用深度模型捕捉隐藏因素，让出行需求分析更准。
Copula-ResLogit: A Deep-Copula Framework for Unobserved Confounding Effects
- 结合残差网络与耦合模型，建模未观测混杂因素。
- 在两个真实数据集上显著降低虚假关联。
- 适合做出行行为分析的学者和政策制定者。
出行需求分析中，未观测因素常导致非因果依赖，掩盖真实因果关系。本文提出可解释的深度联合建模框架 Copula-ResLogit，融合残差神经网络（ResNet）的灵活性与耦合模型对依赖关系的捕捉能力。该方法先通过传统耦合函数检测未观测混杂，再利用深度学习组件缓解隐藏关联。研究在两个案例中验证：一是虚拟现实下行人过街时压力水平与等待时间的关系，二是伦敦出行行为数据中出行方式选择与出行距离的依赖关系。结果表明，Copula-ResLogit 显著减少甚至消除这些依赖，证明残差层能有效建模隐藏混杂效应。
原文摘要 · Abstract (English)
A key challenge in travel demand analysis is the presence of unobserved factors that may generate non-causal dependencies, obscuring the true causal effects. To address the issue, the study introduces a novel deep learning based fully interpretable joint modelling framework, Copula-ResLogit, which integrates the flexibility of Residual Neural Network (ResNet) architectures with the dependence capturing capabilities of copula models. This hybrid structure enables us to first detect unobserved confounding through traditional copula function based joint modelling and then mitigate these hidden associations by incorporating deep learning components. The study applies this framework to two case studies, including the relationship between stress levels and wait time of pedestrians when crossing mid block in VR and the dependencies between travel mode choice and travel distance in London travel behaviour data. Results show that Copula-ResLogit substantially reduces or eliminates the dependencies, demonstrating the ability of residual layers to account for hidden confounding effects.
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