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Advanced Ship Trajectory Prediction and Route Planning
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Advanced Ship Trajectory Prediction and Route Planning

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 3382

Special Issue Editor

Prof. Dr. Nam-kyun Im

E-Mail Website
Guest Editor
Division of Navigation Science, Mokpo National Maritime University, Mokpo City 58628, Jeonnam, Republic of Korea
Interests: AI-based technologies for autonomous ship operations; ship maneuverability, stability, and control; maritime traffic engineering and ship safety environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

This Special Issue explores advanced methods for ship trajectory prediction and route planning with a focus on practical needs in today’s maritime world. Key themes of interest include route planning for Maritime Autonomous Surface Ships (MASSs), traffic-flow-based trajectory studies using AIS data, and ship maneuvering dynamics for predicting vessel states in complex conditions. We welcome studies that combine theory with application, such as AI-based modeling, real-time decision support, and traffic management strategies. By bringing together these perspectives, this issue aims to provide insights that improve safety, efficiency, and autonomy in modern maritime navigation

Prof. Dr. Nam-kyun Im
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • maritime autonomous surface ships (MASSs)
  • AIS-based traffic analysis 
  • ship maneuvering dynamics 
  • route planning 
  • ship trajectory prediction

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Published Papers (3 papers)

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Research

21 pages, 11718 KB  
Article
A Method to Infer Customary Routes via Analysis of the Movement Importance of Ship Trajectories Calculated Using TF-IDF
by Seung Sim, Jun-Rae Cho, Jae-Ryong Jung, Jong-Hwa Baek and Deuk-Jae Cho
J. Mar. Sci. Eng. 2026, 14(1), 29; https://doi.org/10.3390/jmse14010029 - 23 Dec 2025
Viewed by 628
Abstract
Ship positional data are widely used for route inference, yet most existing studies rely on automatic identification system data, which contain irregular transmission intervals and limit the ability to capture vessel-specific operational habits and subtle route choices. This study addresses these limitations by [...] Read more.
Ship positional data are widely used for route inference, yet most existing studies rely on automatic identification system data, which contain irregular transmission intervals and limit the ability to capture vessel-specific operational habits and subtle route choices. This study addresses these limitations by proposing a methodology to infer customary routes using periodic 3 s ship position data collected through the Korean e-Navigation system based on long-term evolution maritime communication. The method comprises three main steps: constructing a sea-area grid with an associated weight map, determining data-driven importance and updating weights, and performing pathfinding. Domestic waters are divided into 100 m grids, and navigable and non-navigable areas are binarized to establish a framework for route exploration. Ship positional data are processed to extract inter-port trajectories, which are then classified by ship size and tidal time zone to account for navigational differences arising from vessel characteristics and tide-dependent accessibility. These trajectories are combined with spatial grids and transformed into a document–word structure, enabling the calculation of movement importance between grid cells using a modified term frequency–inverse document frequency measure. The resulting weights are applied to a pathfinding graph to derive routes that reflect vessel size and tidal conditions. The effectiveness of the proposed method is evaluated by computing cosine similarity between the inferred routes and actual trajectories. Full article
(This article belongs to the Special Issue Advanced Ship Trajectory Prediction and Route Planning)
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22 pages, 5462 KB  
Article
Ship Motion State Recognition Using Trajectory Image Modeling and CNN-Lite
by Shuaibing Zhao, Zongshun Tian, Yuefeng Lu, Peng Xie, Xueyuan Li, Yu Yan and Bo Liu
J. Mar. Sci. Eng. 2025, 13(12), 2327; https://doi.org/10.3390/jmse13122327 - 8 Dec 2025
Viewed by 837
Abstract
Intelligent recognition of ship motion states is a key technology for achieving smart maritime supervision and optimized port scheduling. To enhance both the modeling efficiency and recognition accuracy of AIS trajectory data, this paper proposes a ship behavior recognition method that integrates trajectory-to-image [...] Read more.
Intelligent recognition of ship motion states is a key technology for achieving smart maritime supervision and optimized port scheduling. To enhance both the modeling efficiency and recognition accuracy of AIS trajectory data, this paper proposes a ship behavior recognition method that integrates trajectory-to-image conversion with a convolutional neural network (CNN) for classifying three typical motion states: mooring, anchoring, and sailing. Firstly, a multi-step preprocessing pipeline is established, incorporating trajectory cleaning, interpolation complementation, and segmentation to ensure data completeness and consistency; secondly, dynamic features—including speed, heading, and temporal progression—are encoded into an RGB three-channel image, which not only preserves the original spatial and temporal information of the trajectory but also strengthens the dimension of the feature expression of the image. Thirdly, the lightweight CNN architecture (CNN-Lite) is designed to automatically extract spatial motion patterns from these images, with data augmentation techniques further enhancing model robustness and generalization across diverse scenarios. Finally, comprehensive comparative experiments are conducted to evaluate the proposed method. On a real-world AIS dataset, the proposed method achieves an accuracy of 91.54%, precision of 91.51%, recall of 91.54%, and F1-score of 91.52%—demonstrating superior or highly competitive performance compared with SVM, KNN, MLSTM, ResNet-50 and Swin-Transformer in both classification accuracy and model stability. These results confirm that constructing dynamic-feature-enriched RGB trajectory images and designing a lightweight CNN can effectively improve ship behavior recognition performance and provide a practical and efficient technical solution for abnormal anchoring detection, maritime traffic monitoring, and development of intelligent shipping systems. Full article
(This article belongs to the Special Issue Advanced Ship Trajectory Prediction and Route Planning)
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17 pages, 2081 KB  
Article
Reconsideration of IMO’s Maneuvering Performance Standards for Large Fishing Vessels
by Su-Hyung Kim and Min-Gyu Lee
J. Mar. Sci. Eng. 2025, 13(12), 2256; https://doi.org/10.3390/jmse13122256 - 27 Nov 2025
Cited by 1 | Viewed by 1547
Abstract
This study evaluates the applicability and limitations of the International Maritime Organization (IMO)’s maneuvering standards (MSC.137(76)) for large fishing vessels under 100 m in length, which are not currently included in the regulation. Full-scale turning circle, zig-zag, and stopping tests were conducted on [...] Read more.
This study evaluates the applicability and limitations of the International Maritime Organization (IMO)’s maneuvering standards (MSC.137(76)) for large fishing vessels under 100 m in length, which are not currently included in the regulation. Full-scale turning circle, zig-zag, and stopping tests were conducted on three representative vessels—a stern trawler, a purse seiner, and a squid-jigging boat—in accordance with ISO 15016:2015 and ITTC procedures. All the vessels satisfied the IMO criteria for their turning and stopping performance; however, the zig-zag tests revealed distinct differences in directional stability. The stern trawler and purse seiner showed excessive first-overshoot angles, indicating over-reactive yaw responses influenced by the hull form and propulsion–rudder interaction, whereas the squid-jigging boat exhibited very small overshoot angles, reflecting strong yaw damping. These patterns correspond with variations in the block coefficient (Cb), Froude number (Fn), and length-to-breadth ratio LBP/B. Although all vessels met the IMO stopping requirements, their deceleration behavior differed due to their hull fullness and reverse-thrust efficiency. Overall, the findings clearly demonstrate a mismatch between merchant-vessel-based IMO standards and the maneuvering characteristics of fishing vessels, which require agility and frequent low-speed operations. The results provide a basis for refining maneuvering prediction methods and developing assessment criteria tailored to fishing vessel design and operational profiles. Full article
(This article belongs to the Special Issue Advanced Ship Trajectory Prediction and Route Planning)
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