GLUE: A Parameter-Tuning-Free Map Updating System

• Main Authors: WU, Hao, TU, Chuanchuan, SUN, Weiwei, ZHENG, Baihua, SU, Hao, WANG, Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: map updating; map inference; trajectory mining; Computer Sciences; Databases and Information Systems; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/2893
• Institution: Singapore Management University

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.