ADFormer applies differential attention plus cluster-based spatial and temporal aggregation to passenger demand forecasting, reporting the best MAE and RMSE on most of nine test settings.
Deep multi-view graph- based network for citywide ride-hailing demand predic- tion.Neurocomputing, 510:79–94,
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ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
ADFormer applies differential attention plus cluster-based spatial and temporal aggregation to passenger demand forecasting, reporting the best MAE and RMSE on most of nine test settings.