Where to put bike counters? Stratifying bicycling patterns in the city using crowdsourced data
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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.
Brum-Bastos , V , Ferster , C J , Nelson , T & Winters , M 2019 , ' Where to put bike counters? Stratifying bicycling patterns in the city using crowdsourced data ' , Transport Findings , vol. 2019 . https://doi.org/10.32866/10828
Copyright © 2019 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY-NC-4.0). View this license’s legal deed at https://creativecommons.org/licenses/by-nc/4.0.
DescriptionThis work was supported by a grant from the Public Health Agency of Canada to BikeMaps.org.
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