Analyzing injury severity of rear-end crashes involving large trucks using a mixed logit model: A case study in North Carolina

Abstract

As one of the most frequently occurring crashes, rear-end crashes often result in injuries and property damage, especially when large trucks are involved. To investigate the contributing factors and the unobserved heterogeneity in such factors, a mixed logit model is developed to analyze rear-end crashes involving large trucks. A dataset containing 7,976 rear-end crashes involving large trucks is collected from Highway Safety Information System (HSIS) in North Carolina between 2005 and 2013. Driver, roadway, and environmental related characteristics are considered in the analysis. Speed limit over 50 mph is found to be better modeled as a random-parameter at specific injury severity levels. Results also indicate that driving under the influence of alcohol or drugs, rural roadways, dark light condition, grade roadway configuration, speed limit over 50 mph will significantly increase the injury severity of large truck involved rear-end crashes. Roadway with traffic control will significantly decrease the injury severity of such crashes. The findings in this study can greatly help traffic agencies and truck companies develop better large truck-involved rear-end crash prevention strategies.