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Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory
| 题名： | Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory |
| 正文语种： | eng |
| 作者： | Tang, Jinjun;Zhang, Xinshao;Yin, Weiqi;Zou, Yajie;Wang, Yinhai |
| 作者单位： | Cent S Univ Sch Traff & Transportat Engn Smart Transport Key Lab Hunan Prov Changsha Peoples R China;Cent S Univ Sch Traff & Transportat Engn Smart Transport Key Lab Hunan Prov Changsha Peoples R China;Cent S Univ Sch Traff & Transportat Engn Sm |
| 关键词： | fuzzy neural network;fuzzy rough set;imputation;K-nearest neighbor;missing values;traffic flow |
| 摘要： | Currently, accurate traffic flow analysis and modeling are important key steps for intelligent transportation system (ITS). Missing traffic flow data are one of the most critical issues in the application of ITS. In this study, a hybrid method combining f |
| 出版年： | 2021 |
| 期刊名称： | Journal of Intelligent Transportation Systems |
| 卷： | 25 |
| 期： | 1/6 |
| 页码： | 439-454 |
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