A tool for creating GTFS transit and OSM pedestrian networks for use in Pandana accessibility analyses.
A tool for computing GTFS transit and OSM pedestrian networks for accessibility analysis.
Integrated AC Transit and BART transit and pedestrian network travel times for Oakland, CA
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.
Citation and academic literature
To cite this tool and for a complete description of the UrbanAccess methodology see the paper below:
Samuel D. Blanchard and Paul Waddell. 2017. “UrbanAccess: Generalized Methodology for Measuring Regional Accessibility with an Integrated Pedestrian and Transit Network.” Transportation Research Record: Journal of the Transportation Research Board. No. 2653. pp. 35–44.
For other related literature see here.
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
UrbanAccess is available on Conda Forge and can be installed with:
conda install urbanaccess -c conda-forge
UrbanAccess is available on PyPI and can be installed with:
pip install urbanaccess
Developers contributing code can install using the develop command rather than install. Make sure you are using the latest version of the codebase 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 and trips and one of either calendar or calendar_dates.
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