Spatio-temporal tree attention network for forecasting traffic flow

doi:10.1016/j.neucom.2026.133032

CSpace

	Spatio-temporal tree attention network for forecasting traffic flow
	Li, Haoran 1; Lv, Zhiqiang 1,2; Ma, Zhaobin 1; Li, Jianbo 1; Ma, Xiaolong 3; Sun, Dongxin 4; Guo, Kangxin 5; Liu, Jun 5
	2026-05-01
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	676 页码:14
摘要	The Intelligent Transport Systems represent a pivotal research area within the broader context of smart city con struction. It constitutes a vital component of the contemporary urban transport system, with the potential to facilitate high-quality development. The prediction of traffic flow represents a significant research area within the field of ITS. It offers a valuable opportunity to develop a robust data foundation for the planning and op timisation of urban traffic road networks. The majority of studies in this field currently employ static graphs and graph neural networks to complete the traffic flow prediction task. The use of static graphs for traffic flow prediction is inadequate for capturing the dynamic spatial and temporal characteristics of the traffic network structure. Furthermore, graph neural networks are only capable of performing local spatial characteristic analy sis. To address the issue of global feature analysis of traffic network topology, multi-layer graph neural networks are required for iterative computation. The number of layers of graph neural networks increases in line with the number of nodes in the traffic network. To address the aforementioned issues, this study proposes a neural network architecture that employs a tree structure for attention computation, namely the Spatio-temporal Tree Attention Network (STTAT). In particular, this study proposes a tree-structured representation of traffic network topology and a tree-structured attention computation method for learning global features of traffic network topol ogy. The proposed model has been evaluated on several real-world traffic datasets, and its performance has been compared with that of several baseline models. The results demonstrate that the proposed model significantly outperforms the baseline models in terms of prediction accuracy.
关键词	Traffic flow prediction Tree attention mechanism Spatio-temporal features Intelligent data analysis
DOI	10.1016/j.neucom.2026.133032
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001695064600001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42784
专题	中国科学院计算技术研究所
通讯作者	Li, Jianbo
作者单位	1.Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Qingdao Hisense TransTech Co Ltd, 399 Songling Rd, Qingdao 266071, Peoples R China 4.Qingdao Metro Grp Co Ltd, Qingdao 266071, Peoples R China 5.Qingdao AIMetro Technol Co Ltd, Qingdao 266071, Peoples R China
推荐引用方式 GB/T 7714	Li, Haoran,Lv, Zhiqiang,Ma, Zhaobin,et al. Spatio-temporal tree attention network for forecasting traffic flow[J]. NEUROCOMPUTING,2026,676:14.
APA	Li, Haoran.,Lv, Zhiqiang.,Ma, Zhaobin.,Li, Jianbo.,Ma, Xiaolong.,...&Liu, Jun.(2026).Spatio-temporal tree attention network for forecasting traffic flow.NEUROCOMPUTING,676,14.
MLA	Li, Haoran,et al."Spatio-temporal tree attention network for forecasting traffic flow".NEUROCOMPUTING 676(2026):14.