IF-matching: towards accurate map-matching with information fusion

Hu, Gang, Shao, Jie, Liu, Fenglin, Wang, Yuan and Shen, Heng Tao (2017) IF-matching: towards accurate map-matching with information fusion. IEEE Transactions on Knowledge and Data Engineering, 29 1: 114-127. doi:10.1109/TKDE.2016.2617326


Author Hu, Gang
Shao, Jie
Liu, Fenglin
Wang, Yuan
Shen, Heng Tao
Title IF-matching: towards accurate map-matching with information fusion
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
1558-2191
Publication date 2017-01-01
Year available 2016
Sub-type Article (original research)
DOI 10.1109/TKDE.2016.2617326
Open Access Status Not yet assessed
Volume 29
Issue 1
Start page 114
End page 127
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Subject 1710 Information Systems
3309 Library and Information Sciences
Abstract With the advance of various location-acquisition technologies, a myriad of GPS trajectories can be collected every day. However, the raw coordinate data captured by sensors often cannot reflect real positions due to many physical constraints and some rules of law. How to accurately match GPS trajectories to roads on a digital map is an important issue. The problem of map-matching is fundamental for many applications. Unfortunately, many existing methods still cannot meet stringent performance requirements in engineering. In particular, low/unstable sampling rate and noisy/lost data are usually big challenges. Information fusion of different data sources is becoming increasingly promising nowadays. As in practice, some other measurements such as speed and moving direction are collected together with the spatial locations acquired, we can make use of not only location coordinates but all data collected. In this paper, we propose a novel model using the related meta-information to describe a moving object, and present an algorithm called IF-Matching for map-matching. It can handle many ambiguous cases which cannot be correctly matched by existing methods. We run our algorithm with taxi trajectory data on a city-wide road network. Compared with two state-of-the-art algorithms of ST-Matching and the winner of GIS Cup 2012, our approach achieves more accurate results.
Keyword GPS trajectories
Information fusion
Map-matching
Road network
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID DP110104227
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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