Evaluation of contributory factors' effects on bicycle-car crash risk at signalized intersections

Bicycle-car accidents at signalized intersections account for a considerable part of bicycle accidents. Factors that have effects on the bicycle-car crash risk at signalized intersections are not well understood. Especially, the safety effects of bicycle facilities are controversial at intersections. This paper aims to identify factors that have significant effects on bicycle-car crash risk at signalized intersections and their effect directions. The negative binomial regression model is used to model associations between the frequency of bicycle-car accidents at signalized intersections and the contributory factors, based on 6-year accident history at 140 signalized intersections in Berlin, Germany. The considered factors include the traffic volume, geometric data, and bicycle-specific and car-specific infrastructures. The factor effect coefficients are estimated by the maximum likelihood approach. The estimated coefficient of the negative binomial dispersion parameter is significant at the level of 0.001. Three factors are identified to have significant effects on the crash risk. The traffic volume of cars across intersections has negative effects on the cycling safety. Regarding bicycle facilities, the number of bicycle lanes on intersection approaches has positive safety effects, whereas the intersections with more bicycle paths is subject to a higher crash risk, though intersection modifications are applied for bicycle paths. The results support the choice of the negative binomial regression over the Poisson regression in this study. The estimated effects provide implications for the cycling safety improvement at signalized intersections.