Bayesian estimation of conflict-based safety performance functions

Most of the current research on road safety relies on the analysis of collision data that is challenged by well-recognized availability and quality issues. Therefore, the use of surrogate safety measures such as traffic conflicts has been gaining acceptance as an alternative or complementary approach to analyze traffic safety from a broader perspective than collision data alone. However, there is a need to develop statistical techniques to analyze conflict data to support various road safety applications. This article discusses the development of conflict-based safety performance functions (SPFs) within the framework of Bayesian statistics. The Bayesian approach was selected as it represents the state-of-the-art technique in the statistical analysis of collisions. In particular, SPFs were developed to predict the number of rear-end conflicts at different intersection approaches. The functions were validated using posterior predictive checking indicators. Data for traffic conflict observations were automatically extracted with computer vision techniques at several urban and suburban intersections in British Columbia (Canada). The results indicate that the models developed have a good fit of the observed conflict data and can offer a useful tool for conducting safety analysis.