  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.
