can capture both local and global dependencies beyond adjacent nodes, thereby reflecting the hidden long-range patterns evolving over time, which enable our model to better capture sharp traffic flow changes. To further incorporate fixed spatial dependencies among sensors into our model, we employ fixed graph convolutions in each spatial transformer and balance it with the learned dynamical spatial dependencies through a gate mechanism.

Long-range temporal dependencies also play an indispensable role traffic forecasting which are usually ignored by previous models. As illustrated in Fig.1(a), in different time steps, there are different spatial dependencies that will be evoked, thus limited range of temporal dependencies will result in significant information loss, leading to poor performance. Furthermore, most existing methods forecast traffic flows in an auto-regressive fashion. Thus, the error-prone predictions are incorporated into the inputs along with previous observations to make further predictions, resulting in an quick error accumulation especially for long-term forecasting. As demonstrated in Fig.2 (a), short-range dependencies may not severely influence the performance of short-term forecasting, as all the observations used for predictions are error-free. However, the long-term forecasting will be significantly degraded, as predictions can only made based on error-prone previous predictions through short-range dependencies, thus resulting in 