Lorena Torres Lahoz, Francisco Camara Pereira, Georges Sfeir, Ioanna Arkoudi, Mayara Moraes Monteiro, Carlos Lima Azevedo
arXiv 20 Feb 2023 · Econometrics · publishedJournal of Choice Modelling (2023) · 18 citations (OpenAlex)
arXiv:2302.09871 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Walker, J., & Ben-Akiva, M (2002) Generalized random utility model | 1.000 | 11 | 4 | 100% |
| 2 | Sifringer, B., Lurkin, V., & Alahi, A (2020) Enhancing discrete choice models with representation learning | 0.644 | 2 | 2 | 100% |
| 3 | Hess, S., Ben-Akiva, M., Gopinath, D., Walker, J (2009) Taste heterogeneity, correlation, and elasticities in latent class choice models, in Transportation Research Board 88th Annual M… | 0.644 | 2 | 2 | 100% |
| 4 | Hess, S (2014) Latent class structures: Taste heterogeneity and beyond, Handbook of Choice Modelling, pp | 0.644 | 2 | 2 | 100% |
| 5 | Arkoudi, I., Azevedo, C. L., & Pereira, F. C (2021) Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance self | 0.644 | 2 | 2 | 100% |
| 6 | Likert, R (1932) A technique for the measurement of attitudes | 0.644 | 2 | 2 | 100% |
| 7 | McFadden, D (1986) The choice theory approach to market research | 0.644 | 2 | 2 | 100% |
| 8 | Motoaki, Y., & Daziano, R. A (2015) A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand | 0.644 | 2 | 2 | 100% |
| 9 | Hurtubia, R., Nguyen, M. H., Glerum, A., & Bierlaire, M (2014) Integrating psychometric indicators in latent class choice models | 0.585 | 3 | 1 | 100% |
| 10 | Frenkel, A., Shiftan, Y., Gal-Tzur, A., Tavory, S. S., Lerner, O., A… (2021) Share More: Shared MObility Rewards - Summary report self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | The Mixed Aggregate Preference Logit Model: A Machine Learning Approach to Modeling Unobserved Heterogeneity in Discrete Choice Analysis | 0.405 | 1 | 1 |