Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
National University of Defense Technology · Hong Kong University of Science and Technology · +5 more institutions
Abstract
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…
Citation impact
- FWCI
- 60.57
- Percentile
- 100%
- References
- 241
Authors
7- GJGuangyin JinCorresponding
National University of Defense Technology
- YLYuxuan Liang
Hong Kong University of Science and Technology, University of Hong Kong
- YFYuchen Fang
University of Electronic Science and Technology of China
- ZSZezhi Shao
Institute of Computing Technology, University of Chinese Academy of Sciences
- JHJincai Huang
National University of Defense Technology
Topics & keywords
- Computer science
- Deep learning
- Graph
- Data science
- Temporal database
- Artificial intelligence
- Machine learning
- Urban computing
- Sustainable cities and communities