Authors:
Yulong Wang
1
;
2
;
3
;
Jingwang Tang
1
;
3
and
Zhe Jia
2
Affiliations:
1
School of Computer Science (National Pilot Software Engineering School), Beijing University of Post and Telecommunications, Beijing, China
;
2
Science and Technology on Communication Networks Laboratory, Shijiazhuang, China
;
3
State Key Laboratory of Networking and Switching Technology, Beijing, China
Keyword(s):
Automatic Identification System, Trajectory Compression, Convolutional Neural Networks, Threshold.
Abstract:
With the Automatic Identification System installed on more and more ships, people can collect a large number of ship-running data, and the relevant maritime departments and shipping companies can also monitor the running status of ships in real-time and schedule at any time. However, it is challenging to compress a large number of ship trajectory data so as to reduce redundant information and save storage space. The existing trajectory compression algorithms manage to find proper thresholds to achieve better compression effect, which is labor-intensive. We propose a new trajectory compression algorithm which utilizes Convolutional Neural Network to perform points classification, and then obtain a compressed trajectory by removing redundant points according to points classification results, and finally reduce the compression error. Our approach does not need to set the threshold manually. Experiments show that our approach outperforms conventional trajectory compression algorithms in
terms of average compression error and fitting degree under the same compression rate, and has certain advantages in time efficiency.
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