A tool for creating GTFS transit and OSM pedestrian networks for use in Pandana accessibility analyses.
Project description
UrbanAccess
A tool for computing GTFS transit and OSM pedestrian networks for accessibility analysis.
Overview
UrbanAccess is tool for creating multi-modal graph networks for use in multi-scale (e.g. address level to the metropolitan level) transit accessibility analyses with the network analysis tool Pandana. UrbanAccess uses open data from General Transit Feed Specification (GTFS) data to represent disparate operational schedule transit networks and pedestrian OpenStreetMap (OSM) data to represent the pedestrian network. UrbanAccess provides a generalized, computationally efficient, and unified accessibility calculation framework by linking tools for: 1) network data acquisition, validation, and processing; 2) computing an integrated pedestrian and transit weighted network graph; and 3) network analysis using Pandana.
UrbanAccess offers the following tools:
GTFS and OSM network data acquisition via APIs
Network data validation and regional network aggregation
Compute network impedance:
by transit schedule day of the week and time of day
by transit mode
by including average passenger headways to approximate passenger transit stop wait time
Integrate pedestrian and transit networks to approximate pedestrian scale accessibility
Resulting networks are designed to be used to compute accessibility metrics using the open source network analysis tool Pandana
Compute cumulative accessibility metrics
Nearest feature analysis using POIs
Let us know what you are working on or if you think you have a great use case by tweeting us at @urbansim or post on the UrbanSim forum.
Current status
UrbanAccess is currently in a alpha release and only compatible with Python 2.x. Further code refinements are expected.
Forthcoming improvements:
Unit tests
Python 3
Reporting bugs
Please report any bugs you encounter via GitHub issues.
Contributing to UrbanAccess
If you have improvements or new features you would like to see in UrbanAccess:
Open a feature request via GitHub issues.
Contribute your code from a fork or branch by using a Pull Request and request a review so it can be considered as an addition to the codebase.
Install the latest release
conda
conda installation is forthcoming.
pip
UrbanAccess is available on PyPI and can be installed with:
pip install -U urbanaccess
Development Installation
UrbanAccess is currently in a alpha release and further code refinements are expected. As such, it is suggested to install using the develop command rather than install. Make sure you are using the latest version of the code base by using git’s git pull inside the cloned repository.
To install UrbanAccess follow these steps:
Git clone the UrbanAccess repo
in the cloned directory run: python setup.py develop
To update to the latest development version:
Use git pull inside the cloned repository
Documentation and demo
Documentation for UrbanAccess can be found here.
A demo jupyter notebook for UrbanAccess can be found in the demo directory.
Minimum GTFS data requirements
The minimum GTFS data types required to use UrbanAccess are: stop_times, stops, routes, calendar, and trips however if there is no calendar, calendar_dates can be used as a replacement.
Citation and academic literature
To cite this tool and for a complete description of the UrbanAccess methodology see the paper below:
For other related literature see here.
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Source Distribution
Built Distribution
Hashes for urbanaccess-0.1.0-py2.py3-none-any.whl
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