A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

• Main Authors: Xiaojun Shao, Xiaoxiang Ma, Feng Chen, Mingtao Song, Xiaodong Pan, Kesi You
• Format: Article
• Language: English
• Published: MDPI AG 2020-01-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: injury severity; truck-involved rear-end collision; random parameter ordered probit
• Online Access: https://www.mdpi.com/1660-4601/17/2/395

Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions.