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Decomposing traffic signals into regular and fluctuating components, then applying separate temporal modules, dramatically improves traffic forecasting accuracy compared to treating traffic as a unified representation.
Modeling high-order interactions among traffic nodes via prototype-guided hypergraph construction significantly boosts spatiotemporal forecasting accuracy.
