This study examines various features affecting the severity associated with commercial motor vehicle (CMV, i.e., large truck and bus) head-on collisions on Kentucky highways. Recent five-year (2015–2019) crash data and variables rarely explored before (e.g., presence of centerline rumble strips, type of passing zone, and terrain type) were collected and prepared using Google Maps. A total of 378 CMV-related head-on collisions were analyzed. The generalized ordered probit (GOP) model was employed to identify the significant factors affecting the severity level resulting from CMV head-on collisions. The model allows the coefficients to vary across the injury severity categories for reliable parameter estimations. From the preliminary investigation, rolling terrains had the highest share of severe CMV head-on crashes (62% and 71% for multilane and two-lane roadways, respectively). The presence of centerline rumble strips could reduce severe crash outcomes along multilane and two-lane facilities. The GOP model identified various significant predictors of minor and severe injuries from CMV head-on crashes. Occupants wearing seatbelt were 39.3% less likely to sustain severe head-on crash injuries. From the roadway characteristics, presence of median cable and concrete barriers could significantly reduce the probability of severe head-on crash injuries, with median cables being more effective. With regard to the driver characteristics, drug impairment and speeding increased the risk of sustaining fatal/serious injuries by 39.5% and 26.4%, respectively. Necessary safety recommendations are proposed to reduce the severity of CMV head-on-related collisions. One example is installing median cable barriers along roadway stretches with a history of head-on CMV-related crashes.
Investigating Head-On Crash Severity Involving Commercial Motor Vehicles in Kentucky
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2675 , 10 ; 133-147
2021-04-04
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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