Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction. (arXiv:2209.03858v1 [cs.LG])

In recent years, graph neural networks (GNNs) combined with variants of
recurrent neural networks (RNNs) have reached state-of-the-art performance in
spatiotemporal forecasting tasks. This is particularly the case for traffic
forecasting, where GNN models use the graph structure of road networks to
account for spatial correlation between links and nodes. Recent solutions are
either based on complex graph operations or avoiding predefined graphs. This
paper proposes a new sequence-to-sequence architecture to extract the
spatiotemporal correlation at multiple levels of abstraction …

arxiv cells graph network neural network prediction recurrent neural network traffic