Spatial-temporal fusion graph framework for trajectory similarity computation

doi:10.1007/s11280-022-01089-0

	Spatial-temporal fusion graph framework for trajectory similarity computation
	Zhou, Silin 1; Han, Peng 2; Yao, Di 3; Chen, Lisi 1; Zhang, Xiangliang 4
	2022-09-22
发表期刊	WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
ISSN	1386-145X
页码	23
摘要	Trajectory similarity computation is an essential operation in many applications of spatial data analysis. In this paper, we study the problem of trajectory similarity computation over spatial network, where the real distances between objects are reflected by the network distance. Unlike previous studies which learn the representation of trajectories in Euclidean space, it requires to capture not only the sequence information of the trajectory but also the structure of spatial network. To this end, we propose GTS, a brand new framework that can jointly learn both factors so as to accurately compute the similarity. It first learns the representation of each point-of-interest (POI) in the road network along with the trajectory information. This is realized by incorporating the distances between POIs and trajectory in the random walk over the spatial network as well as the loss function. Then the trajectory representation is learned by a Graph Neural Network model to identify neighboring POIs within the same trajectory, together with an LSTM model to capture the sequence information in the trajectory. On the basis of it, we also develop the GTS(+) extension to support similarity metrics that involve both spatial and temporal information. We conduct comprehensive evaluation on several real world datasets. The experimental results demonstrate that our model substantially outperforms all existing approaches.
关键词	Trajectory Similarity search Spatial network Deep learning Spatio-temporal
DOI	10.1007/s11280-022-01089-0
收录类别	SCI
语种	英语
资助项目	NSFC[U21B2046]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:000856593500001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19421
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Lisi
作者单位	1.Univ Elect Sci & Technol China, Chengdu, Peoples R China 2.Aalborg Univ, Aalborg, Denmark 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Univ Notre Dame, Notre Dame, IN 46556 USA
推荐引用方式 GB/T 7714	Zhou, Silin,Han, Peng,Yao, Di,et al. Spatial-temporal fusion graph framework for trajectory similarity computation[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2022:23.
APA	Zhou, Silin,Han, Peng,Yao, Di,Chen, Lisi,&Zhang, Xiangliang.(2022).Spatial-temporal fusion graph framework for trajectory similarity computation.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,23.
MLA	Zhou, Silin,et al."Spatial-temporal fusion graph framework for trajectory similarity computation".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2022):23.