一种用于交通预测的注意力时空图神经网络

doi:10.19665/j.issn1001-2400.2023.01.019

Abstract

Abstract:

With the development of urbanization,traffic prediction plays an important role in the application of traffic planning and urban management.However,in the task of traffic prediction,it is still a great challenge to capture the highly nonlinear and complex spatio-temporal dependencies of traffic data.In order to effectively capture the time dynamics and global spatial correlation of traffic data and satisfy both long-term and short-term prediction tasks,an attention based spatial-temporal graph neural network for traffic prediction is designed.First,the attention mechanism is introduced to adjust the importance of adjacent roads and non-adjacent roads,which is beneficial to integrating global spatial information.Then,the spatial-temporal correlations are captured by graph convolutional networks and gated linear units with extended causal convolution.Experimental results on two real data sets PeMSD7(M) and PEMS-BAY show that the network model can improve the accuracy of both long and short-term traffic prediction.

Key words: traffic prediction, spatial-temporal correlativity, mechanisms of attention, graph convolutional networks

CLC Number:

TP391

GAN Ping, NONG Liping, ZHANG Wenhui, LIN Jiming, WANG Junyi. Attention spatial-temporal graph neural network for traffic prediction[J].Journal of Xidian University, 2023, 50(1): 168-176.

Figures/Tables 10

References 19

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数据集	#Nods	#Edges	#Time Steps
PeMSD7(M)	228	832	12 672
PEMS-BAY	325	2 369	52 116

模型	PeMSD7(M)(15/30/60 min)
模型	MAE	RMSE	MAPE/%
HA	4.01	7.20	10.61
ARIMA	5.55/5.86/6.75	9.00/9.13/9.72	12.92/13.94/16.92
FC-LSTM	3.57/3.94/4.35	6.20/7.03/7.93	8.60/9.55/10.61
STGCN	2.25/3.03/4.01	4.04/5.70/7.55	5.26/7.33/9.78
GraphWaveNet	2.14/2.80/3.42	4.01/5.48/6.72	4.93/6.89/8.57
LSGCN	2.22/2.96/3.81	3.98/5.47/7.09	5.14/7.18//9.60
STTN	2.14/2.70/3.31	4.04/5.37/6.51	5.05/6.68/7.98
AST-GCN	1.89/2.42/3.09	3.36/4.37/5.46	4.14/5.32/6.88
模型	PEMS-BAY(15/30/60 min)
模型	MAE	RMSE	MAPE/%
HA	2.88	5.59	6.80
ARIMA	1.62/2.23/3.38	3.30/4.76/6.50	3.50/5.40/8.30
FC-LSTM	2.05/2.20/2.37	4.19/4.55/4.96	4.80/5.20/5.70
STGCN	1.39/1.84/2.42	2.92/4.12/5.33	3.00/4.22/5.58
GraphWaveNet	1.30/1.63/1.95	2.73/3.67/4.63	2.74/3.70/4.52
STTN	1.36/1.67/1.95	2.87/3.79/4.50	2.89/3.78/4.58
AST-GCN	1.22/1.52/1.86	2.54/3.20/3.99	2.39/3.12/3.75

图注意力层数	MAE(15/30/60 min)	RMSE(15/30/60 min)	MAPE(15/30/60 min)	训练时间(epoch)/s
1	1.89/2.42/3.09	3.36/4.37/5.46	4.14/5.32/6.88	15
2	1.91/2.41/3.42	3.57/4.68/5.86	4.35/5.59/7.27	24
3	1.90/2.85/3.54	3.70/4.77/5.99	4.57/5.81/7.48	37

模型	参数量	训练时间(epoch)/s	测试时间/s
STGCN	582 783	55	51
STTN	483 421	269	257
AST-GCN	659 387	81	78

Attention spatial-temporal graph neural network for traffic prediction

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