交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 56-66.DOI: 10.16097/j.cnki.1009-6744.2025.01.006

• 智能交通系统与信息技术 • 上一篇    下一篇

城市道路智能网联汽车专用车道设置研究

王连震，马志飞，程国柱*，郑夫水   

  1. 东北林业大学，土木与交通学院，哈尔滨150040
  • 收稿日期:2024-07-30 修回日期:2024-09-17 接受日期:2024-09-23 出版日期:2025-02-25 发布日期:2025-02-21
  • 作者简介:王连震(1985—)，男，山东禹城人，副教授，博士。
  • 基金资助:
    中央高校基本科研业务费专项资金(2572023CT21-03)；长安大学中央高校基本科研业务费专项资金(300102344501-1-1)。

Urban Road Dedicated Lane for Connected and Automated Vehicles

WANG Lianzhen, MA Zhifei, CHENG Guozhu*, ZHENG Fushui   

  1. School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China
  • Received:2024-07-30 Revised:2024-09-17 Accepted:2024-09-23 Online:2025-02-25 Published:2025-02-21
  • Supported by:
    Fundamental Research Funds for the Central Universities of Ministry of Education of China (2572023CT21-03)；The Fundamental Research Funds for theCentral Universities, CHD (300102344501-1-1)。

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摘要： 为科学有效地设置城市道路智能网联汽车专用车道，减少智能网联汽车与人工驾驶汽车的路权冲突，提高安全和效率，本文建立城市道路智能网联汽车专用车道双层规划模型。上层模型以总出行时间最少和交通事故率最低为优化目标，将是否设置智能网联汽车专用车道作为决策变量，采用具有增强精英保留算子的遗传算法(Strengthen Elitist GeneticAlgorithm, SEGA)进行求解。下层模型基于用户均衡原则进行交通流分配，采用嵌入黄金分割法的Frank-Wolfe算法求解。使用Nguyen-Dupuis网络作为案例研究，验证模型及SEGA算法的有效性，并分析不同渗透率、通行能力和OD总量对总出行时间和交通事故率的影响。算例表明：渗透率为10%和80%时，设置城市道路智能网联汽车(ConnectedandAutomatedVehicles,CAV)专用车道均会导致总出行时间升高；渗透率为40%时，可以实现总出行时间降低2.68%。渗透率低于20%时，事故率会不同程度增加；渗透率为40%时，事故率降低14.30%；渗透率为80%时，事故率降低5.77%。随着OD总量的增加，总出行时间降低幅度在0~6%之间，变化趋势不明显。低交通需求(14400 veh·h-1)时，事故率降低13.45%；中交通需求(21600 veh·h-1)时，事故率降低3.16%；高交通需求(31200 veh·h-1)时，事故率升高10.35%。设置城市道路CAV专用车道的最优条件为中低交通需求水平下，渗透率为40%时。研究成果可为制定城市道路CAV专用车道设置方案提供理论依据。

关键词: 城市交通, 专用车道, 双层规划, 设置优化, 智能网联汽车

Abstract: In order to scientifically and effectively set up dedicated lanes for connected and automated vehicles (CAV), reduce conflicts between human-driven vehicles and CAV, and improve the safety and efficiency, this paper proposes a bi-level optimization model for urban road dedicated lanes for the CAV. The upper model has two optimization objectives, which aim to reduce the total travel time cost and reduce the traffic accident rate. The model also considers whether to set up a dedicated lane for CAV as a decision variable and uses the Strengthened Elitist Genetic Algorithm (SEGA) to solve the problem. The distribution of traffic flow in the lower model follows the user equilibrium principle. The lower model is solved using the Frank-Wolfe algorithm integrated with the golden section search. The Nguyen-Dupuis network serves as a case study to evaluate the model's effectiveness and the SEGA algorithm, and the effects of different penetration rates, capacity and total origin-destination (OD) demand on total travel time and traffic accident rate are analyzed. The results of the example show that the establishment of dedicated lanes for CAV will lead to an increase of total travel time regardless of the penetration rate of 10% or 80%. The total travel time decreases by 2.68% when the permeability is 40%. The accident rate would increase to different extents, when the penetration rate is below 20%. When the penetration rate is 40%, the accident rate decreases by 14.30%, and when the penetration rate is 80%, the accident rate decreases by 5.77%. With the increase of total OD demand, the total travel time would decrease by 0 to 6%, and the trend is insignificant. The accident rate reduces by 13.45% at low traffic demand (i.e., 14400 vehicles per hour), and reduces by 3.16% at medium traffic demand (i.e., 21600 vehicles per hour), and reduces by 10.35% at high traffic demand (i.e., 31200 vehicles per hour). The most suitable condition for establishing the CAV dedicated lanes is when traffic demand is low and the penetration rate is around 40%. The findings of this study can provide a theoretical foundation for establishing urban roads dedicated lanes for CAV.

Key words: urban traffic, dedicated lane, bilevel programming, setting optimization, connected and automated vehicles(CAV)

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