Bayesian hierarchical modelling of traffic barrier crash severity

Severe vehicle crashes have resulted in a large-scale social and economic loss. As a result, the reduction of those crashes has become one of the key objectives of policy makers. Although traffic barriers have been utilized to reduce the run-of-road crash severity, still those crashes account for a high number of severe crashes. Previous studies of traffic barrier crashes often either ignore the heterogeneity across different traffic barrier types, or just focus on specific types of traffic barriers. Thus, this study developed a Bayesian Hierarchical model (BHM) to identify the contributory factors impacting the severity of traffic barrier crashes while accounting for that heterogeneity. The assessment of model fit, inter-class correlation (ICC) coefficient, and deviance information criterion (DIC) all favoured the use of the BHM. Besides accounting for the heterogeneity between barrier types, the interaction across variables shoulder width and traffic barrier height were incorporated into the analysis. Due to the differences in traffic barrier design and vehicle performance across different roadway classifications, only Wyoming interstate traffic barrier were considered. Results indicated that there is an important interaction term between traffic barrier height and shoulder width so that the impact of these two predictors should not be separated. In addition, having a citation record, negotiating a curve, being a female driver, non-speed compliance, alcohol involvement, and showing emotional signs at the time of crash were factors increasing the severity of traffic barrier crashes. On the other hand, having a turn before hitting a traffic barrier, being a younger driver, and driving in adverse weather conditions were factors that significantly decrease the severity of traffic barrier crashes.