Attitudes and Latent Class Choice Models using Machine learning
Abstract
Latent Class Choice Models (LCCM) are extensions of discrete choice models (DCMs) that capture unobserved heterogeneity in the choice process by segmenting the population based on the assumption of preference similarities. We present a method of efficiently incorporating attitudinal indicators in the specification of LCCM, by introducing Artificial Neural Networks (ANN) to formulate latent variables constructs. This formulation overcomes structural equations in its capability of exploring the relationship between the attitudinal indicators and the decision choice, given the Machine Learning (ML) flexibility and power in capturing unobserved and complex behavioural features, such as attitudes and beliefs. All of this while still maintaining the consistency of the theoretical assumptions presented in the Generalized Random Utility model and the interpretability of the estimated parameters. We test our proposed framework for estimating a Car-Sharing (CS) service subscription choice with stated preference data from Copenhagen, Denmark. The results show that our proposed approach provides a complete and realistic segmentation, which helps design better policies.
Cite
@article{arxiv.2302.09871,
title = {Attitudes and Latent Class Choice Models using Machine learning},
author = {Lorena Torres Lahoz and Francisco Camara Pereira and Georges Sfeir and Ioanna Arkoudi and Mayara Moraes Monteiro and Carlos Lima Azevedo},
journal= {arXiv preprint arXiv:2302.09871},
year = {2023}
}
Comments
25 pages, 8 figures