In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models

Public railway transport systems play a crucial role in servicing the global society and are the transport backbone of a sustainable economy. While a significant effort has been devoted to predict inter-station trains movements to support stakeholders (i.e., infrastructure managers, train operators, and travellers) decisions, the problem of predicting in-station movements, while being crucial to improve train dispatching (i.e., empowering human or automatic dispatchers), has been far more less investigated. In fact, stations are the most critical points in a railway network: even small improvements in the estimation of the duration of trains movements can remarkably enhance the dispatching efficiency in coping with the increase in capacity demand and with delays. In this work we will first leverage on state of the art shallow models, fed by domain experts with domain specific features, to improve the current predictive systems. Then, we will leverage on a customised deep multi scale model able to automatically learn the representation and improve the accuracy of the shallow models. Results on real-world data coming from the Italian railway network will support our proposal.