基于改进贝叶斯网络的城市道路交通事故概率建模

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-09-14 DOI:10.1080/19427867.2024.2447167

Minqing Zhu , Peng Shi , Hongjun Cui , Xueqing Li

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引用次数: 0

摘要

城市道路交通事故严重威胁着人类生命财产安全。本研究利用随机森林（Random Forest， RF）算法识别路段事故和交叉口事故的显著危险因素，构建了基于改进贝叶斯网络（IBN）的城市道路交通事故概率预测模型。其次，提出了一种城市道路事故易发点的识别方法。研究结果表明：(1)影响路段和交叉口事故概率的因素存在显著差异。(2)当影响因素的不同组合发生变化时，路段和交叉口的事故概率也会发生变化。最后，基于天津市路段和交叉口数据，确定事故概率阈值分别为10.28%和6.69%，能够准确识别城市道路上的事故易发点。

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Modeling the traffic accident probability of Urban roads based on an improved Bayesian network

Urban road traffic accidents are severely threatening the safety of human life and property. In this study, the Random Forest (RF) algorithm was used to identify the significant risk factors of road section accidents and intersection accidents, and a probability prediction model of urban road traffic accidents based on the improved Bayesian network (IBN) was constructed. Next, a method is proposed to identify the accident-prone points on urban roads. The study results showed that: (1) There are significant differences in the factors influencing the probability of accidents at road sections and intersections. (2) When different combinations of influencing factors change, the probability of accidents at road sections and intersections also changes. Finally, based on the data from road sections and intersections in Tianjin, accident probability thresholds of 10.28% and 6.69% respectively have been determined, which can accurately identify the accident-prone points on urban roads.

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来源期刊

Transportation Letters-The International Journal of Transportation Research TRANSPORTATION SCIENCE & TECHNOLOGY-

CiteScore

6.40

自引率

14.30%

发文量

审稿时长

>12 weeks

期刊介绍： Transportation Letters: The International Journal of Transportation Research is a quarterly journal that publishes high-quality peer-reviewed and mini-review papers as well as technical notes and book reviews on the state-of-the-art in transportation research. The focus of Transportation Letters is on analytical and empirical findings, methodological papers, and theoretical and conceptual insights across all areas of research. Review resource papers that merge descriptions of the state-of-the-art with innovative and new methodological, theoretical, and conceptual insights spanning all areas of transportation research are invited and of particular interest.