Extracting 4-Attributes Vessel Courses from AIS Data with PQK-Means and Topic Model
Pages 129 - 135
Abstract
AIS (Automatic Identification System) data received from moving vessels over an area of interest can be of very much interest for deriving maritime trajectory patterns. In this paper, a novel approach to extract course patterns from AIS data of vessels is presented. From machine learning and natural language processing principles, a topic model might be used for extracting implicit patterns underlying massive and unstructured collection of incoming data. To apply topic model to AIS data, PQk-means vector quantization to convert AIS data record to code documents is introduced. Then, a topic model is applied to extract course patterns from AIS data. In fact, courses, not only encompasses trajectory locations, but also headings and speeds, are recognized by the proposed algorithm. The performance of PQk-means is evaluated using the relative root mean square error and elapsed time. 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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Index Terms
- Extracting 4-Attributes Vessel Courses from AIS Data with PQK-Means and Topic Model
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Published In
November 2019
192 pages
ISBN:9781450372015
DOI:10.1145/3372454
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In-Cooperation
- Shandong Univ.: Shandong University
- The University of Versailles Saint-Quentin: The University of Versailles Saint-Quentin, Versailles, France
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 January 2020
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ICBDR 2019
ICBDR 2019: 2019 The 3rd International Conference on Big Data Research
November 20 - 22, 2019
Cergy-Pontoise, France
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