Table 3 Comparison of different models on the highway traffic flow dataset.
From: Spatio-temporal transformer and graph convolutional networks based traffic flow prediction
Methods | PEMS04 | PEMS07 | PEMS08 | ||||||
---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | MAPE | MAE | RMSE | MAPE | MAE | RMSE | MAPE | |
LSTM | 26.91 | 42.90 | 17.60 | 30.24 | 48.67 | 12.56 | 21.49 | 33.87 | 13.09 |
DCRNN | 23.70 | 36.70 | 15.54 | 24.94 | 37.71 | 10.89 | 18.35 | 27.87 | 11.97 |
GWN | 20.11 | 31.79 | 14.14 | 22.24 | 35.10 | 9.70 | 14.89 | 23.64 | 9.62 |
AGCRN | 19.76 | 32.61 | 12.95 | 20.92 | 34.73 | 8.82 | 16.31 | 25.88 | 10.29 |
ASTGCN | 21.25 | 33.65 | 13.93 | 24.53 | 38.12 | 10.55 | 18.27 | 28.43 | 11.05 |
STGCN | 24.58 | 38.28 | 16.79 | 29.34 | 45.74 | 13.42 | 19.52 | 29.91 | 12.87 |
ASTGNN | 18.32 | 31.00 | 12.29 | 19.23 | 32.79 | 8.43 | 12.96 | 22.83 | 8,85 |
STWave | 18.50 | 30.39 | 12.43 | 19.94 | 33.88 | 8.38 | 13.42 | 23.40 | 8.90 |
ST-ABC | 19.61 | 30.76 | 13.58 | 21.88 | 34.19 | 9.55 | 14.97 | 23.77 | 10.29 |
ASTTN | 18.51 | 30.20 | 12.21 | 20.05 | 32.94 | 7.94 | 15.07 | 24.10 | 8.60 |
TSGDC | 18.80 | 31.08 | 12.67 | 19.96 | 33.25 | 8.54 | 14.12 | 23.39 | 9.63 |
PDFormer | 18.32 | 29.97 | 12.10 | 19.83 | 32.87 | 8.53 | 13.58 | 23.51 | 9.05 |
IEEAformer | 18.22 | 30.31 | 11.99 | 19.11 | 32.60 | 8.01 | 13.49 | 23.20 | 8.89 |
TSTGNN | 19.06 | 30.52 | 12.72 | 20.40 | 33.83 | 8.72 | 15.69 | 24.72 | 9.86 |
TDMGCN | 18.18 | 30.32 | 12.02 | 18.66 | 32.01 | 7.92 | 12.86 | 22.73 | 8.94 |