It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme.

中文翻译：

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互联车辆的多车道时空轨迹优化方法（MSTTOM）

公认的是，互联车辆技术对交通管理系统具有广泛的影响。为了缓解城市拥堵并提高道路通行能力，本文提出了一种多车道时空轨迹优化方法（MSTTOM），以通过考虑车辆安全，交通能力，燃油效率和驾驶员舒适度来充分发挥互联车辆的潜力。在该MSTTOM中，制定了连接车辆的动态特性，车辆状态向量，优化的目标函数和约束条件。基于庞特里亚金的最大原理和强化学习（RL），优化了求解轨迹问题的方法。测量具有单向四车道截面的典型交叉场景，并收集24小时内的数据进行测试。