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

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  • Gelhausen, Marc Christopher

Abstract

The purpose of the paper is to present a novel approach of a general airport and access mode choice model. Based on data of the German Air Traveller Survey 2003 with a sample size of about 210.000 passengers interviewed at 21 airports a three-stage nested logit model has been estimated in a first step. 7 different access modes to the airport are modelled, subdivided into four private and three public travel modes. The model includes 7 different market segments: Domestic, European and Intercontinental travel, each segment split up into private and business travel. The European private travel segment is further subdivided into short stay trips and holiday travel. The aim is to develop a generally applicable airport and access mode choice model. Thereby it is possible to analyse future in terms of new airport constellations and new airport access modes. To achieve this, Kohonens Self-Organizing-Maps are used to identify different airport clusters and assign every airport to the appropriate cluster. Based on these airport clusters the aforementioned nested logit model has been estimated. In a second step, neural networks are applied to the problem of airport and access mode choice. On the basis of neural networks a new kind of discrete choice model called "Generalized Neural Logit Model" has been developed. To optimize the network structure genetic algorithms have been applied. Such a model fits into the structure of a General Extreme Value model and satisfies the condition of utility maximization. A second airport and access mode choice model based on the Generalized Neural Logit Model and the airport clusters has been estimated. Although the former approach showed for most market segments a good model fit, the new approach showed a significant increase in model fit especially for those market segments the model fits of which in the nested logit model were less satisfying.

Suggested Citation

  • Gelhausen, Marc Christopher, 2006. "Airport and Access Mode Choice in Germany: A Generalized Neural Logit Model Approach," MPRA Paper 4236, University Library of Munich, Germany, revised Sep 2006.
    • Handle: RePEc:pra:mprapa:4236
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    File URL: https://mpra.ub.uni-muenchen.de/11956/3/MPRA_paper_11956.pdf
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    References listed on IDEAS

    as
    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 9780521766555, September.
    3. Gaundry, Marc J. I. & Dagenais, Marcel G., 1979. "The dogit model," Transportation Research Part B: Methodological, Elsevier, vol. 13(2), pages 105-111, June.
    4. Yves Bentz & Dwight Merunka, 2000. "Neural networks and the multinomial logit for brand choice modelling: a hybrid approach," Post-Print hal-01822273, HAL.
    5. Gelhausen, Marc Christopher & Wilken, Dieter, 2006. "Airport and Access Mode Choice : A Generalized Nested Logit Model Approach," MPRA Paper 4311, University Library of Munich, Germany, revised 2006.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Gelhausen, Marc Christopher, 2006. "Flughafen- und Zugangsverkehrsmittelwahl in Deutschland - Ein verallgemeinerter Nested Logit-Ansatz," MPRA Paper 16002, University Library of Munich, Germany.
    2. Kristoffersson, Ida & Berglund , Svante, 2020. "Modelling connection trips to long-distance travel : state-of-the-art and directions for future research," Papers 2020:5, Research Programme in Transport Economics.

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    More about this item

    Keywords

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

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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