Exploring safety effects on urban expressway diverging areas: crash risk estimation considering extreme conflict types

Previous research solely employed a single type of conflict extremes for crash estimation, without considering the joint impact of multiple types of conflict extremes on crash risk. Therefore, two analysis frameworks based on conflict extremes were proposed: separate modeling and cooperative modeling. Based on the trajectories from five diverging areas, longitudinal and lateral conflicts were extracted. Then, a Bayesian hierarchical model for joint multi-location conflict extremes was constructed. Next, the threshold for conflict extremes was determined using automatic mean residual life plots, and a link function was established between the logarithmic scale parameter and dynamic and static variables. Finally, model parameters were estimated using the Markov Chain Monte Carlo simulation method, and a comparative analysis of crash probabilities and overall risks for diverging areas in the two frameworks was conducted by the fitted distributions. The results show that density differences, speed differences, and the ratio of large vehicles are important covariates explaining the non-stationarity of conflict extremes. In terms of crash probability, significant covariates exhibit stronger explanatory power for longitudinal conflicts compared to lateral conflicts. At the overall risk level, the accuracy of the separate modeling is higher compared to the cooperative modeling.