Research Article
Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms
Table 1
Traffic volume prediction under all conditions.
| Model | Input resolution (minutes) | 0.5 | 5 | 15 | Accuracy | RMSE | MAE | Accuracy | RMSE | MAE | Accuracy | RMSE | MAE |
| ANN | 0.906 (0.01) | 34.5 (11.7) | 23.8 (8.6) | 0.889 (0.01) | 44.5 (16.9) | 30.1 (11.5) | 0.865 (0.013) | 53.6 (24.8) | 37.3 (16.9) | RF | 0.910 (0.01) | 31.2 (11.7) | 22.2 (8.5) | 0.904 (0.01) | 34.0 (11.2) | 23.8 (8.5) | 0.890 (0.013) | 39.9 (13.3) | 28.1 (9.7) | SVR | 0.905 (0.01) | 34.7 (12.2) | 24.4 (8.8) | 0.894 (0.01) | 39.5 (14.5) | 27.9 (10.6) | 0.882 (0.007) | 43.7 (16.3) | 30.9 (11.9) | Historical avg. | 0.806 (0.01) | 79.7 (35.7) | 43.5 (17.4) | 0.806 (0.01) | 79.7 (35.7) | 43.5 (17.4) | 0.806 (0.01) | 79.7 (35.7) | 43.5 (17.4) | ARIMA | 0.839 (0.02) | 54.6 (18.3) | 39.1 (13.2) | 0.879 (0.01) | 43.8 (15.6) | 30.6 (11.4) | 0.881 (0.01) | 44.3 (16.3) | 30.1 (11.4) |
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Standard deviations across segments are reported in parentheses and numbers in boldface show the best results.
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