Attitudes and Latent Class Choice Models using Machine learning

Lorena Torres Lahoz*, Francisco Camara Pereira, Georges Sfeir, Ioanna Arkoudi, Mayara Moraes Monteiro, Carlos Lima Azevedo

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Article number100452
JournalJournal of Choice Modelling
Volume49
Number of pages17
ISSN1755-5345
DOIs
Publication statusPublished - 2023

Keywords

  • Machine learning
  • Latent Class Choice Models
  • Car-Sharing
  • Psychometric Indicators
  • Deep Learning

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