Abstract
This study proposes a solution to the problem of inaccurate and time-consuming ship trajectory prediction caused by frequent ship maneuvering in complex waterways. The proposed solution is a ship trajectory prediction model that uses a difference long short-term memory neural network (D-LSTM). To improve prediction performance and reduce time dependence, the model combines the other variables of dynamic time features in the ship’s Automatic Identification System (AIS) data with nonlinear elements in the sequence data. The effectiveness of this method is demonstrated by comparing its accuracy to other commonly used time series modeling techniques. The results show that the proposed model significantly reduces training time and improves prediction accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
