This article 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 algorithm with standard likelihood maximization routines. If access to general maximum-likelihood software is not available, the multinomial-logit based Expectation-Maximization 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.
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