Using trajectory data to explore roadway characterization for bikeshare network

ABSTRACT

The rapid expansion of bikeshare programs nationwide provides opportunities to gain insights on the optimal development of multimodal networks and bike-friendly environments. The profusion of trajectory-level data produced by bikeshare systems allows for information extraction on users' route preferences and, if modeled properly, will lead to a greater understanding of road characteristics that are appealing to bikeshare users. Leveraging Global Positioning System (GPS) data obtained from the GREENbike program, this study proposes a method to characterize roadways (e.g. collector, peripheral road, attractive road, and local road) on the basis of a variety of network centrality functions. The methodology is able to uncover the structure of the underlying transportation network and identify locations of critical bicycle infrastructures. A series of centrality measures, including degree, shortest-path betweenness, and random-walk betweenness centrality are implemented to determine the roadway classifications. Their suitability and usability for this purpose is then explored and discussed at length through a sensitivity analysis. The method can be applied to any bikeshare system that has access to trajectory-level (i.e. GPS, crowdsourcing) data for identifying road attributes that are appealing to bike users. Results can effectively guide future investment choices.

KEYWORDS:

• bikeshare
• centrality
• network modeling
• road classification
• trajectory

Acknowledgments

This paper is based on the research project entitled First Mile Last Mile Strategies for Transit systems, jointly sponsored by Utah Transit Authority and the Mountain Plain Consortium (MPC) of the U.S. DOT University Transportation Centers program. The authors would like to thank UA for their support and feedback on this study. The authors would also like to extend sincere thanks to Will Becker and Ben Bolte with GREENbike for providing the valuable bikeshare dataset to facilitate this research. The work presented in this paper remains the sole responsibility of the authors.

Additional information

Funding

This work is supported by the Utah Transit Authority.