Optimizing passengers’ experience: A goal-oriented reinforcement learning speed control approach for urban railway trains

Abstract

Prolonged vibration can be uncomfortable for passengers utilizing urban rail transit systems. This study proposes an automatic speed control framework for urban railway trains to reduce vertical vibrations experienced by passengers. We suggest the concept of the “segmented comfort speed limit” to represent the vertical passing comfort of oncoming sections. This speed limit is calculated from 1/3 octave band acceleration and smoothed through lag-type speed control mode. The deep deterministic policy gradient algorithm with hindsight experience replay mechanism (HER-DDPG) is designed, to balance safety, comfort, and energy efficiency driving. Verify the speed control framework based on HER-DDPG through the rail data collected from Dalian Metro Line 12. Compared with the DDPG-based model, the vertical comfort is improved by 2.34%, and the longitudinal acceleration and total energy consumption are reduced by 45% and 8.1%. Compared with the real-world train control trajectory, HER-DDPG improves vertical comfort by 9.76% and reduces energy consumption by 12.4%. The results show that the proposed framework can effectively improve the ride experience of passengers.