Research map / Graph learning / Knowledge and temporal graphs
STREETS: A Novel Camera Network Dataset for Traffic Flow
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add 2-hop neighbor camera data (K=2) to the features instead of own camera only (K=0) (ablation) | SVR, RFR, LR · STREETS 15 min horizon | MAEc/MAE: 3.05/3.25 -> 2.96/3.20 (SVR, 7/16) | Helped |
| Add 2-hop neighborhood to ANN features (ablation) | ANN · STREETS 15 min horizon | MAEc/MAE: 3.21/3.40 -> 3.52/3.73 (7/16) | Hurt |
| Condition predictions on queue vs free-flow state (MAEc instead of MAE) (ablation) | SVR, RFR, LR · STREETS | MAE: 3.25 -> 3.05 (SVR K=0, 7/16, 15 min) | Helped |
| Use nonlinear models (RFR, SVR) instead of linear regression | traffic prediction · STREETS | MAE: 4.80/5.05 -> 4.07/4.28 (LR vs SVR, 7/15, K=0, L=3) | Helped |
| Increase history length L from 3 to 6 (ablation) | SVR · STREETS 15 min horizon | MAEc/MAE: 3.05/3.25 -> 3.14/3.32 (K=0, 7/16) | No clear effect |
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