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:隐性分类选择模型(LCM)是独立选择模型(DCMs)的延伸,它反映了选择过程中未观察到的异质性,根据偏好相似性假设对人口进行分割。我们提出了一个方法,通过引入人工神经网络(ANN)来制定潜在的变量结构,有效地将态度指标纳入LCM规格中。这一公式克服了探讨态度指标和决策选择之间关系的结构方程式,因为机器学习(ML)在捕捉未观察到的复杂行为特征(例如态度和信仰)方面的灵活性和力量。所有这些都保持了普遍随机功用模型中所提出的理论假设的一致性和估计参数的可解释性。我们用丹麦哥本哈根的公开的优惠数据测试了我们估算汽车共享服务订阅选择的拟议框架。结果显示,我们拟议的方法提供了一种完整和现实的分化,有助于设计更好的政策。