Research map / Probabilistic and scientific modeling / Molecules and proteins
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use STDEN's physics-guided potential-energy-field differential equation instead of the strongest baseline | Traffic flow prediction · GT-221, 15 min | MAE: 0.962 -> 0.865 | Helped |
| Use STDEN's physics-guided potential-energy-field differential equation instead of the strongest baseline | Traffic flow prediction · WRS-393, 15 min | MAE: 0.792 -> 0.730 | Helped |
| Use STDEN's physics-guided potential-energy-field differential equation instead of the strongest baseline | Traffic flow prediction · ZGC-564, 15 min | MAE: 0.708 -> 0.623 | Helped |
| Replace the potential-energy-field DE with a fully connected network (unknown physics) (ablation) | STDEN · All three datasets | MAE | Hurt |
| Remove the energy volume factor φ from the potential-energy-field DE (ablation) | STDEN · All three datasets | MAE | Hurt |
| Increase the ODE solver's number of function evaluations | STDEN · GT-221, WRS-393, and ZGC-564 | MAE | Helped |
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