Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction

Table 1

Compares the performance of MBAF with other baseline models at 15 min, 30 min, and 60 min.

Data	Models	15 min			30 min			60 min
		MAE	RMSE	MAPE (%)	MAE	RMSE	MAPE (%)	MAE	RMSE	MAPE (%)
METR-LA	HA	4.16	7.80	13.0	4.16	7.80	13.0	4.16	7.80	13.0
	ARIMA	3.99	8.21	9.60	5.15	10.45	12.70	6.90	13.23	17.40
	SVR	3.54	7.84	8.94	4.44	9.82	11.64	5.65	12.07	15.92
	FC-LSTM	3.44	6.30	9.60	3.77	7.23	10.90	4.37	8.69	13.20
	WaveNet	2.99	5.89	8.04	3.59	7.28	10.25	4.45	8.93	13.62
	DCRNN	2.77	5.38	7.30	3.15	6.45	8.80	3.60	7.60	10.50
	GGRU	2.71	5.24	6.99	3.12	6.36	8.56	3.64	7.65	10.62
	STGCN	2.88	5.74	7.62	3.47	7.24	9.57	4.59	9.40	12.70
	Graph WaveNet	2.69	5.15	6.90	3.07	6.22	8.37	3.53	7.37	10.01
	MBAF-GCN	2.69	5.13	6.86	3.02	6.06	8.14	3.45	7.13	9.98
PEMS-BAY	HA	2.88	5.59	6.80	2.88	5.59	6.80	2.88	5.59	6.80
	ARIMA	1.62	3.30	3.50	2.33	4.76	5.40	3.38	6.50	8.30
	SVR	1.53	3.38	3.49	2.01	4.63	4.78	2.64	6.03	6.72
	FC-LSTM	2.05	4.19	4.80	2.20	4.55	5.20	2.37	4.96	5.70
	WaveNet	1.39	3.01	2.91	1.83	4.21	4.16	2.35	5.43	5.87
	DCRNN	1.38	2.95	2.90	1.74	3.97	3.90	2.07	4.74	4.90
	GGRU	—	—	—	—	—	—	—	—	—
	STGCN	1.36	2.96	2.90	1.81	4.27	4.17	2.49	5.69	5.79
	Graph WaveNet	1.30	2.74	2.73	1.63	3.70	3.67	1.95	4.52	4.63
	MBAF-GCN	1.30	2.75	2.77	1.49	3.55	3.39	1.87	4.38	4.41

The MBAF-GCN model is superior to the comparison model.