FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction

TL;DR

FAST is a unified framework combining attention and state-space models to improve scalable spatiotemporal traffic prediction, balancing accuracy and efficiency.

Contribution

It introduces a novel architecture integrating temporal attention, Mamba-based spatial modeling, and multi-source embeddings for enhanced traffic forecasting.

Findings

FAST outperforms baselines on PeMS datasets in MAE and RMSE.

FAST achieves up to 4.3% lower RMSE than strongest baselines.

FAST demonstrates a good balance of accuracy, scalability, and generalization.

Abstract

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. We propose FAST, a unified framework that combines attention and state-space modeling for scalable spatiotemporal traffic forecasting. FAST adopts a Temporal-Spatial-Temporal architecture, where temporal attention modules capture both short- and long-term temporal patterns, and a Mamba-based spatial module models long-range inter-sensor dependencies with linear complexity. To better represent heterogeneous traffic contexts, FAST further introduces a learnable multi-source spatiotemporal embedding that integrates historical traffic flow, temporal context, and node-level information, together with a multi-level skip prediction mechanism for hierarchical feature fusion. Experiments on PeMS04, PeMS07, and PeMS08 show that FAST consistently outperforms strong baselines from Transformer-, GNN-, attention-, and Mamba-based families. In particular, FAST achieves the best MAE and RMSE on all three benchmarks, with up to 4.3\% lower RMSE and 2.8\% lower MAE than the strongest baseline, demonstrating a favorable balance between accuracy, scalability, and generalization.