PDLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Pressure and Dynamic Light Duration
This addresses congestion and delays in urban traffic management, representing an incremental improvement over existing DRL-based traffic control algorithms.
The paper tackles the problem of inefficient traffic light control at urban intersections by proposing PDlight, a deep reinforcement learning algorithm that uses a novel reward function (PRCOL) incorporating both incoming vehicle pressure and outgoing lane capacity. Simulation results show PDlight reduces average travel time compared to state-of-the-art methods like PressLight and Colight under various conditions.
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.