Urban traffic network is a typical complex network. Traffic states data (e.g., traffic flow, traffic occupancy, traffic speed, etc.) has strong temporal and spatial correlation. To accurately predict urban traffic state, it is very important to extract the road features in the traffic network. The existing methods use separated temporal and spatial components or Spatio-temporal fusion components to predict traffic. Graph Convolution Network (GCN) is usually used to obtain the correlation between spatial nodes or Spatio-temporal nodes. However, the message aggregation method of GCN cannot assign different weights to neighbor nodes. While Graph Attention Network (GAT) can pay attention to different neighbor nodes. To better explain the existing traffic prediction models, we carried out experiments on the model framework based on GCN. We use a new proposed GAT instead of GCN, and find that the new GAT has better performance in multi-features traffic prediction tasks. We also made a theoretical analysis on the improvement of the performance and carried out experiments on four real datasets, which can provide strong support for the theoretical analysis. Our method improves the interpretability of the Graph Neural Network (GNN) model in extracting spatial features of the traffic networks.