Crash risk : how cycling flow can help explain crash data.
20210376 ST [electronic version only]
Accident Analysis and Prevention, Vol. 105 (August 2017), p. 21-29, 22 ref.
|Samenvatting||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, exposure data from 11 roadside stations were used 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, these 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. (Author/publisher)|
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