Residual Graph Convolutional Recurrent Networks For Multi-step Traffic Flow Forecasting. (arXiv:2205.01480v1 [cs.LG])

Traffic flow forecasting is essential for traffic planning, control and
management. The main challenge of traffic forecasting tasks is accurately
capturing traffic networks' spatial and temporal correlation. Although there
are many traffic forecasting methods, most of them still have limitations in
capturing spatial and temporal correlations. To improve traffic forecasting
accuracy, we propose a new Spatial-temporal forecasting model, namely the
Residual Graph Convolutional Recurrent Network (RGCRN). The model uses our
proposed Residual Graph Convolutional Network (ResGCN) to capture the
fine-grained …

arxiv flow forecasting graph networks traffic