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Neural Network Modeling of Constrained Spatial Interaction Flows: Design, Estimation, and Performance Issues

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Abstract:

In this paper a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedure for parameter estimation, such as the Alopex procedure. A benchmark comparison against the standard origin constrained gravity model and the two–stage neural network approach, suggested by Openshaw (1998), illustrates the superiority of the proposed model in terms of the generalization performance measured by ARV and SRMSE.

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/1467-9787.00288

Affiliations: 1: Vienna University of Economics and Business Administration, Austria manfred.fischer@wu–wien.ac.at 2: Vienna University of Economics and Business Administration, Austria martin.reismann@wu–wien.ac.at 3: Vienna University of Economics and Business Administration, Austria

Publication date: February 1, 2003

bpl/jors/2003/00000043/00000001/art00002
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