A comparative study of factors associated with motorcycle crash severities under different causal scenarios
Emmanuel Kofi Adanu, Abhay Lidbe, Jun Liu, Steven Jones
Abstract
This study was carried out to examine the factors associated with motorcycle crash severity in Alabama, under different manner of crash and causal scenarios using mixed logit modeling. Three crash mechanisms were considered in this study: single-vehicle motorcycle crash with motorcyclist at fault, multi-vehicle collision between a motorcycle and another vehicle with motorcyclist being at fault, and motorcyclist not at fault in a collision between a motorcycle and another vehicle. The model estimation results showed that crashes that happened in rural areas were more likely to be severe, irrespective of the causal unit or manner of collision. The results also show that fatigue among motorcyclists was associated with severe injury, whereas driver fatigue was linked to no injury outcome. Further, it was found that risky behaviors such as speeding, driving/riding under the influence of alcohol or drugs, driving/riding with invalid license were significantly associated with severe injury outcome. Developing the injury-severity models based on the segmented crash data has helped to reveal some similarities and differences in crash outcomes based on the crash mechanism and the at-fault road user. It is expected that these findings would provide a data-driven evidence to improve motorcycle safety in the state.