# Attitudes and Latent Class Choice Models using Machine learning

arXiv: https://arxiv.org/abs/2302.09871 · HTML: https://mlsanity.com/paper/2302.09871/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance](https://mlsanity.com/paper/2109.12042/) (2021)
- [Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling](https://mlsanity.com/paper/1706.00505/) (2017)
- [Rethinking travel behavior modeling representations through embeddings](https://mlsanity.com/paper/1909.00154/) (2019)
- [Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach](https://mlsanity.com/paper/2007.02739/) (2020)
- [Gaussian Process Latent Class Choice Models](https://mlsanity.com/paper/2101.12252/) (2021)

## Built on by (most cited first, 2 of 2)

- [Aligning LLM with human travel choices: a persona-based embedding learning approach](https://mlsanity.com/paper/2505.19003/) (2025)
- [Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs](https://mlsanity.com/paper/2312.14724/) (2023)

## Abstract (arXiv)

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.