the learned parameters.

Note that we naturally use the physical connectivity and distances among sensors to construct graph, so that we can explicitly explore the road topology information through graph convolution.

### Dynamical Graph Convolution Layer

Previous GCNs-based models such as [@yu2018spatio] [@li2018dcrnn_traffic] can only model fixed spatial dependencies. To capture the hidden spatial dependencies evolving time, here, we propose a novel dynamical graph convolution network to dynamically calculate spatial dependencies in learned latent high-dimensional subspaces. Specifically, we learn linear mappings to project input features of each node to latent high-dimensional subspaces and then the spatial dependencies are dynamically computed by a self-attention mechanism between the projected features. Through such mechanism, we can efficiently model the dynamical spatial dependencies according to the changing input graph signals. Notably, the learned dependencies are directed and we can also learn multiple linear mappings to model spatial dependencies in various representation subspaces as to reveal more hidden spatial dependencies influenced by different relationships factors.

Here, we adopt the self-attention to dynamically model the spatial dependencies among sensors. Self-attention mechanism is widely used in computer vision and natural language processing tasks. It proves powerful to model the relationships among individual items. In GAT, self-attention mechanism 