Using empirical traffic trajectory data for crash risk evaluation under three-phase traffic theory framework.

This study employed surrogate safety measures to evaluate the crash risks in different traffic phases and phase transitions according to the three-phase theory. The analysis was conducted from a microscopic perspective based on empirical vehicle trajectory data collected from the Interstate 80 in California, USA, and the Yingtian Expressway in Nanjing, China. Traffic phases were identified based on traffic flow variables and their correlations. Two advanced crash risk indexes from vehicle trajectories were conducted to evaluate the safety performance in each traffic state. The effects of various traffic flow variables (i.e. flow rate, density, average speed) on crash risks were explored based on speed-density plane, speed-flow plane and flow-density plane. Three regression models were developed to quantify the effects of traffic flow variables and traffic states on crash risks. The results show significant disparities of safety performance among different traffic states. Synchronized flow and wide moving jam are found to be the most dangerous phases. High density and low speed are associated with high crash risk. The best crash risk prediction performance is achieved when integrating both traffic phases and traffic parameters.