This study proposes a novel distributed predictive cruise control (PCC) algorithm based on reinforcement learning. The algorithm aims at reducing idle time and maintaining an adjustable speed depending on the traffic signals. The effectiveness of the proposed approach has been validated through Paramics microscopic traffic simulations by proposing a scenario in Statesboro, Georgia. For different traffic demands, the travel time and fuel consumption rate of vehicles are compared between non-PCC and PCC algorithms. Microscopic traffic simulation results demonstrate that the proposed PCC algorithm will reduce the fuel consumption rate by 4.24% and decrease the average travel time by 3.78%.

中文翻译：

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基于强化学习和微观交通仿真验证的分布式预测巡航控制

这项研究提出了一种新的基于强化学习的分布式预测巡航控制（PCC）算法。该算法旨在减少空闲时间并根据交通信号维持可调节的速度。拟议方法的有效性已通过Paramics微观交通仿真通过在佐治亚州Statesboro提出的方案进行了验证。针对不同的交通需求，在非PCC和PCC算法之间比较了车辆的行驶时间和燃油消耗率。微观交通仿真结果表明，提出的PCC算法将使燃油消耗率降低4.24％，平均旅行时间减少3.78％。