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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 <https://renaissanceplanning.github.io/emma-docs/>`_)
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 <https://renaissanceplanning.github.io/WestStation/>`_

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**

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

.. toctree::
   :maxdepth: 2
   :caption: Contents:
  
   README
   Step1
   Step2
   Step3
   Step4
   Step5
   Step6
   Step7


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`