Four dimensional (4D) flight trajectories play an important role in air traffic future plans. In this paper, the time and altitude variables in 4D trajectories are analyzed for their characteristics, and the procedure of preprocessing flight trajectory data is provided, and support vector regression and decision tree regression are introduced to build the prediction models for trajectory time and altitude, respectively. It is demonstrated by the experiments on actual flight trajectory data that the proposed method can improve the 4D trajectory prediction accuracy effectively.


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    Titel :

    Flight trajectory time and altitude prediction based on support vector and decision tree regressions


    Beteiligte:
    Xiao, Yingchao (Autor:in) / Ma, Yuanyuan (Autor:in) / Xu, Qiucheng (Autor:in) / Ding, Hui (Autor:in)

    Kongress:

    Fifth International Workshop on Pattern Recognition ; 2020 ; Chengdu,China


    Erschienen in:

    Proc. SPIE ; 11526


    Erscheinungsdatum :

    24.06.2020





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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