Train Overtaking Prediction in Railway Networks: A Big Data Perspective

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.