As new forms of industries emerge in marine spaces due to global environmental protection trends and the promotion of recreational activities, traditional navigation areas for vessels are also being affected. To ensure safe vessel passage and effective vessel traffic management in response to evolving maritime environments, navigational routes for ships must be exclusively established. Numerous studies have attempted to derive shipping routes from historical vessel-traffic data; however, the final forms of polygon shapes representing routes have not been produced. Hence, this study aimed to extract polygonal routes based on densely trafficked maritime areas. Data collected from the automatic identification system (AIS) onboard maritime vessels were utilized to analyze dense navigational areas, which were then divided into 1 km grids. Through a spatial-temporal analysis method utilized from The European Marine Observation and Data Network (EMODnet), the occupancy time of the vessels in each grid was calculated to extract dense traffic areas. The dense traffic areas extracted in grid form were processed using the geographical identification system to create polygonal routes by smoothing and simplification. The resulting polygons exhibited different shapes depending on the analysis period. To extract a unique representative route polygon, the CRiteria Importance Through Intercriteria Correlation (CRITIC) method was employed to calculate the shape similarity based on the centroid, shape index, and overlapping area ratio of the polygons. The extracted representative polygon demonstrated the highest shape similarity compared with the other polygons and was utilized by 95.93% of the vessels navigating the analyzed area. The study results can contribute to identifying essential areas for vessels in maritime zones by proposing representative shipping routes.

This study addresses the challenges of effectively reflecting maritime traffic flows in Korea’s MSP. Maritime traffic intensity zones were identified through a spatio-temporal density analysis and the application of standardization techniques. This study proposes unified extraction standards for maritime traffic intensity zones and provides quantitative indicators for MSP based on the occupancy rates of Automatic Identification System (AIS) data. The results of the spatio-temporal density analysis were polygonized and represented at 10% intervals. A suitable maritime traffic intensity zone was then selected based on the occupancy rates of ship counts and ship points within each polygon, followed by a statistical review. Consequently, the maritime traffic intensity zone with an upper 20% density was identified, and the zone exhibiting an upper 10% density was designated as the primary maritime traffic intensity zone. This approach facilitates improved navigation and port area reflections, as anticipated from the implementation of unified extraction standards and methods for maritime traffic intensity zones established by local governments in Korea.

Accurately identifying fishing vessel behavior is essential for preventing collisions with merchant vessels and ensuring maritime safety. However, this task remains challenging due to diverse fishing operations, ambiguous boundaries between fishing and navigating states, and limited availability of labeled data. We aimed to develop a scalable and data-efficient framework for classifying fishing vessel behavior using unlabeled Automatic Identification System (AIS) trajectory data. Raw AIS data from fishing vessels operating along the South Korean coast were preprocessed, and Hermite interpolation was applied to generate uniform 1-s interval trajectories. Trajectories were segmented into 10-min windows, and 11 domain-based features were extracted from each trajectory, including Menger curvature, Steaming Intention, Directionality, speed statistics, and point proximity. A SimCLR-based self-supervised learning model with tabular data augmentations (Gaussian jittering and feature dropout) was employed to learn embeddings. Optimal hyperparameters (τ = 0.2, batch size N = 1,024) were identified via ablation studies. The embeddings were then classified using an AutoML framework (AutoGluon) for final behavior classification, minimizing manual intervention in model selection and hyperparameter tuning. The proposed method achieved an F1-score of 0.870 and accuracy of 0.926 on the test set. Validation across three sea areas (East, Yellow, and South Seas) yielded F1-scores up to 0.974. A 1 km × 1 km grid-based spatial density analysis further identified high-intensity fishing zones across the South Korean Exclusive Economic Zone. The framework provides an effective and scalable solution for fishing vessel behavior classification and maritime traffic management.