Analysis of The ship's automatic identification system information and models for constructing spatio-temporal algorithms for predicting vessel traffic

被引:0
|
作者
Astrein, Vadim V. [1 ,2 ]
Kondratiev, Sergey I. [1 ,3 ]
Boran-Keshishian, Anastas L. [1 ,4 ]
Astreina, Ludmila B. [1 ,2 ]
Filatov, Viktor I. [1 ,5 ]
机构
[1] Admiral Ushakov Maritime State, Novorossiysk, Russia
[2] Admiral Ushakov Maritime State Univ, Dept Nav, 93 Lenina Ave, Novorossiysk, Russia
[3] Admiral Ushakov Maritime State Univ, Rector, 93 Lenina Ave, Novorossiysk, Russia
[4] Admiral Ushakov Maritime State Univ, Dept Nav, Vice Rector Convent Training & Branches, 93 Lenina Ave, Novorossiysk, Russia
[5] Admiral Ushakov Maritime State Univ, 93 Lenina Ave, Novorossiysk, Russia
来源
关键词
autonomous navigation; automatic identification system; real-time monitoring and forecasting of vessel movement; preliminary analysis of AIS data; vessel movement models; TARGET;
D O I
10.37220/MIT.2024.63.1.020
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
The accuracy of solving navigation safety problems is determined by two factors: the accuracy of the initial data and the degree of adequacy of the mathematical models used. The ship's automatic identification system (AIS) has clear advantages over radar/ARPA in terms of data accuracy for predicting vessel movements. This data can be used to build Decision Support Systems for Maritime Autonomous Vessels (MANS) to track traffic routes and predict collisions in real time. AIS data makes it possible to describe movement in coordinate form, in the form of a onedimensional time series of observations of the vessel's location (x, y), identify trends in changes in movement dynamics (increase, decrease or constancy) and observe a series of periods of ship movement with different dynamics, when stationary time series change to non-stationary and vice versa. A change in movement modes is accompanied by a disorder in the time series, i.e. detection of changes in the properties of the observed parametric series occurring at an unknown point in time. Determining the moment of disorder due to the inertia of the vessel is a rather difficult task. To solve it, the method of finding the likelihood ratio of the threshold value of some decision rule after crossing the corresponding values of the time series (x,y) can be used. There is no universal method for forecasting time series; each method finds its application for different types of movement and different types of time series. Time series (x,y) can be used in various models: physical, machine learning and hybrid models. Today there is no implementation of this or that model in real time. This problem can be solved by developing hybrid models, which are a combination of spatiotemporal algorithms for predicting the movement of vessels at sea to obtain better vessel movement estimation results. Keywords: autonomous navigation, automatic identification system, real-time monitoring and forecasting of vessel movement, preliminary analysis of AIS data, vessel movement models.
引用
收藏
页码:159 / 170
页数:12
相关论文
共 30 条
  • [1] A Spatio-Temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data
    Ahmed, Imtiaz
    Jun, Mikyoung
    Ding, Yu
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (11) : 20783 - 20797
  • [2] On the Use of ON/OFF Traffic Models for Spatio-Temporal Analysis of Wireless Networks
    Marvi, Murk
    Aijaz, Adnan
    Khurram, Muhammad
    IEEE COMMUNICATIONS LETTERS, 2019, 23 (07) : 1219 - 1222
  • [3] Model term selection for spatio-temporal system identification using mutual information
    Wang, Shu
    Wei, Hua-Liang
    Coca, Daniel
    Billings, Stephen A.
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2013, 44 (02) : 223 - 231
  • [4] Methodology for Predicting Maritime Traffic Ship Emissions Using Automatic Identification System Data
    da Silva, Joao N. Ribeiro
    Santos, Tiago A.
    Teixeira, Angelo P.
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2024, 12 (02)
  • [5] Analysis on urban traffic status based on improved spatio-temporal Moran's I
    Chen Shao-Kuan
    Wei Wei
    Mao Bao-Hua
    Guan Wei
    ACTA PHYSICA SINICA, 2013, 62 (14)
  • [6] Analysis with Automatic Identification System Data of Vessel Traffic Characteristics in the Singapore Strait
    Meng, Qiang
    Weng, Jinxian
    Li, Suyi
    TRANSPORTATION RESEARCH RECORD, 2014, (2426) : 33 - 43
  • [7] Identification of environmental determinants for spatio-temporal patterns of norovirus outbreaks in Korea using a geographic information system and binary response models
    Kim, Jin Hwi
    Lee, Dong Hoon
    Joo, Yongsung
    Zoh, Kyung Duk
    Ko, Gwangpyo
    Kang, Joo-Hyon
    SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 569 : 291 - 299
  • [8] Geographic information system based spatio-temporal dengue fever cluster analysis and mapping
    Mala, Shuchi
    Jat, Mahesh Kumar
    EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES, 2019, 22 (03): : 297 - 304
  • [9] Spatio-temporal analysis of riverbank changes using remote sensing and geographic information system
    Kumar, S. M. Shravan
    Pandey, Manish
    Shukla, Anoop Kumar
    PHYSICS AND CHEMISTRY OF THE EARTH, 2024, 136
  • [10] A real-time vision system for automatic traffic monitoring based on 2D spatio-temporal images
    Zhu, ZG
    Yang, B
    Xu, GY
    Shi, DJ
    THIRD IEEE WORKSHOP ON APPLICATIONS OF COMPUTER VISION - WACV '96, PROCEEDINGS, 1996, : 162 - 167