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A predictive model of train delays on a railway line

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  • Chao Wen
  • Weiwei Mou
  • Ping Huang
  • Zhongcan Li

Abstract

Delay prediction is an important issue associated with train timetabling and dispatching. Based on real‐world operation records, accurate forecasting of delays is of immense significance in train operation and decisions of dispatchers. In this study, we established a model that illustrates the interaction between train delays and their affecting factors via train describer records on a Dutch railway line. Based on the main factors that affect train delay and the time series trend, we determined the independent and dependent variables. A long short‐term memory (LSTM) prediction model in which the actual delay time corresponded to the dependent variable was established via Python. Finally, the prediction accuracy of the random forest model and artificial neural network model was compared. The results indicated that the LSTM model outperformed the other two models.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:3:p:470-488
    DOI: 10.1002/for.2639
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    References listed on IDEAS

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    1. Huisman, Tijs & Boucherie, Richard J. & van Dijk, Nico M., 2002. "A solvable queueing network model for railway networks and its validation and applications for the Netherlands," European Journal of Operational Research, Elsevier, vol. 142(1), pages 30-51, October.
    2. Gorman, Michael F., 2009. "Statistical estimation of railroad congestion delay," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(3), pages 446-456, May.
    3. Malavasi, Gabriele & Ricci, Stefano, 2001. "Simulation of stochastic elements in railway systems using self-learning processes," European Journal of Operational Research, Elsevier, vol. 131(2), pages 262-272, June.
    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. 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.
    6. Olsson, Nils O.E. & Haugland, Hans, 2004. "Influencing factors on train punctuality--results from some Norwegian studies," Transport Policy, Elsevier, vol. 11(4), pages 387-397, October.
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    Cited by:

    1. Sobrie, Léon & Verschelde, Marijn & Hennebel, Veerle & Roets, Bart, 2023. "Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1201-1217.
    2. 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).
    3. Luo, Jie & Wen, Chao & Peng, Qiyuan & Qin, Yong & Huang, Ping, 2023. "Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).

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