Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making.

Automatic Identification System (AIS) data offer essential insights into maritime traffic patterns; however, effective visualization tools for decision-making remain limited. This study presents an integrated visualization processing method to support ship operators by identifying maritime traffic behavior information, such as traffic density, direction, and flow in specific sea navigational areas. We analyzed AIS dynamic data from a specific sea area, calculated ship density distributions across a grid lattice, and obtained visualizations of traffic-dense areas as heat maps. Using the density-based spatial clustering of applications with a noise algorithm, we detected traffic direction at each grid point, which was visualized in the form of directional arrows, and clustered ship trajectories to identify representative traffic flows. The visualizations were integrated and overlaid onto an S-57-based electronic nautical map for Mokpo’s entry and exit routes, revealing primary shipping lanes and critical inflection points within the target area. This integrated visualization method simultaneously displays traffic density, flow, and customary routes. It is adapted for the electronic nautical chart (S-101) under the next-generation hydrographic information standard (S-100), which can be used as a tool to support decision-making for ship operators.

Navigational obstacles can impede safe vessel operation and cause secondary accidents, neces-sitating their rapid removal. To conduct these removal operations efficiently, it is necessary to comprehensively assess the effects of obstacles on both collision and pollution risks. However, comprehensive risk assessments that simultaneously consider both collision and pollution risks are lacking. In this study, a navigational obstacle risk assessment system based on the fuzzy set theory was developed to support the rapid removal of obstacles. The system comprises three main components: (i) a collision risk assessment model utilizing Monte Carlo simulation, (ii) a pollution risk assessment model employing a hazard management structure for sinking vessels, and (iii) a fuzzy set-based evaluation system that integrates risk indices derived from these models to infer the comprehensive risk. The results of the simulation-based case study confirm that the developed system provides reliable risk assessment by comprehensively considering both the collision and pollution risks. The system is sensitive to changes in the risk levels and effectively reflects the realistic conditions, thereby overcoming the limitations of existing research. This demonstrates its potential as a valuable tool for decision making for rapid removal of navigational obstacles.

Navigational obstacles can impede safe vessel operation and cause secondary accidents, necessitating their rapid removal. To conduct these removal operations efficiently, it is necessary to comprehensively assess the effects of obstacles on both collision and pollution risks. However, comprehensive risk assessments that simultaneously consider both collision and pollution risks are lacking. In this study, a navigational obstacle risk assessment system based on the fuzzy set theory was developed to support the rapid removal of obstacles. The system comprises three main components: (i) a collision risk assessment model utilizing Monte Carlo simulation, (ii) a pollution risk assessment model employing a hazard management structure for sinking vessels, and (iii) a fuzzy set-based evaluation system that integrates risk indices derived from these models to infer the comprehensive risk. The results of the simulation-based case study confirm that the developed system provides reliable risk assessment by comprehensively considering both the collision and pollution risks. The system is sensitive to changes in the risk levels and effectively reflects the realistic conditions, thereby overcoming the limitations of existing research. This demonstrates its potential as a valuable tool for decision-making for rapid removal of navigational obstacles.

The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity ($\mathit{O}\left(\mathit{N}\right)$). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion.