- Conference Article
- 10.1109/itsc60802.2025.11423118
Online Planning of High-Speed Train Operation Curve Based on Dynamic Programming-Deep Q Network
- Nov 18, 2025
- Qianying Ma + 6 more +6
Rapid online planning of train operation strategy in response to emergencies is a critical means of ensuring the safe and efficient operation for high-speed railways. However, it is difficult to achieve punctual, comfortable, and energy-saving operation under emergency scenarios due to the properties of high speeds, long inter-station distances, and the presence of long continuous gradients. This paper proposes an online planning approach of train operation curve based on dynamic programmingdeep Q network (DP-DQN). Firstly, the dynamic programming (DP) algorithm is used to optimize some train operation curves, and the state quadruples it contains are put into the memory buffer and incorporated into the reward function to guide and accelerate the network training. Then, the action mask from reinforcement learning is used to filter out the actions that cause the train speed to exceed the Automatic Train Protection speed, enabling fast and safe online planning of the speed curve under emergencies. Based on the actual data from a high-speed railway line, the proposed approach is evaluated in offline optimization. Experimental results show that, compared with mixed integer linear programming (MILP) and particle swarm optimization (PSO) algorithms, the DP-DQN approach yields small errors in train operation punctuality and stopping accuracy, better passenger comfort, and a reduction of energy consumption by 2.99% and 12.30%, respectively. Additionally, emergency scenarios involving temporary speed limit, traction/braking failure and emergency stop are simulated and analyzed. While DP-DQN results in slightly higher energy consumption than MILP in some scenarios, it significantly reduces train delays, ensures stopping accuracy, and completes online strategy planning within one second, which meets the real-time requirements of online planning.
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