Traffic flow prediction method based on graph discrete attention network, medium and equipment

The invention discloses a traffic flow prediction method based on a graph discrete attention network, a medium and equipment, and the method comprises the steps: carrying out the flow statistics of traffic big data, and carrying out the short-time prediction of future traffic flow according to a designed algorithm model. According to the method, the time and space characteristics of traffic flow are comprehensively considered, the space characteristics are represented through a graph discrete attention mechanism, and the time sequence characteristics are represented by using the architecture of the multi-layer encoder sequence to the multi-layer decoder sequence, so that a complete traffic flow model is constructed, and a road traffic flow prediction model can be obtained through a training algorithm model. The result shows that the model constructed by the invention can accurately predict future traffic flow data of the traffic monitoring points and can characterize the dynamic change of the flow between the