Research map / Graph learning / Knowledge and temporal graphs
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MLC-PPF instead of LS-KNN | Sydney PPF prediction · Sydney PPF, average across time periods | MAE: 21.20 -> 9.84 | Helped |
| Use MLC-PPF instead of LS-KNN | Sydney PPF prediction · Sydney PPF, average across time periods | NRMSE: 7.01% -> 2.21% | Helped |
| Use MLC-PPF instead of WITF | Cold-start recommendation · Amazon Book | MAE: 0.740 -> 0.396 | Helped |
| Use MLC-PPF instead of WITF | Cold-start recommendation · Amazon Music | MAE: 0.716 -> 0.582 | Helped |
| Set the number of nearest neighbors to k=2 (ablation) | MLC-PPF · Sydney PPF | MAE | Helped |
| Vary k within 1 to 5 (ablation) | MLC-PPF · Sydney PPF | MAE | No clear effect |
| Set the ABS guidance factor λ to 10⁻¹ (ablation) | MLC-PPF · Sydney PPF | MAE | Helped |
| Vary λ within 10⁻⁵ to 10⁰ (ablation) | MLC-PPF · Sydney PPF | MAE | No clear effect |
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