A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning
https://orcid.org/0000-0003-3225-0576
Under the condition of urban rail transit uncertainty of passenger demand and the high frequency of departure intervals, this study presents an innovative real-time urban rail transit (URT) train service scheduling control framework. In the context of a bidirectional urban rail transit line, a high-fidelity urban rail transit simulation environment was constructed. Within this environment, an advantage actor–critic (A2C) reinforcement learning approach was utilized to train a suitable strategy aimed at minimizing both passenger waiting costs and transit authority operational expenses. Subject to specific constraints, the strategy is designed to generate real-time train schedule based on the representation of traffic state using station congestion levels and train positions. Experimental results on Lines 3 and S7 of Nanjing Metro demonstrated the agent’s effectiveness in achieving high-performance schedules across various scenarios. This research integrates deep reinforcement learning into the optimization of dynamic traffic systems, showing great potential for enhancing the efficiency and resilience of urban transport systems.
Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning
https://orcid.org/0000-0003-3225-0576
Under the condition of urban rail transit uncertainty of passenger demand and the high frequency of departure intervals, this study presents an innovative real-time urban rail transit (URT) train service scheduling control framework. In the context of a bidirectional urban rail transit line, a high-fidelity urban rail transit simulation environment was constructed. Within this environment, an advantage actor–critic (A2C) reinforcement learning approach was utilized to train a suitable strategy aimed at minimizing both passenger waiting costs and transit authority operational expenses. Subject to specific constraints, the strategy is designed to generate real-time train schedule based on the representation of traffic state using station congestion levels and train positions. Experimental results on Lines 3 and S7 of Nanjing Metro demonstrated the agent’s effectiveness in achieving high-performance schedules across various scenarios. This research integrates deep reinforcement learning into the optimization of dynamic traffic systems, showing great potential for enhancing the efficiency and resilience of urban transport systems.
Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning
https://orcid.org/0000-0003-3225-0576
Under the condition of urban rail transit uncertainty of passenger demand and the high frequency of departure intervals, this study presents an innovative real-time urban rail transit (URT) train service scheduling control framework. In the context of a bidirectional urban rail transit line, a high-fidelity urban rail transit simulation environment was constructed. Within this environment, an advantage actor–critic (A2C) reinforcement learning approach was utilized to train a suitable strategy aimed at minimizing both passenger waiting costs and transit authority operational expenses. Subject to specific constraints, the strategy is designed to generate real-time train schedule based on the representation of traffic state using station congestion levels and train positions. Experimental results on Lines 3 and S7 of Nanjing Metro demonstrated the agent’s effectiveness in achieving high-performance schedules across various scenarios. This research integrates deep reinforcement learning into the optimization of dynamic traffic systems, showing great potential for enhancing the efficiency and resilience of urban transport systems.
Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning
J. Transp. Eng., Part A: Systems
Wen, Longhui (author) / Zhou, Wei (author) / Liu, Jiajun (author) / Ren, Gang (author) / Zhang, Ning (author)
2024-09-01
Article (Journal)
Electronic Resource
English
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