IGSNRR OpenIR
A ST-CRF Map-Matching Method for Low-Frequency Floating Car Data
Liu, Xiliang1,2; Liu, Kang1,3; Li, Mingxiao1,3; Lu, Feng1,4
2017-05-01
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
Volume18Issue:5Pages:1241-1254
Corresponding AuthorLu, Feng(luf@lreis.ac.cn)
AbstractIntegrating 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.
KeywordMap matching conditional random field label-bias problem floating car data trajectory robustness
DOI10.1109/TITS.2016.2604484
WOS KeywordVEHICLE DATA ; ALGORITHM ; GPS ; TRANSPORT ; PATH
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; China Postdoctoral Science Foundation
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000400901400019
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62662
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Feng
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, 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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu, Xiliang]'s Articles
[Liu, Kang]'s Articles
[Li, Mingxiao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Xiliang]'s Articles
[Liu, Kang]'s Articles
[Li, Mingxiao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Xiliang]'s Articles
[Liu, Kang]'s Articles
[Li, Mingxiao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.