Published In
Journal of Ocean Engineering and Science
Document Type
Article
Publication Date
4-1-2025
Subjects
Automatic identification system, Maritime situational awareness, Recurrent neural networks, Vessel trajectory prediction
Abstract
Maritime situational awareness tasks such as port management, collision avoidance, and search-and-rescue missions rely on accurate knowledge of vessel locations. The availability of historical vessel trajectory data through the Automatic Identification System (AIS) has enabled the development of prediction methods, with a recent focus on trajectory prediction via recurrent neural networks (RNNs) and other deep learning architectures. While these methods have shown promising performance benefits over kinematic and clustering-based models, comparing among RNN-based models remains difficult due to variations in evaluation datasets, region sizes, vessel types, and numerous other design choices. As a result, it is not clear whether recent methods based on highly-sophisticated network architectures are necessary to achieve strong prediction performance. In this work, we present a simple fusion-based RNN approach to vessel trajectory prediction that allows for easy incorporation of exogenous variables. We perform an extensive ablation study to measure the impact of various modeling choices, including preprocessing, loss functions, and the choice of features, as well as the first usage of surface current information in vessel trajectory prediction. We demonstrate that our approach achieves state-of-the-art performance on three large regions off the United States coast, obtaining an improvement of up to 0.88 km over competing methods when predicting three hours into the future. We conclude that our simple architecture can outperform more complicated architectures while incurring a lower memory cost. Further, we show that the choice of loss function and the inclusion of surface current information both have significant impact on prediction performance.
Rights
Copyright (c) 2025 The Authors Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1016/j.joes.2024.01.002
Persistent Identifier
https://archives.pdx.edu/ds/psu/43600
Publisher
Elsevier BV
Citation Details
Slaughter, I., Charla, J. L., Siderius, M., & Lipor, J. (2025). Vessel trajectory prediction with recurrent neural networks: An evaluation of datasets, features, and architectures. Journal of Ocean Engineering and Science, 10(2), 229–238.