STTD: spatial-temporal transformer with double recurrent graph convolutional cooperative network for traffic flow prediction

Traffic flow prediction is an important part of ITS, accurate traffic flow prediction plays a crucial role in the development of ITS. It can not only effectively avoid traffic problems such as traffic congestion, but also provide a database for other complex tasks. With the increase of population and vehicles, the huge amount of traffic data brings a huge challenge to the accuracy of traffic model prediction. Most of the existing methods use some simple spatial-temporal components and static graph structures, and do not distinguish between normal and abnormal traffic flow signals, which is difficult to capture complex temporal and spatial correlations. To solve this problem, we propose a spatial-temporal Transformer with double recurrent graph convolutional cooperative network (STTDGRU) for traffic flow prediction. The model uses the double graph convolutional gated recurrent module based on dynamic graphs to capture the temporal and spatial correlations, and then uses the spatial-temporal Transformer to capture the temporal and spatial correlations in depth, then predicts more accurate traffic flow by integrating multi-level temporal and spatial features. We also use a residual separation superposition mechanism to separate normal and abnormal traffic flow signals and learn the abnormal signals separately. We conduct extensive experiments on four real datasets to demonstrate the effectiveness of the proposed model and its competitiveness with some current methods.