As cities continue to grow, expanding metro networks becomes essential for optimizing urban transportation efficiency. Therefore, scientific strategic planning of metro networks is indispensable. This study proposes a deep reinforcement learning (DRL) approach to discover the optimal planning strategy for metro network extension. The model integrates multi-source data into the reward function while customizing the state and action spaces to reflect the unique characteristics of metro networks. A policy network is developed using an Encoder-Decoder framework, with the parameters being updated by an Actor-Critic framework based on policy gradient. A comprehensive performance index is proposed to evaluate the vulnerability and service capacity of planned networks. The proposed method is validated through a case study on the Hangzhou metro system. The results demonstrate that the proposed DRL can result in optimal planned networks that outperform the actually implemented network with a maximum Performance Improvement Percentage of 19.87 \%. The DRL-based optimization framework proposed for metro network extension planning is anticipated to enhance adaptability towards urban development and increase resilience.
BT - APPLIED SOFT COMPUTING DO - 10.1016/j.asoc.2025.113163 N2 -As cities continue to grow, expanding metro networks becomes essential for optimizing urban transportation efficiency. Therefore, scientific strategic planning of metro networks is indispensable. This study proposes a deep reinforcement learning (DRL) approach to discover the optimal planning strategy for metro network extension. The model integrates multi-source data into the reward function while customizing the state and action spaces to reflect the unique characteristics of metro networks. A policy network is developed using an Encoder-Decoder framework, with the parameters being updated by an Actor-Critic framework based on policy gradient. A comprehensive performance index is proposed to evaluate the vulnerability and service capacity of planned networks. The proposed method is validated through a case study on the Hangzhou metro system. The results demonstrate that the proposed DRL can result in optimal planned networks that outperform the actually implemented network with a maximum Performance Improvement Percentage of 19.87 \%. The DRL-based optimization framework proposed for metro network extension planning is anticipated to enhance adaptability towards urban development and increase resilience.
PY - 2025 T2 - APPLIED SOFT COMPUTING TI - Network extension planning towards resilient urban critical infrastructures using deep reinforcement learning ER -