Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Videos
Videos
Thesis submission
Thesis submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Contact us
Contact us
The Vessel route pattern extraction and anomaly detection from AIS data
Download
index.pdf
Date
2019
Author
Boztepe, Gözde
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
11
views
8
downloads
Cite This
The need for a variety of auxiliary analytical tools to enhance marine safety and marine status awareness has been expressed by various platforms. There are lots of data sources breaking out while the ship is on cruising. Automatic Identification System (AIS) device that is widely used in vessels, is one of them. It broadcasts information such as type of ship, identity number, state, destination, estimated time of arrival (ETA), location, speed, direction, cargo. In this study, to aid operators while sailing, the trajectory extraction and anomaly detection tool have been developed. The AIS messages are used to improve a system for safe navigation. Three different approaches are applied for the prediction of the vessel trajectories. Later, movements that have not matched the route patterns and unusual stop anomalies have been examined.
Subject Keywords
Ships
,
Ships Automatic identification systems.
,
Keywords: AIS
,
trajectory
,
anomaly
,
LSTM
,
vessel
,
maritime.
URI
http://etd.lib.metu.edu.tr/upload/12623897/index.pdf
https://hdl.handle.net/11511/44461
Collections
Graduate School of Natural and Applied Sciences, Thesis
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
G. Boztepe, “The Vessel route pattern extraction and anomaly detection from AIS data,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., 2019.