GLUE: A Parameter-Tuning-Free Map Updating System
Conference Proceeding Article
Map data are widely used in mobile services, but most maps might not be complete. Updating the map automatically is an important problem because road networks are frequently changed with the development of the city. This paper studies the problem of recovering missing road segments via GPS trajectories, especially low sampled data. Our approach takes the GPS noise into consideration and proposes an effective self-adaptive algorithm. Besides, we propose theoretical models behind all the important parameters to enable self-adaptive parameter setting. To the best of our knowledge, this is the first work that addresses the parameter setting issue successfully to make sure our approach is free of parameter-tuning. In addition, we also propose a quantitative evaluation method for map updating problem. The result shows our algorithm has a much better performance than the existing approaches.
map updating, map inference, trajectory mining
Computer Sciences | Databases and Information Systems | Transportation
Data Management and Analytics
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management: October 19-23, Melbourne
City or Country
WU, Hao; TU, Chuanchuan; SUN, Weiwei; ZHENG, Baihua; SU, Hao; and WANG, Wei.
GLUE: A Parameter-Tuning-Free Map Updating System. (2015). CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management: October 19-23, Melbourne. 683-692. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2893