[1]王玥骄,卢霄娟,郑诗禹,等.智能网联环境下高速公路事故临近区可变限速控制[J].长安大学学报(自然科学版),2025,45(3):152-162.[doi:10.19721/j.cnki.1671-8879.2025.03.013]
　WANG Yue-jiao,LU Xiao-juan,ZHENG Shi-yu,et al.Variable speed limit control for expressway accident vicinity area in intelligent connected environment[J].Journal of Chang’an University (Natural Science Edition),2025,45(3):152-162.[doi:10.19721/j.cnki.1671-8879.2025.03.013]
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智能网联环境下高速公路事故临近区可变限速控制()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第45卷
期数:
2025年3期
页码:
152-162
栏目:
交通工程
出版日期:
2025-05-31

文章信息/Info

Title:
Variable speed limit control for expressway accident vicinity area in intelligent connected environment
文章编号:
1671-8879(2025)03-0152-11
作者:
王玥骄1; 卢霄娟2; 郑诗禹3; 赵文鼎4; 李维佳5
(1. 同济大学 道路与交通工程教育部重点实验室,上海 201804; 2. 兰州交通大学 交通运输学院,甘肃 兰州 730070; 3. 长安大学 运输工程学院,陕西 西安 710064; 4. 中交第二公路勘察设计研究院有限公司,湖北 武汉 430056; 5.长安大学 建筑学院,陕西 西安 710064)
Author(s):
WANG Yue-jiao1;  LU Xiao-juan2;  ZHENG Shi-yu3;  ZHAO Wen-ding4;  LI Wei-jia5
(1. Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University,Shanghai 201804, China; 2. School of Traffic and Transportation, Lanzhou Jiaotong University,Lanzhou 730070, Gansu, China; 3. School of Transportation Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; 4. CCCC Second Highway Consultants Co., Ltd., Wuhan 430056, Hubei, China; 5. School of Architecture,Chang'an University, Xi'an 710064, Shaanxi, China)
关键词:
交通工程;  可变限速控制;  深度强化学习;  高速公路事故区;  智能网联交通
Keywords:
traffic engineering;  variable speed limit control;  deep reinforcement learning;  expressway accident area;  intelligent connected transportation
分类号:
U491.54
DOI:
10.19721/j.cnki.1671-8879.2025.03.013
文献标志码:
A
摘要:
为提高高速公路事故临近区可变限速(VSL)在网联自动驾驶车辆(CAV)和人工驾驶车辆(HDV)混合交通环境中的控制效果,基于双延迟深度确定性策略梯度(TD3)算法能自动适应复杂环境和CAV可控性的优势,利用智能网联环境实时监测了被控路段的交通流状态,确定了算法的动作空间、状态空间和奖励函数,并提出了基于动态延迟策略更新TD3算法的事故临近区TD3可变限速(TD3-VSL)控制策略; 以G75兰海高速公路某双向四车道区间为研究对象,基于SUMO仿真平台构建了符合实际道路特征的交通流模拟环境,参照实地采集的交通数据标定了仿真参数; 设计了无动态限速控制、反馈限速控制和TD3-VSL控制这3种对比试验方案,以验证不同控制策略的有效性,并对比分析了不同CAV渗透率下的下游交通量和车辆速度标准差。研究结果表明:相较于无动态限速控制和反馈限速控制,TD3-VSL控制策略在拥堵发生后能更有效地提升通行效率,速度标准差均值分别下降了58.4%和75.8%,发布的限速标准差分别降低了28.3%; 随着CAV渗透率的提高,TD3-VSL控制策略的效果进一步改善,交通拥堵情况得到缓解,当CAV渗透率为40%时,车速标准差较纯人工驾驶场景降低了44.11%; CAV渗透率提高至80%时,降幅进一步达到54.46%。研究成果为智能网联环境下高速公路安全管理提供了理论依据,对减少交通事故发生率、优化路网运行效率具有参考价值。
Abstract:
To improve the control effect of variable speed limit(VSL)for expressway accident vicinity area in mixed traffic environment with connected and autonomous vehicle(CAV)and human-driven vehicle(HDV), based on the advantages of the twin delayed deep deterministic policy gradient(TD3)algorithm in automatically adapting to complex environments and the controllability of CAV, the intelligent connected environment was applied to monitor the traffic flow state of the controlled road section in real time, and the action space, state space and reward function of the algorithm were determined. The TD3-variable speed limit(TD3-VSL )control strategy based on the dynamic delay strategy to update the TD3 algorithm was proposed. Taking a two-way four-lane section of G75 Lanzhou-Haikou Expressway as the research object, a traffic flow simulation environment conformed to actual road characteristics was constructed through the SUMO simulation platform. The simulation parameters were calibrated with reference to the traffic data collected in the field. Three comparative experimental schemes such as no dynamic speed limit control, feedback speed limit control and TD3-VSL control were designed to verify the effectiveness of different control strategies. The downstream traffic volumes and vehicle speed standard deviations under different CAV penetration rates were compared and analyzed. The research results show that compared with the no dynamic speed limit control strategy and feedback speed limit control, the TD3-VSL control strategy can more effectively improve the traffic efficiency after the congestion occurs. The mean speed standard deviation reduces by 58.4% and 75.8%, respectively, and the published speed limit standard deviation reduces by 28.3%. With the increase in the CAV penetration rate, the control effect of TD3-VSL further improves, and the traffic congestion alleviates. When the CAV penetration rate is 40%, the vehicle speed standard deviation is 44.11% lower than that of the pure human driving scene. When the CAV penetration rate increases to 80%, the decrease is further up to 54.46%. The research results provide a theoretical basis for expressway safety management in intelligent connected environment, and have reference value for reducing the incidence of traffic accidents and optimizing the operation efficiency of road network.2 tabs, 10 figs, 31 refs.

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备注/Memo

备注/Memo:
收稿日期:2024-11-05
基金项目:甘肃省科技计划项目(22ZD6GA010); 国家自然科学基金项目(52272316); 陕西省自然科学基础研究计划项目(2023-JC-YB-332); 陕西省教育厅服务地方专项计划项目(22JE0104)
作者简介:王玥骄(1999-),女,陕西西安人,工学博士研究生,E-mail:2111233@tongii.edu.cn。
通信作者:李维佳(1992-),女,陕西渭南人,讲师,工学博士,E-mail:weijiali@chd.edu.cn。
更新日期/Last Update: 2025-05-30