We investigate the problem of analyzing the train movements in Large-Scale Railway Networks for the purpose of understanding and predicting their behaviour. We focus on different important aspects: the Running Time of a train between two stations, the Dwell Time of a train in a station, the Train Delay, and the Penalty Costs associated to a delay. Two main approaches exist in literature to study these aspects. One is based on the knowledge of the network and the experience of the operators. The other one is based on the analysis of the historical data about the network with advanced data analytics methods. In this paper, we will propose an hybrid approach in order to address the limitations of the current solutions. In fact, experience-based models are interpretable and robust but not really able to take into account all the factors which influence train movements resulting in low accuracy. From the other side, Data-Driven models are usually not easy to interpret, nor robust to infrequent events, and require a representative amount of data which is not always available if the phenomenon under examination changes too fast. Results on real world data coming from the Italian railway network will show that the proposed solution outperforms both state-of-the-art experience and Data-Driven based systems in terms of interpretability, robustness, ability to handle non recurrent events and changes in the behaviour of the network, and ability to consider complex and exogenous information.

Large-scale railway networks train movements: A dynamic, interpretable, and robust hybrid data analytics system

Lulli A.;Oneto L.;Anguita D.
2019-01-01

Abstract

We investigate the problem of analyzing the train movements in Large-Scale Railway Networks for the purpose of understanding and predicting their behaviour. We focus on different important aspects: the Running Time of a train between two stations, the Dwell Time of a train in a station, the Train Delay, and the Penalty Costs associated to a delay. Two main approaches exist in literature to study these aspects. One is based on the knowledge of the network and the experience of the operators. The other one is based on the analysis of the historical data about the network with advanced data analytics methods. In this paper, we will propose an hybrid approach in order to address the limitations of the current solutions. In fact, experience-based models are interpretable and robust but not really able to take into account all the factors which influence train movements resulting in low accuracy. From the other side, Data-Driven models are usually not easy to interpret, nor robust to infrequent events, and require a representative amount of data which is not always available if the phenomenon under examination changes too fast. Results on real world data coming from the Italian railway network will show that the proposed solution outperforms both state-of-the-art experience and Data-Driven based systems in terms of interpretability, robustness, ability to handle non recurrent events and changes in the behaviour of the network, and ability to consider complex and exogenous information.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/950900
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