@article{ref1,
title="Harnessing the power of Machine learning for AIS Data-Driven maritime Research: a comprehensive review",
journal="Transportation research part E: logistics and transportation review",
year="2024",
author="Yang, Ying and Liu, Yang and Li, Guorong and Zhang, Zekun and Liu, Yanbin",
volume="183",
number="",
pages="e103426-e103426",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1366-5545",
doi="10.1016/j.tre.2024.103426",
url="http://dx.doi.org/10.1016/j.tre.2024.103426"
}