Graph Neural Networks (GNNs) have attracted increasing attention due to the significant representation learning capacity for graphs. The traffic forecasting is a typical graph representation learning task, but it is challenging to model the complex spatial and temporal relationships in traffics. Traditional spectral approaches get filters based on the eigendecomposition, which depends on the Laplacian matrix of the graph. However, these approaches have expensive matrix operation on graph convolutions neural networks and are insufficient to tackle the spatial dependency. In this paper, we propose a novel graph neural network-Spatial-Temporal Multi-head Graph ATtention network (ST-MGAT), to deal with the traffic forecasting problem. We build convolutions on the graph directly. We consider the features of neighborhood nodes and the weights of the edges to generate new node representation. More specifically, there are two main modules: i) Temporal convolution blocks to capture the dynamic time correlations; ii) Graph attention networks to capture the dynamic spatial relations between nodes. Experimental results show that our model achieves up to 13% improvement over the state-of-The-Art approaches in short-Term, medium-Term, and long-Term highway traffic forecasting. 1Code is available at https://github.com/Kelang-Tian/ST-Mgat