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)


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.


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



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).


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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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