Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review
Journal article, 2024

Automatic Identification System (AIS) data holds immense research value in the maritime industry because of its massive scale and the ability to reveal the spatial–temporal variation patterns of vessels. Unfortunately, its potential has long been limited by traditional methodologies. The emergence of machine learning (ML) offers a promising avenue to unlock the full potential of AIS data. In recent years, there has been a growing interest among researchers in leveraging ML to analyze and utilize AIS data. This paper, therefore, provides a comprehensive review of ML applications using AIS data and offers valuable suggestions for future research, such as constructing benchmark AIS datasets, exploring more deep learning (DL) and deep reinforcement learning (DRL) applications on AIS-based studies, and developing large-scale ML models trained by AIS data.

AIS data

Machine learning

Collision avoidance

Anomaly detection

Maritime research

Trajectory prediction

Energy efficiency

Author

Ying Yang

Shanghai University

Yang Liu

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Guorong Li

Tsinghua University

Zekun Zhang

Shanghai Maritime University

Yanbin Liu

Tsinghua University

Transportation Research Part E: Logistics and Transportation Review

1366-5545 (ISSN)

Vol. 183 103426

Subject Categories

Transport Systems and Logistics

Business Administration

DOI

10.1016/j.tre.2024.103426

More information

Latest update

2/9/2024 9