Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach. (November 2021)

• Record Type: Journal Article
• Authors: Haq, Muhammad Tahmidul; Zlatkovic, Milan; Ksaibati, Khaled
• Is Part Of: Accident analysis and prevention. Volume 162(2021)
• Publication Date: 2021-11
• Subjects: Commercial truck safety -- Truck driver injury -- Truck configuration -- Bayesian inference -- Unobserved heterogeneity -- Intra-crash correlation; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2021.106392
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Truck-involved crashes result in more severe injuries. Truck configuration has significant impacts, but is not well presented in current practice. Involvement of heavy and HAZMAT trucks result in more severe injuries. Inadequate driver behavior results in more severe crashes. Abstract: For the last decade, disaggregate modeling approach has been frequently practiced to analyze truck-involved crash injury severity. This included truck-involved crashes based on single and multi-vehicles, rural and urban locations, time of day variations, roadway classification, lighting, and weather conditions. However, analyzing commercial truck driver injury severity based on truck configuration is still missing. This paper aims to fill this knowledge gap by undertaking an extensive assessment of truck driver injury severity in truck-involved crashes based on various truck configurations (i.e. single-unit truck with two or more axles, single-unit truck pulling a trailer, semi-trailer/tractor, and double trailer/tractor) using ten years (2007–2016) of Wyoming crash data through hierarchical Bayesian random intercept approach. The log-likelihood ratio tests were conducted to justify that separate models by various truck configurations are warranted. The results obtained from the individual models demonstrate considerable differences among the four truck configuration models. The age, gender, and residency of the truck driver, multi-vehicles involvement, license restriction, runoff road, work zones, presence of junctions, and median type were found to have significantly different impacts on the driver injury severity. These differences in both the combination and the magnitude of the impact of variables justified the importance of examining truck driver injury severity for different truck configuration types. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (24%–42%) of intra-crash correlation (effects of the common crash-specific unobserved factors) in driver injury severity within the same crash. Finally, based on the findings of this study, several potential countermeasures are suggested.