AIS-Based Vessel Trajectory Compression: A Systematic Review and Software Development

With the advancement of satellite and 5G communication technologies, vehicles can transmit and exchange data from anywhere in the world. It has resulted in the generation of massive spatial trajectories, particularly from the Automatic Identification System (AIS) for surface vehicles. The massive AI...

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Bibliographic Details
Published in:IEEE open journal of vehicular technology 2024, Vol.5, p.1193-1214
Main Authors: Liu, Ryan Wen, Zhou, Shiqi, Yin, Shangkun, Shu, Yaqing, Liang, Maohan
Format: Article
Language:English
Subjects:
Approximation algorithms
Artificial intelligence
Automatic identification system (AIS)
Compression algorithms
error metrics
Real-time systems
Safety
similarity measure
Software
Trajectory
trajectory compression
vessel trajectory
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Summary:With the advancement of satellite and 5G communication technologies, vehicles can transmit and exchange data from anywhere in the world. It has resulted in the generation of massive spatial trajectories, particularly from the Automatic Identification System (AIS) for surface vehicles. The massive AIS data lead to high storage requirements and computing costs, as well as low data transmission efficiency. These challenges highlight the critical importance of vessel trajectory compression for surface vehicles. However, the complexity and diversity of vessel trajectories and behaviors make trajectory compression imperative and challenging in maritime applications. Therefore, trajectory compression has been one of the hot spots in research on trajectory data mining. The major purpose of this work is to provide a comprehensive reference source for beginners involved in vessel trajectory compression. The current trajectory compression methods could be broadly divided into two types, batch (offline) and online modes. The principles and pseudo-codes of these methods will be provided and discussed in detail. In addition, compressive experiments on several publicly available data sets have been implemented to evaluate the batch and online compression methods in terms of computation time, compression ratio, trajectory similarity, and trajectory length loss rate. Finally, we develop a flexible and open software, called AISCompress , for AIS-based batch and online vessel trajectory compression. The conclusions and associated future works are also given to inspire future applications in vessel trajectory compression.
ISSN:2644-1330
2644-1330
DOI:10.1109/OJVT.2024.3443675