Abstract
Due to the increasing complexity in transportation systems, one needs to search for different ways to model the separate components of these systems. A general transportation system comprises components/models concerning mode choice, travel duration, trip distance, departure time, accompanying individuals, etc. This paper tries to discover whether semi- and nonlinear models bring an added value to transportation analysis in general and mode choice modelling in particular. Linear (logistic regression), semi-linear (multiple fractional polynomials) and nonlinear (support vector machines and classification and regression trees) models are applied to several binary settings and compared to each other based on sensitivity (i.e. the proportion of positive cases that are predicted correctly). In general, one can state that on skewed data sets, linear and semi-linear models tend to perform better, whereas on more balanced data sets both nonlinear models yield better results. Future research will take a closer look at other extensions of the well-established linear regression model.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agresti, A.: An Introduction to Categorical Data Analysis. Wiley Series in Probability and Statistics. Wiley, Chichester (1996)
Arentze, T.A., Timmermans, H.J.P.: Albatross: A Learning-Based Transportation Oriented Simulation System. Eindhoven University of Technology (2000)
Ben Akiva, M., Lerman, S.R.: Discrete Choice Analysis Theory and Application to Travel Demand. MIT Press, Cambridge (1985)
Bhat, C.R., Guo, J.: A mixed spatially correlated Logit model: formulation and application to residential choice modeling. Paper presented at the 82nd Annual Meeting of the Transportation Research Board, Washington, D.C (2003)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth Statistics/Probability Series (1984)
Box, G.E.P., Tidwell, P.W.: Transformations of the independent variables. Technom. 4, 531–550 (1962)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)
Friedman, J.H.: Multivariate adaptive regression splines. An. Stat. 19(1), 1–67 (1991)
Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman and Hall, London (1990)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning; Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, Heidelberg (2001)
Hensher, D.A., Ton, T.T.: A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice. Transp. Res. 36E, 155–172 (2000)
Hensher, D., Greene, W.H.: The mixed logit model: The state of practice. Transportation 30(2), 133–176 (2003)
Koppelman, F., Wen, C.-H.: The Paired Combinatorial Logit model: Properties, estimation and application. Transp. Res. B. 34(2), 75–89 (2000)
Moons, E., Wets, G., Aerts, M.: Nonlinear models in transportation. In: Proc. of Conf. on Progress in Activity-Based Analysis, Maastricht, The Netherlands (2004a)
Moons, E., Aerts, M., Wets, G.: The application of fractional polynomials and support vector machines in transportation analysis. Paper accepted for presentation at the Joint Stat. Meetings, Toronto, Canada (2004b)
Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W.: Applied Linear Statistical Models. Irwin (1996)
Nijkamp, P., Reggiani, A., Tritapepe, T.: Modelling inter-urban transport flows in Italy: A Comparison between neural network analysis and logit analysis. Transpn. Res.-C 4(6), 323–338 (1996)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, California (1988)
Reinsch, C.H.: Smoothing by spline functions. Num. Math. 10, 177–183 (1967)
Royston, P., Altman, D.G.: Regression using fractional polynomials of continuous covariates: parsimonious parametric modeling. Appl. Stat. 43, 429–467 (1994)
Royston, P., Ambler, G., Sauerbrei, W.: The use of fractional polynomials to model continuous risk variables in epidemiology. Int. J. of Epi. 28, 964–974 (1999)
Sadek, A.W.: Artificial Intelligence Applications in Transportation. In: Transportation Research Circular E-C113, TRB (2007)
Sauerbrei, W., Royston, P.: Building multivariable prognostic and diagnostic models: transformation of the predictors using fractional polynomials. J. R. Stat. Soc., Series A 162, 71–94 (1999)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1996)
Zhang, H.P., Singer, B.: Recursive Partitioning in the Health Sciences. Springer, New York (1999)
Zurada, J.M.: Introduction to Artificial Neural Systems. W. Publishing Company (1992)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moons, E., Wets, G., Aerts, M. (2007). Nonlinear Models for Determining Mode Choice. In: Neves, J., Santos, M.F., Machado, J.M. (eds) Progress in Artificial Intelligence. EPIA 2007. Lecture Notes in Computer Science(), vol 4874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77002-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-77002-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77000-8
Online ISBN: 978-3-540-77002-2
eBook Packages: Computer ScienceComputer Science (R0)