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A Generalized Neural Logit Model for Airport and Access Mode Choice in Germany


  • Gelhausen, Marc Christopher


The purpose of this paper is to present a new kind of discrete choice model called "Generalized Neural Logit Model" applied exemplarily to the case of airport and access mode choice. This approach employs neural networks to model the utility function of a discrete choice model and correlations within the alternative set and genetic algorithms to optimize the network structure. To evaluate the new approach the application case of airport and access mode choice is chosen. Benchmark for the Generalized Neural Logit Model is a nested logit approach. The estimated market segment specific airport and access mode choice models are generally applicable to any number of airports and combinations of airports and access modes. Thereby it is possible to analyse future scenarios in terms of new airport constellations and new airport access modes. To achieve this, Kohonen’s Self-Organizing-Maps are used to identify different airport clusters and assign every airport to the appropriate cluster. Although the nested logit model show a good model fit for most market segments, the Generalized Neural Logit approach produces a significant increase in model fit especially for those market segments whose nested logit model show less satisfying results.

Suggested Citation

  • Gelhausen, Marc Christopher, 2007. "A Generalized Neural Logit Model for Airport and Access Mode Choice in Germany," MPRA Paper 4313, University Library of Munich, Germany, revised 2007.
  • Handle: RePEc:pra:mprapa:4313

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    References listed on IDEAS

    1. Wilken, Dieter & Berster, Peter & Gelhausen, Marc Christopher, 2005. "Airport Choice in Germany - New Empirical Evidence of the German Air Traveller Survey 2003," MPRA Paper 5631, University Library of Munich, Germany.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
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    More about this item


    Airport and access mode choice model; Artificial neural networks; Concept of alternative groups; Discrete choice model; Generalized Neural Logit Model; Kohonen’s Self-Organizing Maps; Nested logit model;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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