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Traffic forecasting is one of the most important problems in the areas of intelligent transportation system, and it is the key link. It plays a major role in transportation service and navigation. However, urban traffic has its own characteristics, and the complex traffic system is highly nonlinear and stochastic, which makes traffic forecasting a very difficult problem. Although many previous methods can make the high performance for predicting in traffic forecasting, the existing research has not fully utilized the influence of spatial and temporal characteristics on prediction. In this article, we put forward a new model called Spatio-temporal multi-attention graph network. Taking into account the similar features of traffic flow every day and the interaction between road network structures, the model takes advantages of the internal dependence between the dynamic spatial network and the time dimension information to improve accuracy of forecasting. Experimental results show that our model is nicer over the others, which has good performance and gain more precision prediction accuracy.
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