Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Semantic Interpretation of Object Activities in a Surveillance System
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Similarity Search for Multidimensional Data Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Efficient detection of motion patterns in spatio-temporal data sets
Proceedings of the 12th annual ACM international workshop on Geographic information systems
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Voronoi Diagram of Polygonal Chains under the Discrete Fréchet Distance
COCOON '08 Proceedings of the 14th annual international conference on Computing and Combinatorics
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
IEEE Transactions on Pattern Analysis and Machine Intelligence
A voronoi diagram approach to autonomous clustering
DS'06 Proceedings of the 9th international conference on Discovery Science
Nonmaterialized motion information in transport networks
ICDT'05 Proceedings of the 10th international conference on Database Theory
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Summarizing trajectories into k-primary corridors: a summary of results
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Graph-Based approaches to clustering network-constrained trajectory data
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Fast and exact network trajectory similarity computation: a case-study on bicycle corridor planning
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
| Hi-index | 0.00 |
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.