A Bikeshare Station Area Typology to Forecast the Station-level Ridership Activity of System Expansion
Corresponding Author: Steven Gehrke, Portland State University
Presented By: Dr. Tim Welch, Georgia Institute of Technology
Coauthors:
- Timothy Welch, Georgia Institute of Technology
Abstract
The continued global introduction and expansion of third-generation bikeshare systems with widely available origin-destination data has opened exciting avenues for bikeshare research. In response, a flux of recent bikeshare studies have examined the sociodemographic determinants and safety or natural environment deterrents of system ridership. An increasing abundance of disaggregate spatial bikeshare data has also spurred recent calls for planning research aimed at extending the utility of these contextual data to model bikeshare demand and trip patterns. Furthermore, as both planners and operators seek to expand bikeshare services into previously unserved contexts, a need exists to provide them with a deeper understanding of the spatial dynamics of bikeshare use.
This study of the Washington, DC metro region’s Capital Bikeshare (CaBi) program answers this call by adopting a latent class cluster analysis framework to classify bikeshare station areas based on variation in the built environment aspects of development pattern, urban design, and transportation infrastructure. Specifically, five station area types were categorized based on the level of activity density, jobs-population balance, housing stock, street connectivity, and rail station access found within one-half mile of the 360 CaBi stations in spring 2016. This proposed bikeshare station area typology was then integrated with the Bikeshare Location and Origin-destination Count (BikeLOC) model to explore a potential expansion of the CaBi system into neighboring jurisdictions and forecast the associated travel patterns between current and modeled stations. BikeLOC is a transportation planning tool designed as a hierarchical recursive model with a series of modules that collect open source and publically available built environment data, predict potential station locations, and estimate ridership activity between current and proposed stations.
This study’s introduction of a bikeshare station area typology based on key built environment indicators permits greater insight into what it is about particular contexts that supports their higher station-level activity. Further, as demonstrated, our proposed typology can be integrated into transportation planning models to not only inform planners and agencies of candidate locations for system expansion, but also provide a sense of the station type and associated ridership activity that may result from siting within a certain neighborhood.