Marine Route Prediction: A Digital Solution for Efficient and Sustainable Maritime Navigation

V. Kamalaveni, K.J. Dharshan, K. Eshwar, G.M. Jersil

doi:https://doi.org/10.59232/DSM-V1I4P101

Research Article | Open Access | Download Full Text

Volume 1 | Issue 4 | Year 2024 | Article Id: DSM-V1I4P101 DOI: https://doi.org/10.59232/DSM-V1I4P101

Marine Route Prediction: A Digital Solution for Efficient and Sustainable Maritime Navigation

V. Kamalaveni, K.J. Dharshan, K. Eshwar, G.M. Jersil

Received	Revised	Accepted	Published
05 Oct 2024	10 Nov 2024	30 Nov 2024	24 Dec 2024

Citation

V. Kamalaveni, K.J. Dharshan, K. Eshwar, G.M. Jersil. “Marine Route Prediction: A Digital Solution for Efficient and Sustainable Maritime Navigation.” DS Journal of Multidisciplinary, vol. 1, no. 4, pp. 1-8, 2024.

Abstract

Efficient and reliable marine route prediction is critical for optimizing maritime navigation, reducing fuel consumption, and minimizing environmental impact. Traditional methods often struggle to account for dynamic factors like weather conditions, ocean currents, and ship characteristics, leading to suboptimal routing. This paper presents a machine learning-based approach to marine route way prediction, leveraging historical data and real-time inputs to enhance route accuracy and efficiency. The proposed methodology integrates key features such as meteorological data, vessel specifications, and sea traffic patterns into predictive models, including Random Forest, Gradient Boosting, and Neural Networks. Results demonstrate a significant improvement in route prediction accuracy, with reduced travel time and enhanced safety metrics. By addressing the challenges of unpredictability and computational complexity, this work contributes to the field of maritime logistics and offers a scalable, data-driven solution for global shipping operations. Future research directions include integrating adaptive algorithms for real-time predictions and expanding the dataset to include diverse maritime regions.

Keywords

Marine route prediction, Maritime navigation, Machine learning algorithms.

References

[1] Ashwin R. et al., “Optimal Ship Route Search Based on Multi-Objective Genetic Algorithm,” 2023 14^th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, pp. 1-5, 2023.

[CrossRef] [Google Scholar] [Publisher Link]

[2] Debabrata Sen, and Chinmaya P. Padhy, “An Approach for Development of a Ship Routing Algorithm for Application in the North Indian Ocean Region,” Applied Ocean Research, vol. 50, pp. 173-191, 2015.

[CrossRef] [Google Scholar] [Publisher Link]

[3] H. Choset, and J. Burdick, “Sensor Based Planning. I. The Generalized Voronoi Graph,” Proceedings of the 1995 IEEE International Conference on Robotics and Automation, Nagoya, Japan, vol. 2, pp. 1649-1655, 1995.

[CrossRef] [Google Scholar] [Publisher Link]

[4] David Hsu et al., “On Finding Narrow Passages with Probabilistic Roadmap Planners,” Robotics: the Algorithmic Perspective: 1998 Workshop on the Algorithmic Foundations of Robotics, 1998.

[Google Scholar] [Publisher Link]

[5] E.W. Dijkstra, “A Note on Two Problems in Connexion with Graphs,” Numerische Mathematik, vol. 1, pp. 269-271, 1959.

[CrossRef] [Google Scholar] [Publisher Link]

[6] Peter E. Hart, Nils J. Nilsson, and Bertram Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100-107, 1968.

[CrossRef] [Google Scholar] [Publisher Link]

[7] Richard Bellman, “On a Routing Problem,” Quarterly of Applied Mathematics, vol. 16, pp. 87-90, 1958.

[CrossRef] [Google Scholar] [Publisher Link]

[8] Joanna Szlapczynska, “Multi-Objective Weather Routing with Customised Criteria and Constraints,” The Journal of Navigation, vol. 68, no. 2, pp. 338-354, 2015.

[CrossRef] [Google Scholar] [Publisher Link]

[9] J. Szłapczyńska, and R. Śmierzchalski, “Multicriteria Optimisation in Weather Routing,” TransNav the International Journal on Marine Navigation and Safety of Sea Transportation, vol. 3, no. 4, pp. 393-400, 2009.

[Google Scholar] [Publisher Link]

[10] Atsuo Maki et al., “A New Weather-Routing System that Accounts for Ship Stability Based on a Real-Coded Genetic Algorithm,” Journal of Marine Science and Technology, vol. 16, pp. 311-322, 2011.

[CrossRef] [Google Scholar] [Publisher Link]