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InfoResearchPeer-reviewed
Robust Traffic Forecasting With Disentangled Spatiotemporal Graph Neural Networks
- Published
- Record updated
Summary
Researchers propose DIST, a disentangled spatiotemporal graph neural network framework for traffic forecasting under distribution shifts caused by exogenous factors. The method explicitly separates latent invariant variables from dynamically evolving spatiotemporal dependencies, and a graph perturbation module simulates topology variations during training. The paper reports that comprehensive real-world experiments demonstrate the approach's superiority, with source code published on GitHub.