This article introduces a multinomial logit model that uses ancillary information to control for uncertainty in both the observed choices made by respondents, and in the attributes of a respondent's choice set. Simulated data are used to compare the performance of this estimator versus simpler...

In order to control for censoring and the integer nature of trip demand, the use of count data models in travel cost analysis is attractive. Two such models, the Poisson and negative binomial, are discussed. Robust estimation techniques that loosen potentially stringent distributional...

The TOBIT estimator of the linear model is commonly used to control for censoring. However, when predicting either demand or consumer surplus, simply substituting the resulting coefficient stimates back into the linear functional form is incorrect. This failure stems from the non-linear impact...

The paper develops a theoretical foundation for using count data models in travel cost analysis. Two micro models are developed: a restricted choice model and a repeated discrete choice model. We show that both models lead to identical welfare measures

This article introduces a multinomial logit model that uses ancillary information to control for uncertainty in both the observed choices made by respondents, and in the attributes of a respondent's choice set. Simulated data are used to compare the performance of this estimator versus simpler...

The reverse logit has been used with some success to predict trip demand, despite the lack of a theoretically plausible foundation. In this article, it is shown that the reverse logit can be derived from demand theory. Specifically, when demand is modeled with a fixed effects Poisson estimator,...