Predicting a Vessel’s Trajectory: A Simple Machine Learning Method

Year 2025, Volume: 6 Issue: 2, 122 - 129, 31.12.2025

Riad Khan , Raju Kundu , Rakin Islam Shah , Rashidul Hasan

https://doi.org/10.54559/amesia.1764517

Abstract

Nowadays, ship safety is a significant concern, with over 50% of ship accidents occurring due to collisions. Therefore, accurately predicting the future position of a ship is essential to avoid such incidents. This is particularly crucial for Autonomous Vessels (AV), as there is no human operator to control the vessel when an obstacle appears in its path. In this study, a procedure was developed to predict a vessel’s future trajectory. Various types of machine learning methods can be employed for this purpose, including the Two-Point Method, Consecutive Average Method, Linear Regression Method, nonlinear regression methods, and deep neural network models. While deep neural network models tend to provide the best results for future position prediction, they necessitate a large dataset. This work employed the simplest machine learning method, namely linear regression, to forecast the future trajectory of a vessel. A computer program using MATLAB was created to predict the future trajectory based on previous GPS positions. The program forecasts future coordinates (Longitude, Latitude) for each second from the current position. The application of weightage and parametric equations was also demonstrated. This method exhibited good prediction accuracy for linear paths but was less effective for curved paths. Nonetheless, this procedure can serve as an initial step in designing Autonomous Vessels, particularly if the AV is intended to follow a straight path.

Keywords

Vessel’s Trajectory , Position Prediction , Linear Regression Method , Parametric Equation , Exponential Weightage.

References

• M. E. Khaled, Y. Kawamura, M. S. Bin Abdullah, A. Banik, A. K. Faruk, M. R. Khan, Assessment of collision and grounding risk at chittagong port, Bangladesh, in: A. B. Abdul Malik (Ed.), 11th International Conference on Marine Technology, Kualalumpur, 2018, pp. 1–10.
• H. Ma, Y. Zuo, T. Li, Vessel navigation behavior analysis and multiple-trajectory prediction model based on AIS data, Journal of Advanced Transportation 2022 (2022) 1–10.
• X. Tian, Y. Suo, Research on ship trajectory prediction method based on difference long short-term memory, Journal of Marine Science and Engineering 11 (9) (2023) 1–18.
• H. Zhou, Y. Chen, S. Zhang, Ship trajectory prediction based on BP neural network, Journal on Artificial Intelligence 1 (1) (2019) 29–36.
• S. Yongfeng, W. Chen, C. Claramunt, S. Yang, A ship trajectory prediction framework based on a recurrent neural network, Sensors 20 (18) (2020) 5133.
• H. Li, H. Jiao, Z. Yang, Ship trajectory prediction based on machine learning and deep learning: A systematic review and methods analysis, Engineering Applications of Artificial Intelligence 126 (3) (2023) 107062.
• J. Park, J. Jeong, Y. S. Park, Ship trajectory prediction based on Bi-LSTM using spectral-clustered AIS data, Journal of Marine Science and Engineering 9 (9) (2021) 1037.
• B. Murray, L. P. Perera, Ship behaviour prediction via trajectory extraction-based clustering for maritime situation awareness, Journal of Ocean Engineering and Science 7 (1) (2022) 1-13.
• D. Gao, Y. Zhu, J. Zhang, Y. He, B. Yan, A novel MP-LSTM method for ship trajectory prediction based on AIS data, Ocean Engineering 228 (2021) 108956.
• K. A. Sørensen, P. Heiselberg, H. Heiselberg, Probabilistic maritime trajectory prediction in complex scenarios using deep learning, Sensors 22 (5) (2022) 2058.
• J. Liu, G. Shi, K. Zhu, Online multiple outputs least-squares support vector regression model of ship trajectory prediction based on automatic information system data and selection mechanism, IEEE Access 8 (2020) 154727–154745.
• H. Tang, Y. Yin, H. Shen, A model for vessel trajectory prediction based on long short-term memory neural network, Journal of Marine Engineering Technology 21 (3) (2019) 136–145.
• S. Hexeberg, A. L. Flåten, B. H. Eriksen, E. F. Brekke, AIS-based vessel trajectory prediction, in: X.R. Li, R. Streit (Eds.), 20th International Conference on Information Fusion, China, 2017, pp. 1– 8.
• K. Bao, J. Bi, M. Gao, Y. Sun, X. Zhang, W. Zhang, An improved ship trajectory prediction based on AIS data using MHA-BiGRU, Journal of Marine Science and Engineering 10 (6) (2022) Article number 804.
• L. Qian, Y. Zheng, L. Li, Y. Ma, C. Zhou, D. Zhang, A new method of inland water ship trajectory prediction based on long short-term memory network optimized by genetic algorithm, Applied Science 12 (8) (2022) Article ID 4073.
• T. A. Volkova, Y. E. Balykina, A. Bespalov, Predicting ship trajectory based on neural networks using AIS data, Journal of Marine Science and Engineering 9 (3) (2021) 254.
• Y. Zheng, L. Li, L. Qian, B. Cheng, W. Hou, Y. Zhuang, Sine-SSA-BP ship trajectory prediction based on chaotic mapping improved sparrow search algorithm, Sensors 23 (2) (2023) 704.
• S. Sarkar, R. Kundu, O. Daramola, B. Stringer, B. Banerjee, G. Nootz, Comparison between deterministic and deep neural network based real-time trajectory prediction of an autonomous surface vehicle, Oceans, 2022, pp. 1–4.
• R. Khan, A. Islam, A. Abdullah, Time and cost effective ship design process using single parent design approach with considerable dimension difference, in: A. Bayatfar (Ed.), 12th International Conference on Marine Technology, Ambon, 2020, pp. 11–19.
• M. R. Khan, R. Kundu, W. Rahman, M. M. Haque and M. R. Ullah, Efficiency study: Contra-rotating propeller system, in: M. A. Nur, M. Ali, S. R. Ahmed (Eds.), International Conference on Mechanical Engineering: Proceedings of the 12th International Conference on Mechanical Engineering, Bangladesh, 2018, 010001.