A data-driven bi-objective matheuristic for energy-optimising timetables in a passenger railway network

Energy-efficient train timetabling (EETT) is essential to achieve the full potential of energy-efficient train control, which can reduce operating costs and contribute to a reduction in CO₂ emissions. This article proposes a bi-objective matheuristic to address the EETT problem for a railway network. To the authors' knowledge, this article is the first to suggest using historical data from train operation to model the actual energy consumption, reflecting the different driving behaviours. The matheuristic employs a genetic algorithm (GA) based on NSGA-II. The GA uses a warm-start method to generate the initial population based on a mixed-integer program. A greedy first-come-first-served fail-fast repair heuristic is used to ensure feasibility throughout the evolution of the population. The objectives taken into account are energy consumption and passenger travel time. The matheuristic was applied to a real-world case from a large North European train operating company. The considered network consists of 107 stations and junctions, and 18 periodic timetables for 9 train lines. The results show that for an entire network, a reduction up to 3.3% in energy consumption and 4.64% in passenger travel time can be achieved. The results are computed in less than a minute, making the approach suitable for integration with a decision support tool.

• Record URL:
  • https://doi.org/10.1016/j.jrtpm.2023.100374
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2210970623000069
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22109706
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Als, Matthias Villads Hinsch
  • 0000-0001-6442-0299
  • Madsen, Mathias Bejlegaard
  • 0000-0002-2447-1670
  • Jensen, Rune Møller
  • 0000-0001-5651-1377
• Publication Date: 2023-6

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 100374
• Serial:
  • Journal of Rail Transport Planning & Management
  • Volume: 26
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2210-9706
  • Serial URL: http://www.sciencedirect.com/science/journal/22109706

Subject/Index Terms

• TRT Terms: Energy consumption; Genetic algorithms; Optimization; Railroad transportation; Timetables; Travel time
• Subject Areas: Energy; Operations and Traffic Management; Planning and Forecasting; Railroads;

Filing Info

• Accession Number: 01889606
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 3 2023 11:39AM