Traffic Flow Prediction Based on Adaptive Dynamic Graph Convolutional Recurrent Network
Aiming at the dynamic relationship between nodes in actual traffic conditions,an adaptive dynamic graph spatio-temporal prediction model TAGGRU was proposed.Based on the encoder-de-coder network structure,the dynamic spatio-temporal characteristics of traffic data were fused and modeled.Combining node embedding and time coding into space-time coding,a dynamic adjacency graph was constructed to represent the time evolution of node relationship.The traffic flow data and the dynamic adjacency matrix were input into the encoder,and the features were extracted by the a-daptive gating cycle unit.An interactive attention module was added between the encoder and the de-coder to transform historical features to generate future feature representations,and the final output was obtained through feature dimension transformation.The results show that the model has better prediction performance.
traffic flow forecastingspatial-temporal embeddingadaptive dynamic graphsgated re-current unit
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