TC-CNN: Trajectory Compression based on Convolutional Neural Network

Yulong Wang, Yulong Wang, Yulong Wang, Jingwang Tang, Jingwang Tang, Zhe Jia

2021

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.

Paper Citation

in Harvard Style

Wang Y., Tang J. and Jia Z. (2021). TC-CNN: Trajectory Compression based on Convolutional Neural Network. In Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-526-5, pages 170-176. DOI: 10.5220/0010577801700176

in Bibtex Style

@conference{delta21,
author={Yulong Wang and Jingwang Tang and Zhe Jia},
title={TC-CNN: Trajectory Compression based on Convolutional Neural Network},
booktitle={Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2021},
pages={170-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010577801700176},
isbn={978-989-758-526-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - TC-CNN: Trajectory Compression based on Convolutional Neural Network
SN - 978-989-758-526-5
AU - Wang Y.
AU - Tang J.
AU - Jia Z.
PY - 2021
SP - 170
EP - 176
DO - 10.5220/0010577801700176