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Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach

Author

Listed:
  • Erfan Hassannayebi

    (Tarbiat Modares University)

  • Seyed Hessameddin Zegordi

    (Tarbiat Modares University)

  • Mohammad Reza Amin-Naseri

    (Tarbiat Modares University)

  • Masoud Yaghini

    (Iran University of Science and Technology)

Abstract

In the setting of public transportation system, improving the service quality as well as robustness against uncertainty through minimizing the total waiting times of passengers is a real issue. This study proposed robust multi-objective stochastic programming models for train timetabling problem in urban rail transit lines. The objective is to minimize the expected value of the passenger waiting times, its variance and the penalty cost function including the capacity violation due to overcrowding. In the proposed formulations, the dynamic and uncertain travel demand is represented by the scenario-based time-varying arrival rates and alighting ratio at stops. Two versions of the robust stochastic programming models are developed and a comparative analysis is conducted to testify the tractability of the models. The effectiveness of the proposed stochastic programming model is demonstrated through the application to line 5 of Tehran underground railway. The outcomes validate the benefits of implementing robust timetables for rail industry. The computational experiments shows significant reductions in expected passenger waiting time of 21.27 %, and cost variance drop of 59.98 % for the passengers, through the proposed robust mathematical modeling approach.

Suggested Citation

  • Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2017. "Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach," Operational Research, Springer, vol. 17(2), pages 435-477, July.
    • Handle: RePEc:spr:operea:v:17:y:2017:i:2:d:10.1007_s12351-016-0232-2
    DOI: 10.1007/s12351-016-0232-2
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