This paper aims to detect convoys in trajectory data with locations sampled at irregular time intervals. In such cases, convoys that exist may not be detected in some algorithms. To avoid this loss of convoy detection due to sampling irregularities, we propose a new method to extract useful position information and, therefore, to find the missing hotspots. We explore three methods, the add-all-points method, the relaxing time method, and the sequence alignment method. The add-all-points method involves adding interpolated points to all trajectories at all times, the relaxing time method introduces flexibility to the temporal dimension which allows to let a trajectory to be measured against the line segments of the second trajectory, and the sequence alignment method uses sequence alignment to reduce the impact of sampling irregularities. The methods are evaluated against a real-world data set. We find that add-all-points method has good performance, but increasing the size of dataset and performing expensive clustering. Relaxing time method and sequence alignment method do not increase the size of dataset. Sequence alignment method detects more convoys than relaxing time method. Therefore, sequence alignment is a promising method to discover convoys without increasing the dataset size.
|Commitee:||Crk, Igor, Matta, John|
|School:||Southern Illinois University at Edwardsville|
|School Location:||United States -- Illinois|
|Source:||MAI 81/7(E), Masters Abstracts International|
|Keywords:||Clustering, Convoy, Sequence alignment, Spatial temporal data|
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