An Endogenous Segmentation Mode Choice Model

This paper uses an endogenous segmentation approach to model mode choice. This approach jointly determines the number of market segments in the travel population, assigns individuals probabilistically to each segment, and develops a distinct mode choice model for each segment group. The author proposes a stable and effective hybrid estimation approach for the endogenous segmentation model that combines an Expectation-Maximization (EM) algorithm with standard likelihood maximization routines. If access to general maximum-likelihood software is not available, the multinomial-logit based EM algorithm can be used in isolation. The endogenous segmentation model and other commonly used models in the travel demand field to capture systematic heterogeneity are estimated using a Canadian intercity mode choice dataset. The results show that the endogenous segmentation model fits the data best and provides intuitively more reasonable results compared to the other approaches. Introduction The estimation of travel mode choice models is an important component of urban and intercity travel demand analysis and has received substantial attention in the transportation literature (see Ben-Akiva and Lerman, 1985). The most widely used model for urban as well as intercity mode choice is the multinomial logit model (MNL). The MNL model is derived from random utility maximizing behavior at the disaggregate individual level. Therefore, ideally, we should estimate the logit model at the individual level and obtain individual-specific parameters for the intrinsic mode biases and for the mode level-of-service attributes. However, the data used for mode choice estimation is usually cross-sectional; that is, there is only one observation per individual. This precludes estimation of the logit parameters at the individual level and constrains the modeler to pool the data across individuals (even in panel data comprising repeated choices from the same individual, the number of observations per individual is rarely sufficient for consistent and efficient estimation of individual-specific parameters). In such pooled estimations, it is important to accommodate differences in intrinsic mode biases (preference heterogeneity) and differences in responsiveness to level-of-service attributes (response heterogeneity) across individuals. In particular, imposing an assumption of preference and response homogeneity in the population is rather strong and is untenable in most cases (Hensher, 1981). Further, if the assumption of homogeneity is imposed when, in fact, there is heterogeneity, the result is biased and inconsistent parameter and choice probability estimates (see Chamberlain, 1980). An issue of interest then is: how can preference and response heterogeneity be incorporated into the multinomial logit model when studying mode choice behavior from crosssectional data? One approach is to estimate a model with (pure) random coefficients where the logit mode bias and level-of-service parameters are assumed to be randomly distributed in the population. This approach ignores any systematic variations in preferences and response across individuals. As such, it cannot be considered as a substitute for careful identification of systematic variations in the population. The random coefficients cannot even be considered as an alternative approach (i.e., alternative to accommodating systematic effects) to account for heterogeneity in choice models; it can only be considered as a potential “add-on” to a model that has attributed as much heterogeneity to systematic variations as possible (Horowitz, 1991 makes a similar point in the context of the use of multinomial probit models in travel demand