基于动态扩散图卷积的交通流量预测算法

doi:10.13229/j.cnki.jdxbgxb.20220888

Abstract

Abstract:

In order to obtain accurate traffic flow prediction results， a traffic flow prediction algorithm based on dynamic diffusion graph convolution was proposed. Firstly， the model used the diffusion graph convolution model to learn the spatial characteristics between different nodes. Secondly， the dynamic adjacency matrix was introduced to ensure that the characteristics of each node at each time can be learned. Once more， the model used the gated recurrent unit to extract the time characteristics of traffic flow data. Finally， residual connection between model levels was used to transfer more original information and enhance the stability of the model. The experimental results on four open data sets can prove the effectiveness of the algorithm in traffic flow prediction tasks.

Key words: artificial intelligence, traffic flow prediction, gated recurrent unit, diffusion graph convolution

CLC Number:

TN18

Pei-guang JING,Yu-dou TIAN,Shao-chu WANG,Yun LI,Yu-ting SU. Traffic flow prediction algorithm based on dynamic diffusion graph convolution[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(6): 1582-1592.

Figures/Tables 10

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模块	MAE	RMSE	MAPE
DGCN	21.08	35.06	13.79
DGCN+DAM	20.93	34.58	13.56
DGCN+DAM+GRU	19.91	32.77	12.92
DDGCN	19.28	31.50	12.72

模型	PeMSD3			PeMSD4			PeMSD7M			PeMSD8
模型	MAE	RMSE	MAPE	MAE	RMSE	MAPE	MAE	RMSE	MAPE	MAE	RMSE	MAPE
HA	20.68	42.46	21.37	22.19	34.33	12.75	2.03	3.87	9.09	18.45	33.32	10.16
VAR	23.99	38.33	22.77	22.53	34.74	12.27	2.38	3.69	8.99	21.87	32.10	13.39
DCRNN	14.49	24.87	16.59	18.88	30.42	14.88	9.86	13.84	28.64	14.69	22.34	9.28
ASTGCN	20.6	33.60	21.04	22.21	34.7	16.03	4.55	7.73	11.71	20.01	30.24	12.8
Graph-Wave	13.58	23.19	12.11	18.66	29.58	13.55	1.76	3.24	4.05	14.35	22.19	9.23
AGCRN	13.21	22.68	12.92	18.21	29.37	12.02	1.80	3.36	4.20	14.17	22.39	9.25
STODE	16.50	27.84	16.69	20.84	32.82	13.77	2.03	3.96	5.06	14.67	25.97	10.62
STG-NCDE	14.35	24.94	14.21	17.46	30.05	14.44	1.73	3.89	4.68	15.02	23.84	9.73
SCINet	14.02	22.34	13.12	18.97	30.58	11.86	1.79	3.35	4.94	14.89	23.32	9.46
DDGCN	13.03	22.53	13.00	18.14	29.14	11.64	1.72	3.63	4.27	14.02	22.64	9.21

模型	PeMSD3			PeMSD4			PeMSD7M			PeMSD8
模型	MAE	RMSE	MAPE	MAE	RMSE	MAPE	MAE	RMSE	MAPE	MAE	RMSE	MAPE
HA	22.77	44.88	24.06	23.31	37.47	15.02	3.36	5.59	10.86	19.85	35.45	12.61
VAR	23.99	38.33	22.77	23.71	36.37	15.89	3.67	5.25	10.76	23.23	33.46	15.66
DCRNN	15.41	26.25	15.16	19.70	30.99	14.08	9.45	14.19	28.82	15.19	23.74	15.96
ASTGCN	20.28	33.24	20.31	24.10	36.92	17.83	4.49	7.59	11.44	21.56	31.94	14.83
Graph-Wave	13.98	24.25	14.75	18.97	30.41	13.37	2.19	4.18	5.25	14.49	23.77	9.73
AGCRN	14.37	25.19	13.71	19.43	31.10	12.94	2.27	4.44	5.54	15.32	24.27	10.01
STODE	16.50	27.84	16.69	20.84	32.82	13.77	2.43	4.26	5.58	15.14	25.97	10.62
STG-NCDE	15.64	26.61	14.92	19.43	32.02	12.78	2.33	4.49	5.60	15.85	25.12	10.02
SCINet	15.48	24.62	14.24	19.02	30.02	11.85	2.61	4.67	5.74	16.03	25.13	9.53
DDGCN	14.12	24.13	13.67	19.05	30.10	11.72	2.08	4.04	5.25	14.61	23.52	9.64

模型	PeMSD3			PeMSD4			PeMSD7M			PeMSD8
模型	MAE	RMSE	MAPE	MAE	RMSE	MAPE	MAE	RMSE	MAPE	MAE	RMSE	MAPE
HA	24.91	46.00	25.81	24.50	39.83	16.58	4.29	7.69	11.56	21.19	36.64	13.79
VAR	23.99	38.33	22.77	25.18	38.36	17.48	4.77	7.32	11.21	24.22	35.13	16.57
DCRNN	16.22	27.73	17.16	20.06	32.52	13.86	3.35	6.89	8.11	16.34	25.47	10.55
ASTGCN	17.86	29.70	17.59	23.09	35.78	16.18	3.61	6.44	9.18	19.17	28.86	13.19
Graph-Wave	22.83	36.76	22.24	19.67	31.38	13.28	2.69	5.17	6.82	15.92	23.82	9.69
AGCRN	16.32	27.73	15.08	19.79	32.29	13.13	2.80	5.61	7.13	15.95	25.29	10.17
STODE	16.50	27.84	16.69	20.84	32.82	13.77	2.97	5.66	7.36	16.81	25.97	10.62
STG-NCDE	15.57	27.35	15.06	19.34	31.10	12.87	2.73	5.41	6.86	15.85	25.12	10.02
SCINet	15.79	27.67	14.58	19.30	31.82	12.06	2.70	5.31	6.81	15.76	24.82	9.91
DDGCN	15.50	27.21	14.56	19.28	31.50	12.72	2.68	5.40	6.80	15.74	24.98	10.08

模型		15 min			30 min			60 min
模型		MAE	RMSE	MAPE	MAE	RMSE	MAPE	MAE	RMSE	MAPE
DDGCN	PeMSD3	13.03	22.53	13.00	14.12	25.23	13.67	15.50	27.21	14.56
	PeMSD4	18.14	29.14	11.64	19.05	30.10	11.72	19.28	31.50	12.72
	PeMSD7M	1.72	3.63	4.27	3.10	2.08	4.04	5.25	5.40	6.80
	PeMSD8	14.02	22.64	9.21	14.61	23.52	9.64	15.74	24.98	10.08
DDGCN¹	PeMSD3	15.64	28.30	15.13	16.14	27.25	14.86	17.13	30.22	16.63
	PeMSD4	18.57	30.31	12.06	19.83	31.98	12.05	21.98	33.83	14.09
	PeMSD7M	2.02	4.30	9.31	3.32	3.23	9.42	3.87	6.89	7.02
	PeMSD8	15.50	23.21	13.15	15.42	24.56	10.29	16.17	25.02	11.00
DDGCN²	PeMSD3	18.26	30.18	16.73	19.73	31.66	18.45	19.12	32.27	19.61
	PeMSD4	19.79	31.37	13.25	19.40	33.19	14.43	21.69	35.13	15.39
	PeMSD7M	2.39	4.63	9.43	3.05	5.06	9.28	3.31	6.65	8.38
	PeMSD8	16.59	25.79	10.55	16.94	27.45	11.49	17.77	28.17	11.34

Traffic flow prediction algorithm based on dynamic diffusion graph convolution

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