Bayesian Network for Motorcycle Crash Severity Analysis
Subasish Das, Valerie Vierkant, Juan Cruz González, Boniphace Kutela, Abbas Sheykhfard
Abstract
Given the lack of protective structural barriers and advanced restraints, motorcyclists are vulnerable road users. In 2020 in the United States, motorcycle-involved fatalities occurred 28 times more frequently per vehicle mile traveled than passenger car occupant fatalities, causing 5,579 motorcycle-related fatalities—the highest number of motorcyclists killed since 1975. By identifying patterns and relationships between key contributing factors, strategies for reducing motorcycle crashes can be developed. In addition to current efforts, additional research must be conducted using innovative avenues, with increased funding. Bayesian networks can better discover the relationships between potential speed compliance variables. This study used six years (2014 to 2019) of motorcycle crash data in Louisiana to determine the conditional probabilities of the influential factors. In addition to the high contribution of alcohol involvement, two-way undivided roadways, 35 to 44 year-old drivers involved in improper driving behaviors, and crash types are the underlying factors associated with a considerable increase in motorcycle crash severity. The findings of this study can also be used for decision making and strategy development for motorcycle safety.