for a forecasting model to effectively and efficiently capture the spatial and temporal dependencies. Generally, traffic forecasting can be classified into two scales: short-term ($\leq 30$ min) and long-term ($\geq 30$ min). Previous approaches such as time-series models [@liu2011discovering] and Kilman filtering models [@lippi2013short] are mostly focus on short-term forecasting and perform quiet well. However, these models are typically based on the stationary assumption which is commonly impractical in long-term forecasting as the traffic flows are naturally highly dynamical. Furthermore, they fail to jointly capture the spatio-temporal correlations in the traffic flows. So that these model fail to effectively forecast long-term traffic flows.

Naturally, traffic networks can be represented as graphs in which the nodes represent traffic sensors and the edges together with its weights are determined by the connectivity as well as Euclidean distances among sensors. Thus traffic flows can be viewed as graph signals evolving with time. As graph neural networks [@atwood2016diffusion] [@defferrard2016convolutional] [@kipf2017semi] have show its power in processing graph-represented data, recent works tend to combine graph neural networks with sequence-learning model to jointly capture spatio-temporal correlations and improve the performance in both short-term and long-term forecasting. [@yu2018spatio] and [@li2018dcrnn_traffic] are the first two graph-based traffic forecasting 