Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data
文献类型：期刊论文
| 作者 | Li, Mingxiao1,2,3; Gao, Song3; Lu, Feng1,4,5; Zhang, Hengcai1 |
| 刊名 | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
 |
| 出版日期 | 2019-09-01 |
| 卷号 | 77页码:10 |
| 关键词 | Trajectory reconstruction Mobile phone data Missing data patterns Data partitioning Machine learning |
| ISSN号 | 0198-9715 |
| DOI | 10.1016/j.compenvurbsys.2019.101346 |
| 通讯作者 | Zhang, Hengcai(zhanghc@lreis.ac.cn) |
| 英文摘要 | Understanding human mobility is significant in many fields, such as geography, transportation, and sociology. Due to the wide spatiotemporal coverage and low operational cost, mobile phone data have been recognized as a major resource for human mobility research. However, due to conflicts between the data sparsity problem of mobile phone data and the requirement of fine-scale solutions, trajectory reconstruction is of considerable importance. Although there have been initial studies on this problem, existing methods rarely consider the effect of similarities among individuals and the patterns of missing data. To address this issue, we propose a multi-criteria data partitioning trajectory reconstruction (MDP-TR) method for large-scale mobile phone data. In the proposed method, a multi-criteria data partitioning (MDP) technique is used to measure the similarity among individuals in near real-time and investigate the spatiotemporal patterns of missing data. With this technique, the trajectory reconstruction from mobile phone data is then conducted with machine learning models. We verified the method using a real mobile phone dataset in a large city. Results indicate that the MDP-TR method outperforms competing methods in both accuracy and robustness. We argue that the MDP-TR method can be effectively utilized for grasping highly dynamic human movement status and improving the spatiotemporal resolution of human mobility research. |
| WOS关键词 | ROUTE-CHOICE ; LOCATION DATA ; PATTERNS ; INFORMATION ; MODEL |
| 资助项目 | National Natural Science Foundation of China[41771436] ; National Key Research and Development Program, China[2016YFB0502104] |
| WOS研究方向 | Computer Science ; Engineering ; Environmental Sciences & Ecology ; Geography ; Operations Research & Management Science ; Public Administration |
| 语种 | 英语 |
| WOS记录号 | WOS:000488657500003 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program, China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/129489]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Hengcai |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Wisconsin, Dept Geog, Geospatial Data Sci Lab, Madison, WI 53706 USA 4.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Mingxiao,Gao, Song,Lu, Feng,et al. Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data[J]. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS,2019,77:10. |
| APA | Li, Mingxiao,Gao, Song,Lu, Feng,&Zhang, Hengcai.(2019).Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data.COMPUTERS ENVIRONMENT AND URBAN SYSTEMS,77,10. |
| MLA | Li, Mingxiao,et al."Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data".COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 77(2019):10. |
入库方式： OAI收割
来源：地理科学与资源研究所
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