Fine and Coarse-grained Graph Flow Neural Network for Traffic Forecasting

Recently, the Intelligent Transportation System has been developed to help relieve traffic congestion, which calls for the need to predict middle and long-term traffic flow accurately. However, existing models can’t effective-ly obtain enough accuracy in middle and long-term prediction. To solve this, we propose a Fine and Coarse-grained Graph Flow Neural Network FCGFNN , which makes better prediction by capturing both fluctuating and stable traffic patterns. Firstly, an asymmetric embedding layer is de-signed to integrate graph structure and temporal dependencies with two di-mensions of data. Then, a Season-Trend Encoder is designed to extract es-sential spatial-temporal features as well as handling non-stationary flows. Finally, the pattern of traffic flow prediction is obtained. Experimental re-sults on two real public traffic datasets shows average performance im-provements of 5.9 , 7.5 and 7.7 across 30-minute, 45-minute and 60-minute prediction intervals.