Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
This work addresses the need for more flexible and accurate choice models in fields like transportation, though it is incremental as it builds on existing mixture model techniques.
The study tackled the problem of improving latent class choice models by proposing a semi-nonparametric approach using mixture models, which resulted in better out-of-sample prediction accuracy and enhanced representation of heterogeneity while maintaining interpretability.
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.