Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to artificial neural systems
Introduction to artificial neural systems
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proceedings of the Second International Workshop on Computational Transportation Science
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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.