In example embodiments, techniques are provided for using machine learning to predict railroad track geometry exceedances to enable proactive maintenance. A machine learning model of a rail operational analytics application may be trained to directly output a probability of future railroad track geometry exceedances for each portion of track of a railroad. Training may be performed using all available railroad track data, and the task of selecting which data is relevant to predicting probability of railroad track geometry exceedances may be devolved to the machine learning model. Further, assumptions about the specific railroad and data characteristics may be avoided, providing the machine learning model flexibility, and allowing for dynamic changes in the problem formulation.


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    Title :

    Techniques for predicting railroad track geometry exceedances


    Contributors:

    Publication date :

    2024-06-25


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B61L Leiten des Eisenbahnverkehrs , GUIDING RAILWAY TRAFFIC / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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