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Stochastic improvement of cyclic railway timetables

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  • Kroon, Leo
  • Maróti, Gábor
  • Helmrich, Mathijn Retel
  • Vromans, Michiel
  • Dekker, Rommert

Abstract

Real-time railway operations are subject to stochastic disturbances. Thus a timetable should be designed in such a way that it can cope with these 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.

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Bibliographic Info

Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

Volume (Year): 42 (2008)
Issue (Month): 6 (July)
Pages: 553-570

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Handle: RePEc:eee:transb:v:42:y:2008:i:6:p:553-570

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References

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  1. Carey, Malachy, 1998. "Optimizing scheduled times, allowing for behavioural response," Transportation Research Part B: Methodological, Elsevier, vol. 32(5), pages 329-342, June.
  2. Huisman, Tijs & Boucherie, Richard J., 2001. "Running times on railway sections with heterogeneous train traffic," Transportation Research Part B: Methodological, Elsevier, vol. 35(3), pages 271-292, March.
  3. Carey, Malachy, 1999. "Ex ante heuristic measures of schedule reliability," Transportation Research Part B: Methodological, Elsevier, vol. 33(7), pages 473-494, September.
  4. Hallowell, Susan F. & Harker, Patrick T., 1998. "Predicting on-time performance in scheduled railroad operations: methodology and application to train scheduling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(4), pages 279-295, May.
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Cited by:
  1. Kroon, L.G. & Peeters, L.W.P. & Wagenaar, J.C. & Zuidwijk, R.A., 2012. "Flexible Connections in PESP Models for Cyclic Passenger Railway Timetabling," ERIM Report Series Research in Management ERS-2012-008-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 Uni.
  2. Cacchiani, Valentina & Caprara, Alberto & Toth, Paolo, 2010. "Scheduling extra freight trains on railway networks," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 215-231, February.
  3. Li, Xiang & Lo, Hong K., 2014. "An energy-efficient scheduling and speed control approach for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 73-89.
  4. Cordone, Roberto & Redaelli, Francesco, 2011. "Optimizing the demand captured by a railway system with a regular timetable," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 430-446, February.

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