models. They largely improve the model performance by introducing the inherent graph topology of a traffic network into sequence-learning models. They integrate spatial-based [@atwood2016diffusion] or spectral-based [@defferrard2016convolutional] Graph Convolutional Networks (GCNs) with convolution-based [@gehring2017convolutional] or Recurrent-neural-networks (RNNs)-based sequence learning models to jointly capture the spatial and temporal dependencies.

However, they have some significant limitations that could be further improved especially for long-term forecasting.

From the perspective of spatial dependencies, the spatial dependencies are dynamical and the dynamics result from two aspects. On one hand, the spatial dependencies between two sensors are influenced by their connectivity and distance. The former one indicates that whether there may be dependencies between them, while the latter one together with the real-time speed determines when the dependencies will be evoked, thus the spatial dependencies should be evolving with time. In fact, this kind of dynamic is a characteristic of traffic forecasting task. As shown in Fig.1 (a), for simplicity, we assume the traffic speeds keep the same across different time steps and all connected sensors are interacted with each other. Taking the central purple node for example, the spatial dependencies between the central node with its neighbor nodes are evolving with time, and the influence 