Attitudes and Latent Class Choice Models using Machine learning

TL;DR

This paper introduces a machine learning-based method using neural networks to enhance latent class choice models by capturing complex attitudinal factors, improving segmentation and policy design.

Contribution

It presents a novel approach integrating neural networks into LCCMs to better model unobserved heterogeneity and attitudes while maintaining theoretical consistency.

Findings

Effective segmentation of car-sharing preferences in Copenhagen

Improved modeling of attitudes and beliefs in choice analysis

Enhanced policy design through realistic behavioral segmentation

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.