An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity

Most previous research on motorcycle accident severity has focused on univariate relationships between severity and an explanatory variable of interest (e.g., helmet use). The potential ambiguity and bias that univariate analyses create in identifying the causality of severity has generated the need for multivariate analyses in which the effects of all factors that influence accident severity are considered. This paper attempts to address this need by presenting a multinomial logit formulation of motorcycle-rider accident severity in single-vehicle collisions. Five levels of severity are considered: (a) property damage only, (b) possible injury, (c) evident injury, (d) disabling injury, and (e) fatality. Using 5-year statewide data on single-vehicle motorcycle accidents from the state of Washington, we estimate a multivariate model of motorcycle-rider severity that considers environmental factors, roadway conditions, vehicle characteristics, and rider attributes. Our findings show that the multinomial logit formulation that we use is a promising approach to evaluate the determinants of motorcycle accident severity.