Enter your email address
Submit
Write your message
1- Master’s Student in Geospatial Information Science, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
2- Assistant Professor, Department of Geomatics Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
3- Associate Professor, Department of Structural and Earthquake Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
2- Assistant Professor, Department of Geomatics Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
3- Associate Professor, Department of Structural and Earthquake Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
Abstract: (36 Views)
Accurate vessel trajectory prediction is a critical requirement for enhancing maritime safety, reducing collision risk, optimizing fuel consumption, and enabling intelligent traffic management at sea. In this study, a comprehensive and data-driven framework is proposed for vessel trajectory prediction, developed using real Automatic Identification System (AIS) data and relevant environmental information such as significant wave height. The dataset includes movement trajectories of cargo vessels, fishing vessels, and passenger vessels within the Gulf of Mexico. A rich and structured dataset was constructed by extracting derived features such as turn rate, acceleration, and geographic coordinates. To model the trajectories, a hybrid architecture based on Long Short-Term Memory (LSTM) networks and an attention mechanism was implemented, enabling the model to effectively learn long-term temporal dependencies and adaptively focus on critical segments of vessel routes. In addition, SHapley Additive exPlanations (SHAP) were used to enhance the interpretability of the model and analyze the contribution of each feature to the prediction process. The feature analysis revealed that latitude, longitude, acceleration, and wave height played the most significant roles in improving predictive accuracy. Experimental evaluation using a held-out test dataset and comparison with a baseline LSTM model demonstrated that the proposed framework reduced the Average Displacement Error (ADE) by approximately 44% and the Final Displacement Error (FDE) by about 39%. These results confirm the effectiveness of combining spatiotemporal deep learning with interpretable feature analysis for precise vessel trajectory forecasting. The proposed framework offers a reliable foundation for developing navigation support systems, autonomous ship routing, smart port management, and maritime traffic forecasting. Furthermore, it can be extended to various types of vessels and other maritime regions.
Keywords: Vessel trajectory prediction, Attention mechanism, LSTM-Attention model, Spatio-temporal modeling, Maritime navigation
Type of Study: Research Paper |
Subject:
Environmental Study
Received: 2025/05/12 | Accepted: 2025/07/9
Received: 2025/05/12 | Accepted: 2025/07/9
فایل صوتی چکیده مقاله [MP3 2966 KB] (1 Download)
References
1. Dodge, S. (2011). Exploring movement using similarity analysis [PhD Thesis, University of Zurich]. https://www.zora.uzh.ch/id/eprint/59842/
2. Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., & Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052-19059. [DOI:10.1073/pnas.0800375105] [PMID] []
3. Liu, X., & Karimi, H. A. (2006). Location awareness through trajectory prediction. Computers, Environment and Urban Systems, 30(6), 741-756. [DOI:10.1016/j.compenvurbsys.2006.02.007]
4. Rodrigue, J.-P. (2020). The geography of transport systems. Routledge. [DOI:10.4324/9780429346323]
5. Grech, M. R., Horberry, T., & Smith, A. (2002). Human Error in Maritime Operations: Analyses of Accident Reports Using the Leximancer Tool. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 46(19), 1718-1721. [DOI:10.1177/154193120204601906]
6. Shin, Y., Kim, N., Lee, H., In, S. Y., Hansen, M., & Yoon, Y. (2024). Deep learning framework for vessel trajectory prediction using auxiliary tasks and convolutional networks. Engineering Applications of Artificial Intelligence, 132, 107936 https://www.sciencedirect.com/science/article/pii/S0952197624000940 [DOI:10.1016/j.engappai.2024.107936]
7. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. https://ieeexplore.ieee.org/abstract/document/6795963/ [DOI:10.1162/neco.1997.9.8.1735] [PMID]
8. Burger, C. N., Kleynhans, W., & Grobler, T. L. (2022). Extended linear regression model for vessel trajectory prediction with a-priori AIS information. Geo-spatial Information Science, 27(1), 202-220. [DOI:10.1080/10095020.2022.2072241]
9. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
10. Rong, H., Teixeira, A. P., & Soares, C. G. (2019). Ship trajectory uncertainty prediction based on a Gaussian Process model. Ocean Engineering, 182, 499-511. [DOI:10.1016/j.oceaneng.2019.04.024]
11. Tang, H., Yin, Y., & Shen, H. (2019). A model for vessel trajectory prediction based on long short-term memory neural network. Journal of Marine Engineering & Technology, 21(3), 136-145. [DOI:10.1080/20464177.2019.1665258]
12. Suo, Y., Chen, W., Claramunt, C., & Yang, S. (2020). A ship trajectory prediction framework based on a recurrent neural network. Sensors, 20(18), 5133. [DOI:10.3390/s20185133] [PMID] []
13. Alizadeh, D., Alesheikh, A. A., & Sharif, M. (2021). Vessel trajectory prediction using historical automatic identification system data. the Journal of Navigation, 74(1), 156-174. [DOI:10.1017/S0373463320000442]
14. Murray, B., & Perera, L. P. (2021). An AIS-based deep learning framework for regional ship behavior prediction. Reliability Engineering & System Safety, 215, 107819. [DOI:10.1016/j.ress.2021.107819]
15. Burger, C. N., Kleynhans, W., & Grobler, T. L. (2022). Extended linear regression model for vessel trajectory prediction with a-priori AIS information. Geo-spatial Information Science, 27(1), 202-220. [DOI:10.1080/10095020.2022.2072241]
16. Sun, Q., Tang, Z., Gao, J., & Zhang, G. (2022). Short-term ship motion attitude prediction based on LSTM and GPR. Applied Ocean Research, 118, 102927. [DOI:10.1016/j.apor.2021.102927]
17. Xiao, Y., Hu, Y., Liu, J., Xiao, Y., & Liu, Q. (2024). An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data. Journal of Marine Science and Engineering, 12(3), 513. [DOI:10.3390/jmse12030513]
18. Li, Y., Yu, Q., & Yang, Z. (2024). Vessel Trajectory Prediction for Enhanced Maritime Navigation Safety: A Novel Hybrid Methodology. Journal of Marine Science and Engineering, 12(8), 1351. [DOI:10.3390/jmse12081351]
19. Mehri, S., Alesheikh, A. A., & Basiri, A. (2023). A context-aware approach for vessels' trajectory prediction. Ocean Engineering, 282, 114916. [DOI:10.1016/j.oceaneng.2023.114916]
20. Employing Traditional Machine Learning Algorithms for Big Data Streams Analysis: The Case of Object Trajectory Prediction, 127 Journal of Systems and Software 249 (Elsevier 2017). https://www.sciencedirect.com/science/article/pii/S016412121630084X [DOI:10.1016/j.jss.2016.06.016]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
International Journal of Maritime Technology is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License.