中文核心期刊
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中国科技核心期刊
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重庆交通大学学报（自然科学版）

重庆交通大学学报（自然科学版） ›› 2026, Vol. 45 ›› Issue (5): 113-122.DOI: 10.3969/j.issn.1674-0696.2026.05.13

• 交通运输+人工智能 • 上一篇    

基于图论和隐马尔科夫模型的山区公路交通风险传播研究

张森1，赵仕林2，赵金3，罗瑛4，秦雅琴2   

  1. (1. 云南省公路科学技术研究院，云南 昆明650051; 2. 昆明理工大学 交通工程学院，云南 昆明 650504; 3. 红河公路局，云南 蒙自 661101；4. 保山公路局， 云南 宝山 678000)
  • 收稿日期:2025-09-24 修回日期:2026-03-10 发布日期:2026-06-08
  • 作者简介:张森（1985—），男，云南昆明人，高级工程师，博士，主要从事道路交通安全方面的研究。E-mail：zhsylshjy@foxmail.com 通信作者：秦雅琴（1972—），女，湖南平江人，教授，博士，主要从事交通安全与仿真方面的研究。E-mail：qinyaqin@kust.edu.cn
  • 基金资助:
    云南省交通运输厅科技创新及示范项目(2023-121)；云南省科学技术厅项目（202501AS070152）；国家自然科学基金项目(71861016)

Traffic Risk Propagation of Mountainous Highway Based on Graph Theory and Hidden Markov Model

ZHANG Sen1, ZHAO Shilin2, ZHAO Jin3, LUO Ying4, QIN Yaqin2   

  1. (1.Yunnan Provincial Highway Science and Technology Research Institute, Kunming 650051, Yunnan, China; 2. School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650504, Yunnan, China; 3. Red River Highway Bureau, Mengzi 661101, Yunnan, China; 4. Baoshan Highway Bureau, Baoshan 678000, Yunnan, China)
  • Received:2025-09-24 Revised:2026-03-10 Published:2026-06-08

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摘要： 针对山区公路交通风险传播的动态性与不确定性特征，笔者提出一种基于图论与隐马尔科夫模型融合的交通风险传播建模方法，构建了山区公路交通风险动态演化理论分析框架。具体而言，首先，通过计算交通风险指标序列数据的变化率，并采用60 min时间窗对序列进行分段并分析指标相关性；进而基于最小生成树（minimum spanning tree, MST）图理论，将指标相关性矩阵映射为无向图结构，以风险指标作为图顶点、风险传播路径作为图边，通过引入相关距离度量方法量化边权值，系统揭示了交通风险网络的核心指标识别及其时变演化规律；最后，结合马尔科夫链的无后效性特征，将风险状态划分为高、中、低三级风险等级，构建了马尔科夫状态转移概率模型，实现了对风险状态转移的定量估计与动态预测。实证结果表明：该方法能够有效解析交通风险状态的变化规律，对观测变量的预测平均绝对百分比误差（mean absolute percentage error, MAPE）为5%，验证了模型的有效性。研究结果可为山区公路交通风险的动态管控提供理论依据与决策支持。

关键词: 交通工程；山区公路；交通风险；图论；隐马尔科夫模型

Abstract: In response to the dynamic and uncertain characteristics of traffic risk propagation of mountain highways, a traffic risk propagation modeling method based on the integration of graph theory and hidden Markov model (HMM) was proposed, and a theoretical analysis framework for the dynamic evolution of traffic risks on mountainous highway was conducted. Specifically, by firstly calculating the change rate of traffic risk index sequence data and then using a 60-minute time window, the sequence was segmented, and the correlation of the indicators was analyzed. Subsequently, based on minimum spanning tree (MST) graph theory, the indicator correlation matrix was mapped into an undirected graph structure, where risk indicators served as graph vertices and risk propagation paths served as graph edges. By introducing correlation distance measurement methods to quantify edge weights, the core indicator identification method and the time-varying evolution patterns of the traffic risk network were systematically revealed. Finally, combining the characteristic of no aftereffects of Markov chains, the risk states were categorized into three levels, including high, medium and low. A Markov state transition probability model was constructed to achieve quantitative estimation and dynamic prediction of risk state transition. Empirical results demonstrate that the proposed method can effectively analyze the changing patterns of traffic risk states, with a mean absolute percentage error (MAPE) of 5% for the prediction of observed variables, verifying the effectiveness of the proposed model. The research results provide theoretical basis and decision-making support for the dynamic management and control of traffic risks on mountainous highway.

Key words: traffic engineering; mountainous highway; traffic risk; graph theory; hidden Markov model

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