Urban Development Policy Making

Urban Development Policy Making

Optimization of Urban Metro Scheduling for Reducing Travel Time and Enhancing Transportation System Efficiency

Document Type : Original Article

Authors
Mohammad Mahdi Mobaraki 1
Ali Roghani Araghi 2
Younes Noorollahi 3
1 M.Sc. Student, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
2 Assistant Professor, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
3 Professor, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
10.22034/judpm.2025.504991.1008
Abstract
In this study, the Grey Wolf Optimizer (GWO) algorithm is effectively employed to optimally schedule an urban metro system. The primary objective of this research is to significantly minimize the total passenger travel time, including waiting time at stations and in-train travel time. The proposed model considers parameters such as passenger arrival rates, stop durations, and the optimization variable h to simultaneously optimize departure and stop times. The implementation results on real-world data indicate that the cost function is significantly reduced from 23,500.75 seconds to 16,939.12 seconds using this approach. Additionally, the reduction in waiting time at high-traffic stations leads to a substantial improvement in system efficiency and passenger satisfaction. This innovative approach can pave the way for developing intelligent solutions in public transportation systems, reducing operational costs, and enhancing metro system performance. The results obtained open new avenues for future research aimed at further improving metro scheduling and management.
Keywords
Optimization
Waiting Time
Travel Time
Metro
Grey Wolf Algorithm

Subjects

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Urban Development Policy Making
Volume 2, Issue 1
Spring 2025
Pages 17-30

  • Receive Date 15 December 2024
  • Revise Date 14 January 2025
  • Accept Date 13 February 2025
  • Publish Date 04 April 2025