Using bicycle app data to develop Safety Performance Functions (SPFs) for bicyclists at intersections: A generic framework

Chen Chen, Haizhong Wang, Josh Roll, Krista Nordback and Yinhai Wang

Transportation Research Part A: Policy and Practice, 2020, vol. 132, issue C, 1034-1052

Abstract: More accurate predictions of bicycle crashes can increase the return on investment from bicycle safety initiatives. One useful tool to understand the association between critical factors and crashes is Safety Performance Functions (SPFs), but most U.S. studies have developed SPFs for motorized vehicles not for bicycles. The objective of this study is to develop SPFs for intersections in a medium- and a large-sized city using bicycle app data (i.e., Strava data), leveraging the rising popularity of social media and mobile phones. Our case studies are from the Portland and Eugene-Springfield metropolitan areas with sizable bike population, which alleviates the challenge of insufficient bicycle volume and crash data. Specifically, in this research (1) bicycle SPFs are created for intersections in medium- and large-sized cities; (2) affordable bicycle app volume data, is used as a surrogate for exposure; (3) bicycle app data is shown to be correlated with automatic bike count station data (p<0.01); (4) bicycle app count shows a significant association with intersection crashes (p<0.01); (5) a general framework for building bicycle SPFs is developed for jurisdictions and the corresponding model is validated to demonstrate predictive ability; and (6) recommendations on infrastructure design and non-motorized policy making are provided with the goal of developing a safer environment for bicyclists.

Keywords: Bicycle safety; Safety Performance Functions (SPF); Bicycle app data; Strava (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.tra.2019.12.034

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