This study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.

中文翻译：

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混网联自动驾驶汽车环境下孤立信号路口协同控制框架


本研究提出了一种在互联和自动驾驶汽车以及互联人类驾驶车辆的混合交通环境下的协同控制框架，该框架可以同时优化孤立路口的信号计时、车道设置和车辆轨迹。首先，考虑到交通需求和不兼容信号的动态，我们分析了每条车道的车辆延误。基于时延分析，建立了车道设置和信号时序的时空资源协同优化模型，以最小化平均时延。随后，在缓冲区中，基于图论的排序和列队模型清晰简洁地表示了车辆从初始状态到目标状态的转换过程，从而实现了列队编队。此外，通过超车区的最优控制模型和跟车模型，将轨迹优化集成到协同控制框架中。仿真实验和敏感性分析证明了所提出的框架在减少平均车辆延误、改善燃料消耗和应对十字路口不断变化的交通需求方面的有效性。