Exploring the latent factors affecting motorcycle crashes by using latent class clustering analysis and multinomial logit models

This study provides robust empirical evidence that motorcycle crash severity in Tainan is shaped by a combination of rider characteristics, environmental conditions, and underlying latent crash patterns. The findings support the development of targeted, cluster-specific safety measures, including risk-tailored rider education, intersection and lighting improvements, and enhanced speed management strategies. Such interventions can help reduce motorcycle crash incidence and mitigate injury severity in high-risk urban environments.