West Station Area Multimodal Accessibility Analysis

Welcome to the documentation for the West Station Area accessibility analysis workflow. The procedures outlined here are written in Python (v. 3.8) and use the Enhanced Multi-modal Accessibility (emma) module and its dependencies to analyze trip generation, mode choice, and trip distribution at a local (census block-level) and regional (TAZ-level) scale.

While this workflow has been developed for analyzing multimodal travel behavior under various scenarios for the West Station Area, the local “window” of analysis can be transfered to any location in the greater Boston region to generate the same outputs and insights.

The WSA Accessibility workflow consists of 7 steps executed in 8 scripts. The steps and associated python scripts are enumerated below. Each script file is configured to be run either as a standalone script (__name__ == “__main__”) or in interactive chunks. Each contains detailed annotations describing the processing done in each chunk and references functions defined in the wsa module available from the West Station git repo

  1. Clean skims (Step1_CleanSkims.py) - process csv OD tables to ensure column names are consistent across scenarios and extraneous rows are excluded.

  2. Import skims (Step2_ImportSkims.py) - convert csv OD tables to emma Skim objects for all modes. Calculate derived values, such as generalized cost.

  3. Summarize access (Step3_SummarizeAccess.py) - using skims and tabular activity data by zone, summarize access to jobs, enrollments, etc.

  4. Trip generation

    1. Regional trips (Step4_TripGen.py) - use regional trip generation rates and TAZ-level activity data to estimate person trip productions and attractions.

    2. Window trips (Step4_TripGenDisag.py) - use block-level activity data and assumptions about trip-making propensity to disaggregate productions and attractions from the TAZ level to the block level in the window area.

  5. Mode choice (Step5_ModeChoice.py) - use access scores and demographic data to apply a series of mode choice models to estimate productions and attractions by mode for TAZs (region) and blocks (window).

  6. Distribution (Step6_Distribution.py) - estimate trip OD patterns by mode and purpose for a given time period, applying K factors if specified.

  7. Estimate TNC trips (Step7_TNC.py) - use assumptions about TNC costs, utilization by purpose, trip substitution by mode, and decay rates to identify OD pairs with high probabilities to substitute trips by a given mode for TNC trips.

Contents:

Indices and tables