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<p>Given the reliance on passive safety control, this study proposes a real-time risk warning model. It integrates the feature selection of the random forest algorithm with the decision optimization of matter-element theory, analyzing 2023 traffic flow data and accident records from California’s PeMS system for I880-N. This identifies correlations among traffic flow factors leading to accidents, revealing patterns in frequent accident zones. An initial indicator system for highway risk states is constructed based on traffic flow, speed, and occupancy rate. Using the random forest method, 13 key warning features are identified and weighted. K-means clustering categorizes risk states into basic levels. Applied to high-accident sections of I880-N, the model aims to improve real-time hazard anticipation and mitigation, shifting from reactive to proactive safety management. The study shows increased variability in occupancy and speed with accidents, and significant traffic flow changes five minutes before accidents. The model’s fit for real-time risk assessment is 0.0056, indicating severe risk (Grade Four). This enhances danger identification, reduces traffic accident rates, and introduces a new paradigm for road risk early warning.</p>

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Advanced Real-time Highway Risk Early Warning System Integrating Comprehensive Weighting Techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月12日

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  • Yun Bai,
  • Weiheng Meng,
  • Yuxuan Gong,
  • Yun Li,
  • Lianyun Gong

摘要

Given the reliance on passive safety control, this study proposes a real-time risk warning model. It integrates the feature selection of the random forest algorithm with the decision optimization of matter-element theory, analyzing 2023 traffic flow data and accident records from California’s PeMS system for I880-N. This identifies correlations among traffic flow factors leading to accidents, revealing patterns in frequent accident zones. An initial indicator system for highway risk states is constructed based on traffic flow, speed, and occupancy rate. Using the random forest method, 13 key warning features are identified and weighted. K-means clustering categorizes risk states into basic levels. Applied to high-accident sections of I880-N, the model aims to improve real-time hazard anticipation and mitigation, shifting from reactive to proactive safety management. The study shows increased variability in occupancy and speed with accidents, and significant traffic flow changes five minutes before accidents. The model’s fit for real-time risk assessment is 0.0056, indicating severe risk (Grade Four). This enhances danger identification, reduces traffic accident rates, and introduces a new paradigm for road risk early warning.