Abstract
Multinomial logit models and nested logit models are limited in that they cannot accommodate unobserved variations in travelers' taste and they do not have flexible substitution patterns among alternatives because of the independence of irrelevant alternatives property. Taking this background into account, traffic demand analysts have recently used the mixed logit model in many studies. Unfortunately, most of the studies in the literature for joint analysis of revealed-preference (RP) and stated-preference (SP) data could not simultaneously resolve the two limitations just mentioned. The mixed logit framework is used to formulate an intercity travel mode choice model for joint RP-SP analysis that accommodates the following behavioral considerations: (a) observed and unobserved heterogeneity across individuals in response to level-of-service (LOS) factors, (b) heteroscedasticity across alternatives, and (c) scale differences between the RP and SP choice contexts. The mixed logit formulation is estimated with the maximum simulated likelihood method that employs quasi-random Halton draws. The formulation is applied to examine the travel behavior responses of users of the HoNam, South Korea, high-speed rail to changes in travel conditions. The empirical results show that there is a significant heteroscedasticity across alternatives and a significant heterogeneity in response to LOS attributes based on both observed and unobserved individual characteristics. There is an improvement in the data-fit statistics when one introduces heterogeneity and heteroscedasticity. These results highlight the need to include heterogeneity and heteroscedasticity within the context of intercity travel mode choice modeling to assist policy decision making.
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