A multivariate probit approach to identifying factors affecting riders’ risky behaviors

Motorcycle crash fatalities have recently become a significant concern for researchers, planners, and safety officials. In 2023, motorcyclists accounted for 24% of all traffic-related fatalities in Iran, underscoring their increased vulnerability due to a lack of protection and the prevalence of high-risk behaviors. Motorcyclists often exhibit a set of interrelated risky behaviors that can trigger crashes and result in the rider being found at fault. This study investigates these interconnected risky behaviors, including riding without a helmet and without a license, and their contribution to crash fault status. Using a crash database from 2021-2022, we developed a multivariate probit model. Our findings indicate a higher likelihood of motorcyclists being at fault during weekends, spring, and increment weather, particularly among young riders under 25, who exhibit less risk aversion. Specific behaviors leading to an increased likelihood of being at fault include traffic rule violations, loss of vehicle control, speeding and overtaking, and unsafe distance. Additionally, motorcyclists were more likely to be at fault in collisions with fixed objects, head-on and rear-end crashes, when no object was involved, and in collisions with bicycles. The study highlights the necessity of training strategies for motorcyclists, especially young riders, to inform them of the effects of risky behaviors on crash risks. It also advocates for stricter enforcement of safety laws. Implementing these measures could significantly reduce fault rates among riders, ultimately decreasing motorcycle crash fatalities.