车联网公交系统动态时空优先控制研究

doi:10.16097/j.cnki.1009-6744.2024.05.006

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (5): 56-64.DOI: 10.16097/j.cnki.1009-6744.2024.05.006

• 智能交通系统与信息技术 • 上一篇下一篇

车联网公交系统动态时空优先控制研究

李哲¹，苟杨扬¹，李震尧¹，李敖²，岑威³，高建平^*1

1. 河南科技大学，车辆与交通工程学院，河南洛阳 471000；2. 河南中烟工业有限责任公司，漯河卷烟厂，河南漯河 462000；3. 宇通客车股份有限公司，郑州 450000

收稿日期:2024-06-20 修回日期:2024-08-01 接受日期:2024-08-13 出版日期:2024-10-25 发布日期:2024-10-22
作者简介:李哲(1992- )，女，河南安阳人，讲师。
基金资助:
郑州市重大科技专项(2021KJZX0060)。

Dynamic Spatiotemporal Priority Control of Connected Vehicles Public Transport System

LI Zhe¹, GOU Yangyang¹, LI Zhenyao¹, LI Ao², CEN Wei³, GAO Jianping^*1

1. College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471000, Henan, China; 2. Luohe Cigarette Factory, Henan Zhongyan Industry Co Ltd, Luohe 462000, Henan, China; 3. Yutong Bus Co Ltd, Zhengzhou 450000, China

Received:2024-06-20 Revised:2024-08-01 Accepted:2024-08-13 Online:2024-10-25 Published:2024-10-22
Supported by:
Zhengzhou Major Science and Technology Project (2021KJZX0060)。

摘要/Abstract

摘要： 为提升公交专用道全时段利用效率，减轻连续公交专用道交叉口节点的车辆延误，本文在分析适用交通流量条件的基础上，从空间和时间两个维度出发，探索车联网环境下公交系统动态时空优先协同控制方法。在空间维度，设置间歇式公交专用进口道，并对清空距离等4个动态区间制定车辆运行控制策略。在时间维度，基于深度强化学习通过绿灯延长和红灯早断的方法动态调整信号配时。使用SUMO和Python构建仿真验证平台，设计原方案、空间优先、时间优先、时空协同优先这4种控制策略和3种饱和度情景的对比仿真实验。结果表明，在饱和度分别为0.2、0.5 和 0.8 时，时空协同优先方案相较于原方案平均延误分别降低了 40.96%、39.93%和 28.20%。低饱和度下，空间优先效果明显；中饱和度下，时间优先效果明显。设置间歇式公交专用进口道可能导致公交自车延误稍有增加，但是整个交叉口的平均延误显著降低。本文提出的车联网公交系统动态时空优先控制方法能够保证公交优先的同时，有效提高交叉口通行效率。

关键词: 智能交通, 动态时空优先, 深度强化学习, 间歇式公交专用进口道, 车联网, 清空距离

Abstract: To improve the utilization efficiency of bus lanes and reduce vehicle delays at intersections of continuous bus lanes, this paper investigates dynamic spatiotemporal priority control of connected public transport systems from spatial and temporal dimensions and analyzes the applicable traffic flow conditions. In the spatial dimension, intermittent bus entrance lanes are introduced and vehicle operation control strategies are formulated for four dynamic intervals, including clearance distance. In the temporal dimension, based on deep reinforcement learning, signal timing is dynamically adjusted through time extension of the green light and time interruption of the red light. A simulation verification platform is constructed using SUMO and Python, and comparative simulation experiments and three saturation scenarios are designed for four control schemes concluding the original scheme, spatial priority scheme, temporal scheme, and spatiotemporal collaborative priority scheme. The results show that at saturation levels of 0.2, 0.5, and 0.8, the spatiotemporal collaborative priority scheme reduces the average delay compared to the original scheme by respectively 40.96% , 39.93% , and 28.20% . At low saturation, the spatial priority effect is obvious; at medium saturation, the temporal effect is obvious. Using intermittent bus entrance lanes may lead to a slight increase in bus delays, but the average delay at the entire intersection is significantly reduced. The proposed dynamic spatiotemporal priority control method for connected vehicle bus systems can effectively improve intersection traffic efficiency while ensuring bus priority.

Key words: intelligent transportation, dynamic spatiotemporal priority, deep reinforcement learning, intermittent bus entrance lane, connected vehicle, clearance distance

中图分类号:

U491

李哲, 苟杨扬, 李震尧, 李敖, 岑威, 高建平. 车联网公交系统动态时空优先控制研究[J]. 交通运输系统工程与信息, 2024, 24(5): 56-64.

LI Zhe, GOU Yangyang, LI Zhenyao, LI Ao, CEN Wei, GAO Jianping. Dynamic Spatiotemporal Priority Control of Connected Vehicles Public Transport System[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(5): 56-64.

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http://www.tseit.org.cn/CN/Y2024/V24/I5/56

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车联网公交系统动态时空优先控制研究

Dynamic Spatiotemporal Priority Control of Connected Vehicles Public Transport System

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