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.

翻译：潜在类别选择模型（LCCM）是离散选择模型（DCM）的扩展，通过基于偏好相似性假设对人群进行细分，来捕捉选择过程中未观测到的异质性。我们提出了一种在LCCM规范中高效整合态度指标的方法，通过引入人工神经网络（ANN）来构建潜在变量构造。鉴于机器学习（ML）在捕捉态度与信念等未观测复杂行为特征方面的灵活性与强大能力，该构造方法在探索态度指标与决策选择之间关系的能力上超越了结构方程模型。同时，该方法仍保持了广义随机效用模型理论假设的一致性以及估计参数的可解释性。我们利用丹麦哥本哈根的陈述偏好数据，对所提出的框架进行了汽车共享（CS）服务订阅选择的验证。结果表明，我们的方法提供了完整且实际有效的细分方案，有助于设计更优的政策。