Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models

Abstract

Roadway safety is an integral part of a functioning infrastructure. A major use of the highway system is the transport of goods. The United States has experienced constant growth in the amount of freight transported by truck in the last few years. Wyoming is experiencing a large increase in truck traffic on its local and county roads due to an increase in oil and gas production. This study explores the involvement of heavy trucks in crashes and their significance as a predictor of crash severity and addresses the effect that large truck traffic is having on the safety of roadways for various road classifications. Studies have been done on the factors involved in and the causation of heavy truck crashes, but none address the causation and effect of roadway classifications on truck crashes. Binary Logit Models (BLM) with Bayesian inferences were utilized to classify heavy truck involvement in severe and non-severe crashes using ten years (2002-2011) of historical crash data in the State of Wyoming. From the final main effects model, various interactions proved to be significant in predicting the severity of crashes and varied depending on the roadway classification. The results indicated the odds of a severe crash increase to 2.3 and 4.5 times when a heavy truck is involved on state and interstate highways respectively. The severity of crashes is significantly increased when road conditions were not clear, icy, and during snowy weather conditions.

Keywords: Bayesian logit model; Heavy truck safety; Inclement weather; Injury severity; Oil and gas.