Wilken, Dieter and Berster, Peter and Gelhausen, Marc Christopher (2005): Airport Choice in Germany - New Empirical Evidence of the German Air Traveller Survey 2003. Published in: Proceedings of the 9th Air Transport Research Society World Congress (2005): pp. 1-29.
Download (1MB) | Preview
The paper deals with the quantitative relationship between the number of air travellers in any region and the airports chosen in Germany in 2003. The purpose of the paper is to present results of an analysis of airport choice behaviour of total air passenger demand in Germany, based on data of the German air traveller survey conducted at 17 international and 5 regional airports. About 210 000 passengers were interviewed about their trip origin, destination, choice of travel mode to the airport, purpose of their journey and further journey and person related attributes. As a result of the analysis so far, the distribution of airports chosen by all passengers coming from any region in Germany can be shown in relation to the journey purpose and destination. Based on these data, logit models have been calibrated for each market segment to forecast airport choice in relation to the accessibility and attractiveness of airports. As a further methodological step the outline of a combined neural and nested logit model of access mode and airport choice is given, which will be calibrated on the basis of the data of the German air traveller survey.
Typically, the nearest airport will be chosen by most travellers, there are, however, on average eight airports serving one region (defined as a Spatial Planning Region, of which there are 97 in Germany). If there is an international airport in a region about two thirds of the demand coming from that region will choose that airport, and about one third will choose to depart from one of seven other airports. Vice versa, each airport attracts passengers coming from almost 40 regions. There is thus an intense interaction between an airport and a large influential area.
|Item Type:||MPRA Paper|
|Institution:||German Aerospace Center (DLR) - Air Transport and Airport Research|
|Original Title:||Airport Choice in Germany - New Empirical Evidence of the German Air Traveller Survey 2003|
|Keywords:||Regional air travel demand; airport choice; air traveller survey; catchment areas of airports; travel route from origin via departing airport to destination area; logit model on airport choice; neural networks|
|Subjects:||R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R4 - Transportation Economics > R41 - Transportation: Demand, Supply, and Congestion ; Travel Time ; Safety and Accidents ; Transportation Noise
C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods
|Depositing User:||Marc Christopher Gelhausen|
|Date Deposited:||06. Nov 2007|
|Last Modified:||16. Feb 2013 02:24|
Arbeitsgemeinschaft Deutscher Verkehrsflughäfen (ADV) (2005), ADV-Monatsstatistik Dezember 2004, Berlin.
Ben-Akiva, M., and Lerman, S. (1985), Discrete Choice Analysis: Theory and Applications to Travel Demand, Cambridge
Bentz, Y., and Merunka, D. (2000), Neural Networks and the Multinomial Logit for Brand Choice Modelling: A Hybrid Approach, Journal of Forecasting , 19, S. 177-200
Bishop, C. M. (2003), Neural Networks for Pattern Recognition, Oxford, S. 319 ff
Fausett, L. (1994), Fundamentals of Neural Networks – Architectures, Algorithms and Applications, Englewood Cliffs
Gaudry, M. J. I. (1981), The Inverse Power Transformation Logit and Dogit Mode Choice Models, Transportation Research, 15 B, S. 97-103
Gelhausen, M. (2003), Evaluation zufallsnutzenbasierter Verfahren und künstlicher neuronaler Netze zur Prognose von Flughafen- und Feedermoduswahl, Aachen
Hecht-Nielsen, R. (1990), Neurocomputing. Reading, MA: Addison-Wesley
Hornik, K., Stinchcombe, M., and White, H. (1989), Multilayer Feedforward Networks are Universal Approximators, Neural Networks, 3, S. 359-366
Hruschka, H., Fettes, W., Probst, M., and Mies, C. (2002), A Flexible Brand Choice Model Based on Neural Net Methodology, OR Spectrum, 24, S. 127-143
Hugo, J. (2000), Modellierung von Mobilitätsdaten mit Methoden der Künstlichen Intelligenz, Zeitschrift für Verkehrswissenschaft, 4, S. 355-377
Kohonen, T. (2001), Self-Organizing Maps, Berlin
Koppelmann, F. S. (1981), Non-Linear Utility Functions in Models of Travel Choice Behaviour, Transportation, 10, S. 127-146
Maier, G., and Weiss, P. (1990), Modelle diskreter Entscheidungen – Theorie und Anwendungen in den Sozial- und Wirtschaftswissenschaften, Wien, S.141 ff
Ortúzar, J. de D., and Willumsen, L. G. ( 2001), Modelling Transport, Chichester
Rojas, R. (1996), Neural Networks, Berlin
Statistisches Bundesamt (2004), Fachserie 8: Verkehr, Reihe 6: Luftverkehr, 2003. Wiesbaden.
Train, K. E. (2003), Discrete Choice Methods with Simulation, Cambridge, S. 38 ff