Research Article
Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network
Table 1
Prediction results of the Bi-GRCN model and other baseline methods.
| T (min) | Metric | Models | HA | ARIMA | SVR | GCN | GRU | Bi-GRCN |
| 15 | RMSE | 4.389446 | 6.998807 | 4.228722 | 5.609166 | 4.802862 | 4.548942 | MAE | 3.006430 | 4.991264 | 2.882457 | 4.410619 | 3.464756 | 3.239479 | Accuracy | 0.699633 | 0.455428 | 0.710631 | 0.616168 | 0.671343 | 0.688718 | var | 0.788113 | −0.000145 | 0.803415 | 0.654015 | 0.746349 | 0.772483 |
| 30 | RMSE | 4.389446 | 6.998712 | 4.252060 | 5.635812 | 4.487037 | 4.369150 | MAE | 3.006430 | 4.990788 | 2.935113 | 4.453700 | 3.218210 | 3.101176 | Accuracy | 0.699633 | 0.455422 | 0.709021 | 0.614327 | 0.692941 | 0.701008 | var | 0.788113 | −0.000347 | 0.802071 | 0.650719 | 0.778851 | 0.790520 |
| 45 | RMSE | 4.389446 | 6.997727 | 4.280222 | 5.664346 | 4.372606 | 4.347006 | MAE | 3.006430 | 4.990163 | 2.978076 | 4.462996 | 3.116036 | 3.077341 | Accuracy | 0.699633 | 0.455437 | 0.707092 | 0.612372 | 0.700769 | 0.702521 | var | 0.788113 | −0.000512 | 0.800080 | 0.647190 | 0.789771 | 0.792385 |
| 60 | RMSE | 4.389446 | 6.989135 | 4.307416 | 5.677034 | 4.333217 | 4.245528 | MAE | 3.006430 | 4.986023 | 3.011925 | 4.489186 | 3.086672 | 3.000904 | Accuracy | 0.699633 | 0.455653 | 0.705231 | 0.611504 | 0.703465 | 0.709466 | var | 0.788113 | 0.000986 | 0.798030 | 0.645671 | 0.793562 | 0.801980 |
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