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Spatial-temporal fusion graph framework for trajectory similarity computation

文献类型：期刊论文

作者Zhou, Silin2; Han, Peng3; Yao, Di4; Chen, Lisi2; Zhang, Xiangliang1
刊名WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
出版日期2022-09-22
页码23
关键词Trajectory Similarity search Spatial network Deep learning Spatio-temporal
ISSN号1386-145X
DOI10.1007/s11280-022-01089-0
英文摘要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.
资助项目NSFC[U21B2046]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000856593500001
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/19420]  
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 Notre Dame, Notre Dame, IN 46556 USA
2.Univ Elect Sci & Technol China, Chengdu, Peoples R China
3.Aalborg Univ, Aalborg, Denmark
4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
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.

入库方式： OAI收割

来源：计算技术研究所

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