Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2016
Abstract
Locating only one GPS position to a road segment accurately is crucial to many location-based services such as mobile taxi-hailing service, geo-tagging, POI check-in, etc. This problem is challenging because of errors including the GPS errors and the digital map errors (misalignment and the same representation of bidirectional roads) and a lack of context information. To the best of our knowledge, no existing work studies this problem directly and the work to reduce GPS signal errors by considering hardware aspect is the most relevant. Consequently, this work is the first attempt to solve the problem of locating one GPS position to a road segment. We study the problem in a data-driven view to make this process ubiquitous by proposing a tractable, efficient and robust generative model. In addition, we extend our solution to the real application scenario, i.e., taxi-hailing service, and propose an approach to further improve the result accuracy by considering destination information. We use the real taxi GPS data to evaluate our approach. The results show that our approach outperforms all the existing approaches significantly while maintaining robustness, and it can achieve an accuracy as high as 90% in some situations.
Discipline
Geographic Information Sciences | Theory and Algorithms
Publication
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016)
First Page
740
Last Page
751
ISBN
978-1-4503-4461-6
Identifier
10.1145/2971648.2971702
Publisher
ACM
City or Country
Heidelberg, Germany
Citation
WU, Hao; SUN, Weiwei; and ZHENG, Baihua.
Is Only One GPS Point Position Sufficient to Locate You to The Road Network Accurately?. (2016). Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016). 740-751.
Available at: https://ink.library.smu.edu.sg/sis_research/3318
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/2971648.2971702