This paper develops a method which relates traffic data to workload situation in qualitative terms such as high, medium and low. The method is based on earlier research in image processing and pattern recognition that has identified measures of second-order statistics which characterize different regions within the image based on gray level patterns. For characterizing air traffic patterns, these measures have been computed using position and velocity of the aircraft within the airspace. In order to relate the patterns in terms of their statistics to the controller's assessment of the workload, a multilayer neural network has been used. Recorded air traffic data using the Center TRACON Automation System and controller's rating of the same data, have been used to train the neural network. It is shown that a trained neural network with statistical measures derived from the traffic as input can be used for predicting air traffic controllers workload that is in agreement with the qualitative assessment of their workload.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Neural network based air traffic controller workload prediction


    Contributors:

    Published in:

    Publication date :

    1999


    Size :

    5 Seiten, 13 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Measures for air traffic controller workload prediction

    Chatterji, Gano / Sridhar, Banavar | AIAA | 2001


    Using Neural Networks to Explore Air Traffic Controller Workload

    Martin, Lynne / Kozon, Thomas / Verma, Savita et al. | NTRS | 2006


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

    Majumdar, A. / Ochieng, W. Y. / Transportation Research Board | British Library Conference Proceedings | 2002



    The Modelling of Air Traffic Controller Workload

    Day, P. O. / Hook, M. K. / Warren, C. et al. | British Library Conference Proceedings | 1993