Artificial Neural Networks. A New Approach to Modelling Interregional Telecommunication Flows

    Publication: Working/Discussion PaperWU Working Paper


    During the last thirty years there has been much research effort in regional science
    devoted to modelling interactions over geographic space. Theoretical approaches for
    studying these phenomena have been modified considerably. This paper suggests a 'new
    modelling approach, based upon a general nested sigmoid neural network model. Its
    feasibility is illustrated in the context of modelling interregional telecommunication traffic in
    Austria and its performance is evaluated in comparison with the classical regression
    approach of the gravity type. The application of this neural network approach may be
    viewed as a three-stage process. The first stage refers to the identification of an
    appropriate network from the family of two-layered feedforward networks with 3 input
    nodes, one layer of (sigmoidal) intermediate nodes and one (sigmoidal) output node
    (logistic activation function). There is no general procedure to address this problem. We
    solved this issue experimentally. The input-output dimensions have been chosen in order
    to make the comparison with the gravity model as close as possible. The second stage
    involves the estimation of the network parameters of the selected neural network model.
    This is perlormed via the adaptive setting of the network parameters (training, estimation)
    by means of the application of a least mean squared error goal and the error back
    propagating technique, a recursive learning procedure using a gradient search to
    minimize the error goal. Particular emphasis is laid on the sensitivity of the network
    perlormance to the choice of the initial network parameters as well as on the problem of
    overlitting. The final stage of applying the neural network approach refers to the testing of
    the interregional teletraffic flows predicted. Prediction quality is analysed by means of two
    perlormance measures, average relative variance and the coefficient of determination, as
    well as by the use of residual analysis. The analysis shows that the neural network model
    approach outperlorms the classical regression approach to modelling telecommunication
    traffic in Austria. (authors' abstract)
    Original languageEnglish
    Place of PublicationVienna
    PublisherWU Vienna University of Economics and Business
    Publication statusPublished - 1 Mar 1994

    Publication series

    NameDiscussion Papers of the Institute for Economic Geography and GIScience

    WU Working Paper Series

    • Discussion Papers of the Institute for Economic Geography and GIScience

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