Comparative modelling of interregional transport flows : applications to multimodal European freight transport
AbstractThis paper aims to compare the descriptive and predictive power of two classes of statistical estimation models for multimodal network flows, viz. the logit model and the neural network model. The application concerns a large data set on inter-regional European freight flows for two commodity categories (food and chemicals). After an exposition of policy issues, methodological and modelling questions and the database, a variety of experiments is carried out. The results show that in general the predictive potential of neural network models is higher than that of logit analysis. The statistical results are also used to investigate the implications of various road tax systems (e.g., e&axes) on various segments of the European road network
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Bibliographic InfoPaper provided by VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics in its series Serie Research Memoranda with number 0002.
Date of creation: 1999
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Web page: http://www.feweb.vu.nl
Other versions of this item:
- Nijkamp, Peter & Reggiani, Aura & Tsang, Wai Fai, 2004. "Comparative modelling of interregional transport flows: Applications to multimodal European freight transport," European Journal of Operational Research, Elsevier, vol. 155(3), pages 584-602, June.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
- L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-03-14 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Hensher, David A. & Ton, Tu T., 2000. "A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(3), pages 155-172, September.
- Longhi, Simonetta & Nijkamp, Peter & Reggiani, Aura & Blien, Uwe, 2002. "Forecasting regional labour markets in Germany: an evaluation of the performance of neural network analysis," ERSA conference papers ersa02p117, European Regional Science Association.
- Giovanni Russo & Aura Reggiani & Peter Nijkamp, 2001. "Modelling and Estimating Modal Share in European Transport," Tinbergen Institute Discussion Papers 01-095/3, Tinbergen Institute.
- Ahmet Kubas & I. Inan & Gokhan Unakitan & E. Erbay, 2008. "The Estimation of the Relationships between Water-Natural Gas Usage and Discharge-Emission Permission by Using Binary Logistic Model for the Industrial Establishments," Quality & Quantity: International Journal of Methodology, Springer, vol. 42(1), pages 35-44, February.
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006.
"New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets,"
Spatial Economic Analysis,
Taylor & Francis Journals, vol. 1(1), pages 7-30.
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets," Tinbergen Institute Discussion Papers 06-020/3, Tinbergen Institute.
- Lucia Rotaris & Romeo Danielis & Igor Sarman & Edoardo Marcucci, 2012. "Testing for nonlinearity in the choice of a freight transport service," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 50, pages 4.
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