Railway incident ranking with machine learning

Evgeni Bikov, Pavel Boyko, Evgeny Sokolov, Dmitry Yarotsky

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Citations (Scopus)

    Abstract

    Modern railway networks include thousands of failure registration devices, and prompt response to detected failures is critical to normal network operation. However, a large share of produced alerts may be formed by false alarms associated with maintenance or faulty diagnostics, thus hindering the processing of actual failures. It is therefore very desirable to perform fast automated intelligent ranking of incidents before they are analyzed by human operators. In this paper we describe a machine-learning-based incident ranking model that we have developed and deployed at the Moscow Railway network (a large network with 500+ stations). The model estimates the probability of failure using multiple features of the incident at hand. The model was constructed using the XGBoost library and a database of 5 million historical incidents. The model shows high accuracy (AUC 0.901) in the deployment environment.

    Original languageEnglish
    Title of host publicationProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    EditorsXuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages601-606
    Number of pages6
    ISBN (Electronic)9781538614174
    DOIs
    Publication statusPublished - 2017
    Event16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
    Duration: 18 Dec 201721 Dec 2017

    Publication series

    NameProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    Volume2017-December

    Conference

    Conference16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
    Country/TerritoryMexico
    CityCancun
    Period18/12/1721/12/17

    Keywords

    • Incident-ranking
    • railway-incidents
    • XGBoost

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