Probabilistic models of motorcyclists’ injury severities in single- and multi-vehicle crashes

Motorcycle fatalities have more than doubled in the United States since 1997—highlighting the need to better understand the many interrelated factors that determine motorcyclists’ crash-injury severities. In this paper, using a detailed crash database from the state of Indiana, we estimate probabilistic models of motorcyclists’ injury severities in single- and multi-vehicle crashes. Nested logit (estimated with full information maximum likelihood) and standard multinomial logit model results show a wide-range of factors significantly influence injury-severity probabilities. Key findings show that increasing motorcyclist age is associated with more severe injuries and that collision type, roadway characteristics, alcohol consumption, helmet use, unsafe speed and other variables play significant roles in crash-injury outcomes.