Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorBrum-Bastos, Vanessa
dc.contributor.authorFerster, Colin J.
dc.contributor.authorNelson, Trisalyn
dc.contributor.authorWinters, Meghan
dc.date.accessioned2020-01-08T09:30:05Z
dc.date.available2020-01-08T09:30:05Z
dc.date.issued2019-11-26
dc.identifier.citationBrum-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/10.32866/10828en
dc.identifier.issn2652-0397
dc.identifier.otherPURE: 265421682
dc.identifier.otherPURE UUID: d2353517-3d0c-4e75-9990-725297cd6845
dc.identifier.otherORCID: /0000-0002-5865-0204/work/67167732
dc.identifier.urihttp://hdl.handle.net/10023/19247
dc.descriptionThis work was supported by a grant from the Public Health Agency of Canada to BikeMaps.org.en
dc.description.abstractWhen 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.
dc.format.extent8
dc.language.isoeng
dc.relation.ispartofTransport Findingsen
dc.rightsCopyright © 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.en
dc.subjectMachine learningen
dc.subjectSmart citiesen
dc.subjectUrban planningen
dc.subjectTransportationen
dc.subjectMobilityen
dc.subjectStravaen
dc.subjectBicyclingen
dc.subjectG Geography (General)en
dc.subject3rd-DASen
dc.subject.lccG1en
dc.titleWhere to put bike counters? Stratifying bicycling patterns in the city using crowdsourced dataen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Geography & Sustainable Developmenten
dc.identifier.doihttps://doi.org/10.32866/10828
dc.description.statusPeer revieweden


This item appears in the following Collection(s)

Show simple item record