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Exploring the latent factors affecting motorcycle crashes by using latent class clustering analysis and multinomial
Chen-Wen Fang1,2, Yang-Kun Ou3, Jia-Jin Jason Chen1
1Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
Traffic Injury Prevention
|February 26, 2026
Summary
Motorcycle crash severity in Tainan is linked to rider age, road conditions, and time of year. Targeted safety measures are needed to reduce injuries in this high-risk urban environment.
Area of Science:
- Traffic Safety
- Injury Epidemiology
- Urban Planning
Background:
- Motorcycle crashes are a major safety concern in Taiwan, especially in Tainan due to high ownership and complex urban environments.
- Understanding crash typologies and injury determinants is crucial for effective safety interventions.
Purpose of the Study:
- To identify latent crash typologies in Tainan.
- To determine key factors influencing motorcycle injury severity across different crash contexts.
Main Methods:
- Analyzed 673 motorcycle crashes from Tainan.
- Utilized Latent Class Clustering (LCC) to define six crash clusters.
- Applied cluster-specific Multinomial Logit (MNL) models to assess injury severity determinants.
Main Results:
- Identified six distinct crash scenarios with variations in rider demographics, lighting, lane configurations, and speed limits.
- Older riders (≥60 years) and crashes on roads with ≤40 km/h speed limits showed higher severe injury risks.
- Winter crashes and specific risk factors demonstrated context-dependent influences on injury severity.
Conclusions:
- Motorcycle crash severity in Tainan is influenced by rider characteristics, environmental factors, and latent crash patterns.
- Findings support developing targeted, cluster-specific safety measures like tailored rider education and improved infrastructure.
- Interventions can reduce crash incidence and mitigate injury severity in high-risk urban settings.
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