Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling

Celikoglu H. B.

MATHEMATICAL AND COMPUTER MODELLING, vol.44, pp.640-658, 2006 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44
  • Publication Date: 2006
  • Doi Number: 10.1016/j.mcm.2006.02.002
  • Page Numbers: pp.640-658


Application of soft computational methods, especially artificial neural networks, in examining individual traveller behaviour is not encountered frequently. In most of the relevant cited papers, the feed-forward back propagation neural network (FFBPNN) models or hybrid models of FFBPNNs are proposed. However the feed-forward back propagation algorithm has some drawbacks, which can easily lead the model to develop in an inaccurate direction. Throughout this study, two different algorithms, radial basis function neural network (RBFNN) and generalized regression neural network (GRNN), are employed to propose a new calibration process for travel mode choice analysis in a transportation modelling framework. The neural network methods are not applied directly to calibrate models but are used as a sub-process for alternative non-linear model specification on utility function.