Crash risk: How cycling flow can help explain crash data. (August 2017)

• Record Type: Journal Article
• Authors: Dozza, Marco
• Is Part Of: Accident analysis and prevention. Volume 105(2017)
• Page Start: 21
• Page End: 29
• Publication Date: 2017-08
• Subjects: Crash risk -- Crash causation -- Road user behavior -- Exposure -- Single-bicycle crashes; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2016.04.033
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Bicycle crashes were combined with detailed exposure data to calculate crash risk. Crash risk is higher 1) at night than in the daytime 2) on weekends than on weekdays. Intoxication may explain single-bicycles crashes during weekends at night. Bicycle use influences crash type on different time scales. Exposure data help select, filter, aggregate, highlight, and normalize crash data. Abstract: Crash databases are commonly queried to infer crash causation, prioritize countermeasures to prevent crashes, and evaluate safety systems. However, crash databases, which may be compiled from police and hospital records, alone cannot provide estimates of crash risk. Moreover, they fail to capture road user behavior before the crash. In Sweden, as in many other countries, crash databases are particularly sterile when it comes to bicycle crashes. In fact, not only are bicycle crashes underreported in police reports, they are also poorly documented in hospital reports. Nevertheless, these reports are irreplaceable sources of information, clearly highlighting the surprising prevalence of single-bicycle crashes and hinting at some cyclist behaviors, such as alcohol consumption, that may increase crash risk. In this study, we used exposure data from 11 roadside stations measuring cyclist flow in Gothenburg to help explain crash data and estimate risk. For instance, our results show that crash risk is greatest at night on weekends, and that this risk is larger for single-bicycle crashes than for crashes between a cyclist and another motorist. This result suggests that the population of night-cyclists on weekend nights is particularly prone to specific crash types, which may be influenced by specific contributing factors (such as alcohol), and may require specific countermeasures. Most importantly, our results demonstrate that detailed exposure data can help select, filter, aggregate, highlight, and normalize crash data to obtain a sharper view of the cycling safety problem, to achieve a more fine-tuned intervention.