Traffic flow prediction algorithm based on dynamic diffusion graph convolution
In order to obtain accurate traffic flow prediction results,a traffic flow prediction algorithm based on dynamic diffusion graph convolution was proposed.Firstly,the model used the diffusion graph convolution model to learn the spatial characteristics between different nodes.Secondly,the dynamic adjacency matrix was introduced to ensure that the characteristics of each node at each time can be learned.Once more,the model used the gated recurrent unit to extract the time characteristics of traffic flow data.Finally,residual connection between model levels was used to transfer more original information and enhance the stability of the model.The experimental results on four open data sets can prove the effectiveness of the algorithm in traffic flow prediction tasks.
万方数据
维普
