Research Article
Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms
Table 4
Traffic volume prediction under nonrecurring congestion conditions.
| Model | Input resolution (minutes) | 0.5 | 5 | 15 | Accuracy | RMSE | MAE | Accuracy | RMSE | MAE | Accuracy | RMSE | MAE |
| ANN | 0.913 (0.01) | 46.9 (16.4) | 33.2 (12.1) | 0.880 (0.02) | 66.5 (24.4) | 46.2 (17.0) | 0.840 (0.03) | 80.2 (29.7) | 59.3 (22.8) | RF | 0.900 (0.01) | 50.1 (17.4) | 37.4 (13.2) | 0.890 (0.01) | 57.3 (19.6) | 42.0 (15.2) | 0.860 (0.02) | 66.6 (21.1) | 50.9 (16.0) | SVR | 0.892 (0.02) | 56.0 (18.9) | 41.0 (14.1) | 0.870 (0.02) | 67.2 (21.4) | 49.7 (16.1) | 0.850 (0.03) | 76.1 (22.9) | 56.4 (17.0) | Historical avg. | 0.139 (0.08) | 232 (109) | 192 (83.6) | 0.139 (0.08) | 232 (109) | 192 (83.6) | 0.139 (0.08) | 232 (109) | 192 (83.6) | ARIMA | 0.851 (0.02) | 73.8 (20.5) | 54.2 (15.5) | 0.670 (0.02) | 176 (157) | 126 (48) | 0.860 (0.02) | 77.7 (30.4) | 51.6 (19.7) |
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Standard deviations across segments are reported in parentheses and numbers in boldface show the best results.
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