With the acceleration of urbanization, traffic forecasting has become an essential role in smart city construction. In the context of spatio-temporal prediction, the key lies in how to model the dependencies of sensors. However, existing works basically only consider the micro relationships between sensors, where the sensors are treated equally, and their macroscopic dependencies are neglected. In this paper, we argue to rethink the sensor's dependency modeling from two hierarchies: regional and global perspectives. Particularly, we merge original sensors with high intra-region correlation as a region node to preserve the inter-region dependency. Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning. In pursuit of the generality and reality of node representations, we incorporate a Meta GCN to calibrate the regional and global nodes in the physical data space. Furthermore, we devise the cross-hierarchy graph convolution to propagate information from different hierarchies. In a nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal prediction method, HIEST, to create and utilize the regional dependency and common spatio-temporal patterns. Extensive experiments have verified the leading performance of our HIEST against state-of-the-art baselines. We publicize the code to ease reproducibility.

翻译：随着城市化进程的加速，交通流量预测已成为智慧城市建设中的关键环节。在时空预测的背景下，核心问题在于如何对传感器间的依赖关系进行建模。然而，现有研究基本仅考虑传感器间的微观关系，将各类传感器视为同等地位，忽略了其宏观依赖关系。本文主张从区域与全局两个层级重新审视传感器的依赖建模：具体而言，我们将区域内高相关性的原始传感器合并为区域节点，以保留区域间依赖关系；随后生成具有代表性的通用时空模式作为全局节点，以反映传感器间的全局依赖关系，并为时空依赖学习提供辅助信息。为追求节点表征的通用性与真实性，我们引入Meta GCN在物理数据空间中对区域节点与全局节点进行校准。进一步地，我们设计跨层级图卷积机制以实现不同层级间的信息传播。综上，本文提出层级信息增强时空预测方法HIEST，通过构建并利用区域依赖关系与通用时空模式实现预测。大量实验证明，HIEST在性能上显著优于当前最先进的基线模型。我们已公开代码以促进研究成果的可复现性。