City metro network expansion based on multi-objective reinforcement learning

This manuscript focuses on investigating the metro network expansion problem, which is formulated as a Markov Decision Process and addressed using a sequential station selection methodology. To identify an effective expansion strategy, the authors introduce a multi-objective reinforcement learning framework, which encompasses objectives such as traffic demands, social equity, and network accessibility. The proposed method can explore the entire city area without limiting the search space, by leveraging reward calculations to fine-tune the policy during the learning process To effectively address the challenges posed by multiple objectives and the curse of dimensionality, the proposed method utilizes an actor-critic framework. The actor is responsible for selecting actions, specifically determining the next metro station to be added to the network. The critic evaluates the performance of the given policy, providing feedback on the quality of the expanded metro network. Furthermore, by integrating the Tchebycheff decomposition method into the actor-critic framework, the proposed method enhances the exploration and optimization of the non-convex metro network expansion problem. The authors' method has been validated through experiments utilizing real-world data and outperforms traditional heuristic algorithms by over 30%. These results compellingly illustrate the superior effectiveness of the authors' proposed method.

• Record URL:
  • https://doi.org/10.1016/j.trc.2024.104880
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X24004017
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Liqing
  • 0000-0001-5657-1812
  • U, Leong Hou
  • 0000-0002-5135-5165
  • Ni, Shaoquan
  • Chen, Dingjun
  • Li, Zhenning
  • Wang, Wenxian
  • Xian, Weizhi
  • 0000-0001-5137-3542
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 104880
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 169
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Accessibility; Demand; Machine learning; Optimization; Subway stations; Urban development
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation; Society;

Filing Info

• Accession Number: 01936456
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 11 2024 9:42AM