A deep reinforcement learning based metro train operation control optimization considering energy conservation and passenger comfort

Abstract As an efficient mode of public transportation, urban rail transit promotes sustainable development by reducing energy consumption and improving service quality. Numerous studies have been conducted on this subject. However, many existing methods struggle to adapt to environmental changes due to the complexity of actual line conditions. Therefore, based on deep deterministic policy gradient (DDPG) and genetic algorithm (GA), a new optimization algorithm GA-DDPG is proposed in this paper. This algorithm utilizes DDPG for continuous action control of trains and employs GA to optimize the noise generation hyperparameter of DDPG, thereby enhancing the learning and adaptive capabilities of the algorithm. Firstly, a reference system is established based on expert knowledge to ensure safe train operation and guide the training of the algorithm model. Then, under the intervention of reference system, the GA-DDPG is used to optimize and adjust the train operation control strategy to achieve a balance in punctuality, energy efficiency, and comfort. Finally, simulation experiment shows that GA-DDPG achieves at least 5.2% energy savings and 51.2% passenger comfort compared to DDPG. More experiments further demonstrate that GA-DDPG possesses better adaptability and robustness than DDPG when the environment changes.