Modeling severities of motorcycle crashes using random parameters

One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes. Researchers have studied the relationships between motorcycle crash severity and crash contributing factors. They are crash characteristics, roadway geometric design features, traffic characteristics, socio-demographics and environmental conditions. However, few researchers considered unobserved heterogeneity effects when modeling motorcycle crash injury severities, let alone interaction effects. In this research, motorcycle crashes in Wyoming that occurred from 2008 to 2017 were analyzed. Specifically, the injury severities of single motorcycle crashes and multiple vehicle crashes involving motorcycles were modeled. The response was whether the motorcycle crash incurred an incapacitating injury or fatality or not. The binary logistic regression and mixed binary logistic regression modeling structures were implemented. The mixed models revealed effects that otherwise would have been undisclosed in the binary logistic regression models' results. According to the results of single motorcycle crashes, the majority of motorcycle-animal crashes and of motorcycle-barrier crashes were likely to be severe relative to other single motorcycle crashes. It was also found that horizontal curves increased the risk of severe injuries. Young riders were found to be less at risk of being gravely injured in single motorcycle crashes than older riders as well. Furthermore, riding under the influence and high posted speed limits increased the odds of severe crashes regardless of whether the crashes were single motorcycle crashes or multiple vehicle crashes involving motorcycles. Additionally, the mixed models uncovered interaction effects and unobserved effects pertaining to speed limits.