YorkSpace has migrated to a new version of its software. Access our Help Resources to learn how to use the refreshed site. Contact diginit@yorku.ca if you have any questions about the migration.
 

Modeling Vessel Behaviours By Clustering Ais Data Using Optimized DBSCAN

Simple item page
dc.contributor.advisorArmenakis, Costas
dc.contributor.advisorMardkheh, Amaneh Jadidi
dc.contributor.authorHan, Xuyang
dc.date.accessioned2021-07-06T12:47:03Z
dc.date.available2021-07-06T12:47:03Z
dc.date.copyright2021-02
dc.date.issued2021-07-06
dc.date.updated2021-07-06T12:47:03Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractToday, maritime transportation represents substantial international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this research thesis, we present an enhanced density-based spatial clustering (DBSCAN) method to model vessel behaviors. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis Distance metric that considers the correlations of the points representing the locations of the vessels. The clustering method is applied to historical Automatic Identification System (AIS) data and generates an action recommendation tool and a model for detecting vessel trajectory anomalies. Two case studies present outcomes from the openly available Gulf of Mexico AIS data, and Saint Lawrence Seaway and Great Lakes AIS licensed data acquired from ORBCOMM (a maritime AIS data provider). This research proposes a framework for modeling AIS data, an algorithm for generating a clustering model of the vessels' trajectories, and a model for detecting vessel trajectory anomalies such as unexpected stops, deviations from regulated routes, or inconsistent speed. This work's findings demonstrate the applicability and scalability of the proposed method for modeling more water regions, contributing to situational awareness, vessel collision prevention, safe navigation, route planning, and detection of vessel behavior anomalies for auto-vessels development.
dc.identifier.urihttp://hdl.handle.net/10315/38453
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectSustainability
dc.subject.keywordsDBSCAN
dc.subject.keywordsVessel Trajectory Clustering
dc.subject.keywordsMahalanobis Metric
dc.subject.keywordsMachine Learning
dc.subject.keywordsMarine Transportation
dc.titleModeling Vessel Behaviours By Clustering Ais Data Using Optimized DBSCAN
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Han_Xuyang_2021_MSc.pdf
Size:
11.1 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Earth & Space Science

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.