期刊
IEEE ACCESS
卷 9, 期 -, 页码 92516-92530出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3092948
关键词
Trajectory; Compression algorithms; Artificial intelligence; Clustering algorithms; Loss measurement; Heuristic algorithms; Shape; Error metrics; lossy compression techniques; similarity measures; simplifying trajectory algorithms; trajectory compression algorithm; trajectory similarity
资金
- European Union [823916, 777695]
- Marie Curie Actions (MSCA) [823916, 777695] Funding Source: Marie Curie Actions (MSCA)
This paper presents various trajectory compression algorithms and evaluates their performance on vessel trajectory data. The experiments show that each algorithm has its own advantages and limitations, and the choice of suitable compression algorithm depends on the application scenario.
Today's industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.
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