交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 92-101.DOI: 10.16097/j.cnki.1009-6744.2025.01.010

• 智能交通系统与信息技术 • 上一篇    下一篇

山区双车道公路弯道路段小客车跟驰状态转移预测

覃文文1a,1b，白碧璇1a，韩春阳1a,1b，戢晓峰1a,1b，谷金晶*2，田毕江3   

  1. 1. 昆明理工大学，a.交通工程学院，b.云南省现代物流工程研究中心，昆明650500；2. 云南大学，信息学院，昆明650500；3.云南省交通规划设计研究院股份有限公司，昆明650200
  • 收稿日期:2024-07-30 修回日期:2024-10-09 接受日期:2024-10-16 出版日期:2025-02-25 发布日期:2025-02-21
  • 作者简介:覃文文(1986—)，男，广西柳州人，讲师，博士。
  • 基金资助:
    国家自然科学基金(52102382, 72461015)；云南省“兴滇英才支持计划”青年人才专项(KKXX202402046)。

Car-following State Transition Prediction on Horizontal Curves of Mountainous Two-lane Roads

QIN Wenwen1a,1b, BAI Bixuan1a, HAN Chunyang1a,1b, JI Xiaofeng1a,1b, GU Jinjing*2, TIAN Bijiang3   

  1. 1a. Faculty of Transportation Engineering, 1b. Yunnan Modern Logistics Engineering Research Center, Kunming University of Science and Technology, Kunming 650500, China; 2. School of Information Science and Engineering, Yunnan University, Kunming 650500, China; 3. Yunnan Institute of Transport Planning and Design Co Ltd, Kunming 650200, China
  • Received:2024-07-30 Revised:2024-10-09 Accepted:2024-10-16 Online:2025-02-25 Published:2025-02-21
  • Supported by:
    National Natural Science Foundation of China (52102382, 72461015)；Yunnan XingDian Talents Plan Young Program (KKXX202402046)。

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摘要： 跟驰状态反映车辆间的跟随风险程度，为预测山区双车道公路弯道路段车辆跟驰状态变化路径，本文利用无人机拍摄视频数据，构建基于高阶马尔可夫链的弯道路段小客车跟驰状态转移预测模型。首先，从视频数据中提取跟驰车辆轨迹特征，采用因子分析法提炼表征跟驰状态的公因子特征；其次，利用K-Means++算法对公因子特征进行聚类，将小客车跟驰状态分为强跟驰、弱跟驰和强弱过渡区间这3种状态；最后，引入高阶马尔可夫链模型预测山区双车道公路小客车跟驰状态转移。结果表明：强跟驰和弱跟驰状态的转移存在状态转移的过渡区间，强跟驰时，前导车对跟驰车有较强的制约性，跟驰车辆速度随前导车变化而发生延迟性变化，随着跟驰状态由强转弱，制约性会逐渐降低；七阶马尔可夫链模型对小客车跟驰状态转移预测的准确率高达97.6%以上；3种跟驰状态的自转移概率分别为97.57%、98.90%和96.74%，状态之间的转移方面，强跟驰与弱跟驰直接转移概率较低，过渡区间在转移模式中占有重要地位。本文提出的方法在预测小客车跟驰状态转移时具有优越性能，研究结果可为研发前车碰撞主动安全预警系统提供方法基础。

关键词: 交通工程, 转移预测, 高阶马尔可夫链, 跟驰状态, 山区双车道公路

Abstract: Car-following states reflect the risk level of vehicle following behavior. To predict the car-following state changes on the curve sections of mountainous two-lane roads, this paper uses video data collected by drones to develop a car-following state transition prediction model based on higher-order Markov chains. First, from the preprocessed data, the car-following trajectory characteristics are extracted, and factor analysis is used to refine common factor characteristics that represent car-following states. Then, the K-Means++ algorithm is utilized to cluster the common factor characteristics. The car-following states are categorized into three states: strong car-following state, weak car-following state, and a transitional zone between strong and weak car-following states. A higher-order Markov chain model is then proposed to predict the car-following state transitions on mountainous two-lane roads. The results show that the transition between strong and weak car-following states involves a state transition process. During strong car-following, the leading car behavior significantly constrains the following car state, causing the following car's speed to change with a delay in response to the leading car. As the car-following state transitions from strong to weak, this constraint gradually decreases. The seventh-order Markov chain model achieves a prediction accuracy of over 97.6% for car-following state transitions. The self-transition probabilities for the three car-following states are respectively 97.57%, 98.90%, and 96.74%. In terms of state transitions, the direct transition probability between strong and weak car-following states is low, with the transitional zone playing an important role in the transition pattern. This proposed method demonstrates good performance in predicting car- following state transitions, and the research results can provide a methodological foundation for the development of active safety pre-warning systems for collisions.

Key words: traffic engineering, transition prediction, higher-order Markov chain, car-following state, mountainous two road

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