When designing bicycle count programs, it can be difficult to know where to locate counters to generate a representative sample of bicycling ridership. Crowdsourced data on ridership has been shown to represent patterns of temporal ridership in dense urban areas. Here we use crowdsourced data and machine learning to categorize street segments into classes of temporal patterns of ridership. We used continuous signal processing to group 3,880 street segments in Ottawa, Ontario into six classes of temporal ridership that varied based on overall volume and daily patterns (commute vs non-commute). Transportation practitioners can use this data to strategically place counters across these strata to efficiently capture bicycling ridership counts that better represent the entire city.


    Access

    Download


    Export, share and cite



    Title :

    Where to Put Bike Counters? Stratifying Bicycling Patterns in the City Using Crowdsourced Data


    Contributors:

    Published in:

    Publication date :

    2019




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Validating crowdsourced bicycling mobility data for supporting city planning

    Leao, Simone Z. / Lieske, Scott N. / Pettit, Chris J. | Taylor & Francis Verlag | 2019


    Crowdsourced data for bicycling research and practice

    Nelson, Trisalyn / Ferster, Colin / Laberee, Karen et al. | Taylor & Francis Verlag | 2021

    Free access

    Crowdsourced bicycling crashes and near misses: trends in Canadian cities

    Laberee, Karen / Nelson, Trisalyn / Branion-Calles, Michael et al. | Taylor & Francis Verlag | 2021

    Free access

    Crowdsourced bicycling crashes and near misses: trends in Canadian cities

    Karen Laberee / Trisalyn Nelson / Michael Branion-Calles et al. | DOAJ | 2021

    Free access

    Modeling Bicycling to Elementary and Junior High Schools with Bike Rack Counts

    Fitch, Dillon T. / Thigpen, Calvin G. / Handy, Susan L. | Transportation Research Record | 2016