Assessing bicycle crash risks controlling for detailed exposure: A Copenhagen case study.

A better understanding of factors associated with bicycle crashes can inform future efforts to limit crash risks. Many previous studies have analysed crash risk based on crash databases. However, these can only provide conditional information on crash risks. A few recent studies have included aggregate flow measures in their crash risk analyses. This study incorporates detailed bicycle flow to investigate factors related to bicycle crashes. Specifically, the study assesses the relative crash risk given various conditions by applying Palm distributions to control for exposure. The study specifically investigates the relationship between weather and time conditions and the relative risk of bicycle crashes at a disaggregate level. The study uses bicycle crash data from police reports of bicycle crashes from 2017-2020 in the greater Copenhagen area (N = 4877). The relations between the bicycle crash risk and the air temperature and wind speeds are found to be highly non-linear. The relative risk of bicycle crashes is elevated at low and high temperatures (0 °C ¿ x, x ¿ 21 °C). The results also show how decreasing visibility relates to increasing bicycle crash risk. Meanwhile, cycling during the early morning peak (7-8) and afternoon peak hours (15-18) is related to an increased risk of bicycle crashes. While some of the effects are likely spurious, they highlight specific conditions associated with higher relative risk. Finally, the results illustrate the increased risk at weekend night times when cyclists are likely to bike under the influence of alcohol. In conclusion, the analysis confirms that visibility, slippery surfaces, and intoxication are all factors associated with a higher risk of bicycle crashes. Hence, it is relevant to consider how infrastructure planning and preventive measures can modify the bicycle environment to minimise these risks.

Factors influencing the injury severity of single-bicycle crashes.

The majority of research on bicyclist injury severity relates to bicycle-motor vehicle crashes, even though single-bicycle crashes make up more than half of bicycle crashes. This study explores the factors related to the injury severity outcome of single-bicycle crashes. We use single-bicycle crash data obtained from medical records collected in the period 2010-2015 combined with road maintenance data. The data includes three injury severity categories: 'severe injury', 'slight injury', 'no injury'. The relation between the factors surrounding single-bicycle crashes and the resulting injury severity is estimated using a latent class ordered probit model. The model estimation identifies three latent classes where the likelihood of cyclist membership depends on the bicyclist's age and gender. Furthermore, several factors appear to affect the likelihood of injuries in single-bicycle crashes. These are the road geometry (i.e. if the crash occurred on a bicycle lane or a road section), maintenance level, and the interaction between road geometry and maintenance level. The findings suggest that single-bicycle crashes on road sections result in more severe injuries than single-bicycle crashes on bicycle lanes. The largest effect is seen when a single-bicycle crash occurs on a road section with a poorly maintained bicycle lane being available. Crashes on low volume roads with few bicyclists are also related to an increased probability of severe injury as well as crashes occurring after dark.