论文标题
pdlight：带压力和动态光持续时间
PDLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Pressure and Dynamic Light Duration
论文作者
论文摘要
城市交叉点上现有的无效和不灵活的交通灯控制通常会导致交通流量充血，并引起许多问题，例如长期延迟和浪费能量。如何找到最佳信号正时策略是城市交通管理中的重大挑战。在本文中，我们提出了PDLIGHT，深入加固学习（DRL）交通灯控制算法，并以新颖的奖励为PRCOL（剩余的载有车道的压力）。 PRCOL可作为对交通控制算法中使用的压力的改善，不仅考虑了即将到来的车道上的车辆数量，而且还考虑了即将离开车道的剩余能力。使用合成和现实世界数据集的仿真结果表明，在固定和动态的绿色光持续时间内，与几种最先进的算法，Presslight和Colight相比，所提出的PDLIGHT会产生较低的平均旅行时间。
Existing ineffective and inflexible traffic light control at urban intersections can often lead to congestion in traffic flows and cause numerous problems, such as long delay and waste of energy. How to find the optimal signal timing strategy is a significant challenge in urban traffic management. In this paper, we propose PDlight, a deep reinforcement learning (DRL) traffic light control algorithm with a novel reward as PRCOL (Pressure with Remaining Capacity of Outgoing Lane). Serving as an improvement over the pressure used in traffic control algorithms, PRCOL considers not only the number of vehicles on the incoming lane but also the remaining capacity of the outgoing lane. Simulation results using both synthetic and real-world data-sets show that the proposed PDlight yields lower average travel time compared with several state-of-the-art algorithms, PressLight and Colight, under both fixed and dynamic green light duration.
