A ST-CRF Map-Matching Method for Low-Frequency Floating Car Data
文献类型：期刊论文
| 作者 | Liu, Xiliang1,2; Liu, Kang2,3; Li, Mingxiao2,3; Lu, Feng2,4 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2017-05-01 |
| 卷号 | 18期号:5页码:1241-1254 |
| 关键词 | Map matching conditional random field label-bias problem floating car data trajectory robustness |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2016.2604484 |
| 通讯作者 | Lu, Feng(luf@lreis.ac.cn) |
| 英文摘要 | Integrating raw Global Position System (GPS) trajectories with a road network is often referred to as a mapmatching problem. However, low-frequency trajectories (e.g., one GPS point for every 1-2 min) have raised many challenges to existing map-matching methods. In this paper, we propose a novel and global spatial-temporal map-matching method called spatial and temporal conditional random field (ST-CRF), which is based on insights relating to: 1) the spatial positioning accuracy of GPS points with the topological information of the underlying road network; 2) the spatial-temporal accessibility of a floating car; 3) the spatial distribution of the middle point between two consecutive GPS points; and 4) the consistency of the driving direction of a GPS trajectory. We construct a conditional random field model and identify the best matching path sequence from all candidate points. A series of experiments conducted for real environments using mass floating car data collected in Beijing and Shanghai shows that the ST-CRF method not only has better performance and robustness than other popular methods (e.g., point-line, ST-matching, and interactive voting-based map-matching methods) in low-frequency map matching but also solves the "label-bias" problem, which has long existed in the map matching of classical hidden Markov-based methods. |
| WOS关键词 | VEHICLE DATA ; ALGORITHM ; GPS ; TRANSPORT ; PATH |
| 资助项目 | National Natural Science Foundation of China[41271408] ; National Natural Science Foundation of China[41601421] ; National Natural Science Foundation of China[41401460] ; China Postdoctoral Science Foundation[2015M581158] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000400901400019 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; China Postdoctoral Science Foundation |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/62661]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lu, Feng |
| 作者单位 | 1.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Xiliang,Liu, Kang,Li, Mingxiao,et al. A ST-CRF Map-Matching Method for Low-Frequency Floating Car Data[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2017,18(5):1241-1254. |
| APA | Liu, Xiliang,Liu, Kang,Li, Mingxiao,&Lu, Feng.(2017).A ST-CRF Map-Matching Method for Low-Frequency Floating Car Data.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,18(5),1241-1254. |
| MLA | Liu, Xiliang,et al."A ST-CRF Map-Matching Method for Low-Frequency Floating Car Data".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 18.5(2017):1241-1254. |
入库方式： OAI收割
来源：地理科学与资源研究所
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