Table 2 Traffic speed prediction results of different methods on METR-LA and PEMS-BAY.

From: Temporal representation learning enhanced dynamic adversarial graph convolutional network for traffic flow prediction

Datasets

Methods

15 min

30 min

60 min

MAE

RMSE

MAPE (%)

MAE

RMSE

MAPE (%)

MAE

RMSE

MAPE (%)

METR-LA

VAR

4.42

7.89

10.20

5.41

9.13

12.7

6.52

10.11

15.80

SVR

3.99

8.45

9.30

5.05

10.87

12.10

6.72

13.76

16.70

ARIMA

3.99

8.21

9.60

5.15

10.45

12.70

6.90

13.23

17.40

LSTM

3.44

6.30

9.60

3.77

7.23

10.90

4.37

8.69

13.20

GCRN

3.03

5.75

8.26

3.54

6.92

10.11

4.32

8.48

13.05

STGCN

2.88

5.74

7.62

3.47

7.24

9.57

4.59

9.40

12.70

DCRNN

2.77

5.38

7.30

3.15

6.45

8.80

3.60

7.59

10.50

ASTGCN(r)

4.86

9.27

9.21

5.43

10.61

10.13

6.51

12.52

11.64

OGCRNN

3.04

5.72

8.20

3.45

6.66

9.56

3.98

7.80

11.35

STSGCN

3.31

7.62

8.06

4.13

9.77

10.29

5.06

11.66

12.91

GWN

2.98

5.90

7.92

3.59

7.29

10.26

4.43

8.97

13.64

Z-GCNETs

3.23

7.48

7.87

3.93

9.40

9.75

4.83

11.57

12.04

AGCRN

3.35

7.72

8.38

4.05

9.58

10.25

4.97

11.74

12.62

TRL-DAG (ours)

2.73

5.32

7.27

3.16

6.41

8.68

3.55

7.37

10.28

PEMS-BAY

VAR

1.74

3.16

3.60

2.32

4.25

5.00

2.93

5.44

6.50

SVR

1.85

3.59

3.80

2.48

5.18

5.50

3.28

7.08

8.00

ARIMA

1.62

3.30

3.50

2.33

4.76

5.40

3.38

6.50

8.30

LSTM

2.05

4.19

4.80

2.20

4.55

5.20

2.37

4.96

5.57

GCRN

1.46

3.06

3.22

1.88

4.17

4.34

2.40

5.36

5.89

STGCN

1.36

2.96

2.90

1.81

4.27

4.17

2.49

5.69

5.79

DCRNN

1.38

2.95

2.90

1.74

3.97

3.90

2.07

4.47

4.90

ASTGCN(r)

1.52

3.13

3.22

2.01

4.27

4.48

2.61

5.42

6.00

OGCRNN

1.48

3.06

3.28

1.83

3.96

4.26

2.16

4.77

5.12

STSGCN

1.44

3.01

3.04

1.83

4.18

4.17

2.26

5.21

5.40

GWN

1.39

3.01

2.89

1.83

4.21

4.11

2.35

5.43

5.78

Z-GCNETs

1.36

2.86

2.88

1.68

3.78

3.79

1.98

4.53

4.60

AGCRN

1.39

2.96

2.98

1.71

3.92

3.87

2.02

4.69

4.70

TRL-DAG (ours)

1.29

2.75

2.78

1.61

3.70

3.66

1.92

4.50

4.56