An Intelligent Train Operation Method Based on Event-Driven Deep Reinforcement Learning

• Published in: IEEE transactions on industrial informatics 2022-10, Vol.18 (10), p.6973-6980
• Main Authors: Zhang, Liqing, U, Leong Hou, Zhou, Mingliang, Li, Zhenning
• Format: Article
• Language: English
• Subjects: Algorithms; Deep deterministic policy gradient (DDPG); Deep learning; deep reinforcement-based train operation (DRTO); Energy consumption; event driven; Heuristic algorithms; Informatics; Machine learning; Optimization; Passenger comfort; Performance evaluation; Q-learning; Rail transportation; Safety
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3138098

Train operation control in urban railways is challenging due to its high dynamics, complex environment, and level of comfort and safety. To address these challenges, in this article, the authors propose a new deep reinforcement-based train operation (DRTO) method which includes: 1) A deterministic deep reinforcement learning algorithm, 2) a dynamic incentive system, which is used to ensure safe operation in a multitrain environment, and 3) an event-driven method, which is used to improve the DRTO performance based on an event-driven strategy. To evaluate the performance, we thoroughly compare the proposed method with other operation control solutions on both synthetic and real datasets. Our results demonstrate that DRTO is effective in: 1) Decreasing the energy consumption of train operation, 2) increasing passenger comfort, and 3) achieving a good tradeoff between efficiency and safety. In addition, the effectiveness of the event-driven strategy and the dynamic incentive system is demonstrated in the experiments.