Research Article
Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms
Table 5
Traffic occupancy prediction under nonrecurring congestion conditions.
| Model | Input resolution (minutes) | 0.5 | 5 | 15 | Accuracy | RMSE | MAE | Accuracy | RMSE | MAE | Accuracy | RMSE | MAE |
| ANN | 0.869 (0.01) | 1.93 (1.27) | 0.94 (0.29) | 0.837 (0.01) | 2.77 (1.72) | 1.32 (0.34) | 0.80 (0.02) | 3.50 (2.19) | 1.63 (0.48) | RF | 0.873 (0.01) | 1.88 (1.22) | 0.91 (0.27) | 0.850 (0.01) | 2.21 (1.42) | 1.07 (0.32) | 0.796 (0.02) | 2.85 (1.83) | 1.42 (0.43) | SVR | 0.858 (0.01) | 1.92 (1.20) | 0.95 (0.23) | 0.828 (0.01) | 2.18 (1.38) | 1.13 (0.28) | 0.73 (0.03) | 2.58 (1.60) | 1.44 (0.31) | Historical avg. | −1.57 (0.83) | 18.0 (7.92) | 16.4 (1.76) | −1.57 (0.83) | 18.0 (7.92) | 16.4 (1.76) | −1.57 (0.83) | 18.0 (7.92) | 16.4 (1.76) |
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Standard deviations across segments are reported in parentheses, and numbers in boldface show the best results.
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