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Optimizing capacity utilization of stations by estimating knock-on train delays

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  • Yuan, Jianxin
  • Hansen, Ingo A.

Abstract

For scheduled train services, a trade-off exists between efficiently utilizing the capacity of railway networks and improving the reliability and punctuality of train operations. This paper proposes a new analytical stochastic model of train delay propagation in stations, which estimates the knock-on delays of trains caused by route conflicts and late transfer connections realistically. The proposed model reflects the constraints of signalling system and train protection operations rules. The stochastic variations of track occupancy times due to the fluctuations of train speed in case of different signal aspects are modelled with conditional probability distributions. The model is solved on the basis of a numerical approximation of the Stieltjes convolution of individual independent distributions and can be integrated into a larger computerized decision support tool for timetable design and train dispatching. Having been validated successfully with empirical data, the model is applied for optimizing the station capacity utilization in a case study of the Dutch railway station The Hague Holland Spoor. The model can determine the maximal frequency of trains passing the critical level crossing with a given maximum knock-on delay at a certain confidence level. It is found that when the scheduled buffer time between train paths at the level crossing decreases, the mean knock-on delay of all passing trains increases exponentially.

Suggested Citation

  • Yuan, Jianxin & Hansen, Ingo A., 2007. "Optimizing capacity utilization of stations by estimating knock-on train delays," Transportation Research Part B: Methodological, Elsevier, vol. 41(2), pages 202-217, February.
  • Handle: RePEc:eee:transb:v:41:y:2007:i:2:p:202-217
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    Cited by:

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    5. Chao Wen & Weiwei Mou & Ping Huang & Zhongcan Li, 2020. "A predictive model of train delays on a railway line," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 470-488, April.
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    7. Nikola Bešinović & Rob M. P. Goverde, 2019. "Stable and robust train routing in station areas with balanced infrastructure capacity occupation," Public Transport, Springer, vol. 11(2), pages 211-236, August.
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    9. Mu, Shi & Dessouky, Maged, 2013. "Efficient dispatching rules on double tracks with heterogeneous train traffic," Transportation Research Part B: Methodological, Elsevier, vol. 51(C), pages 45-64.
    10. Jens Parbo & Otto Anker Nielsen & Carlo Giacomo Prato, 2016. "Passenger Perspectives in Railway Timetabling: A Literature Review," Transport Reviews, Taylor & Francis Journals, vol. 36(4), pages 500-526, July.
    11. Agbelie, Bismark & Libnao, Kathleen, 2018. "Unobserved heterogeneity analysis of rail transit incident delays," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 39-43.
    12. Gert Janssenswillen & Benoît Depaire & Sabine Verboven, 2018. "Detecting train reroutings with process mining," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(1), pages 1-24, March.
    13. Lu Yang & Leishan Zhou & Hanxiao Zhou & Chang Han & Wenqiang Zhao, 2023. "A Lagrangian Method for Calculation of Passing Capacity on a Railway Hub Station," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    14. Huang, Ping & Wen, Chao & Fu, Liping & Lessan, Javad & Jiang, Chaozhe & Peng, Qiyuan & Xu, Xinyue, 2020. "Modeling train operation as sequences: A study of delay prediction with operation and weather data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    15. Ingo Hansen, 2009. "Introduction," Networks and Spatial Economics, Springer, vol. 9(1), pages 1-5, March.
    16. Harshad Khadilkar, 2017. "Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks," Transportation Science, INFORMS, vol. 51(4), pages 1161-1176, November.
    17. Tiong, Kah Yong & Ma, Zhenliang & Palmqvist, Carl-William, 2023. "Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    18. Jovanović, Predrag & Kecman, Pavle & Bojović, Nebojša & Mandić, Dragomir, 2017. "Optimal allocation of buffer times to increase train schedule robustness," European Journal of Operational Research, Elsevier, vol. 256(1), pages 44-54.
    19. Franciszek Restel & Łukasz Wolniewicz & Matea Mikulčić, 2021. "Method for Designing Robust and Energy Efficient Railway Schedules," Energies, MDPI, vol. 14(24), pages 1-12, December.
    20. Huang, Ping & Guo, Jingwei & Liu, Shu & Corman, Francesco, 2024. "Explainable train delay propagation: A graph attention network approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    21. Krüger, Niclas A. & Vierth , Inge & Fakhraei Roudsari, Farzad, 2013. "Spatial, temporal and size distribution of freight train delays: evidence from Sweden," Working papers in Transport Economics 2013:8, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    22. Zhengwen Liao & Ce Mu, 2023. "Assessing the Compatibility of Railway Station Layouts and Mixed Heterogeneous Traffic Patterns by Optimization-Based Capacity Estimation," Mathematics, MDPI, vol. 11(17), pages 1-29, August.
    23. Bernal, Margarita & Welch, Eric W. & Sriraj, P.S., 2016. "The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 11-21.
    24. Bhatia, Vinod & Sharma, Seema, 2021. "Expense based performance analysis and resource rationalization: Case of Indian Railways," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).

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