The invention discloses a statistical learning model based gate position allocation method. The statistical learning model based gate position allocation method comprises the steps of adopting a prior probability prediction model, producing a take-off time difference value probability set, a take-off time difference value probability set and a landing time difference value probability set according to the historical flying situation of a certain flight, predicting arrival time probability distribution and parking apron airside leisure degree of the flight and accordingly performing flight gate position allocation. The statistical learning model based gate position allocation method is based on airside allocation, facilitates adjustment on only close gate positions of flights and shortens walking distances of passengers. In addition, overall evaluation on the using situation of gate positions is facilitated, allocation can be performed according to the planned landing time probability of the flights based on probability allocation, the gate position allocation accuracy is improved, gate position adjustment times caused by flight delay is decreased, and meanwhile the satisfaction degree of the passengers is improved. The utilization rate of the gate positions is comprehensively improved, and reasonable allocation of gate position resources is ensured.


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    Titel :

    Statistical learning model based gate position allocation method


    Beteiligte:
    WANG LEI (Autor:in) / RU XINGXING (Autor:in) / LI YAN (Autor:in) / HEI XINHONG (Autor:in) / FEI RONG (Autor:in)

    Erscheinungsdatum :

    2015-07-01


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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