Exploring the motorcycle crash risks and riders' risk profiles: Evidence from the motorcycle crash causation study
Year: 2026
Type: article
Abstract: Motorcyclists are among the most vulnerable road users in the United States, facing disproportionately high crash and fatality rates, while many states prioritize motorcycle safety through the Strategic Highway Safety Plan. Despite this alarming trend, crash reports often lack critical insights into rider behavior and contributing risk factors. National crash data highlights an alarming trend where motorcycle crashes and fatality rates significantly exceed those of passenger cars, with sharp increases in recent years. However, existing crash reports often lack critical details about motorcycle operator behavior and risk factors, limiting efforts to develop effective safety interventions. This study aims to bridge this gap by analyzing the Federal Highway Administration's Motorcycle Crash Causation Study dataset, incorporating both crash-involved motorcyclists and paired control groups who were not in the crashes. By applying a random parameter logit model to estimate crash likelihood and a random parameter Weibull model to assess hazard duration until crash occurrence, this research identifies key contributing factors. Findings reveal that rider age, annual mileage, prior crash experience, passenger presence, travel speed, licensing status, motorcycle maintenance practices, and riding tasks play pivotal roles in influencing motorcycle crash risks. These insights underscore the urgent need for targeted motorcycle rider training, policy enhancements, and proactive safety interventions. By collaborating with state and local stakeholders, decision-makers can implement strategies that reduce motorcycle-related crashes, ultimately improving roadway safety for all users.
Source: IATSS Research
Authors: Mouyid Islam, Xiaobing Li
Institutions: Michigan Department of Transportation, University of Kentucky
Language: English
Topic: Traffic and Road Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7125612054