MA-GCN: A Memory Augmented Graph Convolutional Network for traffic prediction. (May 2023) - Peng, Dunlu; Zhang, Yongsheng
Abstract: Traffic forecasting is a particularly challenging and important application direction in the field of spatial–temporal prediction. However, it is difficult for existing models to accurately capture the long time dependence of traffic data and the complex spatial dependence of road network. To solve these two issues, in this work, we propose a new deep learning framework — Memory Augmented Graph Convolutional Network (MA-GCN), which combines graph convolutional network (GCN) with differential neural computer (DNC). In the model, GCN is used to learn the complex road network structure to capture the spatial dependence, while DNC is applied to learn the long-term dynamic changes of traffic data to capture the long time dependence. Based on this, the traffic prediction is implemented, and the experimental evaluation is carried out on two public datasets, PeMSD4 and PeMSD8. The results show that the MA-GCN model is superior to the comparative models on several evaluation metrics.