The invention belongs to the field of intelligent transportation, and discloses a
traffic prediction method based on an attention temporal graph convolutional network. The method includes the following steps that: firstly, an
urban road network is modeled as a graph structure, nodes of the graph represent road sections, edges are connection relationships between the road sections, and the
time series of each road section is described as attribute characteristics of the nodes; secondly, the temporal and spatial characteristics of the
traffic flow are captured by using an attention temporal graph convolutional
network model, the temporal variation trend of the
traffic flow on urban roads is learned by using gated cycle units to capture the time dependence, and the global temporal variation trend of the
traffic flow is learned by using an attention mechanism; and then, the traffic flow state at different times on each road section is obtained by using a fully connected layer; and finally,different evaluation indexes are used to estimate the difference between the real value and the predicted value of the traffic flow on the urban roads and further estimate the prediction ability of the model. Experiments prove that the method provided by the invention can effectively realize tasks of predicting the traffic flow on the urban roads.