Using trajectory data to explore roadway characterization for bikeshare network

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 p...

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Bibliographic Details
Published inJournal of intelligent transportation systems Vol. 22; no. 6; pp. 530 - 546
Main Authors Liu, Xiaoyue Cathy, Taylor, Jeffrey, Porter, Richard J., Wei, Ran
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.11.2018
Taylor & Francis Ltd
Subjects
Bicycles
bikeshare
centrality
Global positioning systems
GPS
Information retrieval
network modeling
road classification
Roads
Roads & highways
Satellite navigation systems
Sensitivity analysis
Shortest-path problems
Trajectories
trajectory
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Summary: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.
ISSN:1547-2450
1547-2442
DOI:10.1080/15472450.2018.1444484