Multinomial Logistic Regression Modeling of Motorcycle Crash Severities and Contributing Factors in Wyoming
Abstract
Motorcycle riders and passengers are much more likely to be killed or severely injured in a crash, and on average about 15% of all traffic fatalities include motorcyclists. Between 2008 and 2019, the average motorcycle crash frequency in Wyoming was 286 crashes/year, 17 of those being fatal. This paper assesses injury severity of motorcycle-related crashes in Wyoming using 12 years of motorcycle crash data and applying multinomial logistic regression modeling to determine the odds ratios for injury severity. Four models were developed and analyzed, based on the setting and the number of vehicles involved. The most common factors affecting injury severity include vehicle maneuver, driver action, junction relation, alcohol, animal and speed involvement, and helmet use. The vicinity of intersections significantly increases the odds of injury crashes in urban areas, and in rural areas with multi-vehicle involvement. Certain vehicle maneuvers are also associated with a more severe crash outcome.
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