learning to non-Euclidean domain. As a variant of GNNs, Graph convolution networks (GCNs) [@atwood2016diffusion] [@defferrard2016convolutional] [@kipf2017semi] generalize classical convolutions to the graph domain, attracting increasing interests from both researchers and practitioners. Recently, GCNs are applied to model the spatial dependencies of traffic flows to explore the inherent traffic topology. For example, STGCN [@yu2018spatio] models spatial dependencies with spectral graph convolutions defined on an undirected graph, while [@li2018dcrnn_traffic] employs diffusion graph convolutions on a directed graph to incorporate the influence of traffic flow directions. However, they both have drawback that their spatial dependencies are fixed once trained, ignoring the dynamic changes of traffic conditions (e.g. rush hours and traffic accidents). Most recently, [@guo2019attention] also proposed to generate dynamical spatial dependencies but their spatial dependencies are not evolving time but the depth of spatial and temporal blocks. [@pan2019urban] models dynamical spatial dependencies by adopting a extra meta learner to summarize the geo-graph features and then the embedded geo-graph features together with GATs [@velivckovic2017graph] are used to generate dynamical spatial dependencies, however, their spatial dependencies are still limited to $k$ nearest neighbors of predefined graph topology that cannot discover hidden patterns of dependencies at various scales beyond local nodes. [@wu2019graph] capture hidden spatial 