A Study of Vessel Trajectory Compression Based on Vector Data Compression Algorithms

With the development of information technology and its vast applications in vessel traffic, such as the popular Automatic Identification System (AIS), a large quantity of vessel trajectory data has been recorded and stored. Vessel traffic has also entered the age of big data. However, the redundancy of data considerably reduces the availability of research and applications, and how to compress these data becomes a problem that needs to be solved. In this paper, several classical vector data compression algorithms are summarized, and the ideas of each algorithm and the steps to compress vessel trajectories are introduced. The vessel trajectory compression experiments based on the algorithms are performed. The results are analyzed, and the characteristics of each algorithm are summarized. The results and conclusions lay the foundation for the selection and improvement of the algorithms in vessel trajectory compression. Through the study of this paper, a systematic theoretical support for the compression of vessel trajectories is provided, which could guide practical applications.