The aim of that paper is to improve theoretic train driving time between stations with use of machine learning and framework TensorFlow. For fluent train traffic management, it is very important to know accurate train arriving time. Then operation can be adapt depending on train delay. We can already calculate theoretic driving time, it includes multiple parameters as distance, angel of curves, angel of ascent and descent, maximal speed limit, engine parameters and driving parameters to save electricity with slowly decelerating before station. But it doesn't include state of railway, train set, weather conditions, weight of passengers,... So, we will use historic data and artificial intelligence to improve prediction to make railway management simpler.


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    Title :

    Improving teoretic train driving time with AI and TensorFlow


    Contributors:
    Krsak, Emil (author) / Kello, Tomas (author)


    Publication date :

    2020-12-15


    Size :

    805903 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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