吉林大学学报(工学版) ›› 2026, Vol. 56 ›› Issue (10): 2606-2616.doi: 10.13229/j.cnki.jdxbgxb.20250374

• 交通运输工程·土木工程 • 上一篇    

网联交叉口信号相位与车辆轨迹联合优化方法

蒋贤才(),马星,郭子豪   

  1. 东北林业大学 土木与交通学院,哈尔滨 150040
  • 收稿日期:2025-04-27 出版日期:2026-10-01 发布日期:2026-10-09
  • 作者简介:蒋贤才(1974-),男,教授,博士.研究方向:道路交通控制.E-mail:jxc023@nefu.edu.cn
  • 基金资助:
    黑龙江省自然科学基金项目(PL2024E012)

Joint optimization method of signal phase and vehicle trajectory at connected intersections

Xian-cai JIANG(),Xing MA,Zi-hao GUO   

  1. School of Civil Engineering and Transportation,Northeast Forestry University,Harbin 150040,China
  • Received:2025-04-27 Online:2026-10-01 Published:2026-10-09

摘要:

为改善传统信号控制滞后响应交通流实时波动的状况,提出了一种网联环境下交叉口信号相位与车辆轨迹联合优化方法。通过协同优化车辆行驶轨迹与信号相位配时方案,动态适配交通流波动,达到提升交叉口通行效率的目的。仿真结果表明:相较于无轨迹优化的信号控制策略,该方法可降低6.45%~16.42%的车均延误以及2.36%~8.39%的停车率;在交通流随机波动较大的场景下,本文方法较混合整数线性规划协同优化方法性能更稳定。进一步分析表明,网联车渗透率越高,本文方法的控制效果越显著。

关键词: 智能交通, 信号相位, 车辆轨迹, 交通流波动, 网联车辆

Abstract:

In order to improve the lag response of traditional signal control methods to real-time fluctuations of traffic flow, a joint optimization method of intersection signal phase and vehicle trajectory in a connected environment was proposed. By collaboratively optimizing the vehicle trajectory and signal phase timing scheme, it can dynamically adapt to traffic flow fluctuations and achieve the purpose of improving the traffic efficiency of intersections. The simulation results show that compared with the signal control strategy without trajectory optimization, the proposed method can reduce the average vehicle delay by 6.45%~16.42% and the parking rate by 2.36%~8.39%. In the scenario of large random fluctuation of traffic flow, the performance of the collaborative optimization method is more stable than that of the mixed integer linear programming. Further analysis shows that the higher the penetration rate of connected vehicles, the more significant the control effectiveness of the proposed method.

Key words: intelligent transportation, signal phase, vehicle trajectory, traffic flow fluctuations, connected vehicles

中图分类号: 

  • U491

图1

JOM-SPVT优化示意"

图2

JOM-SPVT相位构建示意"

图3

车辆轨迹优化示意"

图4

简化车辆轨迹优化示意"

图5

JOM-SPVT优化流程"

表1

仿真参数"

参 数取值
道路限速/(m·s-1)16
最低限速/(m·s-1)5
交叉口限速/(m·s-1)11
最大加速度/(m·s-2)4
期望加速度/(m·s-2)3
车辆长度/m4
直行及右转车流最短绿灯时间/s15
直行及右转车流最长绿灯时间/s70
左转车流最短绿灯时间/s12
左转车流最长绿灯时间/s36
基准东西左转相位绿灯时间/s20
基准东西直行及右转相位绿灯时间/s40
基准南北左转相位绿灯时间/s25
基准南北直行及右转相位绿灯时间/s48
连续车流之间的安全距离/m3.6
CAV反应时间/s0.1
CHV反应时间/s1
CAV-CAV和CHV-CAV饱和车头时距/s1
CAV-CHV和CHV-CHV饱和车头时距/s2
CHV平均速度/(m·s-1)10
仿真时长/s3 600

表2

各进口道交通流量 (pcu/h)"

进口道左转直行与右转
CHVCAVCHVCAV
东7582360690
南6691385530
西8876356610
北8599368663

表3

车均延误与停车率"

流量方案进口相位进口相位
1234
低BCM东22.0/0.8122.5/0.82南24.1/0.8323.2/0.80
西22.9/0.8323.7/0.79北24.4/0.8122.7/0.75
JOM-SPVT东18.9/0.7318.3/0.71南20.2/0.7519.4/0.72
西18.4/0.7419.5/0.72北18.8/0.7721.3/0.76
中BCM东30.2/0.8631.7/0.86南31.4/0.8532.6/0.86
西31.2/0.8732.5/0.85北32.1/0.8732.2/0.88
JOM-SPVT东29.3/0.8125.0/0.83南26.0/0.8227.3/0.81
西28.7/0.7926.3/0.81北26.3/0.7826.7/0.77
高BCM东36.4/0.9337.3/0.92南35.7/0.9036.6/0.92
西35.4/0.9432.7/0.92北34.4/0.9133.5/0.93
JOM-SPVT东34.0/0.8931.0/0.92南35.1/0.9030.8/0.88
西33.9/0.9034.2/0.91北32.0/0.8932.8/0.91

图6

轨迹优化对比"

图7

负二项分布下不同方案的延误变化趋势"

图8

东西直行相位非对称系数阈值对车均延误的影响"

图9

CAV渗透率对延误的影响"

图10

CAV渗透率对停车率的影响"

[1] Liu C, Liu Z, Xu Z, et al. A multistep cooperative lane change strategy for connected and autonomous vehicle platoons departing from dedicated lanes[J]. Transportation Research Part C: Emerging Technologies, 2024, 165: 104720.
[2] Shang Y, Zhu F, Jiang R, et al. Trajectory planning at a signalized road section in a mixed traffic environment considering lane-changing of CAVs and stochasticity of HDVs[J]. Transportation Research Part C: Emerging Technologies, 2024, 158: 104441.
[3] Duan L, Wei Y, Dong S, et al. Centralized vehicle trajectory planning on general platoon sorting problem with multi-vehicle lane changing[J]. Transportation research part C: Emerging Technologies, 2023, 154: 104273.
[4] Tajalli M, Niroumand R, Hajbabaie A. Distributed cooperative trajectory and lane changing optimization of connected automated vehicles: freeway segments with lane drop[J]. Transportation Research Part C: Emerging Technologies, 2022, 143: 103761.
[5] Liu Q, Zhang K, Li M, et al. Integrated optimization of traffic signal timings and vehicle trajectories considering mandatory lane-changing at isolated intersections[J]. Transportation Research Part C: Emerging Technologies, 2024, 163: 104614.
[6] Yao S, Zhou Y, Friedrich B, et al. Planning trajectories for connected and automated vehicle platoon on curved roads: a two-dimensional cooperative approach[J]. Transportation Research Part C: Emerging Technologies, 2024, 165: 104718.
[7] 蒲云, 徐银, 刘海旭, 等. 考虑多车影响的智能网联车跟驰模型[J]. 吉林大学学报:工学版, 2024, 54(5): 1285-1292.
Pu Yun, Xu Yin, Liu Hai-xu,et al. An improved car⁃following model for connected and automated vehicles considering impact of multiple vehicles[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1285-1292.
[8] 姚昌宇, 柳祖鹏, 何雅琴, 等. 基于NEMA双环相位与合流相位的感应控制策略[J]. 公路交通科技, 2021, 38(3): 105-112.
Yao Chang-yu, Liu Zu-peng, He Ya-qin, et al. Inductive control strategy based on NEMA double-loop phase and confluence phase[J]. Journal of Highway and Transportation Research and Development, 2021, 38(3): 105-112.
[9] 何烈云, 周妍, 黄少泽, 等. 叠加放行信号控制方式适用条件及效益评价[J]. 重庆交通大学学报: 自然科学版, 2023, 42(1): 113-119.
He Lie-yun, Zhou Yan, Huang Shao-ze, et al. Application conditions and benefit evaluation of superimposed release signal control mode[J]. Journal of Chongqing Jiaotong University(Natural Science Edition), 2023, 42(1): 113-119.
[10] 刘晓佳, 李子木. 设置移位左转车道的不对称交通流信号交叉口设计及优化[J]. 重庆交通大学学报: 自然科学版, 2023, 42(2): 86-92, 143.
Liu Xiao-jia, Li Zi-mu. Design and optimization of asymmetric traffic flow signal intersection with shifted left-turn lane[J]. Journal of Chongqing Jiaotong University(Natural Science Edition), 2023, 42(2): 86-92, 143.
[11] 蒋贤才,于晨. 信号交叉口不对称交通流的优化控制方法[J]. 交通运输系统工程与信息, 2018, 18(6): 48-54.
Jiang Xian-cai, Yu Chen. The optimal control method of asymmetric traffic flow at signalized intersection[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(6): 48-54.
[12] 陆丽萍, 程垦, 褚端峰, 等. 基于竞争循环双Q网络的自适应交通信号控制[J]. 中国公路学报, 2022, 35(8): 267-277.
Lu Li-ping, Cheng Ken, Chu Duan-feng, et al. Adaptive traffic signal control based on dueling recurrent double Q network[J]. China Journal of Highway and Transport, 2022, 35(8): 267-277.
[13] Mo Z, Li W, Fu Y, et al. CVLight: Decentralized learning for adaptive traffic signal control with connected vehicles[J]. Transportation Research Part C: Emerging Technologies, 2022, 141: 103728.
[14] 张玺君,聂生元, 李喆, 等. 基于自注意力机制的深度强化学习交通信号控制[J]. 交通运输系统工程与信息, 2024, 24(2): 96-104.
Zhang Xi-jun, Nie Sheng-yuan, Li Zhe, et al. Traffic signal control based on deep reinforcement learning based on self-attention mechanism[J]. Transportation Systems Engineering and Information, 2024, 24(2): 96-104.
[15] Li W, Zhu T, Feng Y. A cooperative perception based adaptive signal control under early deployment of connected and automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2024, 169: 104860.
[16] Jiang X, Shang Q. A dynamic CAV-dedicated lane allocation method with the joint optimization of signal timing parameters and smooth trajectory in a mixed traffic environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 24(6): 6436-6449.
[17] Panwai S, Dia H. Comparative evaluation of microscopic car-following behaviour[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(3): 314-325.
[18] 蒋贤才,徐慧智. 混合驾驶环境下交叉口空间资源动态控制方法[J]. 交通运输系统工程与信息, 2023, 23(6): 63-73.
Jiang Xian-cai, Xu Hui-zhi. Dynamic control method of intersection space resources in mixed driving environment[J]. Transportation Systems Engineering and Information, 2023, 23(6): 63-73.
[19] Liu M, Zhao J, Hoogendoorn S, et al. A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersections[J]. Transportation Research Part C: Emerging Technologies, 2022, 134: 103459.
[1] 王江锋,贺丹,罗冬宇,李云飞,齐崇楷,闫学东. 基于交互信息融合网络的车辆行驶风险预测模型[J]. 吉林大学学报(工学版), 2026, 56(8): 2177-2190.
[2] 赵红星,刘书英,聂江龙,梁瑞艳,何瑞春. 考虑弯道条件的同质与异质交通流稳定性分析[J]. 吉林大学学报(工学版), 2026, 56(2): 393-406.
[3] 郭洪艳,谢鸿楠,孟庆瑜. 单车智能条件下综合车辆运动状态的轨迹预测方法[J]. 吉林大学学报(工学版), 2026, 56(10): 2535-2545.
[4] 单飞,李辉,孙浩,聂世刚,申忠虎. 基于改进simAM-YOLOv8的路面多病害识别方法[J]. 吉林大学学报(工学版), 2026, 56(1): 219-230.
[5] 吴德华,陈荣峰. 智能网联客货混合交通流特性及集聚换道策略[J]. 吉林大学学报(工学版), 2025, 55(8): 2588-2596.
[6] 侯越,郭劲松,林伟,张迪,武月,张鑫. 分割可跨越车道分界线的多视角视频车速提取方法[J]. 吉林大学学报(工学版), 2025, 55(5): 1692-1704.
[7] 刘照霞,付锐,牛世峰. 基于极值理论与智能网联信息的超车风险评估[J]. 吉林大学学报(工学版), 2025, 55(3): 925-937.
[8] 江晟,王祎笛,谢睿麟,夏淼磊. 基于门控循环网络时空关联的交通事故预测模型[J]. 吉林大学学报(工学版), 2025, 55(3): 954-962.
[9] 李立,鲍宇健,杨文臣,楚庆玲,汪贵平. 路侧多源感知数据集规范化构建方法[J]. 吉林大学学报(工学版), 2025, 55(2): 529-536.
[10] 王喆,范文波,刘昕,杨欢,宋现敏,杨柏婷. 基于改进开普勒优化算法的区域停车分配模型[J]. 吉林大学学报(工学版), 2025, 55(12): 3907-3917.
[11] 程国柱,陈永胜. 智能网联环境下高速公路异质交通流建模及仿真[J]. 吉林大学学报(工学版), 2025, 55(11): 3583-3592.
[12] 杨楠,肖军. 序列二次规划算法下城市智能交通运行节能优化控制[J]. 吉林大学学报(工学版), 2024, 54(8): 2223-2228.
[13] 张辉,温鑫,陈海宇,黄世淳. 基于激光测距传感器的车辆轨迹全息感知方法[J]. 吉林大学学报(工学版), 2024, 54(8): 2378-2384.
[14] 李立,吴晓强,杨文臣,周瑞杰,汪贵平. 基于路侧毫米波雷达的群体车辆目标识别与跟踪[J]. 吉林大学学报(工学版), 2024, 54(7): 2104-2114.
[15] 杨秀建,贾晓寒,张生斌. 考虑汽车队列动态特性的混合交通流特性[J]. 吉林大学学报(工学版), 2024, 54(4): 947-958.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!