交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 90-103.DOI: 10.16097/j.cnki.1009-6744.2026.01.009

• 智能交通系统与信息技术 • 上一篇    下一篇

面向混合交通流的逆向可变车道左转车辆轨迹优化控制

甘佐贤*1，刘雅新1，秦严严2   

  1. 1. 大连海事大学，交通运输工程学院，辽宁大连116026；2.重庆交通大学，交通运输学院，重庆400074
  • 收稿日期:2025-10-17 修回日期:2025-12-17 接受日期:2025-12-23 出版日期:2026-02-25 发布日期:2026-02-15
  • 作者简介:甘佐贤（1989—），男，湖北黄冈人，副教授，博士。
  • 基金资助:
    国家自然科学基金(52302387)。

Trajectory Optimization Control for Left-Turn Vehicles at Reversible Lane Intersections Under Mixed Traffic Flow

GAN Zuoxian*1, LIU Yaxin1, QIN Yanyan2   

  1. 1. College of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China; 2. College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
  • Received:2025-10-17 Revised:2025-12-17 Accepted:2025-12-23 Online:2026-02-25 Published:2026-02-15
  • Supported by:
    National Natural Science Foundation of China (52302387)。

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摘要： 为缓解逆向可变车道交叉口左转流压力导致的通行效率低、运行不稳定及能耗问题，本文考虑不同自动化水平下自动驾驶车辆（Autonomous Vehicle,AV）与网联自动驾驶车辆（Connected and Autonomous Vehicle, CAV）的混行趋势，构建一种可变车道背景下左转车辆的轨迹控制方法。在控制时域内建立以通行效率最大化与能耗最小化为目标的多目标轨迹优化模型，引入AV与CAV在信息感知和协同能力上的差异，实现动态调控车队加减速和变道行为，并采用分支定界算法求解变道信息与行驶轨迹。进一步设计持续型、集中型与均衡型3类典型交通情境，结合不同CAV渗透率进行数值模拟分析。结果表明：轨迹优化控制模型能够有效改善车队的纵向时空分布，降低停车次数与队列波动；在持续型、集中型与均衡型情境下，分别在CAV渗透率为20%、50%与100%时效果最佳，相较于能耗优先与效率优先方法，持续型场景综合指标分别下降约51.5%与38.9%，集中型分别下降16.2%与40.8%，均衡型情境分别下降10.7%与33.2%；同时，轨迹控制模型在车头时距与可变车道长度变化下表现出较强的鲁棒性。

关键词: 智能交通, 轨迹优化, 多目标优化, 逆向可变车道, 自动驾驶车辆, 混合交通流

Abstract: To improve the traffic efficiency, unstable operations, and increased energy consumption caused by left-turn demand at intersections with reversible lanes, this study proposes a trajectory control method for left-turn vehicles under mixed traffic with autonomous vehicles (AVs) and connected autonomous vehicles (CAVs). A multi-objective trajectory optimization model is developed to maximize traffic efficiency and minimize energy consumption. By incorporating the differences between AVs and CAVs in perception accuracy and cooperative capability, the model enables dynamic regulation of vehicle acceleration, deceleration, and lane-changing behaviors. A branch-and-bound algorithm is applied to jointly determine lane-changing strategies and vehicle trajectories. Furthermore, three typical traffic scenarios—continuous, concentrated, and balanced—are designed, and the simulation experiments are conducted under various CAV penetration rates. The results show that the proposed trajectory control model effectively improves the spatiotemporal distribution of vehicle platoons, reducing stop frequency and queue oscillations. The optimal effects occur at CAV penetration rates of 20%, 50%, and 100% under the continuous, concentrated, and balanced scenarios, respectively. In the continuous scenario, the proposed method reduces the comprehensive index by about 51.5% and 38.9% compared with the energy-first and efficiency-first strategies. The corresponding improvements are observed in the concentrated scenario, with reductions of 16.2% and 40.8%, respectively. In the balanced scenario, with reductions of 10.7% and 33.2% of the comprehensive index, respectively. Moreover, the trajectory control model demonstrates strong robustness to variations in headway and variable-lane length.

Key words: intelligent transportation, trajectory optimization, multi-objective optimization, reverse convertible lane, autonomous vehicles, mixed traffic flow

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