Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2015
Abstract
The accuracy and completeness of a digital map plays a critical role in determining the quality of most location-based services. Unfortunately, road networks change frequently. Consequently, we study the issue of automatic map update in this paper. We propose a system called COBWEB which takes all the unmatched trajectories as input and generates the missing road segments with both the geometry properties and topology features well preserved. We conduct a comprehensive experimental study via real trajectory data generated by roughly 15,000 taxis in Singapore within a 5-month period. Compared with existing work, COBWEB demonstrates a better and more stable performance and a stronger resilience to various sampling rates and data sizes.
Keywords
map update, map inference, GPS trajectories, GPS noise, low sampling rate, map matching
Discipline
Computer Sciences | Databases and Information Systems
Publication
UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
First Page
927
Last Page
937
Identifier
10.1145/2750858.2804286
Publisher
ACM
City or Country
New York
Citation
Zhangqing Shan; Hao Wu; Weiwei Sun; and ZHENG, Baihua.
COBWEB: A Robust Map Update System using GPS Trajectories. (2015). UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 927-937.
Available at: https://ink.library.smu.edu.sg/sis_research/2697
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dl.acm.org/citation.cfm?id=2750858.2804286