Mining port operation information from AIS data

Jussi Steenari, Lucy Ellen Lwakatare, Jukka Nurminen, Jaakko Talonen and Teemu Manderbacka

A chapter in Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New Era, 2022, pp 657-678 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: 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.

Keywords: Port; Logistics (search for similar items in EconPapers)
Date: 2022
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https://www.econstor.eu/bitstream/10419/267202/1/hicl-2021-33-657.pdf (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:267202

DOI: 10.15480/882.4705

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