TS-STNN: Spatial-temporal neural network based on tree structure for traffic flow prediction

doi:10.1016/j.tre.2023.103251

	TS-STNN: Spatial-temporal neural network based on tree structure for traffic flow prediction
	Lv, Yang 1; Lv, Zhiqiang 1,2; Cheng, Zesheng 1; Zhu, Zhanqi 3; Rashidi, Taha Hossein 4
	2023-09-01
发表期刊	TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
ISSN	1366-5545
卷号	177 页码:16
摘要	Traffic flow prediction effectively supports the sustainable expansion and operation of modern transport networks, one of the emerging research areas in intelligent transportation systems. Currently, most common traffic flow prediction methods use deep learning spatial-temporal models based on graph convolution theory, which cannot deeply explore the spatial hierarchy and directional information of traffic flow data due to their structural characteristics. To address this problem, a spatial-temporal neural network based on tree structure (TS-STNN) is created to anticipate future traffic flow at a specific time at a target location. The principle of this method is to use the characteristics of the tree structure to construct a plane tree matrix with hierarchical and directional features, which is finally fused into a spatial tree matrix to extract the spatial information. Meanwhile, the temporal correlation of traffic flow data in the traffic network is analyzed by TS-STNN using Gated Recurrent Units (GRUs). By comparing with the existing baseline methods, it is verified that the TS-STNN model has high prediction accuracy in both Random Uniformly Distributed (RND) and Small-Scale Aggregation of Node Distributed (SSAND) scenarios. It is further demonstrated through ablation experiments that the developed tree convolution module greatly impacts the TS-STNN accuracy.
DOI	10.1016/j.tre.2023.103251
收录类别	SCI
语种	英语
WOS研究方向	Business & Economics ; Engineering ; Operations Research & Management Science ; Transportation
WOS类目	Economics ; Engineering, Civil ; Operations Research & Management Science ; Transportation ; Transportation Science & Technology
WOS记录号	WOS:001067594100001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21166
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cheng, Zesheng
作者单位	1.Qingdao Univ, Coll Comp Sci & Technol, Qingdao, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Vanderbilt Univ, Sch Engn, Nashville, TN USA 4.UNSW, Sch Civil & Environm Engn, Res Ctr Integrated Transport Innovat rCITI, Sydney, Australia
推荐引用方式 GB/T 7714	Lv, Yang,Lv, Zhiqiang,Cheng, Zesheng,et al. TS-STNN: Spatial-temporal neural network based on tree structure for traffic flow prediction[J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW,2023,177:16.
APA	Lv, Yang,Lv, Zhiqiang,Cheng, Zesheng,Zhu, Zhanqi,&Rashidi, Taha Hossein.(2023).TS-STNN: Spatial-temporal neural network based on tree structure for traffic flow prediction.TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW,177,16.
MLA	Lv, Yang,et al."TS-STNN: Spatial-temporal neural network based on tree structure for traffic flow prediction".TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 177(2023):16.