Attitudes and Latent Class Choice Models using Machine learning
This work addresses the challenge of modeling complex behavioral features like attitudes in discrete choice models for transportation policy, but it is incremental as it builds on existing LCCM frameworks with ML enhancements.
The authors tackled the problem of capturing unobserved heterogeneity in choice models by efficiently incorporating attitudinal indicators using Artificial Neural Networks in Latent Class Choice Models, applied to car-sharing subscription data from Copenhagen, resulting in a complete and realistic segmentation for policy design.
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.