A combined traffic flow forecasting model based on graph convolutional network and attention mechanism

doi:10.1142/S0129183121501588

	A combined traffic flow forecasting model based on graph convolutional network and attention mechanism
	Zhang, Hong; Chen, Linlong; Cao, Jie; Zhang, Xijun; Kan, Sunan
刊名	INTERNATIONAL JOURNAL OF MODERN PHYSICS C
	2021-12-01
卷号	32 期号:12
关键词	Traffic flow forecasting deep learning attention mechanism graph convolutional network spatiotemporal characteristics
ISSN号	0129-1831
DOI	10.1142/S0129183121501588
英文摘要	Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation, but due to the complex spatiotemporal characteristics of traffic flow, its forecasting has always been difficult. Deep learning can learn the deep spatiotemporal characteristics of traffic flow from a large amount of data. Deep learning can learn the deep spatiotemporal characteristics of traffic flow from a large amount of data. This paper establishes a novel combination forecasting model GGCN-SA based on deep learning for traffic flow to effectively capture the spatiotemporal characteristics of traffic flow and improve forecasting accuracy. The model captures the spatial correlation of the road traffic network through the graph convolutional network (GCN), captures the time dependence of the traffic flow through the gated recursive unit (GRU), and further introduces the soft attention mechanism (Soft Attention) to aggregate different neighborhoods Spatio-temporal information within the range to enhance the model's ability to characterize the temporal and spatial characteristics of traffic flow. A large number of experiments have been conducted on the METR-LA and SZ-taxi data sets. The experimental results show that the GGCN-SA model proposed in this paper has better forecasting performance compared with the baseline methods.
WOS研究方向	Computer Science ; Physics
语种	英语
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
WOS记录号	WOS:000729847400005
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/154975]
专题	计算机与通信学院
作者单位	Lanzhou Univ Technol, Coll Comp & Commun, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Hong,Chen, Linlong,Cao, Jie,et al. A combined traffic flow forecasting model based on graph convolutional network and attention mechanism[J]. INTERNATIONAL JOURNAL OF MODERN PHYSICS C,2021,32(12).
APA	Zhang, Hong,Chen, Linlong,Cao, Jie,Zhang, Xijun,&Kan, Sunan.(2021).A combined traffic flow forecasting model based on graph convolutional network and attention mechanism.INTERNATIONAL JOURNAL OF MODERN PHYSICS C,32(12).
MLA	Zhang, Hong,et al."A combined traffic flow forecasting model based on graph convolutional network and attention mechanism".INTERNATIONAL JOURNAL OF MODERN PHYSICS C 32.12(2021).