Multi-factor influence-based ship trajectory prediction analysis via deep learning

Jiao, H, Li, H, Lam, JSL, Gao, X and Yang, Z (2025) Multi-factor influence-based ship trajectory prediction analysis via deep learning. Journal of Marine Engineering and Technology. pp. 1-19. ISSN 2046-4177

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Abstract

The trajectory prediction research based on deep learning methods shows more substantial competitiveness than classical ones in the context of big data analysis methods. However, the relevant literature fails to explain the collective impact of multiple influential factors identified from Automatic Identification System (AIS) data, including latitude, longitude, Course Over Ground (COG), and Speed Over Ground (SOG). To fill in this research gap, six classical deep learning methods are newly employed to conduct ship trajectory prediction, taking into account multiple influential factors for the first time. Two real AIS datasets collected from water areas of high representation are chosen to test and analyse the performance of the six deep learning models against seven indexes. The experimental results reveal that both the traditional factors of longitude and latitude and the newly incorporated ones of SOG and COG play a key role in trajectory prediction. Moreover, the effect of SOG on the accuracy of prediction results is greater than that of COG. Furthermore, the advantages and disadvantages of the six trajectory prediction models revealed by the experimental results provide useful insights into the best-fit method under different circumstances of traffic management involving Maritime Autonomous Surface Ships (MASS).

Item Type:	Article
Uncontrolled Keywords:	4605 Data Management and Data Science; 46 Information and Computing Sciences; 40 Engineering; Data Science; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; 4007 Control engineering, mechatronics and robotics; 4015 Maritime engineering; 4602 Artificial intelligence
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Engineering
Publisher:	Taylor and Francis Group
Date of acceptance:	23 April 2025
Date of first compliant Open Access:	29 May 2025
Date Deposited:	29 May 2025 11:14
Last Modified:	29 May 2025 11:30
DOI or ID number:	10.1080/20464177.2025.2498815
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26459

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