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.


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    Title :

    Using trajectory data to explore roadway characterization for bikeshare network


    Contributors:

    Published in:

    Publication date :

    2018-11-02


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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