Flight delays have been a serious problem in the national airspace system costing about $30B per year. About 70 of the delays are attributed to weather and upto two thirds of these are avoidable. Better decision support tools would reduce these delays and improve air traffic management tools. Such tools would benefit from models of weather impacts on the airspace operations. This presentation discusses use of machine learning methods to mine various types of weather and traffic data to develop such models.


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    Titel :

    Models of Weather Impact on Air Traffic


    Beteiligte:
    Kulkarni, Deepak (Autor:in) / Wang, Yao (Autor:in)

    Kongress:

    Machine Learning Workshop ; 2017 ; Moffett Field, CA, United States


    Erscheinungsdatum :

    2017-08-29


    Medientyp :

    Sonstige


    Format :

    Keine Angabe


    Sprache :

    Englisch




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