The role of striking and struck vehicles in side crashes between vehicles: Bayesian bivariate probit analysis in China.

• The impact of influencing factors on injury severity of side crashes at intersections and role of striking and struck vehicles are investigated. • Bayesian bivariate probit model is proposed to accommodate the correlation between the injury severity levels of striking and struck vehicles, and Bayesian approach via Markov Chain Monte Carlo (MCMC) is employed to estimate the model. • The Bayesian bivariate probit model can effectively address the correlation and provide better model fit than Bayesian univariate probit model. • The findings can help designers and management departments improve the safety at intersections. Side crashes between vehicles which usually lead to high casualties and property loss, rank first among total crashes in China. This paper aims to identify the factors associated with injury severity of side crashes at intersections and to provide suggestions for developing countermeasures to mitigate the levels of injuries. In order to investigate the role of striking and struck vehicles in side crashes simultaneously, bivariate probit model was proposed and Bayesian approach was employed to evaluate the model, compared to the corresponding univariate probit model. Crash data from Beijing, China for the period 2009–2012 were used to carry out the statistical analysis. Based on the investigation with vehicles and data analysis on events, 130 intersection side crash cases were selected to form a specific dataset. Then, the influence of human, vehicles, roadway and environmental variables on crash severity was examined by means of bivariate probit regression within Bayesian framework. The effects of the factors on striking vehicle drivers and struck vehicle drivers were considered separately and simultaneously to find more targeted conclusions. The statistical analysis revealed vehicle type, lane number, no non-motorized lane and speeding have the corresponding influence on the injury severity of striking vehicles, while time of day and vehicle type of struck vehicles increased the likelihood of being injured. From the results it can be concluded that there indeed exists correlation between striking and struck vehicles in side crashes, although the correlation is not so strong. Importantly, Bayesian bivariate probit model can address the role of striking and struck vehicles in side crashes simultaneously and can accommodate the correlation clearly, which extends the range of univariate probit analysis. The general and empirical countermeasures are presented to improve the safety at intersections. [ABSTRACT FROM AUTHOR]