Connected automated vehicle trajectory optimization along signalized arterial: A decentralized approach under mixed traffic environment
Connected automated vehicle trajectory optimization along signalized arterial: A decentralized approach under mixed traffic environment
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DOI：
10.1016/j.trc.2022.103918
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Qinzheng Wang;Yaobang Gong;X. Yang
中科院分区：
文献类型：
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作者：
Qinzheng Wang;Yaobang Gong;X. Yang
轨迹优化作为一项关键的联网自动车辆（CAV）运行任务，具有缓解交通拥堵、降低能源消耗、提高交通运行效率的潜力。本研究提出了一种分散式方法，用于在人车 (HV) 和 CAV 共存的混合交通环境下沿着信号干线在纵向和横向维度上优化 CAV 轨迹。更具体地说，开发了一个两阶段模型，根据下游交叉口的交通信号计划和周围车辆的轨迹信息来优化 CAV 轨迹。第一阶段的制定是为了粗略估计单个 CAV 沿着这条干线行驶并最少停靠所需的最短行驶时间。然后，第二阶段模型旨在优化 CAV 的纵向和横向行为，以最大限度地减少延误和换道成本。该模型采用动态规划算法进行求解，以满足实时优化的需要。滚动地平线方法适用于根据不断变化的交通状况动态实施所提出的模型。在现实世界的动脉上进行了数值实验，以评估模型的性能。通过将优化轨迹与无优化基准进行比较，所提出的模型可以减少 CAV 的平均停止延迟。此外，还可以减少高压车辆和混合交通的停车延误。
Trajectory optimization, as a key connected automated vehicles (CAVs) operation task, has the potential to mitigate traffic congestion, lower energy consumption, and increase the efficiency of traffic operation. This study proposes a decentralized approach to optimization CAV trajectories in both longitudinal and lateral dimensions along a signalized arterial under the mixed traffic environment, where human vehicles (HVs) and CAVs co-exist. More specifically, a 2-stage model is developed to optimize CAV trajectories based on traffic signal plans of downstream intersections and trajectory information of surrounding vehicles. The stage-1 is formulated to provide a rough estimate of the minimal travel time required for a single CAV traveling along this arterial with minimum stops. The stage-2 model is then designed to optimize the longitudinal and lateral behavior of CAVs with the objective of minimizing delay and lane-changing costs. This model is solved by a dynamic programming algorithm to satisfy the real-time optimization needs. A rolling horizon approach is adapted to dynamically implement the proposed model in light of changing traffic conditions. Numerical experiments have been conducted on a real-world arterial to evaluate the model performances. By comparing the optimized trajectories to the no optimization benchmark, the proposed model can reduce average stop delays of CAVs. Moreover, it can also reduce the stop delays of HVs and mixed traffic.