Keywords: maritime safety, route planning, big Data, automatic identification system, graph algorithms, shortest path
Ships route planning in heavy-traffic marine area based on historical data
UDC 004.8
DOI: 10.26102/2310-6018/2022.38.3.014
The paper considers the problem of planning a route for sea vessel shifting. Under the conditions of heavy traffic, navigators should follow the traffic scheme accepted in this defined water area. Such a pattern may not be officially established while representing collective experience in navigation. In this case, route planning based on the data on the movement of other ships that had been in this water area before (the same idea underlies the methods of "big data" tasks) appears to be productive. In the papers published earlier, such route planning employed a cluster analysis of retrospective data on the movement of ships, which involved dividing the water area into sections and isolating their characteristic values of speeds and courses. The problem with this approach was the choice of partitioning parameters, which had to be set for each specific water area separately. This paper proposes another approach when the graph of potential routes includes a selection of the trajectories of individual ships that had been previously implemented in the selected water area. The article regards a method for constructing such a graph of possible routes, estimates the number of its vertices and edges, and gives recommendations on the choice of a method for finding the shortest path on this graph. A possible method premised on the notion of combining straight and maneuverable sections of vessel traffic that can be applied to interpolate the missing data required to build a graph is discussed. Examples of route planning in a number of real water areas are given: Vladivostok, Tokyo Bay, the Tsugaru Strait.
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Keywords: maritime safety, route planning, big Data, automatic identification system, graph algorithms, shortest path
For citation: Grinyak V.M., Devyatisilnyi A.S. Ships route planning in heavy-traffic marine area based on historical data. Modeling, Optimization and Information Technology. 2022;10(3). URL: https://moitvivt.ru/ru/journal/pdf?id=1221 DOI: 10.26102/2310-6018/2022.38.3.014 (In Russ).
Received 26.08.2022
Revised 14.09.2022
Accepted 22.09.2022
Published 30.09.2022