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Train Overtaking Prediction in Railway Networks: A Big Data Perspective

  • Luca OnetoEmail author
  • Irene Buselli
  • Alessandro Lulli
  • Renzo Canepa
  • Simone Petralli
  • Davide Anguita
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)

Abstract

Every time two or more trains are in the wrong relative position on the railway network because of maintenance, delays or other causes, it is required to decide if, where, and when to make them overtake. This is a quite complex problem that is tackled every day by the train operators exploiting their knowledge and experience since no effective automatic tools are available for large scale railway networks. In this work we propose a train overtaking hybrid prediction system. Our model is hybrid in the sense that it is able to both encapsulate the experience of the operators and integrate this knowledge with information coming from the historical data about the railway network using state-of-the-art data-driven techniques. Results on real world data coming from the Italian railway network will show that the proposed solution outperforms the fully data-driven approach and could help the operators in timely identify and schedule the best train overtaking solution.

Keywords

Railway network Train overtaking Big data Data-Driven Models Hybrid models 

Notes

Acknowledgments

This research has been supported by the European Union through the projects IN2DREAMS (European Union’s Horizon 2020 research and innovation programme under grant agreement 777596).

References

  1. 1.
    Albrecht, T.: Reducing power peaks and energy consumption in rail transit systems by simultaneous train running time control. WIT Trans. State-of-Art Sci. Eng. 39 (2010)Google Scholar
  2. 2.
    Barta, J., Rizzoli, A.E., Salani, M., Gambardella, L.M.: Statistical modelling of delays in a rail freight transportation network. In: Proceedings of the Winter Simulation Conference (2012)Google Scholar
  3. 3.
    Berger, A., Gebhardt, A., Müller-Hannemann, M., Ostrowski, M.: Stochastic delay prediction in large train networks. In: OASIcs-OpenAccess Series in Informatics, vol. 20 (2011)Google Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  5. 5.
    Bryan, J., Weisbrod, G.E., Martland, C.D.: Rail freight solutions to roadway congestion: final report and guidebook. Transp. Res. Board (2007)Google Scholar
  6. 6.
    Daamen, W., Goverde, R.M.P., Hansen, I.A.: Non-discriminatory automatic registration of knock-on train delays. Netw. Spat. Econ. 9(1), 47–61 (2009)CrossRefGoogle Scholar
  7. 7.
    D’Ariano, A.: Improving real-time train dispatching: models, algorithms and applications. TRAIL Research School (2008)Google Scholar
  8. 8.
    D’Ariano, A., Pranzo, M.: An advanced real-time train dispatching system for minimizing the propagation of delays in a dispatching area under severe disturbances. Netw. Spat. Econ. 9(1), 63–84 (2009)CrossRefGoogle Scholar
  9. 9.
    Fang, W., Yang, S., Yao, X.: A survey on problem models and solution approaches to rescheduling in railway networks. IEEE Trans. Intell. Transp. Syst. 16(6), 2997–3016 (2015)CrossRefGoogle Scholar
  10. 10.
    Ghofrani, F., He, Q., Goverde, R.M., Liu, X.: Recent applications of big data analytics in railway transportation systems: a survey. Transp. Res. Part C Emerg. Technol. 90, 226–246 (2018)CrossRefGoogle Scholar
  11. 11.
    Goverde, R.M.P., Meng, L.: Advanced monitoring and management information of railway operations. J. Rail Transp. Plan. Manag. 1(2), 69–79 (2011)Google Scholar
  12. 12.
    Hansen, I.A., Goverde, R.M.P., Van Der Meer, D.J.: Online train delay recognition and running time prediction. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1783–1788 (2010)Google Scholar
  13. 13.
    Kecman, P., Goverde, R.M.P.: Process mining of train describer event data and automatic conflict identification. In: Computers in Railways XIII: Computer System Design and Operation in the Railway and Other Transit Systems, vol. 127, p. 227 (2013)Google Scholar
  14. 14.
    Kecman, P., Goverde, R.M.P.: Online data-driven adaptive prediction of train event times. IEEE Trans. Intell. Transp. Syst. 16(1), 465–474 (2015)CrossRefGoogle Scholar
  15. 15.
    Ko, H., Koseki, T., Miyatake, M.: Application of dynamic programming to the optimization of the running profile of a train. WIT Trans. Built Environ. 74 (2004)Google Scholar
  16. 16.
    Lamorgese, L., Mannino, C.: An exact decomposition approach for the real-time train dispatching problem. Oper. Res. 63(1), 48–64 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Lukaszewicz, P.: Energy consumption and running time for trains. Ph.D. thesis, Doctoral Thesis. Railway Technology, Department of Vehicle Engineering, Royal Institute of Technology, Stockholm (2001)Google Scholar
  18. 18.
    Lulli, A., Oneto, L., Canepa, R., Petralli, S., Anguita, D.: Large-scale railway networks train movements: a dynamic, interpretable, and robust hybrid data analytics system. In: IEEE International Conference on Data Science and Advanced Analytics (2018)Google Scholar
  19. 19.
    Marković, N., Milinković, S., Tikhonov, K.S., Schonfeld, P.: Analyzing passenger train arrival delays with support vector regression. Transp. Res. Part C Emerg. Technol. 56, 251–262 (2015)CrossRefGoogle Scholar
  20. 20.
    Milinković, S., Marković, M., Vesković, S., Ivić, M., Pavlović, N.: A fuzzy petri net model to estimate train delays. Simul. Model. Pract. Theory 33, 144–157 (2013)CrossRefGoogle Scholar
  21. 21.
    Oneto, L., Fumeo, E., Clerico, G., Canepa, R., Papa, F., Dambra, C., Mazzino, N., Anguita, D.: Advanced analytics for train delay prediction systems by including exogenous weather data. In: IEEE International Conference on Data Science and Advanced Analytics (2016)Google Scholar
  22. 22.
  23. 23.
    Trabo, I., Landex, A., Nielsen, O.A., Schneider-Tilli, J.E.: Cost benchmarking of railway projects in europe-can it help to reduce costs? In: International Seminar on Railway Operations Modelling and Analysis-RailCopenhagen (2013)Google Scholar
  24. 24.
    Wang, R., Work, D.B.: Data driven approaches for passenger train delay estimation. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), pp. 535–540 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Luca Oneto
    • 1
    Email author
  • Irene Buselli
    • 1
  • Alessandro Lulli
    • 1
  • Renzo Canepa
    • 2
  • Simone Petralli
    • 2
  • Davide Anguita
    • 1
  1. 1.DIBRIS, University of GenoaGenoaItaly
  2. 2.Rete Ferroviaria Italiana S.p.A.GenoaItaly

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