Traffic Flow Prediction Using Deep Learning Techniques in Urban Road Networks
17 mars 2025
À propos de cet article
Publié en ligne: 17 mars 2025
Reçu: 15 oct. 2024
Accepté: 12 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0832
Mots clés
© 2025 Yilin Han, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Scalability Analysis Results on METR-LA Dataset
| Number of Sensors | 50 | 100 | 200 |
|---|---|---|---|
| MAE (Proposed, mph) | 2.31 | 2.43 | 2.51 |
| RMSE (Proposed, mph) | 4.52 | 4.67 | 4.80 |
| MAE (ST-GCN, mph) | 2.45 | 2.60 | 2.78 |
| RMSE (ST-GCN, mph) | 4.76 | 4.98 | 5.24 |
| MAE (LSTM, mph) | 2.68 | 2.83 | 3.01 |
| RMSE (LSTM, mph) | 5.05 | 5.32 | 5.60 |
Computational Efficiency Analysis
| Model | Training Time (s/epoch) | Inference Time (ms/batch) | GPU Memory (GB) |
|---|---|---|---|
| Proposed Model | 1.8 | 42 | 6.2 |
| ST-GCN | 2.6 | 53 | 6.8 |
| T-GCN | 2.9 | 56 | 7.1 |
| LSTM | 3.1 | 59 | 7.2 |
Robustness to Missing Data
| Missing Data Rate | 10% | 20% | 30% |
|---|---|---|---|
| MAE (Proposed, mph) | 2.45 | 2.62 | 2.84 |
| RMSE (Proposed, mph) | 4.69 | 4.92 | 5.18 |
| MAE (T-GCN, mph) | 2.61 | 2.82 | 3.12 |
| RMSE (T-GCN, mph) | 4.88 | 5.16 | 5.54 |
| MAE (LSTM, mph) | 2.85 | 3.12 | 3.45 |
| RMSE (LSTM, mph) | 5.15 | 5.46 | 5.84 |