The National Center for Statistics and Analysis (NCSA) and the National Highway Traffic Safety Administration (NHTSA) suggest that head-on crashes are disproportionately represented in fatal crashes on two-lane highways, which constitutes a substantial proportion of the highway network in the US. This paper focuses on analyzing the correlation between head-on crash severity and the potential causal factors, such as the geometric characteristics of the road segment, weather conditions, road surface conditions, and time of occurrence. Ordered Probit models were estimated for datasets describing two-lane roads in Connecticut. It was found that a wet roadway surface and narrow road segments are significantly correlated with more severe head-on crashes. A high density of access points and a nighttime occurrence for the crash are significantly correlated with more severe cases. Pavement width is found to be the most consistent factor, possibly because a wider road offers more space to avoid a direct head-on impact, thus reducing the severity of the crash. Also, the vehicle braking performance is important, as suggested by the higher probability of severe head-on crashes on wet surfaces. The analysis results may be used by practitioners to understand the trade-off between geometric design decisions and head-on crash severity. Furthermore, identifying correlated factors will help to better explain the crash phenomenon and in turn can institute safer roadway design standards.
Abstract