The platooning management technology for Connected Automated Vehicles (CAVs) can potentially increase the efficiency of the traffic system. However, the randomness in the spatial distribution of CAVs poses a new challenge for CAVs’ platooning strategy in mixed traffic flow. To effectively utilize the advantages of platooning technology, this paper proposes a platoon-aware cooperative lane-changing (PCLC) strategy inspired by platoon formation. This strategy focuses on the formation of CAV platoons in mixed traffic flow and restructures the spatial distribution of CAVs on the road segment. First, the cooperative lane-changing behavior of CAVs is modeled based on vehicle-to-vehicle communication, and a gap suitable for lane-changing is created by controlling the acceleration of cooperative CAVs to complete the lane-changing process smoothly. Meanwhile, considering the maximum platoon size, a decision-making framework of cooperative lane-changing for CAVs is designed. Then, considering the change in spatial distribution of CAVs (i.e., platoon intensity) caused by PCLC, the impact of PCLC on traffic capacity is analyzed from theoretical and numerical verification perspectives. Finally, simulation experiments are conducted to investigate the impact of PCLC on lane-changing indicators, traffic flow stability, and traffic efficiency. The simulation experiments show that compared with the discretionary lane-changing (DLC) operation, the PCLC strategy may reduce the lane-changing rate and raise the average platoon size on road segments, increasing road capacity. In addition, the vehicles on the road segment have modest swings in average speed and acceleration, proving the safety and stability of the proposed strategy. Particularly, scenarios with moderate to heavy traffic flow and fairly balanced CAVs penetration rates favor the effectiveness of this strategy. With 50% CAVs and moderate traffic density, the lane capacity increases by 450 veh/h and improves by 10.135%. These findings support the ability of PCLC strategy to reduce traffic congestion and effectively explore traffic management strategies for mixed traffic flow.

中文翻译：

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混合交通流中互联自动车辆的队列感知协作变道策略

联网自动车辆（CAV）编队管理技术可以潜在地提高交通系统的效率，但CAV空间分布的随机性对CAV在混合交通流中的编队策略提出了新的挑战。为了有效利用队列技术的优势，受队列编队的启发，本文提出了一种队列感知协作变道（PCLC）策略。该策略的重点是在混合交通流中形成 CAV 排，并重组 CAV 在路段上的空间分布。首先，基于车辆间通信对CAV的协同换道行为进行建模，通过控制协同CAV的加速度来创造适合换道的间隙，以平稳地完成换道过程。同时，考虑最大排规模，设计了CAV协同换道决策框架。然后，考虑PCLC引起的CAV空间分布（即编队强度）的变化，从理论和数值验证的角度分析了PCLC对通行能力的影响。最后通过仿真实验研究PCLC对换道指标、交通流稳定性和交通效率的影响。仿真实验表明，与自主换道（DLC）操作相比，PCLC策略可以降低换道率并增加路段的平均队列规模，从而增加道路通行能力。此外，该路段上的车辆平均速度和加速度波动较小，证明了该策略的安全性和稳定性。特别是，中等到大交通流量和相当平衡的 CAV 渗透率的场景有利于该策略的有效性。在 CAV 比例为 50% 且交通密度适中的情况下，车道容量增加 450 辆/小时，提高 10,135%。这些发现支持了PCLC策略减少交通拥堵并有效探索混合交通流的交通管理策略的能力。