
Clustering of Marine Vessel Trajectory Data for Routes Planning through Water Areas with Heavy Traffic
Author(s) -
V. M. Grinyak,
А.В. Шуленина,
A. S. Devyatisil’nyi
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/988/2/022054
Subject(s) - cluster analysis , grid , computer science , graph , set (abstract data type) , graph theory , shortest path problem , data mining , point (geometry) , operations research , theoretical computer science , geography , mathematics , artificial intelligence , geometry , combinatorics , programming language , geodesy
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.