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Dimitrios Kaklis, Ioannis Kontopoulos, Iraklis Varlamis, Ioannis Z. Emiris and Takis Varelas
Trajectory data holds pivotal importance in the shipping industry and transcend their significance in various domains, including transportation, health care, tourism, surveillance, and security. In the maritime domain, improved predictions for estimated ...
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Mingze Li, Bing Li, Zhigang Qi, Jiashuai Li and Jiawei Wu
Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emer...
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Wenbo Zhao, Dezhi Wang, Kai Gao, Jiani Wu and Xinghua Cheng
Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to...
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Paolo Massimo Buscema, Giulia Massini, Giovanbattista Raimondi, Giuseppe Caporaso, Marco Breda and Riccardo Petritoli
The automatic identification system (AIS) facilitates the monitoring of ship movements and provides essential input parameters for traffic safety. Previous studies have employed AIS data to detect behavioral anomalies and classify vessel types using supe...
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Fan Zhang, Baoxin Yuan, Liang Huang, Yuanqiao Wen, Xue Yang, Rongxin Song and Pieter van Gelder
Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method t...
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Bakht Zaman, Dusica Marijan and Tetyana Kholodna
The availability of automatic identification system (AIS) data for tracking vessels has paved the way for improvements in maritime safety and efficiency. However, one of the main challenges in using AIS data is often the low quality of the data. Practica...
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Lin Ma, Guoyou Shi, Weifeng Li and Dapeng Jiang
Ship trajectory data can be used in most marine-related research, and most ship trajectory data come from AIS. The large number of ships and the short reporting period of AIS have resulted in a huge amount of ship trajectory data, which has caused a cert...
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Naifeng Wen, Yundong Long, Rubo Zhang, Guanqun Liu, Wenjie Wan and Dian Jiao
This research introduces a two-stage deep reinforcement learning approach for the cooperative path planning of unmanned surface vehicles (USVs). The method is designed to address cooperative collision-avoidance path planning while adhering to the Interna...
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Je Ho Hwang and Dal Won Kang
Maritime shipping is considered a major air pollutant that directly affects global warming and climate change. This study aims to design an emission control route (ECR) that can contribute to long-term initiatives along with emission prevention policies....
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Xiang Zhang, Yuchuan Zhou and Lianying Li
Recognizing vessel navigation patterns plays a vital role in understanding maritime traffic behaviors, managing and planning vessel activities, spotting outliers, and predicting traffic. However, the growth in trajectory data and the complexity of mariti...
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