Stochastic improvement of cyclic railway timetables
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.
Volume (Year): 42 (2008)
Issue (Month): 6 (July)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Carey, Malachy, 1998. "Optimizing scheduled times, allowing for behavioural response," Transportation Research Part B: Methodological, Elsevier, vol. 32(5), pages 329-342, June.
- 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.
- Carey, Malachy, 1999. "Ex ante heuristic measures of schedule reliability," Transportation Research Part B: Methodological, Elsevier, vol. 33(7), pages 473-494, September.
- 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.
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