Package: urbanaccess
UrbanAccessA 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, CAOverviewUrbanAccess 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 APIsNetwork data validation and regional network aggregationCompute network impedance:by transit schedule day of the week and time of dayby transit modeby including average passenger headways to approximate passenger transit stop wait timeIntegrate pedestrian and transit networks to approximate pedestrian scale accessibilityResulting networks are designed to be used to compute accessibility
metrics using the open source network analysis toolPandanaCompute cumulative accessibility metricsNearest feature analysis using POIsLet us know what you are working on or if you think you have a great use
case by tweeting us at@urbansimor post on the UrbanSimforum.Citation and academic literatureTo 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 seehere.Reporting bugsPlease report any bugs you encounter viaGitHub
issues.Contributing to UrbanAccessIf you have improvements or new features you would like to see in
UrbanAccess:Open a feature request viaGitHub 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 releasecondaUrbanAccess is available on Conda Forge and can be installed with:conda install urbanaccess -c conda-forgepipUrbanAccess is available on PyPI and can be installed with:pip install urbanaccessDevelopment InstallationDevelopers contributing code can install using thedevelopcommand rather thaninstall. Make sure you are using the latest version of the codebase by using git’sgit pullinside the cloned repository.To install UrbanAccess follow these steps:Git clone theUrbanAccess repoin the cloned directory run:python setup.py developTo update to the latest development version:Usegit pullinside the cloned repositoryDocumentation and demoDocumentation for UrbanAccess can be foundhere.A demo jupyter notebook for UrbanAccess can be found in thedemo
directory.Minimum GTFS data requirementsThe minimumGTFS data
typesrequired to use
UrbanAccess are:stop_times,stops,routesandtripsand
one of eithercalendarorcalendar_dates.Related UDST librariesPandanaOSMnet