Analysis of Motorcycle Crashes in Texas with Multinomial Logit Model

Abstract

Motorcyclists accounted for 15% of all traffic-related deaths in Texas in 2008. This proportion increased threefold during the past decade. Knowledge of the associated causes of motorcycle crashes and the factors that contributed to the severity of injuries to motorcyclists involved in crashes is useful in suggesting approaches for reducing their frequency and severity. In this study, crash data from police-reported motorcycle crashes in Texas were used to estimate multinomial logit models to identify differences in factors likely to affect the severity of crash injuries of motorcyclists. In addition, probabilistic models of the injury severity of motorcyclists in urban and rural crashes were estimated. Average direct and cross-pseudoelasticity results supported the development of probabilistic models for identifying factors that significantly influenced injury severity in urban and rural motorcycle crashes. Key findings showed that alcohol, gender, lighting, and presence of both horizontal and vertical curves played significant roles in injury outcomes of motorcyclist crashes in urban areas. Similar factors were found to have significantly affected the injury severity of motorcyclists in rural areas, but older riders (older than 55), single-vehicle crashes, angular crashes, and divided highways also affected injury severity outcomes in rural motorcycle crashes. From the study findings, recommendations to reduce the severity of motorcyclists’ crash injuries are presented.