ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 11. Vol. 29. 2023

DOI: 10.17587/it.29.604-611

V. M. Grinyak, Dr.Sc. (Tech.), Professor,
Vladivostok State University, Vladivostok, Russian Federation,
A. S. Devyatisilnyi, Dr.Sc. (Tech.), Professor,
Institute of Automation and Control Processes, FEBRAS, Vladivostok, Russian Federation,
V. A. Petrov, Ph.D. (Tech.), Assistant Professor,
Maritime State University named after admiral G. I. Nevelskoy, Vladivostok, Russian Federation

Ships Route Planning Based on Navigation Historical Data and Traffic Intensity

This work is about maritime safety. The paper considers the problem of planning a route for a vessel to cross water areas with heavy traffic. It should be borne in mind that the trajectory of the vessel should be consistent with established navigational practices and collective navigation experience. In this case, it seems productive to plan a route based on data on the movement of other ships that were in the water area earlier (the same idea underlies the methods of "big data" tasks). In the works published earlier, such route planning was based on a cluster analysis of retrospective data on the movement of ships, which involved dividing the water area into sections and highlighting characteristic values of speeds and courses in them. The problem with this approach was the choice of partitioning parameters, which had to be set for each specific water area separately. In this paper, another approach is proposed, when the graph of possible routes includes a selection of the trajectories of individual ships that were previously implemented in the selected water area. This work is a further development of methods for solving the problem of ship route planning in areas with heavy traffic. The proposed method is based on the formation of a graph of possible routes from a set of intersecting broken lines, each of which represents a previously implemented route. Each edge of the graph is assigned a measure of its "popularity", which characterizes the proximity of other edges to it. The shortest path on a weighted graph is constructed considering not only the geometric length of the edges, but also the measure of their "popularity". The route found in this way will lie among the most frequently used trajectories. Samples of route panning for Vladivostok water area are presenting in this paper.
Keywords: marine traffic control, unmanned navigation, e-navigation, route planning, traffic intensity, big data, graph algorithms, automatic identification system

P. 604-611

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