Analysis of crash severity using hierarchical binomial logit model

Abstract

Crash severity is a concern in traffic safety. In purpose of proposing efficient safety strategies to reduce accident severity, the relationship between injury severity and risk factors should be clearly explored. The purpose of this study is to identify the effects of time related factors, road features, and vehicle-driver characteristics on crash severity at signalized intersections by using hierarchical binomial logit model and accident data in Singapore from 2003 to 2007. The results indicate that crashes at night, with high speed limit or at intersection with presence of red light camera vitally increase the severity while wet road surface variables reduce the injury. Vehicle movement variable also significantly affects the crash severity. This study also finds that Honda manufacture is safer than other vehicle makes. With driver characteristics, driver gender and age is also positively associated with crash severity, while Involvement of offending party increase crash severity. Based on the findings, countermeasures are developed to improve road safety.