异质交通流下交叉口信号及车辆轨迹融合控制模型

doi:10.16097/j.cnki.1009-6744.2025.04.010

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (4): 96-103.DOI: 10.16097/j.cnki.1009-6744.2025.04.010

• 智能交通系统与信息技术 • 上一篇下一篇

异质交通流下交叉口信号及车辆轨迹融合控制模型

王海涌^*，张丹，王孟琳，田爱爱

兰州交通大学，电子与信息工程学院，兰州730070

收稿日期:2025-05-06 修回日期:2025-06-17 接受日期:2025-07-07 出版日期:2025-08-25 发布日期:2025-08-25
作者简介:王海涌(1974—)，男，甘肃会宁人，教授，博士。
基金资助:
国家自然科学基金(52062028)。

Integrated Control Model for Intersection Signal and Vehicle Trajectory Under Heterogeneous Traffic Flows

WANG Haiyong^*, ZHANG Dan,WANG Menglin,TIAN Aiai

School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Received:2025-05-06 Revised:2025-06-17 Accepted:2025-07-07 Online:2025-08-25 Published:2025-08-25
Supported by:
National Natural Science Foundation of China (52062028)。

摘要/Abstract

摘要： 在异质交通流背景下，针对交通信号调度与车辆轨迹规划协同问题，本文提出集信号和轨迹于一体的融合控制模型。该模型采用竞争双深度Q网络算法(Dueling Double Deep Q Network,D3QN)，通过深度强化学习技术对交通信号和车辆轨迹进行同步优化，旨在实现交通效率与生态驾驶的双重目标，并基于SUMO(Simulation of Urban Mobility)仿真平台对模型进行全面验证。仿真结果表明：与基准模型相比，单一优化策略虽然能在一定程度上改善交叉口性能，但存在整体效率提升受限的问题；本文提出的融合控制模型结合了宏观交通流与微观车辆行为的优化，使车均延误降低66.99%，燃油消耗减少11.26%，同时CO2等污染物排放量也显著减少。进一步的敏感性分析揭示了系统性能随网联自动驾驶汽车(Connectedand Autonomous Vehicles, CAV)渗透率的变化规律修正：当渗透率达到一定水平后，性能提升幅度逐渐减小，且模型在不同交通流量条件下均展现出稳定的优化效果，这一结果证实了该控制方法在城市交叉口环境中的适应性和鲁棒性。

关键词: 城市交通, 信号控制, 轨迹规划, 交叉口, 异质交通流

Abstract: Under heterogeneous traffic conditions, this study proposes an integrated control model which simultaneously optimizes signals and trajectories to address the coordination problem between traffic signal control and vehicle trajectory planning. The model employs a Dueling Double Deep Q-Network (D3QN) through deep reinforcement learning approach to achieve the dual objectives of improving traffic efficiency and promoting eco-driving. The comprehensive validation of proposed model was conducted using the SUMO simulation platform. The simulation results show that, compared to the baseline model, although single-objective optimization strategies can partially enhance the performance of intersection, there are some limitations in overall efficiency improvement. In contrast, the proposed integrated control model effectively combines the optimization of macroscopic traffic flow with microscopic vehicle behavior adjustment, achieving a 66.99% reduction in average vehicle delay, an 11.26% decrease in fuel consumption, and significant reductions in CO2 and other pollutant emissions. Further sensitivity analyses reveal the performance of system trends under varying CAV penetration rate, indicating that performance gains gradually plateau beyond certain penetration thresholds. Moreover, the model demonstrates stable optimization effects under different traffic demand conditions, confirming its adaptability and robustness for urban intersection environments.

Key words: urban traffic, signal control, trajectory planning, intersection, heterogeneous traffic flow

中图分类号:

U491

王海涌, 张丹, 王孟琳, 田爱爱. 异质交通流下交叉口信号及车辆轨迹融合控制模型[J]. 交通运输系统工程与信息, 2025, 25(4): 96-103.

WANG Haiyong, ZHANG Dan, WANG Menglin, TIAN Aiai. Integrated Control Model for Intersection Signal and Vehicle Trajectory Under Heterogeneous Traffic Flows[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(4): 96-103.

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链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2025.04.010

http://www.tseit.org.cn/CN/Y2025/V25/I4/96

参考文献

[1]吕红星.智能网联新能源汽车:汽车产业变革的驱动力[N].中国经济时报, 2025-03-18(001). [LÜHX. Intelligent connected new energy vehicles: The driving force behind the automotive industry transformation[N]. China Economic Times, 2025-03-18(001).]

[2] SHABAB K R, ALI S M, ZAKI M H. Deep reinforcement learning-based short-term traffic signal optimizing using disaggregated vehicle data[J]. Data Science for Transportation, 2023, 5(2): 13.

[3] HUANG Z, LIU Y, LIANG C, et al. AMM: Adaptive modularized reinforcement model for multi-city traffic signal control[J]. arXiv Preprint arXiv, 2025: 2501.02548.

[4]金志琦,张正华,姜邦宇,等.基于改进D3QN的单点交叉口信号控制研究[J]. 无线电工程,2025, 55(1): 28 35. [JIN Z Q, ZHANG Z H, JIANG B Y, et al. Research on single intersection signal control based on improved D3QN[J]. Radio Engineering, 2025, 55(1): 28-35.]

[5] GUO Q, LI L, BAN X J. Urban traffic signal control with connected and automated vehicles: A survey[J]. Transportation Research Part C: Emerging Technologies, 2019, 101: 313-334.

[6] REN Y, LAN Z, LIU L, et al. EMSIN: Enhanced multistream interaction network for vehicle trajectory prediction[J]. IEEE Transactions on Fuzzy Systems, 2024, 33(1): 54-68.

[7] WU Z, GAO Z, HAO W, et al. An optimal longitudinal control strategy of platoons using improved particle swarm optimization[J]. Journal of Advanced Transportation, 2020, 2020(1): 8822117.

[8] CHENG Y, HU X, CHEN K, et al. Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach[J]. Journal of Intelligent Transportation Systems, 2023, 27(3): 396-410.

[9] FENG L, ZHAO X, CHEN Z, et al. An adaptive coupled control method based on vehicles platooning for intersection controller and vehicle trajectories in mixed traffic[J]. IET Intelligent Transport Systems, 2024, 18(8): 1459-1476.

[10] 孙伟, 张梦雅,马成元,等.新型混合交通交叉口信号与车辆轨迹协同控制方法[J].交通运输系统工程与信息, 2023, 23(1): 97-105. [SUN W, ZHANG M Y, MA C Y, et al. A novel cooperative control method for hybrid traffic intersection signals and vehicle trajectories[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 97-105.]

[11] LADOSZ P, WENG L, KIM M, et al. Exploration in deep reinforcement learning: A survey[J]. Information Fusion, 2022, 85: 1-22.

[12] ZHANG X, HE Z, ZHU Y, et al. Coupled vehicle-signal control based on stackelberg game enabled multi-agent reinforcement learning in mixed traffic environment[J]. Physica A: Statistical Mechanics and its Applications, 2025, 658: 130289.

[13] ZHU M, WANG Y, PU Z, et al. Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving[J]. Transportation Research Part C: Emerging Technologies, 2020, 117: 102662.

[14] LI J, FOTOUHI A, PAN W, et al. Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties[J]. Energy, 2023, 279: 128139.

[15] 刘春禹, 刘永红,罗霞,等.混合交通流环境下单交叉口自动驾驶车辆轨迹优化[J].交通运输系统工程与信息, 2022, 22(2): 154-162. [LIU C Y, LIU Y H, LUO X, et al. Trajectory optimization of autonomous vehicles at single intersections under mixed traffic flow environment [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 154-162.]

异质交通流下交叉口信号及车辆轨迹融合控制模型

Integrated Control Model for Intersection Signal and Vehicle Trajectory Under Heterogeneous Traffic Flows

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