Predicting when a flight may be delayed is crucial in creating a more productive airline. The complexity of the air transportation system, the abundance of forecast methods, and the deluge of flight data made the creation of accurate prediction models for flight delays arduous. This study provides a comprehensive overview of the methods previously employed to develop flight delay prediction models within this setting. Delays caused by airlines have a significant impact on the economy and the business world. Airlines, airports, and passengers all suffer when flights are delayed. We provide linear regression techniques and other machine learning algorithms. Predicting flight delays using machine learning algorithms is the focus of this study. The aviation industry is a major contributor to many countries' economies, and air travel is the most convenient and time-efficient mode of transportation available. Delays in flights are never convenient, but they can be extremely frustrating when they cause passengers to miss their connecting flights. People would have more time to reschedule subsequent flights if they knew in advance whether there would be a delay, and even more so if they knew how long the delay may be.


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    Titel :

    A Flight Delay Prediction Model Using Machine Learning and Temporal Convolutional Networks


    Beteiligte:
    K, Vijay (Autor:in) / Sr, Deepak (Autor:in) / T, Deepika (Autor:in) / K, Vivitha (Autor:in) / S, Jayashree (Autor:in)


    Erscheinungsdatum :

    01.11.2023


    Format / Umfang :

    4194733 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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