Transformer-GCN model reconstructs high-resolution GPS trajectories from low-resolution inputs, reporting 0.198 km average Fréchet distance on Beijing data and outperforming map-matching and LSTM baselines.
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CROSS-Net combines multiview graph neural networks with VAE-based feature disentanglement to predict taxi demand in previously unseen urban regions.
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Restoring Super-High Resolution GPS Mobility Data
Transformer-GCN model reconstructs high-resolution GPS trajectories from low-resolution inputs, reporting 0.198 km average Fréchet distance on Beijing data and outperforming map-matching and LSTM baselines.
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CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement
CROSS-Net combines multiview graph neural networks with VAE-based feature disentanglement to predict taxi demand in previously unseen urban regions.