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Stochastic Improvement of Cyclic Railway Timetables

Author

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  • Kroon, L.G.
  • Dekker, R.
  • Maróti, G.
  • Retel Helmrich, M.
  • Vromans, M.J.C.M.

Abstract

Real-time railway operations are subject to stochastic disturbances. However, a railway timetable is a deterministic plan. Thus a timetable should be designed in such a way that it can cope with the stochastic disturbances as well as possible. For that purpose, a timetable usually contains time supplements in several process times and buffer times between pairs of consecutive trains. This paper describes a Stochastic Optimization Model that can be used to allocate the time supplements and the buffer times in a given timetable in such a way that the timetable becomes maximally robust against stochastic disturbances. The Stochastic Optimization Model was tested on several instances of NS Reizigers, the main operator of passenger trains in the Netherlands. Moreover, a timetable that was computed by the model was operated in practice in a timetable experiment on the so-called “Zaanlijn”. The results show that the average delays of trains can often be reduced significantly by applying relatively small modifications to a given timetable.

Suggested Citation

  • Kroon, L.G. & Dekker, R. & Maróti, G. & Retel Helmrich, M. & Vromans, M.J.C.M., 2006. "Stochastic Improvement of Cyclic Railway Timetables," ERIM Report Series Research in Management ERS-2006-067-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:8291
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Railway Timetabling; Robustness; Stochastic Optimization;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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