The main purpose of this paper is to improve the accuracy of the prediction of aircraft delay time. Using K-Medoids clustering algorithm to cluster the time intervals between arrival and departure time based on historical data, and then use the principal component analysis to reduce the dimensionality of the data. Finally, The Bayesian Classifier is used for classification processing to predict the category of the time interval to which the data belongs. The improved Bayesian Classifier has greatly improved accuracy and reduced errors, then we can use this data to predict the actual departure time of the aircraft. It is convenient for us to predict the delay time of the aircraft, and the prediction result is more accurate.


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    Titel :

    Aircraft arrival and departure time interval prediction based on improved Bayesian classifier


    Beteiligte:
    Ladaci, Samir (Herausgeber:in) / Kaswan, Suresh (Herausgeber:in) / Xie, Juzan (Autor:in) / Yin, Bo (Autor:in) / Lu, Zhaoyang (Autor:in)

    Kongress:

    International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023) ; 2023 ; Xiamen, China


    Erschienen in:

    Proc. SPIE ; 12759


    Erscheinungsdatum :

    10.08.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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