Latent class choice model with a flexible class membership component: A mixture model approach

This study presents a Latent Class Choice Model (LCCM) with a flexible class membership component. Specifically, it formulates the latent classes using Gaussian-Bernoulli mixture models and investigates the impact of such formulation on the representation of heterogeneity in the choice process, goodness-of-fit measures and out-of-sample prediction accuracy of the choice models. Mixture models are model-based clustering techniques that have been widely used in areas such as machine learning, data mining and pattern recognition for clustering and classification problems. An Expectation-Maximization (EM) algorithm is derived for the estimation of the proposed model. Using two different case studies on travel mode choice behavior, the proposed model is compared to traditional discrete choice models on the basis of parameter estimates’ signs, values of time, statistical goodness-of-fit measures, and cross-validation tests. Results show that mixture models improve the overall performance of latent class choice models by providing better out-of-sample predication accuracy in addition to better representations of heterogeneity without weakening the behavioral and economic interpretability of the choice models.