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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 287))

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Abstract

Slack time allocation, heterogeneity of train paths, and capacity utilization have great impact on the reliability of train timetable. Aiming at the factor of slack time allocation, we proposed a two-stage stochastic programming with recourse model to optimize slack time allocation in train timetable on high-speed passenger dedicated lines (PDL), in which the first-stage model formalizes the slack time allocation in the train timetabling phase, while the second-stage model simulates the execution of train timetable with consideration of “train dispatching” behaviors. A genetic algorithm was designed and implemented to solve the mathematical model. Numerical experiments were conducted to test effects of the proposed method. The results show that slack time allocation has great impact on the train timetable reliability and the presented method in this paper could improve the quality of slack time allocation by 10 % on average.

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Acknowledgments

This paper is jointly supported by “the Fundamental Research Funds for the Central Universities” (2011JBM252) and “the Postdoc Research Funding of China” (2011M500228). The authors would like to thank Dr. Zhou from University of Utah, USA, for his professional and constructive comments on this work. The authors are of course responsible for all the results and opinions expressed in this paper.

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Correspondence to Yong-tao Niu .

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Niu, Yt., Meng, Ly. (2014). Optimizing Slack Time Allocation in Train Timetable: A Two-Stage Stochastic Recourse Model. In: Jia, L., Liu, Z., Qin, Y., Zhao, M., Diao, L. (eds) Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume I. Lecture Notes in Electrical Engineering, vol 287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53778-3_23

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  • DOI: https://doi.org/10.1007/978-3-642-53778-3_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53777-6

  • Online ISBN: 978-3-642-53778-3

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