A heteroscedastic extreme value model of intercity travel mode choice

Abstract

Estimation of disaggregate mode choice models to estimate the ridership share on a proposed new (or improved) intercity travel service and to identify the modes from which existing intercity travelers will be diverted to the new or upgraded service constitutes a critical part of evaluating alternative travel service proposals to alleviate intercity travel congestion. This paper develops a new heteroscedastic extreme value model of intercity mode choice that overcomes the ‘independence of irrelevant alternatives’ (IIA) property of the commonly used multinomial logit model. The proposed model allows a more flexible cross-elasticity structure among alternatives than the nested logit model. It is also simple, intuitive and much less of a computational burden than the multinomial probit model. The paper discusses the non-IIA property of the heteroscedastic extreme value model and presents an efficient and accurate Gaussian quadrature technique to estimate the heteroscedastic model using the maximum likelihood method. The multinomial logit, alternative nested logit structures, and the heteroscedastic model are estimated to examine the impact of improved rail service on business travel in the Toronto-Montreal corridor. The nested logit structures are either inconsistent with utility maximization principles or are not significantly better than the multinomial logit model. The heteroscedastic extreme value model, however, is found to be superior to the multinomial logit model. The heteroscedastic model predicts smaller increases in rail shares and smaller decreases in non-rail shares than the multinomial logit in response to rail-service improvements. It also suggests a larger percentage decrease in air share and a smaller percentage decrease in auto share than the multinomial logit. Thus, the multinomial logit model is likely to provide overly optimistic projections of rail ridership and revenue, and of alleviation in inter-city travel congestion in general, and highway traffic congestion in particular. These findings point to the limitations of the multinomial logit and nested logit models in studying intercity mode choice behavior and to the usefulness of the heteroscedastic model proposed in this paper.