With the advent of ubiquitous computing, we can easily acquire the locations of moving objects. This paper studies clustering problems for trajectory data that is constrained by the road network. While many trajectory clustering algorithms have been proposed, they do not consider the spatial proximity of objects across the road network. For this kind of data, we propose a new distance measure that reflects the spatial proximity of vehicle trajectories on the road network, and an efficient clustering method that reduces the number of distance computations during the clustering process. Experimental results demonstrate that our proposed method correctly identifies clusters using real-life trajectory data yet reduces the distance computations by up to 80% against the baseline algorithm.
|Title of host publication||Database Systems for Advanced Applications - 15th International Conference, DASFAA 2010, Proceedings|
|Number of pages||15|
|Publication status||Published - 2010|
|Event||15th International Conference on Database Systems for Advanced Applications, DASFAA 2010 - Tsukuba, Japan|
Duration: 2010 Apr 1 → 2010 Apr 4
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||15th International Conference on Database Systems for Advanced Applications, DASFAA 2010|
|Period||10/4/1 → 10/4/4|
Bibliographical noteFunding Information:
This work was supported by Microsoft Research Asia and Engineering Research Center of Excellence Program of Korea Ministry of Education, Science and Technology (MEST) / Korea Science and Engineering Foundation (KOSEF), grant number R11-2008-007-03003-0.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)