Crash risk: How cycling flow can help explain crash data

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.