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<p> The global elderly population is projected to double by 2050 due to increasing life expectancy, raising road safety concerns because of age-related physical and cognitive decline. Detailed data on traffic crashes of elderly drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a hybrid analytical framework combining Random Forest and multivariate logit models was used to explore crash characteristics and important influences affecting injury severity among elderly drivers in Shanghai between 2018 and 2022. The results indicate that 31.38%, 28.28%, 13.11%, and 27.23% of accident injuries are caused by human, vehicle, road, and environmental factors. Among the various single factors, risk awareness, vehicle speed, airbag status, road signal conditions, and time of collision significantly affected elderly drivers’ crash injury levels, and their preference for low-cost, older vehicles with outdated safety features further exacerbated these risks. This study comprehensively analyzed elderly drivers’ crash data to reveal injury risks and severity across different traffic situations, suggesting safety improvements that serve as the foundation for developing age-appropriate driver assistance systems and enhancing road traffic safety management to reduce challenges faced by older people worldwide.</p>

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Elderly Drivers’ Traffic Accident Injuries and Contributing Factors: Findings from Real World Data

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Shiyuan Liu,
  • Yubin Qian,
  • Xianguo Qu,
  • Jiejie Xu,
  • Wenhao Hu

摘要

The global elderly population is projected to double by 2050 due to increasing life expectancy, raising road safety concerns because of age-related physical and cognitive decline. Detailed data on traffic crashes of elderly drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a hybrid analytical framework combining Random Forest and multivariate logit models was used to explore crash characteristics and important influences affecting injury severity among elderly drivers in Shanghai between 2018 and 2022. The results indicate that 31.38%, 28.28%, 13.11%, and 27.23% of accident injuries are caused by human, vehicle, road, and environmental factors. Among the various single factors, risk awareness, vehicle speed, airbag status, road signal conditions, and time of collision significantly affected elderly drivers’ crash injury levels, and their preference for low-cost, older vehicles with outdated safety features further exacerbated these risks. This study comprehensively analyzed elderly drivers’ crash data to reveal injury risks and severity across different traffic situations, suggesting safety improvements that serve as the foundation for developing age-appropriate driver assistance systems and enhancing road traffic safety management to reduce challenges faced by older people worldwide.