Da-Un Jang

Coastal waters are characterized by complex spatial constraints, including narrow channels, irregular shorelines, and shallow-water areas. To address these challenges, this study proposes an Empirical Probabilistic Roadmap (EPRM) method for ship path planning based on Automatic Identification System (AIS) trajectory data. Historical AIS data of a regularly operated coastal passenger ferry near Mokpo, Republic of Korea, were cleaned, interpolated, and resampled at common longitude intervals. An empirical navigable space was then generated from the outer boundaries of the trajectories, and nodes were preferentially sampled within it according to the trajectory density. In addition, predicted tidal levels were compared with an empirically derived tidal threshold to select an appropriate empirical navigable space for the time of operation, thereby reflecting historically observed tide-dependent route selection in the path-planning process. The proposed method was compared with four benchmark path-planning algorithms, namely the A* graph-search algorithm, the Probabilistic Roadmap (PRM), the Rapidly-exploring Random Tree (RRT), and its asymptotically optimal variant RRT*, in terms of path length, search time, and similarity to actual AIS trajectories. Trajectory similarity was assessed using Modified Hausdorff Distance (MHD), Dynamic Time Warping (DTW), and Longest Common Subsequence (LCSS). The results showed that the EPRM generated paths with small run-to-run variability and the highest similarity to actual AIS trajectories among the compared algorithms under the study conditions, although its computational advantage was limited to the path-query phase once the roadmap had been constructed. Moreover, different paths to the same destination were generated as the normal and detour empirical navigable spaces were selected according to tidal conditions. It should be noted that the AIS-derived space represents historically used waters rather than a verified safe-navigation area, and its integration with bathymetric and under-keel clearance information is required for operational use.

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.

Map space composition is the first step in ship route planning. In this study, a map modeling method for path planning is proposed. This method incorporates the safety margin based on the theory of geographic space existing in coastal waters, maneuvering space according to ship characteristics, and the psychological buffer space of a ship navigator. First, the obstacle area was segmented using the binary method—a segmentation method—based on the international standard electronic chart image. Next, the margin space was incorporated through the morphological algorithm for the obstacle area. Finally, to minimize the space lost during the route search, the boundary simplification of the obstacle area was performed through the concave hull method. The experimental results of the proposed method resulted in a map that minimized the area lost due to obstacles. In addition, it was found that the distance and path-finding time were reduced compared to the conventional convex hull method. The study shows that the map modeling method is feasible, and that it can be applied to path planning.

Ship route planning is a crucial activity that must consider not only the safety of the ship but also the safe passage of nearby ships in the same space and time. This study aims to provide general route-planning guidance to shipping traffic by improving conventional sampling-based route-planning algorithms in accordance with the maritime environment from a ship operator’s perspective. The obstacle safety margin in a marine environment can be reflected in a binary image map space based on an electronic navigational chart. Consequently, an initial route was created using the probabilistic road map (PRM) algorithm in the configured map space to increase the speed of the conventional sampling-based route-planning algorithm. Based on the initial route created, a designated space—that is, a multi-elliptical area—was created to limit the route-search range. After searching the final route in the designated space based on the rapidly-exploring random tree (RRT*) algorithm, optimal route planning could be achieved by generating a collision-free space graph to remove unnecessary nodes from the searched final route. The simulation results showed that the route was shortened by approximately 33.7 km compared with the conventional RRT* algorithm, and the calculation time was shortened by approximately 2.5 times.