A deep learning method to predict ship short-term trajectory for proactive maritime traffic management

Quandang Ma, Zhouyu Lian, Xu Du, Yuting Jiang, Ahmad BahooToroody and Mingyang Zhang

Reliability Engineering and System Safety, 2026, vol. 265, issue PB

Abstract: With the rapid growth of global maritime trade and the increasing density of vessel traffic, the risk of ship collisions has become a growing concern, especially in busy and complex waterways. To support proactive maritime traffic management and enhance navigational safety, this paper presents a deep learning-based approach for short-term ship trajectory prediction. Specifically, we propose a multi-scale fusion Temporal Convolutional Network (TCN) that learns vessel movement patterns using data from the Automatic Identification System (AIS). The model captures key motion features—such as trajectory changes, speed, acceleration, turning angles, and rotation rate—to better reflect the dynamic behavior of ships. By combining short-term variations with longer-term trends, the proposed TCN model achieves more accurate and reliable predictions. Experiments on real AIS datasets demonstrate that our method outperforms existing techniques in both accuracy and robustness, particularly in complex coastal environments. This research contributes to smarter traffic control and safer maritime navigation, and supports the development of intelligent maritime systems.

Keywords: Maritime safety; Trajectory prediction; Motion feature analysis; Multi-scale fusion; AIS data (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832025007422
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:265:y:2026:i:pb:s0951832025007422

DOI: 10.1016/j.ress.2025.111542

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().