A Noise-Immune Boosting Framework for Short-Term Traffic Flow Forecasting

Table 1

Comparison of prediction performances of various models.

		A1	A2	A4	A8
DT	MAPE	14.76	11.60	12.89	12.62
	RMSE	246.58	204.06	236.02	203.22
ANN	MAPE	10.67	10.89	10.73	11.34
	RMSE	287.60	249.35	233.91	150.91
SVR	MAPE	8.44	10.20	8.44	8.67
	RMSE	215.07	247.05	170.35	130.48
GBDT	MAPE	7.87	6.87	7.94	7.76
	RMSE	182.67	127.42	138.94	111.07
SVRGSA [23]	MAPE	11.15	9.42	10.65	11.81
	RMSE	284.97	192.68	213.69	161.07
SVRPSO [24]	MAPE	11.63	10.08	10.99	12.20
	RMSE	300.97	205.94	224.63	163.95
OiKF [4]	MAPE	8.57	7.93	10.61	11.56
	RMSE	203.34	154.98	184.96	132.41
XGBoost	MAPE	5.64	4.85	6.87	6.70
	RMSE	157.11	111.01	132.30	100.38