Driver injury severity analysis of work zone crashes: A Bayesian hierarchical generalized ordered probit approach

Abstract

Highway work zones are locations where severe traffic crashes tend to occur. Most of the extant research on work zone crash severity neglects the discrepancy in the injuries sustained by different drivers involved in the same crash. Admittedly, it is essential to analyze crash-level factors to their highest injury severity; but it is equally important to understand driver-level contributing factors to their injury severity to establish effective safety countermeasures to minimize drivers’ injury severity. Thus, this research aims to identify the factors with significant impacts on driver injury severity of work zone crashes and estimate their effects on each severity level. Data of 3,880 drivers involved in 2,134 work zone crashes are obtained from the Crash Report Sampling System (CRSS) database of the United States and employed for the empirical investigation. A Bayesian hierarchical generalized ordered probit model is advocated for analyzing the driver injury severity. Model performance indices suggest that the advocated hierarchical model is superior to the generalized ordered probit model, and considerable within-crash correlation is found across the observed driver injury severity. The estimated parameters show that driver age and gender, alcohol use, vehicle age and type, speeding and speed limit, weather condition, lighting condition, and crash type have significant effects on the driver injury severity in work zone crashes. Marginal effects of the significant factors on each divided injury severity level are also estimated. Countermeasures are proposed from the results to reduce severe injuries sustained by drivers involved in work zone crashes.