The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during training. While deep learning models have shown promise in learning complex spatial and contextual dependencies, it remains unclear which neural network architectures are most effective at generalizing to unseen layouts. To address this, we conduct a comparative study of Graph Neural Networks, Convolutional Neural Networks, and standard feedforward Neural Networks using synthetic data generated from a simplified and synthetic pocket park environment. Beginning with this illustrative case study, allows for controlled analysis of each model's ability to transfer learned preference patterns to unseen spatial scenarios. The models are evaluated based on their capacity to predict preferences influenced by heterogeneous physical, environmental, and social features. Generalizability score is calculated using the area under the precision-recall curve for the seen and unseen layouts. This generalizability score is appropriate for imbalanced data, providing insights into the suitability of each neural network architecture for preference-aware human behavior modeling in unseen built environments.

翻译：预测人类在建筑环境中的空间偏好对于发展网络-物理-社会基础设施系统至关重要。该领域的一个重大挑战是偏好模型的泛化能力，特别是其在预测训练期间未遇到的环境配置中的偏好时的有效性。尽管深度学习模型在学习复杂的空间和上下文依赖关系方面显示出潜力，但哪种神经网络架构最能有效泛化到未见布局仍不明确。为此，我们使用基于简化的合成口袋公园环境生成的合成数据，对图神经网络、卷积神经网络和标准前馈神经网络进行了比较研究。从这个示例性案例研究入手，可以控制性地分析每个模型将学习到的偏好模式迁移到未见空间场景的能力。模型的评估基于其预测受异质物理、环境和社交特征影响的偏好的能力。泛化性得分通过计算已见和未见布局的精确率-召回率曲线下面积得出。该泛化性得分适用于不平衡数据，为每种神经网络架构在未见建筑环境中进行偏好感知的人类行为建模的适用性提供了见解。