Optimized traffic flow prediction model based on space-time diagram convolutional network

The invention relates to an optimized traffic flow prediction model based on a space-time diagram convolutional network. Traffic flow prediction is defined as follows: for a specific road network structure, traffic flow data of several time steps in the future are predicted according to traffic flow data of several time steps recorded historically, wherein the model establishment comprises spatial correlation modeling; the structure of the graph is represented through a self-adaptive adjacency matrix obtained through model training; time correlation modeling is carried out, the calculation process of the gate and the hidden state of the GRU is full-connection operation, and GCN is used for replacing the gate and the hidden state of the GRU; a TPA mechanism is introduced; loss function is adopted, and the purpose of designing and training the model is to minimize an error between a model prediction value and a real value of a road node. According to the method, the accurate prediction precision of the short-ti