Comparing Crowdsourced Near-Miss and Collision Cycling Data and Official Bike Safety Reporting. Issue 1 (2017)

• Record Type: Journal Article
• Authors: Branion-Calles, Michael; Nelson, Trisalyn; Winters, Meghan
• Is Part Of: Transportation research record. Volume 2662:Issue 1(2017)
• Page Start: 1
• Page End: 11
• Publication Date: 2017
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2662-01
• Languages: English
• ISSNs: 0361-1981

Abstract

Official sources of cyclist safety data suffer from underreporting and bias. Crowdsourced safety data have the potential to supplement official sources and to provide new data on near-miss incidents. BikeMaps.org is a global online mapping tool that allows cyclists to record the location and details of near misses and collisions they experience. However, little is known about how the characteristics of near-miss and collision events compare. Further, the question remains whether the characteristics of crowdsourced collision data are similar to those of collision data captured by official insurance reports. The objectives of this study were twofold: ( a ) to assess similarities and differences in near misses and collisions reported to BikeMaps.org and ( b ) to assess similarities and differences in collisions reported to BikeMaps.org and to an official insurance data set. Logistic regression was used first to model the odds of crowdsourced near-miss reports as opposed to collision reports and then to model the odds of crowdsourced as opposed to official insurance collision reports, as a function of incident circumstances. The results indicated higher odds of crowdsourced reports of near misses than of crowdsourced collision reports for commute trips, interactions with motor vehicles, and in locations without bicycle-specific facilities. In addition, relative to insurance reports, crowdsourced collision reports were associated with peak traffic hours, nonintersection locations, and locations where bicycle facilities were present. These analyses indicated that crowdsourced collision data have potential to fill in gaps in reports to official collision sources and that crowdsourced near-miss reporting may be influenced by perceptions of risk.