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Table 4 Comparison of the performance of different methods on the test data set

From: Traffic flow prediction based on depthwise separable convolution fusion network

Method(L + W + E + T)

MAE

RAE

(× 100)

RMSE

RRSE

(× 100)

MAPE

R2

(× 100)

Trainable params

FFN(SConv1D + earlyTGTR)

148.7

63.0

227.7

67.8

15.5

54.1

198101

FFN(SConv1D + middleTGTR)

150.0

63.5

228.6

68.0

15.7

53.7

197641

FFN(SConv1D + lateTGTR)

152.1

64.4

231.0

68.7

16.0

52.8

197641

FFN(SConv1D + none)

151.6

64.2

229.3

68.2

15.9

53.4

196911

FFN(Conv1D + none)

154.5

65.4

230.2

68.5

16.4

53.1

205821

DL-LSTM [19]

160.8

–

233.5

–

16.7

51.7

–

DL-FC [19]

152.6

64.6

232.1

69.1

16.1

52.3

238313