What does crowdsourced data tell us about bicycling injury? A case study in a mid-sized Canadian city

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dc.contributor.author Fischer, Jaimy
dc.contributor.author Nelson, Trisalyn
dc.contributor.author Laberee, Karen
dc.contributor.author Winters, Meghan
dc.date.accessioned 2020-08-22T00:17:40Z
dc.date.available 2020-08-22T00:17:40Z
dc.date.copyright 2020 en_US
dc.date.issued 2020
dc.identifier.citation Fischer, J., Nelson, T., Laberee, K., & Winters, M. (2020). What does crowdsourced data tell us about bicycling injury? A case study in a mid-sized Canadian city. Accident Analysis & Prevention, 145, 1-8. https://doi.org/10.1016/j.aap.2020.105695. en_US
dc.identifier.uri https://doi.org/10.1016/j.aap.2020.105695
dc.identifier.uri http://hdl.handle.net/1828/12018
dc.description.abstract With only ∼20 % of bicycling crashes captured in official databases, studies on bicycling safety can be limited. New datasets on bicycling incidents are available via crowdsourcing applications, with opportunity for analyses that characterize reporting patterns. Our goal was to characterize patterns of injury in crowdsourced bicycle incident reports from BikeMaps.org. We extracted 281 incidents reported on the BikeMaps.org global mapping platform and analyzed 21 explanatory variables representing personal, trip, route, and crash characteristics. We used a balanced random forest classifier to classify three outcomes: (i) collisions resulting in injury requiring medical treatment; (ii) collisions resulting in injury but the bicyclist did not seek medical treatment; and (iii) collisions that did not result in injury. Results indicate the ranked importance and direction of relationship for explanatory variables. By knowing conditions that are most associated with injury we can target interventions to reduce future risk. The most important reporting pattern overall was the type of object the bicyclist collided with. Increased probability of injury requiring medical treatment was associated with collisions with animals, train tracks, transient hazards, and left-turning motor vehicles. Falls, right hooks, and doorings were associated with incidents where the bicyclist was injured but did not seek medical treatment, and conflicts with pedestrians and passing motor vehicles were associated with minor collisions with no injuries. In Victoria, British Columbia, Canada, bicycling safety would be improved by additional infrastructure to support safe left turns and around train tracks. Our findings support previous research using hospital admissions data that demonstrate how non-motor vehicle crashes can lead to bicyclist injury and that route characteristics and conditions are factors in bicycling collisions. Crowdsourced data have potential to fill gaps in official data such as insurance, police, and hospital reports. en_US
dc.description.sponsorship This BikeMaps.org research and outreach has been funded by a grant from the Public Health Agency of Canada (PHAC). We acknowledge Taylor Denouden, Darren Boss, Colin Ferster, and Ayan Mitra in creating and maintaining the technology used to collect BikeMaps.org incident data, and the Capital Regional District for their support of outreach. We thank all members of the BikeMaps.org team whose outreach has helped BikeMaps.org reach a broad number of bicyclists in numerous locations. We also thank everyone who took the time to report an incident on BikeMaps.org. en_US
dc.language.iso en en_US
dc.publisher Accident Analysis & Prevention en_US
dc.subject Bicycling safety en_US
dc.subject Citizen science en_US
dc.subject Crowdsourced en_US
dc.subject Injury en_US
dc.title What does crowdsourced data tell us about bicycling injury? A case study in a mid-sized Canadian city en_US
dc.type Article en_US
dc.description.scholarlevel Faculty en_US
dc.description.reviewstatus Reviewed en_US

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