information of nodes as well as the temporal information of each observations are inherently lost. As a matter of fact, they both play an important role in modeling spatial dependencies, as illustrated in Fig 1(a), the distance and time determine whether a spatial dependency between two node should be utilized for future predictions. In transformer [@NIPS2017_7181], prior positional embedding is adopted to inject ’position’ information into the input sequences. Here, we adopt learnable spatial and temporal positional embedding layer to learn spatial temporal embedding into each node feature. Specifically, a dictionary ${\mathcal D}^{\mathcal S}\in {\mathcal R^{N\times N}}$ and ${\mathcal D}^{\mathcal T}\in {\mathcal R^{N\times T}}$ is learned as spatial position embedding matrix and temporal position embedding matrix respectively. We initialize the dictionaries with the graph adjacent matrix and one-hot time encode,respectively, as graph adjacent matrix contains the connectivity as well as distance information which is important for spatial dependencies modeling while one hot time encode can well inject the time step information into each node. The dictionaries are then updated during training. The input features are concatenated with the dictionaries as $X^{i}=[X^{\mathcal S}, {\mathcal D}^{\mathcal S}, {\mathcal D}^{\mathcal T}]$ before feeding into following graph convolution layers, where $X^{\mathcal S}$ is the 