UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions

arXiv:2602.14049 · cs.LG, cs.AI · Submitted 2026-02-15 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-02-15

Updated: 2026-10-06

Code: https://github.com/yw3576/UniST-Pred

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
• STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling
• GMAN: A Graph Multi-Attention Network for Traffic Prediction

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