P.Y. Boyko, E.M. Bikov, E.I. Sokolov, D.A. Yarotsky Application of Machine Learning to Incident Ranking at Moscow Railway
P.Y. Boyko, E.M. Bikov, E.I. Sokolov, D.A. Yarotsky Application of Machine Learning to Incident Ranking at Moscow Railway


Moscow Railway, a large railway network including 8800 kilometers of track and 549 stations, is equipped with tens of thousands of devices for automatic registration of system failures. Alerts produced by these devices are processed by operators of the Infrastructure Management Center. The alert flow is very intense and creates a significant stress on the operators while about 97% of the signals turn out to be false alarms. To optimize the operation of the Center we have used machine learning to develop an advanced automated incident ranking model that estimates the probability of an actual failure from multiple features of the registered incident. The model was trained as an ensemble of decision trees by the algorithm XGBoost using a database of 5 million historical incidents. The model has been integrated into the software infrastructure of the Center and is used in the daily work of operators.


railroad monitoring, incident ranking, machine learning, feature engineering, ensemble of decision trees, XGBoost.

PP. 43-53.


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