Road networks are critical infrastructures underpinning intelligent transportation systems and their related applications. Effective representation learning of road networks remains challenging due to the complex interplay between spatial structures and frequency characteristics in traffic patterns. Existing graph neural networks for modeling road networks predominantly fall into two paradigms: spatial-based methods that capture local topology but tend to over-smooth representations, and spectral-based methods that analyze global frequency components but often overlook localized variations. This spatial-spectral misalignment limits their modeling capacity for road networks exhibiting both coarse global trends and fine-grained local fluctuations. To bridge this gap, we propose HiFiNet, a novel hierarchical frequency-decomposition graph neural network that unifies spatial and spectral modeling. HiFiNet constructs a multi-level hierarchy of virtual nodes to enable localized frequency analysis, and employs a decomposition-updating-reconstruction framework with a topology-aware graph transformer to separately model and fuse low- and high-frequency signals. Theoretically justified and empirically validated on multiple real-world datasets across four downstream tasks, HiFiNet demonstrates superior performance and generalization ability in capturing effective road network representations.

翻译：道路网络是支撑智能交通系统及其相关应用的关键基础设施。由于交通模式中空间结构与频率特性之间存在复杂的相互作用，道路网络的有效表示学习仍然具有挑战性。现有的用于建模道路网络的图神经网络主要分为两种范式：基于空间的方法能够捕获局部拓扑结构，但容易导致表示过度平滑；基于谱的方法能够分析全局频率分量，但常常忽略局部变化。这种空间-谱错位限制了它们对同时呈现粗粒度全局趋势和细粒度局部波动的道路网络的建模能力。为弥合这一差距，我们提出了HiFiNet，一种新颖的层次化频率分解图神经网络，它统一了空间和谱建模。HiFiNet构建了一个多层次的虚拟节点层级结构，以实现局部频率分析，并采用一个包含拓扑感知图Transformer的分解-更新-重构框架，分别建模并融合低频和高频信号。在多个真实世界数据集上，通过四项下游任务的理论论证和实证验证，HiFiNet在捕获有效的道路网络表示方面展现出卓越的性能和泛化能力。