Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

doi:10.1109/TKDE.2023.3333824

	Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
	Jin, Guangyin 1,2; Liang, Yuxuan 3; Fang, Yuchen 4; Shao, Zezhi 5; Huang, Jincai 6; Zhang, Junbo 7; Zheng, Yu 7
	2024-10-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	36 期号:10 页码:5388-5408
摘要	With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work.
关键词	Surveys Task analysis Time series analysis Transportation Topology Graph neural networks Deep learning predictive learning spatio-temporal data mining time series urban computing
DOI	10.1109/TKDE.2023.3333824
收录类别	SCI
语种	英语
资助项目	Guangzhou Municiple Science and Technology Project[2023A03J0011] ; National Natural Science Foundation of China[62172034] ; National Natural Science Foundation of China[72242106] ; Beijing Natural Science Foundation[4212021]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001313362200027
出版者	IEEE COMPUTER SOC
引用统计	被引频次：25[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/2XEOYT63/39576
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liang, Yuxuan
作者单位	1.Natl Innovat Inst Def Technol, Beijing 100071, Peoples R China 2.Natl Univ Def Technol, Changsha 410003, Peoples R China 3.Hong Kong Univ Sci & Technol Guangzhou, Intelligent Transportat Thrust, Guangzhou 511442, Peoples R China 4.Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611730, Peoples R China 5.Univ Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China 6.Natl Univ Def Technol, Coll Syst Engn, Changsha 410003, Peoples R China 7.JD Technol, JD Intelligent Cities Res & JD iCity, Beijing 100176, Peoples R China
推荐引用方式 GB/T 7714	Jin, Guangyin,Liang, Yuxuan,Fang, Yuchen,et al. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2024,36(10):5388-5408.
APA	Jin, Guangyin.,Liang, Yuxuan.,Fang, Yuchen.,Shao, Zezhi.,Huang, Jincai.,...&Zheng, Yu.(2024).Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,36(10),5388-5408.
MLA	Jin, Guangyin,et al."Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 36.10(2024):5388-5408.