A tensor decomposition method based on embedded geographic meta-knowledge for urban traffic flow imputation
文献类型：期刊论文
| 作者 | Luo, Xiaoyue2,3; Cheng, Shifen2,3; Wang, Lizeng2,3; Liang, Yuxuan4; Lu, Feng1,2,3,5 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2024-11-29 |
| 卷号 | N/A |
| 关键词 | Traffic flow imputation geographic meta-knowledge spatial weight matrix tensor decomposition spatial heterogeneity |
| DOI | 10.1080/13658816.2024.2434665 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Accurate and reliable traffic flow data are essential for intelligent transportation systems; however, limitations arising from hardware and communication costs often lead to missing data. Tensor decomposition is widely used to address these issues. However, existing imputation methods employ a fixed geographic feature similarity matrix to constrain the tensor decomposition process, which fails to accurately capture the spatial heterogeneity of traffic flows, thus limiting the imputation accuracy and robustness. This study proposes a tensor decomposition method embedded with geographic meta-knowledge (Meta-TD) to accurately determine the spatial heterogeneity of traffic flows. The key innovation is establishing a dynamic relationship between the geographic meta-knowledge and spatial heterogeneity of traffic flows, and then using the spatial heterogeneity of the traffic flows to constrain the tensor decomposition process. Experimental results based on real urban traffic flows demonstrated the superiority of Meta-TD over fifteen baseline models under random, block, and long time-series missing patterns, achieving reductions in MAE, RMSE, and MAPE of 6.97-97.05%, 3.33-94.68%, and 0.72-90.89%, respectively. Notably, Meta-TD maintained high accuracy for sudden changes in traffic flow states, evidencing its robustness to varying missing data rates and distribution patterns. This adaptability makes it highly suitable for complex and dynamic urban traffic environments. |
| WOS关键词 | NETWORK ; PREDICTION |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| WOS记录号 | WOS:001368526000001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210453]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Cheng, Shifen |
| 作者单位 | 1.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Hong Kong Univ Sci & Technol Guangzhou, Intelligent Transportat Thrust, Guangzhou, Peoples R China 5.Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Xiaoyue,Cheng, Shifen,Wang, Lizeng,et al. A tensor decomposition method based on embedded geographic meta-knowledge for urban traffic flow imputation[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2024,N/A. |
| APA | Luo, Xiaoyue,Cheng, Shifen,Wang, Lizeng,Liang, Yuxuan,&Lu, Feng.(2024).A tensor decomposition method based on embedded geographic meta-knowledge for urban traffic flow imputation.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A. |
| MLA | Luo, Xiaoyue,et al."A tensor decomposition method based on embedded geographic meta-knowledge for urban traffic flow imputation".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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