Multi-Objective Energy-Efficient Train Timetable Optimization in Urban Rail Transit via a Hybrid DRL-NSGA-III Framework
Urban rail transit systems face a pronounced contradiction between escalating traction energy consumption and passenger service quality under the carbon peaking and carbon neutrality goals. This study proposes a hybrid optimization framework offline combining deep reinforcement learning (DRL) and Non-dominated Sorting Genetic Algorithm III (NSGA-III) for multi-objective train timetable optimization in urban rail transit systems. The proposed model simultaneously considers energy consumption, passenger waiting time, and regenerative braking energy utilization. To address the limitations of traditional evolutionary and learning-based methods, a two-stage optimization architecture is developed. In the offline stage, NSGA-III is used to generate high-quality Pareto-optimal solutions, which are utilized to initialize the experience replay buffer of a Double Deep Q-Network (Double DQN). In the online stage, the DRL agent performs adaptive timetable adjustments under dynamic passenger demand. NSGA-III is used exclusively in the offline stage for Pareto solution generation and DQN pre-training; no NSGA-III optimization is performed during online execution. Experimental results on a real-world metro case study demonstrate that the proposed method achieves significant improvements in energy efficiency and service quality compared with baseline methods. The results confirm that the hybrid framework provides an effective and scalable solution for real-time metro timetable optimization problems.