OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Comparison of Distance Measures for Planar Curves
Algorithmica
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Clustering Multidimensional Trajectories based on Shape and Velocity
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Detecting Commuting Patterns by Clustering Subtrajectories
ISAAC '08 Proceedings of the 19th International Symposium on Algorithms and Computation
Mining sub-trajectory cliques to find frequent routes
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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With technology advancement and increasing popularity of location-aware devices, trajectory data are ubiquitous in the real world. Trajectory corridor, as one of the moving patterns, is composed of concatenated sub-trajectory clusters which help analyze the behaviors of moving objects. In this paper we adopt a three-phase approach to discover trajectory corridors using Fréchet distance as a dissimilarity measurement. First, trajectories are segmented into sub-trajectories using meshing-grids. In the second phase, a hierarchical method is utilized to cluster intra-grid sub-trajectories for each grid cell. Finally, local clusters in each single grid cell are concatenated to construct trajectory corridors. By utilizing a grid structure, the segmentation and concatenation need only single traversing of trajectories or grid cells. Experiments demonstrate that the unsupervised algorithm correctly discovers trajectory corridors from the real trajectory data. The trajectory corridors using Fréchet distance with temporal information are different from those having only spatial information. By choosing an appropriate grid size, the computing time could be reduced significantly because the number of sub-trajectories in a single grid cell is a dominant factor influencing the speed of the algorithms.