Development of a Real-time Crash Risk Prediction Model Incorporating the Various Crash Mechanisms across Different Traffic States

ABSTRACT<b><i>Objective:</i></b> This study aimed to identify the traffic flow variables contributing to crash risks under different traffic states, and to develop a real-time crash risk model incorporating the varying crash mechanisms across different traffic states.<b><i>Methods:</i></b> The crash, traffic and geometric data were collected on the I-880N freeway in California, United States in 2008 and 2009. This study considered four different traffic states in the Wu's four-phase traffic theory. They are free fluid traffic, bunched fluid traffic, bunched congested traffic and standing congested traffic. Several different statistical methods were used to accomplish the research objective.<b><i>Results:</i></b> The preliminary analysis showed that traffic states significantly affected crash likelihood, collision type, and injury severity. The nonlinear canonical correlation analysis (NLCCA) was conducted to identify the underlying phenomena that made certain traffic states more hazardous than others. The results suggested that different traffic states were associated with various collision types and injury severities. The matching of traffic flow characteristics and crash characteristics in NLCCA revealed how traffic states affected traffic safety. The logistic regression analyses showed that the contributing factors to crash risks were quite different across various traffic states. To incorporate the varying crash mechanisms across different traffic states, the random-parameters logistic regression was used to develop the real-time crash risk model. Bayesian inference based on Markov chain Monte Carlo (MCMC) simulations was used for model estimation. The parameters of traffic flow variables in the model were allowed to vary across different traffic states. Compared with the standard logistic regression model, the proposed model significantly improved the goodness-of-fit and the predictive performance.<b><i>Conclusions:</i></b> These results can promote a better understanding of the relationship between traffic flow characteristics and crash risks, which is valuable knowledge in the pursuit of improving traffic safety on freeways through the use of dynamic safety management systems.