Traffic Prediction On Nycbike2
Métriques
MAE @ in
MAE @ out
MAPE (%) @ in
MAPE (%) @ out
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | MAE @ in | MAE @ out | MAPE (%) @ in | MAPE (%) @ out | Paper Title | Repository |
|---|---|---|---|---|---|---|
| AGCRN | 5.18 | 4.79 | 27.14 | 26.17 | Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting | |
| ST-SSL | 5.04 | 4.71 | 22.54 | 21.17 | Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction | |
| STFGNN | 5.80 | 5.51 | 30.73 | 29.98 | Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting | |
| STSGCN | 5.25 | 4.94 | 29.26 | 28.02 | Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting |
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