Signal Priority Control for Trams Using Deep Reinforcement Learning

Current trams-priority signal control systems have many problems, such as low adaptability to real-time traffic changes and high complexity in optimization solutions, etc. In this paper, an active signal priority control model is proposed for the trams based on deep reinforcement learning. Considering the traffic demands from tram and general vehicles, it can reduce the traffic delay of general vehicles while minimizing the need for trams to stop at the intersection. Real-time traffic information is used to dynamically adjust the sequence of traffic signals throughout the whole passing process of the tram, without relying on the complex traffic modeling. We use deep Q-network algorithm for problem-solving, and adopt dueling network, double Q network, and prioritized experience replay to improve the learning performance. Experiments based on SUMO have demonstrated that the proposed model can excellently improve the efficiency of trams and general vehicles simultaneously. Copyright © 2019 Acta Automatica Sinica. All rights reserved.