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汽车安全与节能学报

汽车安全与节能学报 ›› 2026, Vol. 17 ›› Issue (3): 380-387.DOI: 10.3969/j.issn.1674-8484.2026.03.010

• 智能驾驶与智慧交通 • 上一篇    下一篇

面向车路协同的信号—路径—速度3层级协同优化控制

陈实1(), 钟绍鹏2,3,*(), 吴建军2, 姜棋瀚1, 徐航1, 邱天润1   

  1. 1 大连理工大学 交通运输系， 大连 116024， 中国
    2 大连理工大学 经济管理学院， 大连 116024， 中国
    3 杭州国际城市学研究中心 浙江省城市治理研究中心， 杭州 311121， 中国
  • 收稿日期:2026-01-19 修回日期:2026-03-29 出版日期:2026-06-30 发布日期:2026-07-02
  • 通讯作者: *钟绍鹏，教授。E-mail：szhong@dlut.edu.cn。
  • 作者简介:陈实（2002—），男（汉），四川，硕士研究生。E-mail：chenshi_tse@mail.dlut.edu.cn。
  • 基金资助:
    国家自然科学基金资助项目(52272308);国家自然科学基金资助项目(71971038);山东省重点研发计划项目(2023CXPT005)

Three-level collaborative optimization control of signal-route-speed for vehicle-road cooperation

CHEN Shi1(), ZHONG Shaopeng2,3,*(), WU Jianjun2, JIANG Qihan1, XU Hang1, QIU Tianrun1   

  1. 1 Department of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China
    2 School of Economics and Management, Dalian University of Technology, Dalian 116024, China
    3 International Urbanology Research Center, Center for Urban Governance of Zhejiang, Hangzhou 311121, China
  • Received:2026-01-19 Revised:2026-03-29 Online:2026-06-30 Published:2026-07-02

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摘要：

为建立多交叉口混合交通流环境智能交通系统，提出了“信号—路径—速度”3层级协同优化控制方法。该方法在上层采用模型预测控制（MPC）滚动求解信号配时问题，并引入信赖域Bayes优化（TuRBO）自适应收缩搜索区域以高效逼近全局最优方案；在中层利用K最短路算法（KSP）生成候选路径集合，结合逐次平均法迭代更新路径流量分配，实现动态路径诱导；在下层基于MPC构建车速规划模型，通过TuRBO在线优化网联车辆的速度轨迹，实现微观车速协同控制。用城市交通仿真软件SUMO仿真。结果表明：在不同网联车辆渗透率下均显著降低总车辆行程时间，相比于无协同优化的基准场景，该方法最佳优化率达67.6%，效果优于单层或双层控制模型。从而，为智能交通系统下的协同控制提供了一种解决方案。

关键词: 智能交通系统, 多层级优化, 协同控制, 智能网联车辆, 最优控制策略

Abstract:

A signal-path-speed three-level collaborative optimization control-method was proposed to develop an intelligent transportation system for multi-intersection mixed traffic flow environments. At the upper level, the model predictive control (MPC) was adopted to solve the signal timing optimization problem in a rolling horizon manner, and the trust region bayesian optimization (TuRBO) was introduced to adaptively shrink the search region for efficiently approaching the global optimum. At the middle level, the K-Shortest Path (KSP) algorithm was employed to generate candidate path sets; and the method of successive averages was combined to iteratively update path flow assignment for dynamic route guidance. At the lower level, a speed planning model was constructed based on the MPC; and the TuRBO was utilized to optimize the speed trajectories of connected vehicles online, achieving microscopic speed collaborative control. The SUMO (simulation of urban mobility) was used to simulate. The results show that compared to the baseline scenario without collaborative optimization, the proposed method significantly reduces the total vehicle travel time under various connected vehicle penetration rates, with the best optimization rate reaching 67.6%, outperforming single-level or two-level control models. Therefore, this method provides a solution for collaborative control in intelligent transportation systems.

Key words: intelligent transportation systems, multi-level optimization, collaborative control, intelligent connected vehicles, optimal control strategies

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