Investigating crash-related injuries between animal-related and motor vehicle in Rural China: Bayesian random parameter probit model considering endogenous variables.

This study intended to investigate the injury severity of animal-vehicle crashes (AVCs) and identify the contributing factors from the perspective of animals. To achieve this objective, Bayesian random parameter probit regression model with endogenous variable was proposed with a case study application on two places in Inner Mongolia, China. First, from the perspective of animals, pooled probit regression model with endogenous variable was presented to address the endogeneity issue. Next, panel data model integrated with probit regression model with endogenous variable was presented in order to accommodate the heterogeneity issue due to unobserved factors within Bayesian framework, and to identify the significant influencing factors of injury severity. Results revealed that the endogenous variable (number of casualty) and vehicle type are significant factors for the animal injury severity. Meanwhile, it is demonstrated that there exists an endogeneity issue between injury severity and the number of casualty; while the panel data model integrated with Bayesian probit regression model accommodates the heterogeneity due to unobserved factors. The findings can contribute to the implementation of more reliable countermeasures to prevent the injury severity from drivers and animals in pastoral areas. [ABSTRACT FROM AUTHOR]