  This study presents a semi-nonparametric Latent Class Choice Model (LCCM)
with a flexible class membership component. The proposed model formulates the
latent classes using mixture models as an alternative approach to the
traditional random utility specification with the aim of comparing the two
approaches on various measures including prediction accuracy and representation
of heterogeneity in the choice process. Mixture models are parametric
model-based clustering techniques that have been widely used in areas such as
machine learning, data mining and patter 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, value 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 prediction accuracy in addition to
better representations of heterogeneity without weakening the behavioral and
economic interpretability of the choice models.
