Analysis of the Spatiotemporal Effects on the Severity of Motorcycle Accidents Without Helmets and Strategies for Building Sustainable Traffic Safety

Jialin Miao, Yiyong Pan () and Kailong Zhao
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Jialin Miao: School of Automotive and Transportation Engineering, Nanjing Forestry University, Nanjing 210037, China
Yiyong Pan: School of Automotive and Transportation Engineering, Nanjing Forestry University, Nanjing 210037, China
Kailong Zhao: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China

Sustainability, 2025, vol. 17, issue 8, 1-22

Abstract: This study analyzes factors influencing injury severity in motorcycle accidents involving non-helmeted riders using Bayesian spatiotemporal logistic models. Five models were developed, four of which incorporated different spatiotemporal configurations, including spatial, temporal, and spatiotemporal interaction error terms. The results indicate that the optimal model integrated Leroux CAR spatial priors, temporal random walks, and interaction terms, achieving 86.74% classification accuracy, with a 3% reduction in the DIC value; obtaining the lowest numerical fit demonstrating spatiotemporal interactions is critical for capturing complex risk patterns (e.g., rain amplifying nighttime collision severity). The results highlight rain (OR = 1.53), age ≥ 50 (OR = 1.90), and bi-directional roads (OR = 1.82) as critical risk factors. Based on these findings, several sustainable traffic safety strategies are proposed. Short-term measures include IoT-based dynamic speed control on high-risk roads and app-enforced helmet checks via ride-hailing platforms. Long-term strategies integrate age-specific behavioral training focusing on hazard perception and reaction time improvement, which reduced elderly fatalities by 18% in Japan’s “Silver Rider” program by directly modifying high-risk riding habits (non-helmets). These solutions, validated by global case studies, demonstrate that helmet use could mitigate over 60% of severe head injuries in these high-risk scenarios, promoting sustainable traffic governance through spatiotemporal risk targeting and helmet enforcement.

Keywords: sustainable traffic safety; accident severity analysis; motorcycle accidents; spatiotemporal combined effects; Bayesian inference (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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