This task might be supported by additional checking facilities, e.g., warning about unsafe water depths. Despite the existence of a number of supporting bridge systems, such a voyage is normally planned manually by the ship’s crew. A waypoint is a single coordinate within a route, at which a vessel stops or changes its course. In the maritime domain, a safe and efficient vessel operation requires a prescient berth to berth voyage planning, resulting in a route that consists of waypoints and legs. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network. The method is evaluated by comparing the results with an on-line voyage planning application. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. Finally, edges connecting these waypoints form the final maritime traffic network. The genetic algorithm with spatial partitioning is used for waypoints discovery. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning.
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