input features to spatial-temporal blocks.

### Fixed Graph Convolution Layer

Graph convolution is a generalization of classical convolution to graph domain. It learns node features by aggregating its neighbors information according to the learned weights and predefined graph, thus it is effective to learn the structure-aware node features. In this paper, we adopt the chebyshev-based graph convolution to capture the fixed spatial dependencies from the prior road topology. Formally, we denote $A \in {\mathbb R^{N\times N}}$ as the adjacent matrix that are calculated via gaussian kernel according to the distances among sensors. Let $ X\in {\mathbb R}^{N\times d}$ denote the input node features that contain real-time traffic conditions of $N$ sensors, and $T_k$ is the order-$k$ Chebyshev polynomials. Also, $D$ is the degree matrix with $D_{ii}=\sum_i{A_{ij}}$, $L=I_n-D^{-1/2}AD^{-1/2}$ is the normalized Laplacian matrix, and ${\mathcal {\widetilde L}}=2L/\lambda_{max} -I_n$ is the scaled Laplacian matrix for chebyshev polynomials, where $\lambda_{max}$ is the largest eigenvalues of $L$.

Then, the structure-aware node features $X^{{\mathcal G}}\in {\mathbb R}^{N\times d^{{\mathcal G}}}$ can be obtained with $K$ order chebyshev-polynomial approximation graph convolution as $$X^{{\mathcal G}}_{:,j}=\sum\limits_{i=1}^{d}\sum\limits_{k=0}^K \theta_{ij,k}T_k({\mathcal {\widetilde L}})X_{:,i} \quad  0 \leq j \leq d_{{\mathcal G}}$$ where $X^{\mathcal G}_{:,i}$ is the $i$-th channel of node features and $\theta_{ij,k}$ is 