ARC Updates

activitysim/abm/models/__init__.py

@@ -22,12 +22,15 @@
 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
 from . import trip_mode_choice
 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

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)

activitysim/abm/models/__init__.py

@@ -22,12 +22,15 @@
 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
 from . import trip_mode_choice
 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

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)

activitysim/abm/models/__init__.py

@@ -22,12 +22,15 @@
 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
 from . import trip_mode_choice
 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

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)

activitysim/abm/models/__init__.py

@@ -22,12 +22,15 @@
 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
 from . import trip_mode_choice
 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

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)

activitysim/abm/models/__init__.py

@@ -22,12 +22,15 @@
 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
 from . import trip_mode_choice
 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

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)

activitysim/abm/models/__init__.py

@@ -22,12 +22,15 @@
 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
 from . import trip_mode_choice
 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

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)

activitysim/abm/models/__init__.py

@@ -22,12 +22,15 @@
 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
 from . import trip_mode_choice
 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

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)

activitysim/abm/models/__init__.py

@@ -22,12 +22,15 @@
 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
 from . import trip_mode_choice
 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

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)