Energy-saving design of underground metro vertical alignment with deep reinforcement learning

Abstract

The design of metro vertical alignment greatly affects train traction energy consumption. This study develops a deep reinforcement learning (DRL) algorithm for an energy-saving design of underground metro vertical alignment and analyzes the influence of train traction energy consumption calculation factors (CFs) on energy-saving design. First, a metro train operation simulation model is adopted using the approximate integral method, and the value range of CFs as input parameters is determined. Second, the 'agent', 'state', 'action', and 'reward' of the metro vertical alignment environment and the corresponding alignment constraints are determined. Third, a Dueling Double Deep Q Network (D3QN) is constructed to design the energy-saving metro vertical alignment. The proposed model is applied to a real-world metro case study, and the influence of CFs (design speed, limiting gradient in reverse slopes, and passenger capacity) on energy-saving design is analyzed. Finally, compared to manual design, the D3QN-based design outperforms GA-based design and DQN-based design, and demonstrates a remarkable energy savings of 6.17%.