Analysis of motorcyclists crash severity using cluster correspondence and hierarchical binary logit models

Abstract

Crashes involving motorcyclists account for a significant portion of traffic-related injuries and fatalities. Despite motorcycles making only three percent of all registered vehicles, motorcyclists account for 14 percent of all roadway fatalities. As the number of motorcyclists increase, there is an urgent need to understand the factors that affect the severity of injuries they sustain in crashes. In this paper, we use cluster correspondence analysis (CCA) and hierarchical binary logit model to explore the factors associated with motorcyclists’ crash injury severities in Utah between 2016 and 2020. Cluster correspondence analysis was used to cluster the crash data into seven groups, while hierarchical binary logit model was used to identify the significant factors that contributed to the injury severity of motorcycle crashes. The results of this study indicate that among the crash-contributing factors the motorcyclist age, roadway alignment, roadside safety systems and temporal factors significantly contribute to motorcyclist crash severities. The model results further account for the correlation of variables within the clusters in the crash data. With the deeper understanding of the relationship between crash factors and injury severity in this study, the findings can help decision makers to implement targeted countermeasures to improve motorcyclist safety.