Ships Route Searching with Respect of Sea Waves Danger

Akmaykin, Denis Alexandrovich; Grinyak, V. M.

doi:10.1088/1755-1315/272/3/032103

Cited by 2 publications

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“…For the considered variant of the route planning problem, taking into account the constraints, the mathematical model can be based on the search for the shortest path on the weighted graph of possible ship routes. This approach is traditional and well proven in this class of problems [3]. The weight of the graph edges is determined by the "desirability" of a particular vessels course for each point of the water area, taking into account the identified movement patterns.…”

Section: Introductionmentioning

confidence: 99%

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Clustering of Marine Vessel Trajectory Data for Routes Planning through Water Areas with Heavy Traffic

Grinyak

Шуленина

Devyatisil’nyi

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The article is devoted to the problem of ensuring the safety of vessel traffic. One of the elements of the traffic organization in areas with heavy navigation is the system of establishing the routes of vessels. This system is a set of restrictions imposed by a certain traffic pattern and rules adopted in a particular water area. The paper considers the problem of planning a transition route for water areas with heavy marine traffic. The planning of the vessels transition route during the movement of the water area with established routes must be carried out taking into account the specified restrictions. A possible way to identify these restrictions is to isolate the movement patterns of a particular sea area from the retrospective information about its traffic. Model representations of such a problem can be formulated on the basis of the idea of clustering the parameters of traffic. The route planning problem model is based on finding the shortest path on a weighted graph. Several ways of constructing such a graph are proposed: a regular grid of vertices and edges; a layered ore random grid of vertices and edges; vertices and edges based on retrospective data. The weight of the edges is proposed to be set as a function of the “desirability” of a particular course of the vessel for each point of the water area, taking into account the identified movement patterns. The paper discusses possible clustering methods and makes a choice in favor of subtractive clustering.

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Section: Introductionmentioning

confidence: 99%

“…However, in practice, it is rarely used because of the high computational complexity and (as a rule) the discreteness of the grid of arguments of the function u (x, y, k, v). A popular heuristic simplification of the problem is its model interpretation by finding the shortest path on the weighted graph of possible routes of the vessel [3].…”

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confidence: 99%

Clustering of Marine Vessel Trajectory Data for Routes Planning through Water Areas with Heavy Traffic

Grinyak

Шуленина

Devyatisil’nyi

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Планирование Маршрутов Судов Через Акватории С Интенсивным Движением

Гриняк,

Шуленина,

Прудникова

et al. 2021

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Статья посвящена проблеме обеспечения безопасности движения судов на морских акваториях. Рассматривается задача планирования маршрута перехода судна через акватории с интенсивным движением, которая представляется актуальной, в том числе в свете перспективного развития беспилотных морских транспортных средств. При движении через акватории с установленными путями планирование маршрута перехода судна следует осуществлять с учетом заданных ограничений. Возможным путем идентификации этих ограничений является выделение паттернов движения конкретной морской акватории из ретроспективной информации о ее трафике. Модельные представления такой задачи могут быть сформулированы на основе идеи о кластеризации параметров движения судов. В основу модели задачи планирования маршрута положен поиск кратчайшего пути на взвешенном графе. Предлагается несколько способов построения такого графа: регулярная сетка вершин и ребер, слоистая сетка вершин и ребер, случайная сетка вершин и ребер, вершины и ребра на основе ретроспективных данных. Вес ребер предлагается задавать как функцию «желательности» того или иного курса судна для каждой точки акватории с учетом выявленных паттернов движения. Для этого акватория разбивается на участки, и для каждого из них выполняется кластеризация курсов и скоростей. В работе обсуждаются возможные методы кластеризации, делается выбор в пользу субтрактивной кластеризации, не требующей предварительного задания количества кластеров. Источником данных о трафике акватории могут служить сервисы Автоматической идентификационной системы. В работе показана возможность использования данных АИС, доступных на специализированных интернет-ресурсах. Несмотря на «разреженность» этих данных, они хорошо отражают сводные особенности трафика акваторий. Приведены примеры планирования маршрутов перехода через Сангарский пролив и Токийский залив. This work is about navigation safety of marine traffic at sea areas. 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. Isolation of established patterns of movement of a specific sea area from retrospective information about its traffic by clustering the parameters of vessel movement is a promising way to identify such an experience. The task is considered relevant due to the promising development of unmanned marine vehicles. Ship's passage routes planning passage should be carried out considering the specified restrictions when moving through the water areas with established routes. Isolation of patterns of movement of a specific marine area from retrospective information about its traffic is a possible way of identifying these restrictions. Model representations of such a problem can be formulated based on the idea of clustering the parameters of ship traffic. The model of the route planning problem is based on finding the shortest path on a weighted graph. There are several ways to construct such a graph: a regular mesh of vertices and edges, a layered mesh of vertices and edges, a random mesh of vertices and edges, vertices and edges based on historical data. The weight of the ribs is proposed to be set as a function of the “desirability” of a particular course of the vessel for each point of the water area, considering the identified movement patterns. The water area is divided into sections and for each of them clustering of rates and velocities is performed. Possible clustering methods are discussed in the paper, and a choice is made in favor of subtractive clustering, which does not require preliminary specification of the number of clusters. Services of the Automatic Identification System can serve as a source of data on water area traffic. The paper shows the possibility of using AIS data available on specialized Internet resources. These data reflect well the summary features of the water area traffic despite their “sparseness”. The historical AIS data of sea traffic at Tokyo Bay and Tsugaru Straight are used for identifying traffic schema and ship routes planning with the model designed under presented research.

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Ships Route Searching with Respect of Sea Waves Danger

Cited by 2 publications

References 5 publications

Clustering of Marine Vessel Trajectory Data for Routes Planning through Water Areas with Heavy Traffic

Clustering of Marine Vessel Trajectory Data for Routes Planning through Water Areas with Heavy Traffic

Планирование Маршрутов Судов Через Акватории С Интенсивным Движением

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