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Article

Trajectory Mining and Routing: A Cross-Sectoral Approach

1
Harokopio Department of Informatics and Telematics, School of Digital Technology, University of Athens, 176 76 Kallithea, Greece
2
Danaos Shipping Co., 185 36 Attica, Greece
3
Athena Research Centre, 151 25 Marousi, Greece
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2024, 12(1), 157; https://doi.org/10.3390/jmse12010157
Submission received: 27 November 2023 / Revised: 3 January 2024 / Accepted: 5 January 2024 / Published: 12 January 2024
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)

Abstract

Trajectory data holds pivotal importance in the shipping industry and transcend their significance in various domains, including transportation, health care, tourism, surveillance, and security. In the maritime domain, improved predictions for estimated time of arrival (ETA) and optimal recommendations for alternate routes when the weather conditions deem it necessary can lead to lower costs, reduced emissions, and an increase in the overall efficiency of the industry. To this end, a methodology that yields optimal route recommendations for vessels is presented and evaluated in comparison with real-world vessel trajectories. The proposed approach utilizes historical vessel tracking data to extract maritime traffic patterns and implements an A* search algorithm on top of these patterns. The experimental results demonstrate that the proposed approach can lead to shorter vessel routes compared to another state-of-the-art routing methodology, resulting in cost savings for the maritime industry. This research not only enhances maritime routing but also demonstrates the broader applicability of trajectory mining, offering insights and solutions for diverse industries reliant on trajectory data.
Keywords: AIS; weather routing optimization; path planning; trajectory mining; dynamic programming AIS; weather routing optimization; path planning; trajectory mining; dynamic programming

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MDPI and ACS Style

Kaklis, D.; Kontopoulos, I.; Varlamis, I.; Emiris, I.Z.; Varelas, T. Trajectory Mining and Routing: A Cross-Sectoral Approach. J. Mar. Sci. Eng. 2024, 12, 157. https://doi.org/10.3390/jmse12010157

AMA Style

Kaklis D, Kontopoulos I, Varlamis I, Emiris IZ, Varelas T. Trajectory Mining and Routing: A Cross-Sectoral Approach. Journal of Marine Science and Engineering. 2024; 12(1):157. https://doi.org/10.3390/jmse12010157

Chicago/Turabian Style

Kaklis, Dimitrios, Ioannis Kontopoulos, Iraklis Varlamis, Ioannis Z. Emiris, and Takis Varelas. 2024. "Trajectory Mining and Routing: A Cross-Sectoral Approach" Journal of Marine Science and Engineering 12, no. 1: 157. https://doi.org/10.3390/jmse12010157

APA Style

Kaklis, D., Kontopoulos, I., Varlamis, I., Emiris, I. Z., & Varelas, T. (2024). Trajectory Mining and Routing: A Cross-Sectoral Approach. Journal of Marine Science and Engineering, 12(1), 157. https://doi.org/10.3390/jmse12010157

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