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Where to put bike counters? Stratifying bicycling patterns in the city using crowdsourced data

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Author(s)

Vanessa Brum-Bastos, Colin J. Ferster, Trisalyn Nelson, Meghan Winters

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Abstract

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.
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Details

Original languageEnglish
Number of pages8
JournalTransport Findings
Volume2019
DOIs
Publication statusPublished - 26 Nov 2019

    Research areas

  • Machine learning, Smart cities, Urban planning, Transportation, Mobility, Strava, Bicycling

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