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Stochastic approximation to the effects of headways on knock-on delays of trains

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  • Carey, Malachy
  • Kwiecinski, Andrzej

Abstract

In train planning and timetabling, the trip time on each link is assumed to depend on the type of train and characteristics of the link, but is usually treated as independent of the time interval (headway) between trains. However, in practice trains are subject to delays from a variety of causes, and since normally they are not allowed to pass each other on a link, any delay to one train may cause "knock-on" delays to following trains. This is especially true of the high density double and multiple track railways in Britain and Europe. The shorter the scheduled headway between trains, the greater is the expected knock-on delay and hence the greater the expected trip times of following trains. We develop simple stochastic approximations to these knock-on train delays. To test and calibrate the approximations, we conduct detailed stochastic simulation of the interaction between trains as they traverse sections of the link. The approximate relationships that we derive between scheduled headways and knock-on delays can be used, for example, (a) to provide correction factors for other stochastic or deterministic models of train planning, dispatching, or control; (b) to adjust train timetables, which are currently produced without explicitly considering the expected knock-on effects, and (c) to make it feasible to conduct larger scale simulations of train networks, by reducing or removing the need to simulate behaviour within each link.

Suggested Citation

  • Carey, Malachy & Kwiecinski, Andrzej, 1994. "Stochastic approximation to the effects of headways on knock-on delays of trains," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 251-267, August.
  • Handle: RePEc:eee:transb:v:28:y:1994:i:4:p:251-267
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    Cited by:

    1. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship route schedule design with sea contingency time and port time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 615-633.
    2. Thomas Spanninger & Beda Büchel & Francesco Corman, 2023. "Train Delay Predictions Using Markov Chains Based on Process Time Deviations and Elastic State Boundaries," Mathematics, MDPI, vol. 11(4), pages 1-23, February.
    3. Zhang, Lang & He, Deqiang & He, Yan & Liu, Bin & Chen, Yanjun & Shan, Sheng, 2022. "Real-time energy saving optimization method for urban rail transit train timetable under delay condition," Energy, Elsevier, vol. 258(C).
    4. 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.
    5. Leachman, Robert C. & Jula, Payman, 2012. "Estimating flow times for containerized imports from Asia to the United States through the Western rail network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 296-309.
    6. Burdett, R.L. & Kozan, E., 2006. "Techniques for absolute capacity determination in railways," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 616-632, September.
    7. Meng, Lingyun & Zhou, Xuesong, 2011. "Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1080-1102, August.
    8. 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.
    9. 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).
    10. Chow, Andy H.F. & Pavlides, Aris, 2018. "Cost functions and multi-objective timetabling of mixed train services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 335-356.
    11. Toru Seo & Kentaro Wada & Daisuke Fukuda, 2023. "Fundamental diagram of urban rail transit considering train–passenger interaction," Transportation, Springer, vol. 50(4), pages 1399-1424, August.
    12. Anupriya, & Graham, Daniel J. & Bansal, Prateek & Hörcher, Daniel & Anderson, Richard, 2023. "Optimal congestion control strategies for near-capacity urban metros: Informing intervention via fundamental diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    13. 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).
    14. Wanqi Wang & Yun Bao & Sihui Long, 2022. "Rescheduling Urban Rail Transit Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains," Sustainability, MDPI, vol. 14(9), pages 1-20, May.
    15. 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.
    16. Mussone, Lorenzo & Wolfler Calvo, Roberto, 2013. "An analytical approach to calculate the capacity of a railway system," European Journal of Operational Research, Elsevier, vol. 228(1), pages 11-23.
    17. Wei, Dali & Liu, Hongchao & Qin, Yong, 2015. "Modeling cascade dynamics of railway networks under inclement weather," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 80(C), pages 95-122.
    18. Meester, Ludolf E. & Muns, Sander, 2007. "Stochastic delay propagation in railway networks and phase-type distributions," Transportation Research Part B: Methodological, Elsevier, vol. 41(2), pages 218-230, February.

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