Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | Transportation Research Part C |
2022 | |
期号 | 99页码:1-1 |
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 |
关键词 | Adaptive graph learning, Traffic prediction, Graph convolutional network, Expectation maximization, Deep learning |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000793721900006 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47496 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
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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2021 Zhang TRC.pdf(2619KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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