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Mining port operation information from AIS data
Citation Link: https://doi.org/10.15480/882.4705
Publikationstyp
Conference Paper
Date Issued
2022-09
Sprache
English
Herausgeber*innen
TORE-DOI
First published in
Number in series
33
Start Page
657
End Page
678
Citation
Hamburg International Conference of Logistics (HICL) 33: 657-678 (2022)
Contribution to Conference
Publisher
epubli
Peer Reviewed
true
Purpose: Ports play a vital role in global trade and commerce. While there is an abundance of analytical studies related to ship operations, less work is available about port operations and infrastructure. Information about them can be complicated and expensive to acquire, especially when done manually. We use an analytical machine learning approach on Automatic Identification System (AIS) data to understand how ports operate.
Methodology: This paper uses the DBSCAN algorithm on AIS data gathered near the Port of Brest, France to detect clusters representing the port’s mooring areas. In addition, exploratory data analyses are per formed on these clusters to gain additional insights into the port infrastructure and operations.
Findings: From Port of Brest, our experiment results identified seven clusters that had defining characteristics, which allowed them to be identified, for example, as dry docks. The clusters created by our approach appear to be situated in the correct places in the port area when inspected visually.
Originality: This paper presents a novel approach to detecting potential mooring areas and how to analyse characteristics of the mooring areas. Similar clustering methods have been used to detect anchoring spots, but this study provides a new approach to getting information on the clusters.
Methodology: This paper uses the DBSCAN algorithm on AIS data gathered near the Port of Brest, France to detect clusters representing the port’s mooring areas. In addition, exploratory data analyses are per formed on these clusters to gain additional insights into the port infrastructure and operations.
Findings: From Port of Brest, our experiment results identified seven clusters that had defining characteristics, which allowed them to be identified, for example, as dry docks. The clusters created by our approach appear to be situated in the correct places in the port area when inspected visually.
Originality: This paper presents a novel approach to detecting potential mooring areas and how to analyse characteristics of the mooring areas. Similar clustering methods have been used to detect anchoring spots, but this study provides a new approach to getting information on the clusters.
Subjects
Port Logistics
DDC Class
330: Wirtschaft
380: Handel, Kommunikation, Verkehr
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Steenari et al. (2022) - Mining Port Operation Information from AIS Data.pdf
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