The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis

The multinomial logit model in discrete choice analysis is widely used in transport research. It has long been known that the Gumbel distribution forms the basis of the multinomial logit model. Although the Gumbel distribution is a good approximation in some applications such as route choice problems, it is chosen mainly for mathematical convenience. This can be restrictive in many other scenarios in practice. In this paper we show that the assumption of the Gumbel distribution can be substantially relaxed to include a large class of distributions that is stable with respect to the minimum operation. The distributions in the class allow heteroscedastic variances. We then seek a transformation that stabilizes the heteroscedastic variances. We show that this leads to a semi-parametric choice model which links the linear combination of travel-related attributes to the choice probabilities via an unknown sensitivity function. This sensitivity function reflects the degree of travelers’ sensitivity to the changes in the combined travel cost. The estimation of the semi-parametric choice model is also investigated and empirical studies are used to illustrate the developed method.

Research highlights
► The assumption of the Gumbel distribution in the multinomial logit model is substantially relaxed to include a large class of distributions.
► The new distribution family is stable with respect to the minimum operation.
► The new distribution family allows heteroscedastic variances that can be stabilized via a suitable transformation.
► A semi-parametric choice model is developed which links the linear combination of travel-related attributes to the choice probabilities via an unknown sensitivity function.
► An algorithm is investigated for the estimation of the semi-parametric choice model based on Bayesian analysis.