AI RESEARCH
FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
arXiv CS.LG
•
ArXi:2604.13453v1 Announce Type: new Traffic forecasting requires modeling complex temporal dynamics and long-range spatial dependencies over large sensor networks. Existing methods typically face a trade-off between expressiveness and efficiency: Transformer-based models capture global dependencies well but suffer from quadratic complexity, while recent selective state-space models are computationally efficient yet less effective at modeling spatial interactions in graph-structured traffic data.