SIMBAD: Spatio-Temporal Traffic Forecasting Robust to Aperiodicity
arXiv:2611.09015v1 Announce Type: new Abstract: Accurate traffic prediction is essential for urban planning but remains challenging due to the irregular patterns caused by unexpected events. While Spatio-Temporal Graph Neural Networks (STGNNs) effectively model periodic data, they often struggle with these…
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