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Multistep Traffic Speed Prediction from Spatial-Temporal Dependencies Using Graph Neural Networks
| 题名： | Multistep Traffic Speed Prediction from Spatial-Temporal Dependencies Using Graph Neural Networks |
| 正文语种： | eng |
| 作者： | Wu, Xuesong;Fang, Jie;Liu, Zhijia;Wu, Xiongwei |
| 作者单位： | Fuzhou Univ Coll Civil Engn Fuzhou 350108 Peoples R China;Fuzhou Univ Coll Civil Engn Fuzhou 350108 Peoples R China;Fuzhou Univ Coll Civil Engn Fuzhou 350108 Peoples R China;Fuzhou Univ Coll Civil Engn Fuzhou 350108 Peoples R China |
| 关键词： | Traffic speed prediction;Deep learning;Graph convolution;Attention mechanism |
| 摘要： | Accurate traffic forecasting on citywide networks is one of the crucial urban data mining applications that accurately provide congestion warning and transportation scheduling. While previous work has made significant efforts to learn traffic temporal dyn |
| 出版年： | 2021 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 147 |
| 期： | 12 |
| 页码： | 04021082.1-04021082.12 |
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