Intra-Trajectory Error Balancing and Inter-Trajectory Feature Point Clustering for Trajectory Compression

The widespread use of locatable devices leads to a sharp increase in the storage of trajectory data, and redundant storage of similar trajectories wastes a large amount of storage resources. The state-of-the-art multiple trajectory compression algorithms are developed to strip the partial informatio...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 37; H. 9; S. 5330 - 5345
Hauptverfasser: Yang, Lei, Cheng, Xin, Liao, Yuwei, Li, Rui, Xie, Guoqi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2025
Schlagworte:
Clustering algorithms
Compression algorithm
Costs
Data mining
Feature extraction
Global Positioning System
Measurement
Redundancy
Roads
similar trajectory
spatio-temporal data
Training
Trajectory
ISSN:1041-4347, 1558-2191
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Zusammenfassung:The widespread use of locatable devices leads to a sharp increase in the storage of trajectory data, and redundant storage of similar trajectories wastes a large amount of storage resources. The state-of-the-art multiple trajectory compression algorithms are developed to strip the partial information of trajectory; however, these algorithms have low compression efficiency because they do not eliminate the redundancy within a single trajectory as much as possible, as well as high time overhead due to matching of reference sub-trajectories. In this study, we propose a new spatio-temporal trajectory compression technique, consisting of intra-trajectory error balancing and inter-trajectory feature point clustering . Intra-trajectory error balancing is achieved through retaining high score (an aggregated metric) trajectory points (i.e., feature points). Furthermore, inter-trajectory feature point clustering realizes the fusion of similar trajectories and extracts the commonality between trajectories. Experiments are performed on five real trajectory datasets, including two road datasets, one airline dataset, and one walking dataset. Compared with the state-of-the-art methods, our compression technique improves the compression ratio by an average of 24.9% under the same error, and reduces the time overhead by at least an order of magnitude.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2025.3579434