: Promoting the safety of commercial trucks by identifying countermeasures that eliminate/reduce the effect of factors that increase the severity of truck-related crashes is crucial. Crash causal factors for rural interstate roads, located within the mountain plains, are
inherently unique compared to urban interstate roads. This is due to the presence of challenging road geometry coupled with severe weather
conditions and high truck traffic volumes. This study investigated Interstate 80 in Wyoming using decision trees, as a data mining approach,
and structural equation model (SEM) as a latent factor modeling approach. SEM was employed to clarify the direct and indirect relationships between endogenous and exogenous variables while accounting for the variation and covariation within and between the constructed
measurement models. Crash severity data were processed to account for factors affecting single vehicles and multivehicle trucks. The results
showed that the interaction with surrounding traffic was the most significant latent variable affecting the crash severity of multivehicle
truck crashes, while adverse weather conditions were the most significant latent variable affecting the crash severity of single-truck crashes.
The results of this study highlighted the importance of increasing the situational awareness of commercial truck drivers with upcoming
hazardous events. This could be performed by communicating information using variable message signs, the 511 application, the commercial vehicle operator portal (CVOP), or the connected vehicle (CV) technologies. |
