Traffic injury prevention · 2026
Exploring the latent factors affecting motorcycle crashes by using latent class clustering analysis and multinomial logit models.
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Fang Chen-Wen, Ou Yang-Kun, Chen Jia-Jin Jason, Chen Wei-Ru
Аннотация
OBJECTIVE: Motorcycle crashes constitute a critical safety issue in Taiwan, particularly in Tainan, a city characterized by exceptionally high motorcycle ownership, dense traffic environments, and complex street network designs. This study aimed to uncover latent crash typologies and identify key determinants of motorcycle injury severity across diverse crash contexts.
METHODS: A total of 673 motorcycle-involved crashes from the Tainan City Traffic Accident Investigation Committee were analyzed. Latent Class Clustering (LCC) was used to classify crashes into six heterogeneous clusters based on rider demographics, roadway characteristics, environmental conditions, and crash configurations. Subsequently, cluster-specific Multinomial Logit (MNL) models were estimated to assess how these factors influence the likelihood of mild, moderate, or severe injury outcomes.
RESULTS: Six distinct crash scenarios were identified, reflecting meaningful heterogeneity across rider age, lighting conditions, lane configurations, and roadway speed limits. Across clusters, older riders (≥60 years) had a substantially higher probability of sustaining severe injuries, while crashes occurring on roadways with ≤40 km/h speed limits showed unexpectedly elevated severity risks, likely attributable to narrow lanes and high conflict density. Seasonal effects were also observed, with winter crashes demonstrating significantly higher odds of severe injury. Notably, several risk factors exerted different magnitudes and directions of influence across clusters, highlighting strong context dependency in motorcycle crash severity mechanisms.
CONCLUSIONS: 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.
METHODS: A total of 673 motorcycle-involved crashes from the Tainan City Traffic Accident Investigation Committee were analyzed. Latent Class Clustering (LCC) was used to classify crashes into six heterogeneous clusters based on rider demographics, roadway characteristics, environmental conditions, and crash configurations. Subsequently, cluster-specific Multinomial Logit (MNL) models were estimated to assess how these factors influence the likelihood of mild, moderate, or severe injury outcomes.
RESULTS: Six distinct crash scenarios were identified, reflecting meaningful heterogeneity across rider age, lighting conditions, lane configurations, and roadway speed limits. Across clusters, older riders (≥60 years) had a substantially higher probability of sustaining severe injuries, while crashes occurring on roadways with ≤40 km/h speed limits showed unexpectedly elevated severity risks, likely attributable to narrow lanes and high conflict density. Seasonal effects were also observed, with winter crashes demonstrating significantly higher odds of severe injury. Notably, several risk factors exerted different magnitudes and directions of influence across clusters, highlighting strong context dependency in motorcycle crash severity mechanisms.
CONCLUSIONS: 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.