When a new system, concept, or tool is proposed in the aviation domain, one concern is the impact that this will have on operator workload. As an experience, workload is difficult to measure in a way that will allow comparison of proposed systems with those already in existence. Chatterji and Sridhar (2001) suggested a method by which airspace parameters can be translated into workload ratings, using a neural network. This approach was employed, and modified to accept input from a non-real time airspace simulation model. The following sections describe the preparations and testing work that will enable comparison of a future airspace concept with a current day baseline in terms of workload levels.


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    Titel :

    Using Neural Networks to Explore Air Traffic Controller Workload


    Beteiligte:
    Martin, Lynne (Autor:in) / Kozon, Thomas (Autor:in) / Verma, Savita (Autor:in) / Lozito, Sandra C. (Autor:in)

    Kongress:

    7th International Conference on Cognitive Modeling ; 2006 ; Trieste, Italy


    Erscheinungsdatum :

    01.01.2006


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Keine Angabe


    Sprache :

    Englisch




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    Factors Affecting Air Traffic Controller Workload: Multivariate Analysis Based on Simulation Modeling of Controller Workload

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