Real-time traffic conflict identification using Bayesian Hierarchical Approach at Merging Area in the Environment of Connected and Autonomous Vehicles

The lane-changing maneuvers of vehicles at freeway merging areas can result in a substantial increase in crash risk. The implementation of a connected and autonomous vehicle (CAV) environment is anticipated to decrease the associated conflict risk of such maneuvers, by utilization of surrounding traffic information. To investigate the potential conflict situations that may arise during multi-vehicle interactions by taking advantage of CAV environment, this study proposes a Bayesian hierarchical algorithm to identify traffic conflicts in real-time during mandatory drivers’ merging behavior. The Bayesian hierarchical model consists of observation vehicle conflict layer and prior distribution layer. The MCMC sampling method was utilized to calibrate the model parameters by generating a Markov chain. Two methods including image discrimination and posterior distribution comparison are combined to ensure reliable convergence diagnosis of Markov chain. The results show that the Markov chain for each parameter gradually stabilizes over iterations and the posterior probability density is consistent with the specified prior function and likelihood function. Additionally, the flexibility of this approach significantly enhances the ability to analyze traffic conflict from the real world using applied statistics. The findings of this research have the potential to encourage cautious driving behaviors and provide useful insights for driver safety in the future, especially in the context of CAVs.