOC,297640230,1 +2381121842,23811218420,7259517,2704261,2976402300,True,shopping,11,5,12.0,WALK_LOC,297640230,2 +2381121845,23811218450,7259517,2704261,2976402300,False,Home,7,11,13.0,TNC_SINGLE,297640230,1 +2381153985,23811539850,7259615,2704310,2976442480,True,shopping,16,23,9.0,WALK_LOC,297644248,1 +2381153989,23811539890,7259615,2704310,2976442480,False,Home,23,16,10.0,WALK_LRF,297644248,1 +2381157593,23811575930,7259626,2704316,2976446990,True,shopping,21,24,8.0,SHARED3FREE,297644699,1 +2381157597,23811575970,7259626,2704316,2976446990,False,Home,24,21,11.0,WALK_LOC,297644699,1 +2381167369,23811673690,7259656,2704331,2976459210,True,othdiscr,10,25,14.0,TNC_SINGLE,297645921,1 +2381167373,23811673730,7259656,2704331,2976459210,False,Home,25,10,19.0,WALK_LOC,297645921,1 +2381167433,23811674330,7259656,2704331,2976459290,True,shopping,11,25,10.0,WALK_LOC,297645929,1 +2381167437,23811674370,7259656,2704331,2976459290,False,shopping,7,11,11.0,WALK_LOC,297645929,1 +2381167438,23811674380,7259656,2704331,2976459290,False,Home,25,7,11.0,WALK_LOC,297645929,2 +2381187729,23811877290,7259718,2704362,2976484660,True,othmaint,10,25,12.0,WALK_LOC,297648466,1 +2381187733,23811877330,7259718,2704362,2976484660,False,Home,25,10,16.0,WALK_LOC,297648466,1 +2382306953,23823069530,7263130,2706068,2977883690,True,work,10,20,9.0,DRIVEALONEFREE,297788369,1 +2382306957,23823069570,7263130,2706068,2977883690,False,Home,20,10,17.0,DRIVEALONEFREE,297788369,1 +2382307193,23823071930,7263131,2706068,2977883990,True,othmaint,9,20,12.0,WALK,297788399,1 +2382307197,23823071970,7263131,2706068,2977883990,False,Home,20,9,14.0,WALK,297788399,1 +2382310121,23823101210,7263140,2706073,2977887650,True,othdiscr,19,20,18.0,SHARED3FREE,297788765,1 +2382310125,23823101250,7263140,2706073,2977887650,False,Home,20,19,19.0,DRIVEALONEFREE,297788765,1 +2382310129,23823101290,7263140,2706073,2977887660,True,othdiscr,11,20,21.0,SHARED2FREE,297788766,1 +2382310133,23823101330,7263140,2706073,2977887660,False,Home,20,11,23.0,SHARED3FREE,297788766,1 +2382310145,23823101450,7263140,2706073,2977887680,True,othmaint,5,20,12.0,WALK_LOC,297788768,1 +2382310149,23823101490,7263140,2706073,2977887680,False,Home,20,5,13.0,WALK_LOC,297788768,1 +2382310185,23823101850,7263140,2706073,2977887730,True,shopping,25,20,14.0,SHARED2FREE,297788773,1 +2382310189,23823101890,7263140,2706073,2977887730,False,Home,20,25,18.0,SHARED2FREE,297788773,1 +2382310209,23823102090,7263140,2706073,2977887760,True,social,14,20,8.0,SHARED3FREE,297788776,1 +2382310213,23823102130,7263140,2706073,2977887760,False,Home,20,14,11.0,SHARED2FREE,297788776,1 +2382310513,23823105130,7263141,2706073,2977888140,True,shopping,11,20,8.0,WALK,297788814,1 +2382310517,23823105170,7263141,2706073,2977888140,False,Home,20,11,17.0,WALK,297788814,1 +2389947449,23899474490,7286425,2717715,2987434310,True,eatout,4,3,15.0,WALK,298743431,1 +2389947453,23899474530,7286425,2717715,2987434310,False,Home,3,4,16.0,WALK,298743431,1 +2389951929,23899519290,7286438,2717722,2987439910,True,shopping,16,3,9.0,WALK,298743991,1 +2389951933,23899519330,7286438,2717722,2987439910,False,Home,3,16,18.0,WALK,298743991,1 +2389952025,23899520250,7286439,2717722,2987440030,True,atwork,12,2,11.0,WALK,298744003,1 +2389952029,23899520290,7286439,2717722,2987440030,False,Work,2,12,13.0,WALK,298744003,1 +2389952193,23899521930,7286439,2717722,2987440240,True,eatout,6,3,18.0,WALK,298744024,1 +2389952194,23899521940,7286439,2717722,2987440240,True,othdiscr,2,6,18.0,WALK,298744024,2 +2389952197,23899521970,7286439,2717722,2987440240,False,eatout,12,2,18.0,WALK_LOC,298744024,1 +2389952198,23899521980,7286439,2717722,2987440240,False,Home,3,12,18.0,WALK_LOC,298744024,2 +2389952305,23899523050,7286439,2717722,2987440380,True,work,2,3,6.0,WALK,298744038,1 +2389952309,23899523090,7286439,2717722,2987440380,False,othmaint,2,2,17.0,WALK,298744038,1 +2389952310,23899523100,7286439,2717722,2987440380,False,Home,3,2,17.0,WALK,298744038,2 +2389984121,23899841210,7286536,2717771,2987480150,True,work,10,10,13.0,WALK,298748015,1 +2389984125,23899841250,7286536,2717771,2987480150,False,Home,10,10,20.0,WALK,298748015,1 +2389993193,23899931930,7286564,2717785,2987491490,True,othdiscr,4,20,14.0,WALK,298749149,1 +2389993197,23899931970,7286564,2717785,2987491490,False,Home,20,4,17.0,WALK,298749149,1 +2389993521,23899935210,7286565,2717785,2987491900,True,othdiscr,12,20,20.0,SHARED3FREE,298749190,1 +2389993525,23899935250,7286565,2717785,2987491900,False,Home,20,12,20.0,SHARED3FREE,298749190,1 +2389993585,23899935850,7286565,2717785,2987491980,True,shopping,11,20,18.0,TNC_SINGLE,298749198,1 +2389993589,23899935890,7286565,2717785,2987491980,False,Home,20,11,18.0,TNC_SINGLE,298749198,1 +2389993633,23899936330,7286565,2717785,2987492040,True,work,11,20,7.0,WALK,298749204,1 +2389993637,23899936370,7286565,2717785,2987492040,False,Home,20,11,17.0,WALK,298749204,1 +2390033041,23900330410,7286686,2717846,2987541300,True,atwork,7,2,10.0,WALK,298754130,1 +2390033045,23900330450,7286686,2717846,2987541300,False,Work,2,7,10.0,WALK,298754130,1 +2390033113,23900331130,7286686,2717846,2987541390,True,eatout,2,23,9.0,SHARED3FREE,298754139,1 +2390033117,23900331170,7286686,2717846,2987541390,False,Home,23,2,10.0,SHARED2FREE,298754139,1 +2390033321,23900333210,7286686,2717846,2987541650,True,eatout,24,23,10.0,WALK,298754165,1 +2390033322,23900333220,7286686,2717846,2987541650,True,work,2,24,10.0,WALK,298754165,2 +2390033325,23900333250,7286686,2717846,2987541650,False,work,23,2,23.0,WALK,298754165,1 +2390033326,23900333260,7286686,2717846,2987541650,False,Home,23,23,23.0,WALK,298754165,2 +2390047473,23900474730,7286730,2717868,2987559340,True,atwork,11,14,14.0,WALK,298755934,1 +2390047477,23900474770,7286730,2717868,2987559340,False,Work,14,11,14.0,WALK,298755934,1 +2390047753,23900477530,7286730,2717868,2987559690,True,work,14,25,7.0,WALK_LOC,298755969,1 +2390047757,23900477570,7286730,2717868,2987559690,False,Home,25,14,17.0,WALK,298755969,1 +2396111553,23961115530,7305218,2727112,2995139440,True,othmaint,21,10,17.0,WALK,299513944,1 +2396111554,23961115540,7305218,2727112,2995139440,True,eatout,16,21,17.0,WALK,299513944,2 +2396111557,23961115570,7305218,2727112,2995139440,False,Home,10,16,17.0,WALK,299513944,1 +2396111609,23961116090,7305218,2727112,2995139510,True,eatout,6,10,17.0,SHARED3FREE,299513951,1 +2396111613,23961116130,7305218,2727112,2995139510,False,Home,10,6,18.0,WALK,299513951,1 +2396111817,23961118170,7305218,2727112,2995139770,True,work,11,10,8.0,WALK,299513977,1 +2396111821,23961118210,7305218,2727112,2995139770,False,Home,10,11,15.0,WALK,299513977,1 +2396111825,23961118250,7305218,2727112,2995139780,True,work,11,10,19.0,WALK,299513978,1 +2396111829,23961118290,7305218,2727112,2995139780,False,Home,10,11,22.0,WALK,299513978,1 +2396112145,23961121450,7305219,2727112,2995140180,True,work,4,10,7.0,TAXI,299514018,1 +2396112149,23961121490,7305219,2727112,2995140180,False,Home,10,4,17.0,WALK_HVY,299514018,1 +2396128593,23961285930,7305270,2727138,2995160740,True,atwork,16,16,14.0,TNC_SINGLE,299516074,1 +2396128597,23961285970,7305270,2727138,2995160740,False,Work,16,16,14.0,TNC_SINGLE,299516074,1 +2396128873,23961288730,7305270,2727138,2995161090,True,othmaint,18,10,6.0,SHARED2FREE,299516109,1 +2396128874,23961288740,7305270,2727138,2995161090,True,work,16,18,7.0,DRIVEALONEFREE,299516109,2 +2396128877,23961288770,7305270,2727138,2995161090,False,Home,10,16,19.0,SHARED2FREE,299516109,1 +2396129089,23961290890,7305271,2727138,2995161360,True,othdiscr,10,10,16.0,WALK,299516136,1 +2396129093,23961290930,7305271,2727138,2995161360,False,Home,10,10,17.0,WALK,299516136,1 +2396129113,23961291130,7305271,2727138,2995161390,True,escort,11,10,12.0,WALK,299516139,1 +2396129114,23961291140,7305271,2727138,2995161390,True,othmaint,5,11,13.0,SHARED3FREE,299516139,2 +2396129117,23961291170,7305271,2727138,2995161390,False,Home,10,5,14.0,DRIVEALONEFREE,299516139,1 +2396129153,23961291530,7305271,2727138,2995161440,True,shopping,12,10,8.0,WALK_LOC,299516144,1 +2396129157,23961291570,7305271,2727138,2995161440,False,Home,10,12,11.0,WALK,299516144,1 +2396129161,23961291610,7305271,2727138,2995161450,True,shopping,2,10,12.0,WALK,299516145,1 +2396129165,23961291650,7305271,2727138,2995161450,False,Home,10,2,12.0,WALK,299516145,1 +2396129201,23961292010,7305271,2727138,2995161500,True,work,13,10,19.0,WALK,299516150,1 +2396129205,23961292050,7305271,2727138,2995161500,False,shopping,16,13,19.0,WALK_LOC,299516150,1 +2396129206,23961292060,7305271,2727138,2995161500,False,Home,10,16,19.0,WALK,299516150,2 +2396142369,23961423690,7305312,2727159,2995177960,True,othmaint,8,21,12.0,WALK,299517796,1 +2396142370,23961423700,7305312,2727159,2995177960,True,atwork,11,8,12.0,WALK,299517796,2 +2396142373,23961423730,7305312,2727159,2995177960,False,Work,21,11,13.0,WALK,299517796,1 +2396142649,23961426490,7305312,2727159,2995178310,True,work,21,10,7.0,WALK,299517831,1 +2396142653,23961426530,7305312,2727159,2995178310,False,Home,10,21,17.0,WALK,299517831,1 +2396142977,23961429770,7305313,2727159,2995178720,True,work,6,10,6.0,WALK,299517872,1 +2396142981,23961429810,7305313,2727159,2995178720,False,Home,10,6,17.0,WALK,299517872,1 +2396151177,23961511770,7305338,2727172,2995188970,True,work,2,10,6.0,WALK_LRF,299518897,1 +2396151181,23961511810,7305338,2727172,2995188970,False,Home,10,2,13.0,WALK,299518897,1 +2396151505,23961515050,7305339,2727172,2995189380,True,othmaint,5,10,9.0,WALK,299518938,1 +2396151506,23961515060,7305339,2727172,2995189380,True,work,16,5,10.0,WALK_LOC,299518938,2 +2396151509,23961515090,7305339,2727172,2995189380,False,Home,10,16,23.0,WALK_LOC,299518938,1 +2396175337,23961753370,7305412,2727209,2995219170,True,othdiscr,16,17,9.0,WALK,299521917,1 +2396175341,23961753410,7305412,2727209,2995219170,False,Home,17,16,11.0,WALK_LRF,299521917,1 +2396175777,23961757770,7305413,2727209,2995219720,True,work,14,17,7.0,WALK_LRF,299521972,1 +2396175781,23961757810,7305413,2727209,2995219720,False,Home,17,14,18.0,WALK_LOC,299521972,1 +2396187145,23961871450,7305448,2727227,2995233930,True,othdiscr,15,17,6.0,WALK,299523393,1 +2396187149,23961871490,7305448,2727227,2995233930,False,Home,17,15,13.0,WALK,299523393,1 +2396187321,23961873210,7305449,2727227,2995234150,True,othdiscr,16,17,12.0,WALK,299523415,1 +2396187322,23961873220,7305449,2727227,2995234150,True,eatout,1,16,12.0,WALK,299523415,2 +2396187325,23961873250,7305449,2727227,2995234150,False,Home,17,1,20.0,WALK,299523415,1 +2396187537,23961875370,7305449,2727227,2995234420,True,escort,7,17,9.0,WALK_LOC,299523442,1 +2396187538,23961875380,7305449,2727227,2995234420,True,shopping,6,7,10.0,WALK,299523442,2 +2396187541,23961875410,7305449,2727227,2995234420,False,Home,17,6,10.0,WALK_LOC,299523442,1 +2396191169,23961911690,7305460,2727233,2995238960,True,social,19,20,8.0,WALK,299523896,1 +2396191173,23961911730,7305460,2727233,2995238960,False,Home,20,19,20.0,WALK,299523896,1 +2396191177,23961911770,7305460,2727233,2995238970,True,othmaint,21,20,20.0,DRIVEALONEFREE,299523897,1 +2396191178,23961911780,7305460,2727233,2995238970,True,social,7,21,20.0,SHARED2FREE,299523897,2 +2396191181,23961911810,7305460,2727233,2995238970,False,Home,20,7,20.0,SHARED2FREE,299523897,1 +2396191257,23961912570,7305461,2727233,2995239070,True,eatout,12,20,11.0,WALK_LOC,299523907,1 +2396191261,23961912610,7305461,2727233,2995239070,False,Home,20,12,13.0,TNC_SINGLE,299523907,1 +2396191521,23961915210,7305461,2727233,2995239400,True,work,16,20,7.0,SHARED2FREE,299523940,1 +2396191525,23961915250,7305461,2727233,2995239400,False,Home,20,16,11.0,DRIVEALONEFREE,299523940,1 +2396201033,23962010330,7305490,2727248,2995251290,True,work,2,20,14.0,WALK_LOC,299525129,1 +2396201037,23962010370,7305490,2727248,2995251290,False,Home,20,2,21.0,WALK_LRF,299525129,1 +2396201097,23962010970,7305491,2727248,2995251370,True,eatout,20,20,18.0,WALK,299525137,1 +2396201101,23962011010,7305491,2727248,2995251370,False,Home,20,20,21.0,WALK,299525137,1 +2396201337,23962013370,7305491,2727248,2995251670,True,social,11,20,8.0,WALK_LOC,299525167,1 +2396201341,23962013410,7305491,2727248,2995251670,False,Home,20,11,16.0,WALK_LOC,299525167,1 +2396217321,23962173210,7305540,2727273,2995271650,True,othdiscr,12,20,15.0,WALK,299527165,1 +2396217325,23962173250,7305540,2727273,2995271650,False,Home,20,12,21.0,WALK,299527165,1 +2396217433,23962174330,7305540,2727273,2995271790,True,work,20,20,11.0,WALK,299527179,1 +2396217437,23962174370,7305540,2727273,2995271790,False,Home,20,20,15.0,DRIVEALONEFREE,299527179,1 +2396217761,23962177610,7305541,2727273,2995272200,True,work,9,20,6.0,WALK,299527220,1 +2396217765,23962177650,7305541,2727273,2995272200,False,Home,20,9,12.0,WALK,299527220,1 +2396217769,23962177690,7305541,2727273,2995272210,True,work,9,20,13.0,WALK,299527221,1 +2396217773,23962177730,7305541,2727273,2995272210,False,Home,20,9,18.0,WALK,299527221,1 +2421624921,24216249210,7383002,2748985,3027031150,True,shopping,11,25,8.0,WALK,302703115,1 +2421624925,24216249250,7383002,2748985,3027031150,False,Home,25,11,17.0,WALK,302703115,1 +2440637657,24406376570,7440968,2758713,3050797070,True,shopping,13,10,7.0,TNC_SINGLE,305079707,1 +2440637661,24406376610,7440968,2758713,3050797070,False,Home,10,13,7.0,TNC_SINGLE,305079707,1 +2440637705,24406377050,7440968,2758713,3050797130,True,othdiscr,15,10,17.0,WALK_LRF,305079713,1 +2440637709,24406377090,7440968,2758713,3050797130,False,Home,10,15,21.0,WALK_LRF,305079713,1 +2440637729,24406377290,7440968,2758713,3050797160,True,othmaint,16,10,13.0,TNC_SINGLE,305079716,1 +2440637733,24406377330,7440968,2758713,3050797160,False,othmaint,2,16,15.0,TNC_SINGLE,305079716,1 +2440637734,24406377340,7440968,2758713,3050797160,False,Home,10,2,16.0,TNC_SINGLE,305079716,2 +2440638097,24406380970,7440969,2758713,3050797620,True,shopping,8,10,11.0,WALK_LOC,305079762,1 +2440638098,24406380980,7440969,2758713,3050797620,True,shopping,20,8,11.0,TNC_SINGLE,305079762,2 +2440638101,24406381010,7440969,2758713,3050797620,False,shopping,16,20,13.0,TNC_SINGLE,305079762,1 +2440638102,24406381020,7440969,2758713,3050797620,False,escort,8,16,13.0,TNC_SINGLE,305079762,2 +2440638103,24406381030,7440969,2758713,3050797620,False,Home,10,8,13.0,TNC_SINGLE,305079762,3 +2440638145,24406381450,7440969,2758713,3050797680,True,work,20,10,16.0,WALK,305079768,1 +2440638149,24406381490,7440969,2758713,3050797680,False,Home,10,20,16.0,WALK_LOC,305079768,1 +2440638473,24406384730,7440970,2758713,3050798090,True,work,9,10,7.0,WALK,305079809,1 +2440638477,24406384770,7440970,2758713,3050798090,False,Home,10,9,18.0,WALK,305079809,1 +2444208361,24442083610,7451854,2760519,3055260450,True,univ,12,7,9.0,WALK_LOC,305526045,1 +2444208365,24442083650,7451854,2760519,3055260450,False,escort,10,12,15.0,WALK_LOC,305526045,1 +2444208366,24442083660,7451854,2760519,3055260450,False,Home,7,10,16.0,WALK_LOC,305526045,2 +2444208377,24442083770,7451854,2760519,3055260470,True,shopping,12,7,16.0,WALK_LOC,305526047,1 +2444208381,24442083810,7451854,2760519,3055260470,False,Home,7,12,16.0,WALK_LOC,305526047,1 +2444209033,24442090330,7451856,2760521,3055261290,True,shopping,17,7,10.0,BIKE,305526129,1 +2444209037,24442090370,7451856,2760521,3055261290,False,Home,7,17,20.0,BIKE,305526129,1 +2444209041,24442090410,7451856,2760521,3055261300,True,shopping,5,7,21.0,WALK,305526130,1 +2444209045,24442090450,7451856,2760521,3055261300,False,Home,7,5,22.0,WALK,305526130,1 +2444228649,24442286490,7451916,2760581,3055285810,True,othdiscr,8,7,14.0,WALK_LOC,305528581,1 +2444228653,24442286530,7451916,2760581,3055285810,False,Home,7,8,23.0,WALK,305528581,1 +2444259201,24442592010,7452009,2760674,3055324000,True,school,6,7,7.0,WALK,305532400,1 +2444259205,24442592050,7452009,2760674,3055324000,False,Home,7,6,13.0,WALK,305532400,1 +2444269697,24442696970,7452041,2760706,3055337120,True,escort,10,9,8.0,WALK,305533712,1 +2444269698,24442696980,7452041,2760706,3055337120,True,univ,9,10,8.0,WALK,305533712,2 +2444269701,24442697010,7452041,2760706,3055337120,False,work,10,9,11.0,WALK,305533712,1 +2444269702,24442697020,7452041,2760706,3055337120,False,Home,9,10,15.0,WALK,305533712,2 +2444277521,24442775210,7452065,2760730,3055346900,True,othdiscr,8,9,11.0,WALK,305534690,1 +2444277525,24442775250,7452065,2760730,3055346900,False,Home,9,8,13.0,WALK,305534690,1 +2444291673,24442916730,7452108,2760773,3055364590,True,univ,10,9,8.0,WALK,305536459,1 +2444291677,24442916770,7452108,2760773,3055364590,False,Home,9,10,11.0,WALK_LOC,305536459,1 +2444303497,24443034970,7452144,2760809,3055379370,True,shopping,8,9,15.0,WALK,305537937,1 +2444303498,24443034980,7452144,2760809,3055379370,True,escort,8,8,15.0,WALK,305537937,2 +2444303499,24443034990,7452144,2760809,3055379370,True,eatout,12,8,15.0,WALK,305537937,3 +2444303500,24443035000,7452144,2760809,3055379370,True,shopping,21,12,15.0,WALK,305537937,4 +2444303501,24443035010,7452144,2760809,3055379370,False,shopping,11,21,15.0,WALK,305537937,1 +2444303502,24443035020,7452144,2760809,3055379370,False,Home,9,11,15.0,WALK,305537937,2 +2444305401,24443054010,7452150,2760815,3055381750,True,othdiscr,10,9,12.0,WALK,305538175,1 +2444305405,24443054050,7452150,2760815,3055381750,False,Home,9,10,14.0,WALK,305538175,1 +2444308417,24443084170,7452159,2760824,3055385520,True,shopping,2,9,12.0,WALK_LRF,305538552,1 +2444308421,24443084210,7452159,2760824,3055385520,False,Home,9,2,12.0,WALK_LRF,305538552,1 +2444320161,24443201610,7452195,2760860,3055400200,True,othmaint,10,9,15.0,WALK,305540020,1 +2444320162,24443201620,7452195,2760860,3055400200,True,othdiscr,14,10,16.0,WALK_LRF,305540020,2 +2444320165,24443201650,7452195,2760860,3055400200,False,Home,9,14,16.0,WALK_LRF,305540020,1 +2444329737,24443297370,7452224,2760889,3055412170,True,shopping,16,9,9.0,TNC_SHARED,305541217,1 +2444329741,24443297410,7452224,2760889,3055412170,False,Home,9,16,12.0,WALK_LRF,305541217,1 +2444329745,24443297450,7452224,2760889,3055412180,True,shopping,11,9,13.0,WALK_LOC,305541218,1 +2444329749,24443297490,7452224,2760889,3055412180,False,Home,9,11,13.0,WALK_LOC,305541218,1 +2444349417,24443494170,7452284,2760949,3055436770,True,shopping,11,9,9.0,WALK,305543677,1 +2444349421,24443494210,7452284,2760949,3055436770,False,Home,9,11,11.0,BIKE,305543677,1 +2444362145,24443621450,7452323,2760988,3055452680,True,othdiscr,11,9,8.0,WALK,305545268,1 +2444362149,24443621490,7452323,2760988,3055452680,False,Home,9,11,12.0,WALK,305545268,1 +2444362169,24443621690,7452323,2760988,3055452710,True,othmaint,9,9,12.0,WALK,305545271,1 +2444362173,24443621730,7452323,2760988,3055452710,False,Home,9,9,15.0,WALK,305545271,1 +2444362177,24443621770,7452323,2760988,3055452720,True,othmaint,7,9,19.0,WALK,305545272,1 +2444362181,24443621810,7452323,2760988,3055452720,False,Home,9,7,21.0,WALK,305545272,1 +2444385761,24443857610,7452395,2761060,3055482200,True,othdiscr,9,9,7.0,WALK,305548220,1 +2444385765,24443857650,7452395,2761060,3055482200,False,Home,9,9,19.0,WALK,305548220,1 +2444431529,24444315290,7452535,2761200,3055539410,True,eatout,2,9,17.0,WALK,305553941,1 +2444431533,24444315330,7452535,2761200,3055539410,False,Home,9,2,18.0,WALK,305553941,1 +2444431681,24444316810,7452535,2761200,3055539600,True,othdiscr,7,9,9.0,WALK,305553960,1 +2444431685,24444316850,7452535,2761200,3055539600,False,Home,9,7,11.0,WALK,305553960,1 +2444431745,24444317450,7452535,2761200,3055539680,True,social,2,9,14.0,SHARED2FREE,305553968,1 +2444431746,24444317460,7452535,2761200,3055539680,True,shopping,17,2,15.0,WALK,305553968,2 +2444431749,24444317490,7452535,2761200,3055539680,False,Home,9,17,16.0,DRIVEALONEFREE,305553968,1 +2444431753,24444317530,7452535,2761200,3055539690,True,shopping,15,9,18.0,DRIVEALONEFREE,305553969,1 +2444431757,24444317570,7452535,2761200,3055539690,False,Home,9,15,18.0,TNC_SINGLE,305553969,1 +2444447841,24444478410,7452584,2761249,3055559800,True,social,11,9,17.0,WALK_LOC,305555980,1 +2444447845,24444478450,7452584,2761249,3055559800,False,Home,9,11,19.0,WALK_LOC,305555980,1 +2444453681,24444536810,7452602,2761267,3055567100,True,othmaint,22,9,9.0,WALK_HVY,305556710,1 +2444453685,24444536850,7452602,2761267,3055567100,False,Home,9,22,15.0,WALK_LRF,305556710,1 +2444469817,24444698170,7452651,2761316,3055587270,True,social,11,10,11.0,WALK,305558727,1 +2444469821,24444698210,7452651,2761316,3055587270,False,Home,10,11,21.0,WALK,305558727,1 +2444477601,24444776010,7452675,2761340,3055597000,True,othdiscr,9,10,16.0,WALK,305559700,1 +2444477605,24444776050,7452675,2761340,3055597000,False,Home,10,9,21.0,WALK,305559700,1 +2444477625,24444776250,7452675,2761340,3055597030,True,othmaint,7,10,8.0,BIKE,305559703,1 +2444477629,24444776290,7452675,2761340,3055597030,False,Home,10,7,12.0,BIKE,305559703,1 +2444481601,24444816010,7452687,2761352,3055602000,True,shopping,5,10,12.0,WALK_LOC,305560200,1 +2444481605,24444816050,7452687,2761352,3055602000,False,shopping,7,5,12.0,WALK_LOC,305560200,1 +2444481606,24444816060,7452687,2761352,3055602000,False,Home,10,7,12.0,WALK_LOC,305560200,2 +2444490897,24444908970,7452716,2761381,3055613620,True,eatout,9,10,6.0,WALK,305561362,1 +2444490901,24444909010,7452716,2761381,3055613620,False,Home,10,9,18.0,WALK,305561362,1 +2444494329,24444943290,7452726,2761391,3055617910,True,othdiscr,12,10,9.0,WALK,305561791,1 +2444494333,24444943330,7452726,2761391,3055617910,False,Home,10,12,12.0,WALK,305561791,1 +2444498113,24444981130,7452738,2761403,3055622640,True,eatout,5,11,16.0,WALK,305562264,1 +2444498117,24444981170,7452738,2761403,3055622640,False,Home,11,5,20.0,WALK,305562264,1 +2444498329,24444983290,7452738,2761403,3055622910,True,shopping,12,11,7.0,WALK,305562291,1 +2444498333,24444983330,7452738,2761403,3055622910,False,Home,11,12,15.0,WALK,305562291,1 +2444519961,24445199610,7452804,2761469,3055649950,True,school,17,11,8.0,WALK_LRF,305564995,1 +2444519965,24445199650,7452804,2761469,3055649950,False,Home,11,17,15.0,WALK_LRF,305564995,1 +2444540097,24445400970,7452866,2761531,3055675120,True,eatout,21,11,9.0,WALK,305567512,1 +2444540101,24445401010,7452866,2761531,3055675120,False,shopping,12,21,13.0,WALK,305567512,1 +2444540102,24445401020,7452866,2761531,3055675120,False,Home,11,12,13.0,WALK_LOC,305567512,2 +2444545825,24445458250,7452883,2761548,3055682280,True,othdiscr,16,13,7.0,WALK,305568228,1 +2444545829,24445458290,7452883,2761548,3055682280,False,Home,13,16,17.0,WALK,305568228,1 +2444551425,24445514250,7452900,2761565,3055689280,True,othmaint,2,15,12.0,WALK,305568928,1 +2444551429,24445514290,7452900,2761565,3055689280,False,Home,15,2,13.0,WALK,305568928,1 +2444594369,24445943690,7453031,2761696,3055742960,True,social,16,17,15.0,WALK,305574296,1 +2444594370,24445943700,7453031,2761696,3055742960,True,othdiscr,9,16,16.0,WALK_LRF,305574296,2 +2444594373,24445943730,7453031,2761696,3055742960,False,Home,17,9,17.0,WALK_LRF,305574296,1 +2444595201,24445952010,7453034,2761699,3055744000,True,eatout,5,17,14.0,SHARED3FREE,305574400,1 +2444595205,24445952050,7453034,2761699,3055744000,False,Home,17,5,17.0,SHARED3FREE,305574400,1 +2444595417,24445954170,7453034,2761699,3055744270,True,shopping,16,17,9.0,WALK,305574427,1 +2444595421,24445954210,7453034,2761699,3055744270,False,Home,17,16,13.0,WALK,305574427,1 +2444597521,24445975210,7453041,2761706,3055746900,True,escort,2,17,7.0,DRIVEALONEFREE,305574690,1 +2444597525,24445975250,7453041,2761706,3055746900,False,escort,4,2,8.0,WALK,305574690,1 +2444597526,24445975260,7453041,2761706,3055746900,False,Home,17,4,9.0,SHARED3FREE,305574690,2 +2444601761,24446017610,7453054,2761719,3055752200,True,eatout,1,17,14.0,WALK,305575220,1 +2444601765,24446017650,7453054,2761719,3055752200,False,Home,17,1,18.0,WALK,305575220,1 +2444617393,24446173930,7453101,2761766,3055771740,True,shopping,11,17,13.0,WALK,305577174,1 +2444617397,24446173970,7453101,2761766,3055771740,False,Home,17,11,15.0,WALK,305577174,1 +2444620281,24446202810,7453110,2761775,3055775350,True,othdiscr,16,17,7.0,WALK,305577535,1 +2444620285,24446202850,7453110,2761775,3055775350,False,Home,17,16,8.0,WALK,305577535,1 +2444620345,24446203450,7453110,2761775,3055775430,True,shopping,17,17,12.0,WALK,305577543,1 +2444620349,24446203490,7453110,2761775,3055775430,False,Home,17,17,21.0,WALK,305577543,1 +2444656097,24446560970,7453219,2761884,3055820120,True,shopping,2,17,11.0,BIKE,305582012,1 +2444656101,24446561010,7453219,2761884,3055820120,False,Home,17,2,16.0,BIKE,305582012,1 +2444660017,24446600170,7453231,2761896,3055825020,True,univ,12,17,15.0,WALK_LOC,305582502,1 +2444660021,24446600210,7453231,2761896,3055825020,False,Home,17,12,17.0,WALK_LRF,305582502,1 +2444698873,24446988730,7453350,2762015,3055873590,True,escort,13,20,8.0,DRIVEALONEFREE,305587359,1 +2444698877,24446988770,7453350,2762015,3055873590,False,Home,20,13,8.0,SHARED3FREE,305587359,1 +2444698881,24446988810,7453350,2762015,3055873600,True,escort,12,20,14.0,WALK,305587360,1 +2444698885,24446988850,7453350,2762015,3055873600,False,Home,20,12,17.0,WALK,305587360,1 +2444709673,24447096730,7453383,2762048,3055887090,True,eatout,9,20,18.0,WALK,305588709,1 +2444709677,24447096770,7453383,2762048,3055887090,False,Home,20,9,23.0,WALK,305588709,1 +2444709681,24447096810,7453383,2762048,3055887100,True,eatout,5,20,23.0,WALK,305588710,1 +2444709685,24447096850,7453383,2762048,3055887100,False,Home,20,5,23.0,WALK,305588710,1 +2444709825,24447098250,7453383,2762048,3055887280,True,othdiscr,7,20,10.0,WALK,305588728,1 +2444709829,24447098290,7453383,2762048,3055887280,False,othdiscr,10,7,17.0,WALK,305588728,1 +2444709830,24447098300,7453383,2762048,3055887280,False,Home,20,10,17.0,WALK,305588728,2 +2444719729,24447197290,7453413,2762078,3055899660,True,shopping,19,20,16.0,WALK,305589966,1 +2444719733,24447197330,7453413,2762078,3055899660,False,Home,20,19,16.0,WALK,305589966,1 +2444720825,24447208250,7453417,2762082,3055901030,True,eatout,7,20,9.0,WALK,305590103,1 +2444720829,24447208290,7453417,2762082,3055901030,False,Home,20,7,13.0,WALK,305590103,1 +2444723121,24447231210,7453424,2762089,3055903900,True,eatout,9,20,10.0,WALK,305590390,1 +2444723125,24447231250,7453424,2762089,3055903900,False,Home,20,9,13.0,WALK,305590390,1 +2444728257,24447282570,7453439,2762104,3055910320,True,shopping,5,20,13.0,WALK_LOC,305591032,1 +2444728261,24447282610,7453439,2762104,3055910320,False,Home,20,5,20.0,WALK_LOC,305591032,1 +2444759073,24447590730,7453533,2762198,3055948840,True,univ,12,21,7.0,WALK_LOC,305594884,1 +2444759077,24447590770,7453533,2762198,3055948840,False,shopping,14,12,13.0,WALK_LOC,305594884,1 +2444759078,24447590780,7453533,2762198,3055948840,False,othmaint,5,14,13.0,WALK,305594884,2 +2444759079,24447590790,7453533,2762198,3055948840,False,escort,5,5,13.0,WALK,305594884,3 +2444759080,24447590800,7453533,2762198,3055948840,False,Home,21,5,14.0,WALK,305594884,4 +2444766961,24447669610,7453557,2762222,3055958700,True,shopping,13,21,17.0,WALK_LOC,305595870,1 +2444766965,24447669650,7453557,2762222,3055958700,False,othmaint,7,13,17.0,WALK_LOC,305595870,1 +2444766966,24447669660,7453557,2762222,3055958700,False,Home,21,7,17.0,WALK_LOC,305595870,2 +2444792217,24447922170,7453634,2762299,3055990270,True,othmaint,12,21,7.0,WALK_LOC,305599027,1 +2444792218,24447922180,7453634,2762299,3055990270,True,shopping,5,12,7.0,WALK,305599027,2 +2444792221,24447922210,7453634,2762299,3055990270,False,Home,21,5,8.0,WALK_LOC,305599027,1 +2463513849,24635138490,7510712,2819377,3079392310,True,work,6,2,6.0,WALK,307939231,1 +2463513853,24635138530,7510712,2819377,3079392310,False,Home,2,6,18.0,WALK,307939231,1 +2463518113,24635181130,7510725,2819390,3079397640,True,work,4,6,14.0,WALK,307939764,1 +2463518117,24635181170,7510725,2819390,3079397640,False,Home,6,4,18.0,WALK,307939764,1 +2463531841,24635318410,7510767,2819432,3079414800,True,shopping,16,6,16.0,SHARED2FREE,307941480,1 +2463531845,24635318450,7510767,2819432,3079414800,False,escort,9,16,17.0,DRIVEALONEFREE,307941480,1 +2463531846,24635318460,7510767,2819432,3079414800,False,Home,6,9,17.0,WALK,307941480,2 +2463531889,24635318890,7510767,2819432,3079414860,True,work,4,6,7.0,DRIVE_LOC,307941486,1 +2463531893,24635318930,7510767,2819432,3079414860,False,Home,6,4,16.0,DRIVE_LOC,307941486,1 +2463535825,24635358250,7510779,2819444,3079419780,True,work,5,6,14.0,WALK,307941978,1 +2463535829,24635358290,7510779,2819444,3079419780,False,Home,6,5,22.0,WALK,307941978,1 +2463546977,24635469770,7510813,2819478,3079433720,True,work,17,6,9.0,WALK_LRF,307943372,1 +2463546981,24635469810,7510813,2819478,3079433720,False,Home,6,17,16.0,WALK_LOC,307943372,1 +2463551153,24635511530,7510826,2819491,3079438940,True,othmaint,4,6,6.0,BIKE,307943894,1 +2463551157,24635511570,7510826,2819491,3079438940,False,Home,6,4,13.0,BIKE,307943894,1 +2463551241,24635512410,7510826,2819491,3079439050,True,work,4,6,14.0,WALK,307943905,1 +2463551245,24635512450,7510826,2819491,3079439050,False,Home,6,4,19.0,WALK,307943905,1 +2463566657,24635666570,7510873,2819538,3079458320,True,work,13,6,7.0,WALK,307945832,1 +2463566661,24635666610,7510873,2819538,3079458320,False,Home,6,13,18.0,WALK,307945832,1 +2463597817,24635978170,7510968,2819633,3079497270,True,shopping,11,6,8.0,WALK_LOC,307949727,1 +2463597818,24635978180,7510968,2819633,3079497270,True,work,9,11,8.0,WALK,307949727,2 +2463597821,24635978210,7510968,2819633,3079497270,False,Home,6,9,19.0,WALK,307949727,1 +2463603393,24636033930,7510985,2819650,3079504240,True,work,9,6,13.0,WALK,307950424,1 +2463603397,24636033970,7510985,2819650,3079504240,False,shopping,5,9,18.0,WALK,307950424,1 +2463603398,24636033980,7510985,2819650,3079504240,False,escort,7,5,19.0,WALK,307950424,2 +2463603399,24636033990,7510985,2819650,3079504240,False,shopping,6,7,19.0,WALK,307950424,3 +2463603400,24636034000,7510985,2819650,3079504240,False,Home,6,6,19.0,WALK,307950424,4 +2463605033,24636050330,7510990,2819655,3079506290,True,work,12,6,8.0,WALK,307950629,1 +2463605037,24636050370,7510990,2819655,3079506290,False,Home,6,12,17.0,WALK,307950629,1 +2463616841,24636168410,7511026,2819691,3079521050,True,work,4,6,7.0,WALK,307952105,1 +2463616845,24636168450,7511026,2819691,3079521050,False,Home,6,4,17.0,WALK,307952105,1 +2463638489,24636384890,7511092,2819757,3079548110,True,work,4,6,7.0,TNC_SINGLE,307954811,1 +2463638493,24636384930,7511092,2819757,3079548110,False,Home,6,4,14.0,TNC_SINGLE,307954811,1 +2463641113,24636411130,7511100,2819765,3079551390,True,work,5,6,7.0,WALK_LOC,307955139,1 +2463641117,24636411170,7511100,2819765,3079551390,False,Home,6,5,16.0,WALK,307955139,1 +2463642313,24636423130,7511104,2819769,3079552890,True,othdiscr,6,6,5.0,WALK,307955289,1 +2463642317,24636423170,7511104,2819769,3079552890,False,Home,6,6,5.0,WALK,307955289,1 +2463642425,24636424250,7511104,2819769,3079553030,True,work,3,6,8.0,WALK,307955303,1 +2463642429,24636424290,7511104,2819769,3079553030,False,escort,25,3,16.0,WALK,307955303,1 +2463642430,24636424300,7511104,2819769,3079553030,False,shopping,5,25,17.0,WALK,307955303,2 +2463642431,24636424310,7511104,2819769,3079553030,False,othmaint,7,5,17.0,WALK,307955303,3 +2463642432,24636424320,7511104,2819769,3079553030,False,Home,6,7,18.0,WALK,307955303,4 +2463650345,24636503450,7511129,2819794,3079562930,True,atwork,15,2,13.0,WALK,307956293,1 +2463650349,24636503490,7511129,2819794,3079562930,False,Work,2,15,14.0,WALK,307956293,1 +2463650513,24636505130,7511129,2819794,3079563140,True,othdiscr,21,6,18.0,WALK,307956314,1 +2463650517,24636505170,7511129,2819794,3079563140,False,Home,6,21,21.0,WALK,307956314,1 +2463650625,24636506250,7511129,2819794,3079563280,True,work,2,6,7.0,WALK,307956328,1 +2463650629,24636506290,7511129,2819794,3079563280,False,Home,6,2,17.0,WALK,307956328,1 +2463663417,24636634170,7511168,2819833,3079579270,True,work,12,6,7.0,WALK_LOC,307957927,1 +2463663421,24636634210,7511168,2819833,3079579270,False,Home,6,12,17.0,WALK_LOC,307957927,1 +2463671289,24636712890,7511192,2819857,3079589110,True,work,9,6,9.0,TNC_SINGLE,307958911,1 +2463671290,24636712900,7511192,2819857,3079589110,True,work,7,9,9.0,WALK_LOC,307958911,2 +2463671293,24636712930,7511192,2819857,3079589110,False,escort,10,7,17.0,TNC_SHARED,307958911,1 +2463671294,24636712940,7511192,2819857,3079589110,False,othmaint,5,10,17.0,TNC_SHARED,307958911,2 +2463671295,24636712950,7511192,2819857,3079589110,False,Home,6,5,18.0,WALK,307958911,3 +2463688721,24636887210,7511246,2819911,3079610900,True,atwork,4,5,11.0,WALK,307961090,1 +2463688725,24636887250,7511246,2819911,3079610900,False,othmaint,16,4,11.0,WALK,307961090,1 +2463688726,24636887260,7511246,2819911,3079610900,False,Work,5,16,11.0,WALK,307961090,2 +2463689001,24636890010,7511246,2819911,3079611250,True,work,5,6,6.0,WALK,307961125,1 +2463689005,24636890050,7511246,2819911,3079611250,False,Home,6,5,17.0,WALK,307961125,1 +2463705073,24637050730,7511295,2819960,3079631340,True,work,5,6,6.0,WALK,307963134,1 +2463705077,24637050770,7511295,2819960,3079631340,False,Home,6,5,11.0,WALK,307963134,1 +2463723113,24637231130,7511350,2820015,3079653890,True,work,16,6,6.0,WALK,307965389,1 +2463723117,24637231170,7511350,2820015,3079653890,False,Home,6,16,16.0,WALK,307965389,1 +2463724009,24637240090,7511353,2820018,3079655010,True,othmaint,5,6,8.0,WALK,307965501,1 +2463724013,24637240130,7511353,2820018,3079655010,False,Home,6,5,10.0,WALK,307965501,1 +2463724073,24637240730,7511353,2820018,3079655090,True,social,5,6,13.0,WALK,307965509,1 +2463724077,24637240770,7511353,2820018,3079655090,False,Home,6,5,18.0,WALK,307965509,1 +2463737873,24637378730,7511395,2820060,3079672340,True,eatout,11,7,8.0,TNC_SINGLE,307967234,1 +2463737874,24637378740,7511395,2820060,3079672340,True,work,8,11,8.0,WALK,307967234,2 +2463737877,24637378770,7511395,2820060,3079672340,False,othmaint,11,8,16.0,WALK,307967234,1 +2463737878,24637378780,7511395,2820060,3079672340,False,Home,7,11,17.0,TNC_SINGLE,307967234,2 +2463751977,24637519770,7511438,2820103,3079689970,True,work,7,8,13.0,WALK,307968997,1 +2463751981,24637519810,7511438,2820103,3079689970,False,Home,8,7,18.0,WALK,307968997,1 +2463755913,24637559130,7511450,2820115,3079694890,True,work,9,8,6.0,WALK,307969489,1 +2463755917,24637559170,7511450,2820115,3079694890,False,Home,8,9,16.0,WALK_LOC,307969489,1 +2463764113,24637641130,7511475,2820140,3079705140,True,work,5,8,9.0,WALK,307970514,1 +2463764117,24637641170,7511475,2820140,3079705140,False,escort,8,5,9.0,WALK,307970514,1 +2463764118,24637641180,7511475,2820140,3079705140,False,work,5,8,19.0,WALK,307970514,2 +2463764119,24637641190,7511475,2820140,3079705140,False,escort,6,5,19.0,WALK,307970514,3 +2463764120,24637641200,7511475,2820140,3079705140,False,Home,8,6,19.0,WALK,307970514,4 +2463766409,24637664090,7511482,2820147,3079708010,True,work,2,8,8.0,WALK,307970801,1 +2463766410,24637664100,7511482,2820147,3079708010,True,work,5,2,8.0,WALK,307970801,2 +2463766413,24637664130,7511482,2820147,3079708010,False,escort,11,5,16.0,WALK_LOC,307970801,1 +2463766414,24637664140,7511482,2820147,3079708010,False,Home,8,11,17.0,WALK,307970801,2 +2463767721,24637677210,7511486,2820151,3079709650,True,work,23,8,7.0,WALK_LRF,307970965,1 +2463767725,24637677250,7511486,2820151,3079709650,False,Home,8,23,17.0,WALK_LRF,307970965,1 +2463772313,24637723130,7511500,2820165,3079715390,True,work,6,8,9.0,WALK,307971539,1 +2463772317,24637723170,7511500,2820165,3079715390,False,Home,8,6,19.0,WALK,307971539,1 +2463786417,24637864170,7511543,2820208,3079733020,True,work,25,8,7.0,WALK,307973302,1 +2463786421,24637864210,7511543,2820208,3079733020,False,Home,8,25,13.0,WALK,307973302,1 +2463787729,24637877290,7511547,2820212,3079734660,True,work,6,8,7.0,WALK,307973466,1 +2463787733,24637877330,7511547,2820212,3079734660,False,Home,8,6,15.0,WALK,307973466,1 +2463794617,24637946170,7511568,2820233,3079743270,True,work,17,8,7.0,WALK_LRF,307974327,1 +2463794621,24637946210,7511568,2820233,3079743270,False,Home,8,17,21.0,WALK_LRF,307974327,1 +2463796913,24637969130,7511575,2820240,3079746140,True,work,17,8,7.0,WALK_LRF,307974614,1 +2463796917,24637969170,7511575,2820240,3079746140,False,Home,8,17,18.0,WALK_LRF,307974614,1 +2463808393,24638083930,7511610,2820275,3079760490,True,work,1,8,9.0,WALK_LRF,307976049,1 +2463808397,24638083970,7511610,2820275,3079760490,False,Home,8,1,18.0,WALK,307976049,1 +2463817793,24638177930,7511639,2820304,3079772240,True,eatout,7,8,7.0,TNC_SHARED,307977224,1 +2463817794,24638177940,7511639,2820304,3079772240,True,othdiscr,10,7,7.0,WALK_LOC,307977224,2 +2463817797,24638177970,7511639,2820304,3079772240,False,Home,8,10,8.0,TNC_SHARED,307977224,1 +2463817905,24638179050,7511639,2820304,3079772380,True,work,9,8,8.0,WALK,307977238,1 +2463817906,24638179060,7511639,2820304,3079772380,True,work,9,9,9.0,WALK,307977238,2 +2463817909,24638179090,7511639,2820304,3079772380,False,Home,8,9,16.0,WALK,307977238,1 +2463823153,24638231530,7511655,2820320,3079778940,True,work,16,8,8.0,WALK,307977894,1 +2463823157,24638231570,7511655,2820320,3079778940,False,Home,8,16,17.0,WALK,307977894,1 +2463834305,24638343050,7511689,2820354,3079792880,True,work,18,8,8.0,WALK,307979288,1 +2463834309,24638343090,7511689,2820354,3079792880,False,eatout,8,18,16.0,WALK,307979288,1 +2463834310,24638343100,7511689,2820354,3079792880,False,Home,8,8,18.0,WALK,307979288,2 +2463856281,24638562810,7511756,2820421,3079820350,True,work,7,8,13.0,WALK,307982035,1 +2463856285,24638562850,7511756,2820421,3079820350,False,Home,8,7,23.0,WALK,307982035,1 +2463865529,24638655290,7511785,2820450,3079831910,True,eatout,1,8,18.0,WALK_LRF,307983191,1 +2463865533,24638655330,7511785,2820450,3079831910,False,Home,8,1,18.0,WALK_LRF,307983191,1 +2463865681,24638656810,7511785,2820450,3079832100,True,othdiscr,11,8,18.0,WALK,307983210,1 +2463865685,24638656850,7511785,2820450,3079832100,False,Home,8,11,23.0,WALK,307983210,1 +2463865793,24638657930,7511785,2820450,3079832240,True,social,7,8,8.0,WALK,307983224,1 +2463865794,24638657940,7511785,2820450,3079832240,True,work,5,7,8.0,WALK,307983224,2 +2463865797,24638657970,7511785,2820450,3079832240,False,othmaint,7,5,11.0,WALK,307983224,1 +2463865798,24638657980,7511785,2820450,3079832240,False,Home,8,7,15.0,WALK,307983224,2 +2463881865,24638818650,7511834,2820499,3079852330,True,escort,2,8,8.0,WALK,307985233,1 +2463881866,24638818660,7511834,2820499,3079852330,True,work,2,2,8.0,WALK,307985233,2 +2463881869,24638818690,7511834,2820499,3079852330,False,Home,8,2,22.0,WALK_LRF,307985233,1 +2463886521,24638865210,7511849,2820514,3079858150,True,eatout,7,8,12.0,WALK,307985815,1 +2463886525,24638865250,7511849,2820514,3079858150,False,Home,8,7,15.0,WALK,307985815,1 +2463886697,24638866970,7511849,2820514,3079858370,True,othmaint,4,8,17.0,WALK,307985837,1 +2463886701,24638867010,7511849,2820514,3079858370,False,Home,8,4,18.0,WALK,307985837,1 +2463886761,24638867610,7511849,2820514,3079858450,True,social,6,8,18.0,SHARED2FREE,307985845,1 +2463886765,24638867650,7511849,2820514,3079858450,False,social,7,6,18.0,WALK,307985845,1 +2463886766,24638867660,7511849,2820514,3079858450,False,social,8,7,18.0,WALK,307985845,2 +2463886767,24638867670,7511849,2820514,3079858450,False,Home,8,8,18.0,WALK,307985845,3 +2463894873,24638948730,7511874,2820539,3079868590,True,othdiscr,12,8,15.0,WALK_LOC,307986859,1 +2463894877,24638948770,7511874,2820539,3079868590,False,Home,8,12,20.0,WALK_LOC,307986859,1 +2463920329,24639203290,7511952,2820617,3079900410,True,escort,22,8,11.0,WALK,307990041,1 +2463920333,24639203330,7511952,2820617,3079900410,False,Home,8,22,12.0,WALK,307990041,1 +2463920569,24639205690,7511952,2820617,3079900710,True,work,4,8,12.0,WALK_LOC,307990071,1 +2463920573,24639205730,7511952,2820617,3079900710,False,Home,8,4,21.0,WALK_LRF,307990071,1 +2463932905,24639329050,7511990,2820655,3079916130,True,atwork,7,7,10.0,WALK,307991613,1 +2463932909,24639329090,7511990,2820655,3079916130,False,Work,7,7,10.0,WALK,307991613,1 +2463933033,24639330330,7511990,2820655,3079916290,True,escort,3,8,7.0,WALK,307991629,1 +2463933034,24639330340,7511990,2820655,3079916290,True,escort,11,3,8.0,WALK_LOC,307991629,2 +2463933035,24639330350,7511990,2820655,3079916290,True,shopping,8,11,8.0,WALK,307991629,3 +2463933036,24639330360,7511990,2820655,3079916290,True,work,7,8,8.0,WALK,307991629,4 +2463933037,24639330370,7511990,2820655,3079916290,False,shopping,8,7,18.0,WALK,307991629,1 +2463933038,24639330380,7511990,2820655,3079916290,False,Home,8,8,18.0,WALK,307991629,2 +2463940249,24639402490,7512012,2820677,3079925310,True,work,5,8,5.0,WALK,307992531,1 +2463940253,24639402530,7512012,2820677,3079925310,False,Home,8,5,23.0,WALK,307992531,1 +2463949761,24639497610,7512041,2820706,3079937200,True,work,9,8,8.0,WALK,307993720,1 +2463949765,24639497650,7512041,2820706,3079937200,False,Home,8,9,18.0,WALK,307993720,1 +2463950417,24639504170,7512043,2820708,3079938020,True,work,2,8,8.0,WALK,307993802,1 +2463950421,24639504210,7512043,2820708,3079938020,False,Home,8,2,17.0,WALK,307993802,1 +2463956977,24639569770,7512063,2820728,3079946220,True,work,15,8,7.0,TNC_SINGLE,307994622,1 +2463956978,24639569780,7512063,2820728,3079946220,True,work,5,15,8.0,WALK_LOC,307994622,2 +2463956981,24639569810,7512063,2820728,3079946220,False,Home,8,5,18.0,WALK_LOC,307994622,1 +2463960537,24639605370,7512074,2820739,3079950670,True,shopping,13,8,18.0,WALK_LOC,307995067,1 +2463960541,24639605410,7512074,2820739,3079950670,False,Home,8,13,21.0,WALK_LRF,307995067,1 +2463960585,24639605850,7512074,2820739,3079950730,True,work,9,8,7.0,WALK,307995073,1 +2463960589,24639605890,7512074,2820739,3079950730,False,Home,8,9,17.0,WALK_LOC,307995073,1 +2463971785,24639717850,7512109,2820774,3079964730,True,atwork,8,2,10.0,WALK,307996473,1 +2463971789,24639717890,7512109,2820774,3079964730,False,work,6,8,10.0,WALK,307996473,1 +2463971790,24639717900,7512109,2820774,3079964730,False,Work,2,6,10.0,WALK,307996473,2 +2463972065,24639720650,7512109,2820774,3079965080,True,work,2,8,8.0,WALK,307996508,1 +2463972069,24639720690,7512109,2820774,3079965080,False,shopping,5,2,18.0,WALK,307996508,1 +2463972070,24639720700,7512109,2820774,3079965080,False,shopping,8,5,19.0,WALK,307996508,2 +2463972071,24639720710,7512109,2820774,3079965080,False,Home,8,8,19.0,WALK,307996508,3 +2463987761,24639877610,7512157,2820822,3079984700,True,shopping,16,8,8.0,WALK_LOC,307998470,1 +2463987765,24639877650,7512157,2820822,3079984700,False,Home,8,16,13.0,WALK,307998470,1 +2463987785,24639877850,7512157,2820822,3079984730,True,social,22,8,17.0,WALK_LRF,307998473,1 +2463987789,24639877890,7512157,2820822,3079984730,False,eatout,13,22,21.0,WALK_LRF,307998473,1 +2463987790,24639877900,7512157,2820822,3079984730,False,Home,8,13,21.0,WALK_LRF,307998473,2 +2463994697,24639946970,7512178,2820843,3079993370,True,work,7,8,7.0,WALK,307999337,1 +2463994701,24639947010,7512178,2820843,3079993370,False,Home,8,7,20.0,WALK,307999337,1 +2464020937,24640209370,7512258,2820923,3080026170,True,work,18,8,13.0,SHARED3FREE,308002617,1 +2464020941,24640209410,7512258,2820923,3080026170,False,Home,8,18,20.0,WALK_LOC,308002617,1 +2464041929,24640419290,7512322,2820987,3080052410,True,work,23,8,7.0,WALK_LOC,308005241,1 +2464041933,24640419330,7512322,2820987,3080052410,False,Home,8,23,15.0,TNC_SINGLE,308005241,1 +2464042585,24640425850,7512324,2820989,3080053230,True,work,9,8,14.0,WALK_LOC,308005323,1 +2464042586,24640425860,7512324,2820989,3080053230,True,work,22,9,15.0,WALK_LRF,308005323,2 +2464042589,24640425890,7512324,2820989,3080053230,False,work,22,22,21.0,WALK,308005323,1 +2464042590,24640425900,7512324,2820989,3080053230,False,Home,8,22,21.0,WALK_LRF,308005323,2 +2464064609,24640646090,7512392,2821057,3080080760,True,atwork,13,1,10.0,WALK,308008076,1 +2464064613,24640646130,7512392,2821057,3080080760,False,Work,1,13,10.0,WALK,308008076,1 +2464064889,24640648890,7512392,2821057,3080081110,True,work,1,8,6.0,WALK,308008111,1 +2464064893,24640648930,7512392,2821057,3080081110,False,Home,8,1,18.0,WALK,308008111,1 +2464097641,24640976410,7512492,2821157,3080122050,True,shopping,20,8,12.0,WALK_LOC,308012205,1 +2464097645,24640976450,7512492,2821157,3080122050,False,Home,8,20,13.0,WALK_LOC,308012205,1 +2464101953,24641019530,7512505,2821170,3080127440,True,work,4,8,20.0,WALK,308012744,1 +2464101957,24641019570,7512505,2821170,3080127440,False,Home,8,4,23.0,WALK,308012744,1 +2464104641,24641046410,7512514,2821179,3080130800,True,eatout,13,8,8.0,WALK,308013080,1 +2464104645,24641046450,7512514,2821179,3080130800,False,Home,8,13,16.0,WALK,308013080,1 +2464104857,24641048570,7512514,2821179,3080131070,True,shopping,6,8,17.0,WALK,308013107,1 +2464104861,24641048610,7512514,2821179,3080131070,False,Home,8,6,19.0,WALK,308013107,1 +2464104881,24641048810,7512514,2821179,3080131100,True,social,9,8,16.0,WALK,308013110,1 +2464104885,24641048850,7512514,2821179,3080131100,False,Home,8,9,16.0,WALK_LOC,308013110,1 +2464119665,24641196650,7512559,2821224,3080149580,True,work,13,8,7.0,WALK_LOC,308014958,1 +2464119669,24641196690,7512559,2821224,3080149580,False,Home,8,13,19.0,WALK,308014958,1 +2464131801,24641318010,7512596,2821261,3080164750,True,work,9,8,7.0,WALK,308016475,1 +2464131805,24641318050,7512596,2821261,3080164750,False,Home,8,9,18.0,WALK,308016475,1 +2464166569,24641665690,7512702,2821367,3080208210,True,work,12,8,7.0,WALK_LOC,308020821,1 +2464166573,24641665730,7512702,2821367,3080208210,False,Home,8,12,17.0,WALK_LOC,308020821,1 +2464170833,24641708330,7512715,2821380,3080213540,True,work,4,8,11.0,WALK,308021354,1 +2464170837,24641708370,7512715,2821380,3080213540,False,Home,8,4,18.0,WALK,308021354,1 +2464184673,24641846730,7512758,2821423,3080230840,True,eatout,4,8,12.0,WALK,308023084,1 +2464184677,24641846770,7512758,2821423,3080230840,False,Home,8,4,12.0,WALK,308023084,1 +2464184825,24641848250,7512758,2821423,3080231030,True,othdiscr,7,8,10.0,WALK,308023103,1 +2464184829,24641848290,7512758,2821423,3080231030,False,Home,8,7,11.0,WALK,308023103,1 +2464184849,24641848490,7512758,2821423,3080231060,True,othmaint,22,8,12.0,WALK_LRF,308023106,1 +2464184853,24641848530,7512758,2821423,3080231060,False,escort,9,22,14.0,WALK_LRF,308023106,1 +2464184854,24641848540,7512758,2821423,3080231060,False,Home,8,9,15.0,WALK,308023106,2 +2464184889,24641848890,7512758,2821423,3080231110,True,shopping,7,8,16.0,WALK,308023111,1 +2464184893,24641848930,7512758,2821423,3080231110,False,Home,8,7,18.0,WALK_LOC,308023111,1 +2464184897,24641848970,7512758,2821423,3080231120,True,shopping,13,8,21.0,WALK_LRF,308023112,1 +2464184901,24641849010,7512758,2821423,3080231120,False,Home,8,13,22.0,WALK_LRF,308023112,1 +2464190905,24641909050,7512777,2821442,3080238630,True,eatout,3,8,15.0,WALK,308023863,1 +2464190909,24641909090,7512777,2821442,3080238630,False,Home,8,3,16.0,WALK,308023863,1 +2464191057,24641910570,7512777,2821442,3080238820,True,othdiscr,12,8,9.0,WALK,308023882,1 +2464191061,24641910610,7512777,2821442,3080238820,False,Home,8,12,10.0,WALK,308023882,1 +2464191121,24641911210,7512777,2821442,3080238900,True,shopping,11,8,10.0,WALK,308023890,1 +2464191125,24641911250,7512777,2821442,3080238900,False,Home,8,11,13.0,WALK,308023890,1 +2464206257,24642062570,7512823,2821488,3080257820,True,work,2,8,5.0,WALK,308025782,1 +2464206261,24642062610,7512823,2821488,3080257820,False,Home,8,2,14.0,WALK,308025782,1 +2464211505,24642115050,7512839,2821504,3080264380,True,work,8,8,6.0,WALK,308026438,1 +2464211509,24642115090,7512839,2821504,3080264380,False,Home,8,8,16.0,WALK,308026438,1 +2464212489,24642124890,7512842,2821507,3080265610,True,work,7,8,12.0,WALK_LOC,308026561,1 +2464212493,24642124930,7512842,2821507,3080265610,False,Home,8,7,17.0,WALK,308026561,1 +2464213777,24642137770,7512846,2821511,3080267220,True,social,9,8,7.0,WALK,308026722,1 +2464213781,24642137810,7512846,2821511,3080267220,False,Home,8,9,20.0,WALK,308026722,1 +2464214065,24642140650,7512847,2821512,3080267580,True,othmaint,22,8,17.0,WALK_LRF,308026758,1 +2464214066,24642140660,7512847,2821512,3080267580,True,eatout,9,22,17.0,WALK_LRF,308026758,2 +2464214067,24642140670,7512847,2821512,3080267580,True,univ,13,9,17.0,WALK_LRF,308026758,3 +2464214069,24642140690,7512847,2821512,3080267580,False,Home,8,13,17.0,WALK_LRF,308026758,1 +2464214129,24642141290,7512847,2821512,3080267660,True,work,12,8,7.0,WALK,308026766,1 +2464214133,24642141330,7512847,2821512,3080267660,False,Home,8,12,14.0,TNC_SINGLE,308026766,1 +2464226313,24642263130,7512885,2821550,3080282890,True,atwork,2,2,10.0,WALK,308028289,1 +2464226317,24642263170,7512885,2821550,3080282890,False,othmaint,7,2,10.0,WALK,308028289,1 +2464226318,24642263180,7512885,2821550,3080282890,False,Work,2,7,10.0,WALK,308028289,2 +2464226593,24642265930,7512885,2821550,3080283240,True,work,2,8,6.0,WALK,308028324,1 +2464226597,24642265970,7512885,2821550,3080283240,False,Home,8,2,15.0,WALK,308028324,1 +2464234401,24642344010,7512909,2821574,3080293000,True,univ,12,8,20.0,WALK_LOC,308029300,1 +2464234405,24642344050,7512909,2821574,3080293000,False,Home,8,12,21.0,WALK_LOC,308029300,1 +2464234465,24642344650,7512909,2821574,3080293080,True,work,5,8,7.0,WALK,308029308,1 +2464234469,24642344690,7512909,2821574,3080293080,False,Home,8,5,11.0,WALK,308029308,1 +2464241417,24642414170,7512931,2821596,3080301770,True,eatout,5,8,14.0,WALK,308030177,1 +2464241421,24642414210,7512931,2821596,3080301770,False,othmaint,9,5,20.0,WALK,308030177,1 +2464241422,24642414220,7512931,2821596,3080301770,False,Home,8,9,20.0,WALK,308030177,2 +2464241441,24642414410,7512931,2821596,3080301800,True,escort,5,8,8.0,WALK_LOC,308030180,1 +2464241445,24642414450,7512931,2821596,3080301800,False,othdiscr,9,5,8.0,WALK_LOC,308030180,1 +2464241446,24642414460,7512931,2821596,3080301800,False,shopping,13,9,8.0,WALK_LOC,308030180,2 +2464241447,24642414470,7512931,2821596,3080301800,False,Home,8,13,9.0,WALK_LRF,308030180,3 +2464246865,24642468650,7512947,2821612,3080308580,True,univ,13,8,8.0,WALK_LOC,308030858,1 +2464246869,24642468690,7512947,2821612,3080308580,False,social,4,13,12.0,WALK_LOC,308030858,1 +2464246870,24642468700,7512947,2821612,3080308580,False,Home,8,4,13.0,WALK_LRF,308030858,2 +2464246929,24642469290,7512947,2821612,3080308660,True,work,7,8,6.0,WALK,308030866,1 +2464246933,24642469330,7512947,2821612,3080308660,False,Home,8,7,6.0,WALK,308030866,1 +2464264313,24642643130,7513000,2821665,3080330390,True,work,7,8,5.0,WALK,308033039,1 +2464264317,24642643170,7513000,2821665,3080330390,False,Home,8,7,14.0,WALK,308033039,1 +2464269561,24642695610,7513016,2821681,3080336950,True,othmaint,8,8,16.0,WALK,308033695,1 +2464269562,24642695620,7513016,2821681,3080336950,True,work,20,8,16.0,WALK,308033695,2 +2464269565,24642695650,7513016,2821681,3080336950,False,othmaint,10,20,21.0,WALK,308033695,1 +2464269566,24642695660,7513016,2821681,3080336950,False,Home,8,10,21.0,WALK,308033695,2 +2464272513,24642725130,7513025,2821690,3080340640,True,work,9,8,6.0,WALK,308034064,1 +2464272517,24642725170,7513025,2821690,3080340640,False,Home,8,9,18.0,WALK_LOC,308034064,1 +2464284649,24642846490,7513062,2821727,3080355810,True,work,16,8,12.0,WALK_LOC,308035581,1 +2464284653,24642846530,7513062,2821727,3080355810,False,othmaint,2,16,21.0,WALK_LOC,308035581,1 +2464284654,24642846540,7513062,2821727,3080355810,False,shopping,16,2,21.0,WALK_LOC,308035581,2 +2464284655,24642846550,7513062,2821727,3080355810,False,Home,8,16,21.0,WALK,308035581,3 +2464292849,24642928490,7513087,2821752,3080366060,True,work,10,8,5.0,WALK,308036606,1 +2464292853,24642928530,7513087,2821752,3080366060,False,Home,8,10,15.0,WALK_LOC,308036606,1 +2464301377,24643013770,7513113,2821778,3080376720,True,work,23,8,7.0,WALK_LRF,308037672,1 +2464301381,24643013810,7513113,2821778,3080376720,False,Home,8,23,21.0,WALK_LRF,308037672,1 +2464315153,24643151530,7513155,2821820,3080393940,True,work,7,8,13.0,WALK,308039394,1 +2464315157,24643151570,7513155,2821820,3080393940,False,Home,8,7,23.0,WALK,308039394,1 +2464328601,24643286010,7513196,2821861,3080410750,True,work,19,8,6.0,WALK,308041075,1 +2464328605,24643286050,7513196,2821861,3080410750,False,Home,8,19,16.0,WALK_LOC,308041075,1 +2464333257,24643332570,7513211,2821876,3080416570,True,eatout,9,8,16.0,WALK,308041657,1 +2464333261,24643332610,7513211,2821876,3080416570,False,Home,8,9,19.0,WALK,308041657,1 +2464333409,24643334090,7513211,2821876,3080416760,True,othdiscr,7,8,11.0,WALK_LOC,308041676,1 +2464333413,24643334130,7513211,2821876,3080416760,False,Home,8,7,15.0,WALK_LOC,308041676,1 +2464333473,24643334730,7513211,2821876,3080416840,True,shopping,11,8,16.0,TNC_SINGLE,308041684,1 +2464333477,24643334770,7513211,2821876,3080416840,False,Home,8,11,16.0,WALK_LOC,308041684,1 +2464343097,24643430970,7513241,2821906,3080428870,True,eatout,7,8,12.0,WALK,308042887,1 +2464343101,24643431010,7513241,2821906,3080428870,False,Home,8,7,12.0,WALK,308042887,1 +2464343249,24643432490,7513241,2821906,3080429060,True,othdiscr,21,8,18.0,WALK,308042906,1 +2464343253,24643432530,7513241,2821906,3080429060,False,Home,8,21,20.0,WALK,308042906,1 +2464343689,24643436890,7513242,2821907,3080429610,True,work,9,8,6.0,WALK_LOC,308042961,1 +2464343693,24643436930,7513242,2821907,3080429610,False,Home,8,9,16.0,TNC_SINGLE,308042961,1 +2464343697,24643436970,7513242,2821907,3080429620,True,work,9,8,16.0,WALK,308042962,1 +2464343701,24643437010,7513242,2821907,3080429620,False,univ,12,9,16.0,WALK_LRF,308042962,1 +2464343702,24643437020,7513242,2821907,3080429620,False,Home,8,12,16.0,WALK_LOC,308042962,2 +2464357185,24643571850,7513284,2821949,3080446480,True,atwork,21,7,10.0,WALK,308044648,1 +2464357189,24643571890,7513284,2821949,3080446480,False,Work,7,21,10.0,WALK,308044648,1 +2464357465,24643574650,7513284,2821949,3080446830,True,work,7,8,7.0,WALK_LOC,308044683,1 +2464357469,24643574690,7513284,2821949,3080446830,False,Home,8,7,15.0,WALK,308044683,1 +2464377473,24643774730,7513345,2822010,3080471840,True,work,1,8,5.0,WALK_LRF,308047184,1 +2464377477,24643774770,7513345,2822010,3080471840,False,Home,8,1,22.0,WALK_LRF,308047184,1 +2464387969,24643879690,7513377,2822042,3080484960,True,work,9,8,8.0,WALK,308048496,1 +2464387973,24643879730,7513377,2822042,3080484960,False,Home,8,9,18.0,WALK,308048496,1 +2464406009,24644060090,7513432,2822097,3080507510,True,work,7,8,5.0,BIKE,308050751,1 +2464406013,24644060130,7513432,2822097,3080507510,False,Home,8,7,15.0,BIKE,308050751,1 +2464412617,24644126170,7513453,2822118,3080515770,True,atwork,16,16,7.0,WALK,308051577,1 +2464412621,24644126210,7513453,2822118,3080515770,False,Work,16,16,7.0,WALK,308051577,1 +2464412897,24644128970,7513453,2822118,3080516120,True,work,16,8,7.0,WALK_LOC,308051612,1 +2464412901,24644129010,7513453,2822118,3080516120,False,Home,8,16,22.0,WALK_LOC,308051612,1 +2464427001,24644270010,7513496,2822161,3080533750,True,work,2,8,6.0,WALK,308053375,1 +2464427005,24644270050,7513496,2822161,3080533750,False,Home,8,2,18.0,WALK_LOC,308053375,1 +2464443401,24644434010,7513546,2822211,3080554250,True,work,2,8,7.0,WALK,308055425,1 +2464443405,24644434050,7513546,2822211,3080554250,False,Home,8,2,14.0,WALK,308055425,1 +2464446025,24644460250,7513554,2822219,3080557530,True,work,9,8,5.0,WALK,308055753,1 +2464446029,24644460290,7513554,2822219,3080557530,False,Home,8,9,16.0,WALK,308055753,1 +2464446241,24644462410,7513555,2822220,3080557800,True,othdiscr,15,8,10.0,WALK,308055780,1 +2464446245,24644462450,7513555,2822220,3080557800,False,Home,8,15,13.0,WALK_LOC,308055780,1 +2464446305,24644463050,7513555,2822220,3080557880,True,shopping,16,8,16.0,WALK,308055788,1 +2464446309,24644463090,7513555,2822220,3080557880,False,eatout,8,16,17.0,WALK,308055788,1 +2464446310,24644463100,7513555,2822220,3080557880,False,Home,8,8,17.0,WALK,308055788,2 +2464449633,24644496330,7513565,2822230,3080562040,True,univ,12,8,9.0,WALK,308056204,1 +2464449634,24644496340,7513565,2822230,3080562040,True,work,25,12,9.0,WALK,308056204,2 +2464449637,24644496370,7513565,2822230,3080562040,False,Home,8,25,18.0,WALK,308056204,1 +2464459145,24644591450,7513594,2822259,3080573930,True,work,13,8,13.0,TNC_SINGLE,308057393,1 +2464459149,24644591490,7513594,2822259,3080573930,False,work,12,13,21.0,TNC_SINGLE,308057393,1 +2464459150,24644591500,7513594,2822259,3080573930,False,Home,8,12,21.0,WALK_LOC,308057393,2 +2464473905,24644739050,7513639,2822304,3080592380,True,work,2,8,10.0,WALK,308059238,1 +2464473909,24644739090,7513639,2822304,3080592380,False,Home,8,2,11.0,WALK,308059238,1 +2464480793,24644807930,7513660,2822325,3080600990,True,shopping,24,8,6.0,BIKE,308060099,1 +2464480794,24644807940,7513660,2822325,3080600990,True,othmaint,24,24,7.0,BIKE,308060099,2 +2464480795,24644807950,7513660,2822325,3080600990,True,work,2,24,8.0,BIKE,308060099,3 +2464480797,24644807970,7513660,2822325,3080600990,False,escort,16,2,16.0,BIKE,308060099,1 +2464480798,24644807980,7513660,2822325,3080600990,False,Home,8,16,16.0,BIKE,308060099,2 +2464508737,24645087370,7513746,2822411,3080635920,True,eatout,5,8,13.0,WALK,308063592,1 +2464508741,24645087410,7513746,2822411,3080635920,False,Home,8,5,16.0,WALK,308063592,1 +2464508761,24645087610,7513746,2822411,3080635950,True,escort,9,8,19.0,WALK,308063595,1 +2464508765,24645087650,7513746,2822411,3080635950,False,Home,8,9,23.0,WALK,308063595,1 +2464508953,24645089530,7513746,2822411,3080636190,True,shopping,13,8,11.0,WALK,308063619,1 +2464508957,24645089570,7513746,2822411,3080636190,False,Home,8,13,13.0,WALK,308063619,1 +2464533929,24645339290,7513822,2822487,3080667410,True,work,9,17,7.0,WALK_LRF,308066741,1 +2464533933,24645339330,7513822,2822487,3080667410,False,Home,17,9,16.0,WALK_LRF,308066741,1 +2464541033,24645410330,7513844,2822509,3080676290,True,othdiscr,22,17,21.0,WALK_LRF,308067629,1 +2464541037,24645410370,7513844,2822509,3080676290,False,Home,17,22,22.0,WALK_LRF,308067629,1 +2464541145,24645411450,7513844,2822509,3080676430,True,work,16,17,7.0,WALK,308067643,1 +2464541149,24645411490,7513844,2822509,3080676430,False,Home,17,16,18.0,WALK,308067643,1 +2464541425,24645414250,7513845,2822510,3080676780,True,shopping,8,17,9.0,WALK_LRF,308067678,1 +2464541429,24645414290,7513845,2822510,3080676780,False,Home,17,8,10.0,WALK_LRF,308067678,1 +2464541433,24645414330,7513845,2822510,3080676790,True,shopping,12,17,14.0,WALK_LOC,308067679,1 +2464541437,24645414370,7513845,2822510,3080676790,False,Home,17,12,16.0,WALK_LRF,308067679,1 +2464541473,24645414730,7513845,2822510,3080676840,True,work,4,17,17.0,WALK_LOC,308067684,1 +2464541474,24645414740,7513845,2822510,3080676840,True,work,22,4,17.0,WALK_LRF,308067684,2 +2464541477,24645414770,7513845,2822510,3080676840,False,Home,17,22,18.0,WALK_LRF,308067684,1 +2464551249,24645512490,7513875,2822540,3080689060,True,univ,9,21,15.0,WALK_LOC,308068906,1 +2464551253,24645512530,7513875,2822540,3080689060,False,Home,21,9,20.0,WALK,308068906,1 +2464551313,24645513130,7513875,2822540,3080689140,True,escort,12,21,10.0,WALK,308068914,1 +2464551314,24645513140,7513875,2822540,3080689140,True,work,12,12,10.0,TNC_SHARED,308068914,2 +2464551317,24645513170,7513875,2822540,3080689140,False,eatout,5,12,14.0,WALK,308068914,1 +2464551318,24645513180,7513875,2822540,3080689140,False,Home,21,5,15.0,WALK,308068914,2 +2464571697,24645716970,7513938,2822603,3080714620,True,atwork,4,1,10.0,WALK,308071462,1 +2464571701,24645717010,7513938,2822603,3080714620,False,Work,1,4,10.0,WALK,308071462,1 +2464571977,24645719770,7513938,2822603,3080714970,True,work,1,21,8.0,WALK_LOC,308071497,1 +2464571981,24645719810,7513938,2822603,3080714970,False,Home,21,1,18.0,WALK_LRF,308071497,1 +2464575257,24645752570,7513948,2822613,3080719070,True,work,24,21,8.0,WALK,308071907,1 +2464575261,24645752610,7513948,2822613,3080719070,False,Home,21,24,18.0,WALK,308071907,1 +2464579849,24645798490,7513962,2822627,3080724810,True,work,21,21,10.0,WALK,308072481,1 +2464579853,24645798530,7513962,2822627,3080724810,False,Home,21,21,17.0,WALK,308072481,1 +2464583129,24645831290,7513972,2822637,3080728910,True,work,5,21,18.0,WALK,308072891,1 +2464583130,24645831300,7513972,2822637,3080728910,True,work,7,5,19.0,WALK_LOC,308072891,2 +2464583133,24645831330,7513972,2822637,3080728910,False,shopping,11,7,18.0,WALK,308072891,1 +2464583134,24645831340,7513972,2822637,3080728910,False,Home,21,11,21.0,WALK,308072891,2 +2464587721,24645877210,7513986,2822651,3080734650,True,work,9,21,8.0,WALK,308073465,1 +2464587725,24645877250,7513986,2822651,3080734650,False,escort,11,9,14.0,WALK_LOC,308073465,1 +2464587726,24645877260,7513986,2822651,3080734650,False,Home,21,11,14.0,WALK,308073465,2 +2464611713,24646117130,7514060,2822725,3080764640,True,atwork,5,12,13.0,WALK,308076464,1 +2464611717,24646117170,7514060,2822725,3080764640,False,Work,12,5,13.0,WALK,308076464,1 +2464611881,24646118810,7514060,2822725,3080764850,True,othdiscr,9,21,21.0,SHARED2FREE,308076485,1 +2464611885,24646118850,7514060,2822725,3080764850,False,Home,21,9,21.0,SHARED2FREE,308076485,1 +2464611993,24646119930,7514060,2822725,3080764990,True,work,12,21,9.0,WALK,308076499,1 +2464611997,24646119970,7514060,2822725,3080764990,False,Home,21,12,19.0,WALK,308076499,1 +2467664249,24676642490,7523366,2832031,3084580310,True,othdiscr,9,3,6.0,WALK,308458031,1 +2467664253,24676642530,7523366,2832031,3084580310,False,Home,3,9,16.0,WALK,308458031,1 +2467679033,24676790330,7523411,2832076,3084598790,True,othmaint,1,7,13.0,WALK,308459879,1 +2467679037,24676790370,7523411,2832076,3084598790,False,Home,7,1,13.0,BIKE,308459879,1 +2467687057,24676870570,7523436,2832101,3084608820,True,eatout,7,7,17.0,WALK,308460882,1 +2467687061,24676870610,7523436,2832101,3084608820,False,Home,7,7,17.0,WALK,308460882,1 +2467687273,24676872730,7523436,2832101,3084609090,True,shopping,10,7,11.0,WALK_LOC,308460909,1 +2467687277,24676872770,7523436,2832101,3084609090,False,Home,7,10,12.0,WALK_LOC,308460909,1 +2467687713,24676877130,7523438,2832103,3084609640,True,eatout,5,7,11.0,WALK,308460964,1 +2467687717,24676877170,7523438,2832103,3084609640,False,Home,7,5,13.0,WALK,308460964,1 +2467705753,24677057530,7523493,2832158,3084632190,True,eatout,12,7,8.0,WALK,308463219,1 +2467705757,24677057570,7523493,2832158,3084632190,False,Home,7,12,14.0,WALK,308463219,1 +2467708705,24677087050,7523502,2832167,3084635880,True,eatout,12,7,13.0,WALK,308463588,1 +2467708709,24677087090,7523502,2832167,3084635880,False,Home,7,12,17.0,WALK,308463588,1 +2467713777,24677137770,7523517,2832182,3084642220,True,othdiscr,9,7,8.0,WALK,308464222,1 +2467713781,24677137810,7523517,2832182,3084642220,False,Home,7,9,13.0,WALK,308464222,1 +2467756745,24677567450,7523648,2832313,3084695930,True,othdiscr,9,10,7.0,SHARED2FREE,308469593,1 +2467756749,24677567490,7523648,2832313,3084695930,False,Home,10,9,15.0,WALK,308469593,1 +2467757425,24677574250,7523650,2832315,3084696780,True,othmaint,8,10,10.0,WALK,308469678,1 +2467757429,24677574290,7523650,2832315,3084696780,False,Home,10,8,13.0,WALK,308469678,1 +2467770873,24677708730,7523691,2832356,3084713590,True,othmaint,5,16,7.0,WALK,308471359,1 +2467770877,24677708770,7523691,2832356,3084713590,False,Home,16,5,10.0,WALK,308471359,1 +2467771241,24677712410,7523692,2832357,3084714050,True,shopping,11,16,20.0,WALK,308471405,1 +2467771245,24677712450,7523692,2832357,3084714050,False,Home,16,11,23.0,WALK,308471405,1 +2467800065,24678000650,7523780,2832445,3084750080,True,othmaint,2,17,9.0,WALK,308475008,1 +2467800069,24678000690,7523780,2832445,3084750080,False,Home,17,2,10.0,WALK_LRF,308475008,1 +2467826345,24678263450,7523860,2832525,3084782930,True,shopping,12,21,9.0,SHARED2FREE,308478293,1 +2467826346,24678263460,7523860,2832525,3084782930,True,shopping,13,12,10.0,DRIVEALONEFREE,308478293,2 +2467826349,24678263490,7523860,2832525,3084782930,False,shopping,16,13,10.0,WALK,308478293,1 +2467826350,24678263500,7523860,2832525,3084782930,False,othdiscr,21,16,10.0,WALK,308478293,2 +2467826351,24678263510,7523860,2832525,3084782930,False,shopping,6,21,10.0,SHARED2FREE,308478293,3 +2467826352,24678263520,7523860,2832525,3084782930,False,Home,21,6,10.0,WALK,308478293,4 +2467826353,24678263530,7523860,2832525,3084782940,True,shopping,16,21,11.0,TNC_SINGLE,308478294,1 +2467826357,24678263570,7523860,2832525,3084782940,False,Home,21,16,11.0,TNC_SHARED,308478294,1 +2467826369,24678263690,7523860,2832525,3084782960,True,social,11,21,11.0,WALK,308478296,1 +2467826373,24678263730,7523860,2832525,3084782960,False,Home,21,11,21.0,WALK,308478296,1 +2467831265,24678312650,7523875,2832540,3084789080,True,shopping,23,21,7.0,WALK,308478908,1 +2467831269,24678312690,7523875,2832540,3084789080,False,shopping,25,23,14.0,WALK,308478908,1 +2467831270,24678312700,7523875,2832540,3084789080,False,Home,21,25,15.0,WALK,308478908,2 +2472943737,24729437370,7539462,2848127,3091179670,True,othdiscr,12,3,11.0,WALK_LOC,309117967,1 +2472943741,24729437410,7539462,2848127,3091179670,False,Home,3,12,15.0,WALK_LOC,309117967,1 +2472945097,24729450970,7539466,2848131,3091181370,True,univ,13,3,8.0,WALK_LOC,309118137,1 +2472945101,24729451010,7539466,2848131,3091181370,False,Home,3,13,15.0,WALK_LOC,309118137,1 +2472945113,24729451130,7539466,2848131,3091181390,True,shopping,8,3,18.0,WALK_LOC,309118139,1 +2472945117,24729451170,7539466,2848131,3091181390,False,shopping,5,8,20.0,WALK_LOC,309118139,1 +2472945118,24729451180,7539466,2848131,3091181390,False,Home,3,5,20.0,WALK,309118139,2 +2472966393,24729663930,7539531,2848196,3091207990,True,othmaint,22,15,11.0,BIKE,309120799,1 +2472966397,24729663970,7539531,2848196,3091207990,False,Home,15,22,13.0,WALK,309120799,1 +2472966697,24729666970,7539532,2848197,3091208370,True,othdiscr,6,15,9.0,WALK,309120837,1 +2472966701,24729667010,7539532,2848197,3091208370,False,Home,15,6,12.0,WALK,309120837,1 +2473003433,24730034330,7539644,2848309,3091254290,True,othdiscr,18,18,14.0,WALK,309125429,1 +2473003437,24730034370,7539644,2848309,3091254290,False,Home,18,18,14.0,WALK,309125429,1 +2473019025,24730190250,7539692,2848357,3091273780,True,eatout,16,18,7.0,WALK,309127378,1 +2473019029,24730190290,7539692,2848357,3091273780,False,Home,18,16,17.0,WALK,309127378,1 +2473024473,24730244730,7539708,2848373,3091280590,True,univ,12,18,10.0,WALK_LOC,309128059,1 +2473024477,24730244770,7539708,2848373,3091280590,False,Home,18,12,21.0,WALK_LOC,309128059,1 +2473035313,24730353130,7539741,2848406,3091294140,True,shopping,11,18,9.0,WALK,309129414,1 +2473035317,24730353170,7539741,2848406,3091294140,False,Home,18,11,15.0,WALK,309129414,1 +2473036257,24730362570,7539744,2848409,3091295320,True,othmaint,9,18,10.0,BIKE,309129532,1 +2473036261,24730362610,7539744,2848409,3091295320,False,Home,18,9,14.0,BIKE,309129532,1 +2473042465,24730424650,7539763,2848428,3091303080,True,othdiscr,25,18,11.0,WALK_LOC,309130308,1 +2473042469,24730424690,7539763,2848428,3091303080,False,shopping,8,25,14.0,WALK_LOC,309130308,1 +2473042470,24730424700,7539763,2848428,3091303080,False,Home,18,8,14.0,WALK_LOC,309130308,2 +2473047081,24730470810,7539777,2848442,3091308850,True,othmaint,13,18,7.0,WALK,309130885,1 +2473047085,24730470850,7539777,2848442,3091308850,False,eatout,7,13,10.0,WALK,309130885,1 +2473047086,24730470860,7539777,2848442,3091308850,False,eatout,18,7,10.0,WALK,309130885,2 +2473047087,24730470870,7539777,2848442,3091308850,False,Home,18,18,10.0,WALK,309130885,3 +2473047089,24730470890,7539777,2848442,3091308860,True,othmaint,1,18,10.0,BIKE,309130886,1 +2473047093,24730470930,7539777,2848442,3091308860,False,Home,18,1,10.0,WALK,309130886,1 +2473068905,24730689050,7539844,2848509,3091336130,True,escort,14,18,10.0,SHARED3FREE,309133613,1 +2473068909,24730689090,7539844,2848509,3091336130,False,Home,18,14,10.0,SHARED3FREE,309133613,1 +2473068913,24730689130,7539844,2848509,3091336140,True,escort,20,18,15.0,SHARED3FREE,309133614,1 +2473068917,24730689170,7539844,2848509,3091336140,False,Home,18,20,16.0,SHARED3FREE,309133614,1 +2473070409,24730704090,7539848,2848513,3091338010,True,shopping,11,18,12.0,TNC_SINGLE,309133801,1 +2473070413,24730704130,7539848,2848513,3091338010,False,Home,18,11,15.0,WALK_LOC,309133801,1 +2473075001,24730750010,7539862,2848527,3091343750,True,social,14,19,17.0,WALK_LOC,309134375,1 +2473075002,24730750020,7539862,2848527,3091343750,True,shopping,5,14,18.0,WALK_LOC,309134375,2 +2473075005,24730750050,7539862,2848527,3091343750,False,Home,19,5,20.0,TNC_SINGLE,309134375,1 +2473093329,24730933290,7539918,2848583,3091366660,True,othmaint,4,19,7.0,WALK_LOC,309136666,1 +2473093333,24730933330,7539918,2848583,3091366660,False,eatout,8,4,13.0,WALK,309136666,1 +2473093334,24730933340,7539918,2848583,3091366660,False,Home,19,8,13.0,WALK_LOC,309136666,2 +2473099865,24730998650,7539938,2848603,3091374830,True,shopping,14,19,12.0,DRIVEALONEFREE,309137483,1 +2473099866,24730998660,7539938,2848603,3091374830,True,othdiscr,16,14,14.0,DRIVEALONEFREE,309137483,2 +2473099869,24730998690,7539938,2848603,3091374830,False,Home,19,16,14.0,TNC_SHARED,309137483,1 +2473107761,24731077610,7539962,2848627,3091384700,True,othmaint,13,19,7.0,TNC_SINGLE,309138470,1 +2473107765,24731077650,7539962,2848627,3091384700,False,Home,19,13,20.0,TNC_SINGLE,309138470,1 +2473132401,24731324010,7540037,2848702,3091415500,True,shopping,5,19,12.0,TNC_SINGLE,309141550,1 +2473132405,24731324050,7540037,2848702,3091415500,False,Home,19,5,16.0,TNC_SINGLE,309141550,1 +2477971081,24779710810,7554789,2863454,3097463850,True,social,1,2,12.0,WALK,309746385,1 +2477971085,24779710850,7554789,2863454,3097463850,False,Home,2,1,23.0,WALK,309746385,1 +2477990409,24779904090,7554848,2863513,3097488010,True,escort,7,20,18.0,WALK,309748801,1 +2477990410,24779904100,7554848,2863513,3097488010,True,shopping,6,7,18.0,WALK,309748801,2 +2477990413,24779904130,7554848,2863513,3097488010,False,Home,20,6,18.0,WALK,309748801,1 diff --git a/activitysim/examples/example_estimation/data_test/survey_data/survey_trips.csv b/activitysim/examples/example_estimation/data_test/survey_data/survey_trips.csv new file mode 100644 index 0000000000..4d3be08e20 --- /dev/null +++ b/activitysim/examples/example_estimation/data_test/survey_data/survey_trips.csv @@ -0,0 +1,9616 @@ +trip_id,person_id,household_id,tour_id,outbound,purpose,destination,origin,depart,trip_mode +8421649,25675,25675,1052706,True,social,4,5,18.0,WALK_LOC +8421650,25675,25675,1052706,True,univ,13,4,19.0,WALK +8421653,25675,25675,1052706,False,shopping,11,13,21.0,WALK_LOC +8421654,25675,25675,1052706,False,univ,14,11,21.0,WALK +8421655,25675,25675,1052706,False,work,15,14,21.0,WALK_LOC +8421656,25675,25675,1052706,False,Home,5,15,21.0,WALK_LOC +8422457,25678,25678,1052807,True,escort,5,6,8.0,WALK +8422461,25678,25678,1052807,False,Home,6,5,9.0,WALK +8422465,25678,25678,1052808,True,escort,7,6,15.0,WALK +8422469,25678,25678,1052808,False,Home,6,7,18.0,WALK +8424249,25683,25683,1053031,True,othmaint,4,6,8.0,WALK +8424253,25683,25683,1053031,False,Home,6,4,21.0,WALK +8424577,25684,25684,1053072,True,othmaint,2,6,14.0,TNC_SINGLE +8424581,25684,25684,1053072,False,Home,6,2,14.0,TNC_SINGLE +8426897,25691,25691,1053362,True,univ,13,6,6.0,WALK_LRF +8426901,25691,25691,1053362,False,Home,6,13,6.0,WALK_LRF +8438001,25725,25725,1054750,True,othdiscr,4,6,7.0,WALK +8438005,25725,25725,1054750,False,Home,6,4,10.0,WALK +8438065,25725,25725,1054758,True,shopping,11,6,15.0,WALK +8438069,25725,25725,1054758,False,Home,6,11,16.0,WALK +8440801,25734,25734,1055100,True,eatout,5,6,11.0,WALK +8440805,25734,25734,1055100,False,Home,6,5,15.0,WALK +8447561,25754,25754,1055945,True,othmaint,5,6,7.0,WALK +8447562,25754,25754,1055945,True,univ,13,5,8.0,WALK +8447565,25754,25754,1055945,False,Home,6,13,14.0,WALK +8447569,25754,25754,1055946,True,univ,13,6,18.0,WALK +8447573,25754,25754,1055946,False,univ,12,13,21.0,WALK +8447574,25754,25754,1055946,False,othmaint,5,12,21.0,WALK +8447575,25754,25754,1055946,False,othmaint,5,5,21.0,WALK +8447576,25754,25754,1055946,False,Home,6,5,21.0,WALK +8450337,25763,25763,1056292,True,escort,7,6,12.0,WALK +8450341,25763,25763,1056292,False,Home,6,7,12.0,WALK +8461337,25796,25796,1057667,True,univ,13,6,5.0,WALK_LRF +8461341,25796,25796,1057667,False,Home,6,13,14.0,WALK_LOC +8476881,25844,25844,1059610,True,eatout,5,6,15.0,WALK +8476885,25844,25844,1059610,False,Home,6,5,16.0,WALK +8503953,25926,25926,1062994,True,othmaint,2,7,15.0,WALK +8503957,25926,25926,1062994,False,Home,7,2,15.0,WALK +8503993,25926,25926,1062999,True,shopping,5,7,11.0,WALK_LOC +8503997,25926,25926,1062999,False,shopping,3,5,12.0,WALK_LOC +8503998,25926,25926,1062999,False,Home,7,3,12.0,TNC_SINGLE +8514801,25959,25959,1064350,True,othmaint,1,7,8.0,WALK_LOC +8514802,25959,25959,1064350,True,escort,14,1,9.0,WALK_LOC +8514803,25959,25959,1064350,True,univ,12,14,9.0,WALK_LOC +8514805,25959,25959,1064350,False,Home,7,12,15.0,WALK +8515937,25963,25963,1064492,True,escort,20,7,17.0,TNC_SINGLE +8515941,25963,25963,1064492,False,Home,7,20,17.0,TNC_SINGLE +8516089,25963,25963,1064511,True,othmaint,22,7,9.0,SHARED3FREE +8516093,25963,25963,1064511,False,shopping,5,22,12.0,WALK +8516094,25963,25963,1064511,False,Home,7,5,12.0,SHARED3FREE +8516097,25963,25963,1064512,True,othmaint,2,7,14.0,WALK +8516101,25963,25963,1064512,False,Home,7,2,16.0,WALK +8523673,25986,25986,1065459,True,shopping,5,7,16.0,WALK +8523677,25986,25986,1065459,False,Home,7,5,16.0,WALK +8530673,26008,26008,1066334,True,eatout,11,7,16.0,WALK +8530677,26008,26008,1066334,False,Home,7,11,16.0,DRIVEALONEFREE +8530825,26008,26008,1066353,True,eatout,7,7,8.0,WALK +8530826,26008,26008,1066353,True,shopping,8,7,9.0,WALK +8530827,26008,26008,1066353,True,othdiscr,10,8,9.0,WALK +8530829,26008,26008,1066353,False,shopping,7,10,10.0,WALK +8530830,26008,26008,1066353,False,Home,7,7,10.0,WALK +8530849,26008,26008,1066356,True,othmaint,13,7,11.0,WALK +8530853,26008,26008,1066356,False,Home,7,13,13.0,WALK +8542681,26044,26044,1067835,True,escort,8,7,16.0,WALK +8542682,26044,26044,1067835,True,eatout,11,8,16.0,WALK_LOC +8542683,26044,26044,1067835,True,univ,12,11,18.0,WALK_LOC +8542685,26044,26044,1067835,False,Home,7,12,18.0,WALK_LOC +8581385,26162,26162,1072673,True,univ,12,8,8.0,WALK_LOC +8581389,26162,26162,1072673,False,Home,8,12,17.0,WALK_LOC +8586257,26177,26177,1073282,True,othdiscr,12,8,10.0,WALK +8586261,26177,26177,1073282,False,Home,8,12,14.0,WALK +8586633,26178,26178,1073329,True,univ,10,8,8.0,WALK +8586637,26178,26178,1073329,False,Home,8,10,11.0,WALK +8586641,26178,26178,1073330,True,univ,10,8,17.0,DRIVEALONEFREE +8586645,26178,26178,1073330,False,eatout,5,10,17.0,DRIVEALONEFREE +8586646,26178,26178,1073330,False,Home,8,5,17.0,WALK +8595113,26204,26204,1074389,True,othdiscr,16,8,14.0,WALK +8595117,26204,26204,1074389,False,Home,8,16,17.0,WALK +8595137,26204,26204,1074392,True,othmaint,8,8,12.0,WALK +8595141,26204,26204,1074392,False,Home,8,8,14.0,WALK +8596297,26208,26208,1074537,True,shopping,7,8,11.0,WALK +8596298,26208,26208,1074537,True,escort,13,7,11.0,WALK +8596301,26208,26208,1074537,False,Home,8,13,11.0,WALK +8603337,26229,26229,1075417,True,othmaint,7,8,6.0,WALK +8603341,26229,26229,1075417,False,escort,8,7,22.0,WALK +8603342,26229,26229,1075417,False,Home,8,8,22.0,WALK +8629073,26308,26308,1078634,True,eatout,15,8,17.0,WALK +8629077,26308,26308,1078634,False,Home,8,15,19.0,WALK +8638737,26337,26337,1079842,True,othdiscr,9,8,18.0,WALK +8638741,26337,26337,1079842,False,Home,8,9,21.0,WALK +8651617,26376,26376,1081452,True,social,11,8,12.0,TNC_SINGLE +8651621,26376,26376,1081452,False,shopping,8,11,17.0,TNC_SINGLE +8651622,26376,26376,1081452,False,Home,8,8,17.0,TNC_SINGLE +8651625,26376,26376,1081453,True,social,7,8,18.0,WALK_LOC +8651629,26376,26376,1081453,False,Home,8,7,21.0,WALK_LOC +8665305,26418,26418,1083163,True,othdiscr,16,8,18.0,WALK +8665309,26418,26418,1083163,False,Home,8,16,19.0,WALK +8665353,26418,26418,1083169,True,univ,9,8,10.0,WALK +8665357,26418,26418,1083169,False,othdiscr,9,9,13.0,WALK +8665358,26418,26418,1083169,False,Home,8,9,13.0,WALK +8684705,26477,26477,1085588,True,shopping,9,8,7.0,WALK +8684706,26477,26477,1085588,True,othdiscr,10,9,8.0,WALK_LOC +8684707,26477,26477,1085588,True,univ,13,10,8.0,WALK_LRF +8684709,26477,26477,1085588,False,othmaint,9,13,12.0,WALK_LRF +8684710,26477,26477,1085588,False,escort,4,9,13.0,WALK_LRF +8684711,26477,26477,1085588,False,othmaint,22,4,13.0,WALK_LRF +8684712,26477,26477,1085588,False,Home,8,22,13.0,WALK_LRF +8684745,26477,26477,1085593,True,social,19,8,17.0,WALK +8684749,26477,26477,1085593,False,Home,8,19,23.0,WALK +8684833,26478,26478,1085604,True,eatout,13,8,11.0,WALK +8684837,26478,26478,1085604,False,Home,8,13,11.0,WALK +8685009,26478,26478,1085626,True,othmaint,10,8,12.0,BIKE +8685013,26478,26478,1085626,False,Home,8,10,13.0,BIKE +8700097,26524,26524,1087512,True,othmaint,11,8,12.0,WALK +8700101,26524,26524,1087512,False,Home,8,11,15.0,BIKE +8721441,26589,26589,1090180,True,univ,10,8,16.0,WALK +8721445,26589,26589,1090180,False,Home,8,10,22.0,WALK +8735889,26633,26633,1091986,True,shopping,9,8,10.0,WALK +8735890,26633,26633,1091986,True,shopping,17,9,12.0,WALK_LRF +8735893,26633,26633,1091986,False,Home,8,17,20.0,WALK_LRF +8753057,26686,26686,1094132,True,eatout,5,8,19.0,WALK +8753061,26686,26686,1094132,False,Home,8,5,19.0,WALK +8753233,26686,26686,1094154,True,othmaint,9,8,12.0,BIKE +8753237,26686,26686,1094154,False,Home,8,9,13.0,WALK +8754913,26691,26691,1094364,True,shopping,16,8,14.0,WALK +8754917,26691,26691,1094364,False,Home,8,16,16.0,WALK +8757145,26698,26698,1094643,True,othdiscr,20,8,8.0,WALK_LOC +8757149,26698,26698,1094643,False,othmaint,16,20,14.0,WALK_LOC +8757150,26698,26698,1094643,False,shopping,5,16,14.0,WALK +8757151,26698,26698,1094643,False,Home,8,5,14.0,WALK +8785049,26783,26783,1098131,True,othmaint,3,8,8.0,WALK +8785053,26783,26783,1098131,False,Home,8,3,12.0,WALK +8787041,26789,26789,1098380,True,univ,12,8,8.0,WALK_LOC +8787045,26789,26789,1098380,False,Home,8,12,8.0,WALK_LOC +8798193,26823,26823,1099774,True,univ,12,8,7.0,WALK_LOC +8798197,26823,26823,1099774,False,social,16,12,11.0,WALK_LOC +8798198,26823,26823,1099774,False,shopping,13,16,11.0,WALK_LOC +8798199,26823,26823,1099774,False,eatout,16,13,11.0,WALK_LOC +8798200,26823,26823,1099774,False,Home,8,16,11.0,WALK_LOC +8801161,26832,26832,1100145,True,shopping,6,8,16.0,WALK +8801165,26832,26832,1100145,False,shopping,11,6,16.0,WALK +8801166,26832,26832,1100145,False,Home,8,11,17.0,WALK +8804097,26841,26841,1100512,True,univ,12,8,16.0,DRIVEALONEFREE +8804101,26841,26841,1100512,False,shopping,19,12,16.0,DRIVEALONEFREE +8804102,26841,26841,1100512,False,Home,8,19,16.0,DRIVEALONEFREE +8805121,26844,26844,1100640,True,social,7,8,8.0,WALK +8805125,26844,26844,1100640,False,othmaint,5,7,12.0,WALK +8805126,26844,26844,1100640,False,Home,8,5,12.0,WALK +8809953,26859,26859,1101244,True,othdiscr,15,8,9.0,WALK +8809957,26859,26859,1101244,False,Home,8,15,14.0,WALK +8812953,26868,26868,1101619,True,univ,12,8,20.0,WALK_LOC +8812957,26868,26868,1101619,False,Home,8,12,20.0,WALK_LOC +8823097,26899,26899,1102887,True,othmaint,4,8,11.0,BIKE +8823101,26899,26899,1102887,False,Home,8,4,14.0,BIKE +8823105,26899,26899,1102888,True,othmaint,13,8,14.0,WALK +8823109,26899,26899,1102888,False,Home,8,13,16.0,WALK +8826529,26910,26910,1103316,True,eatout,12,8,9.0,WALK +8826533,26910,26910,1103316,False,Home,8,12,11.0,WALK +8826745,26910,26910,1103343,True,shopping,16,8,13.0,WALK +8826749,26910,26910,1103343,False,Home,8,16,16.0,WALK +8826753,26910,26910,1103344,True,shopping,9,8,16.0,BIKE +8826757,26910,26910,1103344,False,Home,8,9,17.0,BIKE +8834097,26933,26933,1104262,True,escort,6,8,7.0,WALK +8834101,26933,26933,1104262,False,Home,8,6,7.0,WALK +8834289,26933,26933,1104286,True,shopping,16,8,12.0,WALK +8834293,26933,26933,1104286,False,Home,8,16,12.0,WALK +8835273,26936,26936,1104409,True,eatout,7,8,10.0,WALK +8835274,26936,26936,1104409,True,social,7,7,10.0,WALK +8835275,26936,26936,1104409,True,shopping,21,7,11.0,WALK +8835277,26936,26936,1104409,False,shopping,5,21,15.0,WALK +8835278,26936,26936,1104409,False,Home,8,5,15.0,WALK +8876257,27061,27061,1109532,True,univ,12,9,16.0,WALK_LRF +8876261,27061,27061,1109532,False,othmaint,5,12,19.0,WALK_LOC +8876262,27061,27061,1109532,False,Home,9,5,19.0,WALK_LOC +8883425,27083,27083,1110428,True,othdiscr,12,9,10.0,WALK_LRF +8883429,27083,27083,1110428,False,Home,9,12,14.0,WALK_LRF +8899497,27132,27132,1112437,True,othdiscr,9,9,9.0,WALK +8899501,27132,27132,1112437,False,Home,9,9,14.0,WALK +8909993,27164,27164,1113749,True,othdiscr,19,9,8.0,WALK +8909997,27164,27164,1113749,False,Home,9,19,23.0,BIKE +8915657,27181,27181,1114457,True,social,16,9,12.0,WALK_LRF +8915661,27181,27181,1114457,False,Home,9,16,14.0,WALK_LRF +8921497,27199,27199,1115187,True,othmaint,9,9,11.0,WALK +8921501,27199,27199,1115187,False,Home,9,9,12.0,WALK +8933633,27236,27236,1116704,True,othmaint,15,9,13.0,WALK_LRF +8933637,27236,27236,1116704,False,shopping,16,15,15.0,WALK +8933638,27236,27236,1116704,False,Home,9,16,15.0,WALK_LRF +8941529,27260,27260,1117691,True,shopping,12,9,8.0,WALK_LRF +8941530,27260,27260,1117691,True,univ,14,12,8.0,WALK +8941533,27260,27260,1117691,False,Home,9,14,16.0,WALK_LRF +8945153,27271,27271,1118144,True,shopping,5,9,10.0,TNC_SINGLE +8945157,27271,27271,1118144,False,Home,9,5,15.0,TNC_SINGLE +8977281,27369,27369,1122160,True,escort,9,9,12.0,WALK +8977282,27369,27369,1122160,True,univ,9,9,13.0,WALK +8977285,27369,27369,1122160,False,Home,9,9,20.0,WALK +8986417,27397,27397,1123302,True,othdiscr,17,9,13.0,WALK_LRF +8986421,27397,27397,1123302,False,Home,9,17,16.0,WALK_LRF +8992321,27415,27415,1124040,True,othdiscr,18,9,11.0,WALK +8992325,27415,27415,1124040,False,Home,9,18,20.0,WALK +8992673,27416,27416,1124084,True,othmaint,16,9,14.0,WALK_LRF +8992677,27416,27416,1124084,False,Home,9,16,14.0,WALK_LRF +9014145,27482,27482,1126768,True,eatout,11,10,14.0,WALK +9014149,27482,27482,1126768,False,Home,10,11,17.0,WALK +9035465,27547,27547,1129433,True,eatout,9,10,18.0,WALK +9035469,27547,27547,1129433,False,Home,10,9,22.0,WALK +9036929,27551,27551,1129616,True,othdiscr,7,10,12.0,WALK +9036933,27551,27551,1129616,False,Home,10,7,16.0,WALK +9042857,27569,27569,1130357,True,othmaint,9,10,8.0,WALK_LOC +9042861,27569,27569,1130357,False,Home,10,9,11.0,TNC_SINGLE +9069249,27650,27650,1133656,True,eatout,14,10,15.0,WALK_LRF +9069253,27650,27650,1133656,False,Home,10,14,16.0,WALK +9075393,27668,27668,1134424,True,social,9,10,8.0,WALK +9075397,27668,27668,1134424,False,Home,10,9,16.0,WALK +9075809,27670,27670,1134476,True,eatout,5,10,16.0,WALK +9075813,27670,27670,1134476,False,Home,10,5,20.0,WALK +9075961,27670,27670,1134495,True,othdiscr,6,10,13.0,WALK +9075962,27670,27670,1134495,True,shopping,16,6,14.0,WALK_LOC +9075963,27670,27670,1134495,True,othdiscr,17,16,14.0,WALK_LRF +9075965,27670,27670,1134495,False,shopping,8,17,16.0,WALK_LRF +9075966,27670,27670,1134495,False,shopping,8,8,16.0,WALK +9075967,27670,27670,1134495,False,Home,10,8,16.0,WALK +9075985,27670,27670,1134498,True,othmaint,5,10,11.0,WALK_LOC +9075986,27670,27670,1134498,True,othmaint,22,5,11.0,TNC_SINGLE +9075989,27670,27670,1134498,False,escort,7,22,12.0,TNC_SINGLE +9075990,27670,27670,1134498,False,othmaint,10,7,12.0,WALK_LOC +9075991,27670,27670,1134498,False,Home,10,10,12.0,TNC_SINGLE +9087441,27705,27705,1135930,True,othdiscr,8,10,18.0,WALK +9087445,27705,27705,1135930,False,Home,10,8,21.0,WALK +9087505,27705,27705,1135938,True,shopping,12,10,11.0,WALK_LOC +9087509,27705,27705,1135938,False,shopping,8,12,16.0,TNC_SHARED +9087510,27705,27705,1135938,False,Home,10,8,16.0,WALK_LOC +9094329,27726,27726,1136791,True,othdiscr,9,10,8.0,WALK +9094333,27726,27726,1136791,False,Home,10,9,10.0,WALK +9098945,27740,27740,1137368,True,othmaint,24,10,13.0,WALK_LRF +9098949,27740,27740,1137368,False,Home,10,24,17.0,WALK_LRF +9123849,27816,27816,1140481,True,othdiscr,17,10,10.0,WALK_LRF +9123853,27816,27816,1140481,False,Home,10,17,14.0,WALK_LRF +9127457,27827,27827,1140932,True,othdiscr,9,10,14.0,WALK +9127461,27827,27827,1140932,False,Home,10,9,16.0,WALK +9134041,27847,27847,1141755,True,othmaint,4,10,7.0,WALK +9134045,27847,27847,1141755,False,Home,10,4,13.0,WALK +9134081,27847,27847,1141760,True,shopping,16,10,15.0,TNC_SINGLE +9134085,27847,27847,1141760,False,Home,10,16,15.0,DRIVEALONEFREE +9144209,27878,27878,1143026,True,othmaint,9,10,14.0,BIKE +9144213,27878,27878,1143026,False,eatout,9,9,16.0,BIKE +9144214,27878,27878,1143026,False,Home,10,9,16.0,BIKE +9192401,28025,28025,1149050,True,othdiscr,3,10,15.0,WALK_LOC +9192405,28025,28025,1149050,False,Home,10,3,22.0,WALK_LRF +9192465,28025,28025,1149058,True,shopping,11,10,13.0,WALK +9192469,28025,28025,1149058,False,eatout,7,11,14.0,WALK +9192470,28025,28025,1149058,False,Home,10,7,14.0,WALK +9196073,28036,28036,1149509,True,shopping,16,10,9.0,TNC_SINGLE +9196077,28036,28036,1149509,False,Home,10,16,10.0,WALK_LOC +9196081,28036,28036,1149510,True,shopping,11,10,14.0,WALK +9196085,28036,28036,1149510,False,Home,10,11,15.0,WALK +9197673,28041,28041,1149709,True,othmaint,25,10,14.0,WALK +9197677,28041,28041,1149709,False,Home,10,25,19.0,WALK +9197977,28042,28042,1149747,True,othdiscr,23,10,18.0,WALK_LOC +9197981,28042,28042,1149747,False,shopping,22,23,20.0,WALK +9197982,28042,28042,1149747,False,Home,10,22,20.0,WALK_LRF +9198001,28042,28042,1149750,True,othmaint,15,10,10.0,WALK +9198005,28042,28042,1149750,False,Home,10,15,15.0,WALK_LRF +9228833,28136,28136,1153604,True,othmaint,5,10,10.0,WALK +9228837,28136,28136,1153604,False,Home,10,5,12.0,WALK +9228841,28136,28136,1153605,True,othmaint,4,10,14.0,WALK +9228845,28136,28136,1153605,False,Home,10,4,18.0,WALK +9228873,28136,28136,1153609,True,shopping,19,10,12.0,WALK +9228877,28136,28136,1153609,False,Home,10,19,13.0,WALK +9239809,28170,28170,1154976,True,eatout,19,10,14.0,WALK +9239813,28170,28170,1154976,False,Home,10,19,14.0,WALK +9282009,28298,28298,1160251,True,shopping,5,11,9.0,WALK +9282013,28298,28298,1160251,False,Home,11,5,13.0,WALK +9303113,28363,28363,1162889,True,eatout,11,11,13.0,WALK +9303117,28363,28363,1162889,False,Home,11,11,14.0,WALK +9303289,28363,28363,1162911,True,othmaint,7,11,12.0,WALK +9303293,28363,28363,1162911,False,Home,11,7,12.0,WALK +9303313,28363,28363,1162914,True,univ,9,11,15.0,WALK +9303317,28363,28363,1162914,False,Home,11,9,20.0,WALK +9308865,28380,28380,1163608,True,othmaint,1,11,11.0,WALK +9308869,28380,28380,1163608,False,Home,11,1,15.0,WALK_LRF +9321961,28420,28420,1165245,True,othdiscr,11,11,8.0,WALK +9321965,28420,28420,1165245,False,Home,11,11,17.0,WALK +9340065,28475,28475,1167508,True,shopping,15,11,11.0,WALK +9340069,28475,28475,1167508,False,Home,11,15,15.0,WALK +9344657,28489,28489,1168082,True,othmaint,7,11,8.0,WALK +9344658,28489,28489,1168082,True,shopping,5,7,10.0,WALK +9344661,28489,28489,1168082,False,Home,11,5,17.0,WALK +9351545,28510,28510,1168943,True,othmaint,9,11,11.0,WALK_LOC +9351546,28510,28510,1168943,True,shopping,16,9,13.0,WALK_LRF +9351549,28510,28510,1168943,False,shopping,25,16,19.0,TNC_SINGLE +9351550,28510,28510,1168943,False,Home,11,25,19.0,TNC_SINGLE +9351553,28510,28510,1168944,True,shopping,12,11,20.0,WALK +9351557,28510,28510,1168944,False,shopping,11,12,20.0,WALK +9351558,28510,28510,1168944,False,Home,11,11,20.0,WALK +9361497,28541,28541,1170187,True,eatout,12,11,16.0,WALK +9361501,28541,28541,1170187,False,Home,11,12,17.0,WALK +9361673,28541,28541,1170209,True,othmaint,21,11,8.0,WALK +9361677,28541,28541,1170209,False,eatout,7,21,14.0,BIKE +9361678,28541,28541,1170209,False,Home,11,7,14.0,BIKE +9366305,28555,28555,1170788,True,shopping,16,11,7.0,WALK +9366309,28555,28555,1170788,False,Home,11,16,20.0,WALK +9396921,28649,28649,1174615,True,eatout,2,13,11.0,WALK +9396925,28649,28649,1174615,False,Home,13,2,16.0,WALK +9397401,28650,28650,1174675,True,othdiscr,9,13,12.0,WALK_LRF +9397405,28650,28650,1174675,False,Home,13,9,15.0,WALK_LRF +9397465,28650,28650,1174683,True,shopping,24,13,9.0,WALK +9397469,28650,28650,1174683,False,Home,13,24,12.0,WALK +9411897,28694,28694,1176487,True,eatout,16,14,10.0,WALK +9411898,28694,28694,1176487,True,shopping,2,16,10.0,WALK +9411901,28694,28694,1176487,False,eatout,7,2,12.0,WALK +9411902,28694,28694,1176487,False,Home,14,7,12.0,WALK +9432369,28757,28757,1179046,True,escort,8,16,13.0,DRIVEALONEFREE +9432373,28757,28757,1179046,False,escort,17,8,14.0,DRIVEALONEFREE +9432374,28757,28757,1179046,False,Home,16,17,14.0,SHARED2FREE +9432521,28757,28757,1179065,True,othmaint,16,16,6.0,WALK +9432525,28757,28757,1179065,False,Home,16,16,6.0,WALK +9432585,28757,28757,1179073,True,social,22,16,14.0,WALK +9432589,28757,28757,1179073,False,Home,16,22,16.0,WALK +9435473,28766,28766,1179434,True,othmaint,21,16,14.0,TNC_SINGLE +9435477,28766,28766,1179434,False,Home,16,21,15.0,DRIVEALONEFREE +9435513,28766,28766,1179439,True,shopping,5,16,9.0,WALK +9435517,28766,28766,1179439,False,Home,16,5,11.0,WALK +9464969,28856,28856,1183121,True,othdiscr,12,16,12.0,WALK +9464973,28856,28856,1183121,False,Home,16,12,13.0,WALK +9483513,28913,28913,1185439,True,eatout,2,16,14.0,WALK +9483517,28913,28913,1185439,False,Home,16,2,17.0,WALK +9491257,28936,28936,1186407,True,univ,14,16,14.0,WALK_LOC +9491261,28936,28936,1186407,False,eatout,9,14,14.0,WALK_LOC +9491262,28936,28936,1186407,False,escort,10,9,15.0,WALK_LOC +9491263,28936,28936,1186407,False,Home,16,10,15.0,WALK_LOC +9492041,28939,28939,1186505,True,social,10,16,6.0,DRIVEALONEFREE +9492042,28939,28939,1186505,True,eatout,22,10,6.0,TNC_SINGLE +9492045,28939,28939,1186505,False,eatout,12,22,6.0,DRIVEALONEFREE +9492046,28939,28939,1186505,False,Home,16,12,6.0,TNC_SHARED +9492193,28939,28939,1186524,True,othdiscr,17,16,15.0,WALK +9492197,28939,28939,1186524,False,Home,16,17,17.0,WALK +9492257,28939,28939,1186532,True,eatout,5,16,9.0,TNC_SINGLE +9492258,28939,28939,1186532,True,shopping,22,5,10.0,TNC_SINGLE +9492261,28939,28939,1186532,False,Home,16,22,13.0,TNC_SINGLE +9492913,28941,28941,1186614,True,shopping,16,16,9.0,WALK +9492917,28941,28941,1186614,False,Home,16,16,16.0,WALK +9495825,28950,28950,1186978,True,othmaint,11,16,10.0,WALK +9495829,28950,28950,1186978,False,shopping,6,11,15.0,WALK +9495830,28950,28950,1186978,False,escort,5,6,15.0,WALK +9495831,28950,28950,1186978,False,Home,16,5,15.0,WALK +9495865,28950,28950,1186983,True,shopping,5,16,9.0,WALK_LOC +9495866,28950,28950,1186983,True,shopping,3,5,10.0,TNC_SINGLE +9495869,28950,28950,1186983,False,Home,16,3,10.0,WALK_LOC +9507785,28987,28987,1188473,True,shopping,6,16,12.0,WALK_LOC +9507786,28987,28987,1188473,True,eatout,7,6,12.0,WALK +9507789,28987,28987,1188473,False,eatout,6,7,12.0,WALK_LOC +9507790,28987,28987,1188473,False,shopping,5,6,20.0,WALK +9507791,28987,28987,1188473,False,Home,16,5,20.0,WALK_LOC +9513513,29004,29004,1189189,True,othdiscr,1,16,9.0,WALK +9513517,29004,29004,1189189,False,Home,16,1,19.0,WALK +9520121,29024,29024,1190015,True,univ,13,16,18.0,WALK +9520125,29024,29024,1190015,False,Home,16,13,23.0,WALK +9551913,29121,29121,1193989,True,othmaint,7,16,12.0,WALK +9551917,29121,29121,1193989,False,shopping,5,7,14.0,WALK +9551918,29121,29121,1193989,False,shopping,16,5,17.0,WALK +9551919,29121,29121,1193989,False,Home,16,16,17.0,WALK +9553857,29127,29127,1194232,True,othdiscr,21,16,9.0,WALK +9553861,29127,29127,1194232,False,Home,16,21,17.0,WALK +9556481,29135,29135,1194560,True,othdiscr,24,16,9.0,WALK +9556485,29135,29135,1194560,False,Home,16,24,14.0,WALK +9558977,29143,29143,1194872,True,escort,8,16,7.0,WALK +9558981,29143,29143,1194872,False,Home,16,8,7.0,WALK +9559105,29143,29143,1194888,True,othdiscr,4,16,14.0,WALK +9559109,29143,29143,1194888,False,Home,16,4,14.0,WALK +9564025,29158,29158,1195503,True,eatout,7,16,10.0,WALK_LOC +9564026,29158,29158,1195503,True,othdiscr,11,7,10.0,WALK_LOC +9564029,29158,29158,1195503,False,othmaint,9,11,13.0,WALK_LOC +9564030,29158,29158,1195503,False,Home,16,9,13.0,WALK_LOC +9567041,29167,29167,1195880,True,shopping,22,16,9.0,WALK +9567045,29167,29167,1195880,False,Home,16,22,16.0,WALK +9581761,29212,29212,1197720,True,othmaint,14,16,10.0,WALK +9581765,29212,29212,1197720,False,Home,16,14,16.0,WALK +9600457,29269,29269,1200057,True,othmaint,16,16,13.0,WALK +9600461,29269,29269,1200057,False,Home,16,16,15.0,WALK +9608065,29292,29292,1201008,True,social,15,16,9.0,WALK +9608069,29292,29292,1201008,False,Home,16,15,17.0,WALK +9608369,29293,29293,1201046,True,shopping,16,16,8.0,WALK +9608373,29293,29293,1201046,False,Home,16,16,16.0,WALK +9619961,29329,29329,1202495,True,eatout,13,16,12.0,WALK +9619965,29329,29329,1202495,False,Home,16,13,14.0,WALK +9620769,29331,29331,1202596,True,othdiscr,17,16,14.0,WALK +9620773,29331,29331,1202596,False,Home,16,17,16.0,WALK +9625713,29346,29346,1203214,True,othmaint,4,16,10.0,WALK +9625717,29346,29346,1203214,False,Home,16,4,11.0,WALK +9630281,29360,29360,1203785,True,eatout,7,16,10.0,WALK +9630282,29360,29360,1203785,True,othdiscr,20,7,11.0,WALK +9630285,29360,29360,1203785,False,Home,16,20,12.0,WALK +9656897,29441,29441,1207112,True,univ,13,16,15.0,WALK +9656901,29441,29441,1207112,False,Home,16,13,16.0,WALK +9667081,29472,29472,1208385,True,shopping,5,16,9.0,WALK +9667085,29472,29472,1208385,False,Home,16,5,10.0,WALK +9667105,29472,29472,1208388,True,social,13,16,11.0,WALK +9667109,29472,29472,1208388,False,Home,16,13,21.0,WALK +9668745,29477,29477,1208593,True,social,5,16,8.0,WALK +9668749,29477,29477,1208593,False,Home,16,5,22.0,WALK +9679857,29511,29511,1209982,True,univ,13,16,8.0,WALK_LOC +9679861,29511,29511,1209982,False,Home,16,13,15.0,WALK_LOC +9686433,29531,29531,1210804,True,shopping,20,17,10.0,WALK_LRF +9686437,29531,29531,1210804,False,Home,17,20,12.0,WALK_LOC +9689385,29540,29540,1211173,True,shopping,19,17,14.0,TNC_SINGLE +9689389,29540,29540,1211173,False,othdiscr,18,19,14.0,TAXI +9689390,29540,29540,1211173,False,Home,17,18,14.0,TNC_SINGLE +9697545,29565,29565,1212193,True,othmaint,5,17,7.0,WALK +9697549,29565,29565,1212193,False,Home,17,5,15.0,WALK_LRF +9705417,29589,29589,1213177,True,othmaint,4,17,16.0,WALK +9705421,29589,29589,1213177,False,Home,17,4,17.0,WALK +9709657,29602,29602,1213707,True,othdiscr,16,17,21.0,TNC_SHARED +9709661,29602,29602,1213707,False,Home,17,16,21.0,WALK +9709721,29602,29602,1213715,True,shopping,16,17,11.0,WALK +9709725,29602,29602,1213715,False,Home,17,16,13.0,WALK +9709729,29602,29602,1213716,True,othdiscr,19,17,17.0,WALK +9709730,29602,29602,1213716,True,eatout,19,19,17.0,WALK +9709731,29602,29602,1213716,True,shopping,19,19,17.0,SHARED2FREE +9709733,29602,29602,1213716,False,Home,17,19,17.0,SHARED2FREE +9709745,29602,29602,1213718,True,social,10,17,13.0,SHARED2FREE +9709749,29602,29602,1213718,False,Home,17,10,13.0,SHARED2FREE +9710969,29606,29606,1213871,True,othdiscr,18,17,15.0,WALK +9710973,29606,29606,1213871,False,Home,17,18,17.0,WALK +9715433,29620,29620,1214429,True,escort,11,17,8.0,SHARED3FREE +9715437,29620,29620,1214429,False,othmaint,4,11,8.0,DRIVEALONEFREE +9715438,29620,29620,1214429,False,Home,17,4,8.0,DRIVEALONEFREE +9717265,29625,29625,1214658,True,shopping,17,17,15.0,WALK +9717269,29625,29625,1214658,False,Home,17,17,15.0,WALK +9728089,29658,29658,1216011,True,shopping,5,17,18.0,WALK +9728093,29658,29658,1216011,False,social,5,5,19.0,WALK +9728094,29658,29658,1216011,False,Home,17,5,19.0,WALK +9779257,29814,29814,1222407,True,shopping,16,17,9.0,WALK +9779261,29814,29814,1222407,False,Home,17,16,10.0,WALK_LRF +9782801,29825,29825,1222850,True,othdiscr,17,17,14.0,WALK +9782805,29825,29825,1222850,False,Home,17,17,16.0,WALK +9782865,29825,29825,1222858,True,shopping,11,17,10.0,WALK +9782869,29825,29825,1222858,False,Home,17,11,13.0,WALK +9806465,29897,29897,1225808,True,univ,14,17,12.0,WALK_LRF +9806469,29897,29897,1225808,False,Home,17,14,13.0,WALK_LOC +9810881,29911,29911,1226360,True,escort,23,17,18.0,SHARED2FREE +9810885,29911,29911,1226360,False,Home,17,23,19.0,SHARED2FREE +9811033,29911,29911,1226379,True,shopping,16,17,10.0,WALK +9811034,29911,29911,1226379,True,othmaint,9,16,11.0,SHARED3FREE +9811037,29911,29911,1226379,False,escort,16,9,11.0,SHARED3FREE +9811038,29911,29911,1226379,False,social,16,16,11.0,WALK +9811039,29911,29911,1226379,False,Home,17,16,11.0,DRIVEALONEFREE +9811097,29911,29911,1226387,True,social,16,17,11.0,WALK +9811101,29911,29911,1226387,False,Home,17,16,14.0,WALK +9811105,29911,29911,1226388,True,escort,2,17,19.0,TNC_SHARED +9811106,29911,29911,1226388,True,social,12,2,19.0,DRIVEALONEFREE +9811109,29911,29911,1226388,False,shopping,16,12,19.0,DRIVEALONEFREE +9811110,29911,29911,1226388,False,shopping,16,16,19.0,WALK +9811111,29911,29911,1226388,False,Home,17,16,19.0,DRIVEALONEFREE +9814025,29920,29920,1226753,True,shopping,5,17,10.0,WALK_LOC +9814029,29920,29920,1226753,False,Home,17,5,10.0,WALK_LRF +9818641,29934,29934,1227330,True,social,15,17,10.0,BIKE +9818645,29934,29934,1227330,False,Home,17,15,16.0,BIKE +9820217,29939,29939,1227527,True,othmaint,4,18,9.0,WALK_LOC +9820221,29939,29939,1227527,False,Home,18,4,15.0,WALK_LOC +9820225,29939,29939,1227528,True,othmaint,13,18,16.0,DRIVEALONEFREE +9820229,29939,29939,1227528,False,Home,18,13,17.0,TNC_SINGLE +9845777,30017,30017,1230722,True,shopping,11,21,12.0,WALK +9845778,30017,30017,1230722,True,othdiscr,19,11,15.0,WALK +9845781,30017,30017,1230722,False,Home,21,19,19.0,WALK +9856801,30051,30051,1232100,True,escort,20,24,13.0,DRIVEALONEFREE +9856805,30051,30051,1232100,False,eatout,3,20,13.0,SHARED3FREE +9856806,30051,30051,1232100,False,Home,24,3,13.0,SHARED3FREE +9856993,30051,30051,1232124,True,shopping,5,24,15.0,WALK +9856997,30051,30051,1232124,False,Home,24,5,20.0,WALK +9862681,30069,30069,1232835,True,eatout,14,25,10.0,WALK +9862685,30069,30069,1232835,False,Home,25,14,10.0,WALK +9862833,30069,30069,1232854,True,othdiscr,11,25,12.0,WALK_LOC +9862837,30069,30069,1232854,False,Home,25,11,15.0,WALK +9862897,30069,30069,1232862,True,shopping,16,25,16.0,WALK_LOC +9862898,30069,30069,1232862,True,shopping,8,16,17.0,WALK_LOC +9862901,30069,30069,1232862,False,Home,25,8,20.0,WALK_LOC +9863881,30072,30072,1232985,True,shopping,5,25,11.0,WALK +9863885,30072,30072,1232985,False,Home,25,5,13.0,WALK +35291145,107594,107594,4411393,True,work,1,1,7.0,WALK +35291149,107594,107594,4411393,False,Home,1,1,19.0,WALK +35302033,107628,107628,4412754,True,eatout,6,6,12.0,WALK +35302037,107628,107628,4412754,False,Home,6,6,12.0,WALK +35303281,107631,107631,4412910,True,work,2,6,16.0,WALK +35303285,107631,107631,4412910,False,Home,6,2,18.0,WALK +35306233,107640,107640,4413279,True,work,2,6,7.0,WALK +35306237,107640,107640,4413279,False,Home,6,2,18.0,WALK +35308529,107647,107647,4413566,True,work,24,6,8.0,WALK +35308533,107647,107647,4413566,False,Home,6,24,18.0,WALK +35312201,107659,107659,4414025,True,eatout,5,6,18.0,WALK +35312205,107659,107659,4414025,False,Home,6,5,18.0,WALK +35312441,107659,107659,4414055,True,social,12,6,19.0,TNC_SHARED +35312445,107659,107659,4414055,False,Home,6,12,20.0,TNC_SHARED +35312465,107659,107659,4414058,True,work,4,6,6.0,TNC_SINGLE +35312469,107659,107659,4414058,False,Home,6,4,15.0,WALK_LOC +35316121,107671,107671,4414515,True,atwork,9,10,10.0,WALK +35316125,107671,107671,4414515,False,work,9,9,14.0,WALK +35316126,107671,107671,4414515,False,work,9,9,14.0,WALK +35316127,107671,107671,4414515,False,Work,10,9,14.0,WALK +35316137,107671,107671,4414517,True,eatout,7,6,18.0,WALK +35316141,107671,107671,4414517,False,Home,6,7,19.0,WALK +35316401,107671,107671,4414550,True,othmaint,9,6,5.0,WALK +35316402,107671,107671,4414550,True,escort,9,9,6.0,WALK +35316403,107671,107671,4414550,True,work,10,9,7.0,WALK +35316405,107671,107671,4414550,False,othmaint,9,10,17.0,WALK +35316406,107671,107671,4414550,False,social,8,9,17.0,WALK +35316407,107671,107671,4414550,False,Home,6,8,17.0,WALK +35331137,107716,107716,4416392,True,social,17,6,14.0,WALK_LRF +35331141,107716,107716,4416392,False,Home,6,17,14.0,WALK_LOC +35337393,107735,107735,4417174,True,othmaint,2,7,9.0,WALK +35337394,107735,107735,4417174,True,work,8,2,10.0,WALK_LOC +35337397,107735,107735,4417174,False,Home,7,8,17.0,WALK_LOC +35339689,107742,107742,4417461,True,work,1,7,6.0,WALK_LRF +35339693,107742,107742,4417461,False,Home,7,1,17.0,WALK +35345329,107760,107760,4418166,True,eatout,6,7,16.0,WALK +35345333,107760,107760,4418166,False,Home,7,6,19.0,WALK +35345593,107760,107760,4418199,True,work,11,7,11.0,WALK +35345597,107760,107760,4418199,False,Home,7,11,15.0,WALK +35353465,107784,107784,4419183,True,work,13,7,8.0,WALK +35353469,107784,107784,4419183,False,Home,7,13,20.0,WALK_LOC +35374345,107848,107848,4421793,True,othdiscr,5,7,20.0,WALK_LOC +35374349,107848,107848,4421793,False,Home,7,5,20.0,WALK +35374457,107848,107848,4421807,True,work,7,7,7.0,WALK +35374461,107848,107848,4421807,False,Home,7,7,19.0,WALK +35377321,107857,107857,4422165,True,othmaint,5,7,15.0,WALK +35377325,107857,107857,4422165,False,Home,7,5,15.0,SHARED2FREE +35377409,107857,107857,4422176,True,work,5,7,5.0,WALK_LOC +35377413,107857,107857,4422176,False,eatout,5,5,15.0,WALK +35377414,107857,107857,4422176,False,Home,7,5,15.0,WALK_LOC +35389545,107894,107894,4423693,True,work,11,7,7.0,WALK_LOC +35389549,107894,107894,4423693,False,Home,7,11,18.0,WALK +35414489,107971,107971,4426811,True,atwork,23,15,11.0,WALK +35414493,107971,107971,4426811,False,Work,15,23,13.0,WALK +35414801,107971,107971,4426850,True,escort,5,7,7.0,WALK +35414802,107971,107971,4426850,True,work,15,5,8.0,WALK +35414805,107971,107971,4426850,False,othmaint,2,15,22.0,WALK_LOC +35414806,107971,107971,4426850,False,Home,7,2,22.0,TNC_SINGLE +35437673,108041,108041,4429709,True,othmaint,9,8,12.0,WALK +35437674,108041,108041,4429709,True,othmaint,22,9,13.0,WALK_LRF +35437677,108041,108041,4429709,False,Home,8,22,16.0,WALK_LRF +35437737,108041,108041,4429717,True,social,24,8,18.0,WALK +35437741,108041,108041,4429717,False,Home,8,24,21.0,WALK +35440057,108048,108048,4430007,True,work,19,8,12.0,WALK +35440061,108048,108048,4430007,False,Home,8,19,20.0,WALK +35441913,108054,108054,4430239,True,othdiscr,5,8,18.0,WALK +35441917,108054,108054,4430239,False,Home,8,5,21.0,WALK +35441937,108054,108054,4430242,True,othmaint,5,8,10.0,BIKE +35441941,108054,108054,4430242,False,Home,8,5,10.0,WALK +35442025,108054,108054,4430253,True,work,4,8,10.0,WALK +35442029,108054,108054,4430253,False,Home,8,4,18.0,WALK +35466625,108129,108129,4433328,True,work,2,8,12.0,SHARED2FREE +35466629,108129,108129,4433328,False,Home,8,2,17.0,WALK_LOC +35466633,108129,108129,4433329,True,work,2,8,17.0,WALK_LOC +35466637,108129,108129,4433329,False,Home,8,2,17.0,WALK +35473073,108149,108149,4434134,True,othdiscr,10,8,18.0,WALK +35473077,108149,108149,4434134,False,Home,8,10,18.0,WALK +35473185,108149,108149,4434148,True,work,13,8,7.0,WALK_LOC +35473189,108149,108149,4434148,False,Home,8,13,17.0,WALK +35473513,108150,108150,4434189,True,work,11,8,6.0,WALK +35473517,108150,108150,4434189,False,Home,8,11,20.0,WALK +35493193,108210,108210,4436649,True,work,4,9,6.0,WALK_LRF +35493197,108210,108210,4436649,False,Home,9,4,15.0,WALK_LRF +35493409,108211,108211,4436676,True,othdiscr,24,9,17.0,BIKE +35493413,108211,108211,4436676,False,Home,9,24,21.0,BIKE +35493433,108211,108211,4436679,True,othmaint,12,9,6.0,WALK_LRF +35493437,108211,108211,4436679,False,Home,9,12,15.0,WALK_LRF +35499097,108228,108228,4437387,True,work,4,9,8.0,WALK +35499101,108228,108228,4437387,False,Home,9,4,17.0,WALK_LRF +35514841,108276,108276,4439355,True,work,21,9,6.0,WALK_LRF +35514845,108276,108276,4439355,False,Home,9,21,21.0,WALK_LOC +35522713,108300,108300,4440339,True,escort,18,9,5.0,WALK_LOC +35522714,108300,108300,4440339,True,work,22,18,7.0,WALK_LRF +35522717,108300,108300,4440339,False,shopping,12,22,16.0,WALK_LOC +35522718,108300,108300,4440339,False,Home,9,12,17.0,WALK_LRF +35528945,108319,108319,4441118,True,work,9,9,6.0,WALK +35528949,108319,108319,4441118,False,Home,9,9,18.0,TNC_SINGLE +35560169,108415,108415,4445021,True,escort,7,9,9.0,WALK +35560170,108415,108415,4445021,True,eatout,7,7,12.0,WALK +35560173,108415,108415,4445021,False,Home,9,7,14.0,WALK +35608649,108562,108562,4451081,True,work,16,9,7.0,WALK_LOC +35608653,108562,108562,4451081,False,Home,9,16,14.0,WALK_LRF +35619145,108594,108594,4452393,True,work,2,9,7.0,TNC_SINGLE +35619149,108594,108594,4452393,False,Home,9,2,19.0,TNC_SINGLE +35625705,108614,108614,4453213,True,work,4,9,9.0,WALK_LRF +35625706,108614,108614,4453213,True,work,17,4,10.0,WALK_LRF +35625709,108614,108614,4453213,False,Home,9,17,20.0,WALK_LRF +35652929,108697,108697,4456616,True,work,4,10,8.0,WALK_LRF +35652933,108697,108697,4456616,False,Home,10,4,17.0,WALK_LRF +35658505,108714,108714,4457313,True,work,1,10,10.0,WALK_LRF +35658509,108714,108714,4457313,False,eatout,9,1,21.0,WALK_LRF +35658510,108714,108714,4457313,False,othmaint,9,9,21.0,WALK +35658511,108714,108714,4457313,False,Home,10,9,21.0,WALK +35660521,108721,108721,4457565,True,atwork,5,5,13.0,WALK +35660525,108721,108721,4457565,False,Work,5,5,13.0,SHARED2FREE +35660801,108721,108721,4457600,True,work,5,10,10.0,WALK +35660805,108721,108721,4457600,False,Home,10,5,19.0,WALK +35663097,108728,108728,4457887,True,escort,5,10,16.0,WALK +35663098,108728,108728,4457887,True,work,2,5,17.0,WALK_LOC +35663101,108728,108728,4457887,False,escort,11,2,19.0,WALK_LOC +35663102,108728,108728,4457887,False,shopping,16,11,19.0,WALK_LOC +35663103,108728,108728,4457887,False,eatout,4,16,19.0,WALK +35663104,108728,108728,4457887,False,Home,10,4,19.0,WALK_LRF +35686057,108798,108798,4460757,True,work,9,10,17.0,TNC_SINGLE +35686061,108798,108798,4460757,False,Home,10,9,18.0,WALK +35704753,108855,108855,4463094,True,work,1,10,7.0,WALK_LRF +35704757,108855,108855,4463094,False,Home,10,1,17.0,WALK_LRF +35707377,108863,108863,4463422,True,work,14,10,7.0,WALK_LRF +35707381,108863,108863,4463422,False,Home,10,14,19.0,WALK_LRF +35725089,108917,108917,4465636,True,escort,9,10,8.0,WALK +35725090,108917,108917,4465636,True,work,14,9,8.0,WALK_LRF +35725093,108917,108917,4465636,False,Home,10,14,21.0,WALK_LRF +35728697,108928,108928,4466087,True,work,15,10,8.0,WALK_LOC +35728701,108928,108928,4466087,False,Home,10,15,18.0,WALK_LRF +35728705,108928,108928,4466088,True,work,15,10,19.0,WALK +35728709,108928,108928,4466088,False,Home,10,15,22.0,WALK +35747113,108985,108985,4468389,True,atwork,15,16,12.0,WALK +35747117,108985,108985,4468389,False,Work,16,15,14.0,WALK +35747345,108985,108985,4468418,True,shopping,19,10,20.0,WALK +35747349,108985,108985,4468418,False,Home,10,19,21.0,WALK +35747393,108985,108985,4468424,True,work,16,10,8.0,WALK_LOC +35747397,108985,108985,4468424,False,shopping,11,16,18.0,WALK_LOC +35747398,108985,108985,4468424,False,Home,10,11,20.0,WALK +35766369,109043,109043,4470796,True,shopping,17,10,21.0,WALK_LRF +35766373,109043,109043,4470796,False,Home,10,17,21.0,WALK_LRF +35766417,109043,109043,4470802,True,work,2,10,8.0,TNC_SHARED +35766421,109043,109043,4470802,False,eatout,8,2,20.0,WALK +35766422,109043,109043,4470802,False,Home,10,8,21.0,WALK_LOC +35778881,109081,109081,4472360,True,escort,8,10,8.0,WALK +35778882,109081,109081,4472360,True,work,9,8,8.0,WALK_LOC +35778885,109081,109081,4472360,False,Home,10,9,18.0,WALK +35797249,109137,109137,4474656,True,work,2,10,9.0,WALK_LRF +35797253,109137,109137,4474656,False,Home,10,2,18.0,WALK_LRF +35797257,109137,109137,4474657,True,work,2,10,18.0,WALK_LRF +35797261,109137,109137,4474657,False,Home,10,2,20.0,WALK_LRF +35828081,109231,109231,4478510,True,work,7,10,14.0,WALK +35828082,109231,109231,4478510,True,work,2,7,15.0,WALK +35828083,109231,109231,4478510,True,work,4,2,15.0,WALK +35828084,109231,109231,4478510,True,work,2,4,15.0,WALK +35828085,109231,109231,4478510,False,shopping,5,2,21.0,WALK +35828086,109231,109231,4478510,False,Home,10,5,21.0,WALK +35830049,109237,109237,4478756,True,work,7,10,11.0,WALK_LOC +35830053,109237,109237,4478756,False,work,9,7,17.0,WALK +35830054,109237,109237,4478756,False,Home,10,9,18.0,WALK +35837593,109260,109260,4479699,True,work,5,10,11.0,WALK +35837597,109260,109260,4479699,False,Home,10,5,15.0,WALK +35839561,109266,109266,4479945,True,work,2,10,6.0,WALK_HVY +35839565,109266,109266,4479945,False,Home,10,2,13.0,WALK_LRF +35853993,109310,109310,4481749,True,work,13,10,7.0,WALK_LRF +35853994,109310,109310,4481749,True,work,4,13,8.0,WALK +35853997,109310,109310,4481749,False,social,11,4,17.0,WALK +35853998,109310,109310,4481749,False,othmaint,7,11,17.0,WALK +35853999,109310,109310,4481749,False,othmaint,9,7,20.0,WALK +35854000,109310,109310,4481749,False,Home,10,9,21.0,WALK_LOC +35854649,109312,109312,4481831,True,work,9,10,8.0,WALK +35854653,109312,109312,4481831,False,Home,10,9,16.0,WALK +35862193,109335,109335,4482774,True,work,7,10,7.0,WALK +35862197,109335,109335,4482774,False,Home,10,7,17.0,WALK +35874985,109374,109374,4484373,True,work,9,10,6.0,WALK +35874989,109374,109374,4484373,False,Home,10,9,19.0,WALK +35886465,109409,109409,4485808,True,work,1,10,12.0,WALK_LRF +35886469,109409,109409,4485808,False,eatout,7,1,21.0,WALK_LOC +35886470,109409,109409,4485808,False,Home,10,7,21.0,WALK_LRF +35892369,109427,109427,4486546,True,work,15,10,7.0,WALK +35892373,109427,109427,4486546,False,Home,10,15,18.0,WALK +35893897,109432,109432,4486737,True,othdiscr,9,10,21.0,WALK +35893901,109432,109432,4486737,False,Home,10,9,21.0,WALK +35893961,109432,109432,4486745,True,shopping,12,10,17.0,WALK_LOC +35893965,109432,109432,4486745,False,Home,10,12,19.0,WALK_LOC +35893969,109432,109432,4486746,True,shopping,5,10,21.0,WALK_LOC +35893973,109432,109432,4486746,False,Home,10,5,21.0,WALK_LOC +35894009,109432,109432,4486751,True,work,1,10,7.0,WALK +35894013,109432,109432,4486751,False,Home,10,1,16.0,WALK +35904745,109465,109465,4488093,True,othmaint,12,10,8.0,WALK_LOC +35904749,109465,109465,4488093,False,Home,10,12,16.0,WALK_LOC +35904753,109465,109465,4488094,True,othmaint,1,10,20.0,WALK +35904757,109465,109465,4488094,False,Home,10,1,20.0,WALK +35910081,109481,109481,4488760,True,work,12,10,8.0,WALK +35910085,109481,109481,4488760,False,Home,10,12,21.0,WALK +35913081,109491,109491,4489135,True,atwork,13,11,11.0,WALK +35913085,109491,109491,4489135,False,Work,11,13,11.0,WALK +35913361,109491,109491,4489170,True,work,11,10,7.0,WALK +35913365,109491,109491,4489170,False,Home,10,11,18.0,WALK +35915281,109497,109497,4489410,True,shopping,5,10,13.0,BIKE +35915285,109497,109497,4489410,False,Home,10,5,14.0,BIKE +35920529,109513,109513,4490066,True,shopping,5,10,11.0,DRIVEALONEFREE +35920533,109513,109513,4490066,False,escort,16,5,13.0,TNC_SHARED +35920534,109513,109513,4490066,False,Home,10,16,13.0,DRIVEALONEFREE +35920537,109513,109513,4490067,True,othmaint,9,10,13.0,WALK_LOC +35920538,109513,109513,4490067,True,othmaint,13,9,13.0,TNC_SINGLE +35920539,109513,109513,4490067,True,shopping,19,13,13.0,WALK_LOC +35920541,109513,109513,4490067,False,Home,10,19,13.0,WALK_LOC +35920577,109513,109513,4490072,True,work,20,10,14.0,WALK +35920581,109513,109513,4490072,False,Home,10,20,21.0,WALK +35922961,109521,109521,4490370,True,escort,9,10,15.0,WALK +35922965,109521,109521,4490370,False,Home,10,9,16.0,WALK +35923153,109521,109521,4490394,True,shopping,12,10,9.0,WALK +35923157,109521,109521,4490394,False,Home,10,12,9.0,BIKE +35925825,109529,109529,4490728,True,work,24,10,5.0,WALK_LRF +35925829,109529,109529,4490728,False,Home,10,24,12.0,WALK +35925833,109529,109529,4490729,True,work,24,10,13.0,WALK_LRF +35925837,109529,109529,4490729,False,Home,10,24,17.0,WALK_LRF +35925889,109530,109530,4490736,True,eatout,19,10,10.0,WALK +35925893,109530,109530,4490736,False,Home,10,19,15.0,WALK +35942553,109580,109580,4492819,True,othmaint,11,10,8.0,WALK +35942554,109580,109580,4492819,True,work,14,11,9.0,WALK +35942557,109580,109580,4492819,False,social,7,14,17.0,WALK +35942558,109580,109580,4492819,False,Home,10,7,18.0,WALK +35948017,109597,109597,4493502,True,othdiscr,20,10,10.0,WALK_LOC +35948021,109597,109597,4493502,False,shopping,9,20,14.0,WALK_LOC +35948022,109597,109597,4493502,False,Home,10,9,14.0,WALK +35948345,109598,109598,4493543,True,othdiscr,22,10,5.0,WALK_LRF +35948349,109598,109598,4493543,False,Home,10,22,13.0,WALK_LOC +35948369,109598,109598,4493546,True,escort,7,10,15.0,TNC_SINGLE +35948370,109598,109598,4493546,True,othmaint,19,7,15.0,TNC_SINGLE +35948373,109598,109598,4493546,False,social,9,19,15.0,WALK_LOC +35948374,109598,109598,4493546,False,Home,10,9,15.0,TNC_SHARED +35955345,109619,109619,4494418,True,work,16,10,8.0,WALK_LOC +35955349,109619,109619,4494418,False,Home,10,16,16.0,WALK_LOC +35968793,109660,109660,4496099,True,work,13,10,10.0,WALK +35968797,109660,109660,4496099,False,Home,10,13,20.0,WALK +35976073,109683,109683,4497009,True,eatout,19,10,19.0,WALK +35976077,109683,109683,4497009,False,Home,10,19,19.0,WALK +35976337,109683,109683,4497042,True,work,1,10,6.0,WALK_LOC +35976341,109683,109683,4497042,False,Home,10,1,18.0,WALK_LOC +35992081,109731,109731,4499010,True,work,4,10,11.0,WALK_LRF +35992082,109731,109731,4499010,True,work,23,4,11.0,WALK +35992085,109731,109731,4499010,False,eatout,15,23,17.0,WALK +35992086,109731,109731,4499010,False,Home,10,15,17.0,WALK_LRF +36003561,109766,109766,4500445,True,work,7,10,8.0,WALK +36003565,109766,109766,4500445,False,Home,10,7,17.0,WALK +36005201,109771,109771,4500650,True,work,20,10,8.0,WALK +36005205,109771,109771,4500650,False,Home,10,20,17.0,WALK_LOC +36013729,109797,109797,4501716,True,eatout,24,10,9.0,WALK_LRF +36013730,109797,109797,4501716,True,work,4,24,9.0,WALK +36013733,109797,109797,4501716,False,Home,10,4,18.0,WALK_LRF +36032097,109853,109853,4504012,True,work,1,11,5.0,WALK +36032101,109853,109853,4504012,False,Home,11,1,19.0,WALK_LRF +36071129,109972,109972,4508891,True,work,14,11,8.0,WALK +36071133,109972,109972,4508891,False,Home,11,14,18.0,WALK +36072441,109976,109976,4509055,True,work,5,11,7.0,WALK_LOC +36072445,109976,109976,4509055,False,Home,11,5,19.0,WALK +36075721,109986,109986,4509465,True,work,5,11,8.0,SHARED2FREE +36075725,109986,109986,4509465,False,Home,11,5,17.0,WALK +36089497,110028,110028,4511187,True,work,8,11,7.0,BIKE +36089501,110028,110028,4511187,False,Home,11,8,13.0,BIKE +36099753,110060,110060,4512469,True,escort,9,11,7.0,TNC_SINGLE +36099757,110060,110060,4512469,False,shopping,1,9,8.0,TNC_SINGLE +36099758,110060,110060,4512469,False,Home,11,1,8.0,WALK_LOC +36101681,110066,110066,4512710,True,atwork,8,2,11.0,WALK +36101685,110066,110066,4512710,False,Work,2,8,11.0,WALK +36101961,110066,110066,4512745,True,work,2,11,6.0,DRIVEALONEFREE +36101965,110066,110066,4512745,False,Home,11,2,17.0,WALK +36104257,110073,110073,4513032,True,work,9,11,7.0,WALK +36104258,110073,110073,4513032,True,work,11,9,8.0,WALK +36104261,110073,110073,4513032,False,Home,11,11,17.0,WALK +36149849,110212,110212,4518731,True,work,16,12,5.0,WALK +36149853,110212,110212,4518731,False,Home,12,16,15.0,DRIVEALONEFREE +36155425,110229,110229,4519428,True,escort,4,12,11.0,WALK +36155426,110229,110229,4519428,True,escort,22,4,11.0,WALK +36155427,110229,110229,4519428,True,work,24,22,11.0,WALK +36155429,110229,110229,4519428,False,Home,12,24,20.0,WALK +36164937,110258,110258,4520617,True,work,12,13,14.0,WALK_LOC +36164938,110258,110258,4520617,True,work,2,12,14.0,TNC_SINGLE +36164941,110258,110258,4520617,False,Home,13,2,23.0,TNC_SINGLE +36172217,110281,110281,4521527,True,eatout,5,14,15.0,WALK +36172221,110281,110281,4521527,False,Home,14,5,16.0,WALK +36172481,110281,110281,4521560,True,work,13,14,7.0,WALK_LOC +36172485,110281,110281,4521560,False,Home,14,13,14.0,WALK +36179257,110302,110302,4522407,True,othdiscr,11,14,18.0,WALK +36179261,110302,110302,4522407,False,Home,14,11,21.0,WALK +36179369,110302,110302,4522421,True,work,13,14,5.0,WALK +36179373,110302,110302,4522421,False,Home,14,13,18.0,WALK_LOC +36184617,110318,110318,4523077,True,work,13,14,6.0,WALK +36184621,110318,110318,4523077,False,Home,14,13,17.0,WALK +36210201,110396,110396,4526275,True,work,1,16,6.0,WALK +36210205,110396,110396,4526275,False,Home,16,1,18.0,WALK +36211841,110401,110401,4526480,True,work,13,16,8.0,TNC_SINGLE +36211845,110401,110401,4526480,False,Home,16,13,18.0,WALK +36211905,110402,110402,4526488,True,eatout,16,16,12.0,SHARED2FREE +36211909,110402,110402,4526488,False,Home,16,16,12.0,WALK +36212081,110402,110402,4526510,True,othmaint,2,16,15.0,WALK +36212085,110402,110402,4526510,False,Home,16,2,15.0,WALK +36212497,110403,110403,4526562,True,work,14,16,7.0,WALK +36212501,110403,110403,4526562,False,Home,16,14,16.0,WALK +36230489,110458,110458,4528811,True,shopping,2,16,13.0,WALK_LOC +36230493,110458,110458,4528811,False,Home,16,2,17.0,WALK_LOC +36230513,110458,110458,4528814,True,social,21,16,9.0,WALK +36230517,110458,110458,4528814,False,Home,16,21,11.0,WALK +36230521,110458,110458,4528815,True,social,22,16,17.0,WALK +36230525,110458,110458,4528815,False,Home,16,22,21.0,WALK +36297777,110663,110663,4537222,True,work,24,16,8.0,WALK +36297781,110663,110663,4537222,False,Home,16,24,17.0,WALK +36305697,110688,110688,4538212,True,atwork,5,22,13.0,WALK +36305701,110688,110688,4538212,False,Work,22,5,13.0,WALK +36305977,110688,110688,4538247,True,othdiscr,16,16,13.0,DRIVEALONEFREE +36305978,110688,110688,4538247,True,work,22,16,13.0,DRIVEALONEFREE +36305981,110688,110688,4538247,False,Home,16,22,21.0,DRIVEALONEFREE +36308929,110697,110697,4538616,True,work,14,16,14.0,WALK +36308933,110697,110697,4538616,False,Home,16,14,21.0,WALK +36313521,110711,110711,4539190,True,work,13,16,8.0,WALK +36313525,110711,110711,4539190,False,Home,16,13,11.0,WALK +36319753,110730,110730,4539969,True,work,2,16,7.0,WALK +36319757,110730,110730,4539969,False,Home,16,2,17.0,WALK +36323689,110742,110742,4540461,True,work,4,16,8.0,WALK +36323693,110742,110742,4540461,False,Home,16,4,16.0,WALK +36348201,110817,110817,4543525,True,othmaint,5,16,14.0,WALK +36348205,110817,110817,4543525,False,Home,16,5,17.0,WALK +36348241,110817,110817,4543530,True,shopping,16,16,13.0,DRIVEALONEFREE +36348245,110817,110817,4543530,False,Home,16,16,14.0,SHARED3FREE +36358017,110847,110847,4544752,True,othdiscr,20,16,10.0,WALK +36358021,110847,110847,4544752,False,Home,16,20,12.0,WALK +36358041,110847,110847,4544755,True,othmaint,9,16,7.0,WALK_LOC +36358045,110847,110847,4544755,False,Home,16,9,10.0,WALK +36358129,110847,110847,4544766,True,work,13,16,12.0,WALK +36358133,110847,110847,4544766,False,othmaint,5,13,19.0,WALK +36358134,110847,110847,4544766,False,Home,16,5,21.0,WALK +36387977,110938,110938,4548497,True,work,16,16,6.0,WALK +36387981,110938,110938,4548497,False,Home,16,16,15.0,WALK +36390601,110946,110946,4548825,True,work,2,16,7.0,WALK +36390605,110946,110946,4548825,False,Home,16,2,15.0,WALK +36391913,110950,110950,4548989,True,work,11,16,8.0,WALK +36391917,110950,110950,4548989,False,Home,16,11,18.0,WALK +36412641,111014,111014,4551580,True,eatout,5,16,13.0,WALK +36412645,111014,111014,4551580,False,Home,16,5,18.0,WALK +36412857,111014,111014,4551607,True,shopping,11,16,10.0,WALK_LOC +36412861,111014,111014,4551607,False,Home,16,11,13.0,WALK_LOC +36415529,111022,111022,4551941,True,work,11,16,7.0,WALK +36415533,111022,111022,4551941,False,Home,16,11,17.0,WALK +36421433,111040,111040,4552679,True,work,10,16,7.0,WALK +36421437,111040,111040,4552679,False,Home,16,10,17.0,TAXI +36443737,111108,111108,4555467,True,work,12,16,8.0,WALK_LOC +36443741,111108,111108,4555467,False,othmaint,5,12,18.0,WALK_LOC +36443742,111108,111108,4555467,False,Home,16,5,19.0,WALK_LOC +36461777,111163,111163,4557722,True,work,5,16,11.0,WALK_LOC +36461781,111163,111163,4557722,False,othmaint,4,5,19.0,WALK +36461782,111163,111163,4557722,False,Home,16,4,20.0,WALK_LOC +36477849,111212,111212,4559731,True,work,12,16,8.0,BIKE +36477853,111212,111212,4559731,False,Home,16,12,17.0,BIKE +36480801,111221,111221,4560100,True,work,9,16,12.0,WALK_LRF +36480805,111221,111221,4560100,False,othdiscr,5,9,18.0,WALK +36480806,111221,111221,4560100,False,Home,16,5,21.0,WALK +36497529,111272,111272,4562191,True,work,18,16,8.0,WALK_LOC +36497533,111272,111272,4562191,False,Home,16,18,18.0,WALK_LOC +36500697,111282,111282,4562587,True,othdiscr,4,16,10.0,WALK +36500701,111282,111282,4562587,False,shopping,3,4,15.0,WALK_LOC +36500702,111282,111282,4562587,False,Home,16,3,15.0,WALK_LOC +36513273,111320,111320,4564159,True,work,22,16,7.0,WALK +36513277,111320,111320,4564159,False,Home,16,22,22.0,WALK +36518521,111336,111336,4564815,True,work,2,16,7.0,WALK_LOC +36518525,111336,111336,4564815,False,Home,16,2,16.0,TNC_SINGLE +36536233,111390,111390,4567029,True,work,1,16,7.0,WALK +36536237,111390,111390,4567029,False,Home,16,1,17.0,WALK +36540169,111402,111402,4567521,True,work,2,16,7.0,WALK +36540173,111402,111402,4567521,False,Home,16,2,14.0,WALK +36568049,111487,111487,4571006,True,work,12,16,6.0,WALK +36568053,111487,111487,4571006,False,Home,16,12,14.0,WALK +36584449,111537,111537,4573056,True,work,17,16,9.0,WALK +36584453,111537,111537,4573056,False,shopping,16,17,13.0,WALK +36584454,111537,111537,4573056,False,work,24,16,13.0,WALK +36584455,111537,111537,4573056,False,Home,16,24,13.0,WALK +36609705,111614,111614,4576213,True,work,1,16,14.0,WALK_LOC +36609709,111614,111614,4576213,False,Home,16,1,21.0,WALK +36613969,111627,111627,4576746,True,othmaint,11,16,13.0,WALK_LOC +36613970,111627,111627,4576746,True,work,16,11,14.0,WALK +36613973,111627,111627,4576746,False,shopping,1,16,15.0,WALK +36613974,111627,111627,4576746,False,Home,16,1,22.0,TNC_SINGLE +36624481,111660,111660,4578060,True,atwork,16,18,12.0,WALK +36624485,111660,111660,4578060,False,othmaint,16,16,14.0,WALK +36624486,111660,111660,4578060,False,shopping,16,16,14.0,WALK +36624487,111660,111660,4578060,False,eatout,7,16,14.0,WALK +36624488,111660,111660,4578060,False,Work,18,7,14.0,WALK +36624745,111660,111660,4578093,True,shopping,16,16,19.0,BIKE +36624749,111660,111660,4578093,False,Home,16,16,19.0,BIKE +36624793,111660,111660,4578099,True,work,18,16,6.0,WALK_LRF +36624797,111660,111660,4578099,False,Home,16,18,17.0,WALK_LRF +36637649,111700,111700,4579706,True,eatout,12,16,16.0,WALK +36637653,111700,111700,4579706,False,Home,16,12,20.0,WALK +36637865,111700,111700,4579733,True,shopping,14,16,11.0,WALK_LOC +36637869,111700,111700,4579733,False,Home,16,14,14.0,WALK_LOC +36642897,111716,111716,4580362,True,eatout,16,16,16.0,WALK +36642901,111716,111716,4580362,False,Home,16,16,21.0,WALK +36643049,111716,111716,4580381,True,othdiscr,16,16,11.0,WALK +36643053,111716,111716,4580381,False,Home,16,16,16.0,WALK +36643161,111716,111716,4580395,True,work,12,16,7.0,DRIVEALONEFREE +36643165,111716,111716,4580395,False,Home,16,12,11.0,DRIVEALONEFREE +36656937,111758,111758,4582117,True,work,16,16,7.0,WALK +36656941,111758,111758,4582117,False,Home,16,16,16.0,WALK +36673665,111809,111809,4584208,True,work,4,16,7.0,WALK +36673669,111809,111809,4584208,False,Home,16,4,17.0,WALK +36680113,111829,111829,4585014,True,othdiscr,11,16,17.0,DRIVEALONEFREE +36680117,111829,111829,4585014,False,Home,16,11,17.0,TNC_SHARED +36680225,111829,111829,4585028,True,work,14,16,6.0,WALK +36680229,111829,111829,4585028,False,Home,16,14,16.0,WALK +36684161,111841,111841,4585520,True,work,11,16,6.0,WALK +36684165,111841,111841,4585520,False,Home,16,11,17.0,WALK +36715321,111936,111936,4589415,True,work,7,16,7.0,WALK_LOC +36715325,111936,111936,4589415,False,Home,16,7,17.0,WALK_LOC +36721769,111956,111956,4590221,True,othdiscr,4,16,7.0,WALK +36721773,111956,111956,4590221,False,Home,16,4,11.0,WALK +36721833,111956,111956,4590229,True,shopping,11,16,12.0,WALK +36721837,111956,111956,4590229,False,Home,16,11,13.0,WALK +36727177,111973,111973,4590897,True,atwork,12,10,10.0,SHARED2FREE +36727181,111973,111973,4590897,False,Work,10,12,10.0,WALK +36727457,111973,111973,4590932,True,escort,12,16,8.0,DRIVEALONEFREE +36727458,111973,111973,4590932,True,eatout,5,12,8.0,WALK +36727459,111973,111973,4590932,True,escort,11,5,8.0,SHARED2FREE +36727460,111973,111973,4590932,True,work,10,11,9.0,WALK +36727461,111973,111973,4590932,False,Home,16,10,18.0,SHARED2FREE +36730473,111983,111983,4591309,True,eatout,2,16,14.0,WALK +36730477,111983,111983,4591309,False,Home,16,2,16.0,WALK +36730625,111983,111983,4591328,True,othdiscr,16,16,14.0,WALK +36730629,111983,111983,4591328,False,Home,16,16,14.0,WALK +36730689,111983,111983,4591336,True,shopping,11,16,18.0,WALK +36730693,111983,111983,4591336,False,Home,16,11,20.0,WALK +36733641,111992,111992,4591705,True,shopping,19,16,10.0,WALK_LOC +36733645,111992,111992,4591705,False,Home,16,19,15.0,WALK_LOC +36740905,112014,112014,4592613,True,work,14,16,7.0,WALK +36740909,112014,112014,4592613,False,Home,16,14,17.0,WALK +36744401,112025,112025,4593050,True,othdiscr,25,16,10.0,WALK +36744405,112025,112025,4593050,False,Home,16,25,12.0,WALK +36744425,112025,112025,4593053,True,othmaint,16,16,15.0,WALK +36744429,112025,112025,4593053,False,Home,16,16,16.0,WALK +36757193,112064,112064,4594649,True,othdiscr,21,16,12.0,DRIVEALONEFREE +36757197,112064,112064,4594649,False,Home,16,21,14.0,TNC_SHARED +36757257,112064,112064,4594657,True,shopping,3,16,16.0,WALK +36757261,112064,112064,4594657,False,Home,16,3,17.0,WALK +36762505,112080,112080,4595313,True,shopping,4,16,21.0,DRIVEALONEFREE +36762509,112080,112080,4595313,False,Home,16,4,22.0,DRIVEALONEFREE +36762553,112080,112080,4595319,True,work,14,16,6.0,WALK +36762557,112080,112080,4595319,False,Home,16,14,16.0,WALK +36767145,112094,112094,4595893,True,work,14,16,11.0,WALK +36767149,112094,112094,4595893,False,Home,16,14,20.0,WALK +36777073,112125,112125,4597134,True,shopping,16,16,7.0,SHARED2FREE +36777074,112125,112125,4597134,True,eatout,3,16,7.0,WALK +36777075,112125,112125,4597134,True,escort,25,3,7.0,WALK +36777077,112125,112125,4597134,False,Home,16,25,7.0,SHARED2FREE +36777313,112125,112125,4597164,True,work,16,16,7.0,WALK +36777317,112125,112125,4597164,False,Home,16,16,18.0,WALK +36782561,112141,112141,4597820,True,work,12,16,10.0,WALK_LOC +36782565,112141,112141,4597820,False,Home,16,12,17.0,WALK +36783217,112143,112143,4597902,True,work,16,16,13.0,WALK +36783218,112143,112143,4597902,True,work,1,16,14.0,WALK_LOC +36783221,112143,112143,4597902,False,shopping,13,1,21.0,WALK +36783222,112143,112143,4597902,False,Home,16,13,22.0,WALK +36800321,112196,112196,4600040,True,atwork,13,19,12.0,WALK +36800325,112196,112196,4600040,False,Work,19,13,13.0,WALK +36800489,112196,112196,4600061,True,othdiscr,11,16,20.0,DRIVEALONEFREE +36800493,112196,112196,4600061,False,eatout,2,11,20.0,SHARED3FREE +36800494,112196,112196,4600061,False,shopping,8,2,20.0,WALK +36800495,112196,112196,4600061,False,Home,16,8,20.0,WALK +36800601,112196,112196,4600075,True,escort,4,16,8.0,WALK +36800602,112196,112196,4600075,True,escort,9,4,9.0,WALK +36800603,112196,112196,4600075,True,eatout,5,9,9.0,WALK_LOC +36800604,112196,112196,4600075,True,work,19,5,10.0,SHARED3FREE +36800605,112196,112196,4600075,False,Home,16,19,19.0,WALK_LOC +36814377,112238,112238,4601797,True,work,16,16,7.0,WALK +36814381,112238,112238,4601797,False,Home,16,16,22.0,WALK +36835305,112302,112302,4604413,True,univ,12,17,18.0,WALK +36835309,112302,112302,4604413,False,othmaint,21,12,21.0,WALK +36835310,112302,112302,4604413,False,Home,17,21,21.0,WALK +36835369,112302,112302,4604421,True,work,7,17,14.0,WALK +36835373,112302,112302,4604421,False,work,22,7,15.0,DRIVEALONEFREE +36835374,112302,112302,4604421,False,escort,17,22,17.0,DRIVEALONEFREE +36835375,112302,112302,4604421,False,Home,17,17,17.0,WALK +36836353,112305,112305,4604544,True,work,4,17,8.0,WALK_LOC +36836357,112305,112305,4604544,False,Home,17,4,17.0,WALK +36842257,112323,112323,4605282,True,work,22,17,8.0,WALK_LRF +36842261,112323,112323,4605282,False,Home,17,22,18.0,WALK_LRF +36847833,112340,112340,4605979,True,work,5,17,8.0,WALK_LOC +36847837,112340,112340,4605979,False,Home,17,5,17.0,WALK_LRF +36849361,112345,112345,4606170,True,othdiscr,22,17,11.0,WALK +36849365,112345,112345,4606170,False,Home,17,22,17.0,WALK +36850457,112348,112348,4606307,True,work,2,17,7.0,WALK_LOC +36850461,112348,112348,4606307,False,Home,17,2,16.0,WALK_LRF +36892657,112477,112477,4611582,True,othdiscr,21,17,18.0,WALK_LOC +36892661,112477,112477,4611582,False,Home,17,21,23.0,WALK_LOC +36892681,112477,112477,4611585,True,othmaint,5,17,9.0,WALK_LOC +36892685,112477,112477,4611585,False,Home,17,5,10.0,WALK +36892721,112477,112477,4611590,True,shopping,9,17,12.0,WALK_LRF +36892722,112477,112477,4611590,True,shopping,11,9,12.0,WALK_LOC +36892725,112477,112477,4611590,False,shopping,8,11,14.0,WALK_LOC +36892726,112477,112477,4611590,False,othmaint,5,8,15.0,WALK_LOC +36892727,112477,112477,4611590,False,Home,17,5,15.0,WALK_LRF +36896769,112490,112490,4612096,True,eatout,16,17,17.0,WALK +36896773,112490,112490,4612096,False,Home,17,16,17.0,WALK +36896945,112490,112490,4612118,True,othmaint,11,17,20.0,SHARED2FREE +36896949,112490,112490,4612118,False,Home,17,11,22.0,TNC_SINGLE +36897033,112490,112490,4612129,True,work,12,17,6.0,WALK_LOC +36897037,112490,112490,4612129,False,Home,17,12,17.0,WALK_LRF +36899329,112497,112497,4612416,True,work,17,17,7.0,WALK +36899333,112497,112497,4612416,False,Home,17,17,17.0,WALK +36900921,112502,112502,4612615,True,shopping,13,17,10.0,WALK +36900925,112502,112502,4612615,False,Home,17,13,11.0,WALK +36900929,112502,112502,4612616,True,shopping,17,17,11.0,WALK +36900933,112502,112502,4612616,False,Home,17,17,13.0,WALK +36906545,112519,112519,4613318,True,work,16,17,7.0,WALK +36906549,112519,112519,4613318,False,Home,17,16,14.0,WALK_LRF +36913761,112541,112541,4614220,True,work,22,17,6.0,WALK +36913765,112541,112541,4614220,False,Home,17,22,16.0,WALK +36916009,112548,112548,4614501,True,shopping,16,17,11.0,WALK +36916013,112548,112548,4614501,False,Home,17,16,14.0,WALK +36920209,112561,112561,4615026,True,othdiscr,9,17,17.0,TNC_SINGLE +36920213,112561,112561,4615026,False,Home,17,9,17.0,DRIVEALONEFREE +36920273,112561,112561,4615034,True,othmaint,9,17,16.0,WALK_LRF +36920274,112561,112561,4615034,True,shopping,16,9,16.0,WALK_LRF +36920277,112561,112561,4615034,False,Home,17,16,16.0,WALK_LRF +36920281,112561,112561,4615035,True,shopping,16,17,18.0,WALK +36920285,112561,112561,4615035,False,Home,17,16,19.0,WALK +36920321,112561,112561,4615040,True,work,5,17,6.0,WALK +36920325,112561,112561,4615040,False,Home,17,5,15.0,WALK_LRF +36924913,112575,112575,4615614,True,work,2,17,11.0,WALK +36924917,112575,112575,4615614,False,Home,17,2,17.0,WALK +36927585,112584,112584,4615948,True,atwork,16,1,12.0,WALK +36927589,112584,112584,4615948,False,shopping,25,16,18.0,WALK +36927590,112584,112584,4615948,False,Work,1,25,18.0,WALK +36927865,112584,112584,4615983,True,work,14,17,11.0,WALK +36927866,112584,112584,4615983,True,eatout,16,14,11.0,WALK +36927867,112584,112584,4615983,True,work,1,16,12.0,WALK +36927869,112584,112584,4615983,False,othdiscr,17,1,18.0,WALK +36927870,112584,112584,4615983,False,Home,17,17,19.0,WALK +36965633,112700,112700,4620704,True,atwork,10,15,11.0,SHARED3FREE +36965637,112700,112700,4620704,False,Work,15,10,11.0,SHARED3FREE +36965913,112700,112700,4620739,True,work,15,17,7.0,WALK_LRF +36965917,112700,112700,4620739,False,Home,17,15,21.0,WALK +36984017,112756,112756,4623002,True,eatout,18,19,15.0,WALK +36984021,112756,112756,4623002,False,Home,19,18,19.0,WALK +36984233,112756,112756,4623029,True,shopping,11,19,13.0,WALK +36984237,112756,112756,4623029,False,escort,9,11,13.0,WALK +36984238,112756,112756,4623029,False,Home,19,9,14.0,WALK_LOC +36989201,112771,112771,4623650,True,work,21,19,7.0,DRIVEALONEFREE +36989205,112771,112771,4623650,False,Home,19,21,12.0,DRIVEALONEFREE +36990401,112775,112775,4623800,True,othdiscr,9,19,7.0,WALK_LOC +36990405,112775,112775,4623800,False,Home,19,9,10.0,TNC_SINGLE +36990513,112775,112775,4623814,True,othdiscr,7,19,11.0,TNC_SINGLE +36990514,112775,112775,4623814,True,work,13,7,12.0,TNC_SINGLE +36990517,112775,112775,4623814,False,othmaint,5,13,18.0,TNC_SINGLE +36990518,112775,112775,4623814,False,Home,19,5,18.0,WALK_LOC +37006017,112823,112823,4625752,True,escort,12,19,15.0,SHARED2FREE +37006021,112823,112823,4625752,False,shopping,17,12,16.0,DRIVEALONEFREE +37006022,112823,112823,4625752,False,Home,19,17,16.0,DRIVEALONEFREE +37006209,112823,112823,4625776,True,shopping,2,19,9.0,WALK_LOC +37006213,112823,112823,4625776,False,Home,19,2,13.0,WALK_LOC +37016425,112854,112854,4627053,True,work,16,19,9.0,WALK +37016429,112854,112854,4627053,False,shopping,11,16,17.0,WALK +37016430,112854,112854,4627053,False,Home,19,11,18.0,WALK +37035121,112911,112911,4629390,True,work,9,19,17.0,WALK_LOC +37035125,112911,112911,4629390,False,othmaint,9,9,22.0,WALK +37035126,112911,112911,4629390,False,Home,19,9,22.0,WALK +37044961,112941,112941,4630620,True,work,22,20,5.0,WALK_LRF +37044965,112941,112941,4630620,False,Home,20,22,14.0,WALK_LRF +37050777,112959,112959,4631347,True,othmaint,4,20,15.0,WALK_LRF +37050781,112959,112959,4631347,False,Home,20,4,17.0,WALK_LRF +37050865,112959,112959,4631358,True,work,21,20,8.0,WALK +37050869,112959,112959,4631358,False,Home,20,21,15.0,WALK +37060377,112988,112988,4632547,True,work,16,21,9.0,WALK +37060381,112988,112988,4632547,False,Home,21,16,18.0,WALK +37065297,113003,113003,4633162,True,escort,4,21,6.0,WALK_LOC +37065298,113003,113003,4633162,True,escort,3,4,7.0,WALK +37065299,113003,113003,4633162,True,work,2,3,7.0,WALK +37065301,113003,113003,4633162,False,Home,21,2,17.0,WALK +37073217,113028,113028,4634152,True,atwork,2,4,10.0,WALK +37073221,113028,113028,4634152,False,shopping,6,2,16.0,WALK +37073222,113028,113028,4634152,False,Work,4,6,16.0,WALK +37073497,113028,113028,4634187,True,work,4,21,7.0,WALK +37073501,113028,113028,4634187,False,Home,21,4,16.0,WALK +37085633,113065,113065,4635704,True,work,14,22,12.0,WALK +37085637,113065,113065,4635704,False,Home,22,14,21.0,WALK +37087553,113071,113071,4635944,True,shopping,16,23,14.0,WALK +37087557,113071,113071,4635944,False,eatout,16,16,14.0,WALK +37087558,113071,113071,4635944,False,othdiscr,16,16,14.0,WALK +37087559,113071,113071,4635944,False,othmaint,25,16,14.0,WALK +37087560,113071,113071,4635944,False,Home,23,25,14.0,WALK +37087577,113071,113071,4635947,True,social,7,23,19.0,WALK +37087581,113071,113071,4635947,False,Home,23,7,20.0,WALK_LRF +37103673,113120,113120,4637959,True,work,13,23,6.0,WALK_LRF +37103677,113120,113120,4637959,False,Home,23,13,20.0,WALK_LRF +37115809,113157,113157,4639476,True,work,7,24,6.0,WALK_LRF +37115813,113157,113157,4639476,False,Home,24,7,21.0,WALK +37116417,113159,113159,4639552,True,shopping,12,25,13.0,WALK_LOC +37116421,113159,113159,4639552,False,Home,25,12,15.0,WALK +69634337,212299,200965,8704292,True,shopping,2,6,10.0,WALK +69634341,212299,200965,8704292,False,Home,6,2,11.0,WALK +69634449,212300,200965,8704306,True,eatout,5,6,20.0,WALK +69634453,212300,200965,8704306,False,Home,6,5,21.0,WALK +69634625,212300,200965,8704328,True,othmaint,11,6,9.0,WALK +69634629,212300,200965,8704328,False,Home,6,11,20.0,BIKE +69659905,212377,201004,8707488,True,univ,9,7,21.0,WALK_LRF +69659909,212377,201004,8707488,False,Home,7,9,21.0,WALK_LRF +69660233,212378,201004,8707529,True,univ,9,7,20.0,WALK_LOC +69660237,212378,201004,8707529,False,work,22,9,21.0,WALK_LRF +69660238,212378,201004,8707529,False,shopping,4,22,22.0,WALK_LRF +69660239,212378,201004,8707529,False,Home,7,4,22.0,WALK_LOC +69663841,212389,201010,8707980,True,univ,12,8,18.0,WALK_LOC +69663845,212389,201010,8707980,False,othmaint,21,12,19.0,WALK_LOC +69663846,212389,201010,8707980,False,Home,8,21,19.0,WALK_LOC +69664169,212390,201010,8708021,True,univ,13,8,15.0,WALK_LOC +69664173,212390,201010,8708021,False,social,4,13,15.0,WALK_LOC +69664174,212390,201010,8708021,False,Home,8,4,16.0,WALK_LRF +69716321,212549,201090,8714540,True,univ,13,8,14.0,WALK_LOC +69716325,212549,201090,8714540,False,Home,8,13,15.0,WALK_LRF +69716649,212550,201090,8714581,True,univ,13,8,7.0,WALK_LOC +69716653,212550,201090,8714581,False,othdiscr,9,13,16.0,WALK_LRF +69716654,212550,201090,8714581,False,Home,8,9,16.0,WALK_LOC +69742561,212629,201130,8717820,True,othmaint,7,8,14.0,WALK_LOC +69742562,212629,201130,8717820,True,escort,11,7,15.0,WALK +69742563,212629,201130,8717820,True,univ,9,11,16.0,WALK +69742565,212629,201130,8717820,False,Home,8,9,17.0,WALK_LOC +69742889,212630,201130,8717861,True,univ,12,8,13.0,WALK_LOC +69742893,212630,201130,8717861,False,othmaint,7,12,21.0,WALK_LOC +69742894,212630,201130,8717861,False,Home,8,7,21.0,WALK +69760929,212685,201158,8720116,True,univ,12,8,19.0,WALK +69760933,212685,201158,8720116,False,univ,13,12,21.0,WALK +69760934,212685,201158,8720116,False,escort,7,13,22.0,WALK +69760935,212685,201158,8720116,False,othmaint,7,7,23.0,WALK +69760936,212685,201158,8720116,False,Home,8,7,23.0,WALK +69761273,212686,201158,8720159,True,social,8,8,19.0,WALK +69761274,212686,201158,8720159,True,shopping,11,8,19.0,WALK_LOC +69761277,212686,201158,8720159,False,Home,8,11,19.0,WALK +69779953,212743,201187,8722494,True,univ,9,9,12.0,WALK +69779957,212743,201187,8722494,False,Home,9,9,17.0,WALK +69780281,212744,201187,8722535,True,othmaint,16,9,16.0,WALK_LRF +69780282,212744,201187,8722535,True,univ,12,16,17.0,WALK_LOC +69780285,212744,201187,8722535,False,social,2,12,20.0,WALK +69780286,212744,201187,8722535,False,othmaint,1,2,20.0,WALK +69780287,212744,201187,8722535,False,Home,9,1,20.0,WALK_LRF +69789417,212772,201201,8723677,True,othdiscr,3,9,8.0,WALK_LRF +69789421,212772,201201,8723677,False,shopping,8,3,17.0,WALK +69789422,212772,201201,8723677,False,Home,9,8,17.0,WALK_LOC +69803521,212815,201223,8725440,True,othdiscr,10,10,7.0,BIKE +69803525,212815,201223,8725440,False,Home,10,10,11.0,BIKE +69803873,212816,201223,8725484,True,othmaint,12,10,14.0,WALK +69803877,212816,201223,8725484,False,Home,10,12,15.0,WALK +69803913,212816,201223,8725489,True,shopping,18,10,15.0,DRIVEALONEFREE +69803917,212816,201223,8725489,False,Home,10,18,16.0,DRIVEALONEFREE +69832945,212905,201268,8729118,True,eatout,9,10,10.0,TNC_SINGLE +69832949,212905,201268,8729118,False,othmaint,9,9,20.0,WALK +69832950,212905,201268,8729118,False,Home,10,9,21.0,WALK +69873057,213027,201329,8734132,True,othdiscr,9,13,9.0,WALK_LRF +69873061,213027,201329,8734132,False,Home,13,9,17.0,WALK_LRF +69873257,213028,201329,8734157,True,escort,24,13,15.0,WALK +69873261,213028,201329,8734157,False,Home,13,24,20.0,WALK +69899385,213107,201369,8737423,True,social,20,21,11.0,WALK +69899389,213107,201369,8737423,False,Home,21,20,13.0,WALK +69899689,213108,201369,8737461,True,shopping,18,21,8.0,WALK +69899693,213108,201369,8737461,False,Home,21,18,10.0,WALK +69900017,213109,201370,8737502,True,shopping,16,21,15.0,WALK +69900018,213109,201370,8737502,True,shopping,16,16,15.0,WALK +69900021,213109,201370,8737502,False,Home,21,16,22.0,WALK +69900041,213109,201370,8737505,True,social,21,21,11.0,WALK +69900045,213109,201370,8737505,False,Home,21,21,11.0,WALK +69906865,213130,201380,8738358,True,othmaint,17,25,12.0,WALK_LRF +69906869,213130,201380,8738358,False,Home,25,17,17.0,WALK_LOC +86565745,263919,226775,10820718,True,work,1,6,8.0,WALK +86565749,263919,226775,10820718,False,Home,6,1,19.0,WALK +86566009,263920,226775,10820751,True,univ,9,6,21.0,WALK_LRF +86566013,263920,226775,10820751,False,escort,12,9,21.0,WALK_LRF +86566014,263920,226775,10820751,False,othmaint,9,12,21.0,WALK_LRF +86566015,263920,226775,10820751,False,othmaint,17,9,21.0,WALK_LRF +86566016,263920,226775,10820751,False,Home,6,17,21.0,WALK_LRF +86572961,263941,226786,10821620,True,work,2,7,6.0,TNC_SHARED +86572965,263941,226786,10821620,False,Home,7,2,17.0,WALK_LOC +86573225,263942,226786,10821653,True,univ,9,7,14.0,WALK +86573229,263942,226786,10821653,False,escort,6,9,21.0,WALK_LRF +86573230,263942,226786,10821653,False,Home,7,6,23.0,WALK_LOC +86581985,263969,226800,10822748,True,escort,7,8,8.0,TNC_SHARED +86581986,263969,226800,10822748,True,shopping,5,7,8.0,WALK +86581987,263969,226800,10822748,True,shopping,5,5,8.0,TAXI +86581989,263969,226800,10822748,False,Home,8,5,8.0,TNC_SHARED +86582473,263970,226800,10822809,True,othdiscr,13,8,8.0,WALK_LOC +86582474,263970,226800,10822809,True,work,1,13,8.0,WALK +86582477,263970,226800,10822809,False,Home,8,1,18.0,TNC_SHARED +86589233,263991,226811,10823654,True,othmaint,7,12,10.0,WALK +86589234,263991,226811,10823654,True,atwork,4,7,10.0,WALK +86589237,263991,226811,10823654,False,Work,12,4,11.0,WALK +86589361,263991,226811,10823670,True,escort,8,8,8.0,WALK +86589362,263991,226811,10823670,True,work,12,8,9.0,WALK_LOC +86589365,263991,226811,10823670,False,shopping,2,12,20.0,WALK +86589366,263991,226811,10823670,False,Home,8,2,20.0,WALK_LOC +86596297,264013,226822,10824537,True,atwork,5,14,11.0,WALK +86596301,264013,226822,10824537,False,Work,14,5,11.0,WALK +86596577,264013,226822,10824572,True,work,14,9,7.0,WALK_LRF +86596581,264013,226822,10824572,False,Home,9,14,17.0,WALK_LRF +86596817,264014,226822,10824602,True,othmaint,4,9,9.0,WALK_LRF +86596821,264014,226822,10824602,False,Home,9,4,18.0,WALK_LRF +86603793,264035,226833,10825474,True,work,15,9,9.0,WALK_LRF +86603797,264035,226833,10825474,False,Home,9,15,20.0,WALK_LRF +86604033,264036,226833,10825504,True,othmaint,9,9,8.0,WALK +86604037,264036,226833,10825504,False,Home,9,9,9.0,WALK +86604073,264036,226833,10825509,True,shopping,11,9,14.0,WALK +86604077,264036,226833,10825509,False,Home,9,11,17.0,WALK +86627409,264107,226869,10828426,True,work,24,9,7.0,WALK_LRF +86627413,264107,226869,10828426,False,Home,9,24,18.0,WALK_LRF +86627625,264108,226869,10828453,True,othdiscr,19,9,10.0,WALK +86627629,264108,226869,10828453,False,Home,9,19,11.0,WALK +86631065,264119,226875,10828883,True,atwork,24,2,12.0,WALK +86631069,264119,226875,10828883,False,Work,2,24,12.0,WALK +86631345,264119,226875,10828918,True,work,2,10,10.0,WALK_LRF +86631349,264119,226875,10828918,False,escort,11,2,12.0,WALK +86631350,264119,226875,10828918,False,social,5,11,12.0,WALK +86631351,264119,226875,10828918,False,eatout,3,5,18.0,WALK +86631352,264119,226875,10828918,False,Home,10,3,18.0,WALK_LRF +86631609,264120,226875,10828951,True,univ,10,10,7.0,WALK +86631613,264120,226875,10828951,False,eatout,9,10,10.0,BIKE +86631614,264120,226875,10828951,False,eatout,19,9,11.0,BIKE +86631615,264120,226875,10828951,False,escort,11,19,11.0,WALK +86631616,264120,226875,10828951,False,Home,10,11,11.0,WALK +86633689,264127,226879,10829211,True,atwork,16,2,13.0,WALK +86633693,264127,226879,10829211,False,Work,2,16,13.0,WALK +86633969,264127,226879,10829246,True,escort,17,10,9.0,WALK_LRF +86633970,264127,226879,10829246,True,work,2,17,10.0,WALK +86633973,264127,226879,10829246,False,othmaint,5,2,13.0,WALK +86633974,264127,226879,10829246,False,Home,10,5,13.0,WALK_LOC +86634185,264128,226879,10829273,True,othdiscr,19,10,13.0,WALK +86634189,264128,226879,10829273,False,Home,10,19,20.0,WALK +86641777,264151,226891,10830222,True,univ,9,10,11.0,WALK +86641781,264151,226891,10830222,False,Home,10,9,19.0,WALK_LOC +86642057,264152,226891,10830257,True,othdiscr,8,10,18.0,WALK +86642061,264152,226891,10830257,False,Home,10,8,19.0,WALK +86653585,264187,226909,10831698,True,univ,13,10,16.0,DRIVEALONEFREE +86653589,264187,226909,10831698,False,Home,10,13,16.0,DRIVEALONEFREE +86653977,264188,226909,10831747,True,work,2,10,7.0,WALK +86653981,264188,226909,10831747,False,Home,10,2,14.0,WALK +86672081,264244,226937,10834010,True,eatout,8,10,12.0,WALK +86672085,264244,226937,10834010,False,Home,10,8,14.0,WALK +86672105,264244,226937,10834013,True,escort,10,10,16.0,WALK +86672109,264244,226937,10834013,False,Home,10,10,17.0,WALK +86673657,264248,226939,10834207,True,othmaint,4,10,7.0,WALK_LRF +86673658,264248,226939,10834207,True,eatout,5,4,8.0,WALK +86673659,264248,226939,10834207,True,escort,2,5,8.0,WALK +86673660,264248,226939,10834207,True,work,22,2,8.0,WALK +86673661,264248,226939,10834207,False,Home,10,22,19.0,WALK_LRF +86680129,264268,226949,10835016,True,othmaint,7,10,13.0,WALK +86680133,264268,226949,10835016,False,Home,10,7,16.0,WALK_LOC +86715969,264377,227004,10839496,True,work,8,10,11.0,WALK +86715973,264377,227004,10839496,False,Home,10,8,19.0,WALK +86716057,264378,227004,10839507,True,escort,9,10,11.0,WALK +86716061,264378,227004,10839507,False,Home,10,9,12.0,WALK +86716249,264378,227004,10839531,True,shopping,11,10,17.0,WALK +86716253,264378,227004,10839531,False,Home,10,11,18.0,WALK +86727777,264413,227022,10840972,True,work,9,10,7.0,WALK +86727781,264413,227022,10840972,False,Home,10,9,18.0,WALK +86728041,264414,227022,10841005,True,univ,9,10,9.0,WALK +86728045,264414,227022,10841005,False,Home,10,9,18.0,WALK_LOC +86759833,264511,227071,10844979,True,othmaint,1,10,9.0,WALK_HVY +86759837,264511,227071,10844979,False,Home,10,1,10.0,WALK_HVY +86759873,264511,227071,10844984,True,shopping,4,10,12.0,WALK_LRF +86759877,264511,227071,10844984,False,Home,10,4,15.0,WALK_LRF +86760161,264512,227071,10845020,True,othmaint,20,10,11.0,WALK +86760165,264512,227071,10845020,False,Home,10,20,12.0,WALK_LOC +86825473,264711,227171,10853184,True,shopping,16,16,12.0,WALK +86825477,264711,227171,10853184,False,Home,16,16,13.0,WALK +86825849,264712,227171,10853231,True,work,2,16,9.0,WALK +86825853,264712,227171,10853231,False,Home,16,2,15.0,WALK +86866129,264835,227233,10858266,True,univ,12,16,8.0,WALK_LOC +86866133,264835,227233,10858266,False,social,5,12,15.0,WALK_LOC +86866134,264835,227233,10858266,False,Home,16,5,17.0,WALK_LOC +86866473,264836,227233,10858309,True,shopping,13,16,8.0,WALK +86866477,264836,227233,10858309,False,shopping,16,13,10.0,WALK +86866478,264836,227233,10858309,False,Home,16,16,10.0,WALK +86881889,264883,227257,10860236,True,shopping,19,19,7.0,WALK +86881893,264883,227257,10860236,False,Home,19,19,12.0,WALK +86882177,264884,227257,10860272,True,othmaint,7,19,11.0,WALK +86882181,264884,227257,10860272,False,Home,19,7,15.0,WALK +86891777,264913,227272,10861472,True,work,1,23,5.0,WALK +86891781,264913,227272,10861472,False,Home,23,1,10.0,WALK +86891785,264913,227272,10861473,True,work,1,23,13.0,WALK +86891789,264913,227272,10861473,False,Home,23,1,18.0,WALK +86891865,264914,227272,10861483,True,escort,14,23,12.0,SHARED2FREE +86891866,264914,227272,10861483,True,escort,17,14,12.0,DRIVEALONEFREE +86891869,264914,227272,10861483,False,eatout,5,17,12.0,DRIVEALONEFREE +86891870,264914,227272,10861483,False,Home,23,5,12.0,SHARED2FREE +86891993,264914,227272,10861499,True,othdiscr,14,23,17.0,WALK_LOC +86891997,264914,227272,10861499,False,othdiscr,16,14,19.0,WALK_LOC +86891998,264914,227272,10861499,False,shopping,12,16,19.0,WALK_LOC +86891999,264914,227272,10861499,False,Home,23,12,19.0,WALK_LOC +86905113,264954,227292,10863139,True,othdiscr,6,25,16.0,WALK +86905117,264954,227292,10863139,False,Home,25,6,19.0,WALK +86905121,264954,227292,10863140,True,othdiscr,13,25,19.0,WALK +86905125,264954,227292,10863140,False,Home,25,13,20.0,WALK +86907761,264962,227296,10863470,True,othmaint,1,25,14.0,WALK +86907765,264962,227296,10863470,False,Home,25,1,19.0,WALK +86907825,264962,227296,10863478,True,escort,7,25,7.0,WALK_LOC +86907826,264962,227296,10863478,True,social,9,7,9.0,WALK_LOC +86907829,264962,227296,10863478,False,Home,25,9,10.0,WALK_LOC +105943049,322997,256314,13242881,True,atwork,9,9,11.0,WALK +105943053,322997,256314,13242881,False,Work,9,9,13.0,WALK +105943329,322997,256314,13242916,True,work,9,7,7.0,WALK +105943333,322997,256314,13242916,False,Home,7,9,14.0,WALK +105943657,322998,256314,13242957,True,work,14,7,7.0,TNC_SINGLE +105943661,322998,256314,13242957,False,Home,7,14,18.0,TNC_SINGLE +105950785,323020,256325,13243848,True,othmaint,6,8,18.0,WALK +105950789,323020,256325,13243848,False,Home,8,6,21.0,WALK +105950873,323020,256325,13243859,True,work,2,8,6.0,WALK +105950877,323020,256325,13243859,False,social,11,2,16.0,WALK +105950878,323020,256325,13243859,False,eatout,5,11,16.0,WALK +105950879,323020,256325,13243859,False,eatout,12,5,17.0,WALK +105950880,323020,256325,13243859,False,Home,8,12,17.0,WALK +105950881,323020,256325,13243860,True,work,2,8,17.0,WALK +105950885,323020,256325,13243860,False,Home,8,2,17.0,WALK +105952889,323027,256329,13244111,True,atwork,2,21,15.0,WALK +105952893,323027,256329,13244111,False,Work,21,2,15.0,WALK +105952977,323027,256329,13244122,True,othdiscr,20,8,16.0,WALK +105952981,323027,256329,13244122,False,Home,8,20,16.0,TNC_SHARED +105953169,323027,256329,13244146,True,work,21,8,6.0,WALK +105953173,323027,256329,13244146,False,Home,8,21,16.0,WALK +105966289,323067,256349,13245786,True,work,2,9,9.0,WALK +105966293,323067,256349,13245786,False,Home,9,2,17.0,WALK +105966505,323068,256349,13245813,True,othdiscr,3,9,8.0,WALK_LRF +105966509,323068,256349,13245813,False,Home,9,3,12.0,WALK_LRF +105966513,323068,256349,13245814,True,othdiscr,15,9,12.0,WALK_LRF +105966517,323068,256349,13245814,False,Home,9,15,13.0,WALK_LRF +105966617,323068,256349,13245827,True,work,16,9,15.0,WALK_LRF +105966621,323068,256349,13245827,False,othdiscr,6,16,21.0,WALK +105966622,323068,256349,13245827,False,Home,9,6,21.0,WALK_LOC +106003681,323181,256406,13250460,True,work,7,10,7.0,WALK_LOC +106003685,323181,256406,13250460,False,Home,10,7,12.0,WALK_LOC +106004009,323182,256406,13250501,True,work,10,10,7.0,WALK +106004013,323182,256406,13250501,False,Home,10,10,13.0,WALK +106029745,323261,256446,13253718,True,escort,5,10,8.0,TNC_SHARED +106029746,323261,256446,13253718,True,othmaint,5,5,8.0,SHARED3FREE +106029747,323261,256446,13253718,True,othmaint,10,5,8.0,TAXI +106029748,323261,256446,13253718,True,othmaint,9,10,8.0,SHARED2FREE +106029749,323261,256446,13253718,False,Home,10,9,8.0,TNC_SHARED +106029833,323261,256446,13253729,True,othmaint,7,10,12.0,BIKE +106029837,323261,256446,13253729,False,Home,10,7,15.0,WALK +106029921,323261,256446,13253740,True,work,4,10,17.0,WALK_LRF +106029925,323261,256446,13253740,False,Home,10,4,20.0,WALK_LRF +106030249,323262,256446,13253781,True,work,4,10,8.0,SHARED3FREE +106030253,323262,256446,13253781,False,eatout,11,4,17.0,WALK +106030254,323262,256446,13253781,False,othmaint,6,11,17.0,WALK +106030255,323262,256446,13253781,False,escort,4,6,17.0,WALK +106030256,323262,256446,13253781,False,Home,10,4,17.0,WALK_LRF +106056817,323343,256487,13257102,True,work,7,10,7.0,WALK +106056821,323343,256487,13257102,False,Home,10,7,17.0,WALK +106057145,323344,256487,13257143,True,work,7,10,7.0,WALK_LOC +106057149,323344,256487,13257143,False,Home,10,7,18.0,WALK_LOC +106060665,323355,256493,13257583,True,othmaint,13,10,7.0,WALK_LRF +106060669,323355,256493,13257583,False,Home,10,13,7.0,WALK_LRF +106061081,323356,256493,13257635,True,work,14,10,12.0,WALK_LRF +106061085,323356,256493,13257635,False,Home,10,14,21.0,WALK_LRF +106064033,323365,256498,13258004,True,work,4,10,7.0,WALK +106064037,323365,256498,13258004,False,Home,10,4,16.0,WALK_LRF +106064313,323366,256498,13258039,True,shopping,21,10,18.0,WALK +106064317,323366,256498,13258039,False,Home,10,21,18.0,WALK +106064361,323366,256498,13258045,True,escort,9,10,7.0,WALK +106064362,323366,256498,13258045,True,social,8,9,7.0,WALK +106064363,323366,256498,13258045,True,work,20,8,8.0,WALK +106064365,323366,256498,13258045,False,Home,10,20,18.0,WALK +106113889,323517,256574,13264236,True,work,13,10,6.0,WALK_LRF +106113893,323517,256574,13264236,False,Home,10,13,16.0,WALK_LRF +106113937,323518,256574,13264242,True,othmaint,4,4,10.0,WALK +106113938,323518,256574,13264242,True,atwork,13,4,10.0,WALK +106113941,323518,256574,13264242,False,shopping,8,13,10.0,WALK +106113942,323518,256574,13264242,False,Work,4,8,10.0,WALK +106114217,323518,256574,13264277,True,work,4,10,6.0,WALK_LRF +106114221,323518,256574,13264277,False,Home,10,4,15.0,WALK_HVY +106119681,323535,256583,13264960,True,othdiscr,9,10,16.0,WALK +106119685,323535,256583,13264960,False,Home,10,9,18.0,WALK_LOC +106119705,323535,256583,13264963,True,othmaint,5,10,16.0,BIKE +106119709,323535,256583,13264963,False,Home,10,5,16.0,BIKE +106119745,323535,256583,13264968,True,shopping,12,10,18.0,WALK +106119749,323535,256583,13264968,False,Home,10,12,19.0,WALK +106119753,323535,256583,13264969,True,shopping,5,10,19.0,WALK_LOC +106119757,323535,256583,13264969,False,Home,10,5,20.0,WALK +106119793,323535,256583,13264974,True,work,16,10,6.0,SHARED2FREE +106119797,323535,256583,13264974,False,Home,10,16,15.0,WALK_LOC +106119841,323536,256583,13264980,True,shopping,8,9,12.0,WALK +106119842,323536,256583,13264980,True,atwork,9,8,12.0,WALK +106119845,323536,256583,13264980,False,Work,9,9,13.0,WALK +106120121,323536,256583,13265015,True,work,9,10,7.0,WALK +106120125,323536,256583,13265015,False,Home,10,9,23.0,WALK +106170305,323689,256660,13271288,True,work,1,10,11.0,WALK_LRF +106170309,323689,256660,13271288,False,Home,10,1,18.0,WALK_LRF +106192609,323757,256694,13274076,True,work,4,13,10.0,WALK +106192613,323757,256694,13274076,False,eatout,16,4,19.0,WALK +106192614,323757,256694,13274076,False,Home,13,16,19.0,WALK +106199169,323777,256704,13274896,True,work,16,16,8.0,WALK +106199173,323777,256704,13274896,False,Home,16,16,17.0,WALK +106199409,323778,256704,13274926,True,othmaint,7,16,7.0,WALK +106199413,323778,256704,13274926,False,Home,16,7,8.0,WALK +106199497,323778,256704,13274937,True,work,11,16,16.0,WALK +106199501,323778,256704,13274937,False,Home,16,11,21.0,BIKE +106201137,323783,256707,13275142,True,work,16,16,7.0,WALK +106201141,323783,256707,13275142,False,Home,16,16,17.0,WALK +106201417,323784,256707,13275177,True,shopping,16,16,10.0,WALK +106201421,323784,256707,13275177,False,Home,16,16,10.0,WALK +106201465,323784,256707,13275183,True,work,16,16,11.0,WALK +106201469,323784,256707,13275183,False,Home,16,16,19.0,WALK +106233001,323881,256756,13279125,True,atwork,2,12,12.0,WALK +106233005,323881,256756,13279125,False,eatout,7,2,12.0,WALK +106233006,323881,256756,13279125,False,Work,12,7,12.0,WALK +106233169,323881,256756,13279146,True,othdiscr,16,16,19.0,WALK +106233173,323881,256756,13279146,False,Home,16,16,20.0,WALK +106233281,323881,256756,13279160,True,work,12,16,6.0,SHARED2FREE +106233285,323881,256756,13279160,False,Home,16,12,18.0,SHARED2FREE +106233609,323882,256756,13279201,True,work,7,16,9.0,WALK_LOC +106233613,323882,256756,13279201,False,Home,16,7,20.0,WALK_LOC +106235249,323887,256759,13279406,True,work,9,16,7.0,WALK_LOC +106235253,323887,256759,13279406,False,Home,16,9,18.0,WALK +106235577,323888,256759,13279447,True,escort,24,16,6.0,WALK +106235578,323888,256759,13279447,True,work,22,24,7.0,WALK +106235581,323888,256759,13279447,False,shopping,16,22,15.0,WALK +106235582,323888,256759,13279447,False,escort,25,16,15.0,WALK_LOC +106235583,323888,256759,13279447,False,Home,16,25,15.0,WALK_LOC +106236433,323891,256761,13279554,True,atwork,4,4,13.0,WALK +106236437,323891,256761,13279554,False,shopping,6,4,14.0,WALK +106236438,323891,256761,13279554,False,Work,4,6,14.0,WALK +106236561,323891,256761,13279570,True,work,4,16,7.0,WALK +106236565,323891,256761,13279570,False,Home,16,4,17.0,WALK +106236625,323892,256761,13279578,True,eatout,16,16,9.0,WALK +106236629,323892,256761,13279578,False,Home,16,16,18.0,WALK +106271329,323997,256814,13283916,True,work,22,16,5.0,WALK +106271333,323997,256814,13283916,False,Home,16,22,21.0,WALK_LOC +106271657,323998,256814,13283957,True,work,18,16,12.0,WALK_LRF +106271661,323998,256814,13283957,False,Home,16,18,16.0,WALK +106287945,324048,256839,13285993,True,othdiscr,22,16,8.0,WALK_LOC +106287949,324048,256839,13285993,False,Home,16,22,15.0,WALK_LOC +106288601,324050,256840,13286075,True,othdiscr,23,16,17.0,WALK +106288605,324050,256840,13286075,False,Home,16,23,21.0,WALK +106288665,324050,256840,13286083,True,shopping,2,16,16.0,BIKE +106288669,324050,256840,13286083,False,Home,16,2,17.0,BIKE +106288713,324050,256840,13286089,True,work,4,16,7.0,WALK +106288717,324050,256840,13286089,False,Home,16,4,16.0,WALK +106289041,324051,256841,13286130,True,work,13,16,6.0,WALK +106289045,324051,256841,13286130,False,Home,16,13,18.0,WALK +106289089,324052,256841,13286136,True,eatout,5,2,10.0,WALK +106289090,324052,256841,13286136,True,escort,7,5,10.0,WALK +106289091,324052,256841,13286136,True,othmaint,7,7,10.0,WALK +106289092,324052,256841,13286136,True,atwork,25,7,10.0,WALK +106289093,324052,256841,13286136,False,Work,2,25,10.0,WALK +106289369,324052,256841,13286171,True,work,2,16,7.0,WALK +106289373,324052,256841,13286171,False,Home,16,2,17.0,WALK +106313969,324127,256879,13289246,True,eatout,13,16,12.0,TNC_SINGLE +106313970,324127,256879,13289246,True,work,4,13,13.0,WALK +106313973,324127,256879,13289246,False,Home,16,4,17.0,WALK +106314297,324128,256879,13289287,True,shopping,16,16,8.0,WALK +106314298,324128,256879,13289287,True,escort,22,16,9.0,TNC_SINGLE +106314299,324128,256879,13289287,True,work,14,22,9.0,TNC_SINGLE +106314301,324128,256879,13289287,False,work,7,14,13.0,TNC_SINGLE +106314302,324128,256879,13289287,False,Home,16,7,13.0,WALK_LOC +106318449,324141,256886,13289806,True,othdiscr,16,16,18.0,WALK +106318453,324141,256886,13289806,False,Home,16,16,19.0,WALK +106318561,324141,256886,13289820,True,escort,24,16,9.0,WALK +106318562,324141,256886,13289820,True,work,10,24,9.0,TNC_SINGLE +106318565,324141,256886,13289820,False,Home,16,10,18.0,TNC_SINGLE +106318889,324142,256886,13289861,True,work,18,16,6.0,WALK +106318893,324142,256886,13289861,False,Home,16,18,17.0,TAXI +106319105,324143,256887,13289888,True,othdiscr,25,16,8.0,WALK +106319109,324143,256887,13289888,False,Home,16,25,15.0,WALK +106323761,324157,256894,13290470,True,shopping,16,16,11.0,WALK +106323765,324157,256894,13290470,False,Home,16,16,11.0,WALK +106324137,324158,256894,13290517,True,work,24,16,5.0,TNC_SHARED +106324141,324158,256894,13290517,False,escort,4,24,15.0,TNC_SHARED +106324142,324158,256894,13290517,False,Home,16,4,16.0,WALK_LOC +106342177,324213,256922,13292772,True,work,16,16,8.0,WALK +106342181,324213,256922,13292772,False,social,12,16,20.0,WALK +106342182,324213,256922,13292772,False,Home,16,12,22.0,WALK +106342505,324214,256922,13292813,True,work,12,16,9.0,WALK_LOC +106342509,324214,256922,13292813,False,escort,14,12,13.0,WALK +106342510,324214,256922,13292813,False,othmaint,13,14,18.0,WALK +106342511,324214,256922,13292813,False,escort,22,13,20.0,WALK_LOC +106342512,324214,256922,13292813,False,Home,16,22,20.0,WALK_LOC +106350705,324239,256935,13293838,True,work,13,16,6.0,WALK +106350709,324239,256935,13293838,False,Home,16,13,17.0,WALK +106350921,324240,256935,13293865,True,othdiscr,12,16,16.0,WALK +106350925,324240,256935,13293865,False,Home,16,12,19.0,WALK +106364393,324281,256956,13295549,True,othmaint,16,16,19.0,WALK +106364397,324281,256956,13295549,False,Home,16,16,22.0,WALK +106364433,324281,256956,13295554,True,shopping,5,16,15.0,WALK +106364437,324281,256956,13295554,False,Home,16,5,19.0,WALK +106364481,324281,256956,13295560,True,work,9,16,7.0,WALK +106364485,324281,256956,13295560,False,Home,16,9,15.0,WALK +106364697,324282,256956,13295587,True,othdiscr,8,16,20.0,WALK_LOC +106364701,324282,256956,13295587,False,Home,16,8,20.0,SHARED2FREE +106364809,324282,256956,13295601,True,work,11,16,7.0,WALK_LOC +106364813,324282,256956,13295601,False,Home,16,11,18.0,WALK +106389953,324359,256995,13298744,True,othdiscr,1,16,14.0,WALK +106389957,324359,256995,13298744,False,Home,16,1,15.0,WALK +106390393,324360,256995,13298799,True,work,9,16,8.0,WALK_LOC +106390397,324360,256995,13298799,False,escort,9,9,18.0,WALK +106390398,324360,256995,13298799,False,escort,16,9,21.0,WALK_LRF +106390399,324360,256995,13298799,False,othmaint,16,16,21.0,WALK +106390400,324360,256995,13298799,False,Home,16,16,21.0,WALK +106421553,324455,257043,13302694,True,escort,5,16,8.0,TNC_SINGLE +106421554,324455,257043,13302694,True,work,21,5,9.0,TNC_SINGLE +106421557,324455,257043,13302694,False,shopping,16,21,17.0,TNC_SINGLE +106421558,324455,257043,13302694,False,Home,16,16,18.0,TNC_SINGLE +106426145,324469,257050,13303268,True,othmaint,1,16,9.0,WALK_LOC +106426146,324469,257050,13303268,True,work,15,1,10.0,WALK +106426149,324469,257050,13303268,False,Home,16,15,16.0,TNC_SHARED +106426473,324470,257050,13303309,True,work,16,16,8.0,WALK +106426477,324470,257050,13303309,False,Home,16,16,18.0,WALK +106464849,324587,257109,13308106,True,work,16,16,6.0,WALK +106464853,324587,257109,13308106,False,Home,16,16,18.0,WALK +106465177,324588,257109,13308147,True,work,17,16,9.0,SHARED2FREE +106465181,324588,257109,13308147,False,escort,17,17,16.0,WALK +106465182,324588,257109,13308147,False,Home,16,17,20.0,WALK +106485841,324651,257141,13310730,True,work,12,16,7.0,WALK +106485845,324651,257141,13310730,False,work,16,12,11.0,WALK +106485846,324651,257141,13310730,False,Home,16,16,18.0,WALK +106486169,324652,257141,13310771,True,work,14,16,7.0,WALK +106486173,324652,257141,13310771,False,Home,16,14,17.0,WALK +106506177,324713,257172,13313272,True,work,16,16,6.0,WALK +106506181,324713,257172,13313272,False,Home,16,16,17.0,WALK +106506225,324714,257172,13313278,True,atwork,13,15,11.0,WALK +106506229,324714,257172,13313278,False,othmaint,12,13,11.0,WALK +106506230,324714,257172,13313278,False,Work,15,12,11.0,WALK +106506393,324714,257172,13313299,True,othdiscr,9,16,18.0,BIKE +106506397,324714,257172,13313299,False,Home,16,9,20.0,BIKE +106506505,324714,257172,13313313,True,work,2,16,8.0,WALK +106506506,324714,257172,13313313,True,work,15,2,8.0,WALK_LOC +106506509,324714,257172,13313313,False,Home,16,15,18.0,WALK_LOC +106508801,324721,257176,13313600,True,work,15,16,6.0,WALK +106508805,324721,257176,13313600,False,Home,16,15,18.0,WALK_LOC +106509017,324722,257176,13313627,True,othdiscr,7,16,21.0,WALK +106509021,324722,257176,13313627,False,Home,16,7,22.0,WALK +106509129,324722,257176,13313641,True,work,12,16,8.0,TNC_SINGLE +106509133,324722,257176,13313641,False,Home,16,12,18.0,WALK +106520609,324757,257194,13315076,True,work,6,16,9.0,WALK +106520613,324757,257194,13315076,False,Home,16,6,18.0,WALK_LOC +106520937,324758,257194,13315117,True,escort,2,16,8.0,WALK +106520938,324758,257194,13315117,True,escort,16,2,9.0,WALK_LOC +106520939,324758,257194,13315117,True,shopping,4,16,9.0,WALK +106520940,324758,257194,13315117,True,work,18,4,12.0,WALK_LOC +106520941,324758,257194,13315117,False,Home,16,18,16.0,WALK_LRF +106534929,324801,257216,13316866,True,shopping,5,16,17.0,WALK +106534930,324801,257216,13316866,True,othdiscr,7,5,19.0,WALK_LOC +106534933,324801,257216,13316866,False,Home,16,7,23.0,WALK_LOC +106535041,324801,257216,13316880,True,work,15,16,9.0,WALK +106535045,324801,257216,13316880,False,Home,16,15,17.0,WALK +106535369,324802,257216,13316921,True,work,8,16,11.0,WALK +106535373,324802,257216,13316921,False,Home,16,8,21.0,WALK +106541977,324823,257227,13317747,True,atwork,17,17,14.0,WALK +106541981,324823,257227,13317747,False,Work,17,17,14.0,WALK +106542257,324823,257227,13317782,True,work,17,16,8.0,WALK +106542261,324823,257227,13317782,False,Home,16,17,18.0,WALK +106542321,324824,257227,13317790,True,eatout,4,16,16.0,WALK +106542325,324824,257227,13317790,False,Home,16,4,19.0,WALK +106542585,324824,257227,13317823,True,work,24,16,7.0,WALK +106542589,324824,257227,13317823,False,Home,16,24,15.0,WALK +106570377,324909,257270,13321297,True,othmaint,7,16,16.0,TNC_SINGLE +106570381,324909,257270,13321297,False,shopping,5,7,16.0,TNC_SHARED +106570382,324909,257270,13321297,False,othmaint,9,5,18.0,TNC_SINGLE +106570383,324909,257270,13321297,False,Home,16,9,19.0,WALK_LOC +106570465,324909,257270,13321308,True,work,4,16,6.0,WALK_LOC +106570469,324909,257270,13321308,False,Home,16,4,15.0,WALK +106623553,325071,257351,13327944,True,shopping,16,16,14.0,WALK +106623557,325071,257351,13327944,False,Home,16,16,16.0,WALK +106623929,325072,257351,13327991,True,work,1,16,7.0,WALK_LOC +106623933,325072,257351,13327991,False,Home,16,1,18.0,WALK_LOC +106638033,325115,257373,13329754,True,work,1,16,7.0,BIKE +106638037,325115,257373,13329754,False,Home,16,1,15.0,BIKE +106638361,325116,257373,13329795,True,work,14,16,10.0,WALK +106638365,325116,257373,13329795,False,Home,16,14,18.0,WALK +106657057,325173,257402,13332132,True,work,9,16,7.0,WALK_LOC +106657061,325173,257402,13332132,False,Home,16,9,16.0,WALK_LRF +106657385,325174,257402,13332173,True,work,15,16,7.0,WALK_LOC +106657389,325174,257402,13332173,False,Home,16,15,18.0,TNC_SINGLE +106680017,325243,257437,13335002,True,work,14,16,8.0,WALK_LOC +106680021,325243,257437,13335002,False,othdiscr,15,14,20.0,WALK_LOC +106680022,325243,257437,13335002,False,Home,16,15,20.0,WALK_LOC +106680345,325244,257437,13335043,True,work,4,16,7.0,SHARED2FREE +106680349,325244,257437,13335043,False,Home,16,4,20.0,WALK_LOC +106712161,325341,257486,13339020,True,work,21,16,12.0,WALK +106712165,325341,257486,13339020,False,Home,16,21,21.0,WALK +106712209,325342,257486,13339026,True,atwork,1,8,13.0,TNC_SINGLE +106712213,325342,257486,13339026,False,Work,8,1,13.0,WALK_LRF +106712489,325342,257486,13339061,True,work,8,16,6.0,WALK +106712493,325342,257486,13339061,False,Home,16,8,17.0,WALK +106723969,325377,257504,13340496,True,work,4,16,6.0,WALK +106723973,325377,257504,13340496,False,Home,16,4,16.0,WALK +106724297,325378,257504,13340537,True,work,14,16,14.0,WALK +106724301,325378,257504,13340537,False,Home,16,14,17.0,WALK +106725849,325383,257507,13340731,True,othmaint,22,16,7.0,WALK +106725853,325383,257507,13340731,False,Home,16,22,15.0,WALK +106725857,325383,257507,13340732,True,othmaint,11,16,18.0,WALK +106725861,325383,257507,13340732,False,Home,16,11,19.0,WALK +106726201,325384,257507,13340775,True,univ,12,16,17.0,WALK +106726205,325384,257507,13340775,False,escort,12,12,17.0,WALK +106726206,325384,257507,13340775,False,Home,16,12,17.0,WALK +106726265,325384,257507,13340783,True,othdiscr,4,16,7.0,WALK +106726266,325384,257507,13340783,True,escort,3,4,7.0,WALK +106726267,325384,257507,13340783,True,othdiscr,25,3,7.0,WALK +106726268,325384,257507,13340783,True,work,1,25,7.0,WALK +106726269,325384,257507,13340783,False,shopping,16,1,10.0,WALK +106726270,325384,257507,13340783,False,work,16,16,10.0,WALK +106726271,325384,257507,13340783,False,eatout,17,16,10.0,WALK +106726272,325384,257507,13340783,False,Home,16,17,10.0,WALK +106741681,325431,257531,13342710,True,shopping,2,16,7.0,WALK_LOC +106741682,325431,257531,13342710,True,work,12,2,8.0,WALK +106741685,325431,257531,13342710,False,eatout,17,12,17.0,WALK_LRF +106741686,325431,257531,13342710,False,shopping,19,17,17.0,TNC_SINGLE +106741687,325431,257531,13342710,False,escort,11,19,17.0,TNC_SHARED +106741688,325431,257531,13342710,False,Home,16,11,17.0,WALK +106741961,325432,257531,13342745,True,shopping,14,16,12.0,WALK_LOC +106741965,325432,257531,13342745,False,Home,16,14,13.0,TNC_SINGLE +106756769,325477,257554,13344596,True,work,16,16,14.0,WALK +106756773,325477,257554,13344596,False,Home,16,16,17.0,WALK +106757097,325478,257554,13344637,True,eatout,16,16,17.0,WALK +106757098,325478,257554,13344637,True,work,2,16,18.0,WALK +106757101,325478,257554,13344637,False,Home,16,2,20.0,WALK +106760657,325489,257560,13345082,True,escort,16,16,15.0,WALK +106760658,325489,257560,13345082,True,shopping,16,16,15.0,WALK +106760661,325489,257560,13345082,False,Home,16,16,15.0,WALK +106760681,325489,257560,13345085,True,social,9,16,14.0,SHARED2FREE +106760685,325489,257560,13345085,False,Home,16,9,14.0,SHARED2FREE +106760945,325490,257560,13345118,True,othmaint,16,16,12.0,WALK +106760946,325490,257560,13345118,True,othmaint,9,16,13.0,WALK_LRF +106760949,325490,257560,13345118,False,Home,16,9,15.0,WALK_LRF +106781697,325553,257592,13347712,True,work,7,16,7.0,WALK +106781701,325553,257592,13347712,False,Home,16,7,17.0,WALK +106782025,325554,257592,13347753,True,work,2,16,7.0,WALK +106782029,325554,257592,13347753,False,Home,16,2,20.0,WALK +106804593,325623,257627,13350574,True,univ,13,16,16.0,TNC_SHARED +106804597,325623,257627,13350574,False,escort,9,13,18.0,TNC_SINGLE +106804598,325623,257627,13350574,False,Home,16,9,19.0,WALK_LRF +106804657,325623,257627,13350582,True,work,17,16,8.0,WALK +106804661,325623,257627,13350582,False,escort,16,17,10.0,WALK +106804662,325623,257627,13350582,False,work,13,16,10.0,WALK +106804663,325623,257627,13350582,False,Home,16,13,10.0,WALK +106804985,325624,257627,13350623,True,work,15,16,6.0,WALK +106804989,325624,257627,13350623,False,Home,16,15,17.0,WALK +106805289,325625,257628,13350661,True,social,3,16,7.0,WALK +106805293,325625,257628,13350661,False,social,2,3,22.0,WALK +106805294,325625,257628,13350661,False,Home,16,2,22.0,WALK +106846641,325751,257691,13355830,True,work,16,17,5.0,WALK +106846645,325751,257691,13355830,False,Home,17,16,18.0,WALK +106846969,325752,257691,13355871,True,work,13,17,8.0,WALK +106846973,325752,257691,13355871,False,Home,17,13,18.0,WALK +106847297,325753,257692,13355912,True,work,7,17,7.0,WALK +106847301,325753,257692,13355912,False,Home,17,7,18.0,WALK +106847625,325754,257692,13355953,True,work,16,17,6.0,WALK +106847629,325754,257692,13355953,False,Home,17,16,16.0,WALK +106859105,325789,257710,13357388,True,work,16,17,8.0,WALK +106859109,325789,257710,13357388,False,Home,17,16,16.0,WALK +106859433,325790,257710,13357429,True,work,12,17,9.0,WALK +106859437,325790,257710,13357429,False,Home,17,12,20.0,WALK +106908305,325939,257785,13363538,True,work,2,19,7.0,WALK_LOC +106908309,325939,257785,13363538,False,Home,19,2,18.0,WALK_LOC +106908633,325940,257785,13363579,True,work,10,19,6.0,WALK +106908637,325940,257785,13363579,False,Home,19,10,19.0,WALK +106947009,326057,257844,13368376,True,work,22,21,7.0,WALK_LOC +106947013,326057,257844,13368376,False,Home,21,22,17.0,WALK_LRF +106947289,326058,257844,13368411,True,shopping,16,21,17.0,WALK +106947293,326058,257844,13368411,False,Home,21,16,18.0,WALK +106947337,326058,257844,13368417,True,work,2,21,6.0,WALK +106947341,326058,257844,13368417,False,Home,21,2,16.0,WALK +126406529,385385,287508,15800816,True,othdiscr,4,6,18.0,WALK +126406530,385385,287508,15800816,True,univ,12,4,19.0,WALK_LOC +126406533,385385,287508,15800816,False,othdiscr,5,12,21.0,WALK_LOC +126406534,385385,287508,15800816,False,Home,6,5,21.0,WALK +126406657,385386,287508,15800832,True,eatout,5,6,10.0,WALK +126406661,385386,287508,15800832,False,Home,6,5,20.0,WALK +126427521,385449,287540,15803440,True,escort,22,7,21.0,WALK_LRF +126427522,385449,287540,15803440,True,univ,9,22,21.0,WALK_LRF +126427525,385449,287540,15803440,False,Home,7,9,21.0,WALK_LRF +126427849,385450,287540,15803481,True,school,8,7,7.0,WALK +126427853,385450,287540,15803481,False,Home,7,8,15.0,WALK +126450497,385519,287575,15806312,True,shopping,16,8,14.0,WALK_LOC +126450501,385519,287575,15806312,False,Home,8,16,14.0,WALK_LOC +126450809,385520,287575,15806351,True,school,3,8,7.0,WALK_LOC +126450813,385520,287575,15806351,False,Home,8,3,18.0,WALK_LOC +126455089,385533,287582,15806886,True,shopping,11,8,11.0,WALK_LOC +126455093,385533,287582,15806886,False,Home,8,11,12.0,WALK +126467553,385571,287601,15808444,True,shopping,11,8,18.0,TNC_SHARED +126467557,385571,287601,15808444,False,Home,8,11,21.0,WALK_LOC +126467865,385572,287601,15808483,True,school,8,8,8.0,WALK +126467869,385572,287601,15808483,False,escort,9,8,13.0,WALK +126467870,385572,287601,15808483,False,eatout,11,9,13.0,WALK +126467871,385572,287601,15808483,False,Home,8,11,13.0,WALK +126479977,385609,287620,15809997,True,othmaint,7,8,14.0,WALK_LOC +126479978,385609,287620,15809997,True,othmaint,9,7,15.0,WALK +126479981,385609,287620,15809997,False,escort,7,9,14.0,WALK_LOC +126479982,385609,287620,15809997,False,Home,8,7,15.0,WALK +126480281,385610,287620,15810035,True,othdiscr,9,8,8.0,WALK +126480282,385610,287620,15810035,True,othdiscr,17,9,9.0,WALK_LRF +126480285,385610,287620,15810035,False,Home,8,17,15.0,WALK_LOC +126509321,385699,287665,15813665,True,eatout,11,8,16.0,SHARED2FREE +126509325,385699,287665,15813665,False,Home,8,11,19.0,SHARED2FREE +126523297,385741,287686,15815412,True,univ,13,8,18.0,WALK +126523301,385741,287686,15815412,False,Home,8,13,18.0,WALK +126523625,385742,287686,15815453,True,escort,7,8,7.0,WALK +126523626,385742,287686,15815453,True,school,9,7,8.0,WALK +126523629,385742,287686,15815453,False,shopping,5,9,23.0,WALK +126523630,385742,287686,15815453,False,Home,8,5,23.0,WALK +126550825,385825,287728,15818853,True,othmaint,8,8,12.0,WALK +126550829,385825,287728,15818853,False,Home,8,8,15.0,WALK +126551177,385826,287728,15818897,True,school,13,8,6.0,WALK_HVY +126551181,385826,287728,15818897,False,social,9,13,14.0,WALK_LRF +126551182,385826,287728,15818897,False,Home,8,9,14.0,WALK_LOC +126608577,386001,287816,15826072,True,escort,22,9,17.0,WALK_LRF +126608578,386001,287816,15826072,True,work,9,22,17.0,WALK_LRF +126608579,386001,287816,15826072,True,univ,12,9,18.0,WALK_LRF +126608581,386001,287816,15826072,False,Home,9,12,23.0,WALK_LRF +126608905,386002,287816,15826113,True,school,10,9,7.0,SHARED2FREE +126608909,386002,287816,15826113,False,Home,9,10,13.0,WALK_LOC +126610345,386007,287819,15826293,True,eatout,8,9,7.0,WALK +126610349,386007,287819,15826293,False,Home,9,8,12.0,WALK +126610561,386007,287819,15826320,True,shopping,16,9,13.0,WALK_LRF +126610565,386007,287819,15826320,False,shopping,16,16,14.0,WALK +126610566,386007,287819,15826320,False,Home,9,16,14.0,WALK_LRF +126610673,386008,287819,15826334,True,eatout,16,9,17.0,WALK_LRF +126610677,386008,287819,15826334,False,Home,9,16,21.0,WALK_LRF +126610873,386008,287819,15826359,True,school,9,9,8.0,WALK +126610877,386008,287819,15826359,False,Home,9,9,13.0,WALK +126614153,386018,287824,15826769,True,school,8,9,7.0,WALK_LOC +126614157,386018,287824,15826769,False,Home,9,8,16.0,WALK_LOC +126628233,386061,287846,15828529,True,othmaint,24,9,8.0,WALK +126628237,386061,287846,15828529,False,Home,9,24,16.0,WALK +126628585,386062,287846,15828573,True,school,13,9,7.0,WALK_LRF +126628589,386062,287846,15828573,False,Home,9,13,15.0,WALK_LRF +126639425,386095,287863,15829928,True,shopping,11,9,6.0,TNC_SINGLE +126639429,386095,287863,15829928,False,Home,9,11,13.0,TNC_SINGLE +126639753,386096,287863,15829969,True,shopping,16,9,9.0,WALK +126639757,386096,287863,15829969,False,Home,9,16,11.0,WALK +126648569,386123,287877,15831071,True,othmaint,7,10,16.0,TNC_SINGLE +126648573,386123,287877,15831071,False,eatout,9,7,19.0,TNC_SINGLE +126648574,386123,287877,15831071,False,Home,10,9,19.0,TNC_SINGLE +126659041,386155,287893,15832380,True,othdiscr,14,10,8.0,WALK +126659045,386155,287893,15832380,False,Home,10,14,12.0,WALK +126659417,386156,287893,15832427,True,escort,9,10,7.0,WALK +126659418,386156,287893,15832427,True,escort,11,9,8.0,WALK +126659419,386156,287893,15832427,True,school,21,11,8.0,WALK +126659421,386156,287893,15832427,False,Home,10,21,21.0,WALK +126685329,386235,287933,15835666,True,othmaint,4,10,8.0,WALK_LRF +126685330,386235,287933,15835666,True,eatout,9,4,9.0,WALK_LRF +126685331,386235,287933,15835666,True,univ,13,9,9.0,WALK_LRF +126685333,386235,287933,15835666,False,Home,10,13,15.0,WALK_LRF +126685657,386236,287933,15835707,True,school,10,10,8.0,WALK +126685661,386236,287933,15835707,False,othmaint,10,10,15.0,WALK +126685662,386236,287933,15835707,False,shopping,7,10,18.0,WALK +126685663,386236,287933,15835707,False,Home,10,7,18.0,WALK +126701745,386285,287958,15837718,True,shopping,5,11,16.0,WALK +126701749,386285,287958,15837718,False,Home,11,5,16.0,WALK +126702057,386286,287958,15837757,True,school,10,11,7.0,WALK_LOC +126702061,386286,287958,15837757,False,Home,11,10,15.0,WALK_LOC +136971385,417595,303613,17121423,True,othmaint,5,2,20.0,DRIVEALONEFREE +136971389,417595,303613,17121423,False,othmaint,9,5,20.0,TNC_SHARED +136971390,417595,303613,17121423,False,Home,2,9,21.0,TAXI +136971425,417595,303613,17121428,True,shopping,4,2,10.0,WALK +136971429,417595,303613,17121428,False,Home,2,4,10.0,WALK +136971473,417595,303613,17121434,True,work,1,2,11.0,WALK +136971474,417595,303613,17121434,True,work,19,1,11.0,DRIVEALONEFREE +136971477,417595,303613,17121434,False,Home,2,19,18.0,DRIVEALONEFREE +137013409,417723,303677,17126676,True,shopping,5,7,14.0,BIKE +137013413,417723,303677,17126676,False,Home,7,5,16.0,BIKE +137013417,417723,303677,17126677,True,shopping,2,7,18.0,WALK +137013421,417723,303677,17126677,False,Home,7,2,20.0,WALK +137013433,417723,303677,17126679,True,social,2,7,20.0,WALK +137013437,417723,303677,17126679,False,Home,7,2,23.0,WALK +137013721,417724,303677,17126715,True,school,7,7,8.0,WALK +137013725,417724,303677,17126715,False,Home,7,7,15.0,WALK +137018657,417739,303685,17127332,True,shopping,5,7,17.0,WALK +137018661,417739,303685,17127332,False,Home,7,5,19.0,WALK +137018769,417740,303685,17127346,True,eatout,6,7,8.0,WALK +137018773,417740,303685,17127346,False,Home,7,6,15.0,WALK +137067905,417889,303760,17133488,True,work,22,8,7.0,DRIVEALONEFREE +137067909,417889,303760,17133488,False,Home,8,22,18.0,DRIVEALONEFREE +137068169,417890,303760,17133521,True,school,9,8,7.0,WALK +137068173,417890,303760,17133521,False,Home,8,9,12.0,WALK +137086929,417947,303789,17135866,True,work,7,9,7.0,WALK_LOC +137086933,417947,303789,17135866,False,Home,9,7,17.0,WALK +137087193,417948,303789,17135899,True,school,10,9,13.0,WALK +137087197,417948,303789,17135899,False,Home,9,10,20.0,WALK_LOC +137090777,417959,303795,17136347,True,othmaint,19,9,10.0,WALK_LOC +137090781,417959,303795,17136347,False,Home,9,19,10.0,WALK_LOC +137090817,417959,303795,17136352,True,shopping,4,9,11.0,WALK +137090821,417959,303795,17136352,False,Home,9,4,17.0,WALK +137091129,417960,303795,17136391,True,school,10,9,7.0,WALK +137091133,417960,303795,17136391,False,Home,9,10,14.0,WALK +137098625,417983,303807,17137328,True,othdiscr,9,9,11.0,WALK +137098629,417983,303807,17137328,False,Home,9,9,16.0,WALK +137098689,417983,303807,17137336,True,shopping,25,9,16.0,WALK +137098693,417983,303807,17137336,False,Home,9,25,18.0,WALK +137100049,417987,303809,17137506,True,work,14,9,7.0,WALK +137100053,417987,303809,17137506,False,Home,9,14,19.0,WALK +137100313,417988,303809,17137539,True,school,9,9,8.0,WALK +137100317,417988,303809,17137539,False,Home,9,9,10.0,WALK +137113169,418027,303829,17139146,True,escort,7,9,7.0,WALK +137113170,418027,303829,17139146,True,work,5,7,8.0,WALK +137113173,418027,303829,17139146,False,shopping,5,5,17.0,WALK +137113174,418027,303829,17139146,False,othmaint,7,5,18.0,WALK +137113175,418027,303829,17139146,False,Home,9,7,18.0,WALK +137113433,418028,303829,17139179,True,school,11,9,7.0,WALK +137113437,418028,303829,17139179,False,Home,9,11,7.0,WALK +137132849,418087,303859,17141606,True,work,14,9,7.0,WALK +137132853,418087,303859,17141606,False,Home,9,14,18.0,WALK +137133065,418088,303859,17141633,True,othdiscr,13,9,16.0,WALK_LRF +137133069,418088,303859,17141633,False,Home,9,13,17.0,WALK_LRF +137133113,418088,303859,17141639,True,school,13,9,8.0,WALK_LRF +137133117,418088,303859,17141639,False,Home,9,13,14.0,WALK_LRF +137174769,418215,303923,17146846,True,univ,10,10,11.0,WALK +137174773,418215,303923,17146846,False,work,10,10,11.0,WALK +137174774,418215,303923,17146846,False,Home,10,10,11.0,WALK +137174833,418215,303923,17146854,True,work,10,10,5.0,WALK +137174837,418215,303923,17146854,False,Home,10,10,10.0,WALK +137175097,418216,303923,17146887,True,school,11,10,7.0,SHARED2FREE +137175101,418216,303923,17146887,False,shopping,9,11,11.0,WALK +137175102,418216,303923,17146887,False,Home,10,9,11.0,SHARED3FREE +137226001,418371,304001,17153250,True,work,1,10,7.0,WALK_LRF +137226005,418371,304001,17153250,False,Home,10,1,17.0,WALK_LRF +137226217,418372,304001,17153277,True,othdiscr,3,10,8.0,WALK +137226221,418372,304001,17153277,False,Home,10,3,19.0,WALK +137231817,418389,304010,17153977,True,othmaint,9,10,8.0,WALK +137231821,418389,304010,17153977,False,Home,10,9,10.0,WALK_LOC +137232169,418390,304010,17154021,True,school,20,10,8.0,WALK_LOC +137232173,418390,304010,17154021,False,Home,10,20,15.0,WALK_LOC +137232209,418390,304010,17154026,True,social,13,10,16.0,WALK_LRF +137232213,418390,304010,17154026,False,Home,10,13,21.0,WALK_LRF +137248721,418441,304036,17156090,True,escort,7,10,7.0,SHARED2FREE +137248725,418441,304036,17156090,False,Home,10,7,7.0,SHARED2FREE +137248873,418441,304036,17156109,True,othmaint,11,10,18.0,WALK +137248877,418441,304036,17156109,False,Home,10,11,20.0,WALK +137248961,418441,304036,17156120,True,work,14,10,7.0,WALK_LRF +137248965,418441,304036,17156120,False,Home,10,14,17.0,WALK_LRF +137249225,418442,304036,17156153,True,school,10,10,8.0,WALK +137249229,418442,304036,17156153,False,Home,10,10,10.0,SHARED3FREE +137271921,418511,304071,17158990,True,work,15,10,7.0,WALK_LOC +137271925,418511,304071,17158990,False,Home,10,15,16.0,WALK_LRF +137272185,418512,304071,17159023,True,school,13,10,10.0,WALK_LRF +137272189,418512,304071,17159023,False,Home,10,13,13.0,WALK_LRF +137282177,418543,304087,17160272,True,escort,7,10,15.0,WALK +137282181,418543,304087,17160272,False,Home,10,7,16.0,WALK +137282329,418543,304087,17160291,True,othmaint,7,10,17.0,BIKE +137282333,418543,304087,17160291,False,Home,10,7,18.0,BIKE +137282369,418543,304087,17160296,True,shopping,19,10,16.0,WALK_LOC +137282373,418543,304087,17160296,False,Home,10,19,16.0,WALK_LOC +137282417,418543,304087,17160302,True,work,9,10,7.0,WALK +137282421,418543,304087,17160302,False,Home,10,9,15.0,WALK +137282681,418544,304087,17160335,True,school,7,10,7.0,WALK +137282685,418544,304087,17160335,False,Home,10,7,21.0,WALK_LRF +137306033,418615,304123,17163254,True,work,19,11,10.0,WALK +137306037,418615,304123,17163254,False,othmaint,10,19,18.0,WALK +137306038,418615,304123,17163254,False,Home,11,10,18.0,WALK +137306273,418616,304123,17163284,True,othmaint,5,11,12.0,WALK +137306277,418616,304123,17163284,False,Home,11,5,14.0,WALK +137327681,418681,304156,17165960,True,work,12,11,7.0,WALK +137327685,418681,304156,17165960,False,Home,11,12,17.0,WALK +137327745,418682,304156,17165968,True,shopping,16,11,8.0,WALK +137327746,418682,304156,17165968,True,eatout,13,16,8.0,WALK +137327749,418682,304156,17165968,False,Home,11,13,15.0,WALK +137333585,418699,304165,17166698,True,work,24,11,7.0,WALK +137333589,418699,304165,17166698,False,shopping,4,24,18.0,WALK +137333590,418699,304165,17166698,False,Home,11,4,18.0,WALK +137355889,418767,304199,17169486,True,work,16,16,6.0,TNC_SINGLE +137355893,418767,304199,17169486,False,Home,16,16,17.0,TNC_SINGLE +137356153,418768,304199,17169519,True,school,17,16,8.0,WALK_LRF +137356157,418768,304199,17169519,False,Home,16,17,11.0,WALK_LRF +137381865,418847,304239,17172733,True,eatout,16,16,12.0,WALK +137381869,418847,304239,17172733,False,Home,16,16,12.0,WALK +137382017,418847,304239,17172752,True,othdiscr,16,16,9.0,WALK +137382021,418847,304239,17172752,False,Home,16,16,10.0,WALK +137382041,418847,304239,17172755,True,othmaint,12,16,12.0,WALK +137382045,418847,304239,17172755,False,Home,16,12,13.0,WALK_LOC +137382081,418847,304239,17172760,True,shopping,16,16,15.0,WALK +137382085,418847,304239,17172760,False,Home,16,16,17.0,WALK +137382393,418848,304239,17172799,True,escort,2,16,8.0,WALK_LOC +137382394,418848,304239,17172799,True,school,8,2,9.0,WALK +137382397,418848,304239,17172799,False,Home,16,8,19.0,WALK_LOC +137405745,418919,304275,17175718,True,work,22,17,11.0,WALK_LOC +137405746,418919,304275,17175718,True,work,16,22,11.0,WALK_LOC +137405749,418919,304275,17175718,False,Home,17,16,21.0,WALK +137405961,418920,304275,17175745,True,othdiscr,10,17,7.0,SHARED3FREE +137405965,418920,304275,17175745,False,eatout,5,10,12.0,SHARED3FREE +137405966,418920,304275,17175745,False,Home,17,5,12.0,SHARED3FREE +137405985,418920,304275,17175748,True,othmaint,16,17,12.0,TNC_SHARED +137405989,418920,304275,17175748,False,Home,17,16,22.0,WALK_LRF +137429273,418991,304311,17178659,True,othmaint,5,19,15.0,WALK_LOC +137429277,418991,304311,17178659,False,shopping,11,5,18.0,WALK +137429278,418991,304311,17178659,False,Home,19,11,18.0,WALK +137429313,418991,304311,17178664,True,shopping,11,19,13.0,WALK +137429317,418991,304311,17178664,False,Home,19,11,13.0,WALK +137429625,418992,304311,17178703,True,school,11,19,11.0,WALK +137429629,418992,304311,17178703,False,Home,19,11,14.0,WALK +154276993,470356,328721,19284624,True,othmaint,16,6,15.0,WALK_LOC +154276997,470356,328721,19284624,False,Home,6,16,22.0,WALK_LOC +154277297,470357,328721,19284662,True,othdiscr,13,6,11.0,WALK_LOC +154277301,470357,328721,19284662,False,Home,6,13,15.0,WALK_LOC +154277361,470357,328721,19284670,True,shopping,18,6,15.0,WALK_LOC +154277365,470357,328721,19284670,False,Home,6,18,15.0,WALK_LOC +154277673,470358,328721,19284709,True,univ,12,6,8.0,WALK +154277677,470358,328721,19284709,False,Home,6,12,12.0,WALK_LOC +175499009,535057,350288,21937376,True,work,15,19,8.0,BIKE +175499013,535057,350288,21937376,False,Home,19,15,15.0,BIKE +175499337,535058,350288,21937417,True,work,9,19,18.0,DRIVEALONEFREE +175499341,535058,350288,21937417,False,social,11,9,18.0,DRIVEALONEFREE +175499342,535058,350288,21937417,False,Home,19,11,19.0,DRIVEALONEFREE +175499665,535059,350288,21937458,True,work,21,19,7.0,WALK +175499669,535059,350288,21937458,False,Home,19,21,13.0,WALK_LOC +175523609,535132,350313,21940451,True,work,16,19,9.0,WALK +175523613,535132,350313,21940451,False,Home,19,16,15.0,WALK +175523617,535132,350313,21940452,True,work,16,19,17.0,WALK +175523621,535132,350313,21940452,False,Home,19,16,18.0,WALK +175523937,535133,350313,21940492,True,work,22,19,8.0,WALK_LOC +175523941,535133,350313,21940492,False,shopping,8,22,10.0,WALK_LRF +175523942,535133,350313,21940492,False,Home,19,8,10.0,WALK +175523945,535133,350313,21940493,True,work,22,19,11.0,WALK_LOC +175523949,535133,350313,21940493,False,eatout,7,22,20.0,WALK_LOC +175523950,535133,350313,21940493,False,Home,19,7,21.0,WALK +175524265,535134,350313,21940533,True,work,22,19,7.0,BIKE +175524269,535134,350313,21940533,False,Home,19,22,10.0,BIKE +181831985,554365,356724,22728998,True,othmaint,7,6,10.0,WALK +181831986,554365,356724,22728998,True,shopping,16,7,10.0,WALK_LOC +181831989,554365,356724,22728998,False,eatout,7,16,16.0,WALK +181831990,554365,356724,22728998,False,Home,6,7,16.0,WALK +181832249,554366,356724,22729031,True,othdiscr,12,6,10.0,WALK +181832253,554366,356724,22729031,False,Home,6,12,13.0,WALK +181832449,554367,356724,22729056,True,escort,9,6,12.0,WALK +181832453,554367,356724,22729056,False,Home,6,9,12.0,WALK +181865097,554466,356757,22733137,True,school,8,8,8.0,WALK +181865101,554466,356757,22733137,False,Home,8,8,16.0,WALK +181916921,554624,356810,22739615,True,univ,13,9,7.0,WALK_LRF +181916925,554624,356810,22739615,False,work,8,13,15.0,WALK_LRF +181916926,554624,356810,22739615,False,Home,9,8,16.0,WALK_LOC +181917289,554625,356810,22739661,True,social,2,9,8.0,WALK +181917293,554625,356810,22739661,False,Home,9,2,18.0,WALK +181927433,554656,356821,22740929,True,shopping,5,17,13.0,WALK +181927437,554656,356821,22740929,False,Home,17,5,19.0,WALK +181927745,554657,356821,22740968,True,school,10,17,7.0,WALK_LRF +181927749,554657,356821,22740968,False,escort,2,10,12.0,WALK_LRF +181927750,554657,356821,22740968,False,social,9,2,12.0,WALK_LRF +181927751,554657,356821,22740968,False,Home,17,9,12.0,WALK_LRF +181928073,554658,356821,22741009,True,school,9,17,10.0,WALK_LRF +181928077,554658,356821,22741009,False,eatout,6,9,18.0,WALK_LOC +181928078,554658,356821,22741009,False,Home,17,6,23.0,WALK_LRF +195037313,594625,370144,24379664,True,work,12,7,5.0,WALK +195037317,594625,370144,24379664,False,shopping,8,12,17.0,WALK +195037318,594625,370144,24379664,False,escort,7,8,17.0,WALK +195037319,594625,370144,24379664,False,eatout,7,7,17.0,WALK +195037320,594625,370144,24379664,False,Home,7,7,17.0,WALK +195037377,594626,370144,24379672,True,eatout,10,7,15.0,WALK +195037381,594626,370144,24379672,False,Home,7,10,17.0,WALK +195037593,594626,370144,24379699,True,shopping,16,7,9.0,WALK +195037597,594626,370144,24379699,False,Home,7,16,12.0,WALK +195037905,594627,370144,24379738,True,school,7,7,8.0,WALK +195037909,594627,370144,24379738,False,Home,7,7,11.0,WALK +195073961,594737,370181,24384245,True,othmaint,7,9,17.0,WALK_LOC +195073965,594737,370181,24384245,False,Home,9,7,19.0,WALK +195074049,594737,370181,24384256,True,work,5,9,8.0,WALK +195074053,594737,370181,24384256,False,Home,9,5,17.0,WALK +195074313,594738,370181,24384289,True,school,10,9,7.0,WALK_LOC +195074317,594738,370181,24384289,False,Home,9,10,18.0,WALK_LOC +195097337,594808,370205,24387167,True,work,14,9,8.0,WALK_LRF +195097341,594808,370205,24387167,False,Home,9,14,17.0,WALK_LRF +195097601,594809,370205,24387200,True,escort,9,9,18.0,WALK +195097602,594809,370205,24387200,True,escort,6,9,18.0,WALK +195097603,594809,370205,24387200,True,univ,9,6,18.0,WALK +195097605,594809,370205,24387200,False,Home,9,9,18.0,WALK +195097929,594810,370205,24387241,True,school,7,9,7.0,WALK_LOC +195097933,594810,370205,24387241,False,Home,9,7,23.0,WALK_LRF +195153185,594979,370262,24394148,True,escort,3,10,8.0,WALK +195153189,594979,370262,24394148,False,Home,10,3,9.0,WALK +195153193,594979,370262,24394149,True,escort,4,10,11.0,DRIVEALONEFREE +195153197,594979,370262,24394149,False,Home,10,4,11.0,DRIVEALONEFREE +195153201,594979,370262,24394150,True,escort,5,10,11.0,WALK +195153205,594979,370262,24394150,False,Home,10,5,12.0,WALK +195153377,594979,370262,24394172,True,shopping,9,10,13.0,WALK +195153378,594979,370262,24394172,True,shopping,10,9,13.0,WALK +195153381,594979,370262,24394172,False,Home,10,10,13.0,WALK +195153689,594980,370262,24394211,True,univ,9,10,16.0,WALK +195153693,594980,370262,24394211,False,work,9,9,16.0,WALK +195153694,594980,370262,24394211,False,Home,10,9,17.0,WALK +195153705,594980,370262,24394213,True,shopping,13,10,13.0,WALK +195153709,594980,370262,24394213,False,shopping,12,13,13.0,WALK +195153710,594980,370262,24394213,False,Home,10,12,13.0,WALK +195154017,594981,370262,24394252,True,school,9,10,7.0,WALK +195154021,594981,370262,24394252,False,Home,10,9,15.0,WALK +195185897,595078,370295,24398237,True,eatout,11,10,8.0,WALK +195185898,595078,370295,24398237,True,work,9,11,10.0,SHARED3FREE +195185901,595078,370295,24398237,False,work,7,9,11.0,WALK +195185902,595078,370295,24398237,False,Home,10,7,15.0,DRIVEALONEFREE +195186161,595079,370295,24398270,True,school,11,10,8.0,SHARED3FREE +195186165,595079,370295,24398270,False,othdiscr,9,11,15.0,SHARED3FREE +195186166,595079,370295,24398270,False,Home,10,9,15.0,SHARED2FREE +195186489,595080,370295,24398311,True,school,10,10,8.0,WALK +195186493,595080,370295,24398311,False,Home,10,10,15.0,WALK +195238049,595237,370348,24404756,True,work,1,10,8.0,WALK +195238053,595237,370348,24404756,False,Home,10,1,17.0,WALK +195238313,595238,370348,24404789,True,school,10,10,8.0,WALK +195238317,595238,370348,24404789,False,eatout,11,10,14.0,WALK +195238318,595238,370348,24404789,False,shopping,11,11,15.0,WALK +195238319,595238,370348,24404789,False,Home,10,11,15.0,WALK +195238641,595239,370348,24404830,True,school,9,10,7.0,WALK +195238645,595239,370348,24404830,False,Home,10,9,14.0,WALK_LOC +195283225,595375,370394,24410403,True,othmaint,9,10,12.0,BIKE +195283229,595375,370394,24410403,False,Home,10,9,15.0,BIKE +195283377,595376,370394,24410422,True,eatout,10,10,16.0,WALK +195283381,595376,370394,24410422,False,Home,10,10,18.0,WALK +195283641,595376,370394,24410455,True,eatout,21,10,8.0,SHARED3FREE +195283642,595376,370394,24410455,True,work,1,21,12.0,WALK +195283645,595376,370394,24410455,False,Home,10,1,16.0,WALK_LRF +195283857,595377,370394,24410482,True,othdiscr,16,10,6.0,WALK +195283861,595377,370394,24410482,False,Home,10,16,17.0,WALK +195351097,595582,370463,24418887,True,othdiscr,14,16,9.0,WALK +195351101,595582,370463,24418887,False,Home,16,14,15.0,WALK +195351537,595583,370463,24418942,True,work,1,16,7.0,WALK_LOC +195351541,595583,370463,24418942,False,Home,16,1,16.0,WALK +195351801,595584,370463,24418975,True,school,16,16,8.0,WALK +195351805,595584,370463,24418975,False,Home,16,16,15.0,WALK +195385257,595686,370497,24423157,True,school,8,21,7.0,WALK_LOC +195385261,595686,370497,24423157,False,Home,21,8,16.0,WALK +195400409,595732,370513,24425051,True,work,15,22,7.0,WALK +195400413,595732,370513,24425051,False,Home,22,15,18.0,WALK +195400473,595733,370513,24425059,True,eatout,12,22,18.0,WALK_LRF +195400477,595733,370513,24425059,False,Home,22,12,20.0,WALK +195400497,595733,370513,24425062,True,escort,11,22,10.0,WALK +195400501,595733,370513,24425062,False,Home,22,11,14.0,WALK +195401001,595734,370513,24425125,True,school,10,22,7.0,WALK_LRF +195401005,595734,370513,24425125,False,Home,22,10,13.0,WALK_LRF +195405065,595747,370518,24425633,True,eatout,2,24,7.0,WALK +195405069,595747,370518,24425633,False,Home,24,2,15.0,WALK +195405609,595748,370518,24425701,True,othmaint,12,24,10.0,WALK +195405610,595748,370518,24425701,True,shopping,4,12,11.0,WALK +195405613,595748,370518,24425701,False,Home,24,4,15.0,WALK +195405937,595749,370518,24425742,True,shopping,11,24,10.0,SHARED2FREE +195405941,595749,370518,24425742,False,Home,24,11,11.0,SHARED2FREE +211327433,644290,386699,26415929,True,work,12,7,12.0,WALK_LOC +211327437,644290,386699,26415929,False,Home,7,12,22.0,WALK_LOC +211327673,644291,386699,26415959,True,othmaint,2,7,19.0,WALK_LOC +211327677,644291,386699,26415959,False,Home,7,2,23.0,TNC_SINGLE +211327761,644291,386699,26415970,True,work,2,7,7.0,WALK +211327765,644291,386699,26415970,False,Home,7,2,17.0,WALK +211328025,644292,386699,26416003,True,school,9,7,13.0,WALK_LOC +211328029,644292,386699,26416003,False,othdiscr,22,9,21.0,WALK_LRF +211328030,644292,386699,26416003,False,Home,7,22,21.0,WALK_LRF +211376457,644440,386749,26422057,True,othmaint,5,9,17.0,WALK +211376461,644440,386749,26422057,False,Home,9,5,17.0,SHARED3FREE +211376585,644440,386749,26422073,True,shopping,17,9,16.0,SHARED2FREE +211376589,644440,386749,26422073,False,Home,9,17,16.0,SHARED2FREE +211376633,644440,386749,26422079,True,work,1,9,5.0,WALK_LRF +211376637,644440,386749,26422079,False,Home,9,1,15.0,WALK_LRF +211376897,644441,386749,26422112,True,school,9,9,8.0,WALK +211376901,644441,386749,26422112,False,Home,9,9,16.0,WALK +211377177,644442,386749,26422147,True,othdiscr,9,9,15.0,WALK +211377181,644442,386749,26422147,False,Home,9,9,16.0,WALK +211377289,644442,386749,26422161,True,work,9,9,6.0,WALK +211377293,644442,386749,26422161,False,Home,9,9,15.0,WALK +211387457,644473,386760,26423432,True,work,13,16,8.0,WALK +211387461,644473,386760,26423432,False,Home,16,13,19.0,WALK +211387465,644473,386760,26423433,True,work,13,16,20.0,WALK +211387469,644473,386760,26423433,False,Home,16,13,23.0,WALK +211387785,644474,386760,26423473,True,work,11,16,6.0,WALK +211387789,644474,386760,26423473,False,Home,16,11,16.0,WALK +211388049,644475,386760,26423506,True,school,16,16,7.0,WALK +211388053,644475,386760,26423506,False,Home,16,16,15.0,WALK +211388201,644476,386761,26423525,True,escort,11,16,5.0,WALK_LOC +211388205,644476,386761,26423525,False,Home,16,11,6.0,WALK_LOC +211388329,644476,386761,26423541,True,othdiscr,16,16,18.0,WALK +211388333,644476,386761,26423541,False,Home,16,16,18.0,WALK +211388353,644476,386761,26423544,True,othmaint,13,16,18.0,WALK +211388357,644476,386761,26423544,False,Home,16,13,19.0,WALK +211388441,644476,386761,26423555,True,work,4,16,7.0,DRIVEALONEFREE +211388445,644476,386761,26423555,False,Home,16,4,17.0,SHARED3FREE +211388721,644477,386761,26423590,True,shopping,16,16,14.0,WALK +211388725,644477,386761,26423590,False,Home,16,16,14.0,WALK +211389033,644478,386761,26423629,True,school,20,16,8.0,WALK_LOC +211389037,644478,386761,26423629,False,shopping,17,20,13.0,WALK +211389038,644478,386761,26423629,False,Home,16,17,23.0,WALK +211407025,644533,386780,26425878,True,othdiscr,8,19,18.0,WALK +211407029,644533,386780,26425878,False,Home,19,8,21.0,WALK +211407137,644533,386780,26425892,True,work,17,19,8.0,WALK +211407141,644533,386780,26425892,False,Home,19,17,17.0,WALK +211407377,644534,386780,26425922,True,othmaint,15,19,15.0,WALK_LOC +211407381,644534,386780,26425922,False,Home,19,15,19.0,TNC_SINGLE +211409057,644539,386782,26426132,True,shopping,11,19,9.0,WALK_LOC +211409058,644539,386782,26426132,True,shopping,13,11,11.0,WALK_LOC +211409061,644539,386782,26426132,False,Home,19,13,13.0,WALK_LOC +211409433,644540,386782,26426179,True,work,7,19,8.0,WALK +211409437,644540,386782,26426179,False,Home,19,7,17.0,WALK +211409697,644541,386782,26426212,True,school,11,19,8.0,WALK +211409701,644541,386782,26426212,False,Home,19,11,13.0,WALK +211447369,644656,386821,26430921,True,othdiscr,15,25,11.0,WALK +211447373,644656,386821,26430921,False,Home,25,15,15.0,WALK +211447433,644656,386821,26430929,True,shopping,2,25,10.0,WALK +211447437,644656,386821,26430929,False,Home,25,2,11.0,WALK +211447809,644657,386821,26430976,True,work,3,25,7.0,WALK +211447813,644657,386821,26430976,False,Home,25,3,18.0,WALK +211448049,644658,386821,26431006,True,othmaint,12,25,10.0,BIKE +211448053,644658,386821,26431006,False,Home,25,12,14.0,BIKE +277530233,846128,431923,34691279,True,school,8,6,8.0,WALK_LOC +277530237,846128,431923,34691279,False,Home,6,8,15.0,WALK_LOC +277530561,846129,431923,34691320,True,school,6,6,8.0,WALK +277530565,846129,431923,34691320,False,Home,6,6,15.0,WALK +277530889,846130,431923,34691361,True,school,9,6,6.0,WALK_HVY +277530893,846130,431923,34691361,False,othmaint,9,9,15.0,WALK +277530894,846130,431923,34691361,False,eatout,2,9,15.0,WALK_HVY +277530895,846130,431923,34691361,False,Home,6,2,15.0,WALK +277530905,846130,431923,34691363,True,othdiscr,6,6,17.0,WALK +277530906,846130,431923,34691363,True,shopping,13,6,17.0,WALK +277530909,846130,431923,34691363,False,Home,6,13,17.0,WALK_LOC +277530929,846130,431923,34691366,True,social,2,6,17.0,TNC_SINGLE +277530933,846130,431923,34691366,False,Home,6,2,18.0,TNC_SHARED +277537929,846152,431929,34692241,True,escort,23,8,7.0,WALK_LOC +277537933,846152,431929,34692241,False,Home,8,23,7.0,TNC_SINGLE +277538081,846152,431929,34692260,True,othmaint,7,8,8.0,WALK +277538085,846152,431929,34692260,False,Home,8,7,15.0,WALK +277538449,846153,431929,34692306,True,shopping,21,8,18.0,DRIVEALONEFREE +277538453,846153,431929,34692306,False,Home,8,21,20.0,DRIVEALONEFREE +277538761,846154,431929,34692345,True,school,9,8,7.0,WALK +277538765,846154,431929,34692345,False,Home,8,9,10.0,WALK +277539041,846155,431929,34692380,True,othdiscr,5,8,20.0,SHARED2FREE +277539045,846155,431929,34692380,False,Home,8,5,22.0,WALK +277539089,846155,431929,34692386,True,school,8,8,8.0,WALK +277539093,846155,431929,34692386,False,Home,8,8,14.0,WALK +277539369,846156,431929,34692421,True,othdiscr,17,8,7.0,WALK_LRF +277539373,846156,431929,34692421,False,Home,8,17,19.0,WALK_LRF +277575121,846265,431952,34696890,True,othdiscr,22,8,15.0,WALK_HVY +277575125,846265,431952,34696890,False,Home,8,22,19.0,WALK_LRF +277575513,846266,431952,34696939,True,shopping,16,8,15.0,WALK +277575517,846266,431952,34696939,False,Home,8,16,16.0,WALK_LOC +277575825,846267,431952,34696978,True,school,13,8,8.0,WALK +277575829,846267,431952,34696978,False,Home,8,13,13.0,WALK_LRF +277576153,846268,431952,34697019,True,escort,10,8,13.0,WALK +277576154,846268,431952,34697019,True,escort,1,10,15.0,WALK_LRF +277576155,846268,431952,34697019,True,othdiscr,10,1,15.0,WALK_LRF +277576156,846268,431952,34697019,True,school,13,10,16.0,WALK_LRF +277576157,846268,431952,34697019,False,Home,8,13,20.0,WALK_LRF +277667337,846546,432008,34708417,True,school,7,9,18.0,WALK_LOC +277667341,846546,432008,34708417,False,Home,9,7,21.0,WALK_LRF +277667665,846547,432008,34708458,True,school,6,9,5.0,WALK +277667669,846547,432008,34708458,False,Home,9,6,19.0,WALK +277690313,846616,432022,34711289,True,shopping,23,9,9.0,WALK_LRF +277690317,846616,432022,34711289,False,Home,9,23,13.0,WALK_LRF +277690953,846618,432022,34711369,True,school,9,9,14.0,WALK +277690957,846618,432022,34711369,False,Home,9,9,21.0,WALK +277750473,846800,432059,34718809,True,escort,7,19,7.0,WALK +277750477,846800,432059,34718809,False,Home,19,7,8.0,WALK +277750977,846801,432059,34718872,True,school,21,19,7.0,BIKE +277750981,846801,432059,34718872,False,Home,19,21,14.0,BIKE +277751305,846802,432059,34718913,True,school,8,19,7.0,WALK +277751309,846802,432059,34718913,False,Home,19,8,10.0,WALK +277751585,846803,432059,34718948,True,othdiscr,12,19,11.0,WALK +277751589,846803,432059,34718948,False,Home,19,12,14.0,WALK +277753425,846809,432061,34719178,True,escort,24,19,8.0,DRIVEALONEFREE +277753429,846809,432061,34719178,False,Home,19,24,8.0,SHARED2FREE +277753729,846810,432061,34719216,True,eatout,11,19,9.0,WALK +277753733,846810,432061,34719216,False,Home,19,11,15.0,WALK +277754273,846811,432061,34719284,True,shopping,16,19,17.0,BIKE +277754277,846811,432061,34719284,False,Home,19,16,20.0,BIKE +277754625,846812,432061,34719328,True,social,5,19,11.0,WALK +277754629,846812,432061,34719328,False,Home,19,5,21.0,WALK +316561641,965126,456554,39570205,True,work,24,9,8.0,WALK_HVY +316561645,965126,456554,39570205,False,Home,9,24,19.0,WALK_LRF +316561881,965127,456554,39570235,True,othmaint,8,9,10.0,WALK +316561882,965127,456554,39570235,True,othmaint,12,8,10.0,WALK +316561885,965127,456554,39570235,False,Home,9,12,10.0,WALK_LRF +316561921,965127,456554,39570240,True,shopping,14,9,10.0,WALK_LRF +316561925,965127,456554,39570240,False,shopping,16,14,20.0,WALK +316561926,965127,456554,39570240,False,shopping,16,16,20.0,WALK +316561927,965127,456554,39570240,False,Home,9,16,20.0,WALK_LRF +316562561,965129,456554,39570320,True,school,8,9,8.0,WALK +316562565,965129,456554,39570320,False,Home,9,8,15.0,SHARED3FREE +316588209,965207,456572,39573526,True,work,9,9,7.0,WALK +316588213,965207,456572,39573526,False,Home,9,9,18.0,WALK +316588473,965208,456572,39573559,True,school,9,9,11.0,WALK +316588477,965208,456572,39573559,False,Home,9,9,16.0,WALK +316600937,965246,456581,39575117,True,escort,9,9,7.0,WALK +316600938,965246,456581,39575117,True,school,13,9,8.0,WALK_LRF +316600941,965246,456581,39575117,False,Home,9,13,17.0,WALK_LRF +316601265,965247,456581,39575158,True,school,9,9,8.0,WALK +316601269,965247,456581,39575158,False,Home,9,9,16.0,WALK +316601593,965248,456581,39575199,True,school,21,9,6.0,WALK_LRF +316601597,965248,456581,39575199,False,othdiscr,22,21,15.0,WALK +316601598,965248,456581,39575199,False,Home,9,22,21.0,WALK_LRF +316601921,965249,456581,39575240,True,school,9,9,8.0,WALK +316601925,965249,456581,39575240,False,Home,9,9,12.0,WALK +316620353,965305,456594,39577544,True,work,22,9,10.0,WALK_LRF +316620357,965305,456594,39577544,False,Home,9,22,17.0,WALK_LRF +316620361,965305,456594,39577545,True,work,22,9,19.0,WALK_LOC +316620365,965305,456594,39577545,False,Home,9,22,19.0,WALK_LOC +316634457,965348,456604,39579307,True,work,9,9,7.0,TNC_SINGLE +316634461,965348,456604,39579307,False,othdiscr,12,9,18.0,TNC_SINGLE +316634462,965348,456604,39579307,False,Home,9,12,19.0,WALK_HVY +316634737,965349,456604,39579342,True,shopping,21,9,17.0,WALK_LOC +316634741,965349,456604,39579342,False,Home,9,21,17.0,WALK_LOC +316635049,965350,456604,39579381,True,school,7,9,9.0,SHARED3FREE +316635053,965350,456604,39579381,False,Home,9,7,18.0,WALK +316635353,965351,456604,39579419,True,othmaint,19,9,18.0,BIKE +316635357,965351,456604,39579419,False,Home,9,19,20.0,WALK +316635377,965351,456604,39579422,True,school,9,9,13.0,WALK +316635381,965351,456604,39579422,False,Home,9,9,16.0,WALK +316659145,965424,456621,39582393,True,escort,9,9,7.0,WALK +316659149,965424,456621,39582393,False,Home,9,9,7.0,WALK +316659153,965424,456621,39582394,True,escort,9,9,17.0,SHARED2FREE +316659154,965424,456621,39582394,True,escort,11,9,17.0,SHARED2FREE +316659157,965424,456621,39582394,False,shopping,13,11,17.0,DRIVEALONEFREE +316659158,965424,456621,39582394,False,Home,9,13,17.0,SHARED2FREE +316659385,965424,456621,39582423,True,work,10,9,7.0,DRIVEALONEFREE +316659389,965424,456621,39582423,False,Home,9,10,17.0,WALK +316659977,965426,456621,39582497,True,school,10,9,8.0,WALK +316659981,965426,456621,39582497,False,Home,9,10,15.0,WALK +316660305,965427,456621,39582538,True,othmaint,9,9,9.0,WALK +316660306,965427,456621,39582538,True,school,9,9,9.0,WALK +316660309,965427,456621,39582538,False,escort,9,9,15.0,WALK +316660310,965427,456621,39582538,False,othmaint,11,9,17.0,WALK +316660311,965427,456621,39582538,False,escort,8,11,17.0,WALK +316660312,965427,456621,39582538,False,Home,9,8,18.0,WALK +316660633,965428,456621,39582579,True,school,9,9,7.0,WALK +316660637,965428,456621,39582579,False,Home,9,9,13.0,WALK +361596585,1102428,484574,45199573,True,othdiscr,11,8,7.0,WALK +361596589,1102428,484574,45199573,False,Home,8,11,14.0,WALK +361596977,1102429,484574,45199622,True,shopping,11,8,10.0,BIKE +361596981,1102429,484574,45199622,False,Home,8,11,15.0,BIKE +361596985,1102429,484574,45199623,True,shopping,2,8,16.0,WALK +361596989,1102429,484574,45199623,False,othmaint,7,2,16.0,WALK +361596990,1102429,484574,45199623,False,shopping,7,7,16.0,WALK +361596991,1102429,484574,45199623,False,social,7,7,16.0,WALK +361596992,1102429,484574,45199623,False,Home,8,7,16.0,WALK +361597265,1102430,484574,45199658,True,othmaint,6,8,12.0,WALK +361597269,1102430,484574,45199658,False,shopping,7,6,12.0,WALK +361597270,1102430,484574,45199658,False,Home,8,7,13.0,WALK +361629497,1102528,484594,45203687,True,work,1,9,8.0,WALK_LRF +361629501,1102528,484594,45203687,False,Home,9,1,17.0,WALK +361630065,1102530,484594,45203758,True,othmaint,12,9,16.0,WALK +361630069,1102530,484594,45203758,False,eatout,7,12,16.0,WALK +361630070,1102530,484594,45203758,False,othmaint,7,7,17.0,WALK +361630071,1102530,484594,45203758,False,Home,9,7,17.0,WALK +361630809,1102532,484594,45203851,True,othmaint,7,9,5.0,WALK +361630810,1102532,484594,45203851,True,work,21,7,7.0,WALK +361630813,1102532,484594,45203851,False,work,5,21,15.0,WALK +361630814,1102532,484594,45203851,False,shopping,5,5,16.0,WALK +361630815,1102532,484594,45203851,False,Home,9,5,16.0,WALK +361631073,1102533,484594,45203884,True,school,10,9,7.0,WALK +361631077,1102533,484594,45203884,False,Home,9,10,15.0,WALK +361647585,1102584,484606,45205948,True,atwork,16,1,10.0,WALK +361647589,1102584,484606,45205948,False,Work,1,16,10.0,WALK +361647865,1102584,484606,45205983,True,work,1,9,5.0,WALK_LRF +361647869,1102584,484606,45205983,False,shopping,5,1,17.0,WALK_LOC +361647870,1102584,484606,45205983,False,shopping,12,5,17.0,WALK +361647871,1102584,484606,45205983,False,Home,9,12,17.0,WALK_LRF +361649113,1102588,484606,45206139,True,school,10,9,7.0,WALK_LOC +361649117,1102588,484606,45206139,False,Home,9,10,15.0,WALK_LOC +361649593,1102590,484606,45206199,True,escort,1,9,10.0,WALK_LRF +361649597,1102590,484606,45206199,False,Home,9,1,10.0,WALK_LRF +361650097,1102591,484606,45206262,True,school,5,9,7.0,WALK +361650101,1102591,484606,45206262,False,Home,9,5,14.0,WALK +361650577,1102593,484606,45206322,True,escort,17,9,14.0,WALK_LRF +361650581,1102593,484606,45206322,False,Home,9,17,17.0,WALK_LRF +361652457,1102598,484608,45206557,True,work,13,9,9.0,WALK +361652461,1102598,484608,45206557,False,Home,9,13,13.0,WALK +361652785,1102599,484608,45206598,True,othmaint,4,9,8.0,BIKE +361652786,1102599,484608,45206598,True,work,9,4,8.0,BIKE +361652789,1102599,484608,45206598,False,Home,9,9,18.0,BIKE +361652849,1102600,484608,45206606,True,eatout,6,9,8.0,WALK +361652853,1102600,484608,45206606,False,Home,9,6,19.0,WALK +361653377,1102601,484608,45206672,True,school,6,9,6.0,WALK_LRF +361653381,1102601,484608,45206672,False,Home,9,6,14.0,WALK_LOC +361653705,1102602,484608,45206713,True,school,6,9,6.0,WALK_LRF +361653709,1102602,484608,45206713,False,Home,9,6,13.0,WALK_LOC +361658689,1102617,484612,45207336,True,work,9,11,8.0,WALK +361658693,1102617,484612,45207336,False,Home,11,9,18.0,WALK +361659281,1102619,484612,45207410,True,school,8,11,7.0,WALK_LOC +361659285,1102619,484612,45207410,False,escort,12,8,11.0,WALK_LOC +361659286,1102619,484612,45207410,False,Home,11,12,12.0,WALK +361659609,1102620,484612,45207451,True,school,8,11,7.0,WALK_LOC +361659613,1102620,484612,45207451,False,Home,11,8,10.0,WALK +361659617,1102620,484612,45207452,True,school,8,11,14.0,WALK +361659621,1102620,484612,45207452,False,Home,11,8,19.0,WALK +361753809,1102907,484670,45219226,True,work,16,17,9.0,WALK +361753813,1102907,484670,45219226,False,othmaint,17,16,17.0,WALK +361753814,1102907,484670,45219226,False,Home,17,17,18.0,WALK +361753897,1102908,484670,45219237,True,escort,25,17,17.0,WALK_LOC +361753901,1102908,484670,45219237,False,Home,17,25,17.0,TNC_SINGLE +361754025,1102908,484670,45219253,True,othdiscr,14,17,18.0,WALK_LOC +361754029,1102908,484670,45219253,False,Home,17,14,21.0,WALK_LRF +361754137,1102908,484670,45219267,True,work,1,17,5.0,WALK +361754141,1102908,484670,45219267,False,Home,17,1,14.0,WALK +361754401,1102909,484670,45219300,True,school,16,17,8.0,WALK_LRF +361754405,1102909,484670,45219300,False,Home,17,16,16.0,WALK_LRF +361754553,1102910,484670,45219319,True,social,7,17,7.0,TNC_SINGLE +361754554,1102910,484670,45219319,True,escort,11,7,7.0,TNC_SINGLE +361754557,1102910,484670,45219319,False,shopping,6,11,7.0,WALK_LOC +361754558,1102910,484670,45219319,False,Home,17,6,7.0,WALK_LRF +361754729,1102910,484670,45219341,True,school,9,17,8.0,WALK_LRF +361754733,1102910,484670,45219341,False,Home,17,9,15.0,WALK_LRF +361760697,1102928,484675,45220087,True,work,15,17,12.0,WALK +361760701,1102928,484675,45220087,False,Home,17,15,22.0,WALK +361760785,1102929,484675,45220098,True,escort,21,17,6.0,TNC_SHARED +361760786,1102929,484675,45220098,True,escort,18,21,7.0,WALK_LOC +361760789,1102929,484675,45220098,False,Home,17,18,7.0,WALK_LOC +361761025,1102929,484675,45220128,True,work,13,17,10.0,WALK +361761026,1102929,484675,45220128,True,work,12,13,14.0,WALK +361761029,1102929,484675,45220128,False,eatout,12,12,19.0,WALK +361761030,1102929,484675,45220128,False,Home,17,12,20.0,WALK +361761289,1102930,484675,45220161,True,school,13,17,13.0,SHARED2FREE +361761293,1102930,484675,45220161,False,Home,17,13,22.0,WALK_LRF +361761617,1102931,484675,45220202,True,school,25,17,8.0,WALK_LOC +361761621,1102931,484675,45220202,False,Home,17,25,16.0,WALK_LRF +361761897,1102932,484675,45220237,True,othdiscr,9,17,18.0,WALK_LRF +361761901,1102932,484675,45220237,False,Home,17,9,22.0,WALK_LRF +361761905,1102932,484675,45220238,True,othdiscr,12,17,22.0,WALK +361761909,1102932,484675,45220238,False,Home,17,12,23.0,WALK +361761945,1102932,484675,45220243,True,school,8,17,8.0,WALK_LRF +361761949,1102932,484675,45220243,False,Home,17,8,15.0,WALK_LRF +361769225,1102954,484680,45221153,True,work,2,21,12.0,WALK +361769229,1102954,484680,45221153,False,Home,21,2,15.0,WALK +361769505,1102955,484680,45221188,True,shopping,5,21,18.0,BIKE +361769509,1102955,484680,45221188,False,othmaint,7,5,18.0,BIKE +361769510,1102955,484680,45221188,False,Home,21,7,19.0,BIKE +361769817,1102956,484680,45221227,True,school,21,21,15.0,WALK +361769821,1102956,484680,45221227,False,Home,21,21,19.0,WALK +361770145,1102957,484680,45221268,True,school,8,21,8.0,SHARED3FREE +361770149,1102957,484680,45221268,False,Home,21,8,15.0,WALK +361779721,1102986,484686,45222465,True,work,4,25,11.0,WALK +361779725,1102986,484686,45222465,False,Home,25,4,20.0,WALK +361780049,1102987,484686,45222506,True,work,21,25,9.0,WALK +361780053,1102987,484686,45222506,False,Home,25,21,19.0,WALK +361780113,1102988,484686,45222514,True,eatout,7,25,17.0,WALK +361780117,1102988,484686,45222514,False,Home,25,7,20.0,WALK +361780313,1102988,484686,45222539,True,school,6,25,7.0,WALK_LOC +361780317,1102988,484686,45222539,False,Home,25,6,15.0,WALK_LOC +361780641,1102989,484686,45222580,True,school,25,25,7.0,WALK +361780645,1102989,484686,45222580,False,Home,25,25,15.0,WALK +393801001,1200612,503499,49225125,True,othmaint,7,5,16.0,SHARED3FREE +393801002,1200612,503499,49225125,True,othmaint,4,7,16.0,SHARED3FREE +393801003,1200612,503499,49225125,True,shopping,22,4,17.0,SHARED3FREE +393801005,1200612,503499,49225125,False,Home,5,22,20.0,DRIVEALONEFREE +393821665,1200675,503562,49227708,True,shopping,11,25,10.0,SHARED3FREE +393821669,1200675,503562,49227708,False,Home,25,11,13.0,WALK +414031713,1262291,565178,51753964,True,shopping,13,2,12.0,WALK +414031717,1262291,565178,51753964,False,Home,2,13,17.0,WALK +414037881,1262310,565197,51754735,True,othdiscr,17,3,19.0,WALK +414037885,1262310,565197,51754735,False,Home,3,17,22.0,WALK +414046737,1262337,565224,51755842,True,othdiscr,22,3,7.0,WALK +414046741,1262337,565224,51755842,False,Home,3,22,18.0,WALK +414056641,1262367,565254,51757080,True,shopping,2,5,9.0,WALK +414056645,1262367,565254,51757080,False,Home,5,2,14.0,WALK +414056649,1262367,565254,51757081,True,social,7,5,16.0,WALK +414056650,1262367,565254,51757081,True,shopping,19,7,16.0,TNC_SHARED +414056653,1262367,565254,51757081,False,Home,5,19,17.0,DRIVEALONEFREE +414070881,1262411,565298,51758860,True,escort,7,5,7.0,WALK +414070885,1262411,565298,51758860,False,social,5,7,8.0,WALK +414070886,1262411,565298,51758860,False,escort,12,5,8.0,WALK +414070887,1262411,565298,51758860,False,Home,5,12,8.0,WALK +414081201,1262442,565329,51760150,True,othmaint,9,5,12.0,WALK +414081205,1262442,565329,51760150,False,Home,5,9,16.0,WALK +414103897,1262511,565398,51762987,True,social,9,6,14.0,WALK_LOC +414103901,1262511,565398,51762987,False,Home,6,9,15.0,WALK_LOC +414109385,1262528,565415,51763673,True,othmaint,6,6,10.0,WALK +414109386,1262528,565415,51763673,True,othdiscr,2,6,11.0,WALK +414109389,1262528,565415,51763673,False,Home,6,2,18.0,WALK +414143889,1262633,565520,51767986,True,shopping,19,6,14.0,SHARED2FREE +414143893,1262633,565520,51767986,False,shopping,5,19,14.0,SHARED2FREE +414143894,1262633,565520,51767986,False,shopping,5,5,14.0,WALK +414143895,1262633,565520,51767986,False,shopping,11,5,14.0,WALK +414143896,1262633,565520,51767986,False,Home,6,11,14.0,SHARED2FREE +414169825,1262712,565599,51771228,True,social,7,6,12.0,WALK +414169829,1262712,565599,51771228,False,Home,6,7,13.0,WALK +414183865,1262755,565642,51772983,True,othmaint,2,7,5.0,WALK +414183869,1262755,565642,51772983,False,Home,7,2,13.0,WALK +414183873,1262755,565642,51772984,True,othmaint,16,7,15.0,WALK_LOC +414183877,1262755,565642,51772984,False,shopping,12,16,16.0,WALK +414183878,1262755,565642,51772984,False,escort,11,12,17.0,WALK_LOC +414183879,1262755,565642,51772984,False,othmaint,5,11,17.0,WALK +414183880,1262755,565642,51772984,False,Home,7,5,17.0,WALK +414192105,1262780,565667,51774013,True,shopping,11,7,10.0,WALK +414192109,1262780,565667,51774013,False,shopping,9,11,12.0,WALK +414192110,1262780,565667,51774013,False,Home,7,9,14.0,WALK +414208137,1262829,565716,51776017,True,othmaint,9,7,10.0,WALK +414208141,1262829,565716,51776017,False,Home,7,9,13.0,WALK +414235073,1262911,565798,51779384,True,shopping,16,7,9.0,WALK_LOC +414235077,1262911,565798,51779384,False,Home,7,16,17.0,TNC_SHARED +414298641,1263105,565992,51787330,True,othdiscr,5,7,16.0,DRIVEALONEFREE +414298645,1263105,565992,51787330,False,Home,7,5,16.0,DRIVEALONEFREE +414314889,1263155,566042,51789361,True,eatout,8,7,7.0,WALK +414314893,1263155,566042,51789361,False,Home,7,8,16.0,WALK +414323241,1263180,566067,51790405,True,othdiscr,9,7,9.0,WALK +414323245,1263180,566067,51790405,False,Home,7,9,17.0,WALK +414342657,1263239,566126,51792832,True,shopping,7,7,13.0,WALK +414342661,1263239,566126,51792832,False,Home,7,7,14.0,WALK +414342681,1263239,566126,51792835,True,social,7,7,16.0,WALK +414342685,1263239,566126,51792835,False,Home,7,7,16.0,WALK +414348017,1263256,566143,51793502,True,eatout,5,7,14.0,WALK +414348021,1263256,566143,51793502,False,Home,7,5,15.0,WALK +414348169,1263256,566143,51793521,True,othdiscr,5,7,15.0,BIKE +414348173,1263256,566143,51793521,False,Home,7,5,21.0,WALK +414393169,1263393,566280,51799146,True,shopping,22,8,16.0,WALK_LRF +414393173,1263393,566280,51799146,False,Home,8,22,19.0,WALK_LRF +414395449,1263400,566287,51799431,True,univ,14,8,18.0,WALK_LOC +414395453,1263400,566287,51799431,False,escort,2,14,20.0,WALK_LOC +414395454,1263400,566287,51799431,False,Home,8,2,21.0,WALK_LRF +414427609,1263498,566385,51803451,True,shopping,5,8,11.0,WALK +414427613,1263498,566385,51803451,False,Home,8,5,13.0,WALK +414428857,1263502,566389,51803607,True,othdiscr,5,8,13.0,WALK +414428861,1263502,566389,51803607,False,Home,8,5,19.0,WALK +414438545,1263532,566419,51804818,True,eatout,13,8,12.0,DRIVEALONEFREE +414438549,1263532,566419,51804818,False,Home,8,13,12.0,SHARED2FREE +414463145,1263607,566494,51807893,True,eatout,9,8,11.0,WALK +414463149,1263607,566494,51807893,False,Home,8,9,19.0,WALK +414477137,1263649,566536,51809642,True,shopping,11,8,16.0,WALK +414477141,1263649,566536,51809642,False,Home,8,11,16.0,WALK +414508737,1263746,566633,51813592,True,eatout,22,8,7.0,WALK +414508741,1263746,566633,51813592,False,Home,8,22,14.0,WALK +414515209,1263765,566652,51814401,True,social,9,8,8.0,WALK +414515213,1263765,566652,51814401,False,Home,8,9,22.0,WALK +414520065,1263780,566667,51815008,True,othmaint,22,8,8.0,WALK +414520069,1263780,566667,51815008,False,Home,8,22,19.0,WALK_LRF +414537489,1263833,566720,51817186,True,shopping,9,8,11.0,WALK_LOC +414537490,1263833,566720,51817186,True,shopping,11,9,12.0,WALK_LOC +414537493,1263833,566720,51817186,False,Home,8,11,13.0,TNC_SINGLE +414562857,1263911,566798,51820357,True,eatout,22,8,14.0,WALK +414562861,1263911,566798,51820357,False,Home,8,22,20.0,WALK +414573921,1263944,566831,51821740,True,social,24,8,10.0,WALK +414573925,1263944,566831,51821740,False,Home,8,24,13.0,WALK +414574993,1263948,566835,51821874,True,eatout,5,8,9.0,WALK +414574997,1263948,566835,51821874,False,Home,8,5,16.0,WALK +414582689,1263971,566858,51822836,True,othdiscr,15,8,8.0,WALK +414582693,1263971,566858,51822836,False,Home,8,15,12.0,WALK +414588265,1263988,566875,51823533,True,othdiscr,5,8,12.0,WALK +414588269,1263988,566875,51823533,False,Home,8,5,18.0,WALK +414624193,1264098,566985,51828024,True,eatout,11,9,15.0,WALK +414624197,1264098,566985,51828024,False,Home,9,11,17.0,WALK +414624369,1264098,566985,51828046,True,othmaint,9,9,11.0,WALK +414624373,1264098,566985,51828046,False,Home,9,9,14.0,WALK +414624409,1264098,566985,51828051,True,shopping,5,9,15.0,TNC_SINGLE +414624413,1264098,566985,51828051,False,shopping,6,5,15.0,DRIVEALONEFREE +414624414,1264098,566985,51828051,False,Home,9,6,15.0,DRIVEALONEFREE +414630641,1264117,567004,51828830,True,shopping,8,9,9.0,WALK +414630645,1264117,567004,51828830,False,Home,9,8,12.0,WALK +414633553,1264126,567013,51829194,True,othdiscr,5,9,10.0,WALK +414633554,1264126,567013,51829194,True,othmaint,24,5,11.0,WALK +414633557,1264126,567013,51829194,False,Home,9,24,15.0,WALK_LRF +414633617,1264126,567013,51829202,True,social,11,9,18.0,WALK +414633621,1264126,567013,51829202,False,Home,9,11,21.0,WALK +414635169,1264131,567018,51829396,True,othdiscr,9,9,12.0,WALK +414635173,1264131,567018,51829396,False,Home,9,9,22.0,WALK +414637529,1264138,567025,51829691,True,shopping,3,9,15.0,WALK_LRF +414637530,1264138,567025,51829691,True,shopping,13,3,15.0,WALK_LOC +414637533,1264138,567025,51829691,False,shopping,16,13,15.0,WALK_LOC +414637534,1264138,567025,51829691,False,Home,9,16,16.0,WALK_LRF +414638449,1264141,567028,51829806,True,othdiscr,7,9,7.0,WALK_LOC +414638453,1264141,567028,51829806,False,Home,9,7,12.0,WALK_LOC +414657865,1264200,567087,51832233,True,shopping,14,9,16.0,WALK_LRF +414657869,1264200,567087,51832233,False,Home,9,14,18.0,WALK_LRF +414665697,1264224,567111,51833212,True,othmaint,4,9,8.0,WALK +414665701,1264224,567111,51833212,False,Home,9,4,11.0,WALK +414667049,1264228,567115,51833381,True,shopping,16,9,13.0,WALK_LRF +414667053,1264228,567115,51833381,False,Home,9,16,15.0,WALK_LRF +414667057,1264228,567115,51833382,True,shopping,16,9,17.0,SHARED3FREE +414667061,1264228,567115,51833382,False,eatout,16,16,17.0,SHARED3FREE +414667062,1264228,567115,51833382,False,Home,9,16,17.0,SHARED3FREE +414686401,1264287,567174,51835800,True,othmaint,22,9,9.0,WALK_LRF +414686402,1264287,567174,51835800,True,shopping,4,22,10.0,WALK_LRF +414686405,1264287,567174,51835800,False,Home,9,4,15.0,WALK_LRF +414686409,1264287,567174,51835801,True,shopping,5,9,15.0,WALK +414686413,1264287,567174,51835801,False,Home,9,5,16.0,WALK +414704769,1264343,567230,51838096,True,shopping,5,9,14.0,WALK +414704773,1264343,567230,51838096,False,Home,9,5,15.0,WALK_LOC +414704777,1264343,567230,51838097,True,shopping,16,9,15.0,WALK_LRF +414704781,1264343,567230,51838097,False,Home,9,16,15.0,WALK_LRF +414731361,1264424,567311,51841420,True,social,9,9,15.0,WALK +414731365,1264424,567311,51841420,False,Home,9,9,16.0,WALK +414756377,1264501,567388,51844547,True,eatout,21,9,16.0,WALK +414756381,1264501,567388,51844547,False,Home,9,21,16.0,WALK +414756577,1264501,567388,51844572,True,escort,9,9,17.0,WALK +414756578,1264501,567388,51844572,True,univ,9,9,17.0,WALK +414756581,1264501,567388,51844572,False,Home,9,9,17.0,WALK +414756593,1264501,567388,51844574,True,shopping,11,9,13.0,WALK +414756597,1264501,567388,51844574,False,Home,9,11,14.0,WALK +414788217,1264598,567485,51848527,True,escort,16,9,9.0,WALK +414788221,1264598,567485,51848527,False,Home,9,16,10.0,WALK +414788369,1264598,567485,51848546,True,othmaint,4,9,11.0,WALK +414788373,1264598,567485,51848546,False,eatout,7,4,13.0,WALK +414788374,1264598,567485,51848546,False,Home,9,7,13.0,WALK +414788409,1264598,567485,51848551,True,shopping,1,9,15.0,WALK_LRF +414788413,1264598,567485,51848551,False,Home,9,1,17.0,WALK_LRF +414788417,1264598,567485,51848552,True,shopping,16,9,18.0,SHARED2FREE +414788421,1264598,567485,51848552,False,Home,9,16,20.0,SHARED2FREE +414797201,1264625,567512,51849650,True,eatout,9,9,8.0,WALK +414797202,1264625,567512,51849650,True,othdiscr,9,9,9.0,WALK +414797205,1264625,567512,51849650,False,Home,9,9,13.0,WALK +414807761,1264657,567544,51850970,True,shopping,19,9,9.0,WALK +414807765,1264657,567544,51850970,False,Home,9,19,10.0,WALK +414819833,1264694,567581,51852479,True,othdiscr,25,10,12.0,WALK_LOC +414819837,1264694,567581,51852479,False,Home,10,25,14.0,WALK_LOC +414828561,1264721,567608,51853570,True,escort,8,10,8.0,DRIVEALONEFREE +414828565,1264721,567608,51853570,False,Home,10,8,8.0,DRIVEALONEFREE +414851321,1264790,567677,51856415,True,othdiscr,9,10,14.0,WALK +414851325,1264790,567677,51856415,False,Home,10,9,18.0,WALK +414851385,1264790,567677,51856423,True,shopping,11,10,11.0,WALK +414851389,1264790,567677,51856423,False,shopping,5,11,11.0,WALK +414851390,1264790,567677,51856423,False,shopping,8,5,11.0,WALK +414851391,1264790,567677,51856423,False,Home,10,8,11.0,WALK +414902489,1264946,567833,51862811,True,othdiscr,10,10,10.0,WALK +414902493,1264946,567833,51862811,False,Home,10,10,17.0,WALK +414945305,1265077,567964,51868163,True,eatout,16,10,16.0,SHARED3FREE +414945309,1265077,567964,51868163,False,Home,10,16,16.0,SHARED3FREE +414945457,1265077,567964,51868182,True,othdiscr,4,10,16.0,WALK +414945461,1265077,567964,51868182,False,Home,10,4,20.0,WALK +414945481,1265077,567964,51868185,True,othmaint,2,10,7.0,WALK +414945485,1265077,567964,51868185,False,Home,10,2,16.0,WALK +414989585,1265212,568099,51873698,True,eatout,12,11,11.0,WALK +414989589,1265212,568099,51873698,False,Home,11,12,14.0,WALK +415001281,1265247,568134,51875160,True,shopping,17,11,15.0,DRIVEALONEFREE +415001285,1265247,568134,51875160,False,Home,11,17,15.0,DRIVEALONEFREE +415018297,1265299,568186,51877287,True,othmaint,1,11,20.0,WALK_LRF +415018301,1265299,568186,51877287,False,Home,11,1,22.0,WALK_LRF +415034673,1265349,568236,51879334,True,othdiscr,2,11,9.0,TNC_SINGLE +415034677,1265349,568236,51879334,False,Home,11,2,11.0,TNC_SINGLE +415034697,1265349,568236,51879337,True,othmaint,7,11,12.0,WALK +415034698,1265349,568236,51879337,True,othmaint,9,7,15.0,WALK_LOC +415034701,1265349,568236,51879337,False,social,9,9,13.0,WALK +415034702,1265349,568236,51879337,False,Home,11,9,15.0,WALK_LOC +415036313,1265354,568241,51879539,True,othdiscr,9,11,14.0,WALK +415036317,1265354,568241,51879539,False,Home,11,9,16.0,WALK +415036377,1265354,568241,51879547,True,shopping,11,11,8.0,WALK +415036381,1265354,568241,51879547,False,othdiscr,7,11,10.0,WALK +415036382,1265354,568241,51879547,False,Home,11,7,10.0,WALK +415037953,1265359,568246,51879744,True,othdiscr,21,11,8.0,WALK +415037957,1265359,568246,51879744,False,Home,11,21,14.0,WALK +415057633,1265419,568306,51882204,True,othdiscr,10,11,9.0,TNC_SINGLE +415057637,1265419,568306,51882204,False,Home,11,10,23.0,WALK_LOC +415066489,1265446,568333,51883311,True,othdiscr,2,12,10.0,WALK +415066493,1265446,568333,51883311,False,Home,12,2,16.0,WALK +415069833,1265456,568343,51883729,True,shopping,4,12,10.0,WALK +415069837,1265456,568343,51883729,False,shopping,6,4,20.0,WALK +415069838,1265456,568343,51883729,False,Home,12,6,20.0,WALK +415097057,1265539,568426,51887132,True,shopping,16,15,14.0,WALK +415097061,1265539,568426,51887132,False,Home,15,16,14.0,WALK +415110529,1265580,568467,51888816,True,social,12,16,9.0,WALK_LOC +415110533,1265580,568467,51888816,False,Home,16,12,17.0,WALK_LOC +415110537,1265580,568467,51888817,True,social,16,16,19.0,TNC_SINGLE +415110541,1265580,568467,51888817,False,Home,16,16,19.0,TNC_SINGLE +415117065,1265600,568487,51889633,True,shopping,23,16,20.0,WALK_LRF +415117069,1265600,568487,51889633,False,Home,16,23,20.0,WALK_LRF +415120785,1265612,568499,51890098,True,eatout,16,16,12.0,WALK +415120789,1265612,568499,51890098,False,shopping,16,16,22.0,WALK +415120790,1265612,568499,51890098,False,Home,16,16,22.0,WALK +415121329,1265613,568500,51890166,True,shopping,5,16,10.0,WALK +415121333,1265613,568500,51890166,False,Home,16,5,10.0,WALK_LOC +415121337,1265613,568500,51890167,True,shopping,5,16,18.0,WALK +415121341,1265613,568500,51890167,False,Home,16,5,18.0,WALK +415121353,1265613,568500,51890169,True,social,14,16,15.0,WALK +415121357,1265613,568500,51890169,False,Home,16,14,15.0,WALK +415154089,1265713,568600,51894261,True,othmaint,15,16,10.0,WALK_LOC +415154093,1265713,568600,51894261,False,othmaint,3,15,12.0,WALK +415154094,1265713,568600,51894261,False,shopping,25,3,13.0,WALK +415154095,1265713,568600,51894261,False,othmaint,4,25,14.0,WALK_LOC +415154096,1265713,568600,51894261,False,Home,16,4,14.0,WALK_LOC +415167929,1265755,568642,51895991,True,social,9,16,9.0,TNC_SHARED +415167933,1265755,568642,51895991,False,Home,16,9,10.0,TNC_SINGLE +415178073,1265786,568673,51897259,True,shopping,21,16,11.0,WALK_LOC +415178077,1265786,568673,51897259,False,othmaint,11,21,14.0,WALK +415178078,1265786,568673,51897259,False,Home,16,11,14.0,WALK_LOC +415182953,1265801,568688,51897869,True,othmaint,16,16,16.0,TNC_SINGLE +415182957,1265801,568688,51897869,False,Home,16,16,20.0,TNC_SINGLE +415192641,1265831,568718,51899080,True,escort,16,16,8.0,WALK +415192645,1265831,568718,51899080,False,shopping,17,16,12.0,WALK +415192646,1265831,568718,51899080,False,Home,16,17,12.0,WALK +415194785,1265837,568724,51899348,True,escort,15,16,20.0,WALK +415194786,1265837,568724,51899348,True,escort,14,15,20.0,WALK +415194787,1265837,568724,51899348,True,univ,14,14,20.0,WALK +415194789,1265837,568724,51899348,False,shopping,16,14,23.0,WALK +415194790,1265837,568724,51899348,False,escort,16,16,23.0,WALK +415194791,1265837,568724,51899348,False,othmaint,17,16,23.0,WALK +415194792,1265837,568724,51899348,False,Home,16,17,23.0,WALK +415203113,1265863,568750,51900389,True,eatout,13,17,9.0,WALK +415203117,1265863,568750,51900389,False,Home,17,13,17.0,WALK +415214745,1265898,568785,51901843,True,othdiscr,20,17,9.0,WALK_LRF +415214749,1265898,568785,51901843,False,Home,17,20,20.0,WALK_LOC +415223993,1265926,568813,51902999,True,shopping,16,17,12.0,WALK +415223997,1265926,568813,51902999,False,Home,17,16,14.0,WALK +415241049,1265978,568865,51905131,True,shopping,5,17,13.0,WALK +415241053,1265978,568865,51905131,False,Home,17,5,13.0,WALK_LRF +415257121,1266027,568914,51907140,True,eatout,16,17,11.0,WALK +415257122,1266027,568914,51907140,True,shopping,16,16,12.0,WALK +415257125,1266027,568914,51907140,False,Home,17,16,16.0,WALK +415263641,1266047,568934,51907955,True,othmaint,16,18,13.0,DRIVEALONEFREE +415263645,1266047,568934,51907955,False,Home,18,16,13.0,DRIVEALONEFREE +415270353,1266068,568955,51908794,True,eatout,5,18,6.0,WALK +415270357,1266068,568955,51908794,False,Home,18,5,15.0,WALK +415270681,1266069,568956,51908835,True,eatout,20,18,15.0,WALK +415270685,1266069,568956,51908835,False,Home,18,20,23.0,WALK +415272561,1266074,568961,51909070,True,othmaint,12,18,12.0,WALK_LOC +415272562,1266074,568961,51909070,True,social,16,12,13.0,WALK_LOC +415272565,1266074,568961,51909070,False,shopping,4,16,14.0,WALK_LOC +415272566,1266074,568961,51909070,False,Home,18,4,15.0,WALK +415274617,1266081,568968,51909327,True,eatout,16,18,9.0,WALK +415274621,1266081,568968,51909327,False,Home,18,16,13.0,WALK +415280873,1266100,568987,51910109,True,escort,16,18,15.0,WALK_LOC +415280877,1266100,568987,51910109,False,Home,18,16,16.0,TNC_SINGLE +415284609,1266111,568998,51910576,True,othdiscr,10,18,11.0,WALK +415284613,1266111,568998,51910576,False,Home,18,10,19.0,WALK +415301209,1266162,569049,51912651,True,escort,8,20,8.0,WALK +415301213,1266162,569049,51912651,False,Home,20,8,11.0,WALK +415301425,1266162,569049,51912678,True,social,20,20,15.0,WALK +415301429,1266162,569049,51912678,False,Home,20,20,15.0,WALK +415305625,1266175,569062,51913203,True,othmaint,12,20,8.0,WALK +415305629,1266175,569062,51913203,False,Home,20,12,16.0,WALK +415313537,1266199,569086,51914192,True,shopping,12,20,15.0,WALK_LOC +415313538,1266199,569086,51914192,True,shopping,15,12,16.0,WALK_LOC +415313541,1266199,569086,51914192,False,shopping,7,15,17.0,WALK_LOC +415313542,1266199,569086,51914192,False,Home,20,7,17.0,WALK_LOC +415341377,1266284,569171,51917672,True,othmaint,11,20,8.0,TNC_SINGLE +415341381,1266284,569171,51917672,False,Home,20,11,9.0,WALK_LOC +415352569,1266318,569205,51919071,True,shopping,24,20,10.0,WALK +415352573,1266318,569205,51919071,False,Home,20,24,14.0,WALK +415358145,1266335,569222,51919768,True,shopping,19,20,12.0,WALK +415358149,1266335,569222,51919768,False,Home,20,19,17.0,WALK +415360769,1266343,569230,51920096,True,shopping,22,20,11.0,SHARED2FREE +415360773,1266343,569230,51920096,False,Home,20,22,13.0,SHARED2FREE +415365625,1266358,569245,51920703,True,othdiscr,8,20,17.0,WALK +415365629,1266358,569245,51920703,False,Home,20,8,18.0,WALK +415365689,1266358,569245,51920711,True,othmaint,10,20,13.0,DRIVEALONEFREE +415365690,1266358,569245,51920711,True,shopping,16,10,13.0,DRIVEALONEFREE +415365693,1266358,569245,51920711,False,eatout,9,16,13.0,TNC_SINGLE +415365694,1266358,569245,51920711,False,shopping,16,9,13.0,DRIVEALONEFREE +415365695,1266358,569245,51920711,False,Home,20,16,13.0,DRIVEALONEFREE +415366017,1266359,569246,51920752,True,shopping,17,20,9.0,WALK +415366021,1266359,569246,51920752,False,Home,20,17,15.0,WALK +415374809,1266386,569273,51921851,True,eatout,8,20,12.0,TNC_SINGLE +415374810,1266386,569273,51921851,True,othdiscr,10,8,13.0,WALK_LOC +415374813,1266386,569273,51921851,False,shopping,5,10,15.0,WALK_LOC +415374814,1266386,569273,51921851,False,social,7,5,15.0,TNC_SINGLE +415374815,1266386,569273,51921851,False,Home,20,7,15.0,WALK_LOC +415381761,1266407,569294,51922720,True,shopping,5,20,16.0,DRIVEALONEFREE +415381765,1266407,569294,51922720,False,Home,20,5,16.0,DRIVEALONEFREE +415385961,1266420,569307,51923245,True,othdiscr,9,20,9.0,WALK +415385965,1266420,569307,51923245,False,Home,20,9,12.0,WALK +415392849,1266441,569328,51924106,True,othmaint,9,20,10.0,WALK +415392850,1266441,569328,51924106,True,othdiscr,9,9,11.0,WALK +415392853,1266441,569328,51924106,False,shopping,11,9,20.0,WALK +415392854,1266441,569328,51924106,False,Home,20,11,20.0,WALK +415397313,1266455,569342,51924664,True,escort,11,20,8.0,WALK +415397317,1266455,569342,51924664,False,Home,20,11,10.0,WALK +415397321,1266455,569342,51924665,True,escort,2,20,14.0,SHARED3FREE +415397325,1266455,569342,51924665,False,shopping,17,2,15.0,DRIVEALONEFREE +415397326,1266455,569342,51924665,False,Home,20,17,15.0,SHARED2FREE +415397505,1266455,569342,51924688,True,shopping,6,20,16.0,WALK +415397509,1266455,569342,51924688,False,Home,20,6,23.0,WALK +415398489,1266458,569345,51924811,True,shopping,5,20,10.0,WALK_LOC +415398493,1266458,569345,51924811,False,Home,20,5,15.0,WALK_LOC +415444737,1266599,569486,51930592,True,shopping,5,20,15.0,WALK +415444741,1266599,569486,51930592,False,Home,20,5,16.0,WALK +415450641,1266617,569504,51931330,True,shopping,16,20,12.0,WALK +415450645,1266617,569504,51931330,False,Home,20,16,13.0,WALK +415459609,1266645,569532,51932451,True,eatout,21,20,11.0,WALK +415459613,1266645,569532,51932451,False,Home,20,21,14.0,WALK +415487641,1266730,569617,51935955,True,othdiscr,3,21,9.0,BIKE +415487645,1266730,569617,51935955,False,Home,21,3,11.0,BIKE +415487665,1266730,569617,51935958,True,othmaint,5,21,14.0,WALK +415487669,1266730,569617,51935958,False,Home,21,5,17.0,WALK +415487705,1266730,569617,51935963,True,shopping,11,21,13.0,TAXI +415487709,1266730,569617,51935963,False,Home,21,11,13.0,DRIVEALONEFREE +415501945,1266774,569661,51937743,True,escort,24,21,8.0,WALK +415501949,1266774,569661,51937743,False,Home,21,24,8.0,WALK +415502425,1266775,569662,51937803,True,othmaint,19,21,9.0,WALK +415502429,1266775,569662,51937803,False,escort,11,19,17.0,WALK +415502430,1266775,569662,51937803,False,eatout,7,11,21.0,WALK +415502431,1266775,569662,51937803,False,Home,21,7,21.0,WALK +415506401,1266787,569674,51938300,True,shopping,11,21,14.0,WALK +415506405,1266787,569674,51938300,False,shopping,11,11,15.0,WALK +415506406,1266787,569674,51938300,False,Home,21,11,17.0,WALK +415535593,1266876,569763,51941949,True,shopping,18,21,13.0,WALK_LOC +415535597,1266876,569763,51941949,False,Home,21,18,20.0,WALK_LOC +415546769,1266910,569797,51943346,True,social,10,21,14.0,WALK +415546773,1266910,569797,51943346,False,Home,21,10,21.0,WALK +415556913,1266941,569828,51944614,True,shopping,5,21,13.0,WALK +415556917,1266941,569828,51944614,False,Home,21,5,14.0,WALK +415558841,1266947,569834,51944855,True,othmaint,13,21,10.0,WALK +415558845,1266947,569834,51944855,False,Home,21,13,11.0,WALK +415559169,1266948,569835,51944896,True,othmaint,8,21,8.0,BIKE +415559173,1266948,569835,51944896,False,Home,21,8,17.0,BIKE +415564113,1266963,569850,51945514,True,escort,14,21,8.0,WALK_LOC +415564114,1266963,569850,51945514,True,escort,5,14,8.0,TAXI +415564115,1266963,569850,51945514,True,univ,9,5,9.0,WALK +415564117,1266963,569850,51945514,False,escort,11,9,14.0,WALK +415564118,1266963,569850,51945514,False,social,5,11,15.0,WALK_LOC +415564119,1266963,569850,51945514,False,Home,21,5,16.0,WALK_LOC +415595401,1267059,569946,51949425,True,eatout,5,21,14.0,WALK +415595405,1267059,569946,51949425,False,Home,21,5,14.0,WALK +415606113,1267091,569978,51950764,True,shopping,13,21,12.0,TNC_SINGLE +415606117,1267091,569978,51950764,False,shopping,16,13,14.0,TNC_SHARED +415606118,1267091,569978,51950764,False,Home,21,16,15.0,TNC_SINGLE +415608737,1267099,569986,51951092,True,shopping,11,21,8.0,WALK +415608741,1267099,569986,51951092,False,shopping,8,11,12.0,WALK +415608742,1267099,569986,51951092,False,othmaint,7,8,12.0,WALK +415608743,1267099,569986,51951092,False,Home,21,7,12.0,WALK +415621185,1267137,570024,51952648,True,univ,12,21,14.0,WALK_LOC +415621189,1267137,570024,51952648,False,Home,21,12,17.0,WALK_LOC +415635745,1267182,570069,51954468,True,eatout,2,21,10.0,WALK +415635749,1267182,570069,51954468,False,Home,21,2,11.0,WALK +415635897,1267182,570069,51954487,True,othdiscr,16,21,12.0,WALK +415635901,1267182,570069,51954487,False,Home,21,16,14.0,WALK +415643313,1267205,570092,51955414,True,escort,20,21,8.0,WALK +415643317,1267205,570092,51955414,False,Home,21,20,9.0,WALK +415643441,1267205,570092,51955430,True,othdiscr,20,21,11.0,WALK_LOC +415643445,1267205,570092,51955430,False,othmaint,9,20,16.0,WALK_LOC +415643446,1267205,570092,51955430,False,Home,21,9,16.0,WALK +415643529,1267205,570092,51955441,True,social,17,21,17.0,WALK_LRF +415643533,1267205,570092,51955441,False,Home,21,17,18.0,WALK_LOC +415645473,1267211,570098,51955684,True,shopping,25,21,13.0,WALK_LOC +415645477,1267211,570098,51955684,False,shopping,7,25,13.0,WALK_LOC +415645478,1267211,570098,51955684,False,Home,21,7,13.0,WALK_LOC +415670385,1267287,570174,51958798,True,univ,12,21,8.0,WALK_LOC +415670389,1267287,570174,51958798,False,Home,21,12,8.0,WALK_LOC +415696577,1267367,570254,51962072,True,othdiscr,14,21,10.0,WALK +415696581,1267367,570254,51962072,False,Home,21,14,14.0,WALK +415706113,1267396,570283,51963264,True,othmaint,15,21,10.0,TNC_SINGLE +415706117,1267396,570283,51963264,False,Home,21,15,10.0,TNC_SINGLE +415711073,1267411,570298,51963884,True,shopping,11,21,10.0,WALK +415711077,1267411,570298,51963884,False,social,7,11,10.0,WALK_LOC +415711078,1267411,570298,51963884,False,Home,21,7,10.0,WALK +415712321,1267415,570302,51964040,True,othdiscr,7,21,12.0,WALK +415712325,1267415,570302,51964040,False,Home,21,7,17.0,WALK +415733377,1267479,570366,51966672,True,shopping,11,21,10.0,DRIVEALONEFREE +415733381,1267479,570366,51966672,False,Home,21,11,10.0,DRIVEALONEFREE +415750065,1267530,570417,51968758,True,othmaint,5,21,16.0,WALK +415750069,1267530,570417,51968758,False,Home,21,5,18.0,WALK +415750105,1267530,570417,51968763,True,shopping,19,21,7.0,WALK +415750109,1267530,570417,51968763,False,Home,21,19,13.0,WALK +415752073,1267536,570423,51969009,True,shopping,21,21,11.0,BIKE +415752077,1267536,570423,51969009,False,Home,21,21,14.0,BIKE +415757345,1267552,570439,51969668,True,social,11,21,9.0,WALK +415757349,1267552,570439,51969668,False,Home,21,11,18.0,WALK +415757913,1267554,570441,51969739,True,othdiscr,3,21,18.0,WALK_LOC +415757917,1267554,570441,51969739,False,Home,21,3,20.0,WALK_LOC +415782513,1267629,570516,51972814,True,othdiscr,15,21,18.0,WALK +415782517,1267629,570516,51972814,False,Home,21,15,23.0,WALK +415782577,1267629,570516,51972822,True,shopping,24,21,14.0,WALK +415782581,1267629,570516,51972822,False,shopping,25,24,18.0,WALK +415782582,1267629,570516,51972822,False,Home,21,25,18.0,WALK +415783825,1267633,570520,51972978,True,othdiscr,6,21,14.0,WALK +415783829,1267633,570520,51972978,False,Home,21,6,17.0,WALK +415783849,1267633,570520,51972981,True,othmaint,11,21,11.0,WALK +415783853,1267633,570520,51972981,False,Home,21,11,12.0,WALK_LOC +415784857,1267636,570523,51973107,True,univ,12,21,18.0,WALK +415784861,1267636,570523,51973107,False,Home,21,12,21.0,WALK_LOC +415814681,1267727,570614,51976835,True,othmaint,7,22,12.0,BIKE +415814682,1267727,570614,51976835,True,othmaint,6,7,12.0,BIKE +415814685,1267727,570614,51976835,False,Home,22,6,13.0,BIKE +415816689,1267733,570620,51977086,True,shopping,16,22,10.0,WALK +415816693,1267733,570620,51977086,False,Home,22,16,16.0,WALK +415823249,1267753,570640,51977906,True,shopping,25,22,9.0,WALK +415823253,1267753,570640,51977906,False,othmaint,5,25,9.0,WALK +415823254,1267753,570640,51977906,False,eatout,1,5,10.0,WALK +415823255,1267753,570640,51977906,False,Home,22,1,10.0,WALK +415830249,1267775,570662,51978781,True,eatout,2,22,15.0,WALK +415830253,1267775,570662,51978781,False,Home,22,2,16.0,WALK +415838297,1267799,570686,51979787,True,othmaint,5,23,11.0,BIKE +415838301,1267799,570686,51979787,False,eatout,8,5,16.0,BIKE +415838302,1267799,570686,51979787,False,Home,23,8,17.0,BIKE +415838305,1267799,570686,51979788,True,othmaint,9,23,18.0,WALK_LOC +415838309,1267799,570686,51979788,False,Home,23,9,19.0,WALK_LOC +415840961,1267807,570694,51980120,True,shopping,5,23,12.0,WALK +415840965,1267807,570694,51980120,False,Home,23,5,14.0,WALK +415841313,1267808,570695,51980164,True,shopping,4,23,13.0,WALK +415841314,1267808,570695,51980164,True,social,24,4,13.0,WALK +415841317,1267808,570695,51980164,False,Home,23,24,15.0,WALK +415845865,1267822,570709,51980733,True,univ,12,23,15.0,DRIVEALONEFREE +415845869,1267822,570709,51980733,False,Home,23,12,15.0,DRIVEALONEFREE +415846977,1267826,570713,51980872,True,eatout,12,23,9.0,WALK +415846981,1267826,570713,51980872,False,Home,23,12,12.0,WALK +415847129,1267826,570713,51980891,True,othdiscr,7,23,12.0,WALK_LRF +415847133,1267826,570713,51980891,False,Home,23,7,20.0,WALK_LRF +415848289,1267830,570717,51981036,True,eatout,11,23,12.0,WALK_LRF +415848293,1267830,570717,51981036,False,shopping,4,11,19.0,WALK +415848294,1267830,570717,51981036,False,Home,23,4,19.0,WALK +415855745,1267852,570739,51981968,True,social,6,24,7.0,WALK +415855749,1267852,570739,51981968,False,Home,24,6,19.0,WALK +415856009,1267853,570740,51982001,True,othmaint,6,24,13.0,TNC_SINGLE +415856013,1267853,570740,51982001,False,Home,24,6,13.0,DRIVEALONEFREE +415856049,1267853,570740,51982006,True,shopping,4,24,10.0,TNC_SINGLE +415856053,1267853,570740,51982006,False,Home,24,4,13.0,TNC_SINGLE +415861913,1267871,570758,51982739,True,othmaint,23,24,8.0,TAXI +415861917,1267871,570758,51982739,False,Home,24,23,14.0,WALK +415862609,1267873,570760,51982826,True,shopping,23,24,12.0,WALK +415862613,1267873,570760,51982826,False,eatout,25,23,13.0,WALK +415862614,1267873,570760,51982826,False,Home,24,25,13.0,WALK +415864273,1267878,570765,51983034,True,social,17,24,6.0,WALK +415864277,1267878,570765,51983034,False,Home,24,17,13.0,WALK +415865233,1267881,570768,51983154,True,shopping,16,24,11.0,WALK +415865237,1267881,570768,51983154,False,Home,24,16,16.0,WALK +415866833,1267886,570773,51983354,True,othmaint,13,24,11.0,WALK +415866837,1267886,570773,51983354,False,Home,24,13,12.0,WALK +415871249,1267900,570787,51983906,True,eatout,16,24,15.0,WALK +415871253,1267900,570787,51983906,False,Home,24,16,17.0,WALK +415875729,1267913,570800,51984466,True,othdiscr,9,24,13.0,WALK_LRF +415875730,1267913,570800,51984466,True,shopping,11,9,13.0,TNC_SHARED +415875733,1267913,570800,51984466,False,eatout,9,11,14.0,WALK_LOC +415875734,1267913,570800,51984466,False,shopping,12,9,15.0,WALK_LRF +415875735,1267913,570800,51984466,False,Home,24,12,15.0,WALK_LOC +415878969,1267923,570810,51984871,True,othmaint,24,24,9.0,WALK +415878973,1267923,570810,51984871,False,Home,24,24,15.0,WALK +415883561,1267937,570824,51985445,True,othmaint,19,24,7.0,WALK_LOC +415883565,1267937,570824,51985445,False,Home,24,19,13.0,WALK_LOC +415907529,1268010,570897,51988441,True,univ,13,25,16.0,DRIVEALONEFREE +415907533,1268010,570897,51988441,False,Home,25,13,17.0,DRIVEALONEFREE +415931753,1268084,570971,51991469,True,othdiscr,22,25,17.0,WALK +415931757,1268084,570971,51991469,False,Home,25,22,19.0,WALK +415939977,1268109,570996,51992497,True,othmaint,14,25,14.0,WALK +415939981,1268109,570996,51992497,False,othmaint,5,14,16.0,WALK +415939982,1268109,570996,51992497,False,Home,25,5,16.0,WALK_LOC +415940017,1268109,570996,51992502,True,shopping,10,25,13.0,BIKE +415940021,1268109,570996,51992502,False,Home,25,10,13.0,BIKE +415956745,1268160,571047,51994593,True,shopping,2,25,12.0,WALK +415956749,1268160,571047,51994593,False,Home,25,2,13.0,WALK +415968905,1268197,571084,51996113,True,social,15,25,16.0,WALK_LOC +415968909,1268197,571084,51996113,False,Home,25,15,17.0,WALK_LOC +444603657,1355498,658385,55575457,True,work,2,2,7.0,WALK +444603661,1355498,658385,55575457,False,Home,2,2,14.0,WALK +444630113,1355579,658466,55578764,True,othdiscr,21,7,7.0,WALK +444630117,1355579,658466,55578764,False,Home,7,21,11.0,WALK +444630121,1355579,658466,55578765,True,othdiscr,2,7,16.0,WALK +444630125,1355579,658466,55578765,False,Home,7,2,18.0,WALK +444630225,1355579,658466,55578778,True,work,9,7,11.0,WALK +444630229,1355579,658466,55578778,False,Home,7,9,16.0,WALK +444666849,1355691,658578,55583356,True,othdiscr,9,9,17.0,WALK +444666853,1355691,658578,55583356,False,Home,9,9,21.0,WALK +444666857,1355691,658578,55583357,True,othdiscr,24,9,21.0,SHARED2FREE +444666861,1355691,658578,55583357,False,Home,9,24,21.0,DRIVEALONEFREE +444666961,1355691,658578,55583370,True,work,24,9,7.0,WALK_LRF +444666965,1355691,658578,55583370,False,Home,9,24,17.0,WALK_LRF +444679377,1355729,658616,55584922,True,shopping,11,10,6.0,WALK +444679381,1355729,658616,55584922,False,Home,10,11,14.0,WALK +444679425,1355729,658616,55584928,True,work,9,10,14.0,WALK +444679429,1355729,658616,55584928,False,escort,8,9,17.0,WALK +444679430,1355729,658616,55584928,False,Home,10,8,18.0,WALK +444683689,1355742,658629,55585461,True,work,2,10,6.0,WALK_LRF +444683693,1355742,658629,55585461,False,Home,10,2,16.0,WALK_HVY +444698185,1355787,658674,55587273,True,eatout,7,11,20.0,WALK +444698189,1355787,658674,55587273,False,Home,11,7,20.0,WALK +444698449,1355787,658674,55587306,True,work,13,11,5.0,WALK +444698453,1355787,658674,55587306,False,eatout,16,13,16.0,WALK_LOC +444698454,1355787,658674,55587306,False,othmaint,7,16,16.0,WALK +444698455,1355787,658674,55587306,False,eatout,9,7,16.0,WALK_LOC +444698456,1355787,658674,55587306,False,Home,11,9,17.0,WALK +444698457,1355787,658674,55587307,True,escort,14,11,18.0,DRIVEALONEFREE +444698458,1355787,658674,55587307,True,work,13,14,18.0,DRIVEALONEFREE +444698461,1355787,658674,55587307,False,escort,5,13,18.0,DRIVEALONEFREE +444698462,1355787,658674,55587307,False,Home,11,5,18.0,DRIVEALONEFREE +444739689,1355913,658800,55592461,True,othmaint,9,17,12.0,WALK_LRF +444739693,1355913,658800,55592461,False,eatout,16,9,13.0,WALK_LRF +444739694,1355913,658800,55592461,False,Home,17,16,14.0,WALK_LOC +444748305,1355939,658826,55593538,True,work,24,19,8.0,TNC_SINGLE +444748309,1355939,658826,55593538,False,Home,19,24,17.0,TNC_SINGLE +444764049,1355987,658874,55595506,True,work,5,20,6.0,TNC_SINGLE +444764053,1355987,658874,55595506,False,Home,20,5,17.0,WALK_LOC +444770281,1356006,658893,55596285,True,work,2,20,12.0,WALK_LOC +444770285,1356006,658893,55596285,False,Home,20,2,19.0,WALK_LOC +444785369,1356052,658939,55598171,True,work,19,20,6.0,WALK +444785373,1356052,658939,55598171,False,Home,20,19,20.0,WALK +444793569,1356077,658964,55599196,True,work,2,20,9.0,SHARED3FREE +444793573,1356077,658964,55599196,False,eatout,13,2,16.0,WALK +444793574,1356077,658964,55599196,False,Home,20,13,17.0,WALK +444812593,1356135,659022,55601574,True,work,23,21,8.0,WALK_LOC +444812597,1356135,659022,55601574,False,Home,21,23,18.0,WALK_LRF +444813465,1356138,659025,55601683,True,othdiscr,16,21,16.0,WALK +444813469,1356138,659025,55601683,False,Home,21,16,18.0,WALK +444853217,1356259,659146,55606652,True,shopping,17,21,9.0,WALK +444853221,1356259,659146,55606652,False,Home,21,17,12.0,WALK +444853265,1356259,659146,55606658,True,work,12,21,18.0,WALK +444853269,1356259,659146,55606658,False,Home,21,12,23.0,WALK +444868073,1356305,659192,55608509,True,atwork,16,7,10.0,WALK +444868077,1356305,659192,55608509,False,Work,7,16,12.0,WALK +444868353,1356305,659192,55608544,True,work,7,21,7.0,WALK +444868357,1356305,659192,55608544,False,Home,21,7,20.0,WALK +444872049,1356317,659204,55609006,True,escort,9,21,11.0,SHARED3FREE +444872053,1356317,659204,55609006,False,escort,10,9,12.0,WALK +444872054,1356317,659204,55609006,False,Home,21,10,12.0,DRIVEALONEFREE +444872177,1356317,659204,55609022,True,escort,8,21,14.0,BIKE +444872178,1356317,659204,55609022,True,othdiscr,20,8,15.0,BIKE +444872181,1356317,659204,55609022,False,Home,21,20,20.0,BIKE +444872617,1356318,659205,55609077,True,work,15,21,7.0,WALK +444872621,1356318,659205,55609077,False,Home,21,15,17.0,WALK +444873929,1356322,659209,55609241,True,eatout,1,21,8.0,WALK +444873930,1356322,659209,55609241,True,work,24,1,9.0,WALK +444873933,1356322,659209,55609241,False,work,4,24,17.0,WALK +444873934,1356322,659209,55609241,False,Home,21,4,18.0,WALK +444873993,1356323,659210,55609249,True,eatout,16,21,15.0,WALK +444873997,1356323,659210,55609249,False,Home,21,16,16.0,WALK +444874145,1356323,659210,55609268,True,othdiscr,6,21,12.0,WALK +444874149,1356323,659210,55609268,False,Home,21,6,14.0,WALK +444874233,1356323,659210,55609279,True,social,9,21,16.0,WALK +444874237,1356323,659210,55609279,False,Home,21,9,21.0,WALK +444875241,1356326,659213,55609405,True,work,2,21,14.0,DRIVEALONEFREE +444875245,1356326,659213,55609405,False,othmaint,5,2,17.0,WALK +444875246,1356326,659213,55609405,False,social,11,5,19.0,WALK +444875247,1356326,659213,55609405,False,eatout,12,11,19.0,WALK +444875248,1356326,659213,55609405,False,Home,21,12,19.0,WALK +444887001,1356362,659249,55610875,True,shopping,11,21,12.0,WALK +444887005,1356362,659249,55610875,False,Home,21,11,14.0,WALK +444887009,1356362,659249,55610876,True,shopping,14,21,17.0,TNC_SINGLE +444887013,1356362,659249,55610876,False,Home,21,14,20.0,WALK_LOC +444896233,1356390,659277,55612029,True,work,10,21,5.0,WALK_LRF +444896237,1356390,659277,55612029,False,Home,21,10,15.0,WALK_LOC +444913617,1356443,659330,55614202,True,work,18,21,7.0,WALK +444913621,1356443,659330,55614202,False,Home,21,18,18.0,WALK +444923129,1356472,659359,55615391,True,work,16,21,8.0,WALK +444923130,1356472,659359,55615391,True,work,18,16,8.0,WALK +444923133,1356472,659359,55615391,False,Home,21,18,23.0,WALK +444928617,1356489,659376,55616077,True,othmaint,19,21,10.0,WALK +444928621,1356489,659376,55616077,False,Home,21,19,12.0,WALK +444928985,1356490,659377,55616123,True,shopping,5,21,13.0,WALK +444928989,1356490,659377,55616123,False,Home,21,5,14.0,WALK +444928993,1356490,659377,55616124,True,escort,16,21,15.0,WALK +444928994,1356490,659377,55616124,True,shopping,12,16,15.0,WALK +444928997,1356490,659377,55616124,False,othmaint,5,12,17.0,WALK +444928998,1356490,659377,55616124,False,escort,7,5,17.0,WALK +444928999,1356490,659377,55616124,False,Home,21,7,17.0,WALK_LOC +444930673,1356495,659382,55616334,True,work,9,21,7.0,WALK +444930677,1356495,659382,55616334,False,Home,21,9,18.0,WALK +444930737,1356496,659383,55616342,True,eatout,22,21,10.0,WALK +444930741,1356496,659383,55616342,False,Home,21,22,15.0,WALK +444938873,1356520,659407,55617359,True,work,5,22,7.0,BIKE +444938877,1356520,659407,55617359,False,Home,22,5,14.0,BIKE +444939073,1356521,659408,55617384,True,atwork,21,17,10.0,WALK +444939077,1356521,659408,55617384,False,Work,17,21,12.0,WALK +444939201,1356521,659408,55617400,True,work,17,22,7.0,WALK_LRF +444939205,1356521,659408,55617400,False,Home,22,17,16.0,WALK_LRF +444942153,1356530,659417,55617769,True,work,2,22,7.0,WALK +444942154,1356530,659417,55617769,True,work,4,2,7.0,WALK +444942157,1356530,659417,55617769,False,shopping,11,4,22.0,WALK_LRF +444942158,1356530,659417,55617769,False,Home,22,11,22.0,WALK_LRF +444944689,1356538,659425,55618086,True,othmaint,3,22,8.0,WALK +444944693,1356538,659425,55618086,False,Home,22,3,10.0,WALK +444944777,1356538,659425,55618097,True,work,2,22,12.0,BIKE +444944781,1356538,659425,55618097,False,Home,22,2,17.0,BIKE +444949913,1356554,659441,55618739,True,othmaint,9,22,8.0,WALK_LRF +444949914,1356554,659441,55618739,True,escort,22,9,9.0,WALK_LRF +444949915,1356554,659441,55618739,True,othdiscr,12,22,10.0,WALK_LRF +444949917,1356554,659441,55618739,False,Home,22,12,10.0,WALK_LOC +444949977,1356554,659441,55618747,True,shopping,15,22,12.0,WALK +444949981,1356554,659441,55618747,False,Home,22,15,12.0,WALK +444961505,1356589,659476,55620188,True,work,7,25,7.0,WALK_LOC +444961509,1356589,659476,55620188,False,Home,25,7,15.0,WALK_LOC +444965441,1356601,659488,55620680,True,work,16,25,9.0,WALK +444965445,1356601,659488,55620680,False,work,4,16,17.0,WALK +444965446,1356601,659488,55620680,False,Home,25,4,18.0,WALK +467005569,1423797,701683,58375696,True,shopping,19,3,11.0,SHARED2FREE +467005573,1423797,701683,58375696,False,shopping,11,19,13.0,SHARED2FREE +467005574,1423797,701683,58375696,False,Home,3,11,13.0,SHARED2FREE +467017361,1423833,701701,58377170,True,othmaint,5,5,9.0,WALK +467017365,1423833,701701,58377170,False,Home,5,5,14.0,WALK +467033913,1423883,701726,58379239,True,social,2,5,11.0,WALK +467033917,1423883,701726,58379239,False,Home,5,2,23.0,WALK +467034177,1423884,701726,58379272,True,othmaint,5,5,7.0,WALK +467034181,1423884,701726,58379272,False,Home,5,5,13.0,WALK +467039161,1423899,701734,58379895,True,social,2,6,13.0,WALK_LOC +467039165,1423899,701734,58379895,False,shopping,16,2,16.0,WALK +467039166,1423899,701734,58379895,False,Home,6,16,16.0,WALK_LOC +467077169,1424015,701792,58384646,True,univ,13,7,9.0,WALK_LOC +467077173,1424015,701792,58384646,False,Home,7,13,16.0,WALK +467077513,1424016,701792,58384689,True,othmaint,10,7,13.0,WALK_LOC +467077514,1424016,701792,58384689,True,shopping,13,10,13.0,WALK_LRF +467077517,1424016,701792,58384689,False,Home,7,13,13.0,TNC_SINGLE +467085017,1424039,701804,58385627,True,othmaint,22,7,7.0,WALK_LOC +467085021,1424039,701804,58385627,False,shopping,16,22,9.0,WALK +467085022,1424039,701804,58385627,False,othmaint,8,16,9.0,WALK_LOC +467085023,1424039,701804,58385627,False,Home,7,8,9.0,WALK +467085385,1424040,701804,58385673,True,shopping,16,7,15.0,WALK_LOC +467085389,1424040,701804,58385673,False,Home,7,16,18.0,TNC_SINGLE +467111321,1424119,701844,58388915,True,social,23,8,9.0,WALK_LOC +467111325,1424119,701844,58388915,False,Home,8,23,14.0,SHARED2FREE +467111409,1424120,701844,58388926,True,eatout,6,8,17.0,WALK +467111413,1424120,701844,58388926,False,Home,8,6,19.0,WALK +467111561,1424120,701844,58388945,True,othdiscr,9,8,8.0,WALK_LOC +467111565,1424120,701844,58388945,False,social,10,9,10.0,WALK_LOC +467111566,1424120,701844,58388945,False,Home,8,10,10.0,WALK_LOC +467124881,1424161,701865,58390610,True,escort,25,8,8.0,SHARED3FREE +467124885,1424161,701865,58390610,False,Home,8,25,8.0,SHARED3FREE +467125033,1424161,701865,58390629,True,othmaint,16,8,8.0,TNC_SHARED +467125037,1424161,701865,58390629,False,Home,8,16,10.0,TNC_SINGLE +467125097,1424161,701865,58390637,True,social,5,8,12.0,WALK +467125101,1424161,701865,58390637,False,Home,8,5,17.0,WALK +467125185,1424162,701865,58390648,True,eatout,16,8,11.0,WALK +467125189,1424162,701865,58390648,False,Home,8,16,13.0,WALK +467125209,1424162,701865,58390651,True,escort,18,8,7.0,TNC_SINGLE +467125213,1424162,701865,58390651,False,Home,8,18,8.0,TNC_SINGLE +467125401,1424162,701865,58390675,True,shopping,18,8,14.0,TNC_SINGLE +467125405,1424162,701865,58390675,False,othdiscr,7,18,14.0,WALK_LOC +467125406,1424162,701865,58390675,False,shopping,16,7,15.0,WALK_LOC +467125407,1424162,701865,58390675,False,eatout,7,16,15.0,TNC_SINGLE +467125408,1424162,701865,58390675,False,Home,8,7,15.0,WALK_LOC +467125425,1424162,701865,58390678,True,social,4,8,16.0,WALK +467125429,1424162,701865,58390678,False,Home,8,4,18.0,WALK +467149673,1424236,701902,58393709,True,shopping,10,8,14.0,WALK_LOC +467149677,1424236,701902,58393709,False,Home,8,10,14.0,WALK_LOC +467169617,1424297,701933,58396202,True,othdiscr,21,8,10.0,WALK +467169621,1424297,701933,58396202,False,Home,8,21,22.0,WALK +467169945,1424298,701933,58396243,True,othdiscr,5,8,12.0,WALK +467169949,1424298,701933,58396243,False,Home,8,5,16.0,WALK +467207185,1424412,701990,58400898,True,eatout,19,8,10.0,WALK +467207189,1424412,701990,58400898,False,Home,8,19,15.0,WALK +467207337,1424412,701990,58400917,True,othdiscr,7,8,16.0,WALK +467207341,1424412,701990,58400917,False,Home,8,7,19.0,WALK +467207401,1424412,701990,58400925,True,shopping,13,8,15.0,BIKE +467207405,1424412,701990,58400925,False,Home,8,13,16.0,BIKE +467229377,1424479,702024,58403672,True,shopping,13,8,11.0,WALK_LOC +467229381,1424479,702024,58403672,False,Home,8,13,11.0,WALK_LRF +467229665,1424480,702024,58403708,True,othmaint,11,8,9.0,WALK +467229669,1424480,702024,58403708,False,Home,8,11,17.0,WALK +467235721,1424499,702034,58404465,True,eatout,6,8,17.0,WALK +467235725,1424499,702034,58404465,False,Home,8,6,21.0,WALK +467235921,1424499,702034,58404490,True,escort,7,8,8.0,WALK +467235922,1424499,702034,58404490,True,univ,12,7,8.0,TAXI +467235925,1424499,702034,58404490,False,shopping,5,12,14.0,WALK_LOC +467235926,1424499,702034,58404490,False,Home,8,5,15.0,WALK +467236265,1424500,702034,58404533,True,shopping,16,8,10.0,WALK_LOC +467236266,1424500,702034,58404533,True,shopping,18,16,11.0,WALK_LRF +467236269,1424500,702034,58404533,False,Home,8,18,23.0,WALK_LRF +467236273,1424500,702034,58404534,True,shopping,21,8,23.0,WALK_LOC +467236277,1424500,702034,58404534,False,Home,8,21,23.0,WALK +467236529,1424501,702035,58404566,True,othdiscr,6,8,17.0,WALK +467236533,1424501,702035,58404566,False,Home,8,6,23.0,WALK +467236617,1424501,702035,58404577,True,social,9,8,13.0,WALK +467236621,1424501,702035,58404577,False,Home,8,9,16.0,WALK +467236921,1424502,702035,58404615,True,shopping,13,8,12.0,WALK +467236925,1424502,702035,58404615,False,othmaint,9,13,21.0,WALK_LRF +467236926,1424502,702035,58404615,False,Home,8,9,21.0,WALK_LOC +467269417,1424601,702085,58408677,True,social,5,8,13.0,WALK +467269421,1424601,702085,58408677,False,Home,8,5,13.0,WALK +467314593,1424739,702154,58414324,True,othdiscr,13,8,9.0,BIKE +467314597,1424739,702154,58414324,False,eatout,25,13,18.0,BIKE +467314598,1424739,702154,58414324,False,Home,8,25,18.0,BIKE +467316625,1424745,702157,58414578,True,shopping,12,8,8.0,WALK +467316626,1424745,702157,58414578,True,shopping,16,12,9.0,DRIVEALONEFREE +467316629,1424745,702157,58414578,False,Home,8,16,16.0,SHARED2FREE +467316889,1424746,702157,58414611,True,othdiscr,7,8,10.0,WALK_LOC +467316893,1424746,702157,58414611,False,eatout,9,7,14.0,WALK_LOC +467316894,1424746,702157,58414611,False,Home,8,9,14.0,WALK_LOC +467353033,1424856,702212,58419129,True,shopping,21,9,10.0,WALK +467353037,1424856,702212,58419129,False,Home,9,21,10.0,WALK +467353041,1424856,702212,58419130,True,shopping,11,9,11.0,WALK +467353045,1424856,702212,58419130,False,shopping,7,11,15.0,WALK +467353046,1424856,702212,58419130,False,othdiscr,7,7,15.0,WALK +467353047,1424856,702212,58419130,False,Home,9,7,15.0,WALK +467364953,1424893,702231,58420619,True,eatout,16,9,9.0,WALK_LOC +467364957,1424893,702231,58420619,False,othdiscr,7,16,13.0,WALK +467364958,1424893,702231,58420619,False,social,7,7,13.0,WALK +467364959,1424893,702231,58420619,False,Home,9,7,13.0,WALK +467365193,1424893,702231,58420649,True,social,5,9,14.0,WALK +467365197,1424893,702231,58420649,False,Home,9,5,14.0,WALK +467365457,1424894,702231,58420682,True,othmaint,9,9,8.0,WALK +467365461,1424894,702231,58420682,False,Home,9,9,14.0,WALK +467399281,1424997,702283,58424910,True,shopping,16,9,11.0,WALK_LOC +467399285,1424997,702283,58424910,False,Home,9,16,11.0,WALK_LOC +467477937,1425237,702403,58434742,True,othdiscr,14,10,13.0,WALK_LRF +467477941,1425237,702403,58434742,False,Home,10,14,16.0,WALK_LRF +467478329,1425238,702403,58434791,True,shopping,11,10,12.0,TNC_SINGLE +467478333,1425238,702403,58434791,False,eatout,9,11,12.0,TNC_SINGLE +467478334,1425238,702403,58434791,False,Home,10,9,12.0,TNC_SHARED +467482593,1425251,702410,58435324,True,shopping,19,10,10.0,WALK +467482597,1425251,702410,58435324,False,Home,10,19,12.0,WALK +467482881,1425252,702410,58435360,True,othmaint,12,10,7.0,WALK +467482885,1425252,702410,58435360,False,shopping,5,12,10.0,WALK +467482886,1425252,702410,58435360,False,shopping,5,5,10.0,WALK +467482887,1425252,702410,58435360,False,Home,10,5,10.0,WALK +467528145,1425390,702479,58441018,True,othmaint,7,11,8.0,WALK +467528149,1425390,702479,58441018,False,Home,11,7,17.0,WALK +467533761,1425407,702488,58441720,True,shopping,14,11,13.0,BIKE +467533765,1425407,702488,58441720,False,Home,11,14,17.0,BIKE +467534049,1425408,702488,58441756,True,shopping,2,11,12.0,TNC_SINGLE +467534050,1425408,702488,58441756,True,othmaint,2,2,13.0,TNC_SINGLE +467534053,1425408,702488,58441756,False,shopping,5,2,15.0,TNC_SINGLE +467534054,1425408,702488,58441756,False,shopping,4,5,15.0,TNC_SINGLE +467534055,1425408,702488,58441756,False,shopping,6,4,15.0,TNC_SINGLE +467534056,1425408,702488,58441756,False,Home,11,6,15.0,WALK_LOC +467573665,1425529,702549,58446708,True,shopping,15,11,10.0,WALK +467573669,1425529,702549,58446708,False,Home,11,15,16.0,WALK +467574393,1425531,702550,58446799,True,othmaint,11,12,8.0,WALK +467574397,1425531,702550,58446799,False,Home,12,11,12.0,WALK +467574569,1425532,702550,58446821,True,escort,10,12,7.0,WALK +467574573,1425532,702550,58446821,False,Home,12,10,7.0,WALK +467614889,1425655,702612,58451861,True,eatout,17,16,10.0,WALK +467614893,1425655,702612,58451861,False,Home,16,17,13.0,WALK +467615369,1425656,702612,58451921,True,othdiscr,12,16,9.0,WALK_LOC +467615373,1425656,702612,58451921,False,Home,16,12,17.0,WALK_LOC +467675481,1425839,702704,58459435,True,social,18,20,12.0,WALK +467675485,1425839,702704,58459435,False,Home,20,18,18.0,WALK +467675745,1425840,702704,58459468,True,othmaint,12,20,10.0,WALK +467675749,1425840,702704,58459468,False,Home,20,12,11.0,WALK +467675785,1425840,702704,58459473,True,shopping,11,20,16.0,WALK +467675789,1425840,702704,58459473,False,Home,20,11,16.0,WALK +467675793,1425840,702704,58459474,True,shopping,21,20,17.0,DRIVEALONEFREE +467675797,1425840,702704,58459474,False,Home,20,21,18.0,TNC_SINGLE +467675809,1425840,702704,58459476,True,social,9,20,14.0,WALK +467675813,1425840,702704,58459476,False,Home,20,9,16.0,WALK +467675897,1425841,702705,58459487,True,eatout,2,20,15.0,WALK +467675901,1425841,702705,58459487,False,Home,20,2,21.0,WALK +467676073,1425841,702705,58459509,True,othmaint,10,20,9.0,WALK +467676077,1425841,702705,58459509,False,Home,20,10,11.0,WALK +467676441,1425842,702705,58459555,True,shopping,9,20,10.0,WALK +467676445,1425842,702705,58459555,False,Home,20,9,16.0,WALK +467688905,1425880,702724,58461113,True,shopping,15,20,8.0,WALK_LRF +467688909,1425880,702724,58461113,False,shopping,9,15,12.0,WALK_LRF +467688910,1425880,702724,58461113,False,shopping,4,9,14.0,WALK_LRF +467688911,1425880,702724,58461113,False,Home,20,4,14.0,WALK_LRF +467688913,1425880,702724,58461114,True,othmaint,22,20,17.0,WALK_LRF +467688914,1425880,702724,58461114,True,shopping,11,22,17.0,WALK_LRF +467688917,1425880,702724,58461114,False,Home,20,11,17.0,TNC_SINGLE +467712481,1425952,702760,58464060,True,othmaint,5,20,9.0,WALK +467712485,1425952,702760,58464060,False,eatout,16,5,10.0,WALK_LOC +467712486,1425952,702760,58464060,False,othmaint,7,16,10.0,WALK +467712487,1425952,702760,58464060,False,Home,20,7,10.0,WALK +467715329,1425961,702765,58464416,True,othdiscr,9,20,11.0,TAXI +467715333,1425961,702765,58464416,False,Home,20,9,14.0,TNC_SHARED +467736929,1426027,702798,58467116,True,othmaint,12,20,10.0,SHARED2FREE +467736930,1426027,702798,58467116,True,escort,13,12,10.0,SHARED2FREE +467736933,1426027,702798,58467116,False,Home,20,13,10.0,SHARED2FREE +467737449,1426028,702798,58467181,True,shopping,5,20,11.0,SHARED2FREE +467737450,1426028,702798,58467181,True,shopping,16,5,11.0,SHARED2FREE +467737453,1426028,702798,58467181,False,escort,10,16,12.0,SHARED2FREE +467737454,1426028,702798,58467181,False,Home,20,10,13.0,SHARED2FREE +467797473,1426211,702890,58474684,True,shopping,15,21,12.0,WALK_LOC +467797477,1426211,702890,58474684,False,shopping,11,15,12.0,WALK_LOC +467797478,1426211,702890,58474684,False,shopping,5,11,13.0,WALK_LOC +467797479,1426211,702890,58474684,False,Home,21,5,13.0,WALK_LOC +467797609,1426212,702890,58474701,True,escort,9,21,8.0,WALK +467797613,1426212,702890,58474701,False,Home,21,9,13.0,WALK +467828241,1426305,702937,58478530,True,othdiscr,21,21,17.0,WALK +467828245,1426305,702937,58478530,False,Home,21,21,23.0,WALK +467828305,1426305,702937,58478538,True,shopping,5,21,15.0,WALK_LOC +467828309,1426305,702937,58478538,False,shopping,25,5,15.0,TNC_SINGLE +467828310,1426305,702937,58478538,False,Home,21,25,15.0,TNC_SINGLE +467828569,1426306,702937,58478571,True,othdiscr,9,21,15.0,WALK_LOC +467828573,1426306,702937,58478571,False,Home,21,9,17.0,WALK_LOC +467857825,1426395,702982,58482228,True,eatout,7,21,10.0,WALK +467857826,1426395,702982,58482228,True,shopping,2,7,11.0,WALK +467857829,1426395,702982,58482228,False,Home,21,2,12.0,WALK +467857961,1426396,702982,58482245,True,escort,10,21,14.0,SHARED2FREE +467857965,1426396,702982,58482245,False,Home,21,10,14.0,DRIVEALONEFREE +467858089,1426396,702982,58482261,True,othdiscr,16,21,14.0,WALK +467858093,1426396,702982,58482261,False,Home,21,16,17.0,WALK +467896337,1426513,703041,58487042,True,escort,11,21,11.0,WALK +467896341,1426513,703041,58487042,False,Home,21,11,17.0,WALK +467896465,1426513,703041,58487058,True,othdiscr,16,21,18.0,WALK +467896469,1426513,703041,58487058,False,Home,21,16,23.0,WALK +467896857,1426514,703041,58487107,True,shopping,5,21,8.0,WALK +467896861,1426514,703041,58487107,False,Home,21,5,14.0,WALK +467896865,1426514,703041,58487108,True,othmaint,16,21,15.0,TNC_SINGLE +467896866,1426514,703041,58487108,True,shopping,19,16,17.0,TNC_SINGLE +467896869,1426514,703041,58487108,False,Home,21,19,17.0,TNC_SINGLE +467907537,1426547,703058,58488442,True,othdiscr,16,21,9.0,WALK +467907541,1426547,703058,58488442,False,Home,21,16,17.0,TNC_SHARED +467914241,1426567,703068,58489280,True,shopping,11,21,8.0,WALK_LOC +467914245,1426567,703068,58489280,False,Home,21,11,12.0,WALK_LOC +467914265,1426567,703068,58489283,True,social,9,21,13.0,WALK +467914269,1426567,703068,58489283,False,Home,21,9,22.0,WALK +467919473,1426583,703076,58489934,True,escort,14,21,18.0,DRIVEALONEFREE +467919474,1426583,703076,58489934,True,escort,10,14,18.0,DRIVEALONEFREE +467919475,1426583,703076,58489934,True,univ,12,10,18.0,DRIVEALONEFREE +467919477,1426583,703076,58489934,False,othmaint,11,12,18.0,WALK +467919478,1426583,703076,58489934,False,Home,21,11,18.0,DRIVEALONEFREE +467919753,1426584,703076,58489969,True,othdiscr,17,21,12.0,WALK_LOC +467919757,1426584,703076,58489969,False,Home,21,17,16.0,WALK_LOC +467919801,1426584,703076,58489975,True,univ,12,21,18.0,TNC_SHARED +467919805,1426584,703076,58489975,False,Home,21,12,18.0,WALK_LOC +467937225,1426637,703103,58492153,True,social,16,21,12.0,WALK +467937229,1426637,703103,58492153,False,Home,21,16,13.0,WALK +467937513,1426638,703103,58492189,True,univ,12,21,8.0,WALK +467937517,1426638,703103,58492189,False,shopping,18,12,15.0,WALK_LOC +467937518,1426638,703103,58492189,False,othmaint,23,18,15.0,WALK_LOC +467937519,1426638,703103,58492189,False,social,22,23,15.0,WALK +467937520,1426638,703103,58492189,False,Home,21,22,15.0,WALK_LOC +467940921,1426649,703109,58492615,True,eatout,4,21,12.0,WALK +467940925,1426649,703109,58492615,False,Home,21,4,15.0,WALK +467941137,1426649,703109,58492642,True,shopping,5,21,18.0,DRIVEALONEFREE +467941141,1426649,703109,58492642,False,social,22,5,19.0,WALK +467941142,1426649,703109,58492642,False,Home,21,22,19.0,TNC_SHARED +467946825,1426667,703118,58493353,True,eatout,8,21,11.0,WALK +467946829,1426667,703118,58493353,False,Home,21,8,12.0,WALK +467947329,1426668,703118,58493416,True,othmaint,9,21,8.0,WALK_LOC +467947333,1426668,703118,58493416,False,Home,21,9,11.0,WALK +467947337,1426668,703118,58493417,True,othmaint,17,21,13.0,WALK +467947341,1426668,703118,58493417,False,eatout,4,17,15.0,WALK +467947342,1426668,703118,58493417,False,Home,21,4,15.0,WALK +467958129,1426701,703135,58494766,True,eatout,7,21,10.0,WALK +467958130,1426701,703135,58494766,True,othdiscr,9,7,10.0,WALK +467958133,1426701,703135,58494766,False,Home,21,9,12.0,WALK +467958481,1426702,703135,58494810,True,othmaint,13,21,9.0,BIKE +467958485,1426702,703135,58494810,False,Home,21,13,13.0,BIKE +467974553,1426751,703160,58496819,True,othmaint,14,21,10.0,WALK_LOC +467974557,1426751,703160,58496819,False,Home,21,14,10.0,WALK +467974881,1426752,703160,58496860,True,othmaint,9,21,7.0,WALK_LOC +467974885,1426752,703160,58496860,False,Home,21,9,11.0,WALK +468006081,1426847,703208,58500760,True,shopping,5,22,10.0,WALK +468006085,1426847,703208,58500760,False,Home,22,5,14.0,WALK +468006369,1426848,703208,58500796,True,othmaint,21,22,7.0,WALK +468006373,1426848,703208,58500796,False,shopping,16,21,13.0,WALK +468006374,1426848,703208,58500796,False,shopping,5,16,13.0,WALK +468006375,1426848,703208,58500796,False,Home,22,5,13.0,WALK +468037441,1426943,703256,58504680,True,othmaint,12,24,10.0,TNC_SINGLE +468037445,1426943,703256,58504680,False,Home,24,12,17.0,TNC_SINGLE +468106945,1427155,703362,58513368,True,eatout,25,25,10.0,WALK +468106949,1427155,703362,58513368,False,othmaint,2,25,13.0,WALK +468106950,1427155,703362,58513368,False,eatout,7,2,13.0,WALK +468106951,1427155,703362,58513368,False,Home,25,7,13.0,WALK +468107433,1427156,703362,58513429,True,shopping,16,25,15.0,WALK +468107437,1427156,703362,58513429,False,shopping,25,16,19.0,WALK_LOC +468107438,1427156,703362,58513429,False,Home,25,25,19.0,WALK +468109753,1427163,703366,58513719,True,social,9,25,12.0,BIKE +468109757,1427163,703366,58513719,False,Home,25,9,22.0,BIKE +468110057,1427164,703366,58513757,True,shopping,5,25,8.0,WALK_LOC +468110061,1427164,703366,58513757,False,Home,25,5,15.0,WALK +468119569,1427193,703381,58514946,True,shopping,14,25,13.0,WALK +468119573,1427193,703381,58514946,False,Home,25,14,19.0,WALK +468119857,1427194,703381,58514982,True,othmaint,14,25,7.0,WALK +468119861,1427194,703381,58514982,False,Home,25,14,13.0,WALK +468119921,1427194,703381,58514990,True,eatout,16,25,16.0,WALK +468119922,1427194,703381,58514990,True,social,2,16,19.0,WALK +468119925,1427194,703381,58514990,False,social,2,2,22.0,WALK +468119926,1427194,703381,58514990,False,shopping,4,2,22.0,WALK +468119927,1427194,703381,58514990,False,social,7,4,22.0,WALK +468119928,1427194,703381,58514990,False,Home,25,7,22.0,WALK +478088193,1457585,718577,59761024,True,work,10,21,6.0,WALK +478088197,1457585,718577,59761024,False,Home,21,10,15.0,WALK +478088473,1457586,718577,59761059,True,shopping,19,21,12.0,WALK +478088477,1457586,718577,59761059,False,Home,21,19,17.0,WALK +478088481,1457586,718577,59761060,True,escort,8,21,17.0,WALK +478088482,1457586,718577,59761060,True,shopping,5,8,17.0,DRIVEALONEFREE +478088485,1457586,718577,59761060,False,Home,21,5,17.0,DRIVEALONEFREE +478088849,1457587,718578,59761106,True,work,2,21,7.0,WALK +478088853,1457587,718578,59761106,False,Home,21,2,18.0,WALK +478088913,1457588,718578,59761114,True,eatout,11,21,9.0,WALK +478088917,1457588,718578,59761114,False,Home,21,11,14.0,WALK +484173857,1476139,727854,60521732,True,shopping,5,7,14.0,TNC_SINGLE +484173861,1476139,727854,60521732,False,Home,7,5,17.0,WALK_LOC +484173905,1476139,727854,60521738,True,work,22,7,7.0,WALK_LOC +484173909,1476139,727854,60521738,False,Home,7,22,14.0,WALK_LOC +484174185,1476140,727854,60521773,True,shopping,5,7,12.0,WALK +484174189,1476140,727854,60521773,False,Home,7,5,14.0,WALK +484206377,1476238,727903,60525797,True,work,20,9,6.0,WALK +484206381,1476238,727903,60525797,False,Home,9,20,21.0,WALK +484219497,1476278,727923,60527437,True,work,16,9,8.0,WALK_LOC +484219501,1476278,727923,60527437,False,Home,9,16,17.0,WALK_LRF +484298521,1476519,728044,60537315,True,othdiscr,22,10,9.0,WALK_LRF +484298522,1476519,728044,60537315,True,social,4,22,9.0,WALK_LRF +484298525,1476519,728044,60537315,False,Home,10,4,19.0,WALK +484315993,1476573,728071,60539499,True,eatout,11,11,18.0,WALK +484315997,1476573,728071,60539499,False,Home,11,11,18.0,WALK +484316257,1476573,728071,60539532,True,work,14,11,6.0,WALK +484316261,1476573,728071,60539532,False,Home,11,14,15.0,WALK_LOC +484316537,1476574,728071,60539567,True,shopping,11,11,15.0,WALK +484316541,1476574,728071,60539567,False,Home,11,11,17.0,WALK +484316561,1476574,728071,60539570,True,social,5,11,5.0,WALK +484316565,1476574,728071,60539570,False,Home,11,5,10.0,WALK +484350369,1476677,728123,60543796,True,work,13,11,9.0,WALK +484350373,1476677,728123,60543796,False,shopping,4,13,14.0,WALK +484350374,1476677,728123,60543796,False,escort,11,4,15.0,WALK +484350375,1476677,728123,60543796,False,escort,11,11,15.0,WALK +484350376,1476677,728123,60543796,False,Home,11,11,15.0,WALK +484350649,1476678,728123,60543831,True,escort,7,11,8.0,WALK +484350650,1476678,728123,60543831,True,shopping,11,7,9.0,WALK +484350653,1476678,728123,60543831,False,Home,11,11,13.0,WALK +484397337,1476821,728195,60549667,True,eatout,16,16,15.0,WALK +484397341,1476821,728195,60549667,False,Home,16,16,18.0,WALK +484397513,1476821,728195,60549689,True,othmaint,23,16,11.0,WALK +484397517,1476821,728195,60549689,False,Home,16,23,14.0,WALK_LRF +484409145,1476857,728213,60551143,True,eatout,14,16,15.0,WALK +484409149,1476857,728213,60551143,False,escort,5,14,20.0,WALK +484409150,1476857,728213,60551143,False,Home,16,5,20.0,WALK +484409385,1476857,728213,60551173,True,social,22,16,13.0,WALK +484409389,1476857,728213,60551173,False,Home,16,22,13.0,WALK +484430289,1476921,728245,60553786,True,othdiscr,9,17,11.0,BIKE +484430293,1476921,728245,60553786,False,Home,17,9,18.0,BIKE +484430681,1476922,728245,60553835,True,eatout,16,17,14.0,WALK +484430682,1476922,728245,60553835,True,shopping,16,16,14.0,WALK +484430685,1476922,728245,60553835,False,Home,17,16,15.0,WALK +484452617,1476989,728279,60556577,True,othmaint,14,20,10.0,WALK_LOC +484452621,1476989,728279,60556577,False,Home,20,14,13.0,WALK_LOC +484452921,1476990,728279,60556615,True,othdiscr,9,20,18.0,WALK +484452925,1476990,728279,60556615,False,Home,20,9,18.0,WALK +484476233,1477061,728315,60559529,True,othmaint,5,20,12.0,BIKE +484476237,1477061,728315,60559529,False,Home,20,5,13.0,BIKE +484476321,1477061,728315,60559540,True,work,23,20,14.0,BIKE +484476325,1477061,728315,60559540,False,Home,20,23,17.0,BIKE +484476601,1477062,728315,60559575,True,shopping,5,20,8.0,WALK +484476605,1477062,728315,60559575,False,Home,20,5,17.0,WALK +484476929,1477063,728316,60559616,True,shopping,5,20,15.0,WALK_LOC +484476933,1477063,728316,60559616,False,Home,20,5,15.0,WALK +484477025,1477064,728316,60559628,True,atwork,11,16,13.0,WALK +484477029,1477064,728316,60559628,False,Work,16,11,13.0,WALK +484477305,1477064,728316,60559663,True,work,16,20,12.0,WALK +484477309,1477064,728316,60559663,False,Home,20,16,22.0,WALK +484533785,1477237,728403,60566723,True,eatout,5,21,14.0,WALK +484533789,1477237,728403,60566723,False,Home,21,5,15.0,WALK +484534001,1477237,728403,60566750,True,shopping,18,21,9.0,TNC_SINGLE +484534005,1477237,728403,60566750,False,Home,21,18,10.0,TNC_SHARED +484534265,1477238,728403,60566783,True,othdiscr,1,21,11.0,WALK +484534269,1477238,728403,60566783,False,othmaint,7,1,11.0,WALK +484534270,1477238,728403,60566783,False,Home,21,7,11.0,WALK +484546401,1477275,728422,60568300,True,othdiscr,21,21,14.0,WALK +484546405,1477275,728422,60568300,False,Home,21,21,16.0,WALK +484560897,1477319,728444,60570112,True,othmaint,10,21,12.0,DRIVEALONEFREE +484560898,1477319,728444,60570112,True,shopping,19,10,14.0,TNC_SINGLE +484560901,1477319,728444,60570112,False,shopping,24,19,21.0,DRIVEALONEFREE +484560902,1477319,728444,60570112,False,Home,21,24,21.0,TNC_SINGLE +484560905,1477319,728444,60570113,True,shopping,16,21,21.0,SHARED3FREE +484560909,1477319,728444,60570113,False,shopping,13,16,21.0,SHARED3FREE +484560910,1477319,728444,60570113,False,othdiscr,16,13,21.0,SHARED3FREE +484560911,1477319,728444,60570113,False,Home,21,16,21.0,SHARED3FREE +484561273,1477320,728444,60570159,True,work,22,21,8.0,DRIVEALONEFREE +484561277,1477320,728444,60570159,False,Home,21,22,15.0,SHARED2FREE +484561281,1477320,728444,60570160,True,othmaint,9,21,15.0,WALK +484561282,1477320,728444,60570160,True,work,22,9,16.0,WALK_LOC +484561285,1477320,728444,60570160,False,shopping,11,22,17.0,WALK_LRF +484561286,1477320,728444,60570160,False,othmaint,8,11,17.0,WALK +484561287,1477320,728444,60570160,False,Home,21,8,17.0,WALK +484576929,1477368,728468,60572116,True,othmaint,8,21,8.0,WALK +484576933,1477368,728468,60572116,False,Home,21,8,9.0,WALK +484577017,1477368,728468,60572127,True,work,2,21,9.0,WALK +484577021,1477368,728468,60572127,False,Home,21,2,19.0,WALK +484611433,1477473,728521,60576429,True,social,5,21,18.0,WALK +484611437,1477473,728521,60576429,False,Home,21,5,21.0,WALK +484611457,1477473,728521,60576432,True,work,5,21,6.0,WALK +484611461,1477473,728521,60576432,False,Home,21,5,17.0,WALK +484621249,1477503,728536,60577656,True,shopping,20,23,13.0,SHARED2FREE +484621253,1477503,728536,60577656,False,othmaint,16,20,13.0,SHARED2FREE +484621254,1477503,728536,60577656,False,Home,23,16,13.0,SHARED2FREE +501577081,1529198,747627,62697135,True,othmaint,4,8,14.0,TNC_SHARED +501577085,1529198,747627,62697135,False,eatout,6,4,18.0,WALK +501577086,1529198,747627,62697135,False,Home,8,6,18.0,TNC_SHARED +501577849,1529200,747627,62697231,True,school,9,8,8.0,WALK_LOC +501577853,1529200,747627,62697231,False,Home,8,9,13.0,WALK_LOC +501580953,1529210,747630,62697619,True,escort,7,8,8.0,WALK +501580954,1529210,747630,62697619,True,escort,2,7,8.0,WALK +501580957,1529210,747630,62697619,False,eatout,5,2,13.0,WALK +501580958,1529210,747630,62697619,False,Home,8,5,13.0,WALK +501580961,1529210,747630,62697620,True,escort,2,8,14.0,WALK +501580965,1529210,747630,62697620,False,social,6,2,14.0,WALK +501580966,1529210,747630,62697620,False,othdiscr,6,6,14.0,WALK +501580967,1529210,747630,62697620,False,Home,8,6,14.0,WALK +501581081,1529210,747630,62697635,True,othdiscr,9,8,15.0,WALK +501581085,1529210,747630,62697635,False,Home,8,9,21.0,WALK +501581785,1529212,747630,62697723,True,school,21,8,12.0,WALK +501581789,1529212,747630,62697723,False,Home,8,21,18.0,WALK +515832417,1572659,763879,64479052,True,othmaint,25,6,7.0,WALK +515832418,1572659,763879,64479052,True,escort,25,25,12.0,WALK +515832419,1572659,763879,64479052,True,shopping,24,25,17.0,WALK +515832421,1572659,763879,64479052,False,shopping,25,24,18.0,WALK +515832422,1572659,763879,64479052,False,escort,25,25,20.0,WALK +515832423,1572659,763879,64479052,False,Home,6,25,20.0,WALK +515838761,1572679,763899,64479845,True,eatout,16,6,18.0,WALK +515838765,1572679,763899,64479845,False,Home,6,16,21.0,WALK +515838977,1572679,763899,64479872,True,shopping,15,6,7.0,WALK +515838981,1572679,763899,64479872,False,Home,6,15,15.0,WALK +515843833,1572694,763914,64480479,True,othdiscr,8,6,11.0,WALK +515843837,1572694,763914,64480479,False,Home,6,8,11.0,WALK +515843881,1572694,763914,64480485,True,univ,13,6,8.0,WALK +515843885,1572694,763914,64480485,False,eatout,2,13,11.0,WALK +515843886,1572694,763914,64480485,False,Home,6,2,11.0,WALK +515843889,1572694,763914,64480486,True,univ,13,6,17.0,DRIVEALONEFREE +515843893,1572694,763914,64480486,False,Home,6,13,17.0,DRIVEALONEFREE +515856953,1572734,763954,64482119,True,othdiscr,10,7,8.0,WALK +515856957,1572734,763954,64482119,False,Home,7,10,9.0,WALK +515859097,1572741,763961,64482387,True,eatout,7,7,8.0,WALK +515859101,1572741,763961,64482387,False,Home,7,7,15.0,WALK +515859337,1572741,763961,64482417,True,social,4,7,18.0,WALK +515859341,1572741,763961,64482417,False,Home,7,4,21.0,WALK +515859953,1572743,763963,64482494,True,univ,12,7,6.0,WALK_LOC +515859957,1572743,763963,64482494,False,othdiscr,8,12,13.0,WALK_LOC +515859958,1572743,763963,64482494,False,Home,7,8,13.0,WALK +515867777,1572767,763987,64483472,True,othdiscr,6,7,16.0,WALK +515867781,1572767,763987,64483472,False,Home,7,6,21.0,WALK +515867785,1572767,763987,64483473,True,othdiscr,7,7,21.0,WALK +515867789,1572767,763987,64483473,False,Home,7,7,21.0,WALK +515867801,1572767,763987,64483475,True,eatout,8,7,9.0,WALK +515867802,1572767,763987,64483475,True,othmaint,7,8,12.0,WALK +515867805,1572767,763987,64483475,False,escort,6,7,13.0,WALK +515867806,1572767,763987,64483475,False,social,14,6,14.0,WALK +515867807,1572767,763987,64483475,False,shopping,7,14,14.0,WALK +515867808,1572767,763987,64483475,False,Home,7,7,14.0,WALK +515867841,1572767,763987,64483480,True,shopping,5,7,8.0,WALK +515867845,1572767,763987,64483480,False,Home,7,5,9.0,WALK +515869921,1572774,763994,64483740,True,eatout,12,8,9.0,WALK +515869925,1572774,763994,64483740,False,Home,8,12,16.0,WALK +515887505,1572827,764047,64485938,True,univ,12,8,7.0,WALK_LOC +515887509,1572827,764047,64485938,False,Home,8,12,15.0,WALK_LOC +515903073,1572875,764095,64487884,True,escort,14,8,7.0,DRIVEALONEFREE +515903077,1572875,764095,64487884,False,Home,8,14,7.0,SHARED3FREE +515903201,1572875,764095,64487900,True,othdiscr,7,8,18.0,WALK +515903205,1572875,764095,64487900,False,Home,8,7,19.0,WALK_LOC +515903249,1572875,764095,64487906,True,univ,12,8,9.0,WALK +515903253,1572875,764095,64487906,False,escort,5,12,15.0,WALK +515903254,1572875,764095,64487906,False,Home,8,5,15.0,WALK +515903265,1572875,764095,64487908,True,shopping,14,8,17.0,TNC_SHARED +515903269,1572875,764095,64487908,False,Home,8,14,17.0,DRIVEALONEFREE +515910137,1572896,764116,64488767,True,work,14,9,13.0,WALK_LRF +515910138,1572896,764116,64488767,True,escort,9,14,14.0,WALK_LRF +515910139,1572896,764116,64488767,True,work,22,9,15.0,WALK_LRF +515910140,1572896,764116,64488767,True,univ,12,22,15.0,WALK_LRF +515910141,1572896,764116,64488767,False,Home,9,12,16.0,WALK_LRF +515920585,1572928,764148,64490073,True,othdiscr,11,9,8.0,WALK +515920589,1572928,764148,64490073,False,escort,7,11,15.0,WALK +515920590,1572928,764148,64490073,False,Home,9,7,15.0,WALK +515921265,1572930,764150,64490158,True,othmaint,9,9,8.0,WALK +515921266,1572930,764150,64490158,True,othmaint,9,9,8.0,WALK +515921269,1572930,764150,64490158,False,Home,9,9,11.0,WALK +515921305,1572930,764150,64490163,True,shopping,7,9,11.0,WALK +515921309,1572930,764150,64490163,False,Home,9,7,16.0,WALK +515979361,1573107,764327,64497420,True,shopping,16,17,11.0,WALK +515979365,1573107,764327,64497420,False,Home,17,16,14.0,WALK +515979385,1573107,764327,64497423,True,social,6,17,18.0,WALK_LOC +515979389,1573107,764327,64497423,False,escort,12,6,18.0,WALK_LOC +515979390,1573107,764327,64497423,False,Home,17,12,18.0,WALK_LOC +518349585,1580334,771554,64793698,True,atwork,13,1,13.0,SHARED3FREE +518349589,1580334,771554,64793698,False,Work,1,13,13.0,SHARED3FREE +518349865,1580334,771554,64793733,True,work,1,5,9.0,WALK +518349869,1580334,771554,64793733,False,Home,5,1,17.0,WALK +534858761,1630666,821886,66857345,True,eatout,14,4,9.0,WALK +534858762,1630666,821886,66857345,True,work,7,14,10.0,WALK +534858763,1630666,821886,66857345,True,work,5,7,10.0,WALK +534858764,1630666,821886,66857345,True,work,6,5,14.0,WALK +534858765,1630666,821886,66857345,False,escort,9,6,17.0,WALK +534858766,1630666,821886,66857345,False,work,9,9,17.0,WALK +534858767,1630666,821886,66857345,False,work,7,9,17.0,WALK +534858768,1630666,821886,66857345,False,Home,4,7,18.0,WALK +534880737,1630733,821953,66860092,True,work,21,6,8.0,WALK +534880741,1630733,821953,66860092,False,Home,6,21,18.0,WALK +534895497,1630778,821998,66861937,True,work,9,6,7.0,TNC_SINGLE +534895501,1630778,821998,66861937,False,Home,6,9,15.0,WALK +534901401,1630796,822016,66862675,True,work,5,6,7.0,WALK +534901405,1630796,822016,66862675,False,Home,6,5,20.0,WALK +534905009,1630807,822027,66863126,True,work,9,6,7.0,TNC_SINGLE +534905013,1630807,822027,66863126,False,Home,6,9,17.0,WALK_LOC +534914193,1630835,822055,66864274,True,work,23,6,6.0,WALK_LOC +534914197,1630835,822055,66864274,False,Home,6,23,15.0,WALK +534971593,1631010,822230,66871449,True,work,16,7,7.0,WALK_LOC +534971597,1631010,822230,66871449,False,Home,7,16,17.0,TNC_SINGLE +534980121,1631036,822256,66872515,True,shopping,13,7,5.0,DRIVEALONEFREE +534980122,1631036,822256,66872515,True,work,16,13,5.0,DRIVEALONEFREE +534980125,1631036,822256,66872515,False,shopping,15,16,15.0,WALK +534980126,1631036,822256,66872515,False,Home,7,15,16.0,SHARED3FREE +534983729,1631047,822267,66872966,True,work,5,7,8.0,TNC_SINGLE +534983733,1631047,822267,66872966,False,shopping,16,5,17.0,TNC_SINGLE +534983734,1631047,822267,66872966,False,Home,7,16,17.0,WALK_LOC +534987401,1631059,822279,66873425,True,eatout,8,7,8.0,WALK +534987405,1631059,822279,66873425,False,Home,7,8,11.0,WALK +534987617,1631059,822279,66873452,True,shopping,3,7,11.0,WALK +534987621,1631059,822279,66873452,False,Home,7,3,15.0,WALK +535000457,1631098,822318,66875057,True,work,9,7,7.0,WALK_LOC +535000461,1631098,822318,66875057,False,Home,7,9,21.0,WALK_LOC +535025713,1631175,822395,66878214,True,eatout,13,7,8.0,DRIVEALONEFREE +535025714,1631175,822395,66878214,True,work,19,13,8.0,DRIVEALONEFREE +535025717,1631175,822395,66878214,False,Home,7,19,21.0,DRIVEALONEFREE +535029321,1631186,822406,66878665,True,work,14,7,7.0,WALK +535029325,1631186,822406,66878665,False,Home,7,14,16.0,WALK_LOC +535029977,1631188,822408,66878747,True,work,15,7,8.0,WALK +535029981,1631188,822408,66878747,False,Home,7,15,19.0,WALK +535036209,1631207,822427,66879526,True,othdiscr,9,7,10.0,WALK +535036210,1631207,822427,66879526,True,work,11,9,10.0,WALK +535036213,1631207,822427,66879526,False,Home,7,11,19.0,WALK +535053265,1631259,822479,66881658,True,work,14,7,9.0,WALK_LOC +535053269,1631259,822479,66881658,False,shopping,5,14,9.0,WALK +535053270,1631259,822479,66881658,False,Home,7,5,16.0,WALK +535065729,1631297,822517,66883216,True,work,24,7,7.0,DRIVEALONEFREE +535065733,1631297,822517,66883216,False,Home,7,24,11.0,DRIVEALONEFREE +535087377,1631363,822583,66885922,True,work,2,8,7.0,WALK +535087381,1631363,822583,66885922,False,Home,8,2,17.0,WALK +535090001,1631371,822591,66886250,True,work,1,8,6.0,WALK_LRF +535090005,1631371,822591,66886250,False,Home,8,1,16.0,WALK_LRF +535125337,1631479,822699,66890667,True,othmaint,16,9,6.0,WALK_LRF +535125341,1631479,822699,66890667,False,Home,9,16,6.0,WALK_LOC +535125425,1631479,822699,66890678,True,work,1,9,8.0,WALK_LRF +535125429,1631479,822699,66890678,False,eatout,5,1,16.0,WALK +535125430,1631479,822699,66890678,False,Home,9,5,17.0,WALK_LOC +535149697,1631553,822773,66893712,True,work,2,9,5.0,WALK_LOC +535149701,1631553,822773,66893712,False,Home,9,2,10.0,WALK_LOC +535149705,1631553,822773,66893713,True,work,2,9,12.0,WALK_LRF +535149709,1631553,822773,66893713,False,Home,9,2,15.0,WALK_LRF +535161505,1631589,822809,66895188,True,work,1,9,9.0,WALK_LRF +535161509,1631589,822809,66895188,False,othmaint,10,1,19.0,WALK_LRF +535161510,1631589,822809,66895188,False,shopping,13,10,19.0,WALK +535161511,1631589,822809,66895188,False,Home,9,13,19.0,WALK_LRF +535161881,1631591,822811,66895235,True,atwork,11,9,12.0,WALK +535161885,1631591,822811,66895235,False,Work,9,11,14.0,WALK +535162161,1631591,822811,66895270,True,work,9,9,7.0,WALK +535162165,1631591,822811,66895270,False,Home,9,9,17.0,WALK +535166753,1631605,822825,66895844,True,othmaint,3,9,8.0,WALK_LRF +535166754,1631605,822825,66895844,True,work,14,3,8.0,WALK_LOC +535166757,1631605,822825,66895844,False,Home,9,14,21.0,WALK_LOC +535170409,1631617,822837,66896301,True,atwork,2,4,13.0,WALK +535170413,1631617,822837,66896301,False,Work,4,2,13.0,WALK +535170689,1631617,822837,66896336,True,work,4,9,10.0,WALK_LOC +535170693,1631617,822837,66896336,False,othmaint,16,4,18.0,WALK +535170694,1631617,822837,66896336,False,Home,9,16,20.0,WALK_LRF +535187137,1631668,822888,66898392,True,eatout,12,25,11.0,WALK +535187138,1631668,822888,66898392,True,atwork,12,12,11.0,WALK +535187141,1631668,822888,66898392,False,Work,25,12,11.0,WALK +535187417,1631668,822888,66898427,True,work,25,9,7.0,WALK_LOC +535187421,1631668,822888,66898427,False,Home,9,25,18.0,WALK_LOC +535190089,1631677,822897,66898761,True,atwork,16,1,10.0,WALK_LOC +535190093,1631677,822897,66898761,False,Work,1,16,13.0,TNC_SINGLE +535190369,1631677,822897,66898796,True,work,1,9,9.0,WALK_LRF +535190373,1631677,822897,66898796,False,Home,9,1,17.0,WALK_LRF +535212297,1631744,822964,66901537,True,shopping,3,9,18.0,WALK +535212301,1631744,822964,66901537,False,Home,9,3,19.0,WALK +535212345,1631744,822964,66901543,True,work,7,9,9.0,WALK +535212349,1631744,822964,66901543,False,Home,9,7,18.0,WALK +535225857,1631786,823006,66903232,True,eatout,13,9,20.0,SHARED3FREE +535225861,1631786,823006,66903232,False,Home,9,13,20.0,SHARED3FREE +535225993,1631786,823006,66903249,True,atwork,4,22,11.0,BIKE +535225997,1631786,823006,66903249,False,Work,22,4,11.0,BIKE +535226073,1631786,823006,66903259,True,shopping,13,9,21.0,TNC_SHARED +535226077,1631786,823006,66903259,False,Home,9,13,22.0,TNC_SHARED +535226121,1631786,823006,66903265,True,work,22,9,7.0,BIKE +535226125,1631786,823006,66903265,False,Home,9,22,20.0,BIKE +535226777,1631788,823008,66903347,True,escort,10,9,5.0,WALK +535226778,1631788,823008,66903347,True,escort,5,10,6.0,TNC_SINGLE +535226779,1631788,823008,66903347,True,work,9,5,12.0,TNC_SINGLE +535226781,1631788,823008,66903347,False,othmaint,2,9,15.0,WALK_HVY +535226782,1631788,823008,66903347,False,shopping,9,2,15.0,TNC_SINGLE +535226783,1631788,823008,66903347,False,eatout,12,9,15.0,WALK_LOC +535226784,1631788,823008,66903347,False,Home,9,12,15.0,WALK_LRF +535238257,1631823,823043,66904782,True,work,12,9,5.0,WALK +535238261,1631823,823043,66904782,False,shopping,11,12,15.0,WALK_LOC +535238262,1631823,823043,66904782,False,Home,9,11,15.0,TNC_SINGLE +535241913,1631835,823055,66905239,True,atwork,15,14,14.0,WALK +535241917,1631835,823055,66905239,False,eatout,7,15,14.0,WALK +535241918,1631835,823055,66905239,False,eatout,5,7,14.0,WALK +535241919,1631835,823055,66905239,False,Work,14,5,14.0,WALK +535242081,1631835,823055,66905260,True,othdiscr,9,9,16.0,WALK +535242085,1631835,823055,66905260,False,Home,9,9,18.0,WALK +535242089,1631835,823055,66905261,True,othdiscr,21,9,21.0,WALK +535242093,1631835,823055,66905261,False,Home,9,21,23.0,WALK +535242097,1631835,823055,66905262,True,othdiscr,1,9,23.0,WALK_LRF +535242101,1631835,823055,66905262,False,Home,9,1,23.0,WALK_LRF +535242193,1631835,823055,66905274,True,work,14,9,6.0,WALK_LRF +535242197,1631835,823055,66905274,False,Home,9,14,15.0,WALK_LRF +535251097,1631863,823083,66906387,True,atwork,7,22,11.0,WALK +535251101,1631863,823083,66906387,False,shopping,25,7,13.0,WALK +535251102,1631863,823083,66906387,False,Work,22,25,13.0,WALK +535251377,1631863,823083,66906422,True,work,22,9,8.0,WALK_HVY +535251381,1631863,823083,66906422,False,Home,9,22,17.0,TNC_SINGLE +535262905,1631899,823119,66907863,True,atwork,21,13,11.0,WALK +535262909,1631899,823119,66907863,False,work,7,21,11.0,WALK +535262910,1631899,823119,66907863,False,Work,13,7,11.0,WALK +535263185,1631899,823119,66907898,True,work,13,9,9.0,WALK +535263189,1631899,823119,66907898,False,Home,9,13,21.0,WALK +535263841,1631901,823121,66907980,True,work,4,9,8.0,WALK +535263845,1631901,823121,66907980,False,Home,9,4,18.0,WALK +535264217,1631903,823123,66908027,True,work,9,5,10.0,WALK +535264218,1631903,823123,66908027,True,othmaint,9,9,10.0,WALK +535264219,1631903,823123,66908027,True,atwork,19,9,10.0,WALK +535264221,1631903,823123,66908027,False,Work,5,19,10.0,WALK +535264497,1631903,823123,66908062,True,work,5,9,6.0,WALK_LRF +535264501,1631903,823123,66908062,False,work,22,5,22.0,WALK +535264502,1631903,823123,66908062,False,Home,9,22,22.0,WALK_LRF +535283537,1631962,823182,66910442,True,atwork,22,2,12.0,WALK +535283541,1631962,823182,66910442,False,work,4,22,14.0,WALK +535283542,1631962,823182,66910442,False,Work,2,4,14.0,WALK +535283849,1631962,823182,66910481,True,work,2,9,6.0,WALK +535283853,1631962,823182,66910481,False,Home,9,2,21.0,WALK +535315337,1632058,823278,66914417,True,work,2,10,8.0,WALK_LOC +535315341,1632058,823278,66914417,False,othmaint,20,2,16.0,WALK_LRF +535315342,1632058,823278,66914417,False,shopping,22,20,16.0,WALK_LRF +535315343,1632058,823278,66914417,False,escort,9,22,16.0,WALK_LRF +535315344,1632058,823278,66914417,False,Home,10,9,16.0,WALK_LOC +535339497,1632132,823352,66917437,True,othdiscr,9,11,10.0,WALK_LOC +535339501,1632132,823352,66917437,False,Home,11,9,11.0,TNC_SINGLE +535339609,1632132,823352,66917451,True,work,4,11,14.0,WALK +535339613,1632132,823352,66917451,False,Home,11,4,21.0,WALK_LRF +535339849,1632133,823353,66917481,True,othmaint,7,11,21.0,WALK +535339850,1632133,823353,66917481,True,othmaint,6,7,21.0,DRIVEALONEFREE +535339853,1632133,823353,66917481,False,othmaint,7,6,21.0,WALK +535339854,1632133,823353,66917481,False,escort,10,7,21.0,DRIVEALONEFREE +535339855,1632133,823353,66917481,False,Home,11,10,21.0,DRIVEALONEFREE +535339937,1632133,823353,66917492,True,work,9,11,7.0,DRIVEALONEFREE +535339941,1632133,823353,66917492,False,Home,11,9,18.0,DRIVEALONEFREE +535341249,1632137,823357,66917656,True,work,13,11,9.0,WALK_LOC +535341253,1632137,823357,66917656,False,escort,13,13,16.0,WALK +535341254,1632137,823357,66917656,False,Home,11,13,16.0,TNC_SINGLE +535351089,1632167,823387,66918886,True,work,9,11,6.0,WALK +535351090,1632167,823387,66918886,True,work,9,9,7.0,WALK +535351091,1632167,823387,66918886,True,work,5,9,7.0,WALK +535351092,1632167,823387,66918886,True,work,11,5,7.0,WALK +535351093,1632167,823387,66918886,False,Home,11,11,17.0,WALK +535351097,1632167,823387,66918887,True,work,11,11,18.0,WALK +535351101,1632167,823387,66918887,False,Home,11,11,21.0,WALK +535354761,1632179,823399,66919345,True,eatout,11,11,21.0,WALK +535354765,1632179,823399,66919345,False,Home,11,11,21.0,WALK +535354937,1632179,823399,66919367,True,othmaint,5,11,9.0,WALK +535354941,1632179,823399,66919367,False,Home,11,5,15.0,WALK +535354977,1632179,823399,66919372,True,shopping,7,11,17.0,WALK +535354978,1632179,823399,66919372,True,shopping,11,7,17.0,WALK +535354981,1632179,823399,66919372,False,Home,11,11,18.0,WALK +535370985,1632228,823448,66921373,True,othdiscr,9,11,20.0,WALK +535370989,1632228,823448,66921373,False,Home,11,9,21.0,WALK +535371097,1632228,823448,66921387,True,work,5,11,6.0,WALK +535371101,1632228,823448,66921387,False,Home,11,5,17.0,WALK_LOC +535377657,1632248,823468,66922207,True,work,9,11,7.0,DRIVEALONEFREE +535377661,1632248,823468,66922207,False,Home,11,9,18.0,DRIVEALONEFREE +535388201,1632281,823501,66923525,True,work,13,13,13.0,WALK +535388202,1632281,823501,66923525,True,atwork,8,13,13.0,WALK +535388205,1632281,823501,66923525,False,Work,13,8,14.0,WALK +535388481,1632281,823501,66923560,True,work,13,12,7.0,WALK +535388485,1632281,823501,66923560,False,Home,12,13,19.0,WALK +535394105,1632299,823519,66924263,True,work,2,2,11.0,WALK +535394106,1632299,823519,66924263,True,atwork,12,2,11.0,WALK +535394109,1632299,823519,66924263,False,Work,2,12,13.0,WALK +535394385,1632299,823519,66924298,True,work,2,12,8.0,WALK +535394389,1632299,823519,66924298,False,work,12,2,23.0,WALK +535394390,1632299,823519,66924298,False,Home,12,12,23.0,WALK +535412425,1632354,823574,66926553,True,work,24,12,8.0,WALK_LOC +535412429,1632354,823574,66926553,False,Home,12,24,17.0,WALK +535414393,1632360,823580,66926799,True,work,11,12,7.0,WALK +535414397,1632360,823580,66926799,False,Home,12,11,17.0,WALK +535416249,1632366,823586,66927031,True,othdiscr,5,12,6.0,WALK +535416253,1632366,823586,66927031,False,Home,12,5,6.0,WALK +535416257,1632366,823586,66927032,True,othdiscr,13,12,19.0,WALK +535416261,1632366,823586,66927032,False,Home,12,13,19.0,WALK +535416313,1632366,823586,66927039,True,shopping,13,12,20.0,WALK +535416317,1632366,823586,66927039,False,shopping,16,13,20.0,WALK +535416318,1632366,823586,66927039,False,Home,12,16,21.0,WALK +535416361,1632366,823586,66927045,True,escort,25,12,7.0,WALK +535416362,1632366,823586,66927045,True,work,24,25,8.0,WALK_LOC +535416365,1632366,823586,66927045,False,othdiscr,8,24,17.0,WALK_LOC +535416366,1632366,823586,66927045,False,shopping,5,8,19.0,WALK_LOC +535416367,1632366,823586,66927045,False,Home,12,5,19.0,TNC_SINGLE +535431777,1632413,823633,66928972,True,shopping,19,14,9.0,TNC_SINGLE +535431778,1632413,823633,66928972,True,work,11,19,9.0,WALK +535431781,1632413,823633,66928972,False,work,2,11,16.0,TNC_SINGLE +535431782,1632413,823633,66928972,False,Home,14,2,19.0,TNC_SINGLE +535432433,1632415,823635,66929054,True,work,4,14,6.0,WALK_LOC +535432437,1632415,823635,66929054,False,Home,14,4,15.0,WALK_LOC +535471465,1632534,823754,66933933,True,work,16,16,7.0,DRIVEALONEFREE +535471469,1632534,823754,66933933,False,Home,16,16,20.0,WALK +535496065,1632609,823829,66937008,True,work,17,17,5.0,WALK +535496069,1632609,823829,66937008,False,Home,17,17,17.0,WALK +535513449,1632662,823882,66939181,True,work,2,17,7.0,WALK_LOC +535513453,1632662,823882,66939181,False,Home,17,2,18.0,WALK_LRF +535523289,1632692,823912,66940411,True,work,2,17,7.0,WALK +535523293,1632692,823912,66940411,False,Home,17,2,18.0,WALK_LRF +535530505,1632714,823934,66941313,True,work,16,17,7.0,WALK +535530509,1632714,823934,66941313,False,Home,17,16,15.0,WALK +535538705,1632739,823959,66942338,True,work,17,17,7.0,WALK +535538709,1632739,823959,66942338,False,Home,17,17,18.0,TNC_SINGLE +535546857,1632764,823984,66943357,True,shopping,25,17,18.0,WALK_LOC +535546861,1632764,823984,66943357,False,Home,17,25,19.0,WALK_LRF +535546905,1632764,823984,66943363,True,work,14,17,7.0,WALK_LRF +535546909,1632764,823984,66943363,False,Home,17,14,16.0,WALK_LOC +535598449,1632922,824142,66949806,True,atwork,9,9,11.0,WALK +535598453,1632922,824142,66949806,False,Work,9,9,11.0,WALK +535598729,1632922,824142,66949841,True,work,9,18,7.0,WALK +535598733,1632922,824142,66949841,False,Home,18,9,18.0,WALK +535599777,1632926,824146,66949972,True,eatout,7,18,18.0,WALK +535599781,1632926,824146,66949972,False,Home,18,7,21.0,WALK +535599929,1632926,824146,66949991,True,othdiscr,7,18,8.0,WALK +535599933,1632926,824146,66949991,False,Home,18,7,15.0,WALK +535604305,1632939,824159,66950538,True,work,6,18,6.0,DRIVEALONEFREE +535604309,1632939,824159,66950538,False,eatout,11,6,13.0,DRIVEALONEFREE +535604310,1632939,824159,66950538,False,Home,18,11,13.0,WALK +535613881,1632969,824189,66951735,True,shopping,11,18,18.0,WALK +535613882,1632969,824189,66951735,True,eatout,9,11,19.0,WALK +535613885,1632969,824189,66951735,False,shopping,11,9,21.0,WALK +535613886,1632969,824189,66951735,False,othdiscr,8,11,21.0,WALK +535613887,1632969,824189,66951735,False,Home,18,8,21.0,WALK +535614033,1632969,824189,66951754,True,othdiscr,9,18,12.0,WALK_LOC +535614037,1632969,824189,66951754,False,Home,18,9,16.0,WALK_LOC +535620049,1632987,824207,66952506,True,work,4,18,15.0,WALK_LOC +535620053,1632987,824207,66952506,False,Home,18,4,22.0,WALK +535636121,1633036,824256,66954515,True,work,2,19,7.0,TNC_SINGLE +535636125,1633036,824256,66954515,False,othdiscr,6,2,17.0,TNC_SINGLE +535636126,1633036,824256,66954515,False,Home,19,6,18.0,TNC_SINGLE +535641369,1633052,824272,66955171,True,work,11,19,6.0,WALK +535641373,1633052,824272,66955171,False,Home,19,11,16.0,WALK +535642353,1633055,824275,66955294,True,othdiscr,7,19,11.0,WALK_LOC +535642354,1633055,824275,66955294,True,work,4,7,13.0,WALK_LOC +535642357,1633055,824275,66955294,False,shopping,20,4,16.0,WALK_LRF +535642358,1633055,824275,66955294,False,Home,19,20,17.0,WALK_LOC +535646945,1633069,824289,66955868,True,escort,9,19,5.0,WALK_LOC +535646946,1633069,824289,66955868,True,work,10,9,16.0,WALK_LOC +535646949,1633069,824289,66955868,False,Home,19,10,16.0,WALK +535647273,1633070,824290,66955909,True,work,5,19,8.0,WALK +535647277,1633070,824290,66955909,False,Home,19,5,18.0,WALK +535656369,1633098,824318,66957046,True,othmaint,24,20,12.0,SHARED2FREE +535656373,1633098,824318,66957046,False,Home,20,24,13.0,SHARED2FREE +535656457,1633098,824318,66957057,True,work,4,20,7.0,WALK_LRF +535656461,1633098,824318,66957057,False,Home,20,4,10.0,WALK_LRF +535663393,1633120,824340,66957924,True,atwork,2,14,12.0,WALK +535663397,1633120,824340,66957924,False,eatout,16,2,13.0,WALK +535663398,1633120,824340,66957924,False,Work,14,16,13.0,WALK +535663673,1633120,824340,66957959,True,work,14,20,9.0,WALK_LOC +535663677,1633120,824340,66957959,False,shopping,11,14,21.0,TNC_SHARED +535663678,1633120,824340,66957959,False,Home,20,11,22.0,WALK +535669577,1633138,824358,66958697,True,work,9,20,8.0,DRIVEALONEFREE +535669581,1633138,824358,66958697,False,work,10,9,17.0,WALK +535669582,1633138,824358,66958697,False,Home,20,10,17.0,DRIVEALONEFREE +535672921,1633149,824369,66959115,True,eatout,13,21,8.0,WALK +535672925,1633149,824369,66959115,False,Home,21,13,14.0,WALK +535681273,1633174,824394,66960159,True,othdiscr,20,21,7.0,DRIVEALONEFREE +535681277,1633174,824394,66960159,False,Home,21,20,7.0,DRIVEALONEFREE +535681337,1633174,824394,66960167,True,shopping,16,21,18.0,WALK +535681341,1633174,824394,66960167,False,Home,21,16,21.0,WALK +535681385,1633174,824394,66960173,True,escort,8,21,8.0,WALK +535681386,1633174,824394,66960173,True,work,12,8,8.0,BIKE +535681389,1633174,824394,66960173,False,Home,21,12,17.0,BIKE +535690897,1633203,824423,66961362,True,work,4,22,6.0,WALK +535690901,1633203,824423,66961362,False,Home,22,4,19.0,WALK +535694417,1633214,824434,66961802,True,othmaint,21,22,16.0,TNC_SINGLE +535694421,1633214,824434,66961802,False,Home,22,21,19.0,TNC_SINGLE +535694457,1633214,824434,66961807,True,shopping,19,22,12.0,WALK_LOC +535694461,1633214,824434,66961807,False,Home,22,19,14.0,TNC_SHARED +535694465,1633214,824434,66961808,True,othmaint,16,22,14.0,WALK_LOC +535694466,1633214,824434,66961808,True,shopping,18,16,14.0,WALK_LRF +535694469,1633214,824434,66961808,False,Home,22,18,15.0,WALK_LRF +535714561,1633276,824496,66964320,True,atwork,1,2,10.0,WALK +535714565,1633276,824496,66964320,False,eatout,3,1,10.0,WALK +535714566,1633276,824496,66964320,False,Work,2,3,10.0,WALK +535714753,1633276,824496,66964344,True,othmaint,23,23,7.0,WALK +535714757,1633276,824496,66964344,False,Home,23,23,9.0,WALK +535714841,1633276,824496,66964355,True,work,2,23,9.0,WALK +535714845,1633276,824496,66964355,False,Home,23,2,18.0,WALK +535720793,1633295,824515,66965099,True,atwork,13,21,10.0,WALK +535720797,1633295,824515,66965099,False,Work,21,13,11.0,SHARED3FREE +535721073,1633295,824515,66965134,True,work,21,24,5.0,WALK_LOC +535721077,1633295,824515,66965134,False,Home,24,21,16.0,WALK_LOC +535728553,1633318,824538,66966069,True,univ,13,24,17.0,DRIVEALONEFREE +535728557,1633318,824538,66966069,False,Home,24,13,18.0,DRIVEALONEFREE +535728617,1633318,824538,66966077,True,work,13,24,7.0,BIKE +535728621,1633318,824538,66966077,False,Home,24,13,13.0,BIKE +535735177,1633338,824558,66966897,True,work,2,25,7.0,DRIVEALONEFREE +535735181,1633338,824558,66966897,False,eatout,2,2,11.0,DRIVEALONEFREE +535735182,1633338,824558,66966897,False,Home,25,2,16.0,DRIVEALONEFREE +535747641,1633376,824596,66968455,True,work,22,25,7.0,WALK +535747645,1633376,824596,66968455,False,Home,25,22,18.0,WALK_LOC +564983897,1722511,906270,70622987,True,social,11,8,11.0,WALK +564983901,1722511,906270,70622987,False,Home,8,11,16.0,WALK +564983905,1722511,906270,70622988,True,social,16,8,17.0,WALK +564983909,1722511,906270,70622988,False,Home,8,16,19.0,WALK +564984137,1722512,906270,70623017,True,othdiscr,20,8,8.0,WALK +564984141,1722512,906270,70623017,False,Home,8,20,16.0,WALK +565004825,1722575,906302,70625603,True,othmaint,11,8,10.0,WALK +565004829,1722575,906302,70625603,False,Home,8,11,18.0,WALK +565005001,1722576,906302,70625625,True,escort,21,8,8.0,WALK +565005005,1722576,906302,70625625,False,othmaint,8,21,8.0,WALK +565005006,1722576,906302,70625625,False,Home,8,8,9.0,WALK +565027169,1722643,906336,70628396,True,shopping,12,9,12.0,WALK +565027173,1722643,906336,70628396,False,Home,9,12,20.0,WALK +565027281,1722644,906336,70628410,True,eatout,21,9,20.0,WALK +565027285,1722644,906336,70628410,False,Home,9,21,21.0,WALK +565027497,1722644,906336,70628437,True,shopping,11,9,8.0,WALK +565027501,1722644,906336,70628437,False,othmaint,9,11,17.0,WALK +565027502,1722644,906336,70628437,False,Home,9,9,18.0,WALK +581938897,1774203,932116,72742362,True,work,2,6,6.0,WALK +581938898,1774203,932116,72742362,True,work,13,2,8.0,WALK_LOC +581938901,1774203,932116,72742362,False,work,24,13,15.0,WALK_LOC +581938902,1774203,932116,72742362,False,Home,6,24,15.0,WALK_LOC +581959169,1774265,932147,72744896,True,escort,7,7,15.0,WALK +581959170,1774265,932147,72744896,True,eatout,5,7,15.0,WALK +581959171,1774265,932147,72744896,True,escort,8,5,16.0,WALK +581959172,1774265,932147,72744896,True,univ,12,8,16.0,WALK +581959173,1774265,932147,72744896,False,othdiscr,9,12,18.0,WALK +581959174,1774265,932147,72744896,False,shopping,5,9,23.0,WALK +581959175,1774265,932147,72744896,False,Home,7,5,23.0,WALK +581959561,1774266,932147,72744945,True,work,14,7,10.0,WALK_LOC +581959565,1774266,932147,72744945,False,Home,7,14,20.0,WALK_LOC +581960545,1774269,932149,72745068,True,work,24,7,7.0,WALK_LOC +581960549,1774269,932149,72745068,False,Home,7,24,17.0,WALK +581960825,1774270,932149,72745103,True,shopping,5,7,16.0,TNC_SINGLE +581960829,1774270,932149,72745103,False,shopping,2,5,17.0,TAXI +581960830,1774270,932149,72745103,False,Home,7,2,17.0,DRIVEALONEFREE +581999905,1774389,932209,72749988,True,work,23,8,14.0,WALK_LOC +581999909,1774389,932209,72749988,False,shopping,23,23,17.0,WALK +581999910,1774389,932209,72749988,False,Home,8,23,20.0,WALK_LOC +582000169,1774390,932209,72750021,True,univ,13,8,17.0,WALK +582000173,1774390,932209,72750021,False,Home,8,13,17.0,WALK_LRF +582009089,1774417,932223,72751136,True,work,10,8,6.0,WALK +582009093,1774417,932223,72751136,False,Home,8,10,16.0,WALK +582009329,1774418,932223,72751166,True,othmaint,17,8,9.0,WALK_LRF +582009333,1774418,932223,72751166,False,Home,8,17,13.0,WALK_LRF +582009745,1774419,932224,72751218,True,work,5,8,6.0,WALK +582009749,1774419,932224,72751218,False,Home,8,5,16.0,WALK +582010025,1774420,932224,72751253,True,escort,3,8,12.0,BIKE +582010026,1774420,932224,72751253,True,shopping,2,3,13.0,BIKE +582010027,1774420,932224,72751253,True,shopping,19,2,13.0,BIKE +582010029,1774420,932224,72751253,False,Home,8,19,15.0,BIKE +582031769,1774487,932258,72753971,True,atwork,9,1,11.0,WALK +582031773,1774487,932258,72753971,False,Work,1,9,11.0,WALK +582032049,1774487,932258,72754006,True,work,1,9,6.0,WALK_LRF +582032053,1774487,932258,72754006,False,Home,9,1,15.0,WALK_LRF +582032137,1774488,932258,72754017,True,escort,7,9,12.0,SHARED2FREE +582032141,1774488,932258,72754017,False,Home,9,7,14.0,WALK +582032145,1774488,932258,72754018,True,escort,10,9,15.0,DRIVEALONEFREE +582032149,1774488,932258,72754018,False,Home,9,10,16.0,SHARED2FREE +582032289,1774488,932258,72754036,True,othmaint,10,9,7.0,WALK +582032293,1774488,932258,72754036,False,Home,9,10,12.0,WALK +582032313,1774488,932258,72754039,True,univ,9,9,17.0,WALK +582032317,1774488,932258,72754039,False,shopping,8,9,21.0,WALK +582032318,1774488,932258,72754039,False,Home,9,8,22.0,WALK +582048385,1774537,932283,72756048,True,univ,9,9,8.0,WALK +582048389,1774537,932283,72756048,False,Home,9,9,13.0,WALK +582054305,1774555,932292,72756788,True,shopping,11,9,14.0,WALK +582054309,1774555,932292,72756788,False,Home,9,11,15.0,DRIVEALONEFREE +582054633,1774556,932292,72756829,True,shopping,11,9,10.0,TNC_SINGLE +582054637,1774556,932292,72756829,False,Home,9,11,13.0,WALK +582081249,1774637,932333,72760156,True,work,23,10,7.0,WALK_LOC +582081253,1774637,932333,72760156,False,Home,10,23,15.0,WALK_LRF +582081529,1774638,932333,72760191,True,othdiscr,7,10,11.0,WALK +582081530,1774638,932333,72760191,True,shopping,24,7,12.0,WALK +582081533,1774638,932333,72760191,False,eatout,6,24,14.0,WALK_LOC +582081534,1774638,932333,72760191,False,Home,10,6,15.0,WALK +582081553,1774638,932333,72760194,True,social,8,10,18.0,WALK +582081557,1774638,932333,72760194,False,Home,10,8,18.0,WALK +582099617,1774693,932361,72762452,True,work,9,11,7.0,WALK +582099621,1774693,932361,72762452,False,Home,11,9,20.0,WALK +582099833,1774694,932361,72762479,True,othdiscr,24,11,10.0,WALK +582099837,1774694,932361,72762479,False,Home,11,24,16.0,WALK +582125577,1774773,932401,72765697,True,atwork,16,5,9.0,SHARED3FREE +582125581,1774773,932401,72765697,False,Work,5,16,9.0,SHARED3FREE +582125809,1774773,932401,72765726,True,shopping,16,13,18.0,SHARED2FREE +582125813,1774773,932401,72765726,False,Home,13,16,18.0,SHARED2FREE +582125857,1774773,932401,72765732,True,work,5,13,8.0,DRIVEALONEFREE +582125861,1774773,932401,72765732,False,shopping,5,5,17.0,WALK +582125862,1774773,932401,72765732,False,Home,13,5,17.0,WALK +582159689,1774877,932453,72769961,True,eatout,5,12,10.0,WALK +582159690,1774877,932453,72769961,True,atwork,1,5,10.0,WALK +582159693,1774877,932453,72769961,False,Work,12,1,10.0,WALK +582159969,1774877,932453,72769996,True,escort,5,24,8.0,WALK_LOC +582159970,1774877,932453,72769996,True,work,12,5,8.0,WALK_LOC +582159973,1774877,932453,72769996,False,Home,24,12,17.0,WALK +582160233,1774878,932453,72770029,True,univ,13,24,9.0,WALK_LOC +582160237,1774878,932453,72770029,False,Home,24,13,16.0,WALK_LOC +582160577,1774879,932454,72770072,True,shopping,12,24,15.0,WALK +582160581,1774879,932454,72770072,False,Home,24,12,15.0,WALK +582160585,1774879,932454,72770073,True,shopping,11,24,18.0,WALK_LOC +582160589,1774879,932454,72770073,False,Home,24,11,18.0,WALK_LOC +582160953,1774880,932454,72770119,True,work,12,24,7.0,WALK +582160957,1774880,932454,72770119,False,Home,24,12,18.0,WALK +614930185,1874787,982408,76866273,True,eatout,8,6,15.0,WALK +614930189,1874787,982408,76866273,False,Home,6,8,16.0,WALK +614930401,1874787,982408,76866300,True,escort,5,6,17.0,WALK_LOC +614930402,1874787,982408,76866300,True,shopping,7,5,17.0,WALK +614930405,1874787,982408,76866300,False,Home,6,7,17.0,WALK +614930497,1874788,982408,76866312,True,atwork,5,21,13.0,WALK +614930501,1874788,982408,76866312,False,Work,21,5,13.0,WALK +614930777,1874788,982408,76866347,True,work,21,6,7.0,WALK +614930781,1874788,982408,76866347,False,Home,6,21,23.0,WALK +614935041,1874801,982415,76866880,True,work,5,6,8.0,WALK +614935045,1874801,982415,76866880,False,Home,6,5,18.0,WALK +614935369,1874802,982415,76866921,True,work,16,6,8.0,WALK_LOC +614935373,1874802,982415,76866921,False,Home,6,16,16.0,WALK_LOC +614942257,1874823,982426,76867782,True,work,2,6,13.0,WALK_LOC +614942261,1874823,982426,76867782,False,Home,6,2,20.0,TNC_SINGLE +614942585,1874824,982426,76867823,True,work,23,6,8.0,BIKE +614942589,1874824,982426,76867823,False,Home,6,23,16.0,BIKE +614954721,1874861,982445,76869340,True,work,9,7,11.0,WALK +614954725,1874861,982445,76869340,False,Home,7,9,21.0,WALK +614954737,1874862,982445,76869342,True,atwork,15,12,12.0,WALK +614954741,1874862,982445,76869342,False,Work,12,15,12.0,WALK +614955049,1874862,982445,76869381,True,work,12,7,8.0,WALK +614955053,1874862,982445,76869381,False,Home,7,12,18.0,WALK +614971057,1874911,982470,76871382,True,othmaint,22,7,21.0,WALK_LRF +614971058,1874911,982470,76871382,True,univ,13,22,21.0,WALK_LRF +614971061,1874911,982470,76871382,False,Home,7,13,21.0,WALK_LOC +614971121,1874911,982470,76871390,True,work,2,7,7.0,WALK_LOC +614971125,1874911,982470,76871390,False,Home,7,2,14.0,WALK +614971449,1874912,982470,76871431,True,work,9,7,6.0,WALK +614971453,1874912,982470,76871431,False,Home,7,9,15.0,WALK +614975057,1874923,982476,76871882,True,work,9,7,9.0,WALK_LOC +614975061,1874923,982476,76871882,False,Home,7,9,18.0,WALK +614975385,1874924,982476,76871923,True,work,16,7,7.0,SHARED3FREE +614975389,1874924,982476,76871923,False,Home,7,16,20.0,WALK_LOC +615009825,1875029,982529,76876228,True,work,2,8,17.0,WALK +615009829,1875029,982529,76876228,False,Home,8,2,21.0,WALK +615010065,1875030,982529,76876258,True,othmaint,21,8,11.0,WALK +615010069,1875030,982529,76876258,False,Home,8,21,14.0,WALK +615032737,1875099,982564,76879092,True,shopping,18,8,8.0,TNC_SINGLE +615032741,1875099,982564,76879092,False,Home,8,18,8.0,TNC_SINGLE +615032745,1875099,982564,76879093,True,shopping,8,8,20.0,TNC_SINGLE +615032749,1875099,982564,76879093,False,Home,8,8,21.0,TNC_SINGLE +615032785,1875099,982564,76879098,True,work,19,8,8.0,TNC_SINGLE +615032789,1875099,982564,76879098,False,escort,12,19,19.0,TNC_SINGLE +615032790,1875099,982564,76879098,False,othmaint,7,12,19.0,TNC_SINGLE +615032791,1875099,982564,76879098,False,Home,8,7,19.0,WALK_LOC +615032833,1875100,982564,76879104,True,atwork,7,4,8.0,WALK_LOC +615032837,1875100,982564,76879104,False,Work,4,7,9.0,TNC_SINGLE +615033113,1875100,982564,76879139,True,work,4,8,6.0,WALK +615033117,1875100,982564,76879139,False,Home,8,4,18.0,TNC_SINGLE +615038409,1875117,982573,76879801,True,atwork,16,18,12.0,WALK +615038413,1875117,982573,76879801,False,eatout,16,16,12.0,WALK +615038414,1875117,982573,76879801,False,Work,18,16,12.0,WALK +615038497,1875117,982573,76879812,True,othdiscr,25,8,8.0,TNC_SINGLE +615038501,1875117,982573,76879812,False,Home,8,25,8.0,TNC_SINGLE +615038601,1875117,982573,76879825,True,othmaint,21,8,18.0,WALK +615038605,1875117,982573,76879825,False,Home,8,21,21.0,WALK_LOC +615038689,1875117,982573,76879836,True,escort,9,8,8.0,TNC_SINGLE +615038690,1875117,982573,76879836,True,work,18,9,9.0,TNC_SINGLE +615038693,1875117,982573,76879836,False,shopping,19,18,17.0,WALK +615038694,1875117,982573,76879836,False,Home,8,19,17.0,WALK_LOC +615038993,1875118,982573,76879874,True,othmaint,7,8,11.0,WALK +615038994,1875118,982573,76879874,True,social,11,7,12.0,WALK +615038997,1875118,982573,76879874,False,Home,8,11,20.0,WALK +615043281,1875131,982580,76880410,True,work,2,9,6.0,WALK_LOC +615043285,1875131,982580,76880410,False,Home,9,2,13.0,WALK_LRF +615043609,1875132,982580,76880451,True,work,8,9,7.0,WALK +615043613,1875132,982580,76880451,False,Home,9,8,18.0,WALK +615056945,1875173,982601,76882118,True,othdiscr,9,9,10.0,WALK +615056949,1875173,982601,76882118,False,Home,9,9,13.0,WALK +615057057,1875173,982601,76882132,True,work,16,9,14.0,BIKE +615057061,1875173,982601,76882132,False,Home,9,16,18.0,BIKE +615057297,1875174,982601,76882162,True,othmaint,2,9,19.0,WALK_LRF +615057301,1875174,982601,76882162,False,Home,9,2,20.0,WALK_LRF +615057385,1875174,982601,76882173,True,work,12,9,8.0,WALK_LRF +615057389,1875174,982601,76882173,False,Home,9,12,18.0,WALK_LRF +615083297,1875253,982641,76885412,True,work,2,10,8.0,WALK_LRF +615083301,1875253,982641,76885412,False,Home,10,2,18.0,WALK_LRF +615083625,1875254,982641,76885453,True,work,5,10,10.0,WALK +615083629,1875254,982641,76885453,False,Home,10,5,21.0,WALK +615109913,1875335,982682,76888739,True,atwork,9,9,13.0,WALK +615109917,1875335,982682,76888739,False,Work,9,9,13.0,WALK +615109929,1875335,982682,76888741,True,eatout,13,11,7.0,WALK +615109933,1875335,982682,76888741,False,Home,11,13,10.0,WALK +615110193,1875335,982682,76888774,True,work,9,11,10.0,WALK +615110197,1875335,982682,76888774,False,Home,11,9,19.0,WALK +615110409,1875336,982682,76888801,True,othdiscr,13,11,8.0,WALK_LOC +615110413,1875336,982682,76888801,False,Home,11,13,14.0,WALK_LOC +615165185,1875503,982766,76895648,True,othdiscr,14,15,11.0,WALK +615165189,1875503,982766,76895648,False,Home,15,14,13.0,WALK +615165625,1875504,982766,76895703,True,work,2,15,18.0,WALK_LOC +615165626,1875504,982766,76895703,True,work,2,2,19.0,WALK +615165627,1875504,982766,76895703,True,work,16,2,19.0,WALK +615165629,1875504,982766,76895703,False,Home,15,16,20.0,WALK +615169233,1875515,982772,76896154,True,work,16,16,7.0,WALK +615169237,1875515,982772,76896154,False,Home,16,16,19.0,WALK +615169561,1875516,982772,76896195,True,work,12,16,5.0,WALK +615169565,1875516,982772,76896195,False,Home,16,12,18.0,WALK_LOC +615204001,1875621,982825,76900500,True,escort,16,16,5.0,WALK +615204002,1875621,982825,76900500,True,work,2,16,6.0,WALK +615204005,1875621,982825,76900500,False,Home,16,2,21.0,WALK +615204281,1875622,982825,76900535,True,shopping,13,16,20.0,TNC_SINGLE +615204285,1875622,982825,76900535,False,Home,16,13,20.0,WALK +615204329,1875622,982825,76900541,True,work,21,16,9.0,WALK_LOC +615204333,1875622,982825,76900541,False,Home,16,21,20.0,WALK_LOC +615236801,1875721,982875,76904600,True,work,10,16,8.0,WALK_LOC +615236805,1875721,982875,76904600,False,Home,16,10,17.0,WALK_LOC +615236865,1875722,982875,76904608,True,escort,3,16,10.0,WALK +615236866,1875722,982875,76904608,True,eatout,14,3,13.0,WALK +615236869,1875722,982875,76904608,False,Home,16,14,17.0,WALK +615245985,1875749,982889,76905748,True,work,13,16,8.0,WALK_LOC +615245989,1875749,982889,76905748,False,othmaint,25,13,19.0,WALK_LOC +615245990,1875749,982889,76905748,False,Home,16,25,19.0,WALK +615246073,1875750,982889,76905759,True,escort,16,16,6.0,WALK +615246077,1875750,982889,76905759,False,Home,16,16,7.0,WALK +615246201,1875750,982889,76905775,True,othdiscr,20,16,18.0,WALK_LOC +615246205,1875750,982889,76905775,False,Home,16,20,18.0,SHARED3FREE +615246313,1875750,982889,76905789,True,work,1,16,8.0,WALK +615246317,1875750,982889,76905789,False,eatout,5,1,15.0,WALK +615246318,1875750,982889,76905789,False,Home,16,5,15.0,WALK +615250577,1875763,982896,76906322,True,work,2,16,13.0,WALK +615250581,1875763,982896,76906322,False,Home,16,2,18.0,WALK +615250857,1875764,982896,76906357,True,shopping,19,16,10.0,WALK_LOC +615250861,1875764,982896,76906357,False,Home,16,19,10.0,WALK_LOC +615255121,1875777,982903,76906890,True,shopping,5,16,8.0,WALK +615255125,1875777,982903,76906890,False,Home,16,5,15.0,WALK +615255473,1875778,982903,76906934,True,social,12,16,18.0,WALK +615255477,1875778,982903,76906934,False,Home,16,12,22.0,WALK +615255497,1875778,982903,76906937,True,work,9,16,7.0,WALK_LOC +615255501,1875778,982903,76906937,False,Home,16,9,17.0,WALK_LRF +615263417,1875803,982916,76907927,True,atwork,2,16,13.0,WALK +615263421,1875803,982916,76907927,False,eatout,4,2,14.0,WALK +615263422,1875803,982916,76907927,False,Work,16,4,14.0,WALK +615263697,1875803,982916,76907962,True,work,16,16,12.0,WALK +615263701,1875803,982916,76907962,False,Home,16,16,21.0,WALK +615264025,1875804,982916,76908003,True,work,15,16,7.0,WALK +615264029,1875804,982916,76908003,False,Home,16,15,14.0,BIKE +615265425,1875809,982919,76908178,True,escort,13,16,7.0,WALK_LOC +615265429,1875809,982919,76908178,False,Home,16,13,7.0,WALK_LOC +615265433,1875809,982919,76908179,True,escort,5,16,13.0,SHARED2FREE +615265437,1875809,982919,76908179,False,Home,16,5,14.0,SHARED2FREE +615265993,1875810,982919,76908249,True,work,2,16,7.0,WALK +615265997,1875810,982919,76908249,False,Home,16,2,20.0,WALK +615266977,1875813,982921,76908372,True,escort,12,16,15.0,WALK_LOC +615266978,1875813,982921,76908372,True,work,1,12,16.0,WALK +615266981,1875813,982921,76908372,False,social,10,1,16.0,TNC_SINGLE +615266982,1875813,982921,76908372,False,escort,22,10,16.0,WALK_LRF +615266983,1875813,982921,76908372,False,escort,21,22,17.0,TNC_SINGLE +615266984,1875813,982921,76908372,False,Home,16,21,17.0,TNC_SINGLE +615267305,1875814,982921,76908413,True,shopping,16,16,8.0,WALK +615267306,1875814,982921,76908413,True,work,13,16,9.0,TNC_SINGLE +615267309,1875814,982921,76908413,False,eatout,5,13,17.0,TNC_SINGLE +615267310,1875814,982921,76908413,False,social,2,5,17.0,WALK +615267311,1875814,982921,76908413,False,Home,16,2,18.0,WALK +615288625,1875879,982954,76911078,True,work,12,16,8.0,SHARED2FREE +615288629,1875879,982954,76911078,False,Home,16,12,18.0,WALK_LOC +615288953,1875880,982954,76911119,True,work,5,16,9.0,WALK +615288957,1875880,982954,76911119,False,Home,16,5,17.0,WALK +615295841,1875901,982965,76911980,True,work,1,17,8.0,WALK +615295845,1875901,982965,76911980,False,shopping,15,1,18.0,WALK +615295846,1875901,982965,76911980,False,Home,17,15,19.0,WALK +615295929,1875902,982965,76911991,True,escort,11,17,10.0,WALK_LRF +615295933,1875902,982965,76911991,False,Home,17,11,10.0,WALK_LRF +615295937,1875902,982965,76911992,True,escort,6,17,16.0,WALK +615295941,1875902,982965,76911992,False,Home,17,6,17.0,WALK +615296057,1875902,982965,76912007,True,shopping,16,17,11.0,WALK +615296058,1875902,982965,76912007,True,escort,12,16,14.0,WALK +615296059,1875902,982965,76912007,True,othdiscr,5,12,15.0,WALK +615296061,1875902,982965,76912007,False,Home,17,5,16.0,WALK +615309353,1875943,982986,76913669,True,eatout,1,17,14.0,WALK +615309357,1875943,982986,76913669,False,shopping,8,1,14.0,WALK_LRF +615309358,1875943,982986,76913669,False,Home,17,8,14.0,WALK_LRF +615309529,1875943,982986,76913691,True,othmaint,2,17,15.0,TAXI +615309530,1875943,982986,76913691,True,othmaint,12,2,15.0,WALK +615309533,1875943,982986,76913691,False,Home,17,12,15.0,WALK_LRF +615309569,1875943,982986,76913696,True,shopping,19,17,12.0,WALK +615309573,1875943,982986,76913696,False,Home,17,19,12.0,WALK +615309665,1875944,982986,76913708,True,atwork,7,13,12.0,WALK +615309669,1875944,982986,76913708,False,shopping,6,7,13.0,WALK +615309670,1875944,982986,76913708,False,eatout,3,6,13.0,WALK +615309671,1875944,982986,76913708,False,Work,13,3,13.0,WALK +615309945,1875944,982986,76913743,True,work,13,17,6.0,WALK_LOC +615309949,1875944,982986,76913743,False,Home,17,13,20.0,WALK +615350289,1876067,983048,76918786,True,work,2,17,8.0,WALK +615350293,1876067,983048,76918786,False,Home,17,2,18.0,WALK +615350553,1876068,983048,76918819,True,univ,9,17,16.0,WALK_LRF +615350557,1876068,983048,76918819,False,escort,16,9,16.0,WALK_LRF +615350558,1876068,983048,76918819,False,Home,17,16,17.0,WALK_LRF +615350617,1876068,983048,76918827,True,work,22,17,7.0,WALK +615350621,1876068,983048,76918827,False,Home,17,22,14.0,WALK +615411361,1876254,983141,76926420,True,eatout,9,19,21.0,WALK +615411365,1876254,983141,76926420,False,Home,19,9,23.0,WALK +615420857,1876283,983156,76927607,True,atwork,16,16,11.0,WALK +615420861,1876283,983156,76927607,False,Work,16,16,11.0,WALK +615421113,1876283,983156,76927639,True,social,21,20,7.0,SHARED3FREE +615421117,1876283,983156,76927639,False,Home,20,21,7.0,SHARED3FREE +615421137,1876283,983156,76927642,True,work,16,20,8.0,WALK +615421141,1876283,983156,76927642,False,Home,20,16,19.0,WALK +615421225,1876284,983156,76927653,True,escort,7,20,7.0,SHARED3FREE +615421229,1876284,983156,76927653,False,shopping,8,7,7.0,WALK +615421230,1876284,983156,76927653,False,Home,20,8,7.0,DRIVEALONEFREE +615421465,1876284,983156,76927683,True,work,5,20,7.0,WALK +615421469,1876284,983156,76927683,False,Home,20,5,18.0,WALK +615426105,1876299,983164,76928263,True,atwork,11,17,12.0,WALK +615426109,1876299,983164,76928263,False,Work,17,11,13.0,WALK +615426385,1876299,983164,76928298,True,work,17,20,7.0,WALK +615426389,1876299,983164,76928298,False,Home,20,17,15.0,WALK +615426601,1876300,983164,76928325,True,othdiscr,7,20,17.0,WALK +615426605,1876300,983164,76928325,False,Home,20,7,22.0,WALK +615426713,1876300,983164,76928339,True,work,18,20,6.0,WALK_LOC +615426717,1876300,983164,76928339,False,Home,20,18,16.0,WALK_LOC +615426721,1876300,983164,76928340,True,work,18,20,16.0,WALK +615426725,1876300,983164,76928340,False,Home,20,18,17.0,WALK +615433601,1876321,983175,76929200,True,work,14,21,7.0,WALK +615433605,1876321,983175,76929200,False,Home,21,14,18.0,WALK +615433841,1876322,983175,76929230,True,othmaint,5,21,8.0,WALK +615433845,1876322,983175,76929230,False,Home,21,5,9.0,WALK +615433929,1876322,983175,76929241,True,work,5,21,9.0,WALK +615433933,1876322,983175,76929241,False,Home,21,5,18.0,SHARED2FREE +615457217,1876393,983211,76932152,True,work,20,21,8.0,WALK +615457221,1876393,983211,76932152,False,Home,21,20,11.0,WALK +615457545,1876394,983211,76932193,True,work,7,21,6.0,WALK +615457549,1876394,983211,76932193,False,Home,21,7,16.0,WALK +615457873,1876395,983212,76932234,True,work,18,21,8.0,WALK +615457877,1876395,983212,76932234,False,Home,21,18,22.0,WALK +615458089,1876396,983212,76932261,True,othdiscr,17,21,14.0,WALK +615458093,1876396,983212,76932261,False,social,17,17,14.0,WALK +615458094,1876396,983212,76932261,False,escort,16,17,14.0,WALK +615458095,1876396,983212,76932261,False,othmaint,5,16,14.0,WALK +615458096,1876396,983212,76932261,False,Home,21,5,14.0,WALK +615458201,1876396,983212,76932275,True,escort,12,21,8.0,WALK_LOC +615458202,1876396,983212,76932275,True,work,4,12,9.0,WALK +615458205,1876396,983212,76932275,False,escort,5,4,12.0,WALK +615458206,1876396,983212,76932275,False,Home,21,5,12.0,WALK +615461497,1876407,983218,76932687,True,atwork,12,8,9.0,WALK +615461501,1876407,983218,76932687,False,shopping,5,12,11.0,WALK +615461502,1876407,983218,76932687,False,work,9,5,11.0,WALK +615461503,1876407,983218,76932687,False,Work,8,9,11.0,WALK +615461809,1876407,983218,76932726,True,work,8,21,8.0,BIKE +615461813,1876407,983218,76932726,False,shopping,21,8,20.0,BIKE +615461814,1876407,983218,76932726,False,Home,21,21,21.0,BIKE +615461825,1876408,983218,76932728,True,atwork,16,5,12.0,WALK +615461829,1876408,983218,76932728,False,Work,5,16,13.0,WALK +615461873,1876408,983218,76932734,True,shopping,16,21,18.0,WALK +615461874,1876408,983218,76932734,True,eatout,14,16,19.0,WALK +615461877,1876408,983218,76932734,False,Home,21,14,21.0,WALK +615462137,1876408,983218,76932767,True,work,5,21,5.0,WALK +615462141,1876408,983218,76932767,False,social,4,5,17.0,WALK_LOC +615462142,1876408,983218,76932767,False,Home,21,4,18.0,WALK +615467057,1876423,983226,76933382,True,work,4,21,8.0,WALK +615467061,1876423,983226,76933382,False,Home,21,4,17.0,WALK +615467361,1876424,983226,76933420,True,social,17,21,9.0,DRIVEALONEFREE +615467362,1876424,983226,76933420,True,social,10,17,11.0,SHARED3FREE +615467365,1876424,983226,76933420,False,Home,21,10,17.0,DRIVEALONEFREE +615472961,1876441,983235,76934120,True,work,15,22,8.0,WALK +615472962,1876441,983235,76934120,True,work,15,15,10.0,WALK +615472965,1876441,983235,76934120,False,work,1,15,17.0,WALK +615472966,1876441,983235,76934120,False,Home,22,1,18.0,WALK +615473177,1876442,983235,76934147,True,othdiscr,10,22,16.0,WALK_LRF +615473181,1876442,983235,76934147,False,Home,22,10,17.0,WALK_LRF +615473201,1876442,983235,76934150,True,othmaint,17,22,12.0,WALK +615473205,1876442,983235,76934150,False,Home,22,17,15.0,WALK_LRF +642393073,1958515,1024272,80299134,True,shopping,18,7,10.0,WALK_LOC +642393077,1958515,1024272,80299134,False,Home,7,18,16.0,WALK_LOC +642412145,1958573,1024301,80301518,True,othdiscr,12,8,11.0,WALK +642412149,1958573,1024301,80301518,False,Home,8,12,20.0,WALK +642412345,1958574,1024301,80301543,True,escort,5,8,15.0,WALK +642412349,1958574,1024301,80301543,False,Home,8,5,16.0,SHARED3FREE +642429073,1958625,1024327,80303634,True,escort,11,8,13.0,WALK +642429077,1958625,1024327,80303634,False,Home,8,11,13.0,WALK +642429201,1958625,1024327,80303650,True,othdiscr,6,8,15.0,WALK +642429205,1958625,1024327,80303650,False,Home,8,6,17.0,WALK +642429265,1958625,1024327,80303658,True,shopping,11,8,9.0,WALK +642429269,1958625,1024327,80303658,False,Home,8,11,10.0,WALK +642429577,1958626,1024327,80303697,True,school,9,8,8.0,WALK +642429581,1958626,1024327,80303697,False,Home,8,9,16.0,WALK +642429857,1958627,1024328,80303732,True,othdiscr,9,8,12.0,WALK_LOC +642429861,1958627,1024328,80303732,False,Home,8,9,14.0,WALK_LOC +642430233,1958628,1024328,80303779,True,escort,8,8,8.0,WALK +642430234,1958628,1024328,80303779,True,social,8,8,8.0,WALK +642430235,1958628,1024328,80303779,True,school,8,8,12.0,WALK +642430237,1958628,1024328,80303779,False,Home,8,8,14.0,WALK +642432481,1958635,1024332,80304060,True,othdiscr,10,8,9.0,WALK +642432485,1958635,1024332,80304060,False,Home,8,10,13.0,WALK +642432857,1958636,1024332,80304107,True,school,13,8,8.0,WALK_LOC +642432861,1958636,1024332,80304107,False,othmaint,9,13,17.0,WALK_LRF +642432862,1958636,1024332,80304107,False,Home,8,9,17.0,WALK +642438449,1958653,1024341,80304806,True,shopping,11,9,15.0,WALK +642438453,1958653,1024341,80304806,False,Home,9,11,17.0,WALK +642438761,1958654,1024341,80304845,True,escort,4,9,10.0,WALK_LRF +642438762,1958654,1024341,80304845,True,school,13,4,12.0,WALK_LOC +642438765,1958654,1024341,80304845,False,escort,8,13,15.0,WALK_LRF +642438766,1958654,1024341,80304845,False,social,22,8,19.0,WALK_LRF +642438767,1958654,1024341,80304845,False,othmaint,4,22,19.0,WALK_LRF +642438768,1958654,1024341,80304845,False,Home,9,4,19.0,WALK_LRF +642446345,1958677,1024353,80305793,True,social,14,9,10.0,WALK_LRF +642446349,1958677,1024353,80305793,False,Home,9,14,11.0,WALK_LRF +642446633,1958678,1024353,80305829,True,school,25,9,6.0,WALK_LRF +642446637,1958678,1024353,80305829,False,Home,9,25,15.0,WALK_LOC +643014353,1960409,1025219,80376794,True,othdiscr,9,5,18.0,WALK +643014357,1960409,1025219,80376794,False,Home,5,9,18.0,WALK +643014417,1960409,1025219,80376802,True,shopping,14,5,6.0,WALK +643014421,1960409,1025219,80376802,False,Home,5,14,6.0,WALK +643014425,1960409,1025219,80376803,True,othmaint,7,5,19.0,WALK_LOC +643014426,1960409,1025219,80376803,True,shopping,6,7,19.0,TNC_SINGLE +643014429,1960409,1025219,80376803,False,Home,5,6,19.0,TNC_SHARED +643014465,1960409,1025219,80376808,True,work,23,5,7.0,WALK_LRF +643014469,1960409,1025219,80376808,False,eatout,13,23,17.0,TNC_SHARED +643014470,1960409,1025219,80376808,False,Home,5,13,18.0,TNC_SINGLE +643014729,1960410,1025219,80376841,True,school,6,5,8.0,WALK +643014733,1960410,1025219,80376841,False,Home,5,6,14.0,WALK_LOC +649296209,1979561,1034795,81162026,True,othdiscr,16,7,18.0,WALK +649296213,1979561,1034795,81162026,False,Home,7,16,18.0,WALK +649296321,1979561,1034795,81162040,True,work,6,7,7.0,TNC_SHARED +649296325,1979561,1034795,81162040,False,Home,7,6,16.0,WALK +649296585,1979562,1034795,81162073,True,school,8,7,8.0,WALK_LOC +649296589,1979562,1034795,81162073,False,Home,7,8,14.0,WALK_LOC +649300801,1979575,1034802,81162600,True,othdiscr,22,7,15.0,WALK_LOC +649300805,1979575,1034802,81162600,False,escort,9,22,18.0,WALK_LOC +649300806,1979575,1034802,81162600,False,Home,7,9,18.0,WALK_LOC +649300913,1979575,1034802,81162614,True,work,5,7,7.0,WALK +649300917,1979575,1034802,81162614,False,Home,7,5,15.0,WALK +649301129,1979576,1034802,81162641,True,othdiscr,12,7,7.0,WALK +649301133,1979576,1034802,81162641,False,Home,7,12,10.0,WALK +649312457,1979611,1034820,81164057,True,eatout,7,9,8.0,WALK +649312461,1979611,1034820,81164057,False,Home,9,7,8.0,WALK +649312721,1979611,1034820,81164090,True,work,9,9,9.0,WALK +649312722,1979611,1034820,81164090,True,othmaint,9,9,9.0,WALK +649312723,1979611,1034820,81164090,True,work,11,9,10.0,WALK +649312725,1979611,1034820,81164090,False,Home,9,11,22.0,WALK +649312985,1979612,1034820,81164123,True,school,9,9,8.0,WALK +649312989,1979612,1034820,81164123,False,Home,9,9,16.0,WALK +649313265,1979613,1034821,81164158,True,othdiscr,17,9,18.0,WALK_LRF +649313269,1979613,1034821,81164158,False,Home,9,17,18.0,WALK_LRF +649313377,1979613,1034821,81164172,True,eatout,20,9,5.0,TNC_SHARED +649313378,1979613,1034821,81164172,True,work,9,20,6.0,WALK +649313381,1979613,1034821,81164172,False,Home,9,9,18.0,TNC_SHARED +649321905,1979639,1034834,81165238,True,work,5,9,9.0,WALK +649321909,1979639,1034834,81165238,False,work,9,5,20.0,WALK +649321910,1979639,1034834,81165238,False,Home,9,9,20.0,WALK +649344209,1979707,1034868,81168026,True,work,9,20,7.0,WALK +649344213,1979707,1034868,81168026,False,Home,20,9,17.0,WALK +649344449,1979708,1034868,81168056,True,othmaint,5,20,17.0,SHARED2FREE +649344453,1979708,1034868,81168056,False,escort,6,5,17.0,SHARED2FREE +649344454,1979708,1034868,81168056,False,Home,20,6,18.0,SHARED2FREE +649344473,1979708,1034868,81168059,True,school,20,20,7.0,WALK +649344477,1979708,1034868,81168059,False,shopping,11,20,16.0,WALK +649344478,1979708,1034868,81168059,False,escort,11,11,17.0,WALK +649344479,1979708,1034868,81168059,False,Home,20,11,17.0,WALK +649348801,1979721,1034875,81168600,True,escort,7,21,6.0,WALK +649348802,1979721,1034875,81168600,True,work,9,7,8.0,WALK_LOC +649348805,1979721,1034875,81168600,False,eatout,8,9,16.0,WALK_LOC +649348806,1979721,1034875,81168600,False,Home,21,8,16.0,WALK +649349065,1979722,1034875,81168633,True,school,8,21,8.0,WALK +649349069,1979722,1034875,81168633,False,Home,21,8,11.0,WALK +649352081,1979731,1034880,81169010,True,work,9,24,7.0,SHARED2FREE +649352085,1979731,1034880,81169010,False,Home,24,9,18.0,WALK_HVY +649352345,1979732,1034880,81169043,True,school,8,24,8.0,SHARED3FREE +649352349,1979732,1034880,81169043,False,escort,5,8,14.0,WALK_LOC +649352350,1979732,1034880,81169043,False,Home,24,5,17.0,WALK_LOC +666869337,2033138,1057653,83358667,True,escort,2,10,13.0,WALK +666869341,2033138,1057653,83358667,False,escort,7,2,13.0,WALK +666869342,2033138,1057653,83358667,False,Home,10,7,13.0,WALK +679341777,2071163,1070328,84917722,True,work,23,11,7.0,SHARED2FREE +679341781,2071163,1070328,84917722,False,Home,11,23,10.0,WALK_LRF +679341785,2071163,1070328,84917723,True,work,23,11,12.0,WALK_LRF +679341789,2071163,1070328,84917723,False,Home,11,23,17.0,WALK_LRF +679342369,2071165,1070328,84917796,True,univ,12,11,8.0,WALK_LOC +679342373,2071165,1070328,84917796,False,Home,11,12,9.0,WALK_LOC +690443945,2105012,1081611,86305493,True,atwork,3,1,12.0,WALK +690443949,2105012,1081611,86305493,False,eatout,3,3,13.0,WALK +690443950,2105012,1081611,86305493,False,Work,1,3,13.0,WALK +690444249,2105012,1081611,86305531,True,work,1,2,7.0,WALK +690444253,2105012,1081611,86305531,False,Home,2,1,9.0,DRIVEALONEFREE +690444257,2105012,1081611,86305532,True,work,1,2,11.0,WALK +690444261,2105012,1081611,86305532,False,Home,2,1,18.0,WALK +690444577,2105013,1081611,86305572,True,work,15,2,6.0,WALK_LOC +690444581,2105013,1081611,86305572,False,Home,2,15,17.0,WALK_LOC +690459993,2105060,1081627,86307499,True,work,10,11,11.0,WALK +690459997,2105060,1081627,86307499,False,Home,11,10,17.0,WALK +690460321,2105061,1081627,86307540,True,work,4,11,6.0,WALK +690460325,2105061,1081627,86307540,False,Home,11,4,15.0,WALK +690460649,2105062,1081627,86307581,True,work,12,11,11.0,WALK_LOC +690460653,2105062,1081627,86307581,False,Home,11,12,22.0,WALK +690461961,2105066,1081629,86307745,True,work,13,11,7.0,TNC_SHARED +690461965,2105066,1081629,86307745,False,Home,11,13,22.0,TNC_SINGLE +690462177,2105067,1081629,86307772,True,othdiscr,24,11,8.0,WALK +690462181,2105067,1081629,86307772,False,Home,11,24,14.0,WALK +690462617,2105068,1081629,86307827,True,work,3,11,7.0,WALK +690462621,2105068,1081629,86307827,False,Home,11,3,18.0,WALK +690462785,2105070,1081630,86307848,True,shopping,7,11,8.0,WALK +690462789,2105070,1081630,86307848,False,Home,11,7,8.0,WALK +690462945,2105069,1081630,86307868,True,work,1,11,5.0,WALK_HVY +690462949,2105069,1081630,86307868,False,Home,11,1,9.0,TNC_SINGLE +690463273,2105070,1081630,86307909,True,work,10,11,8.0,WALK +690463277,2105070,1081630,86307909,False,shopping,11,10,13.0,WALK_LOC +690463278,2105070,1081630,86307909,False,Home,11,11,13.0,WALK +690463601,2105071,1081630,86307950,True,work,16,11,9.0,WALK_LOC +690463605,2105071,1081630,86307950,False,Home,11,16,16.0,WALK_LOC +695855153,2121509,1087110,86981894,True,othdiscr,4,8,11.0,WALK +695855157,2121509,1087110,86981894,False,Home,8,4,15.0,WALK +695855529,2121510,1087110,86981941,True,school,16,8,8.0,WALK_LOC +695855533,2121510,1087110,86981941,False,othdiscr,7,16,17.0,WALK_LOC +695855534,2121510,1087110,86981941,False,Home,8,7,18.0,WALK_LOC +695855873,2121511,1087110,86981984,True,shopping,25,8,11.0,WALK +695855877,2121511,1087110,86981984,False,Home,8,25,12.0,BIKE +695860097,2121524,1087115,86982512,True,othmaint,9,8,14.0,WALK +695860101,2121524,1087115,86982512,False,Home,8,9,16.0,WALK +695860105,2121524,1087115,86982513,True,othmaint,1,8,20.0,WALK_LRF +695860109,2121524,1087115,86982513,False,Home,8,1,22.0,WALK_LRF +695860137,2121524,1087115,86982517,True,shopping,17,8,12.0,SHARED2FREE +695860141,2121524,1087115,86982517,False,shopping,5,17,12.0,SHARED2FREE +695860142,2121524,1087115,86982517,False,Home,8,5,12.0,WALK +695860273,2121525,1087115,86982534,True,escort,16,8,11.0,WALK_LOC +695860277,2121525,1087115,86982534,False,Home,8,16,12.0,TNC_SHARED +695860401,2121525,1087115,86982550,True,othdiscr,2,8,13.0,WALK +695860405,2121525,1087115,86982550,False,Home,8,2,16.0,WALK +695860777,2121526,1087115,86982597,True,school,9,8,7.0,WALK +695860781,2121526,1087115,86982597,False,Home,8,9,15.0,WALK +708087089,2158802,1099541,88510886,True,atwork,8,2,14.0,WALK +708087093,2158802,1099541,88510886,False,Work,2,8,14.0,WALK +708087281,2158802,1099541,88510910,True,othmaint,5,9,17.0,DRIVEALONEFREE +708087285,2158802,1099541,88510910,False,Home,9,5,17.0,TNC_SHARED +708087369,2158802,1099541,88510921,True,work,2,9,7.0,TNC_SHARED +708087373,2158802,1099541,88510921,False,Home,9,2,17.0,WALK_LRF +708087585,2158803,1099541,88510948,True,othdiscr,9,9,17.0,WALK +708087589,2158803,1099541,88510948,False,Home,9,9,21.0,WALK +708087633,2158803,1099541,88510954,True,school,10,9,12.0,WALK +708087637,2158803,1099541,88510954,False,Home,9,10,16.0,WALK_LOC +708087961,2158804,1099541,88510995,True,school,9,9,8.0,WALK +708087965,2158804,1099541,88510995,False,Home,9,9,13.0,WALK +708087969,2158804,1099541,88510996,True,school,9,9,14.0,WALK +708087973,2158804,1099541,88510996,False,Home,9,9,17.0,WALK +708121809,2158907,1099576,88515226,True,work,9,10,7.0,WALK_LOC +708121813,2158907,1099576,88515226,False,Home,10,9,17.0,WALK +708122073,2158908,1099576,88515259,True,escort,5,10,8.0,WALK_LOC +708122074,2158908,1099576,88515259,True,escort,6,5,8.0,WALK_LOC +708122075,2158908,1099576,88515259,True,school,8,6,9.0,WALK_LOC +708122077,2158908,1099576,88515259,False,Home,10,8,17.0,WALK_LOC +708122401,2158909,1099576,88515300,True,school,21,10,8.0,WALK_LOC +708122405,2158909,1099576,88515300,False,Home,10,21,15.0,WALK_LOC +708171009,2159057,1099626,88521376,True,work,2,20,7.0,WALK_LOC +708171013,2159057,1099626,88521376,False,shopping,16,2,18.0,WALK +708171014,2159057,1099626,88521376,False,Home,20,16,18.0,WALK_LOC +708171273,2159058,1099626,88521409,True,univ,9,20,16.0,WALK_LOC +708171277,2159058,1099626,88521409,False,Home,20,9,16.0,WALK_LOC +708171601,2159059,1099626,88521450,True,school,20,20,8.0,WALK +708171605,2159059,1099626,88521450,False,Home,20,20,13.0,WALK +708253665,2159309,1099710,88531708,True,work,24,25,8.0,WALK +708253666,2159309,1099710,88531708,True,work,4,24,9.0,WALK +708253669,2159309,1099710,88531708,False,shopping,16,4,16.0,WALK +708253670,2159309,1099710,88531708,False,shopping,5,16,16.0,WALK_LOC +708253671,2159309,1099710,88531708,False,work,5,5,17.0,WALK +708253672,2159309,1099710,88531708,False,Home,25,5,17.0,WALK +708253945,2159310,1099710,88531743,True,shopping,12,25,10.0,WALK +708253949,2159310,1099710,88531743,False,Home,25,12,12.0,WALK +708254081,2159311,1099710,88531760,True,escort,10,25,13.0,SHARED3FREE +708254085,2159311,1099710,88531760,False,Home,25,10,15.0,SHARED3FREE +728000577,2219513,1119778,91000072,True,work,2,3,8.0,WALK +728000581,2219513,1119778,91000072,False,shopping,5,2,17.0,WALK +728000582,2219513,1119778,91000072,False,Home,3,5,20.0,WALK +728000905,2219514,1119778,91000113,True,work,24,3,6.0,WALK +728000909,2219514,1119778,91000113,False,Home,3,24,18.0,WALK +728001169,2219515,1119778,91000146,True,school,13,3,8.0,WALK +728001173,2219515,1119778,91000146,False,Home,3,13,15.0,WALK +728016321,2219561,1119794,91002040,True,work,5,7,6.0,WALK +728016325,2219561,1119794,91002040,False,Home,7,5,16.0,WALK +728016369,2219562,1119794,91002046,True,atwork,15,13,12.0,WALK +728016373,2219562,1119794,91002046,False,Work,13,15,13.0,WALK +728016649,2219562,1119794,91002081,True,work,13,7,6.0,WALK_LRF +728016653,2219562,1119794,91002081,False,othmaint,9,13,17.0,WALK +728016654,2219562,1119794,91002081,False,othdiscr,6,9,18.0,WALK_LOC +728016655,2219562,1119794,91002081,False,othmaint,3,6,18.0,WALK +728016656,2219562,1119794,91002081,False,Home,7,3,18.0,WALK_LOC +728016913,2219563,1119794,91002114,True,school,11,7,9.0,WALK_LOC +728016917,2219563,1119794,91002114,False,Home,7,11,16.0,WALK_LOC +728025177,2219588,1119803,91003147,True,work,7,7,6.0,WALK +728025181,2219588,1119803,91003147,False,Home,7,7,17.0,WALK +728025505,2219589,1119803,91003188,True,work,17,7,7.0,WALK_LOC +728025509,2219589,1119803,91003188,False,Home,7,17,10.0,WALK +728025769,2219590,1119803,91003221,True,escort,5,7,14.0,WALK +728025770,2219590,1119803,91003221,True,school,7,5,14.0,WALK +728025773,2219590,1119803,91003221,False,eatout,7,7,14.0,WALK +728025774,2219590,1119803,91003221,False,Home,7,7,23.0,WALK +728033049,2219612,1119811,91004131,True,shopping,2,7,8.0,WALK +728033050,2219612,1119811,91004131,True,work,17,2,9.0,WALK +728033053,2219612,1119811,91004131,False,Home,7,17,21.0,WALK +728033377,2219613,1119811,91004172,True,work,9,7,9.0,WALK +728033378,2219613,1119811,91004172,True,work,11,9,10.0,WALK +728033381,2219613,1119811,91004172,False,escort,6,11,18.0,WALK +728033382,2219613,1119811,91004172,False,Home,7,6,18.0,WALK +728033641,2219614,1119811,91004205,True,school,8,7,7.0,SHARED2FREE +728033645,2219614,1119811,91004205,False,escort,7,8,16.0,WALK +728033646,2219614,1119811,91004205,False,Home,7,7,17.0,WALK +728088153,2219780,1119867,91011019,True,work,1,9,7.0,WALK_LRF +728088157,2219780,1119867,91011019,False,Home,9,1,19.0,WALK_HVY +728088201,2219781,1119867,91011025,True,atwork,4,1,14.0,WALK +728088205,2219781,1119867,91011025,False,Work,1,4,15.0,WALK +728088481,2219781,1119867,91011060,True,work,1,9,6.0,WALK_LRF +728088485,2219781,1119867,91011060,False,Home,9,1,17.0,WALK_LRF +728114721,2219861,1119894,91014340,True,work,11,11,9.0,WALK +728114725,2219861,1119894,91014340,False,shopping,5,11,20.0,WALK +728114726,2219861,1119894,91014340,False,Home,11,5,21.0,WALK +728115049,2219862,1119894,91014381,True,escort,12,11,7.0,WALK_LOC +728115050,2219862,1119894,91014381,True,eatout,16,12,8.0,WALK_LOC +728115051,2219862,1119894,91014381,True,work,22,16,8.0,WALK_LOC +728115053,2219862,1119894,91014381,False,escort,6,22,18.0,TAXI +728115054,2219862,1119894,91014381,False,shopping,2,6,18.0,WALK +728115055,2219862,1119894,91014381,False,shopping,12,2,19.0,WALK_LOC +728115056,2219862,1119894,91014381,False,Home,11,12,20.0,WALK +728115313,2219863,1119894,91014414,True,school,9,11,8.0,WALK +728115317,2219863,1119894,91014414,False,Home,11,9,10.0,WALK +728159001,2219996,1119939,91019875,True,work,1,16,7.0,WALK +728159005,2219996,1119939,91019875,False,Home,16,1,18.0,WALK +728159049,2219997,1119939,91019881,True,atwork,11,2,13.0,WALK +728159053,2219997,1119939,91019881,False,Work,2,11,13.0,WALK +728159329,2219997,1119939,91019916,True,work,2,16,7.0,SHARED2FREE +728159333,2219997,1119939,91019916,False,Home,16,2,18.0,WALK +728159569,2219998,1119939,91019946,True,othmaint,9,16,15.0,WALK_HVY +728159573,2219998,1119939,91019946,False,Home,16,9,17.0,TNC_SINGLE +728159593,2219998,1119939,91019949,True,school,8,16,7.0,WALK_LOC +728159597,2219998,1119939,91019949,False,Home,16,8,13.0,WALK_LOC +728162937,2220008,1119943,91020367,True,work,3,16,8.0,WALK +728162941,2220008,1119943,91020367,False,Home,16,3,18.0,WALK +728163265,2220009,1119943,91020408,True,work,12,16,7.0,WALK +728163269,2220009,1119943,91020408,False,Home,16,12,11.0,WALK +728163273,2220009,1119943,91020409,True,work,12,16,14.0,WALK +728163277,2220009,1119943,91020409,False,Home,16,12,16.0,WALK +728163481,2220010,1119943,91020435,True,othdiscr,21,16,16.0,SHARED3FREE +728163485,2220010,1119943,91020435,False,Home,16,21,21.0,SHARED3FREE +728163529,2220010,1119943,91020441,True,school,16,16,6.0,WALK +728163533,2220010,1119943,91020441,False,Home,16,16,14.0,WALK +728184537,2220074,1119965,91023067,True,shopping,16,16,17.0,WALK +728184541,2220074,1119965,91023067,False,Home,16,16,19.0,WALK +728184585,2220074,1119965,91023073,True,work,13,16,6.0,WALK_LOC +728184589,2220074,1119965,91023073,False,Home,16,13,16.0,WALK_LOC +728184865,2220075,1119965,91023108,True,shopping,16,16,11.0,TNC_SINGLE +728184869,2220075,1119965,91023108,False,Home,16,16,13.0,TNC_SINGLE +728184873,2220075,1119965,91023109,True,shopping,5,16,21.0,WALK_LOC +728184877,2220075,1119965,91023109,False,Home,16,5,21.0,WALK_LOC +728184913,2220075,1119965,91023114,True,work,14,16,13.0,WALK +728184917,2220075,1119965,91023114,False,Home,16,14,20.0,WALK_LOC +728184921,2220075,1119965,91023115,True,work,14,16,20.0,TNC_SINGLE +728184925,2220075,1119965,91023115,False,Home,16,14,20.0,WALK +728185129,2220076,1119965,91023141,True,othdiscr,12,16,13.0,WALK +728185133,2220076,1119965,91023141,False,Home,16,12,16.0,WALK +728185177,2220076,1119965,91023147,True,school,13,16,9.0,WALK_LOC +728185181,2220076,1119965,91023147,False,Home,16,13,13.0,WALK_LOC +728221737,2220188,1120003,91027717,True,escort,13,16,15.0,TNC_SINGLE +728221741,2220188,1120003,91027717,False,Home,16,13,18.0,TNC_SINGLE +728221977,2220188,1120003,91027747,True,work,14,16,5.0,WALK +728221981,2220188,1120003,91027747,False,Home,16,14,11.0,WALK +728222305,2220189,1120003,91027788,True,work,14,16,13.0,WALK +728222309,2220189,1120003,91027788,False,Home,16,14,18.0,WALK +728222569,2220190,1120003,91027821,True,school,18,16,7.0,WALK_LRF +728222573,2220190,1120003,91027821,False,Home,16,18,16.0,WALK_LRF +728258145,2220299,1120040,91032268,True,escort,22,19,18.0,WALK_LRF +728258149,2220299,1120040,91032268,False,Home,19,22,21.0,WALK_LRF +728258337,2220299,1120040,91032292,True,shopping,16,19,11.0,SHARED2FREE +728258338,2220299,1120040,91032292,True,social,11,16,11.0,SHARED2FREE +728258339,2220299,1120040,91032292,True,shopping,16,11,12.0,SHARED2FREE +728258341,2220299,1120040,91032292,False,eatout,11,16,14.0,SHARED2FREE +728258342,2220299,1120040,91032292,False,othmaint,10,11,14.0,SHARED2FREE +728258343,2220299,1120040,91032292,False,Home,19,10,14.0,DRIVEALONEFREE +728258713,2220300,1120040,91032339,True,work,11,19,7.0,WALK +728258717,2220300,1120040,91032339,False,Home,19,11,16.0,WALK +728258977,2220301,1120040,91032372,True,school,9,19,7.0,WALK +728258981,2220301,1120040,91032372,False,Home,19,9,15.0,WALK +728268929,2220332,1120051,91033616,True,atwork,25,5,15.0,WALK +728268933,2220332,1120051,91033616,False,Work,5,25,15.0,WALK +728268969,2220332,1120051,91033621,True,escort,9,20,15.0,WALK_LOC +728268973,2220332,1120051,91033621,False,Home,20,9,15.0,TNC_SINGLE +728269209,2220332,1120051,91033651,True,work,5,20,6.0,WALK +728269213,2220332,1120051,91033651,False,Home,20,5,15.0,WALK +728269537,2220333,1120051,91033692,True,work,4,20,8.0,WALK_LRF +728269538,2220333,1120051,91033692,True,othmaint,16,4,9.0,WALK_LOC +728269539,2220333,1120051,91033692,True,work,2,16,9.0,WALK +728269541,2220333,1120051,91033692,False,othdiscr,8,2,17.0,WALK_LOC +728269542,2220333,1120051,91033692,False,eatout,9,8,17.0,WALK +728269543,2220333,1120051,91033692,False,escort,8,9,17.0,WALK_LOC +728269544,2220333,1120051,91033692,False,Home,20,8,21.0,WALK_LOC +728269801,2220334,1120051,91033725,True,school,8,20,7.0,WALK_LOC +728269805,2220334,1120051,91033725,False,Home,20,8,15.0,WALK +728311521,2220461,1120094,91038940,True,work,4,25,7.0,WALK +728311525,2220461,1120094,91038940,False,Home,25,4,17.0,WALK +728311849,2220462,1120094,91038981,True,othmaint,4,25,5.0,WALK +728311850,2220462,1120094,91038981,True,work,13,4,6.0,WALK +728311853,2220462,1120094,91038981,False,Home,25,13,15.0,WALK_LOC +728312113,2220463,1120094,91039014,True,school,22,25,6.0,WALK_LRF +728312117,2220463,1120094,91039014,False,Home,25,22,6.0,WALK_LRF +739441545,2254394,1131405,92430193,True,work,13,7,7.0,WALK +739441549,2254394,1131405,92430193,False,Home,7,13,20.0,WALK +739441809,2254395,1131405,92430226,True,school,13,7,5.0,WALK_LRF +739441813,2254395,1131405,92430226,False,Home,7,13,23.0,WALK_LOC +739442153,2254396,1131405,92430269,True,shopping,15,7,20.0,WALK +739442157,2254396,1131405,92430269,False,Home,7,15,20.0,WALK +739442201,2254396,1131405,92430275,True,work,17,7,8.0,WALK +739442205,2254396,1131405,92430275,False,Home,7,17,18.0,WALK +739453113,2254430,1131417,92431639,True,escort,2,7,15.0,WALK_LOC +739453117,2254430,1131417,92431639,False,escort,6,2,18.0,TNC_SINGLE +739453118,2254430,1131417,92431639,False,othmaint,16,6,18.0,WALK_LOC +739453119,2254430,1131417,92431639,False,eatout,17,16,18.0,WALK_LRF +739453120,2254430,1131417,92431639,False,Home,7,17,18.0,WALK_LOC +739453241,2254430,1131417,92431655,True,othdiscr,10,7,8.0,WALK +739453245,2254430,1131417,92431655,False,Home,7,10,14.0,WALK +739453945,2254432,1131417,92431743,True,school,13,7,6.0,WALK_LRF +739453949,2254432,1131417,92431743,False,Home,7,13,10.0,WALK_LOC +739453985,2254432,1131417,92431748,True,othdiscr,8,7,11.0,WALK +739453986,2254432,1131417,92431748,True,social,6,8,11.0,WALK +739453989,2254432,1131417,92431748,False,Home,7,6,21.0,WALK +772521937,2355249,1152868,96565242,True,shopping,11,10,18.0,DRIVEALONEFREE +772521941,2355249,1152868,96565242,False,Home,10,11,20.0,TNC_SHARED +772522313,2355250,1152868,96565289,True,work,13,10,8.0,WALK_LRF +772522317,2355250,1152868,96565289,False,shopping,14,13,16.0,WALK +772522318,2355250,1152868,96565289,False,othdiscr,9,14,17.0,WALK_LRF +772522319,2355250,1152868,96565289,False,Home,10,9,17.0,TAXI +772522401,2355251,1152868,96565300,True,escort,10,10,12.0,WALK +772522405,2355251,1152868,96565300,False,Home,10,10,13.0,WALK +772522905,2355252,1152868,96565363,True,univ,9,10,15.0,WALK +772522909,2355252,1152868,96565363,False,othmaint,9,9,15.0,WALK +772522910,2355252,1152868,96565363,False,Home,10,9,15.0,WALK +772523233,2355253,1152868,96565404,True,school,10,10,5.0,WALK +772523237,2355253,1152868,96565404,False,Home,10,10,15.0,WALK +772523561,2355254,1152868,96565445,True,school,6,10,6.0,WALK_HVY +772523565,2355254,1152868,96565445,False,Home,10,6,18.0,WALK_LOC +772523577,2355254,1152868,96565447,True,shopping,11,10,21.0,WALK +772523581,2355254,1152868,96565447,False,Home,10,11,22.0,WALK +772523889,2355255,1152868,96565486,True,school,19,10,7.0,SHARED2FREE +772523893,2355255,1152868,96565486,False,Home,10,19,17.0,SHARED2FREE +772524001,2355256,1152868,96565500,True,atwork,2,2,11.0,WALK +772524005,2355256,1152868,96565500,False,eatout,4,2,11.0,WALK +772524006,2355256,1152868,96565500,False,Work,2,4,11.0,WALK +772524281,2355256,1152868,96565535,True,work,2,10,7.0,WALK +772524285,2355256,1152868,96565535,False,Home,10,2,20.0,WALK_LRF +772542889,2355313,1152879,96567861,True,othmaint,18,20,17.0,TNC_SINGLE +772542893,2355313,1152879,96567861,False,Home,20,18,17.0,TNC_SINGLE +772542977,2355313,1152879,96567872,True,work,5,20,8.0,WALK +772542981,2355313,1152879,96567872,False,Home,20,5,16.0,WALK +772543193,2355314,1152879,96567899,True,othdiscr,9,20,20.0,WALK +772543197,2355314,1152879,96567899,False,Home,20,9,22.0,WALK +772543305,2355314,1152879,96567913,True,work,10,20,7.0,WALK +772543309,2355314,1152879,96567913,False,Home,20,10,18.0,WALK +772543585,2355315,1152879,96567948,True,shopping,5,20,10.0,WALK +772543589,2355315,1152879,96567948,False,Home,20,5,11.0,WALK +772543913,2355316,1152879,96567989,True,shopping,11,20,18.0,WALK +772543917,2355316,1152879,96567989,False,shopping,10,11,20.0,WALK +772543918,2355316,1152879,96567989,False,Home,20,10,22.0,WALK +806380921,2458478,1173900,100797615,True,othmaint,17,8,11.0,WALK_LRF +806380925,2458478,1173900,100797615,False,Home,8,17,13.0,WALK_LRF +806382017,2458481,1173900,100797752,True,school,8,8,8.0,WALK +806382021,2458481,1173900,100797752,False,Home,8,8,16.0,WALK +806388153,2458502,1173905,100798519,True,shopping,5,8,8.0,SHARED2FREE +806388157,2458502,1173905,100798519,False,Home,8,5,8.0,SHARED2FREE +806388225,2458500,1173905,100798528,True,othmaint,7,8,12.0,WALK +806388229,2458500,1173905,100798528,False,Home,8,7,20.0,WALK +806388401,2458501,1173905,100798550,True,escort,16,8,15.0,WALK_LOC +806388405,2458501,1173905,100798550,False,Home,8,16,16.0,WALK_LOC +806388905,2458502,1173905,100798613,True,social,5,8,8.0,WALK +806388906,2458502,1173905,100798613,True,school,8,5,8.0,WALK +806388909,2458502,1173905,100798613,False,Home,8,8,18.0,WALK +806389233,2458503,1173905,100798654,True,school,8,8,8.0,WALK +806389237,2458503,1173905,100798654,False,Home,8,8,14.0,WALK +841865537,2566663,1196291,105233192,True,escort,8,21,8.0,WALK_LOC +841865541,2566663,1196291,105233192,False,shopping,11,8,8.0,TNC_SINGLE +841865542,2566663,1196291,105233192,False,Home,21,11,8.0,TNC_SHARED +841865545,2566663,1196291,105233193,True,escort,21,21,12.0,TNC_SINGLE +841865549,2566663,1196291,105233193,False,Home,21,21,13.0,TNC_SINGLE +841866369,2566665,1196291,105233296,True,school,9,21,7.0,WALK_LOC +841866373,2566665,1196291,105233296,False,othdiscr,16,9,17.0,WALK_LOC +841866374,2566665,1196291,105233296,False,Home,21,16,17.0,WALK_LOC +841866697,2566666,1196291,105233337,True,school,21,21,8.0,WALK +841866701,2566666,1196291,105233337,False,Home,21,21,15.0,WALK +841867025,2566667,1196291,105233378,True,school,9,21,8.0,WALK +841867029,2566667,1196291,105233378,False,Home,21,9,14.0,WALK +841867177,2566668,1196291,105233397,True,escort,10,21,6.0,DRIVEALONEFREE +841867181,2566668,1196291,105233397,False,Home,21,10,6.0,SHARED3FREE +841867417,2566668,1196291,105233427,True,work,16,21,6.0,WALK +841867421,2566668,1196291,105233427,False,Home,21,16,21.0,WALK +841877257,2566698,1196298,105234657,True,work,1,25,6.0,WALK_LOC +841877261,2566698,1196298,105234657,False,Home,25,1,17.0,WALK_LOC +841877849,2566700,1196298,105234731,True,school,25,25,7.0,WALK +841877853,2566700,1196298,105234731,False,Home,25,25,15.0,WALK +841878177,2566701,1196298,105234772,True,school,3,25,8.0,WALK +841878181,2566701,1196298,105234772,False,Home,25,3,13.0,WALK +841878505,2566702,1196298,105234813,True,school,6,25,12.0,WALK_LOC +841878509,2566702,1196298,105234813,False,shopping,13,6,20.0,WALK_LOC +841878510,2566702,1196298,105234813,False,Home,25,13,20.0,WALK_LOC +841893217,2566747,1196308,105236652,True,othdiscr,6,25,11.0,WALK +841893221,2566747,1196308,105236652,False,Home,25,6,16.0,WALK +841893593,2566748,1196308,105236699,True,school,25,25,7.0,WALK +841893597,2566748,1196308,105236699,False,Home,25,25,15.0,WALK +841898529,2566763,1196312,105237316,True,shopping,16,25,12.0,WALK +841898533,2566763,1196312,105237316,False,Home,25,16,19.0,WALK_LOC +841909289,2566796,1196319,105238661,True,othdiscr,14,25,14.0,WALK +841909293,2566796,1196319,105238661,False,Home,25,14,17.0,WALK +841909465,2566797,1196319,105238683,True,eatout,6,25,10.0,WALK +841909469,2566797,1196319,105238683,False,Home,25,6,14.0,WALK +841909777,2566798,1196319,105238722,True,atwork,13,14,10.0,WALK_LOC +841909781,2566798,1196319,105238722,False,Work,14,13,10.0,WALK +841909945,2566798,1196319,105238743,True,othdiscr,12,25,18.0,WALK_LOC +841909949,2566798,1196319,105238743,False,eatout,7,12,20.0,WALK +841909950,2566798,1196319,105238743,False,Home,25,7,20.0,WALK_LOC +841910057,2566798,1196319,105238757,True,work,14,25,7.0,SHARED2FREE +841910061,2566798,1196319,105238757,False,Home,25,14,17.0,WALK +841910321,2566799,1196319,105238790,True,school,25,25,8.0,WALK +841910325,2566799,1196319,105238790,False,Home,25,25,18.0,WALK +857896777,2615538,1206644,107237097,True,work,10,25,8.0,WALK_LOC +857896781,2615538,1206644,107237097,False,Home,25,10,18.0,WALK_LOC +857897105,2615539,1206644,107237138,True,work,4,25,8.0,WALK +857897109,2615539,1206644,107237138,False,Home,25,4,18.0,TNC_SHARED +857897369,2615540,1206644,107237171,True,univ,12,25,7.0,WALK_LOC +857897373,2615540,1206644,107237171,False,Home,25,12,14.0,WALK_LOC +857897697,2615541,1206644,107237212,True,school,13,25,7.0,WALK_LOC +857897701,2615541,1206644,107237212,False,Home,25,13,15.0,WALK_LOC +900829073,2746430,1234020,112603634,True,atwork,5,4,12.0,WALK +900829077,2746430,1234020,112603634,False,Work,4,5,15.0,WALK +900829241,2746430,1234020,112603655,True,othdiscr,2,10,20.0,WALK_LRF +900829245,2746430,1234020,112603655,False,Home,10,2,21.0,TNC_SINGLE +900829353,2746430,1234020,112603669,True,othdiscr,7,10,5.0,WALK +900829354,2746430,1234020,112603669,True,work,4,7,7.0,WALK_LOC +900829357,2746430,1234020,112603669,False,Home,10,4,17.0,WALK_HVY +900829681,2746431,1234020,112603710,True,shopping,5,10,7.0,WALK +900829682,2746431,1234020,112603710,True,work,14,5,8.0,TNC_SINGLE +900829685,2746431,1234020,112603710,False,Home,10,14,17.0,TNC_SINGLE +900829745,2746432,1234020,112603718,True,eatout,12,10,18.0,WALK +900829749,2746432,1234020,112603718,False,Home,10,12,22.0,WALK +900829945,2746432,1234020,112603743,True,school,9,10,8.0,WALK_LOC +900829949,2746432,1234020,112603743,False,Home,10,9,14.0,WALK_LOC +900830273,2746433,1234020,112603784,True,escort,8,10,8.0,WALK_LOC +900830274,2746433,1234020,112603784,True,school,9,8,8.0,WALK_LOC +900830277,2746433,1234020,112603784,False,social,20,9,17.0,WALK_LOC +900830278,2746433,1234020,112603784,False,Home,10,20,18.0,WALK_LOC +900830289,2746433,1234020,112603786,True,shopping,14,10,19.0,WALK_LOC +900830293,2746433,1234020,112603786,False,Home,10,14,20.0,WALK_LRF +900851377,2746498,1234034,112606422,True,atwork,9,9,13.0,WALK +900851381,2746498,1234034,112606422,False,Work,9,9,14.0,WALK +900851657,2746498,1234034,112606457,True,work,9,10,5.0,SHARED2FREE +900851661,2746498,1234034,112606457,False,social,9,9,21.0,WALK +900851662,2746498,1234034,112606457,False,Home,10,9,22.0,WALK +900851985,2746499,1234034,112606498,True,work,12,10,11.0,WALK +900851986,2746499,1234034,112606498,True,work,2,12,12.0,WALK +900851989,2746499,1234034,112606498,False,shopping,7,2,20.0,WALK +900851990,2746499,1234034,112606498,False,Home,10,7,21.0,WALK +900852249,2746500,1234034,112606531,True,school,20,10,7.0,WALK_LOC +900852253,2746500,1234034,112606531,False,Home,10,20,15.0,WALK_LOC +900852529,2746501,1234034,112606566,True,othdiscr,11,10,9.0,WALK +900852533,2746501,1234034,112606566,False,Home,10,11,11.0,WALK +900852577,2746501,1234034,112606572,True,school,10,10,11.0,SHARED2FREE +900852581,2746501,1234034,112606572,False,Home,10,10,18.0,WALK +900966521,2746849,1234104,112620815,True,eatout,9,10,15.0,WALK +900966525,2746849,1234104,112620815,False,Home,10,9,20.0,WALK +900967113,2746850,1234104,112620889,True,work,16,10,5.0,WALK_LRF +900967117,2746850,1234104,112620889,False,othmaint,22,16,15.0,WALK +900967118,2746850,1234104,112620889,False,Home,10,22,15.0,WALK_HVY +900967705,2746852,1234104,112620963,True,escort,5,10,8.0,WALK_LOC +900967706,2746852,1234104,112620963,True,school,9,5,8.0,WALK +900967709,2746852,1234104,112620963,False,Home,10,9,18.0,WALK_LOC +900982137,2746896,1234114,112622767,True,school,9,11,7.0,SHARED3FREE +900982141,2746896,1234114,112622767,False,Home,11,9,14.0,WALK +901053049,2747112,1234158,112631631,True,work,14,11,9.0,WALK_LOC +901053053,2747112,1234158,112631631,False,Home,11,14,16.0,WALK_LOC +901053353,2747113,1234158,112631669,True,social,11,11,12.0,WALK +901053357,2747113,1234158,112631669,False,Home,11,11,19.0,WALK +901053641,2747114,1234158,112631705,True,univ,10,11,16.0,WALK +901053645,2747114,1234158,112631705,False,Home,11,10,16.0,WALK_LOC +901053705,2747114,1234158,112631713,True,work,1,11,5.0,WALK +901053709,2747114,1234158,112631713,False,Home,11,1,10.0,WALK +901053793,2747115,1234158,112631724,True,escort,11,11,10.0,DRIVEALONEFREE +901053797,2747115,1234158,112631724,False,shopping,8,11,11.0,SHARED2FREE +901053798,2747115,1234158,112631724,False,Home,11,8,12.0,DRIVEALONEFREE +901053969,2747115,1234158,112631746,True,school,13,11,6.0,WALK_LOC +901053973,2747115,1234158,112631746,False,Home,11,13,6.0,SHARED2FREE +901054297,2747116,1234158,112631787,True,school,13,11,8.0,WALK_LOC +901054301,2747116,1234158,112631787,False,Home,11,13,18.0,WALK +901054625,2747117,1234158,112631828,True,school,13,11,7.0,WALK_LOC +901054629,2747117,1234158,112631828,False,Home,11,13,13.0,SHARED3FREE +901054953,2747118,1234158,112631869,True,school,10,11,7.0,WALK +901054957,2747118,1234158,112631869,False,Home,11,10,14.0,WALK +901076273,2747183,1234171,112634534,True,school,18,18,7.0,WALK +901076277,2747183,1234171,112634534,False,Home,18,18,11.0,WALK +901095361,2747241,1234184,112636920,True,work,22,20,10.0,WALK_LRF +901095365,2747241,1234184,112636920,False,work,9,22,18.0,WALK_LRF +901095366,2747241,1234184,112636920,False,othmaint,13,9,19.0,WALK_LRF +901095367,2747241,1234184,112636920,False,Home,20,13,19.0,WALK +901095641,2747242,1234184,112636955,True,shopping,12,20,11.0,WALK +901095645,2747242,1234184,112636955,False,Home,20,12,15.0,WALK +901096017,2747243,1234184,112637002,True,work,12,20,5.0,WALK +901096021,2747243,1234184,112637002,False,Home,20,12,15.0,WALK +901096081,2747244,1234184,112637010,True,eatout,9,20,18.0,WALK +901096085,2747244,1234184,112637010,False,Home,20,9,21.0,WALK +901096609,2747245,1234184,112637076,True,school,10,20,10.0,WALK +901096613,2747245,1234184,112637076,False,Home,20,10,14.0,WALK +901096937,2747246,1234184,112637117,True,school,20,20,8.0,WALK +901096941,2747246,1234184,112637117,False,Home,20,20,15.0,WALK +943687097,2877094,1259325,117960887,True,shopping,16,9,13.0,BIKE +943687101,2877094,1259325,117960887,False,Home,9,16,13.0,BIKE +943687105,2877094,1259325,117960888,True,shopping,22,9,17.0,WALK_LRF +943687109,2877094,1259325,117960888,False,Home,9,22,18.0,WALK_LOC +943687145,2877094,1259325,117960893,True,work,9,9,19.0,WALK +943687149,2877094,1259325,117960893,False,othdiscr,9,9,19.0,WALK +943687150,2877094,1259325,117960893,False,shopping,11,9,21.0,WALK +943687151,2877094,1259325,117960893,False,Home,9,11,22.0,WALK +943687473,2877095,1259325,117960934,True,escort,11,9,9.0,WALK +943687474,2877095,1259325,117960934,True,othmaint,21,11,9.0,WALK +943687475,2877095,1259325,117960934,True,work,19,21,9.0,WALK +943687477,2877095,1259325,117960934,False,social,9,19,21.0,WALK +943687478,2877095,1259325,117960934,False,othmaint,9,9,22.0,WALK +943687479,2877095,1259325,117960934,False,social,8,9,22.0,WALK +943687480,2877095,1259325,117960934,False,Home,9,8,22.0,WALK +943687737,2877096,1259325,117960967,True,school,16,9,7.0,WALK_LOC +943687741,2877096,1259325,117960967,False,Home,9,16,15.0,WALK_LRF +943688129,2877097,1259325,117961016,True,work,9,9,6.0,WALK +943688133,2877097,1259325,117961016,False,Home,9,9,20.0,WALK +943749401,2877284,1259353,117968675,True,univ,10,10,15.0,WALK +943749405,2877284,1259353,117968675,False,Home,10,10,17.0,WALK +943749465,2877284,1259353,117968683,True,work,13,10,7.0,WALK_LRF +943749469,2877284,1259353,117968683,False,eatout,9,13,11.0,WALK_LRF +943749470,2877284,1259353,117968683,False,work,9,9,11.0,WALK +943749471,2877284,1259353,117968683,False,Home,10,9,11.0,WALK +943750385,2877287,1259353,117968798,True,school,10,10,10.0,WALK +943750389,2877287,1259353,117968798,False,Home,10,10,15.0,WALK +943811457,2877473,1259382,117976432,True,work,13,16,7.0,WALK +943811461,2877473,1259382,117976432,False,Home,16,13,16.0,TNC_SINGLE +943812377,2877476,1259382,117976547,True,school,10,16,8.0,WALK_LOC +943812381,2877476,1259382,117976547,False,Home,16,10,21.0,WALK_LRF +943825777,2877517,1259389,117978222,True,othdiscr,18,17,15.0,WALK +943825781,2877517,1259389,117978222,False,Home,17,18,15.0,WALK +943825889,2877517,1259389,117978236,True,work,13,17,15.0,WALK +943825893,2877517,1259389,117978236,False,Home,17,13,21.0,WALK +943825937,2877518,1259389,117978242,True,atwork,12,16,12.0,WALK +943825941,2877518,1259389,117978242,False,shopping,16,12,14.0,WALK +943825942,2877518,1259389,117978242,False,Work,16,16,14.0,WALK +943826217,2877518,1259389,117978277,True,escort,16,17,11.0,WALK +943826218,2877518,1259389,117978277,True,work,16,16,11.0,WALK +943826221,2877518,1259389,117978277,False,othdiscr,17,16,15.0,WALK +943826222,2877518,1259389,117978277,False,Home,17,17,20.0,WALK +943826761,2877520,1259389,117978345,True,othdiscr,9,17,17.0,SHARED3FREE +943826765,2877520,1259389,117978345,False,Home,17,9,19.0,WALK_LRF +943826809,2877520,1259389,117978351,True,school,20,17,8.0,WALK_LRF +943826813,2877520,1259389,117978351,False,Home,17,20,15.0,WALK_LOC +943827249,2877522,1259390,117978406,True,atwork,5,13,14.0,WALK +943827253,2877522,1259390,117978406,False,Work,13,5,14.0,WALK +943827481,2877522,1259390,117978435,True,shopping,5,17,20.0,WALK +943827485,2877522,1259390,117978435,False,Home,17,5,20.0,WALK +943827529,2877522,1259390,117978441,True,work,13,17,8.0,WALK +943827533,2877522,1259390,117978441,False,Home,17,13,20.0,WALK +943827809,2877523,1259390,117978476,True,shopping,19,17,17.0,WALK_LOC +943827813,2877523,1259390,117978476,False,eatout,4,19,17.0,WALK_LOC +943827814,2877523,1259390,117978476,False,eatout,9,4,18.0,TNC_SHARED +943827815,2877523,1259390,117978476,False,othmaint,12,9,18.0,WALK_LRF +943827816,2877523,1259390,117978476,False,Home,17,12,18.0,WALK_LRF +943827857,2877523,1259390,117978482,True,work,24,17,9.0,WALK_LOC +943827861,2877523,1259390,117978482,False,Home,17,24,16.0,WALK_LOC +943828185,2877524,1259390,117978523,True,work,17,17,6.0,WALK +943828189,2877524,1259390,117978523,False,Home,17,17,15.0,WALK +943828449,2877525,1259390,117978556,True,school,9,17,8.0,WALK_LRF +943828453,2877525,1259390,117978556,False,Home,17,9,15.0,WALK_LRF +943828777,2877526,1259390,117978597,True,school,18,17,7.0,WALK_LRF +943828781,2877526,1259390,117978597,False,Home,17,18,14.0,WALK_LRF +943855409,2877607,1259403,117981926,True,work,5,17,8.0,WALK +943855413,2877607,1259403,117981926,False,shopping,12,5,20.0,WALK +943855414,2877607,1259403,117981926,False,Home,17,12,20.0,WALK +943855673,2877608,1259403,117981959,True,school,10,17,7.0,WALK_LRF +943855677,2877608,1259403,117981959,False,Home,17,10,15.0,WALK_LRF +943856001,2877609,1259403,117982000,True,school,18,17,8.0,WALK_LRF +943856005,2877609,1259403,117982000,False,Home,17,18,13.0,WALK_LRF +943856329,2877610,1259403,117982041,True,school,9,17,8.0,WALK_LRF +943856333,2877610,1259403,117982041,False,Home,17,9,16.0,WALK_LRF +943856809,2877612,1259403,117982101,True,escort,16,17,16.0,TNC_SHARED +943856813,2877612,1259403,117982101,False,Home,17,16,17.0,WALK_LRF +943856817,2877612,1259403,117982102,True,escort,17,17,18.0,TNC_SHARED +943856821,2877612,1259403,117982102,False,Home,17,17,19.0,TNC_SHARED +943856961,2877612,1259403,117982120,True,othmaint,9,17,18.0,DRIVEALONEFREE +943856965,2877612,1259403,117982120,False,Home,17,9,18.0,DRIVEALONEFREE +943857049,2877612,1259403,117982131,True,work,24,17,5.0,WALK +943857053,2877612,1259403,117982131,False,Home,17,24,16.0,WALK +943857065,2877613,1259403,117982133,True,atwork,25,13,14.0,WALK +943857069,2877613,1259403,117982133,False,Work,13,25,14.0,WALK +943857377,2877613,1259403,117982172,True,work,13,17,14.0,WALK +943857381,2877613,1259403,117982172,False,Home,17,13,23.0,WALK +943857425,2877614,1259403,117982178,True,atwork,4,5,14.0,WALK +943857429,2877614,1259403,117982178,False,Work,5,4,14.0,WALK +943857705,2877614,1259403,117982213,True,othdiscr,12,17,7.0,WALK +943857706,2877614,1259403,117982213,True,work,5,12,8.0,WALK +943857709,2877614,1259403,117982213,False,Home,17,5,21.0,WALK_LRF +943910249,2877775,1259428,117988781,True,eatout,17,17,17.0,WALK +943910253,2877775,1259428,117988781,False,Home,17,17,17.0,WALK +943910273,2877775,1259428,117988784,True,escort,12,17,14.0,TNC_SINGLE +943910277,2877775,1259428,117988784,False,Home,17,12,14.0,TNC_SINGLE +943910513,2877775,1259428,117988814,True,work,22,17,7.0,WALK_LRF +943910517,2877775,1259428,117988814,False,Home,17,22,13.0,WALK +943910577,2877776,1259428,117988822,True,eatout,4,17,17.0,WALK_LRF +943910581,2877776,1259428,117988822,False,Home,17,4,20.0,WALK_LRF +943910777,2877776,1259428,117988847,True,school,10,17,7.0,WALK_LRF +943910781,2877776,1259428,117988847,False,Home,17,10,14.0,WALK_LRF +943911169,2877777,1259428,117988896,True,work,11,17,12.0,WALK_LRF +943911173,2877777,1259428,117988896,False,othdiscr,14,11,22.0,WALK +943911174,2877777,1259428,117988896,False,Home,17,14,22.0,WALK_LOC +943911433,2877778,1259428,117988929,True,school,18,17,8.0,WALK_LRF +943911437,2877778,1259428,117988929,False,Home,17,18,17.0,WALK_LRF +943911737,2877779,1259428,117988967,True,othmaint,21,17,7.0,TNC_SHARED +943911741,2877779,1259428,117988967,False,Home,17,21,14.0,TNC_SINGLE +943912153,2877780,1259428,117989019,True,work,2,17,9.0,WALK_LOC +943912157,2877780,1259428,117989019,False,Home,17,2,18.0,WALK +943912481,2877781,1259428,117989060,True,work,12,17,12.0,WALK +943912485,2877781,1259428,117989060,False,Home,17,12,20.0,WALK +943931505,2877839,1259438,117991438,True,work,4,25,7.0,TAXI +943931509,2877839,1259438,117991438,False,Home,25,4,11.0,WALK +943932097,2877841,1259438,117991512,True,univ,12,25,8.0,WALK_LOC +943932101,2877841,1259438,117991512,False,Home,25,12,8.0,WALK_LOC +943932105,2877841,1259438,117991513,True,univ,12,25,13.0,WALK_LOC +943932109,2877841,1259438,117991513,False,Home,25,12,17.0,WALK_LOC +943932489,2877842,1259438,117991561,True,work,14,25,12.0,WALK_LOC +943932493,2877842,1259438,117991561,False,Home,25,14,18.0,WALK +943932753,2877843,1259438,117991594,True,univ,13,25,18.0,WALK_LOC +943932757,2877843,1259438,117991594,False,Home,25,13,19.0,WALK_LOC +943932817,2877843,1259438,117991602,True,work,1,25,8.0,WALK_LOC +943932821,2877843,1259438,117991602,False,work,1,1,12.0,TNC_SINGLE +943932822,2877843,1259438,117991602,False,work,14,1,12.0,WALK +943932823,2877843,1259438,117991602,False,Home,25,14,12.0,WALK_LOC +943933081,2877844,1259438,117991635,True,school,25,25,8.0,WALK +943933085,2877844,1259438,117991635,False,Home,25,25,13.0,WALK +963058409,2936153,1285862,120382301,True,othmaint,13,3,12.0,BIKE +963058413,2936153,1285862,120382301,False,Home,3,13,15.0,WALK +963058417,2936153,1285862,120382302,True,othmaint,10,3,17.0,WALK_LRF +963058421,2936153,1285862,120382302,False,Home,3,10,18.0,WALK_LOC +963058449,2936153,1285862,120382306,True,shopping,11,3,12.0,WALK_LOC +963058453,2936153,1285862,120382306,False,Home,3,11,12.0,WALK_LOC +963063961,2936170,1285879,120382995,True,othdiscr,14,5,12.0,WALK +963063965,2936170,1285879,120382995,False,Home,5,14,17.0,WALK +963075793,2936206,1285915,120384474,True,othmaint,7,6,11.0,WALK +963075797,2936206,1285915,120384474,False,Home,6,7,20.0,WALK +963087753,2936243,1285952,120385969,True,eatout,16,6,10.0,DRIVEALONEFREE +963087757,2936243,1285952,120385969,False,Home,6,16,12.0,SHARED2FREE +963087777,2936243,1285952,120385972,True,escort,25,6,13.0,SHARED3FREE +963087781,2936243,1285952,120385972,False,Home,6,25,13.0,SHARED2FREE +963087905,2936243,1285952,120385988,True,social,12,6,15.0,WALK +963087906,2936243,1285952,120385988,True,othdiscr,5,12,16.0,SHARED2FREE +963087909,2936243,1285952,120385988,False,eatout,6,5,16.0,DRIVEALONEFREE +963087910,2936243,1285952,120385988,False,Home,6,6,16.0,WALK +963087929,2936243,1285952,120385991,True,othmaint,7,6,14.0,WALK +963087933,2936243,1285952,120385991,False,Home,6,7,15.0,WALK +963093481,2936260,1285969,120386685,True,othdiscr,9,6,16.0,WALK +963093485,2936260,1285969,120386685,False,Home,6,9,16.0,WALK +963093545,2936260,1285969,120386693,True,shopping,16,6,10.0,WALK +963093549,2936260,1285969,120386693,False,Home,6,16,14.0,WALK +963097921,2936274,1285983,120387240,True,eatout,6,6,16.0,WALK +963097925,2936274,1285983,120387240,False,Home,6,6,16.0,WALK +963098073,2936274,1285983,120387259,True,othdiscr,10,6,13.0,SHARED2FREE +963098077,2936274,1285983,120387259,False,Home,6,10,16.0,WALK +963098137,2936274,1285983,120387267,True,othmaint,8,6,13.0,TNC_SINGLE +963098138,2936274,1285983,120387267,True,shopping,16,8,13.0,TNC_SINGLE +963098141,2936274,1285983,120387267,False,eatout,6,16,13.0,DRIVEALONEFREE +963098142,2936274,1285983,120387267,False,shopping,6,6,13.0,TNC_SHARED +963098143,2936274,1285983,120387267,False,Home,6,6,13.0,TNC_SHARED +963107673,2936303,1286012,120388459,True,social,9,6,11.0,WALK +963107677,2936303,1286012,120388459,False,Home,6,9,22.0,WALK +963143993,2936414,1286123,120392999,True,othdiscr,8,7,16.0,WALK +963143997,2936414,1286123,120392999,False,Home,7,8,17.0,WALK +963178457,2936519,1286228,120397307,True,othmaint,20,7,8.0,WALK +963178461,2936519,1286228,120397307,False,Home,7,20,13.0,WALK +963187137,2936546,1286255,120398392,True,eatout,9,8,11.0,WALK +963187141,2936546,1286255,120398392,False,Home,8,9,14.0,WALK +963188601,2936550,1286259,120398575,True,othdiscr,6,8,14.0,WALK +963188605,2936550,1286259,120398575,False,Home,8,6,17.0,WALK +963188665,2936550,1286259,120398583,True,shopping,11,8,12.0,WALK +963188669,2936550,1286259,120398583,False,Home,8,11,13.0,WALK +963196865,2936575,1286284,120399608,True,shopping,1,8,8.0,WALK +963196869,2936575,1286284,120399608,False,Home,8,1,14.0,WALK +963221073,2936649,1286358,120402634,True,othdiscr,1,9,11.0,WALK_LOC +963221077,2936649,1286358,120402634,False,Home,9,1,16.0,WALK_LRF +963252953,2936746,1286455,120406619,True,shopping,18,9,11.0,WALK +963252957,2936746,1286455,120406619,False,Home,9,18,14.0,WALK +963256521,2936757,1286466,120407065,True,othmaint,9,9,10.0,TNC_SINGLE +963256525,2936757,1286466,120407065,False,Home,9,9,10.0,DRIVEALONEFREE +963256561,2936757,1286466,120407070,True,othmaint,8,9,15.0,WALK +963256562,2936757,1286466,120407070,True,shopping,7,8,15.0,DRIVEALONEFREE +963256563,2936757,1286466,120407070,True,shopping,10,7,17.0,TNC_SHARED +963256565,2936757,1286466,120407070,False,shopping,13,10,17.0,DRIVEALONEFREE +963256566,2936757,1286466,120407070,False,shopping,18,13,17.0,TNC_SINGLE +963256567,2936757,1286466,120407070,False,Home,9,18,17.0,DRIVEALONEFREE +963275521,2936815,1286524,120409440,True,othdiscr,6,10,9.0,WALK_LOC +963275525,2936815,1286524,120409440,False,Home,10,6,17.0,WALK_LOC +963285425,2936845,1286554,120410678,True,othmaint,8,11,13.0,WALK_LOC +963285426,2936845,1286554,120410678,True,othmaint,6,8,13.0,WALK +963285427,2936845,1286554,120410678,True,shopping,16,6,14.0,WALK_LOC +963285429,2936845,1286554,120410678,False,Home,11,16,15.0,WALK_LOC +963285433,2936845,1286554,120410679,True,shopping,16,11,16.0,TNC_SINGLE +963285437,2936845,1286554,120410679,False,social,24,16,17.0,WALK_LOC +963285438,2936845,1286554,120410679,False,shopping,7,24,17.0,TNC_SINGLE +963285439,2936845,1286554,120410679,False,Home,11,7,17.0,TNC_SINGLE +963286193,2936848,1286557,120410774,True,eatout,9,11,14.0,WALK +963286197,2936848,1286557,120410774,False,Home,11,9,15.0,TNC_SHARED +963286345,2936848,1286557,120410793,True,escort,10,11,15.0,DRIVEALONEFREE +963286346,2936848,1286557,120410793,True,othdiscr,11,10,16.0,DRIVEALONEFREE +963286349,2936848,1286557,120410793,False,Home,11,11,16.0,DRIVEALONEFREE +963294281,2936872,1286581,120411785,True,shopping,16,14,17.0,WALK +963294285,2936872,1286581,120411785,False,Home,14,16,17.0,WALK +963307401,2936912,1286621,120413425,True,othmaint,17,17,9.0,WALK +963307402,2936912,1286621,120413425,True,othmaint,1,17,10.0,DRIVEALONEFREE +963307403,2936912,1286621,120413425,True,othdiscr,19,1,10.0,SHARED2FREE +963307404,2936912,1286621,120413425,True,shopping,12,19,10.0,DRIVEALONEFREE +963307405,2936912,1286621,120413425,False,Home,17,12,10.0,WALK +963311993,2936926,1286635,120413999,True,shopping,21,20,15.0,WALK +963311997,2936926,1286635,120413999,False,Home,20,21,16.0,WALK +963321441,2936955,1286664,120415180,True,othdiscr,15,22,9.0,WALK +963321445,2936955,1286664,120415180,False,Home,22,15,18.0,WALK +963357697,2937066,1286775,120419712,True,eatout,16,23,14.0,WALK +963357701,2937066,1286775,120419712,False,Home,23,16,17.0,WALK +963357913,2937066,1286775,120419739,True,shopping,11,23,12.0,SHARED2FREE +963357917,2937066,1286775,120419739,False,shopping,16,11,12.0,DRIVEALONEFREE +963357918,2937066,1286775,120419739,False,Home,23,16,12.0,SHARED2FREE +963371297,2937107,1286816,120421412,True,othdiscr,5,25,11.0,WALK +963371301,2937107,1286816,120421412,False,Home,25,5,14.0,WALK +969922769,2957081,1306790,121240346,True,othdiscr,6,3,18.0,WALK +969922773,2957081,1306790,121240346,False,Home,3,6,20.0,WALK +969922881,2957081,1306790,121240360,True,work,22,3,6.0,WALK +969922885,2957081,1306790,121240360,False,Home,3,22,15.0,WALK +969937641,2957126,1306835,121242205,True,work,2,5,6.0,WALK_LOC +969937642,2957126,1306835,121242205,True,work,21,2,7.0,WALK +969937645,2957126,1306835,121242205,False,Home,5,21,17.0,WALK_LOC +969949777,2957163,1306872,121243722,True,work,9,6,18.0,WALK +969949781,2957163,1306872,121243722,False,Home,6,9,20.0,WALK +969951697,2957169,1306878,121243962,True,shopping,2,7,18.0,WALK +969951701,2957169,1306878,121243962,False,Home,7,2,20.0,WALK +969951745,2957169,1306878,121243968,True,escort,7,7,6.0,WALK +969951746,2957169,1306878,121243968,True,work,4,7,8.0,WALK +969951749,2957169,1306878,121243968,False,Home,7,4,16.0,WALK +969957321,2957186,1306895,121244665,True,work,2,7,7.0,WALK +969957325,2957186,1306895,121244665,False,Home,7,2,19.0,WALK +969965537,2957212,1306921,121245692,True,atwork,9,9,12.0,WALK +969965541,2957212,1306921,121245692,False,shopping,11,9,15.0,WALK +969965542,2957212,1306921,121245692,False,Work,9,11,15.0,WALK +969965849,2957212,1306921,121245731,True,work,9,7,8.0,WALK +969965853,2957212,1306921,121245731,False,Home,7,9,17.0,TNC_SINGLE +969970113,2957225,1306934,121246264,True,eatout,6,7,5.0,WALK +969970114,2957225,1306934,121246264,True,work,1,6,7.0,WALK +969970117,2957225,1306934,121246264,False,othmaint,8,1,17.0,WALK_LRF +969970118,2957225,1306934,121246264,False,othmaint,2,8,17.0,WALK +969970119,2957225,1306934,121246264,False,work,2,2,18.0,WALK +969970120,2957225,1306934,121246264,False,Home,7,2,18.0,WALK_LOC +970006193,2957335,1307044,121250774,True,work,13,11,6.0,DRIVEALONEFREE +970006197,2957335,1307044,121250774,False,Home,11,13,18.0,DRIVEALONEFREE +970010985,2957350,1307059,121251373,True,atwork,25,2,13.0,WALK +970010989,2957350,1307059,121251373,False,Work,2,25,13.0,WALK +970011113,2957350,1307059,121251389,True,work,2,17,8.0,WALK +970011117,2957350,1307059,121251389,False,Home,17,2,18.0,WALK +970013409,2957357,1307066,121251676,True,work,5,17,7.0,BIKE +970013413,2957357,1307066,121251676,False,Home,17,5,19.0,BIKE +970013785,2957359,1307068,121251723,True,atwork,5,12,11.0,WALK +970013789,2957359,1307068,121251723,False,Work,12,5,13.0,WALK +970014065,2957359,1307068,121251758,True,work,12,17,7.0,WALK +970014069,2957359,1307068,121251758,False,Home,17,12,17.0,WALK_LRF +970028873,2957405,1307114,121253609,True,atwork,11,14,9.0,WALK +970028877,2957405,1307114,121253609,False,Work,14,11,9.0,WALK +970029153,2957405,1307114,121253644,True,escort,7,20,8.0,TNC_SINGLE +970029154,2957405,1307114,121253644,True,work,14,7,8.0,WALK +970029157,2957405,1307114,121253644,False,social,3,14,18.0,TNC_SINGLE +970029158,2957405,1307114,121253644,False,Home,20,3,19.0,WALK_LRF +970032105,2957414,1307123,121254013,True,work,4,20,7.0,WALK_LRF +970032109,2957414,1307123,121254013,False,Home,20,4,18.0,WALK_LRF +970054953,2957484,1307193,121256869,True,othdiscr,14,21,10.0,WALK_LOC +970054957,2957484,1307193,121256869,False,Home,21,14,11.0,WALK_LOC +970068729,2957526,1307235,121258591,True,othdiscr,17,21,11.0,WALK +970068733,2957526,1307235,121258591,False,othmaint,16,17,17.0,WALK_LOC +970068734,2957526,1307235,121258591,False,othdiscr,7,16,17.0,WALK_LOC +970068735,2957526,1307235,121258591,False,othdiscr,11,7,17.0,WALK_LOC +970068736,2957526,1307235,121258591,False,Home,21,11,17.0,WALK_LOC +970070153,2957530,1307239,121258769,True,escort,9,21,8.0,WALK_LOC +970070154,2957530,1307239,121258769,True,work,4,9,8.0,WALK_LRF +970070157,2957530,1307239,121258769,False,Home,21,4,19.0,WALK_LRF +970098689,2957617,1307326,121262336,True,work,4,23,12.0,WALK +970098693,2957617,1307326,121262336,False,Home,23,4,17.0,WALK +970102185,2957628,1307337,121262773,True,othdiscr,23,23,10.0,WALK +970102189,2957628,1307337,121262773,False,Home,23,23,13.0,WALK +970102297,2957628,1307337,121262787,True,work,24,23,14.0,WALK +970102301,2957628,1307337,121262787,False,Home,23,24,21.0,WALK +970103673,2957633,1307342,121262959,True,eatout,4,23,12.0,WALK +970103677,2957633,1307342,121262959,False,Home,23,4,13.0,WALK +970103889,2957633,1307342,121262986,True,shopping,20,23,14.0,DRIVEALONEFREE +970103893,2957633,1307342,121262986,False,shopping,13,20,14.0,TNC_SINGLE +970103894,2957633,1307342,121262986,False,Home,23,13,14.0,TNC_SINGLE +970103897,2957633,1307342,121262987,True,othmaint,2,23,14.0,WALK +970103898,2957633,1307342,121262987,True,shopping,16,2,14.0,WALK +970103901,2957633,1307342,121262987,False,othmaint,2,16,15.0,WALK +970103902,2957633,1307342,121262987,False,escort,2,2,16.0,WALK +970103903,2957633,1307342,121262987,False,shopping,25,2,16.0,WALK +970103904,2957633,1307342,121262987,False,Home,23,25,16.0,WALK +970103937,2957633,1307342,121262992,True,work,14,23,7.0,DRIVEALONEFREE +970103941,2957633,1307342,121262992,False,Home,23,14,10.0,DRIVEALONEFREE +970110121,2957652,1307361,121263765,True,shopping,5,23,5.0,TNC_SINGLE +970110125,2957652,1307361,121263765,False,Home,23,5,5.0,TNC_SINGLE +970110129,2957652,1307361,121263766,True,shopping,12,23,17.0,TNC_SINGLE +970110130,2957652,1307361,121263766,True,shopping,5,12,17.0,DRIVEALONEFREE +970110133,2957652,1307361,121263766,False,Home,23,5,17.0,DRIVEALONEFREE +970110169,2957652,1307361,121263771,True,work,2,23,7.0,DRIVEALONEFREE +970110173,2957652,1307361,121263771,False,Home,23,2,15.0,DRIVEALONEFREE +970119681,2957681,1307390,121264960,True,work,7,25,8.0,BIKE +970119685,2957681,1307390,121264960,False,Home,25,7,17.0,BIKE +970120993,2957685,1307394,121265124,True,work,15,25,11.0,WALK +970120997,2957685,1307394,121265124,False,Home,25,15,18.0,WALK_LOC +970123945,2957694,1307403,121265493,True,work,2,25,8.0,WALK +970123949,2957694,1307403,121265493,False,Home,25,2,18.0,WALK +989462777,3016654,1340847,123682847,True,shopping,17,8,11.0,DRIVEALONEFREE +989462781,3016654,1340847,123682847,False,shopping,6,17,12.0,TNC_SINGLE +989462782,3016654,1340847,123682847,False,shopping,10,6,12.0,DRIVEALONEFREE +989462783,3016654,1340847,123682847,False,shopping,11,10,12.0,DRIVEALONEFREE +989462784,3016654,1340847,123682847,False,Home,8,11,12.0,TNC_SINGLE +989462785,3016654,1340847,123682848,True,shopping,20,8,16.0,TNC_SINGLE +989462789,3016654,1340847,123682848,False,othmaint,13,20,16.0,DRIVEALONEFREE +989462790,3016654,1340847,123682848,False,othdiscr,5,13,16.0,DRIVEALONEFREE +989462791,3016654,1340847,123682848,False,Home,8,5,16.0,TAXI +989463041,3016655,1340847,123682880,True,othdiscr,9,8,11.0,TNC_SINGLE +989463045,3016655,1340847,123682880,False,eatout,20,9,14.0,TAXI +989463046,3016655,1340847,123682880,False,Home,8,20,14.0,TNC_SINGLE +989497353,3016760,1340900,123687169,True,escort,16,10,9.0,WALK +989497357,3016760,1340900,123687169,False,Home,10,16,14.0,WALK +989497505,3016760,1340900,123687188,True,othmaint,4,10,14.0,WALK_LOC +989497509,3016760,1340900,123687188,False,Home,10,4,15.0,WALK_HVY +989497657,3016761,1340900,123687207,True,eatout,9,10,10.0,WALK +989497661,3016761,1340900,123687207,False,Home,10,9,13.0,WALK +989497833,3016761,1340900,123687229,True,othmaint,9,10,13.0,WALK +989497837,3016761,1340900,123687229,False,Home,10,9,16.0,WALK +989521121,3016832,1340936,123690140,True,othmaint,6,22,6.0,WALK +989521125,3016832,1340936,123690140,False,Home,22,6,10.0,WALK +989521129,3016832,1340936,123690141,True,othmaint,17,22,12.0,WALK +989521133,3016832,1340936,123690141,False,Home,22,17,12.0,WALK +989521137,3016832,1340936,123690142,True,othmaint,6,22,15.0,SHARED3FREE +989521141,3016832,1340936,123690142,False,Home,22,6,15.0,DRIVEALONEFREE +989521297,3016833,1340936,123690162,True,escort,5,22,14.0,TNC_SINGLE +989521301,3016833,1340936,123690162,False,shopping,5,5,14.0,TNC_SINGLE +989521302,3016833,1340936,123690162,False,Home,22,5,15.0,WALK_LOC +989521489,3016833,1340936,123690186,True,shopping,13,22,12.0,TNC_SHARED +989521490,3016833,1340936,123690186,True,shopping,16,13,13.0,TNC_SINGLE +989521493,3016833,1340936,123690186,False,Home,22,16,13.0,TAXI +1004039689,3061096,1363068,125504961,True,othdiscr,18,3,7.0,WALK_LOC +1004039693,3061096,1363068,125504961,False,Home,3,18,17.0,WALK_LOC +1004040129,3061097,1363068,125505016,True,work,2,3,8.0,WALK +1004040133,3061097,1363068,125505016,False,shopping,5,2,18.0,WALK +1004040134,3061097,1363068,125505016,False,Home,3,5,19.0,WALK_LOC +1004064489,3061172,1363106,125508061,True,escort,22,7,8.0,WALK +1004064493,3061172,1363106,125508061,False,Home,7,22,8.0,WALK +1004064969,3061173,1363106,125508121,True,othmaint,11,7,7.0,WALK +1004064973,3061173,1363106,125508121,False,Home,7,11,7.0,WALK +1004065057,3061173,1363106,125508132,True,work,12,7,8.0,BIKE +1004065061,3061173,1363106,125508132,False,Home,7,12,17.0,BIKE +1004089417,3061248,1363144,125511177,True,escort,3,8,9.0,WALK +1004089421,3061248,1363144,125511177,False,Home,8,3,9.0,WALK +1004089873,3061249,1363144,125511234,True,othdiscr,12,8,12.0,WALK +1004089877,3061249,1363144,125511234,False,Home,8,12,17.0,WALK +1004113753,3061322,1363181,125514219,True,othmaint,4,10,8.0,TNC_SINGLE +1004113757,3061322,1363181,125514219,False,Home,10,4,8.0,TNC_SINGLE +1004113977,3061323,1363181,125514247,True,atwork,19,2,13.0,SHARED2FREE +1004113981,3061323,1363181,125514247,False,Work,2,19,13.0,SHARED2FREE +1004114257,3061323,1363181,125514282,True,escort,8,10,8.0,WALK +1004114258,3061323,1363181,125514282,True,work,2,8,9.0,WALK +1004114261,3061323,1363181,125514282,False,Home,10,2,17.0,WALK_LRF +1004119881,3061341,1363190,125514985,True,atwork,3,1,12.0,WALK +1004119885,3061341,1363190,125514985,False,Work,1,3,13.0,WALK +1004120161,3061341,1363190,125515020,True,work,1,10,7.0,WALK +1004120165,3061341,1363190,125515020,False,Home,10,1,21.0,WALK_LRF +1004147713,3061425,1363232,125518464,True,work,21,10,7.0,WALK_LOC +1004147717,3061425,1363232,125518464,False,Home,10,21,16.0,WALK +1004159737,3061462,1363251,125519967,True,othdiscr,20,11,18.0,WALK_LOC +1004159741,3061462,1363251,125519967,False,Home,11,20,21.0,WALK_LOC +1004159849,3061462,1363251,125519981,True,work,10,11,13.0,WALK +1004159853,3061462,1363251,125519981,False,Home,11,10,16.0,WALK +1004159913,3061463,1363251,125519989,True,eatout,14,11,17.0,WALK +1004159917,3061463,1363251,125519989,False,shopping,5,14,20.0,WALK +1004159918,3061463,1363251,125519989,False,Home,11,5,20.0,WALK +1004160153,3061463,1363251,125520019,True,social,16,11,7.0,WALK +1004160157,3061463,1363251,125520019,False,Home,11,16,11.0,WALK +1004166409,3061482,1363261,125520801,True,work,9,11,6.0,WALK_LOC +1004166413,3061482,1363261,125520801,False,Home,11,9,15.0,WALK +1004246161,3061726,1363383,125530770,True,atwork,16,12,12.0,WALK +1004246165,3061726,1363383,125530770,False,Work,12,16,14.0,WALK +1004246441,3061726,1363383,125530805,True,work,12,20,7.0,WALK_LOC +1004246445,3061726,1363383,125530805,False,Home,20,12,17.0,WALK_LOC +1004254593,3061751,1363395,125531824,True,shopping,5,20,11.0,WALK +1004254597,3061751,1363395,125531824,False,Home,20,5,13.0,WALK +1004301497,3061894,1363467,125537687,True,shopping,20,24,12.0,TNC_SHARED +1004301501,3061894,1363467,125537687,False,Home,24,20,13.0,DRIVEALONEFREE +1004301761,3061895,1363467,125537720,True,othdiscr,9,24,17.0,WALK_HVY +1004301765,3061895,1363467,125537720,False,Home,24,9,19.0,WALK_HVY +1004301785,3061895,1363467,125537723,True,othmaint,7,24,15.0,WALK +1004301789,3061895,1363467,125537723,False,Home,24,7,16.0,WALK +1004301873,3061895,1363467,125537734,True,work,25,24,6.0,WALK +1004301877,3061895,1363467,125537734,False,Home,24,25,13.0,WALK +1008051809,3073328,1369184,126006476,True,othmaint,9,22,11.0,WALK_HVY +1008051813,3073328,1369184,126006476,False,Home,22,9,15.0,WALK_LRF +1008051897,3073328,1369184,126006487,True,work,4,22,15.0,BIKE +1008051901,3073328,1369184,126006487,False,Home,22,4,22.0,BIKE +1008052225,3073329,1369184,126006528,True,work,11,22,7.0,WALK_LRF +1008052229,3073329,1369184,126006528,False,Home,22,11,21.0,WALK_LRF +1008053209,3073332,1369186,126006651,True,work,12,22,7.0,WALK +1008053213,3073332,1369186,126006651,False,Home,22,12,14.0,WALK_LOC +1008053273,3073333,1369186,126006659,True,eatout,3,22,16.0,WALK +1008053277,3073333,1369186,126006659,False,Home,22,3,16.0,WALK +1008053449,3073333,1369186,126006681,True,othmaint,22,22,16.0,TNC_SINGLE +1008053453,3073333,1369186,126006681,False,Home,22,22,17.0,TNC_SINGLE +1008053489,3073333,1369186,126006686,True,shopping,7,22,15.0,DRIVEALONEFREE +1008053493,3073333,1369186,126006686,False,Home,22,7,15.0,DRIVEALONEFREE +1008053497,3073333,1369186,126006687,True,shopping,2,22,17.0,WALK +1008053501,3073333,1369186,126006687,False,Home,22,2,19.0,WALK +1008053537,3073333,1369186,126006692,True,escort,3,22,7.0,WALK_LOC +1008053538,3073333,1369186,126006692,True,work,14,3,7.0,WALK_LOC +1008053541,3073333,1369186,126006692,False,escort,2,14,14.0,WALK +1008053542,3073333,1369186,126006692,False,Home,22,2,15.0,WALK_LOC +1011081065,3082564,1373802,126385133,True,escort,17,16,17.0,WALK +1011081069,3082564,1373802,126385133,False,Home,16,17,17.0,WALK +1011081073,3082564,1373802,126385134,True,escort,16,16,17.0,TNC_SHARED +1011081077,3082564,1373802,126385134,False,Home,16,16,18.0,TNC_SINGLE +1011081633,3082565,1373802,126385204,True,work,18,16,7.0,WALK +1011081637,3082565,1373802,126385204,False,Home,16,18,18.0,WALK +1011109513,3082650,1373845,126388689,True,work,2,21,6.0,WALK +1011109517,3082650,1373845,126388689,False,Home,21,2,19.0,WALK +1011109561,3082651,1373845,126388695,True,atwork,22,5,13.0,TNC_SINGLE +1011109565,3082651,1373845,126388695,False,Work,5,22,13.0,TNC_SINGLE +1011109577,3082651,1373845,126388697,True,eatout,1,21,21.0,DRIVEALONEFREE +1011109581,3082651,1373845,126388697,False,Home,21,1,21.0,SHARED2FREE +1011109793,3082651,1373845,126388724,True,shopping,16,21,18.0,WALK +1011109797,3082651,1373845,126388724,False,Home,21,16,20.0,WALK +1011109841,3082651,1373845,126388730,True,work,5,21,7.0,WALK +1011109845,3082651,1373845,126388730,False,Home,21,5,18.0,WALK +1011158057,3082798,1373919,126394757,True,work,11,25,6.0,WALK +1011158061,3082798,1373919,126394757,False,Home,25,11,17.0,WALK +1011158385,3082799,1373919,126394798,True,work,25,25,8.0,WALK +1011158389,3082799,1373919,126394798,False,Home,25,25,21.0,WALK +1020944745,3112636,1384875,127618093,True,othmaint,13,10,10.0,SHARED2FREE +1020944749,3112636,1384875,127618093,False,othmaint,4,13,12.0,SHARED2FREE +1020944750,3112636,1384875,127618093,False,Home,10,4,13.0,TNC_SINGLE +1020944873,3112636,1384875,127618109,True,shopping,1,10,15.0,WALK_LRF +1020944877,3112636,1384875,127618109,False,othmaint,7,1,16.0,WALK_LOC +1020944878,3112636,1384875,127618109,False,Home,10,7,16.0,WALK_LOC +1020945313,3112638,1384875,127618164,True,eatout,11,10,10.0,WALK +1020945317,3112638,1384875,127618164,False,Home,10,11,13.0,WALK +1020959305,3112680,1384889,127619913,True,shopping,14,11,20.0,WALK_LOC +1020959309,3112680,1384889,127619913,False,Home,11,14,22.0,TNC_SINGLE +1020959353,3112680,1384889,127619919,True,escort,13,11,10.0,BIKE +1020959354,3112680,1384889,127619919,True,work,16,13,10.0,BIKE +1020959357,3112680,1384889,127619919,False,Home,11,16,20.0,BIKE +1020978753,3112740,1384909,127622344,True,atwork,13,14,10.0,WALK +1020978757,3112740,1384909,127622344,False,othmaint,14,13,10.0,WALK +1020978758,3112740,1384909,127622344,False,Work,14,14,10.0,WALK +1020978945,3112740,1384909,127622368,True,othmaint,16,21,14.0,BIKE +1020978949,3112740,1384909,127622368,False,Home,21,16,17.0,BIKE +1020979033,3112740,1384909,127622379,True,work,14,21,7.0,WALK +1020979037,3112740,1384909,127622379,False,Home,21,14,13.0,WALK_LOC +1025529329,3126613,1389534,128191166,True,shopping,11,16,12.0,WALK_LOC +1025529333,3126613,1389534,128191166,False,Home,16,11,13.0,TNC_SINGLE +1025529337,3126613,1389534,128191167,True,shopping,16,16,18.0,WALK +1025529341,3126613,1389534,128191167,False,Home,16,16,18.0,WALK +1025529705,3126614,1389534,128191213,True,work,9,16,8.0,WALK_LOC +1025529709,3126614,1389534,128191213,False,Home,16,9,18.0,WALK_LRF +1029292673,3138091,1392610,128661584,True,othmaint,22,25,19.0,WALK_LOC +1029292677,3138091,1392610,128661584,False,Home,25,22,21.0,WALK +1029292801,3138087,1392610,128661600,True,shopping,5,25,13.0,WALK +1029292805,3138087,1392610,128661600,False,eatout,6,5,16.0,BIKE +1029292806,3138087,1392610,128661600,False,Home,25,6,16.0,BIKE +1029294097,3138091,1392610,128661762,True,school,18,25,7.0,WALK_LOC +1029294101,3138091,1392610,128661762,False,Home,25,18,16.0,WALK_LOC +1029294425,3138092,1392610,128661803,True,school,24,25,8.0,WALK +1029294429,3138092,1392610,128661803,False,Home,25,24,19.0,WALK_LOC +1029294705,3138093,1392610,128661838,True,othdiscr,10,25,21.0,WALK_LRF +1029294709,3138093,1392610,128661838,False,Home,25,10,21.0,WALK_LRF +1029294729,3138093,1392610,128661841,True,othmaint,22,25,10.0,WALK_LOC +1029294733,3138093,1392610,128661841,False,Home,25,22,16.0,WALK_LOC +1029295409,3138095,1392610,128661926,True,univ,13,25,13.0,WALK_LOC +1029295413,3138095,1392610,128661926,False,Home,25,13,13.0,WALK_LOC +1041329465,3174784,1400319,130166183,True,work,2,10,8.0,WALK +1041329469,3174784,1400319,130166183,False,Home,10,2,19.0,WALK +1041385601,3174956,1400342,130173200,True,atwork,9,9,13.0,WALK +1041385605,3174956,1400342,130173200,False,Work,9,9,13.0,WALK +1041385881,3174956,1400342,130173235,True,work,9,10,8.0,WALK +1041385885,3174956,1400342,130173235,False,Home,10,9,21.0,WALK +1041386209,3174957,1400342,130173276,True,work,22,10,7.0,WALK_LRF +1041386213,3174957,1400342,130173276,False,Home,10,22,16.0,WALK_LRF +1041386273,3174958,1400342,130173284,True,eatout,8,10,15.0,WALK +1041386277,3174958,1400342,130173284,False,Home,10,8,17.0,WALK +1041386777,3174959,1400342,130173347,True,othmaint,6,10,13.0,SHARED2FREE +1041386781,3174959,1400342,130173347,False,Home,10,6,14.0,DRIVEALONEFREE +1041386785,3174959,1400342,130173348,True,othmaint,7,10,17.0,WALK +1041386789,3174959,1400342,130173348,False,Home,10,7,17.0,WALK +1041386841,3174959,1400342,130173355,True,social,1,10,17.0,WALK_LRF +1041386845,3174959,1400342,130173355,False,Home,10,1,19.0,WALK_LRF +1041386953,3174960,1400342,130173369,True,escort,13,10,14.0,WALK +1041386957,3174960,1400342,130173369,False,Home,10,13,23.0,WALK +1041387145,3174960,1400342,130173393,True,shopping,11,10,12.0,WALK +1041387149,3174960,1400342,130173393,False,Home,10,11,14.0,WALK +1041387409,3174961,1400342,130173426,True,othdiscr,9,10,14.0,BIKE +1041387413,3174961,1400342,130173426,False,Home,10,9,16.0,BIKE +1041387785,3174962,1400342,130173473,True,school,10,10,8.0,WALK +1041387789,3174962,1400342,130173473,False,Home,10,10,13.0,WALK +1041387937,3174963,1400342,130173492,True,escort,5,10,16.0,SHARED2FREE +1041387941,3174963,1400342,130173492,False,Home,10,5,17.0,WALK +1041388113,3174963,1400342,130173514,True,school,20,10,8.0,WALK +1041388117,3174963,1400342,130173514,False,Home,10,20,15.0,WALK +1041388129,3174963,1400342,130173516,True,othdiscr,11,10,17.0,WALK +1041388130,3174963,1400342,130173516,True,shopping,16,11,18.0,WALK +1041388133,3174963,1400342,130173516,False,Home,10,16,21.0,WALK +1041419553,3175059,1400360,130177444,True,othdiscr,24,11,13.0,WALK +1041419557,3175059,1400360,130177444,False,Home,11,24,17.0,WALK +1041419993,3175060,1400360,130177499,True,work,25,11,6.0,WALK +1041419997,3175060,1400360,130177499,False,Home,11,25,20.0,WALK +1041420585,3175062,1400360,130177573,True,school,21,11,8.0,WALK +1041420589,3175062,1400360,130177573,False,othdiscr,8,21,17.0,WALK_LOC +1041420590,3175062,1400360,130177573,False,escort,7,8,17.0,WALK +1041420591,3175062,1400360,130177573,False,Home,11,7,17.0,WALK_LOC +1041420713,3175063,1400360,130177589,True,eatout,16,11,9.0,WALK +1041420717,3175063,1400360,130177589,False,Home,11,16,18.0,WALK +1041420721,3175063,1400360,130177590,True,eatout,16,11,18.0,WALK +1041420725,3175063,1400360,130177590,False,Home,11,16,18.0,WALK +1041472561,3175221,1400387,130184070,True,escort,12,21,10.0,TNC_SINGLE +1041472565,3175221,1400387,130184070,False,Home,21,12,17.0,WALK_LOC +1041473081,3175222,1400387,130184135,True,shopping,5,21,7.0,WALK +1041473085,3175222,1400387,130184135,False,Home,21,5,12.0,WALK +1041473105,3175222,1400387,130184138,True,social,11,21,14.0,WALK +1041473109,3175222,1400387,130184138,False,Home,21,11,17.0,WALK +1041473113,3175222,1400387,130184139,True,social,5,21,20.0,WALK +1041473117,3175222,1400387,130184139,False,Home,21,5,20.0,WALK +1041473457,3175223,1400387,130184182,True,work,5,21,7.0,WALK +1041473461,3175223,1400387,130184182,False,Home,21,5,16.0,WALK +1041473673,3175224,1400387,130184209,True,eatout,10,21,8.0,WALK_LOC +1041473674,3175224,1400387,130184209,True,othdiscr,9,10,8.0,WALK_LOC +1041473677,3175224,1400387,130184209,False,escort,7,9,22.0,WALK_LOC +1041473678,3175224,1400387,130184209,False,Home,21,7,22.0,WALK_LOC +1041473873,3175225,1400387,130184234,True,escort,16,21,8.0,WALK +1041473877,3175225,1400387,130184234,False,Home,21,16,9.0,WALK +1045757945,3188286,1402915,130719743,True,othmaint,16,10,15.0,TNC_SHARED +1045757949,3188286,1402915,130719743,False,eatout,13,16,16.0,WALK +1045757950,3188286,1402915,130719743,False,Home,10,13,18.0,WALK +1045757977,3188286,1402915,130719747,True,social,16,10,15.0,TNC_SHARED +1045757981,3188286,1402915,130719747,False,Home,10,16,15.0,WALK_LOC +1045759345,3188290,1402915,130719918,True,othmaint,14,10,18.0,WALK +1045759349,3188290,1402915,130719918,False,Home,10,14,20.0,WALK +1045759433,3188290,1402915,130719929,True,work,9,10,11.0,WALK +1045759437,3188290,1402915,130719929,False,Home,10,9,15.0,WALK +1045760025,3188292,1402915,130720003,True,school,11,10,7.0,WALK +1045760029,3188292,1402915,130720003,False,Home,10,11,15.0,WALK +1045760633,3188294,1402915,130720079,True,othdiscr,4,10,11.0,WALK_LRF +1045760637,3188294,1402915,130720079,False,Home,10,4,13.0,WALK_LRF +1045761009,3188295,1402915,130720126,True,school,17,10,7.0,WALK_LRF +1045761013,3188295,1402915,130720126,False,Home,10,17,15.0,WALK_LRF +1045795841,3188401,1402932,130724480,True,work,14,17,5.0,WALK +1045795845,3188401,1402932,130724480,False,Home,17,14,16.0,WALK +1045796169,3188402,1402932,130724521,True,work,14,17,14.0,WALK +1045796173,3188402,1402932,130724521,False,Home,17,14,16.0,WALK +1045796497,3188403,1402932,130724562,True,work,5,17,6.0,WALK_LRF +1045796501,3188403,1402932,130724562,False,Home,17,5,18.0,WALK_LOC +1045796825,3188404,1402932,130724603,True,work,4,17,8.0,WALK +1045796829,3188404,1402932,130724603,False,escort,14,4,18.0,WALK +1045796830,3188404,1402932,130724603,False,escort,16,14,18.0,WALK +1045796831,3188404,1402932,130724603,False,othmaint,16,16,19.0,WALK +1045796832,3188404,1402932,130724603,False,Home,17,16,19.0,WALK +1045797025,3188405,1402932,130724628,True,atwork,16,11,11.0,WALK +1045797029,3188405,1402932,130724628,False,eatout,5,16,14.0,WALK +1045797030,3188405,1402932,130724628,False,othmaint,7,5,14.0,WALK +1045797031,3188405,1402932,130724628,False,othmaint,9,7,14.0,WALK +1045797032,3188405,1402932,130724628,False,Work,11,9,14.0,WALK +1045797153,3188405,1402932,130724644,True,work,11,17,7.0,WALK +1045797157,3188405,1402932,130724644,False,Home,17,11,17.0,SHARED2FREE +1045798465,3188409,1402933,130724808,True,work,11,17,5.0,WALK +1045798469,3188409,1402933,130724808,False,shopping,13,11,23.0,WALK +1045798470,3188409,1402933,130724808,False,Home,17,13,23.0,WALK +1045822217,3188483,1402945,130727777,True,othdiscr,24,25,9.0,WALK +1045822221,3188483,1402945,130727777,False,Home,25,24,9.0,TNC_SINGLE +1045822409,3188482,1402945,130727801,True,work,6,25,8.0,WALK +1045822410,3188482,1402945,130727801,True,work,24,6,9.0,WALK +1045822413,3188482,1402945,130727801,False,Home,25,24,17.0,WALK +1045822737,3188483,1402945,130727842,True,work,2,25,9.0,WALK +1045822741,3188483,1402945,130727842,False,Home,25,2,17.0,WALK_LOC +1045823001,3188484,1402945,130727875,True,univ,12,25,16.0,WALK +1045823005,3188484,1402945,130727875,False,shopping,5,12,17.0,WALK +1045823006,3188484,1402945,130727875,False,othmaint,7,5,17.0,WALK +1045823007,3188484,1402945,130727875,False,Home,25,7,17.0,WALK +1045823065,3188484,1402945,130727883,True,work,2,25,9.0,WALK +1045823069,3188484,1402945,130727883,False,Home,25,2,12.0,WALK_LOC +1045823393,3188485,1402945,130727924,True,escort,24,25,10.0,BIKE +1045823394,3188485,1402945,130727924,True,work,2,24,12.0,BIKE +1045823397,3188485,1402945,130727924,False,eatout,5,2,23.0,BIKE +1045823398,3188485,1402945,130727924,False,Home,25,5,23.0,BIKE +1047989721,3195090,1406850,130998715,True,othdiscr,7,3,10.0,WALK +1047989725,3195090,1406850,130998715,False,Home,3,7,16.0,WALK +1047995345,3195107,1406867,130999418,True,univ,12,5,7.0,WALK +1047995349,3195107,1406867,130999418,False,Home,5,12,7.0,WALK +1047995353,3195107,1406867,130999419,True,shopping,16,5,11.0,DRIVEALONEFREE +1047995354,3195107,1406867,130999419,True,othmaint,5,16,11.0,DRIVEALONEFREE +1047995355,3195107,1406867,130999419,True,univ,12,5,11.0,DRIVEALONEFREE +1047995357,3195107,1406867,130999419,False,Home,5,12,11.0,DRIVEALONEFREE +1047996937,3195112,1406872,130999617,True,othdiscr,16,5,10.0,WALK_LOC +1047996941,3195112,1406872,130999617,False,Home,5,16,13.0,WALK +1048009073,3195149,1406909,131001134,True,othdiscr,11,6,12.0,WALK +1048009077,3195149,1406909,131001134,False,Home,6,11,21.0,WALK +1048034657,3195227,1406987,131004332,True,othdiscr,9,8,11.0,WALK +1048034661,3195227,1406987,131004332,False,Home,8,9,14.0,WALK +1048054057,3195286,1407046,131006757,True,univ,9,9,8.0,WALK +1048054061,3195286,1407046,131006757,False,Home,9,9,10.0,WALK +1048055697,3195291,1407051,131006962,True,univ,10,9,18.0,WALK +1048055701,3195291,1407051,131006962,False,eatout,9,10,19.0,WALK_LOC +1048055702,3195291,1407051,131006962,False,Home,9,9,19.0,WALK +1048062209,3195311,1407071,131007776,True,othdiscr,22,9,16.0,TNC_SINGLE +1048062213,3195311,1407071,131007776,False,shopping,6,22,18.0,TNC_SINGLE +1048062214,3195311,1407071,131007776,False,othdiscr,8,6,18.0,TNC_SINGLE +1048062215,3195311,1407071,131007776,False,Home,9,8,18.0,WALK_LOC +1048062233,3195311,1407071,131007779,True,othmaint,2,9,18.0,WALK_LRF +1048062237,3195311,1407071,131007779,False,Home,9,2,20.0,WALK_LRF +1048062257,3195311,1407071,131007782,True,shopping,5,9,8.0,WALK_LOC +1048062258,3195311,1407071,131007782,True,univ,10,5,8.0,WALK_LOC +1048062261,3195311,1407071,131007782,False,Home,9,10,11.0,WALK_LOC +1048062273,3195311,1407071,131007784,True,shopping,5,9,12.0,TNC_SINGLE +1048062277,3195311,1407071,131007784,False,eatout,7,5,14.0,TNC_SINGLE +1048062278,3195311,1407071,131007784,False,shopping,11,7,15.0,WALK_LOC +1048062279,3195311,1407071,131007784,False,shopping,21,11,15.0,TNC_SINGLE +1048062280,3195311,1407071,131007784,False,Home,9,21,15.0,WALK_LOC +1048069425,3195333,1407093,131008678,True,othdiscr,16,17,12.0,SHARED2FREE +1048069429,3195333,1407093,131008678,False,Home,17,16,13.0,WALK +1048069449,3195333,1407093,131008681,True,social,13,17,14.0,SHARED2FREE +1048069450,3195333,1407093,131008681,True,othmaint,2,13,15.0,DRIVEALONEFREE +1048069453,3195333,1407093,131008681,False,Home,17,2,16.0,DRIVEALONEFREE +1048070785,3195337,1407097,131008848,True,univ,12,17,13.0,WALK_LOC +1048070789,3195337,1407097,131008848,False,shopping,4,12,16.0,WALK_LOC +1048070790,3195337,1407097,131008848,False,Home,17,4,17.0,WALK_LRF +1048070801,3195337,1407097,131008850,True,shopping,21,17,11.0,SHARED3FREE +1048070805,3195337,1407097,131008850,False,shopping,5,21,13.0,DRIVEALONEFREE +1048070806,3195337,1407097,131008850,False,shopping,7,5,13.0,WALK +1048070807,3195337,1407097,131008850,False,shopping,11,7,13.0,SHARED3FREE +1048070808,3195337,1407097,131008850,False,Home,17,11,13.0,SHARED3FREE +1048075529,3195352,1407112,131009441,True,escort,8,18,13.0,TNC_SINGLE +1048075533,3195352,1407112,131009441,False,shopping,7,8,13.0,TNC_SINGLE +1048075534,3195352,1407112,131009441,False,shopping,9,7,13.0,WALK_LOC +1048075535,3195352,1407112,131009441,False,Home,18,9,13.0,TNC_SINGLE +1048078633,3195361,1407121,131009829,True,othmaint,10,18,11.0,WALK +1048078637,3195361,1407121,131009829,False,Home,18,10,14.0,DRIVEALONEFREE +1048097633,3195419,1407179,131012204,True,othdiscr,10,19,9.0,WALK +1048097637,3195419,1407179,131012204,False,Home,19,10,11.0,WALK +1060119649,3232072,1443832,132514956,True,atwork,4,16,16.0,WALK +1060119653,3232072,1443832,132514956,False,Work,16,4,16.0,WALK +1060119841,3232072,1443832,132514980,True,othmaint,15,3,6.0,WALK +1060119845,3232072,1443832,132514980,False,Home,3,15,6.0,WALK +1060119929,3232072,1443832,132514991,True,work,16,3,7.0,DRIVEALONEFREE +1060119933,3232072,1443832,132514991,False,Home,3,16,16.0,SHARED2FREE +1060121897,3232078,1443838,132515237,True,work,5,4,19.0,WALK +1060121901,3232078,1443838,132515237,False,Home,4,5,22.0,WALK +1060131737,3232108,1443868,132516467,True,work,16,6,7.0,WALK +1060131741,3232108,1443868,132516467,False,Home,6,16,17.0,WALK +1060140657,3232136,1443896,132517582,True,social,7,6,16.0,WALK +1060140658,3232136,1443896,132517582,True,eatout,22,7,18.0,WALK_LOC +1060140661,3232136,1443896,132517582,False,shopping,5,22,17.0,WALK_LOC +1060140662,3232136,1443896,132517582,False,Home,6,5,18.0,WALK +1060140833,3232136,1443896,132517604,True,escort,12,6,16.0,WALK_LOC +1060140834,3232136,1443896,132517604,True,escort,8,12,16.0,WALK_LOC +1060140835,3232136,1443896,132517604,True,othmaint,9,8,16.0,WALK_LOC +1060140837,3232136,1443896,132517604,False,shopping,7,9,16.0,WALK_LOC +1060140838,3232136,1443896,132517604,False,Home,6,7,16.0,WALK +1060140921,3232136,1443896,132517615,True,work,17,6,7.0,WALK_LOC +1060140925,3232136,1443896,132517615,False,Home,6,17,16.0,WALK_LOC +1060153713,3232175,1443935,132519214,True,work,7,6,7.0,WALK +1060153717,3232175,1443935,132519214,False,Home,6,7,18.0,WALK +1060171097,3232228,1443988,132521387,True,work,2,7,6.0,WALK_LOC +1060171101,3232228,1443988,132521387,False,Home,7,2,17.0,WALK_LOC +1060174377,3232238,1443998,132521797,True,work,22,7,9.0,WALK_LOC +1060174381,3232238,1443998,132521797,False,shopping,5,22,17.0,TNC_SHARED +1060174382,3232238,1443998,132521797,False,Home,7,5,17.0,WALK +1060187561,3232279,1444039,132523445,True,eatout,5,7,15.0,WALK +1060187565,3232279,1444039,132523445,False,Home,7,5,18.0,WALK +1060195369,3232302,1444062,132524421,True,work,16,7,8.0,WALK +1060195373,3232302,1444062,132524421,False,Home,7,16,20.0,WALK +1060200833,3232319,1444079,132525104,True,othdiscr,2,7,18.0,WALK +1060200837,3232319,1444079,132525104,False,Home,7,2,22.0,WALK +1060200897,3232319,1444079,132525112,True,shopping,4,7,17.0,DRIVEALONEFREE +1060200898,3232319,1444079,132525112,True,shopping,5,4,17.0,SHARED3FREE +1060200901,3232319,1444079,132525112,False,Home,7,5,17.0,SHARED3FREE +1060200945,3232319,1444079,132525118,True,work,9,7,7.0,WALK +1060200949,3232319,1444079,132525118,False,Home,7,9,16.0,WALK +1060205209,3232332,1444092,132525651,True,work,2,7,7.0,TNC_SINGLE +1060205213,3232332,1444092,132525651,False,Home,7,2,18.0,TNC_SHARED +1060225545,3232394,1444154,132528193,True,work,4,8,10.0,WALK +1060225549,3232394,1444154,132528193,False,Home,8,4,21.0,WALK +1060254145,3232482,1444242,132531768,True,eatout,2,9,21.0,WALK_LRF +1060254149,3232482,1444242,132531768,False,Home,9,2,22.0,WALK_LRF +1060254385,3232482,1444242,132531798,True,social,2,9,20.0,DRIVEALONEFREE +1060254389,3232482,1444242,132531798,False,Home,9,2,20.0,DRIVEALONEFREE +1060254409,3232482,1444242,132531801,True,work,9,9,8.0,BIKE +1060254413,3232482,1444242,132531801,False,Home,9,9,19.0,TAXI +1060255393,3232485,1444245,132531924,True,work,8,9,7.0,WALK +1060255397,3232485,1444245,132531924,False,Home,9,8,19.0,WALK +1060257249,3232491,1444251,132532156,True,shopping,6,9,11.0,WALK_LOC +1060257250,3232491,1444251,132532156,True,othdiscr,2,6,14.0,WALK_LOC +1060257253,3232491,1444251,132532156,False,Home,9,2,15.0,WALK_LRF +1060257257,3232491,1444251,132532157,True,othdiscr,8,9,17.0,WALK_LOC +1060257261,3232491,1444251,132532157,False,Home,9,8,19.0,TNC_SINGLE +1060259329,3232497,1444257,132532416,True,work,10,9,7.0,WALK +1060259333,3232497,1444257,132532416,False,Home,9,10,17.0,WALK +1060269057,3232527,1444287,132533632,True,othdiscr,11,9,17.0,WALK +1060269061,3232527,1444287,132533632,False,Home,9,11,19.0,WALK +1060269169,3232527,1444287,132533646,True,work,2,9,6.0,WALK_LRF +1060269173,3232527,1444287,132533646,False,Home,9,2,16.0,WALK_LRF +1060284145,3232573,1444333,132535518,True,othdiscr,5,9,12.0,BIKE +1060284149,3232573,1444333,132535518,False,Home,9,5,15.0,BIKE +1060284257,3232573,1444333,132535532,True,work,17,9,18.0,BIKE +1060284261,3232573,1444333,132535532,False,Home,9,17,20.0,BIKE +1060290489,3232592,1444352,132536311,True,work,20,9,9.0,WALK +1060290493,3232592,1444352,132536311,False,Home,9,20,19.0,WALK +1060291345,3232595,1444355,132536418,True,atwork,4,7,14.0,WALK +1060291349,3232595,1444355,132536418,False,Work,7,4,14.0,WALK +1060291361,3232595,1444355,132536420,True,othdiscr,12,9,10.0,WALK_LRF +1060291365,3232595,1444355,132536420,False,Home,9,12,12.0,WALK_LRF +1060291473,3232595,1444355,132536434,True,work,7,9,14.0,WALK +1060291477,3232595,1444355,132536434,False,eatout,5,7,19.0,WALK +1060291478,3232595,1444355,132536434,False,Home,9,5,20.0,WALK +1060302625,3232629,1444389,132537828,True,work,14,9,8.0,WALK +1060302629,3232629,1444389,132537828,False,Home,9,14,18.0,WALK +1060316793,3232673,1444433,132539599,True,eatout,14,9,21.0,WALK_LRF +1060316797,3232673,1444433,132539599,False,Home,9,14,22.0,WALK_LRF +1060316945,3232673,1444433,132539618,True,othdiscr,13,9,9.0,WALK_LRF +1060316949,3232673,1444433,132539618,False,Home,9,13,10.0,WALK_LRF +1060317057,3232673,1444433,132539632,True,work,9,9,11.0,WALK +1060317061,3232673,1444433,132539632,False,Home,9,9,19.0,WALK +1060353137,3232783,1444543,132544142,True,work,9,10,6.0,WALK +1060353141,3232783,1444543,132544142,False,Home,10,9,17.0,WALK +1060353185,3232784,1444544,132544148,True,eatout,8,13,11.0,WALK +1060353186,3232784,1444544,132544148,True,shopping,5,8,11.0,WALK +1060353187,3232784,1444544,132544148,True,atwork,2,5,11.0,WALK +1060353189,3232784,1444544,132544148,False,Work,13,2,13.0,WALK +1060353465,3232784,1444544,132544183,True,work,13,10,9.0,WALK_LRF +1060353469,3232784,1444544,132544183,False,othmaint,16,13,19.0,WALK_LOC +1060353470,3232784,1444544,132544183,False,Home,10,16,19.0,WALK_LOC +1060359745,3232804,1444564,132544968,True,atwork,17,14,11.0,WALK +1060359749,3232804,1444564,132544968,False,othmaint,12,17,14.0,WALK +1060359750,3232804,1444564,132544968,False,Work,14,12,14.0,WALK +1060359913,3232804,1444564,132544989,True,othdiscr,9,11,17.0,WALK +1060359917,3232804,1444564,132544989,False,Home,11,9,20.0,WALK +1060360025,3232804,1444564,132545003,True,work,14,11,7.0,DRIVEALONEFREE +1060360029,3232804,1444564,132545003,False,Home,11,14,17.0,DRIVEALONEFREE +1060405665,3232944,1444704,132550708,True,atwork,9,9,14.0,WALK +1060405669,3232944,1444704,132550708,False,Work,9,9,14.0,WALK +1060405833,3232944,1444704,132550729,True,othdiscr,14,14,5.0,WALK +1060405837,3232944,1444704,132550729,False,Home,14,14,6.0,WALK +1060405945,3232944,1444704,132550743,True,work,9,14,7.0,DRIVEALONEFREE +1060405949,3232944,1444704,132550743,False,Home,14,9,18.0,DRIVEALONEFREE +1060421689,3232992,1444752,132552711,True,work,14,16,7.0,WALK +1060421693,3232992,1444752,132552711,False,Home,16,14,13.0,WALK +1060433825,3233029,1444789,132554228,True,shopping,24,16,8.0,WALK +1060433826,3233029,1444789,132554228,True,othmaint,1,24,8.0,WALK +1060433827,3233029,1444789,132554228,True,work,14,1,12.0,WALK +1060433829,3233029,1444789,132554228,False,shopping,5,14,18.0,WALK +1060433830,3233029,1444789,132554228,False,Home,16,5,18.0,WALK_LOC +1060436449,3233037,1444797,132554556,True,work,2,16,7.0,WALK +1060436453,3233037,1444797,132554556,False,Home,16,2,18.0,WALK_LOC +1060440105,3233049,1444809,132555013,True,atwork,15,14,8.0,WALK +1060440109,3233049,1444809,132555013,False,Work,14,15,8.0,WALK +1060440385,3233049,1444809,132555048,True,work,14,16,7.0,TNC_SINGLE +1060440389,3233049,1444809,132555048,False,Home,16,14,19.0,TNC_SINGLE +1060450881,3233081,1444841,132556360,True,escort,6,17,7.0,DRIVEALONEFREE +1060450882,3233081,1444841,132556360,True,work,25,6,8.0,WALK +1060450885,3233081,1444841,132556360,False,eatout,11,25,16.0,DRIVEALONEFREE +1060450886,3233081,1444841,132556360,False,Home,17,11,17.0,DRIVEALONEFREE +1060456849,3233100,1444860,132557106,True,eatout,17,17,18.0,WALK +1060456853,3233100,1444860,132557106,False,Home,17,17,22.0,WALK +1060456873,3233100,1444860,132557109,True,escort,18,17,17.0,DRIVEALONEFREE +1060456877,3233100,1444860,132557109,False,Home,17,18,17.0,SHARED3FREE +1060457113,3233100,1444860,132557139,True,eatout,5,17,8.0,WALK +1060457114,3233100,1444860,132557139,True,work,6,5,9.0,WALK +1060457117,3233100,1444860,132557139,False,shopping,4,6,13.0,WALK +1060457118,3233100,1444860,132557139,False,Home,17,4,17.0,WALK +1060461113,3233113,1444873,132557639,True,eatout,13,17,18.0,WALK +1060461117,3233113,1444873,132557639,False,Home,17,13,19.0,WALK +1060461265,3233113,1444873,132557658,True,othdiscr,17,17,15.0,WALK +1060461269,3233113,1444873,132557658,False,Home,17,17,17.0,WALK +1060461377,3233113,1444873,132557672,True,work,22,17,7.0,WALK +1060461381,3233113,1444873,132557672,False,Home,17,22,11.0,WALK +1060461385,3233113,1444873,132557673,True,work,22,17,11.0,WALK +1060461389,3233113,1444873,132557673,False,othmaint,12,22,13.0,WALK +1060461390,3233113,1444873,132557673,False,Home,17,12,14.0,WALK +1060467937,3233133,1444893,132558492,True,work,15,17,7.0,WALK_LOC +1060467941,3233133,1444893,132558492,False,Home,17,15,19.0,TNC_SINGLE +1060467985,3233134,1444894,132558498,True,atwork,12,1,13.0,SHARED2FREE +1060467989,3233134,1444894,132558498,False,eatout,15,12,13.0,SHARED2FREE +1060467990,3233134,1444894,132558498,False,Work,1,15,13.0,WALK +1060468265,3233134,1444894,132558533,True,escort,5,17,5.0,TNC_SINGLE +1060468266,3233134,1444894,132558533,True,work,1,5,5.0,SHARED3FREE +1060468269,3233134,1444894,132558533,False,Home,17,1,19.0,WALK_LRF +1060487177,3233192,1444952,132560897,True,othdiscr,24,17,9.0,SHARED3FREE +1060487181,3233192,1444952,132560897,False,Home,17,24,23.0,WALK_LRF +1060489913,3233200,1444960,132561239,True,work,2,17,8.0,SHARED3FREE +1060489917,3233200,1444960,132561239,False,Home,17,2,18.0,WALK_LRF +1060516369,3233281,1445041,132564546,True,othdiscr,9,17,11.0,WALK_LRF +1060516373,3233281,1445041,132564546,False,Home,17,9,11.0,SHARED3FREE +1060516809,3233282,1445042,132564601,True,work,22,17,7.0,WALK_LRF +1060516813,3233282,1445042,132564601,False,shopping,4,22,20.0,WALK_LRF +1060516814,3233282,1445042,132564601,False,Home,17,4,20.0,WALK +1060521313,3233296,1445056,132565164,True,othmaint,9,17,18.0,DRIVEALONEFREE +1060521317,3233296,1445056,132565164,False,shopping,16,9,18.0,WALK +1060521318,3233296,1445056,132565164,False,escort,12,16,19.0,WALK +1060521319,3233296,1445056,132565164,False,social,2,12,19.0,SHARED3FREE +1060521320,3233296,1445056,132565164,False,Home,17,2,19.0,SHARED3FREE +1060521401,3233296,1445056,132565175,True,work,13,17,7.0,WALK +1060521405,3233296,1445056,132565175,False,Home,17,13,18.0,WALK +1060523369,3233302,1445062,132565421,True,work,12,17,7.0,WALK +1060523373,3233302,1445062,132565421,False,Home,17,12,18.0,WALK +1060524745,3233307,1445067,132565593,True,eatout,2,17,18.0,WALK +1060524749,3233307,1445067,132565593,False,Home,17,2,20.0,WALK +1060524961,3233307,1445067,132565620,True,shopping,11,17,11.0,WALK_LOC +1060524965,3233307,1445067,132565620,False,Home,17,11,18.0,WALK_LOC +1060535177,3233338,1445098,132566897,True,work,4,17,8.0,DRIVEALONEFREE +1060535181,3233338,1445098,132566897,False,Home,17,4,11.0,DRIVEALONEFREE +1060535185,3233338,1445098,132566898,True,work,4,17,13.0,WALK_LOC +1060535189,3233338,1445098,132566898,False,Home,17,4,17.0,WALK +1060546657,3233373,1445133,132568332,True,work,12,17,11.0,WALK_LOC +1060546661,3233373,1445133,132568332,False,Home,17,12,18.0,WALK +1060572617,3233453,1445213,132571577,True,eatout,5,2,11.0,WALK +1060572618,3233453,1445213,132571577,True,eatout,7,5,11.0,WALK +1060572619,3233453,1445213,132571577,True,atwork,4,7,11.0,WALK +1060572621,3233453,1445213,132571577,False,Work,2,4,14.0,WALK +1060572897,3233453,1445213,132571612,True,work,2,17,6.0,WALK_LRF +1060572901,3233453,1445213,132571612,False,Home,17,2,18.0,WALK_LRF +1060575585,3233462,1445222,132571948,True,eatout,5,17,13.0,WALK +1060575589,3233462,1445222,132571948,False,Home,17,5,13.0,WALK +1060575737,3233462,1445222,132571967,True,othdiscr,14,17,19.0,WALK +1060575741,3233462,1445222,132571967,False,Home,17,14,19.0,WALK_LOC +1060575801,3233462,1445222,132571975,True,shopping,16,17,15.0,WALK +1060575805,3233462,1445222,132571975,False,Home,17,16,18.0,WALK_LRF +1060575809,3233462,1445222,132571976,True,shopping,13,17,20.0,WALK_LOC +1060575813,3233462,1445222,132571976,False,Home,17,13,23.0,WALK_LRF +1060575849,3233462,1445222,132571981,True,work,22,17,8.0,TNC_SINGLE +1060575853,3233462,1445222,132571981,False,Home,17,22,13.0,TNC_SINGLE +1060581097,3233478,1445238,132572637,True,work,4,17,6.0,WALK +1060581101,3233478,1445238,132572637,False,Home,17,4,16.0,WALK +1060590281,3233506,1445266,132573785,True,work,16,18,7.0,WALK_LRF +1060590285,3233506,1445266,132573785,False,Home,18,16,18.0,WALK_LOC +1060601481,3233541,1445301,132575185,True,atwork,5,9,10.0,WALK +1060601485,3233541,1445301,132575185,False,eatout,7,5,10.0,WALK +1060601486,3233541,1445301,132575185,False,work,9,7,10.0,WALK +1060601487,3233541,1445301,132575185,False,eatout,9,9,10.0,WALK +1060601488,3233541,1445301,132575185,False,Work,9,9,10.0,WALK +1060601649,3233541,1445301,132575206,True,othdiscr,21,18,18.0,WALK +1060601653,3233541,1445301,132575206,False,Home,18,21,23.0,WALK +1060601761,3233541,1445301,132575220,True,work,9,18,7.0,WALK +1060601765,3233541,1445301,132575220,False,Home,18,9,17.0,WALK +1060603401,3233546,1445306,132575425,True,work,9,18,8.0,WALK_LOC +1060603405,3233546,1445306,132575425,False,Home,18,9,13.0,TNC_SHARED +1060609961,3233566,1445326,132576245,True,work,13,18,6.0,DRIVEALONEFREE +1060609965,3233566,1445326,132576245,False,Home,18,13,17.0,DRIVEALONEFREE +1060612305,3233574,1445334,132576538,True,atwork,18,19,13.0,SHARED3FREE +1060612309,3233574,1445334,132576538,False,Work,19,18,13.0,WALK +1060612585,3233574,1445334,132576573,True,work,19,18,8.0,WALK +1060612589,3233574,1445334,132576573,False,Home,18,19,15.0,WALK +1060613129,3233576,1445336,132576641,True,othdiscr,23,18,16.0,WALK_LRF +1060613133,3233576,1445336,132576641,False,Home,18,23,18.0,WALK_LRF +1060613241,3233576,1445336,132576655,True,work,12,18,8.0,TNC_SINGLE +1060613242,3233576,1445336,132576655,True,work,19,12,8.0,WALK_LOC +1060613245,3233576,1445336,132576655,False,Home,18,19,13.0,WALK +1060615097,3233582,1445342,132576887,True,othdiscr,19,19,7.0,WALK +1060615101,3233582,1445342,132576887,False,Home,19,19,10.0,WALK +1060615209,3233582,1445342,132576901,True,work,4,19,11.0,WALK_LOC +1060615213,3233582,1445342,132576901,False,Home,19,4,17.0,WALK_LOC +1060618489,3233592,1445352,132577311,True,escort,5,19,13.0,DRIVEALONEFREE +1060618490,3233592,1445352,132577311,True,work,1,5,13.0,DRIVEALONEFREE +1060618493,3233592,1445352,132577311,False,othmaint,5,1,18.0,SHARED2FREE +1060618494,3233592,1445352,132577311,False,Home,19,5,21.0,SHARED2FREE +1060640793,3233660,1445420,132580099,True,work,21,21,10.0,WALK +1060640797,3233660,1445420,132580099,False,Home,21,21,21.0,WALK +1060643745,3233669,1445429,132580468,True,work,14,21,8.0,WALK +1060643749,3233669,1445429,132580468,False,Home,21,14,17.0,WALK +1060644049,3233670,1445430,132580506,True,othmaint,9,21,9.0,WALK +1060644050,3233670,1445430,132580506,True,social,4,9,10.0,WALK_LRF +1060644053,3233670,1445430,132580506,False,Home,21,4,17.0,WALK_LRF +1060644057,3233670,1445430,132580507,True,social,6,21,18.0,SHARED2FREE +1060644061,3233670,1445430,132580507,False,Home,21,6,20.0,SHARED2FREE +1060645385,3233674,1445434,132580673,True,work,5,21,7.0,WALK +1060645389,3233674,1445434,132580673,False,Home,21,5,18.0,WALK +1060664473,3233733,1445493,132583059,True,othdiscr,2,22,16.0,WALK +1060664474,3233733,1445493,132583059,True,eatout,7,2,16.0,SHARED2FREE +1060664477,3233733,1445493,132583059,False,Home,22,7,16.0,SHARED2FREE +1060664649,3233733,1445493,132583081,True,escort,8,22,9.0,TAXI +1060664650,3233733,1445493,132583081,True,othmaint,9,8,9.0,TNC_SHARED +1060664653,3233733,1445493,132583081,False,othdiscr,10,9,14.0,TNC_SINGLE +1060664654,3233733,1445493,132583081,False,Home,22,10,14.0,DRIVEALONEFREE +1060664657,3233733,1445493,132583082,True,othmaint,2,22,14.0,WALK +1060664661,3233733,1445493,132583082,False,Home,22,2,16.0,WALK +1060664737,3233733,1445493,132583092,True,othmaint,4,22,16.0,WALK +1060664738,3233733,1445493,132583092,True,work,7,4,17.0,WALK_LOC +1060664741,3233733,1445493,132583092,False,shopping,9,7,18.0,WALK_LOC +1060664742,3233733,1445493,132583092,False,shopping,4,9,23.0,WALK_LRF +1060664743,3233733,1445493,132583092,False,Home,22,4,23.0,WALK +1060689009,3233807,1445567,132586126,True,work,24,24,11.0,WALK +1060689013,3233807,1445567,132586126,False,Home,24,24,17.0,WALK +1075792993,3279856,1486878,134474124,True,othmaint,13,6,11.0,WALK_LOC +1075792997,3279856,1486878,134474124,False,Home,6,13,11.0,WALK_LOC +1075793361,3279857,1486878,134474170,True,shopping,11,6,8.0,WALK +1075793365,3279857,1486878,134474170,False,Home,6,11,20.0,WALK +1075806089,3279896,1486898,134475761,True,othdiscr,22,8,11.0,WALK_LRF +1075806093,3279896,1486898,134475761,False,othdiscr,10,22,15.0,WALK_LRF +1075806094,3279896,1486898,134475761,False,othdiscr,9,10,15.0,WALK_LOC +1075806095,3279896,1486898,134475761,False,Home,8,9,15.0,WALK_LOC +1075806481,3279897,1486898,134475810,True,shopping,5,8,13.0,WALK +1075806485,3279897,1486898,134475810,False,Home,8,5,16.0,WALK +1075810705,3279910,1486905,134476338,True,othmaint,7,8,13.0,TNC_SINGLE +1075810709,3279910,1486905,134476338,False,Home,8,7,15.0,WALK_LOC +1075811073,3279911,1486905,134476384,True,shopping,21,8,8.0,WALK_LOC +1075811077,3279911,1486905,134476384,False,Home,8,21,16.0,WALK_LOC +1075825201,3279954,1486927,134478150,True,social,16,25,15.0,TNC_SINGLE +1075825205,3279954,1486927,134478150,False,Home,25,16,18.0,TNC_SINGLE +1075825465,3279955,1486927,134478183,True,othmaint,2,25,11.0,WALK_LOC +1075825469,3279955,1486927,134478183,False,Home,25,2,21.0,WALK_LOC +1091730553,3328446,1511173,136466319,True,shopping,11,8,12.0,WALK_LOC +1091730557,3328446,1511173,136466319,False,Home,8,11,16.0,TNC_SHARED +1091730929,3328447,1511173,136466366,True,work,23,8,6.0,SHARED2FREE +1091730933,3328447,1511173,136466366,False,Home,8,23,15.0,SHARED2FREE +1091733225,3328454,1511177,136466653,True,work,23,8,8.0,WALK_LOC +1091733229,3328454,1511177,136466653,False,Home,8,23,17.0,WALK_LRF +1091747609,3328498,1511199,136468451,True,shopping,8,8,10.0,TNC_SINGLE +1091747613,3328498,1511199,136468451,False,Home,8,8,13.0,TNC_SINGLE +1091747705,3328499,1511199,136468463,True,atwork,2,16,11.0,WALK +1091747709,3328499,1511199,136468463,False,Work,16,2,13.0,WALK +1091747985,3328499,1511199,136468498,True,work,16,8,6.0,SHARED2FREE +1091747989,3328499,1511199,136468498,False,Home,8,16,22.0,WALK_LOC +1091748857,3328502,1511201,136468607,True,othdiscr,16,8,9.0,TNC_SINGLE +1091748861,3328502,1511201,136468607,False,Home,8,16,10.0,TNC_SINGLE +1091748921,3328502,1511201,136468615,True,shopping,1,8,13.0,DRIVEALONEFREE +1091748925,3328502,1511201,136468615,False,social,16,1,13.0,WALK +1091748926,3328502,1511201,136468615,False,Home,8,16,13.0,DRIVEALONEFREE +1091749017,3328503,1511201,136468627,True,atwork,19,19,10.0,WALK +1091749021,3328503,1511201,136468627,False,Work,19,19,13.0,WALK +1091749297,3328503,1511201,136468662,True,work,19,8,6.0,DRIVEALONEFREE +1091749301,3328503,1511201,136468662,False,Home,8,19,16.0,DRIVEALONEFREE +1091770617,3328568,1511234,136471327,True,work,1,8,6.0,WALK_LRF +1091770621,3328568,1511234,136471327,False,Home,8,1,17.0,WALK +1091770681,3328569,1511234,136471335,True,eatout,13,8,14.0,WALK +1091770685,3328569,1511234,136471335,False,Home,8,13,16.0,WALK +1091770897,3328569,1511234,136471362,True,eatout,9,8,10.0,WALK_LOC +1091770898,3328569,1511234,136471362,True,shopping,4,9,10.0,WALK_LRF +1091770901,3328569,1511234,136471362,False,Home,8,4,10.0,WALK_LRF +1091777553,3328590,1511245,136472194,True,atwork,8,15,10.0,WALK +1091777557,3328590,1511245,136472194,False,Work,15,8,13.0,WALK +1091777625,3328590,1511245,136472203,True,eatout,9,9,18.0,SHARED2FREE +1091777629,3328590,1511245,136472203,False,Home,9,9,21.0,WALK +1091777785,3328590,1511245,136472223,True,shopping,4,9,16.0,BIKE +1091777789,3328590,1511245,136472223,False,Home,9,4,16.0,BIKE +1091777833,3328590,1511245,136472229,True,work,15,9,6.0,WALK +1091777837,3328590,1511245,136472229,False,Home,9,15,16.0,WALK +1091784961,3328612,1511256,136473120,True,othmaint,13,9,7.0,WALK_LRF +1091784965,3328612,1511256,136473120,False,Home,9,13,10.0,WALK_HVY +1091785377,3328613,1511256,136473172,True,work,1,9,6.0,WALK +1091785381,3328613,1511256,136473172,False,Home,9,1,18.0,WALK +1091794889,3328642,1511271,136474361,True,escort,8,9,8.0,WALK +1091794890,3328642,1511271,136474361,True,work,18,8,9.0,WALK +1091794893,3328642,1511271,136474361,False,Home,9,18,18.0,WALK +1091813801,3328700,1511300,136476725,True,othdiscr,19,11,18.0,TNC_SINGLE +1091813805,3328700,1511300,136476725,False,Home,11,19,18.0,TNC_SINGLE +1091814241,3328701,1511300,136476780,True,escort,5,11,8.0,WALK_LOC +1091814242,3328701,1511300,136476780,True,work,2,5,8.0,WALK_LOC +1091814245,3328701,1511300,136476780,False,Home,11,2,17.0,TNC_SINGLE +1091820425,3328720,1511310,136477553,True,shopping,5,11,16.0,WALK +1091820429,3328720,1511310,136477553,False,Home,11,5,17.0,WALK +1091820521,3328721,1511310,136477565,True,atwork,16,10,14.0,WALK +1091820525,3328721,1511310,136477565,False,Work,10,16,14.0,WALK +1091820801,3328721,1511310,136477600,True,escort,9,11,9.0,WALK +1091820802,3328721,1511310,136477600,True,work,10,9,10.0,WALK +1091820805,3328721,1511310,136477600,False,work,11,10,17.0,WALK +1091820806,3328721,1511310,136477600,False,escort,8,11,18.0,WALK +1091820807,3328721,1511310,136477600,False,work,8,8,18.0,WALK +1091820808,3328721,1511310,136477600,False,Home,11,8,18.0,WALK +1091823665,3328730,1511315,136477958,True,othmaint,2,11,14.0,BIKE +1091823669,3328730,1511315,136477958,False,Home,11,2,15.0,BIKE +1091823841,3328731,1511315,136477980,True,escort,13,11,7.0,WALK +1091823845,3328731,1511315,136477980,False,Home,11,13,10.0,WALK +1091824033,3328731,1511315,136478004,True,shopping,5,11,12.0,WALK +1091824034,3328731,1511315,136478004,True,shopping,16,5,13.0,WALK +1091824037,3328731,1511315,136478004,False,Home,11,16,13.0,WALK +1091831561,3328754,1511327,136478945,True,othmaint,5,11,8.0,WALK +1091831562,3328754,1511327,136478945,True,univ,10,5,9.0,WALK_LOC +1091831565,3328754,1511327,136478945,False,Home,11,10,12.0,WALK_LOC +1091831953,3328755,1511327,136478994,True,work,1,11,7.0,WALK +1091831957,3328755,1511327,136478994,False,Home,11,1,15.0,WALK +1091860209,3328842,1511371,136482526,True,othmaint,5,5,12.0,WALK +1091860210,3328842,1511371,136482526,True,atwork,16,5,12.0,WALK +1091860213,3328842,1511371,136482526,False,Work,5,16,13.0,WALK +1091860489,3328842,1511371,136482561,True,work,5,21,6.0,WALK +1091860493,3328842,1511371,136482561,False,Home,21,5,16.0,WALK_LOC +1091860705,3328843,1511371,136482588,True,othdiscr,6,21,11.0,WALK +1091860709,3328843,1511371,136482588,False,Home,21,6,13.0,WALK +1091860769,3328843,1511371,136482596,True,shopping,8,21,13.0,WALK +1091860773,3328843,1511371,136482596,False,eatout,6,8,19.0,WALK +1091860774,3328843,1511371,136482596,False,Home,21,6,19.0,WALK +1091872209,3328878,1511389,136484026,True,othdiscr,14,23,9.0,WALK +1091872210,3328878,1511389,136484026,True,othmaint,6,14,9.0,WALK_LOC +1091872213,3328878,1511389,136484026,False,shopping,25,6,9.0,WALK +1091872214,3328878,1511389,136484026,False,escort,1,25,9.0,WALK +1091872215,3328878,1511389,136484026,False,Home,23,1,9.0,WALK +1091872577,3328879,1511389,136484072,True,othmaint,2,23,8.0,WALK +1091872578,3328879,1511389,136484072,True,shopping,24,2,15.0,WALK +1091872581,3328879,1511389,136484072,False,shopping,25,24,20.0,WALK +1091872582,3328879,1511389,136484072,False,othmaint,2,25,20.0,WALK +1091872583,3328879,1511389,136484072,False,Home,23,2,21.0,WALK +1091888697,3328928,1511414,136486087,True,work,24,25,13.0,WALK +1091888701,3328928,1511414,136486087,False,Home,25,24,22.0,WALK +1091888977,3328929,1511414,136486122,True,shopping,11,25,8.0,WALK +1091888981,3328929,1511414,136486122,False,Home,25,11,10.0,WALK +1091889305,3328930,1511415,136486163,True,shopping,13,25,13.0,WALK +1091889309,3328930,1511415,136486163,False,Home,25,13,13.0,WALK +1091889633,3328931,1511415,136486204,True,shopping,12,25,17.0,WALK_LOC +1091889637,3328931,1511415,136486204,False,othmaint,9,12,18.0,TNC_SINGLE +1091889638,3328931,1511415,136486204,False,shopping,8,9,18.0,WALK_LOC +1091889639,3328931,1511415,136486204,False,Home,25,8,18.0,TNC_SINGLE +1091889681,3328931,1511415,136486210,True,work,24,25,8.0,WALK +1091889685,3328931,1511415,136486210,False,Home,25,24,15.0,WALK +1146245233,3494650,1594275,143280654,True,atwork,15,16,10.0,WALK +1146245237,3494650,1594275,143280654,False,Work,16,15,10.0,WALK +1146245513,3494650,1594275,143280689,True,work,16,5,8.0,WALK +1146245517,3494650,1594275,143280689,False,Home,5,16,17.0,WALK +1146245841,3494651,1594275,143280730,True,work,22,5,6.0,WALK_LOC +1146245845,3494651,1594275,143280730,False,Home,5,22,21.0,WALK_LRF +1146246169,3494652,1594276,143280771,True,work,4,6,7.0,BIKE +1146246173,3494652,1594276,143280771,False,Home,6,4,18.0,BIKE +1146246497,3494653,1594276,143280812,True,work,7,6,7.0,WALK +1146246501,3494653,1594276,143280812,False,Home,6,7,16.0,WALK +1146269081,3494722,1594311,143283635,True,shopping,9,6,10.0,WALK +1146269085,3494722,1594311,143283635,False,Home,6,9,21.0,WALK_LRF +1146269345,3494723,1594311,143283668,True,othdiscr,14,6,10.0,WALK +1146269349,3494723,1594311,143283668,False,Home,6,14,10.0,WALK +1146269369,3494723,1594311,143283671,True,othmaint,6,6,12.0,TNC_SHARED +1146269373,3494723,1594311,143283671,False,escort,9,6,13.0,WALK_LOC +1146269374,3494723,1594311,143283671,False,eatout,7,9,13.0,TNC_SINGLE +1146269375,3494723,1594311,143283671,False,escort,8,7,14.0,WALK_LOC +1146269376,3494723,1594311,143283671,False,Home,6,8,14.0,TNC_SINGLE +1146297337,3494808,1594354,143287167,True,work,2,7,7.0,WALK +1146297341,3494808,1594354,143287167,False,Home,7,2,17.0,WALK +1146297385,3494809,1594354,143287173,True,atwork,14,4,10.0,WALK +1146297389,3494809,1594354,143287173,False,Work,4,14,10.0,WALK +1146297401,3494809,1594354,143287175,True,eatout,2,7,19.0,WALK +1146297405,3494809,1594354,143287175,False,Home,7,2,20.0,WALK +1146297665,3494809,1594354,143287208,True,work,4,7,7.0,WALK +1146297669,3494809,1594354,143287208,False,Home,7,4,17.0,WALK +1146328169,3494902,1594401,143291021,True,work,2,7,11.0,WALK +1146328173,3494902,1594401,143291021,False,Home,7,2,15.0,WALK +1146328473,3494903,1594401,143291059,True,othdiscr,10,7,17.0,TNC_SINGLE +1146328474,3494903,1594401,143291059,True,social,14,10,18.0,WALK_LRF +1146328477,3494903,1594401,143291059,False,Home,7,14,19.0,WALK_LOC +1146328497,3494903,1594401,143291062,True,work,14,7,8.0,WALK +1146328498,3494903,1594401,143291062,True,eatout,16,14,8.0,WALK +1146328499,3494903,1594401,143291062,True,work,4,16,9.0,WALK +1146328501,3494903,1594401,143291062,False,escort,5,4,13.0,WALK_LOC +1146328502,3494903,1594401,143291062,False,shopping,8,5,14.0,WALK +1146328503,3494903,1594401,143291062,False,Home,7,8,14.0,WALK +1146347193,3494960,1594430,143293399,True,work,13,7,7.0,WALK +1146347197,3494960,1594430,143293399,False,shopping,21,13,18.0,WALK +1146347198,3494960,1594430,143293399,False,Home,7,21,18.0,TNC_SINGLE +1146347409,3494961,1594430,143293426,True,othdiscr,10,7,16.0,WALK_LOC +1146347413,3494961,1594430,143293426,False,Home,7,10,16.0,SHARED3FREE +1146347473,3494961,1594430,143293434,True,shopping,5,7,18.0,WALK +1146347477,3494961,1594430,143293434,False,Home,7,5,21.0,WALK +1146347521,3494961,1594430,143293440,True,work,23,7,7.0,WALK +1146347525,3494961,1594430,143293440,False,Home,7,23,16.0,WALK +1146360033,3495000,1594450,143295004,True,atwork,5,5,12.0,WALK +1146360037,3495000,1594450,143295004,False,eatout,5,5,18.0,WALK +1146360038,3495000,1594450,143295004,False,Work,5,5,18.0,WALK +1146360313,3495000,1594450,143295039,True,work,5,7,8.0,WALK +1146360317,3495000,1594450,143295039,False,Home,7,5,18.0,WALK +1146360377,3495001,1594450,143295047,True,eatout,13,7,9.0,WALK +1146360381,3495001,1594450,143295047,False,Home,7,13,9.0,WALK +1146360641,3495001,1594450,143295080,True,work,2,7,10.0,WALK +1146360645,3495001,1594450,143295080,False,Home,7,2,19.0,WALK +1146384961,3495076,1594488,143298120,True,escort,2,4,11.0,WALK +1146384962,3495076,1594488,143298120,True,atwork,22,2,11.0,WALK +1146384965,3495076,1594488,143298120,False,Work,4,22,11.0,WALK +1146385241,3495076,1594488,143298155,True,escort,8,9,6.0,WALK +1146385242,3495076,1594488,143298155,True,work,4,8,7.0,WALK +1146385245,3495076,1594488,143298155,False,Home,9,4,18.0,TNC_SINGLE +1146385305,3495077,1594488,143298163,True,eatout,9,9,20.0,WALK +1146385309,3495077,1594488,143298163,False,Home,9,9,20.0,WALK +1146385521,3495077,1594488,143298190,True,shopping,4,9,18.0,WALK +1146385525,3495077,1594488,143298190,False,Home,9,4,19.0,WALK_LOC +1146385569,3495077,1594488,143298196,True,work,14,9,8.0,WALK_LRF +1146385570,3495077,1594488,143298196,True,work,2,14,8.0,WALK +1146385573,3495077,1594488,143298196,False,Home,9,2,18.0,WALK_LRF +1146386553,3495080,1594490,143298319,True,work,8,9,6.0,WALK +1146386557,3495080,1594490,143298319,False,Home,9,8,11.0,WALK +1146386561,3495080,1594490,143298320,True,work,8,9,13.0,BIKE +1146386565,3495080,1594490,143298320,False,Home,9,8,17.0,BIKE +1146386881,3495081,1594490,143298360,True,work,2,9,7.0,DRIVEALONEFREE +1146386885,3495081,1594490,143298360,False,Home,9,2,19.0,SHARED2FREE +1146433017,3495222,1594561,143304127,True,othdiscr,17,9,17.0,WALK_LRF +1146433021,3495222,1594561,143304127,False,Home,9,17,17.0,WALK_LRF +1146433457,3495223,1594561,143304182,True,work,6,9,8.0,SHARED2FREE +1146433461,3495223,1594561,143304182,False,Home,9,6,15.0,WALK_LOC +1146460681,3495306,1594603,143307585,True,shopping,11,10,10.0,WALK_LOC +1146460682,3495306,1594603,143307585,True,work,7,11,11.0,WALK +1146460685,3495306,1594603,143307585,False,Home,10,7,21.0,WALK_LRF +1146461009,3495307,1594603,143307626,True,work,9,10,10.0,WALK_LOC +1146461010,3495307,1594603,143307626,True,eatout,7,9,10.0,WALK +1146461011,3495307,1594603,143307626,True,work,9,7,12.0,WALK +1146461013,3495307,1594603,143307626,False,Home,10,9,21.0,TNC_SINGLE +1146472377,3495342,1594621,143309047,True,othdiscr,15,10,16.0,WALK_LRF +1146472381,3495342,1594621,143309047,False,Home,10,15,22.0,WALK_LRF +1146472489,3495342,1594621,143309061,True,work,11,10,7.0,WALK_LOC +1146472493,3495342,1594621,143309061,False,Home,10,11,16.0,WALK +1146472537,3495343,1594621,143309067,True,othmaint,6,9,14.0,WALK +1146472538,3495343,1594621,143309067,True,shopping,16,6,14.0,WALK +1146472539,3495343,1594621,143309067,True,escort,16,16,14.0,WALK +1146472540,3495343,1594621,143309067,True,atwork,16,16,14.0,WALK +1146472541,3495343,1594621,143309067,False,Work,9,16,14.0,WALK +1146472817,3495343,1594621,143309102,True,work,9,10,13.0,WALK +1146472821,3495343,1594621,143309102,False,Home,10,9,17.0,WALK +1146474041,3495347,1594623,143309255,True,othmaint,7,10,8.0,BIKE +1146474042,3495347,1594623,143309255,True,eatout,25,7,8.0,BIKE +1146474043,3495347,1594623,143309255,True,othmaint,12,25,8.0,BIKE +1146474045,3495347,1594623,143309255,False,Home,10,12,8.0,BIKE +1146474129,3495347,1594623,143309266,True,work,2,10,8.0,WALK_LRF +1146474133,3495347,1594623,143309266,False,Home,10,2,18.0,WALK_LRF +1146475769,3495352,1594626,143309471,True,work,12,10,7.0,WALK +1146475773,3495352,1594626,143309471,False,Home,10,12,16.0,WALK +1146476097,3495353,1594626,143309512,True,work,22,10,7.0,WALK +1146476101,3495353,1594626,143309512,False,Home,10,22,16.0,WALK +1146489545,3495394,1594647,143311193,True,work,13,10,9.0,WALK_LRF +1146489549,3495394,1594647,143311193,False,Home,10,13,12.0,WALK_HVY +1146489553,3495394,1594647,143311194,True,work,13,10,12.0,WALK +1146489557,3495394,1594647,143311194,False,Home,10,13,15.0,WALK_LRF +1146489825,3495395,1594647,143311228,True,shopping,19,10,10.0,TNC_SINGLE +1146489829,3495395,1594647,143311228,False,shopping,16,19,12.0,TNC_SINGLE +1146489830,3495395,1594647,143311228,False,Home,10,16,14.0,TNC_SINGLE +1146489873,3495395,1594647,143311234,True,work,21,10,8.0,DRIVEALONEFREE +1146489877,3495395,1594647,143311234,False,Home,10,21,10.0,SHARED2FREE +1146498025,3495420,1594660,143312253,True,escort,6,10,18.0,WALK_LOC +1146498026,3495420,1594660,143312253,True,shopping,16,6,18.0,TNC_SINGLE +1146498029,3495420,1594660,143312253,False,Home,10,16,20.0,WALK_LOC +1146498073,3495420,1594660,143312259,True,work,9,10,8.0,WALK +1146498077,3495420,1594660,143312259,False,Home,10,9,17.0,WALK +1146498121,3495421,1594660,143312265,True,atwork,11,4,14.0,WALK +1146498125,3495421,1594660,143312265,False,Work,4,11,14.0,WALK +1146498401,3495421,1594660,143312300,True,work,4,10,8.0,WALK +1146498405,3495421,1594660,143312300,False,Home,10,4,17.0,WALK_LRF +1146535465,3495534,1594717,143316933,True,work,18,10,14.0,WALK +1146535469,3495534,1594717,143316933,False,Home,10,18,17.0,WALK +1146535473,3495534,1594717,143316934,True,othmaint,11,10,19.0,WALK +1146535474,3495534,1594717,143316934,True,work,8,11,19.0,WALK +1146535475,3495534,1594717,143316934,True,eatout,21,8,19.0,WALK +1146535476,3495534,1594717,143316934,True,work,18,21,19.0,WALK +1146535477,3495534,1594717,143316934,False,Home,10,18,19.0,WALK +1146535553,3495535,1594717,143316944,True,escort,10,10,8.0,WALK +1146535557,3495535,1594717,143316944,False,Home,10,10,11.0,WALK +1146535561,3495535,1594717,143316945,True,escort,11,10,13.0,TNC_SINGLE +1146535565,3495535,1594717,143316945,False,Home,10,11,13.0,TNC_SINGLE +1146535681,3495535,1594717,143316960,True,othdiscr,7,10,17.0,WALK +1146535685,3495535,1594717,143316960,False,Home,10,7,19.0,WALK +1146535745,3495535,1594717,143316968,True,othmaint,9,10,16.0,WALK_LOC +1146535746,3495535,1594717,143316968,True,shopping,16,9,16.0,WALK_LOC +1146535749,3495535,1594717,143316968,False,shopping,5,16,17.0,WALK_LOC +1146535750,3495535,1594717,143316968,False,Home,10,5,17.0,WALK_LOC +1146547273,3495570,1594735,143318409,True,work,3,11,6.0,WALK_LOC +1146547277,3495570,1594735,143318409,False,Home,11,3,10.0,WALK +1146547281,3495570,1594735,143318410,True,shopping,5,11,11.0,WALK +1146547282,3495570,1594735,143318410,True,work,3,5,12.0,TNC_SINGLE +1146547285,3495570,1594735,143318410,False,Home,11,3,14.0,TNC_SINGLE +1146547601,3495571,1594735,143318450,True,work,1,11,8.0,WALK +1146547605,3495571,1594735,143318450,False,Home,11,1,15.0,WALK_LOC +1146572745,3495648,1594774,143321593,True,othdiscr,16,11,16.0,WALK +1146572749,3495648,1594774,143321593,False,Home,11,16,18.0,WALK +1146572857,3495648,1594774,143321607,True,work,9,11,6.0,WALK +1146572861,3495648,1594774,143321607,False,Home,11,9,10.0,WALK +1146572865,3495648,1594774,143321608,True,work,9,11,13.0,WALK +1146572869,3495648,1594774,143321608,False,work,9,9,14.0,WALK +1146572870,3495648,1594774,143321608,False,Home,11,9,14.0,WALK +1146584009,3495682,1594791,143323001,True,work,1,11,9.0,WALK +1146584013,3495682,1594791,143323001,False,Home,11,1,20.0,WALK +1146584337,3495683,1594791,143323042,True,work,7,11,7.0,WALK_LOC +1146584341,3495683,1594791,143323042,False,Home,11,7,20.0,WALK_LOC +1146585977,3495688,1594794,143323247,True,work,12,11,8.0,WALK +1146585981,3495688,1594794,143323247,False,Home,11,12,18.0,WALK +1146586257,3495689,1594794,143323282,True,shopping,19,11,20.0,DRIVEALONEFREE +1146586261,3495689,1594794,143323282,False,Home,11,19,20.0,DRIVEALONEFREE +1146586305,3495689,1594794,143323288,True,work,23,11,8.0,WALK_LOC +1146586309,3495689,1594794,143323288,False,Home,11,23,18.0,WALK_LOC +1146589257,3495698,1594799,143323657,True,work,2,11,7.0,SHARED2FREE +1146589261,3495698,1594799,143323657,False,Home,11,2,18.0,WALK +1146589585,3495699,1594799,143323698,True,work,12,11,7.0,WALK +1146589589,3495699,1594799,143323698,False,Home,11,12,10.0,WALK +1146602113,3495738,1594819,143325264,True,eatout,10,11,12.0,WALK +1146602117,3495738,1594819,143325264,False,Home,11,10,12.0,WALK +1146606201,3495750,1594825,143325775,True,othdiscr,9,11,10.0,WALK +1146606205,3495750,1594825,143325775,False,Home,11,9,21.0,WALK +1146606641,3495751,1594825,143325830,True,work,1,11,9.0,WALK_LOC +1146606645,3495751,1594825,143325830,False,Home,11,1,19.0,WALK +1146620745,3495794,1594847,143327593,True,work,4,11,8.0,WALK +1146620749,3495794,1594847,143327593,False,Home,11,4,17.0,WALK +1146621073,3495795,1594847,143327634,True,work,17,11,6.0,WALK +1146621077,3495795,1594847,143327634,False,Home,11,17,19.0,WALK +1146626385,3495812,1594856,143328298,True,eatout,13,11,7.0,WALK +1146626389,3495812,1594856,143328298,False,Home,11,13,14.0,WALK +1146626977,3495813,1594856,143328372,True,work,9,11,8.0,WALK +1146626981,3495813,1594856,143328372,False,Home,11,9,18.0,TAXI +1146658681,3495910,1594905,143332335,True,othdiscr,16,12,12.0,WALK +1146658685,3495910,1594905,143332335,False,Home,12,16,15.0,WALK +1146658793,3495910,1594905,143332349,True,work,2,12,17.0,WALK +1146658797,3495910,1594905,143332349,False,Home,12,2,20.0,WALK +1146659033,3495911,1594905,143332379,True,othmaint,13,12,17.0,TNC_SINGLE +1146659037,3495911,1594905,143332379,False,Home,12,13,21.0,TNC_SINGLE +1146659073,3495911,1594905,143332384,True,shopping,4,12,17.0,WALK +1146659077,3495911,1594905,143332384,False,Home,12,4,17.0,WALK +1146659121,3495911,1594905,143332390,True,work,2,12,8.0,WALK +1146659125,3495911,1594905,143332390,False,eatout,1,2,16.0,WALK +1146659126,3495911,1594905,143332390,False,Home,12,1,17.0,WALK +1146693281,3496016,1594958,143336660,True,atwork,13,22,12.0,WALK +1146693285,3496016,1594958,143336660,False,Work,22,13,13.0,WALK +1146693561,3496016,1594958,143336695,True,work,22,15,7.0,TNC_SINGLE +1146693565,3496016,1594958,143336695,False,Home,15,22,17.0,WALK_LRF +1146693889,3496017,1594958,143336736,True,work,15,15,10.0,WALK +1146693893,3496017,1594958,143336736,False,Home,15,15,20.0,WALK +1146697497,3496028,1594964,143337187,True,work,22,15,7.0,WALK +1146697501,3496028,1594964,143337187,False,Home,15,22,12.0,WALK +1146697505,3496028,1594964,143337188,True,othmaint,24,15,13.0,SHARED3FREE +1146697506,3496028,1594964,143337188,True,work,22,24,13.0,SHARED3FREE +1146697509,3496028,1594964,143337188,False,work,22,22,17.0,SHARED3FREE +1146697510,3496028,1594964,143337188,False,Home,15,22,18.0,WALK +1146697713,3496029,1594964,143337214,True,othdiscr,15,15,10.0,WALK +1146697717,3496029,1594964,143337214,False,Home,15,15,18.0,WALK +1146701433,3496040,1594970,143337679,True,work,15,16,7.0,WALK +1146701437,3496040,1594970,143337679,False,Home,16,15,20.0,WALK +1146701481,3496041,1594970,143337685,True,eatout,7,1,9.0,WALK +1146701482,3496041,1594970,143337685,True,atwork,1,7,9.0,WALK +1146701485,3496041,1594970,143337685,False,Work,1,1,9.0,WALK +1146701649,3496041,1594970,143337706,True,othdiscr,13,16,18.0,WALK +1146701653,3496041,1594970,143337706,False,Home,16,13,20.0,WALK +1146701761,3496041,1594970,143337720,True,work,1,16,8.0,WALK +1146701765,3496041,1594970,143337720,False,Home,16,1,18.0,WALK +1146713897,3496078,1594989,143339237,True,escort,2,16,8.0,WALK_LOC +1146713898,3496078,1594989,143339237,True,work,4,2,8.0,WALK +1146713901,3496078,1594989,143339237,False,shopping,23,4,10.0,TNC_SINGLE +1146713902,3496078,1594989,143339237,False,Home,16,23,16.0,TNC_SINGLE +1146713945,3496079,1594989,143339243,True,atwork,11,21,10.0,WALK +1146713949,3496079,1594989,143339243,False,Work,21,11,10.0,WALK +1146714225,3496079,1594989,143339278,True,work,21,16,8.0,TNC_SINGLE +1146714229,3496079,1594989,143339278,False,othmaint,20,21,18.0,TNC_SINGLE +1146714230,3496079,1594989,143339278,False,Home,16,20,19.0,WALK_LOC +1146717553,3496090,1594995,143339694,True,atwork,12,2,14.0,WALK +1146717557,3496090,1594995,143339694,False,Work,2,12,14.0,WALK +1146717833,3496090,1594995,143339729,True,work,2,16,6.0,WALK +1146717837,3496090,1594995,143339729,False,Home,16,2,17.0,WALK +1146717897,3496091,1594995,143339737,True,eatout,17,16,12.0,WALK +1146717901,3496091,1594995,143339737,False,Home,16,17,13.0,WALK +1146718113,3496091,1594995,143339764,True,shopping,10,16,11.0,WALK_LOC +1146718117,3496091,1594995,143339764,False,Home,16,10,11.0,TNC_SINGLE +1146748665,3496184,1595042,143343583,True,work,23,16,8.0,WALK +1146748669,3496184,1595042,143343583,False,Home,16,23,18.0,WALK +1146748993,3496185,1595042,143343624,True,work,4,16,7.0,BIKE +1146748997,3496185,1595042,143343624,False,Home,16,4,15.0,BIKE +1146759161,3496216,1595058,143344895,True,work,1,16,7.0,WALK +1146759165,3496216,1595058,143344895,False,Home,16,1,21.0,WALK +1146759489,3496217,1595058,143344936,True,work,4,16,8.0,WALK +1146759493,3496217,1595058,143344936,False,Home,16,4,18.0,WALK +1146788401,3496306,1595103,143348550,True,shopping,16,16,11.0,WALK +1146788402,3496306,1595103,143348550,True,atwork,16,16,11.0,WALK +1146788405,3496306,1595103,143348550,False,Work,16,16,13.0,WALK +1146788681,3496306,1595103,143348585,True,work,16,17,7.0,WALK +1146788685,3496306,1595103,143348585,False,Home,17,16,21.0,WALK +1146788769,3496307,1595103,143348596,True,escort,22,17,7.0,SHARED2FREE +1146788773,3496307,1595103,143348596,False,Home,17,22,7.0,DRIVEALONEFREE +1146792617,3496318,1595109,143349077,True,work,14,17,16.0,WALK_LOC +1146792621,3496318,1595109,143349077,False,Home,17,14,20.0,WALK_LOC +1146792665,3496319,1595109,143349083,True,atwork,15,14,12.0,WALK +1146792669,3496319,1595109,143349083,False,eatout,16,15,14.0,WALK +1146792670,3496319,1595109,143349083,False,escort,2,16,14.0,WALK +1146792671,3496319,1595109,143349083,False,Work,14,2,14.0,WALK +1146792945,3496319,1595109,143349118,True,work,14,17,8.0,WALK_LOC +1146792949,3496319,1595109,143349118,False,escort,2,14,17.0,WALK +1146792950,3496319,1595109,143349118,False,Home,17,2,18.0,WALK +1146793649,3496322,1595111,143349206,True,atwork,25,22,11.0,WALK +1146793653,3496322,1595111,143349206,False,eatout,2,25,14.0,WALK +1146793654,3496322,1595111,143349206,False,Work,22,2,14.0,WALK +1146793929,3496322,1595111,143349241,True,work,22,17,9.0,TNC_SINGLE +1146793933,3496322,1595111,143349241,False,shopping,5,22,16.0,TNC_SINGLE +1146793934,3496322,1595111,143349241,False,Home,17,5,17.0,TNC_SINGLE +1146794257,3496323,1595111,143349282,True,work,10,17,5.0,WALK_LRF +1146794261,3496323,1595111,143349282,False,Home,17,10,16.0,WALK_LOC +1146807393,3496364,1595132,143350924,True,atwork,3,1,13.0,WALK +1146807397,3496364,1595132,143350924,False,Work,1,3,14.0,WALK +1146807657,3496364,1595132,143350957,True,shopping,12,17,10.0,WALK +1146807661,3496364,1595132,143350957,False,shopping,16,12,11.0,WALK +1146807662,3496364,1595132,143350957,False,shopping,16,16,11.0,WALK +1146807663,3496364,1595132,143350957,False,Home,17,16,11.0,WALK +1146807665,3496364,1595132,143350958,True,shopping,16,17,11.0,TNC_SINGLE +1146807669,3496364,1595132,143350958,False,Home,17,16,13.0,TNC_SINGLE +1146807705,3496364,1595132,143350963,True,work,1,17,13.0,WALK_LOC +1146807709,3496364,1595132,143350963,False,othdiscr,18,1,17.0,WALK_LRF +1146807710,3496364,1595132,143350963,False,Home,17,18,20.0,WALK_LOC +1146808033,3496365,1595132,143351004,True,escort,5,17,8.0,WALK +1146808034,3496365,1595132,143351004,True,work,14,5,9.0,WALK +1146808037,3496365,1595132,143351004,False,escort,4,14,19.0,WALK +1146808038,3496365,1595132,143351004,False,eatout,2,4,19.0,WALK +1146808039,3496365,1595132,143351004,False,escort,16,2,19.0,WALK +1146808040,3496365,1595132,143351004,False,Home,17,16,19.0,WALK +1146826073,3496420,1595160,143353259,True,work,14,17,8.0,WALK_LRF +1146826077,3496420,1595160,143353259,False,othdiscr,8,14,17.0,WALK_LOC +1146826078,3496420,1595160,143353259,False,Home,17,8,18.0,WALK_LRF +1146826081,3496420,1595160,143353260,True,work,14,17,18.0,WALK +1146826085,3496420,1595160,143353260,False,Home,17,14,21.0,WALK +1146826401,3496421,1595160,143353300,True,work,18,17,9.0,DRIVEALONEFREE +1146826405,3496421,1595160,143353300,False,shopping,16,18,16.0,DRIVEALONEFREE +1146826406,3496421,1595160,143353300,False,work,2,16,19.0,DRIVEALONEFREE +1146826407,3496421,1595160,143353300,False,othmaint,1,2,19.0,DRIVEALONEFREE +1146826408,3496421,1595160,143353300,False,Home,17,1,19.0,DRIVEALONEFREE +1146860561,3496526,1595213,143357570,True,atwork,10,18,11.0,WALK +1146860565,3496526,1595213,143357570,False,work,5,10,16.0,WALK +1146860566,3496526,1595213,143357570,False,Work,18,5,16.0,WALK +1146860841,3496526,1595213,143357605,True,work,18,17,8.0,WALK +1146860845,3496526,1595213,143357605,False,escort,17,18,16.0,WALK +1146860846,3496526,1595213,143357605,False,Home,17,17,19.0,WALK +1146867401,3496546,1595223,143358425,True,work,22,17,7.0,TNC_SINGLE +1146867405,3496546,1595223,143358425,False,escort,8,22,14.0,WALK_LOC +1146867406,3496546,1595223,143358425,False,escort,4,8,19.0,WALK +1146867407,3496546,1595223,143358425,False,Home,17,4,19.0,WALK_LRF +1146867465,3496547,1595223,143358433,True,eatout,20,17,18.0,WALK_LRF +1146867469,3496547,1595223,143358433,False,Home,17,20,18.0,WALK_LOC +1146867729,3496547,1595223,143358466,True,work,13,17,9.0,WALK +1146867733,3496547,1595223,143358466,False,Home,17,13,18.0,WALK +1146914353,3496690,1595295,143364294,True,atwork,11,20,15.0,WALK +1146914357,3496690,1595295,143364294,False,Work,20,11,15.0,WALK +1146914633,3496690,1595295,143364329,True,work,20,20,6.0,WALK +1146914637,3496690,1595295,143364329,False,Home,20,20,17.0,WALK +1146916601,3496696,1595298,143364575,True,work,22,20,8.0,WALK_LRF +1146916605,3496696,1595298,143364575,False,Home,20,22,18.0,WALK_LRF +1146916665,3496697,1595298,143364583,True,eatout,11,20,8.0,WALK +1146916669,3496697,1595298,143364583,False,Home,20,11,13.0,WALK +1146917913,3496700,1595300,143364739,True,work,9,20,7.0,DRIVEALONEFREE +1146917917,3496700,1595300,143364739,False,Home,20,9,16.0,WALK +1146918241,3496701,1595300,143364780,True,escort,10,20,8.0,WALK_LOC +1146918242,3496701,1595300,143364780,True,work,4,10,8.0,WALK_LRF +1146918245,3496701,1595300,143364780,False,social,16,4,14.0,WALK_LOC +1146918246,3496701,1595300,143364780,False,Home,20,16,16.0,WALK_LOC +1146944809,3496782,1595341,143368101,True,work,17,21,8.0,WALK +1146944813,3496782,1595341,143368101,False,Home,21,17,18.0,WALK +1146945025,3496783,1595341,143368128,True,othdiscr,16,21,11.0,WALK +1146945029,3496783,1595341,143368128,False,Home,21,16,18.0,WALK +1179519753,3596096,1644998,147439969,True,shopping,11,6,12.0,BIKE +1179519757,3596096,1644998,147439969,False,shopping,11,11,12.0,BIKE +1179519758,3596096,1644998,147439969,False,Home,6,11,13.0,WALK +1179520065,3596097,1644998,147440008,True,school,8,6,8.0,WALK +1179520069,3596097,1644998,147440008,False,Home,6,8,18.0,WALK +1179530841,3596130,1645015,147441355,True,othdiscr,16,7,14.0,WALK +1179530845,3596130,1645015,147441355,False,Home,7,16,17.0,WALK +1179531217,3596131,1645015,147441402,True,school,7,7,14.0,WALK +1179531221,3596131,1645015,147441402,False,Home,7,7,16.0,WALK +1179567641,3596242,1645071,147445955,True,shopping,5,8,15.0,WALK_LOC +1179567645,3596242,1645071,147445955,False,Home,8,5,15.0,WALK_LOC +1179567953,3596243,1645071,147445994,True,school,8,8,6.0,WALK +1179567957,3596243,1645071,147445994,False,Home,8,8,13.0,WALK +1179607441,3596364,1645132,147450930,True,eatout,5,9,14.0,WALK +1179607445,3596364,1645132,147450930,False,Home,9,5,14.0,WALK +1179607657,3596364,1645132,147450957,True,shopping,22,9,19.0,WALK_LRF +1179607661,3596364,1645132,147450957,False,shopping,8,22,20.0,WALK_LRF +1179607662,3596364,1645132,147450957,False,eatout,2,8,20.0,WALK_LOC +1179607663,3596364,1645132,147450957,False,Home,9,2,20.0,WALK_LRF +1179608009,3596365,1645132,147451001,True,social,9,9,7.0,TNC_SINGLE +1179608013,3596365,1645132,147451001,False,Home,9,9,18.0,TNC_SINGLE +1179624713,3596416,1645158,147453089,True,shopping,11,9,9.0,WALK +1179624717,3596416,1645158,147453089,False,Home,9,11,13.0,WALK +1179640921,3596466,1645183,147455115,True,escort,19,11,8.0,WALK +1179640925,3596466,1645183,147455115,False,Home,11,19,10.0,WALK +1179640929,3596466,1645183,147455116,True,escort,5,11,15.0,WALK +1179640933,3596466,1645183,147455116,False,Home,11,5,16.0,WALK +1179641113,3596466,1645183,147455139,True,shopping,11,11,16.0,WALK +1179641117,3596466,1645183,147455139,False,Home,11,11,16.0,WALK +1179641249,3596467,1645183,147455156,True,escort,12,11,14.0,WALK +1179641253,3596467,1645183,147455156,False,Home,11,12,14.0,WALK +1179643697,3596474,1645187,147455462,True,escort,9,11,12.0,WALK_LOC +1179643698,3596474,1645187,147455462,True,othmaint,5,9,14.0,TNC_SINGLE +1179643701,3596474,1645187,147455462,False,eatout,22,5,17.0,WALK_LOC +1179643702,3596474,1645187,147455462,False,Home,11,22,17.0,TNC_SINGLE +1179644001,3596475,1645187,147455500,True,othdiscr,17,11,10.0,WALK_LOC +1179644005,3596475,1645187,147455500,False,shopping,5,17,21.0,WALK +1179644006,3596475,1645187,147455500,False,Home,11,5,21.0,WALK +1183490521,3608202,1651051,147936315,True,shopping,22,7,15.0,WALK_LOC +1183490525,3608202,1651051,147936315,False,Home,7,22,19.0,TNC_SINGLE +1183490833,3608203,1651051,147936354,True,school,13,7,5.0,WALK_HVY +1183490837,3608203,1651051,147936354,False,Home,7,13,10.0,WALK_LOC +1183497081,3608222,1651061,147937135,True,shopping,11,7,11.0,WALK_LOC +1183497085,3608222,1651061,147937135,False,Home,7,11,12.0,WALK_LOC +1183497393,3608223,1651061,147937174,True,school,9,7,7.0,WALK +1183497397,3608223,1651061,147937174,False,shopping,6,9,15.0,WALK_LOC +1183497398,3608223,1651061,147937174,False,Home,7,6,23.0,WALK_LOC +1183522601,3608300,1651100,147940325,True,othdiscr,21,7,18.0,WALK +1183522605,3608300,1651100,147940325,False,Home,7,21,18.0,WALK +1183522665,3608300,1651100,147940333,True,shopping,13,7,14.0,WALK +1183522669,3608300,1651100,147940333,False,Home,7,13,16.0,WALK +1183522977,3608301,1651100,147940372,True,school,13,7,6.0,WALK_LRF +1183522981,3608301,1651100,147940372,False,Home,7,13,14.0,WALK_LOC +1183546329,3608372,1651136,147943291,True,work,20,8,8.0,WALK +1183546333,3608372,1651136,147943291,False,shopping,21,20,8.0,WALK +1183546334,3608372,1651136,147943291,False,work,10,21,18.0,WALK +1183546335,3608372,1651136,147943291,False,Home,8,10,18.0,WALK +1183546545,3608373,1651136,147943318,True,escort,4,8,10.0,WALK +1183546546,3608373,1651136,147943318,True,othdiscr,16,4,10.0,SHARED3FREE +1183546549,3608373,1651136,147943318,False,Home,8,16,15.0,SHARED3FREE +1183546593,3608373,1651136,147943324,True,school,8,8,16.0,WALK +1183546597,3608373,1651136,147943324,False,Home,8,8,16.0,WALK +1183550001,3608384,1651142,147943750,True,eatout,5,8,9.0,WALK +1183550005,3608384,1651142,147943750,False,Home,8,5,11.0,WALK +1183550265,3608384,1651142,147943783,True,escort,5,8,11.0,WALK +1183550266,3608384,1651142,147943783,True,work,4,5,11.0,WALK +1183550269,3608384,1651142,147943783,False,shopping,5,4,16.0,WALK_LOC +1183550270,3608384,1651142,147943783,False,othdiscr,11,5,21.0,TNC_SINGLE +1183550271,3608384,1651142,147943783,False,Home,8,11,21.0,TNC_SINGLE +1183550529,3608385,1651142,147943816,True,eatout,25,8,5.0,WALK_LOC +1183550530,3608385,1651142,147943816,True,school,6,25,7.0,WALK +1183550533,3608385,1651142,147943816,False,Home,8,6,13.0,WALK_LOC +1183562449,3608422,1651161,147945306,True,atwork,18,10,13.0,WALK +1183562453,3608422,1651161,147945306,False,Work,10,18,13.0,WALK +1183562681,3608422,1651161,147945335,True,shopping,5,8,9.0,WALK_LOC +1183562685,3608422,1651161,147945335,False,Home,8,5,11.0,WALK_LOC +1183562729,3608422,1651161,147945341,True,work,5,8,13.0,WALK +1183562730,3608422,1651161,147945341,True,work,10,5,13.0,WALK +1183562733,3608422,1651161,147945341,False,Home,8,10,20.0,WALK +1183562993,3608423,1651161,147945374,True,school,13,8,7.0,WALK_LOC +1183562997,3608423,1651161,147945374,False,Home,8,13,14.0,WALK_LRF +1183563009,3608423,1651161,147945376,True,eatout,7,8,15.0,WALK_LOC +1183563010,3608423,1651161,147945376,True,shopping,5,7,16.0,WALK_LOC +1183563013,3608423,1651161,147945376,False,shopping,7,5,23.0,WALK_LOC +1183563014,3608423,1651161,147945376,False,Home,8,7,23.0,TNC_SINGLE +1183613897,3608578,1651239,147951737,True,work,13,9,8.0,WALK_LRF +1183613901,3608578,1651239,147951737,False,Home,9,13,19.0,WALK_LRF +1183614161,3608579,1651239,147951770,True,school,9,9,7.0,WALK +1183614165,3608579,1651239,147951770,False,Home,9,9,15.0,WALK +1183629553,3608626,1651263,147953694,True,othmaint,9,9,10.0,TNC_SINGLE +1183629557,3608626,1651263,147953694,False,shopping,16,9,13.0,WALK_LRF +1183629558,3608626,1651263,147953694,False,othmaint,10,16,13.0,TNC_SHARED +1183629559,3608626,1651263,147953694,False,Home,9,10,13.0,WALK_LOC +1183629561,3608626,1651263,147953695,True,othmaint,2,9,13.0,WALK_LRF +1183629565,3608626,1651263,147953695,False,Home,9,2,16.0,WALK_LOC +1183629905,3608627,1651263,147953738,True,school,11,9,7.0,WALK_LOC +1183629909,3608627,1651263,147953738,False,shopping,21,11,17.0,WALK +1183629910,3608627,1651263,147953738,False,Home,9,21,17.0,WALK_LOC +1183678185,3608774,1651337,147959773,True,work,10,11,7.0,WALK +1183678189,3608774,1651337,147959773,False,Home,11,10,17.0,WALK +1183678449,3608775,1651337,147959806,True,school,11,11,7.0,WALK +1183678453,3608775,1651337,147959806,False,Home,11,11,15.0,WALK +1183682497,3608788,1651344,147960312,True,atwork,25,14,13.0,WALK +1183682501,3608788,1651344,147960312,False,Work,14,25,13.0,WALK +1183682777,3608788,1651344,147960347,True,work,14,16,7.0,WALK +1183682781,3608788,1651344,147960347,False,Home,16,14,13.0,WALK +1183682785,3608788,1651344,147960348,True,work,14,16,17.0,WALK +1183682789,3608788,1651344,147960348,False,Home,16,14,21.0,WALK +1183682993,3608789,1651344,147960374,True,othdiscr,12,16,12.0,WALK +1183682997,3608789,1651344,147960374,False,Home,16,12,18.0,WALK +1183692617,3608818,1651359,147961577,True,eatout,23,18,15.0,WALK_LRF +1183692618,3608818,1651359,147961577,True,work,15,23,15.0,WALK +1183692621,3608818,1651359,147961577,False,Home,18,15,18.0,WALK_LRF +1183692881,3608819,1651359,147961610,True,school,17,18,8.0,WALK_LRF +1183692885,3608819,1651359,147961610,False,othmaint,1,17,17.0,WALK_LRF +1183692886,3608819,1651359,147961610,False,Home,18,1,17.0,WALK_LRF +1183701209,3608845,1651372,147962651,True,eatout,18,20,13.0,WALK +1183701213,3608845,1651372,147962651,False,Home,20,18,20.0,WALK +1183701409,3608845,1651372,147962676,True,school,25,20,6.0,WALK_LRF +1183701413,3608845,1651372,147962676,False,Home,20,25,13.0,WALK_LOC +1183713673,3608883,1651391,147964209,True,eatout,9,21,16.0,WALK +1183713677,3608883,1651391,147964209,False,Home,21,9,18.0,WALK +1183713873,3608883,1651391,147964234,True,school,18,21,7.0,TNC_SHARED +1183713877,3608883,1651391,147964234,False,othdiscr,15,18,15.0,WALK_LOC +1183713878,3608883,1651391,147964234,False,Home,21,15,15.0,WALK_LOC +1183723449,3608912,1651406,147965431,True,work,3,21,14.0,TNC_SINGLE +1183723450,3608912,1651406,147965431,True,work,6,3,15.0,WALK +1183723453,3608912,1651406,147965431,False,escort,7,6,17.0,WALK +1183723454,3608912,1651406,147965431,False,Home,21,7,18.0,WALK_LOC +1183723713,3608913,1651406,147965464,True,school,11,21,6.0,WALK +1183723717,3608913,1651406,147965464,False,social,12,11,14.0,WALK_LOC +1183723718,3608913,1651406,147965464,False,Home,21,12,15.0,WALK_LOC +1183724481,3608916,1651408,147965560,True,atwork,14,14,11.0,WALK +1183724485,3608916,1651408,147965560,False,Work,14,14,13.0,WALK +1183724761,3608916,1651408,147965595,True,work,14,21,8.0,TNC_SINGLE +1183724762,3608916,1651408,147965595,True,work,13,14,8.0,TNC_SINGLE +1183724763,3608916,1651408,147965595,True,work,14,13,9.0,WALK_LOC +1183724765,3608916,1651408,147965595,False,Home,21,14,18.0,WALK_LOC +1183725025,3608917,1651408,147965628,True,school,11,21,8.0,WALK_LOC +1183725029,3608917,1651408,147965628,False,Home,21,11,13.0,WALK_LOC +1183727385,3608924,1651412,147965923,True,work,7,21,8.0,WALK +1183727389,3608924,1651412,147965923,False,Home,21,7,12.0,WALK +1183727649,3608925,1651412,147965956,True,school,10,21,6.0,WALK_LRF +1183727653,3608925,1651412,147965956,False,othdiscr,22,10,15.0,WALK_LRF +1183727654,3608925,1651412,147965956,False,Home,21,22,15.0,WALK_LOC +1183737881,3608956,1651428,147967235,True,work,2,25,8.0,WALK +1183737885,3608956,1651428,147967235,False,shopping,25,2,16.0,WALK +1183737886,3608956,1651428,147967235,False,shopping,5,25,17.0,WALK +1183737887,3608956,1651428,147967235,False,work,4,5,18.0,WALK +1183737888,3608956,1651428,147967235,False,Home,25,4,18.0,WALK +1183738145,3608957,1651428,147967268,True,school,9,25,7.0,WALK_LOC +1183738149,3608957,1651428,147967268,False,eatout,10,9,17.0,WALK_LOC +1183738150,3608957,1651428,147967268,False,Home,25,10,20.0,WALK_LOC +1183738257,3608958,1651429,147967282,True,escort,5,4,8.0,WALK +1183738258,3608958,1651429,147967282,True,atwork,4,5,8.0,WALK +1183738261,3608958,1651429,147967282,False,Work,4,4,10.0,WALK +1183738537,3608958,1651429,147967317,True,work,4,25,7.0,WALK_LOC +1183738541,3608958,1651429,147967317,False,work,5,4,17.0,WALK_LOC +1183738542,3608958,1651429,147967317,False,Home,25,5,17.0,WALK_LOC +1183738801,3608959,1651429,147967350,True,school,25,25,11.0,WALK +1183738805,3608959,1651429,147967350,False,Home,25,25,20.0,WALK +1183739193,3608960,1651430,147967399,True,work,5,25,7.0,WALK +1183739197,3608960,1651430,147967399,False,Home,25,5,18.0,WALK_LOC +1183739457,3608961,1651430,147967432,True,school,25,25,7.0,WALK +1183739461,3608961,1651430,147967432,False,Home,25,25,13.0,WALK +1210300633,3689940,1680413,151287579,True,work,5,10,8.0,WALK +1210300637,3689940,1680413,151287579,False,Home,10,5,20.0,WALK_LOC +1210300873,3689941,1680413,151287609,True,othmaint,22,10,10.0,TNC_SINGLE +1210300877,3689941,1680413,151287609,False,eatout,16,22,10.0,TNC_SINGLE +1210300878,3689941,1680413,151287609,False,Home,10,16,10.0,TNC_SINGLE +1210301177,3689942,1680413,151287647,True,othdiscr,9,10,16.0,TNC_SINGLE +1210301181,3689942,1680413,151287647,False,Home,10,9,18.0,WALK_LOC +1210301289,3689942,1680413,151287661,True,work,22,10,7.0,WALK_LRF +1210301293,3689942,1680413,151287661,False,Home,10,22,15.0,WALK_LRF +1210332825,3690039,1680446,151291603,True,atwork,11,4,10.0,WALK +1210332829,3690039,1680446,151291603,False,othmaint,4,11,10.0,WALK +1210332830,3690039,1680446,151291603,False,Work,4,4,10.0,WALK +1210333105,3690039,1680446,151291638,True,work,4,10,9.0,WALK +1210333109,3690039,1680446,151291638,False,Home,10,4,20.0,WALK +1210333345,3690040,1680446,151291668,True,othmaint,13,10,8.0,WALK_LRF +1210333349,3690040,1680446,151291668,False,Home,10,13,15.0,WALK_LRF +1210333369,3690040,1680446,151291671,True,univ,9,10,16.0,WALK_LOC +1210333373,3690040,1680446,151291671,False,Home,10,9,16.0,WALK_LOC +1210333761,3690041,1680446,151291720,True,work,9,10,7.0,WALK +1210333765,3690041,1680446,151291720,False,Home,10,9,17.0,WALK +1229739273,3749205,1700168,153717409,True,atwork,22,14,12.0,TNC_SINGLE +1229739277,3749205,1700168,153717409,False,Work,14,22,12.0,WALK +1229739553,3749205,1700168,153717444,True,work,14,12,7.0,WALK +1229739557,3749205,1700168,153717444,False,Home,12,14,18.0,WALK +1229739881,3749206,1700168,153717485,True,work,2,12,8.0,WALK +1229739885,3749206,1700168,153717485,False,Home,12,2,18.0,WALK +1229739929,3749207,1700168,153717491,True,atwork,5,12,10.0,WALK +1229739933,3749207,1700168,153717491,False,work,7,5,13.0,WALK +1229739934,3749207,1700168,153717491,False,Work,12,7,13.0,WALK +1229740209,3749207,1700168,153717526,True,work,12,12,8.0,WALK +1229740213,3749207,1700168,153717526,False,Home,12,12,18.0,WALK +1229765809,3749286,1700195,153720726,True,atwork,6,21,13.0,WALK +1229765813,3749286,1700195,153720726,False,Work,21,6,14.0,WALK +1229766121,3749286,1700195,153720765,True,work,21,18,7.0,WALK +1229766125,3749286,1700195,153720765,False,Home,18,21,20.0,WALK +1229766449,3749287,1700195,153720806,True,work,12,18,8.0,WALK +1229766453,3749287,1700195,153720806,False,Home,18,12,17.0,WALK +1229766777,3749288,1700195,153720847,True,work,5,18,8.0,WALK +1229766781,3749288,1700195,153720847,False,Home,18,5,17.0,WALK +1229786505,3749349,1700216,153723313,True,atwork,21,15,10.0,WALK +1229786509,3749349,1700216,153723313,False,Work,15,21,10.0,TNC_SINGLE +1229786785,3749349,1700216,153723348,True,work,15,21,7.0,TNC_SINGLE +1229786789,3749349,1700216,153723348,False,Home,21,15,18.0,TNC_SINGLE +1237441273,3772686,1707995,154680159,True,shopping,21,21,20.0,TNC_SHARED +1237441277,3772686,1707995,154680159,False,Home,21,21,21.0,TNC_SINGLE +1237441321,3772686,1707995,154680165,True,work,12,21,9.0,WALK +1237441325,3772686,1707995,154680165,False,escort,11,12,11.0,WALK +1237441326,3772686,1707995,154680165,False,Home,21,11,18.0,WALK +1237441585,3772687,1707995,154680198,True,school,20,21,7.0,WALK +1237441589,3772687,1707995,154680198,False,Home,21,20,15.0,WALK +1237441913,3772688,1707995,154680239,True,school,9,21,6.0,WALK_HVY +1237441917,3772688,1707995,154680239,False,Home,21,9,12.0,WALK_LOC +1275963937,3890133,1747144,159495492,True,work,11,1,7.0,WALK +1275963941,3890133,1747144,159495492,False,Home,1,11,17.0,WALK +1275964177,3890134,1747144,159495522,True,othmaint,6,1,8.0,WALK +1275964181,3890134,1747144,159495522,False,Home,1,6,13.0,WALK +1275964529,3890135,1747144,159495566,True,school,8,1,7.0,WALK_LRF +1275964533,3890135,1747144,159495566,False,othmaint,23,8,17.0,WALK_LRF +1275964534,3890135,1747144,159495566,False,Home,1,23,17.0,WALK_LOC +1275964537,3890135,1747144,159495567,True,school,8,1,18.0,WALK_LRF +1275964541,3890135,1747144,159495567,False,Home,1,8,18.0,WALK_LRF +1275965905,3890139,1747146,159495738,True,work,16,3,5.0,WALK_LOC +1275965909,3890139,1747146,159495738,False,escort,5,16,15.0,WALK_LOC +1275965910,3890139,1747146,159495738,False,eatout,13,5,15.0,WALK +1275965911,3890139,1747146,159495738,False,Home,3,13,19.0,WALK +1275965953,3890140,1747146,159495744,True,atwork,21,4,11.0,WALK +1275965957,3890140,1747146,159495744,False,eatout,4,21,12.0,WALK +1275965958,3890140,1747146,159495744,False,Work,4,4,12.0,WALK +1275966121,3890140,1747146,159495765,True,othdiscr,11,3,20.0,WALK_LOC +1275966125,3890140,1747146,159495765,False,Home,3,11,21.0,WALK_LOC +1275966129,3890140,1747146,159495766,True,othdiscr,11,3,21.0,WALK +1275966133,3890140,1747146,159495766,False,Home,3,11,23.0,WALK +1275966137,3890140,1747146,159495767,True,shopping,7,3,23.0,WALK +1275966138,3890140,1747146,159495767,True,othdiscr,17,7,23.0,WALK_LRF +1275966141,3890140,1747146,159495767,False,Home,3,17,23.0,WALK +1275966233,3890140,1747146,159495779,True,work,4,3,6.0,WALK +1275966237,3890140,1747146,159495779,False,Home,3,4,19.0,WALK +1275966497,3890141,1747146,159495812,True,school,9,3,7.0,WALK_LRF +1275966501,3890141,1747146,159495812,False,Home,3,9,15.0,WALK_LRF +1275982193,3890189,1747162,159497774,True,shopping,5,6,9.0,WALK +1275982194,3890189,1747162,159497774,True,othdiscr,12,5,9.0,WALK +1275982197,3890189,1747162,159497774,False,Home,6,12,22.0,WALK +1275999233,3890241,1747180,159499904,True,eatout,5,2,11.0,WALK +1275999234,3890241,1747180,159499904,True,atwork,1,5,11.0,WALK +1275999237,3890241,1747180,159499904,False,Work,2,1,14.0,WALK +1275999361,3890241,1747180,159499920,True,work,2,7,7.0,TNC_SINGLE +1275999365,3890241,1747180,159499920,False,work,24,2,17.0,TNC_SINGLE +1275999366,3890241,1747180,159499920,False,eatout,6,24,17.0,TNC_SINGLE +1275999367,3890241,1747180,159499920,False,social,8,6,17.0,TNC_SINGLE +1275999368,3890241,1747180,159499920,False,Home,7,8,19.0,WALK_LOC +1275999577,3890242,1747180,159499947,True,othdiscr,5,7,12.0,WALK +1275999581,3890242,1747180,159499947,False,Home,7,5,20.0,WALK +1276001049,3890247,1747182,159500131,True,atwork,7,6,11.0,WALK +1276001053,3890247,1747182,159500131,False,Work,6,7,12.0,WALK +1276001329,3890247,1747182,159500166,True,work,6,7,6.0,WALK +1276001333,3890247,1747182,159500166,False,Home,7,6,16.0,WALK +1276001657,3890248,1747182,159500207,True,work,2,7,7.0,WALK +1276001661,3890248,1747182,159500207,False,Home,7,2,17.0,WALK +1276001921,3890249,1747182,159500240,True,school,6,7,8.0,WALK +1276001925,3890249,1747182,159500240,False,Home,7,6,13.0,WALK +1276044625,3890379,1747226,159505578,True,work,7,9,7.0,WALK +1276044629,3890379,1747226,159505578,False,Home,9,7,21.0,WALK_LOC +1276044713,3890380,1747226,159505589,True,escort,8,9,17.0,TNC_SINGLE +1276044717,3890380,1747226,159505589,False,Home,9,8,17.0,TNC_SINGLE +1276044953,3890380,1747226,159505619,True,work,2,9,7.0,WALK_HVY +1276044957,3890380,1747226,159505619,False,Home,9,2,15.0,WALK_HVY +1276045217,3890381,1747226,159505652,True,school,8,9,7.0,WALK +1276045221,3890381,1747226,159505652,False,Home,9,8,16.0,WALK +1276099641,3890547,1747282,159512455,True,othmaint,7,10,21.0,WALK_LOC +1276099645,3890547,1747282,159512455,False,Home,10,7,22.0,WALK_LRF +1276099729,3890547,1747282,159512466,True,escort,9,10,8.0,WALK_LOC +1276099730,3890547,1747282,159512466,True,work,23,9,8.0,WALK_LOC +1276099733,3890547,1747282,159512466,False,othmaint,9,23,18.0,WALK_LRF +1276099734,3890547,1747282,159512466,False,Home,10,9,19.0,WALK +1276100057,3890548,1747282,159512507,True,work,10,10,7.0,WALK +1276100061,3890548,1747282,159512507,False,Home,10,10,17.0,WALK +1276100273,3890549,1747282,159512534,True,othdiscr,8,10,10.0,WALK +1276100277,3890549,1747282,159512534,False,Home,10,8,10.0,WALK +1276100321,3890549,1747282,159512540,True,school,21,10,12.0,WALK +1276100325,3890549,1747282,159512540,False,Home,10,21,20.0,WALK_LOC +1276102401,3890556,1747285,159512800,True,atwork,5,24,11.0,WALK +1276102405,3890556,1747285,159512800,False,Work,24,5,13.0,WALK +1276102681,3890556,1747285,159512835,True,shopping,11,10,6.0,TNC_SINGLE +1276102682,3890556,1747285,159512835,True,work,22,11,7.0,WALK_LOC +1276102683,3890556,1747285,159512835,True,work,24,22,7.0,TNC_SINGLE +1276102685,3890556,1747285,159512835,False,Home,10,24,17.0,WALK_HVY +1276103009,3890557,1747285,159512876,True,work,9,10,9.0,WALK_LOC +1276103013,3890557,1747285,159512876,False,Home,10,9,18.0,TNC_SINGLE +1276120113,3890610,1747303,159515014,True,atwork,1,12,9.0,WALK_LOC +1276120117,3890610,1747303,159515014,False,Work,12,1,9.0,TNC_SINGLE +1276120393,3890610,1747303,159515049,True,work,12,10,5.0,WALK_LRF +1276120397,3890610,1747303,159515049,False,Home,10,12,20.0,WALK_LOC +1276120721,3890611,1747303,159515090,True,work,4,10,13.0,WALK_LOC +1276120722,3890611,1747303,159515090,True,work,14,4,13.0,WALK_LOC +1276120725,3890611,1747303,159515090,False,Home,10,14,16.0,TNC_SINGLE +1276120985,3890612,1747303,159515123,True,school,11,10,11.0,WALK_LOC +1276120989,3890612,1747303,159515123,False,Home,10,11,19.0,WALK_LOC +1276144993,3890685,1747328,159518124,True,work,16,10,16.0,DRIVEALONEFREE +1276144997,3890685,1747328,159518124,False,social,16,16,16.0,DRIVEALONEFREE +1276144998,3890685,1747328,159518124,False,Home,10,16,16.0,WALK +1276145321,3890686,1747328,159518165,True,work,15,10,7.0,WALK_LRF +1276145325,3890686,1747328,159518165,False,othmaint,7,15,17.0,WALK_LOC +1276145326,3890686,1747328,159518165,False,othmaint,9,7,20.0,WALK +1276145327,3890686,1747328,159518165,False,Home,10,9,20.0,WALK +1276145585,3890687,1747328,159518198,True,school,10,10,8.0,WALK +1276145589,3890687,1747328,159518198,False,Home,10,10,14.0,WALK +1276155817,3890718,1747339,159519477,True,work,22,10,13.0,WALK_LRF +1276155821,3890718,1747339,159519477,False,othdiscr,9,22,16.0,WALK +1276155822,3890718,1747339,159519477,False,othmaint,4,9,16.0,WALK_LOC +1276155823,3890718,1747339,159519477,False,escort,9,4,19.0,TAXI +1276155824,3890718,1747339,159519477,False,Home,10,9,20.0,WALK +1276156097,3890719,1747339,159519512,True,shopping,2,10,18.0,WALK_LRF +1276156101,3890719,1747339,159519512,False,Home,10,2,21.0,WALK_LRF +1276156145,3890719,1747339,159519518,True,work,22,10,6.0,WALK_LRF +1276156149,3890719,1747339,159519518,False,Home,10,22,16.0,WALK_LRF +1276156409,3890720,1747339,159519551,True,school,10,10,8.0,WALK +1276156413,3890720,1747339,159519551,False,Home,10,10,14.0,WALK +1276186145,3890811,1747370,159523268,True,social,5,10,7.0,WALK +1276186146,3890811,1747370,159523268,True,othmaint,12,5,7.0,SHARED2FREE +1276186149,3890811,1747370,159523268,False,Home,10,12,7.0,SHARED2FREE +1276186321,3890811,1747370,159523290,True,work,11,10,7.0,WALK +1276186325,3890811,1747370,159523290,False,Home,10,11,21.0,WALK +1276186585,3890812,1747370,159523323,True,school,10,10,7.0,WALK +1276186589,3890812,1747370,159523323,False,Home,10,10,14.0,WALK +1276186697,3890813,1747370,159523337,True,escort,8,21,14.0,WALK +1276186698,3890813,1747370,159523337,True,atwork,7,8,14.0,WALK +1276186701,3890813,1747370,159523337,False,Work,21,7,14.0,WALK +1276186977,3890813,1747370,159523372,True,work,21,10,7.0,WALK_LOC +1276186981,3890813,1747370,159523372,False,Home,10,21,22.0,WALK +1276221745,3890919,1747406,159527718,True,work,7,11,8.0,WALK +1276221749,3890919,1747406,159527718,False,Home,11,7,19.0,WALK +1276221793,3890920,1747406,159527724,True,atwork,5,18,10.0,WALK +1276221797,3890920,1747406,159527724,False,Work,18,5,11.0,WALK +1276222073,3890920,1747406,159527759,True,work,18,11,7.0,WALK +1276222077,3890920,1747406,159527759,False,eatout,8,18,17.0,WALK_LOC +1276222078,3890920,1747406,159527759,False,Home,11,8,17.0,WALK_LOC +1276222337,3890921,1747406,159527792,True,school,16,11,7.0,WALK +1276222341,3890921,1747406,159527792,False,Home,11,16,11.0,WALK +1276229617,3890943,1747414,159528702,True,work,12,11,8.0,WALK +1276229621,3890943,1747414,159528702,False,Home,11,12,22.0,WALK +1276229705,3890944,1747414,159528713,True,escort,8,11,6.0,WALK +1276229709,3890944,1747414,159528713,False,Home,11,8,6.0,WALK +1276230209,3890945,1747414,159528776,True,school,9,11,12.0,TAXI +1276230213,3890945,1747414,159528776,False,escort,12,9,17.0,WALK +1276230214,3890945,1747414,159528776,False,Home,11,12,17.0,SHARED3FREE +1276235521,3890961,1747420,159529440,True,work,12,11,7.0,DRIVEALONEFREE +1276235525,3890961,1747420,159529440,False,Home,11,12,18.0,DRIVEALONEFREE +1276235849,3890962,1747420,159529481,True,work,1,11,8.0,WALK +1276235853,3890962,1747420,159529481,False,Home,11,1,18.0,WALK_LRF +1276236113,3890963,1747420,159529514,True,escort,11,11,8.0,WALK +1276236114,3890963,1747420,159529514,True,school,8,11,8.0,WALK +1276236117,3890963,1747420,159529514,False,Home,11,8,17.0,WALK +1276281769,3891102,1747467,159535221,True,work,11,16,7.0,WALK_LOC +1276281773,3891102,1747467,159535221,False,Home,16,11,16.0,WALK_LOC +1276282049,3891103,1747467,159535256,True,shopping,16,16,13.0,WALK +1276282053,3891103,1747467,159535256,False,Home,16,16,16.0,WALK +1276282313,3891104,1747467,159535289,True,othdiscr,2,16,14.0,WALK +1276282317,3891104,1747467,159535289,False,Home,16,2,15.0,WALK +1276282321,3891104,1747467,159535290,True,othdiscr,19,16,16.0,WALK_LOC +1276282325,3891104,1747467,159535290,False,Home,16,19,21.0,WALK_LOC +1276282361,3891104,1747467,159535295,True,school,17,16,7.0,WALK_LRF +1276282365,3891104,1747467,159535295,False,Home,16,17,14.0,WALK_LRF +1276292313,3891135,1747478,159536539,True,atwork,2,23,11.0,WALK +1276292317,3891135,1747478,159536539,False,othmaint,3,2,13.0,WALK +1276292318,3891135,1747478,159536539,False,Work,23,3,13.0,WALK +1276292329,3891135,1747478,159536541,True,othmaint,14,16,15.0,WALK +1276292330,3891135,1747478,159536541,True,eatout,13,14,18.0,WALK +1276292333,3891135,1747478,159536541,False,Home,16,13,21.0,WALK +1276292593,3891135,1747478,159536574,True,work,23,16,5.0,WALK +1276292597,3891135,1747478,159536574,False,eatout,1,23,13.0,WALK +1276292598,3891135,1747478,159536574,False,Home,16,1,13.0,WALK +1276292857,3891136,1747478,159536607,True,school,13,16,8.0,WALK +1276292861,3891136,1747478,159536607,False,othmaint,13,13,18.0,WALK +1276292862,3891136,1747478,159536607,False,Home,16,13,18.0,WALK +1276293185,3891137,1747478,159536648,True,school,11,16,8.0,SHARED2FREE +1276293189,3891137,1747478,159536648,False,Home,16,11,16.0,SHARED2FREE +1276315225,3891204,1747501,159539403,True,social,8,17,9.0,DRIVEALONEFREE +1276315226,3891204,1747501,159539403,True,work,2,8,11.0,DRIVEALONEFREE +1276315229,3891204,1747501,159539403,False,Home,17,2,20.0,DRIVEALONEFREE +1276315617,3891206,1747501,159539452,True,eatout,19,17,18.0,SHARED2FREE +1276315621,3891206,1747501,159539452,False,Home,17,19,21.0,SHARED2FREE +1276315817,3891206,1747501,159539477,True,school,25,17,7.0,WALK +1276315821,3891206,1747501,159539477,False,Home,17,25,15.0,SHARED2FREE +1276373281,3891381,1747560,159546660,True,work,10,25,6.0,WALK_LRF +1276373285,3891381,1747560,159546660,False,Home,25,10,16.0,WALK_LOC +1276373609,3891382,1747560,159546701,True,work,1,25,7.0,WALK_LOC +1276373613,3891382,1747560,159546701,False,Home,25,1,16.0,WALK +1276373873,3891383,1747560,159546734,True,school,25,25,8.0,WALK +1276373877,3891383,1747560,159546734,False,Home,25,25,12.0,WALK +1276384809,3891417,1747572,159548101,True,atwork,13,22,13.0,WALK +1276384813,3891417,1747572,159548101,False,Work,22,13,13.0,WALK +1276384913,3891417,1747572,159548114,True,othmaint,17,25,14.0,SHARED3FREE +1276384914,3891417,1747572,159548114,True,othmaint,19,17,15.0,TNC_SHARED +1276384917,3891417,1747572,159548114,False,shopping,19,19,16.0,SHARED2FREE +1276384918,3891417,1747572,159548114,False,othmaint,18,19,16.0,SHARED2FREE +1276384919,3891417,1747572,159548114,False,eatout,11,18,16.0,TNC_SHARED +1276384920,3891417,1747572,159548114,False,Home,25,11,16.0,SHARED3FREE +1276385089,3891417,1747572,159548136,True,work,22,25,6.0,WALK +1276385093,3891417,1747572,159548136,False,Home,25,22,14.0,WALK +1276385353,3891418,1747572,159548169,True,school,25,25,8.0,WALK +1276385357,3891418,1747572,159548169,False,Home,25,25,14.0,WALK +1276385745,3891419,1747572,159548218,True,work,5,25,5.0,WALK +1276385749,3891419,1747572,159548218,False,othmaint,7,5,20.0,WALK +1276385750,3891419,1747572,159548218,False,Home,25,7,21.0,WALK +1368108865,4171063,1809932,171013608,True,othdiscr,9,9,10.0,WALK +1368108869,4171063,1809932,171013608,False,Home,9,9,16.0,WALK +1368109305,4171064,1809932,171013663,True,work,15,9,6.0,TNC_SINGLE +1368109309,4171064,1809932,171013663,False,Home,9,15,17.0,TNC_SINGLE +1368109681,4171066,1809932,171013710,True,atwork,5,6,10.0,WALK +1368109685,4171066,1809932,171013710,False,shopping,5,5,10.0,WALK +1368109686,4171066,1809932,171013710,False,Work,6,5,10.0,WALK +1368109961,4171066,1809932,171013745,True,work,6,9,8.0,WALK +1368109965,4171066,1809932,171013745,False,Home,9,6,17.0,WALK +1368110225,4171067,1809932,171013778,True,shopping,12,9,12.0,WALK_LRF +1368110226,4171067,1809932,171013778,True,univ,13,12,13.0,WALK +1368110229,4171067,1809932,171013778,False,Home,9,13,16.0,WALK_LRF +1368110617,4171068,1809932,171013827,True,work,11,9,5.0,WALK_LOC +1368110621,4171068,1809932,171013827,False,Home,9,11,18.0,WALK +1368110897,4171069,1809932,171013862,True,shopping,5,9,18.0,WALK +1368110901,4171069,1809932,171013862,False,Home,9,5,19.0,WALK +1368110945,4171069,1809932,171013868,True,escort,24,9,5.0,WALK_LRF +1368110946,4171069,1809932,171013868,True,work,5,24,5.0,WALK +1368110949,4171069,1809932,171013868,False,shopping,6,5,18.0,WALK +1368110950,4171069,1809932,171013868,False,Home,9,6,18.0,WALK_LOC +1368111161,4171070,1809932,171013895,True,othdiscr,20,9,10.0,WALK +1368111165,4171070,1809932,171013895,False,Home,9,20,10.0,WALK_LOC +1368111537,4171071,1809932,171013942,True,school,13,9,7.0,WALK_LRF +1368111541,4171071,1809932,171013942,False,Home,9,13,11.0,WALK_LRF +1368154305,4171202,1809952,171019288,True,eatout,5,11,14.0,WALK +1368154309,4171202,1809952,171019288,False,Home,11,5,14.0,WALK +1368154785,4171203,1809952,171019348,True,othdiscr,22,11,17.0,WALK_LRF +1368154789,4171203,1809952,171019348,False,Home,11,22,19.0,WALK_LRF +1368154897,4171203,1809952,171019362,True,work,24,11,7.0,WALK +1368154901,4171203,1809952,171019362,False,Home,11,24,17.0,WALK +1368155097,4171204,1809952,171019387,True,atwork,7,5,14.0,WALK +1368155101,4171204,1809952,171019387,False,Work,5,7,14.0,WALK +1368155177,4171204,1809952,171019397,True,shopping,16,11,6.0,TNC_SHARED +1368155181,4171204,1809952,171019397,False,Home,11,16,6.0,DRIVEALONEFREE +1368155225,4171204,1809952,171019403,True,work,5,11,7.0,WALK +1368155229,4171204,1809952,171019403,False,Home,11,5,20.0,WALK +1368155553,4171205,1809952,171019444,True,escort,11,11,8.0,WALK +1368155554,4171205,1809952,171019444,True,work,10,11,9.0,WALK +1368155557,4171205,1809952,171019444,False,othmaint,11,10,17.0,WALK +1368155558,4171205,1809952,171019444,False,eatout,7,11,17.0,WALK +1368155559,4171205,1809952,171019444,False,Home,11,7,18.0,WALK +1368289969,4171615,1810015,171036246,True,univ,12,16,13.0,WALK +1368289973,4171615,1810015,171036246,False,Home,16,12,17.0,WALK +1368290273,4171616,1810015,171036284,True,othmaint,3,16,9.0,WALK +1368290277,4171616,1810015,171036284,False,Home,16,3,11.0,WALK +1368290689,4171617,1810015,171036336,True,social,12,16,9.0,WALK +1368290690,4171617,1810015,171036336,True,work,15,12,12.0,WALK +1368290693,4171617,1810015,171036336,False,Home,16,15,18.0,WALK +1368291297,4171619,1810015,171036412,True,shopping,1,16,13.0,WALK +1368291301,4171619,1810015,171036412,False,shopping,16,1,14.0,WALK +1368291302,4171619,1810015,171036412,False,Home,16,16,15.0,WALK +1368291609,4171620,1810015,171036451,True,school,8,16,7.0,WALK_LOC +1368291613,4171620,1810015,171036451,False,Home,16,8,15.0,WALK_LOC +1368292281,4171622,1810015,171036535,True,shopping,19,16,9.0,WALK +1368292285,4171622,1810015,171036535,False,Home,16,19,15.0,WALK +1368292657,4171623,1810015,171036582,True,shopping,16,16,9.0,WALK +1368292658,4171623,1810015,171036582,True,work,21,16,9.0,WALK +1368292661,4171623,1810015,171036582,False,Home,16,21,13.0,WALK +1387047257,4228802,1821121,173380907,True,othdiscr,24,9,13.0,WALK_LRF +1387047261,4228802,1821121,173380907,False,othmaint,5,24,17.0,TNC_SINGLE +1387047262,4228802,1821121,173380907,False,Home,9,5,17.0,TNC_SINGLE +1387047305,4228802,1821121,173380913,True,univ,13,9,20.0,WALK_LRF +1387047309,4228802,1821121,173380913,False,othmaint,7,13,20.0,WALK_LOC +1387047310,4228802,1821121,173380913,False,othmaint,12,7,20.0,WALK_LOC +1387047311,4228802,1821121,173380913,False,Home,9,12,20.0,WALK_LRF +1387047633,4228803,1821121,173380954,True,school,8,9,9.0,WALK +1387047637,4228803,1821121,173380954,False,Home,9,8,17.0,WALK +1387047977,4228804,1821121,173380997,True,shopping,8,9,11.0,WALK +1387047981,4228804,1821121,173380997,False,Home,9,8,15.0,WALK +1387047985,4228804,1821121,173380998,True,shopping,11,9,18.0,SHARED3FREE +1387047989,4228804,1821121,173380998,False,shopping,12,11,19.0,SHARED3FREE +1387047990,4228804,1821121,173380998,False,Home,9,12,19.0,DRIVEALONEFREE +1387051441,4228816,1821124,173381430,True,eatout,10,9,6.0,WALK +1387051442,4228816,1821124,173381430,True,othdiscr,20,10,7.0,SHARED3FREE +1387051445,4228816,1821124,173381430,False,Home,9,20,7.0,SHARED3FREE +1387051897,4228816,1821124,173381487,True,school,16,9,8.0,WALK_LOC +1387051901,4228816,1821124,173381487,False,Home,9,16,16.0,WALK_LRF +1387052225,4228817,1821124,173381528,True,school,8,9,7.0,WALK +1387052229,4228817,1821124,173381528,False,Home,9,8,14.0,WALK +1387052553,4228818,1821124,173381569,True,school,9,9,8.0,WALK +1387052557,4228818,1821124,173381569,False,Home,9,9,13.0,WALK +1387066281,4228860,1821133,173383285,True,othdiscr,20,11,8.0,WALK +1387066285,4228860,1821133,173383285,False,Home,11,20,10.0,WALK +1387066305,4228860,1821133,173383288,True,othmaint,7,11,11.0,WALK +1387066309,4228860,1821133,173383288,False,Home,11,7,20.0,WALK +1387066657,4228861,1821133,173383332,True,school,20,11,7.0,BIKE +1387066661,4228861,1821133,173383332,False,Home,11,20,14.0,WALK +1387067313,4228863,1821133,173383414,True,escort,8,11,8.0,WALK_LOC +1387067314,4228863,1821133,173383414,True,school,7,8,9.0,WALK +1387067317,4228863,1821133,173383414,False,eatout,14,7,17.0,WALK_LOC +1387067318,4228863,1821133,173383414,False,Home,11,14,17.0,WALK +1387067465,4228864,1821133,173383433,True,escort,6,11,17.0,SHARED3FREE +1387067469,4228864,1821133,173383433,False,Home,11,6,18.0,WALK +1387067617,4228864,1821133,173383452,True,othmaint,5,11,20.0,DRIVEALONEFREE +1387067621,4228864,1821133,173383452,False,Home,11,5,20.0,TAXI +1387067641,4228864,1821133,173383455,True,univ,9,11,8.0,WALK +1387067645,4228864,1821133,173383455,False,social,10,9,14.0,WALK +1387067646,4228864,1821133,173383455,False,Home,11,10,15.0,WALK +1387067657,4228864,1821133,173383457,True,shopping,5,11,16.0,WALK +1387067661,4228864,1821133,173383457,False,Home,11,5,16.0,WALK +1387095409,4228949,1821151,173386926,True,othmaint,3,22,10.0,TNC_SHARED +1387095413,4228949,1821151,173386926,False,Home,22,3,13.0,TNC_SINGLE +1387095537,4228949,1821151,173386942,True,shopping,16,22,18.0,WALK +1387095541,4228949,1821151,173386942,False,Home,22,16,18.0,WALK +1387095865,4228950,1821151,173386983,True,shopping,5,22,17.0,TNC_SINGLE +1387095869,4228950,1821151,173386983,False,Home,22,5,17.0,TNC_SHARED +1387096785,4228953,1821151,173387098,True,othdiscr,18,22,14.0,TNC_SINGLE +1387096789,4228953,1821151,173387098,False,Home,22,18,15.0,TNC_SINGLE +1387097313,4228955,1821152,173387164,True,shopping,16,22,15.0,SHARED2FREE +1387097314,4228955,1821152,173387164,True,escort,12,16,15.0,SHARED2FREE +1387097317,4228955,1821152,173387164,False,Home,22,12,15.0,DRIVEALONEFREE +1387097641,4228956,1821152,173387205,True,escort,3,22,8.0,WALK +1387097645,4228956,1821152,173387205,False,Home,22,3,9.0,WALK +1387097769,4228956,1821152,173387221,True,othdiscr,11,22,14.0,WALK +1387097773,4228956,1821152,173387221,False,Home,22,11,19.0,WALK +1387097969,4228957,1821152,173387246,True,escort,16,22,10.0,SHARED3FREE +1387097973,4228957,1821152,173387246,False,Home,22,16,10.0,SHARED3FREE +1387098489,4228958,1821152,173387311,True,shopping,16,22,13.0,WALK +1387098493,4228958,1821152,173387311,False,Home,22,16,13.0,WALK +1419657457,4328223,1842600,177457182,True,work,13,10,8.0,WALK_LRF +1419657461,4328223,1842600,177457182,False,Home,10,13,16.0,WALK_LRF +1419657873,4328225,1842600,177457234,True,escort,9,10,5.0,WALK_LOC +1419657877,4328225,1842600,177457234,False,Home,10,9,6.0,TNC_SINGLE +1419657881,4328225,1842600,177457235,True,escort,5,10,8.0,WALK +1419657885,4328225,1842600,177457235,False,Home,10,5,11.0,WALK +1419658377,4328226,1842600,177457297,True,school,9,10,7.0,WALK_LOC +1419658381,4328226,1842600,177457297,False,social,11,9,16.0,WALK +1419658382,4328226,1842600,177457297,False,Home,10,11,17.0,WALK_LOC +1419658529,4328227,1842600,177457316,True,escort,16,10,11.0,TNC_SINGLE +1419658533,4328227,1842600,177457316,False,Home,10,16,12.0,TNC_SINGLE +1419658537,4328227,1842600,177457317,True,escort,11,10,17.0,TNC_SINGLE +1419658541,4328227,1842600,177457317,False,othmaint,1,11,17.0,WALK_LRF +1419658542,4328227,1842600,177457317,False,Home,10,1,17.0,TNC_SINGLE +1419659097,4328228,1842601,177457387,True,work,24,10,9.0,WALK_LRF +1419659101,4328228,1842601,177457387,False,work,1,24,16.0,WALK +1419659102,4328228,1842601,177457387,False,eatout,9,1,19.0,WALK_LOC +1419659103,4328228,1842601,177457387,False,Home,10,9,19.0,WALK_LOC +1419659361,4328229,1842601,177457420,True,school,8,10,8.0,WALK_LOC +1419659365,4328229,1842601,177457420,False,Home,10,8,13.0,WALK_LOC +1419659689,4328230,1842601,177457461,True,school,10,10,7.0,WALK +1419659693,4328230,1842601,177457461,False,Home,10,10,15.0,WALK +1419660017,4328231,1842601,177457502,True,univ,9,10,18.0,WALK_LOC +1419660021,4328231,1842601,177457502,False,othmaint,9,9,21.0,WALK +1419660022,4328231,1842601,177457502,False,Home,10,9,21.0,WALK_LOC +1500781697,4575553,1896787,187597712,True,work,4,3,13.0,WALK +1500781701,4575553,1896787,187597712,False,shopping,5,4,18.0,WALK +1500781702,4575553,1896787,187597712,False,Home,3,5,23.0,WALK +1500781913,4575554,1896787,187597739,True,othdiscr,10,3,16.0,WALK_LRF +1500781917,4575554,1896787,187597739,False,Home,3,10,22.0,WALK_LRF +1500782025,4575554,1896787,187597753,True,work,1,3,6.0,WALK +1500782029,4575554,1896787,187597753,False,Home,3,1,15.0,WALK +1500782289,4575555,1896787,187597786,True,school,9,3,8.0,WALK_LRF +1500782293,4575555,1896787,187597786,False,Home,3,9,16.0,WALK_LRF +1500782617,4575556,1896787,187597827,True,school,10,3,8.0,WALK_LRF +1500782621,4575556,1896787,187597827,False,othmaint,24,10,17.0,WALK_LRF +1500782622,4575556,1896787,187597827,False,Home,3,24,17.0,WALK +1500898201,4575909,1896857,187612275,True,eatout,2,24,16.0,TAXI +1500898205,4575909,1896857,187612275,False,social,5,2,20.0,WALK +1500898206,4575909,1896857,187612275,False,Home,24,5,20.0,WALK +1500898681,4575910,1896857,187612335,True,othdiscr,2,24,8.0,WALK +1500898685,4575910,1896857,187612335,False,Home,24,2,10.0,WALK +1500898857,4575911,1896857,187612357,True,eatout,4,24,21.0,WALK +1500898861,4575911,1896857,187612357,False,Home,24,4,22.0,WALK +1500899009,4575911,1896857,187612376,True,othdiscr,15,24,10.0,WALK +1500899013,4575911,1896857,187612376,False,Home,24,15,21.0,WALK +1517029505,4625089,1907359,189628688,True,work,20,11,8.0,WALK +1517029509,4625089,1907359,189628688,False,social,12,20,12.0,WALK +1517029510,4625089,1907359,189628688,False,Home,11,12,12.0,WALK +1517029513,4625089,1907359,189628689,True,work,20,11,12.0,DRIVEALONEFREE +1517029517,4625089,1907359,189628689,False,Home,11,20,17.0,DRIVEALONEFREE +1517029593,4625090,1907359,189628699,True,escort,23,11,8.0,SHARED2FREE +1517029597,4625090,1907359,189628699,False,Home,11,23,8.0,SHARED2FREE +1517029601,4625090,1907359,189628700,True,escort,4,11,16.0,WALK_LOC +1517029605,4625090,1907359,189628700,False,Home,11,4,17.0,WALK_LRF +1517029609,4625090,1907359,189628701,True,escort,16,11,17.0,WALK_LOC +1517029613,4625090,1907359,189628701,False,Home,11,16,17.0,TNC_SINGLE +1517029785,4625090,1907359,189628723,True,shopping,13,11,9.0,DRIVEALONEFREE +1517029789,4625090,1907359,189628723,False,Home,11,13,10.0,DRIVEALONEFREE +1517030161,4625091,1907359,189628770,True,work,12,11,11.0,WALK +1517030165,4625091,1907359,189628770,False,Home,11,12,16.0,WALK +1517030425,4625092,1907359,189628803,True,school,9,11,7.0,WALK +1517030429,4625092,1907359,189628803,False,Home,11,9,15.0,WALK +1517030753,4625093,1907359,189628844,True,school,10,11,7.0,SHARED3FREE +1517030757,4625093,1907359,189628844,False,Home,11,10,14.0,WALK_LOC +1517030865,4625094,1907359,189628858,True,atwork,9,15,13.0,SHARED2FREE +1517030869,4625094,1907359,189628858,False,social,7,9,13.0,WALK +1517030870,4625094,1907359,189628858,False,Work,15,7,13.0,SHARED2FREE +1517031145,4625094,1907359,189628893,True,work,15,11,6.0,WALK +1517031149,4625094,1907359,189628893,False,Home,11,15,19.0,WALK_LOC +1561922537,4761958,1931827,195240317,True,work,14,8,7.0,WALK +1561922541,4761958,1931827,195240317,False,Home,8,14,20.0,WALK +1561922865,4761959,1931827,195240358,True,work,1,8,11.0,WALK +1561922869,4761959,1931827,195240358,False,Home,8,1,18.0,WALK_LRF +1561923129,4761960,1931827,195240391,True,school,6,8,7.0,WALK +1561923133,4761960,1931827,195240391,False,Home,8,6,17.0,WALK +1561923457,4761961,1931827,195240432,True,school,13,8,8.0,WALK_LOC +1561923461,4761961,1931827,195240432,False,Home,8,13,16.0,WALK_LRF 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+1562221893,4762871,1931922,195277736,False,Home,16,8,10.0,WALK +1562222049,4762872,1931922,195277756,True,atwork,12,4,16.0,BIKE +1562222053,4762872,1931922,195277756,False,eatout,7,12,16.0,BIKE +1562222054,4762872,1931922,195277756,False,Work,4,7,16.0,BIKE +1562222329,4762872,1931922,195277791,True,work,4,16,7.0,BIKE +1562222333,4762872,1931922,195277791,False,Home,16,4,18.0,WALK +1562222609,4762873,1931922,195277826,True,shopping,16,16,13.0,WALK +1562222613,4762873,1931922,195277826,False,Home,16,16,13.0,WALK +1562298337,4763104,1931947,195287292,True,othmaint,21,16,13.0,DRIVEALONEFREE +1562298341,4763104,1931947,195287292,False,Home,16,21,15.0,TNC_SHARED +1562300393,4763110,1931947,195287549,True,work,11,16,7.0,WALK +1562300397,4763110,1931947,195287549,False,Home,16,11,19.0,WALK_LOC +1562300721,4763111,1931947,195287590,True,work,23,16,7.0,WALK +1562300725,4763111,1931947,195287590,False,Home,16,23,19.0,WALK +1562300769,4763112,1931947,195287596,True,atwork,13,4,10.0,WALK +1562300773,4763112,1931947,195287596,False,Work,4,13,10.0,WALK +1562301049,4763112,1931947,195287631,True,work,4,16,7.0,WALK_LOC +1562301053,4763112,1931947,195287631,False,Home,16,4,16.0,WALK +1562342793,4763240,1931961,195292849,True,escort,1,17,14.0,WALK_LOC +1562342797,4763240,1931961,195292849,False,Home,17,1,14.0,TNC_SINGLE +1562343689,4763242,1931961,195292961,True,work,4,17,8.0,WALK +1562343693,4763242,1931961,195292961,False,Home,17,4,17.0,WALK +1562344017,4763243,1931961,195293002,True,work,22,17,7.0,WALK_LRF +1562344021,4763243,1931961,195293002,False,Home,17,22,17.0,WALK_LRF +1562344281,4763244,1931961,195293035,True,school,13,17,6.0,WALK_LRF +1562344285,4763244,1931961,195293035,False,Home,17,13,10.0,WALK +1562344673,4763245,1931961,195293084,True,work,4,17,9.0,WALK +1562344674,4763245,1931961,195293084,True,work,10,4,10.0,WALK_LRF +1562344677,4763245,1931961,195293084,False,escort,4,10,17.0,WALK_LRF +1562344678,4763245,1931961,195293084,False,escort,16,4,17.0,WALK 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+1562347497,4763254,1931962,195293437,True,atwork,10,9,12.0,WALK +1562347501,4763254,1931962,195293437,False,Work,9,10,13.0,WALK +1562347625,4763254,1931962,195293453,True,work,9,17,11.0,WALK_LRF +1562347629,4763254,1931962,195293453,False,Home,17,9,18.0,WALK_LRF +1562347689,4763255,1931962,195293461,True,eatout,12,17,17.0,WALK +1562347693,4763255,1931962,195293461,False,Home,17,12,19.0,WALK +1562347889,4763255,1931962,195293486,True,school,11,17,7.0,WALK +1562347893,4763255,1931962,195293486,False,Home,17,11,15.0,WALK +1562348281,4763256,1931962,195293535,True,work,21,17,14.0,WALK +1562348285,4763256,1931962,195293535,False,Home,17,21,20.0,WALK +1562348481,4763257,1931962,195293560,True,atwork,16,16,8.0,WALK +1562348485,4763257,1931962,195293560,False,Work,16,16,8.0,WALK +1562348609,4763257,1931962,195293576,True,work,16,17,8.0,WALK +1562348613,4763257,1931962,195293576,False,Home,17,16,17.0,WALK +1562349201,4763259,1931962,195293650,True,school,18,17,7.0,WALK_LRF +1562349205,4763259,1931962,195293650,False,Home,17,18,14.0,WALK_LRF +1562349313,4763260,1931962,195293664,True,work,13,2,11.0,WALK +1562349314,4763260,1931962,195293664,True,atwork,22,13,11.0,WALK +1562349317,4763260,1931962,195293664,False,Work,2,22,11.0,WALK +1562349593,4763260,1931962,195293699,True,work,2,17,7.0,WALK +1562349597,4763260,1931962,195293699,False,Home,17,2,15.0,WALK +1562396825,4763404,1931978,195299603,True,shopping,16,17,13.0,WALK_LRF +1562396826,4763404,1931978,195299603,True,work,2,16,14.0,WALK +1562396829,4763404,1931978,195299603,False,Home,17,2,22.0,WALK_LRF +1562397417,4763406,1931978,195299677,True,work,14,17,18.0,WALK +1562397418,4763406,1931978,195299677,True,escort,16,14,19.0,WALK +1562397419,4763406,1931978,195299677,True,univ,14,16,19.0,WALK +1562397421,4763406,1931978,195299677,False,Home,17,14,21.0,WALK +1562397481,4763406,1931978,195299685,True,work,23,17,13.0,WALK +1562397485,4763406,1931978,195299685,False,Home,17,23,17.0,WALK +1562397745,4763407,1931978,195299718,True,school,18,17,8.0,WALK_LRF +1562397749,4763407,1931978,195299718,False,Home,17,18,15.0,WALK_LRF +1562397761,4763407,1931978,195299720,True,shopping,14,17,15.0,WALK +1562397765,4763407,1931978,195299720,False,Home,17,14,20.0,WALK +1562398137,4763408,1931978,195299767,True,escort,16,17,10.0,WALK_LRF +1562398138,4763408,1931978,195299767,True,othmaint,2,16,10.0,WALK +1562398139,4763408,1931978,195299767,True,eatout,12,2,10.0,SHARED2FREE +1562398140,4763408,1931978,195299767,True,work,16,12,13.0,WALK +1562398141,4763408,1931978,195299767,False,escort,11,16,17.0,WALK +1562398142,4763408,1931978,195299767,False,eatout,11,11,17.0,WALK +1562398143,4763408,1931978,195299767,False,eatout,10,11,18.0,WALK +1562398144,4763408,1931978,195299767,False,Home,17,10,19.0,WALK_LRF +1562398185,4763409,1931978,195299773,True,escort,5,7,10.0,WALK +1562398186,4763409,1931978,195299773,True,atwork,10,5,10.0,WALK +1562398189,4763409,1931978,195299773,False,Work,7,10,13.0,WALK +1562398465,4763409,1931978,195299808,True,work,7,17,7.0,WALK +1562398469,4763409,1931978,195299808,False,Home,17,7,20.0,WALK_LOC +1562398793,4763410,1931978,195299849,True,escort,12,17,8.0,WALK +1562398794,4763410,1931978,195299849,True,work,12,12,8.0,WALK +1562398797,4763410,1931978,195299849,False,Home,17,12,23.0,WALK +1562399449,4763412,1931978,195299931,True,work,4,17,9.0,WALK +1562399453,4763412,1931978,195299931,False,Home,17,4,20.0,WALK +1562408145,4763439,1931982,195301018,True,shopping,11,17,18.0,TNC_SINGLE +1562408146,4763439,1931982,195301018,True,shopping,18,11,18.0,TNC_SINGLE +1562408149,4763439,1931982,195301018,False,Home,17,18,21.0,TNC_SINGLE +1562408353,4763440,1931982,195301044,True,atwork,7,21,9.0,WALK +1562408357,4763440,1931982,195301044,False,shopping,9,7,9.0,WALK +1562408358,4763440,1931982,195301044,False,Work,21,9,9.0,WALK +1562408633,4763440,1931982,195301079,True,work,21,17,7.0,WALK +1562408637,4763440,1931982,195301079,False,Home,17,21,12.0,WALK +1562409225,4763442,1931982,195301153,True,school,8,17,8.0,WALK_LRF +1562409229,4763442,1931982,195301153,False,Home,17,8,16.0,WALK_LRF +1562409553,4763443,1931982,195301194,True,school,13,17,6.0,WALK_LRF +1562409557,4763443,1931982,195301194,False,Home,17,13,12.0,WALK_LOC +1562409881,4763444,1931982,195301235,True,school,17,17,8.0,WALK +1562409885,4763444,1931982,195301235,False,Home,17,17,15.0,WALK +1562409993,4763445,1931982,195301249,True,othmaint,9,5,10.0,WALK +1562409994,4763445,1931982,195301249,True,atwork,8,9,10.0,WALK +1562409997,4763445,1931982,195301249,False,Work,5,8,11.0,WALK +1562410273,4763445,1931982,195301284,True,work,5,17,5.0,WALK +1562410277,4763445,1931982,195301284,False,Home,17,5,16.0,WALK +1562414273,4763458,1931984,195301784,True,eatout,16,17,15.0,WALK +1562414277,4763458,1931984,195301784,False,Home,17,16,20.0,WALK +1562414449,4763458,1931984,195301806,True,othmaint,13,17,10.0,WALK +1562414453,4763458,1931984,195301806,False,Home,17,13,15.0,WALK +1562416457,4763464,1931984,195302057,True,shopping,16,17,15.0,WALK +1562416461,4763464,1931984,195302057,False,Home,17,16,17.0,WALK +1562416833,4763465,1931984,195302104,True,work,9,17,7.0,SHARED2FREE +1562416837,4763465,1931984,195302104,False,Home,17,9,17.0,WALK_LRF +1562417161,4763466,1931984,195302145,True,work,8,17,13.0,WALK_LOC +1562417165,4763466,1931984,195302145,False,Home,17,8,22.0,WALK_LRF +1562435481,4763522,1931991,195304435,True,shopping,19,20,19.0,WALK +1562435485,4763522,1931991,195304435,False,Home,20,19,20.0,WALK +1562435769,4763523,1931991,195304471,True,othmaint,14,20,18.0,WALK_LOC +1562435773,4763523,1931991,195304471,False,Home,20,14,19.0,WALK_LOC +1562435857,4763523,1931991,195304482,True,work,5,20,9.0,WALK +1562435861,4763523,1931991,195304482,False,Home,20,5,17.0,WALK_LOC +1562436185,4763524,1931991,195304523,True,work,9,20,6.0,WALK +1562436189,4763524,1931991,195304523,False,Home,20,9,13.0,WALK +1562436193,4763524,1931991,195304524,True,work,9,20,15.0,SHARED2FREE +1562436197,4763524,1931991,195304524,False,Home,20,9,17.0,WALK +1562436513,4763525,1931991,195304564,True,work,1,20,7.0,WALK_HVY +1562436517,4763525,1931991,195304564,False,Home,20,1,16.0,WALK +1562436521,4763525,1931991,195304565,True,work,4,20,16.0,DRIVEALONEFREE +1562436522,4763525,1931991,195304565,True,work,14,4,17.0,WALK +1562436523,4763525,1931991,195304565,True,work,1,14,17.0,DRIVEALONEFREE +1562436525,4763525,1931991,195304565,False,Home,20,1,18.0,SHARED3FREE +1562436729,4763526,1931991,195304591,True,othdiscr,5,20,18.0,WALK_LOC +1562436733,4763526,1931991,195304591,False,Home,20,5,20.0,WALK +1562436777,4763526,1931991,195304597,True,school,13,20,6.0,WALK_LRF +1562436781,4763526,1931991,195304597,False,Home,20,13,16.0,WALK_LOC +1562437169,4763527,1931991,195304646,True,work,6,20,7.0,SHARED2FREE +1562437173,4763527,1931991,195304646,False,Home,20,6,17.0,WALK +1562437433,4763528,1931991,195304679,True,school,20,20,11.0,WALK +1562437437,4763528,1931991,195304679,False,Home,20,20,17.0,WALK +1562437825,4763529,1931991,195304728,True,work,9,20,7.0,WALK +1562437829,4763529,1931991,195304728,False,Home,20,9,18.0,WALK +1562438089,4763530,1931991,195304761,True,school,7,20,7.0,WALK +1562438093,4763530,1931991,195304761,False,Home,20,7,13.0,WALK +1562438433,4763531,1931991,195304804,True,shopping,12,20,14.0,WALK +1562438437,4763531,1931991,195304804,False,Home,20,12,15.0,WALK +1562438825,4763533,1931991,195304853,True,atwork,11,10,7.0,WALK +1562438829,4763533,1931991,195304853,False,Work,10,11,7.0,WALK +1562439137,4763533,1931991,195304892,True,work,10,20,7.0,WALK +1562439141,4763533,1931991,195304892,False,Home,20,10,10.0,WALK +1562439145,4763533,1931991,195304893,True,work,10,20,13.0,WALK +1562439149,4763533,1931991,195304893,False,Home,20,10,20.0,WALK +1562468377,4763623,1932002,195308547,True,atwork,24,2,10.0,WALK +1562468381,4763623,1932002,195308547,False,eatout,5,24,13.0,WALK +1562468382,4763623,1932002,195308547,False,shopping,8,5,13.0,WALK +1562468383,4763623,1932002,195308547,False,Work,2,8,13.0,WALK +1562468657,4763623,1932002,195308582,True,work,2,21,7.0,DRIVEALONEFREE +1562468661,4763623,1932002,195308582,False,Home,21,2,18.0,WALK +1562468985,4763624,1932002,195308623,True,work,19,21,7.0,WALK_LOC +1562468989,4763624,1932002,195308623,False,Home,21,19,17.0,WALK +1562469577,4763626,1932002,195308697,True,school,8,21,8.0,WALK +1562469581,4763626,1932002,195308697,False,Home,21,8,15.0,WALK +1562469905,4763627,1932002,195308738,True,school,13,21,13.0,WALK +1562469909,4763627,1932002,195308738,False,Home,21,13,21.0,WALK +1562470625,4763629,1932002,195308828,True,work,10,21,8.0,WALK +1562470629,4763629,1932002,195308828,False,Home,21,10,18.0,WALK +1562489977,4763688,1932009,195311247,True,work,2,21,9.0,WALK +1562489981,4763688,1932009,195311247,False,Home,21,2,16.0,WALK +1562490257,4763689,1932009,195311282,True,shopping,25,21,12.0,WALK_LOC +1562490261,4763689,1932009,195311282,False,Home,21,25,15.0,WALK_LOC +1562490569,4763690,1932009,195311321,True,school,17,21,9.0,WALK_LRF +1562490573,4763690,1932009,195311321,False,Home,21,17,15.0,WALK_LOC +1562490897,4763691,1932009,195311362,True,school,25,21,10.0,WALK +1562490901,4763691,1932009,195311362,False,Home,21,25,15.0,WALK +1562491225,4763692,1932009,195311403,True,school,7,21,8.0,WALK_LOC +1562491229,4763692,1932009,195311403,False,Home,21,7,12.0,WALK_LOC +1562491705,4763694,1932009,195311463,True,escort,6,21,7.0,WALK_LOC +1562491709,4763694,1932009,195311463,False,Home,21,6,13.0,WALK_LOC +1562491713,4763694,1932009,195311464,True,escort,21,21,15.0,TNC_SHARED +1562491717,4763694,1932009,195311464,False,Home,21,21,15.0,TNC_SINGLE +1562492009,4763695,1932009,195311501,True,eatout,5,21,12.0,WALK +1562492013,4763695,1932009,195311501,False,Home,21,5,13.0,WALK +1562492601,4763696,1932009,195311575,True,work,23,21,9.0,WALK_LRF +1562492605,4763696,1932009,195311575,False,Home,21,23,12.0,WALK_LRF +1562492609,4763696,1932009,195311576,True,escort,9,21,17.0,DRIVEALONEFREE +1562492610,4763696,1932009,195311576,True,work,23,9,17.0,DRIVEALONEFREE +1562492613,4763696,1932009,195311576,False,othmaint,4,23,21.0,DRIVEALONEFREE +1562492614,4763696,1932009,195311576,False,Home,21,4,21.0,SHARED2FREE +1579966097,4816969,1945964,197495762,True,othmaint,9,6,13.0,WALK_LOC +1579966098,4816969,1945964,197495762,True,shopping,5,9,14.0,TNC_SHARED +1579966101,4816969,1945964,197495762,False,Home,6,5,16.0,WALK_LOC +1579968857,4816978,1945973,197496107,True,escort,7,6,10.0,TNC_SINGLE +1579968861,4816978,1945973,197496107,False,shopping,5,7,10.0,WALK_LOC +1579968862,4816978,1945973,197496107,False,Home,6,5,10.0,WALK_LOC +1579969377,4816979,1945974,197496172,True,shopping,13,6,12.0,WALK +1579969381,4816979,1945974,197496172,False,Home,6,13,14.0,WALK_LOC +1579999841,4817072,1946067,197499980,True,othmaint,12,23,13.0,DRIVEALONEFREE +1579999842,4817072,1946067,197499980,True,othmaint,5,12,14.0,TNC_SHARED +1579999845,4817072,1946067,197499980,False,Home,23,5,14.0,DRIVEALONEFREE +1582193025,4823759,1952754,197774128,True,othdiscr,5,7,10.0,WALK +1582193026,4823759,1952754,197774128,True,social,16,5,11.0,WALK +1582193027,4823759,1952754,197774128,True,escort,16,16,11.0,WALK +1582193029,4823759,1952754,197774128,False,Home,7,16,20.0,WALK +1582193033,4823759,1952754,197774129,True,escort,7,7,21.0,WALK +1582193037,4823759,1952754,197774129,False,Home,7,7,21.0,WALK +1582199057,4823777,1952772,197774882,True,othdiscr,9,7,13.0,TNC_SINGLE +1582199061,4823777,1952772,197774882,False,Home,7,9,16.0,TNC_SINGLE +1582199081,4823777,1952772,197774885,True,othmaint,5,7,8.0,WALK +1582199085,4823777,1952772,197774885,False,Home,7,5,10.0,WALK +1582204745,4823794,1952789,197775593,True,work,12,12,7.0,WALK +1582204749,4823794,1952789,197775593,False,Home,12,12,16.0,TNC_SINGLE +1582205641,4823797,1952792,197775705,True,othmaint,7,14,9.0,WALK +1582205645,4823797,1952792,197775705,False,Home,14,7,10.0,WALK +1582205729,4823797,1952792,197775716,True,work,12,14,11.0,BIKE +1582205733,4823797,1952792,197775716,False,Home,14,12,17.0,BIKE +1582227113,4823863,1952858,197778389,True,eatout,9,20,12.0,SHARED3FREE +1582227117,4823863,1952858,197778389,False,Home,20,9,21.0,SHARED3FREE +1582228249,4823866,1952861,197778531,True,othdiscr,24,20,15.0,DRIVEALONEFREE +1582228253,4823866,1952861,197778531,False,Home,20,24,17.0,SHARED2FREE +1582228361,4823866,1952861,197778545,True,work,20,20,5.0,SHARED2FREE +1582228365,4823866,1952861,197778545,False,shopping,19,20,11.0,SHARED2FREE +1582228366,4823866,1952861,197778545,False,Home,20,19,11.0,DRIVEALONEFREE +1582231689,4823877,1952872,197778961,True,atwork,25,13,11.0,WALK +1582231693,4823877,1952872,197778961,False,Work,13,25,11.0,WALK +1582231969,4823877,1952872,197778996,True,work,13,20,8.0,WALK_LOC +1582231973,4823877,1952872,197778996,False,Home,20,13,20.0,WALK_LOC +1582240825,4823904,1952899,197780103,True,work,8,21,7.0,WALK +1582240829,4823904,1952899,197780103,False,Home,21,8,17.0,WALK +1582241217,4823906,1952901,197780152,True,eatout,11,21,15.0,WALK +1582241221,4823906,1952901,197780152,False,Home,21,11,20.0,WALK +1582249729,4823932,1952927,197781216,True,atwork,17,2,18.0,WALK +1582249733,4823932,1952927,197781216,False,work,14,17,18.0,WALK +1582249734,4823932,1952927,197781216,False,eatout,25,14,18.0,WALK +1582249735,4823932,1952927,197781216,False,eatout,7,25,18.0,WALK +1582249736,4823932,1952927,197781216,False,Work,2,7,18.0,WALK +1582250009,4823932,1952927,197781251,True,work,2,22,7.0,TNC_SINGLE +1582250013,4823932,1952927,197781251,False,Home,22,2,19.0,WALK +1591190697,4851191,1967890,198898837,True,eatout,4,6,16.0,WALK +1591190701,4851191,1967890,198898837,False,Home,6,4,17.0,WALK +1591190913,4851191,1967890,198898864,True,social,7,6,17.0,WALK +1591190914,4851191,1967890,198898864,True,shopping,5,7,17.0,WALK +1591190917,4851191,1967890,198898864,False,Home,6,5,20.0,WALK +1591194457,4851202,1967896,198899307,True,othdiscr,18,7,15.0,WALK +1591194461,4851202,1967896,198899307,False,Home,7,18,19.0,WALK +1591194481,4851202,1967896,198899310,True,othmaint,16,7,12.0,WALK_LOC +1591194485,4851202,1967896,198899310,False,Home,7,16,14.0,WALK_LOC +1591194521,4851202,1967896,198899315,True,shopping,11,7,8.0,WALK +1591194525,4851202,1967896,198899315,False,Home,7,11,10.0,WALK +1591223913,4851292,1967941,198902989,True,othmaint,19,8,14.0,TNC_SHARED +1591223917,4851292,1967941,198902989,False,Home,8,19,14.0,SHARED3FREE +1591224025,4851292,1967941,198903003,True,work,10,8,8.0,WALK_LOC +1591224026,4851292,1967941,198903003,True,univ,9,10,9.0,WALK_LOC +1591224029,4851292,1967941,198903003,False,work,7,9,14.0,WALK_LOC +1591224030,4851292,1967941,198903003,False,shopping,5,7,14.0,WALK +1591224031,4851292,1967941,198903003,False,escort,10,5,14.0,WALK_LOC +1591224032,4851292,1967941,198903003,False,Home,8,10,14.0,WALK_LOC +1591224305,4851293,1967941,198903038,True,othdiscr,11,8,12.0,WALK_LOC +1591224309,4851293,1967941,198903038,False,Home,8,11,13.0,WALK_LOC +1591224329,4851293,1967941,198903041,True,othmaint,9,8,14.0,WALK +1591224333,4851293,1967941,198903041,False,Home,8,9,16.0,WALK +1591224353,4851293,1967941,198903044,True,univ,13,8,18.0,WALK_LOC +1591224357,4851293,1967941,198903044,False,work,1,13,18.0,WALK_LOC +1591224358,4851293,1967941,198903044,False,Home,8,1,18.0,WALK_LRF +1591224369,4851293,1967941,198903046,True,shopping,1,8,17.0,DRIVEALONEFREE +1591224373,4851293,1967941,198903046,False,Home,8,1,17.0,DRIVEALONEFREE +1591268433,4851428,1968009,198908554,True,eatout,12,22,8.0,WALK +1591268437,4851428,1968009,198908554,False,Home,22,12,13.0,WALK +1591268649,4851428,1968009,198908581,True,shopping,5,22,14.0,WALK +1591268653,4851428,1968009,198908581,False,Home,22,5,16.0,WALK +1591269001,4851429,1968009,198908625,True,social,1,22,16.0,WALK +1591269005,4851429,1968009,198908625,False,Home,22,1,21.0,WALK +1591305321,4851540,1968065,198913165,True,othdiscr,25,25,13.0,WALK +1591305325,4851540,1968065,198913165,False,Home,25,25,15.0,WALK +1591305649,4851541,1968065,198913206,True,othdiscr,13,25,9.0,WALK +1591305653,4851541,1968065,198913206,False,Home,25,13,11.0,WALK +1591305657,4851541,1968065,198913207,True,social,23,25,11.0,WALK +1591305658,4851541,1968065,198913207,True,othdiscr,25,23,11.0,WALK +1591305661,4851541,1968065,198913207,False,Home,25,25,11.0,TNC_SHARED +1591305665,4851541,1968065,198913208,True,othdiscr,2,25,14.0,WALK +1591305669,4851541,1968065,198913208,False,Home,25,2,17.0,WALK +1592785913,4856054,1970322,199098239,True,othdiscr,22,20,15.0,WALK_LRF +1592785917,4856054,1970322,199098239,False,Home,20,22,20.0,WALK_HVY +1592786073,4856055,1970322,199098259,True,atwork,25,12,10.0,WALK +1592786077,4856055,1970322,199098259,False,Work,12,25,10.0,WALK +1592786089,4856055,1970322,199098261,True,eatout,12,20,18.0,SHARED3FREE +1592786093,4856055,1970322,199098261,False,Home,20,12,19.0,SHARED3FREE +1592786353,4856055,1970322,199098294,True,work,12,20,8.0,SHARED3FREE +1592786357,4856055,1970322,199098294,False,Home,20,12,18.0,SHARED2FREE +1592789697,4856066,1970328,199098712,True,eatout,16,21,11.0,WALK +1592789701,4856066,1970328,199098712,False,Home,21,16,19.0,WALK +1592790009,4856067,1970328,199098751,True,atwork,7,4,11.0,SHARED3FREE +1592790013,4856067,1970328,199098751,False,Work,4,7,11.0,SHARED3FREE +1592790025,4856067,1970328,199098753,True,eatout,12,21,15.0,TNC_SINGLE +1592790029,4856067,1970328,199098753,False,Home,21,12,15.0,DRIVEALONEFREE +1592790241,4856067,1970328,199098780,True,shopping,25,21,16.0,SHARED2FREE +1592790242,4856067,1970328,199098780,True,shopping,7,25,16.0,WALK +1592790245,4856067,1970328,199098780,False,eatout,9,7,16.0,SHARED2FREE +1592790246,4856067,1970328,199098780,False,shopping,11,9,16.0,DRIVEALONEFREE +1592790247,4856067,1970328,199098780,False,shopping,11,11,16.0,SHARED2FREE +1592790248,4856067,1970328,199098780,False,Home,21,11,16.0,SHARED2FREE +1592790289,4856067,1970328,199098786,True,work,4,21,6.0,WALK +1592790293,4856067,1970328,199098786,False,Home,21,4,15.0,WALK +1592798225,4856092,1970341,199099778,True,eatout,5,22,9.0,WALK +1592798229,4856092,1970341,199099778,False,Home,22,5,16.0,WALK +1592798233,4856092,1970341,199099779,True,shopping,8,22,17.0,WALK_LRF +1592798234,4856092,1970341,199099779,True,eatout,4,8,18.0,WALK +1592798237,4856092,1970341,199099779,False,shopping,5,4,20.0,WALK +1592798238,4856092,1970341,199099779,False,Home,22,5,20.0,WALK +1592798553,4856093,1970341,199099819,True,eatout,21,22,20.0,WALK +1592798557,4856093,1970341,199099819,False,Home,22,21,22.0,WALK +1592798769,4856093,1970341,199099846,True,shopping,18,22,13.0,SHARED2FREE +1592798773,4856093,1970341,199099846,False,Home,22,18,17.0,DRIVEALONEFREE +1601200585,4881709,1983149,200150073,True,othmaint,7,2,13.0,WALK +1601200586,4881709,1983149,200150073,True,atwork,25,7,13.0,WALK +1601200589,4881709,1983149,200150073,False,Work,2,25,13.0,WALK +1601200865,4881709,1983149,200150108,True,work,2,7,8.0,WALK +1601200869,4881709,1983149,200150108,False,Home,7,2,17.0,WALK_LOC +1601222137,4881774,1983182,200152767,True,shopping,5,8,10.0,TNC_SINGLE +1601222141,4881774,1983182,200152767,False,Home,8,5,10.0,TNC_SINGLE +1601222145,4881774,1983182,200152768,True,shopping,2,8,13.0,TNC_SINGLE +1601222149,4881774,1983182,200152768,False,shopping,11,2,17.0,TNC_SINGLE +1601222150,4881774,1983182,200152768,False,Home,8,11,17.0,TNC_SINGLE +1601222465,4881775,1983182,200152808,True,shopping,16,8,18.0,WALK +1601222469,4881775,1983182,200152808,False,othmaint,7,16,19.0,WALK +1601222470,4881775,1983182,200152808,False,Home,8,7,19.0,WALK +1601223449,4881778,1983184,200152931,True,othdiscr,9,12,18.0,WALK_LRF +1601223450,4881778,1983184,200152931,True,shopping,11,9,18.0,WALK_LOC +1601223453,4881778,1983184,200152931,False,Home,12,11,18.0,WALK_LOC +1601223561,4881779,1983184,200152945,True,eatout,12,12,7.0,WALK +1601223565,4881779,1983184,200152945,False,Home,12,12,15.0,WALK +1601233969,4881810,1983200,200154246,True,escort,7,20,10.0,WALK_LOC +1601233970,4881810,1983200,200154246,True,social,12,7,12.0,TNC_SINGLE +1601233973,4881810,1983200,200154246,False,Home,20,12,17.0,TNC_SINGLE +1601233977,4881810,1983200,200154247,True,social,6,20,17.0,WALK_LOC +1601233981,4881810,1983200,200154247,False,social,13,6,17.0,WALK +1601233982,4881810,1983200,200154247,False,Home,20,13,17.0,WALK_LOC +1601234057,4881811,1983200,200154257,True,eatout,8,20,12.0,WALK +1601234061,4881811,1983200,200154257,False,Home,20,8,13.0,WALK +1601234321,4881811,1983200,200154290,True,work,2,20,13.0,TNC_SINGLE +1601234325,4881811,1983200,200154290,False,work,4,2,15.0,WALK_LOC +1601234326,4881811,1983200,200154290,False,shopping,5,4,16.0,TNC_SINGLE +1601234327,4881811,1983200,200154290,False,Home,20,5,16.0,WALK +1601264825,4881904,1983247,200158103,True,work,16,22,10.0,TNC_SINGLE +1601264826,4881904,1983247,200158103,True,escort,5,16,11.0,TNC_SINGLE +1601264827,4881904,1983247,200158103,True,work,2,5,12.0,WALK_LOC +1601264828,4881904,1983247,200158103,True,work,22,2,15.0,WALK_LOC +1601264829,4881904,1983247,200158103,False,escort,16,22,17.0,TNC_SINGLE +1601264830,4881904,1983247,200158103,False,escort,16,16,17.0,TNC_SINGLE +1601264831,4881904,1983247,200158103,False,Home,22,16,17.0,TNC_SINGLE +1601265217,4881906,1983248,200158152,True,eatout,11,22,10.0,DRIVEALONEFREE +1601265221,4881906,1983248,200158152,False,Home,22,11,14.0,SHARED3FREE +1601265241,4881906,1983248,200158155,True,escort,10,22,8.0,SHARED3FREE +1601265245,4881906,1983248,200158155,False,othmaint,10,10,9.0,SHARED3FREE +1601265246,4881906,1983248,200158155,False,escort,21,10,9.0,SHARED3FREE +1601265247,4881906,1983248,200158155,False,Home,22,21,9.0,SHARED3FREE +1601265545,4881907,1983248,200158193,True,eatout,25,22,12.0,TNC_SINGLE +1601265549,4881907,1983248,200158193,False,Home,22,25,12.0,WALK_LOC +1601265697,4881907,1983248,200158212,True,othdiscr,18,22,14.0,WALK_LRF +1601265701,4881907,1983248,200158212,False,shopping,16,18,15.0,WALK_LRF +1601265702,4881907,1983248,200158212,False,Home,22,16,15.0,WALK +1601265761,4881907,1983248,200158220,True,shopping,5,22,17.0,WALK +1601265765,4881907,1983248,200158220,False,Home,22,5,22.0,WALK +1601298017,4882006,1983298,200162252,True,eatout,16,24,7.0,WALK +1601298021,4882006,1983298,200162252,False,Home,24,16,17.0,WALK +1601298329,4882007,1983298,200162291,True,atwork,5,10,10.0,SHARED2FREE +1601298333,4882007,1983298,200162291,False,Work,10,5,13.0,WALK +1601298609,4882007,1983298,200162326,True,shopping,7,24,8.0,WALK_LOC +1601298610,4882007,1983298,200162326,True,work,10,7,8.0,WALK +1601298613,4882007,1983298,200162326,False,othmaint,12,10,22.0,WALK +1601298614,4882007,1983298,200162326,False,Home,24,12,22.0,WALK +1601299505,4882010,1983300,200162438,True,othmaint,9,25,11.0,BIKE +1601299509,4882010,1983300,200162438,False,Home,25,9,11.0,BIKE +1610697993,4910664,1997089,201337249,True,othdiscr,1,9,10.0,TNC_SINGLE +1610697997,4910664,1997089,201337249,False,eatout,14,1,13.0,TNC_SHARED +1610697998,4910664,1997089,201337249,False,Home,9,14,13.0,WALK_LOC +1610698369,4910665,1997089,201337296,True,school,13,9,8.0,WALK_LRF +1610698373,4910665,1997089,201337296,False,Home,9,13,18.0,WALK_LRF +1610698697,4910666,1997089,201337337,True,univ,9,9,6.0,WALK +1610698701,4910666,1997089,201337337,False,othmaint,9,9,13.0,WALK +1610698702,4910666,1997089,201337337,False,Home,9,9,13.0,WALK +1616285169,4927698,2002767,202035646,True,othmaint,11,20,8.0,TNC_SHARED +1616285173,4927698,2002767,202035646,False,Home,20,11,13.0,TNC_SINGLE +1616285177,4927698,2002767,202035647,True,othmaint,4,20,14.0,WALK +1616285181,4927698,2002767,202035647,False,Home,20,4,16.0,WALK +1616285913,4927700,2002767,202035739,True,work,14,20,7.0,DRIVEALONEFREE +1616285917,4927700,2002767,202035739,False,Home,20,14,17.0,DRIVEALONEFREE +1623789481,4950577,2010076,202973685,True,othmaint,6,8,7.0,BIKE +1623789485,4950577,2010076,202973685,False,Home,8,6,13.0,BIKE +1623789489,4950577,2010076,202973686,True,othmaint,1,8,17.0,WALK +1623789493,4950577,2010076,202973686,False,Home,8,1,19.0,WALK_LOC +1623789833,4950578,2010076,202973729,True,school,6,8,7.0,WALK +1623789837,4950578,2010076,202973729,False,Home,8,6,12.0,WALK_LOC +1623798313,4950604,2010083,202974789,True,othdiscr,7,8,8.0,WALK +1623798317,4950604,2010083,202974789,False,Home,8,7,20.0,WALK +1623798993,4950606,2010083,202974874,True,othmaint,10,8,10.0,TNC_SHARED +1623798997,4950606,2010083,202974874,False,Home,8,10,11.0,WALK_LOC +1649585081,5029222,2025165,206198135,True,shopping,16,16,15.0,WALK +1649585085,5029222,2025165,206198135,False,Home,16,16,20.0,WALK +1649585457,5029223,2025165,206198182,True,work,1,16,6.0,WALK +1649585461,5029223,2025165,206198182,False,Home,16,1,12.0,WALK_LOC +1649585785,5029224,2025165,206198223,True,work,2,16,9.0,WALK +1649585789,5029224,2025165,206198223,False,Home,16,2,18.0,WALK +1649586113,5029225,2025165,206198264,True,work,1,16,17.0,WALK +1649586117,5029225,2025165,206198264,False,Home,16,1,20.0,WALK +1649586377,5029226,2025165,206198297,True,school,16,16,12.0,WALK +1649586381,5029226,2025165,206198297,False,Home,16,16,16.0,WALK +1649586705,5029227,2025165,206198338,True,univ,12,16,8.0,SHARED2FREE +1649586709,5029227,2025165,206198338,False,Home,16,12,14.0,WALK_LOC +1649634281,5029372,2025189,206204285,True,shopping,3,16,8.0,WALK +1649634285,5029372,2025189,206204285,False,Home,16,3,16.0,WALK +1649634985,5029374,2025189,206204373,True,escort,4,16,8.0,WALK +1649634986,5029374,2025189,206204373,True,work,2,4,9.0,WALK +1649634989,5029374,2025189,206204373,False,eatout,16,2,13.0,WALK +1649634990,5029374,2025189,206204373,False,Home,16,16,16.0,WALK +1649635313,5029375,2025189,206204414,True,work,2,16,7.0,WALK +1649635317,5029375,2025189,206204414,False,Home,16,2,16.0,WALK +1649635577,5029376,2025189,206204447,True,school,8,16,8.0,WALK_LOC +1649635581,5029376,2025189,206204447,False,Home,16,8,16.0,WALK_LOC +1649635905,5029377,2025189,206204488,True,univ,14,16,12.0,WALK_LOC +1649635909,5029377,2025189,206204488,False,social,13,14,17.0,WALK_LOC +1649635910,5029377,2025189,206204488,False,escort,5,13,17.0,WALK_LOC +1649635911,5029377,2025189,206204488,False,othmaint,2,5,17.0,WALK_LOC +1649635912,5029377,2025189,206204488,False,Home,16,2,17.0,WALK +1649687185,5029534,2025217,206210898,True,social,12,2,11.0,WALK +1649687186,5029534,2025217,206210898,True,escort,7,12,11.0,WALK +1649687187,5029534,2025217,206210898,True,atwork,11,7,11.0,WALK +1649687189,5029534,2025217,206210898,False,Work,2,11,11.0,WALK +1649687465,5029534,2025217,206210933,True,work,2,16,7.0,WALK +1649687469,5029534,2025217,206210933,False,Home,16,2,20.0,WALK +1649688121,5029536,2025217,206211015,True,work,16,16,6.0,WALK +1649688125,5029536,2025217,206211015,False,Home,16,16,17.0,WALK +1649688337,5029537,2025217,206211042,True,othdiscr,11,16,10.0,WALK +1649688341,5029537,2025217,206211042,False,Home,16,11,19.0,WALK +1649688713,5029538,2025217,206211089,True,school,17,16,7.0,WALK_LRF +1649688717,5029538,2025217,206211089,False,Home,16,17,15.0,WALK_LRF +1649688993,5029539,2025217,206211124,True,othdiscr,2,16,13.0,WALK +1649688997,5029539,2025217,206211124,False,Home,16,2,15.0,WALK +1649689369,5029540,2025217,206211171,True,univ,13,16,17.0,TNC_SHARED +1649689373,5029540,2025217,206211171,False,Home,16,13,17.0,TNC_SINGLE +1649689521,5029541,2025217,206211190,True,escort,2,16,7.0,WALK_LOC +1649689525,5029541,2025217,206211190,False,Home,16,2,7.0,TNC_SHARED +1649689529,5029541,2025217,206211191,True,escort,2,16,12.0,WALK +1649689533,5029541,2025217,206211191,False,Home,16,2,13.0,WALK +1649689649,5029541,2025217,206211206,True,othdiscr,7,16,9.0,WALK_LOC +1649689653,5029541,2025217,206211206,False,Home,16,7,10.0,WALK_LOC +1649713049,5029612,2025228,206214131,True,work,2,16,11.0,WALK +1649713053,5029612,2025228,206214131,False,Home,16,2,11.0,WALK +1649713265,5029613,2025228,206214158,True,othdiscr,20,16,18.0,WALK_LOC +1649713269,5029613,2025228,206214158,False,Home,16,20,21.0,WALK_LRF +1649713377,5029613,2025228,206214172,True,escort,5,16,5.0,TNC_SINGLE +1649713378,5029613,2025228,206214172,True,work,12,5,6.0,TNC_SINGLE +1649713381,5029613,2025228,206214172,False,eatout,9,12,17.0,WALK_LRF +1649713382,5029613,2025228,206214172,False,Home,16,9,17.0,TNC_SINGLE +1649713425,5029614,2025228,206214178,True,othmaint,16,4,11.0,WALK +1649713426,5029614,2025228,206214178,True,atwork,24,16,11.0,WALK +1649713429,5029614,2025228,206214178,False,Work,4,24,11.0,WALK +1649713705,5029614,2025228,206214213,True,work,4,16,8.0,TNC_SINGLE +1649713709,5029614,2025228,206214213,False,Home,16,4,18.0,WALK_LOC +1649713753,5029615,2025228,206214219,True,atwork,16,5,10.0,WALK +1649713757,5029615,2025228,206214219,False,Work,5,16,10.0,WALK +1649714033,5029615,2025228,206214254,True,work,5,16,5.0,TNC_SINGLE +1649714037,5029615,2025228,206214254,False,Home,16,5,14.0,TNC_SINGLE +1649714041,5029615,2025228,206214255,True,work,5,16,15.0,WALK +1649714045,5029615,2025228,206214255,False,Home,16,5,18.0,WALK +1649714297,5029616,2025228,206214287,True,school,13,16,8.0,WALK_LOC +1649714301,5029616,2025228,206214287,False,Home,16,13,13.0,WALK_LOC +1649721249,5029637,2025233,206215156,True,work,5,16,8.0,WALK +1649721253,5029637,2025233,206215156,False,Home,16,5,17.0,WALK +1649721297,5029638,2025233,206215162,True,shopping,25,2,9.0,WALK +1649721298,5029638,2025233,206215162,True,atwork,3,25,9.0,WALK +1649721301,5029638,2025233,206215162,False,Work,2,3,11.0,WALK +1649721577,5029638,2025233,206215197,True,work,2,16,7.0,WALK +1649721581,5029638,2025233,206215197,False,Home,16,2,19.0,WALK +1649721905,5029639,2025233,206215238,True,escort,14,16,8.0,WALK +1649721906,5029639,2025233,206215238,True,work,4,14,9.0,WALK +1649721909,5029639,2025233,206215238,False,Home,16,4,17.0,WALK +1649722169,5029640,2025233,206215271,True,school,10,16,16.0,WALK_LOC +1649722173,5029640,2025233,206215271,False,Home,16,10,23.0,WALK_LRF +1649777993,5029810,2025265,206222249,True,work,4,17,17.0,WALK +1649777997,5029810,2025265,206222249,False,Home,17,4,18.0,WALK +1649778257,5029811,2025265,206222282,True,school,11,17,7.0,WALK_LRF +1649778261,5029811,2025265,206222282,False,Home,17,11,10.0,WALK_LRF +1649778273,5029811,2025265,206222284,True,shopping,19,17,12.0,WALK +1649778277,5029811,2025265,206222284,False,Home,17,19,15.0,WALK +1649778601,5029812,2025265,206222325,True,shopping,11,17,8.0,WALK +1649778605,5029812,2025265,206222325,False,Home,17,11,12.0,WALK +1649778609,5029812,2025265,206222326,True,shopping,4,17,13.0,WALK +1649778613,5029812,2025265,206222326,False,Home,17,4,13.0,WALK +1649778625,5029812,2025265,206222328,True,othdiscr,9,17,14.0,SHARED2FREE +1649778626,5029812,2025265,206222328,True,othdiscr,9,9,15.0,WALK +1649778627,5029812,2025265,206222328,True,social,5,9,15.0,SHARED2FREE +1649778629,5029812,2025265,206222328,False,Home,17,5,19.0,SHARED2FREE +1649778697,5029813,2025265,206222337,True,atwork,22,5,15.0,SHARED3FREE +1649778701,5029813,2025265,206222337,False,Work,5,22,15.0,SHARED3FREE +1649778977,5029813,2025265,206222372,True,work,5,17,8.0,TAXI +1649778981,5029813,2025265,206222372,False,Home,17,5,15.0,WALK_LRF +1649779193,5029814,2025265,206222399,True,othdiscr,2,17,14.0,WALK +1649779197,5029814,2025265,206222399,False,Home,17,2,18.0,WALK_LRF +1649779241,5029814,2025265,206222405,True,school,9,17,8.0,WALK_LRF +1649779245,5029814,2025265,206222405,False,Home,17,9,14.0,WALK_LRF +1649779569,5029815,2025265,206222446,True,school,18,17,7.0,WALK_LRF +1649779573,5029815,2025265,206222446,False,Home,17,18,13.0,WALK_LRF +1649779961,5029816,2025265,206222495,True,work,12,17,7.0,SHARED2FREE +1649779965,5029816,2025265,206222495,False,Home,17,12,18.0,WALK +1649785033,5029832,2025269,206223129,True,othmaint,9,17,7.0,WALK_LRF +1649785037,5029832,2025269,206223129,False,eatout,4,9,7.0,WALK +1649785038,5029832,2025269,206223129,False,Home,17,4,7.0,WALK +1649786457,5029836,2025269,206223307,True,school,10,17,8.0,WALK_LRF +1649786461,5029836,2025269,206223307,False,Home,17,10,13.0,WALK_LRF +1649786801,5029837,2025269,206223350,True,shopping,16,17,15.0,WALK +1649786805,5029837,2025269,206223350,False,Home,17,16,17.0,WALK +1649786825,5029837,2025269,206223353,True,social,4,17,17.0,WALK +1649786829,5029837,2025269,206223353,False,Home,17,4,19.0,WALK +1649787177,5029838,2025269,206223397,True,work,23,17,7.0,WALK_LRF +1649787181,5029838,2025269,206223397,False,Home,17,23,17.0,WALK +1649787441,5029839,2025269,206223430,True,school,11,17,16.0,WALK_LRF +1649787445,5029839,2025269,206223430,False,Home,17,11,23.0,WALK_LRF +1649787769,5029840,2025269,206223471,True,school,21,17,8.0,WALK +1649787773,5029840,2025269,206223471,False,Home,17,21,17.0,WALK +1649787849,5029841,2025269,206223481,True,eatout,6,22,11.0,WALK +1649787850,5029841,2025269,206223481,True,shopping,5,6,11.0,WALK +1649787851,5029841,2025269,206223481,True,atwork,8,5,11.0,WALK +1649787853,5029841,2025269,206223481,False,Work,22,8,17.0,WALK +1649788161,5029841,2025269,206223520,True,work,22,17,6.0,WALK +1649788165,5029841,2025269,206223520,False,Home,17,22,19.0,WALK +1652036993,5036698,2027742,206504624,True,eatout,12,15,16.0,WALK +1652036997,5036698,2027742,206504624,False,Home,15,12,21.0,WALK +1652037017,5036698,2027742,206504627,True,escort,11,15,14.0,SHARED3FREE +1652037018,5036698,2027742,206504627,True,escort,16,11,14.0,SHARED3FREE +1652037021,5036698,2027742,206504627,False,othmaint,16,16,14.0,SHARED3FREE +1652037022,5036698,2027742,206504627,False,Home,15,16,15.0,WALK +1652037145,5036698,2027742,206504643,True,othdiscr,17,15,10.0,WALK +1652037149,5036698,2027742,206504643,False,othmaint,16,17,11.0,WALK +1652037150,5036698,2027742,206504643,False,shopping,16,16,11.0,WALK +1652037151,5036698,2027742,206504643,False,eatout,16,16,11.0,WALK +1652037152,5036698,2027742,206504643,False,Home,15,16,11.0,WALK +1652045737,5036724,2027768,206505717,True,shopping,2,24,8.0,WALK +1652045741,5036724,2027768,206505717,False,Home,24,2,17.0,WALK +1658748793,5057160,2048204,207343599,True,work,23,5,7.0,TNC_SINGLE +1658748797,5057160,2048204,207343599,False,Home,5,23,15.0,WALK_LRF +1658753433,5057175,2048219,207344179,True,atwork,9,23,12.0,SHARED2FREE +1658753437,5057175,2048219,207344179,False,Work,23,9,14.0,SHARED2FREE +1658753449,5057175,2048219,207344181,True,eatout,22,6,18.0,WALK +1658753453,5057175,2048219,207344181,False,Home,6,22,19.0,WALK +1658753713,5057175,2048219,207344214,True,work,23,6,5.0,WALK_LOC +1658753717,5057175,2048219,207344214,False,Home,6,23,16.0,WALK_LOC +1658754041,5057176,2048220,207344255,True,work,2,6,7.0,TNC_SHARED +1658754042,5057176,2048220,207344255,True,work,17,2,9.0,WALK_LOC +1658754045,5057176,2048220,207344255,False,social,12,17,17.0,TNC_SINGLE +1658754046,5057176,2048220,207344255,False,Home,6,12,17.0,TNC_SINGLE +1658758257,5057189,2048233,207344782,True,shopping,11,6,11.0,WALK_LOC +1658758261,5057189,2048233,207344782,False,othdiscr,9,11,12.0,WALK_LOC +1658758262,5057189,2048233,207344782,False,social,8,9,12.0,WALK_LOC +1658758263,5057189,2048233,207344782,False,Home,6,8,12.0,WALK_LOC +1658777569,5057248,2048292,207347196,True,othmaint,2,6,14.0,WALK_LOC +1658777573,5057248,2048292,207347196,False,shopping,25,2,14.0,WALK +1658777574,5057248,2048292,207347196,False,Home,6,25,14.0,WALK +1658777657,5057248,2048292,207347207,True,work,14,6,7.0,TNC_SHARED +1658777661,5057248,2048292,207347207,False,Home,6,14,12.0,WALK_LOC +1658789793,5057285,2048329,207348724,True,work,7,6,8.0,WALK +1658789797,5057285,2048329,207348724,False,Home,6,7,18.0,WALK +1658800289,5057317,2048361,207350036,True,work,2,6,6.0,TNC_SINGLE +1658800293,5057317,2048361,207350036,False,Home,6,2,17.0,WALK +1658802169,5057323,2048367,207350271,True,othmaint,9,7,18.0,WALK +1658802173,5057323,2048367,207350271,False,Home,7,9,18.0,WALK +1658802177,5057323,2048367,207350272,True,othmaint,24,7,18.0,WALK +1658802181,5057323,2048367,207350272,False,Home,7,24,20.0,WALK +1658802209,5057323,2048367,207350276,True,shopping,15,7,16.0,SHARED2FREE +1658802213,5057323,2048367,207350276,False,Home,7,15,16.0,DRIVEALONEFREE +1658802257,5057323,2048367,207350282,True,work,5,7,6.0,WALK +1658802261,5057323,2048367,207350282,False,Home,7,5,15.0,WALK +1658806913,5057338,2048382,207350864,True,eatout,10,7,20.0,WALK +1658806917,5057338,2048382,207350864,False,Home,7,10,20.0,WALK +1658807089,5057338,2048382,207350886,True,othmaint,7,7,18.0,WALK +1658807093,5057338,2048382,207350886,False,Home,7,7,18.0,WALK +1658807129,5057338,2048382,207350891,True,shopping,25,7,21.0,WALK +1658807133,5057338,2048382,207350891,False,Home,7,25,21.0,WALK +1658807153,5057338,2048382,207350894,True,social,6,7,18.0,WALK_LOC +1658807157,5057338,2048382,207350894,False,Home,7,6,19.0,TNC_SINGLE +1658807177,5057338,2048382,207350897,True,work,2,7,6.0,WALK +1658807181,5057338,2048382,207350897,False,Home,7,2,17.0,WALK +1658813801,5057359,2048403,207351725,True,eatout,7,7,18.0,TNC_SINGLE +1658813805,5057359,2048403,207351725,False,Home,7,7,20.0,TNC_SINGLE +1658814065,5057359,2048403,207351758,True,work,14,7,7.0,WALK +1658814069,5057359,2048403,207351758,False,Home,7,14,17.0,WALK_LOC +1658819265,5057375,2048419,207352408,True,shopping,21,7,18.0,TNC_SINGLE +1658819269,5057375,2048419,207352408,False,Home,7,21,23.0,TNC_SINGLE +1658819313,5057375,2048419,207352414,True,work,14,7,8.0,WALK +1658819317,5057375,2048419,207352414,False,Home,7,14,15.0,WALK +1658829153,5057405,2048449,207353644,True,othdiscr,14,7,10.0,TNC_SINGLE +1658829154,5057405,2048449,207353644,True,work,1,14,10.0,TNC_SINGLE +1658829157,5057405,2048449,207353644,False,eatout,12,1,18.0,WALK +1658829158,5057405,2048449,207353644,False,Home,7,12,19.0,TNC_SINGLE +1658833633,5057419,2048463,207354204,True,othdiscr,10,7,11.0,WALK +1658833637,5057419,2048463,207354204,False,Home,7,10,14.0,WALK +1658835665,5057425,2048469,207354458,True,shopping,19,7,12.0,SHARED3FREE +1658835669,5057425,2048469,207354458,False,Home,7,19,13.0,WALK +1658839649,5057437,2048481,207354956,True,work,22,7,8.0,SHARED2FREE +1658839653,5057437,2048481,207354956,False,Home,7,22,18.0,WALK_LOC +1658851673,5057474,2048518,207356459,True,othdiscr,17,7,15.0,WALK_LOC +1658851677,5057474,2048518,207356459,False,Home,7,17,19.0,WALK_LOC +1658873649,5057541,2048585,207359206,True,othdiscr,4,7,20.0,WALK +1658873653,5057541,2048585,207359206,False,Home,7,4,23.0,WALK +1658873761,5057541,2048585,207359220,True,eatout,5,7,9.0,WALK_LOC +1658873762,5057541,2048585,207359220,True,shopping,16,5,10.0,WALK +1658873763,5057541,2048585,207359220,True,work,22,16,10.0,WALK +1658873765,5057541,2048585,207359220,False,shopping,11,22,18.0,WALK_LOC +1658873766,5057541,2048585,207359220,False,othmaint,5,11,18.0,WALK_LOC +1658873767,5057541,2048585,207359220,False,work,8,5,19.0,WALK +1658873768,5057541,2048585,207359220,False,Home,7,8,19.0,WALK_LOC +1658900001,5057621,2048665,207362500,True,work,9,10,6.0,WALK +1658900005,5057621,2048665,207362500,False,Home,10,9,18.0,WALK +1658916617,5057672,2048716,207364577,True,othdiscr,1,11,15.0,WALK_LRF +1658916621,5057672,2048716,207364577,False,Home,11,1,17.0,WALK_LRF +1658916705,5057672,2048716,207364588,True,social,10,11,14.0,TNC_SINGLE +1658916709,5057672,2048716,207364588,False,Home,11,10,14.0,TNC_SINGLE +1658922193,5057689,2048733,207365274,True,othdiscr,10,11,14.0,WALK +1658922197,5057689,2048733,207365274,False,escort,9,10,15.0,WALK_LOC +1658922198,5057689,2048733,207365274,False,Home,11,9,15.0,WALK_LOC +1658922353,5057690,2048734,207365294,True,atwork,17,23,11.0,WALK +1658922357,5057690,2048734,207365294,False,Work,23,17,12.0,WALK +1658922369,5057690,2048734,207365296,True,eatout,6,11,16.0,TNC_SINGLE +1658922373,5057690,2048734,207365296,False,Home,11,6,16.0,DRIVEALONEFREE +1658922393,5057690,2048734,207365299,True,escort,25,11,7.0,WALK +1658922397,5057690,2048734,207365299,False,Home,11,25,7.0,WALK +1658922633,5057690,2048734,207365329,True,work,23,11,7.0,DRIVEALONEFREE +1658922637,5057690,2048734,207365329,False,Home,11,23,16.0,DRIVEALONEFREE +1658938329,5057738,2048782,207367291,True,shopping,16,11,20.0,DRIVEALONEFREE +1658938333,5057738,2048782,207367291,False,shopping,10,16,20.0,TNC_SINGLE +1658938334,5057738,2048782,207367291,False,shopping,16,10,20.0,TNC_SHARED +1658938335,5057738,2048782,207367291,False,shopping,16,16,20.0,WALK +1658938336,5057738,2048782,207367291,False,Home,11,16,20.0,TNC_SINGLE +1658938377,5057738,2048782,207367297,True,work,7,11,8.0,WALK +1658938381,5057738,2048782,207367297,False,Home,11,7,17.0,WALK +1658943953,5057755,2048799,207367994,True,work,5,11,7.0,WALK +1658943957,5057755,2048799,207367994,False,Home,11,5,17.0,WALK_LOC +1658947233,5057765,2048809,207368404,True,othmaint,11,11,9.0,WALK +1658947234,5057765,2048809,207368404,True,work,9,11,10.0,DRIVEALONEFREE +1658947237,5057765,2048809,207368404,False,othdiscr,9,9,18.0,DRIVEALONEFREE +1658947238,5057765,2048809,207368404,False,Home,11,9,19.0,DRIVEALONEFREE +1658948497,5057769,2048813,207368562,True,shopping,16,11,9.0,WALK_LOC +1658948501,5057769,2048813,207368562,False,Home,11,16,10.0,TNC_SINGLE +1658948545,5057769,2048813,207368568,True,shopping,11,11,11.0,TNC_SINGLE +1658948546,5057769,2048813,207368568,True,work,12,11,12.0,TNC_SINGLE +1658948549,5057769,2048813,207368568,False,Home,11,12,19.0,WALK_LOC +1658951873,5057780,2048824,207368984,True,atwork,17,13,10.0,WALK +1658951877,5057780,2048824,207368984,False,othmaint,16,17,15.0,WALK +1658951878,5057780,2048824,207368984,False,Work,13,16,15.0,WALK +1658952153,5057780,2048824,207369019,True,work,13,11,7.0,TNC_SINGLE +1658952157,5057780,2048824,207369019,False,Home,11,13,17.0,WALK +1658953185,5057784,2048828,207369148,True,work,5,21,10.0,WALK +1658953186,5057784,2048828,207369148,True,atwork,7,5,10.0,WALK +1658953189,5057784,2048828,207369148,False,Work,21,7,11.0,WALK +1658953465,5057784,2048828,207369183,True,shopping,11,11,8.0,WALK +1658953466,5057784,2048828,207369183,True,escort,11,11,8.0,WALK +1658953467,5057784,2048828,207369183,True,work,21,11,10.0,WALK +1658953469,5057784,2048828,207369183,False,Home,11,21,21.0,WALK +1658958433,5057800,2048844,207369804,True,atwork,5,21,14.0,WALK +1658958437,5057800,2048844,207369804,False,Work,21,5,14.0,WALK +1658958713,5057800,2048844,207369839,True,work,21,11,8.0,WALK +1658958717,5057800,2048844,207369839,False,Home,11,21,19.0,WALK +1658963681,5057816,2048860,207370460,True,atwork,5,13,15.0,WALK +1658963685,5057816,2048860,207370460,False,Work,13,5,15.0,WALK +1658963697,5057816,2048860,207370462,True,eatout,2,11,8.0,WALK +1658963701,5057816,2048860,207370462,False,Home,11,2,8.0,WALK +1658963849,5057816,2048860,207370481,True,othdiscr,8,11,5.0,WALK +1658963853,5057816,2048860,207370481,False,Home,11,8,6.0,WALK +1658963961,5057816,2048860,207370495,True,work,13,11,8.0,WALK +1658963965,5057816,2048860,207370495,False,Home,11,13,22.0,WALK +1658977129,5057857,2048901,207372141,True,atwork,16,1,18.0,TNC_SINGLE +1658977133,5057857,2048901,207372141,False,Work,1,16,18.0,TNC_SINGLE +1658977409,5057857,2048901,207372176,True,work,1,11,6.0,WALK_HVY +1658977413,5057857,2048901,207372176,False,Home,11,1,19.0,WALK +1658978393,5057860,2048904,207372299,True,work,18,11,6.0,WALK_LOC +1658978397,5057860,2048904,207372299,False,Home,11,18,16.0,TNC_SHARED +1658996105,5057914,2048958,207374513,True,work,20,14,15.0,WALK_LOC +1658996109,5057914,2048958,207374513,False,shopping,11,20,18.0,WALK_LOC +1658996110,5057914,2048958,207374513,False,Home,14,11,18.0,WALK +1658997481,5057919,2048963,207374685,True,eatout,5,14,18.0,WALK +1658997485,5057919,2048963,207374685,False,Home,14,5,19.0,WALK +1658997505,5057919,2048963,207374688,True,escort,5,14,17.0,WALK +1658997509,5057919,2048963,207374688,False,Home,14,5,18.0,WALK +1658997633,5057919,2048963,207374704,True,othdiscr,15,14,12.0,WALK +1658997637,5057919,2048963,207374704,False,Home,14,15,15.0,WALK +1658997697,5057919,2048963,207374712,True,shopping,11,14,8.0,WALK +1658997701,5057919,2048963,207374712,False,Home,14,11,11.0,WALK +1658997705,5057919,2048963,207374713,True,shopping,10,14,15.0,WALK +1658997709,5057919,2048963,207374713,False,Home,14,10,16.0,WALK +1658998401,5057921,2048965,207374800,True,work,16,14,9.0,WALK +1658998405,5057921,2048965,207374800,False,Home,14,16,19.0,WALK +1659002057,5057933,2048977,207375257,True,atwork,25,6,9.0,WALK +1659002061,5057933,2048977,207375257,False,Work,6,25,10.0,WALK +1659002289,5057933,2048977,207375286,True,shopping,8,14,17.0,WALK_LOC +1659002293,5057933,2048977,207375286,False,Home,14,8,20.0,WALK +1659002337,5057933,2048977,207375292,True,work,6,14,6.0,WALK +1659002341,5057933,2048977,207375292,False,Home,14,6,16.0,WALK +1659008289,5057952,2048996,207376036,True,atwork,5,13,10.0,WALK +1659008293,5057952,2048996,207376036,False,Work,13,5,10.0,WALK +1659008569,5057952,2048996,207376071,True,work,13,14,7.0,WALK +1659008573,5057952,2048996,207376071,False,Home,14,13,17.0,WALK +1659012553,5057965,2049009,207376569,True,atwork,13,1,10.0,WALK_LOC +1659012557,5057965,2049009,207376569,False,Work,1,13,12.0,WALK_LOC +1659012833,5057965,2049009,207376604,True,work,1,15,8.0,WALK_LOC +1659012837,5057965,2049009,207376604,False,Home,15,1,18.0,WALK +1659015785,5057974,2049018,207376973,True,work,18,15,8.0,WALK_LRF +1659015789,5057974,2049018,207376973,False,Home,15,18,18.0,WALK_LRF +1659016441,5057976,2049020,207377055,True,work,5,15,5.0,WALK +1659016445,5057976,2049020,207377055,False,Home,15,5,13.0,WALK +1659021049,5057991,2049035,207377631,True,atwork,16,12,12.0,WALK +1659021053,5057991,2049035,207377631,False,Work,12,16,13.0,WALK +1659021361,5057991,2049035,207377670,True,escort,16,15,8.0,DRIVEALONEFREE +1659021362,5057991,2049035,207377670,True,work,12,16,9.0,DRIVEALONEFREE +1659021365,5057991,2049035,207377670,False,othmaint,24,12,13.0,DRIVEALONEFREE +1659021366,5057991,2049035,207377670,False,Home,15,24,13.0,WALK +1659034529,5058032,2049076,207379316,True,atwork,16,12,14.0,WALK +1659034533,5058032,2049076,207379316,False,Work,12,16,14.0,WALK +1659034809,5058032,2049076,207379351,True,work,16,16,7.0,WALK +1659034810,5058032,2049076,207379351,True,work,12,16,9.0,WALK +1659034813,5058032,2049076,207379351,False,Home,16,12,16.0,WALK_LOC +1659061705,5058114,2049158,207382713,True,eatout,17,17,14.0,WALK +1659061706,5058114,2049158,207382713,True,work,12,17,14.0,WALK_LOC +1659061709,5058114,2049158,207382713,False,othmaint,9,12,16.0,WALK_LRF +1659061710,5058114,2049158,207382713,False,escort,6,9,16.0,WALK +1659061711,5058114,2049158,207382713,False,Home,17,6,17.0,WALK_LRF +1659063721,5058121,2049165,207382965,True,atwork,21,4,14.0,WALK +1659063725,5058121,2049165,207382965,False,Work,4,21,15.0,WALK +1659064001,5058121,2049165,207383000,True,work,4,17,7.0,WALK +1659064005,5058121,2049165,207383000,False,Home,17,4,18.0,WALK +1659073073,5058149,2049193,207384134,True,othdiscr,22,17,18.0,WALK +1659073077,5058149,2049193,207384134,False,Home,17,22,21.0,WALK +1659073185,5058149,2049193,207384148,True,work,23,17,9.0,TNC_SINGLE +1659073189,5058149,2049193,207384148,False,Home,17,23,18.0,TNC_SINGLE +1659076137,5058158,2049202,207384517,True,work,14,17,5.0,WALK_LRF +1659076141,5058158,2049202,207384517,False,Home,17,14,9.0,WALK +1659076145,5058158,2049202,207384518,True,work,14,17,9.0,WALK +1659076149,5058158,2049202,207384518,False,Home,17,14,14.0,WALK +1659094505,5058214,2049258,207386813,True,work,13,17,12.0,WALK +1659094509,5058214,2049258,207386813,False,shopping,11,13,18.0,WALK +1659094510,5058214,2049258,207386813,False,othmaint,17,11,21.0,DRIVEALONEFREE +1659094511,5058214,2049258,207386813,False,othmaint,2,17,21.0,SHARED2FREE +1659094512,5058214,2049258,207386813,False,Home,17,2,21.0,DRIVEALONEFREE +1659109641,5058261,2049305,207388705,True,atwork,19,18,8.0,WALK +1659109645,5058261,2049305,207388705,False,othmaint,10,19,10.0,WALK +1659109646,5058261,2049305,207388705,False,Work,18,10,10.0,WALK +1659109921,5058261,2049305,207388740,True,work,18,17,8.0,WALK +1659109925,5058261,2049305,207388740,False,Home,17,18,19.0,WALK_LOC +1659115825,5058279,2049323,207389478,True,work,15,17,8.0,WALK +1659115829,5058279,2049323,207389478,False,work,9,15,17.0,TNC_SINGLE +1659115830,5058279,2049323,207389478,False,Home,17,9,17.0,TNC_SINGLE +1659127681,5058316,2049360,207390960,True,atwork,9,4,12.0,WALK +1659127685,5058316,2049360,207390960,False,Work,4,9,13.0,WALK +1659127961,5058316,2049360,207390995,True,escort,5,18,8.0,WALK_LOC +1659127962,5058316,2049360,207390995,True,work,4,5,9.0,WALK +1659127965,5058316,2049360,207390995,False,Home,18,4,18.0,WALK_LRF +1659134537,5058337,2049381,207391817,True,shopping,17,18,11.0,WALK +1659134538,5058337,2049381,207391817,True,atwork,17,17,11.0,WALK +1659134541,5058337,2049381,207391817,False,Work,18,17,11.0,WALK +1659134553,5058337,2049381,207391819,True,atwork,22,18,11.0,WALK +1659134557,5058337,2049381,207391819,False,eatout,5,22,16.0,WALK +1659134558,5058337,2049381,207391819,False,Work,18,5,16.0,WALK +1659134737,5058337,2049381,207391842,True,othdiscr,7,18,21.0,TNC_SHARED +1659134741,5058337,2049381,207391842,False,Home,18,7,21.0,DRIVEALONEFREE +1659134849,5058337,2049381,207391856,True,work,18,18,8.0,WALK +1659134853,5058337,2049381,207391856,False,Home,18,18,20.0,WALK +1659143377,5058363,2049407,207392922,True,work,2,18,12.0,WALK_LOC +1659143381,5058363,2049407,207392922,False,Home,18,2,14.0,WALK_LOC +1659143385,5058363,2049407,207392923,True,work,2,18,15.0,WALK_LOC +1659143389,5058363,2049407,207392923,False,othmaint,5,2,17.0,WALK +1659143390,5058363,2049407,207392923,False,Home,18,5,18.0,WALK_LOC +1659155841,5058401,2049445,207394480,True,work,9,18,7.0,WALK_LOC +1659155845,5058401,2049445,207394480,False,Home,18,9,17.0,WALK_LOC +1659172289,5058452,2049496,207396536,True,atwork,1,15,8.0,WALK +1659172293,5058452,2049496,207396536,False,othmaint,2,1,8.0,WALK +1659172294,5058452,2049496,207396536,False,Work,15,2,8.0,WALK +1659172569,5058452,2049496,207396571,True,work,15,19,7.0,WALK_LOC +1659172573,5058452,2049496,207396571,False,Home,19,15,20.0,WALK_LOC +1659179849,5058475,2049519,207397481,True,eatout,11,20,11.0,WALK +1659179853,5058475,2049519,207397481,False,Home,20,11,11.0,WALK +1659189361,5058504,2049548,207398670,True,eatout,5,20,13.0,WALK +1659189365,5058504,2049548,207398670,False,Home,20,5,18.0,WALK +1659189537,5058504,2049548,207398692,True,othmaint,5,20,9.0,WALK_LOC +1659189541,5058504,2049548,207398692,False,Home,20,5,11.0,WALK_LRF +1659195857,5058523,2049567,207399482,True,work,19,21,8.0,WALK +1659195861,5058523,2049567,207399482,False,shopping,11,19,15.0,WALK +1659195862,5058523,2049567,207399482,False,shopping,5,11,18.0,WALK +1659195863,5058523,2049567,207399482,False,Home,21,5,18.0,WALK +1659215209,5058582,2049626,207401901,True,work,7,21,8.0,WALK +1659215213,5058582,2049626,207401901,False,work,8,7,14.0,WALK +1659215214,5058582,2049626,207401901,False,Home,21,8,14.0,DRIVEALONEFREE +1659216849,5058587,2049631,207402106,True,work,2,21,5.0,WALK +1659216853,5058587,2049631,207402106,False,Home,21,2,15.0,WALK +1659218537,5058593,2049637,207402317,True,atwork,14,14,9.0,WALK +1659218541,5058593,2049637,207402317,False,shopping,5,14,9.0,WALK +1659218542,5058593,2049637,207402317,False,Work,14,5,9.0,WALK +1659218817,5058593,2049637,207402352,True,work,14,21,9.0,WALK +1659218821,5058593,2049637,207402352,False,Home,21,14,16.0,WALK +1659219145,5058594,2049638,207402393,True,escort,9,21,8.0,DRIVEALONEFREE +1659219146,5058594,2049638,207402393,True,work,13,9,8.0,DRIVEALONEFREE +1659219149,5058594,2049638,207402393,False,Home,21,13,17.0,WALK +1659225905,5058615,2049659,207403238,True,atwork,16,4,12.0,DRIVEALONEFREE +1659225909,5058615,2049659,207403238,False,Work,4,16,13.0,DRIVEALONEFREE +1659226033,5058615,2049659,207403254,True,escort,2,22,7.0,DRIVEALONEFREE +1659226034,5058615,2049659,207403254,True,work,4,2,8.0,DRIVEALONEFREE +1659226037,5058615,2049659,207403254,False,work,14,4,13.0,DRIVEALONEFREE +1659226038,5058615,2049659,207403254,False,othdiscr,9,14,18.0,DRIVEALONEFREE +1659226039,5058615,2049659,207403254,False,work,12,9,19.0,DRIVEALONEFREE +1659226040,5058615,2049659,207403254,False,Home,22,12,19.0,DRIVEALONEFREE +1659230297,5058628,2049672,207403787,True,work,2,22,7.0,WALK +1659230301,5058628,2049672,207403787,False,Home,22,2,18.0,WALK +1659247401,5058681,2049725,207405925,True,atwork,7,5,10.0,WALK +1659247405,5058681,2049725,207405925,False,Work,5,7,10.0,WALK +1659247681,5058681,2049725,207405960,True,work,5,23,7.0,WALK_LRF +1659247685,5058681,2049725,207405960,False,Home,23,5,18.0,WALK_LRF +1659250353,5058690,2049734,207406294,True,atwork,17,22,13.0,WALK +1659250357,5058690,2049734,207406294,False,eatout,5,17,13.0,WALK +1659250358,5058690,2049734,207406294,False,Work,22,5,13.0,WALK +1659250633,5058690,2049734,207406329,True,work,22,24,6.0,WALK +1659250637,5058690,2049734,207406329,False,Home,24,22,15.0,WALK +1659253633,5058700,2049744,207406704,True,atwork,5,1,12.0,TNC_SINGLE +1659253637,5058700,2049744,207406704,False,Work,1,5,13.0,WALK_LOC +1659253865,5058700,2049744,207406733,True,shopping,21,24,18.0,DRIVEALONEFREE +1659253869,5058700,2049744,207406733,False,escort,22,21,18.0,TNC_SHARED +1659253870,5058700,2049744,207406733,False,Home,24,22,18.0,DRIVEALONEFREE +1659253913,5058700,2049744,207406739,True,work,1,24,6.0,WALK_LOC +1659253917,5058700,2049744,207406739,False,Home,24,1,18.0,WALK +1685502985,5138728,2098510,210687873,True,othdiscr,17,5,21.0,WALK +1685502989,5138728,2098510,210687873,False,Home,5,17,23.0,WALK +1685503009,5138728,2098510,210687876,True,othmaint,7,5,18.0,WALK +1685503013,5138728,2098510,210687876,False,Home,5,7,20.0,WALK +1685503097,5138728,2098510,210687887,True,work,16,5,6.0,DRIVE_LOC +1685503101,5138728,2098510,210687887,False,Home,5,16,18.0,DRIVE_LOC +1685503313,5138729,2098510,210687914,True,othdiscr,9,5,7.0,WALK +1685503317,5138729,2098510,210687914,False,Home,5,9,9.0,WALK +1685503377,5138729,2098510,210687922,True,escort,7,5,11.0,WALK_LOC +1685503378,5138729,2098510,210687922,True,shopping,11,7,12.0,WALK_LOC +1685503381,5138729,2098510,210687922,False,escort,8,11,19.0,TNC_SINGLE +1685503382,5138729,2098510,210687922,False,othmaint,7,8,19.0,TNC_SINGLE +1685503383,5138729,2098510,210687922,False,shopping,16,7,19.0,TNC_SINGLE +1685503384,5138729,2098510,210687922,False,Home,5,16,19.0,WALK_LOC +1685503401,5138729,2098510,210687925,True,social,5,5,11.0,WALK +1685503405,5138729,2098510,210687925,False,Home,5,5,11.0,WALK +1685553609,5138882,2098587,210694201,True,work,11,9,6.0,WALK +1685553613,5138882,2098587,210694201,False,Home,9,11,17.0,WALK +1685578537,5138958,2098625,210697317,True,work,15,9,8.0,WALK_LOC +1685578541,5138958,2098625,210697317,False,Home,9,15,18.0,WALK_LRF +1685578817,5138959,2098625,210697352,True,shopping,5,9,15.0,BIKE +1685578821,5138959,2098625,210697352,False,Home,9,5,18.0,BIKE +1685605153,5139040,2098666,210700644,True,atwork,7,20,10.0,SHARED2FREE +1685605157,5139040,2098666,210700644,False,shopping,5,7,14.0,SHARED2FREE +1685605158,5139040,2098666,210700644,False,Work,20,5,14.0,SHARED2FREE +1685605433,5139040,2098666,210700679,True,work,20,9,8.0,WALK +1685605437,5139040,2098666,210700679,False,Home,9,20,20.0,WALK +1685605713,5139041,2098666,210700714,True,shopping,11,9,13.0,TNC_SINGLE +1685605717,5139041,2098666,210700714,False,Home,9,11,16.0,WALK_LOC +1685632329,5139122,2098707,210704041,True,work,2,9,13.0,WALK_LRF +1685632330,5139122,2098707,210704041,True,shopping,5,2,14.0,WALK +1685632331,5139122,2098707,210704041,True,work,2,5,14.0,WALK_LOC +1685632333,5139122,2098707,210704041,False,othmaint,7,2,17.0,WALK_LOC +1685632334,5139122,2098707,210704041,False,shopping,5,7,18.0,WALK +1685632335,5139122,2098707,210704041,False,escort,8,5,18.0,WALK +1685632336,5139122,2098707,210704041,False,Home,9,8,18.0,WALK +1685632545,5139123,2098707,210704068,True,othdiscr,11,9,12.0,WALK +1685632549,5139123,2098707,210704068,False,Home,9,11,15.0,WALK +1685780457,5139574,2098933,210722557,True,atwork,21,13,11.0,SHARED2FREE +1685780461,5139574,2098933,210722557,False,Work,13,21,12.0,SHARED3FREE +1685780585,5139574,2098933,210722573,True,work,13,21,8.0,WALK +1685780589,5139574,2098933,210722573,False,shopping,11,13,17.0,WALK +1685780590,5139574,2098933,210722573,False,Home,21,11,17.0,WALK +1685780865,5139575,2098933,210722608,True,shopping,24,21,12.0,WALK +1685780869,5139575,2098933,210722608,False,escort,5,24,14.0,WALK +1685780870,5139575,2098933,210722608,False,Home,21,5,14.0,WALK +1690202353,5153055,2105673,211275294,True,work,2,21,7.0,WALK +1690202357,5153055,2105673,211275294,False,Home,21,2,20.0,WALK +1690203057,5153058,2105675,211275382,True,atwork,19,24,10.0,WALK_LOC +1690203061,5153058,2105675,211275382,False,eatout,8,19,10.0,TNC_SINGLE +1690203062,5153058,2105675,211275382,False,Work,24,8,10.0,TNC_SINGLE +1690203337,5153058,2105675,211275417,True,othmaint,11,21,8.0,WALK_LOC +1690203338,5153058,2105675,211275417,True,escort,13,11,9.0,WALK +1690203339,5153058,2105675,211275417,True,work,24,13,9.0,WALK +1690203341,5153058,2105675,211275417,False,Home,21,24,17.0,WALK_LOC +1690203385,5153059,2105675,211275423,True,atwork,13,13,12.0,WALK +1690203389,5153059,2105675,211275423,False,work,7,13,14.0,WALK +1690203390,5153059,2105675,211275423,False,Work,13,7,14.0,WALK +1690203665,5153059,2105675,211275458,True,escort,22,21,8.0,WALK_LOC +1690203666,5153059,2105675,211275458,True,shopping,16,22,9.0,WALK_LOC +1690203667,5153059,2105675,211275458,True,work,13,16,9.0,WALK +1690203669,5153059,2105675,211275458,False,Home,21,13,19.0,WALK +1766790681,5386556,2222424,220848835,True,work,2,6,8.0,WALK +1766790685,5386556,2222424,220848835,False,othmaint,4,2,16.0,WALK +1766790686,5386556,2222424,220848835,False,escort,11,4,16.0,WALK +1766790687,5386556,2222424,220848835,False,shopping,5,11,17.0,WALK +1766790688,5386556,2222424,220848835,False,Home,6,5,17.0,WALK +1766791009,5386557,2222424,220848876,True,othmaint,5,6,8.0,WALK +1766791010,5386557,2222424,220848876,True,work,1,5,8.0,WALK +1766791013,5386557,2222424,220848876,False,Home,6,1,16.0,WALK +1766817313,5386638,2222465,220852164,True,eatout,4,6,10.0,WALK +1766817317,5386638,2222465,220852164,False,Home,6,4,10.0,WALK +1766817465,5386638,2222465,220852183,True,othdiscr,7,6,14.0,WALK +1766817469,5386638,2222465,220852183,False,Home,6,7,17.0,WALK +1766817793,5386639,2222465,220852224,True,othdiscr,12,6,9.0,WALK +1766817797,5386639,2222465,220852224,False,Home,6,12,10.0,WALK +1766817817,5386639,2222465,220852227,True,othmaint,9,6,11.0,WALK_LOC +1766817821,5386639,2222465,220852227,False,Home,6,9,12.0,WALK_LOC +1766817857,5386639,2222465,220852232,True,shopping,19,6,14.0,WALK_LOC +1766817861,5386639,2222465,220852232,False,Home,6,19,18.0,WALK_LOC +1766872681,5386806,2222549,220859085,True,work,6,7,11.0,WALK +1766872685,5386806,2222549,220859085,False,Home,7,6,20.0,WALK +1766872897,5386807,2222549,220859112,True,othdiscr,12,7,8.0,WALK +1766872901,5386807,2222549,220859112,False,Home,7,12,11.0,WALK +1766872921,5386807,2222549,220859115,True,othmaint,3,7,11.0,WALK +1766872925,5386807,2222549,220859115,False,Home,7,3,20.0,WALK_LOC +1766880929,5386832,2222562,220860116,True,atwork,14,16,10.0,WALK +1766880933,5386832,2222562,220860116,False,work,16,14,10.0,WALK +1766880934,5386832,2222562,220860116,False,Work,16,16,10.0,WALK +1766881209,5386832,2222562,220860151,True,work,16,7,7.0,WALK_LOC +1766881213,5386832,2222562,220860151,False,Home,7,16,12.0,TNC_SINGLE +1766881273,5386833,2222562,220860159,True,eatout,2,7,10.0,WALK +1766881277,5386833,2222562,220860159,False,Home,7,2,12.0,WALK +1766881449,5386833,2222562,220860181,True,othmaint,5,7,13.0,WALK +1766881453,5386833,2222562,220860181,False,Home,7,5,13.0,WALK +1766903249,5386900,2222596,220862906,True,eatout,5,7,12.0,WALK +1766903253,5386900,2222596,220862906,False,Home,7,5,19.0,WALK +1766903465,5386900,2222596,220862933,True,shopping,6,7,10.0,BIKE +1766903469,5386900,2222596,220862933,False,Home,7,6,11.0,BIKE +1766903841,5386901,2222596,220862980,True,othmaint,7,7,21.0,WALK +1766903842,5386901,2222596,220862980,True,escort,7,7,21.0,WALK +1766903843,5386901,2222596,220862980,True,work,7,7,21.0,WALK +1766903844,5386901,2222596,220862980,True,work,9,7,21.0,WALK_LRF +1766903845,5386901,2222596,220862980,False,Home,7,9,21.0,WALK_LRF +1766907841,5386914,2222603,220863480,True,eatout,8,7,20.0,WALK +1766907845,5386914,2222603,220863480,False,Home,7,8,21.0,WALK +1766907993,5386914,2222603,220863499,True,othdiscr,11,7,15.0,WALK +1766907997,5386914,2222603,220863499,False,Home,7,11,20.0,WALK +1766908001,5386914,2222603,220863500,True,othdiscr,22,7,21.0,WALK_LRF +1766908005,5386914,2222603,220863500,False,social,9,22,21.0,WALK_LRF +1766908006,5386914,2222603,220863500,False,Home,7,9,21.0,WALK_LRF +1766908017,5386914,2222603,220863502,True,othmaint,7,7,10.0,WALK +1766908021,5386914,2222603,220863502,False,Home,7,7,14.0,WALK +1766908433,5386915,2222603,220863554,True,work,2,7,8.0,WALK +1766908437,5386915,2222603,220863554,False,Home,7,2,18.0,WALK +1766911713,5386925,2222608,220863964,True,work,7,7,11.0,WALK +1766911717,5386925,2222608,220863964,False,Home,7,7,23.0,TNC_SINGLE +1766917289,5386942,2222617,220864661,True,work,19,7,7.0,TNC_SINGLE +1766917293,5386942,2222617,220864661,False,Home,7,19,21.0,TNC_SINGLE +1766917617,5386943,2222617,220864702,True,work,22,7,8.0,SHARED2FREE +1766917621,5386943,2222617,220864702,False,social,7,22,12.0,WALK_LOC +1766917622,5386943,2222617,220864702,False,shopping,25,7,16.0,WALK +1766917623,5386943,2222617,220864702,False,Home,7,25,21.0,WALK +1766956369,5387062,2222677,220869546,True,shopping,3,4,10.0,WALK +1766956370,5387062,2222677,220869546,True,atwork,24,3,10.0,WALK +1766956373,5387062,2222677,220869546,False,Work,4,24,10.0,WALK +1766956649,5387062,2222677,220869581,True,work,4,7,9.0,WALK +1766956653,5387062,2222677,220869581,False,social,7,4,17.0,WALK +1766956654,5387062,2222677,220869581,False,Home,7,7,18.0,WALK +1766956697,5387063,2222677,220869587,True,atwork,5,13,11.0,WALK +1766956701,5387063,2222677,220869587,False,Work,13,5,13.0,WALK +1766956977,5387063,2222677,220869622,True,work,13,7,5.0,WALK_LRF +1766956981,5387063,2222677,220869622,False,Home,7,13,17.0,WALK_LOC +1766963865,5387084,2222688,220870483,True,work,13,7,7.0,WALK +1766963869,5387084,2222688,220870483,False,Home,7,13,20.0,WALK +1766964193,5387085,2222688,220870524,True,work,7,7,7.0,WALK +1766964197,5387085,2222688,220870524,False,Home,7,7,16.0,WALK +1766972769,5387112,2222702,220871596,True,othmaint,24,24,10.0,WALK +1766972770,5387112,2222702,220871596,True,atwork,2,24,10.0,WALK +1766972773,5387112,2222702,220871596,False,Work,24,2,10.0,WALK +1766973049,5387112,2222702,220871631,True,work,24,7,7.0,WALK +1766973053,5387112,2222702,220871631,False,Home,7,24,16.0,WALK_LOC +1766973377,5387113,2222702,220871672,True,work,24,7,6.0,WALK_LOC +1766973381,5387113,2222702,220871672,False,Home,7,24,14.0,TNC_SINGLE +1766973705,5387114,2222703,220871713,True,work,10,7,8.0,WALK_LOC +1766973709,5387114,2222703,220871713,False,Home,7,10,17.0,WALK_LOC +1766973985,5387115,2222703,220871748,True,shopping,18,7,12.0,TNC_SINGLE +1766973986,5387115,2222703,220871748,True,shopping,11,18,14.0,TNC_SINGLE +1766973989,5387115,2222703,220871748,False,othmaint,11,11,20.0,TNC_SINGLE +1766973990,5387115,2222703,220871748,False,Home,7,11,20.0,TNC_SINGLE +1766983265,5387144,2222718,220872908,True,atwork,16,14,11.0,WALK +1766983269,5387144,2222718,220872908,False,Work,14,16,11.0,WALK +1766983545,5387144,2222718,220872943,True,escort,6,9,8.0,WALK_LOC +1766983546,5387144,2222718,220872943,True,work,14,6,9.0,WALK_LOC +1766983549,5387144,2222718,220872943,False,shopping,16,14,18.0,WALK +1766983550,5387144,2222718,220872943,False,Home,9,16,18.0,WALK_LRF +1766983809,5387145,2222718,220872976,True,univ,9,9,21.0,WALK +1766983813,5387145,2222718,220872976,False,eatout,9,9,21.0,WALK +1766983814,5387145,2222718,220872976,False,Home,9,9,21.0,WALK +1766983873,5387145,2222718,220872984,True,work,15,9,7.0,WALK_LRF +1766983877,5387145,2222718,220872984,False,work,9,15,14.0,WALK_LRF +1766983878,5387145,2222718,220872984,False,Home,9,9,14.0,WALK +1766986561,5387154,2222723,220873320,True,eatout,8,9,17.0,WALK +1766986565,5387154,2222723,220873320,False,Home,9,8,20.0,WALK +1766986825,5387154,2222723,220873353,True,work,22,9,7.0,SHARED2FREE +1766986829,5387154,2222723,220873353,False,Home,9,22,16.0,SHARED3FREE +1766986873,5387155,2222723,220873359,True,atwork,4,2,8.0,WALK +1766986877,5387155,2222723,220873359,False,work,7,4,8.0,WALK +1766986878,5387155,2222723,220873359,False,Work,2,7,8.0,WALK +1766987153,5387155,2222723,220873394,True,escort,7,9,5.0,WALK_LRF +1766987154,5387155,2222723,220873394,True,eatout,9,7,6.0,WALK_LOC +1766987155,5387155,2222723,220873394,True,work,2,9,6.0,WALK_LRF +1766987157,5387155,2222723,220873394,False,Home,9,2,16.0,WALK_LRF +1766997977,5387188,2222740,220874747,True,work,17,9,7.0,BIKE +1766997981,5387188,2222740,220874747,False,Home,9,17,12.0,BIKE +1766997985,5387188,2222740,220874748,True,work,17,9,13.0,WALK_LRF +1766997989,5387188,2222740,220874748,False,Home,9,17,18.0,WALK_LRF +1766998025,5387189,2222740,220874753,True,atwork,13,13,13.0,WALK +1766998029,5387189,2222740,220874753,False,Work,13,13,13.0,WALK +1766998305,5387189,2222740,220874788,True,work,13,9,8.0,WALK +1766998309,5387189,2222740,220874788,False,Home,9,13,18.0,WALK +1767003833,5387206,2222749,220875479,True,shopping,21,9,18.0,WALK +1767003837,5387206,2222749,220875479,False,Home,9,21,20.0,WALK_LOC +1767003881,5387206,2222749,220875485,True,work,4,9,5.0,TNC_SINGLE +1767003885,5387206,2222749,220875485,False,escort,8,4,17.0,WALK +1767003886,5387206,2222749,220875485,False,Home,9,8,18.0,WALK +1767004209,5387207,2222749,220875526,True,work,11,9,7.0,TNC_SINGLE +1767004213,5387207,2222749,220875526,False,Home,9,11,16.0,TNC_SINGLE +1767013721,5387236,2222764,220876715,True,work,1,9,7.0,WALK_LRF +1767013725,5387236,2222764,220876715,False,shopping,8,1,16.0,WALK_LRF +1767013726,5387236,2222764,220876715,False,Home,9,8,16.0,WALK_LOC +1767013785,5387237,2222764,220876723,True,eatout,18,9,7.0,WALK +1767013789,5387237,2222764,220876723,False,Home,9,18,10.0,WALK +1767015425,5387242,2222767,220876928,True,eatout,10,9,21.0,WALK +1767015429,5387242,2222767,220876928,False,Home,9,10,21.0,WALK +1767015689,5387242,2222767,220876961,True,work,4,9,8.0,WALK +1767015693,5387242,2222767,220876961,False,Home,9,4,20.0,WALK +1767016017,5387243,2222767,220877002,True,work,5,9,11.0,WALK +1767016021,5387243,2222767,220877002,False,Home,9,5,15.0,WALK +1767031345,5387290,2222791,220878918,True,othmaint,24,9,8.0,TNC_SINGLE +1767031349,5387290,2222791,220878918,False,Home,9,24,11.0,WALK_LOC +1767031433,5387290,2222791,220878929,True,work,10,9,17.0,WALK_LOC +1767031437,5387290,2222791,220878929,False,Home,9,10,23.0,WALK +1767031761,5387291,2222791,220878970,True,work,2,9,9.0,WALK_HVY +1767031765,5387291,2222791,220878970,False,escort,25,2,18.0,TNC_SINGLE +1767031766,5387291,2222791,220878970,False,Home,9,25,19.0,WALK_LOC +1767046897,5387338,2222815,220880862,True,atwork,19,1,13.0,SHARED2FREE +1767046901,5387338,2222815,220880862,False,Work,1,19,13.0,SHARED2FREE +1767047177,5387338,2222815,220880897,True,work,1,9,6.0,WALK_HVY +1767047181,5387338,2222815,220880897,False,Home,9,1,16.0,WALK_LRF +1767047225,5387339,2222815,220880903,True,atwork,7,5,12.0,WALK +1767047229,5387339,2222815,220880903,False,Work,5,7,13.0,WALK +1767047393,5387339,2222815,220880924,True,othdiscr,7,9,16.0,SHARED2FREE +1767047397,5387339,2222815,220880924,False,escort,9,7,16.0,WALK +1767047398,5387339,2222815,220880924,False,Home,9,9,16.0,WALK +1767047505,5387339,2222815,220880938,True,work,5,9,6.0,WALK +1767047509,5387339,2222815,220880938,False,Home,9,5,15.0,WALK +1767057673,5387370,2222831,220882209,True,work,22,9,7.0,WALK_LRF +1767057677,5387370,2222831,220882209,False,Home,9,22,18.0,WALK_LRF +1767057721,5387371,2222831,220882215,True,atwork,16,19,10.0,TNC_SINGLE +1767057725,5387371,2222831,220882215,False,Work,19,16,12.0,TNC_SINGLE +1767057737,5387371,2222831,220882217,True,eatout,11,9,6.0,WALK +1767057741,5387371,2222831,220882217,False,Home,9,11,6.0,WALK +1767058001,5387371,2222831,220882250,True,work,19,9,7.0,WALK_LOC +1767058005,5387371,2222831,220882250,False,Home,9,19,20.0,WALK_LOC +1767066745,5387398,2222845,220883343,True,othdiscr,4,9,15.0,WALK +1767066749,5387398,2222845,220883343,False,Home,9,4,17.0,WALK +1767066857,5387398,2222845,220883357,True,work,10,9,6.0,WALK +1767066861,5387398,2222845,220883357,False,Home,9,10,15.0,WALK +1767066873,5387399,2222845,220883359,True,shopping,5,10,12.0,WALK +1767066874,5387399,2222845,220883359,True,atwork,16,5,12.0,WALK +1767066877,5387399,2222845,220883359,False,Work,10,16,13.0,WALK +1767067185,5387399,2222845,220883398,True,work,10,9,6.0,WALK +1767067189,5387399,2222845,220883398,False,Home,9,10,21.0,WALK +1767091785,5387474,2222883,220886473,True,work,24,9,8.0,WALK_LRF +1767091789,5387474,2222883,220886473,False,shopping,5,24,21.0,WALK_LOC +1767091790,5387474,2222883,220886473,False,Home,9,5,21.0,WALK +1767092113,5387475,2222883,220886514,True,work,14,9,7.0,WALK_LRF +1767092117,5387475,2222883,220886514,False,Home,9,14,18.0,WALK_LRF +1767092817,5387478,2222885,220886602,True,eatout,6,2,10.0,WALK +1767092818,5387478,2222885,220886602,True,atwork,1,6,10.0,WALK +1767092821,5387478,2222885,220886602,False,Work,2,1,10.0,WALK +1767093097,5387478,2222885,220886637,True,work,2,9,6.0,WALK +1767093101,5387478,2222885,220886637,False,Home,9,2,20.0,WALK +1767093113,5387479,2222885,220886639,True,atwork,16,19,15.0,WALK +1767093117,5387479,2222885,220886639,False,work,12,16,15.0,WALK +1767093118,5387479,2222885,220886639,False,Work,19,12,15.0,WALK +1767093313,5387479,2222885,220886664,True,othdiscr,16,9,12.0,WALK_LRF +1767093317,5387479,2222885,220886664,False,Home,9,16,14.0,WALK_LRF +1767093425,5387479,2222885,220886678,True,work,19,9,14.0,WALK +1767093429,5387479,2222885,220886678,False,othmaint,9,19,16.0,WALK +1767093430,5387479,2222885,220886678,False,othmaint,9,9,17.0,WALK +1767093431,5387479,2222885,220886678,False,social,9,9,17.0,WALK +1767093432,5387479,2222885,220886678,False,Home,9,9,17.0,WALK +1767124961,5387576,2222934,220890620,True,atwork,5,4,10.0,WALK +1767124965,5387576,2222934,220890620,False,eatout,7,5,10.0,WALK +1767124966,5387576,2222934,220890620,False,work,7,7,10.0,WALK +1767124967,5387576,2222934,220890620,False,Work,4,7,10.0,WALK +1767125241,5387576,2222934,220890655,True,work,4,9,10.0,WALK_LRF +1767125245,5387576,2222934,220890655,False,Home,9,4,20.0,WALK_LRF +1767125569,5387577,2222934,220890696,True,work,24,9,7.0,WALK_LOC +1767125573,5387577,2222934,220890696,False,Home,9,24,17.0,WALK_LRF +1767134425,5387604,2222948,220891803,True,work,6,9,7.0,TNC_SINGLE +1767134429,5387604,2222948,220891803,False,Home,9,6,17.0,TNC_SINGLE +1767134705,5387605,2222948,220891838,True,shopping,13,9,9.0,WALK_LRF +1767134709,5387605,2222948,220891838,False,shopping,5,13,10.0,TNC_SINGLE +1767134710,5387605,2222948,220891838,False,Home,9,5,10.0,TNC_SHARED +1767182857,5387752,2223022,220897857,True,othdiscr,2,9,12.0,WALK_LRF +1767182861,5387752,2223022,220897857,False,Home,9,2,16.0,WALK_LRF +1767182865,5387752,2223022,220897858,True,othdiscr,15,9,17.0,WALK_HVY +1767182869,5387752,2223022,220897858,False,Home,9,15,22.0,TNC_SINGLE +1767182945,5387752,2223022,220897868,True,social,1,9,16.0,WALK +1767182949,5387752,2223022,220897868,False,Home,9,1,17.0,WALK_LRF +1767182985,5387753,2223022,220897873,True,atwork,16,16,14.0,WALK +1767182989,5387753,2223022,220897873,False,Work,16,16,14.0,WALK +1767183297,5387753,2223022,220897912,True,work,16,9,14.0,TNC_SINGLE +1767183301,5387753,2223022,220897912,False,Home,9,16,17.0,TNC_SINGLE +1767183313,5387754,2223023,220897914,True,atwork,7,21,12.0,WALK +1767183317,5387754,2223023,220897914,False,Work,21,7,14.0,WALK +1767183537,5387754,2223023,220897942,True,othmaint,16,9,5.0,WALK_LRF +1767183541,5387754,2223023,220897942,False,escort,24,16,7.0,WALK_LOC +1767183542,5387754,2223023,220897942,False,Home,9,24,7.0,WALK_LRF +1767183625,5387754,2223023,220897953,True,work,21,9,8.0,WALK +1767183629,5387754,2223023,220897953,False,Home,9,21,18.0,WALK +1767183633,5387754,2223023,220897954,True,work,21,9,18.0,TNC_SINGLE +1767183637,5387754,2223023,220897954,False,escort,8,21,18.0,WALK_LOC +1767183638,5387754,2223023,220897954,False,Home,9,8,19.0,TNC_SINGLE +1767183905,5387755,2223023,220897988,True,shopping,11,9,20.0,SHARED3FREE +1767183909,5387755,2223023,220897988,False,Home,9,11,20.0,WALK +1767183953,5387755,2223023,220897994,True,work,11,9,6.0,WALK +1767183957,5387755,2223023,220897994,False,Home,9,11,16.0,WALK +1767186249,5387762,2223027,220898281,True,work,4,9,7.0,WALK_HVY +1767186253,5387762,2223027,220898281,False,Home,9,4,17.0,WALK_LRF +1767186577,5387763,2223027,220898322,True,escort,10,9,8.0,WALK +1767186578,5387763,2223027,220898322,True,work,14,10,8.0,WALK_LRF +1767186581,5387763,2223027,220898322,False,othmaint,13,14,17.0,WALK +1767186582,5387763,2223027,220898322,False,Home,9,13,17.0,WALK_LRF +1767211713,5387840,2223066,220901464,True,atwork,4,5,18.0,WALK +1767211717,5387840,2223066,220901464,False,Work,5,4,18.0,WALK +1767211833,5387840,2223066,220901479,True,work,5,9,6.0,WALK +1767211837,5387840,2223066,220901479,False,Home,9,5,16.0,WALK +1767211841,5387840,2223066,220901480,True,work,5,9,16.0,WALK +1767211845,5387840,2223066,220901480,False,othdiscr,9,5,18.0,WALK_LOC +1767211846,5387840,2223066,220901480,False,shopping,11,9,20.0,WALK_LOC +1767211847,5387840,2223066,220901480,False,univ,9,11,20.0,WALK_LOC +1767211848,5387840,2223066,220901480,False,Home,9,9,21.0,WALK +1767211881,5387841,2223066,220901485,True,atwork,2,22,10.0,WALK +1767211885,5387841,2223066,220901485,False,work,2,2,12.0,WALK +1767211886,5387841,2223066,220901485,False,Work,22,2,12.0,WALK +1767212161,5387841,2223066,220901520,True,work,22,9,7.0,WALK_LRF +1767212165,5387841,2223066,220901520,False,Home,9,22,21.0,WALK_LRF +1767281745,5388054,2223173,220910218,True,atwork,15,13,10.0,WALK +1767281749,5388054,2223173,220910218,False,eatout,16,15,13.0,WALK +1767281750,5388054,2223173,220910218,False,Work,13,16,13.0,WALK +1767282025,5388054,2223173,220910253,True,shopping,12,10,9.0,WALK_LOC +1767282026,5388054,2223173,220910253,True,work,13,12,10.0,WALK_LOC +1767282029,5388054,2223173,220910253,False,shopping,5,13,17.0,WALK_LOC +1767282030,5388054,2223173,220910253,False,othmaint,13,5,18.0,WALK_LOC +1767282031,5388054,2223173,220910253,False,Home,10,13,18.0,WALK_LRF +1767282041,5388055,2223173,220910255,True,work,22,23,12.0,WALK +1767282042,5388055,2223173,220910255,True,atwork,22,22,12.0,WALK +1767282045,5388055,2223173,220910255,False,Work,23,22,14.0,WALK +1767282353,5388055,2223173,220910294,True,eatout,16,10,9.0,WALK_LOC +1767282354,5388055,2223173,220910294,True,work,23,16,10.0,WALK +1767282357,5388055,2223173,220910294,False,othdiscr,16,23,16.0,TNC_SINGLE +1767282358,5388055,2223173,220910294,False,Home,10,16,18.0,WALK_LOC +1767287273,5388070,2223181,220910909,True,work,5,10,13.0,WALK +1767287277,5388070,2223181,220910909,False,Home,10,5,19.0,WALK +1767287601,5388071,2223181,220910950,True,work,1,10,8.0,WALK_LRF +1767287605,5388071,2223181,220910950,False,othmaint,12,1,19.0,WALK +1767287606,5388071,2223181,220910950,False,shopping,13,12,19.0,WALK_LOC +1767287607,5388071,2223181,220910950,False,othmaint,9,13,19.0,WALK_LRF +1767287608,5388071,2223181,220910950,False,Home,10,9,19.0,WALK +1767309577,5388138,2223215,220913697,True,work,23,10,8.0,WALK_LOC +1767309581,5388138,2223215,220913697,False,Home,10,23,18.0,TNC_SINGLE +1767309857,5388139,2223215,220913732,True,shopping,13,10,13.0,WALK_LRF +1767309861,5388139,2223215,220913732,False,Home,10,13,13.0,WALK_LRF +1767335401,5388217,2223254,220916925,True,othmaint,20,10,8.0,WALK +1767335405,5388217,2223254,220916925,False,Home,10,20,8.0,WALK +1767335489,5388217,2223254,220916936,True,work,19,10,8.0,WALK +1767335493,5388217,2223254,220916936,False,Home,10,19,17.0,WALK +1767337785,5388224,2223258,220917223,True,work,10,10,7.0,TNC_SINGLE +1767337789,5388224,2223258,220917223,False,eatout,11,10,17.0,TNC_SINGLE +1767337790,5388224,2223258,220917223,False,Home,10,11,18.0,TNC_SINGLE +1767338113,5388225,2223258,220917264,True,work,5,10,7.0,TNC_SINGLE +1767338117,5388225,2223258,220917264,False,Home,10,5,18.0,WALK +1767344721,5388246,2223269,220918090,True,atwork,13,13,11.0,WALK +1767344725,5388246,2223269,220918090,False,Work,13,13,11.0,WALK +1767345001,5388246,2223269,220918125,True,work,13,10,8.0,WALK_LRF +1767345005,5388246,2223269,220918125,False,Home,10,13,18.0,WALK_LRF +1767345329,5388247,2223269,220918166,True,work,1,10,8.0,WALK_LRF +1767345333,5388247,2223269,220918166,False,Home,10,1,19.0,WALK_LRF +1767345985,5388249,2223270,220918248,True,work,10,10,7.0,WALK +1767345989,5388249,2223270,220918248,False,Home,10,10,15.0,WALK +1767353249,5388272,2223282,220919156,True,atwork,20,9,16.0,WALK +1767353253,5388272,2223282,220919156,False,Work,9,20,16.0,SHARED3FREE +1767353265,5388272,2223282,220919158,True,eatout,12,11,17.0,WALK +1767353269,5388272,2223282,220919158,False,Home,11,12,19.0,WALK +1767353529,5388272,2223282,220919191,True,work,9,11,8.0,WALK_LOC +1767353533,5388272,2223282,220919191,False,Home,11,9,17.0,TNC_SINGLE +1767353857,5388273,2223282,220919232,True,work,14,11,7.0,WALK +1767353861,5388273,2223282,220919232,False,Home,11,14,17.0,TNC_SINGLE +1767381081,5388356,2223324,220922635,True,work,13,11,8.0,WALK +1767381085,5388356,2223324,220922635,False,Home,11,13,19.0,WALK +1767381425,5388358,2223325,220922678,True,atwork,14,11,12.0,WALK +1767381429,5388358,2223325,220922678,False,Work,11,14,14.0,WALK +1767381737,5388358,2223325,220922717,True,escort,13,11,8.0,TNC_SINGLE +1767381738,5388358,2223325,220922717,True,work,11,13,10.0,WALK +1767381741,5388358,2223325,220922717,False,eatout,2,11,18.0,WALK +1767381742,5388358,2223325,220922717,False,Home,11,2,19.0,WALK_LOC +1767382065,5388359,2223325,220922758,True,work,2,11,14.0,WALK +1767382069,5388359,2223325,220922758,False,Home,11,2,18.0,WALK +1767389609,5388382,2223337,220923701,True,escort,11,11,5.0,SHARED3FREE +1767389610,5388382,2223337,220923701,True,work,12,11,5.0,SHARED3FREE +1767389613,5388382,2223337,220923701,False,escort,20,12,16.0,SHARED3FREE +1767389614,5388382,2223337,220923701,False,Home,11,20,16.0,WALK +1767389657,5388383,2223337,220923707,True,atwork,16,21,12.0,WALK +1767389661,5388383,2223337,220923707,False,Work,21,16,14.0,WALK +1767389937,5388383,2223337,220923742,True,work,21,11,7.0,WALK +1767389941,5388383,2223337,220923742,False,Home,11,21,20.0,WALK +1767427657,5388498,2223395,220928457,True,escort,5,11,7.0,WALK +1767427658,5388498,2223395,220928457,True,work,4,5,8.0,WALK_LOC +1767427661,5388498,2223395,220928457,False,othmaint,5,4,15.0,WALK +1767427662,5388498,2223395,220928457,False,Home,11,5,19.0,WALK +1767427873,5388499,2223395,220928484,True,othdiscr,10,11,11.0,WALK +1767427877,5388499,2223395,220928484,False,Home,11,10,13.0,WALK +1767427985,5388499,2223395,220928498,True,escort,21,11,13.0,SHARED2FREE +1767427986,5388499,2223395,220928498,True,work,16,21,13.0,WALK +1767427989,5388499,2223395,220928498,False,Home,11,16,20.0,WALK_LOC +1767478825,5388654,2223473,220934853,True,escort,21,12,7.0,WALK +1767478826,5388654,2223473,220934853,True,work,24,21,8.0,SHARED2FREE +1767478829,5388654,2223473,220934853,False,eatout,25,24,19.0,DRIVEALONEFREE +1767478830,5388654,2223473,220934853,False,shopping,13,25,20.0,DRIVEALONEFREE +1767478831,5388654,2223473,220934853,False,Home,12,13,20.0,DRIVEALONEFREE +1767479153,5388655,2223473,220934894,True,work,1,12,18.0,TNC_SINGLE +1767479157,5388655,2223473,220934894,False,Home,12,1,21.0,WALK_LRF +1767485337,5388674,2223483,220935667,True,shopping,13,12,14.0,WALK +1767485341,5388674,2223483,220935667,False,Home,12,13,16.0,WALK +1767485625,5388675,2223483,220935703,True,othmaint,13,12,6.0,WALK_LOC +1767485629,5388675,2223483,220935703,False,Home,12,13,6.0,TNC_SINGLE +1767485633,5388675,2223483,220935704,True,othmaint,11,12,16.0,WALK +1767485637,5388675,2223483,220935704,False,Home,12,11,16.0,WALK +1767485665,5388675,2223483,220935708,True,shopping,16,12,18.0,SHARED3FREE +1767485669,5388675,2223483,220935708,False,Home,12,16,19.0,SHARED3FREE +1767485713,5388675,2223483,220935714,True,work,16,12,7.0,WALK_LOC +1767485717,5388675,2223483,220935714,False,Home,12,16,16.0,WALK_LOC +1767514249,5388762,2223527,220939281,True,work,3,14,8.0,WALK +1767514253,5388762,2223527,220939281,False,Home,14,3,19.0,WALK +1767514449,5388763,2223527,220939306,True,atwork,11,19,13.0,WALK +1767514453,5388763,2223527,220939306,False,eatout,9,11,13.0,WALK +1767514454,5388763,2223527,220939306,False,Work,19,9,13.0,WALK +1767514577,5388763,2223527,220939322,True,escort,5,14,8.0,WALK +1767514578,5388763,2223527,220939322,True,work,19,5,9.0,WALK_LRF +1767514581,5388763,2223527,220939322,False,othmaint,9,19,17.0,WALK +1767514582,5388763,2223527,220939322,False,Home,14,9,17.0,WALK_LRF +1767515953,5388768,2223530,220939494,True,eatout,11,14,14.0,WALK +1767515957,5388768,2223530,220939494,False,Home,14,11,18.0,WALK +1767516233,5388769,2223530,220939529,True,atwork,2,2,11.0,WALK +1767516237,5388769,2223530,220939529,False,Work,2,2,11.0,WALK +1767516265,5388769,2223530,220939533,True,atwork,2,2,12.0,WALK +1767516269,5388769,2223530,220939533,False,shopping,16,2,18.0,WALK +1767516270,5388769,2223530,220939533,False,Work,2,16,18.0,WALK +1767516433,5388769,2223530,220939554,True,othdiscr,12,14,6.0,WALK +1767516437,5388769,2223530,220939554,False,Home,14,12,6.0,WALK +1767516497,5388769,2223530,220939562,True,shopping,2,14,21.0,WALK_LOC +1767516501,5388769,2223530,220939562,False,Home,14,2,22.0,TNC_SHARED +1767516545,5388769,2223530,220939568,True,work,2,14,7.0,WALK +1767516549,5388769,2223530,220939568,False,shopping,5,2,18.0,WALK +1767516550,5388769,2223530,220939568,False,Home,14,5,21.0,WALK +1767518577,5388776,2223534,220939822,True,eatout,22,14,12.0,BIKE +1767518581,5388776,2223534,220939822,False,Home,14,22,13.0,BIKE +1767519057,5388777,2223534,220939882,True,othdiscr,8,14,18.0,WALK +1767519061,5388777,2223534,220939882,False,Home,14,8,19.0,WALK +1767519169,5388777,2223534,220939896,True,work,24,14,6.0,WALK +1767519173,5388777,2223534,220939896,False,Home,14,24,17.0,WALK +1767524745,5388794,2223543,220940593,True,work,12,14,7.0,WALK_LOC +1767524749,5388794,2223543,220940593,False,Home,14,12,17.0,TAXI +1767525073,5388795,2223543,220940634,True,work,2,14,5.0,WALK +1767525077,5388795,2223543,220940634,False,Home,14,2,11.0,WALK +1767525081,5388795,2223543,220940635,True,work,2,14,13.0,WALK +1767525085,5388795,2223543,220940635,False,Home,14,2,16.0,WALK +1767532569,5388818,2223555,220941571,True,shopping,2,15,16.0,WALK +1767532573,5388818,2223555,220941571,False,Home,15,2,16.0,WALK +1767532577,5388818,2223555,220941572,True,shopping,13,15,16.0,WALK +1767532581,5388818,2223555,220941572,False,Home,15,13,18.0,WALK +1767532617,5388818,2223555,220941577,True,work,19,15,8.0,WALK +1767532621,5388818,2223555,220941577,False,Home,15,19,16.0,WALK +1767532705,5388819,2223555,220941588,True,escort,9,15,6.0,WALK +1767532709,5388819,2223555,220941588,False,Home,15,9,7.0,WALK +1767532833,5388819,2223555,220941604,True,othdiscr,9,15,12.0,WALK_LRF +1767532837,5388819,2223555,220941604,False,Home,15,9,14.0,WALK_LRF +1767532921,5388819,2223555,220941615,True,social,2,15,16.0,WALK +1767532925,5388819,2223555,220941615,False,Home,15,2,23.0,WALK +1767613305,5389064,2223678,220951663,True,work,21,16,6.0,WALK_LOC +1767613309,5389064,2223678,220951663,False,Home,16,21,17.0,WALK_LOC +1767613633,5389065,2223678,220951704,True,work,2,16,5.0,WALK +1767613637,5389065,2223678,220951704,False,Home,16,2,18.0,WALK +1767666161,5389226,2223759,220958270,True,atwork,2,1,13.0,WALK +1767666165,5389226,2223759,220958270,False,Work,1,2,13.0,WALK +1767666233,5389226,2223759,220958279,True,eatout,13,16,20.0,TNC_SHARED +1767666237,5389226,2223759,220958279,False,Home,16,13,20.0,TNC_SHARED +1767666441,5389226,2223759,220958305,True,work,1,16,9.0,WALK +1767666445,5389226,2223759,220958305,False,Home,16,1,18.0,WALK +1767666769,5389227,2223759,220958346,True,work,23,16,6.0,WALK +1767666773,5389227,2223759,220958346,False,Home,16,23,20.0,WALK +1767676657,5389258,2223775,220959582,True,atwork,4,21,12.0,TNC_SINGLE +1767676661,5389258,2223775,220959582,False,Work,21,4,13.0,TNC_SINGLE +1767676937,5389258,2223775,220959617,True,othdiscr,21,16,8.0,WALK +1767676938,5389258,2223775,220959617,True,work,21,21,8.0,TNC_SINGLE +1767676941,5389258,2223775,220959617,False,shopping,5,21,13.0,WALK_LOC +1767676942,5389258,2223775,220959617,False,eatout,9,5,23.0,TNC_SINGLE +1767676943,5389258,2223775,220959617,False,Home,16,9,23.0,TNC_SINGLE +1767677001,5389259,2223775,220959625,True,eatout,16,16,10.0,WALK +1767677005,5389259,2223775,220959625,False,Home,16,16,12.0,WALK +1767677153,5389259,2223775,220959644,True,eatout,9,16,9.0,SHARED2FREE +1767677154,5389259,2223775,220959644,True,eatout,7,9,9.0,WALK +1767677155,5389259,2223775,220959644,True,othdiscr,11,7,10.0,SHARED2FREE +1767677157,5389259,2223775,220959644,False,Home,16,11,10.0,SHARED2FREE +1767677217,5389259,2223775,220959652,True,shopping,22,16,12.0,WALK_LOC +1767677221,5389259,2223775,220959652,False,Home,16,22,15.0,WALK_LOC +1767677265,5389259,2223775,220959658,True,work,4,16,16.0,WALK +1767677269,5389259,2223775,220959658,False,Home,16,4,20.0,WALK +1767704489,5389342,2223817,220963061,True,work,15,16,8.0,TNC_SHARED +1767704493,5389342,2223817,220963061,False,Home,16,15,17.0,WALK_LOC +1767704537,5389343,2223817,220963067,True,shopping,7,4,10.0,WALK +1767704538,5389343,2223817,220963067,True,atwork,2,7,10.0,WALK +1767704541,5389343,2223817,220963067,False,Work,4,2,10.0,WALK +1767704769,5389343,2223817,220963096,True,eatout,11,16,19.0,TNC_SHARED +1767704770,5389343,2223817,220963096,True,shopping,5,11,20.0,WALK_LOC +1767704771,5389343,2223817,220963096,True,shopping,16,5,20.0,TNC_SINGLE +1767704773,5389343,2223817,220963096,False,Home,16,16,20.0,TNC_SINGLE +1767704817,5389343,2223817,220963102,True,othdiscr,12,16,7.0,WALK +1767704818,5389343,2223817,220963102,True,work,4,12,8.0,TNC_SINGLE +1767704821,5389343,2223817,220963102,False,Home,16,4,18.0,TNC_SINGLE +1767712361,5389366,2223829,220964045,True,work,2,16,10.0,WALK +1767712362,5389366,2223829,220964045,True,work,14,2,11.0,WALK +1767712365,5389366,2223829,220964045,False,shopping,5,14,17.0,WALK +1767712366,5389366,2223829,220964045,False,Home,16,5,20.0,WALK +1767712689,5389367,2223829,220964086,True,work,14,16,7.0,WALK +1767712693,5389367,2223829,220964086,False,Home,16,14,17.0,WALK +1767718921,5389386,2223839,220964865,True,work,17,16,6.0,WALK +1767718925,5389386,2223839,220964865,False,Home,16,17,16.0,WALK +1767718985,5389387,2223839,220964873,True,eatout,4,16,6.0,WALK +1767718989,5389387,2223839,220964873,False,Home,16,4,6.0,WALK +1767719137,5389387,2223839,220964892,True,othdiscr,9,16,7.0,WALK +1767719141,5389387,2223839,220964892,False,Home,16,9,7.0,WALK +1767719249,5389387,2223839,220964906,True,work,25,16,9.0,WALK_LOC +1767719253,5389387,2223839,220964906,False,Home,16,25,21.0,WALK_LOC +1767746473,5389470,2223881,220968309,True,work,4,16,7.0,WALK_LOC +1767746477,5389470,2223881,220968309,False,Home,16,4,17.0,WALK +1767746801,5389471,2223881,220968350,True,work,5,16,9.0,WALK +1767746805,5389471,2223881,220968350,False,Home,16,5,17.0,WALK +1767779273,5389570,2223931,220972409,True,work,4,16,7.0,WALK_LOC 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+1767933765,5390041,2224166,220991720,False,Home,17,9,18.0,TNC_SINGLE +1767947209,5390082,2224187,220993401,True,work,13,17,7.0,WALK_LOC +1767947213,5390082,2224187,220993401,False,Home,17,13,18.0,WALK_LOC +1767947537,5390083,2224187,220993442,True,work,2,17,9.0,WALK +1767947541,5390083,2224187,220993442,False,Home,17,2,22.0,WALK +1767960329,5390122,2224207,220995041,True,eatout,16,17,10.0,TNC_SINGLE +1767960330,5390122,2224207,220995041,True,work,14,16,10.0,WALK +1767960333,5390122,2224207,220995041,False,Home,17,14,19.0,TNC_SINGLE +1767960657,5390123,2224207,220995082,True,work,23,17,7.0,TNC_SINGLE +1767960661,5390123,2224207,220995082,False,Home,17,23,21.0,TNC_SINGLE +1767992193,5390220,2224256,220999024,True,atwork,5,15,12.0,WALK +1767992197,5390220,2224256,220999024,False,Work,15,5,14.0,WALK +1767992473,5390220,2224256,220999059,True,work,15,17,7.0,WALK +1767992477,5390220,2224256,220999059,False,Home,17,15,16.0,WALK +1767999033,5390240,2224266,220999879,True,work,22,17,5.0,WALK +1767999037,5390240,2224266,220999879,False,work,10,22,17.0,WALK_LRF +1767999038,5390240,2224266,220999879,False,shopping,21,10,17.0,WALK +1767999039,5390240,2224266,220999879,False,eatout,16,21,17.0,WALK +1767999040,5390240,2224266,220999879,False,Home,17,16,18.0,WALK +1767999361,5390241,2224266,220999920,True,work,11,17,9.0,WALK_LRF +1767999362,5390241,2224266,220999920,True,work,5,11,9.0,WALK_LOC +1767999365,5390241,2224266,220999920,False,social,5,5,17.0,WALK +1767999366,5390241,2224266,220999920,False,shopping,7,5,17.0,WALK_LOC +1767999367,5390241,2224266,220999920,False,Home,17,7,18.0,WALK_LOC +1768001545,5390248,2224270,221000193,True,othdiscr,20,17,18.0,WALK_LRF +1768001549,5390248,2224270,221000193,False,Home,17,20,21.0,WALK_LRF +1768001657,5390248,2224270,221000207,True,work,19,17,8.0,WALK +1768001661,5390248,2224270,221000207,False,Home,17,19,18.0,WALK_LOC +1768001705,5390249,2224270,221000213,True,atwork,1,9,12.0,WALK +1768001709,5390249,2224270,221000213,False,Work,9,1,13.0,WALK +1768001721,5390249,2224270,221000215,True,eatout,11,17,15.0,SHARED2FREE +1768001725,5390249,2224270,221000215,False,Home,17,11,17.0,SHARED2FREE +1768001985,5390249,2224270,221000248,True,work,9,17,6.0,BIKE +1768001989,5390249,2224270,221000248,False,Home,17,9,15.0,BIKE +1768005313,5390260,2224276,221000664,True,atwork,24,2,11.0,WALK +1768005317,5390260,2224276,221000664,False,eatout,25,24,14.0,WALK +1768005318,5390260,2224276,221000664,False,othmaint,7,25,14.0,WALK +1768005319,5390260,2224276,221000664,False,Work,2,7,14.0,WALK +1768005569,5390260,2224276,221000696,True,social,12,17,19.0,WALK +1768005573,5390260,2224276,221000696,False,Home,17,12,23.0,WALK +1768005593,5390260,2224276,221000699,True,work,2,17,8.0,WALK +1768005597,5390260,2224276,221000699,False,Home,17,2,18.0,WALK +1768005921,5390261,2224276,221000740,True,work,18,17,8.0,WALK +1768005925,5390261,2224276,221000740,False,Home,17,18,18.0,WALK +1768010841,5390276,2224284,221001355,True,work,2,17,14.0,WALK +1768010845,5390276,2224284,221001355,False,othmaint,16,2,19.0,WALK +1768010846,5390276,2224284,221001355,False,shopping,16,16,19.0,WALK +1768010847,5390276,2224284,221001355,False,work,2,16,20.0,WALK +1768010848,5390276,2224284,221001355,False,Home,17,2,20.0,WALK +1768011169,5390277,2224284,221001396,True,work,5,17,7.0,WALK +1768011173,5390277,2224284,221001396,False,Home,17,5,18.0,WALK +1768014649,5390288,2224290,221001831,True,atwork,5,4,11.0,WALK +1768014653,5390288,2224290,221001831,False,Work,4,5,11.0,WALK +1768014777,5390288,2224290,221001847,True,work,4,17,7.0,SHARED2FREE +1768014781,5390288,2224290,221001847,False,Home,17,4,17.0,SHARED2FREE +1768015105,5390289,2224290,221001888,True,work,4,17,7.0,WALK_LOC +1768015109,5390289,2224290,221001888,False,Home,17,4,17.0,WALK_LRF +1768021057,5390308,2224300,221002632,True,work,12,4,14.0,WALK +1768021058,5390308,2224300,221002632,True,atwork,12,12,14.0,WALK +1768021061,5390308,2224300,221002632,False,Work,4,12,14.0,WALK +1768021337,5390308,2224300,221002667,True,work,4,17,14.0,SHARED2FREE +1768021341,5390308,2224300,221002667,False,Home,17,4,18.0,WALK +1768044689,5390380,2224336,221005586,True,eatout,5,17,18.0,WALK +1768044693,5390380,2224336,221005586,False,Home,17,5,18.0,WALK +1768044745,5390380,2224336,221005593,True,eatout,18,17,18.0,TNC_SHARED +1768044749,5390380,2224336,221005593,False,Home,17,18,21.0,SHARED2FREE +1768044953,5390380,2224336,221005619,True,work,14,17,5.0,WALK +1768044957,5390380,2224336,221005619,False,Home,17,14,17.0,WALK +1768045281,5390381,2224336,221005660,True,work,1,17,8.0,WALK +1768045285,5390381,2224336,221005660,False,othmaint,16,1,17.0,WALK_LOC +1768045286,5390381,2224336,221005660,False,Home,17,16,18.0,WALK +1768098089,5390542,2224417,221012261,True,work,15,17,6.0,WALK +1768098093,5390542,2224417,221012261,False,Home,17,15,16.0,WALK +1768098137,5390543,2224417,221012267,True,othmaint,24,14,10.0,WALK +1768098138,5390543,2224417,221012267,True,atwork,1,24,10.0,WALK +1768098141,5390543,2224417,221012267,False,Work,14,1,13.0,WALK +1768098417,5390543,2224417,221012302,True,work,14,17,8.0,TNC_SINGLE +1768098421,5390543,2224417,221012302,False,escort,5,14,17.0,WALK_LOC +1768098422,5390543,2224417,221012302,False,Home,17,5,18.0,WALK_LOC +1768159561,5390730,2224511,221019945,True,othdiscr,24,17,19.0,TNC_SINGLE +1768159565,5390730,2224511,221019945,False,Home,17,24,21.0,TNC_SINGLE +1768159753,5390730,2224511,221019969,True,work,24,17,6.0,WALK +1768159757,5390730,2224511,221019969,False,Home,17,24,17.0,WALK +1768159769,5390731,2224511,221019971,True,atwork,24,14,12.0,WALK +1768159773,5390731,2224511,221019971,False,Work,14,24,13.0,WALK +1768160081,5390731,2224511,221020010,True,work,14,17,8.0,WALK +1768160085,5390731,2224511,221020010,False,Home,17,14,19.0,WALK +1768184417,5390806,2224549,221023052,True,eatout,12,17,8.0,WALK +1768184421,5390806,2224549,221023052,False,Home,17,12,10.0,WALK +1768184569,5390806,2224549,221023071,True,othdiscr,16,17,12.0,WALK +1768184573,5390806,2224549,221023071,False,Home,17,16,12.0,WALK_LOC +1768184593,5390806,2224549,221023074,True,othmaint,22,17,12.0,DRIVEALONEFREE +1768184597,5390806,2224549,221023074,False,Home,17,22,15.0,SHARED2FREE +1768184601,5390806,2224549,221023075,True,othmaint,5,17,15.0,TNC_SINGLE +1768184605,5390806,2224549,221023075,False,Home,17,5,15.0,TNC_SINGLE +1768184681,5390806,2224549,221023085,True,work,11,17,16.0,WALK_LRF +1768184685,5390806,2224549,221023085,False,Home,17,11,21.0,WALK_LOC +1768185009,5390807,2224549,221023126,True,work,16,17,8.0,WALK +1768185013,5390807,2224549,221023126,False,Home,17,16,18.0,WALK +1768206985,5390874,2224583,221025873,True,work,12,17,8.0,SHARED2FREE +1768206986,5390874,2224583,221025873,True,work,5,12,9.0,WALK +1768206989,5390874,2224583,221025873,False,Home,17,5,20.0,WALK +1768207313,5390875,2224583,221025914,True,work,2,17,7.0,WALK +1768207317,5390875,2224583,221025914,False,Home,17,2,17.0,WALK +1768236897,5390966,2224629,221029612,True,eatout,6,17,21.0,WALK +1768236901,5390966,2224629,221029612,False,Home,17,6,21.0,WALK +1768237161,5390966,2224629,221029645,True,work,16,17,8.0,WALK +1768237165,5390966,2224629,221029645,False,shopping,16,16,17.0,WALK +1768237166,5390966,2224629,221029645,False,work,9,16,18.0,WALK_LRF +1768237167,5390966,2224629,221029645,False,Home,17,9,19.0,WALK_LOC +1768237489,5390967,2224629,221029686,True,work,17,17,6.0,WALK +1768237493,5390967,2224629,221029686,False,Home,17,17,16.0,WALK +1768253561,5391016,2224654,221031695,True,work,18,17,8.0,WALK_LRF +1768253565,5391016,2224654,221031695,False,Home,17,18,16.0,WALK_LOC +1768253889,5391017,2224654,221031736,True,work,6,17,7.0,WALK_LOC +1768253893,5391017,2224654,221031736,False,Home,17,6,20.0,WALK_LOC +1768256561,5391026,2224659,221032070,True,shopping,16,16,14.0,WALK +1768256562,5391026,2224659,221032070,True,atwork,2,16,14.0,WALK 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+1768567677,5391974,2225133,221070959,False,Home,22,10,13.0,WALK_LRF +1768567737,5391974,2225133,221070967,True,shopping,1,22,13.0,TNC_SINGLE +1768567741,5391974,2225133,221070967,False,Home,22,1,14.0,TNC_SINGLE +1768567801,5391975,2225133,221070975,True,atwork,5,23,11.0,WALK +1768567805,5391975,2225133,221070975,False,Work,23,5,13.0,WALK +1768568113,5391975,2225133,221071014,True,work,23,22,7.0,WALK +1768568117,5391975,2225133,221071014,False,Home,22,23,17.0,WALK +1768581561,5392016,2225154,221072695,True,work,15,22,8.0,TNC_SHARED +1768581565,5392016,2225154,221072695,False,Home,22,15,19.0,WALK +1768581889,5392017,2225154,221072736,True,work,22,22,8.0,TNC_SHARED +1768581893,5392017,2225154,221072736,False,Home,22,22,17.0,WALK +1768595993,5392060,2225176,221074499,True,work,4,22,8.0,WALK +1768595997,5392060,2225176,221074499,False,Home,22,4,20.0,WALK_LRF +1768596001,5392060,2225176,221074500,True,work,4,22,22.0,WALK +1768596005,5392060,2225176,221074500,False,Home,22,4,23.0,WALK +1768596209,5392061,2225176,221074526,True,othdiscr,21,22,17.0,TNC_SINGLE +1768596213,5392061,2225176,221074526,False,Home,22,21,19.0,DRIVEALONEFREE +1768596321,5392061,2225176,221074540,True,othmaint,24,22,10.0,WALK +1768596322,5392061,2225176,221074540,True,work,11,24,12.0,WALK_LOC +1768596325,5392061,2225176,221074540,False,escort,3,11,17.0,WALK +1768596326,5392061,2225176,221074540,False,Home,22,3,17.0,WALK_LOC +1768636385,5392184,2225238,221079548,True,atwork,7,8,12.0,WALK +1768636389,5392184,2225238,221079548,False,Work,8,7,12.0,WALK +1768636665,5392184,2225238,221079583,True,work,8,23,8.0,WALK_LOC +1768636669,5392184,2225238,221079583,False,Home,23,8,17.0,WALK_LRF +1768636881,5392185,2225238,221079610,True,othdiscr,10,23,16.0,WALK_LRF +1768636885,5392185,2225238,221079610,False,Home,23,10,20.0,WALK_LRF +1768636993,5392185,2225238,221079624,True,work,16,23,7.0,WALK +1768636997,5392185,2225238,221079624,False,Home,23,16,16.0,WALK 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+1868570937,5696862,2344811,233571367,True,othdiscr,9,11,14.0,WALK +1868570941,5696862,2344811,233571367,False,Home,11,9,18.0,WALK +1868571001,5696862,2344811,233571375,True,shopping,10,11,9.0,WALK +1868571005,5696862,2344811,233571375,False,Home,11,10,13.0,WALK +1868571377,5696863,2344811,233571422,True,work,1,11,8.0,WALK +1868571381,5696863,2344811,233571422,False,Home,11,1,19.0,WALK +1868628825,5697039,2344870,233578603,True,atwork,14,14,12.0,WALK +1868628829,5697039,2344870,233578603,False,Work,14,14,13.0,WALK +1868629105,5697039,2344870,233578638,True,work,14,16,8.0,WALK +1868629109,5697039,2344870,233578638,False,work,14,14,23.0,TNC_SINGLE +1868629110,5697039,2344870,233578638,False,Home,16,14,23.0,WALK +1868629433,5697040,2344870,233578679,True,work,2,16,6.0,WALK +1868629437,5697040,2344870,233578679,False,Home,16,2,15.0,WALK +1868629761,5697041,2344870,233578720,True,work,2,16,7.0,WALK_LOC +1868629765,5697041,2344870,233578720,False,Home,16,2,18.0,WALK_LOC 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+1868660317,5697135,2344902,233582539,False,Work,20,11,10.0,WALK +1868660593,5697135,2344902,233582574,True,work,20,16,6.0,WALK +1868660597,5697135,2344902,233582574,False,Home,16,20,17.0,WALK_LOC +1868660921,5697136,2344902,233582615,True,work,18,16,11.0,WALK +1868660925,5697136,2344902,233582615,False,othmaint,17,18,22.0,WALK +1868660926,5697136,2344902,233582615,False,Home,16,17,22.0,WALK_LRF +1868661249,5697137,2344902,233582656,True,work,1,16,7.0,WALK_LOC +1868661253,5697137,2344902,233582656,False,Home,16,1,13.0,WALK +1868719521,5697315,2344962,233589940,True,othdiscr,25,16,9.0,TNC_SINGLE +1868719525,5697315,2344962,233589940,False,Home,16,25,12.0,TNC_SINGLE +1868719697,5697316,2344962,233589962,True,eatout,14,16,10.0,WALK +1868719701,5697316,2344962,233589962,False,Home,16,14,16.0,WALK +1868720177,5697317,2344962,233590022,True,othdiscr,16,16,7.0,WALK +1868720181,5697317,2344962,233590022,False,Home,16,16,12.0,WALK +1868727505,5697339,2344970,233590938,True,work,4,16,8.0,WALK +1868727509,5697339,2344970,233590938,False,Home,16,4,19.0,WALK +1868727833,5697340,2344970,233590979,True,work,2,16,7.0,WALK +1868727837,5697340,2344970,233590979,False,Home,16,2,17.0,WALK +1868728113,5697341,2344970,233591014,True,shopping,11,16,15.0,WALK +1868728117,5697341,2344970,233591014,False,Home,16,11,15.0,WALK +1868750137,5697408,2344993,233593767,True,work,16,16,6.0,WALK +1868750141,5697408,2344993,233593767,False,Home,16,16,14.0,WALK +1868750465,5697409,2344993,233593808,True,work,13,16,11.0,WALK +1868750466,5697409,2344993,233593808,True,work,21,13,12.0,DRIVEALONEFREE +1868750469,5697409,2344993,233593808,False,Home,16,21,21.0,WALK +1868750793,5697410,2344993,233593849,True,work,22,16,14.0,WALK +1868750797,5697410,2344993,233593849,False,Home,16,22,18.0,WALK +1868760961,5697441,2345004,233595120,True,work,2,16,5.0,WALK +1868760965,5697441,2345004,233595120,False,Home,16,2,16.0,WALK +1868761289,5697442,2345004,233595161,True,work,23,16,9.0,BIKE 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+1868791749,5697535,2345035,233598968,False,Home,17,16,17.0,WALK_LRF +1868791753,5697535,2345035,233598969,True,shopping,5,17,17.0,TNC_SINGLE +1868791757,5697535,2345035,233598969,False,Home,17,5,18.0,TNC_SINGLE +1868792121,5697536,2345035,233599015,True,work,14,17,10.0,WALK +1868792125,5697536,2345035,233599015,False,Home,17,14,20.0,WALK +1868815081,5697606,2345059,233601885,True,work,16,17,8.0,WALK +1868815085,5697606,2345059,233601885,False,Home,17,16,15.0,WALK +1868815129,5697607,2345059,233601891,True,atwork,13,20,10.0,WALK +1868815133,5697607,2345059,233601891,False,Work,20,13,10.0,WALK +1868815409,5697607,2345059,233601926,True,work,20,17,6.0,WALK +1868815413,5697607,2345059,233601926,False,Home,17,20,17.0,WALK +1868815457,5697608,2345059,233601932,True,atwork,21,17,12.0,WALK +1868815461,5697608,2345059,233601932,False,Work,17,21,12.0,WALK +1868815737,5697608,2345059,233601967,True,escort,16,17,9.0,TNC_SINGLE +1868815738,5697608,2345059,233601967,True,othdiscr,17,16,9.0,WALK +1868815739,5697608,2345059,233601967,True,work,17,17,10.0,WALK +1868815741,5697608,2345059,233601967,False,Home,17,17,19.0,TNC_SHARED +1868843617,5697693,2345088,233605452,True,work,6,17,10.0,TNC_SINGLE +1868843621,5697693,2345088,233605452,False,Home,17,6,18.0,WALK_LOC +1868843945,5697694,2345088,233605493,True,work,4,17,7.0,WALK +1868843949,5697694,2345088,233605493,False,Home,17,4,21.0,WALK_LRF +1868844273,5697695,2345088,233605534,True,work,19,17,8.0,WALK +1868844277,5697695,2345088,233605534,False,work,2,19,17.0,TNC_SINGLE +1868844278,5697695,2345088,233605534,False,othdiscr,5,2,17.0,WALK +1868844279,5697695,2345088,233605534,False,Home,17,5,18.0,WALK_LOC +1868907297,5697888,2345153,233613412,True,atwork,12,2,13.0,WALK +1868907301,5697888,2345153,233613412,False,eatout,3,12,13.0,WALK +1868907302,5697888,2345153,233613412,False,Work,2,3,13.0,WALK +1868907465,5697888,2345153,233613433,True,othdiscr,17,17,8.0,WALK +1868907469,5697888,2345153,233613433,False,Home,17,17,8.0,WALK +1868907577,5697888,2345153,233613447,True,work,2,17,9.0,WALK +1868907581,5697888,2345153,233613447,False,Home,17,2,20.0,WALK +1868907905,5697889,2345153,233613488,True,eatout,25,17,13.0,WALK_LOC +1868907906,5697889,2345153,233613488,True,work,2,25,13.0,WALK +1868907909,5697889,2345153,233613488,False,Home,17,2,23.0,WALK_LRF +1868908233,5697890,2345153,233613529,True,work,14,17,8.0,WALK +1868908237,5697890,2345153,233613529,False,social,12,14,18.0,WALK +1868908238,5697890,2345153,233613529,False,Home,17,12,19.0,WALK +1868914465,5697909,2345160,233614308,True,work,13,17,8.0,WALK +1868914469,5697909,2345160,233614308,False,Home,17,13,17.0,WALK +1868914513,5697910,2345160,233614314,True,atwork,12,22,15.0,WALK +1868914517,5697910,2345160,233614314,False,work,16,12,15.0,WALK +1868914518,5697910,2345160,233614314,False,eatout,23,16,15.0,WALK +1868914519,5697910,2345160,233614314,False,Work,22,23,15.0,WALK +1868914793,5697910,2345160,233614349,True,work,22,17,5.0,TNC_SINGLE +1868914797,5697910,2345160,233614349,False,work,24,22,21.0,TNC_SINGLE +1868914798,5697910,2345160,233614349,False,Home,17,24,21.0,TNC_SINGLE +1868915121,5697911,2345160,233614390,True,work,5,17,7.0,WALK +1868915125,5697911,2345160,233614390,False,Home,17,5,13.0,WALK_LRF +1868915129,5697911,2345160,233614391,True,work,5,17,15.0,WALK_LOC +1868915133,5697911,2345160,233614391,False,Home,17,5,18.0,WALK_LRF +1868973505,5698089,2345220,233621688,True,work,5,23,8.0,WALK +1868973509,5698089,2345220,233621688,False,Home,23,5,18.0,WALK +1868973833,5698090,2345220,233621729,True,work,2,23,7.0,WALK +1868973837,5698090,2345220,233621729,False,Home,23,2,17.0,WALK +1889746617,5761422,2366331,236218327,True,othdiscr,15,8,12.0,WALK_LOC +1889746621,5761422,2366331,236218327,False,Home,8,15,19.0,WALK_LOC +1889747057,5761423,2366331,236218382,True,eatout,5,8,8.0,WALK +1889747058,5761423,2366331,236218382,True,work,13,5,10.0,WALK +1889747061,5761423,2366331,236218382,False,work,4,13,21.0,WALK +1889747062,5761423,2366331,236218382,False,Home,8,4,21.0,WALK +1889747337,5761424,2366331,236218417,True,shopping,5,8,12.0,WALK +1889747338,5761424,2366331,236218417,True,othmaint,7,5,12.0,WALK +1889747339,5761424,2366331,236218417,True,othmaint,7,7,13.0,WALK +1889747340,5761424,2366331,236218417,True,shopping,11,7,13.0,WALK +1889747341,5761424,2366331,236218417,False,shopping,5,11,21.0,WALK +1889747342,5761424,2366331,236218417,False,Home,8,5,21.0,WALK +1889783089,5761533,2366368,236222886,True,shopping,11,8,10.0,WALK +1889783093,5761533,2366368,236222886,False,othmaint,7,11,20.0,BIKE +1889783094,5761533,2366368,236222886,False,escort,6,7,20.0,BIKE +1889783095,5761533,2366368,236222886,False,Home,8,6,20.0,BIKE +1889783729,5761535,2366368,236222966,True,school,9,8,7.0,WALK +1889783733,5761535,2366368,236222966,False,Home,8,9,15.0,WALK +1889784121,5761536,2366369,236223015,True,work,12,8,8.0,WALK +1889784122,5761536,2366369,236223015,True,work,5,12,9.0,WALK +1889784125,5761536,2366369,236223015,False,othdiscr,9,5,21.0,WALK +1889784126,5761536,2366369,236223015,False,Home,8,9,21.0,WALK +1889784209,5761537,2366369,236223026,True,escort,3,8,8.0,TNC_SINGLE +1889784213,5761537,2366369,236223026,False,shopping,25,3,9.0,WALK_LOC +1889784214,5761537,2366369,236223026,False,Home,8,25,9.0,WALK_LOC +1889839929,5761707,2366426,236229991,True,atwork,21,4,10.0,WALK +1889839933,5761707,2366426,236229991,False,Work,4,21,10.0,WALK +1889840161,5761707,2366426,236230020,True,shopping,11,21,18.0,WALK +1889840165,5761707,2366426,236230020,False,shopping,11,11,18.0,TAXI +1889840166,5761707,2366426,236230020,False,Home,21,11,18.0,TNC_SHARED +1889840209,5761707,2366426,236230026,True,work,4,21,8.0,WALK +1889840213,5761707,2366426,236230026,False,Home,21,4,17.0,WALK_LRF +1889840425,5761708,2366426,236230053,True,othdiscr,14,21,10.0,WALK +1889840429,5761708,2366426,236230053,False,Home,21,14,13.0,WALK +1889840489,5761708,2366426,236230061,True,shopping,19,21,18.0,DRIVEALONEFREE +1889840493,5761708,2366426,236230061,False,Home,21,19,21.0,TNC_SHARED +1889840801,5761709,2366426,236230100,True,school,6,21,9.0,WALK_LOC +1889840805,5761709,2366426,236230100,False,Home,21,6,14.0,WALK_LOC +1889870601,5761800,2366457,236233825,True,othdiscr,16,21,10.0,WALK +1889870605,5761800,2366457,236233825,False,Home,21,16,12.0,WALK_LOC +1889870665,5761800,2366457,236233833,True,shopping,25,21,15.0,WALK_LOC +1889870669,5761800,2366457,236233833,False,Home,21,25,15.0,WALK_LOC +1889870929,5761801,2366457,236233866,True,othdiscr,4,21,8.0,WALK_LOC +1889870933,5761801,2366457,236233866,False,shopping,22,4,19.0,WALK_LRF +1889870934,5761801,2366457,236233866,False,Home,21,22,19.0,WALK_LRF +1889871321,5761802,2366457,236233915,True,shopping,25,21,9.0,WALK_LOC +1889871325,5761802,2366457,236233915,False,Home,21,25,17.0,WALK_LOC +1931558857,5888898,2408823,241444857,True,work,22,16,7.0,WALK +1931558861,5888898,2408823,241444857,False,Home,16,22,22.0,WALK +1931559185,5888899,2408823,241444898,True,escort,16,16,8.0,WALK +1931559186,5888899,2408823,241444898,True,work,4,16,10.0,WALK +1931559189,5888899,2408823,241444898,False,work,2,4,16.0,WALK +1931559190,5888899,2408823,241444898,False,escort,14,2,16.0,WALK +1931559191,5888899,2408823,241444898,False,Home,16,14,16.0,WALK +1931559449,5888900,2408823,241444931,True,school,16,16,8.0,WALK +1931559453,5888900,2408823,241444931,False,Home,16,16,10.0,WALK +1931566449,5888922,2408831,241445806,True,atwork,18,20,12.0,WALK +1931566453,5888922,2408831,241445806,False,Work,20,18,12.0,WALK +1931566729,5888922,2408831,241445841,True,work,20,16,7.0,WALK +1931566733,5888922,2408831,241445841,False,Home,16,20,18.0,WALK +1931567321,5888924,2408831,241445915,True,school,16,16,7.0,WALK +1931567325,5888924,2408831,241445915,False,Home,16,16,23.0,WALK +1931567665,5888925,2408832,241445958,True,shopping,18,16,9.0,TNC_SINGLE +1931567669,5888925,2408832,241445958,False,Home,16,18,10.0,WALK_LRF +1931568305,5888927,2408832,241446038,True,eatout,11,16,7.0,WALK_LOC +1931568306,5888927,2408832,241446038,True,school,8,11,8.0,WALK_LOC +1931568309,5888927,2408832,241446038,False,Home,16,8,21.0,WALK_LOC +1931574601,5888946,2408839,241446825,True,work,8,16,7.0,BIKE +1931574605,5888946,2408839,241446825,False,Home,16,8,18.0,BIKE +1931574929,5888947,2408839,241446866,True,work,4,16,7.0,WALK +1931574933,5888947,2408839,241446866,False,Home,16,4,20.0,WALK +1931575193,5888948,2408839,241446899,True,school,9,16,8.0,WALK_LOC +1931575197,5888948,2408839,241446899,False,Home,16,9,18.0,WALK_LRF +1931580505,5888964,2408845,241447563,True,work,13,16,7.0,WALK +1931580509,5888964,2408845,241447563,False,Home,16,13,17.0,WALK +1931580593,5888965,2408845,241447574,True,escort,7,16,7.0,WALK_LOC +1931580597,5888965,2408845,241447574,False,Home,16,7,9.0,WALK_LOC +1931580601,5888965,2408845,241447575,True,escort,15,16,11.0,SHARED2FREE +1931580605,5888965,2408845,241447575,False,escort,18,15,11.0,SHARED2FREE +1931580606,5888965,2408845,241447575,False,othdiscr,25,18,11.0,SHARED2FREE +1931580607,5888965,2408845,241447575,False,escort,5,25,11.0,SHARED2FREE +1931580608,5888965,2408845,241447575,False,Home,16,5,11.0,SHARED2FREE +1931580809,5888965,2408845,241447601,True,social,20,16,13.0,WALK +1931580813,5888965,2408845,241447601,False,Home,16,20,20.0,WALK +1931581049,5888966,2408845,241447631,True,othdiscr,9,16,21.0,WALK_LRF +1931581053,5888966,2408845,241447631,False,shopping,16,9,22.0,WALK_LRF +1931581054,5888966,2408845,241447631,False,othdiscr,10,16,22.0,WALK_LRF +1931581055,5888966,2408845,241447631,False,Home,16,10,22.0,WALK_LRF +1931581097,5888966,2408845,241447637,True,eatout,2,16,7.0,WALK_LOC +1931581098,5888966,2408845,241447637,True,school,9,2,8.0,WALK_LRF +1931581101,5888966,2408845,241447637,False,Home,16,9,21.0,WALK_LRF +1931621721,5889090,2408887,241452715,True,othdiscr,4,17,11.0,WALK +1931621725,5889090,2408887,241452715,False,Home,17,4,19.0,WALK +1931622161,5889091,2408887,241452770,True,work,2,17,10.0,WALK +1931622165,5889091,2408887,241452770,False,Home,17,2,20.0,WALK_LOC +1931622425,5889092,2408887,241452803,True,school,6,17,7.0,WALK_LOC +1931622429,5889092,2408887,241452803,False,Home,17,6,11.0,WALK_LRF +1931638281,5889141,2408904,241454785,True,atwork,4,1,11.0,WALK +1931638285,5889141,2408904,241454785,False,eatout,6,4,11.0,WALK +1931638286,5889141,2408904,241454785,False,eatout,7,6,11.0,WALK +1931638287,5889141,2408904,241454785,False,Work,1,7,11.0,WALK +1931638561,5889141,2408904,241454820,True,work,1,18,7.0,WALK +1931638565,5889141,2408904,241454820,False,Home,18,1,18.0,WALK +1931638889,5889142,2408904,241454861,True,work,9,18,8.0,WALK +1931638893,5889142,2408904,241454861,False,shopping,11,9,16.0,WALK +1931638894,5889142,2408904,241454861,False,shopping,5,11,16.0,WALK_LOC +1931638895,5889142,2408904,241454861,False,Home,18,5,16.0,WALK +1931639153,5889143,2408904,241454894,True,school,9,18,6.0,WALK +1931639157,5889143,2408904,241454894,False,Home,18,9,14.0,WALK +1931639545,5889144,2408905,241454943,True,escort,9,18,7.0,DRIVEALONEFREE +1931639546,5889144,2408905,241454943,True,escort,16,9,8.0,DRIVEALONEFREE +1931639547,5889144,2408905,241454943,True,work,16,16,8.0,DRIVEALONEFREE +1931639549,5889144,2408905,241454943,False,othdiscr,16,16,17.0,DRIVEALONEFREE +1931639550,5889144,2408905,241454943,False,Home,18,16,17.0,DRIVEALONEFREE +1931639873,5889145,2408905,241454984,True,eatout,9,18,9.0,DRIVEALONEFREE +1931639874,5889145,2408905,241454984,True,othmaint,13,9,10.0,DRIVEALONEFREE +1931639875,5889145,2408905,241454984,True,othdiscr,14,13,10.0,DRIVEALONEFREE +1931639876,5889145,2408905,241454984,True,work,16,14,12.0,WALK +1931639877,5889145,2408905,241454984,False,Home,18,16,22.0,DRIVEALONEFREE +1931640137,5889146,2408905,241455017,True,school,8,18,11.0,WALK +1931640141,5889146,2408905,241455017,False,Home,18,8,18.0,WALK +2086467113,6361180,2506726,260808389,True,escort,10,16,13.0,WALK_LOC +2086467117,6361180,2506726,260808389,False,othdiscr,5,10,14.0,TNC_SINGLE +2086467118,6361180,2506726,260808389,False,Home,16,5,14.0,TNC_SINGLE +2086467681,6361181,2506726,260808460,True,work,23,16,7.0,WALK +2086467685,6361181,2506726,260808460,False,Home,16,23,18.0,WALK +2086467769,6361182,2506726,260808471,True,escort,19,16,11.0,TNC_SINGLE +2086467773,6361182,2506726,260808471,False,Home,16,19,12.0,TNC_SINGLE +2086467921,6361182,2506726,260808490,True,othmaint,16,16,13.0,TNC_SINGLE +2086467925,6361182,2506726,260808490,False,Home,16,16,16.0,TAXI +2086467961,6361182,2506726,260808495,True,shopping,1,16,16.0,TNC_SHARED +2086467965,6361182,2506726,260808495,False,Home,16,1,16.0,TNC_SINGLE +2086468337,6361183,2506726,260808542,True,work,11,16,7.0,WALK +2086468341,6361183,2506726,260808542,False,Home,16,11,16.0,WALK +2107104641,6424099,2518598,263388080,True,social,24,22,12.0,WALK +2107104645,6424099,2518598,263388080,False,social,24,24,15.0,WALK +2107104646,6424099,2518598,263388080,False,Home,22,24,16.0,TNC_SHARED +2107104785,6424099,2518598,263388098,True,work,2,22,5.0,WALK +2107104789,6424099,2518598,263388098,False,Home,22,2,5.0,WALK +2107104793,6424099,2518598,263388099,True,work,2,22,6.0,WALK +2107104797,6424099,2518598,263388099,False,Home,22,2,6.0,WALK +2107104873,6424100,2518598,263388109,True,escort,5,22,8.0,WALK +2107104877,6424100,2518598,263388109,False,Home,22,5,11.0,WALK +2107104881,6424100,2518598,263388110,True,escort,5,22,17.0,DRIVEALONEFREE +2107104885,6424100,2518598,263388110,False,Home,22,5,17.0,DRIVEALONEFREE +2107105001,6424100,2518598,263388125,True,othdiscr,9,22,17.0,WALK_LRF +2107105005,6424100,2518598,263388125,False,Home,22,9,20.0,WALK_LRF +2107105505,6424102,2518598,263388188,True,eatout,24,22,8.0,WALK +2107105509,6424102,2518598,263388188,False,Home,22,24,10.0,WALK +2153537257,6565667,2549865,269192157,True,othdiscr,8,18,15.0,SHARED3FREE +2153537261,6565667,2549865,269192157,False,eatout,5,8,15.0,WALK +2153537262,6565667,2549865,269192157,False,Home,18,5,15.0,SHARED2FREE +2153537537,6565663,2549865,269192192,True,escort,12,18,7.0,DRIVEALONEFREE +2153537541,6565663,2549865,269192192,False,Home,18,12,7.0,DRIVEALONEFREE +2153537777,6565663,2549865,269192222,True,work,21,18,7.0,DRIVEALONEFREE +2153537781,6565663,2549865,269192222,False,eatout,11,21,17.0,DRIVEALONEFREE +2153537782,6565663,2549865,269192222,False,Home,18,11,18.0,DRIVEALONEFREE +2153538041,6565664,2549865,269192255,True,school,13,18,7.0,WALK +2153538045,6565664,2549865,269192255,False,Home,18,13,12.0,WALK_LOC +2153538369,6565665,2549865,269192296,True,school,9,18,8.0,WALK +2153538373,6565665,2549865,269192296,False,Home,18,9,16.0,WALK +2153538497,6565666,2549865,269192312,True,eatout,21,18,14.0,SHARED2FREE +2153538501,6565666,2549865,269192312,False,Home,18,21,15.0,WALK +2153538697,6565666,2549865,269192337,True,school,7,18,17.0,WALK +2153538701,6565666,2549865,269192337,False,Home,18,7,18.0,WALK +2153539025,6565667,2549865,269192378,True,school,25,18,7.0,WALK_LOC +2153539029,6565667,2549865,269192378,False,Home,18,25,15.0,WALK_LOC +2153539353,6565668,2549865,269192419,True,school,16,18,8.0,WALK_LRF +2153539357,6565668,2549865,269192419,False,Home,18,16,15.0,WALK_LRF +2153539681,6565669,2549865,269192460,True,school,18,18,7.0,WALK +2153539685,6565669,2549865,269192460,False,Home,18,18,13.0,WALK +2258627097,6886058,2621752,282328387,True,escort,10,16,18.0,TNC_SINGLE +2258627101,6886058,2621752,282328387,False,Home,16,10,18.0,TNC_SINGLE +2258627337,6886058,2621752,282328417,True,work,16,16,8.0,WALK +2258627341,6886058,2621752,282328417,False,Home,16,16,18.0,WALK +2258627729,6886060,2621752,282328466,True,eatout,11,16,19.0,SHARED2FREE +2258627733,6886060,2621752,282328466,False,Home,16,11,20.0,WALK +2258627929,6886060,2621752,282328491,True,school,21,16,7.0,WALK_LOC +2258627933,6886060,2621752,282328491,False,Home,16,21,16.0,WALK_LOC +2258628257,6886061,2621752,282328532,True,school,16,16,11.0,WALK +2258628261,6886061,2621752,282328532,False,Home,16,16,20.0,WALK +2258628265,6886061,2621752,282328533,True,eatout,2,16,20.0,WALK +2258628266,6886061,2621752,282328533,True,school,16,2,20.0,WALK +2258628269,6886061,2621752,282328533,False,Home,16,16,21.0,WALK +2258673305,6886199,2621785,282334163,True,atwork,25,24,13.0,WALK +2258673309,6886199,2621785,282334163,False,Work,24,25,13.0,WALK +2258673585,6886199,2621785,282334198,True,work,24,25,7.0,WALK +2258673589,6886199,2621785,282334198,False,Home,25,24,22.0,WALK +2258673673,6886200,2621785,282334209,True,escort,18,25,7.0,TNC_SINGLE +2258673677,6886200,2621785,282334209,False,Home,25,18,8.0,TNC_SHARED +2258673801,6886200,2621785,282334225,True,othdiscr,16,25,21.0,WALK_LOC +2258673805,6886200,2621785,282334225,False,Home,25,16,21.0,WALK_LOC +2258673825,6886200,2621785,282334228,True,othmaint,9,25,9.0,WALK +2258673829,6886200,2621785,282334228,False,Home,25,9,9.0,WALK +2258673865,6886200,2621785,282334233,True,shopping,16,25,21.0,TNC_SHARED +2258673869,6886200,2621785,282334233,False,Home,25,16,21.0,TNC_SINGLE +2258673913,6886200,2621785,282334239,True,work,5,25,9.0,WALK +2258673917,6886200,2621785,282334239,False,othmaint,24,5,17.0,WALK +2258673918,6886200,2621785,282334239,False,eatout,23,24,18.0,WALK +2258673919,6886200,2621785,282334239,False,Home,25,23,18.0,WALK +2258674177,6886201,2621785,282334272,True,school,9,25,7.0,WALK +2258674181,6886201,2621785,282334272,False,Home,25,9,13.0,WALK +2258674505,6886202,2621785,282334313,True,school,25,25,8.0,WALK +2258674509,6886202,2621785,282334313,False,Home,25,25,19.0,WALK +2258674833,6886203,2621785,282334354,True,school,25,25,8.0,WALK +2258674837,6886203,2621785,282334354,False,Home,25,25,18.0,WALK +2357979937,7188963,2677133,294747492,True,escort,9,9,8.0,TNC_SINGLE +2357979941,7188963,2677133,294747492,False,Home,9,9,9.0,TNC_SHARED +2357979945,7188963,2677133,294747493,True,escort,14,9,16.0,DRIVEALONEFREE +2357979949,7188963,2677133,294747493,False,Home,9,14,16.0,SHARED2FREE +2357980505,7188964,2677133,294747563,True,escort,5,9,13.0,WALK_LOC +2357980506,7188964,2677133,294747563,True,eatout,2,5,13.0,WALK +2357980507,7188964,2677133,294747563,True,othmaint,4,2,14.0,WALK +2357980508,7188964,2677133,294747563,True,work,23,4,17.0,WALK_LRF +2357980509,7188964,2677133,294747563,False,othmaint,7,23,22.0,SHARED3FREE +2357980510,7188964,2677133,294747563,False,Home,9,7,22.0,WALK_LOC +2357980833,7188965,2677133,294747604,True,work,13,9,7.0,WALK +2357980837,7188965,2677133,294747604,False,Home,9,13,14.0,WALK +2357980841,7188965,2677133,294747605,True,work,13,9,14.0,WALK +2357980845,7188965,2677133,294747605,False,Home,9,13,14.0,WALK +2357980881,7188966,2677133,294747610,True,atwork,8,6,10.0,WALK +2357980885,7188966,2677133,294747610,False,Work,6,8,10.0,WALK +2357981161,7188966,2677133,294747645,True,work,6,9,7.0,WALK +2357981165,7188966,2677133,294747645,False,Home,9,6,18.0,WALK +2357981425,7188967,2677133,294747678,True,school,10,9,7.0,WALK +2357981429,7188967,2677133,294747678,False,Home,9,10,20.0,WALK +2357981553,7188968,2677133,294747694,True,eatout,5,9,15.0,WALK +2357981557,7188968,2677133,294747694,False,Home,9,5,18.0,WALK +2357981753,7188968,2677133,294747719,True,school,10,9,6.0,WALK +2357981757,7188968,2677133,294747719,False,Home,9,10,15.0,WALK +2357982145,7188969,2677133,294747768,True,work,7,9,6.0,WALK +2357982149,7188969,2677133,294747768,False,Home,9,7,15.0,WALK +2358106737,7189349,2677180,294763342,True,shopping,13,10,17.0,WALK +2358106741,7189349,2677180,294763342,False,Home,10,13,18.0,WALK +2358107001,7189350,2677180,294763375,True,escort,9,10,12.0,WALK +2358107002,7189350,2677180,294763375,True,othmaint,8,9,13.0,WALK +2358107003,7189350,2677180,294763375,True,othdiscr,21,8,13.0,WALK +2358107005,7189350,2677180,294763375,False,social,7,21,19.0,WALK +2358107006,7189350,2677180,294763375,False,othdiscr,8,7,19.0,WALK +2358107007,7189350,2677180,294763375,False,Home,10,8,19.0,WALK +2358107009,7189350,2677180,294763376,True,othdiscr,16,10,19.0,SHARED3FREE +2358107013,7189350,2677180,294763376,False,Home,10,16,20.0,SHARED3FREE +2358107049,7189350,2677180,294763381,True,school,9,10,7.0,WALK +2358107053,7189350,2677180,294763381,False,Home,10,9,10.0,WALK +2358107201,7189351,2677180,294763400,True,escort,10,10,8.0,TNC_SINGLE +2358107205,7189351,2677180,294763400,False,Home,10,10,12.0,TNC_SHARED +2358107721,7189352,2677180,294763465,True,shopping,5,10,17.0,WALK_LOC +2358107725,7189352,2677180,294763465,False,shopping,20,5,17.0,TNC_SINGLE +2358107726,7189352,2677180,294763465,False,Home,10,20,18.0,TNC_SINGLE +2358107769,7189352,2677180,294763471,True,work,17,10,8.0,SHARED3FREE +2358107773,7189352,2677180,294763471,False,Home,10,17,16.0,SHARED3FREE +2358107985,7189353,2677180,294763498,True,othdiscr,9,10,17.0,WALK +2358107989,7189353,2677180,294763498,False,Home,10,9,21.0,WALK +2358108377,7189354,2677180,294763547,True,shopping,19,10,15.0,WALK +2358108381,7189354,2677180,294763547,False,Home,10,19,19.0,WALK +2358108753,7189355,2677180,294763594,True,work,16,10,7.0,WALK +2358108757,7189355,2677180,294763594,False,Home,10,16,18.0,WALK +2358108841,7189356,2677180,294763605,True,escort,25,10,8.0,SHARED2FREE +2358108845,7189356,2677180,294763605,False,Home,10,25,11.0,DRIVEALONEFREE +2358230769,7189727,2677226,294778846,True,work,17,17,5.0,WALK +2358230773,7189727,2677226,294778846,False,Home,17,17,18.0,WALK +2358230985,7189728,2677226,294778873,True,othdiscr,8,17,8.0,WALK_LRF +2358230986,7189728,2677226,294778873,True,othdiscr,22,8,9.0,WALK_LRF +2358230989,7189728,2677226,294778873,False,Home,17,22,9.0,WALK_LRF +2358231033,7189728,2677226,294778879,True,school,13,17,9.0,WALK_LOC +2358231037,7189728,2677226,294778879,False,Home,17,13,22.0,WALK_LRF +2358231361,7189729,2677226,294778920,True,school,25,17,8.0,WALK_LOC +2358231365,7189729,2677226,294778920,False,Home,17,25,10.0,WALK_LRF +2358231953,7189731,2677226,294778994,True,atwork,9,13,10.0,WALK +2358231957,7189731,2677226,294778994,False,eatout,7,9,17.0,WALK +2358231958,7189731,2677226,294778994,False,Work,13,7,17.0,WALK +2358232081,7189731,2677226,294779010,True,work,13,17,7.0,WALK_LOC +2358232085,7189731,2677226,294779010,False,Home,17,13,17.0,WALK +2358232673,7189733,2677226,294779084,True,school,13,17,7.0,WALK +2358232677,7189733,2677226,294779084,False,Home,17,13,23.0,WALK_LRF +2358233393,7189735,2677226,294779174,True,work,2,17,8.0,WALK +2358233397,7189735,2677226,294779174,False,Home,17,2,19.0,WALK_LRF +2358233721,7189736,2677226,294779215,True,work,1,17,8.0,WALK +2358233725,7189736,2677226,294779215,False,Home,17,1,17.0,WALK +2358233937,7189737,2677226,294779242,True,othdiscr,9,17,7.0,WALK_LOC +2358233941,7189737,2677226,294779242,False,Home,17,9,19.0,WALK_LRF +2358234313,7189738,2677226,294779289,True,school,13,17,6.0,WALK_LRF +2358234317,7189738,2677226,294779289,False,Home,17,13,15.0,WALK_LOC +2371727225,7230875,2683536,296465903,True,othmaint,5,25,6.0,WALK +2371727229,7230875,2683536,296465903,False,Home,25,5,13.0,WALK +2372886681,7234410,2687071,296610835,True,othdiscr,10,4,17.0,TNC_SINGLE +2372886685,7234410,2687071,296610835,False,Home,4,10,19.0,TNC_SINGLE +2372886729,7234410,2687071,296610841,True,escort,5,4,11.0,WALK_LOC +2372886730,7234410,2687071,296610841,True,univ,12,5,12.0,WALK_LOC +2372886733,7234410,2687071,296610841,False,shopping,13,12,11.0,WALK_LOC +2372886734,7234410,2687071,296610841,False,Home,4,13,12.0,WALK_LOC +2372886745,7234410,2687071,296610843,True,shopping,19,4,14.0,TNC_SINGLE +2372886749,7234410,2687071,296610843,False,Home,4,19,14.0,TNC_SHARED +2372889657,7234419,2687080,296611207,True,othmaint,8,5,14.0,WALK +2372889661,7234419,2687080,296611207,False,Home,5,8,15.0,WALK +2372898665,7234447,2687108,296612333,True,eatout,9,8,12.0,WALK +2372898669,7234447,2687108,296612333,False,Home,8,9,14.0,WALK +2372933585,7234553,2687214,296616698,True,othdiscr,17,8,14.0,WALK +2372933589,7234553,2687214,296616698,False,Home,8,17,19.0,WALK +2372939553,7234571,2687232,296617444,True,othdiscr,10,8,9.0,TNC_SINGLE +2372939554,7234571,2687232,296617444,True,othdiscr,4,10,10.0,TNC_SINGLE +2372939555,7234571,2687232,296617444,True,shopping,11,4,10.0,TNC_SINGLE +2372939557,7234571,2687232,296617444,False,Home,8,11,12.0,WALK_LOC +2372971961,7234670,2687331,296621495,True,othdiscr,20,8,14.0,WALK +2372971965,7234670,2687331,296621495,False,Home,8,20,14.0,WALK +2372971985,7234670,2687331,296621498,True,othmaint,5,8,15.0,WALK +2372971989,7234670,2687331,296621498,False,Home,8,5,22.0,WALK +2372980665,7234697,2687358,296622583,True,eatout,12,20,15.0,WALK +2372980669,7234697,2687358,296622583,False,Home,20,12,17.0,WALK +2372988425,7234720,2687381,296623553,True,shopping,11,20,17.0,DRIVEALONEFREE +2372988429,7234720,2687381,296623553,False,Home,20,11,18.0,TAXI +2373007081,7234777,2687438,296625885,True,othmaint,25,25,13.0,WALK +2373007085,7234777,2687438,296625885,False,shopping,22,25,17.0,WALK +2373007086,7234777,2687438,296625885,False,Home,25,22,17.0,WALK +2374992793,7240831,2693492,296874099,True,othmaint,22,5,15.0,WALK +2374992797,7240831,2693492,296874099,False,Home,5,22,16.0,WALK +2374992881,7240831,2693492,296874110,True,work,1,5,6.0,WALK +2374992885,7240831,2693492,296874110,False,Home,5,1,14.0,WALK +2374992889,7240831,2693492,296874111,True,work,1,5,16.0,WALK +2374992893,7240831,2693492,296874111,False,escort,17,1,17.0,WALK +2374992894,7240831,2693492,296874111,False,Home,5,17,18.0,WALK +2374995065,7240838,2693499,296874383,True,othdiscr,12,5,18.0,WALK +2374995069,7240838,2693499,296874383,False,Home,5,12,18.0,WALK +2374995089,7240838,2693499,296874386,True,othmaint,5,5,17.0,TNC_SINGLE +2374995093,7240838,2693499,296874386,False,Home,5,5,18.0,TNC_SINGLE +2374995177,7240838,2693499,296874397,True,work,2,5,7.0,WALK +2374995181,7240838,2693499,296874397,False,Home,5,2,17.0,WALK +2375040721,7240977,2693638,296880090,True,shopping,5,20,18.0,WALK +2375040725,7240977,2693638,296880090,False,Home,20,5,19.0,WALK +2375040769,7240977,2693638,296880096,True,work,22,20,6.0,WALK_LRF +2375040773,7240977,2693638,296880096,False,Home,20,22,18.0,WALK_LRF +2375041769,7240981,2693642,296880221,True,atwork,5,23,11.0,WALK +2375041773,7240981,2693642,296880221,False,shopping,25,5,11.0,WALK +2375041774,7240981,2693642,296880221,False,Work,23,25,11.0,WALK +2375041801,7240981,2693642,296880225,True,atwork,25,23,12.0,WALK +2375041805,7240981,2693642,296880225,False,shopping,25,25,15.0,WALK +2375041806,7240981,2693642,296880225,False,Work,23,25,15.0,WALK +2375041969,7240981,2693642,296880246,True,othdiscr,5,20,21.0,WALK_LOC +2375041973,7240981,2693642,296880246,False,Home,20,5,21.0,WALK_LOC +2375042081,7240981,2693642,296880260,True,work,23,20,7.0,WALK_LRF +2375042085,7240981,2693642,296880260,False,Home,20,23,19.0,WALK_LRF +2375054281,7241019,2693680,296881785,True,eatout,5,22,12.0,DRIVEALONEFREE +2375054285,7241019,2693680,296881785,False,Home,22,5,14.0,TNC_SINGLE +2375054305,7241019,2693680,296881788,True,escort,16,22,15.0,DRIVEALONEFREE +2375054309,7241019,2693680,296881788,False,eatout,16,16,16.0,DRIVEALONEFREE +2375054310,7241019,2693680,296881788,False,Home,22,16,16.0,SHARED3FREE +2375054433,7241019,2693680,296881804,True,othdiscr,9,22,7.0,DRIVEALONEFREE +2375054437,7241019,2693680,296881804,False,Home,22,9,12.0,TNC_SHARED +2381109009,7259478,2704242,297638626,True,othmaint,5,7,7.0,WALK +2381109013,7259478,2704242,297638626,False,Home,7,5,12.0,WALK +2381109313,7259479,2704242,297638664,True,othdiscr,12,7,13.0,WALK +2381109317,7259479,2704242,297638664,False,Home,7,12,16.0,WALK +2381110033,7259481,2704243,297638754,True,shopping,16,7,15.0,TNC_SHARED +2381110037,7259481,2704243,297638754,False,othmaint,7,16,15.0,WALK_LOC +2381110038,7259481,2704243,297638754,False,shopping,8,7,15.0,WALK +2381110039,7259481,2704243,297638754,False,Home,7,8,15.0,WALK_LOC +2381116265,7259500,2704253,297639533,True,shopping,20,7,8.0,DRIVEALONEFREE +2381116269,7259500,2704253,297639533,False,shopping,11,20,19.0,SHARED3FREE +2381116270,7259500,2704253,297639533,False,Home,7,11,19.0,SHARED3FREE +2381121297,7259516,2704261,297640162,True,eatout,5,7,15.0,WALK +2381121301,7259516,2704261,297640162,False,Home,7,5,17.0,WALK +2381121473,7259516,2704261,297640184,True,othmaint,5,7,15.0,BIKE +2381121477,7259516,2704261,297640184,False,Home,7,5,15.0,BIKE +2381121537,7259516,2704261,297640192,True,social,5,7,15.0,WALK_LOC +2381121541,7259516,2704261,297640192,False,social,8,5,15.0,WALK +2381121542,7259516,2704261,297640192,False,Home,7,8,15.0,WALK_LOC +2381121841,7259517,2704261,297640230,True,shopping,5,7,12.0,WALK_LOC +2381121842,7259517,2704261,297640230,True,shopping,11,5,12.0,WALK_LOC +2381121845,7259517,2704261,297640230,False,Home,7,11,13.0,TNC_SINGLE +2381153985,7259615,2704310,297644248,True,shopping,16,23,9.0,WALK_LOC +2381153989,7259615,2704310,297644248,False,Home,23,16,10.0,WALK_LRF +2381157593,7259626,2704316,297644699,True,shopping,21,24,8.0,SHARED3FREE +2381157597,7259626,2704316,297644699,False,Home,24,21,11.0,WALK_LOC +2381167369,7259656,2704331,297645921,True,othdiscr,10,25,14.0,TNC_SINGLE +2381167373,7259656,2704331,297645921,False,Home,25,10,19.0,WALK_LOC +2381167433,7259656,2704331,297645929,True,shopping,11,25,10.0,WALK_LOC +2381167437,7259656,2704331,297645929,False,shopping,7,11,11.0,WALK_LOC +2381167438,7259656,2704331,297645929,False,Home,25,7,11.0,WALK_LOC +2381187729,7259718,2704362,297648466,True,othmaint,10,25,12.0,WALK_LOC +2381187733,7259718,2704362,297648466,False,Home,25,10,16.0,WALK_LOC +2382306953,7263130,2706068,297788369,True,work,10,20,9.0,DRIVEALONEFREE +2382306957,7263130,2706068,297788369,False,Home,20,10,17.0,DRIVEALONEFREE +2382307193,7263131,2706068,297788399,True,othmaint,9,20,12.0,WALK +2382307197,7263131,2706068,297788399,False,Home,20,9,14.0,WALK +2382310121,7263140,2706073,297788765,True,othdiscr,19,20,18.0,SHARED3FREE +2382310125,7263140,2706073,297788765,False,Home,20,19,19.0,DRIVEALONEFREE +2382310129,7263140,2706073,297788766,True,othdiscr,11,20,21.0,SHARED2FREE +2382310133,7263140,2706073,297788766,False,Home,20,11,23.0,SHARED3FREE +2382310145,7263140,2706073,297788768,True,othmaint,5,20,12.0,WALK_LOC +2382310149,7263140,2706073,297788768,False,Home,20,5,13.0,WALK_LOC +2382310185,7263140,2706073,297788773,True,shopping,25,20,14.0,SHARED2FREE +2382310189,7263140,2706073,297788773,False,Home,20,25,18.0,SHARED2FREE +2382310209,7263140,2706073,297788776,True,social,14,20,8.0,SHARED3FREE +2382310213,7263140,2706073,297788776,False,Home,20,14,11.0,SHARED2FREE +2382310513,7263141,2706073,297788814,True,shopping,11,20,8.0,WALK +2382310517,7263141,2706073,297788814,False,Home,20,11,17.0,WALK +2389947449,7286425,2717715,298743431,True,eatout,4,3,15.0,WALK +2389947453,7286425,2717715,298743431,False,Home,3,4,16.0,WALK +2389951929,7286438,2717722,298743991,True,shopping,16,3,9.0,WALK +2389951933,7286438,2717722,298743991,False,Home,3,16,18.0,WALK +2389952025,7286439,2717722,298744003,True,atwork,12,2,11.0,WALK +2389952029,7286439,2717722,298744003,False,Work,2,12,13.0,WALK +2389952193,7286439,2717722,298744024,True,eatout,6,3,18.0,WALK +2389952194,7286439,2717722,298744024,True,othdiscr,2,6,18.0,WALK +2389952197,7286439,2717722,298744024,False,eatout,12,2,18.0,WALK_LOC +2389952198,7286439,2717722,298744024,False,Home,3,12,18.0,WALK_LOC +2389952305,7286439,2717722,298744038,True,work,2,3,6.0,WALK +2389952309,7286439,2717722,298744038,False,othmaint,2,2,17.0,WALK +2389952310,7286439,2717722,298744038,False,Home,3,2,17.0,WALK +2389984121,7286536,2717771,298748015,True,work,10,10,13.0,WALK +2389984125,7286536,2717771,298748015,False,Home,10,10,20.0,WALK +2389993193,7286564,2717785,298749149,True,othdiscr,4,20,14.0,WALK +2389993197,7286564,2717785,298749149,False,Home,20,4,17.0,WALK +2389993521,7286565,2717785,298749190,True,othdiscr,12,20,20.0,SHARED3FREE +2389993525,7286565,2717785,298749190,False,Home,20,12,20.0,SHARED3FREE +2389993585,7286565,2717785,298749198,True,shopping,11,20,18.0,TNC_SINGLE +2389993589,7286565,2717785,298749198,False,Home,20,11,18.0,TNC_SINGLE +2389993633,7286565,2717785,298749204,True,work,11,20,7.0,WALK +2389993637,7286565,2717785,298749204,False,Home,20,11,17.0,WALK +2390033041,7286686,2717846,298754130,True,atwork,7,2,10.0,WALK +2390033045,7286686,2717846,298754130,False,Work,2,7,10.0,WALK +2390033113,7286686,2717846,298754139,True,eatout,2,23,9.0,SHARED3FREE +2390033117,7286686,2717846,298754139,False,Home,23,2,10.0,SHARED2FREE +2390033321,7286686,2717846,298754165,True,eatout,24,23,10.0,WALK +2390033322,7286686,2717846,298754165,True,work,2,24,10.0,WALK +2390033325,7286686,2717846,298754165,False,work,23,2,23.0,WALK +2390033326,7286686,2717846,298754165,False,Home,23,23,23.0,WALK +2390047473,7286730,2717868,298755934,True,atwork,11,14,14.0,WALK +2390047477,7286730,2717868,298755934,False,Work,14,11,14.0,WALK +2390047753,7286730,2717868,298755969,True,work,14,25,7.0,WALK_LOC +2390047757,7286730,2717868,298755969,False,Home,25,14,17.0,WALK +2396111553,7305218,2727112,299513944,True,othmaint,21,10,17.0,WALK +2396111554,7305218,2727112,299513944,True,eatout,16,21,17.0,WALK +2396111557,7305218,2727112,299513944,False,Home,10,16,17.0,WALK +2396111609,7305218,2727112,299513951,True,eatout,6,10,17.0,SHARED3FREE +2396111613,7305218,2727112,299513951,False,Home,10,6,18.0,WALK +2396111817,7305218,2727112,299513977,True,work,11,10,8.0,WALK +2396111821,7305218,2727112,299513977,False,Home,10,11,15.0,WALK +2396111825,7305218,2727112,299513978,True,work,11,10,19.0,WALK +2396111829,7305218,2727112,299513978,False,Home,10,11,22.0,WALK +2396112145,7305219,2727112,299514018,True,work,4,10,7.0,TAXI +2396112149,7305219,2727112,299514018,False,Home,10,4,17.0,WALK_HVY +2396128593,7305270,2727138,299516074,True,atwork,16,16,14.0,TNC_SINGLE +2396128597,7305270,2727138,299516074,False,Work,16,16,14.0,TNC_SINGLE +2396128873,7305270,2727138,299516109,True,othmaint,18,10,6.0,SHARED2FREE +2396128874,7305270,2727138,299516109,True,work,16,18,7.0,DRIVEALONEFREE +2396128877,7305270,2727138,299516109,False,Home,10,16,19.0,SHARED2FREE +2396129089,7305271,2727138,299516136,True,othdiscr,10,10,16.0,WALK +2396129093,7305271,2727138,299516136,False,Home,10,10,17.0,WALK +2396129113,7305271,2727138,299516139,True,escort,11,10,12.0,WALK +2396129114,7305271,2727138,299516139,True,othmaint,5,11,13.0,SHARED3FREE +2396129117,7305271,2727138,299516139,False,Home,10,5,14.0,DRIVEALONEFREE +2396129153,7305271,2727138,299516144,True,shopping,12,10,8.0,WALK_LOC +2396129157,7305271,2727138,299516144,False,Home,10,12,11.0,WALK +2396129161,7305271,2727138,299516145,True,shopping,2,10,12.0,WALK +2396129165,7305271,2727138,299516145,False,Home,10,2,12.0,WALK +2396129201,7305271,2727138,299516150,True,work,13,10,19.0,WALK +2396129205,7305271,2727138,299516150,False,shopping,16,13,19.0,WALK_LOC +2396129206,7305271,2727138,299516150,False,Home,10,16,19.0,WALK +2396142369,7305312,2727159,299517796,True,othmaint,8,21,12.0,WALK +2396142370,7305312,2727159,299517796,True,atwork,11,8,12.0,WALK +2396142373,7305312,2727159,299517796,False,Work,21,11,13.0,WALK +2396142649,7305312,2727159,299517831,True,work,21,10,7.0,WALK +2396142653,7305312,2727159,299517831,False,Home,10,21,17.0,WALK +2396142977,7305313,2727159,299517872,True,work,6,10,6.0,WALK +2396142981,7305313,2727159,299517872,False,Home,10,6,17.0,WALK +2396151177,7305338,2727172,299518897,True,work,2,10,6.0,WALK_LRF +2396151181,7305338,2727172,299518897,False,Home,10,2,13.0,WALK +2396151505,7305339,2727172,299518938,True,othmaint,5,10,9.0,WALK +2396151506,7305339,2727172,299518938,True,work,16,5,10.0,WALK_LOC +2396151509,7305339,2727172,299518938,False,Home,10,16,23.0,WALK_LOC +2396175337,7305412,2727209,299521917,True,othdiscr,16,17,9.0,WALK +2396175341,7305412,2727209,299521917,False,Home,17,16,11.0,WALK_LRF +2396175777,7305413,2727209,299521972,True,work,14,17,7.0,WALK_LRF +2396175781,7305413,2727209,299521972,False,Home,17,14,18.0,WALK_LOC +2396187145,7305448,2727227,299523393,True,othdiscr,15,17,6.0,WALK +2396187149,7305448,2727227,299523393,False,Home,17,15,13.0,WALK +2396187321,7305449,2727227,299523415,True,othdiscr,16,17,12.0,WALK +2396187322,7305449,2727227,299523415,True,eatout,1,16,12.0,WALK +2396187325,7305449,2727227,299523415,False,Home,17,1,20.0,WALK +2396187537,7305449,2727227,299523442,True,escort,7,17,9.0,WALK_LOC +2396187538,7305449,2727227,299523442,True,shopping,6,7,10.0,WALK +2396187541,7305449,2727227,299523442,False,Home,17,6,10.0,WALK_LOC +2396191169,7305460,2727233,299523896,True,social,19,20,8.0,WALK +2396191173,7305460,2727233,299523896,False,Home,20,19,20.0,WALK +2396191177,7305460,2727233,299523897,True,othmaint,21,20,20.0,DRIVEALONEFREE +2396191178,7305460,2727233,299523897,True,social,7,21,20.0,SHARED2FREE +2396191181,7305460,2727233,299523897,False,Home,20,7,20.0,SHARED2FREE +2396191257,7305461,2727233,299523907,True,eatout,12,20,11.0,WALK_LOC +2396191261,7305461,2727233,299523907,False,Home,20,12,13.0,TNC_SINGLE +2396191521,7305461,2727233,299523940,True,work,16,20,7.0,SHARED2FREE +2396191525,7305461,2727233,299523940,False,Home,20,16,11.0,DRIVEALONEFREE +2396201033,7305490,2727248,299525129,True,work,2,20,14.0,WALK_LOC +2396201037,7305490,2727248,299525129,False,Home,20,2,21.0,WALK_LRF +2396201097,7305491,2727248,299525137,True,eatout,20,20,18.0,WALK +2396201101,7305491,2727248,299525137,False,Home,20,20,21.0,WALK +2396201337,7305491,2727248,299525167,True,social,11,20,8.0,WALK_LOC +2396201341,7305491,2727248,299525167,False,Home,20,11,16.0,WALK_LOC +2396217321,7305540,2727273,299527165,True,othdiscr,12,20,15.0,WALK +2396217325,7305540,2727273,299527165,False,Home,20,12,21.0,WALK +2396217433,7305540,2727273,299527179,True,work,20,20,11.0,WALK +2396217437,7305540,2727273,299527179,False,Home,20,20,15.0,DRIVEALONEFREE +2396217761,7305541,2727273,299527220,True,work,9,20,6.0,WALK +2396217765,7305541,2727273,299527220,False,Home,20,9,12.0,WALK +2396217769,7305541,2727273,299527221,True,work,9,20,13.0,WALK +2396217773,7305541,2727273,299527221,False,Home,20,9,18.0,WALK +2421624921,7383002,2748985,302703115,True,shopping,11,25,8.0,WALK +2421624925,7383002,2748985,302703115,False,Home,25,11,17.0,WALK +2440637657,7440968,2758713,305079707,True,shopping,13,10,7.0,TNC_SINGLE +2440637661,7440968,2758713,305079707,False,Home,10,13,7.0,TNC_SINGLE +2440637705,7440968,2758713,305079713,True,othdiscr,15,10,17.0,WALK_LRF +2440637709,7440968,2758713,305079713,False,Home,10,15,21.0,WALK_LRF +2440637729,7440968,2758713,305079716,True,othmaint,16,10,13.0,TNC_SINGLE +2440637733,7440968,2758713,305079716,False,othmaint,2,16,15.0,TNC_SINGLE +2440637734,7440968,2758713,305079716,False,Home,10,2,16.0,TNC_SINGLE +2440638097,7440969,2758713,305079762,True,shopping,8,10,11.0,WALK_LOC +2440638098,7440969,2758713,305079762,True,shopping,20,8,11.0,TNC_SINGLE +2440638101,7440969,2758713,305079762,False,shopping,16,20,13.0,TNC_SINGLE +2440638102,7440969,2758713,305079762,False,escort,8,16,13.0,TNC_SINGLE +2440638103,7440969,2758713,305079762,False,Home,10,8,13.0,TNC_SINGLE +2440638145,7440969,2758713,305079768,True,work,20,10,16.0,WALK +2440638149,7440969,2758713,305079768,False,Home,10,20,16.0,WALK_LOC +2440638473,7440970,2758713,305079809,True,work,9,10,7.0,WALK +2440638477,7440970,2758713,305079809,False,Home,10,9,18.0,WALK +2444208361,7451854,2760519,305526045,True,univ,12,7,9.0,WALK_LOC +2444208365,7451854,2760519,305526045,False,escort,10,12,15.0,WALK_LOC +2444208366,7451854,2760519,305526045,False,Home,7,10,16.0,WALK_LOC +2444208377,7451854,2760519,305526047,True,shopping,12,7,16.0,WALK_LOC +2444208381,7451854,2760519,305526047,False,Home,7,12,16.0,WALK_LOC +2444209033,7451856,2760521,305526129,True,shopping,17,7,10.0,BIKE +2444209037,7451856,2760521,305526129,False,Home,7,17,20.0,BIKE +2444209041,7451856,2760521,305526130,True,shopping,5,7,21.0,WALK +2444209045,7451856,2760521,305526130,False,Home,7,5,22.0,WALK +2444228649,7451916,2760581,305528581,True,othdiscr,8,7,14.0,WALK_LOC +2444228653,7451916,2760581,305528581,False,Home,7,8,23.0,WALK +2444259201,7452009,2760674,305532400,True,school,6,7,7.0,WALK +2444259205,7452009,2760674,305532400,False,Home,7,6,13.0,WALK +2444269697,7452041,2760706,305533712,True,escort,10,9,8.0,WALK +2444269698,7452041,2760706,305533712,True,univ,9,10,8.0,WALK +2444269701,7452041,2760706,305533712,False,work,10,9,11.0,WALK +2444269702,7452041,2760706,305533712,False,Home,9,10,15.0,WALK +2444277521,7452065,2760730,305534690,True,othdiscr,8,9,11.0,WALK +2444277525,7452065,2760730,305534690,False,Home,9,8,13.0,WALK +2444291673,7452108,2760773,305536459,True,univ,10,9,8.0,WALK +2444291677,7452108,2760773,305536459,False,Home,9,10,11.0,WALK_LOC +2444303497,7452144,2760809,305537937,True,shopping,8,9,15.0,WALK +2444303498,7452144,2760809,305537937,True,escort,8,8,15.0,WALK +2444303499,7452144,2760809,305537937,True,eatout,12,8,15.0,WALK +2444303500,7452144,2760809,305537937,True,shopping,21,12,15.0,WALK +2444303501,7452144,2760809,305537937,False,shopping,11,21,15.0,WALK +2444303502,7452144,2760809,305537937,False,Home,9,11,15.0,WALK +2444305401,7452150,2760815,305538175,True,othdiscr,10,9,12.0,WALK +2444305405,7452150,2760815,305538175,False,Home,9,10,14.0,WALK +2444308417,7452159,2760824,305538552,True,shopping,2,9,12.0,WALK_LRF +2444308421,7452159,2760824,305538552,False,Home,9,2,12.0,WALK_LRF +2444320161,7452195,2760860,305540020,True,othmaint,10,9,15.0,WALK +2444320162,7452195,2760860,305540020,True,othdiscr,14,10,16.0,WALK_LRF +2444320165,7452195,2760860,305540020,False,Home,9,14,16.0,WALK_LRF +2444329737,7452224,2760889,305541217,True,shopping,16,9,9.0,TNC_SHARED +2444329741,7452224,2760889,305541217,False,Home,9,16,12.0,WALK_LRF +2444329745,7452224,2760889,305541218,True,shopping,11,9,13.0,WALK_LOC +2444329749,7452224,2760889,305541218,False,Home,9,11,13.0,WALK_LOC +2444349417,7452284,2760949,305543677,True,shopping,11,9,9.0,WALK +2444349421,7452284,2760949,305543677,False,Home,9,11,11.0,BIKE +2444362145,7452323,2760988,305545268,True,othdiscr,11,9,8.0,WALK +2444362149,7452323,2760988,305545268,False,Home,9,11,12.0,WALK +2444362169,7452323,2760988,305545271,True,othmaint,9,9,12.0,WALK +2444362173,7452323,2760988,305545271,False,Home,9,9,15.0,WALK +2444362177,7452323,2760988,305545272,True,othmaint,7,9,19.0,WALK +2444362181,7452323,2760988,305545272,False,Home,9,7,21.0,WALK +2444385761,7452395,2761060,305548220,True,othdiscr,9,9,7.0,WALK +2444385765,7452395,2761060,305548220,False,Home,9,9,19.0,WALK +2444431529,7452535,2761200,305553941,True,eatout,2,9,17.0,WALK +2444431533,7452535,2761200,305553941,False,Home,9,2,18.0,WALK +2444431681,7452535,2761200,305553960,True,othdiscr,7,9,9.0,WALK +2444431685,7452535,2761200,305553960,False,Home,9,7,11.0,WALK +2444431745,7452535,2761200,305553968,True,social,2,9,14.0,SHARED2FREE +2444431746,7452535,2761200,305553968,True,shopping,17,2,15.0,WALK +2444431749,7452535,2761200,305553968,False,Home,9,17,16.0,DRIVEALONEFREE +2444431753,7452535,2761200,305553969,True,shopping,15,9,18.0,DRIVEALONEFREE +2444431757,7452535,2761200,305553969,False,Home,9,15,18.0,TNC_SINGLE +2444447841,7452584,2761249,305555980,True,social,11,9,17.0,WALK_LOC +2444447845,7452584,2761249,305555980,False,Home,9,11,19.0,WALK_LOC +2444453681,7452602,2761267,305556710,True,othmaint,22,9,9.0,WALK_HVY +2444453685,7452602,2761267,305556710,False,Home,9,22,15.0,WALK_LRF +2444469817,7452651,2761316,305558727,True,social,11,10,11.0,WALK +2444469821,7452651,2761316,305558727,False,Home,10,11,21.0,WALK +2444477601,7452675,2761340,305559700,True,othdiscr,9,10,16.0,WALK +2444477605,7452675,2761340,305559700,False,Home,10,9,21.0,WALK +2444477625,7452675,2761340,305559703,True,othmaint,7,10,8.0,BIKE +2444477629,7452675,2761340,305559703,False,Home,10,7,12.0,BIKE +2444481601,7452687,2761352,305560200,True,shopping,5,10,12.0,WALK_LOC +2444481605,7452687,2761352,305560200,False,shopping,7,5,12.0,WALK_LOC +2444481606,7452687,2761352,305560200,False,Home,10,7,12.0,WALK_LOC +2444490897,7452716,2761381,305561362,True,eatout,9,10,6.0,WALK +2444490901,7452716,2761381,305561362,False,Home,10,9,18.0,WALK +2444494329,7452726,2761391,305561791,True,othdiscr,12,10,9.0,WALK +2444494333,7452726,2761391,305561791,False,Home,10,12,12.0,WALK +2444498113,7452738,2761403,305562264,True,eatout,5,11,16.0,WALK +2444498117,7452738,2761403,305562264,False,Home,11,5,20.0,WALK +2444498329,7452738,2761403,305562291,True,shopping,12,11,7.0,WALK +2444498333,7452738,2761403,305562291,False,Home,11,12,15.0,WALK +2444519961,7452804,2761469,305564995,True,school,17,11,8.0,WALK_LRF +2444519965,7452804,2761469,305564995,False,Home,11,17,15.0,WALK_LRF +2444540097,7452866,2761531,305567512,True,eatout,21,11,9.0,WALK +2444540101,7452866,2761531,305567512,False,shopping,12,21,13.0,WALK +2444540102,7452866,2761531,305567512,False,Home,11,12,13.0,WALK_LOC +2444545825,7452883,2761548,305568228,True,othdiscr,16,13,7.0,WALK +2444545829,7452883,2761548,305568228,False,Home,13,16,17.0,WALK +2444551425,7452900,2761565,305568928,True,othmaint,2,15,12.0,WALK +2444551429,7452900,2761565,305568928,False,Home,15,2,13.0,WALK +2444594369,7453031,2761696,305574296,True,social,16,17,15.0,WALK +2444594370,7453031,2761696,305574296,True,othdiscr,9,16,16.0,WALK_LRF +2444594373,7453031,2761696,305574296,False,Home,17,9,17.0,WALK_LRF +2444595201,7453034,2761699,305574400,True,eatout,5,17,14.0,SHARED3FREE +2444595205,7453034,2761699,305574400,False,Home,17,5,17.0,SHARED3FREE +2444595417,7453034,2761699,305574427,True,shopping,16,17,9.0,WALK +2444595421,7453034,2761699,305574427,False,Home,17,16,13.0,WALK +2444597521,7453041,2761706,305574690,True,escort,2,17,7.0,DRIVEALONEFREE +2444597525,7453041,2761706,305574690,False,escort,4,2,8.0,WALK +2444597526,7453041,2761706,305574690,False,Home,17,4,9.0,SHARED3FREE +2444601761,7453054,2761719,305575220,True,eatout,1,17,14.0,WALK +2444601765,7453054,2761719,305575220,False,Home,17,1,18.0,WALK +2444617393,7453101,2761766,305577174,True,shopping,11,17,13.0,WALK +2444617397,7453101,2761766,305577174,False,Home,17,11,15.0,WALK +2444620281,7453110,2761775,305577535,True,othdiscr,16,17,7.0,WALK +2444620285,7453110,2761775,305577535,False,Home,17,16,8.0,WALK +2444620345,7453110,2761775,305577543,True,shopping,17,17,12.0,WALK +2444620349,7453110,2761775,305577543,False,Home,17,17,21.0,WALK +2444656097,7453219,2761884,305582012,True,shopping,2,17,11.0,BIKE +2444656101,7453219,2761884,305582012,False,Home,17,2,16.0,BIKE +2444660017,7453231,2761896,305582502,True,univ,12,17,15.0,WALK_LOC +2444660021,7453231,2761896,305582502,False,Home,17,12,17.0,WALK_LRF +2444698873,7453350,2762015,305587359,True,escort,13,20,8.0,DRIVEALONEFREE +2444698877,7453350,2762015,305587359,False,Home,20,13,8.0,SHARED3FREE +2444698881,7453350,2762015,305587360,True,escort,12,20,14.0,WALK +2444698885,7453350,2762015,305587360,False,Home,20,12,17.0,WALK +2444709673,7453383,2762048,305588709,True,eatout,9,20,18.0,WALK +2444709677,7453383,2762048,305588709,False,Home,20,9,23.0,WALK +2444709681,7453383,2762048,305588710,True,eatout,5,20,23.0,WALK +2444709685,7453383,2762048,305588710,False,Home,20,5,23.0,WALK +2444709825,7453383,2762048,305588728,True,othdiscr,7,20,10.0,WALK +2444709829,7453383,2762048,305588728,False,othdiscr,10,7,17.0,WALK +2444709830,7453383,2762048,305588728,False,Home,20,10,17.0,WALK +2444719729,7453413,2762078,305589966,True,shopping,19,20,16.0,WALK +2444719733,7453413,2762078,305589966,False,Home,20,19,16.0,WALK +2444720825,7453417,2762082,305590103,True,eatout,7,20,9.0,WALK +2444720829,7453417,2762082,305590103,False,Home,20,7,13.0,WALK +2444723121,7453424,2762089,305590390,True,eatout,9,20,10.0,WALK +2444723125,7453424,2762089,305590390,False,Home,20,9,13.0,WALK +2444728257,7453439,2762104,305591032,True,shopping,5,20,13.0,WALK_LOC +2444728261,7453439,2762104,305591032,False,Home,20,5,20.0,WALK_LOC +2444759073,7453533,2762198,305594884,True,univ,12,21,7.0,WALK_LOC +2444759077,7453533,2762198,305594884,False,shopping,14,12,13.0,WALK_LOC +2444759078,7453533,2762198,305594884,False,othmaint,5,14,13.0,WALK +2444759079,7453533,2762198,305594884,False,escort,5,5,13.0,WALK +2444759080,7453533,2762198,305594884,False,Home,21,5,14.0,WALK +2444766961,7453557,2762222,305595870,True,shopping,13,21,17.0,WALK_LOC +2444766965,7453557,2762222,305595870,False,othmaint,7,13,17.0,WALK_LOC +2444766966,7453557,2762222,305595870,False,Home,21,7,17.0,WALK_LOC +2444792217,7453634,2762299,305599027,True,othmaint,12,21,7.0,WALK_LOC +2444792218,7453634,2762299,305599027,True,shopping,5,12,7.0,WALK +2444792221,7453634,2762299,305599027,False,Home,21,5,8.0,WALK_LOC +2463513849,7510712,2819377,307939231,True,work,6,2,6.0,WALK +2463513853,7510712,2819377,307939231,False,Home,2,6,18.0,WALK +2463518113,7510725,2819390,307939764,True,work,4,6,14.0,WALK +2463518117,7510725,2819390,307939764,False,Home,6,4,18.0,WALK +2463531841,7510767,2819432,307941480,True,shopping,16,6,16.0,SHARED2FREE +2463531845,7510767,2819432,307941480,False,escort,9,16,17.0,DRIVEALONEFREE +2463531846,7510767,2819432,307941480,False,Home,6,9,17.0,WALK +2463531889,7510767,2819432,307941486,True,work,4,6,7.0,DRIVE_LOC +2463531893,7510767,2819432,307941486,False,Home,6,4,16.0,DRIVE_LOC +2463535825,7510779,2819444,307941978,True,work,5,6,14.0,WALK +2463535829,7510779,2819444,307941978,False,Home,6,5,22.0,WALK +2463546977,7510813,2819478,307943372,True,work,17,6,9.0,WALK_LRF +2463546981,7510813,2819478,307943372,False,Home,6,17,16.0,WALK_LOC +2463551153,7510826,2819491,307943894,True,othmaint,4,6,6.0,BIKE +2463551157,7510826,2819491,307943894,False,Home,6,4,13.0,BIKE +2463551241,7510826,2819491,307943905,True,work,4,6,14.0,WALK +2463551245,7510826,2819491,307943905,False,Home,6,4,19.0,WALK +2463566657,7510873,2819538,307945832,True,work,13,6,7.0,WALK +2463566661,7510873,2819538,307945832,False,Home,6,13,18.0,WALK +2463597817,7510968,2819633,307949727,True,shopping,11,6,8.0,WALK_LOC +2463597818,7510968,2819633,307949727,True,work,9,11,8.0,WALK +2463597821,7510968,2819633,307949727,False,Home,6,9,19.0,WALK +2463603393,7510985,2819650,307950424,True,work,9,6,13.0,WALK +2463603397,7510985,2819650,307950424,False,shopping,5,9,18.0,WALK +2463603398,7510985,2819650,307950424,False,escort,7,5,19.0,WALK +2463603399,7510985,2819650,307950424,False,shopping,6,7,19.0,WALK +2463603400,7510985,2819650,307950424,False,Home,6,6,19.0,WALK +2463605033,7510990,2819655,307950629,True,work,12,6,8.0,WALK +2463605037,7510990,2819655,307950629,False,Home,6,12,17.0,WALK +2463616841,7511026,2819691,307952105,True,work,4,6,7.0,WALK +2463616845,7511026,2819691,307952105,False,Home,6,4,17.0,WALK +2463638489,7511092,2819757,307954811,True,work,4,6,7.0,TNC_SINGLE +2463638493,7511092,2819757,307954811,False,Home,6,4,14.0,TNC_SINGLE +2463641113,7511100,2819765,307955139,True,work,5,6,7.0,WALK_LOC +2463641117,7511100,2819765,307955139,False,Home,6,5,16.0,WALK +2463642313,7511104,2819769,307955289,True,othdiscr,6,6,5.0,WALK +2463642317,7511104,2819769,307955289,False,Home,6,6,5.0,WALK +2463642425,7511104,2819769,307955303,True,work,3,6,8.0,WALK +2463642429,7511104,2819769,307955303,False,escort,25,3,16.0,WALK +2463642430,7511104,2819769,307955303,False,shopping,5,25,17.0,WALK +2463642431,7511104,2819769,307955303,False,othmaint,7,5,17.0,WALK +2463642432,7511104,2819769,307955303,False,Home,6,7,18.0,WALK +2463650345,7511129,2819794,307956293,True,atwork,15,2,13.0,WALK +2463650349,7511129,2819794,307956293,False,Work,2,15,14.0,WALK +2463650513,7511129,2819794,307956314,True,othdiscr,21,6,18.0,WALK +2463650517,7511129,2819794,307956314,False,Home,6,21,21.0,WALK +2463650625,7511129,2819794,307956328,True,work,2,6,7.0,WALK +2463650629,7511129,2819794,307956328,False,Home,6,2,17.0,WALK +2463663417,7511168,2819833,307957927,True,work,12,6,7.0,WALK_LOC +2463663421,7511168,2819833,307957927,False,Home,6,12,17.0,WALK_LOC +2463671289,7511192,2819857,307958911,True,work,9,6,9.0,TNC_SINGLE +2463671290,7511192,2819857,307958911,True,work,7,9,9.0,WALK_LOC +2463671293,7511192,2819857,307958911,False,escort,10,7,17.0,TNC_SHARED +2463671294,7511192,2819857,307958911,False,othmaint,5,10,17.0,TNC_SHARED +2463671295,7511192,2819857,307958911,False,Home,6,5,18.0,WALK +2463688721,7511246,2819911,307961090,True,atwork,4,5,11.0,WALK +2463688725,7511246,2819911,307961090,False,othmaint,16,4,11.0,WALK +2463688726,7511246,2819911,307961090,False,Work,5,16,11.0,WALK +2463689001,7511246,2819911,307961125,True,work,5,6,6.0,WALK +2463689005,7511246,2819911,307961125,False,Home,6,5,17.0,WALK +2463705073,7511295,2819960,307963134,True,work,5,6,6.0,WALK +2463705077,7511295,2819960,307963134,False,Home,6,5,11.0,WALK +2463723113,7511350,2820015,307965389,True,work,16,6,6.0,WALK +2463723117,7511350,2820015,307965389,False,Home,6,16,16.0,WALK +2463724009,7511353,2820018,307965501,True,othmaint,5,6,8.0,WALK +2463724013,7511353,2820018,307965501,False,Home,6,5,10.0,WALK +2463724073,7511353,2820018,307965509,True,social,5,6,13.0,WALK +2463724077,7511353,2820018,307965509,False,Home,6,5,18.0,WALK +2463737873,7511395,2820060,307967234,True,eatout,11,7,8.0,TNC_SINGLE +2463737874,7511395,2820060,307967234,True,work,8,11,8.0,WALK +2463737877,7511395,2820060,307967234,False,othmaint,11,8,16.0,WALK +2463737878,7511395,2820060,307967234,False,Home,7,11,17.0,TNC_SINGLE +2463751977,7511438,2820103,307968997,True,work,7,8,13.0,WALK +2463751981,7511438,2820103,307968997,False,Home,8,7,18.0,WALK +2463755913,7511450,2820115,307969489,True,work,9,8,6.0,WALK +2463755917,7511450,2820115,307969489,False,Home,8,9,16.0,WALK_LOC +2463764113,7511475,2820140,307970514,True,work,5,8,9.0,WALK +2463764117,7511475,2820140,307970514,False,escort,8,5,9.0,WALK +2463764118,7511475,2820140,307970514,False,work,5,8,19.0,WALK +2463764119,7511475,2820140,307970514,False,escort,6,5,19.0,WALK +2463764120,7511475,2820140,307970514,False,Home,8,6,19.0,WALK +2463766409,7511482,2820147,307970801,True,work,2,8,8.0,WALK +2463766410,7511482,2820147,307970801,True,work,5,2,8.0,WALK +2463766413,7511482,2820147,307970801,False,escort,11,5,16.0,WALK_LOC +2463766414,7511482,2820147,307970801,False,Home,8,11,17.0,WALK +2463767721,7511486,2820151,307970965,True,work,23,8,7.0,WALK_LRF +2463767725,7511486,2820151,307970965,False,Home,8,23,17.0,WALK_LRF +2463772313,7511500,2820165,307971539,True,work,6,8,9.0,WALK +2463772317,7511500,2820165,307971539,False,Home,8,6,19.0,WALK +2463786417,7511543,2820208,307973302,True,work,25,8,7.0,WALK +2463786421,7511543,2820208,307973302,False,Home,8,25,13.0,WALK +2463787729,7511547,2820212,307973466,True,work,6,8,7.0,WALK +2463787733,7511547,2820212,307973466,False,Home,8,6,15.0,WALK +2463794617,7511568,2820233,307974327,True,work,17,8,7.0,WALK_LRF +2463794621,7511568,2820233,307974327,False,Home,8,17,21.0,WALK_LRF +2463796913,7511575,2820240,307974614,True,work,17,8,7.0,WALK_LRF +2463796917,7511575,2820240,307974614,False,Home,8,17,18.0,WALK_LRF +2463808393,7511610,2820275,307976049,True,work,1,8,9.0,WALK_LRF +2463808397,7511610,2820275,307976049,False,Home,8,1,18.0,WALK +2463817793,7511639,2820304,307977224,True,eatout,7,8,7.0,TNC_SHARED +2463817794,7511639,2820304,307977224,True,othdiscr,10,7,7.0,WALK_LOC +2463817797,7511639,2820304,307977224,False,Home,8,10,8.0,TNC_SHARED +2463817905,7511639,2820304,307977238,True,work,9,8,8.0,WALK +2463817906,7511639,2820304,307977238,True,work,9,9,9.0,WALK +2463817909,7511639,2820304,307977238,False,Home,8,9,16.0,WALK +2463823153,7511655,2820320,307977894,True,work,16,8,8.0,WALK +2463823157,7511655,2820320,307977894,False,Home,8,16,17.0,WALK +2463834305,7511689,2820354,307979288,True,work,18,8,8.0,WALK +2463834309,7511689,2820354,307979288,False,eatout,8,18,16.0,WALK +2463834310,7511689,2820354,307979288,False,Home,8,8,18.0,WALK +2463856281,7511756,2820421,307982035,True,work,7,8,13.0,WALK +2463856285,7511756,2820421,307982035,False,Home,8,7,23.0,WALK +2463865529,7511785,2820450,307983191,True,eatout,1,8,18.0,WALK_LRF +2463865533,7511785,2820450,307983191,False,Home,8,1,18.0,WALK_LRF +2463865681,7511785,2820450,307983210,True,othdiscr,11,8,18.0,WALK +2463865685,7511785,2820450,307983210,False,Home,8,11,23.0,WALK +2463865793,7511785,2820450,307983224,True,social,7,8,8.0,WALK +2463865794,7511785,2820450,307983224,True,work,5,7,8.0,WALK +2463865797,7511785,2820450,307983224,False,othmaint,7,5,11.0,WALK +2463865798,7511785,2820450,307983224,False,Home,8,7,15.0,WALK +2463881865,7511834,2820499,307985233,True,escort,2,8,8.0,WALK +2463881866,7511834,2820499,307985233,True,work,2,2,8.0,WALK +2463881869,7511834,2820499,307985233,False,Home,8,2,22.0,WALK_LRF +2463886521,7511849,2820514,307985815,True,eatout,7,8,12.0,WALK +2463886525,7511849,2820514,307985815,False,Home,8,7,15.0,WALK +2463886697,7511849,2820514,307985837,True,othmaint,4,8,17.0,WALK +2463886701,7511849,2820514,307985837,False,Home,8,4,18.0,WALK +2463886761,7511849,2820514,307985845,True,social,6,8,18.0,SHARED2FREE +2463886765,7511849,2820514,307985845,False,social,7,6,18.0,WALK +2463886766,7511849,2820514,307985845,False,social,8,7,18.0,WALK +2463886767,7511849,2820514,307985845,False,Home,8,8,18.0,WALK +2463894873,7511874,2820539,307986859,True,othdiscr,12,8,15.0,WALK_LOC +2463894877,7511874,2820539,307986859,False,Home,8,12,20.0,WALK_LOC +2463920329,7511952,2820617,307990041,True,escort,22,8,11.0,WALK +2463920333,7511952,2820617,307990041,False,Home,8,22,12.0,WALK +2463920569,7511952,2820617,307990071,True,work,4,8,12.0,WALK_LOC +2463920573,7511952,2820617,307990071,False,Home,8,4,21.0,WALK_LRF +2463932905,7511990,2820655,307991613,True,atwork,7,7,10.0,WALK +2463932909,7511990,2820655,307991613,False,Work,7,7,10.0,WALK +2463933033,7511990,2820655,307991629,True,escort,3,8,7.0,WALK +2463933034,7511990,2820655,307991629,True,escort,11,3,8.0,WALK_LOC +2463933035,7511990,2820655,307991629,True,shopping,8,11,8.0,WALK +2463933036,7511990,2820655,307991629,True,work,7,8,8.0,WALK +2463933037,7511990,2820655,307991629,False,shopping,8,7,18.0,WALK +2463933038,7511990,2820655,307991629,False,Home,8,8,18.0,WALK +2463940249,7512012,2820677,307992531,True,work,5,8,5.0,WALK +2463940253,7512012,2820677,307992531,False,Home,8,5,23.0,WALK +2463949761,7512041,2820706,307993720,True,work,9,8,8.0,WALK +2463949765,7512041,2820706,307993720,False,Home,8,9,18.0,WALK +2463950417,7512043,2820708,307993802,True,work,2,8,8.0,WALK +2463950421,7512043,2820708,307993802,False,Home,8,2,17.0,WALK +2463956977,7512063,2820728,307994622,True,work,15,8,7.0,TNC_SINGLE +2463956978,7512063,2820728,307994622,True,work,5,15,8.0,WALK_LOC +2463956981,7512063,2820728,307994622,False,Home,8,5,18.0,WALK_LOC +2463960537,7512074,2820739,307995067,True,shopping,13,8,18.0,WALK_LOC +2463960541,7512074,2820739,307995067,False,Home,8,13,21.0,WALK_LRF +2463960585,7512074,2820739,307995073,True,work,9,8,7.0,WALK +2463960589,7512074,2820739,307995073,False,Home,8,9,17.0,WALK_LOC +2463971785,7512109,2820774,307996473,True,atwork,8,2,10.0,WALK +2463971789,7512109,2820774,307996473,False,work,6,8,10.0,WALK +2463971790,7512109,2820774,307996473,False,Work,2,6,10.0,WALK +2463972065,7512109,2820774,307996508,True,work,2,8,8.0,WALK +2463972069,7512109,2820774,307996508,False,shopping,5,2,18.0,WALK +2463972070,7512109,2820774,307996508,False,shopping,8,5,19.0,WALK +2463972071,7512109,2820774,307996508,False,Home,8,8,19.0,WALK +2463987761,7512157,2820822,307998470,True,shopping,16,8,8.0,WALK_LOC +2463987765,7512157,2820822,307998470,False,Home,8,16,13.0,WALK +2463987785,7512157,2820822,307998473,True,social,22,8,17.0,WALK_LRF +2463987789,7512157,2820822,307998473,False,eatout,13,22,21.0,WALK_LRF +2463987790,7512157,2820822,307998473,False,Home,8,13,21.0,WALK_LRF +2463994697,7512178,2820843,307999337,True,work,7,8,7.0,WALK +2463994701,7512178,2820843,307999337,False,Home,8,7,20.0,WALK +2464020937,7512258,2820923,308002617,True,work,18,8,13.0,SHARED3FREE +2464020941,7512258,2820923,308002617,False,Home,8,18,20.0,WALK_LOC +2464041929,7512322,2820987,308005241,True,work,23,8,7.0,WALK_LOC +2464041933,7512322,2820987,308005241,False,Home,8,23,15.0,TNC_SINGLE +2464042585,7512324,2820989,308005323,True,work,9,8,14.0,WALK_LOC +2464042586,7512324,2820989,308005323,True,work,22,9,15.0,WALK_LRF +2464042589,7512324,2820989,308005323,False,work,22,22,21.0,WALK +2464042590,7512324,2820989,308005323,False,Home,8,22,21.0,WALK_LRF +2464064609,7512392,2821057,308008076,True,atwork,13,1,10.0,WALK +2464064613,7512392,2821057,308008076,False,Work,1,13,10.0,WALK +2464064889,7512392,2821057,308008111,True,work,1,8,6.0,WALK +2464064893,7512392,2821057,308008111,False,Home,8,1,18.0,WALK +2464097641,7512492,2821157,308012205,True,shopping,20,8,12.0,WALK_LOC +2464097645,7512492,2821157,308012205,False,Home,8,20,13.0,WALK_LOC +2464101953,7512505,2821170,308012744,True,work,4,8,20.0,WALK +2464101957,7512505,2821170,308012744,False,Home,8,4,23.0,WALK +2464104641,7512514,2821179,308013080,True,eatout,13,8,8.0,WALK +2464104645,7512514,2821179,308013080,False,Home,8,13,16.0,WALK +2464104857,7512514,2821179,308013107,True,shopping,6,8,17.0,WALK +2464104861,7512514,2821179,308013107,False,Home,8,6,19.0,WALK +2464104881,7512514,2821179,308013110,True,social,9,8,16.0,WALK +2464104885,7512514,2821179,308013110,False,Home,8,9,16.0,WALK_LOC +2464119665,7512559,2821224,308014958,True,work,13,8,7.0,WALK_LOC +2464119669,7512559,2821224,308014958,False,Home,8,13,19.0,WALK +2464131801,7512596,2821261,308016475,True,work,9,8,7.0,WALK +2464131805,7512596,2821261,308016475,False,Home,8,9,18.0,WALK +2464166569,7512702,2821367,308020821,True,work,12,8,7.0,WALK_LOC +2464166573,7512702,2821367,308020821,False,Home,8,12,17.0,WALK_LOC +2464170833,7512715,2821380,308021354,True,work,4,8,11.0,WALK +2464170837,7512715,2821380,308021354,False,Home,8,4,18.0,WALK +2464184673,7512758,2821423,308023084,True,eatout,4,8,12.0,WALK +2464184677,7512758,2821423,308023084,False,Home,8,4,12.0,WALK +2464184825,7512758,2821423,308023103,True,othdiscr,7,8,10.0,WALK +2464184829,7512758,2821423,308023103,False,Home,8,7,11.0,WALK +2464184849,7512758,2821423,308023106,True,othmaint,22,8,12.0,WALK_LRF +2464184853,7512758,2821423,308023106,False,escort,9,22,14.0,WALK_LRF +2464184854,7512758,2821423,308023106,False,Home,8,9,15.0,WALK +2464184889,7512758,2821423,308023111,True,shopping,7,8,16.0,WALK +2464184893,7512758,2821423,308023111,False,Home,8,7,18.0,WALK_LOC +2464184897,7512758,2821423,308023112,True,shopping,13,8,21.0,WALK_LRF +2464184901,7512758,2821423,308023112,False,Home,8,13,22.0,WALK_LRF +2464190905,7512777,2821442,308023863,True,eatout,3,8,15.0,WALK +2464190909,7512777,2821442,308023863,False,Home,8,3,16.0,WALK +2464191057,7512777,2821442,308023882,True,othdiscr,12,8,9.0,WALK +2464191061,7512777,2821442,308023882,False,Home,8,12,10.0,WALK +2464191121,7512777,2821442,308023890,True,shopping,11,8,10.0,WALK +2464191125,7512777,2821442,308023890,False,Home,8,11,13.0,WALK +2464206257,7512823,2821488,308025782,True,work,2,8,5.0,WALK +2464206261,7512823,2821488,308025782,False,Home,8,2,14.0,WALK +2464211505,7512839,2821504,308026438,True,work,8,8,6.0,WALK +2464211509,7512839,2821504,308026438,False,Home,8,8,16.0,WALK +2464212489,7512842,2821507,308026561,True,work,7,8,12.0,WALK_LOC +2464212493,7512842,2821507,308026561,False,Home,8,7,17.0,WALK +2464213777,7512846,2821511,308026722,True,social,9,8,7.0,WALK +2464213781,7512846,2821511,308026722,False,Home,8,9,20.0,WALK +2464214065,7512847,2821512,308026758,True,othmaint,22,8,17.0,WALK_LRF +2464214066,7512847,2821512,308026758,True,eatout,9,22,17.0,WALK_LRF +2464214067,7512847,2821512,308026758,True,univ,13,9,17.0,WALK_LRF +2464214069,7512847,2821512,308026758,False,Home,8,13,17.0,WALK_LRF +2464214129,7512847,2821512,308026766,True,work,12,8,7.0,WALK +2464214133,7512847,2821512,308026766,False,Home,8,12,14.0,TNC_SINGLE +2464226313,7512885,2821550,308028289,True,atwork,2,2,10.0,WALK +2464226317,7512885,2821550,308028289,False,othmaint,7,2,10.0,WALK +2464226318,7512885,2821550,308028289,False,Work,2,7,10.0,WALK +2464226593,7512885,2821550,308028324,True,work,2,8,6.0,WALK +2464226597,7512885,2821550,308028324,False,Home,8,2,15.0,WALK +2464234401,7512909,2821574,308029300,True,univ,12,8,20.0,WALK_LOC +2464234405,7512909,2821574,308029300,False,Home,8,12,21.0,WALK_LOC +2464234465,7512909,2821574,308029308,True,work,5,8,7.0,WALK +2464234469,7512909,2821574,308029308,False,Home,8,5,11.0,WALK +2464241417,7512931,2821596,308030177,True,eatout,5,8,14.0,WALK +2464241421,7512931,2821596,308030177,False,othmaint,9,5,20.0,WALK +2464241422,7512931,2821596,308030177,False,Home,8,9,20.0,WALK +2464241441,7512931,2821596,308030180,True,escort,5,8,8.0,WALK_LOC +2464241445,7512931,2821596,308030180,False,othdiscr,9,5,8.0,WALK_LOC +2464241446,7512931,2821596,308030180,False,shopping,13,9,8.0,WALK_LOC +2464241447,7512931,2821596,308030180,False,Home,8,13,9.0,WALK_LRF +2464246865,7512947,2821612,308030858,True,univ,13,8,8.0,WALK_LOC +2464246869,7512947,2821612,308030858,False,social,4,13,12.0,WALK_LOC +2464246870,7512947,2821612,308030858,False,Home,8,4,13.0,WALK_LRF +2464246929,7512947,2821612,308030866,True,work,7,8,6.0,WALK +2464246933,7512947,2821612,308030866,False,Home,8,7,6.0,WALK +2464264313,7513000,2821665,308033039,True,work,7,8,5.0,WALK +2464264317,7513000,2821665,308033039,False,Home,8,7,14.0,WALK +2464269561,7513016,2821681,308033695,True,othmaint,8,8,16.0,WALK +2464269562,7513016,2821681,308033695,True,work,20,8,16.0,WALK +2464269565,7513016,2821681,308033695,False,othmaint,10,20,21.0,WALK +2464269566,7513016,2821681,308033695,False,Home,8,10,21.0,WALK +2464272513,7513025,2821690,308034064,True,work,9,8,6.0,WALK +2464272517,7513025,2821690,308034064,False,Home,8,9,18.0,WALK_LOC +2464284649,7513062,2821727,308035581,True,work,16,8,12.0,WALK_LOC +2464284653,7513062,2821727,308035581,False,othmaint,2,16,21.0,WALK_LOC +2464284654,7513062,2821727,308035581,False,shopping,16,2,21.0,WALK_LOC +2464284655,7513062,2821727,308035581,False,Home,8,16,21.0,WALK +2464292849,7513087,2821752,308036606,True,work,10,8,5.0,WALK +2464292853,7513087,2821752,308036606,False,Home,8,10,15.0,WALK_LOC +2464301377,7513113,2821778,308037672,True,work,23,8,7.0,WALK_LRF +2464301381,7513113,2821778,308037672,False,Home,8,23,21.0,WALK_LRF +2464315153,7513155,2821820,308039394,True,work,7,8,13.0,WALK +2464315157,7513155,2821820,308039394,False,Home,8,7,23.0,WALK +2464328601,7513196,2821861,308041075,True,work,19,8,6.0,WALK +2464328605,7513196,2821861,308041075,False,Home,8,19,16.0,WALK_LOC +2464333257,7513211,2821876,308041657,True,eatout,9,8,16.0,WALK +2464333261,7513211,2821876,308041657,False,Home,8,9,19.0,WALK +2464333409,7513211,2821876,308041676,True,othdiscr,7,8,11.0,WALK_LOC +2464333413,7513211,2821876,308041676,False,Home,8,7,15.0,WALK_LOC +2464333473,7513211,2821876,308041684,True,shopping,11,8,16.0,TNC_SINGLE +2464333477,7513211,2821876,308041684,False,Home,8,11,16.0,WALK_LOC +2464343097,7513241,2821906,308042887,True,eatout,7,8,12.0,WALK +2464343101,7513241,2821906,308042887,False,Home,8,7,12.0,WALK +2464343249,7513241,2821906,308042906,True,othdiscr,21,8,18.0,WALK +2464343253,7513241,2821906,308042906,False,Home,8,21,20.0,WALK +2464343689,7513242,2821907,308042961,True,work,9,8,6.0,WALK_LOC +2464343693,7513242,2821907,308042961,False,Home,8,9,16.0,TNC_SINGLE +2464343697,7513242,2821907,308042962,True,work,9,8,16.0,WALK +2464343701,7513242,2821907,308042962,False,univ,12,9,16.0,WALK_LRF +2464343702,7513242,2821907,308042962,False,Home,8,12,16.0,WALK_LOC +2464357185,7513284,2821949,308044648,True,atwork,21,7,10.0,WALK +2464357189,7513284,2821949,308044648,False,Work,7,21,10.0,WALK +2464357465,7513284,2821949,308044683,True,work,7,8,7.0,WALK_LOC +2464357469,7513284,2821949,308044683,False,Home,8,7,15.0,WALK +2464377473,7513345,2822010,308047184,True,work,1,8,5.0,WALK_LRF +2464377477,7513345,2822010,308047184,False,Home,8,1,22.0,WALK_LRF +2464387969,7513377,2822042,308048496,True,work,9,8,8.0,WALK +2464387973,7513377,2822042,308048496,False,Home,8,9,18.0,WALK +2464406009,7513432,2822097,308050751,True,work,7,8,5.0,BIKE +2464406013,7513432,2822097,308050751,False,Home,8,7,15.0,BIKE +2464412617,7513453,2822118,308051577,True,atwork,16,16,7.0,WALK +2464412621,7513453,2822118,308051577,False,Work,16,16,7.0,WALK +2464412897,7513453,2822118,308051612,True,work,16,8,7.0,WALK_LOC +2464412901,7513453,2822118,308051612,False,Home,8,16,22.0,WALK_LOC +2464427001,7513496,2822161,308053375,True,work,2,8,6.0,WALK +2464427005,7513496,2822161,308053375,False,Home,8,2,18.0,WALK_LOC +2464443401,7513546,2822211,308055425,True,work,2,8,7.0,WALK +2464443405,7513546,2822211,308055425,False,Home,8,2,14.0,WALK +2464446025,7513554,2822219,308055753,True,work,9,8,5.0,WALK +2464446029,7513554,2822219,308055753,False,Home,8,9,16.0,WALK +2464446241,7513555,2822220,308055780,True,othdiscr,15,8,10.0,WALK +2464446245,7513555,2822220,308055780,False,Home,8,15,13.0,WALK_LOC +2464446305,7513555,2822220,308055788,True,shopping,16,8,16.0,WALK +2464446309,7513555,2822220,308055788,False,eatout,8,16,17.0,WALK +2464446310,7513555,2822220,308055788,False,Home,8,8,17.0,WALK +2464449633,7513565,2822230,308056204,True,univ,12,8,9.0,WALK +2464449634,7513565,2822230,308056204,True,work,25,12,9.0,WALK +2464449637,7513565,2822230,308056204,False,Home,8,25,18.0,WALK +2464459145,7513594,2822259,308057393,True,work,13,8,13.0,TNC_SINGLE +2464459149,7513594,2822259,308057393,False,work,12,13,21.0,TNC_SINGLE +2464459150,7513594,2822259,308057393,False,Home,8,12,21.0,WALK_LOC +2464473905,7513639,2822304,308059238,True,work,2,8,10.0,WALK +2464473909,7513639,2822304,308059238,False,Home,8,2,11.0,WALK +2464480793,7513660,2822325,308060099,True,shopping,24,8,6.0,BIKE +2464480794,7513660,2822325,308060099,True,othmaint,24,24,7.0,BIKE +2464480795,7513660,2822325,308060099,True,work,2,24,8.0,BIKE +2464480797,7513660,2822325,308060099,False,escort,16,2,16.0,BIKE +2464480798,7513660,2822325,308060099,False,Home,8,16,16.0,BIKE +2464508737,7513746,2822411,308063592,True,eatout,5,8,13.0,WALK +2464508741,7513746,2822411,308063592,False,Home,8,5,16.0,WALK +2464508761,7513746,2822411,308063595,True,escort,9,8,19.0,WALK +2464508765,7513746,2822411,308063595,False,Home,8,9,23.0,WALK +2464508953,7513746,2822411,308063619,True,shopping,13,8,11.0,WALK +2464508957,7513746,2822411,308063619,False,Home,8,13,13.0,WALK +2464533929,7513822,2822487,308066741,True,work,9,17,7.0,WALK_LRF +2464533933,7513822,2822487,308066741,False,Home,17,9,16.0,WALK_LRF +2464541033,7513844,2822509,308067629,True,othdiscr,22,17,21.0,WALK_LRF +2464541037,7513844,2822509,308067629,False,Home,17,22,22.0,WALK_LRF +2464541145,7513844,2822509,308067643,True,work,16,17,7.0,WALK +2464541149,7513844,2822509,308067643,False,Home,17,16,18.0,WALK +2464541425,7513845,2822510,308067678,True,shopping,8,17,9.0,WALK_LRF +2464541429,7513845,2822510,308067678,False,Home,17,8,10.0,WALK_LRF +2464541433,7513845,2822510,308067679,True,shopping,12,17,14.0,WALK_LOC +2464541437,7513845,2822510,308067679,False,Home,17,12,16.0,WALK_LRF +2464541473,7513845,2822510,308067684,True,work,4,17,17.0,WALK_LOC +2464541474,7513845,2822510,308067684,True,work,22,4,17.0,WALK_LRF +2464541477,7513845,2822510,308067684,False,Home,17,22,18.0,WALK_LRF +2464551249,7513875,2822540,308068906,True,univ,9,21,15.0,WALK_LOC +2464551253,7513875,2822540,308068906,False,Home,21,9,20.0,WALK +2464551313,7513875,2822540,308068914,True,escort,12,21,10.0,WALK +2464551314,7513875,2822540,308068914,True,work,12,12,10.0,TNC_SHARED +2464551317,7513875,2822540,308068914,False,eatout,5,12,14.0,WALK +2464551318,7513875,2822540,308068914,False,Home,21,5,15.0,WALK +2464571697,7513938,2822603,308071462,True,atwork,4,1,10.0,WALK +2464571701,7513938,2822603,308071462,False,Work,1,4,10.0,WALK +2464571977,7513938,2822603,308071497,True,work,1,21,8.0,WALK_LOC +2464571981,7513938,2822603,308071497,False,Home,21,1,18.0,WALK_LRF +2464575257,7513948,2822613,308071907,True,work,24,21,8.0,WALK +2464575261,7513948,2822613,308071907,False,Home,21,24,18.0,WALK +2464579849,7513962,2822627,308072481,True,work,21,21,10.0,WALK +2464579853,7513962,2822627,308072481,False,Home,21,21,17.0,WALK +2464583129,7513972,2822637,308072891,True,work,5,21,18.0,WALK +2464583130,7513972,2822637,308072891,True,work,7,5,19.0,WALK_LOC +2464583133,7513972,2822637,308072891,False,shopping,11,7,18.0,WALK +2464583134,7513972,2822637,308072891,False,Home,21,11,21.0,WALK +2464587721,7513986,2822651,308073465,True,work,9,21,8.0,WALK +2464587725,7513986,2822651,308073465,False,escort,11,9,14.0,WALK_LOC +2464587726,7513986,2822651,308073465,False,Home,21,11,14.0,WALK +2464611713,7514060,2822725,308076464,True,atwork,5,12,13.0,WALK +2464611717,7514060,2822725,308076464,False,Work,12,5,13.0,WALK +2464611881,7514060,2822725,308076485,True,othdiscr,9,21,21.0,SHARED2FREE +2464611885,7514060,2822725,308076485,False,Home,21,9,21.0,SHARED2FREE +2464611993,7514060,2822725,308076499,True,work,12,21,9.0,WALK +2464611997,7514060,2822725,308076499,False,Home,21,12,19.0,WALK +2467664249,7523366,2832031,308458031,True,othdiscr,9,3,6.0,WALK +2467664253,7523366,2832031,308458031,False,Home,3,9,16.0,WALK +2467679033,7523411,2832076,308459879,True,othmaint,1,7,13.0,WALK +2467679037,7523411,2832076,308459879,False,Home,7,1,13.0,BIKE +2467687057,7523436,2832101,308460882,True,eatout,7,7,17.0,WALK +2467687061,7523436,2832101,308460882,False,Home,7,7,17.0,WALK +2467687273,7523436,2832101,308460909,True,shopping,10,7,11.0,WALK_LOC +2467687277,7523436,2832101,308460909,False,Home,7,10,12.0,WALK_LOC +2467687713,7523438,2832103,308460964,True,eatout,5,7,11.0,WALK +2467687717,7523438,2832103,308460964,False,Home,7,5,13.0,WALK +2467705753,7523493,2832158,308463219,True,eatout,12,7,8.0,WALK +2467705757,7523493,2832158,308463219,False,Home,7,12,14.0,WALK +2467708705,7523502,2832167,308463588,True,eatout,12,7,13.0,WALK +2467708709,7523502,2832167,308463588,False,Home,7,12,17.0,WALK +2467713777,7523517,2832182,308464222,True,othdiscr,9,7,8.0,WALK +2467713781,7523517,2832182,308464222,False,Home,7,9,13.0,WALK +2467756745,7523648,2832313,308469593,True,othdiscr,9,10,7.0,SHARED2FREE +2467756749,7523648,2832313,308469593,False,Home,10,9,15.0,WALK +2467757425,7523650,2832315,308469678,True,othmaint,8,10,10.0,WALK +2467757429,7523650,2832315,308469678,False,Home,10,8,13.0,WALK +2467770873,7523691,2832356,308471359,True,othmaint,5,16,7.0,WALK +2467770877,7523691,2832356,308471359,False,Home,16,5,10.0,WALK +2467771241,7523692,2832357,308471405,True,shopping,11,16,20.0,WALK +2467771245,7523692,2832357,308471405,False,Home,16,11,23.0,WALK +2467800065,7523780,2832445,308475008,True,othmaint,2,17,9.0,WALK +2467800069,7523780,2832445,308475008,False,Home,17,2,10.0,WALK_LRF +2467826345,7523860,2832525,308478293,True,shopping,12,21,9.0,SHARED2FREE +2467826346,7523860,2832525,308478293,True,shopping,13,12,10.0,DRIVEALONEFREE +2467826349,7523860,2832525,308478293,False,shopping,16,13,10.0,WALK +2467826350,7523860,2832525,308478293,False,othdiscr,21,16,10.0,WALK +2467826351,7523860,2832525,308478293,False,shopping,6,21,10.0,SHARED2FREE +2467826352,7523860,2832525,308478293,False,Home,21,6,10.0,WALK +2467826353,7523860,2832525,308478294,True,shopping,16,21,11.0,TNC_SINGLE +2467826357,7523860,2832525,308478294,False,Home,21,16,11.0,TNC_SHARED +2467826369,7523860,2832525,308478296,True,social,11,21,11.0,WALK +2467826373,7523860,2832525,308478296,False,Home,21,11,21.0,WALK +2467831265,7523875,2832540,308478908,True,shopping,23,21,7.0,WALK +2467831269,7523875,2832540,308478908,False,shopping,25,23,14.0,WALK +2467831270,7523875,2832540,308478908,False,Home,21,25,15.0,WALK +2472943737,7539462,2848127,309117967,True,othdiscr,12,3,11.0,WALK_LOC +2472943741,7539462,2848127,309117967,False,Home,3,12,15.0,WALK_LOC +2472945097,7539466,2848131,309118137,True,univ,13,3,8.0,WALK_LOC +2472945101,7539466,2848131,309118137,False,Home,3,13,15.0,WALK_LOC +2472945113,7539466,2848131,309118139,True,shopping,8,3,18.0,WALK_LOC +2472945117,7539466,2848131,309118139,False,shopping,5,8,20.0,WALK_LOC +2472945118,7539466,2848131,309118139,False,Home,3,5,20.0,WALK +2472966393,7539531,2848196,309120799,True,othmaint,22,15,11.0,BIKE +2472966397,7539531,2848196,309120799,False,Home,15,22,13.0,WALK +2472966697,7539532,2848197,309120837,True,othdiscr,6,15,9.0,WALK +2472966701,7539532,2848197,309120837,False,Home,15,6,12.0,WALK +2473003433,7539644,2848309,309125429,True,othdiscr,18,18,14.0,WALK +2473003437,7539644,2848309,309125429,False,Home,18,18,14.0,WALK +2473019025,7539692,2848357,309127378,True,eatout,16,18,7.0,WALK +2473019029,7539692,2848357,309127378,False,Home,18,16,17.0,WALK +2473024473,7539708,2848373,309128059,True,univ,12,18,10.0,WALK_LOC +2473024477,7539708,2848373,309128059,False,Home,18,12,21.0,WALK_LOC +2473035313,7539741,2848406,309129414,True,shopping,11,18,9.0,WALK +2473035317,7539741,2848406,309129414,False,Home,18,11,15.0,WALK +2473036257,7539744,2848409,309129532,True,othmaint,9,18,10.0,BIKE +2473036261,7539744,2848409,309129532,False,Home,18,9,14.0,BIKE +2473042465,7539763,2848428,309130308,True,othdiscr,25,18,11.0,WALK_LOC +2473042469,7539763,2848428,309130308,False,shopping,8,25,14.0,WALK_LOC +2473042470,7539763,2848428,309130308,False,Home,18,8,14.0,WALK_LOC +2473047081,7539777,2848442,309130885,True,othmaint,13,18,7.0,WALK +2473047085,7539777,2848442,309130885,False,eatout,7,13,10.0,WALK +2473047086,7539777,2848442,309130885,False,eatout,18,7,10.0,WALK +2473047087,7539777,2848442,309130885,False,Home,18,18,10.0,WALK +2473047089,7539777,2848442,309130886,True,othmaint,1,18,10.0,BIKE +2473047093,7539777,2848442,309130886,False,Home,18,1,10.0,WALK +2473068905,7539844,2848509,309133613,True,escort,14,18,10.0,SHARED3FREE +2473068909,7539844,2848509,309133613,False,Home,18,14,10.0,SHARED3FREE +2473068913,7539844,2848509,309133614,True,escort,20,18,15.0,SHARED3FREE +2473068917,7539844,2848509,309133614,False,Home,18,20,16.0,SHARED3FREE +2473070409,7539848,2848513,309133801,True,shopping,11,18,12.0,TNC_SINGLE +2473070413,7539848,2848513,309133801,False,Home,18,11,15.0,WALK_LOC +2473075001,7539862,2848527,309134375,True,social,14,19,17.0,WALK_LOC +2473075002,7539862,2848527,309134375,True,shopping,5,14,18.0,WALK_LOC +2473075005,7539862,2848527,309134375,False,Home,19,5,20.0,TNC_SINGLE +2473093329,7539918,2848583,309136666,True,othmaint,4,19,7.0,WALK_LOC +2473093333,7539918,2848583,309136666,False,eatout,8,4,13.0,WALK +2473093334,7539918,2848583,309136666,False,Home,19,8,13.0,WALK_LOC +2473099865,7539938,2848603,309137483,True,shopping,14,19,12.0,DRIVEALONEFREE +2473099866,7539938,2848603,309137483,True,othdiscr,16,14,14.0,DRIVEALONEFREE +2473099869,7539938,2848603,309137483,False,Home,19,16,14.0,TNC_SHARED +2473107761,7539962,2848627,309138470,True,othmaint,13,19,7.0,TNC_SINGLE +2473107765,7539962,2848627,309138470,False,Home,19,13,20.0,TNC_SINGLE +2473132401,7540037,2848702,309141550,True,shopping,5,19,12.0,TNC_SINGLE +2473132405,7540037,2848702,309141550,False,Home,19,5,16.0,TNC_SINGLE +2477971081,7554789,2863454,309746385,True,social,1,2,12.0,WALK +2477971085,7554789,2863454,309746385,False,Home,2,1,23.0,WALK +2477990409,7554848,2863513,309748801,True,escort,7,20,18.0,WALK +2477990410,7554848,2863513,309748801,True,shopping,6,7,18.0,WALK +2477990413,7554848,2863513,309748801,False,Home,20,6,18.0,WALK diff --git a/activitysim/examples/example_estimation/scripts/extract_survey_data.py b/activitysim/examples/example_estimation/scripts/extract_survey_data.py index 2dcc812a5a..cea3d8c251 100644 --- a/activitysim/examples/example_estimation/scripts/extract_survey_data.py +++ b/activitysim/examples/example_estimation/scripts/extract_survey_data.py @@ -48,11 +48,11 @@ joint_tour_participants = pd.read_csv(os.path.join(input_dir, inputs['joint_tour_participants'])) households = households[ - ['household_id', 'TAZ', 'income', 'hhsize', 'HHT', 'auto_ownership', 'num_workers'] + ['household_id', 'home_zone_id', 'income', 'hhsize', 'HHT', 'auto_ownership', 'num_workers'] ] persons = persons[ ['person_id', 'household_id', 'age', 'PNUM', 'sex', - 'pemploy', 'pstudent', 'ptype', 'school_taz', 'workplace_taz', 'free_parking_at_work'] + 'pemploy', 'pstudent', 'ptype', 'school_zone_id', 'workplace_zone_id', 'free_parking_at_work'] ] tours = tours[ ['tour_id', 'person_id', 'household_id', 'tour_type', 'tour_category', @@ -67,8 +67,10 @@ tours.to_csv(os.path.join(output_dir, surveys['tours']), index=False) joint_tour_participants.to_csv(os.path.join(output_dir, surveys['joint_tour_participants']), index=False) -# household_id,TAZ,income,PERSONS,HHT,VEHICL,workers -raw_households = households[['household_id', 'TAZ', 'income', 'hhsize', 'HHT', 'auto_ownership', 'num_workers']] +# household_id,home_zone_id,income,PERSONS,HHT,VEHICL,workers +raw_households = households[ + ['household_id', 'home_zone_id', 'income', 'hhsize', 'HHT', 'auto_ownership', 'num_workers'] +] raw_households = raw_households.rename({'hhsize': 'PERSONS', 'num_workers': 'workers', 'auto_ownership': 'VEHICL'}) raw_households.to_csv(os.path.join(data_dir, 'households.csv'), index=False) diff --git a/activitysim/examples/example_estimation/scripts/infer.py b/activitysim/examples/example_estimation/scripts/infer.py index 6bc78ee57d..96c3eaca29 100644 --- a/activitysim/examples/example_estimation/scripts/infer.py +++ b/activitysim/examples/example_estimation/scripts/infer.py @@ -4,13 +4,13 @@ import sys import os import logging +import yaml import numpy as np import pandas as pd from activitysim.abm.models.util import tour_frequency as tf from activitysim.core.util import reindex -from activitysim.abm.tables import constants logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) @@ -20,6 +20,8 @@ ch.setFormatter(logging.Formatter('%(levelname)s - %(message)s')) logger.addHandler(ch) +CONSTANTS = {} + SURVEY_TOUR_ID = 'survey_tour_id' SURVEY_PARENT_TOUR_ID = 'survey_parent_tour_id' SURVEY_PARTICIPANT_ID = 'survey_participant_id' @@ -391,7 +393,7 @@ def set_tour_index(tours, parent_tour_num_col, is_joint): reindex(persons.mandatory_tour_frequency, mandatory_tours.person_id) is_worker = \ reindex(persons.pemploy, mandatory_tours.person_id).\ - isin([constants.PEMPLOY_FULL, constants.PEMPLOY_PART]) + isin([CONSTANTS['PEMPLOY_FULL'], CONSTANTS['PEMPLOY_PART']]) work_and_school_and_worker = (mandatory_tour_frequency == 'work_and_school') & is_worker # calculate tour_num for work tours (required to set_tour_index for atwork subtours) @@ -602,6 +604,9 @@ def infer(configs_dir, input_dir, output_dir): data_dir = args[0] configs_dir = args[1] +with open(os.path.join(configs_dir, 'constants.yaml')) as stream: + CONSTANTS = yaml.load(stream, Loader=yaml.SafeLoader) + input_dir = os.path.join(data_dir, 'survey_data/') output_dir = input_dir diff --git a/activitysim/examples/example_manifest.yaml b/activitysim/examples/example_manifest.yaml index a806ec1d32..eda2c0cbd6 100644 --- a/activitysim/examples/example_manifest.yaml +++ b/activitysim/examples/example_manifest.yaml @@ -8,6 +8,7 @@ - example_mtc/configs_mp - example_mtc/output - example_mtc/README.MD + - name: example_test description: data and configs for the ActivitySim test system # activitysim create -e example_test -d test_example_test @@ -17,6 +18,7 @@ - example_mtc/configs - example_mtc/configs_mp - example_mtc/output + - name: example_mtc_full description: Full 1475-zone dataset for the MTC region with 2.8M households and 7.5M persons # activitysim create -e example_mtc_full -d test_example_mtc_full @@ -34,6 +36,7 @@ data/persons.csv - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/mtc_data_full/land_use.csv data/land_use.csv + - name: example_mtc_sf description: San Francisco MTC dataset with 190 zones, 400k households and 900k persons # activitysim create -e example_mtc_sf -d test_example_mtc_sf @@ -51,6 +54,7 @@ data/persons.csv - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/mtc_data_sf/land_use.csv data/land_use.csv + - name: example_estimation description: Estimation mode 25 zone example # activitysim create -e example_estimation -d test_example_estimation @@ -63,6 +67,7 @@ - example_mtc/data/skims.omx data_test/skims.omx - example_mtc/output + - name: example_estimation_sf description: Estimation mode San Francisco MTC dataset with 190 zones, 2k households and 8k persons # activitysim create -e example_estimation_sf -d test_example_estimation_sf @@ -74,4 +79,228 @@ - example_estimation/data_sf - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/mtc_data_sf/skims.omx data_sf/skims.omx - - example_mtc/output \ No newline at end of file + - example_mtc/output + +- name: example_2_zone + description: 2 zone system test example based on TM1 + # activitysim create -e example_2_zone -d test_example_2_zone + # activitysim run -c configs_local -c configs_2_zone -c configs -d data_2 -o output_2 + include: + - example_mtc/configs + - example_multiple_zone/configs_2_zone + - example_multiple_zone/configs_local + - example_multiple_zone/data_2 + - example_multiple_zone/output_2 + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_2/households.csv + data_2/households.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_2/land_use.csv + data_2/land_use.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_2/maz.csv + data_2/maz.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_2/maz_to_maz_bike.csv + data_2/maz_to_maz_bike.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_2/maz_to_maz_walk.csv + data_2/maz_to_maz_walk.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_2/persons.csv + data_2/persons.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_2/taz.csv + data_2/taz.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_2/taz_skims.omx + data_2/taz_skims.omx + +- name: example_3_zone + description: 3 zone system test example based on TM1 + # activitysim create -e example_3_zone -d test_example_3_zone + # activitysim run -c configs_local -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_static.yaml + # activitysim run -c configs_local -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_mp.yaml + include: + - example_mtc/configs + - example_multiple_zone/configs_3_zone + - example_multiple_zone/configs_local + - example_multiple_zone/data_3 + - example_multiple_zone/output_3 + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/households.csv + data_3/households.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/land_use.csv + data_3/land_use.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/maz.csv + data_3/maz.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/maz_to_maz_bike.csv + data_3/maz_to_maz_bike.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/maz_to_maz_walk.csv + data_3/maz_to_maz_walk.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/maz_to_tap_bike.csv + data_3/maz_to_tap_bike.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/maz_to_tap_drive.csv + data_3/maz_to_tap_drive.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/maz_to_tap_walk.csv + data_3/maz_to_tap_walk.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/persons.csv + data_3/persons.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/tap.csv + data_3/tap.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/tap_skims.omx + data_3/tap_skims.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/taz.csv + data_3/taz.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3/taz_skims.omx + data_3/taz_skims.omx + +- name: example_3_marin + description: Marin TM2 work tour mode choice cropped to one county for testing + # activitysim create -e example_3_marin -d test_example_3_marin + # activitysim run -c configs_3_zone_marin -d data_3_marin -o output_3_marin + # activitysim run -c configs_3_zone_marin -d data_3_marin -o output_3_marin -s settings_mp.yaml + include: + - example_multiple_zone/configs_3_zone_marin + - example_multiple_zone/data_3_marin + - example_multiple_zone/output_3_marin + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/HWYSKMAM_taz_rename.omx + data_3_marin/HWYSKMAM_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/HWYSKMEA_taz_rename.omx + data_3_marin/HWYSKMEA_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/HWYSKMEV_taz_rename.omx + data_3_marin/HWYSKMEV_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/HWYSKMMD_taz_rename.omx + data_3_marin/HWYSKMMD_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/HWYSKMPM_taz_rename.omx + data_3_marin/HWYSKMPM_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/access.csv + data_3_marin/access.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/households_asim.csv + data_3_marin/households_asim.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/maz_data_asim.csv + data_3_marin/maz_data_asim.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/maz_maz_bike.csv + data_3_marin/maz_maz_bike.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/maz_maz_walk.csv + data_3_marin/maz_maz_walk.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/maz_tap_walk.csv + data_3_marin/maz_tap_walk.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/maz_taz.csv + data_3_marin/maz_taz.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/maz_taz_tap_drive.csv + data_3_marin/maz_taz_tap_drive.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/persons_asim.csv + data_3_marin/persons_asim.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/tap_data.csv + data_3_marin/tap_data.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/tap_lines.csv + data_3_marin/tap_lines.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_AM_SET1_rename.omx + data_3_marin/transit_skims_AM_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_AM_SET2_rename.omx + data_3_marin/transit_skims_AM_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_AM_SET3_rename.omx + data_3_marin/transit_skims_AM_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_EA_SET1_rename.omx + data_3_marin/transit_skims_EA_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_EA_SET2_rename.omx + data_3_marin/transit_skims_EA_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_EA_SET3_rename.omx + data_3_marin/transit_skims_EA_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_EV_SET1_rename.omx + data_3_marin/transit_skims_EV_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_EV_SET2_rename.omx + data_3_marin/transit_skims_EV_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_EV_SET3_rename.omx + data_3_marin/transit_skims_EV_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_MD_SET1_rename.omx + data_3_marin/transit_skims_MD_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_MD_SET2_rename.omx + data_3_marin/transit_skims_MD_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_MD_SET3_rename.omx + data_3_marin/transit_skims_MD_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_PM_SET1_rename.omx + data_3_marin/transit_skims_PM_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_PM_SET2_rename.omx + data_3_marin/transit_skims_PM_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/transit_skims_PM_SET3_rename.omx + data_3_marin/transit_skims_PM_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin/work_tours.csv + data_3_marin/work_tours.csv + +- name: example_3_marin_full + description: Marin TM2 work tour mode choice for the 9 county MTC region + # activitysim create -e example_3_marin_full -d test_example_3_marin_full + # activitysim run -c configs_3_zone_marin_full -c configs_3_zone_marin -d data_3_marin_full -o output_3_marin_full -s settings_mp.yaml + include: + - example_multiple_zone/configs_3_zone_marin + - example_multiple_zone/configs_3_zone_marin_full + - example_multiple_zone/data_3_marin_full + - example_multiple_zone/output_3_marin_full + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/HWYSKMAM_taz_rename.omx + data_3_marin_full/HWYSKMAM_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/HWYSKMEA_taz_rename.omx + data_3_marin_full/HWYSKMEA_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/HWYSKMEV_taz_rename.omx + data_3_marin_full/HWYSKMEV_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/HWYSKMMD_taz_rename.omx + data_3_marin_full/HWYSKMMD_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/HWYSKMPM_taz_rename.omx + data_3_marin_full/HWYSKMPM_taz_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/access.csv + data_3_marin_full/access.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/households_asim.csv + data_3_marin_full/households_asim.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/maz_data_asim.csv + data_3_marin_full/maz_data_asim.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/maz_maz_bike.csv + data_3_marin_full/maz_maz_bike.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/maz_maz_walk.csv + data_3_marin_full/maz_maz_walk.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/maz_tap_walk.csv + data_3_marin_full/maz_tap_walk.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/maz_taz.csv + data_3_marin_full/maz_taz.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/maz_taz_tap_drive.csv + data_3_marin_full/maz_taz_tap_drive.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/persons_asim.csv + data_3_marin_full/persons_asim.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/tap_data.csv + data_3_marin_full/tap_data.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/tap_lines.csv + data_3_marin_full/tap_lines.csv + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_AM_SET1_rename.omx + data_3_marin_full/transit_skims_AM_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_AM_SET2_rename.omx + data_3_marin_full/transit_skims_AM_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_AM_SET3_rename.omx + data_3_marin_full/transit_skims_AM_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_EA_SET1_rename.omx + data_3_marin_full/transit_skims_EA_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_EA_SET2_rename.omx + data_3_marin_full/transit_skims_EA_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_EA_SET3_rename.omx + data_3_marin_full/transit_skims_EA_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_EV_SET1_rename.omx + data_3_marin_full/transit_skims_EV_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_EV_SET2_rename.omx + data_3_marin_full/transit_skims_EV_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_EV_SET3_rename.omx + data_3_marin_full/transit_skims_EV_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_MD_SET1_rename.omx + data_3_marin_full/transit_skims_MD_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_MD_SET2_rename.omx + data_3_marin_full/transit_skims_MD_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_MD_SET3_rename.omx + data_3_marin_full/transit_skims_MD_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_PM_SET1_rename.omx + data_3_marin_full/transit_skims_PM_SET1_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_PM_SET2_rename.omx + data_3_marin_full/transit_skims_PM_SET2_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/transit_skims_PM_SET3_rename.omx + data_3_marin_full/transit_skims_PM_SET3_rename.omx + - https://media.githubusercontent.com/media/RSGInc/activitysim_resources/master/data_3_marin_full/work_tours.csv + data_3_marin_full/work_tours.csv + +- name: example_mtc_arc_extensions + description: 25-zone example for the MTC region with ARC extensions + # activitysim create -e example_mtc_arc_extensions -d test_example_mtc_arc_extensions + # activitysim run -c configs_arc -c configs -o output -d data + include: + - example_mtc/data + - example_mtc/configs + - example_mtc/configs_arc + - example_mtc/output + - example_mtc/README.MD \ No newline at end of file diff --git a/activitysim/examples/example_mtc/.gitignore b/activitysim/examples/example_mtc/.gitignore index b865095f38..487afbcc25 100644 --- a/activitysim/examples/example_mtc/.gitignore +++ b/activitysim/examples/example_mtc/.gitignore @@ -1,3 +1,4 @@ override_configs/ simulation_mp.py -notes.txt \ No newline at end of file +notes.txt +output_*/ diff --git a/activitysim/examples/example_mtc/configs/annotate_households.csv b/activitysim/examples/example_mtc/configs/annotate_households.csv index d61dbca662..e7b590be17 100644 --- a/activitysim/examples/example_mtc/configs/annotate_households.csv +++ b/activitysim/examples/example_mtc/configs/annotate_households.csv @@ -3,7 +3,7 @@ Description,Target,Expression ,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" #,,FIXME households.income can be negative - so we clip? income_in_thousands,income_in_thousands,(households.income / 1000).clip(lower=0) -income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)" +income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[INCOME_SEGMENT_LOW, INCOME_SEGMENT_MED, INCOME_SEGMENT_HIGH, INCOME_SEGMENT_VERYHIGH]).astype(int)" #,, ,_MIN_VOT,setting('min_value_of_time') ,_MAX_VOT,setting('max_value_of_time') @@ -25,10 +25,7 @@ num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', perso num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" -non_family,non_family,households.HHT.isin(constants.HHT_NONFAMILY) -family,family,households.HHT.isin(constants.HHT_FAMILY) -home_is_urban,home_is_urban,"reindex(land_use.area_type, households.TAZ) < setting('urban_threshold')" -home_is_rural,home_is_rural,"reindex(land_use.area_type, households.TAZ) > setting('rural_threshold')" -#,, default for work and school location logsums before auto_ownership model is run -#,auto_ownership,households.VEHICL -#home_taz,home_taz,households.TAZ +non_family,non_family,households.HHT.isin(HHT_NONFAMILY) +family,family,households.HHT.isin(HHT_FAMILY) +home_is_urban,home_is_urban,"reindex(land_use.area_type, households.home_zone_id) < setting('urban_threshold')" +home_is_rural,home_is_rural,"reindex(land_use.area_type, households.home_zone_id) > setting('urban_threshold')" \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/annotate_households_cdap.csv b/activitysim/examples/example_mtc/configs/annotate_households_cdap.csv index 93f1db2ec0..44b4fdcbfd 100644 --- a/activitysim/examples/example_mtc/configs/annotate_households_cdap.csv +++ b/activitysim/examples/example_mtc/configs/annotate_households_cdap.csv @@ -3,7 +3,7 @@ Description,Target,Expression num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == constants.PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0) diff --git a/activitysim/examples/example_mtc/configs/annotate_households_workplace.csv b/activitysim/examples/example_mtc/configs/annotate_households_workplace.csv index 1b53e91daf..a28f92732b 100644 --- a/activitysim/examples/example_mtc/configs/annotate_households_workplace.csv +++ b/activitysim/examples/example_mtc/configs/annotate_households_workplace.csv @@ -2,4 +2,4 @@ Description,Target,Expression #,, annotate households table after workplace_location model has run #,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio ,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0) -#,,handle persons with no locatcion +#,,handle persons with no location diff --git a/activitysim/examples/example_mtc/configs/annotate_landuse.csv b/activitysim/examples/example_mtc/configs/annotate_landuse.csv index 229833a503..91e0927747 100644 --- a/activitysim/examples/example_mtc/configs/annotate_landuse.csv +++ b/activitysim/examples/example_mtc/configs/annotate_landuse.csv @@ -3,3 +3,5 @@ Description,Target,Expression household_density,household_density,land_use.TOTHH / (land_use.RESACRE + land_use.CIACRE) employment_density,employment_density,land_use.TOTEMP / (land_use.RESACRE + land_use.CIACRE) density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) +,is_cbd,land_use.area_type == 1 + diff --git a/activitysim/examples/example_mtc/configs/annotate_persons.csv b/activitysim/examples/example_mtc/configs/annotate_persons.csv index a54b944818..7a5f497c91 100644 --- a/activitysim/examples/example_mtc/configs/annotate_persons.csv +++ b/activitysim/examples/example_mtc/configs/annotate_persons.csv @@ -5,34 +5,34 @@ age_16_p,age_16_p,persons.age >= 16 adult,adult,persons.age >= 18 male,male,persons.sex == 1 female,female,persons.sex == 2 -presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == constants.PTYPE_NONWORK)" -presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == constants.PTYPE_RETIRED)" -presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == constants.PTYPE_PRESCHOOL)" -presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == constants.PTYPE_DRIVING)" -presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == constants.PTYPE_SCHOOL)" -presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==constants.PEMPLOY_FULL)" -presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==constants.PEMPLOY_PART)" -presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == constants.PTYPE_UNIVERSITY)" -student_is_employed,student_is_employed,"(persons.ptype.isin([constants.PTYPE_UNIVERSITY, constants.PTYPE_DRIVING]) & persons.pemploy.isin([constants.PEMPLOY_FULL, constants.PEMPLOY_PART]))" -nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([constants.PTYPE_FULL, constants.PTYPE_PART, constants.PTYPE_NONWORK, constants.PTYPE_RETIRED]) & persons.pstudent.isin([constants.PSTUDENT_GRADE_OR_HIGH, constants.PSTUDENT_UNIVERSITY]))" +presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)" +presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)" +presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)" +presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)" +presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)" +presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)" +presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)" +presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)" +student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))" +nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))" #,, #,, FIXME - if person is a university student but has school age student category value then reset student category value -,pstudent,"persons.pstudent.where(persons.ptype!=constants.PTYPE_UNIVERSITY, constants.PSTUDENT_UNIVERSITY)" +,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)" #,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student -,pstudent,"pstudent.where(persons.ptype!=constants.PTYPE_FULL, constants.PSTUDENT_NOT)" +,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)" #,, FIXME if student category is non-student and employment is student then reset student category value to student -,pstudent,"pstudent.where((persons.ptype!=constants.PTYPE_DRIVING) & (persons.ptype!=constants.PTYPE_SCHOOL), constants.PSTUDENT_GRADE_OR_HIGH)" +,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)" #,, -is_student,is_student,"pstudent.isin([constants.PSTUDENT_GRADE_OR_HIGH, constants.PSTUDENT_UNIVERSITY])" -preschool age can go to preschool,is_student,"is_student.where(persons.age > constants.GRADE_SCHOOL_MIN_AGE, True)" -preschool age can go to preschool,pstudent,"pstudent.where(persons.age > constants.GRADE_SCHOOL_MIN_AGE, constants.PSTUDENT_GRADE_OR_HIGH)" -is_gradeschool,is_gradeschool,(pstudent == constants.PSTUDENT_GRADE_OR_HIGH) & (persons.age <= constants.GRADE_SCHOOL_MAX_AGE) -is_highschool,is_highschool,(pstudent == constants.PSTUDENT_GRADE_OR_HIGH) & (persons.age > constants.GRADE_SCHOOL_MAX_AGE) -is_university,is_university,pstudent == constants.PSTUDENT_UNIVERSITY -school_segment gradeschool,school_segment,"np.where(is_gradeschool, constants.SCHOOL_SEGMENT_GRADE, constants.SCHOOL_SEGMENT_NONE)" -school_segment highschool,school_segment,"np.where(is_highschool, constants.SCHOOL_SEGMENT_HIGH, school_segment)" -school_segment university,school_segment,"np.where(is_university, constants.SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" +is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])" +preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)" +preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)" +is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) +is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) +is_university,is_university,pstudent == PSTUDENT_UNIVERSITY +#school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)" +#school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +#school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" #,, -is_worker,is_worker,"persons.pemploy.isin([constants.PEMPLOY_FULL, constants.PEMPLOY_PART])" +is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])" #,, -home_taz,home_taz,"reindex(households.TAZ, persons.household_id)" +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_cdap.csv b/activitysim/examples/example_mtc/configs/annotate_persons_cdap.csv index d66166bc68..2ad5e56a6b 100644 --- a/activitysim/examples/example_mtc/configs/annotate_persons_cdap.csv +++ b/activitysim/examples/example_mtc/configs/annotate_persons_cdap.csv @@ -1,6 +1,6 @@ Description,Target,Expression #,, annotate persons table after cdap model has run -travel_active,travel_active,persons.cdap_activity != constants.CDAP_ACTIVITY_HOME -under16_not_at_school,under16_not_at_school,"persons.ptype.isin([constants.PTYPE_SCHOOL, constants.PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" -has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == constants.PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" -has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == constants.PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" +travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME +under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" +has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" +has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_school.csv b/activitysim/examples/example_mtc/configs/annotate_persons_school.csv index 3b33e893f5..553b124c3b 100644 --- a/activitysim/examples/example_mtc/configs/annotate_persons_school.csv +++ b/activitysim/examples/example_mtc/configs/annotate_persons_school.csv @@ -1,9 +1,7 @@ Description,Target,Expression #,, annotate persons table after school_location model has run -local scalar distance skim,_DISTANCE_SKIM,"skim_dict.get('DIST')" -,distance_to_school,"np.where(persons.school_taz>=0,_DISTANCE_SKIM.get(persons.home_taz, persons.school_taz),np.nan)" +,distance_to_school,"np.where(persons.school_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, 'DIST'),np.nan)" #,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD -local scalar distance skim,_SOVMD_SKIM,"skim_dict.get(('SOV_TIME', 'MD'))" -temp auto_time_to_school,_auto_time_to_school,"_SOVMD_SKIM.get(persons.home_taz, persons.school_taz)" -temp auto_time_return,_auto_time_return,"_SOVMD_SKIM.get(persons.school_taz, persons.home_taz)" -free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_taz>=0,_auto_time_to_school + _auto_time_return,0)" +temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_TIME', 'MD'))" +temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" +free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id>=0,_auto_time_to_school + _auto_time_return,0)" diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_workplace.csv b/activitysim/examples/example_mtc/configs/annotate_persons_workplace.csv index e2b85eccba..88ef9487f3 100644 --- a/activitysim/examples/example_mtc/configs/annotate_persons_workplace.csv +++ b/activitysim/examples/example_mtc/configs/annotate_persons_workplace.csv @@ -1,38 +1,29 @@ Description,Target,Expression #,, annotate persons table after workplace_location model has run -local scalar distance skim,_DISTANCE_SKIM,skim_dict.get('DIST') -,distance_to_work,"np.where(persons.workplace_taz>=0,_DISTANCE_SKIM.get(persons.home_taz, persons.workplace_taz),np.nan)" -workplace_in_cbd,workplace_in_cbd,"reindex(land_use.area_type, persons.workplace_taz) < setting('cbd_threshold')" -work_taz_area_type,work_taz_area_type,"reindex(land_use.area_type, persons.workplace_taz)" +,distance_to_work,"np.where(persons.workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DIST'),np.nan)" +workplace_in_cbd,workplace_in_cbd,"reindex(land_use.area_type, persons.workplace_zone_id) < setting('cbd_threshold')" +work_zone_area_type,work_zone_area_type,"reindex(land_use.area_type, persons.workplace_zone_id)" #,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD -local scalar distance skim,_SOVMD_SKIM,"skim_dict.get(('SOV_TIME', 'MD'))" #,,roundtrip_auto_time_to_work -,_auto_time_home_to_work,"_SOVMD_SKIM.get(persons.home_taz, persons.workplace_taz)" -,_auto_time_work_to_home,"_SOVMD_SKIM.get(persons.workplace_taz, persons.home_taz)" -,roundtrip_auto_time_to_work,"np.where(persons.workplace_taz>=0,_auto_time_home_to_work + _auto_time_work_to_home,0)" +,_auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_TIME', 'MD'))" +,_auto_time_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" +,roundtrip_auto_time_to_work,"np.where(persons.workplace_zone_id>=0,_auto_time_home_to_work + _auto_time_work_to_home,0)" #,,_roundtrip_walk_time_to_work ,_MAX_TIME_TO_WORK,999 ,_WALK_SPEED_MPH,3 -,_DISTWALK_SKIM,skim_dict.get(('DISTWALK')) -,_walk_time_home_to_work,"60 * _DISTWALK_SKIM.get(persons.home_taz, persons.workplace_taz)/_WALK_SPEED_MPH" -,_walk_time_work_to_home,"60 * _DISTWALK_SKIM.get(persons.workplace_taz, persons.home_taz)/_WALK_SPEED_MPH" +,_walk_time_home_to_work,"60 * skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" +,_walk_time_work_to_home,"60 * skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" ,_work_walk_available,(_walk_time_home_to_work > 0) & (_walk_time_work_to_home > 0) ,_roundtrip_walk_time_to_work,"np.where(_work_walk_available, _walk_time_home_to_work + _walk_time_work_to_home, _MAX_TIME_TO_WORK)" #,,_roundtrip_transit_time_to_work -,_IVT_SKIM,"skim_dict.get(('WLK_TRN_WLK_IVT', 'MD'))" -,_transit_ivt_home_to_work,"_IVT_SKIM.get(persons.home_taz, persons.workplace_taz)/100" -,_transit_ivt_work_to_home,"_IVT_SKIM.get(persons.workplace_taz, persons.home_taz)/100" +,_transit_ivt_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_IVT', 'MD'))/100" +,_transit_ivt_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_IVT', 'MD'))/100" ,_work_transit_available,(_transit_ivt_home_to_work > 0) & (_transit_ivt_work_to_home > 0) -,_IWAIT_SKIM,"skim_dict.get(('WLK_TRN_WLK_IWAIT', 'MD'))" -,_transit_iwait,"_IWAIT_SKIM.get(persons.home_taz, persons.workplace_taz)/100 + _IWAIT_SKIM.get(persons.workplace_taz, persons.home_taz)/100" -,_XWAIT_SKIM,"skim_dict.get(('WLK_TRN_WLK_XWAIT', 'MD'))" -,_transit_xwait,"_XWAIT_SKIM.get(persons.home_taz, persons.workplace_taz)/100 + _XWAIT_SKIM.get(persons.workplace_taz, persons.home_taz)/100" -,_WAUX_SKIM,"skim_dict.get(('WLK_TRN_WLK_WAUX', 'MD'))" -,_transit_waux,"_WAUX_SKIM.get(persons.home_taz, persons.workplace_taz)/100 + _WAUX_SKIM.get(persons.workplace_taz, persons.home_taz)/100" -,_WACC_SKIM,"skim_dict.get(('WLK_TRN_WLK_WACC', 'MD'))" -,_transit_wacc,"_WACC_SKIM.get(persons.home_taz, persons.workplace_taz)/100 + _WACC_SKIM.get(persons.workplace_taz, persons.home_taz)/100" -,_WEGR_SKIM,"skim_dict.get(('WLK_TRN_WLK_WEGR', 'MD'))" -,_transit_wegr,"_WEGR_SKIM.get(persons.home_taz, persons.workplace_taz)/100 + _WEGR_SKIM.get(persons.workplace_taz, persons.home_taz)/100" +,_transit_iwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_IWAIT', 'MD'))/100 + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_IWAIT', 'MD'))/100" +,_transit_xwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_XWAIT', 'MD'))/100 + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_XWAIT', 'MD'))/100" +,_transit_waux,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WAUX', 'MD'))/100 + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WAUX', 'MD'))/100" +,_transit_wacc,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WACC', 'MD'))/100 + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WACC', 'MD'))/100" +,_transit_wegr,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WEGR', 'MD'))/100 + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WEGR', 'MD'))/100" ,_roundtrip_transit_time_to_work,_transit_ivt_home_to_work + _transit_ivt_work_to_home + _transit_iwait + _transit_xwait + _transit_waux + _transit_wacc + _transit_wegr #,,work_auto_savings_ratio ,_min_work_walk_transit,"np.where(_work_transit_available, np.minimum(_roundtrip_transit_time_to_work, _roundtrip_walk_time_to_work), _roundtrip_walk_time_to_work)" diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_destination.yaml b/activitysim/examples/example_mtc/configs/atwork_subtour_destination.yaml index 09fae573c9..ea7a48e378 100644 --- a/activitysim/examples/example_mtc/configs/atwork_subtour_destination.yaml +++ b/activitysim/examples/example_mtc/configs/atwork_subtour_destination.yaml @@ -9,12 +9,12 @@ SAMPLE_SIZE: 30 SIMULATE_CHOOSER_COLUMNS: - person_id - income_segment - - workplace_taz + - workplace_zone_id LOGSUM_SETTINGS: tour_mode_choice.yaml # model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: workplace_taz +CHOOSER_ORIG_COL_NAME: workplace_zone_id ALT_DEST_COL_NAME: alt_dest IN_PERIOD: 14 OUT_PERIOD: 14 diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_frequency.csv b/activitysim/examples/example_mtc/configs/atwork_subtour_frequency.csv index 2533f88c32..06e9f8878f 100644 --- a/activitysim/examples/example_mtc/configs/atwork_subtour_frequency.csv +++ b/activitysim/examples/example_mtc/configs/atwork_subtour_frequency.csv @@ -15,9 +15,9 @@ util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_part util_log_of_the_work_tour_duration,@np.log(df.duration+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business -util_workplace_urban_area_dummy,work_taz_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business -util_workplace_suburban_area_dummy,(work_taz_area_type>3) & (work_taz_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business +util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business +util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business -util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,coefficient_at_work_sub_tour_alternative_specific_constant_eat,coefficient_at_work_sub_tour_alternative_specific_constant_business1,coefficient_at_work_sub_tour_alternative_specific_constant_maint,coefficient_at_work_sub_tour_alternative_specific_constant_business2,coefficient_at_work_sub_tour_alternative_specific_constant_eat_business \ No newline at end of file +util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,coefficient_at_work_sub_tour_alternative_specific_constant_eat,coefficient_at_work_sub_tour_alternative_specific_constant_business1,coefficient_at_work_sub_tour_alternative_specific_constant_maint,coefficient_at_work_sub_tour_alternative_specific_constant_business2,coefficient_at_work_sub_tour_alternative_specific_constant_eat_business diff --git a/activitysim/examples/example_mtc/configs/cdap.yaml b/activitysim/examples/example_mtc/configs/cdap.yaml index 3a34aa21f7..a1fb793e46 100644 --- a/activitysim/examples/example_mtc/configs/cdap.yaml +++ b/activitysim/examples/example_mtc/configs/cdap.yaml @@ -13,6 +13,15 @@ CONSTANTS: SCHOOL: 7 PRESCHOOL: 8 +PERSON_TYPE_MAP: + WORKER: + - 1 + - 2 + CHILD: + - 6 + - 7 + - 8 + annotate_persons: SPEC: annotate_persons_cdap DF: persons diff --git a/activitysim/examples/example_mtc/configs/constants.yaml b/activitysim/examples/example_mtc/configs/constants.yaml new file mode 100644 index 0000000000..7d3864fd9d --- /dev/null +++ b/activitysim/examples/example_mtc/configs/constants.yaml @@ -0,0 +1,63 @@ +## ActivitySim +## See full license in LICENSE.txt. + + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H diff --git a/activitysim/examples/example_mtc/configs/free_parking_annotate_persons_preprocessor.csv b/activitysim/examples/example_mtc/configs/free_parking_annotate_persons_preprocessor.csv index 841b147502..7c3f555f88 100644 --- a/activitysim/examples/example_mtc/configs/free_parking_annotate_persons_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/free_parking_annotate_persons_preprocessor.csv @@ -1,2 +1,2 @@ Description,Target,Expression -,workplace_county_id,"reindex(land_use.county_id, persons.workplace_taz)" +,workplace_county_id,"reindex(land_use.county_id, persons.workplace_zone_id)" diff --git a/activitysim/examples/example_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv b/activitysim/examples/example_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv index 74cff52ccc..0dd67e97a9 100644 --- a/activitysim/examples/example_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv @@ -9,14 +9,14 @@ logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_ove logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) #,, ,_HH_PERSON_COUNT,"lambda exp, households, persons: persons.query(exp).groupby('household_id').size().reindex(households.index).fillna(0)" -,_num_full,"_HH_PERSON_COUNT('ptype == %s' % constants.PTYPE_FULL, households, persons)" -,_num_part,"_HH_PERSON_COUNT('ptype == %s' % constants.PTYPE_PART, households, persons)" +,_num_full,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_FULL, households, persons)" +,_num_part,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PART, households, persons)" ,num_full_max3,"_num_full.clip(0,3)" ,num_part_max3,"_num_part.clip(0,3)" -,num_univ_max3,"_HH_PERSON_COUNT('ptype == %s' % constants.PTYPE_UNIVERSITY, households, persons).clip(0,3)" -,num_nonwork_max3,"_HH_PERSON_COUNT('ptype == %s' % constants.PTYPE_NONWORK, households, persons).clip(0,3)" -,num_preschool_max3,"_HH_PERSON_COUNT('ptype == %s' % constants.PTYPE_PRESCHOOL, households, persons).clip(0,3)" -,num_school_max3,"_HH_PERSON_COUNT('ptype == %s' % constants.PTYPE_SCHOOL, households, persons).clip(0,3)" -,num_driving_max3,"_HH_PERSON_COUNT('ptype == %s' % constants.PTYPE_DRIVING, households, persons).clip(0,3)" +,num_univ_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_UNIVERSITY, households, persons).clip(0,3)" +,num_nonwork_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_NONWORK, households, persons).clip(0,3)" +,num_preschool_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PRESCHOOL, households, persons).clip(0,3)" +,num_school_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_SCHOOL, households, persons).clip(0,3)" +,num_driving_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_DRIVING, households, persons).clip(0,3)" #,, ,more_cars_than_workers,households.auto_ownership > (_num_full + _num_part) diff --git a/activitysim/examples/example_mtc/configs/joint_tour_destination.yaml b/activitysim/examples/example_mtc/configs/joint_tour_destination.yaml index 36d67bc306..7cd1645876 100644 --- a/activitysim/examples/example_mtc/configs/joint_tour_destination.yaml +++ b/activitysim/examples/example_mtc/configs/joint_tour_destination.yaml @@ -1,35 +1 @@ - -SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv -SPEC: non_mandatory_tour_destination.csv -COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv - -SEGMENTS: - - shopping - - othmaint - - othdiscr - - eatout - - social - - -SAMPLE_SIZE: 30 - -SIMULATE_CHOOSER_COLUMNS: - - tour_type - - TAZ - - household_id - -LOGSUM_SETTINGS: tour_mode_choice.yaml - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: TAZ -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 14 -OUT_PERIOD: 14 - -SIZE_TERM_SELECTOR: non_mandatory - -# optional (comment out if not desired) -DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample +include_settings: non_mandatory_tour_destination.yaml \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv b/activitysim/examples/example_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv index 68a1de8c44..af42d07490 100644 --- a/activitysim/examples/example_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv @@ -8,19 +8,19 @@ Description,Target,Expression ,time_window_overlap_child,_HH_OVERLAPS['cc'] ,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] #,, -,cdap_home_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(constants.PEMPLOY_FULL, 'H', households, persons).clip(0,3)" -,cdap_home_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(constants.PEMPLOY_PART, 'H', households, persons).clip(0,3)" +,cdap_home_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'H', households, persons).clip(0,3)" +,cdap_home_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'H', households, persons).clip(0,3)" ,cdap_home_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'H', households, persons).clip(0,3)" ,cdap_home_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'H', households, persons).clip(0,3)" ,cdap_home_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'H', households, persons).clip(0,3)" ,cdap_home_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'H', households, persons).clip(0,3)" -,cdap_nonmand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(constants.PEMPLOY_FULL, 'N', households, persons).clip(0,3)" -,cdap_nonmand_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(constants.PEMPLOY_PART, 'N', households, persons).clip(0,3)" +,cdap_nonmand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'N', households, persons).clip(0,3)" +,cdap_nonmand_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'N', households, persons).clip(0,3)" ,cdap_nonmand_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'N', households, persons).clip(0,3)" ,cdap_nonmand_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'N', households, persons).clip(0,3)" ,cdap_nonmand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'N', households, persons).clip(0,3)" ,cdap_nonmand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'N', households, persons).clip(0,3)" -,cdap_mand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(constants.PEMPLOY_FULL, 'M', households, persons).clip(0,3)" +,cdap_mand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'M', households, persons).clip(0,3)" ,cdap_mand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'M', households, persons).clip(0,3)" ,cdap_mand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'M', households, persons).clip(0,3)" ,income_between_50_and_100,(households.income > 50000) & (households.income <= 100000) @@ -29,4 +29,4 @@ Description,Target,Expression logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) -nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.TAZ)" +nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.home_zone_id)" diff --git a/activitysim/examples/example_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/activitysim/examples/example_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv index 9fe268b447..be9b64a944 100644 --- a/activitysim/examples/example_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv @@ -7,13 +7,13 @@ logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_ove logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) #,, -,person_is_full,participants.ptype == constants.PTYPE_FULL -,person_is_part,participants.ptype == constants.PTYPE_PART -,person_is_univ,participants.ptype == constants.PTYPE_UNIVERSITY -,person_is_nonwork,participants.ptype == constants.PTYPE_NONWORK -,person_is_driving,participants.ptype == constants.PTYPE_DRIVING -,person_is_school,participants.ptype == constants.PTYPE_SCHOOL -,person_is_preschool,participants.ptype == constants.PTYPE_PRESCHOOL +,person_is_full,participants.ptype == PTYPE_FULL +,person_is_part,participants.ptype == PTYPE_PART +,person_is_univ,participants.ptype == PTYPE_UNIVERSITY +,person_is_nonwork,participants.ptype == PTYPE_NONWORK +,person_is_driving,participants.ptype == PTYPE_DRIVING +,person_is_school,participants.ptype == PTYPE_SCHOOL +,person_is_preschool,participants.ptype == PTYPE_PRESCHOOL ,tour_type_is_eat,participants.tour_type=='eat' ,tour_type_is_disc,participants.tour_type=='disc' ,tour_composition_is_adults,participants.composition=='adults' diff --git a/activitysim/examples/example_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/example_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv index db01451b08..d239c39ca6 100644 --- a/activitysim/examples/example_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv @@ -1,9 +1,8 @@ Description,Target,Expression destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, joint_tours.destination) < setting('cbd_threshold')) * 1" #,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD -local scalar distance skim,_SOVMD_SKIM,"skim_dict.get(('SOV_TIME', 'MD'))" -temp auto_time_to_destination,_auto_time_to_destination,"_SOVMD_SKIM.get(joint_tours.origin, joint_tours.destination)" -temp auto_time_return,_auto_time_return,"_SOVMD_SKIM.get(joint_tours.destination, joint_tours.origin)" +temp auto_time_to_destination,_auto_time_to_destination,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, ('SOV_TIME', 'MD'))" +temp auto_time_return,_auto_time_return,"skim_dict.lookup(joint_tours.destination, joint_tours.origin, ('SOV_TIME', 'MD'))" free flow roundtrip_auto_time,roundtrip_auto_time,"_auto_time_to_destination + _auto_time_return" #"number of joint tours that this joint tours point_person participates in",, ,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), joint_tours.person_id)" diff --git a/activitysim/examples/example_mtc/configs/mandatory_tour_frequency.csv b/activitysim/examples/example_mtc/configs/mandatory_tour_frequency.csv index c4714eeb29..848bbf77aa 100644 --- a/activitysim/examples/example_mtc/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/example_mtc/configs/mandatory_tour_frequency.csv @@ -97,5 +97,5 @@ util_availability_retired,Unavailable: Retired,ptype == 5,,,,coef_unavailable,co util_availability_driving_age_child,Unavailable: Driving-age child,ptype == 6,coef_unavailable,coef_unavailable,,, util_availability_pre_driving_age_student,Unavailable: Pre-driving age child who is in school,ptype == 7,,coef_unavailable,,,coef_unavailable util_availability_pre_driving_age_not_in_school,Unavailable: Pre-driving age child who is not in school,ptype == 8,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable -util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_taz > -1),coef_unavailable,coef_unavailable,,,coef_unavailable -util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_taz > -1),,,coef_unavailable,coef_unavailable,coef_unavailable +util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_zone_id > -1),coef_unavailable,coef_unavailable,,,coef_unavailable +util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_zone_id > -1),,,coef_unavailable,coef_unavailable,coef_unavailable diff --git a/activitysim/examples/example_mtc/configs/mandatory_tour_scheduling.yaml b/activitysim/examples/example_mtc/configs/mandatory_tour_scheduling.yaml index 70c3540275..871f159ca7 100644 --- a/activitysim/examples/example_mtc/configs/mandatory_tour_scheduling.yaml +++ b/activitysim/examples/example_mtc/configs/mandatory_tour_scheduling.yaml @@ -13,9 +13,9 @@ SIMULATE_CHOOSER_COLUMNS: - is_worker - is_student - is_university - - workplace_taz - - school_taz - - TAZ + - workplace_zone_id + - school_zone_id + - home_zone_id LOGSUM_SETTINGS: tour_mode_choice.yaml @@ -34,9 +34,9 @@ SPEC_SEGMENTS: 'SPEC': tour_scheduling_school.csv 'COEFFICIENTS': tour_scheduling_school_coeffs.csv -#CHOOSER_ORIG_COL_NAME: TAZ +#CHOOSER_ORIG_COL_NAME: home_zone_id DESTINATION_FOR_TOUR_PURPOSE: - work: workplace_taz - school: school_taz - univ: school_taz + work: workplace_zone_id + school: school_zone_id + univ: school_zone_id diff --git a/activitysim/examples/example_mtc/configs/network_los.yaml b/activitysim/examples/example_mtc/configs/network_los.yaml new file mode 100644 index 0000000000..f15a1170ea --- /dev/null +++ b/activitysim/examples/example_mtc/configs/network_los.yaml @@ -0,0 +1,17 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +#alternate dir to read/write skim cache (defaults to output_dir) +#cache_dir: data/cache + +zone_system: 1 + +taz_skims: skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.yaml b/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.yaml index c4077a439a..2a64e50443 100644 --- a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.yaml +++ b/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.yaml @@ -26,13 +26,13 @@ SEGMENTS: SIMULATE_CHOOSER_COLUMNS: - tour_type - - TAZ + - home_zone_id - person_id LOGSUM_SETTINGS: tour_mode_choice.yaml # model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: TAZ +CHOOSER_ORIG_COL_NAME: home_zone_id ALT_DEST_COL_NAME: alt_dest IN_PERIOD: 14 OUT_PERIOD: 14 diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination_coeffs.csv b/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination_coeffs.csv index bf02b811f8..4096d6e3ae 100644 --- a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination_coeffs.csv +++ b/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination_coeffs.csv @@ -3,7 +3,7 @@ coef_mode_logsum,0.6755,F coef_escort_dist_0_2,-0.1499,F coef_eatout_dist_0_2,-0.5609,F coef_eatout_social_0_2,-0.5609,F -coef_eatout_dist_0_2,-0.7841,F +#coef_eatout_dist_0_2,-0.7841,F coef_othdiscr_dist_0_2,-0.1677,F coef_escort_dist_2_5,-0.8671,F coef_shopping_dist_2_5,-0.5655,F diff --git a/activitysim/examples/example_mtc/configs/school_location.yaml b/activitysim/examples/example_mtc/configs/school_location.yaml index d3df3b34be..14f0c8a1af 100644 --- a/activitysim/examples/example_mtc/configs/school_location.yaml +++ b/activitysim/examples/example_mtc/configs/school_location.yaml @@ -1,19 +1,19 @@ SAMPLE_SIZE: 30 SIMULATE_CHOOSER_COLUMNS: - - TAZ + - home_zone_id - school_segment - household_id # model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: TAZ +CHOOSER_ORIG_COL_NAME: home_zone_id ALT_DEST_COL_NAME: alt_dest IN_PERIOD: 14 OUT_PERIOD: 8 -DEST_CHOICE_COLUMN_NAME: school_taz +DEST_CHOICE_COLUMN_NAME: school_zone_id # comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table -DEST_CHOICE_LOGSUM_COLUMN_NAME: school_taz_logsum +DEST_CHOICE_LOGSUM_COLUMN_NAME: school_location_logsum # comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table DEST_CHOICE_SAMPLE_TABLE_NAME: school_location_sample @@ -56,6 +56,12 @@ SEGMENT_IDS: highschool: 2 gradeschool: 1 + +segment_preprocessor: + SPEC: school_location_segment_choosers_preprocessor + DF: persons + + # model adds these tables (informational - not added if commented out) SHADOW_PRICE_TABLE: school_shadow_prices MODELED_SIZE_TABLE: school_modeled_size diff --git a/activitysim/examples/example_mtc/configs/school_location_segment_choosers_preprocessor.csv b/activitysim/examples/example_mtc/configs/school_location_segment_choosers_preprocessor.csv new file mode 100644 index 0000000000..77ff28ba9d --- /dev/null +++ b/activitysim/examples/example_mtc/configs/school_location_segment_choosers_preprocessor.csv @@ -0,0 +1,5 @@ +Description,Target,Expression +school_segment gradeschool,school_segment,"np.where(persons.is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)" +school_segment highschool,school_segment,"np.where(persons.is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +school_segment university,school_segment,"np.where(persons.is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" + diff --git a/activitysim/examples/example_mtc/configs/settings.yaml b/activitysim/examples/example_mtc/configs/settings.yaml index 69887d8f9a..1faaf86faf 100644 --- a/activitysim/examples/example_mtc/configs/settings.yaml +++ b/activitysim/examples/example_mtc/configs/settings.yaml @@ -1,5 +1,24 @@ + + # input tables +# +# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) +# as well as for created tables (tours, joint_tour_participants, trips) +# e.g. the households table must have an index column 'household_id' and the foreign key to households in the +# persons table is also household_id. This naming convention allows activitysim to intuit the relationship +# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips +# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by +# household_id. +# +# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id +# you cannot change the well-known name of the index by modifying this directive. However, if your input file +# has a different id column name, you can rename it to the required index name with the rename_columns directive. +# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. +# input_table_list: + # + # households (table index 'household_id') + # - tablename: households filename: households.csv index_col: household_id @@ -8,13 +27,17 @@ input_table_list: PERSONS: hhsize workers: num_workers VEHICL: auto_ownership + TAZ: home_zone_id keep_columns: - - TAZ + - home_zone_id - income - hhsize - HHT - auto_ownership - num_workers + # + # persons (table index 'person_id') + # - tablename: persons filename: persons.csv index_col: person_id @@ -28,11 +51,14 @@ input_table_list: - pemploy - pstudent - ptype + # + # land_use (table index 'zone_id') + # - tablename: land_use filename: land_use.csv - index_col: TAZ + index_col: zone_id rename_columns: - ZONE: TAZ + TAZ: zone_id COUNTY: county_id keep_columns: - DISTRICT @@ -60,10 +86,6 @@ input_table_list: - TOPOLOGY - TERMINAL - -# input skims -skims_file: skims.omx - # convert input CSVs to HDF5 format and save to outputs directory # create_input_store: True @@ -87,15 +109,9 @@ check_for_variability: False use_shadow_pricing: False # turn writing of sample_tables on and off for all models -# want_dest_choice_sample_tables: False - +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False -# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) -#read_skim_cache: True -# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -#write_skim_cache: True -#alternate dir to read/write skim cache (defaults to output_dir) -#skim_cache_dir: data/cache # - tracing @@ -146,7 +162,6 @@ models: - write_trip_matrices - write_tables - output_tables: h5_store: False action: include @@ -164,33 +179,7 @@ output_tables: # area_types less than this are considered urban urban_threshold: 4 cbd_threshold: 2 -rural_threshold: 6 - -# upperEA Upper limit on time of day for the Early morning time period 5 -# upperAM Upper limit on time of day for the AM peak time period 10 -# upperMD Upper limit on time of day for the Midday time period 15 -# upperPM Upper limit on time of day for the PM time peak period 19 - -skim_time_periods: - period_minutes: 60 - periods: - - 0 - - 6 - - 11 - - 16 - - 20 - - 24 - labels: - - EA - - AM - - MD - - PM - - EV - # - value of time - -# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) - min_value_of_time: 1 max_value_of_time: 50 distributed_vot_mu: 0.684 diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/example_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv index e11704c918..d7b2f1d61f 100644 --- a/activitysim/examples/example_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv @@ -10,9 +10,9 @@ Description,Target,Expression #,, ,is_joint,df.tour_category=='joint' ,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()" -,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % constants.PEMPLOY_FULL, persons), df.household_id)" -,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % constants.PEMPLOY_PART, persons), df.household_id)" -,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % constants.PSTUDENT_NOT, persons), df.household_id)" +,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)" +,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)" +,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)" Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)" Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)" Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)" @@ -35,7 +35,7 @@ Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency. #,, Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)" Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)" -tourStartsInPeakPeriod,_tour_starts_in_peak,(skim_time_period_label(df.start) == 'AM') | (skim_time_period_label(df.start) == 'PM') +tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') AccesibilityAtOrigin fallback,hhacc,0 AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))" AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)" diff --git a/activitysim/examples/example_mtc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/example_mtc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv index 7feb6c8e1a..e6c5e2d011 100644 --- a/activitysim/examples/example_mtc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -23,13 +23,13 @@ local,_DF_IS_TOUR,'tour_type' in df.columns ,dest_topology,"reindex(land_use.TOPOLOGY, df[dest_col_name])" ,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])" ,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" -# FIXME no transit subzones so all zones short walk to transit,, -,_walk_transit_origin,True -,_walk_transit_destination,True -,walk_transit_available,_walk_transit_origin & _walk_transit_destination -,drive_transit_available,_walk_transit_destination & (df.auto_ownership > 0) -,origin_walk_time,shortWalk*60/walkSpeed -,destination_walk_time,shortWalk*60/walkSpeed +# FIXME no transit subzones for ONE_ZONE version, so all zones short walk to transit,, +,_origin_distance_to_transit,"reindex(land_use.access_dist_transit, df[orig_col_name]) if 'access_dist_transit' in land_use else shortWalk" +,_destination_distance_to_transit,"reindex(land_use.access_dist_transit, df[dest_col_name]) if 'access_dist_transit' in land_use else shortWalk" +,walk_transit_available,(_origin_distance_to_transit > 0) & (_destination_distance_to_transit > 0) +,drive_transit_available,(_destination_distance_to_transit > 0) & (df.auto_ownership > 0) +,origin_walk_time,_origin_distance_to_transit*60/walkSpeed +,destination_walk_time,_destination_distance_to_transit*60/walkSpeed # RIDEHAIL,, ,origin_density_measure,"(reindex(land_use.TOTPOP, df[orig_col_name]) + reindex(land_use.TOTEMP, df[orig_col_name])) / (reindex(land_use.TOTACRE, df[orig_col_name]) / 640)" ,dest_density_measure,"(reindex(land_use.TOTPOP, df[dest_col_name]) + reindex(land_use.TOTEMP, df[dest_col_name])) / (reindex(land_use.TOTACRE, df[dest_col_name]) / 640)" diff --git a/activitysim/examples/example_mtc/configs/tour_mode_choice_coeffs.csv b/activitysim/examples/example_mtc/configs/tour_mode_choice_coeffs.csv index 0d21e4d550..c5d9a264a2 100644 --- a/activitysim/examples/example_mtc/configs/tour_mode_choice_coeffs.csv +++ b/activitysim/examples/example_mtc/configs/tour_mode_choice_coeffs.csv @@ -1,4 +1,5 @@ coefficient_name,value,constrain +coef_one,1,T coef_nest_root,1.00,T coef_nest_AUTO,0.72,T coef_nest_AUTO_DRIVEALONE,0.35,T @@ -304,4 +305,4 @@ walk_transit_CBD_ASC_atwork,0.564,F drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F drive_transit_CBD_ASC_school_univ,0.672,F drive_transit_CBD_ASC_work,1.1,F -drive_transit_CBD_ASC_atwork,0.564,F \ No newline at end of file +drive_transit_CBD_ASC_atwork,0.564,F diff --git a/activitysim/examples/example_mtc/configs/tour_mode_choice_coeffs_template.csv b/activitysim/examples/example_mtc/configs/tour_mode_choice_coeffs_template.csv index c5a9584aa5..b1b009a3f0 100644 --- a/activitysim/examples/example_mtc/configs/tour_mode_choice_coeffs_template.csv +++ b/activitysim/examples/example_mtc/configs/tour_mode_choice_coeffs_template.csv @@ -1,5 +1,6 @@ coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork -#same for all sergments,,,,,,,,,, +#same for all segments,,,,,,,,,, +coef_one,,,,,,,,,, coef_nest_root,,,,,,,,,, coef_nest_AUTO,,,,,,,,,, coef_nest_AUTO_DRIVEALONE,,,,,,,,,, diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_atwork_preprocessor.csv b/activitysim/examples/example_mtc/configs/tour_scheduling_atwork_preprocessor.csv index c44e65d051..5c9c77403c 100644 --- a/activitysim/examples/example_mtc/configs/tour_scheduling_atwork_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/tour_scheduling_atwork_preprocessor.csv @@ -1,3 +1,3 @@ Description,Target,Expression ,sovtimemd,"od_skims[('SOV_TIME', 'MD')]" -,sovtimemd_t,"do_skims[('SOV_TIME', 'MD')]" +,sovtimemd_t,"od_skims.reverse(('SOV_TIME', 'MD'))" diff --git a/activitysim/examples/example_mtc/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/example_mtc/configs/trip_destination_annotate_trips_preprocessor.csv index 63230e69f4..0217a68555 100644 --- a/activitysim/examples/example_mtc/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/trip_destination_annotate_trips_preprocessor.csv @@ -2,7 +2,7 @@ Description,Target,Expression #,, ,tour_mode,"reindex(tours.tour_mode, df.tour_id)" ,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" -,trip_period,skim_time_period_label(_tod) +,trip_period,network_los.skim_time_period_label(_tod) ,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" #,,not needed as school is not chosen as an intermediate trip destination #,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" diff --git a/activitysim/examples/example_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv b/activitysim/examples/example_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv index 36691df163..782116aa99 100644 --- a/activitysim/examples/example_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv +++ b/activitysim/examples/example_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv @@ -1,5 +1,5 @@ Description,Target,Expression #,, ,ptype,"reindex(persons.ptype, df.person_id)" -,person_type,ptype.map(constants.PTYPE_NAME) +,person_type,ptype.map(PTYPE_NAME) ,start,"reindex_i(tours.start, df.tour_id)" diff --git a/activitysim/examples/example_mtc/configs/workplace_location.yaml b/activitysim/examples/example_mtc/configs/workplace_location.yaml index f4776ac30a..71c1b74d5f 100644 --- a/activitysim/examples/example_mtc/configs/workplace_location.yaml +++ b/activitysim/examples/example_mtc/configs/workplace_location.yaml @@ -2,7 +2,7 @@ SAMPLE_SIZE: 30 SIMULATE_CHOOSER_COLUMNS: - income_segment - - TAZ + - home_zone_id SAMPLE_SPEC: workplace_location_sample.csv SPEC: workplace_location.csv @@ -13,12 +13,12 @@ LOGSUM_PREPROCESSOR: nontour_preprocessor LOGSUM_TOUR_PURPOSE: work # model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: TAZ +CHOOSER_ORIG_COL_NAME: home_zone_id ALT_DEST_COL_NAME: alt_dest IN_PERIOD: 17 OUT_PERIOD: 8 -DEST_CHOICE_COLUMN_NAME: workplace_taz +DEST_CHOICE_COLUMN_NAME: workplace_zone_id # comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum diff --git a/activitysim/examples/example_mtc/configs/write_trip_matrices.yaml b/activitysim/examples/example_mtc/configs/write_trip_matrices.yaml index 28bd72bef2..a9f14a336d 100644 --- a/activitysim/examples/example_mtc/configs/write_trip_matrices.yaml +++ b/activitysim/examples/example_mtc/configs/write_trip_matrices.yaml @@ -274,4 +274,4 @@ CONSTANTS: last_hour: 2 # SHARED2 and SHARED3 Occupancies OCC_SHARED2: 2.0 - OCC_SHARED3: 3.33 \ No newline at end of file + OCC_SHARED3: 3.33 diff --git a/activitysim/examples/example_mtc/configs_arc/parking_location_choice.csv b/activitysim/examples/example_mtc/configs_arc/parking_location_choice.csv new file mode 100644 index 0000000000..78d345ce12 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/parking_location_choice.csv @@ -0,0 +1,2 @@ +Description,Expression,mandatory_free,mandatory_pay,nonmandatory +Distance-Parking Zone to Destination,@pd_skims['DIST'],-0.4048,-4.366,-0.2572 diff --git a/activitysim/examples/example_mtc/configs_arc/parking_location_choice.yaml b/activitysim/examples/example_mtc/configs_arc/parking_location_choice.yaml new file mode 100644 index 0000000000..3cf661d979 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/parking_location_choice.yaml @@ -0,0 +1,36 @@ +METADATA: + CHOOSER: trips + INPUT: + persons: + trips: + tours: + OUTPUT: + trips: + - parking_taz + +SPECIFICATION: parking_location_choice.csv +COEFFICIENTS: parking_location_choice_coeffs.csv + +PREPROCESSOR: + SPEC: parking_location_choice_annotate_trips_preprocessor + DF: trips + TABLES: + - land_use + - persons + - tours + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_park_eligible +CHOOSER_SEGMENT_COLUMN_NAME: parking_segment + +ALTERNATIVE_FILTER_COLUMN_NAME: is_cbd +TRIP_DEPARTURE_PERIOD: depart + +SEGMENTS: + - mandatory_free + - mandatory_pay + - nonmandatory + +ALT_DEST_COL_NAME: parking_taz +TRIP_ORIGIN: origin +TRIP_DESTINATION: destination diff --git a/activitysim/examples/example_mtc/configs_arc/parking_location_choice_annotate_trips_preprocessor.csv b/activitysim/examples/example_mtc/configs_arc/parking_location_choice_annotate_trips_preprocessor.csv new file mode 100644 index 0000000000..291d281389 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/parking_location_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,10 @@ +Description,Target,Expression +#,, +,_area_type,"reindex(land_use.area_type, df.destination)" +,is_cbd,_area_type == 1 +,is_drive,"df.trip_mode.isin(['DRIVEALONEFREE', 'DRIVEALONEPAY', 'SHARED2FREE', 'SHARED2PAY', 'SHARED3FREE', 'SHARED3PAY'])" +,is_park_eligible, is_cbd & is_drive +,tour_category,"reindex(tours.tour_category, df.tour_id)" +,_free_parking,"reindex(persons.free_parking_at_work, df.person_id)" +,parking_segment,"np.where(tour_category == 'mandatory', np.where(_free_parking,'mandatory_free', 'mandatory_pay'),'nonmandatory')" +,trip_period,network_los.skim_time_period_label(df.depart) diff --git a/activitysim/examples/example_mtc/configs_arc/parking_location_choice_coeffs.csv b/activitysim/examples/example_mtc/configs_arc/parking_location_choice_coeffs.csv new file mode 100644 index 0000000000..1e3f0fbf46 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/parking_location_choice_coeffs.csv @@ -0,0 +1 @@ +coefficient_name,value,constrain diff --git a/activitysim/examples/example_mtc/configs_arc/settings.yaml b/activitysim/examples/example_mtc/configs_arc/settings.yaml new file mode 100644 index 0000000000..5cde84bbf8 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/settings.yaml @@ -0,0 +1,39 @@ + +inherit_settings: True + +models: + - initialize_landuse + - compute_accessibility + - initialize_households + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling_choice + - trip_departure_choice + - trip_mode_choice + - parking_location + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables diff --git a/activitysim/examples/example_mtc/configs_arc/trip_departure_choice.csv b/activitysim/examples/example_mtc/configs_arc/trip_departure_choice.csv new file mode 100644 index 0000000000..a460044f27 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/trip_departure_choice.csv @@ -0,0 +1,8 @@ +Description,Expression,outbound,inbound +StopTimeWork,@(df['stop_time_duration'] * df['is_work'].astype(int)).astype(int),0.933020,0.933020 +StopTimeSchool,@(df['stop_time_duration'] * df['is_school'].astype(int)).astype(int),0.370260,0.370260 +StopTimeEatOut,@(df['stop_time_duration'] * df['is_eatout'].astype(int)).astype(int),0.994840,0.994840 +StopTimeMainen,@(df['stop_time_duration'] * df['is_other_maintenance'].astype(int)).astype(int),0.254180,0.254180 +StopTimeShop,@(df['stop_time_duration'] * df['is_shopping'].astype(int)).astype(int),0.619340,0.619340 +StopTimeSocial,@(df['stop_time_duration'] * df['is_social'].astype(int)).astype(int),0.784420,0.784420 +StopTimeDiscre,@(df['stop_time_duration'] * df['is_othdisc'].astype(int)).astype(int),1.277000,1.277000 diff --git a/activitysim/examples/example_mtc/configs_arc/trip_departure_choice.yaml b/activitysim/examples/example_mtc/configs_arc/trip_departure_choice.yaml new file mode 100644 index 0000000000..daf657bcce --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/trip_departure_choice.yaml @@ -0,0 +1,20 @@ +METADATA: + CHOOSER: tours + INPUT: + persons: + trips: + tours: + OUTPUT: + trips: + - start_period + - end_period + +SPECIFICATION: trip_departure_choice.csv +COEFFICIENTS: trip_departure_choice_coeff.csv + + +PREPROCESSOR: + SPEC: trip_departure_choice_preprocessor + DF: trips + TABLES: + - tours \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs_arc/trip_departure_choice_preprocessor.csv b/activitysim/examples/example_mtc/configs_arc/trip_departure_choice_preprocessor.csv new file mode 100644 index 0000000000..1a2ecccab0 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/trip_departure_choice_preprocessor.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +,tripFFT,"od_skims['SOV_TIME', 'MD']" +,is_work,trips['purpose']=='work' +,is_school,trips['purpose']=='school' +,is_eatout,trips['purpose']=='eatout' +,is_other_maintenance,trips['purpose']=='othmaint' +,is_shopping,trips['purpose']=='shopping' +,is_social,trips['purpose']=='social' +,is_othdisc,trips['purpose']=='othdisc' diff --git a/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice.csv b/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice.csv new file mode 100644 index 0000000000..be457f8a31 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice.csv @@ -0,0 +1,128 @@ +Description,Expression,stage_one +Alternative is Invalid if leg time is longer than total time,@(df['main_leg_duration']>df['duration']).astype(int),-999 +Discretionary tour-ASC for Legtime = 0,@(df['main_leg_duration'] == 0)&(df['tour_type']=='othdiscr'),-6.5884 +"Discretionary tour,ASC for Legtime = 1",@(df['main_leg_duration'] == 1)&(df['tour_type']=='othdiscr'),-5.0326 +"Discretionary tour,ASC for Legtime = 2",@(df['main_leg_duration'] == 2)&(df['tour_type']=='othdiscr'),-2.0526 +"Discretionary tour,ASC for Legtime = 3",@(df['main_leg_duration'] == 3)&(df['tour_type']=='othdiscr'),-1.0313 +"Discretionary tour,ASC for Legtime = 4",@(df['main_leg_duration'] == 4)&(df['tour_type']=='othdiscr'),-0.46489 +Discretionary tour - Main Leg time,@df['tour_type']=='othdiscr',0.060382 +Eatout tour - Shift,@df['tour_type']=='eatout',-0.7508 +Eatout tour - Main leg time,@df['tour_type']=='eatout',0.53247 +"Maintenance tour,ASC for Legtime = 0",@(df['main_leg_duration'] == 0)&(df['tour_type']=='othmaint'),-3.6079 +"Maintenance tour,ASC for Legtime = 1",@(df['main_leg_duration'] == 1)&(df['tour_type']=='othmaint'),-1.9376 +"Maintenance tour,ASC for Legtime = 2",@(df['main_leg_duration'] == 2)&(df['tour_type']=='othmaint'),-0.99484 +"Maintenance tour,ASC for Legtime = 3",@(df['main_leg_duration'] == 3)&(df['tour_type']=='othmaint'),-0.29166 +"Maintenance tour,ASC for Legtime = 4",@(df['main_leg_duration'] == 4)&(df['tour_type']=='othmaint'),0.18669 +Maintenance tour - Main leg time,@df['tour_type']=='othmaint',-0.03572 +"School tour,ASC for Legtime = 14",@(df['main_leg_duration'] == 14)&(df['tour_type']=='school'),1.2449 +"School tour,ASC for Legtime = 15",@(df['main_leg_duration'] == 15)&(df['tour_type']=='school'),1.8492 +"School tour,ASC for Legtime = 16",@(df['main_leg_duration'] == 16)&(df['tour_type']=='school'),2.0672 +"School tour,ASC for Legtime = 17",@(df['main_leg_duration'] == 17)&(df['tour_type']=='school'),1.8571 +"School tour,ASC for Legtime = 18",@(df['main_leg_duration'] == 18)&(df['tour_type']=='school'),1.3826 +"School tour,ASC for Legtime = 19",@(df['main_leg_duration'] == 19)&(df['tour_type']=='school'),0.92034 +"School tour,ASC for Legtime = 20",@(df['main_leg_duration'] == 20)&(df['tour_type']=='school'),0.37001 +School tour - Main Leg time,@df['tour_type']=='school',1.7393 +School tour - Shift,@df['tour_type']=='school',-1.5696 +School tour - Shift,@df['tour_type']=='school',-0.43764 +"Escort tour,ASC for Legtime = 0",@(df['main_leg_duration'] == 0).astype(int)*(df['tour_type']=='escort'),-1.2273 +"Escort tour,ASC for Legtime = 1",@(df['main_leg_duration'] == 1)&(df['tour_type']=='escort'),0.48815 +"Escort tour,ASC for Legtime = 2",@(df['main_leg_duration'] == 2)&(df['tour_type']=='escort'),0.37136 +"Escort tour,ASC for Legtime = 3",@(df['main_leg_duration'] == 3)&(df['tour_type']=='escort'),-0.29005 +Escort tour - Main Leg time,@df['tour_type']=='escort',-0.005499 +"Shopping tour,ASC for Legtime = 0",@(df['main_leg_duration'] == 0)&(df['tour_type']=='shopping'),-4.5136 +"Shopping tour,ASC for Legtime = 1",@(df['main_leg_duration'] == 1)&(df['tour_type']=='shopping'),-1.8461 +"Shopping tour,ASC for Legtime = 2",@(df['main_leg_duration'] == 2)&(df['tour_type']=='shopping'),-0.81101 +"Shopping tour,ASC for Legtime = 3",@(df['main_leg_duration'] == 3)&(df['tour_type']=='shopping'),-0.42265 +"Shopping tour,ASC for Legtime = 4",@(df['main_leg_duration'] == 4)&(df['tour_type']=='shopping'),-0.25089 +Shopping tour - Main Leg time,@df['tour_type']=='shopping',-0.30597 +Social tour - Main Leg time,@df['tour_type']=='social',1.1482 +Social tour - Shift,@df['tour_type']=='social',-0.94185 +University tour - Main Leg time,@df['tour_type']=='univ',0.56244 +University tour - Shift,@df['tour_type']=='univ',-0.55984 +University tour - Shift,@df['tour_type']=='univ',-0.22445 +Work tour - Main Leg time,@df['tour_type']=='work',0.45055 +Work tour - Shift,@df['tour_type']=='work',-0.27206 +Work tour - Shift,@df['tour_type']=='work',0.009149 +"Work tour,ASC for Legtime = 17",@(df['main_leg_duration'] == 17)&(df['tour_type']=='work'),0.12954 +"Work tour,ASC for Legtime = 18",@(df['main_leg_duration'] == 18)&(df['tour_type']=='work'),0.54498 +"Work tour,ASC for Legtime = 19",@(df['main_leg_duration'] == 19)&(df['tour_type']=='work'),0.64445 +"Work tour,ASC for Legtime = 20",@(df['main_leg_duration'] == 20)&(df['tour_type']=='work'),0.56793 +"Work tour,ASC for Legtime = 21",@(df['main_leg_duration'] == 21)&(df['tour_type']=='work'),0.16153 +"Work tour,ASC for Legtime = 22",@(df['main_leg_duration'] == 22)&(df['tour_type']=='work'),-0.15183 +Work tour - Shift,@df['tour_type']=='work',-0.57964 +# main leg time - main leg free flow travel time,,0.00387 +# main leg time *SIN(2?*TourStartPeriod/48),,-0.007568 +# main leg time *COS(2?*TourStartPeriod/48),,0.11681 +# main leg time *SIN(4?*TourStartPeriod/48),,0.019579 +# main leg time *COS(4?*TourStartPeriod/48),,0.01919 +# main leg time - full time worker's work tour ,fullTimeWorker&df['tour_type']=='work',0.037065 +Calibration,@(df['main_leg_duration'] == 19)&(df['tour_type']=='work'),0.5253 +Calibration,@(df['main_leg_duration'] == 20)&(df['tour_type']=='work'),0.7719 +Calibration,@(df['main_leg_duration'] == 21)&(df['tour_type']=='work'),1.0697 +Calibration,@(df['main_leg_duration'] == 22)&(df['tour_type']=='work'),1.2412 +Calibration,@(df['main_leg_duration'] == 23)&(df['tour_type']=='work'),1.1888 +Calibration,@(df['main_leg_duration'] == 16)&(df['tour_type']=='school'),0.3565 +Calibration,@(df['main_leg_duration'] == 17)&(df['tour_type']=='school'),0.5677 +Calibration,@(df['main_leg_duration'] == 18)&(df['tour_type']=='school'),0.8005 +Calibration,@(df['main_leg_duration'] == 19)&(df['tour_type']=='school'),0.7861 +Calibration,@(df['main_leg_duration'] == 0)&(df['tour_type']=='escort'),-2.8173 +Calibration,@(df['main_leg_duration'] == 1)&(df['tour_type']=='escort'),-0.359 +Calibration,@(df['main_leg_duration'] == 2)&(df['tour_type']=='escort'),1.2018 +Calibration,@(df['main_leg_duration'] == 3)&(df['tour_type']=='escort'),1.6866 +Calibration,@(df['main_leg_duration'] == 0)&(df['tour_type']=='othmaint'),-3.3465 +Calibration,@(df['main_leg_duration'] == 1)&(df['tour_type']=='othmaint'),-1.511 +Calibration,@(df['main_leg_duration'] == 2)&(df['tour_type']=='othmaint'),-0.4784 +Calibration,@(df['main_leg_duration'] == 3)&(df['tour_type']=='othmaint'),0.0637 +Calibration,@(df['main_leg_duration'] == 4)&(df['tour_type']=='othmaint'),0.4645 +Calibration,@(df['main_leg_duration'] == 0)&(df['tour_type']=='shopping'),-2.0645 +Calibration,@(df['main_leg_duration'] == 1)&(df['tour_type']=='shopping'),-1.0205 +Calibration,@(df['main_leg_duration'] == 2)&(df['tour_type']=='shopping'),-0.0582 +Calibration,@(df['main_leg_duration'] == 3)&(df['tour_type']=='shopping'),0.5533 +Calibration,@(df['main_leg_duration'] == 0)&(df['tour_type']=='eatout'),-100 +Calibration,@(df['main_leg_duration'] == 1)&(df['tour_type']=='eatout'),-100 +Calibration,@(df['main_leg_duration'] == 2)&(df['tour_type']=='eatout'),-6.8372 +Calibration,@(df['main_leg_duration'] == 3)&(df['tour_type']=='eatout'),-0.3319 +Calibration,@(df['main_leg_duration'] == 4)&(df['tour_type']=='eatout'),0.8709 +Calibration,@(df['main_leg_duration'] == 5)&(df['tour_type']=='eatout'),1.2215 +Calibration,@(df['main_leg_duration'] == 6)&(df['tour_type']=='eatout'),1.0655 +Calibration,@(df['main_leg_duration'] == 0)&(df['tour_type']=='social'),-5.9111 +Calibration,@(df['main_leg_duration'] == 1)&(df['tour_type']=='social'),-2.9703 +Calibration,@(df['main_leg_duration'] == 2)&(df['tour_type']=='social'),-1.5087 +Calibration,@(df['main_leg_duration'] == 0)&(df['tour_type']=='at_work'),-1.988 +Calibration,@(df['main_leg_duration'] == 1)&(df['tour_type']=='at_work'),0.1619 +Calibration,@(df['main_leg_duration'] == 2)&(df['tour_type']=='at_work'),0.335 +Calibration,@(df['main_leg_duration'] == 3)&(df['tour_type']=='at_work'),1.0155 +# OUTBOUND LEG COMPONENTS,, +alternative is invalid if leg time is longer than total tour time,@(df['outbound_duration']>df['duration']).astype(int),-999 +alternative is invalid if leg time>0 yet there is no stop on the leg,@(df['num_outbound_stops']==0)&(df['outbound_duration']>0),-999 +outbound leg time * outbound leg free flow travel time,"@(df['outbound_duration']*od_skims['SOV_TIME', 'MD'])",0.0058104 +# outbound leg time *SIN(2?*TourStartPeriod/48),outboundLegTime*@fourierSin1,-0.20702 +# outbound leg time *COS(2?*TourStartPeriod/48),outboundLegTime*@fourierCos1,0.18594 +# outbound leg time *SIN(4?*TourStartPeriod/48),outboundLegTime*@fourierSin2,-0.11703 +# outbound leg time *COS(4?*TourStartPeriod/48),outboundLegTime*@fourierCos2,-0.014628 +Average Stop Time,"@np.where(df['num_outbound_stops'] > 0,df['outbound_duration'] / df['num_outbound_stops'],0)",-0.31564 +Calibration,@(df['num_outbound_stops']==1)&(df['outbound_duration'] ==0),-0.723010589 +Calibration,@(df['num_outbound_stops']==1)&(df['outbound_duration'] ==1),0.792121459 +Calibration,@(df['num_outbound_stops']==2)&(df['outbound_duration'] ==0),-4.854181844 +Calibration,@(df['num_outbound_stops']==2)&(df['outbound_duration'] ==1),-0.181033741 +Calibration,@(df['num_outbound_stops']==2)&(df['outbound_duration'] ==2),0.967315884 +Calibration,@(df['num_outbound_stops']==2)&(df['outbound_duration'] ==3),0.467052643 +Calibration,@(df['num_outbound_stops']==3)&(df['outbound_duration'] ==0),-15.05439781 +Calibration,@(df['num_outbound_stops']==3)&(df['outbound_duration'] ==1),-4.807075147 +Calibration,@(df['num_outbound_stops']==3)&(df['outbound_duration'] ==2),-0.127915425 +Calibration,@(df['num_outbound_stops']==3)&(df['outbound_duration'] ==3),0.30556271 +# INBOUND LEG COMPONENTS,, +alternative is invalid if leg time is longer than total tour time,@(df['inbound_duration']>df['duration']).astype(int),-999 +alternative is invalid if leg time>0 yet there is no stop on the leg,@(df['num_inbound_stops']==0)&(df['inbound_duration']>0),-999 +inbound leg time * inbound leg free flow travel time,"@(df['inbound_duration']*do_skims['SOV_TIME', 'MD'])",0.002936 +Average Stop Time,"@np.where(df['num_inbound_stops'] > 0,df['inbound_duration'] / df['num_inbound_stops'],0)",-0.446440 +Calibration,@(df['num_inbound_stops']==1)&(df['inbound_duration'] ==0),-1.927130 +Calibration,@(df['num_inbound_stops']==1)&(df['inbound_duration'] ==1),0.291882 +Calibration,@(df['num_inbound_stops']==2)&(df['inbound_duration'] ==0),-6.934284 +Calibration,@(df['num_inbound_stops']==2)&(df['inbound_duration'] ==1),-1.325881 +Calibration,@(df['num_inbound_stops']==2)&(df['inbound_duration'] ==2),0.479435 +Calibration,@(df['num_inbound_stops']==2)&(df['inbound_duration'] ==3),0.474259 +Calibration,@(df['num_inbound_stops']==3)&(df['inbound_duration'] ==0),-14.253409 +Calibration,@(df['num_inbound_stops']==3)&(df['inbound_duration'] ==1),-8.055671 +Calibration,@(df['num_inbound_stops']==3)&(df['inbound_duration'] ==2),-2.151257 +Calibration,@(df['num_inbound_stops']==3)&(df['inbound_duration'] ==3),0.378101 diff --git a/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice.yaml b/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice.yaml new file mode 100644 index 0000000000..e4b545a4ad --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice.yaml @@ -0,0 +1,19 @@ +METADATA: + CHOOSER: tours + INPUT: + persons: + trips: + tours: + OUTPUT: + trips: + - start_period + - end_period + +SPECIFICATION: trip_scheduling_choice.csv +COEFFICIENTS: trip_scheduling_choice_coeff.csv + +PREPROCESSOR: + SPEC: trip_scheduling_choice_preprocessor + DF: tours + TABLES: + - trips \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice_preprocessor.csv b/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice_preprocessor.csv new file mode 100644 index 0000000000..909d2f7251 --- /dev/null +++ b/activitysim/examples/example_mtc/configs_arc/trip_scheduling_choice_preprocessor.csv @@ -0,0 +1,4 @@ +Description,Target,Expression +,tour_outbound_dist,"od_skims['DIST']" +,tour_inbound_dist,"do_skims['DIST']" +,main_leg_dist,"obib_skims['DIST']" diff --git a/activitysim/examples/example_mtc/configs_mp/settings.yaml b/activitysim/examples/example_mtc/configs_mp/settings.yaml index 073162d9e0..d277ce92d8 100644 --- a/activitysim/examples/example_mtc/configs_mp/settings.yaml +++ b/activitysim/examples/example_mtc/configs_mp/settings.yaml @@ -12,7 +12,7 @@ fail_fast: True # - full sample - 2875192 households on 64 processor 432 GiB RAM #households_sample_size: 0 -#chunk_size: 80000000000 +chunk_size: 80000000000 #num_processes: 60 @@ -23,19 +23,20 @@ strict: False mem_tick: 30 use_shadow_pricing: False -households_sample_size: 2000 -chunk_size: 0 +# households_sample_size: 2000 +#chunk_size: 0 num_processes: 2 # - ------------------------- +# not recommended or supported for multiprocessing want_dest_choice_sample_tables: False #read_skim_cache: True #write_skim_cache: True # - tracing -trace_hh_id: +#trace_hh_id: trace_od: # to resume after last successful checkpoint, specify resume_after: _ diff --git a/activitysim/examples/example_mtc/data/land_use.csv b/activitysim/examples/example_mtc/data/land_use.csv index 2572f5bea5..b27dde6e32 100644 --- a/activitysim/examples/example_mtc/data/land_use.csv +++ b/activitysim/examples/example_mtc/data/land_use.csv @@ -1,4 +1,4 @@ -ZONE,DISTRICT,SD,COUNTY,TOTHH,HHPOP,TOTPOP,EMPRES,SFDU,MFDU,HHINCQ1,HHINCQ2,HHINCQ3,HHINCQ4,TOTACRE,RESACRE,CIACRE,SHPOP62P,TOTEMP,AGE0004,AGE0519,AGE2044,AGE4564,AGE65P,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL,ZERO,hhlds,sftaz,gqpop +TAZ,DISTRICT,SD,COUNTY,TOTHH,HHPOP,TOTPOP,EMPRES,SFDU,MFDU,HHINCQ1,HHINCQ2,HHINCQ3,HHINCQ4,TOTACRE,RESACRE,CIACRE,SHPOP62P,TOTEMP,AGE0004,AGE0519,AGE2044,AGE4564,AGE65P,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL,ZERO,hhlds,sftaz,gqpop 1,1,1,1,46,74,82,37,1,60,15,13,9,9,20.3,1.0,15.0,0.23800000000000002,27318,3,7,31,27,14,224,21927,2137,2254,18,758,284.01965,932.83514,0,0.0,0.0,0.0,3,5.89564,0,46,1,8 2,1,1,1,134,214,240,107,5,147,57,32,24,21,31.1,1.0,24.79297,0.23800000000000002,42078,8,19,89,81,43,453,33422,4399,2948,56,800,269.6431,885.61682,0,0.0,0.0,0.0,1,5.84871,0,134,2,26 3,1,1,1,267,427,476,214,9,285,101,86,40,40,14.7,1.0,2.31799,0.23800000000000002,2445,16,38,177,160,85,93,1159,950,211,0,32,218.08298,716.27252,0,0.0,0.0,0.0,1,5.53231,0,267,3,49 diff --git a/activitysim/examples/example_mtc/notebooks/getting_started.ipynb b/activitysim/examples/example_mtc/notebooks/getting_started.ipynb index 008e18c765..b3e3170abe 100644 --- a/activitysim/examples/example_mtc/notebooks/getting_started.ipynb +++ b/activitysim/examples/example_mtc/notebooks/getting_started.ipynb @@ -112,11 +112,11 @@ "copying configs_mp ...\n", "copying output ...\n", "copying README.MD ...\n", - "copied! new project files are in C:\\projects\\development\\activitysim_rsg\\notebooks\\example\n", + "copied! new project files are in c:\\test\\activitysim\\activitysim\\examples\\example_mtc\\notebooks\\example\n", "the copied example can be run with\n", "\n", " activitysim run -w example\n", - "C:\\projects\\development\\activitysim_rsg\\notebooks\\example\n" + "c:\\test\\activitysim\\activitysim\\examples\\example_mtc\\notebooks\\example\n" ] } ], @@ -170,169 +170,516 @@ "INFO - run single process simulation\n", "INFO - open_pipeline\n", "INFO - Set random seed base to 0\n", - "INFO - Time to execute open_pipeline : 0.013 seconds (0.0 minutes)\n", + "INFO - Time to execute open_pipeline : 0.048 seconds (0.0 minutes)\n", + "INFO - init_trace file_name mem.csv\n", + "INFO - trace_memory_info #MEM pipeline.run before preload_injectables rss: 0.11GB used: 7.8 GB percent: 49.2%\n", "INFO - preload_injectables\n", - "INFO - Time to execute preload_injectables : 0.016 seconds (0.0 minutes)\n", + "INFO - Time to execute preload_injectables : 0.124 seconds (0.0 minutes)\n", + "INFO - trace_memory_info #MEM pipeline.run before run_models rss: 0.11GB used: 7.8 GB percent: 49.2%\n", + "INFO - #run_model running step initialize_landuse\n", "INFO - Reading CSV file data\\land_use.csv\n", - "INFO - renaming columns: {'ZONE': 'TAZ', 'COUNTY': 'county_id'}\n", - "INFO - keeping columns: ['DISTRICT', 'SD', 'county_id', 'TOTHH', 'TOTPOP', 'TOTACRE', 'RESACRE', 'CIACRE', 'TOTEMP', 'AGE0519', 'RETEMPN', 'FPSEMPN', 'HEREMPN', 'OTHEMPN', 'AGREMPN', 'MWTEMPN', 'PRKCST', 'OPRKCST', 'area_type', 'HSENROLL', 'COLLFTE', 'COLLPTE', 'TOPOLOGY', 'TERMINAL']\n", - "INFO - keeping columns: ['DISTRICT', 'SD', 'county_id', 'TOTHH', 'TOTPOP', 'TOTACRE', 'RESACRE', 'CIACRE', 'TOTEMP', 'AGE0519', 'RETEMPN', 'FPSEMPN', 'HEREMPN', 'OTHEMPN', 'AGREMPN', 'MWTEMPN', 'PRKCST', 'OPRKCST', 'area_type', 'HSENROLL', 'COLLFTE', 'COLLPTE', 'TOPOLOGY', 'TERMINAL']\n", - "INFO - land_use index name: TAZ\n", + "INFO - land_use index name: zone_id\n", "INFO - loaded land_use (25, 24)\n", - "INFO - annotated land_use SPEC annotate_landuse\n", - "INFO - loading skim_dict from data\\skims.omx\n", - "INFO - allocating shared buffer skim_skims_0 for 826 skims (skim size: (25, 25) * 4 bytes = 516250) total size: 2065000 (2.0 MB)\n", - "INFO - load_skims loaded skims from data\\skims.omx\n", - "INFO - Time to execute read_skims_from_omx : 0.585 seconds (0.0 minutes)\n", - "INFO - block_name skim_skims_0 bytes 2065000 (2.0 MB)\n", + "INFO - initialize_landuse - annotating land_use SPEC annotate_landuse\n", + "INFO - Network_LOS using skim_dict_factory: NumpyArraySkimFactory\n", + "INFO - trace_memory_info #MEM network_los.load_data before create_skim_dicts rss: 0.12GB used: 7.81 GB percent: 49.3%\n", + "INFO - allocate_skim_buffer shared False taz shape (826, 25, 25) total size: 2065000 (1.97 MB)\n", + "INFO - _read_skims_from_omx data\\skims.omx\n", + "INFO - _read_skims_from_omx loaded 826 skims from skims.omx\n", + "INFO - writing skim cache taz (826, 25, 25) to output\\cache\\cached_taz.mmap\n", + "INFO - load_skims_to_buffer taz shape (826, 25, 25)\n", + "INFO - get_skim_data taz SkimData shape (826, 25, 25)\n", + "INFO - SkimDict init taz\n", + "INFO - SkimDict.build_3d_skim_block_offset_table registered 167 3d keys\n", + "INFO - trace_memory_info network_los.load_data after create_skim_dicts rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info pipeline.run after initialize_landuse rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - #run_model running step compute_accessibility\n", "INFO - Running compute_accessibility with 25 dest zones\n", "INFO - Running compute_accessibility with 25 dest zones 25 orig zones\n", - "INFO - init AccessibilitySkims with 25 dest zones 25 orig zones omx_shape (25, 25)\n", - "INFO - init AccessibilitySkims with 25 dest zones 25 orig zones omx_shape (25, 25)\n", + "INFO - {trace_label} added {len(results.columns} columns\n", + "INFO - trace_memory_info pipeline.run after compute_accessibility rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - #run_model running step initialize_households\n", "INFO - Reading CSV file data\\households.csv\n", - "INFO - renaming columns: {'HHID': 'household_id', 'PERSONS': 'hhsize', 'workers': 'num_workers', 'VEHICL': 'auto_ownership'}\n", - "INFO - keeping columns: ['TAZ', 'income', 'hhsize', 'HHT', 'auto_ownership', 'num_workers']\n", - "INFO - keeping columns: ['TAZ', 'income', 'hhsize', 'HHT', 'auto_ownership', 'num_workers']\n", "INFO - households index name: household_id\n", "INFO - full household list contains 5000 households\n", "INFO - sampling 100 of 5000 households\n", "INFO - loaded households (100, 7)\n", "INFO - tracing household id 2223759 in 100 households\n", - "adding table households.household_id to traceable_table_indexes\n", "INFO - register households: added 1 new ids to 0 existing trace ids\n", "INFO - register households: tracing new ids [2223759] in households\n", "INFO - Reading CSV file data\\persons.csv\n", - "INFO - renaming columns: {'PERID': 'person_id'}\n", - "INFO - keeping columns: ['household_id', 'age', 'PNUM', 'sex', 'pemploy', 'pstudent', 'ptype']\n", - "INFO - keeping columns: ['household_id', 'age', 'PNUM', 'sex', 'pemploy', 'pstudent', 'ptype']\n", "INFO - persons index name: person_id\n", "INFO - loaded persons (167, 7)\n", - "adding table persons.person_id to traceable_table_indexes\n", "INFO - register persons: added 2 new ids to 0 existing trace ids\n", "INFO - register persons: tracing new ids [5389226, 5389227] in persons\n", - "INFO - annotated persons SPEC annotate_persons\n", - "INFO - annotated households SPEC annotate_households\n", - "INFO - annotated persons SPEC annotate_persons_after_hh\n", - "INFO - SkimStack.__init__ loaded 167 keys with 823 total skims\n", + "100 unique household_ids in persons\n", + "100 unique household_ids in households\n", + "INFO - initialize_households - annotating persons SPEC annotate_persons\n", + "INFO - initialize_households - annotating households SPEC annotate_households\n", + "INFO - initialize_households - annotating persons SPEC annotate_persons_after_hh\n", + "INFO - trace_memory_info initialize_households after shadow_pricing.add_size_tables rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info pipeline.run after initialize_households rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - #run_model running step school_location\n", "INFO - Running school_location.i1.sample.university with 17 persons\n", - "INFO - Running chunk 1 of 1 size 17\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 17 choosers\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.add.interaction_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", "INFO - Running eval_interaction_utilities on 425 rows\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.add.interaction_utilities rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.del.interaction_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.add.utilities rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.del.interaction_utilities rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.add.probs rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.del.utilities rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.add.choices_df rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.del.probs rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.add.choices_df rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.sample.university.interaction_sample.add.choices_df rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers school_location.i1.sample.university.interaction_sample number_of_rows: 17 observed_row_size: 200 num_chunks: 1\n", "INFO - Running school_location.i1.logsums.university with 62 rows\n", - "INFO - Running chunk 1 of 1 size 62\n", - "INFO - Time to execute eval_utilities : 0.572 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 62 choosers\n", + "INFO - Running chunk 1 of 1 with 62 of 62 choosers\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - #chunk_history adaptive_chunked_choosers school_location.i1.logsums.university.compute_logsums.simple_simulate_logsums number_of_rows: 62 observed_row_size: 322 num_chunks: 1\n", "INFO - Running school_location.i1.simulate.university with 17 persons\n", - "INFO - Running chunk 1 of 1 size 17\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 17 choosers and 62 alternatives\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.interaction_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", "INFO - Running eval_interaction_utilities on 62 rows\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.interaction_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.del.interaction_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.sample_counts rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.del.sample_counts rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.padded_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.del.interaction_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.utilities_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.del.padded_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.probs rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.logsums rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.del.utilities_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.positions rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.rands rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.del.probs rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.simulate.university.interaction_sample_simulate.add.choices rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts school_location.i1.simulate.university.interaction_sample_simulate number_of_rows: 17 observed_row_size: 48 num_chunks: 1\n", "INFO - Running school_location.i1.sample.highschool with 5 persons\n", - "INFO - Running chunk 1 of 1 size 5\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 5 choosers\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.add.interaction_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", "INFO - Running eval_interaction_utilities on 125 rows\n", - "INFO - Running school_location.i1.logsums.highschool with 10 rows\n", - "INFO - Running chunk 1 of 1 size 10\n", - "INFO - Time to execute eval_utilities : 0.833 seconds (0.0 minutes)\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.add.interaction_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.del.interaction_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.add.utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.del.interaction_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.add.probs rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.del.utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.add.choices_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.del.probs rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.add.choices_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.sample.highschool.interaction_sample.add.choices_df rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - #chunk_history adaptive_chunked_choosers school_location.i1.sample.highschool.interaction_sample number_of_rows: 5 observed_row_size: 200 num_chunks: 1\n", + "INFO - Running school_location.i1.logsums.highschool with 10 rows\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 10 choosers\n", + "INFO - Running chunk 1 of 1 with 10 of 10 choosers\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.13GB used: 7.79 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers school_location.i1.logsums.highschool.compute_logsums.simple_simulate_logsums number_of_rows: 10 observed_row_size: 322 num_chunks: 1\n", "INFO - Running school_location.i1.simulate.highschool with 5 persons\n", - "INFO - Running chunk 1 of 1 size 5\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 5 choosers and 10 alternatives\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.interaction_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", "INFO - Running eval_interaction_utilities on 10 rows\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.interaction_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.del.interaction_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.sample_counts rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.del.sample_counts rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.padded_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.del.interaction_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.utilities_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.del.padded_utilities rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.probs rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.logsums rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.del.utilities_df rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.positions rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.rands rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.del.probs rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info school_location.i1.simulate.highschool.interaction_sample_simulate.add.choices rss: 0.13GB used: 7.82 GB percent: 49.3%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts school_location.i1.simulate.highschool.interaction_sample_simulate number_of_rows: 5 observed_row_size: 26 num_chunks: 1\n", "INFO - Running school_location.i1.sample.gradeschool with 17 persons\n", - "INFO - Running chunk 1 of 1 size 17\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 17 choosers\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.add.interaction_df rss: 0.13GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 425 rows\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.add.interaction_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.del.interaction_df rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.add.utilities rss: 0.13GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.del.interaction_utilities rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.add.probs rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.del.utilities rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.add.choices_df rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.del.probs rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.add.choices_df rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.sample.gradeschool.interaction_sample.add.choices_df rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers school_location.i1.sample.gradeschool.interaction_sample number_of_rows: 17 observed_row_size: 200 num_chunks: 1\n", "INFO - Running school_location.i1.logsums.gradeschool with 168 rows\n", - "INFO - Running chunk 1 of 1 size 168\n", - "INFO - Time to execute eval_utilities : 0.547 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 168 choosers\n", + "INFO - Running chunk 1 of 1 with 168 of 168 choosers\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.13GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.13GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.13GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.13GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.13GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.13GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.13GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.13GB used: 7.87 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers school_location.i1.logsums.gradeschool.compute_logsums.simple_simulate_logsums number_of_rows: 168 observed_row_size: 322 num_chunks: 1\n", "INFO - Running school_location.i1.simulate.gradeschool with 17 persons\n", - "INFO - Running chunk 1 of 1 size 17\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 17 choosers and 168 alternatives\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.interaction_df rss: 0.13GB used: 7.87 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 168 rows\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.interaction_utilities rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.del.interaction_df rss: 0.13GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.sample_counts rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.del.sample_counts rss: 0.13GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.padded_utilities rss: 0.13GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.del.interaction_utilities rss: 0.13GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.utilities_df rss: 0.13GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.del.padded_utilities rss: 0.13GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.probs rss: 0.13GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.logsums rss: 0.13GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.del.utilities_df rss: 0.13GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.positions rss: 0.13GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.rands rss: 0.13GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.del.probs rss: 0.13GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info school_location.i1.simulate.gradeschool.interaction_sample_simulate.add.choices rss: 0.13GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts school_location.i1.simulate.gradeschool.interaction_sample_simulate number_of_rows: 17 observed_row_size: 129 num_chunks: 1\n", "INFO - write_trace_files iteration 1\n", - "INFO - school_taz_logsum top 10 value counts:\n", - "10.539866 1\n", - "12.125021 1\n", - "13.609502 1\n", - "11.178622 1\n", - "20.578601 1\n", + "INFO - school_location_logsum top 10 value counts:\n", + "14.273560 1\n", "20.361189 1\n", - "20.519581 1\n", - "10.165962 1\n", - "13.956237 1\n", - "12.278788 1\n", + "20.578601 1\n", + "14.105825 1\n", + "10.770754 1\n", + "10.705934 1\n", + "12.125021 1\n", + "19.950674 1\n", + "11.748869 1\n", + "12.526989 1\n", "Name: logsum, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after school_location rss: 0.13GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step workplace_location\n", "INFO - Running workplace_location.i1.sample.work_low with 37 persons\n", - "INFO - Running chunk 1 of 1 size 37\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 37 choosers\n", + "INFO - Running chunk 1 of 1 with 37 of 37 choosers\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.add.interaction_df rss: 0.13GB used: 7.84 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 925 rows\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.add.interaction_utilities rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.del.interaction_df rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.add.utilities rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.del.interaction_utilities rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.add.probs rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.del.utilities rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.add.choices_df rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.del.probs rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.add.choices_df rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_low.interaction_sample.add.choices_df rss: 0.13GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers workplace_location.i1.sample.work_low.interaction_sample number_of_rows: 37 observed_row_size: 175 num_chunks: 1\n", "INFO - Running workplace_location.i1.logsums.work_low with 504 rows\n", - "INFO - Running chunk 1 of 1 size 504\n", - "INFO - Time to execute eval_utilities : 0.567 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 504 choosers\n", + "INFO - Running chunk 1 of 1 with 504 of 504 choosers\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.13GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers workplace_location.i1.logsums.work_low.compute_logsums.simple_simulate_logsums number_of_rows: 504 observed_row_size: 310 num_chunks: 1\n", "INFO - Running workplace_location.i1.simulate.work_low with 37 persons\n", - "INFO - Running chunk 1 of 1 size 37\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 37 choosers and 504 alternatives\n", + "INFO - Running chunk 1 of 1 with 37 of 37 choosers\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 504 rows\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.del.interaction_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.sample_counts rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.del.sample_counts rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.padded_utilities rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.del.interaction_utilities rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.utilities_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.del.padded_utilities rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.probs rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.logsums rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.del.utilities_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.positions rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.rands rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.del.probs rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_low.interaction_sample_simulate.add.choices rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts workplace_location.i1.simulate.work_low.interaction_sample_simulate number_of_rows: 37 observed_row_size: 164 num_chunks: 1\n", "INFO - Running workplace_location.i1.sample.work_med with 26 persons\n", - "INFO - Running chunk 1 of 1 size 26\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 26 choosers\n", + "INFO - Running chunk 1 of 1 with 26 of 26 choosers\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.add.interaction_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", "INFO - Running eval_interaction_utilities on 650 rows\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.add.interaction_utilities rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.del.interaction_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.add.utilities rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.del.interaction_utilities rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.add.probs rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.del.utilities rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.add.choices_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.del.probs rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.add.choices_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_med.interaction_sample.add.choices_df rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - #chunk_history adaptive_chunked_choosers workplace_location.i1.sample.work_med.interaction_sample number_of_rows: 26 observed_row_size: 175 num_chunks: 1\n", "INFO - Running workplace_location.i1.logsums.work_med with 367 rows\n", - "INFO - Running chunk 1 of 1 size 367\n", - "INFO - Time to execute eval_utilities : 0.553 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 367 choosers\n", + "INFO - Running chunk 1 of 1 with 367 of 367 choosers\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers workplace_location.i1.logsums.work_med.compute_logsums.simple_simulate_logsums number_of_rows: 367 observed_row_size: 310 num_chunks: 1\n", "INFO - Running workplace_location.i1.simulate.work_med with 26 persons\n", - "INFO - Running chunk 1 of 1 size 26\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 26 choosers and 367 alternatives\n", + "INFO - Running chunk 1 of 1 with 26 of 26 choosers\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.interaction_df rss: 0.13GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 367 rows\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.interaction_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.del.interaction_df rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.sample_counts rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.del.sample_counts rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.padded_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.del.interaction_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.utilities_df rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.del.padded_utilities rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.probs rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.logsums rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.del.utilities_df rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.positions rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.rands rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.del.probs rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_med.interaction_sample_simulate.add.choices rss: 0.13GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts workplace_location.i1.simulate.work_med.interaction_sample_simulate number_of_rows: 26 observed_row_size: 170 num_chunks: 1\n", "INFO - Running workplace_location.i1.sample.work_high with 16 persons\n", - "INFO - Running chunk 1 of 1 size 16\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 16 choosers\n", + "INFO - Running chunk 1 of 1 with 16 of 16 choosers\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.add.interaction_df rss: 0.13GB used: 7.81 GB percent: 49.3%\n", "INFO - Running eval_interaction_utilities on 400 rows\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.add.interaction_utilities rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.del.interaction_df rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.add.utilities rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.del.interaction_utilities rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.add.probs rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.del.utilities rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.add.choices_df rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.del.probs rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.add.choices_df rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_high.interaction_sample.add.choices_df rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - #chunk_history adaptive_chunked_choosers workplace_location.i1.sample.work_high.interaction_sample number_of_rows: 16 observed_row_size: 175 num_chunks: 1\n", "INFO - Running workplace_location.i1.logsums.work_high with 226 rows\n", - "INFO - Running chunk 1 of 1 size 226\n", - "INFO - Time to execute eval_utilities : 0.559 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 226 choosers\n", + "INFO - Running chunk 1 of 1 with 226 of 226 choosers\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.13GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers workplace_location.i1.logsums.work_high.compute_logsums.simple_simulate_logsums number_of_rows: 226 observed_row_size: 310 num_chunks: 1\n", "INFO - Running workplace_location.i1.simulate.work_high with 16 persons\n", - "INFO - Running chunk 1 of 1 size 16\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 16 choosers and 226 alternatives\n", + "INFO - Running chunk 1 of 1 with 16 of 16 choosers\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.interaction_df rss: 0.13GB used: 7.81 GB percent: 49.2%\n", "INFO - Running eval_interaction_utilities on 226 rows\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.interaction_utilities rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.del.interaction_df rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.sample_counts rss: 0.13GB used: 7.81 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.del.sample_counts rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.padded_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.del.interaction_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.utilities_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.del.padded_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.probs rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.logsums rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.del.utilities_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.positions rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.rands rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.del.probs rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_high.interaction_sample_simulate.add.choices rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts workplace_location.i1.simulate.work_high.interaction_sample_simulate number_of_rows: 16 observed_row_size: 170 num_chunks: 1\n", "INFO - Running workplace_location.i1.sample.work_veryhigh with 18 persons\n", - "INFO - Running chunk 1 of 1 size 18\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 18 choosers\n", + "INFO - Running chunk 1 of 1 with 18 of 18 choosers\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.add.interaction_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", "INFO - Running eval_interaction_utilities on 450 rows\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.add.interaction_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.del.interaction_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.add.utilities rss: 0.13GB used: 7.79 GB percent: 49.1%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.del.interaction_utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.add.probs rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.del.utilities rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.add.choices_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.del.probs rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.add.choices_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.sample.work_veryhigh.interaction_sample.add.choices_df rss: 0.13GB used: 7.8 GB percent: 49.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers workplace_location.i1.sample.work_veryhigh.interaction_sample number_of_rows: 18 observed_row_size: 175 num_chunks: 1\n", "INFO - Running workplace_location.i1.logsums.work_veryhigh with 253 rows\n", - "INFO - Running chunk 1 of 1 size 253\n", - "INFO - Time to execute eval_utilities : 0.548 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 253 choosers\n", + "INFO - Running chunk 1 of 1 with 253 of 253 choosers\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.14GB used: 7.8 GB percent: 49.2%\n", + "INFO - trace_memory_info workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.14GB used: 7.81 GB percent: 49.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums number_of_rows: 253 observed_row_size: 310 num_chunks: 1\n", "INFO - Running workplace_location.i1.simulate.work_veryhigh with 18 persons\n", - "INFO - Running chunk 1 of 1 size 18\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 18 choosers and 253 alternatives\n", + "INFO - Running chunk 1 of 1 with 18 of 18 choosers\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.interaction_df rss: 0.14GB used: 7.81 GB percent: 49.3%\n", "INFO - Running eval_interaction_utilities on 253 rows\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.interaction_utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.del.interaction_df rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.sample_counts rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.del.sample_counts rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.padded_utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.del.interaction_utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.utilities_df rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.del.padded_utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.probs rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.logsums rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.del.utilities_df rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.positions rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.rands rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.del.probs rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.add.choices rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate number_of_rows: 18 observed_row_size: 169 num_chunks: 1\n", "INFO - write_trace_files iteration 1\n", "INFO - workplace_location_logsum top 10 value counts:\n", - "13.744453 1\n", + "15.316797 1\n", + "15.641096 1\n", "13.574187 1\n", - "15.371088 1\n", - "15.576300 1\n", - "14.410616 1\n", - "15.500614 1\n", - "15.551819 1\n", - "15.656738 1\n", - "13.802811 1\n", - "15.601574 1\n", + "15.586471 1\n", + "15.430079 1\n", + "15.412180 1\n", + "14.011234 1\n", + "15.522393 1\n", + "15.725096 1\n", + "13.564067 1\n", "Name: logsum, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after workplace_location rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - #run_model running step auto_ownership_simulate\n", "INFO - Running auto_ownership_simulate with 100 households\n", - "INFO - Running chunk 1 of 1 size 100\n", - "INFO - Time to execute eval_utilities : 0.069 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 100 choosers\n", + "INFO - Running chunk 1 of 1 with 100 of 100 choosers\n", + "INFO - trace_memory_info auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info auto_ownership_simulate.simple_simulate.eval_mnl.add.utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info auto_ownership_simulate.simple_simulate.eval_mnl.add.probs rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info auto_ownership_simulate.simple_simulate.eval_mnl.del.utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info auto_ownership_simulate.simple_simulate.eval_mnl.del.probs rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - #chunk_history adaptive_chunked_choosers auto_ownership_simulate.simple_simulate number_of_rows: 100 observed_row_size: 34 num_chunks: 1\n", "INFO - auto_ownership top 10 value counts:\n", "0 60\n", "1 40\n", "Name: auto_ownership, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after auto_ownership_simulate rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - #run_model running step free_parking\n", "INFO - Running free_parking with 97 persons\n", - "INFO - Running chunk 1 of 1 size 97\n", - "INFO - Time to execute eval_utilities : 0.0 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 97 choosers\n", + "INFO - Running chunk 1 of 1 with 97 of 97 choosers\n", + "INFO - trace_memory_info free_parking.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info free_parking.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info free_parking.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info free_parking.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info free_parking.simple_simulate.eval_mnl.add.utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info free_parking.simple_simulate.eval_mnl.add.probs rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info free_parking.simple_simulate.eval_mnl.del.utilities rss: 0.14GB used: 7.81 GB percent: 49.3%\n", + "INFO - trace_memory_info free_parking.simple_simulate.eval_mnl.del.probs rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - #chunk_history adaptive_chunked_choosers free_parking.simple_simulate number_of_rows: 97 observed_row_size: 10 num_chunks: 1\n", "INFO - free_parking top 10 value counts:\n", "False 163\n", "True 4\n", "Name: free_parking_at_work, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after free_parking rss: 0.14GB used: 7.82 GB percent: 49.3%\n", + "INFO - #run_model running step cdap_simulate\n", "INFO - Pre-building cdap specs\n", - "INFO - Time to execute build_cdap_spec hh_size 2 : 0.265 seconds (0.0 minutes)\n", - "INFO - Time to execute build_cdap_spec hh_size 3 : 0.826 seconds (0.0 minutes)\n", - "INFO - Time to execute build_cdap_spec hh_size 4 : 1.808 seconds (0.0 minutes)\n", - "INFO - Time to execute build_cdap_spec hh_size 5 : 3.978 seconds (0.1 minutes)\n", + "INFO - Time to execute build_cdap_spec hh_size 2 : 0.537 seconds (0.0 minutes)\n", + "INFO - Time to execute build_cdap_spec hh_size 3 : 1.245 seconds (0.0 minutes)\n", + "INFO - Time to execute build_cdap_spec hh_size 4 : 3.603 seconds (0.1 minutes)\n", + "INFO - Time to execute build_cdap_spec hh_size 5 : 9.064 seconds (0.2 minutes)\n", "INFO - Running cdap_simulate with 167 persons\n", - "INFO - Running chunk 1 of 1 with 167 persons\n", - "INFO - Time to execute eval_utilities : 0.24 seconds (0.0 minutes)\n", + "INFO - Running chunk 1 of 1 with 100 of 100 choosers\n", + "INFO - trace_memory_info cdap.cdap.add.persons rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.add.indiv_utils rss: 0.15GB used: 7.83 GB percent: 49.4%\n", "INFO - build_cdap_spec returning cached injectable spec cdap_spec_2\n", - "INFO - Time to execute eval_utilities : 0.102 seconds (0.0 minutes)\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", "INFO - build_cdap_spec returning cached injectable spec cdap_spec_3\n", - "INFO - Time to execute eval_utilities : 0.316 seconds (0.0 minutes)\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", "INFO - build_cdap_spec returning cached injectable spec cdap_spec_4\n", - "INFO - Time to execute eval_utilities : 0.77 seconds (0.0 minutes)\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", "INFO - build_cdap_spec returning cached injectable spec cdap_spec_5\n", - "INFO - Time to execute eval_utilities : 1.654 seconds (0.0 minutes)\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.eval_utils.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.del.indiv_utils rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.add.hh_activity_choices rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.add.persons rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info cdap.cdap.del.persons rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id cdap.cdap number_of_rows: 100 observed_row_size: 258 num_chunks: 1\n", "INFO - cdap_activity top 10 value counts:\n", "M 89\n", "N 48\n", @@ -350,10 +697,20 @@ "7 0 10 1 11\n", "8 0 6 0 6\n", "All 30 89 48 167\n", + "INFO - trace_memory_info pipeline.run after cdap_simulate rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #run_model running step mandatory_tour_frequency\n", "INFO - Running mandatory_tour_frequency with 89 persons\n", - "INFO - Running chunk 1 of 1 size 89\n", - "INFO - Time to execute eval_utilities : 0.508 seconds (0.0 minutes)\n", - "adding table tours.tour_id to traceable_table_indexes\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 89 choosers\n", + "INFO - Running chunk 1 of 1 with 89 of 89 choosers\n", + "INFO - trace_memory_info mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_frequency.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_frequency.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_frequency.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_frequency.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_frequency.simple_simulate number_of_rows: 89 observed_row_size: 103 num_chunks: 1\n", "INFO - register tours: added 2 new ids to 0 existing trace ids\n", "INFO - register tours: tracing new ids [220958305, 220958346] in tours\n", "INFO - mandatory_tour_frequency top 10 value counts:\n", @@ -363,115 +720,438 @@ "work_and_school 4\n", "work2 2\n", "Name: mandatory_tour_frequency, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after mandatory_tour_frequency rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #run_model running step mandatory_tour_scheduling\n", "DEBUG - @inject timetable\n", "INFO - Running mandatory_tour_scheduling with 95 tours\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work schedule_tours running 67 tour choices\n", - "INFO - Running chunk 1 of 1 size 67\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 67 choosers\n", + "INFO - Running chunk 1 of 1 with 67 of 67 choosers\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work schedule_tours running 67 tour choices\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.add.tours rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "tours (67, 35) alts (190, 3)\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.add.alt_tdd rss: 0.15GB used: 7.83 GB percent: 49.4%\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums compute_logsums for 5695 choosers5695 alts\n", - "INFO - Running chunk 1 of 1 size 5695\n", - "INFO - Time to execute eval_utilities : 0.817 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 67\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 5695 choosers\n", + "INFO - Running chunk 1 of 1 with 5695 of 5695 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.16GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.17GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.17GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.16GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.16GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.16GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.16GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.16GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums number_of_rows: 5695 observed_row_size: 320 num_chunks: 1\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.add.tours rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 67 choosers and 12730 alternatives\n", + "INFO - Running chunk 1 of 1 with 67 of 67 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", "INFO - Running eval_interaction_utilities on 12730 rows\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate number_of_rows: 67 observed_row_size: 9347 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work number_of_rows: 67 observed_row_size: 27145 num_chunks: 1\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school schedule_tours running 17 tour choices\n", - "INFO - Running chunk 1 of 1 size 17\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 17 choosers\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school schedule_tours running 17 tour choices\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.add.tours rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "tours (17, 35) alts (190, 3)\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.add.alt_tdd rss: 0.14GB used: 7.84 GB percent: 49.4%\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums compute_logsums for 1445 choosers1445 alts\n", - "INFO - Running chunk 1 of 1 size 1445\n", - "INFO - Time to execute eval_utilities : 0.634 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 17\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1445 choosers\n", + "INFO - Running chunk 1 of 1 with 1445 of 1445 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.logsums.simple_simulate_logsums number_of_rows: 1445 observed_row_size: 332 num_chunks: 1\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.add.tours rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 17 choosers and 3230 alternatives\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 3230 rows\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school.interaction_sample_simulate number_of_rows: 17 observed_row_size: 9347 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.school number_of_rows: 17 observed_row_size: 28165 num_chunks: 1\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ schedule_tours running 5 tour choices\n", - "INFO - Running chunk 1 of 1 size 5\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 5 choosers\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ schedule_tours running 5 tour choices\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.add.tours rss: 0.14GB used: 7.86 GB percent: 49.6%\n", + "tours (5, 35) alts (190, 3)\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.add.alt_tdd rss: 0.14GB used: 7.86 GB percent: 49.6%\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums compute_logsums for 425 choosers425 alts\n", - "INFO - Running chunk 1 of 1 size 425\n", - "INFO - Time to execute eval_utilities : 0.608 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 5\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 425 choosers\n", + "INFO - Running chunk 1 of 1 with 425 of 425 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.14GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.logsums.simple_simulate_logsums number_of_rows: 425 observed_row_size: 332 num_chunks: 1\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.add.tours rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 5 choosers and 950 alternatives\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 950 rows\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.del.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.sample_counts rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.del.sample_counts rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.padded_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.del.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.utilities_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.del.padded_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.del.utilities_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.positions rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.rands rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.del.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate.add.choices rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ.interaction_sample_simulate number_of_rows: 5 observed_row_size: 9347 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.univ number_of_rows: 5 observed_row_size: 28165 num_chunks: 1\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work schedule_tours running 2 tour choices\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 2 choosers\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work schedule_tours running 2 tour choices\n", - "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums compute_logsums for 83 choosers83 alts\n", - "INFO - Running chunk 1 of 1 size 83\n", - "INFO - Time to execute eval_utilities : 0.579 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.add.tours rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "tours (2, 35) alts (190, 3)\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.add.alt_tdd rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums compute_logsums for 75 choosers75 alts\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 75 choosers\n", + "INFO - Running chunk 1 of 1 with 75 of 75 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.logsums.simple_simulate_logsums number_of_rows: 75 observed_row_size: 321 num_chunks: 1\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.add.tours rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 2 choosers and 175 alternatives\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 175 rows\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work.interaction_sample_simulate number_of_rows: 2 observed_row_size: 4325 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work number_of_rows: 2 observed_row_size: 12010 num_chunks: 1\n", "INFO - skipping empty segment school\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ schedule_tours running 4 tour choices\n", - "INFO - Running chunk 1 of 1 size 4\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 4 choosers\n", + "INFO - Running chunk 1 of 1 with 4 of 4 choosers\n", "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ schedule_tours running 4 tour choices\n", - "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums compute_logsums for 179 choosers179 alts\n", - "INFO - Running chunk 1 of 1 size 179\n", - "INFO - Time to execute eval_utilities : 0.648 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 4\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.add.tours rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "tours (4, 35) alts (190, 3)\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.add.alt_tdd rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums compute_logsums for 161 choosers161 alts\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 161 choosers\n", + "INFO - Running chunk 1 of 1 with 161 of 161 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.logsums.simple_simulate_logsums number_of_rows: 161 observed_row_size: 332 num_chunks: 1\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.add.tours rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 4 choosers and 366 alternatives\n", + "INFO - Running chunk 1 of 1 with 4 of 4 choosers\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.interaction_df rss: 0.14GB used: 7.89 GB percent: 49.8%\n", "INFO - Running eval_interaction_utilities on 366 rows\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.interaction_utilities rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.del.interaction_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.sample_counts rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.del.sample_counts rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.padded_utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.del.interaction_utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.utilities_df rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.del.padded_utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.probs rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.del.utilities_df rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.positions rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.rands rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.del.probs rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate.add.choices rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ.interaction_sample_simulate number_of_rows: 4 observed_row_size: 4521 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ number_of_rows: 4 observed_row_size: 13362 num_chunks: 1\n", + "INFO - trace_memory_info pipeline.run after mandatory_tour_scheduling rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - #run_model running step joint_tour_frequency\n", "INFO - Running joint_tour_frequency with 36 multi-person households\n", "DEBUG - @inject timetable\n", - "INFO - Running chunk 1 of 1 size 36\n", - "INFO - Time to execute eval_utilities : 0.214 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 36 choosers\n", + "INFO - Running chunk 1 of 1 with 36 of 36 choosers\n", + "INFO - trace_memory_info joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_frequency.simple_simulate.eval_mnl.add.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_frequency.simple_simulate.eval_mnl.add.probs rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_frequency.simple_simulate.eval_mnl.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_frequency.simple_simulate.eval_mnl.del.probs rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_frequency.simple_simulate number_of_rows: 36 observed_row_size: 91 num_chunks: 1\n", "INFO - register tours: added 1 new ids to 2 existing trace ids\n", "INFO - register tours: tracing new ids [220958279] in tours\n", "INFO - joint_tour_frequency top 10 value counts:\n", "0_tours 97\n", - "1_Shop 1\n", "1_Disc 1\n", + "1_Shop 1\n", "1_Eat 1\n", "Name: joint_tour_frequency, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after joint_tour_frequency rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - #run_model running step joint_tour_composition\n", "INFO - Running joint_tour_composition with 3 joint tours\n", "DEBUG - @inject timetable\n", - "INFO - Running chunk 1 of 1 size 3\n", - "INFO - Time to execute eval_utilities : 0.065 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info joint_tour_composition.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_composition.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_composition.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_composition.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_composition.simple_simulate.eval_mnl.add.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_composition.simple_simulate.eval_mnl.add.probs rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_composition.simple_simulate.eval_mnl.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_composition.simple_simulate.eval_mnl.del.probs rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_composition.simple_simulate number_of_rows: 3 observed_row_size: 24 num_chunks: 1\n", "INFO - joint_tour_composition top 10 value counts:\n", "adults 2\n", "children 1\n", "Name: composition, dtype: int64\n", - "adding table joint_tour_participants.participant_id to traceable_table_indexes\n", + "INFO - trace_memory_info pipeline.run after joint_tour_composition rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #run_model running step joint_tour_participation\n", "INFO - register joint_tour_participants: added 2 new ids to 0 existing trace ids\n", "INFO - register joint_tour_participants: tracing new ids [22095827901, 22095827902] in joint_tour_participants\n", "INFO - Running joint_tours_participation with 8 potential participants (candidates)\n", "DEBUG - @inject timetable\n", - "INFO - Running chunk 1 of 1 size 8\n", - "INFO - Time to execute eval_utilities : 0.316 seconds (0.0 minutes)\n", - "INFO - joint_tour_participation.simple_simulate.eval_mnl.participants_chooser 3 joint tours to satisfy.\n", - "INFO - joint_tour_participation.simple_simulate.eval_mnl.participants_chooser iteration 1 : 3 joint tours satisfied 0 remaining\n", - "INFO - joint_tour_participation.simple_simulate.eval_mnl.participants_chooser 1 iterations to satisfy all joint tours.\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info joint_tour_participation.eval_mnl.eval_utils.add.expression_values rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_participation.eval_mnl.eval_utils.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.3%\n", + "INFO - trace_memory_info joint_tour_participation.eval_mnl.eval_utils.del.expression_values rss: 0.14GB used: 7.83 GB percent: 49.3%\n", + "INFO - trace_memory_info joint_tour_participation.eval_mnl.eval_utils.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.3%\n", + "INFO - trace_memory_info joint_tour_participation.eval_mnl.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.3%\n", + "INFO - trace_memory_info joint_tour_participation.eval_mnl.add.probs rss: 0.14GB used: 7.83 GB percent: 49.3%\n", + "INFO - trace_memory_info joint_tour_participation.eval_mnl.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.3%\n", + "INFO - joint_tour_participation.eval_mnl.participants_chooser 3 joint tours to satisfy.\n", + "INFO - joint_tour_participation.eval_mnl.participants_chooser iteration 1 : 3 joint tours satisfied 0 remaining\n", + "INFO - joint_tour_participation.eval_mnl.participants_chooser 1 iterations to satisfy all joint tours.\n", + "INFO - trace_memory_info joint_tour_participation.eval_mnl.del.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id joint_tour_participation number_of_rows: 3 observed_row_size: 144 num_chunks: 1\n", + "INFO - trace_memory_info pipeline.run after joint_tour_participation rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #run_model running step joint_tour_destination\n", "INFO - Running segment 'shopping' of 1 joint_tours 25 alternatives\n", - "INFO - running non_mandatory_tour_destination.sample.shopping with 1 tours\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - running joint_tour_destination.sample.shopping with 1 tours\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.add.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 25 rows\n", - "INFO - Running non_mandatory_tour_destination.logsums.shopping with 11 rows\n", - "INFO - Running chunk 1 of 1 size 11\n", - "INFO - Time to execute eval_utilities : 0.554 seconds (0.0 minutes)\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.add.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.del.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.del.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.add.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.add.choices_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.del.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.add.choices_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.shopping.interaction_sample.add.choices_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_destination.sample.shopping.interaction_sample number_of_rows: 1 observed_row_size: 150 num_chunks: 1\n", + "INFO - Running joint_tour_destination.logsums.shopping with 11 rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 11 choosers\n", + "INFO - Running chunk 1 of 1 with 11 of 11 choosers\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums number_of_rows: 11 observed_row_size: 310 num_chunks: 1\n", "INFO - Running tour_destination_simulate with 1 persons\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 11 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 11 rows\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.del.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.sample_counts rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.del.sample_counts rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.padded_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.del.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.utilities_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.del.padded_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.logsums rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.del.utilities_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.positions rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.rands rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.del.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.shopping.interaction_sample_simulate.add.choices rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts joint_tour_destination.simulate.shopping.interaction_sample_simulate number_of_rows: 1 observed_row_size: 121 num_chunks: 1\n", "INFO - Running segment 'othmaint' of 0 joint_tours 25 alternatives\n", - "INFO - non_mandatory_tour_destination skipping segment othmaint: no choosers\n", + "INFO - joint_tour_destination skipping segment othmaint: no choosers\n", "INFO - Running segment 'othdiscr' of 1 joint_tours 25 alternatives\n", - "INFO - running non_mandatory_tour_destination.sample.othdiscr with 1 tours\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - running joint_tour_destination.sample.othdiscr with 1 tours\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.add.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 25 rows\n", - "INFO - Running non_mandatory_tour_destination.logsums.othdiscr with 15 rows\n", - "INFO - Running chunk 1 of 1 size 15\n", - "INFO - Time to execute eval_utilities : 0.558 seconds (0.0 minutes)\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.add.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.del.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.del.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.add.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.add.choices_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.del.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.add.choices_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.othdiscr.interaction_sample.add.choices_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_destination.sample.othdiscr.interaction_sample number_of_rows: 1 observed_row_size: 150 num_chunks: 1\n", + "INFO - Running joint_tour_destination.logsums.othdiscr with 15 rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 15 choosers\n", + "INFO - Running chunk 1 of 1 with 15 of 15 choosers\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums number_of_rows: 15 observed_row_size: 310 num_chunks: 1\n", "INFO - Running tour_destination_simulate with 1 persons\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 15 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 15 rows\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts joint_tour_destination.simulate.othdiscr.interaction_sample_simulate number_of_rows: 1 observed_row_size: 165 num_chunks: 1\n", "INFO - Running segment 'eatout' of 1 joint_tours 25 alternatives\n", - "INFO - running non_mandatory_tour_destination.sample.eatout with 1 tours\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - running joint_tour_destination.sample.eatout with 1 tours\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.add.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 25 rows\n", - "INFO - Running non_mandatory_tour_destination.logsums.eatout with 15 rows\n", - "INFO - Running chunk 1 of 1 size 15\n", - "INFO - Time to execute eval_utilities : 0.549 seconds (0.0 minutes)\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.del.interaction_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.add.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.add.probs rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.del.utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.add.choices_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.del.probs rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.add.choices_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.sample.eatout.interaction_sample.add.choices_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_destination.sample.eatout.interaction_sample number_of_rows: 1 observed_row_size: 150 num_chunks: 1\n", + "INFO - Running joint_tour_destination.logsums.eatout with 16 rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 16 choosers\n", + "INFO - Running chunk 1 of 1 with 16 of 16 choosers\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums number_of_rows: 16 observed_row_size: 310 num_chunks: 1\n", "INFO - Running tour_destination_simulate with 1 persons\n", - "INFO - Running chunk 1 of 1 size 1\n", - "INFO - Running eval_interaction_utilities on 15 rows\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 16 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - Running eval_interaction_utilities on 16 rows\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.del.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.sample_counts rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.del.sample_counts rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.padded_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.del.interaction_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.utilities_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.del.padded_utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.logsums rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.del.utilities_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.positions rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.rands rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.del.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_destination.simulate.eatout.interaction_sample_simulate.add.choices rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts joint_tour_destination.simulate.eatout.interaction_sample_simulate number_of_rows: 1 observed_row_size: 176 num_chunks: 1\n", "INFO - Running segment 'social' of 0 joint_tours 25 alternatives\n", - "INFO - non_mandatory_tour_destination skipping segment social: no choosers\n", + "INFO - joint_tour_destination skipping segment social: no choosers\n", "INFO - Running segment 'escort' of 0 joint_tours 25 alternatives\n", - "INFO - non_mandatory_tour_destination skipping segment escort: no choosers\n", + "INFO - joint_tour_destination skipping segment escort: no choosers\n", "INFO - destination summary:\n", "count 3.000000\n", "mean 14.000000\n", @@ -480,936 +1160,4993 @@ "25% 9.000000\n", "50% 13.000000\n", "75% 18.500000\n", - "max 24.000000\n", + "max 24.000000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Name: destination, dtype: float64\n", + "INFO - trace_memory_info pipeline.run after joint_tour_destination rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #run_model running step joint_tour_scheduling\n", "INFO - Running joint_tour_scheduling with 3 joint tours\n", "DEBUG - @inject timetable\n", "INFO - schedule_tours %s tours not monotonic_increasing - sorting df\n", "INFO - joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1 schedule_tours running 3 tour choices\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", "INFO - joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1 schedule_tours running 3 tour choices\n", - "INFO - Running chunk 1 of 1 size 3\n", - "INFO - Running eval_interaction_utilities on 60 rows\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.add.tours rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "tours (3, 144) alts (190, 3)\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.add.alt_tdd rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.add.tours rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 3 choosers and 65 alternatives\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - Running eval_interaction_utilities on 65 rows\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate number_of_rows: 3 observed_row_size: 3526 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1 number_of_rows: 3 observed_row_size: 3526 num_chunks: 1\n", + "INFO - trace_memory_info pipeline.run after joint_tour_scheduling rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #run_model running step non_mandatory_tour_frequency\n", "DEBUG - @inject timetable\n", "INFO - Running non_mandatory_tour_frequency with 137 persons\n", "INFO - Running segment 'PTYPE_FULL' of size 48\n", - "INFO - Running chunk 1 of 1 size 48\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 48 choosers\n", + "INFO - Running chunk 1 of 1 with 48 of 48 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.add.interaction_df rss: 0.15GB used: 7.83 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 4608 rows\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.add.interaction_utilities rss: 0.15GB used: 7.83 GB percent: 49.4%\n", + "interaction_df (4608, 144)\n", + "interaction_utilities (4608, 1)\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.del.interaction_df rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.add.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.add.probs rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.del.utilities rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.add.positions rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.add.rands rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.add.choices rss: 0.14GB used: 7.83 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate number_of_rows: 48 observed_row_size: 13920 num_chunks: 1\n", "INFO - Running segment 'PTYPE_PART' of size 26\n", - "INFO - Running chunk 1 of 1 size 26\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 26 choosers\n", + "INFO - Running chunk 1 of 1 with 26 of 26 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 2496 rows\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "interaction_df (2496, 144)\n", + "interaction_utilities (2496, 1)\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate.interaction_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_frequency.PTYPE_PART.interaction_simulate number_of_rows: 26 observed_row_size: 13920 num_chunks: 1\n", "INFO - Running segment 'PTYPE_UNIVERSITY' of size 16\n", - "INFO - Running chunk 1 of 1 size 16\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 16 choosers\n", + "INFO - Running chunk 1 of 1 with 16 of 16 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 1536 rows\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "interaction_df (1536, 144)\n", + "interaction_utilities (1536, 1)\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate.interaction_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_frequency.PTYPE_UNIVERSITY.interaction_simulate number_of_rows: 16 observed_row_size: 13920 num_chunks: 1\n", "INFO - Running segment 'PTYPE_NONWORK' of size 17\n", - "INFO - Running chunk 1 of 1 size 17\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 17 choosers\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 1632 rows\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "interaction_df (1632, 144)\n", + "interaction_utilities (1632, 1)\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate.interaction_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_frequency.PTYPE_NONWORK.interaction_simulate number_of_rows: 17 observed_row_size: 13920 num_chunks: 1\n", "INFO - Running segment 'PTYPE_RETIRED' of size 12\n", - "INFO - Running chunk 1 of 1 size 12\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 12 choosers\n", + "INFO - Running chunk 1 of 1 with 12 of 12 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 1152 rows\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "interaction_df (1152, 144)\n", + "interaction_utilities (1152, 1)\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate.interaction_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_frequency.PTYPE_RETIRED.interaction_simulate number_of_rows: 12 observed_row_size: 13920 num_chunks: 1\n", "INFO - Running segment 'PTYPE_DRIVING' of size 1\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 96 rows\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "interaction_df (96, 144)\n", + "interaction_utilities (96, 1)\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate.interaction_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_frequency.PTYPE_DRIVING.interaction_simulate number_of_rows: 1 observed_row_size: 13920 num_chunks: 1\n", "INFO - Running segment 'PTYPE_SCHOOL' of size 11\n", - "INFO - Running chunk 1 of 1 size 11\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 11 choosers\n", + "INFO - Running chunk 1 of 1 with 11 of 11 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 1056 rows\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "interaction_df (1056, 144)\n", + "interaction_utilities (1056, 1)\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate.interaction_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_frequency.PTYPE_SCHOOL.interaction_simulate number_of_rows: 11 observed_row_size: 13920 num_chunks: 1\n", "INFO - Running segment 'PTYPE_PRESCHOOL' of size 6\n", - "INFO - Running chunk 1 of 1 size 6\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 6 choosers\n", + "INFO - Running chunk 1 of 1 with 6 of 6 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 576 rows\n", - "INFO - extend_tour_counts increased tour count by 5 from 90 to 95\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "interaction_df (576, 144)\n", + "interaction_utilities (576, 1)\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate.interaction_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_frequency.PTYPE_PRESCHOOL.interaction_simulate number_of_rows: 6 observed_row_size: 13920 num_chunks: 1\n", + "INFO - extend_tour_counts increased tour count by 5 from 89 to 94\n", "WARNING - register tours: no rows with household_id in [2223759].\n", "INFO - register tours: added 0 new ids to 3 existing trace ids\n", "INFO - register tours: tracing new ids [] in tours\n", "INFO - non_mandatory_tour_frequency top 10 value counts:\n", "0 102\n", - "16 18\n", + "16 17\n", + "8 9\n", "1 8\n", - "8 8\n", "4 7\n", "2 3\n", - "9 3\n", "20 3\n", - "12 2\n", - "17 2\n", + "9 3\n", + "17 3\n", + "24 2\n", "Name: non_mandatory_tour_frequency, dtype: int64\n", "WARNING - slice_canonically: no rows in non_mandatory_tour_frequency.non_mandatory_tours with household_id == [2223759]\n", - "INFO - running non_mandatory_tour_destination.sample.shopping with 31 tours\n", - "INFO - Running chunk 1 of 1 size 31\n", - "INFO - Running eval_interaction_utilities on 775 rows\n", - "INFO - Running non_mandatory_tour_destination.logsums.shopping with 378 rows\n", - "INFO - Running chunk 1 of 1 size 378\n", - "INFO - Time to execute eval_utilities : 0.575 seconds (0.0 minutes)\n", - "INFO - Running tour_destination_simulate with 31 persons\n", - "INFO - Running chunk 1 of 1 size 31\n", - "INFO - Running eval_interaction_utilities on 378 rows\n", - "INFO - running non_mandatory_tour_destination.sample.othmaint with 19 tours\n", - "INFO - Running chunk 1 of 1 size 19\n", - "INFO - Running eval_interaction_utilities on 475 rows\n", - "INFO - Running non_mandatory_tour_destination.logsums.othmaint with 272 rows\n", - "INFO - Running chunk 1 of 1 size 272\n", - "INFO - Time to execute eval_utilities : 0.555 seconds (0.0 minutes)\n", - "INFO - Running tour_destination_simulate with 19 persons\n", - "INFO - Running chunk 1 of 1 size 19\n", - "INFO - Running eval_interaction_utilities on 272 rows\n", + "INFO - trace_memory_info pipeline.run after non_mandatory_tour_frequency rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step non_mandatory_tour_destination\n", + "INFO - running non_mandatory_tour_destination.sample.shopping with 30 tours\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 30 choosers\n", + "INFO - Running chunk 1 of 1 with 30 of 30 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 750 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.shopping.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.sample.shopping.interaction_sample number_of_rows: 30 observed_row_size: 150 num_chunks: 1\n", + "INFO - Running non_mandatory_tour_destination.logsums.shopping with 366 rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 366 choosers\n", + "INFO - Running chunk 1 of 1 with 366 of 366 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.logsums.shopping.compute_logsums.simple_simulate_logsums number_of_rows: 366 observed_row_size: 310 num_chunks: 1\n", + "INFO - Running tour_destination_simulate with 30 persons\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 30 choosers and 366 alternatives\n", + "INFO - Running chunk 1 of 1 with 30 of 30 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 366 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_destination.simulate.shopping.interaction_sample_simulate number_of_rows: 30 observed_row_size: 135 num_chunks: 1\n", + "INFO - running non_mandatory_tour_destination.sample.othmaint with 20 tours\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 20 choosers\n", + "INFO - Running chunk 1 of 1 with 20 of 20 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 500 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othmaint.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.sample.othmaint.interaction_sample number_of_rows: 20 observed_row_size: 150 num_chunks: 1\n", + "INFO - Running non_mandatory_tour_destination.logsums.othmaint with 286 rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 286 choosers\n", + "INFO - Running chunk 1 of 1 with 286 of 286 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.logsums.othmaint.compute_logsums.simple_simulate_logsums number_of_rows: 286 observed_row_size: 310 num_chunks: 1\n", + "INFO - Running tour_destination_simulate with 20 persons\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 20 choosers and 286 alternatives\n", + "INFO - Running chunk 1 of 1 with 20 of 20 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 286 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_destination.simulate.othmaint.interaction_sample_simulate number_of_rows: 20 observed_row_size: 158 num_chunks: 1\n", "INFO - running non_mandatory_tour_destination.sample.othdiscr with 19 tours\n", - "INFO - Running chunk 1 of 1 size 19\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 19 choosers\n", + "INFO - Running chunk 1 of 1 with 19 of 19 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 475 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.othdiscr.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.sample.othdiscr.interaction_sample number_of_rows: 19 observed_row_size: 150 num_chunks: 1\n", "INFO - Running non_mandatory_tour_destination.logsums.othdiscr with 277 rows\n", - "INFO - Running chunk 1 of 1 size 277\n", - "INFO - Time to execute eval_utilities : 0.571 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 277 choosers\n", + "INFO - Running chunk 1 of 1 with 277 of 277 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.logsums.othdiscr.compute_logsums.simple_simulate_logsums number_of_rows: 277 observed_row_size: 310 num_chunks: 1\n", "INFO - Running tour_destination_simulate with 19 persons\n", - "INFO - Running chunk 1 of 1 size 19\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 19 choosers and 277 alternatives\n", + "INFO - Running chunk 1 of 1 with 19 of 19 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 277 rows\n", - "INFO - running non_mandatory_tour_destination.sample.eatout with 16 tours\n", - "INFO - Running chunk 1 of 1 size 16\n", - "INFO - Running eval_interaction_utilities on 400 rows\n", - "INFO - Running non_mandatory_tour_destination.logsums.eatout with 216 rows\n", - "INFO - Running chunk 1 of 1 size 216\n", - "INFO - Time to execute eval_utilities : 0.549 seconds (0.0 minutes)\n", - "INFO - Running tour_destination_simulate with 16 persons\n", - "INFO - Running chunk 1 of 1 size 16\n", - "INFO - Running eval_interaction_utilities on 216 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_destination.simulate.othdiscr.interaction_sample_simulate number_of_rows: 19 observed_row_size: 161 num_chunks: 1\n", + "INFO - running non_mandatory_tour_destination.sample.eatout with 15 tours\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 15 choosers\n", + "INFO - Running chunk 1 of 1 with 15 of 15 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 375 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.eatout.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.sample.eatout.interaction_sample number_of_rows: 15 observed_row_size: 150 num_chunks: 1\n", + "INFO - Running non_mandatory_tour_destination.logsums.eatout with 203 rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 203 choosers\n", + "INFO - Running chunk 1 of 1 with 203 of 203 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums number_of_rows: 203 observed_row_size: 310 num_chunks: 1\n", + "INFO - Running tour_destination_simulate with 15 persons\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 15 choosers and 203 alternatives\n", + "INFO - Running chunk 1 of 1 with 15 of 15 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.interaction_df rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 203 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.interaction_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.del.interaction_df rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.sample_counts rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.del.sample_counts rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.padded_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.del.interaction_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.utilities_df rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.del.padded_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.probs rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.logsums rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.del.utilities_df rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.positions rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.rands rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.del.probs rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.add.choices rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate number_of_rows: 15 observed_row_size: 149 num_chunks: 1\n", "INFO - running non_mandatory_tour_destination.sample.social with 7 tours\n", - "INFO - Running chunk 1 of 1 size 7\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 7 choosers\n", + "INFO - Running chunk 1 of 1 with 7 of 7 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.add.interaction_df rss: 0.14GB used: 7.85 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 175 rows\n", - "INFO - Running non_mandatory_tour_destination.logsums.social with 91 rows\n", - "INFO - Running chunk 1 of 1 size 91\n", - "INFO - Time to execute eval_utilities : 0.549 seconds (0.0 minutes)\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.add.interaction_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.del.interaction_df rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.add.utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.del.interaction_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.add.probs rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.add.choices_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.del.probs rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.add.choices_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.social.interaction_sample.add.choices_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.sample.social.interaction_sample number_of_rows: 7 observed_row_size: 150 num_chunks: 1\n", + "INFO - Running non_mandatory_tour_destination.logsums.social with 97 rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 97 choosers\n", + "INFO - Running chunk 1 of 1 with 97 of 97 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.logsums.social.compute_logsums.simple_simulate_logsums number_of_rows: 97 observed_row_size: 310 num_chunks: 1\n", "INFO - Running tour_destination_simulate with 7 persons\n", - "INFO - Running chunk 1 of 1 size 7\n", - "INFO - Running eval_interaction_utilities on 91 rows\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 7 choosers and 97 alternatives\n", + "INFO - Running chunk 1 of 1 with 7 of 7 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.interaction_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 97 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.interaction_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.del.interaction_df rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.sample_counts rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.del.sample_counts rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.padded_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.del.interaction_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.utilities_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.del.padded_utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.probs rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.logsums rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.del.utilities_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.positions rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.rands rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.del.probs rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.social.interaction_sample_simulate.add.choices rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_destination.simulate.social.interaction_sample_simulate number_of_rows: 7 observed_row_size: 153 num_chunks: 1\n", "INFO - running non_mandatory_tour_destination.sample.escort with 3 tours\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.add.interaction_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", "INFO - Running eval_interaction_utilities on 75 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.add.interaction_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.del.interaction_df rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.add.utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.del.interaction_utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.add.probs rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.add.choices_df rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.del.probs rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.add.choices_df rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.sample.escort.interaction_sample.add.choices_df rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.sample.escort.interaction_sample number_of_rows: 3 observed_row_size: 150 num_chunks: 1\n", "INFO - Running non_mandatory_tour_destination.logsums.escort with 41 rows\n", - "INFO - Running chunk 1 of 1 size 41\n", - "INFO - Time to execute eval_utilities : 0.549 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 41 choosers\n", + "INFO - Running chunk 1 of 1 with 41 of 41 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_destination.logsums.escort.compute_logsums.simple_simulate_logsums number_of_rows: 41 observed_row_size: 308 num_chunks: 1\n", "INFO - Running tour_destination_simulate with 3 persons\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 3 choosers and 41 alternatives\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", "INFO - Running eval_interaction_utilities on 41 rows\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_destination.simulate.escort.interaction_sample_simulate number_of_rows: 3 observed_row_size: 151 num_chunks: 1\n", "WARNING - slice_canonically: no rows in non_mandatory_tour_destination with household_id == [2223759]\n", - "INFO - Running non_mandatory_tour_scheduling with 193 tours\n", + "INFO - trace_memory_info pipeline.run after non_mandatory_tour_destination rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - #run_model running step non_mandatory_tour_scheduling\n", + "INFO - Running non_mandatory_tour_scheduling with 192 tours\n", "DEBUG - @inject timetable\n", "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1 schedule_tours running 65 tour choices\n", - "INFO - Running chunk 1 of 1 size 65\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 65 choosers\n", + "INFO - Running chunk 1 of 1 with 65 of 65 choosers\n", "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1 schedule_tours running 65 tour choices\n", - "INFO - Running chunk 1 of 1 size 65\n", - "INFO - Running eval_interaction_utilities on 10073 rows\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.add.tours rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "tours (65, 26) alts (190, 3)\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.add.alt_tdd rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.add.tours rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 65 choosers and 9970 alternatives\n", + "INFO - Running chunk 1 of 1 with 65 of 65 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 9970 rows\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.interaction_sample_simulate number_of_rows: 65 observed_row_size: 5857 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1 number_of_rows: 65 observed_row_size: 5857 num_chunks: 1\n", "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2 schedule_tours running 22 tour choices\n", - "INFO - Running chunk 1 of 1 size 22\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 22 choosers\n", + "INFO - Running chunk 1 of 1 with 22 of 22 choosers\n", "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2 schedule_tours running 22 tour choices\n", - "INFO - Running chunk 1 of 1 size 22\n", - "INFO - Running eval_interaction_utilities on 1716 rows\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.add.tours rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "tours (22, 26) alts (190, 3)\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.add.alt_tdd rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.add.tours rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 22 choosers and 1685 alternatives\n", + "INFO - Running chunk 1 of 1 with 22 of 22 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 1685 rows\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.interaction_sample_simulate number_of_rows: 22 observed_row_size: 2939 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2 number_of_rows: 22 observed_row_size: 2939 num_chunks: 1\n", "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3 schedule_tours running 6 tour choices\n", - "INFO - Running chunk 1 of 1 size 6\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 6 choosers\n", + "INFO - Running chunk 1 of 1 with 6 of 6 choosers\n", "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3 schedule_tours running 6 tour choices\n", - "INFO - Running chunk 1 of 1 size 6\n", - "INFO - Running eval_interaction_utilities on 217 rows\n", - "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4 schedule_tours running 2 tour choices\n", - "INFO - Running chunk 1 of 1 size 2\n", - "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4 schedule_tours running 2 tour choices\n", - "INFO - Running chunk 1 of 1 size 2\n", - "INFO - Running eval_interaction_utilities on 40 rows\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.add.tours rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "tours (6, 26) alts (190, 3)\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.add.alt_tdd rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.add.tours rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 6 choosers and 200 alternatives\n", + "INFO - Running chunk 1 of 1 with 6 of 6 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 200 rows\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3.interaction_sample_simulate number_of_rows: 6 observed_row_size: 1295 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3 number_of_rows: 6 observed_row_size: 1295 num_chunks: 1\n", + "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4 schedule_tours running 1 tour choices\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4 schedule_tours running 1 tour choices\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.add.tours rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "tours (1, 26) alts (190, 3)\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.add.alt_tdd rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.add.tours rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 18 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 18 rows\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.84 GB percent: 49.4%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4.interaction_sample_simulate number_of_rows: 1 observed_row_size: 712 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4 number_of_rows: 1 observed_row_size: 712 num_chunks: 1\n", "WARNING - slice_canonically: no rows in non_mandatory_tour_scheduling with household_id == [2223759]\n", - "INFO - Running tour_mode_choice with 193 tours\n", + "INFO - trace_memory_info pipeline.run after non_mandatory_tour_scheduling rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - #run_model running step tour_mode_choice_simulate\n", + "INFO - Running tour_mode_choice with 192 tours\n", "INFO - tour_types top 10 value counts:\n", "work 69\n", - "shopping 32\n", + "shopping 31\n", "school 26\n", + "othmaint 20\n", "othdiscr 20\n", - "othmaint 19\n", - "eatout 17\n", + "eatout 16\n", "social 7\n", "escort 3\n", "Name: tour_type, dtype: int64\n", - "INFO - tour_mode_choice_simulate tour_type 'eatout' (17 tours)\n", - "INFO - Running chunk 1 of 1 size 17\n", - "INFO - Time to execute eval_utilities : 0.733 seconds (0.0 minutes)\n", + "INFO - tour_mode_choice_simulate tour_type 'eatout' (16 tours)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 16 choosers\n", + "INFO - Running chunk 1 of 1 with 16 of 16 choosers\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.add.raw_utilities rss: 0.14GB used: 7.84 GB percent: 49.4%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.del.raw_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.add.nested_probabilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.add.logsums rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.add.base_probabilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.del.nested_probabilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.eatout.simple_simulate.eval_nl.del.base_probabilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.eatout.simple_simulate number_of_rows: 16 observed_row_size: 310 num_chunks: 1\n", "INFO - tour_mode_choice_simulate eatout choices_df top 10 value counts:\n", - "WALK 13\n", - "WALK_LRF 2\n", - "WALK_LOC 1\n", + "WALK 12\n", + "WALK_LRF 3\n", "DRIVEALONEFREE 1\n", "Name: tour_mode, dtype: int64\n", "INFO - tour_mode_choice_simulate tour_type 'escort' (3 tours)\n", - "INFO - Running chunk 1 of 1 size 3\n", - "INFO - Time to execute eval_utilities : 0.686 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.add.raw_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.del.raw_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.add.nested_probabilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.add.logsums rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.add.base_probabilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.del.nested_probabilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.escort.simple_simulate.eval_nl.del.base_probabilities rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.escort.simple_simulate number_of_rows: 3 observed_row_size: 308 num_chunks: 1\n", "INFO - tour_mode_choice_simulate escort choices_df top 10 value counts:\n", - "SHARED2FREE 1\n", "WALK_LOC 1\n", "TNC_SINGLE 1\n", + "SHARED2FREE 1\n", "Name: tour_mode, dtype: int64\n", "INFO - tour_mode_choice_simulate tour_type 'othdiscr' (20 tours)\n", - "INFO - Running chunk 1 of 1 size 20\n", - "INFO - Time to execute eval_utilities : 0.702 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 20 choosers\n", + "INFO - Running chunk 1 of 1 with 20 of 20 choosers\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.14GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.14GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.add.raw_utilities rss: 0.14GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.del.raw_utilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.add.nested_probabilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.add.logsums rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.add.base_probabilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.del.nested_probabilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othdiscr.simple_simulate.eval_nl.del.base_probabilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.othdiscr.simple_simulate number_of_rows: 20 observed_row_size: 310 num_chunks: 1\n", "INFO - tour_mode_choice_simulate othdiscr choices_df top 10 value counts:\n", "WALK 9\n", "WALK_LRF 3\n", "DRIVEALONEFREE 2\n", "WALK_LOC 2\n", - "SHARED3FREE 1\n", - "TNC_SINGLE 1\n", - "TAXI 1\n", + "TNC_SINGLE 2\n", "WALK_HVY 1\n", + "SHARED3FREE 1\n", "Name: tour_mode, dtype: int64\n", - "INFO - tour_mode_choice_simulate tour_type 'othmaint' (19 tours)\n", - "INFO - Running chunk 1 of 1 size 19\n", - "INFO - Time to execute eval_utilities : 0.691 seconds (0.0 minutes)\n", + "INFO - tour_mode_choice_simulate tour_type 'othmaint' (20 tours)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 20 choosers\n", + "INFO - Running chunk 1 of 1 with 20 of 20 choosers\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.add.raw_utilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.del.raw_utilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.add.nested_probabilities rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.add.logsums rss: 0.14GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.14GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.add.base_probabilities rss: 0.14GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.del.nested_probabilities rss: 0.14GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info tour_mode_choice.othmaint.simple_simulate.eval_nl.del.base_probabilities rss: 0.14GB used: 7.92 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.othmaint.simple_simulate number_of_rows: 20 observed_row_size: 310 num_chunks: 1\n", "INFO - tour_mode_choice_simulate othmaint choices_df top 10 value counts:\n", "WALK 10\n", + "BIKE 4\n", "TNC_SINGLE 4\n", - "WALK_LOC 2\n", - "BIKE 2\n", "TAXI 1\n", + "WALK_LOC 1\n", "Name: tour_mode, dtype: int64\n", - "INFO - tour_mode_choice_simulate tour_type 'school' (26 tours)\n", - "INFO - Running chunk 1 of 1 size 26\n", - "INFO - Time to execute eval_utilities : 0.687 seconds (0.0 minutes)\n", + "INFO - tour_mode_choice_simulate tour_type 'school' (17 tours)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 17 choosers\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.school.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.school.simple_simulate number_of_rows: 17 observed_row_size: 322 num_chunks: 1\n", "INFO - tour_mode_choice_simulate school choices_df top 10 value counts:\n", - "WALK_LOC 10\n", - "WALK 8\n", - "WALK_LRF 5\n", - "SHARED3FREE 2\n", - "WALK_HVY 1\n", + "WALK 7\n", + "WALK_LOC 5\n", + "WALK_LRF 3\n", + "WALK_HVY 1\n", + "SHARED3FREE 1\n", "Name: tour_mode, dtype: int64\n", - "INFO - tour_mode_choice_simulate tour_type 'shopping' (32 tours)\n", - "INFO - Running chunk 1 of 1 size 32\n", - "INFO - Time to execute eval_utilities : 0.685 seconds (0.0 minutes)\n", + "INFO - tour_mode_choice_simulate tour_type 'shopping' (31 tours)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 31 choosers\n", + "INFO - Running chunk 1 of 1 with 31 of 31 choosers\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.shopping.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.shopping.simple_simulate number_of_rows: 31 observed_row_size: 310 num_chunks: 1\n", "INFO - tour_mode_choice_simulate shopping choices_df top 10 value counts:\n", "WALK 13\n", "WALK_LRF 7\n", "WALK_LOC 5\n", "DRIVEALONEFREE 2\n", - "TNC_SINGLE 2\n", "TAXI 2\n", "SHARED2FREE 1\n", + "TNC_SINGLE 1\n", "Name: tour_mode, dtype: int64\n", "INFO - tour_mode_choice_simulate tour_type 'social' (7 tours)\n", - "INFO - Running chunk 1 of 1 size 7\n", - "INFO - Time to execute eval_utilities : 0.733 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 7 choosers\n", + "INFO - Running chunk 1 of 1 with 7 of 7 choosers\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.add.raw_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.del.raw_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.add.nested_probabilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.add.logsums rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.add.base_probabilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.del.nested_probabilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.social.simple_simulate.eval_nl.del.base_probabilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.social.simple_simulate number_of_rows: 7 observed_row_size: 310 num_chunks: 1\n", "INFO - tour_mode_choice_simulate social choices_df top 10 value counts:\n", "WALK 2\n", "WALK_LRF 2\n", + "TAXI 1\n", "WALK_LOC 1\n", "TNC_SINGLE 1\n", - "TAXI 1\n", + "Name: tour_mode, dtype: int64\n", + "INFO - tour_mode_choice_simulate tour_type 'univ' (9 tours)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 9 choosers\n", + "INFO - Running chunk 1 of 1 with 9 of 9 choosers\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.add.raw_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.del.raw_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.add.nested_probabilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.add.logsums rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.add.base_probabilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.del.nested_probabilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.univ.simple_simulate.eval_nl.del.base_probabilities rss: 0.14GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.univ.simple_simulate number_of_rows: 9 observed_row_size: 322 num_chunks: 1\n", + "INFO - tour_mode_choice_simulate univ choices_df top 10 value counts:\n", + "WALK 4\n", + "WALK_LOC 3\n", + "TNC_SHARED 1\n", + "WALK_LRF 1\n", "Name: tour_mode, dtype: int64\n", "INFO - tour_mode_choice_simulate tour_type 'work' (69 tours)\n", - "INFO - Running chunk 1 of 1 size 69\n", - "INFO - Time to execute eval_utilities : 0.708 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 69 choosers\n", + "INFO - Running chunk 1 of 1 with 69 of 69 choosers\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info tour_mode_choice.work.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers tour_mode_choice.work.simple_simulate number_of_rows: 69 observed_row_size: 310 num_chunks: 1\n", "INFO - tour_mode_choice_simulate work choices_df top 10 value counts:\n", "WALK 24\n", "WALK_LOC 14\n", - "WALK_LRF 13\n", - "TNC_SINGLE 6\n", + "WALK_LRF 12\n", + "TNC_SINGLE 7\n", "BIKE 4\n", - "WALK_HVY 3\n", "DRIVEALONEFREE 3\n", + "WALK_HVY 3\n", "SHARED2FREE 1\n", "SHARED3FREE 1\n", "Name: tour_mode, dtype: int64\n", "INFO - tour_mode_choice_simulate all tour type choices top 10 value counts:\n", - "WALK 79\n", - "WALK_LOC 36\n", - "WALK_LRF 32\n", - "TNC_SINGLE 15\n", + "WALK 81\n", + "WALK_LOC 32\n", + "WALK_LRF 31\n", + "TNC_SINGLE 16\n", "DRIVEALONEFREE 8\n", - "BIKE 6\n", + "BIKE 8\n", "WALK_HVY 5\n", - "TAXI 5\n", - "SHARED3FREE 4\n", + "TAXI 4\n", "SHARED2FREE 3\n", + "SHARED3FREE 3\n", "Name: tour_mode, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after tour_mode_choice_simulate rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step atwork_subtour_frequency\n", "INFO - Running atwork_subtour_frequency with 69 work tours\n", - "INFO - Running chunk 1 of 1 size 69\n", - "INFO - Time to execute eval_utilities : 0.1 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 69 choosers\n", + "INFO - Running chunk 1 of 1 with 69 of 69 choosers\n", + "INFO - trace_memory_info atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_frequency.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_frequency.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_frequency.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_frequency.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers atwork_subtour_frequency.simple_simulate number_of_rows: 69 observed_row_size: 28 num_chunks: 1\n", "INFO - register tours: added 1 new ids to 3 existing trace ids\n", "INFO - register tours: tracing new ids [220958270] in tours\n", "INFO - atwork_subtour_frequency top 10 value counts:\n", " 132\n", - "no_subtours 61\n", - "eat 7\n", - "business1 1\n", + "no_subtours 60\n", + "eat 8\n", + "maint 1\n", "Name: atwork_subtour_frequency, dtype: int64\n", - "INFO - Running atwork_subtour_location_sample with 8 tours\n", - "INFO - Running chunk 1 of 1 size 8\n", - "INFO - Running eval_interaction_utilities on 200 rows\n", - "INFO - Running atwork_subtour_destination.logsums with 109 rows\n", - "INFO - Running chunk 1 of 1 size 109\n", - "INFO - Time to execute eval_utilities : 0.564 seconds (0.0 minutes)\n", - "INFO - Running atwork_subtour_destination_simulate with 8 persons\n", - "INFO - Running chunk 1 of 1 size 8\n", - "INFO - Running eval_interaction_utilities on 109 rows\n", + "INFO - trace_memory_info pipeline.run after atwork_subtour_frequency rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step atwork_subtour_destination\n", + "INFO - Running atwork_subtour_location_sample with 9 tours\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 9 choosers\n", + "INFO - Running chunk 1 of 1 with 9 of 9 choosers\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 225 rows\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.add.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.del.utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.del.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers atwork_subtour_destination.sample.interaction_sample number_of_rows: 9 observed_row_size: 150 num_chunks: 1\n", + "INFO - Running atwork_subtour_destination.logsums with 124 rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 124 choosers\n", + "INFO - Running chunk 1 of 1 with 124 of 124 choosers\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums number_of_rows: 124 observed_row_size: 318 num_chunks: 1\n", + "INFO - Running atwork_subtour_destination_simulate with 9 persons\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 9 choosers and 124 alternatives\n", + "INFO - Running chunk 1 of 1 with 9 of 9 choosers\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 124 rows\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_destination.simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.84 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts atwork_subtour_destination.simulate.interaction_sample_simulate number_of_rows: 9 observed_row_size: 166 num_chunks: 1\n", "INFO - destination summary:\n", - "count 8.000000\n", - "mean 11.000000\n", - "std 7.801099\n", + "count 9.000000\n", + "mean 10.444444\n", + "std 7.333333\n", "min 2.000000\n", - "25% 6.500000\n", - "50% 9.500000\n", - "75% 16.000000\n", + "25% 7.000000\n", + "50% 8.000000\n", + "75% 15.000000\n", "max 25.000000\n", "Name: destination, dtype: float64\n", - "INFO - Running atwork_subtour_scheduling with 8 tours\n", - "INFO - atwork_subtour_scheduling.tour_1 schedule_tours running 8 tour choices\n", - "INFO - Running chunk 1 of 1 size 8\n", - "INFO - atwork_subtour_scheduling.tour_1 schedule_tours running 8 tour choices\n", - "INFO - Running chunk 1 of 1 size 8\n", - "INFO - Running eval_interaction_utilities on 459 rows\n", - "INFO - Running atwork_subtour_mode_choice with 8 subtours\n", + "INFO - trace_memory_info pipeline.run after atwork_subtour_destination rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step atwork_subtour_scheduling\n", + "INFO - Running atwork_subtour_scheduling with 9 tours\n", + "INFO - atwork_subtour_scheduling.tour_1 schedule_tours running 9 tour choices\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 9 choosers\n", + "INFO - Running chunk 1 of 1 with 9 of 9 choosers\n", + "INFO - atwork_subtour_scheduling.tour_1 schedule_tours running 9 tour choices\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.add.tours rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "tours (9, 155) alts (190, 3)\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.add.alt_tdd rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.add.tours rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 9 choosers and 536 alternatives\n", + "INFO - Running chunk 1 of 1 with 9 of 9 choosers\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 536 rows\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_scheduling.tour_1.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts atwork_subtour_scheduling.tour_1.interaction_sample_simulate number_of_rows: 9 observed_row_size: 10103 num_chunks: 1\n", + "INFO - #chunk_history adaptive_chunked_choosers atwork_subtour_scheduling.tour_1 number_of_rows: 9 observed_row_size: 10103 num_chunks: 1\n", + "INFO - trace_memory_info pipeline.run after atwork_subtour_scheduling rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step atwork_subtour_mode_choice\n", + "INFO - Running atwork_subtour_mode_choice with 9 subtours\n", "INFO - atwork_subtour_mode_choice tour_type top 10 value counts:\n", - "eat 7\n", - "business 1\n", + "eat 8\n", + "maint 1\n", "Name: tour_type, dtype: int64\n", - "INFO - Running chunk 1 of 1 size 8\n", - "INFO - Time to execute eval_utilities : 0.701 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 9 choosers\n", + "INFO - Running chunk 1 of 1 with 9 of 9 choosers\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info atwork_subtour_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers atwork_subtour_mode_choice.simple_simulate number_of_rows: 9 observed_row_size: 318 num_chunks: 1\n", "INFO - atwork_subtour_mode_choice choices top 10 value counts:\n", - "WALK 8\n", + "WALK 9\n", "Name: tour_mode, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after atwork_subtour_mode_choice rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step stop_frequency\n", "INFO - stop_frequency segments top 10 value counts:\n", "work 69\n", - "shopping 32\n", + "shopping 31\n", + "othmaint 20\n", "othdiscr 20\n", - "othmaint 19\n", "school 17\n", - "eatout 17\n", + "eatout 16\n", "univ 9\n", - "atwork 8\n", + "atwork 9\n", "social 7\n", "escort 3\n", "Name: primary_purpose, dtype: int64\n", - "INFO - stop_frequency running segment atwork with 8 chooser rows\n", - "INFO - Running chunk 1 of 1 size 8\n", - "INFO - Time to execute eval_utilities : 0.062 seconds (0.0 minutes)\n", - "INFO - stop_frequency running segment eatout with 17 chooser rows\n", - "INFO - Running chunk 1 of 1 size 17\n", - "INFO - Time to execute eval_utilities : 0.316 seconds (0.0 minutes)\n", + "INFO - stop_frequency running segment atwork with 9 chooser rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 9 choosers\n", + "INFO - Running chunk 1 of 1 with 9 of 9 choosers\n", + "INFO - trace_memory_info stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.atwork.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.atwork.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.atwork.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.atwork.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.atwork.simple_simulate number_of_rows: 9 observed_row_size: 32 num_chunks: 1\n", + "INFO - stop_frequency running segment eatout with 16 chooser rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 16 choosers\n", + "INFO - Running chunk 1 of 1 with 16 of 16 choosers\n", + "INFO - trace_memory_info stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.eatout.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.eatout.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.eatout.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.eatout.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.eatout.simple_simulate number_of_rows: 16 observed_row_size: 67 num_chunks: 1\n", "INFO - stop_frequency running segment escort with 3 chooser rows\n", - "INFO - Running chunk 1 of 1 size 3\n", - "INFO - Time to execute eval_utilities : 0.264 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info stop_frequency.escort.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.escort.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.escort.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.escort.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.escort.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.escort.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.escort.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.escort.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.escort.simple_simulate number_of_rows: 3 observed_row_size: 61 num_chunks: 1\n", "INFO - stop_frequency running segment othdiscr with 20 chooser rows\n", - "INFO - Running chunk 1 of 1 size 20\n", - "INFO - Time to execute eval_utilities : 0.265 seconds (0.0 minutes)\n", - "INFO - stop_frequency running segment othmaint with 19 chooser rows\n", - "INFO - Running chunk 1 of 1 size 19\n", - "INFO - Time to execute eval_utilities : 0.254 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 20 choosers\n", + "INFO - Running chunk 1 of 1 with 20 of 20 choosers\n", + "INFO - trace_memory_info stop_frequency.othdiscr.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othdiscr.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othdiscr.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othdiscr.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othdiscr.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othdiscr.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othdiscr.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othdiscr.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.othdiscr.simple_simulate number_of_rows: 20 observed_row_size: 63 num_chunks: 1\n", + "INFO - stop_frequency running segment othmaint with 20 chooser rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 20 choosers\n", + "INFO - Running chunk 1 of 1 with 20 of 20 choosers\n", + "INFO - trace_memory_info stop_frequency.othmaint.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othmaint.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othmaint.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othmaint.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othmaint.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othmaint.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othmaint.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.othmaint.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.othmaint.simple_simulate number_of_rows: 20 observed_row_size: 64 num_chunks: 1\n", "INFO - stop_frequency running segment school with 17 chooser rows\n", - "INFO - Running chunk 1 of 1 size 17\n", - "INFO - Time to execute eval_utilities : 0.238 seconds (0.0 minutes)\n", - "INFO - stop_frequency running segment shopping with 32 chooser rows\n", - "INFO - Running chunk 1 of 1 size 32\n", - "INFO - Time to execute eval_utilities : 0.268 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 17 choosers\n", + "INFO - Running chunk 1 of 1 with 17 of 17 choosers\n", + "INFO - trace_memory_info stop_frequency.school.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.school.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.school.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.school.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.school.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.school.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.school.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.school.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.school.simple_simulate number_of_rows: 17 observed_row_size: 58 num_chunks: 1\n", + "INFO - stop_frequency running segment shopping with 31 chooser rows\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 31 choosers\n", + "INFO - Running chunk 1 of 1 with 31 of 31 choosers\n", + "INFO - trace_memory_info stop_frequency.shopping.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.shopping.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.shopping.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.shopping.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.shopping.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.shopping.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.shopping.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.shopping.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.shopping.simple_simulate number_of_rows: 31 observed_row_size: 64 num_chunks: 1\n", "INFO - stop_frequency running segment social with 7 chooser rows\n", - "INFO - Running chunk 1 of 1 size 7\n", - "INFO - Time to execute eval_utilities : 0.301 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 7 choosers\n", + "INFO - Running chunk 1 of 1 with 7 of 7 choosers\n", + "INFO - trace_memory_info stop_frequency.social.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.social.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.social.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.social.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.social.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.social.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.social.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.social.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.social.simple_simulate number_of_rows: 7 observed_row_size: 67 num_chunks: 1\n", "INFO - stop_frequency running segment univ with 9 chooser rows\n", - "INFO - Running chunk 1 of 1 size 9\n", - "INFO - Time to execute eval_utilities : 0.216 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 9 choosers\n", + "INFO - Running chunk 1 of 1 with 9 of 9 choosers\n", + "INFO - trace_memory_info stop_frequency.univ.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.univ.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.univ.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.univ.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.univ.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.univ.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.univ.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.univ.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.univ.simple_simulate number_of_rows: 9 observed_row_size: 58 num_chunks: 1\n", "INFO - stop_frequency running segment work with 69 chooser rows\n", - "INFO - Running chunk 1 of 1 size 69\n", - "INFO - Time to execute eval_utilities : 0.234 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 69 choosers\n", + "INFO - Running chunk 1 of 1 with 69 of 69 choosers\n", + "INFO - trace_memory_info stop_frequency.work.simple_simulate.eval_mnl.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.work.simple_simulate.eval_mnl.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.work.simple_simulate.eval_mnl.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.work.simple_simulate.eval_mnl.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.work.simple_simulate.eval_mnl.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.work.simple_simulate.eval_mnl.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.work.simple_simulate.eval_mnl.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info stop_frequency.work.simple_simulate.eval_mnl.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers stop_frequency.work.simple_simulate number_of_rows: 69 observed_row_size: 59 num_chunks: 1\n", "INFO - stop_frequency top 10 value counts:\n", "0out_0in 156\n", "0out_1in 14\n", "1out_0in 11\n", - "0out_2in 7\n", + "0out_2in 8\n", "1out_1in 3\n", "3out_0in 3\n", "1out_3in 2\n", - "0out_3in 2\n", - "2out_2in 1\n", + "0out_3in 1\n", "3out_2in 1\n", + "2out_0in 1\n", "dtype: int64\n", - "adding table trips.trip_id to traceable_table_indexes\n", "INFO - register trips: added 8 new ids to 0 existing trace ids\n", "INFO - register trips: tracing new ids [1767666441, 1767666445, 1767666769, 1767666773, 1767666233, 1767666237, 1767666161, 1767666165] in trips\n", + "INFO - trace_memory_info pipeline.run after stop_frequency rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step trip_purpose\n", "INFO - assign purpose to 201 last outbound trips\n", "INFO - assign purpose to 201 last inbound trips\n", - "INFO - assign purpose to 79 intermediate trips\n", - "INFO - Running chunk 1 of 1 size 79\n", - "INFO - Running trip_destination with 481 trips\n", + "INFO - assign purpose to 78 intermediate trips\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 78 choosers\n", + "INFO - Running chunk 1 of 1 with 78 of 78 choosers\n", + "INFO - trace_memory_info trip_purpose.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_purpose number_of_rows: 78 observed_row_size: 29 num_chunks: 1\n", + "INFO - trace_memory_info pipeline.run after trip_purpose rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #run_model running step trip_destination\n", + "INFO - Running trip_destination with 480 trips\n", "INFO - Running trip_destination.trip_num_1 with 52 trips\n", - "INFO - choose_trip_destination trip_destination.trip_num_1.atwork with 4 trips\n", - "INFO - Running trip_destination.trip_num_1.atwork.trip_destination_sample with 4 trips\n", - "INFO - Running chunk 1 of 1 size 4\n", - "INFO - Running eval_interaction_utilities on 100 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.atwork.trip_destination_sample : 0.153 seconds (0.0 minutes)\n", - "INFO - Running trip_destination.trip_num_1.atwork.compute_logsums with 53 samples\n", - "INFO - Running chunk 1 of 1 size 53\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 53\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.atwork.compute_logsums : 1.602 seconds (0.0 minutes)\n", - "INFO - Running trip_destination_simulate with 4 trips\n", - "INFO - Running chunk 1 of 1 size 4\n", - "INFO - Running eval_interaction_utilities on 53 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.atwork.trip_destination_simulate : 0.185 seconds (0.0 minutes)\n", + "INFO - choose_trip_destination trip_destination.trip_num_1.atwork with 5 trips\n", + "INFO - Running trip_destination.trip_num_1.atwork.trip_destination_sample with 5 trips\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 5 choosers\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 125 rows\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.atwork.trip_destination_sample.interaction_sample number_of_rows: 5 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.atwork.trip_destination_sample : 1.241 seconds (0.0 minutes)\n", + "INFO - Running trip_destination.trip_num_1.atwork.compute_logsums with 68 samples\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 68 choosers\n", + "INFO - Running chunk 1 of 1 with 68 of 68 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.atwork.compute_logsums.od.simple_simulate_logsums number_of_rows: 68 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 68 choosers\n", + "INFO - Running chunk 1 of 1 with 68 of 68 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.atwork.compute_logsums.dp.simple_simulate_logsums number_of_rows: 68 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.atwork.compute_logsums : 5.91 seconds (0.1 minutes)\n", + "INFO - Running trip_destination_simulate with 5 trips\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 5 choosers and 68 alternatives\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - Running eval_interaction_utilities on 68 rows\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.atwork.trip_dest_simulate.interaction_sample_simulate number_of_rows: 5 observed_row_size: 300 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.atwork.trip_destination_simulate : 2.018 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_1.eatout with 1 trips\n", "INFO - Running trip_destination.trip_num_1.eatout.trip_destination_sample with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 25 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.eatout.trip_destination_sample : 0.154 seconds (0.0 minutes)\n", - "INFO - Running trip_destination.trip_num_1.eatout.compute_logsums with 16 samples\n", - "INFO - Running chunk 1 of 1 size 16\n", - "INFO - Time to execute eval_utilities : 0.547 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 16\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.eatout.compute_logsums : 1.614 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.eatout.trip_destination_sample.interaction_sample number_of_rows: 1 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.eatout.trip_destination_sample : 2.302 seconds (0.0 minutes)\n", + "INFO - Running trip_destination.trip_num_1.eatout.compute_logsums with 15 samples\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 15 choosers\n", + "INFO - Running chunk 1 of 1 with 15 of 15 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.eatout.compute_logsums.od.simple_simulate_logsums number_of_rows: 15 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 15 choosers\n", + "INFO - Running chunk 1 of 1 with 15 of 15 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.eatout.compute_logsums.dp.simple_simulate_logsums number_of_rows: 15 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.eatout.compute_logsums : 6.463 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", - "INFO - Running eval_interaction_utilities on 16 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.eatout.trip_destination_simulate : 0.182 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 15 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - Running eval_interaction_utilities on 15 rows\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.eatout.trip_dest_simulate.interaction_sample_simulate number_of_rows: 1 observed_row_size: 330 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.eatout.trip_destination_simulate : 1.464 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_1.othdiscr with 1 trips\n", "INFO - Running trip_destination.trip_num_1.othdiscr.trip_destination_sample with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.88 GB percent: 49.7%\n", "INFO - Running eval_interaction_utilities on 25 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.othdiscr.trip_destination_sample : 0.152 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.othdiscr.trip_destination_sample.interaction_sample number_of_rows: 1 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.othdiscr.trip_destination_sample : 1.664 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_1.othdiscr.compute_logsums with 15 samples\n", - "INFO - Running chunk 1 of 1 size 15\n", - "INFO - Time to execute eval_utilities : 0.549 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 15\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.othdiscr.compute_logsums : 1.604 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 15 choosers\n", + "INFO - Running chunk 1 of 1 with 15 of 15 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.othdiscr.compute_logsums.od.simple_simulate_logsums number_of_rows: 15 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 15 choosers\n", + "INFO - Running chunk 1 of 1 with 15 of 15 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.othdiscr.compute_logsums.dp.simple_simulate_logsums number_of_rows: 15 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.othdiscr.compute_logsums : 6.614 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 15 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 15 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.othdiscr.trip_destination_simulate : 0.182 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.othdiscr.trip_dest_simulate.interaction_sample_simulate number_of_rows: 1 observed_row_size: 330 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.othdiscr.trip_destination_simulate : 1.801 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_1.othmaint with 3 trips\n", "INFO - Running trip_destination.trip_num_1.othmaint.trip_destination_sample with 3 trips\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 75 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.othmaint.trip_destination_sample : 0.155 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.othmaint.trip_destination_sample.interaction_sample number_of_rows: 3 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.othmaint.trip_destination_sample : 1.239 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_1.othmaint.compute_logsums with 45 samples\n", - "INFO - Running chunk 1 of 1 size 45\n", - "INFO - Time to execute eval_utilities : 0.546 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 45\n", - "INFO - Time to execute eval_utilities : 0.541 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.othmaint.compute_logsums : 1.61 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 45 choosers\n", + "INFO - Running chunk 1 of 1 with 45 of 45 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.othmaint.compute_logsums.od.simple_simulate_logsums number_of_rows: 45 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 45 choosers\n", + "INFO - Running chunk 1 of 1 with 45 of 45 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.othmaint.compute_logsums.dp.simple_simulate_logsums number_of_rows: 45 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.othmaint.compute_logsums : 5.655 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 3 trips\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 3 choosers and 45 alternatives\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 45 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.othmaint.trip_destination_simulate : 0.181 seconds (0.0 minutes)\n", - "INFO - choose_trip_destination trip_destination.trip_num_1.school with 4 trips\n", - "INFO - Running trip_destination.trip_num_1.school.trip_destination_sample with 4 trips\n", - "INFO - Running chunk 1 of 1 size 4\n", - "INFO - Running eval_interaction_utilities on 100 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.school.trip_destination_sample : 0.152 seconds (0.0 minutes)\n", - "INFO - Running trip_destination.trip_num_1.school.compute_logsums with 51 samples\n", - "INFO - Running chunk 1 of 1 size 51\n", - "INFO - Time to execute eval_utilities : 0.548 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 51\n", - "INFO - Time to execute eval_utilities : 0.549 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.school.compute_logsums : 1.614 seconds (0.0 minutes)\n", - "INFO - Running trip_destination_simulate with 4 trips\n", - "INFO - Running chunk 1 of 1 size 4\n", - "INFO - Running eval_interaction_utilities on 51 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.school.trip_destination_simulate : 0.172 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.othmaint.trip_dest_simulate.interaction_sample_simulate number_of_rows: 3 observed_row_size: 330 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.othmaint.trip_destination_simulate : 1.723 seconds (0.0 minutes)\n", + "INFO - choose_trip_destination trip_destination.trip_num_1.school with 3 trips\n", + "INFO - Running trip_destination.trip_num_1.school.trip_destination_sample with 3 trips\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - Running eval_interaction_utilities on 75 rows\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.school.trip_destination_sample.interaction_sample number_of_rows: 3 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.school.trip_destination_sample : 1.177 seconds (0.0 minutes)\n", + "INFO - Running trip_destination.trip_num_1.school.compute_logsums with 42 samples\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 42 choosers\n", + "INFO - Running chunk 1 of 1 with 42 of 42 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.school.compute_logsums.od.simple_simulate_logsums number_of_rows: 42 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 42 choosers\n", + "INFO - Running chunk 1 of 1 with 42 of 42 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.school.compute_logsums.dp.simple_simulate_logsums number_of_rows: 42 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.school.compute_logsums : 5.804 seconds (0.1 minutes)\n", + "INFO - Running trip_destination_simulate with 3 trips\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 3 choosers and 42 alternatives\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - Running eval_interaction_utilities on 42 rows\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.school.trip_dest_simulate.interaction_sample_simulate number_of_rows: 3 observed_row_size: 308 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.school.trip_destination_simulate : 1.487 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_1.shopping with 10 trips\n", "INFO - Running trip_destination.trip_num_1.shopping.trip_destination_sample with 10 trips\n", - "INFO - Running chunk 1 of 1 size 10\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 10 choosers\n", + "INFO - Running chunk 1 of 1 with 10 of 10 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 250 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.shopping.trip_destination_sample : 0.17 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.shopping.trip_destination_sample.interaction_sample number_of_rows: 10 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.shopping.trip_destination_sample : 1.279 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_1.shopping.compute_logsums with 124 samples\n", - "INFO - Running chunk 1 of 1 size 124\n", - "INFO - Time to execute eval_utilities : 0.532 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 124\n", - "INFO - Time to execute eval_utilities : 0.539 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.shopping.compute_logsums : 1.604 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 124 choosers\n", + "INFO - Running chunk 1 of 1 with 124 of 124 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.shopping.compute_logsums.od.simple_simulate_logsums number_of_rows: 124 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 124 choosers\n", + "INFO - Running chunk 1 of 1 with 124 of 124 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.shopping.compute_logsums.dp.simple_simulate_logsums number_of_rows: 124 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.shopping.compute_logsums : 6.538 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 10 trips\n", - "INFO - Running chunk 1 of 1 size 10\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 10 choosers and 124 alternatives\n", + "INFO - Running chunk 1 of 1 with 10 of 10 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 124 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.shopping.trip_destination_simulate : 0.2 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.shopping.trip_dest_simulate.interaction_sample_simulate number_of_rows: 10 observed_row_size: 273 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.shopping.trip_destination_simulate : 1.8 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_1.social with 3 trips\n", "INFO - Running trip_destination.trip_num_1.social.trip_destination_sample with 3 trips\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.94 GB percent: 50.0%\n", "INFO - Running eval_interaction_utilities on 75 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.social.trip_destination_sample : 0.147 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.social.trip_destination_sample.interaction_sample number_of_rows: 3 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.social.trip_destination_sample : 1.574 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_1.social.compute_logsums with 42 samples\n", - "INFO - Running chunk 1 of 1 size 42\n", - "INFO - Time to execute eval_utilities : 0.545 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 42\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.social.compute_logsums : 1.626 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 42 choosers\n", + "INFO - Running chunk 1 of 1 with 42 of 42 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.94 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.94 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.94 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.94 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.social.compute_logsums.od.simple_simulate_logsums number_of_rows: 42 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 42 choosers\n", + "INFO - Running chunk 1 of 1 with 42 of 42 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.94 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.social.compute_logsums.dp.simple_simulate_logsums number_of_rows: 42 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.social.compute_logsums : 7.434 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 3 trips\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 3 choosers and 42 alternatives\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", "INFO - Running eval_interaction_utilities on 42 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.social.trip_destination_simulate : 0.182 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.social.trip_dest_simulate.interaction_sample_simulate number_of_rows: 3 observed_row_size: 308 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.social.trip_destination_simulate : 1.691 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_1.univ with 5 trips\n", "INFO - Running trip_destination.trip_num_1.univ.trip_destination_sample with 5 trips\n", - "INFO - Running chunk 1 of 1 size 5\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 5 choosers\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.89 GB percent: 49.8%\n", "INFO - Running eval_interaction_utilities on 125 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.univ.trip_destination_sample : 0.162 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.univ.trip_destination_sample.interaction_sample number_of_rows: 5 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.univ.trip_destination_sample : 1.276 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_1.univ.compute_logsums with 72 samples\n", - "INFO - Running chunk 1 of 1 size 72\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 72\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.univ.compute_logsums : 1.633 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 72 choosers\n", + "INFO - Running chunk 1 of 1 with 72 of 72 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.univ.compute_logsums.od.simple_simulate_logsums number_of_rows: 72 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 72 choosers\n", + "INFO - Running chunk 1 of 1 with 72 of 72 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.univ.compute_logsums.dp.simple_simulate_logsums number_of_rows: 72 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.univ.compute_logsums : 5.989 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 5 trips\n", - "INFO - Running chunk 1 of 1 size 5\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 5 choosers and 72 alternatives\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 72 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.univ.trip_destination_simulate : 0.18 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.univ.trip_dest_simulate.interaction_sample_simulate number_of_rows: 5 observed_row_size: 317 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.univ.trip_destination_simulate : 1.792 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_1.work with 21 trips\n", "INFO - Running trip_destination.trip_num_1.work.trip_destination_sample with 21 trips\n", - "INFO - Running chunk 1 of 1 size 21\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 21 choosers\n", + "INFO - Running chunk 1 of 1 with 21 of 21 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 525 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.work.trip_destination_sample : 0.161 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.work.trip_destination_sample.interaction_sample number_of_rows: 21 observed_row_size: 425 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.work.trip_destination_sample : 1.187 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_1.work.compute_logsums with 269 samples\n", - "INFO - Running chunk 1 of 1 size 269\n", - "INFO - Time to execute eval_utilities : 0.556 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 269\n", - "INFO - Time to execute eval_utilities : 0.55 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_1.work.compute_logsums : 1.649 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 269 choosers\n", + "INFO - Running chunk 1 of 1 with 269 of 269 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.work.compute_logsums.od.simple_simulate_logsums number_of_rows: 269 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 269 choosers\n", + "INFO - Running chunk 1 of 1 with 269 of 269 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_1.work.compute_logsums.dp.simple_simulate_logsums number_of_rows: 269 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.work.compute_logsums : 5.294 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 21 trips\n", - "INFO - Running chunk 1 of 1 size 21\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 21 choosers and 269 alternatives\n", + "INFO - Running chunk 1 of 1 with 21 of 21 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 269 rows\n", - "INFO - Time to execute trip_destination.trip_num_1.work.trip_destination_simulate : 0.184 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_1.work.trip_dest_simulate.interaction_sample_simulate number_of_rows: 21 observed_row_size: 282 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_1.work.trip_destination_simulate : 1.672 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_2 with 19 trips\n", "INFO - choose_trip_destination trip_destination.trip_num_2.atwork with 2 trips\n", "INFO - Running trip_destination.trip_num_2.atwork.trip_destination_sample with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 2 choosers\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 50 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.atwork.trip_destination_sample : 0.161 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.atwork.trip_destination_sample.interaction_sample number_of_rows: 2 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.atwork.trip_destination_sample : 1.267 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_2.atwork.compute_logsums with 23 samples\n", - "INFO - Running chunk 1 of 1 size 23\n", - "INFO - Time to execute eval_utilities : 0.547 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 23\n", - "INFO - Time to execute eval_utilities : 0.569 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_2.atwork.compute_logsums : 1.722 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 23 choosers\n", + "INFO - Running chunk 1 of 1 with 23 of 23 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.94 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.atwork.compute_logsums.od.simple_simulate_logsums number_of_rows: 23 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 23 choosers\n", + "INFO - Running chunk 1 of 1 with 23 of 23 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 8.33 GB percent: 52.5%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.atwork.compute_logsums.dp.simple_simulate_logsums number_of_rows: 23 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.atwork.compute_logsums : 7.57 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 2 choosers and 23 alternatives\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.7%\n", "INFO - Running eval_interaction_utilities on 23 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.atwork.trip_destination_simulate : 0.185 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_2.atwork.trip_dest_simulate.interaction_sample_simulate number_of_rows: 2 observed_row_size: 265 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.atwork.trip_destination_simulate : 1.931 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_2.othmaint with 2 trips\n", "INFO - Running trip_destination.trip_num_2.othmaint.trip_destination_sample with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 2 choosers\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", "INFO - Running eval_interaction_utilities on 50 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.othmaint.trip_destination_sample : 0.162 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.othmaint.trip_destination_sample.interaction_sample number_of_rows: 2 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.othmaint.trip_destination_sample : 1.24 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_2.othmaint.compute_logsums with 27 samples\n", - "INFO - Running chunk 1 of 1 size 27\n", - "INFO - Time to execute eval_utilities : 0.546 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 27\n", - "INFO - Time to execute eval_utilities : 0.561 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_2.othmaint.compute_logsums : 1.627 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 27 choosers\n", + "INFO - Running chunk 1 of 1 with 27 of 27 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.othmaint.compute_logsums.od.simple_simulate_logsums number_of_rows: 27 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 27 choosers\n", + "INFO - Running chunk 1 of 1 with 27 of 27 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.othmaint.compute_logsums.dp.simple_simulate_logsums number_of_rows: 27 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.othmaint.compute_logsums : 6.837 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 2 choosers and 27 alternatives\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", "INFO - Running eval_interaction_utilities on 27 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.othmaint.trip_destination_simulate : 0.194 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_2.othmaint.trip_dest_simulate.interaction_sample_simulate number_of_rows: 2 observed_row_size: 311 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.othmaint.trip_destination_simulate : 1.973 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_2.shopping with 5 trips\n", "INFO - Running trip_destination.trip_num_2.shopping.trip_destination_sample with 5 trips\n", - "INFO - Running chunk 1 of 1 size 5\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 5 choosers\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", "INFO - Running eval_interaction_utilities on 125 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.shopping.trip_destination_sample : 0.152 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.shopping.trip_destination_sample.interaction_sample number_of_rows: 5 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.shopping.trip_destination_sample : 1.351 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_2.shopping.compute_logsums with 68 samples\n", - "INFO - Running chunk 1 of 1 size 68\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 68\n", - "INFO - Time to execute eval_utilities : 0.54 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_2.shopping.compute_logsums : 1.633 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 68 choosers\n", + "INFO - Running chunk 1 of 1 with 68 of 68 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.shopping.compute_logsums.od.simple_simulate_logsums number_of_rows: 68 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 68 choosers\n", + "INFO - Running chunk 1 of 1 with 68 of 68 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.shopping.compute_logsums.dp.simple_simulate_logsums number_of_rows: 68 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.shopping.compute_logsums : 5.586 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 5 trips\n", - "INFO - Running chunk 1 of 1 size 5\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 5 choosers and 68 alternatives\n", + "INFO - Running chunk 1 of 1 with 5 of 5 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.88 GB percent: 49.7%\n", "INFO - Running eval_interaction_utilities on 68 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.shopping.trip_destination_simulate : 0.183 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_2.shopping.trip_dest_simulate.interaction_sample_simulate number_of_rows: 5 observed_row_size: 313 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.shopping.trip_destination_simulate : 1.65 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_2.social with 1 trips\n", "INFO - Running trip_destination.trip_num_2.social.trip_destination_sample with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", "INFO - Running eval_interaction_utilities on 25 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.social.trip_destination_sample : 0.16 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.social.trip_destination_sample.interaction_sample number_of_rows: 1 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.social.trip_destination_sample : 1.26 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_2.social.compute_logsums with 10 samples\n", - "INFO - Running chunk 1 of 1 size 10\n", - "INFO - Time to execute eval_utilities : 0.546 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 10\n", - "INFO - Time to execute eval_utilities : 0.528 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_2.social.compute_logsums : 1.633 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 10 choosers\n", + "INFO - Running chunk 1 of 1 with 10 of 10 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.social.compute_logsums.od.simple_simulate_logsums number_of_rows: 10 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 10 choosers\n", + "INFO - Running chunk 1 of 1 with 10 of 10 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.social.compute_logsums.dp.simple_simulate_logsums number_of_rows: 10 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.social.compute_logsums : 8.892 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 10 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", "INFO - Running eval_interaction_utilities on 10 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.social.trip_destination_simulate : 0.247 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_2.social.trip_dest_simulate.interaction_sample_simulate number_of_rows: 1 observed_row_size: 230 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.social.trip_destination_simulate : 2.059 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_2.univ with 3 trips\n", "INFO - Running trip_destination.trip_num_2.univ.trip_destination_sample with 3 trips\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 3 choosers\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", "INFO - Running eval_interaction_utilities on 75 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.univ.trip_destination_sample : 0.144 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.univ.trip_destination_sample.interaction_sample number_of_rows: 3 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.univ.trip_destination_sample : 1.382 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_2.univ.compute_logsums with 38 samples\n", - "INFO - Running chunk 1 of 1 size 38\n", - "INFO - Time to execute eval_utilities : 0.543 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 38\n", - "INFO - Time to execute eval_utilities : 0.539 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_2.univ.compute_logsums : 1.608 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 38 choosers\n", + "INFO - Running chunk 1 of 1 with 38 of 38 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.97 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.97 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.97 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.univ.compute_logsums.od.simple_simulate_logsums number_of_rows: 38 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 38 choosers\n", + "INFO - Running chunk 1 of 1 with 38 of 38 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.97 GB percent: 50.3%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.univ.compute_logsums.dp.simple_simulate_logsums number_of_rows: 38 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.univ.compute_logsums : 6.068 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 3 trips\n", - "INFO - Running chunk 1 of 1 size 3\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 3 choosers and 38 alternatives\n", + "INFO - Running chunk 1 of 1 with 3 of 3 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", "INFO - Running eval_interaction_utilities on 38 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.univ.trip_destination_simulate : 0.212 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_2.univ.trip_dest_simulate.interaction_sample_simulate number_of_rows: 3 observed_row_size: 292 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.univ.trip_destination_simulate : 1.798 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_2.work with 6 trips\n", "INFO - Running trip_destination.trip_num_2.work.trip_destination_sample with 6 trips\n", - "INFO - Running chunk 1 of 1 size 6\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 6 choosers\n", + "INFO - Running chunk 1 of 1 with 6 of 6 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", "INFO - Running eval_interaction_utilities on 150 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.work.trip_destination_sample : 0.157 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.work.trip_destination_sample.interaction_sample number_of_rows: 6 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.work.trip_destination_sample : 1.218 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_2.work.compute_logsums with 78 samples\n", - "INFO - Running chunk 1 of 1 size 78\n", - "INFO - Time to execute eval_utilities : 0.555 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 78\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_2.work.compute_logsums : 1.627 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 78 choosers\n", + "INFO - Running chunk 1 of 1 with 78 of 78 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.work.compute_logsums.od.simple_simulate_logsums number_of_rows: 78 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 78 choosers\n", + "INFO - Running chunk 1 of 1 with 78 of 78 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_2.work.compute_logsums.dp.simple_simulate_logsums number_of_rows: 78 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.work.compute_logsums : 5.166 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 6 trips\n", - "INFO - Running chunk 1 of 1 size 6\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 6 choosers and 78 alternatives\n", + "INFO - Running chunk 1 of 1 with 6 of 6 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", "INFO - Running eval_interaction_utilities on 78 rows\n", - "INFO - Time to execute trip_destination.trip_num_2.work.trip_destination_simulate : 0.193 seconds (0.0 minutes)\n", - "INFO - Running trip_destination.trip_num_3 with 8 trips\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_2.work.trip_dest_simulate.interaction_sample_simulate number_of_rows: 6 observed_row_size: 299 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_2.work.trip_destination_simulate : 1.648 seconds (0.0 minutes)\n", + "INFO - Running trip_destination.trip_num_3 with 7 trips\n", "INFO - choose_trip_destination trip_destination.trip_num_3.atwork with 2 trips\n", "INFO - Running trip_destination.trip_num_3.atwork.trip_destination_sample with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 2 choosers\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", "INFO - Running eval_interaction_utilities on 50 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.atwork.trip_destination_sample : 0.163 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.atwork.trip_destination_sample.interaction_sample number_of_rows: 2 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.atwork.trip_destination_sample : 1.152 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_3.atwork.compute_logsums with 28 samples\n", - "INFO - Running chunk 1 of 1 size 28\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 28\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_3.atwork.compute_logsums : 1.602 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 28 choosers\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.atwork.compute_logsums.od.simple_simulate_logsums number_of_rows: 28 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 28 choosers\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.atwork.compute_logsums.dp.simple_simulate_logsums number_of_rows: 28 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.atwork.compute_logsums : 5.631 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 2 choosers and 28 alternatives\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", "INFO - Running eval_interaction_utilities on 28 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.atwork.trip_destination_simulate : 0.181 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_3.atwork.trip_dest_simulate.interaction_sample_simulate number_of_rows: 2 observed_row_size: 322 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.atwork.trip_destination_simulate : 2.417 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_3.othmaint with 1 trips\n", - "INFO - Running trip_destination.trip_num_3.othmaint.trip_destination_sample with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running trip_destination.trip_num_3.othmaint.trip_destination_sample with 1 trips\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.94 GB percent: 50.1%\n", "INFO - Running eval_interaction_utilities on 25 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.othmaint.trip_destination_sample : 0.154 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.94 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.othmaint.trip_destination_sample.interaction_sample number_of_rows: 1 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.othmaint.trip_destination_sample : 1.508 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_3.othmaint.compute_logsums with 12 samples\n", - "INFO - Running chunk 1 of 1 size 12\n", - "INFO - Time to execute eval_utilities : 0.536 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 12\n", - "INFO - Time to execute eval_utilities : 0.538 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_3.othmaint.compute_logsums : 1.639 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 12 choosers\n", + "INFO - Running chunk 1 of 1 with 12 of 12 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.othmaint.compute_logsums.od.simple_simulate_logsums number_of_rows: 12 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 12 choosers\n", + "INFO - Running chunk 1 of 1 with 12 of 12 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.96 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.othmaint.compute_logsums.dp.simple_simulate_logsums number_of_rows: 12 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.othmaint.compute_logsums : 6.9 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 12 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.95 GB percent: 50.1%\n", "INFO - Running eval_interaction_utilities on 12 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.othmaint.trip_destination_simulate : 0.181 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_3.othmaint.trip_dest_simulate.interaction_sample_simulate number_of_rows: 1 observed_row_size: 276 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.othmaint.trip_destination_simulate : 1.562 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_3.social with 1 trips\n", "INFO - Running trip_destination.trip_num_3.social.trip_destination_sample with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.95 GB percent: 50.1%\n", "INFO - Running eval_interaction_utilities on 25 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.social.trip_destination_sample : 0.163 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.95 GB percent: 50.1%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.95 GB percent: 50.2%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.social.trip_destination_sample.interaction_sample number_of_rows: 1 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.social.trip_destination_sample : 1.469 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_3.social.compute_logsums with 16 samples\n", - "INFO - Running chunk 1 of 1 size 16\n", - "INFO - Time to execute eval_utilities : 0.536 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 16\n", - "INFO - Time to execute eval_utilities : 0.54 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_3.social.compute_logsums : 1.589 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 16 choosers\n", + "INFO - Running chunk 1 of 1 with 16 of 16 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.94 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.social.compute_logsums.od.simple_simulate_logsums number_of_rows: 16 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 16 choosers\n", + "INFO - Running chunk 1 of 1 with 16 of 16 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.social.compute_logsums.dp.simple_simulate_logsums number_of_rows: 16 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.social.compute_logsums : 6.382 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 1 trips\n", - "INFO - Running chunk 1 of 1 size 1\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 16 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", "INFO - Running eval_interaction_utilities on 16 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.social.trip_destination_simulate : 0.187 seconds (0.0 minutes)\n", - "INFO - choose_trip_destination trip_destination.trip_num_3.univ with 2 trips\n", - "INFO - Running trip_destination.trip_num_3.univ.trip_destination_sample with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", - "INFO - Running eval_interaction_utilities on 50 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.univ.trip_destination_sample : 0.172 seconds (0.0 minutes)\n", - "INFO - Running trip_destination.trip_num_3.univ.compute_logsums with 27 samples\n", - "INFO - Running chunk 1 of 1 size 27\n", - "INFO - Time to execute eval_utilities : 0.542 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 27\n", - "INFO - Time to execute eval_utilities : 0.537 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_3.univ.compute_logsums : 1.595 seconds (0.0 minutes)\n", - "INFO - Running trip_destination_simulate with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", - "INFO - Running eval_interaction_utilities on 27 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.univ.trip_destination_simulate : 0.173 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_3.social.trip_dest_simulate.interaction_sample_simulate number_of_rows: 1 observed_row_size: 368 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.social.trip_destination_simulate : 2.199 seconds (0.0 minutes)\n", + "INFO - choose_trip_destination trip_destination.trip_num_3.univ with 1 trips\n", + "INFO - Running trip_destination.trip_num_3.univ.trip_destination_sample with 1 trips\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 1 choosers\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - Running eval_interaction_utilities on 25 rows\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.univ.trip_destination_sample.interaction_sample number_of_rows: 1 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.univ.trip_destination_sample : 1.572 seconds (0.0 minutes)\n", + "INFO - Running trip_destination.trip_num_3.univ.compute_logsums with 14 samples\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 14 choosers\n", + "INFO - Running chunk 1 of 1 with 14 of 14 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.univ.compute_logsums.od.simple_simulate_logsums number_of_rows: 14 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 14 choosers\n", + "INFO - Running chunk 1 of 1 with 14 of 14 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.92 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.univ.compute_logsums.dp.simple_simulate_logsums number_of_rows: 14 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.univ.compute_logsums : 8.081 seconds (0.1 minutes)\n", + "INFO - Running trip_destination_simulate with 1 trips\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 1 choosers and 14 alternatives\n", + "INFO - Running chunk 1 of 1 with 1 of 1 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - Running eval_interaction_utilities on 14 rows\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_3.univ.trip_dest_simulate.interaction_sample_simulate number_of_rows: 1 observed_row_size: 322 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.univ.trip_destination_simulate : 1.754 seconds (0.0 minutes)\n", "INFO - choose_trip_destination trip_destination.trip_num_3.work with 2 trips\n", "INFO - Running trip_destination.trip_num_3.work.trip_destination_sample with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 2 choosers\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.add.interaction_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", "INFO - Running eval_interaction_utilities on 50 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.work.trip_destination_sample : 0.166 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.add.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.del.interaction_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.add.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.del.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.add.probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.del.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.del.probs rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample.add.choices_df rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.work.trip_destination_sample.interaction_sample number_of_rows: 2 observed_row_size: 450 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.work.trip_destination_sample : 1.434 seconds (0.0 minutes)\n", "INFO - Running trip_destination.trip_num_3.work.compute_logsums with 27 samples\n", - "INFO - Running chunk 1 of 1 size 27\n", - "INFO - Time to execute eval_utilities : 0.535 seconds (0.0 minutes)\n", - "INFO - Running chunk 1 of 1 size 27\n", - "INFO - Time to execute eval_utilities : 0.535 seconds (0.0 minutes)\n", - "INFO - Time to execute trip_destination.trip_num_3.work.compute_logsums : 1.588 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 27 choosers\n", + "INFO - Running chunk 1 of 1 with 27 of 27 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.93 GB percent: 50.0%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.work.compute_logsums.od.simple_simulate_logsums number_of_rows: 27 observed_row_size: 400 num_chunks: 1\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 27 choosers\n", + "INFO - Running chunk 1 of 1 with 27 of 27 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.expression_values rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.add.utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.expression_values rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.eval_utils.del.utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.raw_utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.add.logsums rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums.eval_nl_logsums.del.nested_exp_utilities rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_destination.trip_num_3.work.compute_logsums.dp.simple_simulate_logsums number_of_rows: 27 observed_row_size: 400 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.work.compute_logsums : 6.258 seconds (0.1 minutes)\n", "INFO - Running trip_destination_simulate with 2 trips\n", - "INFO - Running chunk 1 of 1 size 2\n", + "INFO - Running adaptive_chunked_choosers_and_alts with chunk_size 0 and 2 choosers and 27 alternatives\n", + "INFO - Running chunk 1 of 1 with 2 of 2 choosers\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.interaction_df rss: 0.15GB used: 7.91 GB percent: 49.8%\n", "INFO - Running eval_interaction_utilities on 27 rows\n", - "INFO - Time to execute trip_destination.trip_num_3.work.trip_destination_simulate : 0.197 seconds (0.0 minutes)\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.interaction_utilities rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.del.interaction_df rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.sample_counts rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.del.sample_counts rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.padded_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.del.interaction_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.utilities_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.del.padded_utilities rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.logsums rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.del.utilities_df rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.positions rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.del.probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_and_alts trip_destination.trip_num_3.work.trip_dest_simulate.interaction_sample_simulate number_of_rows: 2 observed_row_size: 311 num_chunks: 1\n", + "INFO - Time to execute trip_destination.trip_num_3.work.trip_destination_simulate : 2.181 seconds (0.0 minutes)\n", + "INFO - trace_memory_info pipeline.run after trip_destination rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #run_model running step trip_purpose_and_destination\n", "INFO - trip_purpose_and_destination - no failed trips from prior model run.\n", - "INFO - trip_scheduling.i1 scheduling 481 trips\n", - "INFO - trip_scheduling.i1 5 failed\n", - "INFO - trip_scheduling.i2 scheduling 14 trips\n", - "INFO - trip_scheduling.i2 1 failed\n", - "INFO - trip_scheduling.i3 scheduling 4 trips\n", + "INFO - trace_memory_info pipeline.run after trip_purpose_and_destination rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #run_model running step trip_scheduling\n", + "INFO - trip_scheduling.i1 scheduling 480 trips\n", + "INFO - Running chunk 1 of 1 with 201 of 201 choosers\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_2.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_2.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_2.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_2.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_3.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_3.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_3.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_3.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_4.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_4.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_4.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.outbound.num_4.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_1.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_1.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_1.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_1.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_1.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_1.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_2.add.rands rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_2.add.failed rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i1.inbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i1 number_of_rows: 201 observed_row_size: 9 num_chunks: 1\n", + "INFO - trip_scheduling.i1 6 failed\n", + "INFO - trip_scheduling.i2 scheduling 17 trips\n", + "INFO - Running chunk 1 of 1 with 148 of 148 choosers\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_3.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_3.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_3.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_3.add.failed rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_4.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_4.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_4.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.outbound.num_4.add.failed rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_1.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_1.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_1.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_1.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_1.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_1.add.failed rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_2.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_2.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_2.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_2.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i2.inbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i2 number_of_rows: 148 observed_row_size: 2 num_chunks: 1\n", + "INFO - trip_scheduling.i2 2 failed\n", + "INFO - trip_scheduling.i3 scheduling 7 trips\n", + "INFO - Running chunk 1 of 1 with 55 of 55 choosers\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_4.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_4.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.outbound.num_4.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_1.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_1.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_1.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_1.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_1.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_1.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_2.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_2.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_2.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i3.inbound.num_2.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i3 number_of_rows: 55 observed_row_size: 2 num_chunks: 1\n", "INFO - trip_scheduling.i3 1 failed\n", "INFO - trip_scheduling.i4 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_2.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_2.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_2.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_2.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_3.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_3.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_3.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_3.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_4.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i4.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i4 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i4 1 failed\n", "INFO - trip_scheduling.i5 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_2.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_2.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_2.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_2.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_3.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_3.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_3.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_3.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_4.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i5.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i5 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i5 1 failed\n", "INFO - trip_scheduling.i6 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i6.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i6 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i6 1 failed\n", "INFO - trip_scheduling.i7 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i7.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i7 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i7 1 failed\n", "INFO - trip_scheduling.i8 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i8.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i8 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i8 1 failed\n", "INFO - trip_scheduling.i9 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i9.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i9 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i9 1 failed\n", "INFO - trip_scheduling.i10 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_4.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i10.outbound.num_4.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i10 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i10 1 failed\n", "INFO - trip_scheduling.i11 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_2.add.choosers rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_2.add.choices rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_2.add.rands rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_2.add.failed rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_3.add.choosers rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_3.add.choices rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_3.add.rands rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_3.add.failed rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_4.add.choosers rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.92 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_4.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_4.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i11.outbound.num_4.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i11 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i11 1 failed\n", "INFO - trip_scheduling.i12 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_2.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_2.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_2.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_2.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_3.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_3.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_3.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_3.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_4.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_4.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_4.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i12.outbound.num_4.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i12 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i12 1 failed\n", "INFO - trip_scheduling.i13 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_2.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_2.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_2.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_2.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_3.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_3.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_3.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_3.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_4.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_4.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_4.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i13.outbound.num_4.add.failed rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i13 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i13 1 failed\n", "INFO - trip_scheduling.i14 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_3.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_3.add.choices rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_3.add.rands rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_4.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.9%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_4.add.rands rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i14.outbound.num_4.add.failed rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i14 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i14 1 failed\n", "INFO - trip_scheduling.i15 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_2.add.choosers rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.91 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i15.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i15 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i15 1 failed\n", "INFO - trip_scheduling.i16 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i16.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i16 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i16 1 failed\n", "INFO - trip_scheduling.i17 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i17.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i17 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i17 1 failed\n", "INFO - trip_scheduling.i18 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_2.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_3.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_3.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_3.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_3.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_4.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_4.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_4.add.rands rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i18.outbound.num_4.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i18 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i18 1 failed\n", "INFO - trip_scheduling.i19 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_2.add.choosers rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_2.add.choices rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_2.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_2.add.failed rss: 0.15GB used: 7.9 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_3.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_3.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_3.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_3.add.failed rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_4.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_4.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_4.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i19.outbound.num_4.add.failed rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i19 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i19 1 failed\n", "INFO - trip_scheduling.i20 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_2.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_2.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_2.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_2.add.failed rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_3.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_3.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_3.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_3.add.failed rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_4.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_4.add.choices rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_4.add.rands rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i20.outbound.num_4.add.failed rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i20 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i20 2 failed\n", "INFO - trip_scheduling.i21 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_2.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.8%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_2.add.choices rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_2.add.rands rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_2.add.failed rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_3.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_3.add.choices rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_3.add.rands rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_3.add.failed rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_4.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_4.add.choices rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_4.add.rands rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i21.outbound.num_4.add.failed rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i21 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i21 1 failed\n", "INFO - trip_scheduling.i22 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_2.add.choosers rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.89 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_2.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_2.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_2.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_3.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_3.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_3.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_3.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_4.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_4.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_4.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i22.outbound.num_4.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i22 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i22 1 failed\n", "INFO - trip_scheduling.i23 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_2.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_2.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_2.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_2.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_3.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_3.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_3.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_3.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_4.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_4.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_4.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i23.outbound.num_4.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i23 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i23 1 failed\n", "INFO - trip_scheduling.i24 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_2.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_2.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_2.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_2.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_3.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_3.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_3.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_3.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_4.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_4.add.choices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_4.add.rands rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i24.outbound.num_4.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i24 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i24 1 failed\n", "INFO - trip_scheduling.i25 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i25.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i25 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i25 1 failed\n", "INFO - trip_scheduling.i26 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i26.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i26 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i26 1 failed\n", "INFO - trip_scheduling.i27 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i27.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i27 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i27 1 failed\n", "INFO - trip_scheduling.i28 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i28.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i28 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i28 1 failed\n", "INFO - trip_scheduling.i29 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i29.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i29 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i29 1 failed\n", "INFO - trip_scheduling.i30 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i30.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i30 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i30 1 failed\n", "INFO - trip_scheduling.i31 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i31.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i31 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i31 1 failed\n", "INFO - trip_scheduling.i32 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i32.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i32 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i32 1 failed\n", "INFO - trip_scheduling.i33 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i33.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i33 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i33 1 failed\n", "INFO - trip_scheduling.i34 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_2.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_2.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_2.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_2.add.failed rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_3.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_3.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_3.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_3.add.failed rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_4.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_4.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_4.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i34.outbound.num_4.add.failed rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i34 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i34 1 failed\n", "INFO - trip_scheduling.i35 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_2.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_2.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_2.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_2.add.failed rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_3.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_3.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_3.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_3.add.failed rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_4.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_4.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_4.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i35.outbound.num_4.add.failed rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i35 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i35 1 failed\n", "INFO - trip_scheduling.i36 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_2.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_2.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_2.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_2.add.failed rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_3.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i36.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i36 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i36 1 failed\n", "INFO - trip_scheduling.i37 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i37.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i37 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i37 1 failed\n", "INFO - trip_scheduling.i38 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i38.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i38 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i38 1 failed\n", "INFO - trip_scheduling.i39 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i39.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i39 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i39 1 failed\n", "INFO - trip_scheduling.i40 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i40.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i40 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i40 1 failed\n", "INFO - trip_scheduling.i41 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i41.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i41 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i41 1 failed\n", "INFO - trip_scheduling.i42 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i42.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i42 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i42 1 failed\n", "INFO - trip_scheduling.i43 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i43.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i43 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i43 1 failed\n", "INFO - trip_scheduling.i44 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i44.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i44 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i44 1 failed\n", "INFO - trip_scheduling.i45 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i45.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i45 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i45 1 failed\n", "INFO - trip_scheduling.i46 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i46.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i46 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i46 1 failed\n", "INFO - trip_scheduling.i47 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i47.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i47 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i47 1 failed\n", "INFO - trip_scheduling.i48 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i48.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i48 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i48 1 failed\n", "INFO - trip_scheduling.i49 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i49.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i49 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i49 2 failed\n", "INFO - trip_scheduling.i50 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i50.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i50 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i50 1 failed\n", "INFO - trip_scheduling.i51 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i51.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i51 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i51 1 failed\n", "INFO - trip_scheduling.i52 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i52.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i52 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i52 1 failed\n", "INFO - trip_scheduling.i53 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i53.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i53 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i53 1 failed\n", "INFO - trip_scheduling.i54 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i54.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i54 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i54 2 failed\n", "INFO - trip_scheduling.i55 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i55.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i55 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i55 1 failed\n", "INFO - trip_scheduling.i56 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i56.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i56 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i56 1 failed\n", "INFO - trip_scheduling.i57 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_3.add.choosers rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i57.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i57 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i57 1 failed\n", "INFO - trip_scheduling.i58 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i58.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i58 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i58 1 failed\n", "INFO - trip_scheduling.i59 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i59.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i59 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i59 1 failed\n", "INFO - trip_scheduling.i60 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i60.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i60 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i60 1 failed\n", "INFO - trip_scheduling.i61 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i61.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i61 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i61 1 failed\n", "INFO - trip_scheduling.i62 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i62.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i62 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i62 1 failed\n", "INFO - trip_scheduling.i63 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i63.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i63 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i63 1 failed\n", "INFO - trip_scheduling.i64 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i64.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i64 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i64 1 failed\n", "INFO - trip_scheduling.i65 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i65.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i65 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i65 1 failed\n", "INFO - trip_scheduling.i66 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i66.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i66 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i66 1 failed\n", "INFO - trip_scheduling.i67 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i67.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i67 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i67 1 failed\n", "INFO - trip_scheduling.i68 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i68.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i68 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i68 1 failed\n", "INFO - trip_scheduling.i69 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i69.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i69 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i69 1 failed\n", "INFO - trip_scheduling.i70 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i70.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i70 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i70 1 failed\n", "INFO - trip_scheduling.i71 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i71.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i71 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i71 1 failed\n", "INFO - trip_scheduling.i72 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i72.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i72 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i72 1 failed\n", "INFO - trip_scheduling.i73 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i73.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i73 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i73 1 failed\n", "INFO - trip_scheduling.i74 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i74.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i74 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i74 1 failed\n", "INFO - trip_scheduling.i75 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i75.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i75 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i75 1 failed\n", "INFO - trip_scheduling.i76 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i76.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i76 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i76 2 failed\n", "INFO - trip_scheduling.i77 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i77.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i77 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i77 2 failed\n", "INFO - trip_scheduling.i78 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i78.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i78 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i78 1 failed\n", "INFO - trip_scheduling.i79 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i79.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i79 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i79 1 failed\n", "INFO - trip_scheduling.i80 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i80.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i80 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i80 1 failed\n", "INFO - trip_scheduling.i81 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i81.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i81 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i81 1 failed\n", "INFO - trip_scheduling.i82 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i82.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i82 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i82 1 failed\n", "INFO - trip_scheduling.i83 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_2.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_2.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_2.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_3.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_4.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_4.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i83.outbound.num_4.add.failed rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i83 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i83 1 failed\n", "INFO - trip_scheduling.i84 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_2.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_3.add.choosers rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_3.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_3.add.rands rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i84.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i84 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i84 1 failed\n", "INFO - trip_scheduling.i85 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i85.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i85 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i85 1 failed\n", "INFO - trip_scheduling.i86 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i86.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i86 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i86 1 failed\n", "INFO - trip_scheduling.i87 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i87.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i87 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i87 1 failed\n", "INFO - trip_scheduling.i88 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_4.add.choices rss: 0.15GB used: 7.85 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i88.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i88 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i88 1 failed\n", "INFO - trip_scheduling.i89 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i89.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i89 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i89 1 failed\n", "INFO - trip_scheduling.i90 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i90.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i90 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "INFO - trip_scheduling.i90 1 failed\n", "INFO - trip_scheduling.i91 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i91.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i91 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i91 1 failed\n", "INFO - trip_scheduling.i92 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i92.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i92 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i92 1 failed\n", "INFO - trip_scheduling.i93 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i93.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i93 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i93 1 failed\n", "INFO - trip_scheduling.i94 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i94.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i94 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i94 1 failed\n", "INFO - trip_scheduling.i95 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i95.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i95 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i95 1 failed\n", "INFO - trip_scheduling.i96 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.5%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i96.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i96 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i96 1 failed\n", "INFO - trip_scheduling.i97 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i97.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i97 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i97 1 failed\n", "INFO - trip_scheduling.i98 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i98.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i98 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i98 1 failed\n", "INFO - trip_scheduling.i99 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_2.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_3.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_3.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_3.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_3.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_4.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_4.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_4.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i99.outbound.num_4.add.failed rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i99 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i99 1 failed\n", "INFO - trip_scheduling.i100 scheduling 4 trips\n", + "INFO - Running chunk 1 of 1 with 28 of 28 choosers\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_2.add.choosers rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_2.add.chooser_probs rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_2.add.choices rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_2.add.rands rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_2.add.failed rss: 0.15GB used: 7.87 GB percent: 49.6%\n", "WARNING - trip_scheduling.i100.outbound.num_2 coercing 0 depart choices to most initial\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_3.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_3.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_3.add.choices rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_3.add.rands rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_3.add.failed rss: 0.15GB used: 7.88 GB percent: 49.7%\n", "WARNING - trip_scheduling.i100.outbound.num_3 coercing 0 depart choices to most initial\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_4.add.choosers rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_4.add.chooser_probs rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_4.add.choices rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_4.add.rands rss: 0.15GB used: 7.87 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_scheduling.i100.outbound.num_4.add.failed rss: 0.15GB used: 7.87 GB percent: 49.7%\n", "INFO - dumping trip_scheduling.i100.outbound.num_4.failed_choosers\n", "WARNING - trip_scheduling.i100.outbound.num_4 coercing 1 depart choices to most initial\n", + "INFO - #chunk_history adaptive_chunked_choosers_by_chunk_id trip_scheduling.i100 number_of_rows: 28 observed_row_size: 3 num_chunks: 1\n", "INFO - trip_scheduling.i100 0 failed\n", - "INFO - Running trip_mode_choice with 481 trips\n", + "INFO - trace_memory_info pipeline.run after trip_scheduling rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #run_model running step trip_mode_choice\n", + "INFO - Running trip_mode_choice with 480 trips\n", "INFO - primary_purpose top 10 value counts:\n", "work 167\n", - "shopping 79\n", - "othmaint 44\n", + "shopping 77\n", + "othmaint 46\n", "othdiscr 41\n", - "school 38\n", - "eatout 35\n", - "univ 28\n", - "atwork 24\n", + "school 37\n", + "eatout 33\n", + "univ 27\n", + "atwork 27\n", "social 19\n", "escort 6\n", "Name: primary_purpose, dtype: int64\n", - "INFO - trip_mode_choice tour_type 'atwork' (24 trips)\n", - "INFO - Running chunk 1 of 1 size 24\n", - "INFO - Time to execute eval_utilities : 0.716 seconds (0.0 minutes)\n", - "INFO - trip_mode_choice tour_type 'eatout' (35 trips)\n", - "INFO - Running chunk 1 of 1 size 35\n", - "INFO - Time to execute eval_utilities : 0.55 seconds (0.0 minutes)\n", + "INFO - trip_mode_choice tour_type 'atwork' (27 trips)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 27 choosers\n", + "INFO - Running chunk 1 of 1 with 27 of 27 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 27 observed_row_size: 400 num_chunks: 1\n", + "INFO - trip_mode_choice tour_type 'eatout' (33 trips)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 33 choosers\n", + "INFO - Running chunk 1 of 1 with 33 of 33 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 33 observed_row_size: 400 num_chunks: 1\n", "INFO - trip_mode_choice tour_type 'escort' (6 trips)\n", - "INFO - Running chunk 1 of 1 size 6\n", - "INFO - Time to execute eval_utilities : 0.561 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 6 choosers\n", + "INFO - Running chunk 1 of 1 with 6 of 6 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 6 observed_row_size: 400 num_chunks: 1\n", "WARNING - slice_canonically: no rows in trip_mode_choice.escort.trip_mode with household_id == [2223759]\n", "INFO - trip_mode_choice tour_type 'othdiscr' (41 trips)\n", - "INFO - Running chunk 1 of 1 size 41\n", - "INFO - Time to execute eval_utilities : 0.716 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 41 choosers\n", + "INFO - Running chunk 1 of 1 with 41 of 41 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.88 GB percent: 49.7%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 41 observed_row_size: 400 num_chunks: 1\n", "WARNING - slice_canonically: no rows in trip_mode_choice.othdiscr.trip_mode with household_id == [2223759]\n", - "INFO - trip_mode_choice tour_type 'othmaint' (44 trips)\n", - "INFO - Running chunk 1 of 1 size 44\n", - "INFO - Time to execute eval_utilities : 0.768 seconds (0.0 minutes)\n", + "INFO - trip_mode_choice tour_type 'othmaint' (46 trips)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 46 choosers\n", + "INFO - Running chunk 1 of 1 with 46 of 46 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 46 observed_row_size: 400 num_chunks: 1\n", "WARNING - slice_canonically: no rows in trip_mode_choice.othmaint.trip_mode with household_id == [2223759]\n", - "INFO - trip_mode_choice tour_type 'school' (38 trips)\n", - "INFO - Running chunk 1 of 1 size 38\n", - "INFO - Time to execute eval_utilities : 0.744 seconds (0.0 minutes)\n", + "INFO - trip_mode_choice tour_type 'school' (37 trips)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 37 choosers\n", + "INFO - Running chunk 1 of 1 with 37 of 37 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 37 observed_row_size: 400 num_chunks: 1\n", "WARNING - slice_canonically: no rows in trip_mode_choice.school.trip_mode with household_id == [2223759]\n", - "INFO - trip_mode_choice tour_type 'shopping' (79 trips)\n", - "INFO - Running chunk 1 of 1 size 79\n", - "INFO - Time to execute eval_utilities : 0.595 seconds (0.0 minutes)\n", + "INFO - trip_mode_choice tour_type 'shopping' (77 trips)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 77 choosers\n", + "INFO - Running chunk 1 of 1 with 77 of 77 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 77 observed_row_size: 400 num_chunks: 1\n", "WARNING - slice_canonically: no rows in trip_mode_choice.shopping.trip_mode with household_id == [2223759]\n", "INFO - trip_mode_choice tour_type 'social' (19 trips)\n", - "INFO - Running chunk 1 of 1 size 19\n", - "INFO - Time to execute eval_utilities : 0.57 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 19 choosers\n", + "INFO - Running chunk 1 of 1 with 19 of 19 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 19 observed_row_size: 400 num_chunks: 1\n", "WARNING - slice_canonically: no rows in trip_mode_choice.social.trip_mode with household_id == [2223759]\n", - "INFO - trip_mode_choice tour_type 'univ' (28 trips)\n", - "INFO - Running chunk 1 of 1 size 28\n", - "INFO - Time to execute eval_utilities : 0.748 seconds (0.0 minutes)\n", + "INFO - trip_mode_choice tour_type 'univ' (27 trips)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 27 choosers\n", + "INFO - Running chunk 1 of 1 with 27 of 27 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 27 observed_row_size: 400 num_chunks: 1\n", "WARNING - slice_canonically: no rows in trip_mode_choice.univ.trip_mode with household_id == [2223759]\n", "INFO - trip_mode_choice tour_type 'work' (167 trips)\n", - "INFO - Running chunk 1 of 1 size 167\n", - "INFO - Time to execute eval_utilities : 0.697 seconds (0.0 minutes)\n", + "INFO - Running adaptive_chunked_choosers with chunk_size 0 and 167 choosers\n", + "INFO - Running chunk 1 of 1 with 167 of 167 choosers\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.add.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.expression_values rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.eval_utils.del.utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.raw_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.logsums rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_exp_utilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.add.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.nested_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info trip_mode_choice.simple_simulate.eval_nl.del.base_probabilities rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #chunk_history adaptive_chunked_choosers trip_mode_choice.simple_simulate number_of_rows: 167 observed_row_size: 400 num_chunks: 1\n", "INFO - tour_modes top 10 value counts:\n", - "WALK 208\n", - "WALK_LRF 83\n", - "WALK_LOC 80\n", - "TNC_SINGLE 36\n", + "WALK 217\n", + "WALK_LRF 78\n", + "WALK_LOC 68\n", + "TNC_SINGLE 38\n", "DRIVEALONEFREE 19\n", - "TAXI 15\n", - "BIKE 14\n", - "WALK_HVY 12\n", - "SHARED3FREE 8\n", + "BIKE 18\n", + "WALK_HVY 14\n", + "TAXI 13\n", "SHARED2FREE 6\n", + "SHARED3FREE 6\n", "Name: tour_mode, dtype: int64\n", "INFO - trip_mode_choice choices top 10 value counts:\n", - "WALK 270\n", - "WALK_LOC 80\n", - "WALK_LRF 55\n", + "WALK 273\n", + "WALK_LOC 72\n", + "WALK_LRF 57\n", "TNC_SINGLE 24\n", + "BIKE 17\n", "DRIVEALONEFREE 13\n", - "BIKE 13\n", - "TNC_SHARED 9\n", + "TNC_SHARED 10\n", "SHARED2FREE 7\n", - "WALK_HVY 5\n", - "SHARED3FREE 5\n", + "WALK_HVY 4\n", + "SHARED3FREE 3\n", "Name: trip_mode, dtype: int64\n", + "INFO - trace_memory_info pipeline.run after trip_mode_choice rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #run_model running step write_data_dictionary\n", + "INFO - trace_memory_info pipeline.run after write_data_dictionary rss: 0.15GB used: 7.86 GB percent: 49.6%\n", + "INFO - #run_model running step track_skim_usage\n", + "INFO - trace_memory_info pipeline.run after track_skim_usage rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #run_model running step write_trip_matrices\n", "INFO - adding 'sample_rate' from households to trips table\n", "INFO - Aggregating trips...\n", "INFO - Finished.\n", @@ -1437,7 +6174,7 @@ "INFO - writing WALK_EXP_DRIVE_EA\n", "INFO - writing WALK_DRIVE_HVY_EA\n", "INFO - writing WALK_COM_DRIVE_EA\n", - "INFO - adding TAZ mapping for 25 zones to trips_ea.omx\n", + "INFO - adding zone_id mapping for 25 zones to trips_ea.omx\n", "INFO - closing output\\trips_ea.omx\n", "INFO - opening output\\trips_am.omx\n", "INFO - writing DRIVEALONEFREE_AM\n", @@ -1463,7 +6200,7 @@ "INFO - writing WALK_EXP_DRIVE_AM\n", "INFO - writing WALK_DRIVE_HVY_AM\n", "INFO - writing WALK_COM_DRIVE_AM\n", - "INFO - adding TAZ mapping for 25 zones to trips_am.omx\n", + "INFO - adding zone_id mapping for 25 zones to trips_am.omx\n", "INFO - closing output\\trips_am.omx\n", "INFO - opening output\\trips_md.omx\n", "INFO - writing DRIVEALONEFREE_MD\n", @@ -1489,7 +6226,7 @@ "INFO - writing WALK_EXP_DRIVE_MD\n", "INFO - writing WALK_DRIVE_HVY_MD\n", "INFO - writing WALK_COM_DRIVE_MD\n", - "INFO - adding TAZ mapping for 25 zones to trips_md.omx\n", + "INFO - adding zone_id mapping for 25 zones to trips_md.omx\n", "INFO - closing output\\trips_md.omx\n", "INFO - opening output\\trips_pm.omx\n", "INFO - writing DRIVEALONEFREE_PM\n", @@ -1515,7 +6252,7 @@ "INFO - writing WALK_EXP_DRIVE_PM\n", "INFO - writing WALK_DRIVE_HVY_PM\n", "INFO - writing WALK_COM_DRIVE_PM\n", - "INFO - adding TAZ mapping for 25 zones to trips_pm.omx\n", + "INFO - adding zone_id mapping for 25 zones to trips_pm.omx\n", "INFO - closing output\\trips_pm.omx\n", "INFO - opening output\\trips_ev.omx\n", "INFO - writing DRIVEALONEFREE_EV\n", @@ -1541,11 +6278,15 @@ "INFO - writing WALK_EXP_DRIVE_EV\n", "INFO - writing WALK_DRIVE_HVY_EV\n", "INFO - writing WALK_COM_DRIVE_EV\n", - "INFO - adding TAZ mapping for 25 zones to trips_ev.omx\n", + "INFO - adding zone_id mapping for 25 zones to trips_ev.omx\n", "INFO - closing output\\trips_ev.omx\n", - "INFO - Time to execute run_model (33 models) : 138.337 seconds (2.3 minutes)\n", + "INFO - trace_memory_info pipeline.run after write_trip_matrices rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - #run_model running step write_tables\n", + "INFO - trace_memory_info pipeline.run after write_tables rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - trace_memory_info #MEM pipeline.run after run_models rss: 0.15GB used: 7.87 GB percent: 49.6%\n", + "INFO - Time to execute run_model (33 models) : 691.017 seconds (11.5 minutes)\n", "INFO - close_pipeline\n", - "INFO - Time to execute all models : 138.365 seconds (2.3 minutes)\n" + "INFO - Time to execute all models : 691.322 seconds (11.5 minutes)\n" ] } ], @@ -1553,390 +6294,6 @@ "!activitysim run -c configs -d data -o output" ] }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "193E7ds2GEVs" - }, - "source": [ - "# Run the Multiprocessor Example\n", - "\n", - "The command below runs the multiprocessor example, which runs in a few minutes. It uses settings inheritance to override setings in the configs folder with settings in the configs_mp folder. This allows for re-using expression files and settings files in the single and multiprocessed setups. The multiprocessed example uses the following additional settings:\n", - "\n", - "```\n", - "chunk_size: 1000000000\n", - "\n", - "num_processes: 2\n", - "\n", - "multiprocess_steps:\n", - " - name: mp_initialize\n", - " begin: initialize_landuse\n", - " - name: mp_households\n", - " begin: school_location\n", - " slice:\n", - " tables:\n", - " - households\n", - " - persons\n", - " - name: mp_summarize\n", - " begin: write_data_dictionary\n", - "\n", - "```\n", - "\n", - "In brief, `num_processes` specifies the number of processors to use and `chunk_size` specifies the size of each batch of choosers data for processing. The `multiprocess_steps` specifies the beginning, middle, and end steps in multiprocessing. The `mp_initialize` step is single processed because there is no `slice` setting. It starts with the `initialize_landuse` submodel and runs until the submodel identified by the next multiprocess submodel starting point, `school_location`. The `mp_households` step is multiprocessed and the households and persons tables are sliced and allocated to processes using the chunking settings. The rest of the submodels are run multiprocessed until the final multiprocess step. The `mp_summarize` step is single processed because there is no `slice` setting and it writes outputs. See [multiprocessing](https://activitysim.github.io/activitysim/core.html#multiprocessing) and [chunk_size](https://activitysim.github.io/activitysim/abmexample.html#chunk-size) for more information. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "colab_type": "code", - "id": "rkI5DhdLF0bn", - "outputId": "9862a11e-c1b3-429e-ba14-903a60a73627" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Configured logging using basicConfig\n", - "INFO:activitysim:Configured logging using basicConfig\n", - "INFO:activitysim.cli.run:using configs_dir: ['configs_mp', 'configs']\n", - "INFO:activitysim.cli.run:using data_dir: ['data']\n", - "INFO:activitysim.cli.run:using output_dir: ['output']\n", - "INFO - activitysim - Read logging configuration from: configs_mp\\logging.yaml\n", - "DEBUG - activitysim.core.config - inherit_settings flag set for settings.yaml in configs_mp\\settings.yaml\n", - "DEBUG - activitysim.core.config - read settings for settings.yaml from configs\\settings.yaml\n", - "INFO - activitysim.cli.run - setting households_sample_size: 100\n", - "INFO - activitysim.cli.run - setting chunk_size: 0\n", - "INFO - activitysim.cli.run - setting multiprocess: True\n", - "INFO - activitysim.cli.run - setting num_processes: 2\n", - "INFO - activitysim.cli.run - setting resume_after: None\n", - "DEBUG - activitysim.core.tracing - delete_output_files ignoring output\\activitysim.log\n", - "[WinError 32] The process cannot access the file because it is being used by another process: 'output\\\\pipeline.h5'\n", - "INFO - activitysim.cli.run - run multiprocess simulation\n", - "INFO - activitysim.core.mp_tasks - Setting num_processes = 0 for step mp_households\n", - "INFO - activitysim.core.mem - init_trace file_name mem.csv\n", - "INFO - activitysim.core.mp_tasks - run_multiprocess fail_fast: True\n", - "INFO - activitysim.core.mp_tasks - allocate_shared_skim_buffer\n", - "DEBUG - activitysim.abm.tables.skims - get_skim_info from data\\skims.omx\n", - "DEBUG - activitysim.abm.tables.skims - get_skim_info skim_dtype omx_shape (25, 25) num_skims 826 num_blocks 1\n", - "INFO - activitysim.abm.tables.skims - allocating shared buffer skim_skims_0 for 516250 ((25, 25)) matrices (2.0 MB)\n", - "INFO - activitysim.core.tracing - Time to execute allocate shared skim buffer : 1.135 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - allocate_shared_shadow_pricing_buffers\n", - "INFO - activitysim.core.input - Reading CSV file data\\land_use.csv\n", - "INFO - activitysim.core.input - land_use table columns: ['ZONE' 'DISTRICT' 'SD' 'COUNTY' 'TOTHH' 'HHPOP' 'TOTPOP' 'EMPRES' 'SFDU'\n", - " 'MFDU' 'HHINCQ1' 'HHINCQ2' 'HHINCQ3' 'HHINCQ4' 'TOTACRE' 'RESACRE'\n", - " 'CIACRE' 'SHPOP62P' 'TOTEMP' 'AGE0004' 'AGE0519' 'AGE2044' 'AGE4564'\n", - " 'AGE65P' 'RETEMPN' 'FPSEMPN' 'HEREMPN' 'OTHEMPN' 'AGREMPN' 'MWTEMPN'\n", - " 'PRKCST' 'OPRKCST' 'area_type' 'HSENROLL' 'COLLFTE' 'COLLPTE' 'TOPOLOGY'\n", - " 'TERMINAL' 'ZERO' 'hhlds' 'sftaz' 'gqpop']\n", - "INFO - activitysim.core.input - land_use table size: (25, 42) 8.3 KB\n", - "INFO - activitysim.core.input - land_use index name: TAZ\n", - "INFO - activitysim.abm.tables.landuse - loaded land_use (25, 41)\n", - "DEBUG - activitysim.core.config - inherit_settings flag set for shadow_pricing.yaml in configs_mp\\shadow_pricing.yaml\n", - "DEBUG - activitysim.core.config - read settings for shadow_pricing.yaml from configs\\shadow_pricing.yaml\n", - "DEBUG - activitysim.abm.tables.shadow_pricing - shadow_pricing_info dtype: \n", - "DEBUG - activitysim.abm.tables.shadow_pricing - shadow_pricing_info block_shapes: OrderedDict([('school', (25, 4)), ('workplace', (25, 5))])\n", - "INFO - activitysim.abm.tables.shadow_pricing - allocating shared buffer school 100 buffer_size (25, 4) bytes 800 (800.0)\n", - "INFO - activitysim.abm.tables.shadow_pricing - buffer_for_shadow_pricing added block school\n", - "INFO - activitysim.abm.tables.shadow_pricing - allocating shared buffer workplace 125 buffer_size (25, 5) bytes 1000 (1000.0)\n", - "INFO - activitysim.abm.tables.shadow_pricing - buffer_for_shadow_pricing added block workplace\n", - "INFO - activitysim.core.tracing - Time to execute allocate shared shadow_pricing buffer : 0.064 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - running sub_process mp_setup_skims\n", - "INFO - activitysim.core.tracing - Time to execute sub_process mp_setup_skims : 6.024 seconds (0.1 minutes)\n", - "INFO - activitysim.core.tracing - Time to execute setup skims : 6.075 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - run_sub_simulations step mp_initialize models resume_after None\n", - "INFO - activitysim.core.mp_tasks - start process mp_initialize\n", - "[WinError 32] The process cannot access the file because it is being used by another process: 'output\\\\pipeline.h5'\n", - "mp_initialize WARNING - activitysim.core.pipeline - Error removing output\\pipeline.h5: [WinError 32] The process cannot access the file because it is being used by another process: 'output\\\\pipeline.h5'\n", - "INFO - activitysim.core.mp_tasks - mp_initialize initialize_landuse : 0.893 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_initialize compute_accessibility : 0.07 seconds (0.0 minutes)\n", - "adding table households.household_id to traceable_table_indexes\n", - "adding table persons.person_id to traceable_table_indexes\n", - "INFO - activitysim.core.mp_tasks - mp_initialize initialize_households : 1.028 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - process mp_initialize completed\n", - "INFO - activitysim.core.mp_tasks - Process mp_initialize completed with exitcode 0\n", - "INFO - activitysim.core.tracing - Time to execute run_sub_simulations step mp_initialize : 4.095 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - running sub_process mp_households_apportion\n", - "INFO - activitysim.core.tracing - Time to execute sub_process mp_households_apportion : 3.011 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - run_sub_simulations step mp_households models resume_after None\n", - "INFO - activitysim.core.mp_tasks - start process mp_households_0\n", - "INFO - activitysim.core.mp_tasks - start process mp_households_1\n", - "mp_households_1 WARNING - activitysim.core.tracing - trace_hh_id 2223759 not in dataframe\n", - "adding table households.household_id to traceable_table_indexes\n", - "mp_households_1 WARNING - activitysim.core.tracing - register persons: no rows with household_id in [].\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 school_location : 11.553 seconds (0.2 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 school_location : 12.768 seconds (0.2 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 workplace_location : 14.82 seconds (0.2 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 auto_ownership_simulate : 0.537 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 free_parking : 0.559 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 workplace_location : 16.757 seconds (0.3 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 auto_ownership_simulate : 0.584 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 free_parking : 0.339 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 cdap_simulate : 35.9 seconds (0.6 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 cdap_simulate : 35.061 seconds (0.6 minutes)\n", - "adding table persons.person_id to traceable_table_indexes\n", - "mp_households_1 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [].\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 mandatory_tour_frequency : 2.97 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 mandatory_tour_frequency : 2.875 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 mandatory_tour_scheduling : 18.834 seconds (0.3 minutes)\n", - "adding table tours.tour_id to traceable_table_indexes\n", - "mp_households_1 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [].\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 joint_tour_frequency : 1.59 seconds (0.0 minutes)\n", - "mp_households_1 WARNING - activitysim.core.tracing - register joint_tour_participants: no rows with household_id in [].\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 joint_tour_composition : 0.927 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 joint_tour_participation : 2.353 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 mandatory_tour_scheduling : 24.107 seconds (0.4 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 joint_tour_frequency : 1.924 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 joint_tour_composition : 0.919 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 joint_tour_participation : 2.19 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 joint_tour_destination : 7.391 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 joint_tour_destination : 4.444 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 joint_tour_scheduling : 2.366 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 joint_tour_scheduling : 2.943 seconds (0.0 minutes)\n", - "adding table joint_tour_participants.participant_id to traceable_table_indexes\n", - " prob pick_count tour_type_id mode_choice_logsum\n", - "tour_id alt_dest \n", - "100798519 4 0.028100 1 2 -0.619635\n", - " 5 0.151353 6 2 -0.524571\n", - " 11 0.157752 5 2 -0.444344\n", - " 13 0.055158 2 2 -0.668666\n", - " 14 0.015094 1 2 -0.733623\n", - " 16 0.194199 3 2 -0.733175\n", - " 17 0.020032 1 2 -0.775685\n", - " 18 0.022745 1 2 -0.615209\n", - " 19 0.064687 2 2 -0.666936\n", - " 21 0.026570 1 2 -0.498372\n", - " 22 0.021632 2 2 -0.773316\n", - "130727777 5 0.055259 3 4 -2.785427\n", - " 6 0.029101 1 4 -2.782740\n", - " 7 0.054684 2 4 -2.925061\n", - " 8 0.039230 1 4 -3.187009\n", - " 9 0.123684 4 4 -3.569094\n", - " 10 0.060143 1 4 -3.684088\n", - " 12 0.050141 1 4 -3.187251\n", - " 13 0.025643 1 4 -3.232057\n", - " 16 0.086455 2 4 -3.240963\n", - " 17 0.042926 1 4 -3.527248\n", - " 19 0.025617 1 4 -3.824914\n", - " 20 0.019363 1 4 -3.749339\n", - " 21 0.038511 2 4 -3.359422\n", - " 24 0.029985 3 4 -2.511566\n", - " 25 0.017205 1 4 -2.141605\n", - "mp_households_1 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [].\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 non_mandatory_tour_frequency : 17.376 seconds (0.3 minutes)\n", - "adding table households.household_id to traceable_table_indexes\n", - "adding table persons.person_id to traceable_table_indexes\n", - "adding table tours.tour_id to traceable_table_indexes\n", - "adding table joint_tour_participants.participant_id to traceable_table_indexes\n", - " prob pick_count tour_type_id mode_choice_logsum\n", - "tour_id alt_dest \n", - "220958279 1 0.030277 1 5 0.073891\n", - " 2 0.057021 1 5 0.149991\n", - " 4 0.043115 1 5 0.114411\n", - " 5 0.100513 2 5 0.076044\n", - " 8 0.017806 1 5 -0.131721\n", - " 9 0.052858 1 5 0.881303\n", - " 10 0.020011 1 5 -0.282984\n", - " 12 0.062115 2 5 0.031664\n", - " 13 0.052364 1 5 0.134034\n", - " 14 0.040011 1 5 0.155959\n", - " 16 0.166000 6 5 -0.224007\n", - " 17 0.024696 2 5 0.673986\n", - " 18 0.017401 2 5 0.393559\n", - " 20 0.011531 1 5 -0.246492\n", - " 21 0.024828 1 5 -0.185322\n", - " 22 0.039916 1 5 0.016920\n", - "mp_households_0 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [2223759].\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in non_mandatory_tour_frequency.non_mandatory_tours with household_id == [2223759]\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 non_mandatory_tour_frequency : 16.092 seconds (0.3 minutes)\n", - " prob pick_count ... mode_choice_logsum size_term\n", - "tour_id alt_dest ... \n", - "1214658 2 0.031578 1 ... 1.453032 453.000\n", - " 4 0.028100 1 ... 1.461359 403.000\n", - " 5 0.151353 3 ... 1.438033 2175.000\n", - " 9 0.008625 1 ... -0.251351 123.000\n", - " 11 0.157752 5 ... 0.785790 2267.000\n", - "... ... ... ... ... ...\n", - "308013110 12 0.065736 2 ... 0.280958 2527.870\n", - " 14 0.026696 4 ... -0.191420 1404.308\n", - " 21 0.026560 1 ... 0.287977 1078.402\n", - " 24 0.018982 2 ... 0.114408 1096.816\n", - " 25 0.008598 1 ... 0.230429 458.784\n", - "\n", - "[450 rows x 5 columns]\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in non_mandatory_tour_destination with household_id == [2223759]\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 non_mandatory_tour_destination : 16.661 seconds (0.3 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 non_mandatory_tour_destination : 20.138 seconds (0.3 minutes)\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in non_mandatory_tour_scheduling with household_id == [2223759]\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 non_mandatory_tour_scheduling : 5.933 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 non_mandatory_tour_scheduling : 4.62 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 tour_mode_choice_simulate : 22.507 seconds (0.4 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 atwork_subtour_frequency : 1.064 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 tour_mode_choice_simulate : 26.546 seconds (0.4 minutes)\n", - " prob pick_count ... mode_choice_logsum size_term\n", - "tour_id alt_dest ... \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15826320 4 0.028160 1 ... -0.185337 403.000\n", - " 5 0.151674 5 ... -0.078774 2175.000\n", - " 6 0.010595 1 ... 0.064716 151.000\n", - " 7 0.017635 1 ... 0.136543 252.000\n", - " 8 0.024048 2 ... 0.266383 344.000\n", - "... ... ... ... ... ...\n", - "207350867 16 0.081638 2 ... 1.235198 1418.382\n", - " 21 0.031663 1 ... 1.489438 514.365\n", - " 22 0.037526 2 ... 1.088449 659.310\n", - " 24 0.021490 1 ... 1.463870 365.961\n", - " 25 0.021808 1 ... 1.727370 360.945\n", - "\n", - "[782 rows x 5 columns]\n", - "mp_households_1 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [].\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 atwork_subtour_frequency : 1.18 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 atwork_subtour_destination : 6.125 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 atwork_subtour_destination : 5.161 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 atwork_subtour_scheduling : 3.813 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 atwork_subtour_scheduling : 2.235 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 atwork_subtour_mode_choice : 4.053 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 atwork_subtour_mode_choice : 3.72 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 stop_frequency : 6.487 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 trip_purpose : 0.252 seconds (0.0 minutes)\n", - " prob pick_count person_id mode_choice_logsum\n", - "tour_id alt_dest \n", - "13286136 2 0.084273 1 324052 1.108713\n", - " 4 0.057217 1 324052 0.871744\n", - " 5 0.120989 2 324052 0.622962\n", - " 8 0.019114 1 324052 0.218815\n", - " 9 0.050739 1 324052 -0.014687\n", - " 11 0.067016 1 324052 0.149299\n", - " 12 0.065655 1 324052 0.485722\n", - " 13 0.049590 2 324052 0.520474\n", - " 15 0.024472 1 324052 0.453201\n", - " 16 0.107866 3 324052 0.437184\n", - " 17 0.014695 1 324052 0.209440\n", - " 18 0.011369 1 324052 -0.080861\n", - " 19 0.012972 2 324052 -0.407002\n", - " 21 0.020611 4 324052 0.130994\n", - " 23 0.019154 1 324052 0.342114\n", - " 25 0.015534 2 324052 0.730235\n", - "66896301 1 0.031347 2 1631617 15.185951\n", - " 2 0.069517 2 1631617 15.644671\n", - " 7 0.047221 1 1631617 15.317551\n", - " 11 0.072955 2 1631617 14.862591\n", - " 12 0.071473 3 1631617 15.189711\n", - " 13 0.053558 2 1631617 15.219791\n", - " 16 0.108477 7 1631617 15.144591\n", - " 17 0.015871 1 1631617 15.106991\n", - " 19 0.014010 1 1631617 14.140672\n", - " 20 0.008314 1 1631617 14.366271\n", - " 22 0.041783 1 1631617 14.964111\n", - " 23 0.015676 1 1631617 15.016751\n", - " 24 0.026783 1 1631617 15.445391\n", - "143309067 1 0.017264 1 3495343 4.699688\n", - " 2 0.037090 1 3495343 5.069908\n", - " 5 0.103623 3 3495343 5.505330\n", - " 7 0.060728 1 3495343 5.821889\n", - " 8 0.035596 2 3495343 6.040981\n", - " 9 0.173955 3 3495343 6.514479\n", - " 11 0.113480 4 3495343 5.774078\n", - " 12 0.050725 1 3495343 5.184436\n", - " 16 0.064158 2 3495343 4.189721\n", - " 19 0.033169 3 3495343 5.190873\n", - " 20 0.022703 3 3495343 5.691072\n", - " 22 0.022293 1 3495343 3.995989\n", - "171036547 3 0.009611 1 4171623 0.207803\n", - " 4 0.037760 2 4171623 0.269144\n", - " 5 0.110507 1 4171623 0.487506\n", - " 7 0.044976 1 4171623 0.333077\n", - " 9 0.072810 2 4171623 0.114520\n", - " 10 0.028606 1 4171623 0.175416\n", - " 11 0.112687 3 4171623 0.595316\n", - " 12 0.069162 1 4171623 0.445733\n", - " 13 0.044581 3 4171623 0.275420\n", - " 16 0.124966 3 4171623 0.319151\n", - " 19 0.031658 1 4171623 0.196034\n", - " 21 0.042589 2 4171623 0.822425\n", - " 22 0.025531 1 4171623 -0.229737\n", - " 23 0.009579 3 4171623 -0.533598\n", - "mp_households_1 WARNING - activitysim.core.tracing - register trips: no rows with household_id in [].\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 stop_frequency : 6.835 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 trip_purpose : 0.332 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 trip_destination : 47.126 seconds (0.8 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 trip_purpose_and_destination : 0.248 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 trip_scheduling : 1.0 seconds (0.0 minutes)\n", - " prob pick_count person_id mode_choice_logsum\n", - "tour_id alt_dest \n", - "66923525 1 0.030519 1 1632281 5.847680\n", - " 2 0.058682 4 1632281 5.955241\n", - " 4 0.050137 2 1632281 6.097234\n", - " 5 0.117526 4 1632281 5.922843\n", - " 7 0.042471 1 1632281 5.479746\n", - "... ... ... ... ...\n", - "307996473 16 0.107866 2 7512109 15.135818\n", - " 20 0.007698 1 7512109 14.244698\n", - " 22 0.051055 2 7512109 15.079418\n", - " 23 0.019154 1 7512109 15.162138\n", - " 24 0.032727 2 7512109 15.594538\n", - "\n", - "[71 rows x 4 columns]\n", - "adding table trips.trip_id to traceable_table_indexes\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.othdiscr.trip_mode with household_id == [2223759]\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.othmaint.trip_mode with household_id == [2223759]\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.school.trip_mode with household_id == [2223759]\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.shopping.trip_mode with household_id == [2223759]\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.social.trip_mode with household_id == [2223759]\n", - "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.univ.trip_mode with household_id == [2223759]\n", - "INFO - activitysim.core.mp_tasks - mp_households_0 trip_mode_choice : 23.1 seconds (0.4 minutes)\n", - "INFO - activitysim.core.mp_tasks - process mp_households_0 completed\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 trip_destination : 95.129 seconds (1.6 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 trip_purpose_and_destination : 0.138 seconds (0.0 minutes)\n", - "adding table trips.trip_id to traceable_table_indexes\n", - "mp_households_1 WARNING - activitysim.abm.models.trip_scheduling - trip_scheduling.i100.outbound.num_2 coercing 0 depart choices to most initial\n", - "mp_households_1 WARNING - activitysim.abm.models.trip_scheduling - trip_scheduling.i100.outbound.num_3 coercing 1 depart choices to most initial\n", - "mp_households_1 WARNING - activitysim.abm.models.trip_scheduling - trip_scheduling.i100.outbound.num_4 coercing 0 depart choices to most initial\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 trip_scheduling : 42.863 seconds (0.7 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_households_1 trip_mode_choice : 17.053 seconds (0.3 minutes)\n", - "INFO - activitysim.core.mp_tasks - process mp_households_1 completed\n", - "INFO - activitysim.core.mp_tasks - Process mp_households_0 completed with exitcode 0\n", - "INFO - activitysim.core.mp_tasks - Process mp_households_1 completed with exitcode 0\n", - "INFO - activitysim.core.tracing - Time to execute run_sub_simulations step mp_households : 348.073 seconds (5.8 minutes)\n", - "INFO - activitysim.core.mp_tasks - running sub_process mp_households_coalesce\n", - "[WinError 32] The process cannot access the file because it is being used by another process: 'output\\\\pipeline.h5'\n", - "mp_households_coalesce WARNING - activitysim.core.pipeline - Error removing output\\pipeline.h5: [WinError 32] The process cannot access the file because it is being used by another process: 'output\\\\pipeline.h5'\n", - "INFO - activitysim.core.tracing - Time to execute sub_process mp_households_coalesce : 6.087 seconds (0.1 minutes)\n", - "INFO - activitysim.core.mp_tasks - run_sub_simulations step mp_summarize models resume_after None\n", - "INFO - activitysim.core.mp_tasks - start process mp_summarize\n", - "adding table households.household_id to traceable_table_indexes\n", - "adding table persons.person_id to traceable_table_indexes\n", - "adding table tours.tour_id to traceable_table_indexes\n", - "adding table joint_tour_participants.participant_id to traceable_table_indexes\n", - "adding table trips.trip_id to traceable_table_indexes\n", - "mp_summarize WARNING - activitysim.core.steps.output - Skipping 'school_shadow_prices': Table not found.\n", - "mp_summarize WARNING - activitysim.core.steps.output - Skipping 'workplace_shadow_prices': Table not found.\n", - "INFO - activitysim.core.mp_tasks - mp_summarize write_data_dictionary : 0.094 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - mp_summarize write_tables : 0.309 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mp_tasks - process mp_summarize completed\n", - "INFO - activitysim.core.mp_tasks - Process mp_summarize completed with exitcode 0\n", - "INFO - activitysim.core.tracing - Time to execute run_sub_simulations step mp_summarize : 2.053 seconds (0.0 minutes)\n", - "INFO - activitysim.core.mem - high water mark rss: 0.28 timestamp: 01/05/2020 11:55:55 label: \n", - "INFO - activitysim.core.mem - high water mark used: 7.81 timestamp: 01/05/2020 11:53:15 label: \n", - "INFO - activitysim.core.tracing - Time to execute all models : 370.939 seconds (6.2 minutes)\n" - ] - } - ], - "source": [ - "!activitysim run -c configs_mp -c configs -d data -o output" - ] - }, { "cell_type": "markdown", "metadata": { @@ -1969,7 +6326,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2014,6 +6371,7 @@ ".\\configs\\cdap_fixed_relative_proportions.csv\n", ".\\configs\\cdap_indiv_and_hhsize1.csv\n", ".\\configs\\cdap_interaction_coefficients.csv\n", + ".\\configs\\constants.yaml\n", ".\\configs\\destination_choice_size_terms.csv\n", ".\\configs\\free_parking.csv\n", ".\\configs\\free_parking.yaml\n", @@ -2043,6 +6401,7 @@ ".\\configs\\mandatory_tour_frequency_alternatives.csv\n", ".\\configs\\mandatory_tour_frequency_coeffs.csv\n", ".\\configs\\mandatory_tour_scheduling.yaml\n", + ".\\configs\\network_los.yaml\n", ".\\configs\\non_mandatory_tour_destination.csv\n", ".\\configs\\non_mandatory_tour_destination.yaml\n", ".\\configs\\non_mandatory_tour_destination_coeffs.csv\n", @@ -2066,6 +6425,7 @@ ".\\configs\\school_location.yaml\n", ".\\configs\\school_location_coeffs.csv\n", ".\\configs\\school_location_sample.csv\n", + ".\\configs\\school_location_segment_choosers_preprocessor.csv\n", ".\\configs\\settings.yaml\n", ".\\configs\\shadow_pricing.yaml\n", ".\\configs\\stop_frequency.yaml\n", @@ -2129,12 +6489,35 @@ ".\\data\\override_hh_ids.csv\n", ".\\data\\persons.csv\n", ".\\data\\skims.omx\n", + ".\\output\\cache\\cached_taz.mmap\n", ".\\output\\log\\.gitignore\n", ".\\output\\log\\activitysim.log\n", + ".\\output\\log\\mem.csv\n", ".\\output\\trace\\.gitignore\n", ".\\output\\trace\\atwork_subtour_destination.csv\n", ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.choosers.csv\n", - ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.expression_values.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_values.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_BIKE.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEFREE.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEPAY.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_COM.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_EXP.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_HVY.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LOC.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LRF.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2FREE.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2PAY.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3FREE.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3PAY.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TAXI.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SHARED.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SINGLE.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_COM.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_EXP.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_HVY.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LOC.csv\n", + ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LRF.csv\n", ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.logsums.csv\n", ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.nested_exp_utilities.csv\n", ".\\output\\trace\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.raw_utilities.csv\n", @@ -2148,7 +6531,7 @@ ".\\output\\trace\\atwork_subtour_destination.sample.interaction_sample.interaction_utilities.csv\n", ".\\output\\trace\\atwork_subtour_destination.sample.interaction_sample.probs.csv\n", ".\\output\\trace\\atwork_subtour_destination.sample.interaction_sample.sampled_alternatives.csv\n", - ".\\output\\trace\\atwork_subtour_destination.sample.interaction_sample.utilities.csv\n", + ".\\output\\trace\\atwork_subtour_destination.sample.interaction_sample.utils.csv\n", ".\\output\\trace\\atwork_subtour_destination.simulate.interaction_sample_simulate.alternatives.csv\n", ".\\output\\trace\\atwork_subtour_destination.simulate.interaction_sample_simulate.choices.csv\n", ".\\output\\trace\\atwork_subtour_destination.simulate.interaction_sample_simulate.choosers.csv\n", @@ -2164,7 +6547,13 @@ ".\\output\\trace\\atwork_subtour_frequency.atwork_subtour_frequency_annotate_tours_preprocessor_locals.csv\n", ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.choices.csv\n", ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.choosers.csv\n", - ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.expression_values.csv\n", + ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_values.csv\n", + ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_business1.csv\n", + ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_business2.csv\n", + ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_eat.csv\n", + ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_eat_business.csv\n", + ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_maint.csv\n", + ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_no_subtours.csv\n", ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.probs.csv\n", ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.rands.csv\n", ".\\output\\trace\\atwork_subtour_frequency.simple_simulate.eval_mnl.utilities.csv\n", @@ -2172,7 +6561,28 @@ ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.base_probabilities.csv\n", ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.choices.csv\n", ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.choosers.csv\n", - ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.expression_values.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_values.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_BIKE.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEFREE.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEPAY.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_COM.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_EXP.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_HVY.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LOC.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LRF.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2FREE.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2PAY.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3FREE.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3PAY.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TAXI.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SHARED.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SINGLE.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_COM.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_EXP.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_HVY.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LOC.csv\n", + ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LRF.csv\n", ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.logsums.csv\n", ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.nested_exp_utilities.csv\n", ".\\output\\trace\\atwork_subtour_mode_choice.simple_simulate.eval_nl.nested_probabilities.csv\n", @@ -2196,25 +6606,32 @@ ".\\output\\trace\\auto_ownership.csv\n", ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.choices.csv\n", ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.choosers.csv\n", - ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.expression_values.csv\n", + ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_values.csv\n", + ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars0.csv\n", + ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars1.csv\n", + ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars2.csv\n", + ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars3.csv\n", + ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars4.csv\n", ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.probs.csv\n", ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.rands.csv\n", ".\\output\\trace\\auto_ownership_simulate.simple_simulate.eval_mnl.utilities.csv\n", ".\\output\\trace\\cdap.annotate_households.annotate_households_cdap.csv\n", 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".\\output\\trace\\joint_tour_scheduling.joint_tour_scheduling_annotate_tours_preprocessor_locals.csv\n", ".\\output\\trace\\joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.alternatives.csv\n", ".\\output\\trace\\joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.choices.csv\n", ".\\output\\trace\\joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.choosers.csv\n", @@ -2272,7 +6762,12 @@ ".\\output\\trace\\mandatory_tour_frequency.persons.csv\n", ".\\output\\trace\\mandatory_tour_frequency.simple_simulate.eval_mnl.choices.csv\n", ".\\output\\trace\\mandatory_tour_frequency.simple_simulate.eval_mnl.choosers.csv\n", - ".\\output\\trace\\mandatory_tour_frequency.simple_simulate.eval_mnl.expression_values.csv\n", + ".\\output\\trace\\mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_values.csv\n", + 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".\\output\\trace\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.utilities.csv\n", ".\\output\\trace\\non_mandatory_tour_frequency.annotated_persons.csv\n", ".\\output\\trace\\non_mandatory_tour_frequency.annotate_persons_nmtf.csv\n", ".\\output\\trace\\non_mandatory_tour_frequency.choosers.csv\n", @@ -2333,14 +6822,13 @@ ".\\output\\trace\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.eval.person_id.5389227.csv\n", ".\\output\\trace\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.eval.raw.csv\n", ".\\output\\trace\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.interaction_df.csv\n", - ".\\output\\trace\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.interaction_utilities.csv\n", + ".\\output\\trace\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.interaction_utils.csv\n", 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".\\output\\trace\\stop_frequency.eatout.simple_simulate.eval_mnl.rands.csv\n", ".\\output\\trace\\stop_frequency.eatout.simple_simulate.eval_mnl.utilities.csv\n", @@ -2366,14 +6886,51 @@ ".\\output\\trace\\stop_frequency.trips.csv\n", ".\\output\\trace\\stop_frequency.work.simple_simulate.eval_mnl.choices.csv\n", ".\\output\\trace\\stop_frequency.work.simple_simulate.eval_mnl.choosers.csv\n", - ".\\output\\trace\\stop_frequency.work.simple_simulate.eval_mnl.expression_values.csv\n", + ".\\output\\trace\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_values.csv\n", + ".\\output\\trace\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_0out_0in.csv\n", + ".\\output\\trace\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_0out_1in.csv\n", + ".\\output\\trace\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_0out_2in.csv\n", + 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".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LOC.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LRF.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2FREE.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2PAY.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3FREE.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3PAY.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_TAXI.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SHARED.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SINGLE.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_COM.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_EXP.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_HVY.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LOC.csv\n", + ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LRF.csv\n", ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.logsums.csv\n", ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.nested_exp_utilities.csv\n", ".\\output\\trace\\tour_mode_choice.eatout.simple_simulate.eval_nl.nested_probabilities.csv\n", @@ -2385,7 +6942,28 @@ ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.base_probabilities.csv\n", ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.choices.csv\n", ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.choosers.csv\n", - ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.expression_values.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_values.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_BIKE.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEFREE.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEPAY.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_COM.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_EXP.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_HVY.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LOC.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LRF.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2FREE.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2PAY.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3FREE.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3PAY.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_TAXI.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SHARED.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SINGLE.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_COM.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_EXP.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_HVY.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LOC.csv\n", + ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LRF.csv\n", ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.logsums.csv\n", ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.nested_exp_utilities.csv\n", ".\\output\\trace\\tour_mode_choice.work.simple_simulate.eval_nl.nested_probabilities.csv\n", @@ -2409,7 +6987,28 @@ ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.base_probabilities.csv\n", ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.choices.csv\n", ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.choosers.csv\n", - ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.expression_values.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_values.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_BIKE.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEFREE.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEPAY.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_COM.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_EXP.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_HVY.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LOC.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LRF.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2FREE.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2PAY.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3FREE.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3PAY.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TAXI.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SHARED.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SINGLE.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_COM.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_EXP.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_HVY.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LOC.csv\n", + ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LRF.csv\n", ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.logsums.csv\n", ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.nested_exp_utilities.csv\n", ".\\output\\trace\\trip_mode_choice.simple_simulate.eval_nl.nested_probabilities.csv\n", @@ -2428,7 +7027,28 @@ ".\\output\\trace\\workplace_location.annotate_persons.annotate_persons_workplace_locals.csv\n", ".\\output\\trace\\workplace_location.csv\n", ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.choosers.csv\n", - ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.expression_values.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_values.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_BIKE.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEFREE.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEPAY.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_COM.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_EXP.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_HVY.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LOC.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LRF.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2FREE.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2PAY.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3FREE.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3PAY.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TAXI.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SHARED.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SINGLE.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_COM.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_EXP.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_HVY.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LOC.csv\n", + ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LRF.csv\n", ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.logsums.csv\n", ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.nested_exp_utilities.csv\n", ".\\output\\trace\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.raw_utilities.csv\n", @@ -2443,7 +7063,7 @@ ".\\output\\trace\\workplace_location.i1.sample.work_veryhigh.interaction_sample.interaction_utilities.csv\n", ".\\output\\trace\\workplace_location.i1.sample.work_veryhigh.interaction_sample.probs.csv\n", ".\\output\\trace\\workplace_location.i1.sample.work_veryhigh.interaction_sample.sampled_alternatives.csv\n", - ".\\output\\trace\\workplace_location.i1.sample.work_veryhigh.interaction_sample.utilities.csv\n", + ".\\output\\trace\\workplace_location.i1.sample.work_veryhigh.interaction_sample.utils.csv\n", ".\\output\\trace\\workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.alternatives.csv\n", ".\\output\\trace\\workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.choices.csv\n", ".\\output\\trace\\workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.choosers.csv\n", @@ -2477,6 +7097,7 @@ ".\\output\\trips_ev.omx\n", ".\\output\\trips_md.omx\n", ".\\output\\trips_pm.omx\n", + ".\\output\\cache\n", ".\\output\\log\n", ".\\output\\trace\n", ".\\README.MD\n", @@ -2543,7 +7164,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2575,7 +7196,7 @@ "- filename: households.csv\n", " index_col: household_id\n", " keep_columns:\n", - " - TAZ\n", + " - home_zone_id\n", " - income\n", " - hhsize\n", " - HHT\n", @@ -2584,6 +7205,7 @@ " rename_columns:\n", " HHID: household_id\n", " PERSONS: hhsize\n", + " TAZ: home_zone_id\n", " VEHICL: auto_ownership\n", " workers: num_workers\n", " tablename: households\n", @@ -2601,7 +7223,7 @@ " PERID: person_id\n", " tablename: persons\n", "- filename: land_use.csv\n", - " index_col: TAZ\n", + " index_col: zone_id\n", " keep_columns:\n", " - DISTRICT\n", " - SD\n", @@ -2629,7 +7251,7 @@ " - TERMINAL\n", " rename_columns:\n", " COUNTY: county_id\n", - " ZONE: TAZ\n", + " TAZ: zone_id\n", " tablename: land_use\n", "max_value_of_time: 50\n", "min_value_of_time: 1\n", @@ -2680,10 +7302,39 @@ " - tours\n", " - trips\n", " - joint_tour_participants\n", - "rural_threshold: 6\n", + "trace_hh_id: 2223759\n", + "trace_od: null\n", + "urban_threshold: 4\n", + "use_shadow_pricing: false\n", + "want_dest_choice_sample_tables: false\n", + "\n" + ] + } + ], + "source": [ + "print(\"Display the settings file.\\n\")\n", + "\n", + "with open(r'configs/settings.yaml') as file:\n", + " file_contents = yaml.load(file, Loader=yaml.FullLoader)\n", + " print(yaml.dump(file_contents))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Display the network_los file.\n", + "\n", + "read_skim_cache: false\n", "skim_time_periods:\n", " labels:\n", " - EA\n", + " - EA\n", " - AM\n", " - MD\n", " - PM\n", @@ -2691,31 +7342,31 @@ " period_minutes: 60\n", " periods:\n", " - 0\n", - " - 6\n", - " - 11\n", - " - 16\n", - " - 20\n", + " - 3\n", + " - 5\n", + " - 9\n", + " - 14\n", + " - 18\n", " - 24\n", - "skims_file: skims.omx\n", - "trace_hh_id: 2223759\n", - "trace_od: null\n", - "urban_threshold: 4\n", - "use_shadow_pricing: false\n", + " time_window: 1440\n", + "taz_skims: skims.omx\n", + "write_skim_cache: true\n", + "zone_system: 1\n", "\n" ] } ], "source": [ - "print(\"Display the settings file.\\n\")\n", + "print(\"Display the network_los file.\\n\")\n", "\n", - "with open(r'configs/settings.yaml') as file:\n", + "with open(r'configs/network_los.yaml') as file:\n", " file_contents = yaml.load(file, Loader=yaml.FullLoader)\n", " print(yaml.dump(file_contents))" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2730,7 +7381,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Input land_use. Primary key: ZONE. Required additional fields depend on the downstream submodels (and expression files).\n" + "Input land_use. Primary key: TAZ. Required additional fields depend on the downstream submodels (and expression files).\n" ] }, { @@ -2754,7 +7405,7 @@ " \n", " \n", " \n", - " ZONE\n", + " TAZ\n", " DISTRICT\n", " SD\n", " COUNTY\n", @@ -3384,32 +8035,32 @@ "" ], "text/plain": [ - " ZONE DISTRICT SD COUNTY TOTHH HHPOP TOTPOP EMPRES SFDU MFDU ... \\\n", - "0 1 1 1 1 46 74 82 37 1 60 ... \n", - "1 2 1 1 1 134 214 240 107 5 147 ... \n", - "2 3 1 1 1 267 427 476 214 9 285 ... \n", - "3 4 1 1 1 151 239 253 117 6 210 ... \n", - "4 5 1 1 1 611 974 1069 476 22 671 ... \n", - "5 6 1 1 1 2240 3311 3963 2052 0 2406 ... \n", - "6 7 1 1 1 3762 5561 6032 3375 0 4174 ... \n", - "7 8 1 1 1 4582 7565 9907 3594 19 4898 ... \n", - "8 9 1 1 1 5545 9494 10171 4672 35 6032 ... \n", - "9 10 1 1 1 5344 9205 9308 5137 5 5663 ... \n", - "10 11 1 1 1 2902 4998 5163 2865 10 2923 ... \n", - "11 12 1 1 1 793 1197 1207 897 0 984 ... \n", - "12 13 1 1 1 102 156 166 60 0 119 ... \n", - "13 14 1 1 1 476 722 728 523 0 552 ... \n", - "14 15 1 1 1 279 422 470 330 0 319 ... \n", - "15 16 1 1 1 6164 10143 10272 8094 15 6720 ... \n", - "16 17 1 1 1 4075 6702 7119 5422 25 4532 ... \n", - "17 18 1 1 1 1034 1705 2069 1266 56 1087 ... \n", - "18 19 1 1 1 843 1387 1608 1023 22 863 ... \n", - "19 20 1 1 1 2006 3346 3691 1668 97 2019 ... \n", - "20 21 1 1 1 3472 5793 6203 2782 117 3520 ... \n", - "21 22 1 1 1 1195 1906 1925 1137 98 1291 ... \n", - "22 23 1 1 1 565 900 906 543 43 628 ... \n", - "23 24 1 1 1 604 972 979 355 0 669 ... \n", - "24 25 1 1 1 1551 3410 3416 1239 131 1553 ... \n", + " TAZ DISTRICT SD COUNTY TOTHH HHPOP TOTPOP EMPRES SFDU MFDU ... \\\n", + "0 1 1 1 1 46 74 82 37 1 60 ... \n", + "1 2 1 1 1 134 214 240 107 5 147 ... \n", + "2 3 1 1 1 267 427 476 214 9 285 ... \n", + "3 4 1 1 1 151 239 253 117 6 210 ... \n", + "4 5 1 1 1 611 974 1069 476 22 671 ... \n", + "5 6 1 1 1 2240 3311 3963 2052 0 2406 ... \n", + "6 7 1 1 1 3762 5561 6032 3375 0 4174 ... \n", + "7 8 1 1 1 4582 7565 9907 3594 19 4898 ... \n", + "8 9 1 1 1 5545 9494 10171 4672 35 6032 ... \n", + "9 10 1 1 1 5344 9205 9308 5137 5 5663 ... \n", + "10 11 1 1 1 2902 4998 5163 2865 10 2923 ... \n", + "11 12 1 1 1 793 1197 1207 897 0 984 ... \n", + "12 13 1 1 1 102 156 166 60 0 119 ... \n", + "13 14 1 1 1 476 722 728 523 0 552 ... \n", + "14 15 1 1 1 279 422 470 330 0 319 ... \n", + "15 16 1 1 1 6164 10143 10272 8094 15 6720 ... \n", + "16 17 1 1 1 4075 6702 7119 5422 25 4532 ... \n", + "17 18 1 1 1 1034 1705 2069 1266 56 1087 ... \n", + "18 19 1 1 1 843 1387 1608 1023 22 863 ... \n", + "19 20 1 1 1 2006 3346 3691 1668 97 2019 ... \n", + "20 21 1 1 1 3472 5793 6203 2782 117 3520 ... \n", + "21 22 1 1 1 1195 1906 1925 1137 98 1291 ... \n", + "22 23 1 1 1 565 900 906 543 43 628 ... \n", + "23 24 1 1 1 604 972 979 355 0 669 ... \n", + "24 25 1 1 1 1551 3410 3416 1239 131 1553 ... \n", "\n", " area_type HSENROLL COLLFTE COLLPTE TOPOLOGY TERMINAL ZERO \\\n", "0 0 0.00000 0.00000 0.00000 3 5.89564 0 \n", @@ -3468,19 +8119,19 @@ "[25 rows x 42 columns]" ] }, - "execution_count": 6, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"Input land_use. Primary key: ZONE. Required additional fields depend on the downstream submodels (and expression files).\")\n", + "print(\"Input land_use. Primary key: TAZ. Required additional fields depend on the downstream submodels (and expression files).\")\n", "pd.read_csv(\"data/land_use.csv\")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -3855,7 +8506,7 @@ "[5000 rows x 47 columns]" ] }, - "execution_count": 7, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -3867,7 +8518,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 40, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -4230,7 +8881,7 @@ "[8212 rows x 20 columns]" ] }, - "execution_count": 8, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -4242,7 +8893,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -4260,7 +8911,7 @@ "Skims. All skims are input via one OMX file. Required skims depend on the downstream submodels (and expression files).\n", "\n", "data/skims.omx (File) ''\n", - "Last modif.: 'Thu May 7 11:46:34 2020'\n", + "Last modif.: 'Tue Dec 29 14:30:17 2020'\n", "Object Tree: \n", "/ (RootGroup) ''\n", "/data (Group) ''\n", @@ -5110,15 +9761,13 @@ "# Outputs\n", "\n", "Run the commands below to: \n", - "* Display the contents of the output data pipeline \n", - "* Gets the households table after auto ownership is run\n", "* Display the output household and person tables\n", "* Display the output tour and trip tables" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -5189,7 +9838,7 @@ " '/accessibility/initialize_landuse']" ] }, - "execution_count": 10, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -5202,7 +9851,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -5241,16 +9890,16 @@ " \n", " \n", " \n", - " TAZ\n", + " home_zone_id\n", " income\n", " hhsize\n", " HHT\n", " auto_ownership\n", " num_workers\n", " sample_rate\n", - " chunk_id\n", " income_in_thousands\n", " income_segment\n", + " median_value_of_time\n", " ...\n", " hh_work_auto_savings_ratio\n", " num_under16_not_at_school\n", @@ -5298,9 +9947,9 @@ " 1\n", " 2\n", " 0.02\n", - " 0\n", " 30.90\n", " 2\n", + " 8.81\n", " ...\n", " 0.399721\n", " 0\n", @@ -5322,9 +9971,9 @@ " 1\n", " 4\n", " 0.02\n", - " 1\n", " 99.70\n", " 3\n", + " 10.44\n", " ...\n", " 0.711955\n", " 0\n", @@ -5346,9 +9995,9 @@ " 1\n", " 1\n", " 0.02\n", - " 2\n", " 58.16\n", " 2\n", + " 8.81\n", " ...\n", " 0.264600\n", " 0\n", @@ -5370,9 +10019,9 @@ " 1\n", " 0\n", " 0.02\n", - " 3\n", " 59.22\n", " 2\n", + " 8.81\n", " ...\n", " 0.000000\n", " 0\n", @@ -5394,9 +10043,9 @@ " 0\n", " 1\n", " 0.02\n", - " 4\n", " 51.00\n", " 2\n", + " 8.81\n", " ...\n", " 0.187061\n", " 0\n", @@ -5442,9 +10091,9 @@ " 1\n", " 0\n", " 0.02\n", - " 95\n", " 6.50\n", " 1\n", + " 6.01\n", " ...\n", " 0.000000\n", " 0\n", @@ -5466,9 +10115,9 @@ " 0\n", " 0\n", " 0.02\n", - " 96\n", " 0.00\n", " 1\n", + " 6.01\n", " ...\n", " 0.000000\n", " 0\n", @@ -5490,9 +10139,9 @@ " 0\n", " 2\n", " 0.02\n", - " 97\n", " 112.50\n", " 4\n", + " 12.86\n", " ...\n", " 0.243357\n", " 0\n", @@ -5514,9 +10163,9 @@ " 1\n", " 1\n", " 0.02\n", - " 98\n", " 145.45\n", " 4\n", + " 12.86\n", " ...\n", " 0.138205\n", " 0\n", @@ -5538,9 +10187,9 @@ " 0\n", " 0\n", " 0.02\n", - " 99\n", " 8.40\n", " 1\n", + " 6.01\n", " ...\n", " 0.000000\n", " 0\n", @@ -5555,112 +10204,126 @@ " \n", " \n", "\n", - "

100 rows × 35 columns

\n", + "

100 rows × 34 columns

\n", "" ], "text/plain": [ - " TAZ income hhsize HHT auto_ownership num_workers \\\n", - "household_id \n", - "982875 16 30900 2 5 1 2 \n", - "1810015 16 99700 9 2 1 4 \n", - "1099626 20 58160 3 1 1 1 \n", - "763879 6 59220 1 4 1 0 \n", - "824207 18 51000 1 4 0 1 \n", - "... ... ... ... ... ... ... \n", - "287819 9 6500 2 3 1 0 \n", - "2832313 10 0 1 0 0 0 \n", - "2222549 7 112500 2 5 0 2 \n", - "2048809 11 145450 1 4 1 1 \n", - "566633 8 8400 1 4 0 0 \n", + " home_zone_id income hhsize HHT auto_ownership num_workers \\\n", + "household_id \n", + "982875 16 30900 2 5 1 2 \n", + "1810015 16 99700 9 2 1 4 \n", + "1099626 20 58160 3 1 1 1 \n", + "763879 6 59220 1 4 1 0 \n", + "824207 18 51000 1 4 0 1 \n", + "... ... ... ... ... ... ... \n", + "287819 9 6500 2 3 1 0 \n", + "2832313 10 0 1 0 0 0 \n", + "2222549 7 112500 2 5 0 2 \n", + "2048809 11 145450 1 4 1 1 \n", + "566633 8 8400 1 4 0 0 \n", "\n", - " sample_rate chunk_id income_in_thousands income_segment ... \\\n", - "household_id ... \n", - "982875 0.02 0 30.90 2 ... \n", - "1810015 0.02 1 99.70 3 ... \n", - "1099626 0.02 2 58.16 2 ... \n", - "763879 0.02 3 59.22 2 ... \n", - "824207 0.02 4 51.00 2 ... \n", - "... ... ... ... ... ... \n", - "287819 0.02 95 6.50 1 ... \n", - "2832313 0.02 96 0.00 1 ... \n", - "2222549 0.02 97 112.50 4 ... \n", - "2048809 0.02 98 145.45 4 ... \n", - "566633 0.02 99 8.40 1 ... \n", + " sample_rate income_in_thousands income_segment \\\n", + "household_id \n", + "982875 0.02 30.90 2 \n", + "1810015 0.02 99.70 3 \n", + "1099626 0.02 58.16 2 \n", + "763879 0.02 59.22 2 \n", + "824207 0.02 51.00 2 \n", + "... ... ... ... \n", + "287819 0.02 6.50 1 \n", + "2832313 0.02 0.00 1 \n", + "2222549 0.02 112.50 4 \n", + "2048809 0.02 145.45 4 \n", + "566633 0.02 8.40 1 \n", "\n", - " hh_work_auto_savings_ratio num_under16_not_at_school \\\n", - "household_id \n", - "982875 0.399721 0 \n", - "1810015 0.711955 0 \n", - "1099626 0.264600 0 \n", - "763879 0.000000 0 \n", - "824207 0.187061 0 \n", - "... ... ... \n", - "287819 0.000000 0 \n", - "2832313 0.000000 0 \n", - "2222549 0.243357 0 \n", - "2048809 0.138205 0 \n", - "566633 0.000000 0 \n", + " median_value_of_time ... hh_work_auto_savings_ratio \\\n", + "household_id ... \n", + "982875 8.81 ... 0.399721 \n", + "1810015 10.44 ... 0.711955 \n", + "1099626 8.81 ... 0.264600 \n", + "763879 8.81 ... 0.000000 \n", + "824207 8.81 ... 0.187061 \n", + "... ... ... ... \n", + "287819 6.01 ... 0.000000 \n", + "2832313 6.01 ... 0.000000 \n", + "2222549 12.86 ... 0.243357 \n", + "2048809 12.86 ... 0.138205 \n", + "566633 6.01 ... 0.000000 \n", "\n", - " num_travel_active num_travel_active_adults \\\n", - "household_id \n", - "982875 2 2 \n", - "1810015 7 6 \n", - "1099626 3 2 \n", - "763879 1 1 \n", - "824207 1 1 \n", - "... ... ... \n", - "287819 2 1 \n", - "2832313 1 1 \n", - "2222549 2 2 \n", - "2048809 1 1 \n", - "566633 1 1 \n", + " num_under16_not_at_school num_travel_active \\\n", + "household_id \n", + "982875 0 2 \n", + "1810015 0 7 \n", + "1099626 0 3 \n", + "763879 0 1 \n", + "824207 0 1 \n", + "... ... ... \n", + "287819 0 2 \n", + "2832313 0 1 \n", + "2222549 0 2 \n", + "2048809 0 1 \n", + "566633 0 1 \n", "\n", - " num_travel_active_preschoolers num_travel_active_children \\\n", - "household_id \n", - "982875 0 0 \n", - "1810015 1 1 \n", - "1099626 1 1 \n", - "763879 0 0 \n", - "824207 0 0 \n", - "... ... ... \n", - "287819 0 1 \n", - "2832313 0 0 \n", - "2222549 0 0 \n", - "2048809 0 0 \n", - "566633 0 0 \n", + " num_travel_active_adults num_travel_active_preschoolers \\\n", + "household_id \n", + "982875 2 0 \n", + "1810015 6 1 \n", + "1099626 2 1 \n", + "763879 1 0 \n", + "824207 1 0 \n", + "... ... ... \n", + "287819 1 0 \n", + "2832313 1 0 \n", + "2222549 2 0 \n", + "2048809 1 0 \n", + "566633 1 0 \n", "\n", - " num_travel_active_non_preschoolers participates_in_jtf_model \\\n", - "household_id \n", - "982875 2 True \n", - "1810015 6 True \n", - "1099626 2 True \n", - "763879 1 False \n", - "824207 1 False \n", - "... ... ... \n", - "287819 2 True \n", - "2832313 1 False \n", - "2222549 2 True \n", - "2048809 1 False \n", - "566633 1 False \n", + " num_travel_active_children num_travel_active_non_preschoolers \\\n", + "household_id \n", + "982875 0 2 \n", + "1810015 1 6 \n", + "1099626 1 2 \n", + "763879 0 1 \n", + "824207 0 1 \n", + "... ... ... \n", + "287819 1 2 \n", + "2832313 0 1 \n", + "2222549 0 2 \n", + "2048809 0 1 \n", + "566633 0 1 \n", "\n", - " joint_tour_frequency num_hh_joint_tours \n", - "household_id \n", - "982875 0_tours 0 \n", - "1810015 0_tours 0 \n", - "1099626 0_tours 0 \n", - "763879 0_tours 0 \n", - "824207 0_tours 0 \n", - "... ... ... \n", - "287819 0_tours 0 \n", - "2832313 0_tours 0 \n", - "2222549 0_tours 0 \n", - "2048809 0_tours 0 \n", - "566633 0_tours 0 \n", + " participates_in_jtf_model joint_tour_frequency \\\n", + "household_id \n", + "982875 True 0_tours \n", + "1810015 True 0_tours \n", + "1099626 True 0_tours \n", + "763879 False 0_tours \n", + "824207 False 0_tours \n", + "... ... ... \n", + "287819 True 0_tours \n", + "2832313 False 0_tours \n", + "2222549 True 0_tours \n", + "2048809 False 0_tours \n", + "566633 False 0_tours \n", "\n", - "[100 rows x 35 columns]" + " num_hh_joint_tours \n", + "household_id \n", + "982875 0 \n", + "1810015 0 \n", + "1099626 0 \n", + "763879 0 \n", + "824207 0 \n", + "... ... \n", + "287819 0 \n", + "2832313 0 \n", + "2222549 0 \n", + "2048809 0 \n", + "566633 0 \n", + "\n", + "[100 rows x 34 columns]" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -5672,7 +10335,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -5712,15 +10375,15 @@ " \n", " \n", " household_id\n", - " TAZ\n", + " home_zone_id\n", " income\n", " hhsize\n", " HHT\n", " auto_ownership\n", " num_workers\n", " sample_rate\n", - " chunk_id\n", " income_in_thousands\n", + " income_segment\n", " ...\n", " hh_work_auto_savings_ratio\n", " num_under16_not_at_school\n", @@ -5745,8 +10408,8 @@ " 1\n", " 2\n", " 0.02\n", - " 0\n", " 30.90\n", + " 2\n", " ...\n", " 0.399721\n", " 0\n", @@ -5769,8 +10432,8 @@ " 1\n", " 4\n", " 0.02\n", - " 1\n", " 99.70\n", + " 3\n", " ...\n", " 0.711955\n", " 0\n", @@ -5793,8 +10456,8 @@ " 1\n", " 1\n", " 0.02\n", - " 2\n", " 58.16\n", + " 2\n", " ...\n", " 0.264600\n", " 0\n", @@ -5817,8 +10480,8 @@ " 1\n", " 0\n", " 0.02\n", - " 3\n", " 59.22\n", + " 2\n", " ...\n", " 0.000000\n", " 0\n", @@ -5841,8 +10504,8 @@ " 0\n", " 1\n", " 0.02\n", - " 4\n", " 51.00\n", + " 2\n", " ...\n", " 0.187061\n", " 0\n", @@ -5889,8 +10552,8 @@ " 1\n", " 0\n", " 0.02\n", - " 95\n", " 6.50\n", + " 1\n", " ...\n", " 0.000000\n", " 0\n", @@ -5913,8 +10576,8 @@ " 0\n", " 0\n", " 0.02\n", - " 96\n", " 0.00\n", + " 1\n", " ...\n", " 0.000000\n", " 0\n", @@ -5937,10 +10600,10 @@ " 0\n", " 2\n", " 0.02\n", - " 97\n", " 112.50\n", + " 4\n", " ...\n", - " 0.243357\n", + " 0.243358\n", " 0\n", " 2\n", " 2\n", @@ -5961,8 +10624,8 @@ " 1\n", " 1\n", " 0.02\n", - " 98\n", " 145.45\n", + " 4\n", " ...\n", " 0.138205\n", " 0\n", @@ -5985,8 +10648,8 @@ " 0\n", " 0\n", " 0.02\n", - " 99\n", " 8.40\n", + " 1\n", " ...\n", " 0.000000\n", " 0\n", @@ -6001,35 +10664,35 @@ " \n", " \n", "\n", - "

100 rows × 36 columns

\n", + "

100 rows × 35 columns

\n", "" ], "text/plain": [ - " household_id TAZ income hhsize HHT auto_ownership num_workers \\\n", - "0 982875 16 30900 2 5 1 2 \n", - "1 1810015 16 99700 9 2 1 4 \n", - "2 1099626 20 58160 3 1 1 1 \n", - "3 763879 6 59220 1 4 1 0 \n", - "4 824207 18 51000 1 4 0 1 \n", - ".. ... ... ... ... ... ... ... \n", - "95 287819 9 6500 2 3 1 0 \n", - "96 2832313 10 0 1 0 0 0 \n", - "97 2222549 7 112500 2 5 0 2 \n", - "98 2048809 11 145450 1 4 1 1 \n", - "99 566633 8 8400 1 4 0 0 \n", + " household_id home_zone_id income hhsize HHT auto_ownership \\\n", + "0 982875 16 30900 2 5 1 \n", + "1 1810015 16 99700 9 2 1 \n", + "2 1099626 20 58160 3 1 1 \n", + "3 763879 6 59220 1 4 1 \n", + "4 824207 18 51000 1 4 0 \n", + ".. ... ... ... ... ... ... \n", + "95 287819 9 6500 2 3 1 \n", + "96 2832313 10 0 1 0 0 \n", + "97 2222549 7 112500 2 5 0 \n", + "98 2048809 11 145450 1 4 1 \n", + "99 566633 8 8400 1 4 0 \n", "\n", - " sample_rate chunk_id income_in_thousands ... \\\n", - "0 0.02 0 30.90 ... \n", - "1 0.02 1 99.70 ... \n", - "2 0.02 2 58.16 ... \n", - "3 0.02 3 59.22 ... \n", - "4 0.02 4 51.00 ... \n", - ".. ... ... ... ... \n", - "95 0.02 95 6.50 ... \n", - "96 0.02 96 0.00 ... \n", - "97 0.02 97 112.50 ... \n", - "98 0.02 98 145.45 ... \n", - "99 0.02 99 8.40 ... \n", + " num_workers sample_rate income_in_thousands income_segment ... \\\n", + "0 2 0.02 30.90 2 ... \n", + "1 4 0.02 99.70 3 ... \n", + "2 1 0.02 58.16 2 ... \n", + "3 0 0.02 59.22 2 ... \n", + "4 1 0.02 51.00 2 ... \n", + ".. ... ... ... ... ... \n", + "95 0 0.02 6.50 1 ... \n", + "96 0 0.02 0.00 1 ... \n", + "97 2 0.02 112.50 4 ... \n", + "98 1 0.02 145.45 4 ... \n", + "99 0 0.02 8.40 1 ... \n", "\n", " hh_work_auto_savings_ratio num_under16_not_at_school num_travel_active \\\n", "0 0.399721 0 2 \n", @@ -6040,7 +10703,7 @@ ".. ... ... ... \n", "95 0.000000 0 2 \n", "96 0.000000 0 1 \n", - "97 0.243357 0 2 \n", + "97 0.243358 0 2 \n", "98 0.138205 0 1 \n", "99 0.000000 0 1 \n", "\n", @@ -6083,10 +10746,10 @@ "98 False 0_tours 0 \n", "99 False 0_tours 0 \n", "\n", - "[100 rows x 36 columns]" + "[100 rows x 35 columns]" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -6098,7 +10761,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -6427,7 +11090,7 @@ " \n", " \n", "\n", - "

167 rows × 64 columns

\n", + "

167 rows × 63 columns

\n", "" ], "text/plain": [ @@ -6496,10 +11159,10 @@ "165 0 1 \n", "166 0 0 \n", "\n", - "[167 rows x 64 columns]" + "[167 rows x 63 columns]" ] }, - "execution_count": 13, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -6511,7 +11174,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -6587,12 +11250,12 @@ " 1\n", " 24.0\n", " ...\n", - " 18.0\n", - " 11.0\n", + " 19.0\n", + " 12.0\n", " NaN\n", " NaN\n", " WALK_LRF\n", - " 5.690144\n", + " 5.706465\n", " no_subtours\n", " NaN\n", " 0out_0in\n", @@ -6616,7 +11279,7 @@ " NaN\n", " NaN\n", " WALK_LRF\n", - " 5.739306\n", + " 5.740181\n", " no_subtours\n", " NaN\n", " 3out_0in\n", @@ -6640,7 +11303,7 @@ " NaN\n", " NaN\n", " WALK_LRF\n", - " 5.767114\n", + " 5.762750\n", " no_subtours\n", " NaN\n", " 0out_0in\n", @@ -6664,7 +11327,7 @@ " NaN\n", " NaN\n", " WALK\n", - " 1.870717\n", + " 1.894204\n", " no_subtours\n", " NaN\n", " 0out_0in\n", @@ -6720,8 +11383,8 @@ " \n", " \n", " 196\n", - " 91019881\n", - " 2219997\n", + " 143309067\n", + " 3495343\n", " eat\n", " 1\n", " 1\n", @@ -6729,23 +11392,23 @@ " 1\n", " atwork\n", " 1\n", - " 11.0\n", + " 16.0\n", " ...\n", - " 13.0\n", + " 14.0\n", " 0.0\n", " NaN\n", - " 13.140266\n", + " 15.544104\n", " WALK\n", - " -0.572441\n", + " 4.189554\n", " NaN\n", - " 91019916.0\n", - " 0out_0in\n", + " 143309102.0\n", + " 3out_0in\n", " atwork\n", " \n", " \n", " 197\n", - " 143309067\n", - " 3495343\n", + " 171036547\n", + " 4171623\n", " eat\n", " 1\n", " 1\n", @@ -6753,38 +11416,38 @@ " 1\n", " atwork\n", " 1\n", - " 16.0\n", + " 7.0\n", " ...\n", - " 14.0\n", + " 10.0\n", " 0.0\n", " NaN\n", - " 15.544104\n", + " 12.963491\n", " WALK\n", - " 4.189554\n", + " -0.212087\n", " NaN\n", - " 143309102.0\n", - " 3out_0in\n", + " 171036582.0\n", + " 0out_1in\n", " atwork\n", " \n", " \n", " 198\n", - " 220897873\n", + " 220897896\n", " 5387753\n", - " business\n", + " maint\n", " 1\n", " 1\n", " 1\n", " 1\n", " atwork\n", " 1\n", - " 16.0\n", + " 15.0\n", " ...\n", " 14.0\n", - " 0.0\n", + " 1.0\n", " NaN\n", - " 15.790724\n", + " 15.821604\n", " WALK\n", - " 6.541524\n", + " 5.934528\n", " NaN\n", " 220897912.0\n", " 0out_0in\n", @@ -6832,7 +11495,7 @@ " NaN\n", " 20.633673\n", " WALK\n", - " -0.468849\n", + " -0.471238\n", " NaN\n", " 307996508.0\n", " 0out_1in\n", @@ -6851,37 +11514,37 @@ "3 13286130 324051 work 1 1 1 \n", "4 13286171 324052 work 1 1 1 \n", ".. ... ... ... ... ... ... \n", - "196 91019881 2219997 eat 1 1 1 \n", - "197 143309067 3495343 eat 1 1 1 \n", - "198 220897873 5387753 business 1 1 1 \n", + "196 143309067 3495343 eat 1 1 1 \n", + "197 171036547 4171623 eat 1 1 1 \n", + "198 220897896 5387753 maint 1 1 1 \n", "199 220958270 5389226 eat 1 1 1 \n", "200 307996473 7512109 eat 1 1 1 \n", "\n", " tour_count tour_category number_of_participants destination ... end \\\n", - "0 1 mandatory 1 24.0 ... 18.0 \n", + "0 1 mandatory 1 24.0 ... 19.0 \n", "1 1 mandatory 1 22.0 ... 19.0 \n", "2 1 mandatory 1 1.0 ... 18.0 \n", "3 1 mandatory 1 13.0 ... 18.0 \n", "4 1 mandatory 1 2.0 ... 17.0 \n", ".. ... ... ... ... ... ... \n", - "196 1 atwork 1 11.0 ... 13.0 \n", - "197 1 atwork 1 16.0 ... 14.0 \n", - "198 1 atwork 1 16.0 ... 14.0 \n", + "196 1 atwork 1 16.0 ... 14.0 \n", + "197 1 atwork 1 7.0 ... 10.0 \n", + "198 1 atwork 1 15.0 ... 14.0 \n", "199 1 atwork 1 2.0 ... 13.0 \n", "200 1 atwork 1 8.0 ... 10.0 \n", "\n", " duration composition destination_logsum tour_mode mode_choice_logsum \\\n", - "0 11.0 NaN NaN WALK_LRF 5.690144 \n", - "1 12.0 NaN NaN WALK_LRF 5.739306 \n", - "2 7.0 NaN NaN WALK_LRF 5.767114 \n", - "3 12.0 NaN NaN WALK 1.870717 \n", + "0 12.0 NaN NaN WALK_LRF 5.706465 \n", + "1 12.0 NaN NaN WALK_LRF 5.740181 \n", + "2 7.0 NaN NaN WALK_LRF 5.762750 \n", + "3 12.0 NaN NaN WALK 1.894204 \n", "4 10.0 NaN NaN WALK_LOC 2.055649 \n", ".. ... ... ... ... ... \n", - "196 0.0 NaN 13.140266 WALK -0.572441 \n", - "197 0.0 NaN 15.544104 WALK 4.189554 \n", - "198 0.0 NaN 15.790724 WALK 6.541524 \n", + "196 0.0 NaN 15.544104 WALK 4.189554 \n", + "197 0.0 NaN 12.963491 WALK -0.212087 \n", + "198 1.0 NaN 15.821604 WALK 5.934528 \n", "199 0.0 NaN 15.712982 WALK 6.339068 \n", - "200 0.0 NaN 20.633673 WALK -0.468849 \n", + "200 0.0 NaN 20.633673 WALK -0.471238 \n", "\n", " atwork_subtour_frequency parent_tour_id stop_frequency primary_purpose \n", "0 no_subtours NaN 0out_0in work \n", @@ -6890,8 +11553,8 @@ "3 no_subtours NaN 0out_0in work \n", "4 eat NaN 0out_0in work \n", ".. ... ... ... ... \n", - "196 NaN 91019916.0 0out_0in atwork \n", - "197 NaN 143309102.0 3out_0in atwork \n", + "196 NaN 143309102.0 3out_0in atwork \n", + "197 NaN 171036582.0 0out_1in atwork \n", "198 NaN 220897912.0 0out_0in atwork \n", "199 NaN 220958305.0 0out_0in atwork \n", "200 NaN 307996508.0 0out_1in atwork \n", @@ -6899,7 +11562,7 @@ "[201 rows x 24 columns]" ] }, - "execution_count": 14, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -6911,7 +11574,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -6979,12 +11642,12 @@ " True\n", " 1\n", " eatout\n", - " 12\n", + " 13\n", " 8\n", " NaN\n", " 11.0\n", " WALK\n", - " -0.244990\n", + " -0.677167\n", " \n", " \n", " 1\n", @@ -6998,11 +11661,11 @@ " 1\n", " Home\n", " 8\n", - " 12\n", + " 13\n", " NaN\n", " 11.0\n", " WALK\n", - " -0.300789\n", + " -0.744125\n", " \n", " \n", " 2\n", @@ -7077,7 +11740,7 @@ " ...\n", " \n", " \n", - " 476\n", + " 475\n", " 2472945113\n", " 7539466\n", " 2848131\n", @@ -7095,7 +11758,7 @@ " 0.820192\n", " \n", " \n", - " 477\n", + " 476\n", " 2472945117\n", " 7539466\n", " 2848131\n", @@ -7107,13 +11770,13 @@ " shopping\n", " 5\n", " 8\n", - " 14.691355\n", + " 14.511304\n", " 20.0\n", " WALK_LOC\n", - " 0.379933\n", + " 0.335476\n", " \n", " \n", - " 478\n", + " 477\n", " 2472945118\n", " 7539466\n", " 2848131\n", @@ -7128,10 +11791,10 @@ " NaN\n", " 20.0\n", " WALK\n", - " 0.942455\n", + " 0.895709\n", " \n", " \n", - " 479\n", + " 478\n", " 2473024473\n", " 7539708\n", " 2848373\n", @@ -7144,12 +11807,12 @@ " 12\n", " 18\n", " NaN\n", - " 10.0\n", + " 11.0\n", " WALK_LOC\n", - " -0.881681\n", + " -0.900429\n", " \n", " \n", - " 480\n", + " 479\n", " 2473024477\n", " 7539708\n", " 2848373\n", @@ -7162,13 +11825,13 @@ " 18\n", " 12\n", " NaN\n", - " 21.0\n", + " 15.0\n", " WALK_LOC\n", - " -0.997212\n", + " -1.116631\n", " \n", " \n", "\n", - "

481 rows × 15 columns

\n", + "

480 rows × 15 columns

\n", "" ], "text/plain": [ @@ -7179,42 +11842,42 @@ "3 8685013 26478 26478 1085626 othmaint 1 \n", "4 8753057 26686 26686 1094132 eatout 1 \n", ".. ... ... ... ... ... ... \n", - "476 2472945113 7539466 2848131 309118139 shopping 1 \n", - "477 2472945117 7539466 2848131 309118139 shopping 1 \n", - "478 2472945118 7539466 2848131 309118139 shopping 2 \n", - "479 2473024473 7539708 2848373 309128059 univ 1 \n", - "480 2473024477 7539708 2848373 309128059 univ 1 \n", + "475 2472945113 7539466 2848131 309118139 shopping 1 \n", + "476 2472945117 7539466 2848131 309118139 shopping 1 \n", + "477 2472945118 7539466 2848131 309118139 shopping 2 \n", + "478 2473024473 7539708 2848373 309128059 univ 1 \n", + "479 2473024477 7539708 2848373 309128059 univ 1 \n", "\n", " outbound trip_count purpose destination origin destination_logsum \\\n", - "0 True 1 eatout 12 8 NaN \n", - "1 False 1 Home 8 12 NaN \n", + "0 True 1 eatout 13 8 NaN \n", + "1 False 1 Home 8 13 NaN \n", "2 True 1 othmaint 10 8 NaN \n", "3 False 1 Home 8 10 NaN \n", "4 True 1 eatout 5 8 NaN \n", ".. ... ... ... ... ... ... \n", - "476 True 1 shopping 8 3 NaN \n", - "477 False 2 shopping 5 8 14.691355 \n", - "478 False 2 Home 3 5 NaN \n", - "479 True 1 univ 12 18 NaN \n", - "480 False 1 Home 18 12 NaN \n", + "475 True 1 shopping 8 3 NaN \n", + "476 False 2 shopping 5 8 14.511304 \n", + "477 False 2 Home 3 5 NaN \n", + "478 True 1 univ 12 18 NaN \n", + "479 False 1 Home 18 12 NaN \n", "\n", " depart trip_mode mode_choice_logsum \n", - "0 11.0 WALK -0.244990 \n", - "1 11.0 WALK -0.300789 \n", + "0 11.0 WALK -0.677167 \n", + "1 11.0 WALK -0.744125 \n", "2 12.0 BIKE 0.227086 \n", "3 13.0 BIKE 0.204142 \n", "4 19.0 WALK 0.683756 \n", ".. ... ... ... \n", - "476 18.0 WALK_LOC 0.820192 \n", - "477 20.0 WALK_LOC 0.379933 \n", - "478 20.0 WALK 0.942455 \n", - "479 10.0 WALK_LOC -0.881681 \n", - "480 21.0 WALK_LOC -0.997212 \n", + "475 18.0 WALK_LOC 0.820192 \n", + "476 20.0 WALK_LOC 0.335476 \n", + "477 20.0 WALK 0.895709 \n", + "478 11.0 WALK_LOC -0.900429 \n", + "479 15.0 WALK_LOC -1.116631 \n", "\n", - "[481 rows x 15 columns]" + "[480 rows x 15 columns]" ] }, - "execution_count": 15, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -7233,7 +11896,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -7264,7 +11927,7 @@ " \n", " \n", " \n", - " TAZ\n", + " zone_id\n", " auPkRetail\n", " auPkTotal\n", " auOpRetail\n", @@ -7633,62 +12296,62 @@ "" ], "text/plain": [ - " TAZ auPkRetail auPkTotal auOpRetail auOpTotal trPkRetail trPkTotal \\\n", - "0 1 9.316494 12.615176 9.307437 12.607849 7.764264 11.145248 \n", - "1 2 9.316898 12.613461 9.304627 12.604209 7.511301 10.950046 \n", - "2 3 9.293217 12.580014 9.286242 12.574902 7.340975 10.787608 \n", - "3 4 9.357349 12.630894 9.348249 12.623586 7.873327 11.224171 \n", - "4 5 9.343551 12.585069 9.333262 12.574554 7.589356 11.082550 \n", - "5 6 9.271350 12.523449 9.265762 12.519698 7.313872 10.504311 \n", - "6 7 9.293194 12.528401 9.286373 12.520416 7.641910 10.805003 \n", - "7 8 9.267844 12.497146 9.262133 12.489886 7.546934 10.834136 \n", - "8 9 9.189503 12.426036 9.184035 12.415460 7.188751 10.303186 \n", - "9 10 9.186004 12.403890 9.180762 12.396344 7.379336 10.548675 \n", - "10 11 9.320093 12.519242 9.315095 12.511758 7.455702 10.875601 \n", - "11 12 9.351591 12.600777 9.340287 12.590073 7.945965 11.204375 \n", - "12 13 9.347596 12.610940 9.337329 12.601590 7.767894 11.121006 \n", - "13 14 9.327288 12.612722 9.319522 12.605843 7.982914 11.205704 \n", - "14 15 9.284935 12.581337 9.277798 12.575463 7.656614 10.997076 \n", - "15 16 9.312159 12.554715 9.309852 12.554250 7.016154 10.534221 \n", - "16 17 9.252513 12.480891 9.251513 12.479315 6.661200 9.844753 \n", - "17 18 9.249360 12.438991 9.248962 12.440035 7.085930 10.252688 \n", - "18 19 9.169029 12.357455 9.169914 12.359584 6.088623 9.295992 \n", - "19 20 9.221743 12.420066 9.218863 12.415977 6.636653 9.801904 \n", - "20 21 9.321916 12.515867 9.325176 12.518961 7.428997 10.580038 \n", - "21 22 9.229652 12.543187 9.219600 12.535205 7.150924 10.713898 \n", - "22 23 9.116150 12.433018 9.107848 12.426055 6.211645 9.770781 \n", - "23 24 9.243797 12.550974 9.230086 12.541350 7.322742 10.850764 \n", - "24 25 9.198262 12.494596 9.191437 12.490872 7.296646 10.729605 \n", + " zone_id auPkRetail auPkTotal auOpRetail auOpTotal trPkRetail \\\n", + "0 1 9.316494 12.615176 9.307437 12.607849 7.764264 \n", + "1 2 9.316898 12.613461 9.304627 12.604209 7.511301 \n", + "2 3 9.293217 12.580014 9.286242 12.574902 7.340975 \n", + "3 4 9.357349 12.630894 9.348249 12.623586 7.873327 \n", + "4 5 9.343551 12.585069 9.333262 12.574554 7.589356 \n", + "5 6 9.271350 12.523449 9.265762 12.519698 7.313872 \n", + "6 7 9.293194 12.528401 9.286373 12.520416 7.641910 \n", + "7 8 9.267844 12.497146 9.262133 12.489886 7.546934 \n", + "8 9 9.189503 12.426036 9.184035 12.415460 7.188751 \n", + "9 10 9.186004 12.403890 9.180762 12.396344 7.379336 \n", + "10 11 9.320093 12.519242 9.315095 12.511758 7.455702 \n", + "11 12 9.351591 12.600777 9.340287 12.590073 7.945965 \n", + "12 13 9.347596 12.610940 9.337329 12.601590 7.767894 \n", + "13 14 9.327288 12.612722 9.319522 12.605843 7.982914 \n", + "14 15 9.284935 12.581337 9.277798 12.575463 7.656614 \n", + "15 16 9.312159 12.554715 9.309852 12.554250 7.016154 \n", + "16 17 9.252513 12.480891 9.251513 12.479315 6.661200 \n", + "17 18 9.249360 12.438991 9.248962 12.440035 7.085930 \n", + "18 19 9.169029 12.357455 9.169914 12.359584 6.088623 \n", + "19 20 9.221743 12.420066 9.218863 12.415977 6.636653 \n", + "20 21 9.321916 12.515867 9.325176 12.518961 7.428997 \n", + "21 22 9.229652 12.543187 9.219600 12.535205 7.150924 \n", + "22 23 9.116150 12.433018 9.107848 12.426055 6.211645 \n", + "23 24 9.243797 12.550974 9.230086 12.541350 7.322742 \n", + "24 25 9.198262 12.494596 9.191437 12.490872 7.296646 \n", "\n", - " trOpRetail trOpTotal nmRetail nmTotal \n", - "0 7.693086 11.037286 8.137361 11.726242 \n", - "1 7.427060 10.763102 8.142717 11.724186 \n", - "2 7.252678 10.574954 8.050369 11.478913 \n", - "3 7.814365 11.135416 8.371197 11.775231 \n", - "4 7.549557 11.027965 8.318059 11.431764 \n", - "5 7.068341 10.251790 7.838241 11.023738 \n", - "6 7.607878 10.752510 8.016915 11.108805 \n", - "7 7.501424 10.779320 7.981951 11.052153 \n", - "8 7.149057 10.260610 7.415630 10.758663 \n", - "9 7.306522 10.495922 7.567826 10.694411 \n", - "10 7.348368 10.762778 8.228287 11.171157 \n", - "11 7.846326 11.074534 8.420518 11.618973 \n", - "12 7.691842 11.012477 8.422746 11.742390 \n", - "13 7.914738 11.096305 8.293606 11.736593 \n", - "14 7.574397 10.914272 8.000487 11.541814 \n", - "15 6.945206 10.442447 8.247303 11.373742 \n", - "16 6.562684 9.735318 7.667142 10.785216 \n", - "17 6.997756 10.137302 7.596636 10.414585 \n", - "18 5.907469 9.101217 7.088693 9.865001 \n", - "19 6.590820 9.753201 7.494197 10.367678 \n", - "20 7.342734 10.454321 8.108436 11.011608 \n", - "21 6.941084 10.463985 7.637908 11.319587 \n", - "22 6.118223 9.687799 6.887640 10.656700 \n", - "23 7.115121 10.577147 7.713628 11.346711 \n", - "24 7.180366 10.549489 7.616518 11.016223 " + " trPkTotal trOpRetail trOpTotal nmRetail nmTotal \n", + "0 11.145248 7.693086 11.037286 8.137361 11.726242 \n", + "1 10.950046 7.427060 10.763102 8.142717 11.724186 \n", + "2 10.787608 7.252678 10.574954 8.050369 11.478913 \n", + "3 11.224171 7.814365 11.135416 8.371197 11.775231 \n", + "4 11.082550 7.549557 11.027965 8.318059 11.431764 \n", + "5 10.504311 7.068341 10.251790 7.838241 11.023738 \n", + "6 10.805003 7.607878 10.752510 8.016915 11.108805 \n", + "7 10.834136 7.501424 10.779320 7.981951 11.052153 \n", + "8 10.303186 7.149057 10.260610 7.415630 10.758663 \n", + "9 10.548675 7.306522 10.495922 7.567826 10.694411 \n", + "10 10.875601 7.348368 10.762778 8.228287 11.171157 \n", + "11 11.204375 7.846326 11.074534 8.420518 11.618973 \n", + "12 11.121006 7.691842 11.012477 8.422746 11.742390 \n", + "13 11.205704 7.914738 11.096305 8.293606 11.736593 \n", + "14 10.997076 7.574397 10.914272 8.000487 11.541814 \n", + "15 10.534221 6.945206 10.442447 8.247303 11.373742 \n", + "16 9.844753 6.562684 9.735318 7.667142 10.785216 \n", + "17 10.252688 6.997756 10.137302 7.596636 10.414585 \n", + "18 9.295992 5.907469 9.101217 7.088693 9.865001 \n", + "19 9.801904 6.590820 9.753201 7.494197 10.367678 \n", + "20 10.580038 7.342734 10.454321 8.108436 11.011608 \n", + "21 10.713898 6.941084 10.463985 7.637908 11.319587 \n", + "22 9.770781 6.118223 9.687799 6.887640 10.656700 \n", + "23 10.850764 7.115121 10.577147 7.713628 11.346711 \n", + "24 10.729605 7.180366 10.549489 7.616518 11.016223 " ] }, - "execution_count": 16, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -7700,7 +12363,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -7810,7 +12473,7 @@ "13072777704 130727777 1402945 3188485 3" ] }, - "execution_count": 17, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -7822,19 +12485,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Destination choice sample logsums table for school location.\n" + "Destination choice sample logsums table for school location if want_dest_choice_sample_tables=True.\n" ] } ], "source": [ - "print(\"Destination choice sample logsums table for school location.\")\n", + "print(\"Destination choice sample logsums table for school location if want_dest_choice_sample_tables=True.\")\n", "if '/school_location_sample/school_location' in pipeline:\n", " pipeline['/school_location_sample/school_location']" ] @@ -7843,14 +12506,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Write trip matrices\n", + "# Trip matrices\n", "\n", "A **write_trip_matrices** step at the end of the model adds boolean indicator columns to the trip table in order to assign each trip into a trip matrix and then aggregates the trip counts and writes OD matrices to OMX (open matrix) files. The coding of trips into trip matrices is done via annotation expressions. " ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -7885,7 +12548,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -7901,7 +12564,28 @@ "text/plain": [ "['output/trace\\\\atwork_subtour_destination.csv',\n", " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.choosers.csv',\n", - " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.expression_values.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_BIKE.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEFREE.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEPAY.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_COM.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_EXP.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_HVY.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LOC.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LRF.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2FREE.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2PAY.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3FREE.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3PAY.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TAXI.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SHARED.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SINGLE.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_COM.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_EXP.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_HVY.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LOC.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LRF.csv',\n", " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.logsums.csv',\n", " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.nested_exp_utilities.csv',\n", " 'output/trace\\\\atwork_subtour_destination.logsums.compute_logsums.simple_simulate_logsums.eval_nl_logsums.raw_utilities.csv',\n", @@ -7915,7 +12599,7 @@ " 'output/trace\\\\atwork_subtour_destination.sample.interaction_sample.interaction_utilities.csv',\n", " 'output/trace\\\\atwork_subtour_destination.sample.interaction_sample.probs.csv',\n", " 'output/trace\\\\atwork_subtour_destination.sample.interaction_sample.sampled_alternatives.csv',\n", - " 'output/trace\\\\atwork_subtour_destination.sample.interaction_sample.utilities.csv',\n", + " 'output/trace\\\\atwork_subtour_destination.sample.interaction_sample.utils.csv',\n", " 'output/trace\\\\atwork_subtour_destination.simulate.interaction_sample_simulate.alternatives.csv',\n", " 'output/trace\\\\atwork_subtour_destination.simulate.interaction_sample_simulate.choices.csv',\n", " 'output/trace\\\\atwork_subtour_destination.simulate.interaction_sample_simulate.choosers.csv',\n", @@ -7931,7 +12615,13 @@ " 'output/trace\\\\atwork_subtour_frequency.atwork_subtour_frequency_annotate_tours_preprocessor_locals.csv',\n", " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_business1.csv',\n", + " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_business2.csv',\n", + " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_eat.csv',\n", + " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_eat_business.csv',\n", + " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_maint.csv',\n", + " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_no_subtours.csv',\n", " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\atwork_subtour_frequency.simple_simulate.eval_mnl.utilities.csv',\n", @@ -7939,7 +12629,28 @@ " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.base_probabilities.csv',\n", " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.choices.csv',\n", " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.choosers.csv',\n", - " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.expression_values.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_BIKE.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEFREE.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEPAY.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_COM.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_EXP.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_HVY.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LOC.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LRF.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2FREE.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2PAY.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3FREE.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3PAY.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TAXI.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SHARED.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SINGLE.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_COM.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_EXP.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_HVY.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LOC.csv',\n", + " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LRF.csv',\n", " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.logsums.csv',\n", " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.nested_exp_utilities.csv',\n", " 'output/trace\\\\atwork_subtour_mode_choice.simple_simulate.eval_nl.nested_probabilities.csv',\n", @@ -7963,25 +12674,32 @@ " 'output/trace\\\\auto_ownership.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars0.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars1.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars2.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars3.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars4.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.utilities.csv',\n", " 'output/trace\\\\cdap.annotate_households.annotate_households_cdap.csv',\n", " 'output/trace\\\\cdap.annotate_persons.annotate_persons_cdap.csv',\n", - " 'output/trace\\\\cdap.cdap.chunk_1.cdap_rank.csv',\n", - " 'output/trace\\\\cdap.cdap.chunk_1.hhsize2_activity_choices.csv',\n", - " 'output/trace\\\\cdap.cdap.chunk_1.hhsize2_choosers.csv',\n", - " 'output/trace\\\\cdap.cdap.chunk_1.hhsize2_probs.csv',\n", - " 'output/trace\\\\cdap.cdap.chunk_1.hhsize2_rands.csv',\n", - " 'output/trace\\\\cdap.cdap.chunk_1.hhsize2_utils.csv',\n", - " 'output/trace\\\\cdap.cdap.chunk_1.indiv_utils.csv',\n", + " 'output/trace\\\\cdap.cdap.cdap_rank.csv',\n", + " 'output/trace\\\\cdap.cdap.hhsize2_activity_choices.csv',\n", + " 'output/trace\\\\cdap.cdap.hhsize2_choosers.csv',\n", + " 'output/trace\\\\cdap.cdap.hhsize2_probs.csv',\n", + " 'output/trace\\\\cdap.cdap.hhsize2_rands.csv',\n", + " 'output/trace\\\\cdap.cdap.hhsize2_utils.csv',\n", + " 'output/trace\\\\cdap.cdap.indiv_utils.csv',\n", " 'output/trace\\\\cdap.csv',\n", " 'output/trace\\\\free_parking.csv',\n", " 'output/trace\\\\free_parking.free_parking_annotate_persons_preprocessor.csv',\n", " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.eval_utils.expression_value_free.csv',\n", + " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.eval_utils.expression_value_pay.csv',\n", " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\free_parking.simple_simulate.eval_mnl.utilities.csv',\n", @@ -7994,35 +12712,108 @@ " 'output/trace\\\\joint_tour_composition.joint_tour_composition_annotate_households_preprocessor_locals.csv',\n", " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.eval_utils.expression_value_adults.csv',\n", + " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.eval_utils.expression_value_children.csv',\n", + " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.eval_utils.expression_value_mixed.csv',\n", " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\joint_tour_composition.simple_simulate.eval_mnl.utilities.csv',\n", " 'output/trace\\\\joint_tour_destination.joint_tours.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.choosers.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_BIKE.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEFREE.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEPAY.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_COM.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_EXP.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_HVY.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LOC.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LRF.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2FREE.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2PAY.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3FREE.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3PAY.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TAXI.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SHARED.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SINGLE.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_COM.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_EXP.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_HVY.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LOC.csv',\n", + " 'output/trace\\\\joint_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LRF.csv',\n", + " 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'output/trace\\\\joint_tour_destination.sample.eatout.interaction_sample.eval.raw.csv',\n", + " 'output/trace\\\\joint_tour_destination.sample.eatout.interaction_sample.interaction_df.csv',\n", + " 'output/trace\\\\joint_tour_destination.sample.eatout.interaction_sample.interaction_utilities.csv',\n", + " 'output/trace\\\\joint_tour_destination.sample.eatout.interaction_sample.probs.csv',\n", + " 'output/trace\\\\joint_tour_destination.sample.eatout.interaction_sample.sampled_alternatives.csv',\n", + " 'output/trace\\\\joint_tour_destination.sample.eatout.interaction_sample.utils.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.alternatives.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.choices.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.choosers.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.eval.person_id.5389226.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.eval.raw.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.interaction_df.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.interaction_utilities.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.logsum.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.probs.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.rands.csv',\n", + " 'output/trace\\\\joint_tour_destination.simulate.eatout.interaction_sample_simulate.utilities.csv',\n", " 'output/trace\\\\joint_tour_frequency.households.csv',\n", " 'output/trace\\\\joint_tour_frequency.joint_tours.csv',\n", " 'output/trace\\\\joint_tour_frequency.joint_tour_frequency_annotate_households_preprocessor.csv',\n", " 'output/trace\\\\joint_tour_frequency.joint_tour_frequency_annotate_households_preprocessor_locals.csv',\n", " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_0_tours.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_1_Disc.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_1_Eat.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_1_Main.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_1_Shop.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_1_Visit.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_DD.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_ED.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_EE.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_EV.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_MD.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_ME.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_MM.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_MV.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_SD.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_SE.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_SM.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_SS.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_SV.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_VD.csv',\n", + " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_2_VV.csv',\n", " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\joint_tour_frequency.simple_simulate.eval_mnl.utilities.csv',\n", " 'output/trace\\\\joint_tour_participation.annotate_persons.annotate_persons_jtp.csv',\n", + " 'output/trace\\\\joint_tour_participation.eval_mnl.choices.csv',\n", + " 'output/trace\\\\joint_tour_participation.eval_mnl.choosers.csv',\n", + " 'output/trace\\\\joint_tour_participation.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\joint_tour_participation.eval_mnl.eval_utils.expression_value_not_participate.csv',\n", + " 'output/trace\\\\joint_tour_participation.eval_mnl.eval_utils.expression_value_participate.csv',\n", + " 'output/trace\\\\joint_tour_participation.eval_mnl.probs.csv',\n", + " 'output/trace\\\\joint_tour_participation.eval_mnl.rands.csv',\n", + " 'output/trace\\\\joint_tour_participation.eval_mnl.utilities.csv',\n", " 'output/trace\\\\joint_tour_participation.joint_tours.csv',\n", " 'output/trace\\\\joint_tour_participation.joint_tour_participation_annotate_participants_preprocessor.csv',\n", " 'output/trace\\\\joint_tour_participation.joint_tour_participation_annotate_participants_preprocessor_locals.csv',\n", " 'output/trace\\\\joint_tour_participation.participants.csv',\n", - " 'output/trace\\\\joint_tour_participation.simple_simulate.eval_mnl.choices.csv',\n", - " 'output/trace\\\\joint_tour_participation.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\joint_tour_participation.simple_simulate.eval_mnl.expression_values.csv',\n", - " 'output/trace\\\\joint_tour_participation.simple_simulate.eval_mnl.probs.csv',\n", - " 'output/trace\\\\joint_tour_participation.simple_simulate.eval_mnl.rands.csv',\n", - " 'output/trace\\\\joint_tour_participation.simple_simulate.eval_mnl.utilities.csv',\n", " 'output/trace\\\\joint_tour_scheduling.csv',\n", " 'output/trace\\\\joint_tour_scheduling.joint_tour_scheduling_annotate_tours_preprocessor.csv',\n", - " 'output/trace\\\\joint_tour_scheduling.joint_tour_scheduling_annotate_tours_preprocessor_locals.csv',\n", " 'output/trace\\\\joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.alternatives.csv',\n", " 'output/trace\\\\joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.choices.csv',\n", " 'output/trace\\\\joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1.interaction_sample_simulate.choosers.csv',\n", @@ -8039,7 +12830,12 @@ " 'output/trace\\\\mandatory_tour_frequency.persons.csv',\n", " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_school1.csv',\n", + " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_school2.csv',\n", + " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_work1.csv',\n", + " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_work2.csv',\n", + " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.eval_utils.expression_value_work_and_school.csv',\n", " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\mandatory_tour_frequency.simple_simulate.eval_mnl.utilities.csv',\n", @@ -8055,39 +12851,33 @@ " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.rands.csv',\n", " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.interaction_sample_simulate.utilities.csv',\n", " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.choosers.csv',\n", - " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.expression_values.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_BIKE.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEFREE.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEPAY.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_COM.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_EXP.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_HVY.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LOC.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LRF.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2FREE.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2PAY.csv',\n", + " 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'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_COM.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_EXP.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_HVY.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LOC.csv',\n", + " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LRF.csv',\n", " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.logsums.csv',\n", " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.nested_exp_utilities.csv',\n", " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.simple_simulate_logsums.eval_nl_logsums.raw_utilities.csv',\n", " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.tour_mode_choice_annotate_choosers_preprocessor.csv',\n", " 'output/trace\\\\mandatory_tour_scheduling.vectorize_tour_scheduling.tour_1.work.logsums.tour_mode_choice_annotate_choosers_preprocessor_locals.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.choosers.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.expression_values.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.logsums.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.nested_exp_utilities.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.logsums.eatout.compute_logsums.simple_simulate_logsums.eval_nl_logsums.raw_utilities.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.logsums.eatout.compute_logsums.tour_mode_choice_annotate_choosers_preprocessor.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.logsums.eatout.compute_logsums.tour_mode_choice_annotate_choosers_preprocessor_locals.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.alternatives.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.choosers.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.eval.person_id.5389226.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.eval.raw.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.interaction_df.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.interaction_utilities.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.probs.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.sampled_alternatives.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.sample.eatout.interaction_sample.utilities.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.alternatives.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.choices.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.choosers.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.eval.person_id.5389226.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.eval.raw.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.interaction_df.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.interaction_utilities.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.logsum.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.probs.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.rands.csv',\n", - " 'output/trace\\\\non_mandatory_tour_destination.simulate.eatout.interaction_sample_simulate.utilities.csv',\n", " 'output/trace\\\\non_mandatory_tour_frequency.annotated_persons.csv',\n", " 'output/trace\\\\non_mandatory_tour_frequency.annotate_persons_nmtf.csv',\n", " 'output/trace\\\\non_mandatory_tour_frequency.choosers.csv',\n", @@ -8100,14 +12890,13 @@ " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.eval.person_id.5389227.csv',\n", " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.eval.raw.csv',\n", " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.interaction_df.csv',\n", - " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.interaction_utilities.csv',\n", + " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.interaction_utils.csv',\n", " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.probs.csv',\n", " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.rands.csv',\n", - " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.utilities.csv',\n", + " 'output/trace\\\\non_mandatory_tour_frequency.PTYPE_FULL.interaction_simulate.interaction_simulate.utils.csv',\n", " 'output/trace\\\\raw.households.csv',\n", " 'output/trace\\\\raw.persons.csv',\n", " 'output/trace\\\\school_location.annotate_persons.annotate_persons_school.csv',\n", - " 'output/trace\\\\school_location.annotate_persons.annotate_persons_school_locals.csv',\n", " 'output/trace\\\\school_location.csv',\n", " 'output/trace\\\\shadow_price_school_desired_size.csv',\n", " 'output/trace\\\\shadow_price_school_modeled_size_1.csv',\n", @@ -8116,13 +12905,45 @@ " 'output/trace\\\\stop_frequency.annotations.csv',\n", " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_0out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_0out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_0out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_0out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_1out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_1out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_1out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_1out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_2out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_2out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_2out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_2out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_3out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_3out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_3out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.eval_utils.expression_value_3out_3in.csv',\n", " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\stop_frequency.atwork.simple_simulate.eval_mnl.utilities.csv',\n", " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_0out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_0out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_0out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_0out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_1out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_1out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_1out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_1out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_2out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_2out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_2out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_2out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_3out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_3out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_3out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.eval_utils.expression_value_3out_3in.csv',\n", " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\stop_frequency.eatout.simple_simulate.eval_mnl.utilities.csv',\n", @@ -8133,14 +12954,51 @@ " 'output/trace\\\\stop_frequency.trips.csv',\n", " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_0out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_0out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_0out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_0out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_1out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_1out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_1out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_1out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_2out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_2out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_2out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_2out_3in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_3out_0in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_3out_1in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_3out_2in.csv',\n", + " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.eval_utils.expression_value_3out_3in.csv',\n", " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\stop_frequency.work.simple_simulate.eval_mnl.utilities.csv',\n", " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.base_probabilities.csv',\n", " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.choices.csv',\n", " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.choosers.csv',\n", - " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.expression_values.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_BIKE.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEFREE.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEPAY.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_COM.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_EXP.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_HVY.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LOC.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LRF.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2FREE.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2PAY.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3FREE.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3PAY.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_TAXI.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SHARED.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SINGLE.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_COM.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_EXP.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_HVY.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LOC.csv',\n", + " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LRF.csv',\n", " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.logsums.csv',\n", " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.nested_exp_utilities.csv',\n", " 'output/trace\\\\tour_mode_choice.eatout.simple_simulate.eval_nl.nested_probabilities.csv',\n", @@ -8152,7 +13010,28 @@ " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.base_probabilities.csv',\n", " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.choices.csv',\n", " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.choosers.csv',\n", - " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.expression_values.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_BIKE.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEFREE.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEPAY.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_COM.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_EXP.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_HVY.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LOC.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LRF.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2FREE.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2PAY.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3FREE.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3PAY.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_TAXI.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SHARED.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SINGLE.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_COM.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_EXP.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_HVY.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LOC.csv',\n", + " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LRF.csv',\n", " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.logsums.csv',\n", " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.nested_exp_utilities.csv',\n", " 'output/trace\\\\tour_mode_choice.work.simple_simulate.eval_nl.nested_probabilities.csv',\n", @@ -8176,7 +13055,28 @@ " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.base_probabilities.csv',\n", " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.choices.csv',\n", " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.choosers.csv',\n", - " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.expression_values.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_BIKE.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEFREE.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVEALONEPAY.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_COM.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_EXP.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_HVY.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LOC.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_DRIVE_LRF.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2FREE.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED2PAY.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3FREE.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_SHARED3PAY.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TAXI.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SHARED.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_TNC_SINGLE.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_COM.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_EXP.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_HVY.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LOC.csv',\n", + " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.eval_utils.expression_value_WALK_LRF.csv',\n", " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.logsums.csv',\n", " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.nested_exp_utilities.csv',\n", " 'output/trace\\\\trip_mode_choice.simple_simulate.eval_nl.nested_probabilities.csv',\n", @@ -8195,7 +13095,28 @@ " 'output/trace\\\\workplace_location.annotate_persons.annotate_persons_workplace_locals.csv',\n", " 'output/trace\\\\workplace_location.csv',\n", " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.choosers.csv',\n", - " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.expression_values.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_BIKE.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEFREE.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVEALONEPAY.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_COM.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_EXP.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_HVY.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LOC.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_DRIVE_LRF.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2FREE.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED2PAY.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3FREE.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_SHARED3PAY.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TAXI.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SHARED.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_TNC_SINGLE.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_COM.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_EXP.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_HVY.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LOC.csv',\n", + " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.eval_utils.expression_value_WALK_LRF.csv',\n", " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.logsums.csv',\n", " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.nested_exp_utilities.csv',\n", " 'output/trace\\\\workplace_location.i1.logsums.work_veryhigh.compute_logsums.simple_simulate_logsums.eval_nl_logsums.raw_utilities.csv',\n", @@ -8210,7 +13131,7 @@ " 'output/trace\\\\workplace_location.i1.sample.work_veryhigh.interaction_sample.interaction_utilities.csv',\n", " 'output/trace\\\\workplace_location.i1.sample.work_veryhigh.interaction_sample.probs.csv',\n", " 'output/trace\\\\workplace_location.i1.sample.work_veryhigh.interaction_sample.sampled_alternatives.csv',\n", - " 'output/trace\\\\workplace_location.i1.sample.work_veryhigh.interaction_sample.utilities.csv',\n", + " 'output/trace\\\\workplace_location.i1.sample.work_veryhigh.interaction_sample.utils.csv',\n", " 'output/trace\\\\workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.alternatives.csv',\n", " 'output/trace\\\\workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.choices.csv',\n", " 'output/trace\\\\workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.choosers.csv',\n", @@ -8225,7 +13146,7 @@ " 'output/trace\\\\workplace_location.i1.simulate.work_veryhigh.interaction_sample_simulate.utilities.csv']" ] }, - "execution_count": 29, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -8237,7 +13158,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -8254,13 +13175,18 @@ "['output/trace\\\\auto_ownership.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.choices.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.choosers.csv',\n", - " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.expression_values.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_values.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars0.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars1.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars2.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars3.csv',\n", + " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_value_cars4.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.probs.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.rands.csv',\n", " 'output/trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.utilities.csv']" ] }, - "execution_count": 30, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -8272,7 +13198,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -8316,7 +13242,7 @@ " \n", " \n", " 1\n", - " TAZ\n", + " home_zone_id\n", " 16\n", " \n", " \n", @@ -8340,53 +13266,53 @@ " ...\n", " \n", " \n", - " 59\n", + " 58\n", " TOPOLOGY\n", " 2\n", " \n", " \n", - " 60\n", + " 59\n", " TERMINAL\n", " 4.75017\n", " \n", " \n", - " 61\n", + " 60\n", " household_density\n", " 71.8980801556283\n", " \n", " \n", - " 62\n", + " 61\n", " employment_density\n", " 273.02374467923295\n", " \n", " \n", - " 63\n", + " 62\n", " density_index\n", " 56.91110757846511\n", " \n", " \n", "\n", - "

64 rows × 2 columns

\n", + "

63 rows × 2 columns

\n", "" ], "text/plain": [ " label value\n", "0 household_id 2223759\n", - "1 TAZ 16\n", + "1 home_zone_id 16\n", "2 income 144100\n", "3 hhsize 2\n", "4 HHT 1\n", ".. ... ...\n", - "59 TOPOLOGY 2\n", - "60 TERMINAL 4.75017\n", - "61 household_density 71.8980801556283\n", - "62 employment_density 273.02374467923295\n", - "63 density_index 56.91110757846511\n", + "58 TOPOLOGY 2\n", + "59 TERMINAL 4.75017\n", + "60 household_density 71.8980801556283\n", + "61 employment_density 273.02374467923295\n", + "62 density_index 56.91110757846511\n", "\n", - "[64 rows x 2 columns]" + "[63 rows x 2 columns]" ] }, - "execution_count": 31, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -8398,7 +13324,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -8430,154 +13356,154 @@ " \n", " \n", " \n", - " Expression\n", - " 2223759\n", + " Label\n", + " 0\n", " \n", " \n", " \n", " \n", " 0\n", - " num_drivers==2\n", + " util_drivers_2\n", " 1.000000\n", " \n", " \n", " 1\n", - " num_drivers==3\n", + " util_drivers_3\n", " 0.000000\n", " \n", " \n", " 2\n", - " num_drivers>3\n", + " util_drivers_4_up\n", " 0.000000\n", " \n", " \n", " 3\n", - " num_children_16_to_17\n", + " util_persons_16_17\n", " 0.000000\n", " \n", " \n", " 4\n", - " num_college_age\n", + " util_persons_18_24\n", " 0.000000\n", " \n", " \n", " 5\n", - " num_young_adults\n", + " util_persons_25_34\n", " 2.000000\n", " \n", " \n", " 6\n", - " num_young_children>0\n", + " util_presence_children_0_4\n", " 0.000000\n", " \n", " \n", " 7\n", - " (num_children_5_to_15+num_children_16_to_17)>0\n", + " util_presence_children_5_17\n", " 0.000000\n", " \n", " \n", " 8\n", - " @df.num_workers.clip(upper=3)\n", + " util_num_workers_clip_3\n", " 2.000000\n", " \n", " \n", " 9\n", - " @df.income_in_thousands.clip(0, 30)\n", + " util_hh_income_0_30k\n", " 30.000000\n", " \n", " \n", " 10\n", - " @(df.income_in_thousands-30).clip(0, 45)\n", + " util_hh_income_30_75k\n", " 45.000000\n", " \n", " \n", " 11\n", - " @(df.income_in_thousands-75).clip(0, 50)\n", + " util_hh_income_75k_up\n", " 50.000000\n", " \n", " \n", " 12\n", - " @(df.num_workers==0)*df.density_index.clip(0, 10)\n", + " util_density_0_10_no_workers\n", " 0.000000\n", " \n", " \n", " 13\n", - " @(df.num_workers==0)*(df.density_index-10).cli...\n", + " util_density_10_up_no_workers\n", " 0.000000\n", " \n", " \n", " 14\n", - " @(df.num_workers>0)*df.density_index.clip(0, 10)\n", + " util_density_0_10_workers\n", " 10.000000\n", " \n", " \n", " 15\n", - " @(df.num_workers>0)*(df.density_index-10).clip(0)\n", + " util_density_10_up_workers\n", " 46.911108\n", " \n", " \n", " 16\n", - " @1\n", + " util_asc\n", " 1.000000\n", " \n", " \n", " 17\n", - " @df.county_id == ID_SAN_FRANCISCO\n", + " util_asc_san_francisco\n", " 1.000000\n", " \n", " \n", " 18\n", - " @df.county_id == ID_SOLANO\n", + " util_asc_solano\n", " 0.000000\n", " \n", " \n", " 19\n", - " @df.county_id == ID_NAPA\n", + " util_asc_napa\n", " 0.000000\n", " \n", " \n", " 20\n", - " @df.county_id == ID_SONOMA\n", + " util_asc_sonoma\n", " 0.000000\n", " \n", " \n", " 21\n", - " @df.county_id == ID_MARIN\n", + " util_asc_marin\n", " 0.000000\n", " \n", " \n", " 22\n", - " (num_workers==0)*(0.66*auPkRetail+0.34*auOpRet...\n", + " util_retail_auto_no_workers\n", " 0.000000\n", " \n", " \n", " 23\n", - " (num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail)\n", + " util_retail_auto_workers\n", " 9.311374\n", " \n", " \n", " 24\n", - " (num_workers==0)*(0.66*trPkRetail+0.34*trOpRet...\n", + " util_retail_transit_no_workers\n", " 0.000000\n", " \n", " \n", " 25\n", - " (num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail)\n", + " util_retail_transit_workers\n", " 6.992032\n", " \n", " \n", " 26\n", - " (num_workers==0)*nmRetail\n", + " util_retail_non_motor_no_workers\n", " 0.000000\n", " \n", " \n", " 27\n", - " (num_workers>0)*nmRetail\n", + " util_retail_non_motor_workers\n", " 8.247303\n", " \n", " \n", " 28\n", - " @np.where(df.num_workers > 0, df.hh_work_auto_...\n", + " util_auto_time_saving_per_worker\n", " 0.232900\n", " \n", " \n", @@ -8585,51 +13511,51 @@ "" ], "text/plain": [ - " Expression 2223759\n", - "0 num_drivers==2 1.000000\n", - "1 num_drivers==3 0.000000\n", - "2 num_drivers>3 0.000000\n", - "3 num_children_16_to_17 0.000000\n", - "4 num_college_age 0.000000\n", - "5 num_young_adults 2.000000\n", - "6 num_young_children>0 0.000000\n", - "7 (num_children_5_to_15+num_children_16_to_17)>0 0.000000\n", - "8 @df.num_workers.clip(upper=3) 2.000000\n", - "9 @df.income_in_thousands.clip(0, 30) 30.000000\n", - "10 @(df.income_in_thousands-30).clip(0, 45) 45.000000\n", - "11 @(df.income_in_thousands-75).clip(0, 50) 50.000000\n", - "12 @(df.num_workers==0)*df.density_index.clip(0, 10) 0.000000\n", - "13 @(df.num_workers==0)*(df.density_index-10).cli... 0.000000\n", - "14 @(df.num_workers>0)*df.density_index.clip(0, 10) 10.000000\n", - "15 @(df.num_workers>0)*(df.density_index-10).clip(0) 46.911108\n", - "16 @1 1.000000\n", - "17 @df.county_id == ID_SAN_FRANCISCO 1.000000\n", - "18 @df.county_id == ID_SOLANO 0.000000\n", - "19 @df.county_id == ID_NAPA 0.000000\n", - "20 @df.county_id == ID_SONOMA 0.000000\n", - "21 @df.county_id == ID_MARIN 0.000000\n", - "22 (num_workers==0)*(0.66*auPkRetail+0.34*auOpRet... 0.000000\n", - "23 (num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail) 9.311374\n", - "24 (num_workers==0)*(0.66*trPkRetail+0.34*trOpRet... 0.000000\n", - "25 (num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail) 6.992032\n", - "26 (num_workers==0)*nmRetail 0.000000\n", - "27 (num_workers>0)*nmRetail 8.247303\n", - "28 @np.where(df.num_workers > 0, df.hh_work_auto_... 0.232900" + " Label 0\n", + "0 util_drivers_2 1.000000\n", + "1 util_drivers_3 0.000000\n", + "2 util_drivers_4_up 0.000000\n", + "3 util_persons_16_17 0.000000\n", + "4 util_persons_18_24 0.000000\n", + "5 util_persons_25_34 2.000000\n", + "6 util_presence_children_0_4 0.000000\n", + "7 util_presence_children_5_17 0.000000\n", + "8 util_num_workers_clip_3 2.000000\n", + "9 util_hh_income_0_30k 30.000000\n", + "10 util_hh_income_30_75k 45.000000\n", + "11 util_hh_income_75k_up 50.000000\n", + "12 util_density_0_10_no_workers 0.000000\n", + "13 util_density_10_up_no_workers 0.000000\n", + "14 util_density_0_10_workers 10.000000\n", + "15 util_density_10_up_workers 46.911108\n", + "16 util_asc 1.000000\n", + "17 util_asc_san_francisco 1.000000\n", + "18 util_asc_solano 0.000000\n", + "19 util_asc_napa 0.000000\n", + "20 util_asc_sonoma 0.000000\n", + "21 util_asc_marin 0.000000\n", + "22 util_retail_auto_no_workers 0.000000\n", + "23 util_retail_auto_workers 9.311374\n", + "24 util_retail_transit_no_workers 0.000000\n", + "25 util_retail_transit_workers 6.992032\n", + "26 util_retail_non_motor_no_workers 0.000000\n", + "27 util_retail_non_motor_workers 8.247303\n", + "28 util_auto_time_saving_per_worker 0.232900" ] }, - "execution_count": 67, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Trace utility expression values for auto ownership.\\n\")\n", - "pd.read_csv(\"output\\\\trace.auto_ownership_simulate.simple_simulate.eval_mnl.expression_values.csv\")" + "pd.read_csv(\"output\\\\trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.eval_utils.expression_values.csv\")" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -8710,7 +13636,7 @@ "5 cars4 -1.326382e+01" ] }, - "execution_count": 32, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -8722,7 +13648,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -8782,12 +13708,12 @@ " \n", " 4\n", " cars3\n", - " 4.295335e-06\n", + " 4.295337e-06\n", " \n", " \n", " 5\n", " cars4\n", - " 9.226827e-07\n", + " 9.226831e-07\n", " \n", " \n", "\n", @@ -8799,18 +13725,18 @@ "1 cars0 5.314431e-01\n", "2 cars1 4.645103e-01\n", "3 cars2 4.041376e-03\n", - "4 cars3 4.295335e-06\n", - "5 cars4 9.226827e-07" + "4 cars3 4.295337e-06\n", + "5 cars4 9.226831e-07" ] }, - "execution_count": 70, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Trace alternative probabilities for auto ownership.\\n\")\n", - "pd.read_csv(\"output\\\\trace.auto_ownership_simulate.simple_simulate.eval_mnl.probs.csv\")" + "pd.read_csv(\"output\\\\trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.probs.csv\")" ] }, { @@ -8939,6 +13865,363 @@ "pd.read_csv(\"output\\\\trace\\\\auto_ownership_simulate.simple_simulate.eval_mnl.choices.csv\")" ] }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "193E7ds2GEVs" + }, + "source": [ + "# Run the Multiprocessor Example\n", + "\n", + "The command below runs the multiprocessor example, which runs in a few minutes. It uses settings inheritance to override setings in the configs folder with settings in the configs_mp folder. This allows for re-using expression files and settings files in the single and multiprocessed setups. The multiprocessed example uses the following additional settings:\n", + "\n", + "```\n", + "num_processes: 2\n", + "chunk_size: 1\n", + "\n", + "multiprocess_steps:\n", + " - name: mp_initialize\n", + " begin: initialize_landuse\n", + " - name: mp_households\n", + " begin: school_location\n", + " slice:\n", + " tables:\n", + " - households\n", + " - persons\n", + " - name: mp_summarize\n", + " begin: write_data_dictionary\n", + "\n", + "```\n", + "\n", + "In brief, `num_processes` specifies the number of processors to use and a `chunk_size` of `1` meaning dynamically calculate the the size of each batch of choosers data for processing. The `multiprocess_steps` specifies the beginning, middle, and end steps in multiprocessing. The `mp_initialize` step is single processed because there is no `slice` setting. It starts with the `initialize_landuse` submodel and runs until the submodel identified by the next multiprocess submodel starting point, `school_location`. The `mp_households` step is multiprocessed and the households and persons tables are sliced and allocated to processes using the chunking settings. The rest of the submodels are run multiprocessed until the final multiprocess step. The `mp_summarize` step is single processed because there is no `slice` setting and it writes outputs. See [multiprocessing](https://activitysim.github.io/activitysim/core.html#multiprocessing) and [chunk_size](https://activitysim.github.io/activitysim/abmexample.html#chunk-size) for more information. " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "rkI5DhdLF0bn", + "outputId": "9862a11e-c1b3-429e-ba14-903a60a73627" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configured logging using basicConfig\n", + "INFO:activitysim:Configured logging using basicConfig\n", + "INFO:activitysim.cli.run:using configs_dir: ['configs_mp', 'configs']\n", + "INFO:activitysim.cli.run:using data_dir: ['data']\n", + "INFO:activitysim.cli.run:using output_dir: ['output']\n", + "INFO - activitysim - Read logging configuration from: configs_mp\\logging.yaml\n", + "INFO - activitysim.cli.run - setting households_sample_size: 100\n", + "INFO - activitysim.cli.run - setting chunk_size: 80000000000\n", + "INFO - activitysim.cli.run - setting multiprocess: True\n", + "INFO - activitysim.cli.run - setting num_processes: 2\n", + "INFO - activitysim.cli.run - setting resume_after: None\n", + "DEBUG - activitysim.core.tracing - delete_output_files ignoring output\\log\\activitysim.log\n", + "[WinError 32] The process cannot access the file because it is being used by another process: 'output\\\\pipeline.h5'\n", + "INFO - activitysim.cli.run - run multiprocess simulation\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - Setting num_processes = 0 for step mp_households\n", + "INFO - activitysim.core.mem - init_trace file_name mem.csv\n", + "INFO - activitysim.core.mem - trace_memory_info run_multiprocess.start rss: 0.11GB used: 7.96 GB percent: 50.2%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - run_multiprocess fail_fast: True\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - allocate_shared_skim_buffer\n", + "DEBUG - activitysim.abm.tables.skims - loading network_los_without_data_loaded injectable\n", + "INFO - activitysim.core.los - Network_LOS using skim_dict_factory: NumpyArraySkimFactory\n", + "INFO - activitysim.core.skim_dict_factory - allocate_skim_buffer shared True taz shape (826, 25, 25) total size: 2065000 (1.97 MB)\n", + "INFO - activitysim.core.tracing - Time to execute allocate shared skim buffer : 1.251 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info allocate_shared_skim_buffer.completed rss: 0.12GB used: 7.96 GB percent: 50.2%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - allocate_shared_shadow_pricing_buffers\n", + "INFO - activitysim.core.input - Reading CSV file data\\land_use.csv\n", + "DEBUG - activitysim.core.input - raw land_use table size: (25, 42) 8.3 KB\n", + "DEBUG - activitysim.core.input - renaming columns: {'TAZ': 'zone_id', 'COUNTY': 'county_id'}\n", + "DEBUG - activitysim.core.input - keeping columns: ['DISTRICT', 'SD', 'county_id', 'TOTHH', 'TOTPOP', 'TOTACRE', 'RESACRE', 'CIACRE', 'TOTEMP', 'AGE0519', 'RETEMPN', 'FPSEMPN', 'HEREMPN', 'OTHEMPN', 'AGREMPN', 'MWTEMPN', 'PRKCST', 'OPRKCST', 'area_type', 'HSENROLL', 'COLLFTE', 'COLLPTE', 'TOPOLOGY', 'TERMINAL']\n", + "DEBUG - activitysim.core.input - land_use table columns: ['DISTRICT' 'SD' 'county_id' 'TOTHH' 'TOTPOP' 'TOTACRE' 'RESACRE' 'CIACRE'\n", + " 'TOTEMP' 'AGE0519' 'RETEMPN' 'FPSEMPN' 'HEREMPN' 'OTHEMPN' 'AGREMPN'\n", + " 'MWTEMPN' 'PRKCST' 'OPRKCST' 'area_type' 'HSENROLL' 'COLLFTE' 'COLLPTE'\n", + " 'TOPOLOGY' 'TERMINAL']\n", + "DEBUG - activitysim.core.input - land_use table size: (25, 24) 4.9 KB\n", + "INFO - activitysim.core.input - land_use index name: zone_id\n", + "INFO - activitysim.abm.tables.landuse - loaded land_use (25, 24)\n", + "DEBUG - activitysim.abm.tables.shadow_pricing - shadow_pricing_info dtype: \n", + "DEBUG - activitysim.abm.tables.shadow_pricing - shadow_pricing_info block_shapes: OrderedDict([('school', (25, 4)), ('workplace', (25, 5))])\n", + "INFO - activitysim.abm.tables.shadow_pricing - allocating shared shadow pricing buffer school 100 buffer_size (25, 4) bytes 800 (800.0)\n", + "INFO - activitysim.abm.tables.shadow_pricing - buffer_for_shadow_pricing added block school\n", + "INFO - activitysim.abm.tables.shadow_pricing - allocating shared shadow pricing buffer workplace 125 buffer_size (25, 5) bytes 1000 (1000.0)\n", + "INFO - activitysim.abm.tables.shadow_pricing - buffer_for_shadow_pricing added block workplace\n", + "INFO - activitysim.core.tracing - Time to execute allocate shared shadow_pricing buffer : 0.118 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info allocate_shared_shadow_pricing_buffers.completed rss: 0.12GB used: 7.97 GB percent: 50.2%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - #run_model running sub_process mp_setup_skims\n", + "INFO - activitysim.core.mem - trace_memory_info mp_setup_skims.start rss: 0.12GB used: 7.97 GB percent: 50.2%\n", + "INFO - activitysim.core.tracing - Time to execute #run_model sub_process mp_setup_skims : 9.017 seconds (0.2 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info #run_model mp_setup_skims completed rss: 0.12GB used: 7.98 GB percent: 50.3%\n", + "INFO - activitysim.core.tracing - Time to execute setup skims : 9.241 seconds (0.2 minutes)\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - run_sub_simulations step mp_initialize models resume_after None\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - start process mp_initialize\n", + "INFO - activitysim.core.mem - trace_memory_info mp_initialize.start rss: 0.17GB used: 8.01 GB percent: 50.5%\n", + "############ mp_tasks - mp_initialize - mp_run_simulation mp_initialize\n", + "[WinError 32] The process cannot access the file because it is being used by another process: 'output\\\\pipeline.h5'\n", + "mp_initialize WARNING - activitysim.core.pipeline - Error removing output\\pipeline.h5: [WinError 32] The process cannot access the file because it is being used by another process: 'output\\\\pipeline.h5'\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_initialize initialize_landuse : 0.986 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_initialize.initialize_landuse.completed rss: 0.24GB used: 8.04 GB percent: 50.7%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_initialize compute_accessibility : 0.179 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_initialize.compute_accessibility.completed rss: 0.24GB used: 8.04 GB percent: 50.7%\n", + "100 unique household_ids in persons\n", + "100 unique household_ids in households\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_initialize initialize_households : 1.644 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_initialize.initialize_households.completed rss: 0.24GB used: 8.05 GB percent: 50.8%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - process mp_initialize completed\n", + "INFO - activitysim.core.mem - trace_memory_info mp_initialize.completed rss: 0.12GB used: 8.03 GB percent: 50.6%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - Process mp_initialize completed with exitcode 0\n", + "INFO - activitysim.core.tracing - Time to execute run_sub_simulations step mp_initialize : 7.599 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - #run_model running sub_process mp_households_apportion\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_apportion.start rss: 0.12GB used: 8.04 GB percent: 50.7%\n", + "INFO - activitysim.core.tracing - Time to execute #run_model sub_process mp_households_apportion : 10.148 seconds (0.2 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info #run_model mp_households_apportion completed rss: 0.12GB used: 8.07 GB percent: 50.9%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - run_sub_simulations step mp_households models resume_after None\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - start process mp_households_0\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.start rss: 0.16GB used: 8.06 GB percent: 50.8%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - start process mp_households_1\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.start rss: 0.22GB used: 8.1 GB percent: 51.1%\n", + "############ mp_tasks - mp_households_1 - mp_run_simulation mp_households\n", + "mp_households_1 WARNING - activitysim.core.tracing - trace_hh_id 2223759 not in dataframe\n", + "mp_households_1 WARNING - activitysim.core.tracing - register persons: no rows with household_id in [].\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 school_location : 27.647 seconds (0.5 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.school_location.completed rss: 0.37GB used: 8.02 GB percent: 50.6%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 school_location : 25.967 seconds (0.4 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.school_location.completed rss: 0.37GB used: 8.02 GB percent: 50.6%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 workplace_location : 43.205 seconds (0.7 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.workplace_location.completed rss: 0.37GB used: 7.84 GB percent: 49.4%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 workplace_location : 44.309 seconds (0.7 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.workplace_location.completed rss: 0.37GB used: 7.84 GB percent: 49.4%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 auto_ownership_simulate : 2.718 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.auto_ownership_simulate.completed rss: 0.37GB used: 7.84 GB percent: 49.4%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 auto_ownership_simulate : 2.638 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.auto_ownership_simulate.completed rss: 0.37GB used: 7.84 GB percent: 49.4%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 free_parking : 2.995 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.free_parking.completed rss: 0.37GB used: 7.84 GB percent: 49.4%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 free_parking : 2.615 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.free_parking.completed rss: 0.37GB used: 7.84 GB percent: 49.4%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 cdap_simulate : 33.842 seconds (0.6 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.cdap_simulate.completed rss: 0.4GB used: 7.85 GB percent: 49.5%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 cdap_simulate : 35.319 seconds (0.6 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.cdap_simulate.completed rss: 0.41GB used: 7.86 GB percent: 49.5%\n", + "mp_households_1 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [].\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 mandatory_tour_frequency : 4.848 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.mandatory_tour_frequency.completed rss: 0.41GB used: 7.86 GB percent: 49.6%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 mandatory_tour_frequency : 5.075 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.mandatory_tour_frequency.completed rss: 0.4GB used: 7.85 GB percent: 49.5%\n", + "tours (31, 35) alts (190, 3)\n", + "tours (12, 35) alts (190, 3)\n", + "tours (3, 35) alts (190, 3)\n", + "tours (2, 35) alts (190, 3)\n", + "mp_households_1 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 39753 observed_row_size 14619 percent_error: 171.9% in mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 mandatory_tour_scheduling : 43.88 seconds (0.7 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.mandatory_tour_scheduling.completed rss: 0.39GB used: 7.83 GB percent: 49.4%\n", + "mp_households_1 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [].\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 joint_tour_frequency : 3.372 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.joint_tour_frequency.completed rss: 0.39GB used: 7.87 GB percent: 49.6%\n", + "############ mp_tasks - mp_households_0 - mp_run_simulation mp_households\n", + "tours (36, 35) alts (190, 3)\n", + "tours (5, 35) alts (190, 3)\n", + "tours (2, 35) alts (190, 3)\n", + "tours (2, 35) alts (190, 3)\n", + "mp_households_0 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 39753 observed_row_size 12010 percent_error: 231.0% in mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.work\n", + "mp_households_1 WARNING - activitysim.core.tracing - register joint_tour_participants: no rows with household_id in [].\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 joint_tour_composition : 2.763 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.joint_tour_composition.completed rss: 0.39GB used: 8.54 GB percent: 53.9%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 joint_tour_participation : 4.599 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.joint_tour_participation.completed rss: 0.39GB used: 7.87 GB percent: 49.6%\n", + "tours (2, 35) alts (190, 3)\n", + "mp_households_0 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 39753 observed_row_size 12104 percent_error: 228.4% in mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2.univ\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 mandatory_tour_scheduling : 58.427 seconds (1.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.mandatory_tour_scheduling.completed rss: 0.39GB used: 7.86 GB percent: 49.6%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 joint_tour_frequency : 5.237 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.joint_tour_frequency.completed rss: 0.39GB used: 7.86 GB percent: 49.6%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 joint_tour_composition : 3.839 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.joint_tour_composition.completed rss: 0.39GB used: 7.86 GB percent: 49.6%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 joint_tour_participation : 5.123 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.joint_tour_participation.completed rss: 0.39GB used: 7.88 GB percent: 49.7%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 joint_tour_destination : 25.883 seconds (0.4 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.joint_tour_destination.completed rss: 0.39GB used: 7.87 GB percent: 49.6%\n", + "tours (2, 144) alts (190, 3)\n", + "mp_households_1 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 28263 observed_row_size 4202 percent_error: 572.6% in joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 joint_tour_scheduling : 6.799 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.joint_tour_scheduling.completed rss: 0.39GB used: 7.86 GB percent: 49.6%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 joint_tour_destination : 14.395 seconds (0.2 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.joint_tour_destination.completed rss: 0.4GB used: 7.87 GB percent: 49.6%\n", + "tours (1, 144) alts (190, 3)\n", + "mp_households_0 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 28263 observed_row_size 2174 percent_error: 1200.0% in joint_tour_scheduling.vectorize_joint_tour_scheduling.tour_1\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 joint_tour_scheduling : 7.434 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.joint_tour_scheduling.completed rss: 0.39GB used: 7.86 GB percent: 49.6%\n", + "interaction_df (2112, 144)\n", + "interaction_utilities (2112, 1)\n", + "interaction_df (1152, 144)\n", + "interaction_utilities (1152, 1)\n", + "interaction_df (960, 144)\n", + "interaction_utilities (960, 1)\n", + "interaction_df (1248, 144)\n", + "interaction_utilities (1248, 1)\n", + "interaction_df (576, 144)\n", + "interaction_utilities (576, 1)\n", + "interaction_df (96, 144)\n", + "interaction_utilities (96, 1)\n", + "interaction_df (768, 144)\n", + "interaction_utilities (768, 1)\n", + "interaction_df (384, 144)\n", + "interaction_utilities (384, 1)\n", + "mp_households_1 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [].\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 non_mandatory_tour_frequency : 35.082 seconds (0.6 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.non_mandatory_tour_frequency.completed rss: 0.39GB used: 7.86 GB percent: 49.6%\n", + "interaction_df (2496, 144)\n", + "interaction_utilities (2496, 1)\n", + "interaction_df (1344, 144)\n", + "interaction_utilities (1344, 1)\n", + "interaction_df (576, 144)\n", + "interaction_utilities (576, 1)\n", + "interaction_df (384, 144)\n", + "interaction_utilities (384, 1)\n", + "interaction_df (576, 144)\n", + "interaction_utilities (576, 1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "interaction_df (288, 144)\n", + "interaction_utilities (288, 1)\n", + "interaction_df (192, 144)\n", + "interaction_utilities (192, 1)\n", + "mp_households_0 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [2223759].\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in non_mandatory_tour_frequency.non_mandatory_tours with household_id == [2223759]\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 non_mandatory_tour_frequency : 30.789 seconds (0.5 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.non_mandatory_tour_frequency.completed rss: 0.4GB used: 7.87 GB percent: 49.6%\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in non_mandatory_tour_destination with household_id == [2223759]\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 non_mandatory_tour_destination : 48.817 seconds (0.8 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.non_mandatory_tour_destination.completed rss: 0.39GB used: 7.73 GB percent: 48.7%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 non_mandatory_tour_destination : 59.418 seconds (1.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.non_mandatory_tour_destination.completed rss: 0.39GB used: 7.74 GB percent: 48.8%\n", + "tours (23, 26) alts (190, 3)\n", + "tours (7, 26) alts (190, 3)\n", + "mp_households_0 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 5916 observed_row_size 3079 percent_error: 92.1% in non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2\n", + "tours (3, 26) alts (190, 3)\n", + "mp_households_0 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 5916 observed_row_size 991 percent_error: 497.0% in non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in non_mandatory_tour_scheduling with household_id == [2223759]\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 non_mandatory_tour_scheduling : 13.647 seconds (0.2 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.non_mandatory_tour_scheduling.completed rss: 0.39GB used: 7.76 GB percent: 49.0%\n", + "tours (42, 26) alts (190, 3)\n", + "tours (15, 26) alts (190, 3)\n", + "mp_households_1 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 5916 observed_row_size 2873 percent_error: 105.9% in non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_2\n", + "tours (3, 26) alts (190, 3)\n", + "mp_households_1 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 5916 observed_row_size 1599 percent_error: 270.0% in non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_3\n", + "tours (1, 26) alts (190, 3)\n", + "mp_households_1 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 5916 observed_row_size 712 percent_error: 730.9% in non_mandatory_tour_scheduling.vectorize_tour_scheduling.tour_4\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 non_mandatory_tour_scheduling : 18.538 seconds (0.3 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.non_mandatory_tour_scheduling.completed rss: 0.39GB used: 7.74 GB percent: 48.8%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 tour_mode_choice_simulate : 56.807 seconds (0.9 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.tour_mode_choice_simulate.completed rss: 0.39GB used: 7.77 GB percent: 49.0%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 atwork_subtour_frequency : 3.834 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.atwork_subtour_frequency.completed rss: 0.39GB used: 7.77 GB percent: 49.0%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 atwork_subtour_destination : 14.491 seconds (0.2 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.atwork_subtour_destination.completed rss: 0.39GB used: 7.78 GB percent: 49.0%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 tour_mode_choice_simulate : 66.718 seconds (1.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.tour_mode_choice_simulate.completed rss: 0.39GB used: 7.78 GB percent: 49.0%\n", + "mp_households_1 WARNING - activitysim.core.tracing - register tours: no rows with household_id in [].\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 atwork_subtour_frequency : 2.657 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.atwork_subtour_frequency.completed rss: 0.39GB used: 7.77 GB percent: 49.0%\n", + "tours (5, 155) alts (190, 3)\n", + "mp_households_0 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 30555 observed_row_size 12449 percent_error: 145.4% in atwork_subtour_scheduling.tour_1\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 atwork_subtour_scheduling : 7.051 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.atwork_subtour_scheduling.completed rss: 0.4GB used: 7.77 GB percent: 49.0%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 atwork_subtour_mode_choice : 9.383 seconds (0.2 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.atwork_subtour_mode_choice.completed rss: 0.39GB used: 7.78 GB percent: 49.0%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 atwork_subtour_destination : 12.136 seconds (0.2 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.atwork_subtour_destination.completed rss: 0.39GB used: 7.77 GB percent: 49.0%\n", + "tours (4, 155) alts (190, 3)\n", + "mp_households_1 WARNING - activitysim.core.chunk - #chunk_history MAX_ROWSIZE_ERROR initial_row_size 30555 observed_row_size 7171 percent_error: 326.1% in atwork_subtour_scheduling.tour_1\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 atwork_subtour_scheduling : 6.947 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.atwork_subtour_scheduling.completed rss: 0.39GB used: 7.78 GB percent: 49.1%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 atwork_subtour_mode_choice : 6.79 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.atwork_subtour_mode_choice.completed rss: 0.39GB used: 7.77 GB percent: 49.0%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 stop_frequency : 25.684 seconds (0.4 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.stop_frequency.completed rss: 0.4GB used: 7.81 GB percent: 49.3%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 trip_purpose : 0.871 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.trip_purpose.completed rss: 0.4GB used: 7.81 GB percent: 49.2%\n", + "mp_households_1 WARNING - activitysim.core.tracing - register trips: no rows with household_id in [].\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 stop_frequency : 25.63 seconds (0.4 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.stop_frequency.completed rss: 0.4GB used: 7.77 GB percent: 49.0%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 trip_purpose : 0.575 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.trip_purpose.completed rss: 0.4GB used: 7.77 GB percent: 49.0%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 trip_destination : 113.391 seconds (1.9 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.trip_destination.completed rss: 0.4GB used: 7.81 GB percent: 49.3%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 trip_purpose_and_destination : 0.531 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.trip_purpose_and_destination.completed rss: 0.4GB used: 7.82 GB percent: 49.3%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 trip_scheduling : 3.197 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.trip_scheduling.completed rss: 0.4GB used: 7.81 GB percent: 49.3%\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.othdiscr.trip_mode with household_id == [2223759]\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.othmaint.trip_mode with household_id == [2223759]\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.school.trip_mode with household_id == [2223759]\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.shopping.trip_mode with household_id == [2223759]\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.social.trip_mode with household_id == [2223759]\n", + "mp_households_0 WARNING - activitysim.core.tracing - slice_canonically: no rows in trip_mode_choice.univ.trip_mode with household_id == [2223759]\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_0 trip_mode_choice : 57.248 seconds (1.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.trip_mode_choice.completed rss: 0.39GB used: 7.76 GB percent: 48.9%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - process mp_households_0 completed\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_0.completed rss: 0.26GB used: 7.66 GB percent: 48.3%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 trip_destination : 228.723 seconds (3.8 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.trip_destination.completed rss: 0.26GB used: 7.74 GB percent: 48.8%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 trip_purpose_and_destination : 0.195 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.trip_purpose_and_destination.completed rss: 0.26GB used: 7.74 GB percent: 48.8%\n", + "mp_households_1 WARNING - activitysim.abm.models.trip_scheduling - trip_scheduling.i100.chunk_1.outbound.num_2 coercing 0 depart choices to most initial\n", + "mp_households_1 WARNING - activitysim.abm.models.trip_scheduling - trip_scheduling.i100.chunk_1.outbound.num_3 coercing 0 depart choices to most initial\n", + "mp_households_1 WARNING - activitysim.abm.models.trip_scheduling - trip_scheduling.i100.chunk_1.outbound.num_4 coercing 1 depart choices to most initial\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 trip_scheduling : 193.594 seconds (3.2 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.trip_scheduling.completed rss: 0.26GB used: 7.69 GB percent: 48.5%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_households_1 trip_mode_choice : 40.499 seconds (0.7 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.trip_mode_choice.completed rss: 0.12GB used: 7.6 GB percent: 47.9%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - process mp_households_1 completed\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_1.completed rss: 0.12GB used: 7.6 GB percent: 47.9%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - Process mp_households_0 completed with exitcode 0\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - Process mp_households_1 completed with exitcode 0\n", + "INFO - activitysim.core.tracing - Time to execute run_sub_simulations step mp_households : 907.148 seconds (15.1 minutes)\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - #run_model running sub_process mp_households_coalesce\n", + "INFO - activitysim.core.mem - trace_memory_info mp_households_coalesce.start rss: 0.12GB used: 7.6 GB percent: 47.9%\n", + "INFO - activitysim.core.tracing - Time to execute #run_model sub_process mp_households_coalesce : 6.009 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info #run_model mp_households_coalesce completed rss: 0.12GB used: 7.6 GB percent: 47.9%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - run_sub_simulations step mp_summarize models resume_after None\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - start process mp_summarize\n", + "INFO - activitysim.core.mem - trace_memory_info mp_summarize.start rss: 0.16GB used: 7.63 GB percent: 48.1%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_summarize write_data_dictionary : 0.151 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_summarize.write_data_dictionary.completed rss: 0.26GB used: 7.69 GB percent: 48.5%\n", + "############ mp_tasks - mp_summarize - mp_run_simulation mp_summarize\n", + "mp_summarize WARNING - activitysim.core.steps.output - Skipping 'school_shadow_prices': Table not found.\n", + "mp_summarize WARNING - activitysim.core.steps.output - Skipping 'workplace_shadow_prices': Table not found.\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_summarize write_trip_matrices : 3.135 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_summarize.write_trip_matrices.completed rss: 0.24GB used: 7.68 GB percent: 48.4%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - mp_summarize write_tables : 0.283 seconds (0.0 minutes)\n", + "INFO - activitysim.core.mem - trace_memory_info mp_summarize.write_tables.completed rss: 0.12GB used: 7.67 GB percent: 48.3%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - process mp_summarize completed\n", + "INFO - activitysim.core.mem - trace_memory_info mp_summarize.completed rss: 0.12GB used: 7.6 GB percent: 47.9%\n", + "INFO - activitysim.core.mp_tasks - mp_tasks - MainProcess - Process mp_summarize completed with exitcode 0\n", + "INFO - activitysim.core.tracing - Time to execute run_sub_simulations step mp_summarize : 8.58 seconds (0.1 minutes)\n", + "INFO - activitysim.core.mem - high water mark rss: 0.41 timestamp: 04/01/2021 11:33:11 label: mp_households_0.cdap_simulate.completed\n", + "INFO - activitysim.core.mem - high water mark used: 8.54 timestamp: 04/01/2021 11:34:05 label: mp_households_1.joint_tour_composition.completed\n", + "INFO - activitysim.core.tracing - Time to execute all models : 951.407 seconds (15.9 minutes)\n" + ] + } + ], + "source": [ + "!activitysim run -c configs_mp -c configs -d data -o output" + ] + }, { "cell_type": "markdown", "metadata": { @@ -8986,7 +14269,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.9" } }, "nbformat": 4, diff --git a/activitysim/examples/example_mtc/notebooks/summarizing_results.ipynb b/activitysim/examples/example_mtc/notebooks/summarizing_results.ipynb index 7fee0ed853..b5e9de83c1 100644 --- a/activitysim/examples/example_mtc/notebooks/summarizing_results.ipynb +++ b/activitysim/examples/example_mtc/notebooks/summarizing_results.ipynb @@ -45,11 +45,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "OUTPUT_DIR = '../test_example_mtc/output'\n", + "OUTPUT_DIR = 'example/output'\n", "VERIF_DIR = os.path.join(OUTPUT_DIR, 'verification')\n", "if not os.path.exists(VERIF_DIR):\n", " os.mkdir(VERIF_DIR)\n", @@ -71,21 +71,21 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "file found: ../test_example_mtc/output\\final_land_use.csv\n", - "file found: ../test_example_mtc/output\\final_accessibility.csv\n", - "file found: ../test_example_mtc/output\\final_households.csv\n", - "file found: ../test_example_mtc/output\\final_persons.csv\n", - "file found: ../test_example_mtc/output\\final_tours.csv\n", - "file found: ../test_example_mtc/output\\final_trips.csv\n", - "file found: ../test_example_mtc/output\\../data/skims.omx\n", - "file found: ../test_example_mtc/output\\../configs\\non_mandatory_tour_frequency_alternatives.csv\n", + "file found: example/output\\final_land_use.csv\n", + "file found: example/output\\final_accessibility.csv\n", + "file found: example/output\\final_households.csv\n", + "file found: example/output\\final_persons.csv\n", + "file found: example/output\\final_tours.csv\n", + "file found: example/output\\final_trips.csv\n", + "file found: example/output\\../data/skims.omx\n", + "file found: example/output\\../configs\\non_mandatory_tour_frequency_alternatives.csv\n", "file found: zone_shapefile\\bayarea_rtaz1454_rev1.shp\n" ] } @@ -128,13 +128,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "DIST_MAT = omx.open_file(DISTANCE_MATRIX_FILE)[\"DIST\"][:]\n", "\n", - "TAZ = pd.read_csv(ZONES_FILE, index_col='TAZ')\n", + "TAZ = pd.read_csv(ZONES_FILE, index_col='zone_id')\n", "HOUSEHOLD = pd.read_csv(HH_FILE, index_col='household_id')\n", "PERSON = pd.read_csv(PERSON_FILE, index_col='person_id')\n", "TOUR = pd.read_csv(TOUR_FILE, index_col='tour_id')\n", @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -203,12 +203,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -218,8 +218,8 @@ } ], "source": [ - "autos = HOUSEHOLD.groupby([\"TAZ\", \"auto_ownership\"]).count()[\"income\"]\n", - "auto_taz = autos.reset_index().pivot_table(index=\"TAZ\", columns=\"auto_ownership\", fill_value=0)\n", + "autos = HOUSEHOLD.groupby([\"home_zone_id\", \"auto_ownership\"]).count()[\"income\"]\n", + "auto_taz = autos.reset_index().pivot_table(index=\"home_zone_id\", columns=\"auto_ownership\", fill_value=0)\n", "\n", "save_verf_csv(auto_taz, 'autos.csv')\n", "\n", @@ -232,32 +232,24 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\matplotlib\\colors.py:527: RuntimeWarning: invalid value encountered in less\n", - " xa[xa < 0] = -1\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 29, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -267,7 +259,7 @@ } ], "source": [ - "auto_shapes = SHAPES.merge(autos.reset_index(), how='left', left_on='TAZ1454', right_on='TAZ')\n", + "auto_shapes = SHAPES.merge(autos.reset_index(), how='left', left_on='TAZ1454', right_on='home_zone_id')\n", "auto_shapes.plot(column='auto_ownership', cmap='Blues',\n", " legend=True,\n", " legend_kwds={'label': \"Autos per Household\"},\n", @@ -283,12 +275,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -326,12 +318,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+9a1v5ZRTTskLL7yQPn36ZOedd2717aDLs+GGG+bSSy/N008/neQfRy9vueWWlqOFo0ePzltvvZULL7wwzz//fAYNGpTx48fnuOOO+9Bzt9UJJ5yQCy64IMcdd9xyjxoee+yxaWpqyq677ppKpZKjjz463/72t1vOHzduXAYNGpQf/OAHGT16dDbYYINst912OeGEE9rtPgB0ZDWV5b35HgAKNnbs2Fx99dWtvozlwzjssMNSqVRyyy23rJHrK9lDDz2UXXfdNU8++eT73oK6yy67ZK+99srll19epekA1n2O4AFQtMWLF+eKK65o+UHwu+++O5dddllOOeWUD33d8+bNyz333JPbbrstf/jDHz78sAVbvHhxnn/++Xzzm9/MQQcdtFqfLwTggwk8AIpWU1OT3/3udxk/fnwWLVqULbbYImPHjs1pp532oa/7Yx/7WBYsWJBzzjkne+211xqYtlzXXntty1t3HekEWHu8RRMAAKAQH/xjRwAAAKwTBB4AAEAhBB4AAEAh1skvWXnhhReqPcI6pbGxMXPnzq32GJ2edag+a9AxWIfqswYdg3WoPmvQMViHVde/f/8VnucIHgAAQCEEHgAAQCEEHgAAQCHWyc/gAQAAnUulUsnixYvT3Nycmpqaao+z1lUqldTW1qa+vn6V7q/AAwAAOrzFixena9euqavrPAnT1NSUxYsXp1u3bm2+jLdoAgAAHV5zc3OnirskqaurS3Nz8ypdRuABAAAdXmd4W+byrOr9FngAAABtsOmmm2b//fdv+fP3v/99hfv+/e9/z7/8y7+043T/0LmOcQIAAEVYdsJn1uj1dZl82wfuU19fn7vuumuN3u6a5ggeAADAavr73/+eQw89NAcccEAOOOCAzJgx4337/OUvf8lBBx2U/fffPyNGjMgzzzyTJPnVr37Vsv2MM87IsmXLPvQ8juABAAC0weLFi7P//vsnSQYNGpTrrrsujY2NufHGG1NfX59nnnkmX/va1/Kb3/ym1eVuuOGGfOlLX8phhx2WJUuWZNmyZXn66adz2223ZcqUKenatWvOPvvs/PrXv87hhx/+oWYUeAAAAG2wvLdoLl26NOeee25mzZqV2tralqNz7zZ06NBcccUVefHFF3PggQdm8ODBuffee/P444/n05/+dJJ/xGNjY+OHnlHgAQAArKbJkyenX79+ueuuu9Lc3JzBgwe/b59DDz00O++8c373u99l5MiRGT9+fCqVSg4//PCcffbZa3Qen8EDAABYTQsXLsxHPvKR1NbW5le/+tVyP0f37LPPZrPNNsuXvvSl7L///nnyySez11575fbbb8/cuXOTJPPnz8/zzz//oedxBA8AAGA1HXfccfnyl7+c22+/PXvuuWe6d+/+vn1uu+22/PrXv05dXV0+8pGPZMyYMWloaMgZZ5yRo446KpVKJXV1dbnooosycODADzVPTaVSqXyoa6iCF154odojrFMaGxtb/mWA6rEO1WcNOgbrUH3WoGOwDtVnDTqGtq7Dm2++udx4Kt3y7nf//v1XuL+3aAIAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABTC7+ABAAB8gHnz5uXzn/98kmTOnDnp0qVL+vTpkyS54447st5661VzvBYCDwAAWOd89mf/Z41e360jt1vp+X369Mldd92VJJkwYUI22GCDnHTSSa32qVQqqVQqqa2t3hslBR5FWdMv9M7ig/6DBgDA8s2ePTtf+tKXMmzYsPz5z3/Oj3/84+y///558sknkyS33nprpk+fnksvvTRz5szJ2Wefnf/3//5famtr853vfCdDhw5do/P4DB4AAMCH8NRTT+Woo47KnXfemY033niF+5133nn5yle+kt/85je56qqrcvrpp6/xWRzBAwAA+BA222yz7LTTTh+43/Tp0/O3v/2t5fSCBQvy1ltvpVu3bmtsFoEHAADwIXTv3r3lf9fW1qZSqbScXrx4cat91/YXsniLJgAAwBpSW1ubXr165Zlnnklzc3P++7//u+W8vfbaK9dff33L6SeeeGLN3/4av0YAAIBO7JxzzsmoUaNyxBFHZJNNNmnZ/t3vfjczZszIiBEjsu++++bnP//5Gr/tmsq7jx+uI1544YVqj7BOaWxszNy5c6s9RrvwLZqrp7N8i2Znei10ZNah+qxBx2Adqs8adAxtXYc333yz1VshO4vl3e/+/fuvcH9H8AAAAAoh8AAAAAoh8AAAAAoh8AAAgA5vHfzqkDViVe+3wAMAADq82traNDU1VXuMdtXU1JTa2lVLNj90DgAAdHj19fVZvHhx3n777dTU1FR7nLWuUqmktrY29fX1q3Q5gQcAAHR4NTU16datW7XH6PC8RRMAAKAQAg8AAKAQ7fYWza997Wupr69PbW1tunTpknHjxuX111/PxIkTM2fOnPTr1y9jxoxJjx492mskAACAorTrZ/DOP//89OzZs+X0lClTMmTIkBxyyCGZMmVKpkyZklGjRrXnSAAAAMWo6ls0Z8yYkeHDhydJhg8fnhkzZlRzHAAAgHVaux7Bu+iii5Ik+++/f0aMGJEFCxakoaEhSdLQ0JCFCxe25zgAAABFabfAu+CCC9KnT58sWLAgF154Yfr379/my06dOjVTp05NkowbNy6NjY1ra8wi1dXVecxYqc7y/PBa6BisQ/VZg47BOlSfNegYrMOa1W6B16dPnyRJr169MmzYsPz1r39Nr169Mn/+/DQ0NGT+/PmtPp/3biNGjMiIESNaTs+dO7ddZi5FY2Ojx4yV6izPD6+FjsE6VJ816BisQ/VZg47BOqy6lR0sa5fP4C1evDhvvfVWy/9+7LHHMmjQoOyyyy6ZNm1akmTatGkZNmxYe4wDAABQpHY5grdgwYJceumlSZJly5Zlr732yk477ZQtt9wyEydOzN13353Gxsaceuqp7TEOAABAkdol8DbaaKOMHz/+fds33HDDjB07tj1GAAAAKF5VfyYBAACANUfgAQAAFELgAQAAFELgAQAAFELgAQAAFKLdfugc2sOv/3BGtUdYN428rdoTAACwBjiCBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUAiBBwAAUIi69ryx5ubmnHXWWenTp0/OOuusvP7665k4cWLmzJmTfv36ZcyYMenRo0d7jgQAAFCMdj2C91//9V8ZMGBAy+kpU6ZkyJAhueKKKzJkyJBMmTKlPccBAAAoSrsF3quvvpo//elP2W+//Vq2zZgxI8OHD0+SDB8+PDNmzGivcQAAAIrTbm/RvP766zNq1Ki89dZbLdsWLFiQhoaGJElDQ0MWLly43MtOnTo1U6dOTZKMGzcujY2Na3/ggtTV1XWax+zlag+wjuosz4/O9FroyKxD9VmDjsE6VJ816Bisw5rVLoH3xz/+Mb169crgwYMzc+bMVb78iBEjMmLEiJbTc+fOXZPjFa+xsdFjxkp1lueH10LHYB2qzxp0DNah+qxBx2AdVl3//v1XeF67BN5f/vKXPPzww/nzn/+cJUuW5K233soVV1yRXr16Zf78+WloaMj8+fPTs2fP9hgHAACgSO0SeEcffXSOPvroJMnMmTPzn//5n/n617+eG264IdOmTcshhxySadOmZdiwYe0xDgAAQJGq+jt4hxxySB577LF8/etfz2OPPZZDDjmkmuMAAACs09r1d/CSZMcdd8yOO+6YJNlwww0zduzY9h4BAACgSFU9ggcAAMCaI/AAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKIfAAAAAKUVftAQBY8z77s/9T7RHWSbeO3K7aIwDAh+IIHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCHqqj0AAGver/9wRrVHWDeNvK3aEwDAh+IIHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCEEHgAAQCHq2rLTTTfdtNztXbt2TZ8+fbLTTjuld+/ea3QwAAAAVk2bjuC9+OKLufXWWzNz5sy89NJLmTlzZm699dbMnj07d911V0aPHp1HHnlkbc8KAADASrTpCF5zc3NOOeWUfOITn2jZNmPGjNx777256KKL8oc//CE/+9nPstNOO621QQEAAFi5Nh3Be/TRR7PLLru02jZ06NCWo3b77LNPXn755TU/HQAAAG3WpsDbeOONc+edd7baduedd2ajjTZKkixcuDDrr7/+mp8OAACANmvTWzRPPPHETJgwIbfeemv69OmTefPmpba2NqeddlqS5IUXXsjnP//5FV5+yZIlOf/889PU1JRly5Zlt912yxFHHJHXX389EydOzJw5c9KvX7+MGTMmPXr0WDP3DAAAoJNpU+ANHjw43//+9/PUU0/ltddeS+/evbPNNtukru4fF99hhx2yww47rPDyXbt2zfnnn5/6+vo0NTVl7Nix2WmnnfLQQw9lyJAhOeSQQzJlypRMmTIlo0aNWjP3DAAAoJNp8+/g1dXVZYcddshuu+2W7bbbLrW1tWlubm7TZWtqalJfX58kWbZsWZYtW5aamprMmDEjw4cPT5IMHz48M2bMWI27AAAAQNLGI3jPPPNMrrvuujz33HNZsmRJq/NW9Bt579Xc3JwzzzwzL730Ug444IBsvfXWWbBgQRoaGpIkDQ0NWbhw4SqODwAAwDvaFHiTJk3K0KFD85WvfGW1v0yltrY248ePzxtvvJFLL700zz33XJsvO3Xq1EydOjVJMm7cuDQ2Nq7WDJ1VXV1dp3nMfJfr6ukszw+vBT5IZ3l+dKbXQkdmHarPGnQM1mHNalPgzZ07N0cddVRqamo+9A1usMEG2WGHHfLII4+kV69emT9/fhoaGjJ//vz07NlzuZcZMWJERowY0Woe2q6xsdFjxkp1lueH1wIfpLM8P7wWOgbrUH3WoGOwDquuf//+KzyvTZ/BGzZsWB599NHVHmDhwoV54403kmf4HhwAACAASURBVPzjGzUff/zxDBgwILvsskumTZuWJJk2bVqGDRu22rcBAADQ2bXpCN7SpUtz6aWXZrvttkvv3r1bnXfyySd/4OXnz5+fSZMmpbm5OZVKJbvvvnuGDh2abbbZJhMnTszdd9+dxsbGnHrqqat3LwAAAGhb4A0cODADBw5c7RvZbLPN8r3vfe992zfccMOMHTt2ta8XAACA/9GmwDv88MPX9hwAAAB8SCsMvFmzZrX8ePkTTzyxwiv46Ec/uuanAgAAYJWtMPCuu+66TJgwIUnyox/9aLn71NTU5Morr1w7kwEAALBKVhh478Rd8o/fwQMAAKBja9PPJNx///3L3f7LX/5yjQ4DAADA6mtT4P385z/Pn//85/dte/jhh9fKUAAAAKy6NgXe2WefncmTJ2fWrFlJkp/85Cd57LHH/MQBAABAB9Kmn0kYMGBA/u3f/i3jx4/Ptttum7lz52bs2LHp3r372p4PAACANlph4C3vpxH++Z//OVOnTs0JJ5yQZ555JomfSQAAAOgoVhh4K/pphK5du+b6669P4mcSAAAAOpIVBt67fxqhubk5tbVt+rgeAAAAVfKB1dbc3JxjjjkmS5cubY95AAAAWE0fGHi1tbXp379/Fi1a1B7zAAAAsJra9C2ae+21Vy655JIceOCB6du3b2pqalrO8yUrAAAAHUObAu/OO+9Mktx8882ttvuSFQAAgI6jTYH37i9cAQAAoGPy1ZgAAACFaNMRvDfffDM333xzZs2alUWLFqVSqbSct6LfywMAAKB9tekI3rXXXpvZs2fnc5/7XF5//fUcf/zxaWxszEEHHbS25wMAAKCN2hR4jz32WE477bQMGzYstbW1GTZsWMaMGZPp06ev7fkAAABoozYFXqVSSffu3ZMk9fX1eeONN9K7d++89NJLa3U4AAAA2q5Nn8HbbLPNMmvWrAwZMiTbbbddrrvuutTX12eTTTZZ2/MBAADQRm06gnfiiSemX79+SZLjjz8+6623Xt54442cfPLJa3U4AAAA2q5NR/A22GCD9OjRI0nSs2fPnHTSSWt1KAAAAFbdSgPvqaeeyoQJE/Laa6+lb9++Of3007PFFlu012wAAACsgpW+RfOGG27I3nvvnQkTJmT33XfPT3/60/aaCwAAgFW00sB7/vnnc/TRR2fgwIE58sgj89xzz7XXXAAAAKyilQZec3Nzamv/sUvXrl3T1NTULkMBAACw6lb6GbwlS5bkyiuvbDn99ttvtzqdxDdpAgAAdBArDbzDDjus1elDDz10rQ4DAADA6ltp4B1++OHtNQcAAAAfUpt+6BwAAICOT+ABAAAUQuABAAAUQuABAAAUYqVfsvKOSqWS3/3ud7nvvvuyaNGiXHrppZk1a1Zee+217LHHHmt7RgAAANqgTUfwbrrppvz+97/PiBEjMnfu3CRJ3759c+utt67V4QAAAGi7NgXetGnTcuaZZ2bPPfdMTU1NkuQjH/lIXnnllbU6HAAAAG3XpsBrbm5OfX19q22LFy9+3zYAAACqp02Bt/POO+ff//3fs3Tp0iT/+EzeTTfdlKFDh67V4QAAAGi7NgXesccem3nz5uULX/hC3nzzzRx77LGZM2dORo4cubbnAwAAoI3a9C2a3bt3zxlnnJEFCxZkzpw5aWxsTO/evdf2bAAAAKyCNv8O3qJFi/Loo49m5syZ6d27d+bNm5dXX311bc4GAADAKmhT4M2aNSunnHJKpk+fnl/96ldJkpdeeimTJ09eq8MBAADQdm0KvOuvvz6nnHJKzj333HTp0iVJstVWW+Vvf/vbWh0OAACAtmtT4M2ZMydDhgxpta2uri7Lli1bK0MBAACw6toUeAMHDswjjzzSatvjjz+eQYMGrZWhAAAAWHVt+hbNY445Jpdcckl23nnnLFmyJNdcc03++Mc/5vTTT1/b8wEAANBGbQq8bbbZJuPHj8/06dNTX1+fxsbGfPe7303fvn3X9nwAAAC00QcGXnNzc77zne/k3HPPzWc/+9n2mAkAAIDV8IGfwautrc0rr7ySSqXSHvMAAACwmtr0JSuf+9znMnny5MyZMyfNzc2t/gAAANAxtOkzeFdffXWS5J577nnfeTfddNOanQgAAIDV0qbAu/LKK9f2HAAAAHxIKw281157Lb17906/fv3aax4AAABW00o/g/eNb3yj1elLL710rQ4DAADA6ltp4L33mzNnzpy5VocBAABg9a008GpqatprDgAAAD6klX4Gb9myZXniiSdaTjc3N7c6nSQf/ehH185kAAAArJKVBl6vXr3yox/9qOV0jx49Wp2uqanxDZsAAAAdxEoDb9KkSe01BwAAAB/SSj+DBwAAwLpD4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABRC4AEAABSirtoDAGVZdsJnqj3CCr1c7QFWosvk26o9AmuY18Lq8VoA+HAcwQMAACiEwAMAACiEwAMAACiEwAMAACiEwAMAAChEu3yL5ty5czNp0qS89tprqampyYgRI/LpT386r7/+eiZOnJg5c+akX79+GTNmTHr06NEeIwEAABSnXQKvS5cuOeaYYzJ48OC89dZbOeuss/Kxj30sf/jDHzJkyJAccsghmTJlSqZMmZJRo0a1x0gAAADFaZe3aDY0NGTw4MFJkm7dumXAgAGZN29eZsyYkeHDhydJhg8fnhkzZrTHOAAAAEVq98/gvfLKK5k9e3a22mqrLFiwIA0NDUn+EYELFy5s73EAAACK0S5v0XzH4sWLM2HChHzhC19I9+7d23y5qVOnZurUqUmScePGpbGxcW2NWKS6urpO85i9XO0B1lFr8vlhDVbPmn6NWofV47VQfZ3l76ukc/393FFZg47BOqxZ7RZ4TU1NmTBhQvbee+/suuuuSZJevXpl/vz5aWhoyPz589OzZ8/lXnbEiBEZMWJEy+m5c+e2y8ylaGxs9JixUp4f1WcNOgbrUH2daQ38/Vx91qBjsA6rrn///is8r13eolmpVHLVVVdlwIABOfjgg1u277LLLpk2bVqSZNq0aRk2bFh7jAMAAFCkdjmC95e//CX33HNPBg0alNNPPz1JctRRR+WQQw7JxIkTc/fdd6exsTGnnnpqe4wDAABQpHYJvO222y6//OUvl3ve2LFj22MEAACA4rX7t2gCAACwdgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQtRVewAAAGDtWnbCZ6o9wgq9XO0BVqLL5NuqPcIqcwQPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEAIPAACgEHXVHgAAgHItO+Ez1R5hhV6u9gAr0WXybdUegXWUI3gAAACFEHgAAACFEHgAAACFEHgAAACFEHgAAACFaJdv0fzhD3+YP/3pT+nVq1cmTJiQJHn99dczceLEzJkzJ/369cuYMWPSo0eP9hgHAACgSO1yBG/ffffNOeec02rblClTMmTIkFxxxRUZMmRIpkyZ0h6jAAAAFKtdAm+HHXZ439G5GTNmZPjw4UmS4cOHZ8aMGe0xCgAAQLGq9hm8BQsWpKGhIUnS0NCQhQsXVmsUAACAIrTLZ/A+rKlTp2bq1KlJknHjxqWxsbHKE61b6urqOs1j9nK1B1hHrcnnhzVYPWv6NWodVo/XQvV1lr+vks7z97PXwurx90LHsC6+RqsWeL169cr8+fPT0NCQ+fPnp2fPnivcd8SIERkxYkTL6blz57bHiMVobGz0mLFSnh/VZw06ButQfZ1pDfz9zMp4bnQMHXUd+vfvv8LzqvYWzV122SXTpk1LkkybNi3Dhg2r1igAAABFaJcjeJdffnlmzZqVRYsW5aSTTsoRRxyRQw45JBMnTszdd9+dxsbGnHrqqe0xCgAAQLHaJfBOOeWU5W4fO3Zse9w8AABAp1C1t2gCAACwZgk8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQgg8AACAQtRVe4BSLDvhM9UeYYVervYAK9Fl8m3VHgEAAIrhCB4AAEAhBB4AAEAhBB4AAEAhBB4AAEAhBB4AAEAhBB4AAEAhBB4AAEAhBB4AAEAhBB4AAEAh6qo9AADA2rLshM9Ue4QVernaA6xEl8m3VXsEYDU5ggcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFAIgQcAAFCIumoP8Mgjj+R//+//nebm5uy333455JBDqj0SAADAOqmqR/Cam5tz3XXX5ZxzzsnEiRNz33335fnnn6/mSAAAAOusqgbeX//612y88cbZaKONUldXlz322CMzZsyo5kgAAADrrKoG3rx589K3b9+W03379s28efOqOBEAAMC6q6qfwatUKu/bVlNT875tU6dOzdSpU5Mk48aNS//+/df6bKvsjoerPQGJdegIrEHHYB2qzxp0DNah+qxBx2AdOo2qHsHr27dvXn311ZbTr776ahoaGt6334gRIzJu3LiMGzeuPccrxllnnVXtEYh16AisQcdgHarPGnQM1qH6rEHHYB3WrKoG3pZbbpkXX3wxr7zySpqamnL//fdnl112qeZIAAAA66yqvkWzS5cuOf7443PRRRelubk5//zP/5xNN920miMBAACss6r+O3j/9E//lH/6p3+q9hhFGzFiRLVHINahI7AGHYN1qD5r0DFYh+qzBh2DdVizairL+6YTAAAA1jlV/QweAAAAa47Ag7Wgubk5999/f7XHgKprbm7ODTfcUO0xAKDTqPpn8FhzfvzjH6/0/OOPP76dJqG2tja//e1vs8cee1R7FKiq2traPPPMM6lUKsv9nVPobJYsWZK5c+d2zN/07QQqlUqmT5+eV155JZ/73Ocyd+7cvPbaa9lqq62qPVqn8oMf/CCjR4/+wG2sHoFXkMGDB1d7BN5lyJAhue2227LHHnukvr6+ZXuPHj2qOFXn0tzcnCuvvDJf//rXqz1Kp7bFFlvke9/7Xnbfffesv/76Ldt33XXXKk7Vedx+++0rPf/ggw9up0l4+OGHc8MNN6SpqSmTJk3K//2//zc33XRTzjzzzGqP1mlce+21qampycyZM/O5z30u9fX1ue6663LxxRdXe7RO5fnnn291urm5Oc8880yVpimPwCvIvvvu2+r0W2+9lZqamlZxQfv5/e9/nyT57W9/27KtpqYmV155ZbVG6nRqa2uzaNGiNDU1pa7Of+6q5fXXX8+GG26YJ554otV2gdc+3nrrrSTJCy+8kL/97W8tvzf7xz/+Mdtvv301R+t0br755lx88cX51re+lSTZfPPNM2fOnOoO1cn89a9/zSWXXJIzzjgjyT/+0bWpqanKU3Uet9xyS2655ZYsWbIkxx13XJJ/HFWtq6vzTZprkP/HU6DnnnsuV155ZV5//fVUKpX07NkzJ598st8YbGeTJk2q9ggk6devX84777wMHTq01T92OGrRfr761a9We4RO7fDDD0+SXHjhhbnkkkvSrVu3lu2XXXZZNUfrdLp06ZLu3btXe4xOrUuXLmlubm55y/jChQu9fbwdHXrooTn00EPz85//PEcffXS1xymWwCvQNddck2OPPTYf/ehHkyQzZ87M1VdfnQsvvLDKk3Uub7/9dm6//fbMnTs3J554Yl588cW88MILGTp0aLVH61QaGhrS0NCQSqXSciSD9vXCCy/k2muvzYIFCzJhwoQ8++yzefjhh/Ov//qv1R6tU5k7d26rI9l1dXWOHrWzTTfdNPfee2+am5vz4osv5je/+U222Wabao/VqRx44IEZP358FixYkBtvvDEPPvhgjjzyyGqP1ekcffTRmTdvXubMmZNly5a1bN9hhx2qOFU5BF6B3n777Za4S5Idd9wxb7/9dhUn6px++MMfZvDgwXnqqaeSJH379s1ll10m8NrZO0cvFi9e7O3KVXL11VfnmGOOyTXXXJMk2WyzzXLFFVcIvHa2zz775JxzzsmwYcNSU1OThx56KPvss0+1x+pUjj/++Pz6179O165d8/3vfz8f//jHvQ7a2d57753Bgwfn8ccfT5KcfvrpGThwYJWn6nx+9rOf5f7778/AgQNbjqDW1NQIvDVE4BXoIx/5SP7jP/6j5S/u6dOnp1+/fv+/vTsPi7Lc/wf+nmEfUAFZPGouYICEiLiChCCdTOto9nXJLEXLlfKbC+JGZGQJSuWGS6a4VWpXi1tH0+MCuFAKmiKoGG6sI3IQhhGGmd8fHp+fc/Sc8z3nwNz2PO/XdXVd89xj+dZpmPk8931/bsGplKe0tBTTp09HZmYmAMDW1lZwImW6dOkSVq9eDb1ej9WrV6OwsBAHDx7EW2+9JTqaYtTV1T3SoU6t5ik9lvbKK68gKCgIeXl5AO4vne3YsaPgVMpiZ2eHUaNGYdSoUaKjKFZ1dTVatGiBsLAwaYz7tC0vKysLn332GWxsbERHkSX+3yxDU6ZMwY4dO5CSkgKTyYTOnTtzD4wA1tbWqKurk+5MlZSU8ANEgLS0NMyfPx/JyckA7jc1uHjxouBUytKsWTOUlJRI74WTJ0/CxcVFcCplqqurg4ODAyIjI1FVVYWysjJ4eHiIjqUYixcvfmS/l0ajgbe3N5577jneCLSAuLg4aLVaODk5wWQyoaamBi4uLmjRogUmTZrEjuQW4unpiYaGBhZ4TYTfNmXIyckJ48ePh06ng1qt5rI0QUaMGIFFixZBq9Vi+fLlyM/PZ6EtiJubm9k1Z48s680338S6detw69YtTJo0CR4eHjzrSICdO3eioKAAxcXFiIyMhMFgwIoVK5CYmCg6mmJ4enqiqqoKffv2BQAcP34cLVq0QFFREdauXcv3hQV07doVvXr1QlBQEADg7NmzyMnJQUhICNavX4+PPvpIcEJlsLW1RWxsLLp06WJ285tnNjcOFngy9HAXTeD+3fOYmBi0a9dOcDJlCQwMRMeOHXH58mWYTCZER0ejefPmomMpTsuWLZGfnw+VSgWDwYB9+/ahTZs2omMpiqurK+Lj46HX62EymeDg4CD9fCLLycrKQnJysnTmmqurKxsPWVhhYSEWLlwoXffo0QMJCQlYuHAhZsyYITCZcly9ehUTJ06Urrt27YqvvvoKY8eORX19vcBkytKjRw/pyBZqfLyNLUMPumimpqYiNTUVY8aMkZobkOVs374dzZo1Q3BwMLp37w4nJycsX75cdCzFmTBhAvbv34+KigpMnjwZhYWFePPNN0XHUpSlS5eioaEB9vb2cHBwQGVlJWeNBLC2toZKpZKWCOr1esGJlKeqqgparVa61mq1qKqqAgAu4bcQJycnfP/99ygvL0d5eTl++OEHODo6wmg0cnWHBUVERCA0NBQ+Pj6IiIiQ/qHGwZ8mMsQumk8GrVaL7777DkOHDkV9fT0++eQTNjQQQK1WY9q0aWZjZWVlaNasmaBEytOzZ0+kpKRg1qxZ0Gq1SE5OxhtvvCE6luKEhIRg3bp1qKmpwcGDB3H48GFERUWJjqUob7zxBuLj49GqVSuYTCaUlZXhrbfegl6vR79+/UTHU4Rp06bhm2++wZIlS2AymeDn54f//d//hdFoxPTp00XHU4xffvkFW7ZsgcFgwKpVq1BYWIjt27dLKwzov6MymUwm0SGocS1ZsgQdO3Y066JZUFCA2bNnC06mLCaTCcuXL0e7du1w4cIFBAUF8XBtAeLj4zF37lzpcOGbN2/i008/RUpKiuBkyvLnP/8ZOTk5KC8vx8SJE+Hr6ys6kiKdO3cOZ8+ehclkQlBQEAIDA0VHUpz6+nrcunULANC6dWs2ViFFiouLQ0JCAt5//32pCdrMmTP52dxIOIMnQ+yiKdbVq1elx4MGDcK6devg6+sLf39/XL16lR26LGzo0KFISkrC3LlzUVRUhJUrVz4yo0dNY8+ePdJjk8mE27dvo0OHDrh8+TIuX77MGx4WZDQasWjRIsTHx7OoE6y4uBhFRUWor6/HtWvXAICzdxZUVVWFH374ATdv3kRdXZ00npCQIDCV8lhZWUk3Xh/4+w6z9J9jgSdDD7pokhhbtmwxu3ZycsKtW7ekcX6IWFZwcDAMBgM+/PBD1NbWIjY2Fn/4wx9Ex1KEv2/g0atXr8eOU9NTq9WwtbWFTqd75EsVWc7OnTuRm5uLmzdvolu3bsjOzoafnx8LPAtavnw5QkNDcebMGUyYMAFHjhxhAzQBnnrqKWRkZMBoNKK4uBg//vgjfHx8RMeSDRZ4MlRUVITdu3ejvLwcDQ0N0jgLC8tISEiA0WjEyZMnERoaKjqOYm3YsMHsura2Fh4eHvjxxx8BsBWzJQwfPhxGoxHbtm3jnrsngI2NDWbOnInAwEDY2dlJ43wvWM7JkyexZMkSxMXFYerUqaisrMSaNWtEx1KUu3fvon///ti3bx/8/f3h7+/P70cCjB8/Ht9++y1sbGywbNkydO3aFf/zP/8jOpZssMCToU8//RR//OMfERUVxY5QgqjVauzfv58FnkB/vxSWS2PFUKvV+O2330THINyfzQ4ODhYdQ9FsbW2hVquhVquh0+nQokULlJWViY6lKA+6lbq4uODMmTNwcXFBRUWF4FTKY2dnh1GjRmHUqFGio8gSCzwZUqvVeP7550XHULwuXbpg165dCA0NNTts3snJSWAq5YiIiIDRaOSeuydAhw4dkJSUhJCQELOZo969ewtMpTxsQS6et7c3ampqEBUVhTlz5sDe3h6dOnUSHUtRXnnlFeh0OrzxxhvYuHEjdDodxo4dKzqWYixevPif7rVjF83GwS6aMvLg4OB9+/ahRYsW6NWrF2xsbKTnWVhYVkxMzCNjKpUKK1euFJBGuRYtWoS4uDieMSVQamrqY8fZ/MkyPvnkE8yYMQMzZ8587BerpUuXCkhFZWVlqK2tRfv27UVHIbKY3NxcAMCpU6dQWVmJZ599FgCQmZkJd3d3vPbaayLjyQYLPBmJiYmBSqXC415SFhakVOvWrcNvv/2G7t27m82ksoMjKcWdO3fg4uKC8vLyxz7v7u5u4UREpHQJCQlYuHDhvxyj/wxvacvIqlWrREeghxgMBhw4cAAXL14EcP/A+eeee44zSRbm4uICFxcXmEwmdm8U5Pbt29iwYQPy8/OhUqng6+uLcePGoWXLlqKjKYKLiwsAYP/+/Xj99dfNntu6desjY0RETa2qqgqlpaXw9PQEcH9Gu6qqSnAq+eAMngz9+c9/xrPPPgtHR0cA95duZmZmYsCAAYKTKcuaNWtgMBikfS/Hjh2DWq3G5MmTxQZTqNraWqhUKrNZPLKMxMREhIWFITw8HACQnp6O9PR0xMfHC06mLHFxcUhKSjIbmzVrFpdoEpHF5eTkYO3atVKBV15ejgkTJiAoKEhwMnngVIIMHTp0CC+88IJ07eTkhEOHDrHAs7CCggIsWbJEug4ICEBsbKzARMp0/fp1rFy5Utqj2qxZM7z99tt46qmnBCdTjqqqKkRGRkrXERER2Lt3r8BEynLgwAHs378fZWVlmDVrljReW1sLX19fgcmU58HPoYfZ29tzZYcF7dmz55ExjUYDLy8vdOjQwfKBFCooKAjLly/HrVu3AABt2rQx6xtB/x3+RJEhk8kEk8kkbaY3Go0wGAyCUymPWq1GSUkJWrVqBQAoLS3lsRUCrFu3DmPGjEFAQAAA4MKFC1i7di0+/PBDwcmUo3nz5jh27BjCwsIAABkZGWjWOZbl0gAAIABJREFUrJngVMoRFhaGoKAgfPnllxg9erQ07uDgwOZbFhYXFwetVgsnJyeYTCbU1NTAxcUFLVq0wKRJk3iciwUUFBTg6tWr6N69OwDgzJkz8Pb2xk8//YQ+ffpgyJAhghMqw9GjR82ur127BgDo16+fiDiywwJPhrp27SqdhadSqXDgwAFOeQvw+uuvY+HChfD09ITJZIJWq8WUKVNEx1Kce/fuScUdcH8v5L179wQmUp4pU6bgiy++wKZNm6BSqeDj48P3ggVpNBpoNBq8++67yMvLQ3FxMSIjI1FVVYWysjJ4eHiIjqgYXbt2Ra9evaTP5LNnzyInJwchISFYv349PvroI8EJ5a+6uhpJSUnScv0RI0YgJSUFCxcuRFxcHAs8CykoKJAe19XV4fz58+jYsSMLvEbCAk+GRo8ejYMHD+LAgQMwmUzo2rUroqKiRMdSnC5dumD58uUoKiqCyWTi8gNBPDw88M0335jt/2LXQMtq3rw5zzZ6AuzcuRMFBQVSgWcwGLBixQokJiaKjqYYV69excSJE6Xrrl274quvvsLYsWNRX18vMJlyaLVasyWxVlZW0Gq1sLW15We0BY0fP97sWqfTYcWKFYLSyA8LPBl6cND5888/j+rqaty+fZtLAwV477330LlzZ3Tu3Bm+vr784BBkypQp2LFjB1JSUmAymeDv78/z1yxs5syZcHZ2hp+fHzp37gw/Pz9oNBrRsRQnKysLycnJUrHt6urKzrIW5uTkhO+//x59+/YFABw/fhyOjo4wGo38nLaQvn37Yv78+ejRowcA4PTp0+jbty/0ej3atm0rOJ1y2draoqSkRHQM2WAXTRl6//33MXv2bBiNRsTGxqJ58+bw9/fH2LFjRUdTlNLSUuTl5eHixYu4fPkybGxs4Ofnh+joaNHRFOXhNswkjlarxcWLF5Gfn4/s7GxoNBqzJkTU9ObOnYuPP/5Y6qap1+uxYMECdtG0oKqqKnzzzTfIy8uDyWSCn58fhg8fDo1GA61WK+3ZpqZVUFCA/Px86TXw9vYWHUlxFi9eLPWKMJlMuHnzJvr06cNjWxoJZ/BkSKfTQaPR4NChQ4iMjMSIESPMOqeRZXh6esLW1hbW1tawtrbGhQsXpG5RZDmpqamoqKiAt7e3NKParl070bEU5fbt29LNjmvXrqFt27bw8/MTHUtxQkJCsG7dOtTU1ODgwYM4fPgw+vfvLzqWojRv3vyRpWkPsLiznI4dO8LFxQVGoxHA/RtQbm5uglMpy+DBg6XHarUa7u7uPBu1EbHAk6GGhgbcuXMHJ06cwKuvvio6jmK98847aNasGcLCwtC/f3+MHz+eS3AEWLhwIQwGA65cuYLc3Fx8/PHH0Ov12Lhxo+hoijF16lR4e3tj6NChZvuPyLIGDx6Mc+fOwcHBAUVFRRg5ciQCAwNFx1KUoqIi7N69G+Xl5WhoaJDGExISBKZSlh9//BHffPMNWrRoAbVaLXUd50y2ZZ05c+aR2bqtW7dyBq+RsMCToWHDhmHRokXw8/NDp06dUFpayjuDAgwcOBB5eXnIzMzEb7/9Bn9/f3Tu3JmvhYU9mDnKy8tDTU0NunfvztkjC0tKSkJeXh4yMjLw/fff4w9/+AP8/f05eyRAYGCgVNQZjUakp6fj2WefFZxKOR50uI6KiuINP0H27duHzz77jEe1CPbrr78+MpaTk8MCr5FwDx5RE9Pr9Th8+DB2796N27dvY/v27aIjKcrIkSPh7e2Nl19+GcHBwTxQWBC9Xi8V2+np6VCpVFi1apXoWIqg0+mwf/9+VFRUoEePHggMDMT+/fuxa9cudOjQAbNnzxYdUTEe7H8kcRYuXIgFCxbAyspKdBRFOnDgAPbv3//I5ENtbS18fX0xbdo0genkgwUeURPZvHkz8vLyoNfr4ePjI3UQZMMPy6qpqUF+fj5yc3NRUFAAtVqNp59+msuXLWjOnDmor6+Hr6+v9D7gURWWk5ycDEdHR/j4+ODXX39FTU0NDAYDxo0bhw4dOoiOpyg7duxAixYt0KtXL7POyjxw3nJWr16NoqIiBAcHm70GL730ksBUyqHT6VBdXY0vv/wSo0ePlsYdHBz4PmhEvJVN1ESefvppDB48GM7OzqKjKJqjoyM8PDyg1WpRUVGB/Px8GAwG0bEUZd68eWjevLnoGIpVWlqKlJQUAEBUVBTefPNNpKamwsHBQXAy5Tl69CgAYNeuXdKYSqXCypUrRUVSHDc3N7i5ucFgMPCzQACNRgONRoNXX30Vzs7OsLGxwYULF3Dt2jX069cPjo6OoiPKAgs8mTEajTh58iRCQ0NFR1G8kJAQ0REI95vdtG7dGn5+fvjjH/+IqVOncpmmhbG4E+vh/9/VajU8PDxY3AnCZcniDR8+XHQEApCSkoLFixejpKQEa9asQffu3bF8+XLMnTtXdDRZ4LccmVGr1di/fz8LPKK/WbZsGZsZkKIVFhZK56CaTCbU1dVh7NixUvfATZs2CU4of+fPn0dAQABOnTr12Od79+5t4UTKk5aWhujoaLPz1x4WFxcnIJVyqdVqWFlZ4dSpUxg0aBAGDhzI/cCNiAWeDHXp0gW7du1CaGgo7O3tpXGubSYleri4Y4MDUiI2dhIvNzcXAQEBOH369GOfZ4HX9MLDwwGYn79G4lhZWSEjIwPHjh2TiuuHjw6h/w4LPBk6fPgwAGD//v3SGNf4i5GXl4fi4mJERkaiqqoKer0eHh4eomMpFntKiXHv3j3s3r0bWq0WkydPRnFxMYqKitC9e3fR0YgsYsSIEQCAyZMnc0WBIF5eXgCA6upqdOvWzazBClne1KlTceDAAQwdOhQeHh4oKyvjkS2NiAWeDHGN/5Nh586dKCgokAo8g8GAFStWIDExUXQ0xQoODhYdQZFSU1Ph5eWFy5cvAwBatmyJTz75hAUeKU5MTAyCgoIQGhqKgICAxy4VpKb1yy+/IC0tDZ07d0bfvn3RtWtXHpkgQNu2bTF+/Hjp2sPDAy+//LLARPLCAk+GDAYDDhw4gIsXLwIAnnnmGTz33HNsLGFhWVlZSE5OlpYeuLq6ora2VnAqZePRCGKUlpZi+vTpyMzMBADY2toKTkQkxrJly/DLL79g//79WLNmDYKDg9G3b1/4+fmJjqYYU6dOhcFgQE5ODjIyMrB+/XoEBgZi8uTJoqMRNRp+45eh9evXw2AwYMCAAQCAY8eOYf369fzhZWHW1tZQqVTSHVq9Xi84kTKdOnUK27Ztw1//+lcAYGMJAaytrVFXVye9F0pKSnjDiRTJ1tYWoaGhCA0NRXV1NdLS0pCQkMB9khZmbW2NoKAgAEBdXR1+/vlnfkciWeEnrAwVFBRgyZIl0nVAQABiY2MFJlKmkJAQrFu3DjU1NTh48CAOHz6MqKgo0bEUZ+vWrYiLi0Pbtm1FR1GsESNGYNGiRdBqtVi+fDny8/MxdepU0bGIhMjNzcXx48eRnZ0Nb29vTJ8+XXQkRcnJyUFmZiYuXLgAf39/9O/fn6+BANevX0e7du1Ex5AtFngypFarUVJSglatWgG4vzyKm7otb/DgwTh37hwcHBxQVFSEkSNHIjAwUHQsxXF2dmZxJ1hgYCA6duyIy5cvw2QyITo6mmfjkSLFxMSgQ4cOCAkJweuvv27W6Zos48iRIwgNDcXEiRPZaEWgzz//HAaDAREREQgLC+MB541MZWJbOdn59ddfkZqaCk9PT5hMJmi1WkyZMgUBAQGioymSTqeD0WiUrnlchWVt3LgRlZWV6Nmzp9mHOduSW9a1a9dQXl5u1gabrwEpjU6ng0ajER2D6IlQXFyMw4cP48SJE+jUqRMiIyN5I7yRsMCTkRMnTiAkJARlZWVwcXFBUVERTCYT2rRpw7tUAvz000/YsWMHbG1toVKppL1fPK7CslJTUx87ziWClpOamorr16+jbdu2ZqsJ+BqQUvzwww8YMmQINmzY8NjnH+4mSE0jPj4eiYmJGDNmjFn3Uu7LFstoNCIrKwsbN26ERqOByWTCqFGjeAPwv8QlmjLy/fffIyQkBCkpKUhKSkL79u1FR1K03bt3IyUlhUvRBGMRId7ly5fx6aefio5BJEybNm0A/P+z2MjyHhxRtHnzZsFJCLi/quPw4cPIzs5Gly5dEBcXBy8vL1RUVGDBggUs8P5LLPBkxMnJCQsXLkRZWRmSkpIeef5Bu36yDE9PT9jZ2YmOoXi3b9/Ghg0bkJ+fD5VKBV9fX4wbNw4tW7YUHU0xfHx8cPPmTe6FJMXq0aMHjEYjbty4gTfeeEN0HMUyGo2IjY1FSkqK6CiKt2HDBkRFReG1114zOzrH1dWVRxo1AhZ4MjJ37lxcvXoVK1euxJ/+9CfRcRTvtddew4IFC/D000+btYTnUhzLSk1NRVhYGGbMmAEASE9PR2pqKuLj4wUnU45+/fph/vz5cHZ2ho2NjbQkaunSpaKjEVmMWq3G1atXRcdQNLVajfbt20Or1cLNzU10HEXr1asXwsPDzcb27duHQYMGPTJO/z4WeDJibW0NHx8ffPjhh1wW+ARYt24dAgIC0K5dO7P1/mRZVVVViIyMlK4jIiKwd+9egYmUZ/Xq1XjnnXf4XiDF69ixI5KSkhASEmK2woPL0Sznzp07mDFjBjp16mT2GnCVk2UdO3YML774otnYkSNHMGjQIEGJ5IUFngyxuHsyWFlZYezYsaJjKF7z5s1x7NgxhIWFAQAyMjLQrFkzwamUxc3NDT169BAdg0i46upqNGvWDOfPnzcbZ4FnOcOHDxcdQdEyMjKQkZHxyHYivV7Pz+ZGxC6aRE3kq6++gru7O7p3727WxZTHJFiWVqvFF198gUuXLkGlUsHHxwfjxo2Du7u76GiKsX79etTU1DzyXuCXWiIiZSkvL0dZWRm+/PJLjB49Whq3t7dH+/btYWVlJTCdfLDAI2oiMTExj4zxmARSIh5VQUr3j45HeIB7s5ve3x+P8Pd4TALJCQs8GXrcB4lGo4G3tzd69uwpIBGR5X3zzTf/9Plhw4ZZKAkRKd2RI0cAAPn5+bh58yZCQ0MBACdPnkTHjh0RHR0tLpzCbN++Hc7OzggPD4fJZEJGRgZqa2sxZMgQ0dEUgecRWgYLPBlau3YtioqK0KdPHwDAqVOn0LZtW9y+fRuenp78IGli58+fR0BAAE6dOvXY57kszTJ27979yNi9e/fwl7/8BXfv3sWWLVsEpFIWHu5MZG7hwoWYP3++1FnZYDBg0aJFSEhIEJxMOebNm4ePPvroX44R/Z6xyYoMlZSU4L333pPWMT///PP48MMPER8fj5kzZwpOJ3+5ubkICAjA6dOnH/s8CzzLePiokNraWuzbtw+HDx9GaGgojxGxEB7uTGSuoqICer1e2out1+tRUVEhOJWyqNVqpKeno2/fvgCAzMxMqNVqwamUp6SkBC1btoSNjQ0uXLiAa9euoV+/fnB0dBQdTRZY4MlQRUUF7t27B41GA+D+rMWdO3egVqvNGhxQ0xgxYgSA+0sAPTw8zJ4rKysTEUmxqqursWfPHqSnp6Nfv35ISkpikxsLetA5087ODiEhIWbPnThxQkQkIqFefvllzJ49G8888wyA+zcE2dXRsqZNm4a0tDSkpaUBAHx9fTFt2jSxoRQoJSUFixcvRklJCdasWYPu3btj+fLlmDt3ruhossACT4aGDBmC2NhYPPPMMzCZTLh48SKGDh0KvV6PLl26iI6nGCkpKWYtgP/RGDWNLVu2ICsrC1FRUUhJSYG9vb3oSIr1/fffP1LgPW6MSO4iIyPRrVs3XL58GQAwevRoODs7C06lLB4eHpg9e7boGIqnVqthZWWFrKwsDBo0CAMHDuTr0ohY4MlQ//790a1bN1y5cgUmkwmjRo2Cq6srAOCNN94QnE7+bt26hRs3bkCn05ntw6utrUV9fb3AZMqyZ88eWFtb49tvv8V3330njXMjt+VkZ2cjOzsbFRUVZvvwamtruSSKFMvZ2ZkNz54QcXFxvOkqiJWVFTIyMnD06FHpkPmGhgbBqeSDBZ5MmUwmNG/eHA0NDSgpKUFJSQn8/f1Fx1KEoqIinDlzBjU1NWb78Ozt7TFp0iSByZRl+/btoiMonouLC7y8vPDLL7+Y7cNzcHDA2LFjBSYjIrr/XYnEmDp1Kg4cOIChQ4fCw8MDZWVlePbZZ0XHkg120ZShrVu34sSJE2jbtq3UglalUkl3SMgyLl26BB8fH9ExiIQzGAxS18CrV6+y6QopTllZ2SN7skm8r7/+Gq+++qroGIpjNBqxcuVK7n1sQlwjI0M///wzPvvsM8ydOxdz5szBnDlzWNwJ8HBxx79/UrIHxR1w/xgXIqVJSUkBAHzwwQeCk9DDWNyJoVarcffuXRgMBtFRZItLNGXI09MTDQ0N7Jj5BOFEOdF9fC+QEplMJuzcuRPFxcXYs2fPI8+/9NJLAlIp06lTp7Bt2zb89a9/BcB92aK4u7sjPj4e3bt3N2uCxvdC42CBJ0O2traIjY1Fly5dzO6c81BhcYKDg0VHIHoiDBs2THQEIot79913kZWVhYaGBtTW1oqOo2hbt25FXFwc2rZtKzqKorm4uMDFxQUmk4nviSbAPXgydOTIkceOR0REWDQHERFwf1nae++99y/HiOQuOzsb3bp1Ex1D0eLj45GYmCg6Bv2NXq/nMUZNgDN4MsRCTqwxY8ZIzW0exmUgpDR1dXWoq6vD3bt3UV1dLY3rdDrcuXNHYDIiMXx9fbFp0yZcvHgRAODv749hw4ZBo9EITqYcXl5e+PTTT9GzZ0+zrSy9e/cWmEp5Ll26hNWrV0Ov12P16tUoLCzEwYMH8dZbb4mOJgss8GQoJibmsQXGypUrBaRRns2bN4uOQPREOHjwIPbu3Ys7d+5gzpw50v47jUaDAQMGCE5HZHmpqalo164dpk+fDgA4duwYUlNTMWvWLMHJlKO2thZ2dnY4d+6c2TgLPMtKS0vD/PnzkZycDADo0KGDdOOD/nss8GRo8eLF0uP6+nqcOHHC7O45Na1/9Xft5ORkoSREYg0aNAgvvPACvv32W+69IwJQWlpqVswNHz4csbGxAhMpz9SpU0VHoL9xc3Mzu1ar2dy/sbDAk6FmzZqZXb/44ouIj4/HyJEjBSVSlri4OKhUqsd2C1SpVJxJJUVRq9XIzs5mgUeE+03Q8vLy4OfnBwDIy8uDra2t4FTKcvv2bWzYsAH5+flQqVTw9fXFuHHj0LJlS9HRFKVly5bSa2AwGLBv3z60adNGdCzZYJMVGbp69ar02GQyoaCgAD/99BOWLFkiMBURKdWOHTvQrl079O7d+7HLx4mUorCwEKtWrYJOpwMAODo6IiYmBu3btxecTDkSExMRFhaG8PBwAEB6ejrS09MRHx8vOJmyVFVVIS0tDb/++itMJhMCAwMxbty4RyYp6D/DGTwZ2rJli/RYrVbD3d1dWu9PlpObm/vYcX9/fwsnIRJrz549uHfvHtRqNWxtbdlwiBSrQ4cOWLJkiVTgsbmK5VVVVSEyMlK6joiIwN69ewUmUq5p06aJjiBbLPBkKCEhQXQEArBr1y7pcX19Pa5cuQIvLy++PqQ4bDxEZI6FnTjNmzfHsWPHEBYWBgDIyMjgrJEACxYsgIeHB0JDQ9G7d284OjqKjiQrXKIpQzqdDjt37mQb5ieMVqvF1q1b8e6774qOQmRx1dXVKCkpQV1dnTTG2WwisjStVosvvvgCly5dgkqlgo+PD8aNGwd3d3fR0RTnypUryMzMxM8//4y2bdsiNDRUWjpL/x0WeDK0dOlStGvXDv369QNwvw3ztWvX2IZZMJPJhFmzZiElJUV0FCKLOnToEPbt24eKigp06NABly5dgo+PD2ezSXHq6+vNzl77R2NESlJVVYXNmzcjPT0d27dvFx1HFrhEU4bYhvnJsGHDBumxyWRCYWEhN9KTIu3btw8ff/wx5s+fj4SEBNy6dQs7duwQHYvI4hYsWICkpKR/OUZNp6qqCgcPHkR5eTkaGhqkcR6fYFk6nQ5ZWVk4fvw4SktL0bNnT3z88ceiY8kGCzwZYhvmJ4OXl5f02MrKCn379pVeEyIlsbW1lX4G1dfXo02bNigqKhKcishyKisrUVFRgbq6Ovz222/SMTq1tbW4d++e4HTKkpycDD8/P3Tp0oXnrgkUGxuLnj17YtiwYfDx8REdR3ZY4MnQhAkTHtuGmSxDq9XCzc0NERERoqMQPRFcXV1RU1ODnj174sMPP4SjoyNcXV1FxyKymJycHBw9ehS3b982azpkb2+PUaNGCUymPPfu3cPrr78uOobirVy5ksfmNCHuwZMho9EItVrNNsyCxMXFScttli5dyr2PRA/Jzc2FTqdDUFAQrK15j5GU5eTJk+jTp4/oGIr29ddfw8fHB8HBwaKjKFJaWhqio6OxePHixxZ4cXFxAlLJDz9dZeidd95Bnz59EBkZibZt24qOozgP3zMpKysTmIRIrLq6Ovz0008oKSlBu3bt0L9/f3bOJEXr3r07MjIyUFZWBqPRKI0PGzZMYCpl2bdvH7777jtYW1vD2tqa53Ja2IMumYMHDxacRN5Y4MnQ0qVLkZmZiTVr1sBkMiEyMhKhoaGcybOQh+9IcfkBKdmqVatgZWWFzp07Izs7Gzdv3sS4ceNExyISJjk5GRqNBl5eXuycKQjP5RTrQX8C3uxrWlyiKXO5ublYtmwZdDodevfujWHDhqFVq1aiY8nayJEjYW9vD5PJhLq6OtjZ2QEA7xKS4sycOVM6FqShoQHz5s1jt0BStIffE0RKlpeXh507d0Kr1aKhoUH6jrRy5UrR0WSBM3gyZDQacebMGRw+fBjl5eX405/+hLCwMOTl5eHjjz/GsmXLREeUNZ7hQnTfw3vsrKysBCYhejL4+Pjg+vXraNeunegoREKtWbMGY8eOhZeXF7uZNgEWeDI0bdo0PPPMMxg8eDB8fX2l8T59+iA3N1dgMiJSksLCQowdOxYApBntsWPHcjabFCsvLw9HjhyBh4cHbGxspPfC0qVLRUeTvbKyMnh4eIiOQX+j0WjQrVs30TFki0s0ZUiv18Pe3l50DCIiInpIeXn5Y8fd3d0tnER5HnS4/uCDD/Dee++JjqN427Ztg9FoRO/evc1Wezx8hjD95ziDJ0Ms7oiIiJ487u7uyMvLQ3FxMSIjI1FVVQW9Xi86liKYTCbs3LkTxcXF2LNnzyPPv/TSSwJSKdeVK1cAAFevXjUbT0hIEBFHdljgEREREVnAzp07UVBQIBV4BoMBK1asQGJiouhosvfuu+8iKysLDQ0NqK2tFR1H8VjINS0WeEREREQWkJWVheTkZOkwZ1dXVxYbFtK6dWu8/PLLaN++Pfd+CfS42dPmzZvDz8+PeyQbEQs8GdLpdNixYwfy8vIA3D9rZNiwYTwHj4iISCBra2uoVCrpjFQuz7Q8X19fbNq0CRcvXgTA70iW9rgbGuXl5fj2228xfPhw9O3bV0Aq+WGTFRlaunQp2rVrh379+gEAjh07hmvXrmHWrFmCkxERESnXrl27UFJSgnPnzuHll1/G4cOHERYWhoEDB4qOphj8jvRkqq6uRmJiIs9KbSQ8eEKGSktLMWLECHh6esLT0xPDhw9HaWmp6FhERESKNnjwYPTp0we9e/dGUVERRo4cyeLOwvgd6cnk5OQEzjk1Hi7RlCFbW1vk5eXBz88PwP1zd2xtbQWnIiIiosDAQAQGBoqOoVj8jvRkOn/+PBwdHUXHkA0u0ZShwsJCrFq1CjqdDiaTCU5OToiJiUH79u1FRyMiIlKcMWPGSPvuHmfTpk0WTKNsD39HAgBHR0d+R7KgmTNnPvJeqK6uhouLC95++220adNGUDJ5YYEnYw9+eHHjMBERkXjbt2+Hs7MzwsPDYTKZkJGRgdraWgwZMkR0NMXhdyQxysvLza5VKhWcnJx4hnMj4xJNGdLpdNi5cyc7RBERET1Bzp49i48++ki6fv755zFv3jwWeALwO5EY7u7uoiMoApusyFBqaiocHBwwffp0TJ8+HQ4ODkhNTRUdi4iISNHUajXS09NhNBphNBqRnp4OtZpfxYiocXGJpgzFxsZiyZIl/3KMiIiILKesrAxpaWnIz88HcP9MtujoaB7wbEH19fWwsbH5l2NEv2dcoilD7BBFRET05PHw8MDs2bNFx1C0BQsWPHLW2uPGiH7PWODJ0IQJEx7bIYqIiIjE+UfbJaZOnWrhJMpTWVmJiooK1NXV4bfffpPOXKutrcW9e/cEpyNqXCzwZEij0WDJkiVmHaLKysoEpyIiIlK24OBg6XF9fT2ysrLg4uIiMJFy5OTk4OjRo7h9+zY2b94sjdvb22PUqFECkxE1Pu7Bk6G4uLhHlho8boyIiIjEMRqNSExMREJCgugoinHy5En06dNHdAyiJsUZPBm5desWbty4AZ1Oh1OnTknjtbW1qK+vF5iMiIiI/l5JSQm0Wq3oGIrSvXt3ZGRkoKysDEajURofNmyYwFREjYsFnowUFRXhzJkzqKmpwenTp6Vxe3t7TJo0SWAyIiIiGjNmDFQqlXTt7OyM0aNHC0ykPMnJydBoNPDy8mLnTJItLtGUoUuXLsHHx0d0DCIiIqInysyZM5GSkiI6BlGT4umaMsTijoiI6MnzwQcf/J/GqOn4+Pjg+vXromMQNSku0SQiIiJqQnV1dairq8Pdu3dRXV0tjet0Oty5c0dgMuXJy8vDkSNH4OHhARsbG5hMJqhUKixdulR0NKJGwyWaRERERE1o37592Lt3L+7cuQNXV1fpDDaNRoOoqCi88MILghMqR3l5+WPH3d3dLZyEqOmwwJORPXv2/NPnX3rpJQslISIior/3448/YuDAgaJjKF5eXh6Ki4sRGRmJqqoq6PV6eHh4iI5F1Gi4RFNGamt9EdDgAAALGElEQVRrAdzvpllQUIAePXoAAE6fPo3OnTuLjEZERKRYV65cgZubm1TcHT16FKdOnYKbmxtGjBgBJycnwQmVY+fOnSgoKJAKPIPBgBUrViAxMVF0NKJGwyYrMjJ8+HAMHz4cd+/eRVJSEsaMGYMxY8Zg8eLFuH37tuh4REREivT555/D2vr+PfXc3Fx8+eWXCA8Ph0ajwdq1awWnU5asrCzExcXBzs4OAODq6irdICeSCxZ4MqTVaqUPEgCwtrb+h2vOiYiIqGkZjUZplu748eOIiopCnz598Oqrr6KkpERwOmWxtraGSqWSziPU6/WCExE1Pi7RlKHw8HDMmzcPPXv2hEqlQlZWFsLDw0XHIiIiUiSj0YiGhgZYWVnh/PnzmDhxotlzZDkhISFYt24dampqcPDgQRw+fBhRUVGiYxE1KjZZkamrV68iLy8PANC5c2d07NhRcCIiIiJl+vbbb5GdnY1mzZpBq9UiKSkJKpUKJSUlWLVqFfd/Wdi5c+dw9uxZmEwmBAUFITAwUHQkokbFAk9GHj5b53G4iZuIiEiMS5cuobKyEoGBgbC3twdwvymaXq+Hl5eX4HREJCcs8GQkJiYGKpVKOl/nwfryB4d4rly5UmQ8IiIiIiHGjBkjfS96nE2bNlkwDVHTYoFHRERERIqwfft2ODs7Izw8HCaTCRkZGaitrcWQIUNERyNqNOyiKSO3bt0CcH//3eP+ISIiIlKys2fPYsCAAXBwcIBGo8Hzzz+PU6dOiY5F1KjYRVNG9uzZg0mTJmHLli2PfT4hIcHCiYiIiIieHGq1Gunp6ejbty8AIDMzE2o15ztIXrhEk4iIiIgUoaysDGlpacjPzwcA+Pr6Ijo6Gh4eHoKTETUeFngylZ+fj/LycjQ0NEhj/fr1E5iIiIiIiIiaGpdoytCKFStQWlqKDh06mC07YIFHRERESpaamvrY8alTp1o4CVHTYYEnQ1evXsUnn3zyT9sBExERESlNcHCw9Li+vh5ZWVlwcXERmIio8bHAk6GnnnoKlZWV/IFFRERE9JA+ffqYXfft2xeJiYmC0hA1DRZ4MrJ48WKoVCro9XrMmDEDnTp1grX1/3+J4+LiBKYjIiIierKUlJRAq9WKjkHUqFjgycjgwYNFRyAiIiJ6Yo0ZM8ZsC4uzszNGjx4tMBFR42MXTRkpKSlBZWUl/Pz8zMZzc3Ph6uqKVq1aCUpGRERERESWwJMdZSQtLQ0ODg6PjNvZ2SEtLc3ygYiIiIieIB988MH/aYzo94xLNGWkvLwc7du3f2Tc29sb5eXlAhIRERERiVdXV4e6ujrcvXsX1dXV0rhOp8OdO3cEJiNqfCzwZKSuru4/eo6IiIhIzg4ePIi9e/fizp07mDNnDh7sUNJoNBgwYIDgdESNiwWejHh7e+PgwYN47rnnzMb/8pe/wMvLS1AqIiIiIrEGDRqEQYMG4ccff8TAgQNFxyFqUmyyIiOVlZVYunQprK2tpYKuoKAABoMBsbGxcHZ2FpyQiIiIyPKuXLkCNzc36bvQ0aNHcerUKbi5uWHEiBFwcnISnJCo8bDAk6Hz58/jxo0bAO4feh4QECA4EREREZE4cXFxiI+Ph5OTE3Jzc7Fs2TKMGzcOhYWFuHXrFmbOnCk6IlGj4RJNGQoICGBRR0RERPQ3RqNRmqU7fvw4oqKi0KdPH/Tp0wexsbGC0xE1Lh6TQERERESyZjQa0dDQAOD+SqeHb4QbjUZRsYiaBGfwiIiIiEjW+vbti/fffx/NmjWDra0tOnfuDAAoKSmBRqMRnI6ocXEPHhERERHJ3qVLl1BZWYnAwEDY29sDAIqKiqDX69ltnGSFBR4REREREZFMcA8eERERERGRTLDAIyIiIiIikgkWeERERERERDLBLppERPREi4mJQWVlJdRqNezt7dGtWzeMHz9eapLwJElPT8e6desA3G+9bjAYYGtrKz2/ZcsWUdGIiEgh2GSFiIieaDExMZg0aRICAwNRUVGBRYsWITg4GKNHj/63/jsNDQ2wsrJqopSPunDhAlasWIE1a9ZY7PckIiLiDB4REf1uuLq6IigoCDdu3AAA6HQ6bNq0CdnZ2VCpVIiMjMSIESOgVqtx5MgRHDp0CN7e3jh69CgGDBiAiIgIrF69GoWFhbC2tkZAQACmT58OAMjPz0daWhqKiorQunVrREdHw9fXFwDw/vvvw8/PDxcuXMC1a9fg4+ODadOmoXnz5v9W/u+++w6FhYXS7wkAn3/+Oezs7DBmzBjEx8fD398fZ8+eRXFxMQICAjBlyhQ4OTkBAPLy8rBlyxbcunUL7u7uGDduHPz9/Rvjr5aIiGSCe/CIiOh3Q6vVIjs7Gx06dAAArFy5ElZWVli+fDmSk5Nx9uxZHDp0SPr1ly9fhqenJ9avX49XXnkFX3/9Nbp27YqNGzdi9erVGDhwIACguroaixcvxsCBA7Fhwwa8+OKLWLx4Me7evSv9tzIzMzFlyhSsX78eBoMBu3fv/rfzh4eH48yZM9DpdAAAg8GAkydPIjw8XPo1x44dw9tvv421a9fCZDJh06ZN0p89OTkZw4cPx4YNGzB69GikpKSYZSQiImKBR0RET7wlS5YgOjoa7733Hvz9/fHKK6+gsrISOTk5iI6Ohr29PVq0aIEXX3wRx48fl/49FxcXDBw4EFZWVrC1tYW1tTXKy8tx584d2Nraws/PDwBw5swZtGrVCuHh4bCyskJYWBhat26N06dPS/+tiIgItG7dGra2tggJCUFhYeG//edo2bIlfHx8cOrUKen3dXFxkQpWAOjXrx/atm0Le3t7jBw5EsePH4fJZMKxY8fQo0cPBAUFQa1WIygoCO3bt0dOTs5/9pdKRESyxCWaRET0xIuNjUVgYKDZ2PXr19HQ0ICJEydKYyaTCS1btpSu3dzczP6d119/HV9//TXmzZsHR0dHvPTSS+jfvz8qKirg7u5u9mvd3d1RUVEhXTs7O0uP7ezsoNfr/6M/S79+/XDkyBFERkYiPT3dbPYOgFl+d3d31NfXo7q6GuXl5cjMzERWVpb0fENDA4KCgv6jHEREJE8s8IiI6HepZcuWsLa2xhdffPF/bp7i7OyMyZMnA7i/ny0xMRH+/v5wdXWVZtUe0Gq1TVI89e7dGxs2bMCNGzeQk5ODcePGmT1/+/Ztsww2NjZwcnJCy5YtERERgQkTJjR6JiIikg8u0SQiot8lFxcXdO3aFZs3b4ZOp4PRaERJSQlyc3P/4b9z4sQJqYBydHQEAKjVanTr1g3FxcXIyMhAQ0MDjh8/jps3byI4OLjRc9vZ2aFXr15YtmwZfH194erqavb80aNHcevWLej1euzYsQMhISFQqVQIDw9HVlYWzp07B6PRiLq6Opw/f95slpGIiIgzeERE9Lv19ttvY9u2bZgxYwZqa2vh6emJIUOG/MNfX1BQgLS0NOh0Ojg7O2PcuHHw8PAAAMyZMwcbN27E559/jlatWmHOnDn/dpfM/6sHyzQHDx78yHPh4eFYsWIFiouL8cwzzyA6OhoA4OHhgdjYWGzbtg2ffvoprKys0KlTJ87oERGRGZ6DR0REZGGlpaWYNWsWPv/8c7MD2+Pj4xEVFYWIiAhx4YiI6HeNSzSJiIgsyGg0Ys+ePQgLCzMr7oiIiBoDCzwiIiIL0el0GDt2LHJzczF8+HDRcYiISIa4RJOIiIiIiEgmOINHREREREQkEyzwiIiIiIiIZIIFHhERERERkUywwCMiIiIiIpIJFnhEREREREQywQKPiIiIiIhIJv4fUj203MADBJ4AAAAASUVORK5CYII=\n", + "image/png": 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33Xeniy66KC9cPtdcc+W5d/PPP3/q379/Ovjgg9PYsWPz9x111FFpqaWWmsaW/NdMEyaarkeowauvvlp3E2YocbZg2LBhdTej6TkO9XMMOgfHoX6OQefgONTPMZjxjsN7772XC5I0k/cm8zsvvPDCU/x+QzQBAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIayDBwAAMBXeeuuttOuuu+avhw4dmrp06ZJ69OiRb994441p1llnnYpH6VgCHgAAMEPa7tdPtuvj/WmP5T/x/ghzt9xyS/76rLPOyouWH3jggS33jxs3Li9mXicBj6K094u8WXzaPzMAACbv0EMPTfPNN1969NFHU+/evVO3bt3aBL9NNtkkXXnllWnRRRdNf/zjH9Nll12Wxo4dm1ZbbbV02mmn5V7A9mQOHgAAwHR47rnn0oABA9KJJ544xe/597//na6//vo0cODA3AsYwe7aa6+djmedPD14AAAA02Gbbbb51J64O++8Mz3yyCNp6623zrfHjBmTevbsOR3POnkCHgAAwHSYc845W76OoPfRRx+13P7ggw/y9YQJE9LOO++cjj322Ol4pk9niCYAAEA7ibl20VMX4vqll17KX2+wwQbphhtuSMOGDcu3R4wYkV555ZV2etb/owcPAACgncQQzD/84Q9p8803T6uuumpacskl8/Zll102HXXUUWn33XfPvXlRbfPUU09Nn//859vpmf9LwAMAAGZIdVYCP/zwwye7fY455ki//e1vJ3vfdtttly8dyRBNAACAQgh4AAAAhRDwAAAACiHgAQAAM4QoTtJsJnzG31nAAwAAZggzzzxzGjduXN3NqEz8rvE7fxaqaAIAADOE2WefPY0ZMyYvHj7TTDPV3ZwO77mLcBe/82ch4AEAADOECHWxDAFTZogmAABAIQQ8AACAQlQ2RPPggw/O40djHGmXLl1Sv3790ujRo1P//v3T0KFDU69evVLfvn1Tt27dqmoSAABAUSqdg3fiiSemeeaZp+X2wIEDU+/evdP222+fv47LnnvuWWWTAAAAilHrEM0hQ4akPn365K/jOm4DAAAwA/TgnXrqqfl68803T5tttlkaOXJk6t69e94W16NGjaqyOQAAAEWpLOCdcsopqUePHjnU/fjHP04LL7zwVP/soEGD8iXE3L2ePXt2VDOL1LVrV/uMT9Qsrymvhc7BcaifY9A5OA71cww6B8ehnfdn+z7clEW4C/POO29aa6210jPPPJO/HjFiRO69i+vW8/Nai96+uDQMGzaskjaX9OHdPuOTNMvfh9dC5+A41M8x6Bwch/o5Bp2D4/DZfVJnWSVz8GK1+ffff7/l64cffjgttthiac0110yDBw/O2+M6gh8AAACduAcvhmWeeeaZ+evx48enDTbYIK266qppqaWWyssk3HrrrTm5H3bYYVU0BwAAoEiVBLwFF1wwnXHGGZNsn3vuudMJJ5xQRRMAAACKV+syCQAAALQfAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFqGyhc6jCtbcfZUdPiz2ut98AAAqgBw8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACtG1yif76KOP0jHHHJN69OiRr0ePHp369++fhg4dmnr16pX69u2bunXrVmWTAAAAilFpD95NN92UFllkkZbbAwcOTL17907nnntuvo7bAAAAdPKAN3z48PTAAw+kTTfdtGXbkCFDUp8+ffLXcR23AQAA6ORDNK+44oq05557pvfff79l28iRI1P37t3z13E9atSoyf7soEGD8iX069cv9ezZs+MbXJCuXbs2zT57o+4GzKCa5e+jmV4LnZnjUD/HoHNwHOrnGHQOjkM778/2fbjJ++c//5nmnXfetOSSS6bHHnvsM//8Zpttli8Nw4YNa8/mFS8+0NpnfJJm+fvwWugcHIf6OQadg+NQP8egc3AcPruFF1643oD31FNPpfvvvz89+OCDaezYsbkXL+bdRegbMWJE7r2L63nmmaeK5gAAABSpkoD3jW98I19C9OD9+c9/Tocccki6+uqr0+DBg9P222+fr9daa60qmgMAAFCkWtfBi2D38MMP57AX13EbAACAGWAdvLDSSivlS5h77rnTCSecUHUTAAAAilRrDx4AAADtR8ADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQnStuwEAtL/tfv2k3ToN/rTH8vYbADM0PXgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhehadwMAaH/X3n6U3Tot9rjefgNghqYHDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACtF1ar7pO9/5zmS3zzLLLGn++edPX/rSl9IWW2yRunTp0q6NAwAAoJ0D3lZbbZXuuOOOfB2BbtiwYekvf/lLWmeddVK3bt3SDTfckIYPH5723HPPqX9mAAAAqg94t99+e/rhD3+YevTo0bJttdVWSz/+8Y/Tz372s7TyyiunU045RcADAADo7HPwRowYkWafffY222abbba8PXzuc59L7777bvu3DgAAgPbtwVtjjTXSGWeckXbYYYc8RDOGYw4cODBvD08//XTq1avXFH9+7Nix6cQTT0zjxo1L48ePz0M7d9lllzR69OjUv3//NHTo0Pzzffv2zUM+AQAA6KCAt//++6drrrkmXXLJJemtt95K3bt3T+uuu27aaaed8v0LLrhgOvbYY6f481GMJQJe9AJGyDvhhBPSqquumu67777Uu3fvtP322+fAGBfz+AAAADow4M0666xpjz32yJfJmW+++T7x52eaaaaWIZ7RgxeX2DZkyJD0ox/9KG/v06dP/lrAAwAA6MCAF1599dX0wgsvpDFjxrTZvskmm0zVz3/00Ufp6KOPTq+//nracsst0zLLLJNGjhyZewNDXI8aNeozNB0AAIDPHPCuvfba9Mc//jEtvvjiubjKtAS8mWeeOc/ji2IsZ555ZnrppZem6ufCoEGD8iX069cv9ezZc6p/lokHuWvXptlnb9TdgBlUs/x9eC3wabwWqFIz/U/qrByDzsFxaOf9OTXfdNNNN6Wf/OQnOeBNr7nmmiutuOKK6aGHHkrzzjtvrsQZvXdxPc8880z2ZzbbbLN8aYh1+Jh68eZhn/FJmuXvw2uBT+O1QJX8T6qfY9A5OA6f3cILLzx9yyTEHLxFFlnksz/z/xdDLxvLKERFzUceeSQ/3pprrpkGDx6ct8f1WmutNc3PAQAA0Oymqgdv1113TZdddlnaeeedc6/bx4defpronbvgggvyPLwJEybkCpyxxMKyyy6bl0m49dZbc3I/7LDDpu23AAAAYOoC3oUXXpiv//a3v01y34ABAz7152No5+mnnz7J9rnnnjsvmQAAAEBFAe/888+f/mcCAACg/oDXq1evDm0EAAAAHRjwLr744nTAAQfkr88777y8MPnkfPe7353+VgAAANBxAW+BBRZo+XqhhRaa7icCAACgpoC3ww47tHz99a9/PXXp0mWS72ksfQAAAED9pmodvHPOOScvb9DaO++8k04++eQOaRQAAAAdFPBirbuLLrqo5fbIkSPTj370o7Taaqt99mcEAACgvoD3ve99L4e6K664Ir399ts53K233nppt91265BGAQAA0EEBL+bfHXbYYemFF17I1xtvvHHacccdP/uzAQAAUH2RlcktjTDnnHPmsPfSSy+1LH5umQQAAIBOHvCmtDTCEkss0WGNAQAAoAMC3s4775yvP/roo3T77benDTbYIM0666zT/kwAAADUOwcvKmheddVVwh0AAEAJRVbWWGONdP/993d0WwAAAOiIIZqtffjhh+lnP/tZWnbZZdP888/fpviKIisAAAAzUMBbdNFF8wUAAIAZPOA1Cq4AAAAwgwe8MG7cuPTqq6+mUaNGtdm+8sort3ujAAAA6KCA9+STT+Y5eDEX7/33309zzDFHGjNmTJ6P11jwHAAAgBmgiuaVV16Ztt1223T55ZfncBfXO+64Y9piiy06un0AAAC0Z8CLoZlbb711m23bb799uvHGG6fyaQAAAOgUAW/OOefMQzPDfPPNl1555ZU0evToPEwTAACAGWgO3tprr50efPDBtMEGG6RNNtkknXTSSalLly5p3XXX7ej2AQAA0J4Bb9999235+mtf+1paZpllco/eF7/4xal8GgAAAGoNeDH37sILL0wvv/xyWmKJJdJBBx2UFlhggbT88st3dLsAAABozzl4l112WQ503//+91OPHj3SFVdc8RkfHgAAgE7Rg/f888+nn//852nWWWdNK664Yg56AAAAzIA9eOPGjcvhLsw+++xp7NixlTQKAACAdu7B+/DDD9OAAQNabkfAa3077Lrrrp/9WQEAAKg24MWyCMOHD2+5vf7667e5DQAAwAwS8KJqJgAAAAXMwQMAAGDGIeABAAAUQsADAAAohIAHAADQDEVWWnv44YfTXXfdlUaOHJmOOeaY9Oyzz6b3338/rbzyyh3ZPgAAANqzB+/mm29Ol1xySfrc5z6XnnjiibwtFkD/3e9+N5VPAwAAQKcIeDfddFM6/vjj0/bbb59mnvm/P7LIIoukV199tUMbBwAAQDsHvBiK2bNnzzbbxo0bl7p2neoRngAAAHSGgLfCCiukgQMHTjJsc6WVVuqQRgEAANBBAe9b3/pWuu+++9LBBx+cxowZk77//e+ne+65J+2zzz6f/RkBAADoEFM1xrJ79+7ptNNOy5Uzhw4dmuaff/609NJLt8zHAwAAoH5TndDGjx+fPvzwwzRhwoS07LLLprFjx+bePAAAAGagHryXXnop/fSnP02zzDJLGj58eFpvvfXS448/ngYPHpz69u3b0W0EAACgvXrwYg28XXfdNZ199tktlTNXXHHF9OSTT07NjwMAANBZAt4rr7ySNtxwwzbbZp999jxMEwAAgBko4PXq1Ss999xzbbY988wzaaGFFuqQRgEAANBBc/BieGa/fv3S5ptvnhc4v+6669Itt9ySDjjggM/+jAAAANTXg7fGGmukY489No0aNSrPvYulEo444oj0xS9+sUMaBQAAQAf04H300Ud5YfOf/exn6dvf/vZnfwYAAAA6Rw9eLGYel1gDDwAAgBl8Dt7WW2+d+vfvn3bYYYfUo0ePNNNMM7Xct+CCC3ZY4wAAAGjngHfZZZfl64cffniS+wYMGDD1zwYAAEA9Ae/tt99O8803nxAHAAAwo8/Bi+IqrZ155pkd2hgAAAA6KOBNmDChze3HHnts2p8JAACA+gJe62IqAAAAzMBz8MaPH58effTRNmvitb4dVl555Y5pGQAAAO0X8Oadd97085//vOV2t27d2tyOHr7zzz//Mz0hAAAANQS8Cy64oGOeFQAAgGrn4AEAADDjEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACF6Fp3A4CyjN9v27qbMEVv1N2AT9DlkuvrbgLtzGth2nTxWgCYLnrwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBCVVNEcNmxYuuCCC9Lbb7+dZpppprTZZpulrbfeOo0ePTr1798/DR06NPXq1Sv17ds3devWrYomAQAAFKeSgNelS5e01157pSWXXDK9//776ZhjjkmrrLJKuv3221Pv3r3T9ttvnwYOHJgve+65ZxVNAgAAKE4lQzS7d++ew12YY4450iKLLJLeeuutNGTIkNSnT5+8Pa7jNgAAADPIHLw333wzPf/882nppZdOI0eOzOEvxPWoUaOqbg4AAEAxKhmi2TBmzJh01llnpX333TfNOeecU/1zgwYNypfQr1+/1LNnz45qYpG6du3aNPvsjbobMINqz78Px6D+Y+A4TDuvhfo1y/tVs70/d1aOQefgOLTz/mzfh5uycePG5XC34YYbprXXXjtvm3feedOIESNy711czzPPPJP92SjKEpfWRVuYevHmYZ/xSfx91M8x6Bwch/o10zHw/lw/x6BzcBw+u4UXXrjeIZoTJkxIF110UZ57t80227RsX3PNNdPgwYPz13G91lprVdEcAACAIlXSg/fUU0+lv//972mxxRZLRx55ZN62++675+qZsUzCrbfempP7YYcdVkVzAAAAilRJwFt++eXT73//+8ned8IJJ1TRBAAAgOJVXkUTAACAjiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhehadwMAAICONX6/bTvtLn6j7gZ8gi6XXP8J93ZOevAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIWYue4GAAAA0D4EPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhehadwMAACjX+P22rbsJU/RG3Q34BF0uub7uJjCD0oMHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhaikiuaFF16YHnjggTTvvPOms846K28bPXp06t+/fxo6dGjq1atX6tu3b+rWrVsVzQEAAChSJT14X/7yl9Nxxx3XZtvAgQNT796907nnnpuv4zYAAACdPOCtuOKKk/TODRkyJPXp0yd/HddxGwAAgBlwDt7IkSNT9+7d89dxPWrUqLqaAgAAUIRK5uBNr0GDBuVL6NevX+rZs2fNLZqxdO3atWn22Rt1N2AG1Z5/H45B/cfAcZh2Xgv1a5b3q2Z6f/a+MG28L3QOPWfA12htAS8KrowYMSL33sX1PPPMM8Xv3WyzzfKlYdiwYVU0sag/TPuMT+Lvo36OQefgONSvmY6B92c+STO9FjqzYZ30OCy88MKdb4jmmmuumQYPHpy/juu11lqrrqYAAAAUoZIevLPPPjs9/vjj6Z133kkHHnhg2mWXXdL222+fl0m49dZb8xmsww47rIqmAAAAFKuSgHfooYdOdvsJJ5xQxdMDAAA0hdqGaAIAANC+BDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhZq67AQAAALQPAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEF3rbkApxu+3bd1NmKI36m7AJ+hyyfV1NwEAAIqhBw8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEF3rbgAAQEcZv9+2nXbnvlF3Az5Bl0uur7sJwDTSgwcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIXoWncDHnrooXT55Zenjz76KG266aZp++23r7tJAAAAM6Rae/Ai1F166aXpuOOOS/3790933XVXeuWVV+psEgAAwAyr1oD3zDPPpIUWWigtuOCCqWvXrmm99dZLQ4YMqbNJAAAAM6xaA95bb72V5p9//pbb8XVsAwAAYAabgzdhwoRJts0000yTbBs0aFC+hH79+qWFF164w9v2md14f90tIDgO9XMMOgfHoX6OQefgONTPMegcHIemUWsPXvTYDR8+vOV2fN29e/dJvm+zzTbLwS4ufHbHHHOM3dYJOA71cww6B8ehfo5B5+A41M8x6Bwch4IC3lJLLZVee+219Oabb6Zx48alu+++O6255pp1NgkAAGCGVesQzS5duqRvfetb6dRTT80VNTfeeOO06KKL1tkkAACAGVbt6+Ctvvrq+ULHiSGu1M9xqJ9j0Dk4DvVzDDoHx6F+jkHn4Di0r5kmTK7SCQAAADOcWufgAQAA0H4EPOgAMaf0hhtusG8BAGiuOXi0n8suu+wT74+CNlRj5plnTvfff3/aZptt7PKag/Y999yT1ltvPcehxmNw/vnnp0MOOcQxgInGjh2bhg0b1jnX9G0CMTPpjjvuyBXcd9ppp3ws3n777bT00kvX3bSmct5556Xvfe97n7qNaSPgFWTJJZesuwm0stxyy6VLL700h4vZZputZbvjVG3Q/stf/iLg1XwM3nnnnbwUTteu3nLqEGvIzjTTTFO8/+ijj66wNc0tTvxdffXV+fVwwQUXpBdeeCENGDDAMajQL3/5y/x6eOyxx3LAm3322fN79WmnnVZlM5reK6+8MsnJwOeee67p90t78W5bkC9/+cttbr///vv5n1j886J6Tz/9dL7+/e9/32b7iSee6HBUqHfv3un666/PIa/1a6Fbt26OQ0V69eqVjj/++LTGGmu0OQZ6uKux7bbb5ut7770391RsuOGG+fZdd92Vjw3Vueaaa3KQ+NGPfpRvf+ELX0hDhw51CCr0zDPPpJ/+9KfpqKOOankviMBNNa677rp8iZ7sffbZp6VXNU4AqqTZfgS8Ar300kt5SNTo0aPzi2aeeeZJ3/3ud60xWDFBrnO47bbb8nX05DXEiY94jVCN7t2750v8P4oTT1RrxRVXzNfRU3TSSSe1bF9zzTX9n6ph/d8555yz6qflY8cgeosavdqjRo36xB5u2tcOO+yQL7/5zW/SN77xDbu3gwh4BfrFL36R9t5777Tyyivn2zEM4eKLL04//vGPa25Zc4kz5b/97W/TiBEj0nHHHZeHI0Sv3iabbFJ305pKDIOiXjvvvHO+HjNmjBEFNYoPsm+88UZacMEF8+2YgxTbqM6iiy6a7rzzzhwwXnvttXTzzTenZZdd1iGo0FZbbZXOOOOMNHLkyPweHfO0d9ttN8egYhHu3nrrrdyDPX78+ElOSDF9BLwCffDBBy3hLqy00kp5G9W68MIL87DZGIoQPve5z6X+/fsLeBWLv/2oaBoT6Q844ID8oerVV1/NwwWpRpzY+PnPf54DXlzHvKNBgwalb3/72w5BhWI4VAwNbAS8+GC13377OQYVimJn1157bZplllnSOeeck774xS+mHXfc0TGoUAxRjrnwjzzySL595JFHps9//vOOQcV+/etfp7vvvjvv+0YPalwLeO1DwCvQAgsskP7whz+kjTbaKN+OalHmWVQvCkvEvK+BAwe2DAuJghNUH7TjzbwxJ3L++edPP/vZzwS8Cl1xxRXpBz/4QTr99NNb5h098cQTVTaBiVZdddV07rnnpv/85z95fyyyyCI5aFCdKLi1++675wv1iOkr8847b9pggw1atikCVb377rsvnX322f4HdRABr0Df+c53cmGPs846K895WWGFFdJBBx1Ud7Oa8o08Ql7jzFQEDHMvqhdD0vr27ZsLSoRZZ521hlbQs2fPNjvByY76erOj5+7AAw/Um91JKprG+8JSSy2VC0z4/9TxompsjOiI4irxGendd9/Nc4Qj9MUoD5WuqxEjCWJoppNMHUPAK1D804phIO+9917+EKWKZj1iHmT0WLz++uu5gmDMdTnssMNqak3zispcUa2r8aEqjody/dWKXtOnnnoqH4M4U37TTTfl3iPq6c3+97//3XJc9GZX/6E23gvWX3/9fDuGqEWwiGHjMVfeGmAdL4bFfulLX8o92uFf//pXeuihh9K6666bl1D4yU9+UkEriJMZMTw2Kl23fk+2ZnP7EPAKr6IZ5p577nTwwQenxRZbrOaWNV/Qjvku8cYdZwljUduYe0S1dtlll3TqqafmM7YxPC2Chh7tasU8rximGRPqo+dolVVWMf+uBnqz6xfvAZOrZBrbnACsRqy1tv/++7cJfFFsJeaofvjhhxW1gvjbjwsdQ8BrkiqasU0VzWrFENkYChJV08Ljjz+eF1ON7VQneiyOOOKI3GsRQXvffffNxT6ozrPPPpsOOeSQNtv++te/pi222MJhqJDe7PpF712cbGoMWY6vG5VMjSyo7uRrzI1v3Ys611xz5cqmho5XJ4rQxeiaeA3ECXDal4BXIFU0O0+vRZRijpAXZwzjDOGxxx5bd7OaTixoG/t99dVXz7djuYqoZipoV+ePf/xjnmfROOn0pz/9KZ94EvCqpTe7fnvttVcesr/QQgvlE06xVEVUk42TTn369Km7eU0hTjZFIbp4f45jsPzyy6fvf//7OeDFfG2qcf/996err746D9uP5YyidzvW6ozPTEy/mSb+cU+Y/oehM4l/WksssUSbKppxBv2oo46quWXNJwqrRO9pfLiNkBGLzlOtBx54IAeK2P8xXDaGL8cbfFRypBrRQxFBe88998xzXaKK46GHHqrHokLx4TXW+4r5Lo3e7GWWWcb/pBrEMMBGJdPouVBYhWYUQS6GJ8dUlkaF5cMPP9zJ13aiB69Aqmh2ripp0aMaVdJi/a/g7FS1oucuzhDGEOX3338/T+qONQmpTpzYiBNMp5xySh4yG2/iH68kSMeKoWd/+ctf8tItjd5s6tFYizOC3osvvpi36b2r9oRTnPSL0RwxRLAhwgbViaWjPl5Z3PtC+xHwCq6iST223Xbbep6YNi677LI2tyPcxRqRN998c77tNdLxYi5w6zfsCNpR6CN6kmL7lVdeWUEraIjeu+uvvz6HvNbVleM9g2pcc801eT52hIvVVlstPfjgg3mIoIBXnSi2Fa+BGN0RUyluv/12Pdk1iPoEd955Zx5dECc94r152WWXraMpRRLwChRnBv/85z/ntY5ijZEGZ6eqseKKK+Z/WFG5MeZaUI+Pr2VkbaPqXXXVVXko4PDhwydZB4/q3Xbbbfk6evIaImjHsGWqESc3GnOzo5rv22+/nS666CK7v0KxPu0mm2ySl2uJ9+u4+HxUvTjJeu211+YpLOecc06uZrrjjjvW0JIyCXgFigISm2++edp0001VhKpxOFTMq4i1CC1uXl+FrgjajTl31CMCRHygjTl41CsKGVCveF+I94e4xPtDrIEXhVaoTqNaaSxuHr14cR1LuFCt2WabLe2+++75QvsT8AoUbxyq09UvzkrFXKNY8yv+kTUYGljtayHO1sbQQCXI6xPFPJ555pm09NJL19iK5vXoo4/mCqb33nvvZO9fe+21K25R81pqqaXSu+++m0/AHnPMMXmorNdFtb7+9a/ncB0VTS+//PL8dayBRz11Cj5OnYL2oYpmQRoLm8ewgzgr+KUvfSmHjAbzLKoV4/qn1LNEdaKK6fPPP5/WWGONNvOOttlmG4ehIlF6POZY9OrVK5/siGGb8QZ/5plnOgYV+P3vf5+XSLjwwgsne38MFaR60XMXc4MXX3xxu5+mEXNQQ5xwiiHKG264Yb5911135feIb3zjG3U2rxgCXkEOPvjg/KFpcitfmGdRj+g5ijmRjXLYepHqKWowOTvvvHPFLWleMR94cuLNnGoDRRQa+rRtAB0t5j2edNJJn7qNaWOIZkHMr+hcYiHnOCaND7HDhg3LITwmdFOdRpCLM+VxoqN1Lx7ViNdALGL75JNP5ttRNdA6hNU766yzJpkLObltAFUsVxFVlRdccMGWk02xjfYh4BXof//3f3OX91xzzdUydDO6vrfccsuaW9Z8FQR/+MMf5p67ED15USnKh6lqvfTSS7nQSmMI89xzz52++93v5hLNVCOGjf/tb3/Lw8bDeeedlzbbbLO01VZbOQQViEW1X3755TzXqPU8vDjpEWuxAVQt5j3GIueNgBcjPWLZCtqHgFeg+CD1la98pc3cu9gm4FUrlqhohLsQX7detoLq5uDFemxRZKLRs3rxxRfnhc+pxq233pqXDWn0nm633Xb55IeAV404uRTVAqO4xz//+c+W7XE8DjjggIpaQWicaGotjoPh+9W54YYbJtkW1a5jKR0jC6qz6qqr5jUJ4wRUWGSRRdrUjWD6CHgFijl4jSIGIUrFx1wwqhVvFj//+c/TRhttlG/fcccd1mKrwQcffNAS7sJKK62Ut1Gd+H8UFU0b4uvJzRWmY6y11lr58vTTT1tIuGZRITCG68eJ13gNROiOMv1RGC3CtvU6O96zzz6bnnvuuVx4K8TJj6huesstt6R11lknn4Ci4w0ePLjN7RdffDFf9+nTx+5vBwJegWKxyMZaeBHy/vrXv+YzJVQrhhrEgsI333xzfiNfYYUV9KLWIApI/OEPf2gTtBX3qNbGG2+cfvCDH+SQEYYMGZIXGqZaESpOPvnkNHLkyDz3Lj5Q3X///RYXrvj9OYYqN96T//Wvf6WHHnoorbvuuumXv/xl+slPflJlc5q2FzWmSjRGFESF2Xg9RHGPCOACXnVBu2Hs2LF5OZclllhCwGsnAl6B9thjjzRo0KAc7CJYxBtKrLlDtZ566qkcspXjr9d3vvOdXCY+3sDj9RBFbpSFr1a8BmK/N4qsxP6PN3KqFUOTY+2vGLYcojx/DJHacccdHYqKRM/R/vvv33I73p9/+9vf5vlI5kNWI3pQWw+J7dKlS94Wi9AbIlidj68JHHOEY3427UPAK3ih87jEmarhw4e3GR5FdevgXXLJJfmsefTeReXAuFiPsFoxBMri8vUaMGBAfg1Er50qpvWJs+QfX1Tbe0O14v//wIED0/rrr59v33333bkgWkylcCyqEfs+RhSsueaa+XbMS41tY8aMSZ///OcragUfFwH79ddft2PaiYBXoKhKdNRRR+U3jCOPPDLNM888+ex5nCGkOlGpMbz11lvpnnvuSZdeemkaMWJE+t3vfucwVCgWd45jEHMsImTEZbHFFnMMKhRDYu+88850+eWX54DXOA6NIZtUIyrIxgeoxvzs+L8U87+oziGHHJKHjJ9xxhl5REGc9Pv+97+f36/79u3rUFRgp512SquttloeZRPHIKZTxPtD4/hQjX79+rX8L4rj8Morr+Q5kLQPC50XKMLd6aefnitnRu9djC8/4ogj0plnnll305rK3//+9zwkLcr0xwereCOPD7XLLrts3U1rOlFk6JlnnkmPP/54nkgfZ2ojbFCtt99+O/dY/PnPf849q7GUCNWJNadieGZ8sI1eo5ifGh9ozUml2USgjv9Hcd3Qs2fPGlvUfOL9uCF6r+P/0Pzzz19ji8qiB69AUYo/eor+8Y9/pN12263u5jStK6+8Mq/vEvPwonJjfJiiehGyn3jiiXwdoSIqp0XYpjoXXXRRPjsblQLjJMfhhx9uDl4N4v/R8ccfn09wxBnzGBIVgVvAq3bJijjBEWt+tV4258QTT6ywFc0tCp9FL2r8P2pU9I2eJCfBqxXVS/fcc8822371q19Nso1pI+AVOvwg1pyKD7Ex3yLO2i600EJ1N6vpxJDMWFw4wkUMy3zttdfyWnjf+9736m5aU4kPTjH8Zvvtt0+rr7669aZq8M477+Qz5dFrFHOQokc7ChtQjSheEBV9Y6hyDIvt3bt3vh1BIwqtbLjhhg5FRRoVrqPwmTl39bjpppvS2Wefnf8PUZ9HHnlkkm1RUVbAax+GaEIHfqiKoVAxDCF6j0aNGpWWWWaZlrl5VCN67RrHIcoyx4eqOA56t6sXvXhRFv7GG2/MgS969uh4MWQ/wnUMD48PVfGaiGHL3/zmNy3sXLEowx8l+qlPLIfwwx/+0EmmmkSF9zjB9PHOh/fffz8tt9xy5kG2Ez140EFOOOGElsqZX/nKV4wtr0ljrlGUwY4ejAh78eGW6kSVuujJjkuEi1h43jDZ6sQHqVgmJETP0f/8z//k4kNzzDFHha0gxBDx+HAba+G1LsmvunJ14v0gitHFiI7Wx8CSRtXYYIMN8jqQv/nNb/KyXg3x/8jroP0IeNBBjOfvHGJIbAyNjUARQ6NiDbbWayDR8R588MFcyXfrrbdOPXr0sMsr1vrvPXqw4wOucFePwYMH5+vrr7++ZVvM/zr//PNralHziWIqcYkTfU72VW/OOefMlxhFM9988+WQ/dhjj6UXX3wxL3IeJ2WZfoZoFiaGPUXp6/XWW6/upkCnYH0pmt2uu+7asv5gFJSI9fBmm222luISURAKoEqxjFcslRAFh6JuRPRuR62CY4891oFoB05jFybOzsbwDwEP/u810WD+C8260Dz1evTRR/PQ5HvvvXey96+99toVt6j5XHHFFWnfffdts/5aa/H+QLXvzVFsK14TMbpjq622yst80T4EvAJFhbQY/hEhr3HWNhjbTLOLHguAqkWRpwh4MR91cgS8jrfRRhvl62233baCZ+PTRLi7884785rBjXDdeukQpo8hmgU6+OCDJ9lmjH896x398pe/TCNHjswFDmJ8+f3335923HHHGlpDiOUqVM+sRwwLjEI3MR8SmpUh4/W777770mqrrdamwAr1VFWOippR3TcKr7z55pt5Xc5Y0ojpJ+BBB66/ttdee6Vf/OIXuUx5iAWeG9XsoFnEiY2rr746FzS44IIL0gsvvJCHDRoSRbP5zne+kysIxgib6NGb3FBBOlZUkI0hsyussEJaf/310xe/+EVLJlAcQzQLFB+i4qxIlCQPK620Utpss81UDqyhxyIWmm/NwrbVi/H9v/71r3NPalBYonrXXHNNOu2003Jp8vCFL3whT6yHZnPOOefkEx4xVz7WgYxS/REyLBtSnaikHJ+TYlHtGCIYI21WWWWVdOCBB1bYCuhYAl6B4p9V/PPacsst8+0Y3xzb/POq1txzz51ef/31ljO0Ud20e/fuFbeCX/3qV7mn6POf/7ydUeNciyiLDc1u1llnzb13cRk9enQu/BGjPRTCqX7pkOhJbZyMHTJkiM9IFEXAK9Czzz6bzjjjjJbbMQwkytFSrVhMOIZn/uc//0kHHHBAXnsq1mSjWrHOjnBXr0UXXTSfKY/5R1EG++abb87zLqBZC67EXKNYH3KppZZKffv2rbtJTSV67u6666689lqsz7nJJps4BjV46aWX0mKLLVbDMzcHAa9AMQwweo4WWmihfPuNN94wNLAGCy64YDr++OPTmDFj8rBACwvXY8kll0z9+/dPa621VptJ9arWVedb3/pWuvbaa/P+jyFqMedFsSGatQhaDFFed91105577tmm0jXVuP3223MP6v7776/QSo0uueSSPNrsy1/+ci6yYoHz9qXISoEeeeSRPIk4AkYEi6hcFxO7oyeP6rz77rtp8ODBea5R69K/8WGX6sRrYUrzMACq9N577xmuDP9fjOi47bbb0j/+8Y9cs2DjjTfO8yGZfgJeQeIFEmcFo9RszPWKMv0R8BZZZBFnqWrwwx/+MC2zzDJ5CELrSmlxtgqabdj4ddddN8nJjjPPPLPGVkF1/vSnP6XtttsuXXbZZZO934m/jhcjak455ZS09957t3lPVnirXjF0P5auuPzyy/PJjzgeu+++u1E208kQzYIMHDgwB7wow//Tn/40Lb744nU3qal9+OGHaZ999qm7GU1v+PDh+UPVU089ld/Ul1tuufTNb34zzT///E2/b6py7rnn5iVDPn6yA5pFnGhtDBmnHhHuwlVXXeUQdAKxNnD03sVc1N69e+diaPH6eOutt/IJctMopo+AV5Bu3bqlk046KffgRcD7OGtOVWvDDTdMgwYNSmussUabHtQ4TlQ7RDPG9x922GH59h133JG3xdlcqjHPPPOkNddc0+6macXff/RUvPzyy/lkB/WIYxBF56xHW7848brpppumb3zjG7m6bEOPHj3SbrvtVmPLyiDgFeTYY49Nzz33XDr//PPT1772tbqb0/SiDHOU6I+haQ3RexHHh+qMGjUqj+tvPUT2xhtvdAgqtMsuu+Q1v2IesEI3NHMBtHiPpt5jEKObojZBz549HYoafelLX0obbbRRm2033XRT2nrrrSfZzmcn4BUWKKL0+I9//ON8xpx6RYiIoWmORb1i/8dakNGLF6Jcf6xRSHViGE7MCY6KafEBq8EQHJrNEksskUfYxHSK2WabrWW710J1RowYkUd0RFGP1sfAKKdqxfvyV7/61UkqnEbAY/oJeAUSKDqHWHut9ZsH9YgKspdeemm68sorcw9qnASJbVQ718KQKEh5cfM4wfToo4+22R0CXnV23nlnf4o1ipOscfn4dKJYUsrJ1/ajiiZ0kFhs/pVXXkkrrbRS7l1tUC2NZhPDM7fZZhsLzgM0uaimHOHuN7/5Tdpjjz1atseakDF8tkuXLjW2rhwCHnSQGGowOZZJqMYf/vCHT7x/p512qqgl9O3bN73++utpgQUWyHPwGmXJLZNAs5jS8ggNTvx1vI8vj/BxMcoDSmGIZpO8kcTaIksttVRaa621amhRcxLk6jW54bEffPBBuvXWW9M777wj4FXouOOOq/LpoNNpLI8Qy7XEyI711lsv377nnnvyvDw6XmN5hAEDBqT55psvF/KIk00xXPD99993CCpiPcJq6MEr0MUXX5wLGqyzzjr59r333puHRsV6YAsuuGDad999a25h2X72s5/lCdyHH374ZM8W6rWoXrx5R3WuCHdR3CCqzM4777w1tKS5vPfee/nkUsw7mhxLhtBsYimjH/zgBy3D9qPw0KmnnppOPPHEmlvWXCecfvKTn3zqNpiR6cErUAyFOuGEE1rGMW+xxRa5smacNYnQQceKRbTDMcccY1fXLILFDTfckNe+69OnT57QLVRUJ6rIxusgqtPFyY44W95gyRCaUSziHMUkGv+H4uvYRnWikm+8J6y//vr59l133dWmui/VfVadf/7587D9xx57LBfjivfpueaayyFoBwJegeLNIoaixZnzEF9HWeD4B9Z6DSo6Rvfu3fP1X/7yl7Tnnnu2uS/Wxfv4NjrG1Vdfne677768kGpUcIwJ3FSrcZLjggsusOthou233z4dddRRufhWePzxx1V1rNghhxySrrjiinwJyy23XN5GteJ9uV+/fjnoRSGuNdZYI58UjDWdmX4CXoG22267dOSRR+Y3kDhj/sQTT6Qddtghnyns3bt33c1rGo888sgk2x566CEBryLRcxfDoK699to2i803CnyYUF+dk08+OY8q+LRtULqNN944rbbaaunf//53vh1VBGM+GNWJYk8RsqlXdDrESLM4ERtr32211VaOSzsS8Aq0ySab5DeQZ555Jn+Y3X333VOPHj3yfXvttVfNrSvfX//619x7F2WAjzjiiDbzwOJMIdWIifTUa+zYsfkSRW1az8OLuXkxqgCaUQQ6Bc86hxg+3notNqoT4S4K3AwePLhlkfnx48c7BO1EwCtUBLtY8DxeLNH9HZcVV1yx7mY1hQ022CCtuuqqk6zxMsccc5j/RVMZNGhQuvHGG3OYi+GajTl4MXx8yy23rLl1QLNrPS+Yah100EH5hHiMMIte1TgpvuGGGzoM7UQVzQLFPK9//OMfuXJmo4pjXDfOkFDPB93NNtvMrqcp3XzzzXn4DTSr+PAaH2LpXH73u9+l3Xbbre5mNJ2PPvoonX/++eY+diA9eAUaMmRIOvvssxVU6URuueUWAY+m1TrcxTIuBxxwQI2tgXoKSsRQQHNPOxfhrr75dzF0P5YJaSwZQvuyVwsUa93F0EwVMzsPw0Dgv5577jm7gqZ8D7jmmmvSa6+9lgtAfdw222xTQ6uaU6wN/Otf/zqNHDky31Z4qx69evXKy3dF9czWVa69FtqHgFegWWedNVfRjIqZrc+MfOtb36qxVc3N8Fj4r5gbDM3m0EMPzdUC4+RrFNyi3mks8Z4c01iod0mpuETA9ppof+bgFej222+f7PYvf/nLFbekOU3u7Gxrzk7RbGJO8Lrrrvup26B0Dz74YK5yTX2i1+iUU05xCDqJWMLLOrXtTw9egQS5ejkTBW0NHDhwkjA3uW1QulgqJ9bgjPVpQ1S33mmnnXJlWaqx5JJLpv79++elKlpPZVl77bUdggo9/fTT6ec//3kOeHH9wgsv5IJ03/72tx2HdiDgFejggw9uqZ7ZWlQsouPtvPPOdjP8/96KuLz11lvpsssua3MSJCbZQ7O58MIL02KLLZb69u2bb//973/P21qvmUrHiv8/s802W3r44YfbbBfwqnXFFVekH/zgB+n000/Pt7/whS+0nPhg+gl4BerXr1/L1x9++GEeCtV6kWE6VusPspNjLiTNIuZXxNny+++/P1+3XhNyn332qbFlUI833nijTZiLE4IxZ55q11+jc+jZs2eb2078tR8Br0Bzzz13m9tf/epX85jzXXfdtaYWNZfWH2ShmcUZ2eitiDPlho7Df4ugPfnkk2n55ZfPuyO+jm1UZ/jw4flE7FNPPZVHO8Ww2W9+85tp/vnndxgqFPu7cQxiuYSbbropLbLIIo5BOxHwCi9DHtWJnn322TzGmWr4IAv/x3pH8H/222+/dMEFF6T33nsv355rrrnytAqqE0NiN9hgg3TYYYfl23fccUfeFifCqfa1EMM0Ywj/gQcemFZZZZX0P//zPw5BOxHwCnT11Ve3+XAVa400xvtTnZNOOmmy20888USHgaZivSP4v17tM844oyXgKa5SvVGjRqWNN964zUnZG2+8sYaWcMghh9gJHUTAK5AA0TnstddeLV+PHTs2L67apUuXGlsE9bDeEbQl2NW7FmcUt4levHDnnXdOMrWFjvfDH/4wLbDAAmm99dbLBW6iN5v2Yx28AsWZwWuuuUYZ5k4avqfUswcAdKxhw4alSy+9NJfpj/lfyy67bJ6DFyMNqNYzzzyT7rrrrjRkyJC88HyEvY022shhaAcCXoHOPPPMXNigT58++XacqXrxxReVYa5Y68qlH330UZ4befnll6dzzjmn6qZA7UOi/vSnP6VXXnkl92Y3GG1As4nK1q3XXpvSNmi294irrroqz4ccMGBA3c0pgiGaBVKGuXM4+uijW76OoZkxFOE73/lOjS2Cepx77rn5zOwDDzyQJ9bffvvteZgUNOOwtJ/+9Kefuo2ODROxoPbQoUPT+PHjW7ZbPqH60Wb33Xdfuvvuu/Pn1lh4/rTTTqu4FeUS8AqkDHP9wz9ibZeolAak9M4776RNNtkkl8FeccUV80XvHc3k7bffztUCowf7+eefzxWuG4tuf/DBBzW3rrnEwtqxTEXv3r2tu1ajWP8xQt1OO+2Uh8nSvgS8AinDXK+okNY4GxvDZVsvagvNqGvXri3FVqIXL67jwy40i4ceeigNHjw4r8EWQ9EaZp999rT77rvX2LLmE4F6zz33rLsZTe/888/PcyDpGAJegWL+nTLM9WmcmQ1vvvlmjS2BzuHrX/96Ho4TlWVjHmp8vc8++9TdLKhMlOKPyz333JPWWWcde75Ga6yxRj7RtPrqqzsONYi17/bdd998InxyAa/19BamnYBXoO9973v5DSTWeYmqRFSr9T8sZ6doZjEc7ZZbbkmvv/567rGLYZqGZtLs4SLK8sfJvyi+1RDD1KhGDBW/7rrr8siCuMRJ2XivvvLKKx2CCjSqZG677bb2dwdSRbNAMaY/ys5GIYP4xxVBLwocWHenGrvuumsedhP7Pj7gzjbbbHm7NxGaTf/+/XOBoRVWWCE9+OCDuQx5lCOHZnXqqafm9+Ill1yyzfyvr33tazW2CiiNgFe4xx9/PJfljyFRsZBknCVcaKGF6m4W0AQOP/zwdNZZZ+Wvo1rdcccdp1ogTa31awKa2ZNPPpnXbI7CdPH+0DgJHnPzmH6GaBYohn3E+PLbbrstlwGOM4MbbLBBfjFFCVrrsAFVFlcJ0ZMHzS6qBb700kt5rjw0s4suuijPxf54bzbtQ8Ar0CGHHJJWWmmlPL55ueWWa9ke8/KiRw+gCi+88EJLMZXGkOW4bbgyzSpOtMb0iVgXNRY3b7wWouIyHSvmPcZ+p3OIocqrrbZa3c0oliGaBRozZkyeAwYAdB4xqmZyYn4qHSuqM0blxpNPPjmdcMIJdnfNfv3rX+cRZzF9qPVoj+jRY/rpwSuQcAcAnU8EuejFe+2113IBtFGjRuWTsnS86C2NOV+x72+44YZJ7t9mm20chgo988wz+fq5555rs12l5fYh4AEAVCACxrPPPtsS8MaNG5fOO++8dMopp9j/HezQQw9N9913Xy7oEdXGqZcg17EEPACACkTAOP3001sWc+7Ro4ewUZGFF144bb/99mnxxRc396tGk+s9nWeeedLyyy9vjmQ7EvAKFEsi/P73v8/DQMKKK66Yl0ewDh4A1CfmGkVRlbgEwzOrF8XnYlHzJ554It/2Galak+s9jbmp1157bdp5553T+uuvX3GLyqTISoGiGleUYO7Tp0++/fe//z29+OKL6Ygjjqi5ZQDQvK6//vr0+uuvp4cffjj3JsVyRrGM0VZbbVV305qGz0id0+jRo/NQ5SiEw/Sz8ESB3njjjbTLLrukBRdcMF/ijEhsAwDqE8sXxZJFUTnw1VdfTbvuuqtwVzGfkTqnbt265UI4tA9DNAs066yz5uGZMZ45xNexDQCo1yqrrJIv1MNnpM7p0UcfTXPNNVfdzSiGIZqFLi58wQUX5Ll4cTYkzoocfPDBeWIxAFCtvffeu2Xe3eTEnDCq/4wUIlT4jFSdww8/fJLXQgzP7N69e/rud7+bFllkkQpbUy4Br2CNf16KqwBA/QYMGJDmm2++tNFGG+UTsHfeeWcuOrHddtvV3bSm4zNSPaKgSmsR9qIjwhrO7csQzUL/acVaOypEAUDn8a9//Sv95Cc/abm9xRZbpOOOO07Aq4GT3/Xo1atXTc/cXBRZKdCFF16Y5phjjtS3b998ia9jGwBQn5lnnjndcccd6aOPPsqX+Dq2AbQnQzQLdOSRR6YzzjjjU7cBANV588030xVXXJGeeuqpljXZ9t13Xws8V+jDDz9Ms8wyy6dugxmZIZoFUiEKADqfBRZYIB111FF1N6Op/fCHP5xkrbXJbYMZmYBXoP3222+yFaIAgPpMabrEQQcdVHFLms/bb7+d3nrrrTR27Nj0/PPPt6y5FkVuPvjgg5pbB+1LwCt04nAMx2xdISqGhQAA9Vl99dXbDAu87777cnl4Ot5DDz2UBg8enIYPH56uuuqqlu1RvXH33Xd3CCiKOXgFOvrooycZajC5bQBAfaLQyimnnJJOPPFEh6Ei99xzT1pnnXXsb4qmB68g//nPf9LLL7+ce+7uvffelu0x/CDOFAIAncfrr7+ehg0bVnczmsoaa6yR1x+MkU0RsBt22mmnGlsF7UvAK8irr76aHnjggfTuu++mf/7zn22GHxxwwAE1tgwA2HvvvfPCzg2x6Pkee+xhx1To9NNPz1NXllxySZUzKZYhmgV6+umn07LLLlt3MwAAOpXDDz88nXXWWXU3AzqU1TULJNwBQOdz8sknT9U2OvYz0ksvvWQXUzRDNAEAOlCU5o/LO++8k0aPHt2yPebMjxgxwr6v0JNPPpluv/32vCZhLG4eyyXEsNkzzzzTcaAYhmgCAHSgm266Kd144405zPXo0aNlDbaYC7bpppumr3zlK/Z/RYYOHTrZ7b169XIMKIaAV5AbbrjhE+/fZpttKmoJAPBxN998c9pqq63smE7Qi/faa6+ljTfeOI0aNSqNGTMm9+hBKQzRLEgsh9Copvnss8+mNddcM9+OiporrLBCnU0DgKb1zDPPpJ49e7aEu1hwO5Yzim277LJL6tatW80tbB7XXHNN/ozUCHjjxo1L5513Xl6PEEqhyEpBdt5553yJMf6xqHmUY45Lv3790vDhw+tuHgA0pUsuuSR17frfc+qPP/54+s1vfpM22mijPETz4osvrrl1zeW+++5LRx99dJptttny7Rgy2zhBDqUQ8AoUi6Y23khCfD2lMecAQMeKBbUbvXR33313nne3zjrrpN122y0vdk514jNRFFVprEcYwzOhNIZoFijOCh533HFprbXWyv/A4mxVbAMA6gl448ePT126dEmPPvpo2n///dvcR3XWXXfd9Itf/CK9++67adCgQem2227LgRtKoshKoZ577rk8iTjE/Lslllii5hYBQHO69tpr04MPPpjmnnvuPMomplHECdjovbvgggvM/6rYww8/nP71r3/laqarrrpqWmWVVapuAnQoAa8grdfWmRyTuAGgHk8//XR6++23c5iYffbZW4qixRDBJZdc0mEB2o2AV5CDDz44nxFsrK/TGF/eWMTz/PPPr7N5AAC1iKJzjc9Fk3PllVdW2BroWAIeAABNYcCAAWm++ebLtQniBPidd96Zq2hut912dTcN2o0qmgX5z3/+0zL/bnIXAIBmFnPvttxyyzTHHHPkZSq22GKLvCYhlEQVzYLccMMN6YADDkhXX331ZO8/8cQTK24RAEDnMfPMM6c77rgjrb/++vn2XXfdlbdBSQzRBACgKbz55pvpiiuuSE899VS+vdxyy6V99903LbDAAjW3DNqPgFeo+McVi5vHujsNffr0qbFFAABARzNEs0DnnXdeeuONN9IXvvCFNsMOBDwAoJldeOGFk91+0EEHVdwS6DgCXoGioMrPfvazTywHDADQbFZfffWWrz/88MN03333pe7du9fYImh/Al6BFl100byYqn9YAAD/Z5111mmzO6LYyimnnGIXURQBryD9+vXLvXZjxoxJhx12WFp66aVT167/d4iPPvroGlsHANC5vP7662nYsGF1NwPalYBXkG233bbuJgAAdFp77713mykssej5HnvsUWOLoP2polnYWagYmrn88su32f7444+nHj16pIUWWqimlgEAAFWwsmNBYl2XOeaYY5Lts802W74PAKCZnXzyyVO1DWZkhmgWJNa9W3zxxSfZvtRSS+X7AACa0dixY/PlnXfeSaNHj27Z/t5776URI0bU2DJofwJeQeIf17TcBwBQskGDBqUbb7wxh7ljjjkmTZgwIW+fc84505Zbbllz66B9CXgFiZ66+Ae22Wabtdl+6623piWXXLKmVgEA1GvrrbfOl5tvvjlttdVWDgdFU2SlIFFg5cwzz8xLIzQC3bPPPpvGjRuXjjzyyFwpCgCg2TzzzDOpZ8+eLZ+FBg8enO699968bZdddkndunWruYXQfgS8Aj366KPp5Zdfbln0fOWVV665RQAA9Ym1gI8//vgc5KK6+DnnnJO++c1vphdeeCH95z//SYcffrjDQzEM0SxQBDqhDgDgvz766KOWXrq77747bbrppmmdddbJlxjlBCWxTAIAAMUHvPHjx7eMdGp9Ijzug5LowQMAoGjrr79++tGPfpTmnnvuNOuss6YVVlghb3/99ddzJU0oiTl4AAAU7+mnn84F6VZZZZU0++yz522vvvpqGjNmjGrjFEXAAwAAKIQ5eAAAAIUQ8AAAAAoh4AEAABRCFU0AZggHH3xwLpAw88wz5wIJq622WvrWt77VUiyhM/nJT36Snnjiifz1uHHj8nXXrv99y91www3T/vvvX1vbACibIisAzDAB74ADDsgV8N5666106qmnptVXXz3tscceU/0YEyZMyJcIiVW54IIL0vzzz5922223yp4TgOalBw+AGU6PHj3Sqquuml5++eWW8udXXXVVeuWVV1KvXr3Svvvum1ZaaaV8X6x9tdxyy6XHH388Pffcc+mss85KTz75ZPrDH/6QRo0aldfFivAVPWux4PF1112X/va3v6WxY8fm54hewlgn680330zf/e5300EHHZQGDBiQ7//qV7+avv71r091u0877bT8mFtttVXLtiOOOCLtsssu6Utf+lK+jrbfdNNN6f33309f/vKXc4BtBNJbb701/fnPf849mUsvvXTuCYzfFwAazMEDYIYzbNiw9OCDD6YvfOELuTevX79+OWhddtllaa+99sohLsJbw9///vcchiIEzjPPPOnyyy9Pxx13XL794x//OD9OuP322/PlxBNPTOeff35eH+vSSy9t89wRDs8555x0/PHH55AYoXJq9enTJ91xxx0tt1944YXc/uiJbBgyZEj+fX7605+m+++/P9122215+3333ZfD5+GHH55++ctfpuWXXz63AwBaE/AAmGGcccYZuYfrhBNOSCuuuGIOdRHeYj5ehKTo6YohnEsttVR64IEHWn4uesIWXXTR1KVLl/w9M800U3rppZdyL1z37t3zfeHOO+9M22yzTVpwwQXz3L5vfOMb6e67707jx49veaydd945zTrrrDkULr744unFF1+c6vavtdZa6bXXXsuXEG1fb731Wubnhe222y5169Yt9ezZM2299dbprrvuytsHDRqUdthhh/T5z38+/x7xdQTEoUOHTtc+BaAshmgCMMM48sgjc4D7eG/ePffck/75z3+2bItA1hiiGWIOXEMEt0MPPTQPdbzooovy8M299947LbLIImnEiBFthjxGyIrHGjlyZMu2+eabr+Xr2WabLffyTa1ZZpklrbvuurkXb6eddsrhLXrkWmvd1mhLtClEkIuex+h1bIj5hNEDaJgmAA0CHgAztAhEMX/uwAMPnOL3RI9dazEPLi7Rg/e73/0uXXzxxenkk0/OvXmte8QiPEZv2bzzzpuGDx/eLu2N3sTzzjsvD7GMgLjsssu2uT+ep9GjGM8fbWqEzeixjN8VAKbEEE0AZmgReKL37qGHHspFUiK0PfbYY1MMZFGgJOa2Rc9bDI2MHr1GEZP1118/3XjjjbmgStz/29/+Nve4RchrLxHo4vmiJ26jjTaa5P7rr78+jR49Ooe7KLYSQzjD5ptvngYOHNhSWOa9995L//jHP9qtXQCUQQ8eADO06Nk66qij0q9+9atcdCTCU1SY3G+//Sb7/TGsMYZnRi9a9OzFXLpvf/vb+b6NN944D4mMIisRFL/4xS/mKprtLYJdVOKMIacft+aaa6ZjjjkmB7jo7dtkk03y9qiyGaHz7LPPzuEvKnv27t07B1AAaLAOHgBUbPDgwbloyimnnNJmeyyTcO6556aFFlrIMQFgmhiiCQAV+uCDD9Jf//rXtNlmm9nvALQ7AQ8AKhLzBGM4aBRt2WCDDex3ANqdIZoAAACF0IMHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACvH/APc8TOQMWbN+AAAAAElFTkSuQmCC\n", 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" ] @@ -364,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -405,12 +397,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -445,12 +437,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -497,12 +489,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -531,12 +523,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -576,20 +568,20 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n", + "C:\\ProgramData\\Anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", + "C:\\ProgramData\\Anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -599,7 +591,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -630,12 +622,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -667,12 +659,12 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -703,20 +695,20 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", + "C:\\ProgramData\\Anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " This is separate from the ipykernel package so we can avoid doing imports until\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", + "C:\\ProgramData\\Anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -726,7 +718,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -751,30 +743,22 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 28, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\matplotlib\\colors.py:527: RuntimeWarning: invalid value encountered in less\n", - " xa[xa < 0] = -1\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 57, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -802,12 +786,12 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -839,12 +823,12 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -881,12 +865,12 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -915,20 +899,20 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", + "C:\\ProgramData\\Anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " This is separate from the ipykernel package so we can avoid doing imports until\n", - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", + "C:\\ProgramData\\Anaconda3\\envs\\asimtest\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -938,7 +922,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -970,12 +954,12 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1007,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1042,16 +1026,29 @@ " household_id\n", " household_id\n", " \n", + " \n", + " stop_frequency\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " 0out_0in\n", - " 15.0\n", + " 14.0\n", " 3.0\n", " 18.0\n", - " 16.0\n", - " 18.0\n", - " 22.0\n", + " 17.0\n", + " 19.0\n", + " 21.0\n", " 5.0\n", " 52.0\n", " 3.0\n", @@ -1063,12 +1060,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 5.0\n", + " 4.0\n", " 3.0\n", " 1.0\n", " 4.0\n", " NaN\n", - " 1.0\n", + " 2.0\n", " \n", " \n", " 0out_2in\n", @@ -1076,7 +1073,7 @@ " NaN\n", " NaN\n", " 1.0\n", - " NaN\n", + " 1.0\n", " 2.0\n", " NaN\n", " 4.0\n", @@ -1089,7 +1086,7 @@ " NaN\n", " NaN\n", " 1.0\n", - " 1.0\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -1192,73 +1189,78 @@ "" ], "text/plain": [ - " (household_id, eatout) (household_id, escort) \\\n", - "0out_0in 15.0 3.0 \n", - "0out_1in NaN NaN \n", - "0out_2in NaN NaN \n", - "0out_3in NaN NaN \n", - "1out_0in 1.0 NaN \n", - "1out_1in NaN NaN \n", - "1out_3in NaN NaN \n", - "2out_0in NaN NaN \n", - "2out_2in NaN NaN \n", - "3out_0in NaN NaN \n", - "3out_2in NaN NaN \n", + " (household_id, eatout) (household_id, escort) \\\n", + "stop_frequency \n", + "0out_0in 14.0 3.0 \n", + "0out_1in NaN NaN \n", + "0out_2in NaN NaN \n", + "0out_3in NaN NaN \n", + "1out_0in 1.0 NaN \n", + "1out_1in NaN NaN \n", + "1out_3in NaN NaN \n", + "2out_0in NaN NaN \n", + "2out_2in NaN NaN \n", + "3out_0in NaN NaN \n", + "3out_2in NaN NaN \n", "\n", - " (household_id, othdiscr) (household_id, othmaint) \\\n", - "0out_0in 18.0 16.0 \n", - "0out_1in NaN NaN \n", - "0out_2in NaN 1.0 \n", - "0out_3in NaN 1.0 \n", - "1out_0in 1.0 1.0 \n", - "1out_1in NaN NaN \n", - "1out_3in NaN NaN \n", - "2out_0in NaN NaN \n", - "2out_2in NaN NaN \n", - "3out_0in NaN NaN \n", - "3out_2in NaN NaN \n", + " (household_id, othdiscr) (household_id, othmaint) \\\n", + "stop_frequency \n", + "0out_0in 18.0 17.0 \n", + "0out_1in NaN NaN \n", + "0out_2in NaN 1.0 \n", + "0out_3in NaN 1.0 \n", + "1out_0in 1.0 1.0 \n", + "1out_1in NaN NaN \n", + "1out_3in NaN NaN \n", + "2out_0in NaN NaN \n", + "2out_2in NaN NaN \n", + "3out_0in NaN NaN \n", + "3out_2in NaN NaN \n", "\n", - " (household_id, school) (household_id, shopping) \\\n", - "0out_0in 18.0 22.0 \n", - "0out_1in 5.0 3.0 \n", - "0out_2in NaN 2.0 \n", - "0out_3in 1.0 NaN \n", - "1out_0in 1.0 2.0 \n", - "1out_1in NaN NaN \n", - "1out_3in NaN NaN \n", - "2out_0in NaN 1.0 \n", - "2out_2in NaN 1.0 \n", - "3out_0in NaN NaN \n", - "3out_2in 1.0 NaN \n", + " (household_id, school) (household_id, shopping) \\\n", + "stop_frequency \n", + "0out_0in 19.0 21.0 \n", + "0out_1in 4.0 3.0 \n", + "0out_2in 1.0 2.0 \n", + "0out_3in NaN NaN \n", + "1out_0in 1.0 2.0 \n", + "1out_1in NaN NaN \n", + "1out_3in NaN NaN \n", + "2out_0in NaN 1.0 \n", + "2out_2in NaN 1.0 \n", + "3out_0in NaN NaN \n", + "3out_2in 1.0 NaN \n", "\n", - " (household_id, social) (household_id, work) household_id \\\n", - "0out_0in 5.0 52.0 3.0 \n", - "0out_1in 1.0 4.0 NaN \n", - "0out_2in NaN 4.0 NaN \n", - "0out_3in NaN NaN NaN \n", - "1out_0in NaN 4.0 NaN \n", - "1out_1in NaN 3.0 NaN \n", - "1out_3in 1.0 1.0 NaN \n", - "2out_0in NaN NaN NaN \n", - "2out_2in NaN NaN NaN \n", - "3out_0in NaN 1.0 NaN \n", - "3out_2in NaN NaN NaN \n", + " (household_id, social) (household_id, work) household_id \\\n", + "stop_frequency \n", + "0out_0in 5.0 52.0 3.0 \n", + "0out_1in 1.0 4.0 NaN \n", + "0out_2in NaN 4.0 NaN \n", + "0out_3in NaN NaN NaN \n", + "1out_0in NaN 4.0 NaN \n", + "1out_1in NaN 3.0 NaN \n", + "1out_3in 1.0 1.0 NaN \n", + "2out_0in NaN NaN NaN \n", + "2out_2in NaN NaN NaN \n", + "3out_0in NaN 1.0 NaN \n", + "3out_2in NaN NaN NaN \n", "\n", - " household_id \n", - "0out_0in 4.0 \n", - "0out_1in 1.0 \n", - "0out_2in NaN \n", - "0out_3in NaN \n", - "1out_0in 1.0 \n", - "1out_1in NaN \n", - "1out_3in NaN \n", - "2out_0in NaN \n", - "2out_2in NaN \n", - "3out_0in 2.0 \n", - "3out_2in NaN " + " household_id \n", + "stop_frequency \n", + "0out_0in 4.0 \n", + "0out_1in 2.0 \n", + "0out_2in NaN \n", + "0out_3in NaN \n", + "1out_0in 1.0 \n", + "1out_1in NaN \n", + "1out_3in NaN \n", + "2out_0in NaN \n", + "2out_2in NaN \n", + "3out_0in 2.0 \n", + "3out_2in NaN " ] }, - "execution_count": 63, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1294,12 +1296,12 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1337,12 +1339,12 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1365,30 +1367,22 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 37, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\programdata\\anaconda3\\envs\\asimtest\\lib\\site-packages\\matplotlib\\colors.py:527: RuntimeWarning: invalid value encountered in less\n", - " xa[xa < 0] = -1\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 66, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -1416,12 +1410,12 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1448,12 +1442,12 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1506,7 +1500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.9" } }, "nbformat": 4, diff --git a/activitysim/examples/example_multiple_zone/.gitignore b/activitysim/examples/example_multiple_zone/.gitignore new file mode 100644 index 0000000000..0eb7da8019 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/.gitignore @@ -0,0 +1,9 @@ +legacy_example/ +data_*/ +install.txt +test.py +notes.txt +output_3_example_marin/ +output_3_example_sf/ +data_marin_full/ +*_local/ diff --git a/activitysim/examples/example_multiple_zone/README.MD b/activitysim/examples/example_multiple_zone/README.MD new file mode 100644 index 0000000000..4b6b3ab6f4 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/README.MD @@ -0,0 +1,4 @@ + +### Multiple zone system setup examples + +See the [examples manifest](https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml) for more information. \ No newline at end of file diff --git a/activitysim/examples/example_multiple_zone/configs_1_zone/network_los.yaml b/activitysim/examples/example_multiple_zone/configs_1_zone/network_los.yaml new file mode 100644 index 0000000000..f63569aa46 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_1_zone/network_los.yaml @@ -0,0 +1,2 @@ +inherit_settings: True + diff --git a/activitysim/examples/example_multiple_zone/configs_1_zone/non_mandatory_tour_destination.yaml b/activitysim/examples/example_multiple_zone/configs_1_zone/non_mandatory_tour_destination.yaml new file mode 100644 index 0000000000..d6df6dd589 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_1_zone/non_mandatory_tour_destination.yaml @@ -0,0 +1,38 @@ +SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv +SPEC: non_mandatory_tour_destination.csv +COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: non_mandatory + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired in tours table) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + + +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 diff --git a/activitysim/examples/example_multiple_zone/configs_1_zone/settings.yaml b/activitysim/examples/example_multiple_zone/configs_1_zone/settings.yaml new file mode 100644 index 0000000000..26abf8b6cf --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_1_zone/settings.yaml @@ -0,0 +1,18 @@ +inherit_settings: True + +output_tables: + h5_store: False + action: include + prefix: 1z_final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +trace_od: [5, 11] + diff --git a/activitysim/examples/example_multiple_zone/configs_2_zone/accessibility.csv b/activitysim/examples/example_multiple_zone/configs_2_zone/accessibility.csv new file mode 100644 index 0000000000..621387555f --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_2_zone/accessibility.csv @@ -0,0 +1,59 @@ +Description,Target,Expression +#,, +#,, auto peak +#,, +#,, assume peak occurs in AM for outbound and PM for inbound +peak round trip distance,_auPkTime,"skim_od[('SOVTOLL_TIME', 'AM')] + skim_do[('SOVTOLL_TIME', 'PM')]" +decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) +auto peak retail,auPkRetail,df.RETEMPN * _decay +auto peak total,auPkTotal,df.TOTEMP * _decay +#,, +#,, auto off-peak +#,, +#,, assume midday occurs entirely in the midday period +off-peak round trip distance,_auOpTime,"skim_od[('SOVTOLL_TIME', 'MD')] + skim_do[('SOVTOLL_TIME', 'MD')]" +decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) +auto off-peak retail,auOpRetail,df.RETEMPN * _decay +auto off-peak total,auOpTotal,df.TOTEMP * _decay +#,, +#,, transit peak +#,, +#,, assume peak outbound transit occurs in AM +o-d peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_WLK_IVT', 'AM')]" +o-d peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_WLK_IWAIT', 'AM')] + skim_od[('WLK_TRN_WLK_XWAIT', 'AM')] + skim_od[('WLK_TRN_WLK_WACC', 'AM')] + skim_od[('WLK_TRN_WLK_WAUX', 'AM')] + skim_od[('WLK_TRN_WLK_WEGR', 'AM')]" +o-d peak transit time,_trPkTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / 100.0 +#,, assume peak inbound transit occurs in PM +d-o peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_WLK_IVT', 'PM')]" +d-o peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_WLK_IWAIT', 'PM')] + skim_do[('WLK_TRN_WLK_XWAIT', 'PM')] + skim_do[('WLK_TRN_WLK_WACC', 'PM')] + skim_do[('WLK_TRN_WLK_WAUX', 'PM')] + skim_do[('WLK_TRN_WLK_WEGR', 'PM')]" +d-o peak transit time,_trPkTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / 100.0 +peak transit time,_trPkTime,_trPkTime_od + _trPkTime_do +round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) +decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) +transit peak retail,trPkRetail,df.RETEMPN * _decay +transit peak total,trPkTotal,df.TOTEMP * _decay +#,, +#,, transit off-peak +#,, +#,, assume off-peak outbound transit occurs in the MD time period +o-d off-peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_WLK_IVT', 'MD')]" +o-d off-peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_WLK_IWAIT', 'MD')] + skim_od[('WLK_TRN_WLK_XWAIT', 'MD')] + skim_od[('WLK_TRN_WLK_WACC', 'MD')] + skim_od[('WLK_TRN_WLK_WAUX', 'MD')] + skim_od[('WLK_TRN_WLK_WEGR', 'MD')]" +o-d off-peak transit time,_trOpTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / 100.0 +#,, assume off-peak inbound transit occurs in the MD time period +d-o off-peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_WLK_IVT', 'MD')]" +d-o off-peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_WLK_IWAIT', 'MD')] + skim_do[('WLK_TRN_WLK_XWAIT', 'MD')] + skim_do[('WLK_TRN_WLK_WACC', 'MD')] + skim_do[('WLK_TRN_WLK_WAUX', 'MD')] + skim_do[('WLK_TRN_WLK_WEGR', 'MD')]" +d-o off-peak transit time,_trOpTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / 100.0 +peak transit time,_trOpTime,_trOpTime_od + _trOpTime_do +#,,FIXME - _rt_available calculation appears to be wrong in mtctm1 accessibility.job +#round trip path is available,_rt_available,(_trOpTime > 0) +round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) +decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) +transit off-peak retail,trOpRetail,df.RETEMPN * _decay +transit off-peak total,trOpTotal,df.TOTEMP * _decay +#,, +#,, non motorized +#,, +non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK'] +round trip path is available,_rt_available,_nmDist <= maximum_walk_distance +decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) +retail accessibility,nmRetail,df.RETEMPN * _decay +total accessibility,nmTotal,df.TOTEMP * _decay diff --git a/activitysim/examples/example_multiple_zone/configs_2_zone/network_los.yaml b/activitysim/examples/example_multiple_zone/configs_2_zone/network_los.yaml new file mode 100644 index 0000000000..5b864a1c46 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_2_zone/network_los.yaml @@ -0,0 +1,31 @@ +inherit_settings: True + +zone_system: 2 + +read_skim_cache: False +write_skim_cache: False + +taz_skims: taz_skims.omx + +maz: maz.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + DIST: 5 + # blend distance of 0 means no blending + DISTBIKE: 0 + DISTWALK: 1 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + blend_distance_skim_name: DIST + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/examples/example_multiple_zone/configs_2_zone/settings.yaml b/activitysim/examples/example_multiple_zone/configs_2_zone/settings.yaml new file mode 100644 index 0000000000..a775eaf685 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_2_zone/settings.yaml @@ -0,0 +1,84 @@ +inherit_settings: True + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id + PERSONS: hhsize + workers: num_workers + VEHICL: auto_ownership + MAZ: home_zone_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + COUNTY: county_id + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL + - access_dist_transit + +#resume_after: initialize_landuse + +output_tables: + h5_store: False + action: include + prefix: 2z_final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +trace_od: [5000, 11000] diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/_bugs.txt b/activitysim/examples/example_multiple_zone/configs_3_zone/_bugs.txt new file mode 100644 index 0000000000..7686d8a6da --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/_bugs.txt @@ -0,0 +1,14 @@ +annotate_persons_workplace.csv + work_auto_savings + work_auto_savings_ration + +annotate_households_workplace.csv + hh_work_auto_savings_ratio + +auto_ownership.csv + util_auto_time_saving_per_worker + + + + + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/accessibility.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/accessibility.csv new file mode 100644 index 0000000000..a14048401b --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/accessibility.csv @@ -0,0 +1,49 @@ +Description,Target,Expression +#,, +#,, auto peak +#,, +#,, assume peak occurs in AM for outbound and PM for inbound +peak round trip distance,_auPkTime,"skim_od[('SOVTOLL_TIME', 'AM')] + skim_do[('SOVTOLL_TIME', 'PM')]" +decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) +auto peak retail,auPkRetail,df.RETEMPN * _decay +auto peak total,auPkTotal,df.TOTEMP * _decay +#,, +#,, auto off-peak +#,, +#,, assume midday occurs entirely in the midday period +off-peak round trip distance,_auOpTime,"skim_od[('SOVTOLL_TIME', 'MD')] + skim_do[('SOVTOLL_TIME', 'MD')]" +decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) +auto off-peak retail,auOpRetail,df.RETEMPN * _decay +auto off-peak total,auOpTotal,df.TOTEMP * _decay +#,, +#,, transit peak +#,, +#,, FIXME - don't need WLK_TRN_WLK_WACC and WLK_TRN_WLK_WEGR as we are using walk_time? +#,, assume peak outbound transit occurs in AM +o-d peak transit time,_trPkTime_od,"tvpb.get_tvpb_best_transit_time(orig=df.orig, dest=df.dest, tod='AM')" +#,, assume peak inbound transit occurs in PM +o-d peak transit time,_trPkTime_do,"tvpb.get_tvpb_best_transit_time(orig=df.dest, dest=df.orig, tod='PM')" +peak transit time,_trPkTime,_trPkTime_od + _trPkTime_do +round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) +decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) +transit peak retail,trPkRetail,df.RETEMPN * _decay +transit peak total,trPkTotal,df.TOTEMP * _decay +####,, +####,, transit off-peak +####,, +####,, assume off-peak inbound and outbound transit occurs in the MD time period +o-d off-peak transit time,_trOpTime_od,"tvpb.get_tvpb_best_transit_time(orig=df.orig, dest=df.dest, tod='MD')" +d-o off-peak transit time,_trOpTime_do,"tvpb.get_tvpb_best_transit_time(orig=df.dest, dest=df.orig, tod='MD')" +off-peak transit time,_trOpTime,_trOpTime_od + _trPkTime_do +round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) +decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) +transit off-peak retail,trOpRetail,df.RETEMPN * _decay +transit off-peak total,trOpTotal,df.TOTEMP * _decay +#,, +#,, non motorized +#,, +non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK'] +round trip path is available,_rt_available,_nmDist <= maximum_walk_distance +decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) +retail accessibility,nmRetail,df.RETEMPN * _decay +total accessibility,nmTotal,df.TOTEMP * _decay diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/annotate_households_workplace.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/annotate_households_workplace.csv new file mode 100644 index 0000000000..92439c9fb0 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/annotate_households_workplace.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +#,, annotate households table after workplace_location model has run +#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio +#,, +#,, FIXME: all we have is a logsum, not an actual transit choice... +##### ,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0) +,hh_work_auto_savings_ratio,float(0.0) diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/annotate_persons_workplace.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/annotate_persons_workplace.csv new file mode 100644 index 0000000000..2779b8a25b --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/annotate_persons_workplace.csv @@ -0,0 +1,38 @@ +Description,Target,Expression +#,, annotate persons table after workplace_location model has run +,distance_to_work,"np.where(persons.workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DIST'),np.nan)" +workplace_in_cbd,workplace_in_cbd,"reindex(land_use.area_type, persons.workplace_zone_id) < setting('cbd_threshold')" +work_zone_area_type,work_zone_area_type,"reindex(land_use.area_type, persons.workplace_zone_id)" +#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD +#,,roundtrip_auto_time_to_work +,_auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_TIME', 'MD'))" +,_auto_time_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" +,roundtrip_auto_time_to_work,"np.where(persons.workplace_zone_id>=0,_auto_time_home_to_work + _auto_time_work_to_home,0)" +#,,_roundtrip_walk_time_to_work +,_MAX_TIME_TO_WORK,999 +,_WALK_SPEED_MPH,3 +,_walk_time_home_to_work,"60 * skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" +,_walk_time_work_to_home,"60 * skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" +,_work_walk_available,(_walk_time_home_to_work > 0) & (_walk_time_work_to_home > 0) +,_roundtrip_walk_time_to_work,"np.where(_work_walk_available, _walk_time_home_to_work + _walk_time_work_to_home, _MAX_TIME_TO_WORK)" +#,,_roundtrip_transit_time_to_work +##### ,_IVT_SKIM,"skim_dict.get(('WLK_TRN_WLK_IVT', 'MD'))" +##### ,_transit_ivt_home_to_work,"_IVT_SKIM.get(persons.home_zone_id, persons.workplace_zone_id)/100" +##### ,_transit_ivt_work_to_home,"_IVT_SKIM.get(persons.workplace_zone_id, persons.home_zone_id)/100" +##### ,_work_transit_available,(_transit_ivt_home_to_work > 0) & (_transit_ivt_work_to_home > 0) +##### ,_IWAIT_SKIM,"skim_dict.get(('WLK_TRN_WLK_IWAIT', 'MD'))" +##### ,_transit_iwait,"_IWAIT_SKIM.get(persons.home_zone_id, persons.workplace_zone_id)/100 + _IWAIT_SKIM.get(persons.workplace_zone_id, persons.home_zone_id)/100" +##### ,_XWAIT_SKIM,"skim_dict.get(('WLK_TRN_WLK_XWAIT', 'MD'))" +##### ,_transit_xwait,"_XWAIT_SKIM.get(persons.home_zone_id, persons.workplace_zone_id)/100 + _XWAIT_SKIM.get(persons.workplace_zone_id, persons.home_zone_id)/100" +##### ,_WAUX_SKIM,"skim_dict.get(('WLK_TRN_WLK_WAUX', 'MD'))" +##### ,_transit_waux,"_WAUX_SKIM.get(persons.home_zone_id, persons.workplace_zone_id)/100 + _WAUX_SKIM.get(persons.workplace_zone_id, persons.home_zone_id)/100" +##### ,_WACC_SKIM,"skim_dict.get(('WLK_TRN_WLK_WACC', 'MD'))" +##### ,_transit_wacc,"_WACC_SKIM.get(persons.home_zone_id, persons.workplace_zone_id)/100 + _WACC_SKIM.get(persons.workplace_zone_id, persons.home_zone_id)/100" +##### ,_WEGR_SKIM,"skim_dict.get(('WLK_TRN_WLK_WEGR', 'MD'))" +##### ,_transit_wegr,"_WEGR_SKIM.get(persons.home_zone_id, persons.workplace_zone_id)/100 + _WEGR_SKIM.get(persons.workplace_zone_id, persons.home_zone_id)/100" +##### ,_roundtrip_transit_time_to_work,_transit_ivt_home_to_work + _transit_ivt_work_to_home + _transit_iwait + _transit_xwait + _transit_waux + _transit_wacc + _transit_wegr +##### #,,work_auto_savings_ratio +##### ,_min_work_walk_transit,"np.where(_work_transit_available, np.minimum(_roundtrip_transit_time_to_work, _roundtrip_walk_time_to_work), _roundtrip_walk_time_to_work)" +##### ,work_auto_savings,"np.where(persons.is_worker, _min_work_walk_transit - roundtrip_auto_time_to_work, 0)" +##### #,,auto savings over walk or transit capped at 120 and normalized to unity +##### ,work_auto_savings_ratio,"(work_auto_savings / 120.0).clip(-1.0, 1.0)" diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/auto_ownership.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/auto_ownership.csv new file mode 100644 index 0000000000..aa30bdb4db --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/auto_ownership.csv @@ -0,0 +1,30 @@ +Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 +util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 +util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3 +util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up +util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 +util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 +util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 +util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4 +util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 +util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 +util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k +util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up +util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up +util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers +util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers +util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers +util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers +util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc +util_asc_san_francisco,San Francisco county,@df.county_id == ID_SAN_FRANCISCO,,coef_cars1_asc_san_francisco,coef_cars2_asc_san_francisco,coef_cars34_asc_san_francisco,coef_cars34_asc_san_francisco +util_asc_solano,Solano county,@df.county_id == ID_SOLANO,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_napa,Napa county,@df.county_id == ID_NAPA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_sonoma,Sonoma county,@df.county_id == ID_SONOMA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_marin,Marin county,@df.county_id == ID_MARIN,,coef_cars1_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin +util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers +util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers +util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers +util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers +util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor +util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor +util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/destination_choice_size_terms.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/destination_choice_size_terms.csv new file mode 100644 index 0000000000..7f70421e85 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/destination_choice_size_terms.csv @@ -0,0 +1,28 @@ +model_selector,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE +workplace,work_low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0 +workplace,work_med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0 +workplace,work_high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0 +workplace,work_veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0 +school,university,0,0,0,0,0,0,0,0,0,0.592,0.408 +school,gradeschool,0,0,0,0,0,0,0,1,0,0,0 +school,highschool,0,0,0,0,0,0,0,0,1,0,0 +non_mandatory,escort,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +#non_mandatory,escort_kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +#non_mandatory,escort_nokids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +non_mandatory,shopping,0,1,0,0,0,0,0,0,0,0,0 +non_mandatory,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 +non_mandatory,othmaint,0,0.482,0,0.518,0,0,0,0,0,0,0 +non_mandatory,social,0,0.522,0,0.478,0,0,0,0,0,0,0 +non_mandatory,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 +atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0 +trip,work,0,1,1,1,1,1,1,0,0,0,0 +trip,escort,0.001,0.225,0,0.144,0,0,0,0.464,0.166,0,0 +trip,shopping,0.001,0.999,0,0,0,0,0,0,0,0,0 +trip,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 +trip,othmaint,0.001,0.481,0,0.518,0,0,0,0,0,0,0 +trip,social,0.001,0.521,0,0.478,0,0,0,0,0,0,0 +trip,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 +trip,univ,0.001,0,0,0,0,0,0,0,0,0.592,0.408 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,, +#trip,gradeschool,0,0,0,0,0,0,0,1,0,0,0 +#trip,highschool,0,0,0,0,0,0,0,0,1,0,0 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/network_los.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone/network_los.yaml new file mode 100644 index 0000000000..091b0ca396 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/network_los.yaml @@ -0,0 +1,157 @@ +inherit_settings: True + +zone_system: 3 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +# rebuild and overwrite existing tap_tap_utilities cache +rebuild_tvpb_cache: False + +# write a csv version of tvpb cache for tracing when checkpointing cache. +# (writes csv file when writing/checkpointing cache i.e. when cached changed) +# (n.b. csv file could be quite large if cache is STATIC!) +trace_tvpb_cache_as_csv: False + +taz_skims: taz_skims.omx + +# we require that skims for all tap_tap sets have unique names +# and can therefor share a single skim_dict without name collision +# e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM +tap_skims: tap_skims.omx + +maz: maz.csv + +tap: tap.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + DIST: 5 + # blend distance of 0 means no blending + DISTBIKE: 0 + DISTWALK: 1 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + blend_distance_skim_name: DIST + +maz_to_tap: + walk: + table: maz_to_tap_walk.csv + drive: + table: maz_to_tap_drive.csv + + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + +# transit virtual path builder settings +TVPB_SETTINGS: + + tour_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + DTW: + access: drive + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + WTD: + access: walk + egress: drive + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + demographic_segment: *demographic_segments + tod: *skim_time_period_labels + access_mode: ['drive', 'walk'] + attributes_as_columns: + - demographic_segment + - tod + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - walk_time + drive: + SPEC: tvpb_utility_drive_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - drive_time + - DIST + CONSTANTS: + C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id + C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id + TVPB_demographic_segments_by_income_segment: + 1: *low_income_segment_id + 2: *low_income_segment_id + 3: *high_income_segment_id + 4: *high_income_segment_id + c_ivt_high_income: -0.028 + c_ivt_low_income: -0.0175 + c_cost_high_income: -0.00112 + c_cost_low_income: -0.00112 + c_wait: 1.5 + c_walk: 1.7 + c_drive: 1.5 + c_auto_operating_cost_per_mile: 18.29 + C_UNAVAILABLE: -999 + C_FASTEST_IVT_MULTIPLIER: 2 + C_FASTEST_COST_MULTIPLIER: 1 + C_CHEAPEST_IVT_MULTIPLIER: 1 + C_CHEAPEST_COST_MULTIPLIER: 500 + C_SHORTEST_IVT_MULTIPLIER: 1 + C_SHORTEST_COST_MULTIPLIER: 1 + C_SHORTEST_DIST_MULTIPLIER: 1 + # illustrate using access mode in tat-tap expressions files + C_DRIVE_TRANSFER_PENALTY: -1 + + accessibility: + units: time + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 1 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_accessibility_tap_tap_.csv +# sets: +# - transit + maz_tap_settings: + walk: + SPEC: tvpb_accessibility_walk_maz_tap.csv + CHOOSER_COLUMNS: + - walk_time + CONSTANTS: + out_of_vehicle_walk_time_weight: 1.5 + out_of_vehicle_wait_time_weight: 2.0 + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/settings.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone/settings.yaml new file mode 100644 index 0000000000..0cde54e8ec --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/settings.yaml @@ -0,0 +1,85 @@ +inherit_settings: True + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id + PERSONS: hhsize + workers: num_workers + VEHICL: auto_ownership + MAZ: home_zone_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + COUNTY: county_id + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL + - access_dist_transit + + +output_tables: + h5_store: False + action: include + prefix: 3z_final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +#trace_od: [5000, 11000] diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/settings_mp.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone/settings_mp.yaml new file mode 100644 index 0000000000..b31aa9d072 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/settings_mp.yaml @@ -0,0 +1,37 @@ +inherit_settings: settings_static.yaml + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + + +# - ------------------------- dev config +multiprocess: True +strict: False +mem_tick: 30 +use_shadow_pricing: False + +num_processes: 2 + +# - ------------------------- + +# not recommended or supported for multiprocessing +want_dest_choice_sample_tables: False + + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_tvpb + begin: initialize_tvpb + chunk_size: 0 + slice: + tables: + - attribute_combinations + - name: mp_models + begin: school_location + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/settings_static.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone/settings_static.yaml new file mode 100644 index 0000000000..e7b878a603 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/settings_static.yaml @@ -0,0 +1,46 @@ +inherit_settings: settings.yaml + + +models: + - initialize_landuse + - compute_accessibility + - initialize_households + # --- STATIC cache prebuild steps + # single-process step to create attribute_combination list + - initialize_los + # multi-processable step to build STATIC cache + # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) + - initialize_tvpb + # --- + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/stop_frequency.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone/stop_frequency.yaml new file mode 100644 index 0000000000..d646e3a3ac --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/stop_frequency.yaml @@ -0,0 +1,32 @@ +LOGIT_TYPE: MNL + +preprocessor: + SPEC: stop_frequency_annotate_tours_preprocessor + DF: tours_merged + TABLES: + - persons + - land_use + - accessibility + + +CONSTANTS: + TRANSIT_MODES: + - WALK_TRANSIT + - DRIVE_TRANSIT + DRIVE_TO_TRANSIT_MODES: + - DRIVE_TRANSIT + NONMOTORIZED_MODES: + - WALK + - BIKE + SHOP_TOUR: shopping + MAINT_TOUR: othmaint + SCHOOL_TOUR: school + EATOUT_TOUR: eatout + SOCIAL_TOUR: social + num_atwork_subtours_map: + no_subtours: 0 + eat: 1 + business1: 1 + maint: 1 + business2: 2 + eat_business: 2 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.csv new file mode 100644 index 0000000000..623001ea5b --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.csv @@ -0,0 +1,189 @@ +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'] + dot_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOVTOLL_DIST'] + dot_skims['SOVTOLL_DIST']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_BTOLL'] + dot_skims['SOVTOLL_BTOLL']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_VTOLL'] + dot_skims['SOVTOLL_VTOLL']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da_multiplier,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,, +util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,, +util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,, +util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['HOV3TOLL_TIME'] + dot_skims['HOV3TOLL_TIME']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3TOLL_DIST'] + dot_skims['HOV3TOLL_DIST']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_BTOLL'] + dot_skims['HOV3TOLL_BTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,, +util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,, +#,Walk,,,,,,,,,,,,,, +#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,,,,coef_ivt,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,,,,coef_ivt,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,, +util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,, +#,Bike,,,,,,,,,,,,,, +#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,,,,coef_ivt,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,,,,coef_ivt,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,, +util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,,,,coef_ivt,,,,, +#,Walk to Local,,,,,,,,,,,,,, +#util_WALK_TRANSIT_Unavailable,WALK_TRANSIT - Unavailable,walk_transit_available == False,,,,,,,,,-999,,,, +util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,"@tvpb_logsum_odt['WTW'] + tvpb_logsum_dot['WTW']",,,,,,,,,coef_one,,,, +util_WALK_TRANSIT_Destination_zone_densityIndex,WALK_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,, +util_WALK_TRANSIT_Topology,WALK_TRANSIT - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,, +util_WALK_TRANSIT_Person_is_less_than_10_years_old,WALK_TRANSIT - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn_multiplier,,,, +#,Drive to Local,,,,,,,,,,,,,, +#util_DRIVE_TRANSIT_Unavailable,DRIVE_TRANSIT - Unavailable,drive_transit_available == False,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,"@tvpb_logsum_odt['DTW'] + tvpb_logsum_dot['WTD']",,,,,,,,,,coef_one,,, +util_DRIVE_TRANSIT_Destination_zone_densityIndex,DRIVE_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,, +util_DRIVE_TRANSIT_Topology,DRIVE_TRANSIT - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,, +util_DRIVE_TRANSIT_Person_is_less_than_10_years_old,DRIVE_TRANSIT - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn_multiplier,,, +#,Taxi,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,coef_ivt,, +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * Taxi_costPerMile +(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, +#,TNC Shared,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt +#,indiv tour ASCs,,,,,,,,,,,,,, +util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,walk_ASC_no_auto,,,,,, +util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_deficient,,,,,, +util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,walk_ASC_auto_sufficient,,,,,, +util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,bike_ASC_no_auto,,,,, +util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_deficient,,,,, +util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,bike_ASC_auto_sufficient,,,,, +util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,, +util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,, +util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,, +util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,, +util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,, +util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,, +util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,walk_transit_ASC_no_auto,,,, +util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,,,, +util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,walk_transit_ASC_auto_sufficient,,,, +util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,drive_transit_ASC_no_auto,,, +util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,drive_transit_ASC_auto_deficient,,, +util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,drive_transit_ASC_auto_sufficient,,, +util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,taxi_ASC_no_auto,, +util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,taxi_ASC_auto_deficient,, +util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,taxi_ASC_auto_sufficient,, +util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,tnc_single_ASC_no_auto, +util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,tnc_single_ASC_auto_deficient, +util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,tnc_single_ASC_auto_sufficient, +util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,tnc_shared_ASC_no_auto +util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,tnc_shared_ASC_auto_deficient +util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient +#,joint tour ASCs,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,, +util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,, +util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,, +util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,, +util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,, +util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,, +util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,, +util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,, +util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,, +util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,, +util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,,,, +util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,,,, +util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,,,, +util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,joint_drive_transit_ASC_no_auto,,, +util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,joint_drive_transit_ASC_auto_deficient,,, +util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,,, +util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,joint_taxi_ASC_no_auto,, +util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,joint_taxi_ASC_auto_deficient,, +util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, +util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,joint_tnc_single_ASC_no_auto, +util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, +util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, +util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto +util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient +util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient +util_Local_bus_ASC,Local bus ASC,1,,,,,,,,,local_bus_ASC,local_bus_ASC,,, +#util_Walk_to_Light_Rail_ASC,Walk to Light Rail ASC,@(df.walk_ferry_available == False),,,,,,,,,,walk_light_rail_ASC,,,,,,,,,,, +#util_Drive_to_Light_Rail_ASC,Drive to Light Rail ASC,@(df.drive_ferry_available == False),,,,,,,,,,,,,,,drive_light_rail_ASC,,,,,, +#util_Walk_to_Ferry_ASC,Walk to Ferry ASC,@df.walk_ferry_available,,,,,,,,,,walk_ferry_ASC,,,,,,,,,,, +#util_Drive_to_Ferry_ASC,Drive to Ferry ASC,@df.drive_ferry_available,,,,,,,,,,,,,,,drive_ferry_ASC,,,,,, +#util_Express_Bus_ASC,Express Bus ASC,1,,,,,,,,,,,express_bus_ASC,,,,,express_bus_ASC,,,,, +#util_Heavy_Rail_ASC,Heavy Rail ASC,1,,,,,,,,,,,,heavy_rail_ASC,,,,,heavy_rail_ASC,,,, +#util_Commuter_Rail,Commuter Rail,1,,,,,,,,,,,,,commuter_rail_ASC,,,,,commuter_rail_ASC,,, +util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,,,, +util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,drive_transit_CBD_ASC,,, +util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,coef_ivt,,, +#, FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, +util_Walk_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,,,,-999,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,,,,-999,,,,, +util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,, +#, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,,, diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.yaml new file mode 100644 index 0000000000..c9f719a636 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.yaml @@ -0,0 +1,186 @@ +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +tvpb_mode_path_types: + DRIVE_TRANSIT: + od: DTW + do: WTD + WALK_TRANSIT: + od: WTW + do: WTW + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - name: DRIVEALONE + coefficient: coef_nest_AUTO_DRIVEALONE + alternatives: + - DRIVEALONEFREE + - DRIVEALONEPAY + - name: SHAREDRIDE2 + coefficient: coef_nest_AUTO_SHAREDRIDE2 + alternatives: + - SHARED2FREE + - SHARED2PAY + - name: SHAREDRIDE3 + coefficient: coef_nest_AUTO_SHAREDRIDE3 + alternatives: + - SHARED3FREE + - SHARED3PAY + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - DRIVE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coeffs.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv + +CONSTANTS: + #valueOfTime: 8.00 + costPerMile: 18.29 + costShareSr2: 1.75 + costShareSr3: 2.50 + waitThresh: 10.00 + walkThresh: 1.50 + shortWalk: 0.333 + longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 6.00 + bikeSpeed: 12.00 + maxCbdAreaTypeThresh: 2 + indivTour: 1.00000 + upperEA: 5 + upperAM: 10 + upperMD: 15 + upperPM: 19 + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 + + ivt_cost_multiplier: 0.6 + ivt_lrt_multiplier: 0.9 + ivt_ferry_multiplier: 0.8 + ivt_exp_multiplier: 1 + ivt_hvy_multiplier: 0.8 + ivt_com_multiplier: 0.7 + walktimeshort_multiplier: 2 + walktimelong_multiplier: 10 + biketimeshort_multiplier: 4 + biketimelong_multiplier: 20 + short_i_wait_multiplier: 2 + long_i_wait_multiplier: 1 + wacc_multiplier: 2 + wegr_multiplier: 2 + waux_multiplier: 2 + dtim_multiplier: 2 + xwait_multiplier: 2 + dacc_ratio: 0 + xfers_wlk_multiplier: 10 + xfers_drv_multiplier: 20 + drvtrn_distpen_0_multiplier: 270 + drvtrn_distpen_max: 15 + density_index_multiplier: -0.2 +# joint_sr2_ASC_no_auto: 0 +# joint_sr2_ASC_auto_deficient: 0 +# joint_sr2_ASC_auto_sufficient: 0 +# joint_drive_transit_ASC_no_auto: 0 + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - age_16_p + - age_16_to_19 + - auto_ownership + - number_of_participants + - tour_category + - num_workers + - value_of_time + - free_parking_at_work + - income_segment + - demographic_segment + - c_ivt_for_segment + - c_cost_for_segment + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000000..3512272849 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,98 @@ +Description,Target,Expression +#,, +local,_DF_IS_TOUR,'tour_type' in df.columns +,number_of_participants,df.number_of_participants if _DF_IS_TOUR else 1 +,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False +#,, +# TVPB,, +#,, +,demographic_segment,df.income_segment.map(TVPB_demographic_segments_by_income_segment) +,c_ivt_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" +,c_cost_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" +#,, + local,_HAVE_PARENT_TOURS,'parent_tour_id' in df.columns +,_parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id) if _HAVE_PARENT_TOURS else ''" +,work_tour_is_drive,"_parent_tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" +,work_tour_is_bike,_parent_tour_mode=='BIKE' +,work_tour_is_SOV,"_parent_tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" +#,, +,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False +,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False +,is_indiv,~is_joint +,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False +,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False +# FIXME why inverse of value of time? need better name?,, +#,c_cost,(0.60 * c_ivt) / df.value_of_time +# ivot * (c_ivt_cost_multiplier * c_ivt) +,ivot,1.0 / df.value_of_time +#,, +,dest_topology,"reindex(land_use.TOPOLOGY, df[dest_col_name])" +,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])" +,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" +# FIXME no transit subzones for ONE_ZONE version, so all zones short walk to transit,, +,_origin_distance_to_transit,"reindex(land_use.access_dist_transit, df[orig_col_name]) if 'access_dist_transit' in land_use else shortWalk" +,_destination_distance_to_transit,"reindex(land_use.access_dist_transit, df[dest_col_name]) if 'access_dist_transit' in land_use else shortWalk" +,walk_transit_available,(_origin_distance_to_transit > 0) & (_destination_distance_to_transit > 0) +,drive_transit_available,(_destination_distance_to_transit > 0) & (df.auto_ownership > 0) +,origin_walk_time,_origin_distance_to_transit*60/walkSpeed +,destination_walk_time,_destination_distance_to_transit*60/walkSpeed +# RIDEHAIL,, +,origin_density_measure,"(reindex(land_use.TOTPOP, df[orig_col_name]) + reindex(land_use.TOTEMP, df[orig_col_name])) / (reindex(land_use.TOTACRE, df[orig_col_name]) / 640)" +,dest_density_measure,"(reindex(land_use.TOTPOP, df[dest_col_name]) + reindex(land_use.TOTEMP, df[dest_col_name])) / (reindex(land_use.TOTACRE, df[dest_col_name]) / 640)" +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime +#,, +,_free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False +,_dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[dest_col_name])" +,_dest_hourly_offpeak_parking_cost,"reindex(land_use.OPRKCST, df[dest_col_name])" +,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)" +,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)" +,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost * df.duration, _hourly_offpeak_parking_cost * df.duration)" +#,, +,distance,od_skims['DIST'] +,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0) +,sovtoll_available,(odt_skims['SOVTOLL_VTOLL']>0) | (dot_skims['SOVTOLL_VTOLL']>0) +,hov2_available,(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME'])>0 +,hov2toll_available,(odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL'])>0 +,hov3_available,(odt_skims['HOV3_TIME']>0) & (dot_skims['HOV3_TIME']>0) +,hov3toll_available,(odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL'])>0 +#,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_WLK_TOTIVT']/100>0) & (dot_skims['WLK_LOC_WLK_TOTIVT']/100>0) +#,walk_commuter_available,walk_transit_available & (odt_skims['WLK_COM_WLK_TOTIVT']/100>0) & (dot_skims['WLK_COM_WLK_TOTIVT']/100>0) & ((odt_skims['WLK_COM_WLK_KEYIVT']/100 + dot_skims['WLK_COM_WLK_KEYIVT']/100)>0) +#,walk_express_available,walk_transit_available & (odt_skims['WLK_EXP_WLK_TOTIVT']/100>0) & (dot_skims['WLK_EXP_WLK_TOTIVT']/100>0) & ((odt_skims['WLK_EXP_WLK_KEYIVT']/100 + dot_skims['WLK_EXP_WLK_KEYIVT']/100)>0) +#,walk_heavyrail_available,walk_transit_available & (odt_skims['WLK_HVY_WLK_TOTIVT']/100>0) & (dot_skims['WLK_HVY_WLK_TOTIVT']/100>0) & ((odt_skims['WLK_HVY_WLK_KEYIVT']/100 + dot_skims['WLK_HVY_WLK_KEYIVT']/100)>0) +#,walk_lrf_available,walk_transit_available & (odt_skims['WLK_LRF_WLK_TOTIVT']/100>0) & (dot_skims['WLK_LRF_WLK_TOTIVT']/100>0) & ((odt_skims['WLK_LRF_WLK_KEYIVT']/100 + dot_skims['WLK_LRF_WLK_KEYIVT']/100)>0) +#,walk_ferry_available,walk_lrf_available & ((odt_skims['WLK_LRF_WLK_FERRYIVT']/100 + dot_skims['WLK_LRF_WLK_FERRYIVT']/100)>0) +#,drive_local_available,drive_transit_available & (odt_skims['DRV_LOC_WLK_TOTIVT']/100>0) & (dot_skims['WLK_LOC_DRV_TOTIVT']/100>0) +#,drive_commuter_available,drive_transit_available & (odt_skims['DRV_COM_WLK_TOTIVT']/100>0) & (dot_skims['WLK_COM_DRV_TOTIVT']/100>0) & ((odt_skims['DRV_COM_WLK_KEYIVT']/100 + dot_skims['WLK_COM_DRV_KEYIVT']/100)>0) +#,drive_express_available,drive_transit_available & (odt_skims['DRV_EXP_WLK_TOTIVT']/100>0) & (dot_skims['WLK_EXP_DRV_TOTIVT']/100>0) & ((odt_skims['DRV_EXP_WLK_KEYIVT']/100 + dot_skims['WLK_EXP_DRV_KEYIVT']/100)>0) +#,drive_heavyrail_available,drive_transit_available & (odt_skims['DRV_HVY_WLK_TOTIVT']/100>0) & (dot_skims['WLK_HVY_DRV_TOTIVT']/100>0) & ((odt_skims['DRV_HVY_WLK_KEYIVT']/100 + dot_skims['WLK_HVY_DRV_KEYIVT']/100)>0) +#,drive_lrf_available,drive_transit_available & (odt_skims['DRV_LRF_WLK_TOTIVT']/100>0) & (dot_skims['WLK_LRF_DRV_TOTIVT']/100>0) & ((odt_skims['DRV_LRF_WLK_KEYIVT']/100 + dot_skims['WLK_LRF_DRV_KEYIVT']/100)>0) +#,drive_ferry_available,drive_lrf_available & ((odt_skims['DRV_LRF_WLK_FERRYIVT']/100 + dot_skims['WLK_LRF_WLK_FERRYIVT']/100)>0) +,walk_transit_available,True +,drive_transit_available,True +#,, +destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, df[dest_col_name]) < setting('cbd_threshold')) * 1" +#,,FIXME diagnostic +#,sov_dist_rt,(odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']) diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.csv new file mode 100644 index 0000000000..8118390145 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.csv @@ -0,0 +1,194 @@ +Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED +#Drive alone no toll,,,,,,,,,,,,,, +DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, +DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,, +DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,, +DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, +DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, +DRIVEALONEFREE - In-vehicle time,@c_ivt*odt_skims['SOV_TIME'],1,,,,,,,,,,,, +DRIVEALONEFREE - Terminal time,@c_walktimeshort * df.total_terminal_time,1,,,,,,,,,,,, +DRIVEALONEFREE - Operating cost,@df.c_cost * costPerMile * odt_skims['SOV_DIST'],1,,,,,,,,,,,, +DRIVEALONEFREE - Parking cost,c_cost * total_parking_cost,1,,,,,,,,,,,, +DRIVEALONEFREE - Bridge toll,@df.c_cost * odt_skims['SOV_BTOLL'],1,,,,,,,,,,,, +DRIVEALONEFREE - Person is between 16 and 19 years old,@c_age1619_da * ((df.age >= 16) & (df.age <= 19)),1,,,,,,,,,,,, +#Drive alone toll,,,,,,,,,,,,,, +DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, +DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,, +DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,, +DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, +DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, +DRIVEALONEPAY - In-vehicle time,@c_ivt*odt_skims['SOVTOLL_TIME'],,1,,,,,,,,,,, +DRIVEALONEPAY - Terminal time,@c_walktimeshort * df.total_terminal_time,,1,,,,,,,,,,, +DRIVEALONEPAY - Operating cost,@df.c_cost * costPerMile * odt_skims['SOVTOLL_DIST'],,1,,,,,,,,,,, +DRIVEALONEPAY - Parking cost,c_cost * total_parking_cost,,1,,,,,,,,,,, +DRIVEALONEPAY - Bridge toll,@df.c_cost * odt_skims['SOVTOLL_BTOLL'],,1,,,,,,,,,,, +DRIVEALONEPAY - Value toll,@df.c_cost * odt_skims['SOVTOLL_VTOLL'],,1,,,,,,,,,,, +DRIVEALONEPAY - Person is between 16 and 19 years old,@c_age1619_da * ((df.age >= 16) & (df.age <= 19)),,1,,,,,,,,,,, +#Shared ride 2,,,,,,,,,,,,,, +SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, +SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, +SHARED2FREE - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'],,,1,,,,,,,,,, +SHARED2FREE - Terminal time,@c_walktimeshort * df.total_terminal_time,,,1,,,,,,,,,, +SHARED2FREE - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV2_DIST'],,,1,,,,,,,,,, +SHARED2FREE - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr2,,,1,,,,,,,,,, +SHARED2FREE - Bridge toll,@df.c_cost * odt_skims['HOV2_BTOLL'] / costShareSr2,,,1,,,,,,,,,, +SHARED2FREE - One person household,@c_hhsize1_sr * (df.hhsize == 1),,,1,,,,,,,,,, +SHARED2FREE - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,,1,,,,,,,,,, +SHARED2FREE - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,,1,,,,,,,,,, +#Shared ride 2 toll,,,,,,,,,,,,,, +SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, +SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, +SHARED2PAY - In-vehicle time,@c_ivt * odt_skims['HOV2TOLL_TIME'],,,,1,,,,,,,,, +SHARED2PAY - Terminal time,@c_walktimeshort * df.total_terminal_time,,,,1,,,,,,,,, +SHARED2PAY - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV2TOLL_DIST'],,,,1,,,,,,,,, +SHARED2PAY - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr2,,,,1,,,,,,,,, +SHARED2PAY - Bridge toll,@df.c_cost * odt_skims['HOV2TOLL_BTOLL'] / costShareSr2,,,,1,,,,,,,,, +SHARED2PAY - Value toll,@df.c_cost * odt_skims['HOV2TOLL_VTOLL'] / costShareSr2,,,,1,,,,,,,,, +SHARED2PAY - One person household,@c_hhsize1_sr * (df.hhsize == 1),,,,1,,,,,,,,, +SHARED2PAY - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,,,1,,,,,,,,, +SHARED2PAY - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,,,1,,,,,,,,, +#Shared ride 3+,,,,,,,,,,,,,, +SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, +SHARED3FREE - In-vehicle time,@c_ivt * odt_skims['HOV3_TIME'],,,,,1,,,,,,,, +SHARED3FREE - Terminal time,@c_walktimeshort * df.total_terminal_time,,,,,1,,,,,,,, +SHARED3FREE - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV3_DIST'],,,,,1,,,,,,,, +SHARED3FREE - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr3,,,,,1,,,,,,,, +SHARED3FREE - Bridge toll,@df.c_cost * odt_skims['HOV3_BTOLL'] / costShareSr3,,,,,1,,,,,,,, +SHARED3FREE - One person household,@c_hhsize1_sr * (df.hhsize == 1),,,,,1,,,,,,,, +SHARED3FREE - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,,,,1,,,,,,,, +SHARED3FREE - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,,,,1,,,,,,,, +#Shared ride 3+ toll,,,,,,,,,,,,,, +SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, +SHARED3PAY - In-vehicle time,@c_ivt * odt_skims['HOV3TOLL_TIME'],,,,,,1,,,,,,, +SHARED3PAY - Terminal time,@c_walktimeshort * df.total_terminal_time,,,,,,1,,,,,,, +SHARED3PAY - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV3TOLL_DIST'],,,,,,1,,,,,,, +SHARED3PAY - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr3,,,,,,1,,,,,,, +SHARED3PAY - Bridge toll,@df.c_cost * odt_skims['HOV3TOLL_BTOLL'] / costShareSr3,,,,,,1,,,,,,, +SHARED3PAY - Value toll,@df.c_cost * odt_skims['HOV3TOLL_VTOLL'] / costShareSr3,,,,,,1,,,,,,, +SHARED3PAY - One person household,@c_hhsize1_sr * (df.hhsize == 1),,,,,,1,,,,,,, +SHARED3PAY - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,,,,,1,,,,,,, +SHARED3PAY - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,,,,,1,,,,,,, +#Walk,,,,,,,,,,,,,, +WALK - Time up to 2 miles,@c_walktimeshort * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,,,,1,,,,,, +WALK - Time beyond 2 of a miles,@c_walktimelong * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,,,,1,,,,,, +WALK - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,1,,,,,, +WALK - Topology,@c_topology_walk * df.trip_topology,,,,,,,1,,,,,, +#Bike,,,,,,,,,,,,,, +BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, +BIKE - Time up to 6 miles,@c_biketimeshort * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,,,,1,,,,, +BIKE - Time beyond 6 of a miles,@c_biketimelong * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,,,,1,,,,, +BIKE - Destination zone densityIndex,@c_density_index*df.density_index,,,,,,,,1,,,,, +BIKE - Topology,@c_topology_bike * df.trip_topology,,,,,,,,1,,,,, +#,Walk to Local,,,,,,,,,,,,, +#WALK_TRANSIT - Unavailable,walk_local_available == False,,,,,,,,,-999,,,, +WALK_TRANSIT - Path logsums,@tvpb_logsum_odt['WTW'],,,,,,,,,1,,,, +WALK_TRANSIT - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,1,,,, +WALK_TRANSIT - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,1,,,, +WALK_TRANSIT - Person is less than 10 years old,@c_age010_trn*(df.age <= 10),,,,,,,,,1,,,, +#,Drive to Local,,,,,,,,,,,,, +#DRIVE_TRANSIT - Unavailable,drive_local_available == False,,,,,,,,,,-999,,, +DRIVE_TRANSIT - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,, +DRIVE_TRANSIT - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,, +DRIVE_TRANSIT - Path logsums,@tvpb_logsum_odt['DTW'],,,,,,,,,,1,,, +DRIVE_TRANSIT - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,1,,, +DRIVE_TRANSIT - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,1,,, +DRIVE_TRANSIT - Person is less than 10 years old,@c_age010_trn*(df.age < 10),,,,,,,,,,1,,, +#Taxi,,,,,,,,,,,,,, +Taxi - In-vehicle time,@c_ivt * odt_skims['HOV2TOLL_TIME'],,,,,,,,,,,1,, +Taxi - Wait time,@c_ivt * 1.5 * df.origTaxiWaitTime,,,,,,,,,,,1,, +Taxi - Tolls,@df.c_cost * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,1,, +Taxi - Bridge toll,@df.c_cost * odt_skims['HOV2TOLL_BTOLL'],,,,,,,,,,,1,, +Taxi - Fare,@df.c_cost * (Taxi_baseFare + odt_skims['HOV2TOLL_DIST'] * Taxi_costPerMile + odt_skims['HOV2TOLL_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,1,, +#TNC Single,,,,,,,,,,,,,, +TNC Single - In-vehicle time,@c_ivt * odt_skims['HOV2TOLL_TIME'] ,,,,,,,,,,,,1, +TNC Single - Wait time,@c_ivt * 1.5 * df.origSingleTNCWaitTime,,,,,,,,,,,,1, +TNC Single - Tolls,@df.c_cost * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,1, +TNC Single - Bridge toll,@df.c_cost * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,1, +TNC Single - Cost,"@df.c_cost * np.maximum(TNC_single_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_single_costPerMile + odt_skims['HOV2TOLL_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,1, +#TNC Shared,,,,,,,,,,,,,, +TNC Shared - In-vehicle time,@c_ivt * odt_skims['HOV2TOLL_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,1 +TNC Shared - Wait time,@c_ivt * 1.5 * df.origSharedTNCWaitTime,,,,,,,,,,,,,1 +TNC Shared - Tolls,@df.c_cost * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,1 +TNC Shared - Bridge toll,@df.c_cost * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,1 +TNC Shared - Cost,"@df.c_cost * np.maximum(TNC_shared_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2TOLL_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,1 +#,,,,,,,,,,,,,, +Auto tour mode availability,tour_mode_is_auto,,,,,,,,-999,-999,-999,,, +Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,-999,-999,-999,,-999,-999,-999,,, +Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,-999,-999,-999,,,-999,-999,,, +Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,-999,,,,,,-999,,,,, +Drive to Transit tour modes availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +Ride hail tour modes availability,tour_mode_is_ride_hail,-999,-999,,,,,,-999,,,,, +#indiv tour ASCs,,,,,,,,,,,,,, +Drive Alone tour mode ASC -- shared ride 2,@sov_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,1,1,,,,,,,,, +Drive Alone tour mode ASC -- shared ride 3+,@sov_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,1,1,,,,,,, +Drive Alone tour mode ASC -- walk,@sov_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,1,,,,,, +Drive Alone tour mode ASC -- ride hail,@sov_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,1,1,1 +Shared Ride 2 tour mode ASC -- shared ride 2,@sr2_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,1,1,,,,,,,,, +Shared Ride 2 tour mode ASC -- shared ride 3+,@sr2_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,1,1,,,,,,, +Shared Ride 2 tour mode ASC -- walk,@sr2_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,1,,,,,, +Shared Ride 2 tour mode ASC -- ride hail,@sr2_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,1,1,1 +Shared Ride 3+ tour mode ASC -- shared ride 2,@sr3p_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,1,1,,,,,,,,, +Shared Ride 3+ tour mode ASC -- shared ride 3+,@sr3p_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,1,1,,,,,,, +Shared Ride 3+ tour mode ASC -- walk,@sr3p_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,1,,,,,, +Shared Ride 3+ tour mode ASC -- ride hail,@sr3p_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,1,1,1 +Walk tour mode ASC -- ride hail,@walk_ASC_rh * df.is_indiv * (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,1,1,1 +Bike tour mode ASC -- walk,@bike_ASC_walk * df.is_indiv * (df.i_tour_mode == I_BIKE_MODE),,,,,,,1,,,,,, +Bike tour mode ASC -- ride hail,@bike_ASC_rh * df.is_indiv * (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,1,1,1 +#Walk to Transit tour mode ASC -- light rail,@walk_transit_ASC_lightrail * (df.is_indiv & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,1,,, +#Walk to Transit tour mode ASC -- ferry,@walk_transit_ASC_ferry * (df.is_indiv & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,1,,, +#Walk to Transit tour mode ASC -- express bus,@walk_transit_ASC_express * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#Walk to Transit tour mode ASC -- heavy rail,@walk_transit_ASC_heavyrail * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#Walk to Transit tour mode ASC -- commuter rail,@walk_transit_ASC_commuter * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#Walk to Transit tour mode ASC -- shared ride 2,@walk_transit_ASC_sr2 * (df.is_indiv & df.tour_mode_is_walk_transit),,,1,1,,,,,,,,, +#Walk to Transit tour mode ASC -- shared ride 3+,@walk_transit_ASC_sr3p * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,1,1,,,,,,, +#Walk to Transit tour mode ASC -- walk,@walk_transit_ASC_walk * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,1,,,,,, +#Walk to Transit tour mode ASC -- ride hail,@walk_transit_ASC_rh * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,1,1,1 +#Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@drive_transit_ASC_lightrail * (df.is_indiv & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,, +#Drive to Transit tour mode ASC -- ferry,@drive_transit_ASC_ferry * (df.is_indiv & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,, +#Drive to Transit tour mode ASC -- express bus,@drive_transit_ASC_express * (df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +#Drive to Transit tour mode ASC -- heavy rail,@drive_transit_ASC_heavyrail * (df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +#Drive to Transit tour mode ASC -- commuter rail,@drive_transit_ASC_commuter * (df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +#Drive to Transit tour mode ASC -- ride hail,@drive_transit_ASC_rh * (df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,1,1,1 +Ride Hail tour mode ASC -- shared ride 2,@ride_hail_ASC_sr2 * (df.is_indiv & df.tour_mode_is_ride_hail),,,1,1,,,,,,,,, +Ride Hail tour mode ASC -- shared ride 3+,@ride_hail_ASC_sr3p * (df.is_indiv & df.tour_mode_is_ride_hail),,,,,1,1,,,,,,, +Ride Hail tour mode ASC -- walk,@ride_hail_ASC_walk * (df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,1,,,,,, +Ride Hail tour mode ASC -- walk to transit,@ride_hail_ASC_walk_transit * (df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,1,1,,, +Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_taxi * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,1,, +Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_tnc_single * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,1, +Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_tnc_shared * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,1 +#joint tour ASCs,,,,,,,,,,,,,, +joint - auto tour mode ASC -- shared ride 2,@joint_auto_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,1,1,,,,,,,,, +joint - auto tour mode ASC -- shared ride 3+,@joint_auto_ASC_sr3p * (df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,1,1,,,,,,, +joint - auto tour mode ASC -- walk,@joint_auto_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,1,,,,,, +joint - auto tour mode ASC -- ride hail,@joint_auto_ASC_rh * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,1,1,1 +joint - Walk tour mode ASC -- ride hail,@joint_walk_ASC_rh * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,1,,,,,, +joint - Bike tour mode ASC -- walk,@joint_bike_ASC_walk * df.is_joint * (df.i_tour_mode == I_BIKE_MODE),,,,,,,1,,,,,, +joint - Bike tour mode ASC -- ride hail,@joint_bike_ASC_rh * df.is_joint * (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,1,1,1 +#joint - Walk to Transit tour mode ASC -- light rail,@joint_walk_transit_ASC_lightrail * (df.is_joint & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,1,,, +#joint - Walk to Transit tour mode ASC -- ferry,@joint_walk_transit_ASC_ferry * (df.is_joint & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,1,,, +#joint - Walk to Transit tour mode ASC -- express bus,@joint_walk_transit_ASC_express * (df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#joint - Walk to Transit tour mode ASC -- heavy rail,@joint_walk_transit_ASC_heavyrail * (df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#joint - Walk to Transit tour mode ASC -- commuter rail,@joint_walk_transit_ASC_commuter * (df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#joint - Walk to Transit tour mode ASC -- shared ride 2,@joint_walk_transit_ASC_sr2 * (df.is_joint & df.tour_mode_is_walk_transit),,,1,1,,,,,,,,, +#joint - Walk to Transit tour mode ASC -- shared ride 3+,@joint_walk_transit_ASC_sr3p * (df.is_joint & df.tour_mode_is_walk_transit),,,,,1,1,,,,,,, +joint - Walk to Transit tour mode ASC -- walk,@joint_walk_transit_ASC_walk * (df.is_joint & df.tour_mode_is_walk_transit),,,,,,,1,,,,,, +joint - Walk to Transit tour mode ASC -- ride hail,@joint_walk_transit_ASC_rh * (df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,1,1,1 +#joint - Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@joint_drive_transit_ASC_lightrail * (df.is_joint & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,, +#joint - Drive to Transit tour mode ASC -- ferry,@joint_drive_transit_ASC_ferry * (df.is_joint & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,, +#joint - Drive to Transit tour mode ASC -- express bus,@joint_drive_transit_ASC_express * (df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +#joint - Drive to Transit tour mode ASC -- heavy rail,@joint_drive_transit_ASC_heavyrail * (df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +#joint - Drive to Transit tour mode ASC -- commuter rail,@joint_drive_transit_ASC_commuter * (df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +joint - Drive to Transit tour mode ASC -- ride hail,@joint_drive_transit_ASC_rh * (df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,1,1,1 +joint - Ride Hail tour mode ASC -- shared ride 2,@joint_ride_hail_ASC_sr2 * (df.is_joint & df.tour_mode_is_ride_hail),,,1,1,,,,,,,,, +joint - Ride Hail tour mode ASC -- shared ride 3+,@joint_ride_hail_ASC_sr3p * (df.is_joint & df.tour_mode_is_ride_hail),,,,,1,1,,,,,,, +joint - Ride Hail tour mode ASC -- walk,@joint_ride_hail_ASC_walk * (df.is_joint & df.tour_mode_is_ride_hail),,,,,,,1,,,,,, +joint - Ride Hail tour mode ASC -- walk to transit,@joint_ride_hail_ASC_walk_transit * (df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,1,1,,, +joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_taxi * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,1,, +joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_tnc_single * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,1, +joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_tnc_shared * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,1 +#,,,,,,,,,,,,,, +Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,,,,-999,,,,,, +Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,,,,-999,,,,, +Origin density index,@(c_origin_density_index*df.origin_density_index).clip(c_origin_density_index_max) if origin_density_applied else 0,,,,,,,1,1,1,1,,1,1 +Walk-express penalty for intermediate stops,@c_walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,,, +TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.yaml new file mode 100644 index 0000000000..33a1e207b7 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.yaml @@ -0,0 +1,181 @@ +SPEC: trip_mode_choice.csv +COEFFICIENTS: trip_mode_choice_coeffs.csv + +tvpb_mode_path_types: + DRIVE_TRANSIT: DTW + WALK_TRANSIT: WTW + + +LOGIT_TYPE: NL + +NESTS: + name: root + coefficient: 1.00 + alternatives: + - name: AUTO + coefficient: 0.72 + alternatives: + - name: DRIVEALONE + coefficient: 0.35 + alternatives: + - DRIVEALONEFREE + - DRIVEALONEPAY + - name: SHAREDRIDE2 + coefficient: 0.35 + alternatives: + - SHARED2FREE + - SHARED2PAY + - name: SHAREDRIDE3 + coefficient: 0.35 + alternatives: + - SHARED3FREE + - SHARED3PAY + - name: NONMOTORIZED + coefficient: 0.72 + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: 0.72 + alternatives: + - WALK_TRANSIT + - DRIVE_TRANSIT + - name: RIDEHAIL + coefficient: 0.36 + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +CONSTANTS: + orig_col_name: origin + dest_col_name: destination + costPerMile: 18.29 + costShareSr2: 1.75 + costShareSr3: 2.50 + waitThresh: 10.00 + walkThresh: 1.00 + shortWalk: 0.333 + longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 6.00 + bikeSpeed: 12.00 +# maxCbdAreaTypeThresh: 2 +# indivTour: 1.00000 +# upperEA: 5 +# upperAM: 10 +# upperMD: 15 +# upperPM: 19 + I_MODE_MAP: + DRIVEALONEFREE: 1 + DRIVEALONEPAY: 2 + SHARED2FREE: 3 + SHARED2PAY: 4 + SHARED3FREE: 5 + SHARED3PAY: 6 + WALK: 7 + BIKE: 8 + WALK_LOC: 9 + WALK_LRF: 10 + WALK_EXP: 11 + WALK_HVY: 12 + WALK_COM: 13 + DRIVE_LOC: 14 + DRIVE_LRF: 15 + DRIVE_EXP: 16 + DRIVE_HVY: 17 + DRIVE_COM: 18 + TAXI: 19 + TNC_SINGLE: 20 + TNC_SHARED: 21 + I_SOV_MODES: [1, 2] + I_SR2_MODES: [3, 4] + I_SR3P_MODES: [5, 6] + I_AUTO_MODES: [1, 2, 3, 4, 5, 6] + I_WALK_MODE: 7 + I_BIKE_MODE: 8 + I_WALK_TRANSIT_MODES: [9, 10, 11, 12, 13] + I_DRIVE_TRANSIT_MODES: [14, 15, 16, 17, 18] + I_RIDE_HAIL_MODES: [19, 20, 21] + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: trip_mode_choice_annotate_trips_preprocessor + DF: df + TABLES: + - land_use + - tours + +# - SPEC: trip_mode_choice_annotate_trips_preprocessor2 +# DF: df +# TABLES: +# - land_use + +# to reduce memory needs filter chooser table to these fields +TOURS_MERGED_CHOOSER_COLUMNS: + - hhsize + - age + - auto_ownership + - number_of_participants + - tour_category + - parent_tour_id + - tour_mode + - duration + - value_of_time + - tour_type + - free_parking_at_work + - income_segment +# - demographic_segment + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv new file mode 100644 index 0000000000..8f777d2461 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,97 @@ +Description,Target,Expression +,is_joint,(df.number_of_participants > 1) +,is_indiv,(df.number_of_participants == 1) +,is_atwork_subtour,~df.parent_tour_id.isnull() +,c_cost,(0.60 * c_ivt) / df.value_of_time +#,, +# TVPB,, +#,, +,demographic_segment,df.income_segment.map(TVPB_demographic_segments_by_income_segment) +,c_ivt_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" +,c_cost_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" + +#,, +#atwork subtours,, +#FIXME tripModeChoice uec wrongly conflates these with tour_mode_is_bike?,, +,parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id).fillna('')" +,work_tour_is_SOV,"parent_tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" +,work_tour_is_bike,parent_tour_mode=='BIKE' +#,, +,i_tour_mode,df.tour_mode.map(I_MODE_MAP) +,tour_mode_is_SOV,i_tour_mode.isin(I_SOV_MODES) +,tour_mode_is_auto,i_tour_mode.isin(I_AUTO_MODES) +,tour_mode_is_walk,i_tour_mode == I_WALK_MODE +,tour_mode_is_bike,i_tour_mode == I_BIKE_MODE +,tour_mode_is_walk_transit,i_tour_mode.isin(I_WALK_TRANSIT_MODES) +,tour_mode_is_drive_transit,i_tour_mode.isin(I_DRIVE_TRANSIT_MODES) +,tour_mode_is_ride_hail,i_tour_mode.isin(I_RIDE_HAIL_MODES) +#,, +,inbound,~df.outbound +,first_trip,df.trip_num == 1 +,last_trip,df.trip_num == df.trip_count +origin terminal time not counted at home,_origin_terminal_time,"np.where(df.outbound & first_trip, 0, reindex(land_use.TERMINAL, df[ORIGIN]))" +dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & last_trip, 0, reindex(land_use.TERMINAL, df[DESTINATION]))" +,total_terminal_time,_origin_terminal_time + _dest_terminal_time +#,, +,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work +,dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[DESTINATION])" +,origin_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[ORIGIN])" +,origin_duration,"np.where(first_trip, np.where(inbound,df.duration * ~free_parking_available,0), 1)" +,dest_duration,"np.where(last_trip, np.where(inbound, df.duration * ~free_parking_available, 0), 1)" +,origin_parking_cost,origin_duration*origin_hourly_peak_parking_cost +,dest_parking_cost,dest_duration*dest_hourly_peak_parking_cost +,total_parking_cost,(origin_parking_cost + dest_parking_cost) / 2.0 +,trip_topology,"np.where(df.outbound, reindex(land_use.TOPOLOGY, df[DESTINATION]), reindex(land_use.TOPOLOGY, df[ORIGIN]))" +,density_index,"np.where(df.outbound, reindex(land_use.density_index, df[DESTINATION]), reindex(land_use.density_index, df[ORIGIN]))" +,origin_density_index,"np.where(df.outbound, reindex(land_use.density_index, df[ORIGIN]), reindex(land_use.density_index, df[DESTINATION]))" +# FIXME no transit subzones so all zones short walk to transit,, +,_walk_transit_origin,True +,_walk_transit_destination,True +,walk_transit_available,_walk_transit_origin & _walk_transit_destination +,drive_transit_available,"np.where(df.outbound, _walk_transit_destination, _walk_transit_origin) & (df.auto_ownership > 0)" +,origin_walk_time,shortWalk*60/walkSpeed +,destination_walk_time,shortWalk*60/walkSpeed +# RIDEHAIL,, +,origin_density_measure,"(reindex(land_use.TOTPOP, df[orig_col_name]) + reindex(land_use.TOTEMP, df[orig_col_name])) / (reindex(land_use.TOTACRE, df[orig_col_name]) / 640)" +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +#,, +,sov_available,odt_skims['SOV_TIME']>0 +,hov2_available,odt_skims['HOV2_TIME']>0 +,hov3_available,odt_skims['HOV3_TIME']>0 +,sovtoll_available,odt_skims['SOVTOLL_VTOLL']>0 +,hov2toll_available,odt_skims['HOV2TOLL_VTOLL']>0 +,hov3toll_available,odt_skims['HOV3TOLL_VTOLL']>0 +#,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_WLK_TOTIVT']/100>0) +#,walk_lrf_available,walk_transit_available & (i_tour_mode >= 10) & (odt_skims['WLK_LRF_WLK_KEYIVT']/100>0) +#,walk_express_available,walk_transit_available & (i_tour_mode >= 11) & (odt_skims['WLK_EXP_WLK_KEYIVT']/100>0) +#,walk_heavyrail_available,walk_transit_available & (i_tour_mode >= 12) & (odt_skims['WLK_HVY_WLK_KEYIVT']/100>0) +#,walk_commuter_available,walk_transit_available & (i_tour_mode >= 13) & (odt_skims['WLK_COM_WLK_KEYIVT']/100>0) +#,drive_local_available_outbound,drive_transit_available & df.outbound & (odt_skims['DRV_LOC_WLK_TOTIVT']/100>0) +#,drive_local_available_inbound,drive_transit_available & ~df.outbound & (odt_skims['WLK_LOC_DRV_TOTIVT']/100>0) +#,drive_lrf_available_outbound,drive_transit_available & df.outbound & (i_tour_mode >= 15) & (odt_skims['DRV_LRF_WLK_KEYIVT']/100>0) +#,drive_lrf_available_inbound,drive_transit_available & ~df.outbound & (i_tour_mode >= 15) & (odt_skims['WLK_LRF_DRV_KEYIVT']/100>0) +#,drive_express_available_outbound,drive_transit_available & df.outbound & (i_tour_mode >= 16) & (odt_skims['DRV_EXP_WLK_KEYIVT']/100>0) +#,drive_express_available_inbound,drive_transit_available & ~df.outbound & (i_tour_mode >= 16) & (odt_skims['WLK_EXP_DRV_KEYIVT']/100>0) +#,drive_heavyrail_available_outbound,drive_transit_available & df.outbound & (i_tour_mode >= 17) & (odt_skims['DRV_HVY_WLK_KEYIVT']/100>0) +#,drive_heavyrail_available_inbound,drive_transit_available & ~df.outbound & (i_tour_mode >= 17) & (odt_skims['WLK_HVY_DRV_KEYIVT']/100>0) +#,drive_commuter_available_outbound,drive_transit_available & df.outbound & (i_tour_mode >= 18) & (odt_skims['DRV_COM_WLK_KEYIVT']/100>0) +#,drive_commuter_available_inbound,drive_transit_available & ~df.outbound & (i_tour_mode >= 18) & (odt_skims['WLK_COM_DRV_KEYIVT']/100>0) +#,walk_ferry_available,walk_lrf_available & (odt_skims['WLK_LRF_WLK_FERRYIVT']/100>0) +#,_drive_ferry_available_outbound,drive_lrf_available_outbound & (odt_skims['DRV_LRF_WLK_FERRYIVT']/100>0) +#,_drive_ferry_available_inbound,drive_lrf_available_inbound & (odt_skims['WLK_LRF_DRV_FERRYIVT']/100>0) +#,drive_ferry_available,"np.where(df.outbound, _drive_ferry_available_outbound, _drive_ferry_available_inbound)" +#,od_dist_walk,od_skims['DISTWALK'] +#,do_dist_walk,od_skims.reverse('DISTWALK') +#,max_dist_walk,od_skims.max('DISTWALK') +#,dist_bike,od_skims['DISTBIKE'] +#,dist_only,od_skims['DIST'] diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_tap_tap_.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_tap_tap_.csv new file mode 100644 index 0000000000..2572c99d90 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_tap_tap_.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +#,, +,_inVehicleTime,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'TRN_IVT_FAST')" +,_iwait,"out_of_vehicle_wait_time_weight * los.get_tappairs3d(df.btap, df.atap, df.tod, 'TRN_IWAIT_FAST')" +,_xwait,"out_of_vehicle_wait_time_weight * los.get_tappairs3d(df.btap, df.atap, df.tod, 'TRN_XWAIT_FAST')" +,_waux,"out_of_vehicle_walk_time_weight * los.get_tappairs3d(df.btap, df.atap, df.tod, 'TRN_WAUX_FAST')" +,_outOfVehicleTime,_iwait + _xwait + _waux +,transit_time,(_inVehicleTime + _outOfVehicleTime) / 100.0 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv new file mode 100644 index 0000000000..35446f0ec8 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv @@ -0,0 +1,2 @@ +Description,Target,Expression +walk time,walk_time,"out_of_vehicle_walk_time_weight * df.walk_time" diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_drive_maz_tap.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_drive_maz_tap.csv new file mode 100644 index 0000000000..9d402a2976 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_drive_maz_tap.csv @@ -0,0 +1,4 @@ +Label,Description,Expression,utility +util_drive_available,walk available,@df.drive_time.isna() * C_UNAVAILABLE,1 +util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_drive * df.drive_time",1 +util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * df.DIST * c_auto_operating_cost_per_mile",1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap.csv new file mode 100644 index 0000000000..afd50f1642 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap.csv @@ -0,0 +1,69 @@ +Label,Description,Expression,fastest,cheapest,shortest +# fastest,,,,, +util_transit_available_fastest,transit_available,@~df.transit_available_fastest * C_UNAVAILABLE,1,, +#,,, FIXME demonstrate that we can use path inor (access and egress modes here),, +util_bus_xfer_fastest,number of transfers,"@C_DRIVE_TRANSFER_PENALTY * (access_mode == 'drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_BOARDS_FAST')-1).clip(0)",1,, +#,,, local bus,, +util_bus_ivt_fastest,local bus in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_FAST')",1,, +util_bus_wait_fastest,local bus wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_FAST')",1,, +util_bus_xwait_fastest,local bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_FAST')",1,, +util_bus_fare_fastest,local bus fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_FAST')",1,, +##,,, commuter bus,, +#util_com_ivt_fastest,commuter bus in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_FAST')",1,, +#util_com_wait_fastest,commuter bus wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_FAST')",1,, +#util_com_xwait_fastest,commuter bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_FAST')",1,, +#util_com_fare_fastest,commuter bus fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_FAST')",1,, +##,,, express,, +#util_exp_ivt_fastest,express in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_FAST')",1,, +#util_exp_wait_fastest,express wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_FAST')",1,, +#util_exp_xwait_fastest,express bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_FAST')",1,, +#util_exp_fare_fastest,express fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_FAST')",1,, +##,,, heavy,, +#util_hvy_ivt_fastest,heavy in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_FAST')",1,, +#util_hvy_wait_fastest,heavy wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_FAST')",1,, +#util_hvy_xwait_fastest,heavy bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_FAST')",1,, +#util_hvy_fare_fastest,heavy fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_FAST')",1,, +## cheapest,,,,, +#util_transit_available_cheapest,transit_available,@~df.transit_available_cheapest * C_UNAVAILABLE,,1, +#,,,, local bus, +util_bus_ivt_cheapest,local bus in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_CHEAP')",,1 +util_bus_wait_cheapest,local bus wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_CHEAP')",,1 +util_bus_xwait_cheapest,local bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_CHEAP')",,1 +util_bus_fare_cheapest,local bus fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_CHEAP')",,1 +##,,,, commuter bus, +#util_com_ivt_cheapest,commuter bus in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_CHEAP')",,1, +#util_com_wait_cheapest,commuter bus wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_CHEAP')",,1, +#util_com_xwait_cheapest,commuter bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_CHEAP')",,1, +#util_com_fare_cheapest,commuter bus fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_CHEAP')",,1, +##,,,, express, +#util_exp_ivt_cheapest,express in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_CHEAP')",,1, +#util_exp_wait_cheapest,express wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_CHEAP')",,1, +#util_exp_xwait_cheapest,express bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_CHEAP')",,1, +#util_exp_fare_cheapest,express fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_CHEAP')",,1, +##,,,, heavy, +#util_hvy_ivt_cheapest,heavy in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_CHEAP')",,1, +#util_hvy_wait_cheapest,heavy wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_CHEAP')",,1, +#util_hvy_xwait_cheapest,heavy bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_CHEAP')",,1, +#util_hvy_fare_cheapest,heavy fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_CHEAP')",,1, +## shortest,,,,, +#util_transit_available_shortest,transit_available,@~df.transit_available_shortest * C_UNAVAILABLE,,,1 +#,,,,, local bus +util_bus_ivt_shortest,local bus in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_SHORT')",,,1 +util_bus_wait_shortest,local bus wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_SHORT')",,,1 +util_bus_xwait_shortest,local bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_SHORT')",,,1 +util_bus_fare_shortest,local bus fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_SHORT')",,,1 +##,,,,, commuter bus +#util_com_ivt_shortest,commuter bus in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_SHORT')",,,1 +#util_com_wait_shortest,commuter bus wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_SHORT')",,,1 +#util_com_xwait_shortest,commuter bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_SHORT')",,,1 +#util_com_fare_shortest,commuter bus fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_SHORT')",,,1 +##,,,,, express +#util_exp_ivt_shortest,express in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_SHORT')",,,1 +#util_exp_wait_shortest,express wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_SHORT')",,,1 +#util_exp_xwait_shortest,express bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_SHORT')",,,1 +#util_exp_fare_shortest,express fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_SHORT')",,,1 +##,,,,, heav +#util_hvy_ivt_shortest,heavy in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_SHORT')",,,1 +#util_hvy_wait_shortest,heavy wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_SHORT')",,,1 +#util_hvy_xwait_shortest,heavy bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_SHORT')",,,1 +#util_hvy_fare_shortest,heavy fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_SHORT')",,,1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000000..cbac046cf0 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv @@ -0,0 +1,26 @@ +Description,Target,Expression +# demographic segment,, +,c_ivt_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" +,c_cost_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" +# fastest,, +,_bus_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_FAST')>0" +,_com_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_FAST')>0" +,_exp_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_FAST')>0" +,_hvy_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_FAST')>0" +,_lrf_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_FAST')>0" +,transit_available_fastest,_bus_available_fastest | _com_available_fastest | _exp_available_fastest | _hvy_available_fastest | _lrf_available_fastest +,not_transit_available_fastest,~transit_available_fastest +# cheapest,, +,_bus_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_CHEAP')>0" +,_com_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_CHEAP')>0" +,_exp_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_CHEAP')>0" +,_hvy_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_CHEAP')>0" +,_lrf_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_CHEAP')>0" +,transit_available_cheapest,_bus_available_cheapest | _com_available_cheapest | _exp_available_cheapest | _hvy_available_cheapest | _lrf_available_cheapest +# shortest,, +,_bus_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_SHORT')>0" +,_com_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_SHORT')>0" +,_exp_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_SHORT')>0" +,_hvy_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_SHORT')>0" +,_lrf_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_SHORT')>0" +,transit_available_shortest,_bus_available_shortest | _com_available_shortest | _exp_available_shortest | _hvy_available_shortest | _lrf_available_shortest diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_walk_maz_tap.csv b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_walk_maz_tap.csv new file mode 100644 index 0000000000..69ff955b0f --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_walk_maz_tap.csv @@ -0,0 +1,3 @@ +Label,Description,Expression,utility +util_walk_available,walk available,@df.walk_time.isna() * C_UNAVAILABLE,1 +util_walk_time,walk time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_walk * df.walk_time",1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_households.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_households.csv new file mode 100644 index 0000000000..c15f4bf597 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_households.csv @@ -0,0 +1,2 @@ +Description,Target,Expression +,num_persons,persons.groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8) diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_persons.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_persons.csv new file mode 100644 index 0000000000..1997ab010c --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_persons.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +#,, annotate persons table after import +is_university,is_university,"type == ""University student""" +is_male,is_male,"df.SEX == 1" +#,, +#home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_tours.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_tours.csv new file mode 100644 index 0000000000..5ba88b101e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_tours.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +,tour_category,tours.tour_category.str.lower() +,tour_purpose,tours.tour_purpose.str.lower() +#,, NOTE: the following might not be correct for all tour types, but is correct for work tours +,tour_type,tours.tour_purpose.str.lower() +,destination,tours.dest_mgra +#,orig_TAZ,"reindex(land_use.TAZ, tours.orig_mgra)" +#,dest_TAZ,"reindex(land_use.TAZ, tours.dest_mgra)" diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/constants.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/constants.yaml new file mode 100755 index 0000000000..626a0c415e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/constants.yaml @@ -0,0 +1,64 @@ +## ActivitySim +## See full license in LICENSE.txt. + +walk_speed: 3.00 + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +#INCOME_SEGMENT_LOW: 1 +#INCOME_SEGMENT_MED: 2 +#INCOME_SEGMENT_HIGH: 3 +#INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/destination_choice_size_terms.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/destination_choice_size_terms.csv new file mode 100644 index 0000000000..7f70421e85 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/destination_choice_size_terms.csv @@ -0,0 +1,28 @@ +model_selector,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE +workplace,work_low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0 +workplace,work_med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0 +workplace,work_high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0 +workplace,work_veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0 +school,university,0,0,0,0,0,0,0,0,0,0.592,0.408 +school,gradeschool,0,0,0,0,0,0,0,1,0,0,0 +school,highschool,0,0,0,0,0,0,0,0,1,0,0 +non_mandatory,escort,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +#non_mandatory,escort_kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +#non_mandatory,escort_nokids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +non_mandatory,shopping,0,1,0,0,0,0,0,0,0,0,0 +non_mandatory,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 +non_mandatory,othmaint,0,0.482,0,0.518,0,0,0,0,0,0,0 +non_mandatory,social,0,0.522,0,0.478,0,0,0,0,0,0,0 +non_mandatory,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 +atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0 +trip,work,0,1,1,1,1,1,1,0,0,0,0 +trip,escort,0.001,0.225,0,0.144,0,0,0,0.464,0.166,0,0 +trip,shopping,0.001,0.999,0,0,0,0,0,0,0,0,0 +trip,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 +trip,othmaint,0.001,0.481,0,0.518,0,0,0,0,0,0,0 +trip,social,0.001,0.521,0,0.478,0,0,0,0,0,0,0 +trip,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 +trip,univ,0.001,0,0,0,0,0,0,0,0,0.592,0.408 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,, +#trip,gradeschool,0,0,0,0,0,0,0,1,0,0,0 +#trip,highschool,0,0,0,0,0,0,0,0,1,0,0 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_households.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_households.yaml new file mode 100644 index 0000000000..0f36c65985 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_households.yaml @@ -0,0 +1,21 @@ +# +annotate_tables: + - tablename: persons + annotate: + SPEC: annotate_persons + DF: persons + TABLES: + - households + - tablename: households + annotate: + SPEC: annotate_households + DF: households + TABLES: + - persons +# - land_use +# - tablename: persons +# annotate: +# SPEC: annotate_persons_after_hh +# DF: persons +# TABLES: +# - households diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_landuse.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_landuse.yaml new file mode 100644 index 0000000000..79b06cc032 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_landuse.yaml @@ -0,0 +1,7 @@ + + +#annotate_tables: +# - tablename: land_use +# annotate: +# SPEC: annotate_landuse +# DF: land_use diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_tours.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_tours.yaml new file mode 100644 index 0000000000..4b626ee360 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_tours.yaml @@ -0,0 +1,7 @@ +# + +annotate_tours: + SPEC: annotate_tours + DF: tours + TABLES: + - land_use diff --git a/other_resources/example_multiple_zone/configs/logging.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/logging.yaml old mode 100644 new mode 100755 similarity index 71% rename from other_resources/example_multiple_zone/configs/logging.yaml rename to activitysim/examples/example_multiple_zone/configs_3_zone_marin/logging.yaml index f2a0e6cb77..33b6a4b1cc --- a/other_resources/example_multiple_zone/configs/logging.yaml +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/logging.yaml @@ -1,54 +1,54 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: !!python/name:logging.DEBUG - handlers: [console, logfile] - - loggers: - - activitysim: - level: !!python/name:logging.DEBUG - handlers: [console, logfile] - propagate: false - - orca: - level: !!python/name:logging.WARN - handlers: [console, logfile] - propagate: false - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: !!python/name:logging.NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: simpleFormatter - #level: !!python/name:logging.NOTSET - level: !!python/name:logging.DEBUG - - formatters: - - simpleFormatter: - class: !!python/name:logging.Formatter - format: '%(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: !!python/name:logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/network_los.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/network_los.yaml new file mode 100755 index 0000000000..7f6ddfcc9a --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/network_los.yaml @@ -0,0 +1,186 @@ +inherit_settings: True + +zone_system: 3 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: True +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +# rebuild and overwrite existing tap_tap_utilities cache +rebuild_tvpb_cache: False + + +# when checkpointing cache. also write a csv version of tvpb cache for tracing +# (writes csv file when writing/checkpointing cache (i.e. when cached changed) even if rebuild_tvpb_cache is False) +# (n.b. csv file could be quite large if cache is STATIC!) +trace_tvpb_cache_as_csv: True + +taz_skims: + - HWYSKMAM_taz_rename.omx + - HWYSKMEA_taz_rename.omx + - HWYSKMEV_taz_rename.omx + - HWYSKMMD_taz_rename.omx + - HWYSKMPM_taz_rename.omx + +tap_skims: + # we require that skims for all tap_tap sets have unique names + # and can therefor share a single skim_dict without name collision + # e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM + - transit_skims_AM_SET1_rename.omx + - transit_skims_AM_SET2_rename.omx + - transit_skims_AM_SET3_rename.omx + - transit_skims_EA_SET1_rename.omx + - transit_skims_EA_SET2_rename.omx + - transit_skims_EA_SET3_rename.omx + - transit_skims_EV_SET1_rename.omx + - transit_skims_EV_SET2_rename.omx + - transit_skims_EV_SET3_rename.omx + - transit_skims_MD_SET1_rename.omx + - transit_skims_MD_SET2_rename.omx + - transit_skims_MD_SET3_rename.omx + - transit_skims_PM_SET1_rename.omx + - transit_skims_PM_SET2_rename.omx + - transit_skims_PM_SET3_rename.omx + + +# FIXME why no taz.csv? +# tas: taz.csv + +maz: maz_taz.csv + +tap: tap_data.csv + +tap_lines: tap_lines.csv + +maz_to_maz: + tables: + - maz_maz_walk.csv + - maz_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + # blend distance of 0 means no blending + WALK_DIST: 0 + BIKE_DIST: 0 + + +maz_to_tap: + walk: + table: maz_tap_walk.csv + # if provided, this column will be used (together with tap_lines table) to trim the near tap set + # to only include the nearest tap to origin when more than one tap serves the same line + tap_line_distance_col: WALK_TRANSIT_DIST + max_dist: 1.2 + drive: + table: maz_taz_tap_drive.csv + # not trimming because drive_maz_tap utility calculations take into account both drive and walk time and cost + # though some sort of trimming appears to have been done as there are not so many of these in marin data + #tap_line_distance_col: DDIST + + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + periods: [0, 12, 20, 30, 38, 48] + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + + +# transit virtual path builder settings +TVPB_SETTINGS: + + tour_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + paths_nest_nesting_coefficient: 1 + DTW: + access: drive + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + paths_nest_nesting_coefficient: 1 + WTD: + access: walk + egress: drive + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + paths_nest_nesting_coefficient: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + demographic_segment: *demographic_segments + tod: *skim_time_period_labels + access_mode: ['drive', 'walk'] + attributes_as_columns: + - demographic_segment + - tod + + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - WALK_TRANSIT_DIST + drive: + SPEC: tvpb_utility_drive_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - DDIST + - DTIME + - WDIST + + CONSTANTS: + C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id + C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id + TVPB_demographic_segments_by_income_segment: + 1: *low_income_segment_id + 2: *low_income_segment_id + 3: *high_income_segment_id + 4: *high_income_segment_id + c_ivt_high_income: -0.016 # use tour constant from TM2 + c_ivt_low_income: -0.016 # use tour constant from TM2 + c_cost_high_income: -0.00112 + c_cost_low_income: -0.00112 + c_auto_operating_cost_per_mile: 18.29 + # constants used in maz_tap and tap_tap utility expressions + c_drive: 1.5 + c_walk: 1.7 + c_fwt: 1.5 + c_waux: 3.677 + c_xwt: 2 + c_xfers1: 30 + c_xfers2: 45 + c_xfers3: 47.026 + # no Express bus alternative-specific constant + c_lrt_asc: -17 # LRT alternative-specific constant + c_fr_asc: -35 # FR alternative-specific constant + c_hr_asc: -22 # Heavy Rail alternative-specific constant + c_cr_asc: -15 # Commuter Rail alternative-specific constant + c_cr20_40: -20 # Commuter Rail distance 20-40 miles + c_cr40plus: -30 # Commuter Rail distance >40 miles + c_drvExpress: -26 # drive to EB constant + c_drvLRT: 2 # drive to LRT constant + c_drvFR: -52 # drive to FR constant + c_drvHeavy: -41 # drive to HR constant + c_drvCR: -52 # drive to CR constant + #"max(IVT/Drive time - 0.3,0)",drvRatio,c_ivt* 6 + C_UNAVAILABLE: -999 + c_walkAcc: 3.0783 # walk to tap time + c_dtim: 2.5724 # drive to tap time + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings.yaml new file mode 100755 index 0000000000..915465d499 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings.yaml @@ -0,0 +1,237 @@ +inherit_settings: True + +# number of households to simulate +#households_sample_size: 200000 +households_sample_size: 500 + +#chunk_size: 4000000000 +chunk_size: 0 + +#trace_hh_id: 662398 +# trace_hh_id: 479617 + +# input tables +input_table_list: + - tablename: households + filename: households_asim.csv + index_col: household_id + rename_columns: + HHID: household_id + MAZ: home_zone_id + keep_columns: + - home_zone_id + - HHINCADJ + - NWRKRS_ESR + - VEH + - NP + #- MTCCountyID + #- HHT + #- BLD + #- TYPE + + - tablename: persons + filename: persons_asim.csv + index_col: person_id + rename_columns: + HHID: household_id + PERID: person_id + keep_columns: + - AGEP + - household_id + - type + - value_of_time + - fp_choice + - SEX + #- SCHL + #- OCCP + #- WKHP + #- WKW + #- EMPLOYED + #- ESR + #- SCHG + + - tablename: land_use + filename: maz_data_asim.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + CountyID: county_id + keep_columns: + - TAZ + - DistID + - ACRES + - POP + - emp_total + - hparkcost + - TERMINALTIME + - county_id + - TotInt + - EmpDen + - RetEmpDen + - DUDen +# - level_0 +# - index +# - MAZ_ORIGINAL +# - TAZ_ORIGINAL +# - DistName +# - CountyID +# - CountyName +# - HH +# - ag +# - art_rec +# - constr +# - eat +# - ed_high +# - ed_k12 +# - ed_oth +# - fire +# - gov +# - health +# - hotel +# - info +# - lease +# - logis +# - man_bio +# - man_lgt +# - man_hvy +# - man_tech +# - natres +# - prof +# - ret_loc +# - ret_reg +# - serv_bus +# - serv_pers +# - serv_soc +# - transp +# - util +# - publicEnrollGradeKto8 +# - privateEnrollGradeKto8 +# - publicEnrollGrade9to12 +# - privateEnrollGrade9to12 +# - comm_coll_enroll +# - EnrollGradeKto8 +# - EnrollGrade9to12 +# - collegeEnroll +# - otherCollegeEnroll +# - AdultSchEnrl +# - hstallsoth +# - hstallssam +# - dstallsoth +# - dstallssam +# - mstallsoth +# - mstallssam +# - park_area +# - numfreehrs +# - dparkcost +# - mparkcost +# - ech_dist +# - hch_dist +# - parkarea +# - MAZ_X +# - MAZ_Y +# - PopDen +# - IntDenBin +# - EmpDenBin +# - DuDenBin +# - PopEmpDenPerMi +# - mgra +# - mgraParkArea +# - lsWgtAvgCostM +# - lsWgtAvgCostD +# - lsWgtAvgCostH + + - tablename: tours + filename: work_tours.csv + # index_col: + rename_columns: + hh_id: household_id + start_period: start + end_period: end + tour_id: tm2_tour_id + tour_mode: tm2_tour_mode + out_btap: tm2_out_btap + out_atap: tm2_out_atap + in_btap: tm2_in_btap + in_atap: tm2_in_atap + out_set: tm2_out_set + in_set: tm2_in_set + keep_columns: + - person_id + - household_id + - tour_category + - tour_purpose + - orig_mgra + - dest_mgra + - start + - end + # ctramp tm2 fields for validation + - tm2_tour_id # really just ordinal position in ctramp tour file, put probably will be useful for validation + - tm2_tour_mode + - tm2_out_btap + - tm2_out_atap + - tm2_in_btap + - tm2_in_atap + - tm2_out_set + - tm2_in_set +# - person_num +# - person_type +# - tour_distance +# - tour_time +# - atWork_freq +# - num_ob_stops +# - num_ib_stops + + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +#resume_after: initialize_tvpb + +models: + - initialize_landuse + - initialize_households + - initialize_tours + # --- STATIC cache prebuild steps + # single-process step to create attribute_combination list + - initialize_los + # multi-processable step to build STATIC cache + # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) + - initialize_tvpb + # --- + - tour_mode_choice_simulate + - write_data_dictionary + - track_skim_usage + - write_tables + - write_summaries + +output_tables: + h5_store: False + action: include + prefix: final_ + # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - attribute_combinations + +output_summaries: + tours: + - tour_mode + - od_path_set + - do_path_set diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings_mp.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings_mp.yaml new file mode 100644 index 0000000000..042a6a2c52 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings_mp.yaml @@ -0,0 +1,71 @@ +inherit_settings: settings.yaml + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + + +# - ------------------------- dev config +multiprocess: True +strict: False +mem_tick: 30 +use_shadow_pricing: False + +# if commented out, inherits setting values from settings.yaml +#households_sample_size: 100 +#chunk_size: 6000000000 + +num_processes: 4 + +# - ------------------------- + +# not recommended or supported for multiprocessing +want_dest_choice_sample_tables: False + +#read_skim_cache: True +#write_skim_cache: True + +# - tracing +trace_hh_id: +trace_od: + +# to resume after last successful checkpoint, specify resume_after: _ +#resume_after: initialize_tvpb + + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_tvpb + begin: initialize_tvpb + #num_processes: 2 + chunk_size: 0 + slice: + tables: + - attribute_combinations + - name: mp_mode_choice + begin: tour_mode_choice_simulate + #num_processes: 2 + #chunk_size: 0 + slice: + tables: + - households + - persons + - tours + - name: mp_summarize + begin: write_data_dictionary + + +output_tables: + action: include + prefix: final_ + # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes + sort: True + tables: + - checkpoints + - households + - persons + - tours + - attribute_combinations + + + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/shadow_pricing.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/shadow_pricing.yaml new file mode 100644 index 0000000000..f37c10d587 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/shadow_pricing.yaml @@ -0,0 +1,34 @@ +#shadow_pricing_models: +# school: school_location +# workplace: workplace_location + +# global switch to enable/disable loading of saved shadow prices +# (ignored if global use_shadow_pricing switch is False) +LOAD_SAVED_SHADOW_PRICES: True + +# number of shadow price iterations for cold start +MAX_ITERATIONS: 10 + +# number of shadow price iterations for warm start (after loading saved shadow_prices) +MAX_ITERATIONS_SAVED: 1 + +# ignore criteria for zones smaller than size_threshold +SIZE_THRESHOLD: 10 + +# zone passes if modeled is within percent_tolerance of predicted_size +PERCENT_TOLERANCE: 5 + +# max percentage of zones allowed to fail +FAIL_THRESHOLD: 10 + +# CTRAMP or daysim +SHADOW_PRICE_METHOD: ctramp +#SHADOW_PRICE_METHOD: daysim + +# ctramp-style shadow_pricing_method parameters +DAMPING_FACTOR: 1 + +# daysim-style shadow_pricing_method parameters +# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? +DAYSIM_ABSOLUTE_TOLERANCE: 50 +DAYSIM_PERCENT_TOLERANCE: 10 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_departure_and_duration_alternatives.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_departure_and_duration_alternatives.csv new file mode 100644 index 0000000000..bddab06b9d --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,191 @@ +start,end +5,5 +5,6 +5,7 +5,8 +5,9 +5,10 +5,11 +5,12 +5,13 +5,14 +5,15 +5,16 +5,17 +5,18 +5,19 +5,20 +5,21 +5,22 +5,23 +6,6 +6,7 +6,8 +6,9 +6,10 +6,11 +6,12 +6,13 +6,14 +6,15 +6,16 +6,17 +6,18 +6,19 +6,20 +6,21 +6,22 +6,23 +7,7 +7,8 +7,9 +7,10 +7,11 +7,12 +7,13 +7,14 +7,15 +7,16 +7,17 +7,18 +7,19 +7,20 +7,21 +7,22 +7,23 +8,8 +8,9 +8,10 +8,11 +8,12 +8,13 +8,14 +8,15 +8,16 +8,17 +8,18 +8,19 +8,20 +8,21 +8,22 +8,23 +9,9 +9,10 +9,11 +9,12 +9,13 +9,14 +9,15 +9,16 +9,17 +9,18 +9,19 +9,20 +9,21 +9,22 +9,23 +10,10 +10,11 +10,12 +10,13 +10,14 +10,15 +10,16 +10,17 +10,18 +10,19 +10,20 +10,21 +10,22 +10,23 +11,11 +11,12 +11,13 +11,14 +11,15 +11,16 +11,17 +11,18 +11,19 +11,20 +11,21 +11,22 +11,23 +12,12 +12,13 +12,14 +12,15 +12,16 +12,17 +12,18 +12,19 +12,20 +12,21 +12,22 +12,23 +13,13 +13,14 +13,15 +13,16 +13,17 +13,18 +13,19 +13,20 +13,21 +13,22 +13,23 +14,14 +14,15 +14,16 +14,17 +14,18 +14,19 +14,20 +14,21 +14,22 +14,23 +15,15 +15,16 +15,17 +15,18 +15,19 +15,20 +15,21 +15,22 +15,23 +16,16 +16,17 +16,18 +16,19 +16,20 +16,21 +16,22 +16,23 +17,17 +17,18 +17,19 +17,20 +17,21 +17,22 +17,23 +18,18 +18,19 +18,20 +18,21 +18,22 +18,23 +19,19 +19,20 +19,21 +19,22 +19,23 +20,20 +20,21 +20,22 +20,23 +21,21 +21,22 +21,23 +22,22 +22,23 +23,23 \ No newline at end of file diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv new file mode 100755 index 0000000000..495f6be57b --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv @@ -0,0 +1,226 @@ +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,VEH == 0,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,AGEP < 16,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['TIMEDA'] + dot_skims['TIMEDA'],coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_TERMINALTIME,DRIVEALONEFREE - Terminal time,@df.origin_terminal_time,coef_walk_access_time,,,,,,,,,,,, +util_DRIVEALONEFREE_TERMINALTIME,DRIVEALONEFREE - Terminal time,@df.dest_terminal_time,coef_walk_egress_time,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTDA'] + dot_skims['DISTDA']),coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLDA'] + dot_skims['BTOLLDA']),coef_ivt,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,VEH == 0,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,AGEP < 16,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['TOLLTIMEDA'] + dot_skims['TOLLTIMEDA'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_TERMINALTIME,DRIVEALONEPAY - Terminal time,@df.origin_terminal_time,,coef_walk_access_time,,,,,,,,,,, +util_DRIVEALONEPAY_TERMINALTIME,DRIVEALONEPAY - Terminal time,@df.dest_terminal_time,,coef_walk_egress_time,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTDA'] + dot_skims['TOLLDISTDA']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLDA'] + dot_skims['TOLLBTOLLDA']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLDA'] + dot_skims['TOLLVTOLLDA']),,coef_ivt,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['TIMES2'] + dot_skims['TIMES2']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_TERMINALTIME,SHARED2FREE - Terminal time,@df.origin_terminal_time,,,coef_walk_access_time,,,,,,,,,, +util_SHARED2FREE_TERMINALTIME,SHARED2FREE - Terminal time,@df.dest_terminal_time,,,coef_walk_egress_time,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS2'] + dot_skims['DISTS2']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS2'] + dot_skims['BTOLLS2']) / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Person_is_between_16_and_24_years_old,SHARED2FREE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,c_age1624_sr2,,,,,,,,,, +util_SHARED2FREE_Person_is_between_41_and_55_years_old,SHARED2FREE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,c_age4155_sr2,,,,,,,,,, +util_SHARED2FREE_Person_is_between_56_and_64_years_old,SHARED2FREE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,c_age5664_sr2,,,,,,,,,, +util_SHARED2FREE_Person_is_between_65plus_years_old,SHARED2FREE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,c_age65pl_sr2,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.NP == 2),,,c_size2_sr2,,,,,,,,,, +util_SHARED2FREE_Three_person_household,SHARED2FREE - Three person household,@(df.NP == 3),,,c_size3_sr2,,,,,,,,,, +util_SHARED2FREE_Four_person_household,SHARED2FREE - Four person household,@(df.NP >= 4),,,c_size4p_sr2,,,,,,,,,, +util_SHARED2FREE_Female,SHARED2FREE - Female,@~df.is_male,,,c_female_sr2,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_TERMINALTIME,SHARED2PAY - Terminal time,@df.origin_terminal_time,,,,coef_walk_access_time,,,,,,,,, +util_SHARED2PAY_TERMINALTIME,SHARED2PAY - Terminal time,@df.dest_terminal_time,,,,coef_walk_egress_time,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Person_is_between_16_and_24_years_old,SHARED2PAY_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,c_age1624_sr2,,,,,,,,, +util_SHARED2PAY_Person_is_between_41_and_55_years_old,SHARED2PAY_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,c_age4155_sr2,,,,,,,,, +util_SHARED2PAY_Person_is_between_56_and_64_years_old,SHARED2PAY_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,c_age5664_sr2,,,,,,,,, +util_SHARED2PAY_Person_is_between_65plus_years_old,SHARED2PAY_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,c_age65pl_sr2,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.NP == 2),,,,c_size2_sr2,,,,,,,,, +util_SHARED2PAY_Three_person_household,SHARED2PAY - Three person household,@(df.NP == 3),,,,c_size3_sr2,,,,,,,,, +util_SHARED2PAY_Four_person_household,SHARED2PAY - Four person household,@(df.NP >= 4),,,,c_size4p_sr2,,,,,,,,, +util_SHARED2PAY_Female,SHARED2PAY - Female,@~df.is_male,,,,c_female_sr2,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['TIMES3'] + dot_skims['TIMES3']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_TERMINALTIME,SHARED3FREE - Terminal time,@df.origin_terminal_time,,,,,coef_walk_access_time,,,,,,,, +util_SHARED3FREE_TERMINALTIME,SHARED3FREE - Terminal time,@df.dest_terminal_time,,,,,coef_walk_egress_time,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS3'] + dot_skims['DISTS3']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS3'] + dot_skims['BTOLLS3']) / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Person_is_between_16_and_24_years_old,SHARED3FREE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,c_age1624_sr3,,,,,,,, +util_SHARED3FREE_Person_is_between_41_and_55_years_old,SHARED3FREE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,c_age4155_sr3,,,,,,,, +util_SHARED3FREE_Person_is_between_56_and_64_years_old,SHARED3FREE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,c_age5664_sr3,,,,,,,, +util_SHARED3FREE_Person_is_between_65plus_years_old,SHARED3FREE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,c_age65pl_sr3,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.NP == 2),,,,,c_size2_sr3p,,,,,,,, +util_SHARED3FREE_Three_person_household,SHARED3FREE - Three person household,@(df.NP == 3),,,,,c_size3_sr3p,,,,,,,, +util_SHARED3FREE_Four_person_household,SHARED3FREE - Four person household,@(df.NP >= 4),,,,,c_size4p_sr3p,,,,,,,, +util_SHARED3FREE_Female,SHARED3FREE - Female,@~df.is_male,,,,,c_female_sr3,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['TOLLTIMES3'] + dot_skims['TOLLTIMES3']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_TERMINALTIME,SHARED3PAY - Terminal time,@df.origin_terminal_time,,,,,,coef_walk_access_time,,,,,,, +util_SHARED3PAY_TERMINALTIME,SHARED3PAY - Terminal time,@df.dest_terminal_time,,,,,,coef_walk_egress_time,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS3'] + dot_skims['TOLLDISTS3']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS3'] + dot_skims['TOLLBTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Person_is_between_16_and_24_years_old,SHARED3PAY_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,c_age1624_sr3,,,,,,, +util_SHARED3PAY_Person_is_between_41_and_55_years_old,SHARED3PAY_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,c_age4155_sr3,,,,,,, +util_SHARED3PAY_Person_is_between_56_and_64_years_old,SHARED3PAY_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,c_age5664_sr3,,,,,,, +util_SHARED3PAY_Person_is_between_65plus_years_old,SHARED3PAY_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,c_age65pl_sr3,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.NP == 2),,,,,,c_size2_sr3p,,,,,,, +util_SHARED3PAY_Three_person_household,SHARED3PAY - Three person household,@(df.NP == 3),,,,,,c_size3_sr3p,,,,,,, +util_SHARED3PAY_Four_person_household,SHARED3PAY - Four person household,@(df.NP >= 4),,,,,,c_size4p_sr3p,,,,,,, +util_SHARED3PAY_Female,SHARED3PAY - Female,@~df.is_male,,,,,,c_female_sr3,,,,,,, +#,Walk,,,,,,,,,,,,,, +util_WALK_Unavailable,WALK - Unavailable,walk_available == False,,,,,,,-999,,,,,, +util_WALK_Time,WALK - walk time,@(od_skims.lookup('WALK_DIST') + od_skims.reverse('WALK_DIST'))*60/walkSpeed,,,,,,,c_walkTime,,,,,, +util_WALK_Person_is_between_16_and_24_years_old,WALK_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,c_age1624_nmot,,,,,, +util_WALK_Person_is_between_41_and_55_years_old,WALK_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,c_age4155_nmot,,,,,, +util_WALK_Person_is_between_56_and_64_years_old,WALK_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,c_age5664_nmot,,,,,, +util_WALK_Person_is_between_65plus_years_old,WALK_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,c_age65pl_nmot,,,,,, +util_WALK_Female,WALK - Female,@~df.is_male,,,,,,,c_female_nmot,,,,,, +util_WALK_Origin_Mix,WALK_Origin_Mix,@df.origin_Mix,,,,,,,c_oMix_nmot,,,,,, +util_WALK_Origin_Intersection_Density,WALK_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,c_oIntDen_nmot,,,,,, +util_WALK_Destination_Employment_Density,WALK_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,c_dEmpDen_nmot,,,,,, +#,Bike,,,,,,,,,,,,,, +util_BIKE_Unavailable,BIKE - Unavailable,bike_available == False,,,,,,,,-999,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, +util_BIKE_Time,BIKE - bike time,@(od_skims.lookup('BIKE_DIST') + od_skims.reverse('BIKE_DIST'))*60/bikeSpeed,,,,,,,,c_bikeTime,,,,, +util_BIKE_Person_is_between_16_and_24_years_old,BIKE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,c_age1624_nmot,,,,, +util_BIKE_Person_is_between_41_and_55_years_old,BIKE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,c_age4155_nmot,,,,, +util_BIKE_Person_is_between_56_and_64_years_old,BIKE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,c_age5664_nmot,,,,, +util_BIKE_Person_is_between_65plus_years_old,BIKE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,c_age65pl_nmot,,,,, +util_BIKE_Female,BIKE - Female,@~df.is_male,,,,,,,,c_female_nmot,,,,, +util_BIKE_Origin_Mix,BIKE_Origin_Mix,@df.origin_Mix,,,,,,,,c_oMix_nmot,,,,, +util_BIKE_Origin_Intersection_Density,BIKE_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,,c_oIntDen_nmot,,,,, +util_BIKE_Destination_Employment_Density,BIKE_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,c_dEmpDen_nmot,,,,, +#,Walk transit,,,,,,,,,,,,,, +util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,@tvpb_logsum_odt['WTW'] + tvpb_logsum_dot['WTW'],,,,,,,,,coef_one,,,, +util_WALK_TRANSIT_Person_is_between_16_and_24_years_old,WALK_TRANSIT_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,,c_age1624_tran,,,, +util_WALK_TRANSIT_Person_is_between_41_and_55_years_old,WALK_TRANSIT_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,,c_age4155_tran,,,, +util_WALK_TRANSIT_Person_is_between_56_and_64_years_old,WALK_TRANSIT_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,,c_age5664_tran,,,, +util_WALK_TRANSIT_Person_is_between_65plus_years_old,WALK_TRANSIT_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,,c_age65pl_tran,,,, +util_WALK_TRANSIT_Female,BIKE - Female,@~df.is_male,,,,,,,,,c_female_tran,,,, +util_WALK_TRANSIT_Origin_Mix,WALK_TRANSIT_Origin_Mix,@df.origin_Mix,,,,,,,,,c_oMix_wtran,,,, +util_WALK_TRANSIT_Origin_Intersection_Density,WALK_TRANSIT_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,,,c_oIntDen_wtran,,,, +util_WALK_TRANSIT_Destination_Employment_Density,WALK_TRANSIT_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,,c_dEmpDen_wtran,,,, +#,Drive transit,,,,,,,,,,,,,, +util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,VEH == 0,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,AGEP < 16,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,@tvpb_logsum_odt['DTW'] + tvpb_logsum_dot['WTD'],,,,,,,,,,coef_one,,, +util_DRIVE_TRANSIT_Person_is_between_16_and_24_years_old,DRIVE_TRANSIT_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,,,c_age1624_tran,,, +util_DRIVE_TRANSIT_Person_is_between_41_and_55_years_old,DRIVE_TRANSIT_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,,,c_age4155_tran,,, +util_DRIVE_TRANSIT_Person_is_between_56_and_64_years_old,DRIVE_TRANSIT_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,,,c_age5664_tran,,, +util_DRIVE_TRANSIT_Person_is_between_65plus_years_old,DRIVE_TRANSIT_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,,,c_age65pl_tran,,, +util_DRIVE_TRANSIT_Female,BIKE - Female,@~df.is_male,,,,,,,,,,c_female_tran,,, +util_DRIVE_TRANSIT_Destination_Employment_Density,DRIVE_TRANSIT_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,,,c_dEmpDen_dtran,,, +#,Taxi,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,coef_ivt,, +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']),,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * Taxi_costPerMile +(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_single_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, +#,TNC Shared,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_shared_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt +#,indiv tour ASCs,,,,,,,,,,,,,, +util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,zeroAutoHH_walk,,,,,, +util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,autoDeficientHH_walk,,,,,, +util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,autoSufficientHH_walk,,,,,, +util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,zeroAutoHH_bike,,,,, +util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,0,,,,, +util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,autoSufficientHH_bike,,,,, +util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,0,0,,,,,,,,, +util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,autoDeficientHH_sr2,autoDeficientHH_sr2,,,,,,,,, +util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,autoSufficientHH_sr2,autoSufficientHH_sr2,,,,,,,,, +util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,zeroAutoHH_sr3,zeroAutoHH_sr3,,,,,,, +util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,autoDeficientHH_sr3,autoDeficientHH_sr3,,,,,,, +util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,autoSufficientHH_sr3,autoSufficientHH_sr3,,,,,,, +util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,zeroAutoHH_wt,,,, +util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,autoDeficientHH_wt,,,, +util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,autoSufficientHH_wt,,,, +util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,zeroAutoHH_kt,,, +util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,autoDeficientHH_dt,,, +util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,autoSufficientHH_dt,,, +util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,0,, +util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,0,, +util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,0,, +util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,0, +util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,0, +util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,0, +util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,,0 +util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,0 +util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,0 +#,joint tour ASCs,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,0,,,,,, +util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,0,,,,,, +util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,0,,,,,, +util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,0,,,,, +util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,0,,,,, +util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,0,,,,, +util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,0,0,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,0,0,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,0,0,,,,,,,,, +util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,0,0,,,,,,, +util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,0,0,,,,,,, +util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,0,0,,,,,,, +util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,0,,,, +util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,0,,,, +util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,0,,,, +util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,0,,, +util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,0,,, +util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,0,,, +util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,0,, +util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,0,, +util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,0,, +util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,0, +util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,0, +util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,0, +util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,,0 +util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,0 +util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,0 +#,calibration,,,,,,,,,,,,,, +util_Walk_to_Transit_dest_CBD_SF,Walk to Transit dest CBD SF,@df.destination_in_cbd_sf,,,,,,,,,asc_wtransit_cbd_sf,,,, +util_Walk_to_Transit_dest_NW_SF,walk to Transit dest NW SF,@df.destination_in_nw_sf,,,,,,,,,asc_wtransit_nw_sf,,,, +util_Walk_to_Transit_dest_SE_SF,Walk to Transit dest SE SF,@df.destination_in_se_sf,,,,,,,,,asc_wtransit_se_sf,,,, +util_Drive_to_Transit_dest_CBD_SF,Drive to Transit dest CBD SF,@df.destination_in_cbd_sf,,,,,,,,,,asc_dtransit_cbd_sf,,, +util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@(500-25*odt_skims['DISTDA']).clip(lower=0),,,,,,,,,,coef_ivt,,, +util_Walk_to_Transit_distance_penalty,Walk to Transit - distance penalty,@(200-133*odt_skims['DISTDA']).clip(lower=0),,,,,,,,,coef_ivt,,,, +util_Transit_Pseudo_area_type_constant,Transit - Pseudo area type constant,@asc_Transit_Pseudo_area_type_constant * (df.daily_parking_cost>0),,,,,,,,,coef_ivt,coef_ivt,,, +util_TM2_Round_2_ASC_adjustment_for_0_Autos_HHs,TM2_Round_2_ASC_adjustment_for_0_Autos_HHs,@(df.is_indiv & (df.VEH == 0)),,,zeroAutoHH_SHARED2HOV,zeroAutoHH_SHARED2PAY,zeroAutoHH_SHARED3HOV,zeroAutoHH_SHARED3PAY,zeroAutoHH_WALK,zeroAutoHH_BIKE,zeroAutoHH_WALK_SET,zeroAutoHH_PNR_SET,,, +util_TM2_Round_2_ASC_adjustment_for_Auto_Defecient_HHs,TM2_Round_2_ASC_adjustment_for_Auto_Defecient_HHs,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,autoDeficientHH_SHARED2HOV,autoDeficientHH_SHARED2PAY,autoDeficientHH_SHARED3HOV,autoDeficientHH_SHARED3PAY,autoDeficientHH_WALK,autoDeficientHH_BIKE,autoDeficientHH_WALK_SET,autoDeficientHH_PNR_SET,,, +util_TM2_Round_2_ASC_adjustment_for_Auto_Sufficient_HHs,TM2_Round_2_ASC_adjustment_for_Auto_Sufficient_HHs,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,autoSufficientHH_SHARED2HOV,autoSufficientHH_SHARED2PAY,autoSufficientHH_SHARED3HOV,autoSufficientHH_SHARED3PAY,autoSufficientHH_WALK,autoSufficientHH_BIKE,autoSufficientHH_WALK_SET,autoSufficientHH_PNR_SET,,, +util_taxi_penalty,taxi penalty,@asc_taxi_penalty,,,,,,,,,,,coef_one,, +util_no_tnc,turn off tnc,1,,,,,,,,,,,,-999,-999 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml new file mode 100755 index 0000000000..281546d286 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml @@ -0,0 +1,188 @@ +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +tvpb_mode_path_types: + DRIVE_TRANSIT: + od: DTW + do: WTD + WALK_TRANSIT: + od: WTW + do: WTW + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - name: DRIVEALONE + coefficient: coef_nest_AUTO_DRIVEALONE + alternatives: + - DRIVEALONEFREE + - DRIVEALONEPAY + - name: SHAREDRIDE2 + coefficient: coef_nest_AUTO_SHAREDRIDE2 + alternatives: + - SHARED2FREE + - SHARED2PAY + - name: SHAREDRIDE3 + coefficient: coef_nest_AUTO_SHAREDRIDE3 + alternatives: + - SHARED3FREE + - SHARED3PAY + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - DRIVE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coeffs.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv + +CONSTANTS: + #valueOfTime: 8.00 + costPerMile: 17.23 + costShareSr2: 1.11 + costShareSr3: 1.25 +# waitThresh: 10.00 + walkThresh: 3.0 +# shortWalk: 0.333 +# longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 12.00 + bikeSpeed: 12.00 +# maxCbdAreaTypeThresh: 2 +# indivTour: 1.00000 +# upperEA: 5 +# upperAM: 10 +# upperMD: 15 +# upperPM: 19 + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 +# + ivt_cost_multiplier: 0.6 +# ivt_lrt_multiplier: 0.9 +# ivt_ferry_multiplier: 0.8 +# ivt_exp_multiplier: 1 +# ivt_hvy_multiplier: 0.8 +# ivt_com_multiplier: 0.7 + walktimeshort_multiplier: 2 +# walktimelong_multiplier: 10 +# biketimeshort_multiplier: 4 +# biketimelong_multiplier: 20 +# short_i_wait_multiplier: 2 +# long_i_wait_multiplier: 1 +# wacc_multiplier: 2 +# wegr_multiplier: 2 +# waux_multiplier: 2 +# dtim_multiplier: 2 +# xwait_multiplier: 2 +# dacc_ratio: 0 +# xfers_wlk_multiplier: 10 +# xfers_drv_multiplier: 20 + drvtrn_distpen_0_multiplier: 270 + drvtrn_distpen_max: 15 +# density_index_multiplier: -0.2 + joint_sr2_ASC_no_auto: 0 + joint_sr2_ASC_auto_deficient: 0 + joint_sr2_ASC_auto_sufficient: 0 + joint_drive_transit_ASC_no_auto: 0 + c_auto_operating_cost_per_mile: 17.23 + + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - age_16_p + - age_16_to_19 + - auto_ownership + - number_of_participants + - tour_category + - num_workers + - value_of_time + - free_parking_at_work + - income_segment + - demographic_segment + - c_ivt_for_segment + - c_cost_for_segment + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv new file mode 100755 index 0000000000..64813a0914 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,87 @@ +Description,Target,Expression, +#,,, +,number_of_participants,1, +,is_joint,False, +#,,, +,_HAVE_PARENT_TOURS,False, +,_parent_tour_mode,False, +,work_tour_is_drive,False, +,work_tour_is_bike,False, +,work_tour_is_SOV,False, +#,,, +,is_mandatory,True, +,is_joint,False, +,is_indiv,~is_joint, +,is_atwork_subtour,False, +,is_escort,False, +#,,, +income_in_thousands,income_in_thousands,(df.HHINCADJ / 1000).clip(lower=0), +income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)", +,demographic_segment,income_segment.map(TVPB_demographic_segments_by_income_segment), +#,c_ivt_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)", +#,c_cost_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)", +#,,, +#,c_cost,(0.60 * c_ivt) / df.value_of_time, +# ivot * (c_ivt_cost_multiplier * c_ivt),,, +,ivot,1.0 / df.value_of_time, +# RIDEHAIL,,, +,origin_density_measure,"(reindex(land_use.POP, df[orig_col_name]) + reindex(land_use.emp_total, df[orig_col_name])) / (reindex(land_use.ACRES, df[orig_col_name]) / 640)", +,dest_density_measure,"(reindex(land_use.POP, df[dest_col_name]) + reindex(land_use.emp_total, df[dest_col_name])) / (reindex(land_use.ACRES, df[dest_col_name]) / 640)", +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime, +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime, +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime, +#,,, +,_free_parking_available,(df.tour_type == 'work') & (df.fp_choice == 1), +,_dest_hourly_peak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", +,_dest_hourly_offpeak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", +,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)", +,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)", +just hourly instead of times duration for now,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost, _hourly_offpeak_parking_cost)", +#,,, +,distance,(odt_skims['DISTDA']), +,sov_available,(odt_skims['TIMEDA']>0) & (dot_skims['TIMEDA']>0), +,sovtoll_available,(odt_skims['TOLLVTOLLDA']>0) | (dot_skims['TOLLVTOLLDA']>0), +,hov2_available,(odt_skims['TIMES2'] + dot_skims['TIMES2'])>0, +,hov2toll_available,(odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2'])>0, +,hov3_available,(odt_skims['TIMES3']>0) & (dot_skims['TIMES3']>0), +,hov3toll_available,(odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3'])>0, +,walk_available,"od_skims.lookup('WALK_DIST').between(0.01, walkThresh) & od_skims.reverse('WALK_DIST').between(0.01, walkThresh)", +,bike_available,"od_skims.lookup('BIKE_DIST').between(0.01, bikeThresh) & od_skims.reverse('BIKE_DIST').between(0.01, bikeThresh)", +#,,, +destination district,destination_in_cbd_sf,"reindex(land_use.DistID, df[dest_col_name])==1", +destination district,destination_in_nw_sf,"reindex(land_use.DistID, df[dest_col_name])==2", +destination district,destination_in_se_sf,"reindex(land_use.DistID, df[dest_col_name])==3", +#,,, +,origin_terminal_time,"reindex(land_use.TERMINALTIME, df[orig_col_name])", +,dest_terminal_time,"reindex(land_use.TERMINALTIME, df[dest_col_name])", +,origin_DUDen,"reindex(land_use.DUDen, df[orig_col_name])", +,dest_DUDen,"reindex(land_use.DUDen, df[dest_col_name])", +,origin_EmpDen,"reindex(land_use.EmpDen, df[orig_col_name])", +,dest_EmpDen,"reindex(land_use.EmpDen, df[dest_col_name])", +,origin_TotInt,"reindex(land_use.TotInt, df[orig_col_name])", +,dest_TotInt,"reindex(land_use.TotInt, df[dest_col_name])", +,origin_Mix,"(origin_DUDen * origin_EmpDen) / np.where((origin_DUDen + origin_EmpDen) > 0, (origin_DUDen + origin_EmpDen), 0.001)", +,dest_Mix,"(dest_DUDen * dest_EmpDen) / np.where((dest_DUDen + dest_EmpDen) > 0, (dest_DUDen + dest_EmpDen), 0.001)", +# diagnostic,,, +#,sov_dist_roundtrip,(odt_skims['DISTDA'] + dot_skims['DISTDA']), \ No newline at end of file diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv new file mode 100755 index 0000000000..5ab2eef91e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv @@ -0,0 +1,392 @@ +coefficient_name,value,constrain +coef_one,1,T +coef_nest_root,1,T +coef_nest_AUTO,0.6,T +coef_nest_AUTO_DRIVEALONE,0.4,T +coef_nest_AUTO_SHAREDRIDE2,0.4,T +coef_nest_AUTO_SHAREDRIDE3,0.4,T +coef_nest_NONMOTORIZED,0.6,T +coef_nest_TRANSIT,0.6,T +coef_nest_RIDEHAIL,0.6,T +coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F +coef_ivt_school_univ,-0.0224,F +coef_ivt_work,-0.016,F +coef_ivt_atwork,-0.0188,F +coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F +coef_topology_walk_multiplier_atwork,7.5,F +coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F +coef_topology_bike_multiplier_atwork,10,F +coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F +coef_topology_trn_multiplier_atwork,2,F +coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age1619_da_multiplier_school_univ,-1.3813,F +coef_age1619_da_multiplier_atwork,0.0032336,F +coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age010_trn_multiplier_school_univ,-1.5548,F +coef_age010_trn_multiplier_atwork,0.000722,F +coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F +coef_age16p_sr_multiplier_school_univ_work_atwork,0,F +coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F +coef_hhsize1_sr_multiplier_work,-0.734588,F +coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F +coef_hhsize2_sr_multiplier_school_univ,-0.6359,F +coef_walk_access_time,-0.03,T +coef_walk_egress_time,-0.03,T +c_age1624_sr2,-0.21388,T +c_age1624_sr3,-1.79023,T +c_age1624_nmot,0.30322,T +c_age1624_tran,0.79472,T +c_age4155_sr2,-0.30638,T +c_age4155_sr3,-0.41025,T +c_age4155_nmot,-0.17752,T +c_age4155_tran,-0.42301,T +c_age5664_sr2,-1.02962,T +c_age5664_sr3,-0.85641,T +c_age5664_nmot,-0.64534,T +c_age5664_tran,-0.44991,T +c_age65pl_sr2,-0.67111,T +c_age65pl_sr3,-1.43462,T +c_age65pl_nmot,-1.45334,T +c_age65pl_tran,-1.1231,T +c_female_sr2,0.59473,T +c_female_sr3,0.84806,T +c_female_tran,0.15779,T +c_female_nmot,0,T +c_size2_sr2,1.06964,T +c_size2_sr3p,-0.46736,T +c_size3_sr2,1.58018,T +c_size3_sr3p,0.65463,T +c_size4p_sr2,1.68839,T +c_size4p_sr3p,1.4987,T +c_walkTime,-0.059,T +c_bikeTime,-0.0492,T +c_oMix_nmot,0.21014,T +c_oMix_wtran,0,T +c_oIntDen_nmot,0.003,T +c_oIntDen_wtran,0,T +c_dEmpDen_nmot,0.02071,T +c_dEmpDen_wtran,0,T +c_dEmpDen_dtran,0,T +walk_ASC_no_auto_eatout,5.1251173,F +walk_ASC_no_auto_escort,2.8012068,F +walk_ASC_no_auto_othdiscr,3.2665946,F +walk_ASC_no_auto_othmaint,1.287299,F +walk_ASC_no_auto_school,18.414557,F +walk_ASC_no_auto_shopping,2.3768773,F +walk_ASC_no_auto_social,1.8680915,F +walk_ASC_no_auto_univ,6.408967,F +walk_ASC_no_auto_work,5.7672157,F +walk_ASC_no_auto_atwork,6.669213,F +walk_ASC_auto_deficient_eatout,3.274605,F +walk_ASC_auto_deficient_escort,-0.90204656,F +walk_ASC_auto_deficient_othdiscr,2.2494075,F +walk_ASC_auto_deficient_othmaint,1.3690404,F +walk_ASC_auto_deficient_school,3.2573624,F +walk_ASC_auto_deficient_shopping,2.2701733,F +walk_ASC_auto_deficient_social,2.870184,F +walk_ASC_auto_deficient_univ,4.50591,F +walk_ASC_auto_deficient_work,2.4010417,F +walk_ASC_auto_deficient_atwork,0.92546093,F +walk_ASC_auto_sufficient_eatout,1.5516903,F +walk_ASC_auto_sufficient_escort,-0.8116066,F +walk_ASC_auto_sufficient_othdiscr,1.2633476,F +walk_ASC_auto_sufficient_othmaint,0.7999634,F +walk_ASC_auto_sufficient_school,0.6476856,F +walk_ASC_auto_sufficient_shopping,0.7312663,F +walk_ASC_auto_sufficient_social,1.7072186,F +walk_ASC_auto_sufficient_univ,1.0607665,F +walk_ASC_auto_sufficient_work,0.053265337,F +walk_ASC_auto_sufficient_atwork,0.677216,F +bike_ASC_no_auto_eatout,0.86807096,F +bike_ASC_no_auto_escort,-0.716212,F +bike_ASC_no_auto_othdiscr,-0.3764232,F +bike_ASC_no_auto_othmaint,1.5394334,F +bike_ASC_no_auto_school,12.098735,F +bike_ASC_no_auto_shopping,0.8341555,F +bike_ASC_no_auto_social,0.02058321,F +bike_ASC_no_auto_univ,4.2945156,F +bike_ASC_no_auto_work,3.1940088,F +bike_ASC_no_auto_atwork,-0.90725845,F +bike_ASC_auto_deficient_eatout,-1.5691106,F +bike_ASC_auto_deficient_escort,-4.527928,F +bike_ASC_auto_deficient_othdiscr,-0.09246834,F +bike_ASC_auto_deficient_othmaint,-1.5184649,F +bike_ASC_auto_deficient_school,-0.5280678,F +bike_ASC_auto_deficient_shopping,-0.87584466,F +bike_ASC_auto_deficient_social,0.6345214,F +bike_ASC_auto_deficient_univ,-0.669235,F +bike_ASC_auto_deficient_work,0.25318968,F +bike_ASC_auto_deficient_atwork,-0.8074083,F +bike_ASC_auto_sufficient_eatout,-1.2003471,F +bike_ASC_auto_sufficient_escort,-5.0631084,F +bike_ASC_auto_sufficient_othdiscr,-1.0714597,F +bike_ASC_auto_sufficient_othmaint,-2.8083024,F +bike_ASC_auto_sufficient_school,-2.1134686,F +bike_ASC_auto_sufficient_shopping,-2.5662103,F +bike_ASC_auto_sufficient_social,-1.368071,F +bike_ASC_auto_sufficient_univ,-1.9397832,F +bike_ASC_auto_sufficient_work,-1.5800232,F +bike_ASC_auto_sufficient_atwork,15.72017,F +sr2_ASC_no_auto_all,0,F +sr2_ASC_auto_deficient_eatout,0.5882345,F +sr2_ASC_auto_deficient_escort,0,F +sr2_ASC_auto_deficient_othdiscr,0.6601513,F +sr2_ASC_auto_deficient_othmaint,0.2621527,F +sr2_ASC_auto_deficient_school,0.12474365,F +sr2_ASC_auto_deficient_shopping,0.24409756,F +sr2_ASC_auto_deficient_social,1.8558528,F +sr2_ASC_auto_deficient_univ,-1.6922346,F +sr2_ASC_auto_deficient_work,-0.33803123,F +sr2_ASC_auto_deficient_atwork,-2.1102421,F +sr2_ASC_auto_sufficient_eatout,0.86280555,F +sr2_ASC_auto_sufficient_escort,0,F +sr2_ASC_auto_sufficient_othdiscr,0.49684617,F +sr2_ASC_auto_sufficient_othmaint,0.25817883,F +sr2_ASC_auto_sufficient_school,-1.6062657,F +sr2_ASC_auto_sufficient_shopping,0.19770707,F +sr2_ASC_auto_sufficient_social,0.5236025,F +sr2_ASC_auto_sufficient_univ,-1.859427,F +sr2_ASC_auto_sufficient_work,-1.0857458,F +sr2_ASC_auto_sufficient_atwork,-1.4450618,F +sr3p_ASC_no_auto_eatout,0.3219998,F +sr3p_ASC_no_auto_escort,-1.8129267,F +sr3p_ASC_no_auto_othdiscr,0.27216902,F +sr3p_ASC_no_auto_othmaint,-0.8031854,F +sr3p_ASC_no_auto_school,-6.0240827,F +sr3p_ASC_no_auto_shopping,-0.27978948,F +sr3p_ASC_no_auto_social,-1.4036902,F +sr3p_ASC_no_auto_univ,-6.056001,F +sr3p_ASC_no_auto_work,-0.5831269,F +sr3p_ASC_no_auto_atwork,0.5826626,F +sr3p_ASC_auto_deficient_eatout,0.04605236,F +sr3p_ASC_auto_deficient_escort,-0.40818766,F +sr3p_ASC_auto_deficient_othdiscr,1.0470966,F +sr3p_ASC_auto_deficient_othmaint,-1.3493925,F +sr3p_ASC_auto_deficient_school,0.7149571,F +sr3p_ASC_auto_deficient_shopping,-0.073370166,F +sr3p_ASC_auto_deficient_social,1.5007243,F +sr3p_ASC_auto_deficient_univ,-1.7277422,F +sr3p_ASC_auto_deficient_work,-0.8527042,F +sr3p_ASC_auto_deficient_atwork,-2.514658,F +sr3p_ASC_auto_sufficient_eatout,0.8468596,F +sr3p_ASC_auto_sufficient_escort,-0.05741253,F +sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F +sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F +sr3p_ASC_auto_sufficient_school,-1.0201935,F +sr3p_ASC_auto_sufficient_shopping,-0.077571295,F +sr3p_ASC_auto_sufficient_social,0.50617886,F +sr3p_ASC_auto_sufficient_univ,-1.9047098,F +sr3p_ASC_auto_sufficient_work,-1.4699702,F +sr3p_ASC_auto_sufficient_atwork,-1.652174,F +walk_transit_ASC_no_auto_eatout,2.5936368,F +walk_transit_ASC_no_auto_escort,-2.2172081,F +walk_transit_ASC_no_auto_othdiscr,2.2437785,F +walk_transit_ASC_no_auto_othmaint,2.5643456,F +walk_transit_ASC_no_auto_school,21.383749,F +walk_transit_ASC_no_auto_shopping,2.1067476,F +walk_transit_ASC_no_auto_social,1.3814651,F +walk_transit_ASC_no_auto_univ,8.786037,F +walk_transit_ASC_no_auto_work,5.0354166,F +walk_transit_ASC_no_auto_atwork,2.7041876,F +walk_transit_ASC_auto_deficient_eatout,-0.03896324,F +walk_transit_ASC_auto_deficient_escort,-4.960704,F +walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F +walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F +walk_transit_ASC_auto_deficient_school,4.120708,F +walk_transit_ASC_auto_deficient_shopping,-0.8476569,F +walk_transit_ASC_auto_deficient_social,0.97444487,F +walk_transit_ASC_auto_deficient_univ,3.1362555,F +walk_transit_ASC_auto_deficient_work,0.65302855,F +walk_transit_ASC_auto_deficient_atwork,-2.9988291,F +walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F +walk_transit_ASC_auto_sufficient_escort,-4.934847,F +walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F +walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F +walk_transit_ASC_auto_sufficient_school,0.74590874,F +walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F +walk_transit_ASC_auto_sufficient_social,-0.3453759,F +walk_transit_ASC_auto_sufficient_univ,0.4731163,F +walk_transit_ASC_auto_sufficient_work,-0.8916507,F +walk_transit_ASC_auto_sufficient_atwork,-3.401027,F +drive_transit_ASC_no_auto_all,0,F +drive_transit_ASC_auto_deficient_eatout,0.5998061,F +drive_transit_ASC_auto_deficient_escort,-1.1537067,F +drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F +drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F +drive_transit_ASC_auto_deficient_school,5.3252654,F +drive_transit_ASC_auto_deficient_shopping,-0.41849178,F +drive_transit_ASC_auto_deficient_social,1.5627195,F +drive_transit_ASC_auto_deficient_univ,1.8501176,F +drive_transit_ASC_auto_deficient_work,0.10081567,F +drive_transit_ASC_auto_deficient_atwork,-998.8196,F +drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F +drive_transit_ASC_auto_sufficient_escort,-4.6014247,F +drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F +drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F +drive_transit_ASC_auto_sufficient_school,1.40135,F +drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F +drive_transit_ASC_auto_sufficient_social,-0.61585575,F +drive_transit_ASC_auto_sufficient_univ,1.3587753,F +drive_transit_ASC_auto_sufficient_work,-1.0045459,F +drive_transit_ASC_auto_sufficient_atwork,-999.21466,F +taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F +taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F +taxi_ASC_no_auto_school_univ,-7,T +taxi_ASC_no_auto_work,4.7291,F +taxi_ASC_no_auto_atwork,4.1021,F +taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F +taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F +taxi_ASC_auto_deficient_school,-0.3338,F +taxi_ASC_auto_deficient_univ,4.2492,F +taxi_ASC_auto_deficient_work,-1.4766,F +taxi_ASC_auto_deficient_atwork,-4.4046,F +taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F +taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F +taxi_ASC_auto_sufficient_school,-2.4294,F +taxi_ASC_auto_sufficient_univ,-0.3131,F +taxi_ASC_auto_sufficient_work,-4.8509,F +taxi_ASC_auto_sufficient_atwork,-2.8804,F +tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F +tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F +tnc_single_ASC_no_auto_school,-7,T +tnc_single_ASC_no_auto_univ,-2.519,F +tnc_single_ASC_no_auto_work,5.7855,F +tnc_single_ASC_no_auto_atwork,4.4982,F +tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F +tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F +tnc_single_ASC_auto_deficient_school,-0.5524,F +tnc_single_ASC_auto_deficient_univ,1.0221,F +tnc_single_ASC_auto_deficient_work,-0.8013,F +tnc_single_ASC_auto_deficient_atwork,-3.7626,F +tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F +tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F +tnc_single_ASC_auto_sufficient_school,-2.8375,F +tnc_single_ASC_auto_sufficient_univ,0.2088,F +tnc_single_ASC_auto_sufficient_work,-4.1946,F +tnc_single_ASC_auto_sufficient_atwork,-2.7988,F +tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F +tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F +tnc_shared_ASC_no_auto_school,-7,T +tnc_shared_ASC_no_auto_univ,-5.8116,F +tnc_shared_ASC_no_auto_work,3.2429,F +tnc_shared_ASC_no_auto_atwork,3.3672,F +tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F +tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F +tnc_shared_ASC_auto_deficient_school,-1.4746,F +tnc_shared_ASC_auto_deficient_univ,3.25,F +tnc_shared_ASC_auto_deficient_work,-2.1435,F +tnc_shared_ASC_auto_deficient_atwork,-4.5089,F +tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F +tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F +tnc_shared_ASC_auto_sufficient_school,-3.7219,F +tnc_shared_ASC_auto_sufficient_univ,-0.9068,F +tnc_shared_ASC_auto_sufficient_work,-5.3575,F +tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F +joint_walk_ASC_no_auto_all,-0.21274701,F +joint_walk_ASC_auto_deficient_all,-1.9607706,F +joint_walk_ASC_auto_sufficient_all,-3.2352157,F +joint_bike_ASC_no_auto_all,-2.8671598,F +joint_bike_ASC_auto_deficient_all,-6.076415,F +joint_bike_ASC_auto_sufficient_all,-6.3760657,F +joint_sr2_ASC_no_auto_all,0,T +joint_sr2_ASC_auto_deficient_all,0,T +joint_sr2_ASC_auto_sufficient_all,0,T +joint_sr3p_ASC_no_auto_all,0.5630671,F +joint_sr3p_ASC_auto_deficient_all,-1.8841692,F +joint_sr3p_ASC_auto_sufficient_all,-2.234826,F +joint_walk_transit_ASC_no_auto_all,0.62292415,F +joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F +joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F +joint_drive_transit_ASC_no_auto_all,0,T +joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F +joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F +joint_taxi_ASC_no_auto_all,-4.5792,F +joint_taxi_ASC_auto_deficient_all,-9.8157,F +joint_taxi_ASC_auto_sufficient_all,-11.7099,T +joint_tnc_single_ASC_no_auto_all,-4.4917,F +joint_tnc_single_ASC_auto_deficient_all,-9.8961,F +joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T +joint_tnc_shared_ASC_no_auto_all,-4.3002,F +joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F +joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T +local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F +local_bus_ASC_school_univ,-0.06508621,F +local_bus_ASC_work,0.06689507,F +walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +walk_light_rail_ASC_school_univ,1.6814003,F +walk_light_rail_ASC_work,0.8255567,F +drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +drive_light_rail_ASC_school_univ,1.6814003,F +drive_light_rail_ASC_work,0.8255567,F +walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +walk_ferry_ASC_school_univ,2.0202317,F +walk_ferry_ASC_work,0.93322605,F +drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +drive_ferry_ASC_school_univ,2.0202317,F +drive_ferry_ASC_work,0.93322605,F +express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F +express_bus_ASC_school_univ,0.32496938,F +express_bus_ASC_work,-0.5165474,F +heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F +heavy_rail_ASC_school_univ,0.96200377,F +heavy_rail_ASC_work,0.64772975,F +commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F +commuter_rail_ASC_school_univ,1.0336206,F +commuter_rail_ASC_work,0.725503,F +walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +walk_transit_CBD_ASC_school_univ,0.672,F +walk_transit_CBD_ASC_work,0.804,F +walk_transit_CBD_ASC_atwork,0.564,F +drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +drive_transit_CBD_ASC_school_univ,0.672,F +drive_transit_CBD_ASC_work,1.1,F +drive_transit_CBD_ASC_atwork,0.564,F +zeroAutoHH_sr3,-0.466,F +zeroAutoHH_walk,6.823,F +zeroAutoHH_bike,3.536,F +zeroAutoHH_wt,10.326,F +zeroAutoHH_kt,8.281,F +autoDeficientHH_sr2,-2.166,F +autoDeficientHH_sr3,-2.58,F +autoDeficientHH_walk,2.794,F +autoDeficientHH_bike,-0.015,F +autoDeficientHH_wt,-0.94,F +autoDeficientHH_dt,-1.706,F +autoDeficientHH_kt,-2.229,F +autoSufficientHH_sr2,-2.582,F +autoSufficientHH_sr3,-2.58,F +autoSufficientHH_walk,0.729,F +autoSufficientHH_bike,-1.434,F +autoSufficientHH_wt,-2.582,F +autoSufficientHH_dt,-2.923,F +autoSufficientHH_kt,-3.493,F +asc_wtransit_cbd_sf,2,F +asc_wtransit_nw_sf,1.25,F +asc_wtransit_se_sf,1.25,F +asc_dtransit_cbd_sf,1.2,F +asc_Transit_Pseudo_area_type_constant,-55,F +asc_taxi_penalty,-10,F +zeroAutoHH_SHARED2HOV,0,F +autoDeficientHH_SHARED2HOV,0.2369,F +autoSufficientHH_SHARED2HOV,0.1127,F +zeroAutoHH_SHARED2PAY,0,F +autoDeficientHH_SHARED2PAY,0.2369,F +autoSufficientHH_SHARED2PAY,0.1127,F +zeroAutoHH_SHARED3HOV,-2.3789,F +autoDeficientHH_SHARED3HOV,0.2982,F +autoSufficientHH_SHARED3HOV,0.1494,F +zeroAutoHH_SHARED3PAY,-2.3789,F +autoDeficientHH_SHARED3PAY,0.2982,F +autoSufficientHH_SHARED3PAY,0.1494,F +zeroAutoHH_WALK,-9.6191,F +autoDeficientHH_WALK,-1.5999,F +autoSufficientHH_WALK,-2.6212,F +zeroAutoHH_BIKE,-8.1503,F +autoDeficientHH_BIKE,-2.1993,F +autoSufficientHH_BIKE,-3.3049,F +zeroAutoHH_WALK_SET,-0.4471,F +autoDeficientHH_WALK_SET,3.6328,F +autoSufficientHH_WALK_SET,1.4325,F +zeroAutoHH_PNR_SET,0,F +autoDeficientHH_PNR_SET,4.0458,F +autoSufficientHH_PNR_SET,2.1645,F diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs_template.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs_template.csv new file mode 100755 index 0000000000..412635c586 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs_template.csv @@ -0,0 +1,174 @@ +coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork +#same for all segments,,,,,,,,,, +coef_one,,,,,,,,,, +coef_nest_root,,,,,,,,,, +coef_nest_AUTO,,,,,,,,,, +coef_nest_AUTO_DRIVEALONE,,,,,,,,,, +coef_nest_AUTO_SHAREDRIDE2,,,,,,,,,, +coef_nest_AUTO_SHAREDRIDE3,,,,,,,,,, +coef_nest_NONMOTORIZED,,,,,,,,,, +coef_nest_TRANSIT,,,,,,,,,, +coef_nest_TRANSIT_WALKACCESS,,,,,,,,,, +coef_nest_TRANSIT_DRIVEACCESS,,,,,,,,,, +coef_nest_RIDEHAIL,,,,,,,,,, +#,,,,,,,,,, +coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork +coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork +coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork +coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork +coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork +coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork +coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork +coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork +coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork +walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork +walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork +walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork +bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork +bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork +bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork +sr2_ASC_no_auto,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all +sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork +sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork +sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork +sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork +sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork +walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork +walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork +walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork +drive_transit_ASC_no_auto,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all +drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient_eatout,drive_transit_ASC_auto_deficient_escort,drive_transit_ASC_auto_deficient_othdiscr,drive_transit_ASC_auto_deficient_othmaint,drive_transit_ASC_auto_deficient_school,drive_transit_ASC_auto_deficient_shopping,drive_transit_ASC_auto_deficient_social,drive_transit_ASC_auto_deficient_univ,drive_transit_ASC_auto_deficient_work,drive_transit_ASC_auto_deficient_atwork +drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient_eatout,drive_transit_ASC_auto_sufficient_escort,drive_transit_ASC_auto_sufficient_othdiscr,drive_transit_ASC_auto_sufficient_othmaint,drive_transit_ASC_auto_sufficient_school,drive_transit_ASC_auto_sufficient_shopping,drive_transit_ASC_auto_sufficient_social,drive_transit_ASC_auto_sufficient_univ,drive_transit_ASC_auto_sufficient_work,drive_transit_ASC_auto_sufficient_atwork +taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork +taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork +taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork +tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork +tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork +tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork +tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork +tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork +tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork +joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all +joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all +joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all +joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all +joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all +joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all +joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all +joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all +joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all +joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all +joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all +joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all +joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all +joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all +joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all +joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all +joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all +joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all +joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all +joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all +joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all +joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all +joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all +joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all +joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all +joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all +joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all +local_bus_ASC,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_work,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork +drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork +#same for all segments,,,,,,,,,, +coef_walk_access_time,,,,,,,,,, +coef_walk_egress_time,,,,,,,,,, +c_age1624_sr2,,,,,,,,,, +c_age1624_sr3,,,,,,,,,, +c_age1624_nmot,,,,,,,,,, +c_age1624_tran,,,,,,,,,, +c_age4155_sr2,,,,,,,,,, +c_age4155_sr3,,,,,,,,,, +c_age4155_nmot,,,,,,,,,, +c_age4155_tran,,,,,,,,,, +c_age5664_sr2,,,,,,,,,, +c_age5664_sr3,,,,,,,,,, +c_age5664_nmot,,,,,,,,,, +c_age5664_tran,,,,,,,,,, +c_age65pl_sr2,,,,,,,,,, +c_age65pl_sr3,,,,,,,,,, +c_age65pl_nmot,,,,,,,,,, +c_age65pl_tran,,,,,,,,,, +c_female_sr2,,,,,,,,,, +c_female_sr3,,,,,,,,,, +c_female_tran,,,,,,,,,, +c_female_nmot,,,,,,,,,, +c_size2_sr2,,,,,,,,,, +c_size2_sr3p,,,,,,,,,, +c_size3_sr2,,,,,,,,,, +c_size3_sr3p,,,,,,,,,, +c_size4p_sr2,,,,,,,,,, +c_size4p_sr3p,,,,,,,,,, +c_walkTime,,,,,,,,,, +c_bikeTime,,,,,,,,,, +c_oMix_nmot,,,,,,,,,, +c_oMix_wtran,,,,,,,,,, +c_oIntDen_nmot,,,,,,,,,, +c_oIntDen_wtran,,,,,,,,,, +c_dEmpDen_nmot,,,,,,,,,, +c_dEmpDen_wtran,,,,,,,,,, +c_dEmpDen_dtran,,,,,,,,,, +zeroAutoHH_sr3,,,,,,,,,, +zeroAutoHH_walk,,,,,,,,,, +zeroAutoHH_bike,,,,,,,,,, +zeroAutoHH_wt,,,,,,,,,, +zeroAutoHH_kt,,,,,,,,,, +autoDeficientHH_sr2,,,,,,,,,, +autoDeficientHH_sr3,,,,,,,,,, +autoDeficientHH_walk,,,,,,,,,, +autoDeficientHH_bike,,,,,,,,,, +autoDeficientHH_wt,,,,,,,,,, +autoDeficientHH_dt,,,,,,,,,, +autoDeficientHH_kt,,,,,,,,,, +autoSufficientHH_sr2,,,,,,,,,, +autoSufficientHH_sr3,,,,,,,,,, +autoSufficientHH_walk,,,,,,,,,, +autoSufficientHH_bike,,,,,,,,,, +autoSufficientHH_wt,,,,,,,,,, +autoSufficientHH_dt,,,,,,,,,, +autoSufficientHH_kt,,,,,,,,,, +asc_wtransit_cbd_sf,,,,,,,,,, +asc_wtransit_nw_sf,,,,,,,,,, +asc_wtransit_se_sf,,,,,,,,,, +asc_dtransit_cbd_sf,,,,,,,,,, +asc_Transit_Pseudo_area_type_constant,,,,,,,,,, +asc_taxi_penalty,,,,,,,,,, +zeroAutoHH_SHARED2HOV,,,,,,,,,, +autoDeficientHH_SHARED2HOV,,,,,,,,,, +autoSufficientHH_SHARED2HOV,,,,,,,,,, +zeroAutoHH_SHARED2PAY,,,,,,,,,, +autoDeficientHH_SHARED2PAY,,,,,,,,,, +autoSufficientHH_SHARED2PAY,,,,,,,,,, +zeroAutoHH_SHARED3HOV,,,,,,,,,, +autoDeficientHH_SHARED3HOV,,,,,,,,,, +autoSufficientHH_SHARED3HOV,,,,,,,,,, +zeroAutoHH_SHARED3PAY,,,,,,,,,, +autoDeficientHH_SHARED3PAY,,,,,,,,,, +autoSufficientHH_SHARED3PAY,,,,,,,,,, +zeroAutoHH_WALK,,,,,,,,,, +autoDeficientHH_WALK,,,,,,,,,, +autoSufficientHH_WALK,,,,,,,,,, +zeroAutoHH_BIKE,,,,,,,,,, +autoDeficientHH_BIKE,,,,,,,,,, +autoSufficientHH_BIKE,,,,,,,,,, +zeroAutoHH_WALK_SET,,,,,,,,,, +autoDeficientHH_WALK_SET,,,,,,,,,, +autoSufficientHH_WALK_SET,,,,,,,,,, +zeroAutoHH_PNR_SET,,,,,,,,,, +autoDeficientHH_PNR_SET,,,,,,,,,, +autoSufficientHH_PNR_SET,,,,,,,,,, diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv new file mode 100755 index 0000000000..04ba9016a7 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv @@ -0,0 +1,3 @@ +Label,Description,Expression,utility +util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_dtim * (df.DTIME + (df.WDIST / 5280 / walk_speed * 60))",1 +util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * (df.DDIST + (df.WDIST / 5280)) * c_auto_operating_cost_per_mile",1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv new file mode 100755 index 0000000000..b7456c856b --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv @@ -0,0 +1,82 @@ +Label,Description,Expression,set1,set2,set3 +# Set 1,,,,, +set1_unavailable,Shut off set if unavailable,@df.not_transit_available_set1,C_UNAVAILABLE,, +set1_ivt,set In-Vehicle Time,@~df.not_transit_available_set1 * df.c_ivt_for_segment * df.totalIVT_set1,1,, +set1_first_wait_time,First wait time,"@~df.not_transit_available_set1 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET1')",1,, +set1_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set1 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET1')",1,, +set1_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set1 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET1')",1,, +set1_fare,set Fare,"@~df.not_transit_available_set1 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET1') * 100",1,, +set1_xfers1,0-1 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers1_set1 * c_xfers1 * df.c_ivt_for_segment,1,, +set1_xfers2,1-2 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers2_set1 * c_xfers2 * df.c_ivt_for_segment,1,, +set1_sfers3,>2 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers3_set1 * c_xfers3 * df.c_ivt_for_segment,1,, +set1_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers1_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, +set1_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers2_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, +set1_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers3_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, +set1_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers1_set1 * (df.c_ivt_for_segment * 5),1,, +set1_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers2_set1 * (df.c_ivt_for_segment * 5),1,, +set1_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers3_set1 * (df.c_ivt_for_segment * 5),1,, +set1_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set1 * (df.crDistance_set1>20) * (df.crDistance_set1<=40) * c_cr20_40 * df.c_ivt_for_segment,1,, +set1_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set1 * (df.crDistance_set1>40) * c_cr40plus * df.c_ivt_for_segment,1,, +set1_CR_drive,drive access to CR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvCR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)",1,, +set1_HR_drive,drive access to HR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvHeavy * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0)",1,, +set1_FR_drive,drive access to FR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvFR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0)",1,, +set1_LRT_drive,drive access to LRT,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvLRT * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0)",1,, +set1_EB_drive,drive access to EB,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvExpress * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0)",1,, +set1_ASC_CR,ASC CR,"@~df.not_transit_available_set1 * c_cr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, +set1_ASC_HR,ASC HR,"@~df.not_transit_available_set1 * c_hr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, +set1_ASC_FR,ASC FR,"@~df.not_transit_available_set1 * c_fr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, +set1_ASC_LRT,ASC LRT,"@~df.not_transit_available_set1 * c_lrt_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, +# Set 2,,,,, +set2_unavailable,Shut off set if unavailable,@df.not_transit_available_set2,,C_UNAVAILABLE, +set2_ivt,set In-Vehicle Time,@~df.not_transit_available_set2 * df.c_ivt_for_segment * df.totalIVT_set2,,1, +set2_first_wait_time,First wait time,"@~df.not_transit_available_set2 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET2')",,1, +set2_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set2 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET2')",,1, +set2_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set2 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET2')",,1, +set2_fare,set Fare,"@~df.not_transit_available_set2 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET2') * 100",,1, +set2_xfers1,0-1 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers1_set2 * c_xfers1 * df.c_ivt_for_segment,,1, +set2_xfers2,1-2 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers2_set2 * c_xfers2 * df.c_ivt_for_segment,,1, +set2_sfers3,>2 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers3_set2 * c_xfers3 * df.c_ivt_for_segment,,1, +set2_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers1_set2 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),,1, +set2_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers2_set2 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),,1, +set2_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * (access_mode=='drive') * df.xfers3_set2 * (df.c_ivt_for_segment * 15),,1, +set2_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers1_set2 * (df.c_ivt_for_segment * 5),,1, +set2_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers2_set2 * (df.c_ivt_for_segment * 5),,1, +set2_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers3_set2 * (df.c_ivt_for_segment * 5),,1, +set2_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set2 * (df.crDistance_set2>20) * (df.crDistance_set2<=40) * c_cr20_40 * df.c_ivt_for_segment,,1, +set2_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set2 * (df.crDistance_set2>40) * c_cr40plus * df.c_ivt_for_segment,,1, +set2_CR_drive,drive access to CR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_drvCR * df.c_ivt_for_segment",,1, +set2_HR_drive,drive access to HR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_drvHeavy * df.c_ivt_for_segment",,1, +set2_FR_drive,drive access to FR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_drvFR * df.c_ivt_for_segment",,1, +set2_LRT_drive,drive access to LRT,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_drvLRT * df.c_ivt_for_segment",,1, +set2_EB_drive,drive access to EB,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) * c_drvExpress * df.c_ivt_for_segment",,1, +set2_ASC_CR,ASC CR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_cr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, +set2_ASC_HR,ASC HR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_hr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, +set2_ASC_FR,ASC FR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_fr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, +set2_ASC_LRT,ASC LRT,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_lrt_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, +# Set 3,,,,, +set3_unavailable,Shut off set if unavailable,@df.not_transit_available_set3,,,C_UNAVAILABLE +set3_ivt,set In-Vehicle Time,@~df.not_transit_available_set3 * df.c_ivt_for_segment * df.totalIVT_set3,,,1 +set3_first_wait_time,First wait time,"@~df.not_transit_available_set3 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET3')",,,1 +set3_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set3 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET3')",,,1 +set3_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set3 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET3')",,,1 +set3_fare,set Fare,"@~df.not_transit_available_set3 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET3') * 100",,,1 +set3_xfers1,0-1 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers1_set3 * c_xfers1 * df.c_ivt_for_segment,,,1 +set3_xfers2,1-2 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers2_set3 * c_xfers2 * df.c_ivt_for_segment,,,1 +set3_sfers3,>2 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers3_set3 * c_xfers3 * df.c_ivt_for_segment,,,1 +set3_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers1_set3 * (df.c_ivt_for_segment * 15),,,1 +set3_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers2_set3 * (df.c_ivt_for_segment * 15),,,1 +set3_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers3_set3 * (df.c_ivt_for_segment * 15),,,1 +set3_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers1_set3 * (df.c_ivt_for_segment * 5),,,1 +set3_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers2_set3 * (df.c_ivt_for_segment * 5),,,1 +set3_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers3_set3 * (df.c_ivt_for_segment * 5),,,1 +set3_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set3 * (df.crDistance_set3>20) * (df.crDistance_set3<=40) * c_cr20_40 * df.c_ivt_for_segment,,,1 +set3_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set3 * (df.crDistance_set3>40) * c_cr40plus * df.c_ivt_for_segment,,,1 +set3_CR_drive,drive access to CR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_drvCR * df.c_ivt_for_segment",,,1 +set3_HR_drive,drive access to HR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_drvHeavy * df.c_ivt_for_segment",,,1 +set3_FR_drive,drive access to FR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_drvFR * df.c_ivt_for_segment",,,1 +set3_LRT_drive,drive access to LRT,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_drvLRT * df.c_ivt_for_segment",,,1 +set3_EB_drive,drive access to EB,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) * c_drvExpress* df.c_ivt_for_segment",,,1 +set3_ASC_CR,ASC CR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_cr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 +set3_ASC_HR,ASC HR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_hr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 +set3_ASC_FR,ASC FR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_fr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 +set3_ASC_LRT,ASC LRT,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_lrt_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv new file mode 100755 index 0000000000..ba5cbe1d30 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv @@ -0,0 +1,44 @@ +Description,Target,Expression +# time of day,,SHOULD BE PASSED IN +# demographic segment,, +,c_ivt_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" +,c_cost_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" +# set1,, +,not_transit_available_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET1')==0" +Total IVT,totalIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') " +IVT on BART,bartIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')" +premium modes used,premiumMode_set1,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)" +only travel by BART,bartOnly_set1,bartIVT_set1 == totalIVT_set1 +Set contains only BART with Xfers,bartWithXfer_set1,"(bartIVT_set1 == totalIVT_set1) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set1,"(premiumMode_set1>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1')>0)" +Number transfers,transfers_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')" +0-1 transfers,xfers1_set1,(transfers_set1>0) & (transfers_set1 <=1) +1-2 transfers,xfers2_set1,(transfers_set1>1) & (transfers_set1 <=2) +>2 transfers,xfers3_set1,(transfers_set1>2) +Commuter Rail Distance in miles [35 mph],crDistance_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') * (35/60)" +# set2,, +,not_transit_available_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET2')==0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')==0)" +Total IVT,totalIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') " +IVT on BART,bartIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')" +premium modes used,premiumMode_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0)" +only travel by BART,bartOnly_set2,bartIVT_set2 == totalIVT_set2 +Set contains only BART with Xfers,bartWithXfer_set2,"(bartIVT_set2 == totalIVT_set2) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set2,"(premiumMode_set2>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2')>0)" +Number transfers,transfers_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')" +0-1 transfers,xfers1_set2,(transfers_set2>0) & (transfers_set2 <=1) +1-2 transfers,xfers2_set2,(transfers_set2>1) & (transfers_set2 <=2) +>2 transfers,xfers3_set2,(transfers_set2>2) +Commuter Rail Distance in miles [35 mph],crDistance_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') * (35/60)" +# set3,, +,not_transit_available_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET3')==0" +Total IVT,totalIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') " +IVT on BART,bartIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')" +premium modes used,premiumMode_set3,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0)" +only travel by BART,bartOnly_set3,bartIVT_set3 == totalIVT_set3 +Set contains only BART with Xfers,bartWithXfer_set3,"(bartIVT_set3 == totalIVT_set3) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set3,"(premiumMode_set3>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3')>0)" +Number transfers,transfers_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')" +0-1 transfers,xfers1_set3,(transfers_set3>0) & (transfers_set3 <=1) +1-2 transfers,xfers2_set3,(transfers_set3>1) & (transfers_set3 <=2) +>2 transfers,xfers3_set3,(transfers_set3>2) +Commuter Rail Distance in miles [35 mph],crDistance_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') * (35/60)" diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv new file mode 100755 index 0000000000..fdb64bd1e9 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv @@ -0,0 +1,4 @@ +Label,Description,Expression,utility +#,,,FIXME column values shouldn't ever be na if different moides have different tables? +#util_walk_available,walk available,@df.walk_time.isna() * C_UNAVAILABLE,1 +util_walk_time,walk time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_walkAcc * df.WALK_TRANSIT_DIST*(60/walk_speed)",1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin_full/settings.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin_full/settings.yaml new file mode 100644 index 0000000000..01849d461e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin_full/settings.yaml @@ -0,0 +1,68 @@ +inherit_settings: True + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + + +# - ------------------------- dev config +multiprocess: True +strict: False +mem_tick: 30 +use_shadow_pricing: False + +households_sample_size: 0 +#chunk_size: 6000000000 +num_processes: 8 + +# - ------------------------- + +# not recommended or supported for multiprocessing +want_dest_choice_sample_tables: False + +read_skim_cache: True +#write_skim_cache: True + +# - tracing +trace_hh_id: +trace_od: + +# to resume after last successful checkpoint, specify resume_after: _ +resume_after: + + + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_tvpb + begin: initialize_tvpb + num_processes: 8 + chunk_size: 0 + slice: + tables: + - attribute_combinations + - name: mp_mode_choice + begin: tour_mode_choice_simulate + num_processes: 8 + #chunk_size: 0 + slice: + tables: + - households + - persons + - tours + - name: mp_summarize + begin: write_data_dictionary + + +output_tables: + action: include + prefix: final_ + tables: + - checkpoints + - households + - persons + - tours + - attribute_combinations + + + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin_full/settings_mp.yaml b/activitysim/examples/example_multiple_zone/configs_3_zone_marin_full/settings_mp.yaml new file mode 100644 index 0000000000..7ada82c922 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_3_zone_marin_full/settings_mp.yaml @@ -0,0 +1,68 @@ +inherit_settings: True + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + + +# - ------------------------- dev config +multiprocess: True +strict: False +mem_tick: 30 +use_shadow_pricing: False + +households_sample_size: 0 +#chunk_size: 6000000000 +num_processes: 32 + +# - ------------------------- + +# not recommended or supported for multiprocessing +want_dest_choice_sample_tables: False + +#read_skim_cache: True +#write_skim_cache: True + +# - tracing +trace_hh_id: 123105 +trace_od: + +# to resume after last successful checkpoint, specify resume_after: _ +resume_after: + + + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_tvpb + begin: initialize_tvpb + num_processes: 20 + chunk_size: 2376277344 # num_taps * num_taps * rowsize / desired_num_chunks = 2376277344 + slice: + tables: + - attribute_combinations + - name: mp_mode_choice + begin: tour_mode_choice_simulate + num_processes: 32 + #chunk_size: 0 + slice: + tables: + - households + - persons + - tours + - name: mp_summarize + begin: write_data_dictionary + + +output_tables: + action: include + prefix: final_ + tables: + - checkpoints + - households + - persons + - tours + - attribute_combinations + + + diff --git a/activitysim/examples/example_multiple_zone/configs_local/logging.yaml b/activitysim/examples/example_multiple_zone/configs_local/logging.yaml new file mode 100644 index 0000000000..df20cf0c7e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_local/logging.yaml @@ -0,0 +1,54 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + diff --git a/activitysim/examples/example_multiple_zone/configs_local/network_los.yaml b/activitysim/examples/example_multiple_zone/configs_local/network_los.yaml new file mode 100644 index 0000000000..25b4092ebf --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_local/network_los.yaml @@ -0,0 +1,18 @@ +inherit_settings: True + +#skim_dict_factory: NumpyArraySkimFactory +##skim_dict_factory: MemMapSkimFactory +# +## read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +#read_skim_cache: True +## write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +#write_skim_cache: True +# +## rebuild and overwrite existing tap_tap_utilities cache +#rebuild_tvpb_cache: True +# +# +## when checkpointing cache. also write a csv version of tvpb cache for tracing +## (writes csv file when writing/checkpointing cache (i.e. when cached changed) even if rebuild_tvpb_cache is False) +## (n.b. csv file could be quite large if cache is STATIC!) +#trace_tvpb_cache_as_csv: False diff --git a/activitysim/examples/example_multiple_zone/configs_local/settings.yaml b/activitysim/examples/example_multiple_zone/configs_local/settings.yaml new file mode 100644 index 0000000000..9883b9fc9b --- /dev/null +++ b/activitysim/examples/example_multiple_zone/configs_local/settings.yaml @@ -0,0 +1,21 @@ +inherit_settings: True + +#households_sample_size: 100000 +households_sample_size: 10 + +#chunk_size: 0 +#chunk_size: 4000000000 + + + +# household with mandatory, non mandatory, atwork_subtours, and joint tours +trace_hh_id: 2848373 + + +# to resume after last successful checkpoint, specify resume_after: _ +#resume_after: write_trip_matrices + +#cleanup_trace_files_on_resume: True + +#trace_od: [5000, 11000] + diff --git a/activitysim/examples/example_multiple_zone/data/households.csv b/activitysim/examples/example_multiple_zone/data/households.csv new file mode 100644 index 0000000000..1e2bf4913e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/data/households.csv @@ -0,0 +1,5001 @@ +HHID,TAZ,SERIALNO,PUMA5,income,PERSONS,HHT,UNITTYPE,NOC,BLDGSZ,TENURE,VEHICL,hinccat1,hinccat2,hhagecat,hsizecat,hfamily,hunittype,hNOCcat,hwrkrcat,h0004,h0511,h1215,h1617,h1824,h2534,h3549,h5064,h6579,h80up,workers,hwork_f,hwork_p,huniv,hnwork,hretire,hpresch,hschpred,hschdriv,htypdwel,hownrent,hadnwst,hadwpst,hadkids,bucketBin,originalPUMA,hmultiunit 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b/activitysim/examples/example_multiple_zone/data/mtc_asim.h5 new file mode 100644 index 0000000000..5ec01031d6 Binary files /dev/null and b/activitysim/examples/example_multiple_zone/data/mtc_asim.h5 differ diff --git a/activitysim/examples/example_multiple_zone/data/override_hh_ids.csv b/activitysim/examples/example_multiple_zone/data/override_hh_ids.csv new file mode 100644 index 0000000000..d15a5aa713 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/data/override_hh_ids.csv @@ -0,0 +1,11 @@ +household_id +982875 +1810015 +1099626 +763879 +824207 +2822230 +2821179 +1196298 +1363467 +386761 diff --git a/activitysim/examples/example_multiple_zone/data/persons.csv b/activitysim/examples/example_multiple_zone/data/persons.csv new file mode 100644 index 0000000000..004c254b9e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/data/persons.csv @@ -0,0 +1,8213 @@ 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b/activitysim/examples/example_multiple_zone/data_3/.gitignore @@ -0,0 +1 @@ +cache/ \ No newline at end of file diff --git a/activitysim/examples/example_multiple_zone/data_3_marin/.gitignore b/activitysim/examples/example_multiple_zone/data_3_marin/.gitignore new file mode 100644 index 0000000000..6e25fa8f10 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/data_3_marin/.gitignore @@ -0,0 +1 @@ +cache/ \ No newline at end of file diff --git a/activitysim/examples/example_multiple_zone/data_3_marin_full/.gitignore b/activitysim/examples/example_multiple_zone/data_3_marin_full/.gitignore new file mode 100644 index 0000000000..6e25fa8f10 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/data_3_marin_full/.gitignore @@ -0,0 +1 @@ +cache/ \ No newline at end of file diff --git a/other_resources/example_multiple_zone/output/.gitignore b/activitysim/examples/example_multiple_zone/output_2/.gitignore similarity index 69% rename from other_resources/example_multiple_zone/output/.gitignore rename to activitysim/examples/example_multiple_zone/output_2/.gitignore index 4a2323ec0a..bf5bf15e3e 100644 --- a/other_resources/example_multiple_zone/output/.gitignore +++ b/activitysim/examples/example_multiple_zone/output_2/.gitignore @@ -3,3 +3,5 @@ *.prof *.h5 *.txt +*.yaml +*.omx diff --git a/activitysim/examples/example_multiple_zone/output_2/trace/.gitignore b/activitysim/examples/example_multiple_zone/output_2/trace/.gitignore new file mode 100644 index 0000000000..8edb806780 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_2/trace/.gitignore @@ -0,0 +1,3 @@ +*.csv +*.log +*.txt diff --git a/activitysim/examples/example_multiple_zone/output_3/.gitignore b/activitysim/examples/example_multiple_zone/output_3/.gitignore new file mode 100644 index 0000000000..bf5bf15e3e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3/.gitignore @@ -0,0 +1,7 @@ +*.csv +*.log +*.prof +*.h5 +*.txt +*.yaml +*.omx diff --git a/activitysim/examples/example_multiple_zone/output_3/cache/.gitignore b/activitysim/examples/example_multiple_zone/output_3/cache/.gitignore new file mode 100644 index 0000000000..3dd2e62f9e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3/cache/.gitignore @@ -0,0 +1,2 @@ +*.mmap +*.feather diff --git a/activitysim/examples/example_multiple_zone/output_3/trace/.gitignore b/activitysim/examples/example_multiple_zone/output_3/trace/.gitignore new file mode 100644 index 0000000000..8edb806780 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3/trace/.gitignore @@ -0,0 +1,3 @@ +*.csv +*.log +*.txt diff --git a/activitysim/examples/example_multiple_zone/output_3_marin/.gitignore b/activitysim/examples/example_multiple_zone/output_3_marin/.gitignore new file mode 100644 index 0000000000..bf5bf15e3e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3_marin/.gitignore @@ -0,0 +1,7 @@ +*.csv +*.log +*.prof +*.h5 +*.txt +*.yaml +*.omx diff --git a/activitysim/examples/example_multiple_zone/output_3_marin/cache/.gitignore b/activitysim/examples/example_multiple_zone/output_3_marin/cache/.gitignore new file mode 100644 index 0000000000..3dd2e62f9e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3_marin/cache/.gitignore @@ -0,0 +1,2 @@ +*.mmap +*.feather diff --git a/activitysim/examples/example_multiple_zone/output_3_marin/trace/.gitignore b/activitysim/examples/example_multiple_zone/output_3_marin/trace/.gitignore new file mode 100644 index 0000000000..8edb806780 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3_marin/trace/.gitignore @@ -0,0 +1,3 @@ +*.csv +*.log +*.txt diff --git a/activitysim/examples/example_multiple_zone/output_3_marin_full/.gitignore b/activitysim/examples/example_multiple_zone/output_3_marin_full/.gitignore new file mode 100644 index 0000000000..bf5bf15e3e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3_marin_full/.gitignore @@ -0,0 +1,7 @@ +*.csv +*.log +*.prof +*.h5 +*.txt +*.yaml +*.omx diff --git a/activitysim/examples/example_multiple_zone/output_3_marin_full/cache/.gitignore b/activitysim/examples/example_multiple_zone/output_3_marin_full/cache/.gitignore new file mode 100644 index 0000000000..3dd2e62f9e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3_marin_full/cache/.gitignore @@ -0,0 +1,2 @@ +*.mmap +*.feather diff --git a/activitysim/examples/example_multiple_zone/output_3_marin_full/trace/.gitignore b/activitysim/examples/example_multiple_zone/output_3_marin_full/trace/.gitignore new file mode 100644 index 0000000000..8edb806780 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/output_3_marin_full/trace/.gitignore @@ -0,0 +1,3 @@ +*.csv +*.log +*.txt diff --git a/activitysim/examples/example_multiple_zone/scripts/marin_crop.py b/activitysim/examples/example_multiple_zone/scripts/marin_crop.py new file mode 100644 index 0000000000..b0f3ca10c9 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/scripts/marin_crop.py @@ -0,0 +1,160 @@ + +# crop marin tvpb example data processing to one county +# Ben Stabler, ben.stabler@rsginc.com, 09/17/20 +# jeff doyle added code to introduce MAZ_OFFSET to avoid confusion (and detect associated errors) between zone types + +import os +import pandas as pd +import openmatrix as omx + +# counties = ["Marin" +# counties = ["San Francisco" +counties = ["Marin", "San Francisco"] + +input_dir = './data_3_marin' +output_dir = './data_3_marin/crop' +MAZ_OFFSET = 100000 + + +def input_path(file_name): + return os.path.join(input_dir, file_name) + + +def output_path(file_name): + return os.path.join(output_dir, file_name) + + +def patch_maz(df, maz_offset): + for c in df.columns: + if c in ['MAZ', 'OMAZ', 'DMAZ', 'mgra', 'orig_mgra', 'dest_mgra']: + df[c] += maz_offset + return df + + +def read_csv(file_name): + df = pd.read_csv(input_path(file_name)) + if MAZ_OFFSET: + df = patch_maz(df, MAZ_OFFSET) + print(f"\n\n{file_name}\n{df}") + return df + + +def to_csv(df, file_name): + df.to_csv(output_path(file_name), index=False) + + +######### +# mazs = read_csv("maz_data_asim.csv") +# taps = read_csv("tap_data.csv") +# +# print(f"max maz {mazs.MAZ.max()}") +# print(f"num maz {len(mazs.MAZ.unique())}") +# print(f"num taz {len(mazs.TAZ.unique())}") +# print(f"num tap {len(taps.TAP.unique())}") +# +# num maz 5952 +# num taz 4735 +# num tap 6216 +######### + + +# 0 - get county zones + +mazs = read_csv("maz_data_asim.csv") + +mazs = mazs[mazs["CountyName"].isin(counties)] +to_csv(mazs, "maz_data_asim.csv") + +maz_taz = mazs[['MAZ', 'TAZ']] +to_csv(mazs, "maz_taz.csv") + +tazs = mazs["TAZ"].unique() +tazs.sort() +tazs_indexes = (tazs - 1).tolist() + +taps = read_csv("tap_data.csv") +taps = taps[['TAP', 'TAZ']].sort_values(by='TAP') +taps = taps[taps["TAZ"].isin(tazs)] +to_csv(taps, "tap_data.csv") + +# 1-based tap_ids +taps_indexes = (taps["TAP"] - 1).tolist() + + +# 2 - maz to tap walk, bike + +maz_tap_walk = read_csv("maz_tap_walk.csv") +maz_maz_walk = read_csv("maz_maz_walk.csv") +maz_maz_bike = read_csv("maz_maz_bike.csv") + +maz_tap_walk = maz_tap_walk[maz_tap_walk["MAZ"].isin(mazs["MAZ"]) & maz_tap_walk["TAP"].isin(taps["TAP"])] +maz_maz_walk = maz_maz_walk[maz_maz_walk["OMAZ"].isin(mazs["MAZ"]) & maz_maz_walk["DMAZ"].isin(mazs["MAZ"])] +maz_maz_bike = maz_maz_bike[maz_maz_bike["OMAZ"].isin(mazs["MAZ"]) & maz_maz_bike["DMAZ"].isin(mazs["MAZ"])] + +to_csv(maz_tap_walk, "maz_tap_walk.csv") +to_csv(maz_maz_walk, "maz_maz_walk.csv") +to_csv(maz_maz_bike, "maz_maz_bike.csv") + + +tap_lines = read_csv("tap_lines.csv") +tap_lines = tap_lines[tap_lines['TAP'].isin(taps["TAP"])] +to_csv(tap_lines, "tap_lines.csv") + +# taz to tap drive data + +taz_tap_drive = read_csv("maz_taz_tap_drive.csv") +taz_tap_drive = taz_tap_drive[taz_tap_drive["MAZ"].isin(mazs["MAZ"]) & taz_tap_drive["TAP"].isin(taps["TAP"])] +to_csv(taz_tap_drive, "maz_taz_tap_drive.csv") + + +# 3 - accessibility data + +access = read_csv("access.csv") +access = access[access["mgra"].isin(mazs["MAZ"])] +to_csv(access, "access.csv") + + +# households + +households = read_csv("households_asim.csv") +households = households[households["MAZ"].isin(mazs["MAZ"])] +to_csv(households, "households_asim.csv") + +# persons + +persons = read_csv("persons_asim.csv") +persons = persons[persons["HHID"].isin(households["HHID"])] +to_csv(persons, "persons_asim.csv") + +# tours file + +work_tours = read_csv("work_tours.csv") +work_tours = work_tours[work_tours["hh_id"].isin(households["HHID"])] +work_tours = work_tours[work_tours["orig_mgra"].isin(mazs["MAZ"]) & work_tours["dest_mgra"].isin(mazs["MAZ"])] +to_csv(work_tours, "work_tours.csv") + +# skims + +time_periods = ["AM", "EA", "EV", "MD", "PM"] +for tp in time_periods: + omx_file_name = 'HWYSKM' + tp + '_taz_rename.omx' + taz_file = omx.open_file(input_path(omx_file_name)) + taz_file_rename = omx.open_file(output_path(omx_file_name), 'w') + taz_file_rename.create_mapping('ZONE', tazs.tolist()) + for mat_name in taz_file.list_matrices(): + taz_file_rename[mat_name] = taz_file[mat_name][tazs_indexes, :][:, tazs_indexes] + print(mat_name) + taz_file.close() + taz_file_rename.close() + +for tp in time_periods: + for skim_set in ["SET1", "SET2", "SET3"]: + omx_file_name = 'transit_skims_' + tp + '_' + skim_set + '_rename.omx' + tap_file = omx.open_file(input_path(omx_file_name)) + tap_file_rename = omx.open_file(output_path(omx_file_name), 'w') + tap_file_rename.create_mapping('ZONE', taps["TAP"].tolist()) + for mat_name in tap_file.list_matrices(): + tap_file_rename[mat_name] = tap_file[mat_name][taps_indexes, :][:, taps_indexes] + print(mat_name) + tap_file.close() + tap_file_rename.close() diff --git a/activitysim/examples/example_multiple_zone/scripts/marin_fix.py b/activitysim/examples/example_multiple_zone/scripts/marin_fix.py new file mode 100644 index 0000000000..6cc1b08d9e --- /dev/null +++ b/activitysim/examples/example_multiple_zone/scripts/marin_fix.py @@ -0,0 +1,91 @@ +# remove some of the asim-style columns added by marin_work_tour_mode_choice.py +# so data input files look 'realistic' - and that work is done instaed by 'import_tours' annotation expression files + +import os +import pandas as pd +import openmatrix as omx + +input_dir = './data_3_marin' +output_dir = './data_3_marin/fix' # don't overwrite - but these files shold replace 'oritinals' + + +def input_path(filenane): + return os.path.join(input_dir, filenane) + + +def output_path(filenane): + return os.path.join(output_dir, filenane) + + +# 0 - get county zones + +mazs = pd.read_csv(input_path("maz_data_asim.csv")) +del mazs['zone_id'] +del mazs['county_id'] +mazs.to_csv(output_path("maz_data_asim.csv"), index=False) + +tazs = mazs["TAZ"].unique() +tazs.sort() +assert ((tazs - 1) == range(len(tazs))).all() + +# MAZ,TAZ +taps = pd.read_csv(input_path("maz_taz.csv")) +# nothing +taps.to_csv(output_path("maz_taz.csv"), index=False) + +taps = pd.read_csv(input_path("tap_data.csv")) +# nothing +taps.to_csv(output_path("tap_data.csv"), index=False) + + +# 2 - nearby skims need headers + +maz_tap_walk = pd.read_csv(input_path("maz_tap_walk.csv")) +maz_maz_walk = pd.read_csv(input_path("maz_maz_walk.csv")) +maz_maz_bike = pd.read_csv(input_path("maz_maz_bike.csv")) + +del maz_tap_walk['TAP.1'] +del maz_maz_walk['DMAZ.1'] +del maz_maz_bike['DMAZ.1'] + +maz_tap_walk.to_csv(output_path("maz_tap_walk.csv"), index=False) +maz_maz_walk.to_csv(output_path("maz_maz_walk.csv"), index=False) +maz_maz_bike.to_csv(output_path("maz_maz_bike.csv"), index=False) + +# 3 - accessibility data + +access = pd.read_csv(input_path("access.csv")) +del access['zone_id'] +access.to_csv(output_path("access.csv"), index=False) + +# 4 - maz to tap drive data + +taz_tap_drive = pd.read_csv(input_path("maz_taz_tap_drive.csv")) + +taz_tap_drive.to_csv(output_path("maz_taz_tap_drive.csv"), index=False) + +# 5 - households + +households = pd.read_csv(input_path("households_asim.csv")) +del households['home_zone_id'] +del households['household_id'] + +households.to_csv(output_path("households_asim.csv"), index=False) + +# 6 - persons + +persons = pd.read_csv(input_path("persons_asim.csv")) +del persons['person_id'] +del persons['household_id'] +del persons['is_university'] +persons.to_csv(output_path("persons_asim.csv"), index=False) + +# 7 - tours file + +work_tours = pd.read_csv(input_path("work_tours.csv")) +del work_tours["household_id"] +del work_tours["destination"] +del work_tours["start"] +del work_tours["end"] +del work_tours["tour_type"] +work_tours.to_csv(output_path("work_tours.csv"), index=False) diff --git a/activitysim/examples/example_multiple_zone/scripts/marin_work_tour_mode_choice_data.py b/activitysim/examples/example_multiple_zone/scripts/marin_work_tour_mode_choice_data.py new file mode 100644 index 0000000000..bfbaefddd3 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/scripts/marin_work_tour_mode_choice_data.py @@ -0,0 +1,148 @@ + +# marin tvpb example data processing +# Ben Stabler, ben.stabler@rsginc.com, 09/17/20 + +import pandas as pd +import openmatrix as omx + +# command to run the underdevelopment example +# python simulation.py -c configs_3_zone_marin -d data_3_marin -o output_3_marin + +# data processing at c:\projects\activitysim\marin + +# 1 - fix skim names, put time periods at end and make all names unique + +time_periods = ["AM", "EA", "EV", "MD", "PM"] +for tp in time_periods: + taz_file = omx.open_file('HWYSKM' + tp + '_taz.omx') + taz_file_rename = omx.open_file('HWYSKM' + tp + '_taz_rename.omx', 'w') + for mat_name in taz_file.list_matrices(): + taz_file_rename[mat_name + "__" + tp] = taz_file[mat_name][:] + print(mat_name + "__" + tp) + taz_file.close() + taz_file_rename.close() + +for tp in time_periods: + for skim_set in ["SET1", "SET2", "SET3"]: + tap_file = omx.open_file('transit_skims_' + tp + '_' + skim_set + '.omx') + tap_file_rename = omx.open_file('transit_skims_' + tp + '_' + skim_set + '_rename.omx', 'w') + for mat_name in tap_file.list_matrices(): + tap_file_rename[mat_name + "_" + skim_set + "__" + tp] = tap_file[mat_name][:] + print(mat_name + '_' + skim_set + "__" + tp) + tap_file.close() + tap_file_rename.close() + +# 2 - nearby skims need headers + +maz_tap_walk = pd.read_csv("2015_test_2019_02_13_Part3/skims/ped_distance_maz_tap.txt", header=None) +maz_maz_walk = pd.read_csv("2015_test_2019_02_13_Part3/skims/ped_distance_maz_maz.txt", header=None) +maz_maz_bike = pd.read_csv("2015_test_2019_02_13_Part3/skims/bike_distance_maz_maz.txt", header=None) + +maz_tap_walk.columns = ["MAZ", "TAP", "TAP", "WALK_TRANSIT_GEN_COST", "WALK_TRANSIT_DIST"] +maz_maz_walk.columns = ["OMAZ", "DMAZ", "DMAZ", "WALK_GEN_COST", "WALK_DIST"] +maz_maz_bike.columns = ["OMAZ", "DMAZ", "DMAZ", "BIKE_GEN_COST", "BIKE_DIST"] + +maz_tap_walk["WALK_TRANSIT_DIST"] = maz_tap_walk["WALK_TRANSIT_DIST"] / 5280 # miles +maz_maz_walk["WALK_DIST"] = maz_maz_walk["WALK_DIST"] / 5280 # miles +maz_maz_bike["BIKE_DIST"] = maz_maz_bike["BIKE_DIST"] / 5280 # miles + +maz_tap_walk[["MAZ", "TAP", "WALK_TRANSIT_DIST"]].to_csv("maz_tap_walk.csv", index=False) +maz_maz_walk[["OMAZ", "DMAZ", "WALK_DIST"]].to_csv("maz_maz_walk.csv", index=False) +maz_maz_bike[["OMAZ", "DMAZ", "BIKE_DIST"]].to_csv("maz_maz_bike.csv", index=False) + +# 3 - maz data + +mazs = pd.read_csv("2015_test_2019_02_13_Part2/landuse/maz_data_withDensity.csv") +pcost = pd.read_csv("2015_test_2019_02_13/ctramp_output/mgraParkingCost.csv") + +mazs = pd.concat([mazs, pcost], axis=1) +mazs = mazs.fillna(0) + +tazs = pd.read_csv("2015_test_2019_02_13_Part2/landuse/taz_data.csv") +tazs = tazs.set_index("TAZ", drop=False) + +mazs["TERMINALTIME"] = tazs["TERMINALTIME"].loc[mazs["TAZ"]].tolist() + +mazs["zone_id"] = mazs["MAZ"] +mazs["county_id"] = mazs["CountyID"] +mazs = mazs.set_index("zone_id", drop=False) + +mazs.to_csv("maz_data_asim.csv", index=False) + +# 4 - accessibility data + +access = pd.read_csv("2015_test_2019_02_13/ctramp_output/accessibilities.csv") +access = access.drop([0]) +access["zone_id"] = access["mgra"] +access = access.set_index("zone_id", drop=False) +access.to_csv("access.csv", index=False) + +# 5 - maz to tap drive data + +taz_tap_drive = pd.read_csv("2015_test_2019_02_13_Part3/skims/drive_maz_taz_tap.csv") + +taz_tap_drive = taz_tap_drive.pivot_table(index=["FTAZ", "TTAP"], values=['DTIME', 'DDIST', "WDIST"], fill_value=0) + +taz_tap_drive.columns = list(map("".join, taz_tap_drive.columns)) +taz_tap_drive = taz_tap_drive.reset_index() +taz_tap_drive = taz_tap_drive.set_index("FTAZ") +taz_tap_drive["TAP"] = taz_tap_drive["TTAP"] + +taz_tap_drive = pd.merge(mazs[["MAZ", "TAZ"]], taz_tap_drive, left_on=['TAZ'], right_on=['FTAZ']) +taz_tap_drive[["MAZ", "TAP", "DDIST", "DTIME", "WDIST"]].to_csv("maz_taz_tap_drive.csv", index=False) + +# 6 - tours file, we just need work tours + +itour = pd.read_csv("2015_test_2019_02_13/ctramp_output/indivTourData_3.csv") +work_tours = itour[itour["tour_purpose"] == "Work"] + +work_tours["tour_id"] = range(1, len(work_tours)+1) +work_tours["household_id"] = work_tours["hh_id"] +work_tours = work_tours.set_index("tour_id", drop=False) + +work_tours["destination"] = work_tours["dest_mgra"] + +work_tours["start"] = work_tours["start_period"] +work_tours["end"] = work_tours["end_period"] +work_tours["tour_type"] = "work" + +work_tours.to_csv("work_tours.csv", index=False) + +# 7 - households + +households = pd.read_csv("2015_test_2019_02_13_Part2/popsyn/households.csv") +households["household_id"] = households["HHID"] +households["home_zone_id"] = households["MAZ"] +households = households.set_index("household_id", drop=False) + +households.to_csv("households_asim.csv", index=False) + +# 8 - persons + +persons = pd.read_csv("2015_test_2019_02_13_Part2/popsyn/persons.csv") +persons["person_id"] = persons["PERID"] +persons["household_id"] = persons["HHID"] +persons = persons.set_index("person_id", drop=False) + +persons_output = pd.read_csv("2015_test_2019_02_13/ctramp_output/personData_3.csv") +persons_output = persons_output.set_index("person_id", drop=False) +persons["type"] = persons_output["type"].loc[persons.index] +persons["value_of_time"] = persons_output["value_of_time"].loc[persons.index] +persons["is_university"] = persons["type"] == "University student" +persons["fp_choice"] = persons_output["fp_choice"] + +persons.to_csv("persons_asim.csv", index=False) + +# 9 - replace existing pipeline tables for restart for now + +# run simple three zone example and get output pipeline and then replace tables before tour mode choice +pipeline = pd.io.pytables.HDFStore('pipeline.h5') +pipeline.keys() + +pipeline['/accessibility/compute_accessibility'] = access # index zone_id +pipeline['/households/joint_tour_frequency'] = households # index household_id +pipeline['/persons/non_mandatory_tour_frequency'] = persons # index person_id +pipeline['/land_use/initialize_landuse'] = mazs # index zone_id +pipeline['/tours/non_mandatory_tour_scheduling'] = work_tours # index tour_id + +pipeline.close() diff --git a/activitysim/examples/example_multiple_zone/scripts/notes.txt b/activitysim/examples/example_multiple_zone/scripts/notes.txt new file mode 100644 index 0000000000..7bc810dbeb --- /dev/null +++ b/activitysim/examples/example_multiple_zone/scripts/notes.txt @@ -0,0 +1,36 @@ + +# for the mtctm1 fudged examples, depending on where you are, run: + +# from top level activitysim repo: +python activitysim/examples/example_multiple_zone/two_zone_example_data.py +python activitysim/examples/example_multiple_zone/three_zone_example_data.py + +# or from this directory: +python two_zone_example_data.py +python three_zone_example_data.py + +python simulation.py -c configs_local -c configs_1_zone -c configs -o output_1 +python simulation.py -c configs_local -c configs_2_zone -c configs -d data_2 -o output_2 +python simulation.py -c configs_local -c configs_3_zone -c configs -d data_3 -o output_3 + +# for the marin data + +you will need the data_3_marin folder with files created/processed by marin_work_tour_mode_choice_data.py + +These were originally created for the purpose of creating a 'fake' pipeline database to resume before tour_mode_choice + +When we decided to implement initialize_tours, I chose to remove some of the asim-style columns +so the data input files would look 'realistic' - and that work is now done instead by 'import_tours' +annotation expression files + +the marin_fix.py script does the job of removing those fields and writes them into a 'fix' folder. +Once they have been created they can replace the original input files. + +You will also need to add (and rename) the tap_lines.csv file to the data_3_marin folder + +the marin_crop.py script has been modified to work with the 'fixed' input files. +marin_crop.py writes a complete data folder into the 'crop' folder (which you will need to create) +which should be copied and renamed to somewhere more appropriate (or you can specify the marin_crop ouput_dir in script) + +python simulation.py -c configs_3_zone_marin -d data_3_marin -o output_3_marin +python simulation.py -c configs_3_zone_marin_full -c configs_3_zone_marin -d data_3_marin_full -o output_3_marin_full -s settings_mp.yaml diff --git a/activitysim/examples/example_multiple_zone/scripts/tvpb_validation.R b/activitysim/examples/example_multiple_zone/scripts/tvpb_validation.R new file mode 100644 index 0000000000..de8eb01356 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/scripts/tvpb_validation.R @@ -0,0 +1,30 @@ + +#mode share +x=read.csv("C:/projects/activitysim/activitysim/examples/example_multiple_zone/output_3_marin_full/final_tours.csv") + +tm2_mode_codes = c("DRIVEALONEFREE","SHARED2FREE","SHARED2FREE","SHARED3FREE","SHARED3FREE","WALK","BIKE","WALK_TRANSIT","DRIVE_TRANSIT","DRIVE_TRANSIT","TAXI") +names(tm2_mode_codes) = c(1,3,4,6,7,9,10,11,12,13,15) +x$tm2_MODE = tm2_mode_codes[match(x$tm2_tour_mode, names(tm2_mode_codes))] +tm2_ms = as.data.frame(round(table(x$tm2_MODE) / nrow(x),2)) + +write.csv(tm2_ms, "c:/projects/tm2_ms.csv", row.names=F) + +asim_ms = as.data.frame(round(table(x$tour_mode) / nrow(x),4)) +write.csv(asim_ms, "c:/projects/asim_ms.csv", row.names=F) + +#taps + +taps=read.csv("C:/projects/activitysim/activitysim/examples/example_multiple_zone/data_3_marin_full/tap_data.csv") + +tm2_out_btap = sort(table(x$tm2_out_btap)) +asim_out_btap = sort(table(x$od_btap)) + +taps = merge(taps, as.data.frame(tm2_out_btap), by.x="TAP", by.y="Var1") +taps = merge(taps, as.data.frame(asim_out_btap), by.x="TAP", by.y="Var1") +taps = taps[c("TAP","Freq.x","Freq.y")] +colnames(taps) = c("TAP","tm2","asim") + +write.csv(taps, "c:/projects/taps.csv", row.names=F) + +table(x[x$tm2_out_btap==674,]$tm2_MODE) +table(as.character(x[x$od_btap==674,]$tour_mode)) diff --git a/activitysim/examples/example_multiple_zone/simulation.py b/activitysim/examples/example_multiple_zone/simulation.py new file mode 100644 index 0000000000..97ca6b6483 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/simulation.py @@ -0,0 +1,15 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse + +from activitysim.cli.run import add_run_args, run + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/activitysim/examples/example_multiple_zone/three_zone_example_data.py b/activitysim/examples/example_multiple_zone/three_zone_example_data.py new file mode 100644 index 0000000000..6e202f7184 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/three_zone_example_data.py @@ -0,0 +1,230 @@ +# Creating the Two Zone Example Data +# +# Transform the TM1 TAZ-based model 25 zone inputs to a two-zone (MAZ and TAZ) set of inputs for software development. +# +# The 25 zones are downtown San Francisco and they are converted to 25 MAZs. +# MAZs 1,2,3,4 are small and adjacent and assigned TAZ 2 and TAP 10002. +# MAZs 13,14,15 are small and adjacent and as signed TAZ 14 and TAP 10014. +# TAZs 1,3,4,13,15 are removed from the final data set. +# +# This script should work for the full TM1 example as well. + +import os +import shutil + +import pandas as pd +import numpy as np +import openmatrix as omx + + +# Create example directory + + +input_data = os.path.join(os.path.dirname(__file__), 'data') +output_data = os.path.join(os.path.dirname(__file__), 'data_3') +MAZ_MULTIPLIER = 1000 +TAP_OFFSET = 90000 + +# ### initialize output data directory + +# new empty output_dir +if os.path.exists(output_data): + # shutil.rmtree(output_data) + # os.makedirs(output_data) + file_type = ('csv', 'omx') + for file_name in os.listdir(output_data): + if file_name.endswith(file_type): + os.unlink(os.path.join(output_data, file_name)) +else: + os.makedirs(output_data) + +# ### Convert tazs to mazs and add transit access distance by mode + +land_use = pd.read_csv(os.path.join(input_data, 'land_use.csv')) + +land_use.insert(loc=0, column='MAZ', value=land_use.TAZ) + +land_use.TAZ = land_use.TAZ.replace([1, 2, 3, 4], 2) +land_use.TAZ = land_use.TAZ.replace([13, 14, 15], 14) + +# make MAZ indexes different from TAZ to drive MAZ/TAZ confusion errors and omisisons +land_use.MAZ *= MAZ_MULTIPLIER + +shortWalk = 0.333 # the tm1 example assumes this distance for transit access +longWalk = 0.667 +land_use['access_dist_transit'] = shortWalk + +# FIXME - could assign longWalk where maz != taz, but then results wodl differe from one-zone +# land_use['access_dist_transit'] =\ +# np.where(land_use.TAZ*MAZ_MULTIPLIER==land_use.MAZ, shortWalk, longWalk) + + +land_use.to_csv(os.path.join(output_data, 'land_use.csv'), index=False) + +# ### Put households in mazs instead of tazs + +households = pd.read_csv(os.path.join(input_data, 'households.csv')) +households.rename(columns={'TAZ': 'MAZ'}, inplace=True) +households.MAZ *= MAZ_MULTIPLIER +households.to_csv(os.path.join(output_data, 'households.csv'), index=False) + +persons = pd.read_csv(os.path.join(input_data, 'persons.csv')) +persons.to_csv(os.path.join(output_data, 'persons.csv'), index=False) + +# ### Create maz file +# one row per maz, currentlyt he only attribute it its containing TAZ + +# FIXME - not clear we need this +maz_df = land_use[['MAZ', 'TAZ']] +maz_df.to_csv(os.path.join(output_data, 'maz.csv'), index=False) +print("maz.csv\n%s" % (maz_df.head(6), )) + +# ### Create taz file + +# TAZ +# 2 +# 5 +# 6 +# 7 + +taz_zone_ids = np.unique(land_use.TAZ) +taz_zone_indexes = (taz_zone_ids-1) +taz_df = pd.DataFrame({'TAZ': taz_zone_ids}, index=taz_zone_indexes) +taz_df.to_csv(os.path.join(output_data, 'taz.csv'), index=False) +print("taz.csv\n%s" % (taz_df.head(6), )) + +# currently this has only the one TAZ column, but the legacy table had: +# index TAZ +# offset int64 +# terminal_time float64 # occasional small integer (1-5), but mostly blank (only if it has a TAP? +# ptype float64 # parking type at TAP? (rarer than terminal_time, never alone) + + +# ### Create maz to maz time/distance + +max_distance_for_walk = 1.0 +max_distance_for_bike = 5.0 + + +with omx.open_file(os.path.join(input_data, 'skims.omx')) as ur_skims: + + # create df with DIST column + maz_to_maz = pd.DataFrame(ur_skims['DIST']).unstack().reset_index() + maz_to_maz.columns = ['OMAZ', 'DMAZ', 'DIST'] + maz_to_maz['OMAZ'] = (maz_to_maz['OMAZ'] + 1) * MAZ_MULTIPLIER + maz_to_maz['DMAZ'] = (maz_to_maz['DMAZ'] + 1) * MAZ_MULTIPLIER + + # additional columns + for c in ['DISTBIKE', 'DISTWALK']: + maz_to_maz[c] = pd.DataFrame(ur_skims[c]).unstack().values + + maz_to_maz.loc[maz_to_maz['DIST'] <= max_distance_for_walk, ['OMAZ', 'DMAZ', 'DISTWALK']].\ + to_csv(os.path.join(output_data, 'maz_to_maz_walk.csv'), index=False) + + maz_to_maz.loc[maz_to_maz['DIST'] <= max_distance_for_bike, ['OMAZ', 'DMAZ', 'DIST', 'DISTBIKE']].\ + to_csv(os.path.join(output_data, 'maz_to_maz_bike.csv'), index=False) + + +######## + + +# create tap file +# currently the only attribute is its containing maz + +taz_zone_labels = taz_df.TAZ.values +tap_zone_labels = taz_zone_labels + TAP_OFFSET +maz_zone_labels = taz_zone_labels * MAZ_MULTIPLIER +tap_df = pd.DataFrame({"TAP": tap_zone_labels, "MAZ": maz_zone_labels}) +tap_df.to_csv(os.path.join(output_data, 'tap.csv'), index=False) + +# create taz_z3 and tap skims +with \ + omx.open_file(os.path.join(input_data, 'skims.omx'), "r") as ur_skims, \ + omx.open_file(os.path.join(output_data, 'taz_skims.omx'), "w") as output_taz_skims_file, \ + omx.open_file(os.path.join(output_data, 'tap_skims.omx'), "w") as output_tap_skims_file: + + for skim_name in ur_skims.list_matrices(): + + ur_skim = ur_skims[skim_name][:] + new_skim = ur_skim[taz_zone_indexes, :][:, taz_zone_indexes] + # print("skim:", skim_name, ": shape", str(new_skim.shape)) + + mode_code = skim_name[0:3] + is_tap_mode = (mode_code == "DRV" or mode_code == "WLK") + is_taz_mode = not is_tap_mode + + if is_tap_mode: + # WLK_TRN_WLK_XWAIT__PM + # 012345678911111111112 + # 01234567890 + access_mode = skim_name[0:3] + transit_mode = skim_name[4:7] + egress_mode = skim_name[8:11] + datum_name = skim_name[12:-4] + tod = skim_name[-2:] + if access_mode == 'WLK' and egress_mode == 'WLK': + for suffix in ['FAST', 'SHORT', 'CHEAP']: + if (suffix == 'FAST') and (datum_name == 'TOTIVT'): + random_variation = np.random.rand(*new_skim.shape)*-0.1 + 1.0 + elif (suffix == 'CHEAP') and (datum_name == 'FAR'): + random_variation = np.random.rand(*new_skim.shape) * -0.5 + 1.0 + else: + random_variation = np.ones_like(new_skim) + + tap_skim_name = f'{transit_mode}_{datum_name}_{suffix}__{tod}' + output_tap_skims_file[tap_skim_name] = new_skim * random_variation + # print(f"tap skim: {skim_name} tap_skim_name: {tap_skim_name}, " + # f"shape: {str(output_tap_skims_file.shape())}") + + if is_taz_mode: + output_taz_skims_file[skim_name] = new_skim + # print("taz skim:", skim_name, ": shape", str(output_taz_skims_file.shape())) + + output_taz_skims_file.create_mapping("taz", taz_zone_labels) + output_tap_skims_file.create_mapping("tap", tap_zone_labels) + +print("taz skims created: " + os.path.join(output_data, 'taz_skims.omx')) +print("tap skims created: " + os.path.join(output_data, 'tap_skims.omx')) + +# Create maz to tap distance file by mode + +with omx.open_file(os.path.join(input_data, 'skims.omx')) as ur_skims: + distance_table = pd.DataFrame(np.transpose(ur_skims['DIST'])).unstack() + distance_table = distance_table.reset_index() + distance_table.columns = ["MAZ", "TAP", "DIST"] + + distance_table['drive_time'] = pd.DataFrame(np.transpose(ur_skims['SOV_TIME__MD'])).unstack().values + + for c in ['DISTBIKE', 'DISTWALK']: + distance_table[c] = pd.DataFrame(np.transpose(ur_skims[c])).unstack().values + +walk_speed = 3 +bike_speed = 10 +drive_speed = 25 +max_distance_for_nearby_taps_walk = 1.0 +max_distance_for_nearby_taps_bike = 5.0 +max_distance_for_nearby_taps_drive = 10.0 + +distance_table["MAZ"] = (distance_table["MAZ"] + 1) * MAZ_MULTIPLIER +distance_table["TAP"] = (distance_table["TAP"] + 1) + TAP_OFFSET + +distance_table["walk_time"] = distance_table["DIST"] * (60 / walk_speed) +distance_table["bike_time"] = distance_table["DIST"] * (60 * bike_speed) + +# FIXME: we are using SOV_TIME__MD - is that right? +distance_table["drive_time"] = distance_table["DIST"] * (60 * drive_speed) + +distance_table = distance_table[distance_table["TAP"].isin(tap_zone_labels)] + + +distance_table.loc[distance_table['DIST'] <= max_distance_for_nearby_taps_walk, + ['MAZ', 'TAP', 'DISTWALK', 'walk_time']]. \ + to_csv(os.path.join(output_data, 'maz_to_tap_walk.csv'), index=False) + +distance_table.loc[distance_table['DIST'] <= max_distance_for_nearby_taps_bike, + ['MAZ', 'TAP', 'DISTBIKE', 'bike_time']]. \ + to_csv(os.path.join(output_data, 'maz_to_tap_bike.csv'), index=False) + +distance_table.loc[distance_table['DIST'] <= max_distance_for_nearby_taps_drive, + ['MAZ', 'TAP', 'DIST', 'drive_time']]. \ + to_csv(os.path.join(output_data, 'maz_to_tap_drive.csv'), index=False) diff --git a/activitysim/examples/example_multiple_zone/two_zone_example_data.py b/activitysim/examples/example_multiple_zone/two_zone_example_data.py new file mode 100644 index 0000000000..8382588fe9 --- /dev/null +++ b/activitysim/examples/example_multiple_zone/two_zone_example_data.py @@ -0,0 +1,144 @@ +# Creating the Two Zone Example Data +# +# Transform the TM1 TAZ-based model 25 zone inputs to a two-zone (MAZ and TAZ) set of inputs for software development. +# +# The 25 zones are downtown San Francisco and they are converted to 25 MAZs. +# MAZs 1,2,3,4 are small and adjacent and assigned TAZ 2 and TAP 10002. +# MAZs 13,14,15 are small and adjacent and as signed TAZ 14 and TAP 10014. +# TAZs 1,3,4,13,15 are removed from the final data set. +# +# This script should work for the full TM1 example as well. + +import os +import shutil + +import pandas as pd +import numpy as np +import openmatrix as omx + + +# Create example directory + +input_data = os.path.join(os.path.dirname(__file__), 'data') +output_data = os.path.join(os.path.dirname(__file__), 'data_2') +MAZ_MULTIPLIER = 1000 + +# ### initialize output data directory + +# new empty output_dir +if os.path.exists(output_data): + # shutil.rmtree(output_data) + # os.makedirs(output_data) + file_type = ('csv', 'omx') + for file_name in os.listdir(output_data): + if file_name.endswith(file_type): + os.unlink(os.path.join(output_data, file_name)) +else: + os.makedirs(output_data) + +# ### Convert tazs to mazs and add transit access distance by mode + +land_use = pd.read_csv(os.path.join(input_data, 'land_use.csv')) + +land_use.insert(loc=0, column='MAZ', value=land_use.TAZ) + +land_use.TAZ = land_use.TAZ.replace([1, 2, 3, 4], 2) +land_use.TAZ = land_use.TAZ.replace([13, 14, 15], 14) + +# make MAZ indexes different from TAZ to drive MAZ/TAZ confusion errors and omisisons +land_use.MAZ *= MAZ_MULTIPLIER + +shortWalk = 0.333 # the tm1 example assumes this distance for transit access +longWalk = 0.667 +land_use['access_dist_transit'] = shortWalk + +# FIXME - could assign longWalk where maz != taz, but then results wodl differe from one-zone +# land_use['access_dist_transit'] =\ +# np.where(land_use.TAZ*MAZ_MULTIPLIER==land_use.MAZ, shortWalk, longWalk) + + +land_use.to_csv(os.path.join(output_data, 'land_use.csv'), index=False) + +# ### Put households in mazs instead of tazs + +households = pd.read_csv(os.path.join(input_data, 'households.csv')) +households.rename(columns={'TAZ': 'MAZ'}, inplace=True) +households.MAZ *= MAZ_MULTIPLIER +households.to_csv(os.path.join(output_data, 'households.csv'), index=False) + +persons = pd.read_csv(os.path.join(input_data, 'persons.csv')) +persons.to_csv(os.path.join(output_data, 'persons.csv'), index=False) + +# ### Create maz correspondence file + +# FIXME - not clear we need this +maz_df = land_use[['MAZ', 'TAZ']] +maz_df.to_csv(os.path.join(output_data, 'maz.csv'), index=False) +print("maz.csv\n%s" % (maz_df.head(6), )) + +# ### Create taz file + +# TAZ +# 2 +# 5 +# 6 +# 7 + +new_zone_labels = np.unique(land_use.TAZ) +new_zone_indexes = (new_zone_labels-1) +taz_df = pd.DataFrame({'TAZ': new_zone_labels}, index=new_zone_indexes) +taz_df.to_csv(os.path.join(output_data, 'taz.csv'), index=False) +print("taz.csv\n%s" % (taz_df.head(6), )) + +# currently this has only the one TAZ column, but the legacy table had: +# index TAZ +# offset int64 +# terminal_time float64 # occasional small integer (1-5), but mostly blank (only if it has a TAP? +# ptype float64 # parking type at TAP? (rarer than terminal_time, never alone) + +# ### Create taz skims + +with omx.open_file(os.path.join(input_data, 'skims.omx'), 'r') as skims_file, \ + omx.open_file(os.path.join(output_data, 'taz_skims.omx'), "w") as output_skims_file: + + skims = skims_file.list_matrices() + num_zones = skims_file.shape()[0] + + # assume zones labels were 1-based in skims file + assert not skims_file.listMappings() + assert num_zones == len(land_use) + + for skim_name in skims_file.list_matrices(): + + old_skim = skims_file[skim_name][:] + new_skim = old_skim[new_zone_indexes, :][:, new_zone_indexes] + output_skims_file[skim_name] = new_skim + # print("skim:", skim_name, ": shape", str(new_skim.shape)) + + output_skims_file.create_mapping("taz", new_zone_labels) + +print("taz skims created: " + os.path.join(output_data, 'taz_skims.omx')) + +# ### Create maz to maz time/distance + +max_distance_for_walk = 1.0 +max_distance_for_bike = 5.0 + + +with omx.open_file(os.path.join(input_data, 'skims.omx')) as skims_file: + + # create df with DIST column + maz_to_maz = pd.DataFrame(np.transpose(skims_file['DIST'])).unstack().reset_index() + maz_to_maz.columns = ['OMAZ', 'DMAZ', 'DIST'] + maz_to_maz['OMAZ'] = (maz_to_maz['OMAZ'] + 1) * MAZ_MULTIPLIER + maz_to_maz['DMAZ'] = (maz_to_maz['DMAZ'] + 1) * MAZ_MULTIPLIER + + # additional columns + for c in ['DISTBIKE', 'DISTWALK']: + maz_to_maz[c] = pd.DataFrame(np.transpose(skims_file[c])).unstack().values + + maz_to_maz.loc[maz_to_maz['DIST'] <= max_distance_for_walk, ['OMAZ', 'DMAZ', 'DISTWALK']].\ + to_csv(os.path.join(output_data, 'maz_to_maz_walk.csv'), index=False) + + maz_to_maz.loc[maz_to_maz['DIST'] <= max_distance_for_bike, ['OMAZ', 'DMAZ', 'DIST', 'DISTBIKE']].\ + to_csv(os.path.join(output_data, 'maz_to_maz_bike.csv'), index=False) diff --git a/docs/abmexample.rst b/docs/abmexample.rst index 167fd93b89..de8f8b451e 100644 --- a/docs/abmexample.rst +++ b/docs/abmexample.rst @@ -29,33 +29,18 @@ individual decision-makers. Space ~~~~~ -TM1 uses the 1454-zone system developed for the MTC trip-based model. The zones are fairly large for the region, +TM1 uses the 1454 TAZ zone system developed for the MTC trip-based model. The zones are fairly large for the region, which may somewhat distort the representation of transit access in mode choice. To ameliorate this problem, the original model zones were further sub-divided into three categories of transit access: short walk, long walk, and not walkable. However, support for transit subzones is not included in the activitysim implementation since the latest generation of activity-based models typically use an improved approach to spatial representation called multiple zone systems. -In brief, under a multiple zone system approach, all households are assigned to microzones (which are smaller than traditional -TAZs) and trips are assigned to origin and destination microzones. When considering network level-of-service (LOS) indicators, -the model uses different spatial resolutions for different travel modes. For example: - - * TAZs are used for auto network modeling and a set of taz-to-taz skims is input to the demand model - * Microzones are used for nearby non-motorized mode (walk and bike) network modeling and skims and a set of nearby maz-to-maz skims is input to the demand model - * Transit access points (TAPs) (or transit catchment areas) are used for transit network modeling and skims and a set of tap-to-tap skims is input to the demand model - * Microzone to transit access point for transit access/egress LOS is input to the demand model - -Since trips are modeled in the demand model from microzone to microzone, but transit network LOS is split across -two input data sets, transit virtual path building (TVPB) is done to generate LOS measures from: - - * the trip origin microzone to a select number of nearby TAPs using microzone to TAP LOS measures - * boarding TAP to alighting TAP LOS measures (TAP to TAP skims) - * alighting TAP to destination microzone using microzone to TAP LOS measures - -The resulting complete transit path LOS for the best, or a bundle of, paths is then used in the demand model -for representing transit LOS at the microzone level. - -Support for multiple zone systems is **NOT YET IMPLEMENTED**, but planned for the next release. For the time being, -all travel is modeled at the TAZ level. +In a multiple zone system approach, households, land use, and trips are modeled at the microzone (MAZ) level. MAZs are smaller +than traditional TAZs and therefore make for a more precise system. However, when considering network level-of-service (LOS) +indicators (e.g. skims), the model uses different spatial resolutions for different travel modes in order to reduce the network +modeling burden. The typical multiple zone system setup is a TAZ zone system for auto travel, a MAZ zone system for +non-motorized travel, and optionally a transit access points (TAPs) zone system for transit. See :ref:`multiple_zone_systems` for +more information. Decision-making units ~~~~~~~~~~~~~~~~~~~~~ @@ -167,19 +152,19 @@ increments, LOS matrices are only created for five aggregate time periods. The t reference the appropriate transport network depending on their trip mode and the mid-point trip time. The definition of time periods for LOS matrices is given below. -+---------------+------------+----------+ -| Time Period | Start Hour | End Hour | -+===============+============+==========+ -| EA | 3 | 6 | -+---------------+------------+----------+ -| AM | 6 | 11 | -+---------------+------------+----------+ -| MD | 11 | 15 | -+---------------+------------+----------+ -| PM | 15 | 20 | -+---------------+------------+----------+ -| EV | 20 | 3 | -+---------------+------------+----------+ ++---------------+------------+ +| Time Period | Start Hour | ++===============+============+ +| EA | 3 | ++---------------+------------+ +| AM | 5 | ++---------------+------------+ +| MD | 9 | ++---------------+------------+ +| PM | 14 | ++---------------+------------+ +| EV | 18 | ++---------------+------------+ Trip modes ~~~~~~~~~~ @@ -276,13 +261,13 @@ The example has the following root folder/file setup: * configs - settings, expressions files, etc. * configs_mp - override settings for the multiprocess configuration - * data - input data such as land use, synthetic population files, and skims + * data - input data such as land use, synthetic population files, and network LOS / skims * output - outputs folder Inputs ~~~~~~ -In order to run the example, you first need the input files in the ``data`` folder as identified in the ``configs\settings.yaml`` file: +In order to run the example, you first need the input files in the ``data`` folder as identified in the ``configs\settings.yaml`` file and the ``configs\network_los.yaml`` file: * input_table_list: the input CSV tables from MTC travel model one: @@ -290,10 +275,10 @@ In order to run the example, you first need the input files in the ``data`` fold * persons - Synthetic population person records for a subset of zones. * land_use - Zone-based land use data (population and employment for example) for a subset of zones. -* skims_file: skims.omx - an OMX matrix file containing the MTC travel model one skim matrices for a subset of zones. +* taz_skims: skims.omx - an OMX matrix file containing the MTC travel model one skim matrices for a subset of zones. The time period for the matrix must be represented at the end of the matrix name and be seperated by a double_underscore (e.g. BUS_IVT__AM indicates base skim BUS_IVT with a time period of AM. These files are used in the tests as well and are in the ``activitysim\abm\test\data`` folder. The full set -of MTC TM1 households, persons, and OMX skims are on the MTC `box account `__. +of MTC TM1 households, persons, and OMX skims are on the ActivitySim `resources repository `__. .. note:: @@ -331,30 +316,33 @@ is the main settings file for the model run. This file includes: * ``h5_tablename`` - table name if reading from HDF5 and different from `tablename` * ``create_input_store`` - write new 'input_data.h5' file to outputs folder using CSVs from `input_table_list` to use for subsequent model runs -* ``skims_file`` - skim matrices in one OMX file * ``households_sample_size`` - number of households to sample and simulate; comment out to simulate all households * ``trace_hh_id`` - trace household id; comment out for no trace * ``trace_od`` - trace origin, destination pair in accessibility calculation; comment out for no trace -* ``chunk_size`` - batch size for processing choosers, see :ref:`chunk_size` +* ``chunk_size`` - batch size for processing choosers and should be set to 1 for dynamic chunking, see :ref:`chunk_size` * ``check_for_variability`` - disable check for variability in an expression result debugging feature in order to speed-up runtime * ``use_shadow_pricing`` - turn shadow_pricing on and off for work and school location * ``output_tables`` - list of output tables to write to CSV or HDF5 * ``want_dest_choice_sample_tables`` - turn writing of sample_tables on and off for all models -* ``read_skim_cache`` - read cached skims (using numpy memmap) from output directory (memmap is faster than omx) -* ``write_skim_cache`` - write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -* ``skim_cache_dir`` - alternate dir to read/write skim cache (defaults to output_dir) * global variables that can be used in expressions tables and Python code such as: * ``urban_threshold`` - urban threshold area type max value * ``county_map`` - mapping of county codes to county names - * ``skim_time_periods`` - time period upper bound values and labels + * ``household_median_value_of_time`` - various household and person value-of-time model settings - * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours)) - * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole factor of ``time_window``. (Default: 60 minutes) - * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment - * ``labels`` - Labels to define names for aggregate periods for skims and assignment +Also in the ``configs`` folder is ``network_los.yaml``, which includes network LOS / skims settings such as: - * ``household_median_value_of_time`` - various household and person value-of-time model settings +* ``zone_system`` - 1 (taz), 2 (maz and taz), or 3 (maz, taz, tap) +* ``taz_skims`` - skim matrices in one OMX file. The time period for the matrix must be represented at the end of the matrix name and be seperated by a double_underscore (e.g. BUS_IVT__AM indicates base skim BUS_IVT with a time period of AM. +* ``skim_time_periods`` - time period upper bound values and labels + + * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours)) + * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole factor of ``time_window``. (Default: 60 minutes) + * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment + * ``labels`` - Labels to define names for aggregate periods for skims and assignment +* ``read_skim_cache`` - read cached skims (using numpy memmap) from output directory (memmap is faster than omx) +* ``write_skim_cache`` - write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +* ``cache_dir`` - alternate dir to read/write skim cache (defaults to output_dir) .. _sub-model-spec-files: @@ -539,6 +527,11 @@ columns indicates the number of non-mandatory tours by purpose. The current set | | - trip_mode_choice_coeffs.csv | | | - trip_mode_choice.csv | +------------------------------------------------+--------------------------------------------------------------------+ +| :ref:`parking_location_choice` | - parking_location_choice.yaml | +| (optional model) | - parking_location_choice_annotate_trips_preprocessor.csv | +| | - parking_location_choice_coeffs.csv | +| | - parking_location_choice.csv | ++------------------------------------------------+--------------------------------------------------------------------+ | :ref:`write_trip_matrices` | - write_trip_matrices.yaml | | | - write_trip_matrices_annotate_trips_preprocessor.csv | +------------------------------------------------+--------------------------------------------------------------------+ @@ -549,14 +542,11 @@ columns indicates the number of non-mandatory tours by purpose. The current set Chunk size ~~~~~~~~~~ -The ``chunk_size`` is the number of doubles in a chunk of the choosers table. It is approximately the number -of rows times the number of columns and it needs to be set to a value that efficiently processes the table with -the available RAM. For example, a chunk size of 1,000,000 could be 100,000 household records with 10 columns of attributes. -Setting the chunk size too high will run into memory errors such as ``OverflowError: Python int -too large to convert to C long.`` Setting the chunk size too low may result in smaller than optimal vector -lengths, which may waste runtime. The chunk size is dependent on the size of the population, the complexity -of the utility expressions, the amount of RAM on the machine, and other problem specific dimensions. Thus, -it needs to be set via experimentation. +The ``chunk_size`` is the number of doubles in a chunk of a choosers table. It is approximately the number +of rows times the number of columns. If set greater than 0, then it is now dynamically calculated by processing a +small sample of households to determine the necessary size for each submodel based on the size of the population, +the complexity of the utility expressions, the amount of RAM on the machine, the number of processors, +and other problem specific dimensions. Logging ~~~~~~~ @@ -699,7 +689,7 @@ machine with 28 cores @ 2.56GHz and 224GB RAM with the configuration below. See :: households_sample_size: 0 - chunk_size: 5000000000 + chunk_size: 1 num_processes: 24 .. note:: @@ -939,8 +929,247 @@ To run the estimation example, do the following: Settings ~~~~~~~~ -Additional settings for running ActivitySim in estimation mode are specified in an ``estimation.yaml`` file that is specified in addition to ``settings.yaml``. The settings are: +Additional settings for running ActivitySim in estimation mode are specified in the ``estimation.yaml`` file. The settings are: * ``enable`` - enable estimation, either True or False * ``bundles`` - the list of submodels for which to write EDBs -* ``survey_tables`` - the list of input ActivitySim format survey tables with observed choices to override model simulation choices in order to write EDBs. These tables are the output of the ``scripts\infer.py`` script that pre-processes the ActivitySim format household travel survey files \ No newline at end of file +* ``survey_tables`` - the list of input ActivitySim format survey tables with observed choices to override model simulation choices in order to write EDBs. These tables are the output of the ``scripts\infer.py`` script that pre-processes the ActivitySim format household travel survey files + +.. _multiple_zone_systems : + +Multiple Zone Systems +--------------------- + +ActivitySim supports models with multiple zone systems. The three versions of multiple zone systems are one-zone, two-zone, and three-zone. + + * **One-zone**: This version is based on TM1 and supports only TAZs. All origins and destinations are represented at the TAZ level, and all skims including auto, transit, and non-motorized times and costs are also represented at the TAZ level. + * **Two-zone**: This version is similar to many DaySim models. It uses microzones (MAZs) for origins and destinations, and TAZs for specification of auto and transit times and costs. Impedance for walk or bike "all the way" from the origin to the destination can be specified at the MAZ level for close together origins and destinations, and at the TAZ level for further origins and destinations. Users can also override transit walk access and egress times with times specified in the MAZ file by transit mode. Careful pre-calculation of the assumed transit walk access and egress time by MAZ and transit mode is required depending on the network scenario. + * **Three-zone**: This version is based on the SANDAG generation of CT-RAMP models. Origins and destinations are represented at the MAZ level. Impedance for walk or bike "all the way" from the origin to the destination can be specified at the MAZ level for close together origins and destinations, and at the TAZ level for further origins and destinations, just like the two-zone system. TAZs are used for auto times and costs. The difference between this system and the two-zone system is that transit times and costs are represented between Transit Access Points (TAPs), which are essentially dummy zones that represent transit stops or clusters of stops. Transit skims are built between TAPs, since there are typically too many MAZs to build skims between them. Often multiple sets of TAP to TAP skims (local bus only, all modes, etc.) are created and input to the demand model for consideration. Walk access and egress times are also calculated between the MAZ and the TAP, and total transit path utilities are assembled from their respective components - from MAZ to first boarding TAP, from first boarding to final alighting TAP, and from alighting TAP to destination MAZ. This assembling is done via the :ref:`transit_virtual_path_builder` (TVPB), which considers all possible combinations of nearby boarding and alighting TAPs for each origin destination MAZ pair. + +Regions that have an interest in more precise transit forecasts may wish to adopt the three-zone approach, while other regions may adopt the one or two-zone approach. The microzone version requires coding households and land use at the microzone level. Typically an all-streets network is used for representation of non-motorized impedances. This requires a routable all-streets network, with centroids and connectors for microzones. If the three-zone system is adopted, procedures need to be developed to code TAPs from transit stops and populate the all-street network with TAP centroids and centroid connectors. A model with transit virtual path building takes longer to run than a traditional +TAZ only model, but it provides a much richer framework for transit modeling. + +Example configurations and inputs for two and three-zone system models are described below. + +.. note:: + The two and three zone system test examples are dummy examples developed from the TM1 example. To develop the two zone system + example, TM1 TAZs were labeled MAZs, each MAZ was assigned a TAZ, and MAZ to MAZ impedance files were created from the + TAZ to TAZ impedances. To develop the three zone example system example, the TM1 TAZ model was further transformed + so select TAZs also became TAPs and TAP to TAP skims and MAZ to TAP impedances files were created. While sufficient for + initial development, these examples were insufficient for validation and performance testing of the new software. + + To finalize development and verification of the multiple zone system and transit virtual path building components, the + `Transportation Authority of Marin County `__ version of MTC's travel model two (TM2) work + tour mode choice model was implemented. This example was also developed to test multiprocessed runtime performance. + The complete runnable setup is available from ActivitySim's command line interface as `example_3_marin_full`. This example + has essentially the same configuration as illustrated by the simpler examples below. + +Examples +~~~~~~~~ + +To run the two zone and three zone system examples, do the following: + +* Activate the correct conda environment if needed +* Create a local copy of the example + +:: + + # simple two zone example + activitysim create -e example_2_zone -d test_example_2_zone + + # simple three zone example + activitysim create -e example_3_zone -d test_example_3_zone + + # Marin TM2 work tour mode choice for the MTC region + activitysim create -e example_3_marin_full -d test_example_3_marin_full + + +* Change to the example directory +* Run the example + +:: + + # simple two zone example + activitysim run -c configs_local -c configs_2_zone -c configs -d data_2 -o output_2 + + # simple three zone example, single process and multiprocess + activitysim run -c configs_local -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_static.yaml + activitysim run -c configs_local -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_mp.yaml + + # Marin TM2 work tour mode choice for the MTC region + activitysim run -c configs_3_zone_marin_full -c configs_3_zone_marin -d data_3_marin_full -o output_3_marin_full -s settings_mp.yaml + + +Settings +~~~~~~~~ + +Additional settings for running ActivitySim with two or three zone systems are specified in the ``network_los.yaml`` file. The settings are: + +**Two Zone** + +The additional two zone system settings and inputs are described and illustrated below. No additional utility expression files or expression revisions are required beyond the one zone approach. The MAZ data is available as zone data and the MAZ to MAZ data is available using the existing skim expressions. Users can specify mode utilities using MAZ data, MAZ to MAZ impedances, and TAZ to TAZ impedances. + +* ``zone_system`` - set to 2 for two zone system +* ``maz`` - MAZ data file, with MAZ ID, TAZ, and land use and other MAZ attributes +* ``maz_to_maz:tables`` - list of MAZ to MAZ impedance tables. These tables are read as pandas DataFrames and the columns are exposed to expressions. +* ``maz_to_maz:max_blend_distance`` - in order to avoid cliff effects, the lookup of MAZ to MAZ impedance can be a blend of origin MAZ to destination MAZ impedance and origin TAZ to destination TAZ impedance up to a max distance. The calculated value is the (MAZ to MAZ distance) * (distance / max distance) * (TAZ to TAZ distance) * (1 - (distance / max distance)). This requires specifying a distance TAZ skim and distance columns from the MAZ to MAZ files. The TAZ skim name and MAZ to MAZ column name need to be the same so the blending can happen on-the-fly. +* ``maz_to_maz:blend_distance_skim_name`` - Identify the distance skim for the blending calculation if different than the blend skim. + +:: + + zone_system: 2 + maz: maz.csv + + maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + max_blend_distance: + DIST: 5 + DISTBIKE: 0 + DISTWALK: 1 + + blend_distance_skim_name: DIST + + +**Three Zone** + +In addition to the extra two zone system settings and inputs above, the following additional settings and inputs are required for a three zone system model. Examples values are illustrated below. + +In ``settings.yaml`` + +* ``models`` - add initialize_los and initialize_tvpb to load network LOS inputs / skims and pre-compute TAP to TAP utilities for TVPB. See :ref:`initialize_los`. + +:: + + models: + - initialize_landuse + - compute_accessibility + - initialize_households + # --- + - initialize_los + - initialize_tvpb + # --- + - school_location + - workplace_location + +In ``network_los.yaml`` + +* ``zone_system`` - set to 3 for three zone system +* ``rebuild_tvpb_cache`` - rebuild and overwrite existing pre-computed TAP to TAP utilities cache +* ``trace_tvpb_cache_as_csv`` - write a CSV version of TVPB cache for tracing +* ``tap_skims`` - TAP to TAP skims OMX file name. The time period for the matrix must be represented at the end of the matrix name and be seperated by a double_underscore (e.g. BUS_IVT__AM indicates base skim BUS_IVT with a time period of AM. +* ``tap`` - TAPs table +* ``tap_lines`` - table of transit line names served for each TAP. This file is used to trimmed the set of nearby TAP for each MAZ so only TAPs that are further away and serve new service are included in the TAP set for consideration. It is a very important file to include as it can considerably reduce runtimes. +* ``maz_to_tap`` - list of MAZ to TAP access/egress impedance files by user defined mode. Examples include "walk" and "drive". The file also includes MAZ to TAP impedances. +* ``maz_to_tap:{walk}:max_dist`` - max distance from MAZ to TAP to consider TAP +* ``maz_to_tap:{walk}:tap_line_distance_col`` - MAZ to TAP data field to use for TAP lines distance filter +* ``demographic_segments`` - list of user defined demographic_segments for pre-computed TVPB impedances. Each chooser is coded with a user defined demographic segment. +* ``TVPB_SETTINGS:units`` - specify the units for calculations, e.g. utility or time. +* ``TVPB_SETTINGS:path_types`` - user defined set of TVPB path types to be calculated and available to the mode choice models. Examples include walk transit walk ("WTW"), drive transit walk ("DTW"), and walk transit drive ("WTD"). +* ``TVPB_SETTINGS:path_types:{WTW}:access`` - access mode for the path type +* ``TVPB_SETTINGS:path_types:{WTW}:egress`` - egress mode for the path type +* ``TVPB_SETTINGS:path_types:{WTW}:max_paths_across_tap_sets`` - max paths to keep across all skim sets, for example, 3 TAP to TAP pairs per origin MAZ destination MAZ pair +* ``TVPB_SETTINGS:path_types:{WTW}:max_paths_per_tap_set`` - max paths to keep per skim set, for example 1 per skim set - all transit submodes, local bus only, etc. + +Unlike the one and two zone system approach, the three zone system approach requires additional expression files for the TVPB. The additional expression files for the TVPB are: + +* ``TVPB_SETTINGS:tap_tap_settings:SPEC`` - TAP to TAP expressions, e.g. tvpb_utility_tap_tap.csv +* ``TVPB_SETTINGS:tap_tap_settings:PREPROCESSOR:SPEC`` - TAP to TAP chooser preprocessor, e.g. tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv +* ``TVPB_SETTINGS:maz_tap_settings:walk:SPEC`` - MAZ to TAP {walk} expressions, e.g. tvpb_utility_walk_maz_tap.csv +* ``TVPB_SETTINGS:maz_tap_settings:drive:SPEC`` - MAZ to TAP {drive} expressions, e.g. tvpb_utility_drive_maz_tap.csv +* ``TVPB_SETTINGS:accessibility:tap_tap_settings:SPEC`` - TAP to TAP expressions for the accessibility calculator, e.g. tvpb_accessibility_tap_tap.csv +* ``TVPB_SETTINGS:accessibility:maz_tap_settings:walk:SPEC`` - MAz to TAP {walk} expressions for the accessibility calculator, e.g. tvpb_accessibility_walk_maz_tap.csv + +Additional settings to configure the TVPB are: + +* ``TVPB_SETTINGS:tap_tap_settings:attribute_segments:demographic_segment`` - TVPB pre-computes TAP to TAP total utilities for demographic segments. These are defined using the attribute_segments keyword. In the example below, the segments are demographic_segment (household income bin), tod (time-of-day), and access_mode (drive, walk). +* ``TVPB_SETTINGS:maz_tap_settings:{walk}:CHOOSER_COLUMNS`` - input impedance columns to expose for TVPB calculations. +* ``TVPB_SETTINGS:maz_tap_settings:{walk}:CONSTANTS`` - constants for TVPB calculations. +* ``accessibility:...`` - for the accessibility model step, the same basic set of TVPB configurations are available. + +:: + + zone_system: 3 + + rebuild_tvpb_cache: False + trace_tvpb_cache_as_csv: False + tap_skims: tap_skims.omx + tap: tap.csv + maz_to_tap: + walk: + table: maz_to_tap_walk.csv + drive: + table: maz_to_tap_drive.csv + + demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + + TVPB_SETTINGS: + tour_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + DTW: + access: drive + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + WTD: + access: walk + egress: drive + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + attribute_segments: + demographic_segment: *demographic_segments + tod: *skim_time_period_labels + access_mode: ['drive', 'walk'] + attributes_as_columns: + - demographic_segment + - tod + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + - walk_time + drive: + SPEC: tvpb_utility_drive_maz_tap.csv + CHOOSER_COLUMNS: + - drive_time + - DIST + CONSTANTS: + c_ivt_high_income: -0.028 + ... + + accessibility: + units: time + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 1 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_accessibility_tap_tap_.csv + maz_tap_settings: + walk: + SPEC: tvpb_accessibility_walk_maz_tap.csv + CHOOSER_COLUMNS: + - walk_time + CONSTANTS: + out_of_vehicle_walk_time_weight: 1.5 + out_of_vehicle_wait_time_weight: 2.0 + +.. note:: + In three zone system mode, the boarding TAP, alighting TAP, and transit skim set is added to the relevant chooser table (e.g. tours and trips) when the chosen mode is transit. \ No newline at end of file diff --git a/docs/core.rst b/docs/core.rst index 2e69bb6b08..f819b18c11 100644 --- a/docs/core.rst +++ b/docs/core.rst @@ -4,10 +4,10 @@ Core Components ActivitySim's core components include features for multiprocessing, data management, utility expressions, choice models, person time window management, and helper -functions. These core components include the multiprocessor, skim matrix manager, the +functions. These core components include the multiprocessor, network LOS (skim) manager, the data pipeline manager, the random number manager, the tracer, sampling methods, simulation methods, model specification readers and expression -evaluators, choice models, timetable, and helper functions. +evaluators, choice models, timetable, transit virtual path builder, and helper functions. .. _multiprocessing_in_detail: @@ -37,17 +37,31 @@ API .. automodule:: activitysim.core.input :members: -.. _skims_in_detail: +.. _los_in_detail: -Skim -~~~~ +LOS +~~~ + +Network Level of Service (LOS) data access + +API +^^^ + +.. automodule:: activitysim.core.los + :members: + +Skims +~~~~~ -Skim matrix data access +Skims data access API ^^^ -.. automodule:: activitysim.core.skim +.. automodule:: activitysim.core.skim_dict_factory + :members: + +.. automodule:: activitysim.core.skim_dictionary :members: .. _pipeline_in_detail: @@ -150,7 +164,7 @@ There are a few conventions for writing expressions in ActivitySim: * global constants are specified in the settings file * comments are specified with ``#`` * you can refer to the current table being operated on as ``df`` -* often an object called ``skims``, ``skims_od``, or similar is available and is used to lookup the relevant skim information. See :ref:`skims_in_detail` for more information. +* often an object called ``skims``, ``skims_od``, or similar is available and is used to lookup the relevant skim information. See :ref:`los_in_detail` for more information. * when editing the CSV files in Excel, use single quote ' or space at the start of a cell to get Excel to accept the expression Example Expressions File @@ -202,6 +216,17 @@ coefficients as columns. Broadly speaking, there are currently four types of mo * :ref:`simulate_with_interaction` choice model - combine the choice expressions with the choice alternatives files since the alternatives are not listed in the expressions file. The :ref:`non_mandatory_tour_destination_choice` model implements this approach. * Combinatorial choice model - first generate a set of alternatives based on a combination of alternatives across choosers, and then make choices. The :ref:`cdap` model implements this approach. +Expressions +~~~~~~~~~~~ + +The expressions class is often used for pre- and post-processor table annotation, which read a CSV file of expression, calculate +a number of additional table fields, and join the fields to the target table. An example table annotation expressions +file is found in the example configuration files for households for the CDAP model - +`annotate_households_cdap.csv `__. + +.. automodule:: activitysim.core.expressions + :members: + Sampling with Interaction ~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -339,7 +364,52 @@ API .. automodule:: activitysim.core.timetable :members: + +.. _transit_virtual_path_builder: + +Transit Virtual Path Builder +---------------------------- + +Transit virtual path builder (TVPB) for three zone system (see :ref:`multiple_zone_systems`) transit path utility calculations. +TAP to TAP skims and walk access and egress times between MAZs and TAPs are input to the +demand model. ActivitySim then assembles the total transit path utility based on the user specified TVPB +expression files for the respective components: + +* from MAZ to first boarding TAP + +* from first boarding to final alighting TAP + +* from alighting TAP to destination MAZ + +This assembling is done via the TVPB, which considers all the possible combinations of nearby boarding and alighting TAPs for each origin +destination MAZ pair and selects the user defined N best paths to represent the transit mode. After selecting N best paths, the logsum across +N best paths is calculated and exposed to the mode choice models and a random number is drawn and a path is chosen. The boarding TAP, +alighting TAP, and TAP to TAP skim set for the chosen path is saved to the chooser table. + +The initialize TVPB submodel (see :ref:`initialize_los`) pre-computes TAP to TAP total utilities for the user defined attribute_segments, +which are typically demographic segment (for example household income bin), time-of-day, and access/egress mode. This submodel can be +run in both single process and multiprocess mode, with single process excellent for development/debugging and multiprocess excellent +for application. ActivitySim saves the pre-calculated TAP to TAP total utilities to a memory mapped cache file for reuse by downstream models +such as tour mode choice. In tour mode choice, the pre-computed TAP to TAP total utilities for the attribute_segment, along with the +access and egress impedances, are used to evaluate the best N TAP pairs for each origin MAZ destination MAZ pair being evaluated. +Assembling the total transit path impedance and then picking the best N is quick since it is done in a de-duplicated manner within +each chunk of multiprocessed choosers. + +A model with TVPB can take considerably longer to run than a traditional TAZ based model since it does an order of magnitude more +calculations. Thus, it is important to be mindful of your approach to your network model as well, especially the number of TAPs +accessible to each MAZ, which is the key determinant of runtime. + +API +~~~ + +.. automodule:: activitysim.core.pathbuilder + :members: + +Cache API +~~~~~~~~~ + +.. automodule:: activitysim.core.pathbuilder_cache + :members: + Helpers ------- @@ -401,7 +471,7 @@ API .. automodule:: activitysim.core.mem :members: - + Output ~~~~~~ @@ -412,7 +482,6 @@ API .. automodule:: activitysim.core.steps.output :members: - Tests ~~~~~ diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst index 75fddda08c..65cfe2b3b1 100644 --- a/docs/gettingstarted.rst +++ b/docs/gettingstarted.rst @@ -41,7 +41,7 @@ Installation :: # required packages for running ActivitySim - conda install cytoolz numpy pandas psutil + conda install cytoolz numpy pandas psutil pyarrow numba conda install -c anaconda pytables pyyaml pip install openmatrix zbox requests @@ -110,7 +110,7 @@ Run the Example ActivitySim includes a :ref:`cli` for creating examples and running the model. -To setup and run the :ref:`example`, do the following: +To setup and run the primary :ref:`example`, do the following: * Open a command prompt * Activate the Anaconda environment with ActivitySim installed (i.e. asimtest) @@ -122,8 +122,9 @@ To setup and run the :ref:`example`, do the following: .. note:: Common configuration settings can be overridden at runtime. See ``activitysim -h``, ``activitysim create -h`` and ``activitysim run -h``. -More complete examples, including the full scale MTC regional demand model are available for creation by typing ``activitysim create -l``. To create -these examples, ActivitySim downloads the large input files from the `ActivitySim resources `__ repository. +More complete examples, including the full scale MTC regional demand model, estimation integration examples, and multiple zone system examples, +are available for creation by typing ``activitysim create -l``. To create these examples, ActivitySim downloads the (large) input files from +the `ActivitySim resources `__ repository. Try the Notebooks ----------------- @@ -148,6 +149,7 @@ The computing hardware required to run a model implemented in the ActivitySim fr * The number of households to be simulated for disaggregate model steps * The number of model zones (for each zone system) for aggregate model steps * The number and size of network skims by mode and time-of-day +* The number of zone systems, see :ref:`multiple_zone_systems` * The desired runtimes ActivitySim framework models use a significant amount of RAM since they store data in-memory to reduce diff --git a/docs/howitworks.rst b/docs/howitworks.rst index 0d809fc3a9..3f2792ec21 100644 --- a/docs/howitworks.rst +++ b/docs/howitworks.rst @@ -2,7 +2,7 @@ How the System Works ==================== -This page describes how the software works, how multiprocessing works, and the example model data schema. +This page describes how the software works, how multiprocessing works, and the primary example model data schema. .. _how_the_system_works: @@ -75,7 +75,7 @@ the ``@inject.step()`` decorator. These steps will eventually be run by the dat #then in accessibility.py @inject.step() - def compute_accessibility(accessibility, skim_dict, land_use, trace_od): + def compute_accessibility(accessibility, network_los, land_use, trace_od): Back in the main ``run`` command, the next steps are to load the tracing, configuration, setting, and pipeline classes to get the system management components up and running. @@ -240,7 +240,7 @@ Now that the persons, households, and other data are in memory, and also annotat for later calculations, the school location model can be run. The school location model is defined in :mod:`activitysim.abm.models.location_choice`. As shown below, the school location model actually uses the ``persons_merged`` table, which includes joined household, land use, and accessibility -tables as well. The school location model also requires the skims dictionary object, which is discussed next. +tables as well. The school location model also requires the network_los object, which is discussed next. Before running the generic iterate location choice function, the model reads the model settings file, which defines various settings, including the expression files, sample size, mode choice logsum calculation settings, time periods for skim lookups, shadow pricing settings, etc. @@ -257,8 +257,7 @@ calculation settings, time periods for skim lookups, shadow pricing settings, et @inject.step() def school_location( persons_merged, persons, households, - skim_dict, skim_stack, - chunk_size, trace_hh_id, locutor + network_los, chunk_size, trace_hh_id, locutor ): trace_label = 'school_location' @@ -267,25 +266,26 @@ calculation settings, time periods for skim lookups, shadow pricing settings, et iterate_location_choice( model_settings, persons_merged, persons, households, - skim_dict, skim_stack, + network_los, chunk_size, trace_hh_id, locutor, trace_label -Deep inside the method calls, the skim matrix lookups required for this model are configured. The following code -sets the keys for looking up the skim values for this model. In this case there is a ``TAZ`` column +Deep inside the method calls, the skim matrix lookups required for this model are configured via ``network_los``. The following +code sets the keys for looking up the skim values for this model. In this case there is a ``TAZ`` column in the households table that is renamed to `TAZ_chooser`` and a ``TAZ`` in the alternatives generation code. The skims are lazy loaded under the name "skims" and are available in the expressions using the ``@skims`` expression. :: - # create wrapper with keys for this lookup - in this case there is a TAZ in the choosers - # and a TAZ in the alternatives which get merged during interaction + # create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers + # and a zone_id in the alternatives which get merged during interaction # (logit.interaction_dataset suffixes duplicate chooser column with '_chooser') # the skims will be available under the name "skims" for any @ expressions - skims = skim_dict.wrap('TAZ_chooser', 'TAZ') + skim_dict = network_los.get_default_skim_dict() + skims = skim_dict.wrap('home_zone_id', 'zone_id') locals_d = { - 'skims': skims + 'skims': skims, } The next step is to call the :func:`activitysim.core.interaction_sample.interaction_sample` function which @@ -321,7 +321,7 @@ the ``LOGIT_TYPE`` setting in the model settings YAML file. The ``auto_ownersh the ``LOGIT_TYPE`` as ``MNL.`` If the expression is a skim matrix, then the entire column of chooser OD pairs is retrieved from the matrix (i.e. numpy array) -in one vectorized step. The ``orig`` and ``dest`` objects in ``self.data[orig, dest]`` in :mod:`activitysim.core.skim` are vectors +in one vectorized step. The ``orig`` and ``dest`` objects in ``self.data[orig, dest]`` in :mod:`activitysim.core.los` are vectors and selecting numpy array items with vector indexes returns a vector. Trace data is also written out if configured (not shown below). :: @@ -370,7 +370,7 @@ then used for the next model step - solving the logsums for the sample. location_sample_df = run_location_logsums( segment_name, choosers, - skim_dict, skim_stack, + network_los, location_sample_df, model_settings, chunk_size, @@ -389,7 +389,7 @@ logsums settings and expression files. The resulting logsums are added to the c choosers, tour_purpose, logsum_settings, model_settings, - skim_dict, skim_stack, + network_los, chunk_size, trace_label) @@ -413,7 +413,7 @@ above and is called as follows: segment_name, choosers, location_sample_df, - skim_dict, + network_los, dest_size_terms, model_settings, chunk_size, @@ -443,7 +443,7 @@ to :ref:`shadow_pricing` for more information. choices = run_location_choice( persons_merged_df, - skim_dict, skim_stack, + network_los, spc, model_settings, chunk_size, trace_hh_id, @@ -538,24 +538,38 @@ the ``tours`` table managed in the data pipeline. This is the same basic patter Vectorized 3D Skim Indexing ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The mode choice model uses the :class:`activitysim.core.skim.SkimStackWrapper` class in addition to the skims (2D) -class. The SkimStackWrapper class represents a collection of skims with a third dimension, which in this case -is time period. Setting up the 3D index for SkimStackWrapper is done as follows: +The mode choice model uses a collection of skims with a third dimension, which in this case +is time period. Setting up the 3D index for skims is done as follows: :: - # setup three skim keys based on columns in the chooser table - # origin, destination, time period; destination, origin, time period; origin, destination - odt_skim_stack_wrapper = skim_stack.wrap(left_key='TAZ', right_key='destination', skim_key="out_period") - dot_skim_stack_wrapper = skim_stack.wrap(left_key='destination', right_key='TAZ', skim_key="in_period") - od_skims = skim_dict.wrap('TAZ', 'destination') - - #pass these into simple_simulate so they can be used in expressions - locals_d = { - "odt_skims": odt_skim_stack_wrapper, - "dot_skims": dot_skim_stack_wrapper, - "od_skims": od_skim_stack_wrapper - } + skim_dict = network_los.get_default_skim_dict() + + # setup skim keys + orig_col_name = 'home_zone_id' + dest_col_name = 'destination' + + out_time_col_name = 'start' + in_time_col_name = 'end' + odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='out_period') + dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='in_period') + odr_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name, + dim3_key='in_period') + dor_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name, + dim3_key='out_period') + od_skim_stack_wrapper = skim_dict.wrap(orig_col_name, dest_col_name) + + skims = { + "odt_skims": odt_skim_stack_wrapper, + "dot_skims": dot_skim_stack_wrapper, + "od_skims": od_skim_stack_wrapper, + 'orig_col_name': orig_col_name, + 'dest_col_name': dest_col_name, + 'out_time_col_name': out_time_col_name, + 'in_time_col_name': in_time_col_name + } When model expressions such as ``@odt_skims['WLK_LOC_WLK_TOTIVT']`` are solved, the ``WLK_LOC_WLK_TOTIVT`` skim matrix values for all chooser table origins, destinations, and @@ -565,7 +579,7 @@ All the skims are preloaded (cached) by the pipeline manager at the beginning of run in order to avoid repeatedly reading the skims from the OMX files on disk. This saves significant model runtime. -See :ref:`skims_in_detail` for more information on skim handling. +See :ref:`los_in_detail` for more information on skim handling. Accessibilities Model ~~~~~~~~~~~~~~~~~~~~~ @@ -723,7 +737,7 @@ Data Schema ----------- The ActivitySim data schema depends on the sub-models implemented. The data schema listed below is for -the example model. These tables and skims are defined in the :mod:`activitysim.abm.tables` package. +the primary TM1 example model. These tables and skims are defined in the :mod:`activitysim.abm.tables` package. .. index:: constants .. index:: households @@ -741,7 +755,7 @@ Data Tables The following tables are currently implemented: * households - household attributes for each household being simulated. Index: ``household_id`` (see ``activitysim.abm.tables.households.py``) - * landuse - zonal land use (such as population and employment) attributes. Index: ``TAZ`` (see ``activitysim.abm.tables.landuse.py``) + * landuse - zonal land use (such as population and employment) attributes. Index: ``zone_id`` (see ``activitysim.abm.tables.landuse.py``) * persons - person attributes for each person being simulated. Index: ``person_id`` (see ``activitysim.abm.tables.persons.py``) * time windows - manages person time windows throughout the simulation. See :ref:`time_windows`. Index: ``person_id`` (see the person_windows table create decorator in ``activitysim.abm.tables.time_windows.py``) * tours - tour attributes for each tour (mandatory, non-mandatory, joint, and atwork-subtour) being simulated. Index: ``tour_id`` (see ``activitysim.abm.models.util.tour_frequency.py``) @@ -1170,7 +1184,7 @@ uses the information stored in the pipeline file to create the table below for a +----------------------------+-------------------------------+---------+------------------------------+------+------+ | persons | workplace_in_cbd | bool | workplace_location | 52 | 271 | +----------------------------+-------------------------------+---------+------------------------------+------+------+ -| persons | work_taz_area_type | float64 | workplace_location | 52 | 271 | +| persons | work_zone_area_type | float64 | workplace_location | 52 | 271 | +----------------------------+-------------------------------+---------+------------------------------+------+------+ | persons | roundtrip_auto_time_to_work | float32 | workplace_location | 52 | 271 | +----------------------------+-------------------------------+---------+------------------------------+------+------+ @@ -1332,14 +1346,6 @@ The skims class defines orca injectables to access the skim matrices. The skims skims from the omx_file on disk. The injectables and omx_file for the example are listed below. The skims are float64 matrix. -+-------------+-----------------+------------------------------------------------------------------------+ -| Table | Type | Creation | -+=============+=================+========================================================================+ -| skim_dict | SkimDict | skims.py defines skim_dict which reads omx_file | -+-------------+-----------------+------------------------------------------------------------------------+ -| skim_stack | SkimStack | skims.py defines skim_stack which calls skim_dict which reads omx_file | -+-------------+-----------------+------------------------------------------------------------------------+ - Skims are named ___