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SimOR

Simulate Oregon (SimOR) - Oregon's Jointly Estimated ActivitySim Model image

Running the model

Setup

  1. Configure setup_environment.bat

    Open setup_environment.bat and set the user-configurable variables at the top of the file:

    VariableDescriptionDefault
    VISUM_PYTHON_DIRFolder containing your Visum 2026 Python interpreterC:\Program Files\PTV Vision\PTV Visum 2026\Exe\Junction_Preview\Python
    INSTALL_PARKINGClone and install sandag_parking (Y or N)Y

    You can run this script on its own to install all dependencies without running the model:

    setup_environment.bat

    It will install UV (if needed), clone and build the required repositories into ext_dependencies/, create the MAZ skimming Python environment from ext_dependencies/maz_skimming/pyproject.toml, and install the necessary Python packages into Visum's Python environment. On subsequent runs it detects existing installs and pulls the latest changes instead of re-cloning.

  2. Place the Visum version file

    Copy your Visum network version file (.ver) into skimming_and_assignment/visum/.

  3. Update configuration files

    Edit the following files to match your local data paths and project settings:

    FilePurpose
    skimming_and_assignment/maz_maz_stop_skims/2zoneSkim_params.yamlNon-motorized skim settings and file paths
    resident/preprocessor_settings.yamlLand use preprocessor input/output paths and network settings

  4. Set user-defined variables in runSIMOR.bat

    Open runSIMOR.bat and update the following variables at the top of the file:

    VariableDescription
    VISUM_VERSION_FILEFilename of the Visum version file
    PROCEDURE_SEQPath to the Visum procedure sequence XML

Running the Pipeline

Run:

runSIMOR.bat

The script automatically calls setup_environment.bat to ensure all dependencies are installed and Python paths are set, then runs the following steps in sequence:

  1. Motorized skims — Using Visum Visum_Runner.py. Automatically outputs required files to run non-motorized skims.
  2. Non-motorized skim preprocessor — Prepares walk network inputs via 2zoneSkim_preprocessor.py.
  3. Non-motorized skims — Computes MAZ-to-MAZ and MAZ-to-stop walk skims via 2zoneSkim.py.
  4. Land use preprocessor — Builds ActivitySim-ready land use table via preprocessor.py.
  5. Run ActivitySim -- Runs ActivitySim -- (not yet implemented)

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Oregon's Jointly Estimated ActivitySim Model

github.com/OrMSC/SimOR/wiki

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