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Ye Xiao, Yupeng Hu, Jizhao Liu, Yi Xiao and Qianzhen Liu
Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical ...
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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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Atefe Sedaghat, Homayoon Arbabkhah, Masood Jafari Kang and Maryam Hamidi
This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to...
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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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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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Zhaojin Yan, Guanghao Yang, Rong He, Hui Yang, Hui Ci and Ran Wang
Automatic identification systems (AIS) provides massive ship trajectory data for maritime traffic management, route planning, and other research. In order to explore the valuable ship traffic characteristics contained implicitly in massive AIS data, a sh...
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Daping Xi, Yuhao Feng, Wenping Jiang, Nai Yang, Xini Hu and Chuyuan Wang
The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic ...
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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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Kristoffer Vinther Olesen, Ahcène Boubekki, Michael C. Kampffmeyer, Robert Jenssen, Anders Nymark Christensen, Sune Hørlück and Line H. Clemmensen
The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models la...
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Chang Liu, Shize Zhang, Lufang Cao and Bin Lin
Automatic identification system (AIS) data record a ship?s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship?s voyage. At present, there are few studies in the literature...
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