Validating the bivariate extreme value modeling approach for road safety estimation with different traffic conflict indicators.

A range of conflict indicators have been developed for traffic conflict observation. The various conflict indicators have been shown in earlier studies to be of different and sometimes independent nature. Therefore, there is a need to combine different indicators to gain better understanding of the underlying severity of traffic events and for more reliable safety analysis. This study proposes a bivariate extreme value model to integrate different traffic conflict indicators for road safety estimation, and the model is validated with actual crash data. Based on video data collected from four signalized intersections in two Canadian cities, computer vision techniques were utilized to identify rear-end traffic conflicts using several indicators. The conflict indicators included: time to collision (TTC), modified time to collision (MTTC), post encroachment time (PET), and deceleration to avoid crash (DRAC). Then bivariate extreme value models were developed for combinations of each two indicators, and the numbers of crashes were estimated from the models and compared to the observed crashes. The results show that most of the estimated crashes are in the range of 95% Poisson confidence interval of observed crashes, which indicates that the bivariate extreme value model is a promising tool for road safety estimation. Moreover, the accuracy of estimated crashes are different for different indicator combinations. The results show that the estimates of TTC&PET are the most accurate, followed by TTC&MTTC, TTC&DRAC, PET&MTTC, PET&DRAC and MTTC&DRAC. A further correlation analysis suggests that a combination of two independent conflict indicators leads to better crash estimation performance.