IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v141y2020ics1366554520306736.html
   My bibliography  Save this article

Modeling train operation as sequences: A study of delay prediction with operation and weather data

Author

Listed:
  • Huang, Ping
  • Wen, Chao
  • Fu, Liping
  • Lessan, Javad
  • Jiang, Chaozhe
  • Peng, Qiyuan
  • Xu, Xinyue

Abstract

This paper presents a carefully designed train delay prediction model, called FCLL-Net, which combines a fully-connected neural network (FCNN) and two long short-term memory (LSTM) components, to capture operational interactions. The performance of FCLL-Net is tested using data from two high speed railway lines in China. The results show that FCLL-Net has significantly improved prediction performance, over 9.4% on both lines, in terms of the selected absolute and relative metrics compared to the commonly used state-of-the-art models. Additionally, the sensitivity analysis demonstrates that interactions of train operations and weather-related features are of great significance to consider in delay prediction models.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:transe:v:141:y:2020:i:c:s1366554520306736
    DOI: 10.1016/j.tre.2020.102022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554520306736
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2020.102022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. M Carey & S Carville, 2000. "Testing schedule performance and reliability for train stations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(6), pages 666-682, June.
    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. Murali, Pavankumar & Dessouky, Maged & Ordóñez, Fernando & Palmer, Kurt, 2010. "A delay estimation technique for single and double-track railroads," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(4), pages 483-495, July.
    5. 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.
    6. 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.
    7. Chao Wen & Zhongcan Li & Javad Lessan & Liping Fu & Ping Huang & Chaozhe Jiang, 2017. "Statistical investigation on train primary delay based on real records: evidence from Wuhan–Guangzhou HSR," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 5(3), pages 170-189, July.
    8. 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.
    9. 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.
    10. Briggs, Keith & Beck, Christian, 2007. "Modelling train delays with q-exponential functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 498-504.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    3. 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.
    4. 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).
    5. Manuel Blanco-Castillo & Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala, 2022. "Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions," Sustainability, MDPI, vol. 14(14), pages 1-26, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. Krier, Betty & Liu, Chia-Mei & McNamara, Brian & Sharpe, Jerrod, 2014. "Individual freight effects, capacity utilization, and Amtrak service quality," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 163-175.
    6. Harshad Khadilkar, 2017. "Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks," Transportation Science, INFORMS, vol. 51(4), pages 1161-1176, November.
    7. 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.
    8. Zhongcan Li & Ping Huang & Chao Wen & Yixiong Tang & Xi Jiang, 2020. "Predictive models for influence of primary delays using high‐speed train operation records," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1198-1212, December.
    9. 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.
    10. Maosheng Li & Zhengqiu Liu & Yonghong Zhang & Weijun Liu & Feng Shi, 2017. "Distribution analysis of train interval journey time employing the censored model with shifting character," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 715-733, March.
    11. Taslimi, Bijan & Babaie Sarijaloo, Farnaz & Liu, Hongcheng & Pardalos, Panos M., 2022. "A novel mixed integer programming model for freight train travel time estimation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 676-688.
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. Vromans, Michiel J.C.M. & Dekker, Rommert & Kroon, Leo G., 2006. "Reliability and heterogeneity of railway services," European Journal of Operational Research, Elsevier, vol. 172(2), pages 647-665, July.
    17. Chao Wen & Zhongcan Li & Javad Lessan & Liping Fu & Ping Huang & Chaozhe Jiang, 2017. "Statistical investigation on train primary delay based on real records: evidence from Wuhan–Guangzhou HSR," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 5(3), pages 170-189, July.
    18. 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.
    19. Eva Nedeliakova & Maria Hudakova & Matej Masar & Lenka Lizbetinova & Renata Stasiak-Betlejewska & Peter Šulko, 2020. "Sustainability of Railway Undertaking Services with Lean Philosophy in Risk Management—Case Study," Sustainability, MDPI, vol. 12(13), pages 1-28, June.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:141:y:2020:i:c:s1366554520306736. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.