Spatial-temporal encoder-decoder model for traffic flow prediction

In order to solve the problem that many traffic flow prediction research methods are unable to comprehensively explore the dynamic hidden correlations in traffic data, the dynamic spatio-temporal variation characteristics were studied and an encoder-decoder-based traffic prediction model was proposed. In the model, both encoder and decoder mainly consisted of multi-head spatio-temporal attention mechanism modules, and a connection attention mechanism was added in between to analyze the spatio-temporal correlations of the road network. The model also used a dynamic embedding module consisting of a combination of both spatio-temporal embedding coding and adaptive graph convolution to analyze the dynamic and static information of nodes. Experiments on two real datasets demonstrate that the spatio-temporal model outperform other models for long- and short-term traffic prediction. Thus, the spatio-temporal encoder-decoder model can effectively handle complex spatio-temporal sequences and improve the traffic flow prediction accuracy.