Intelligent air traffic management systems will be necessary for future ATC (Air Traffic Control), which can manage air traffic flows and flight schedules efficiently in a real-time fashion. To meet this objective, an automated decision support system is described. This system consists of several distributed decision-makers, and uses the concept learning scheme using neural networks. The system has the capability to find a suboptimal solution without interrupting the actual operations, in order to deal with various constraints. Simulation studies show that the proposed scheduling strategy works rather more efficiently than the current ATC procedures based on fixed heuristic rules.


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    Title :

    Real-time decision support for air traffic management, utilizing machine learning


    Additional title:

    Ein Echtzeit-Entscheidungshilfesystem mit maschinellem Lernen für die Steuerung des Luftverkehrs


    Contributors:
    Nogami, J. (author) / Nakasuka, S. (author) / Tanabe, T. (author)

    Published in:

    Control Engineering Practice ; 4 , 8 ; 1129-1141


    Publication date :

    1996


    Size :

    13 Seiten, 12 Bilder, 8 Tabellen, 18 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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