Ship global path planning using jump point search and maritime traffic route extraction DOI
Lichao Yang, Jingxian Liu, Zhao Liu

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер 284, С. 127885 - 127885

Опубликована: Май 7, 2025

Язык: Английский

A data mining-then-predict method for proactive maritime traffic management by machine learning DOI Creative Commons
Zhao Liu, Wanli Chen, Cong Liu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 135, С. 108696 - 108696

Опубликована: Июнь 7, 2024

Язык: Английский

Процитировано

12
Enabling autonomous navigation: adaptive multi-source risk quantification in maritime transportation DOI Creative Commons
Lichao Yang, Jingxian Liu, Quanlin Zhou

и другие.

Reliability Engineering & System Safety, Год журнала: 2025, Номер unknown, С. 111118 - 111118

Опубликована: Апрель 1, 2025

Процитировано

1
A spatial-temporal attention method for the prediction of multi ship time headways using AIS data DOI Creative Commons

Quandang Ma,

Xu Du,

Mingyang Zhang

и другие.

Ocean Engineering, Год журнала: 2024, Номер 311, С. 118927 - 118927

Опубликована: Авг. 19, 2024

Ship Time Headway (STH) is the time interval between two consecutive ships arriving in same water area. It serves as a crucial indicator for visually measuring probability of ship congestion and frequency passage busy waterways. Accurately predicting STH effective maritime traffic management. In this paper, we propose deep learning method aimed at simultaneously multiple areas (multi-STH). This integrates Variational Mode Decomposition (VMD) algorithm with Spatial-Temporal Attention Graph Convolution Network (STAGCN) to deeply capture complex spatial-temporal features STHs each areas. sequences were obtained from Automatic Identification System (AIS) reach, ensuring that these remained numerically continuous on timeline. The VMD was employed decompose into multi-feature inputs STAGCN, training model conjunction inland waterway network patterns variation Extensive experiments demonstrate proposed prediction surpasses accuracy robustness other existing methods, exhibiting excellent performance various multi-STH study accounts inherent correlation waterways, substantially improving efficiency compared single-waterway prediction. may have potential provide useful support be practical significance enhancing safety waterways navigation.

Язык: Английский

Процитировано

5
A minimum remote operator demand estimation model for collision risk response: Manpower planning for remote operation centers DOI
Taewoong Hwang, Ik-Hyun Youn

Ocean Engineering, Год журнала: 2025, Номер 319, С. 120276 - 120276

Опубликована: Янв. 2, 2025

Язык: Английский

Процитировано

0
Ship global path planning using jump point search and maritime traffic route extraction DOI
Lichao Yang, Jingxian Liu, Zhao Liu

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер 284, С. 127885 - 127885

Опубликована: Май 7, 2025

Язык: Английский

Процитировано

0