AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks | |
Wei Zhang; Fenghua Zhu; Yisheng Lv; Chang Tan; Wen Liu; Xin Zhang; Fei-Yue Wang | |
刊名 | Transportation Research Part C |
2022 | |
期号 | 99页码:1-1 |
关键词 | Adaptive graph learning, Traffic prediction, Graph convolutional network, Expectation maximization, Deep learning |
英文摘要 | With well-defined graphs, graph convolution based spatiotemporal neural networks for traffic prediction have achieved great performance in numerous tasks. Compared to other methods, the networks can exploit the latent spatial dependencies between nodes according to the djacency |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/47496] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
通讯作者 | Fenghua Zhu; Yisheng Lv |
推荐引用方式 GB/T 7714 | Wei Zhang,Fenghua Zhu,Yisheng Lv,et al. AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks[J]. Transportation Research Part C,2022(99):1-1. |
APA | Wei Zhang.,Fenghua Zhu.,Yisheng Lv.,Chang Tan.,Wen Liu.,...&Fei-Yue Wang.(2022).AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks.Transportation Research Part C(99),1-1. |
MLA | Wei Zhang,et al."AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks".Transportation Research Part C .99(2022):1-1. |
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