research-article

Extracting Courses of Vessels from AIS Data and Real-Time Warning Against Off-Course

Authors:

Christophe Claramunt,

Abdel-Ouahab BoudraaAuthors Info & Claims

ICBDR '18: Proceedings of the 2nd International Conference on Big Data Research

Pages 62 - 69

https://doi.org/10.1145/3291801.3291823

Published: 27 October 2018 Publication History

Abstract

Automatic Identification System (AIS) broadcasts real-time information from moving vessels at sea. AIS data collected from moving vessels over an area of interest can rapidly generates large datasets, while such data can be also of very much interest for deriving maritime trajectory patterns or anomaly events. Besides a number of route discovery and anomaly detection techniques currently derived from AIS data, it appears, that from machine learning and natural language processing principles, a topic model might provide a novel approach for extracting implicit patterns underlying massive and unstructured collection of incoming data considered as documents. Inspired by this idea, we first applied a topic model to extract course patterns from AIS data, and then developed an algorithm-based approach for real-time warning against off course. In fact, a course not only encompasses trajectory locations, but also headings. The potential of the approach is illustrated by a series of experimental results derived from practical AIS data set in a region of North West France.

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Cited By

Fujino IClaramunt C(2023)Navigation pattern extraction from AIS trajectory big data via topic modelJournal of Navigation10.1017/S0373463323000206(1-19)Online publication date: 10-Jul-2023
https://doi.org/10.1017/S0373463323000206
Asborno MHernandez SMitchell KYves M(2022)Inland waterway network mapping of AIS data for freight transportation planningJournal of Navigation10.1017/S037346332100095375:2(251-272)Online publication date: 13-Jan-2022
https://doi.org/10.1017/S0373463321000953
Kolley LRückert NKastner MJahn CFischer K(2022)Robust berth scheduling using machine learning for vessel arrival time predictionFlexible Services and Manufacturing Journal10.1007/s10696-022-09462-x35:1(29-69)Online publication date: 1-Sep-2022
https://doi.org/10.1007/s10696-022-09462-x
Show More Cited By

Index Terms

Extracting Courses of Vessels from AIS Data and Real-Time Warning Against Off-Course
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
      2. Data stream mining
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Interpolation

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cover image ACM Other conferences

ICBDR '18: Proceedings of the 2nd International Conference on Big Data Research

October 2018

221 pages

ISBN:9781450364768

DOI:10.1145/3291801

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shandong Univ.: Shandong University
University of Queensland: University of Queensland
Dalian Maritime University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2018

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ICBDR 2018

ICBDR 2018: 2018 The 2nd International Conference on Big Data Research

October 27 - 29, 2018

Weihai, China

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Cited By

Fujino IClaramunt C(2023)Navigation pattern extraction from AIS trajectory big data via topic modelJournal of Navigation10.1017/S0373463323000206(1-19)Online publication date: 10-Jul-2023
https://doi.org/10.1017/S0373463323000206
Asborno MHernandez SMitchell KYves M(2022)Inland waterway network mapping of AIS data for freight transportation planningJournal of Navigation10.1017/S037346332100095375:2(251-272)Online publication date: 13-Jan-2022
https://doi.org/10.1017/S0373463321000953
Kolley LRückert NKastner MJahn CFischer K(2022)Robust berth scheduling using machine learning for vessel arrival time predictionFlexible Services and Manufacturing Journal10.1007/s10696-022-09462-x35:1(29-69)Online publication date: 1-Sep-2022
https://doi.org/10.1007/s10696-022-09462-x
Rawson ABrito MSabeur ZTran-Thanh L(2021)From Conventional to Machine Learning Methods for Maritime Risk AssessmentTransNav, the International Journal on Marine Navigation and Safety of Sea Transportation10.1271615:4(757-764)Online publication date: 2021
https://doi.org/10.12716
Rawson ABrito MSabeur ZTran-Thanh L(2021)A machine learning approach for monitoring ship safety in extreme weather eventsSafety Science10.1016/j.ssci.2021.105336141(105336)Online publication date: Sep-2021
https://doi.org/10.1016/j.ssci.2021.105336
Asborno MHernandez SYves M(2021)GIS-based identification and visualization of multimodal freight transportation catchment areasTransportation10.1007/s11116-020-10155-348:6(2939-2968)Online publication date: 2-Jan-2021
https://doi.org/10.1007/s11116-020-10155-3
Rätsep MParnell KSoomere TKruusmaa MRistolainen ATuhtan J(2020)Using Spectrograms from Underwater Total Pressure Sensors to Detect Passing Vessels in a Coastal EnvironmentJournal of Atmospheric and Oceanic Technology10.1175/JTECH-D-19-0192.137:8(1353-1363)Online publication date: 1-Aug-2020
https://doi.org/10.1175/JTECH-D-19-0192.1
Alizadeh DAlesheikh ASharif M(2020)Prediction of vessels locations and maritime traffic using similarity measurement of trajectoryAnnals of GIS10.1080/19475683.2020.184043427:2(151-162)Online publication date: 4-Nov-2020
https://doi.org/10.1080/19475683.2020.1840434
Alizadeh DAlesheikh ASharif M(2020)Vessel Trajectory Prediction Using Historical Automatic Identification System DataJournal of Navigation10.1017/S037346332000044274:1(156-174)Online publication date: 26-Aug-2020
https://doi.org/10.1017/S0373463320000442

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