The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.
Bayesian Hierarchical Random Intercept Model Based on Three Parameter Gamma Distribution
