Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling
文献类型：期刊论文
| 作者 | Zhu, Kemin2; Liu, Junli2; Song, Xianfeng1,2,4; Wang, Weifeng2; Chen, Hao3 |
| 刊名 | JOURNAL OF ADVANCED TRANSPORTATION
 |
| 出版日期 | 2021-01-27 |
| 卷号 | 2021页码:12 |
| ISSN号 | 0197-6729 |
| DOI | 10.1155/2021/1586010 |
| 通讯作者 | Song, Xianfeng(xfsong@ucas.ac.cn) |
| 英文摘要 | Mobile phone data have become a critical data source for transportation research. While a cell-id trajectory was routinely reorganized by International Mobile Subscriber Identity (IMSI), it potentially allows to analyze transportation behaviors and social interaction of total population, with a full temporal coverage at low cost. However, cell-id trajectory is often sparse due to low reporting frequency and uncertainness of mobile holders' position. So, the cell-id trajectory refinement has been recognized as challenging work to further facilitate trajectory data mining. This paper presents a comprehensive approach to identify cell-id trajectories of public service vehicles (PSVs) from large volume of trajectories and further refines these cell-id trajectories by a heuristic global optimization approach. The modified longest common subsequence (LCSS) method is used to match a cell-id trajectory and a public transportation route (PTR) and correspondingly calculates their similarities for determining whether the trajectory is PSV mode or not. Taking full advantages of the nature of a PSV tends to move on the PTR in uniform motion to meet a prescript visit to stops, a heuristic global optimization approach is deployed to build a spatiotemporal model of a PSV motion, which estimates new locations of cell-id trajectories on the PTR. The approach was finally tested using Beijing cellular network signaling datasets. The precision of PSV trajectory detection is 90%, and the recall is 88%. Evaluated by our GNSS-logged trajectories, the mean absolute error (MAE) of refined PSV trajectories is 144.5 m and the standard deviation (St. Dev) is 81.8 m. It shows a significant improvement in comparison of traditional interpolation methods. |
| 资助项目 | National Key Research and Development Foundation of China[2017YFB0503702] ; National Key Research and Development Foundation of China[2017YFB0503605] ; 973 Program[2013CB733402] ; National Natural Science Foundation of China[41601486] ; National Natural Science Foundation of China[40771167] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000617606600002 |
| 出版者 | WILEY-HINDAWI |
| 资助机构 | National Key Research and Development Foundation of China ; 973 Program ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/160669]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Song, Xianfeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Huaiyin Normal Univ, Sch Urban & Environm Sci, Huaian 223001, Jiangsu, Peoples R China 4.Chinese Acad Sci, Key Lab Quantitat Remote Sensing Informat Technol, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Kemin,Liu, Junli,Song, Xianfeng,et al. Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling[J]. JOURNAL OF ADVANCED TRANSPORTATION,2021,2021:12. |
| APA | Zhu, Kemin,Liu, Junli,Song, Xianfeng,Wang, Weifeng,&Chen, Hao.(2021).Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling.JOURNAL OF ADVANCED TRANSPORTATION,2021,12. |
| MLA | Zhu, Kemin,et al."Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling".JOURNAL OF ADVANCED TRANSPORTATION 2021(2021):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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