Data-driven timetable design and passenger flow control optimization in metro lines

As travel demands in metro systems continue to grow rapidly, the mismatch between passenger demand and metro capacity has become a critical challenge in metro operations. To address this issue, this paper investigates the collaborative optimization of train timetables and station-based passenger flow control under stochastic demand, which aims to minimize the total system cost while ensuring an adequate service level to each station. The authors formulate the research problem as a stochastic mixed-integer programming model with expected travel time cost constraints for each station and translate it into a multi-objective attainability problem by imposing a target on the objective value. The authors develop an efficient operation policy that determines the timetable and flow control decisions in response to each demand scenario, satisfying the objective and service level targets in the long term when feasible. The authors conduct extensive numerical experiments on both synthetic and real-world transit data to evaluate the performance of their approach. The results demonstrate that the authors' approach outperforms the benchmark first-come-first-served policy in terms of efficiency and service fairness under both exogenous and endogenous demand distributions. The improvement achieved by the authors' approach is attributed to the prioritization of short trips over long ones, effectively exploiting the reusable nature of train capacity.

• Record URL:
  • https://doi.org/10.1016/j.trc.2024.104761
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X24002821
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2024 The Author(s). Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Liang, Jinpeng
  • 0000-0003-4807-6074
  • Ren, Mengxue
  • Huang, Kang
  • 0000-0003-3422-8057
  • Gao, Ziyou
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 104761
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 166
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Optimization; Passenger handling; Passenger traffic; Public transit; Timetables; Traffic flow
• Geographic Terms: Beijing (China)
• Subject Areas: Operations and Traffic Management; Passenger Transportation; Public Transportation;

Filing Info

• Accession Number: 01927986
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 22 2024 3:11PM