Gelhausen, Marc Christopher (2006): Airport and Access Mode Choice in Germany: A Generalized Neural Logit Model Approach. Published in: Proceedings of the 2006 European Transport Conference (2006): pp. 1-32.
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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.
|Item Type:||MPRA Paper|
|Institution:||German Aerospace Center (DLR), Air Transport and Airport Research|
|Original Title:||Airport and Access Mode Choice in Germany: A Generalized Neural Logit Model Approach|
|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|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C25 - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
|Depositing User:||Marc Christopher Gelhausen|
|Date Deposited:||07. Dec 2008 15:19|
|Last Modified:||21. Feb 2013 15:58|
Anders, U. (1997) Statistische neuronale Netze, München.
Bhat, Ch. R. (2003) Econometric Choice Formulations: Alternative Model Structures, Estimation Techniques and Emerging Directions, paper presented at the 10th International Conference on Travel Behaviour Research.
Ben-Akiva, M., Lerman, S. (1985) Discrete Choice Analysis: Theory and Applications to Travel Demand, Cambridge.
Bentz, Y., Merunka, D. (2000) Neural Networks and the Multinomial Logit for Brand Choice Modelling: A Hybrid Approach, Journal of Forecasting, vol. 19 177-200
Berster, P., Focke, H., Pabst, H., Wilken, D. (2005) Gemeinsame Auswertung der ADV-Fluggastbefragung 2003, Köln (unveröffentlicht).
Bishop, C. M. (2003) Neural Networks for Pattern Recognition, Oxford.
Domencich, T. A., McFadden, D. (1975). Urban Travel Demand - A Behavioral Analysis, New York.
Fausett, L. (1994) Fundamentals of Neural Networks - Architectures, Algorithms and Applications, Englewood Cliffs
Gaudry, M. J. I., Dagenais, M. G. (1979) The Dogit Model, Transportation Research, vol. 13 B 105-111.
Gelhausen, M. (2003) Evaluation zufallsnutzenbasierter Verfahren und künstlicher neuronaler Netze zur Prognose von Flughafen- und Feedermoduswahl, Aachen
Gelhausen, M., Wilken, D. (2006) Airport and Access Mode Choice - A Generalized Nested Logit Model Approach, paper presented at the 10th Air Transport Research Society Conference 2006 in Nagoya, Japan.
Hornik, K., Stinchcombe, M., White, H. (1989) Multilayer Feedforward Networks are Universal Approximators, Neural Networks, vol. 3 359-366.
Hruschka, H., Fettes, W., Probst, M., Mies, Ch. (2002) A Flexible Brand Choice Model Based on Neural Net Methodology - A Comparison to the Linear Utility Multinomial Logit Model and its Latent Class Extension, OR Spectrum, vol. 24 127 – 143.
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., Jackel, L. D. (1989) Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol. 1 541-551.
Maier, G., Weiss, P. (1990) Modelle diskreter Entscheidungen - Theorie und Anwendung in den Sozial- und Wirtschaftswissenschaften, Wien.
McCarthy, P. S. (1997) The Role of Captivity in Aggregate Share Models of Intercity Passenger Travel, Journal of Transport Economics and Policy, 31 (3) 293-308.
McFadden, D. (1974) Conditional Logit Analysis of Qualitative Choice Behavior, in: Zarembka, P.: Frontiers in Econometrics 105-142, New York.
McFadden, D. (1978) Modelling the Choice of Residental Location, in: Karlqvist, A., Lundqvist, L., Snickars, F., Weibull, J.: Spatial Interaction Theory and Planning Models 75-96, Amsterdam.
Miller, G. F., Todd, P. M., Hedge, S. U. (1989) Designing Neural Networks Using Genetic Algorithms, in: Schaffer, J. D.: Proceedings of the 3rd International Conference on Genetic Algorithms 379 – 384.
Ortuzar, J. de D., Willumsen, L. G. (2001) Modelling Transport, London. Probst, M. (2002) Neuronale Netze zur Bestimmung nichtlinearer Nutzenfunktionen in Markenwahlmodellen, Regensburg.
Rumelhardt, D. E., Hinton, G. E., Williams, R. J. (1986) Learning Internal Representations by Error Propagation, in: Rumelhart, D. E., McClelland, J. L.: Parallel Distributed Processing: Exploration in the Microstructure of Cognition vol. 1: Foundations 318 – 362, Cambridge.
Train, K. E. (2003) Discrete Choice Methods with Simulation, Cambridge.
Wilken, D., Berster, P., Gelhausen, M. (2005). Airport Choice in Germany – New Empirical Evidence of the German Air Traveller Survey 2003, paper presented at the 9th Air Transport Research Society Conference 2005 in Rio de Janeiro, Brazil.
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Airport and Access Mode Choice in Germany: A Generalized Neural Logit Model Approach. (deposited 24. Jul 2007)
- Airport and Access Mode Choice in Germany: A Generalized Neural Logit Model Approach. (deposited 07. Dec 2008 15:19) [Currently Displayed]