Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

Xuyang Han, Costas Armenakis and Mojgan Jadidi
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Xuyang Han: Geomatics Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
Costas Armenakis: Geomatics Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
Mojgan Jadidi: Geomatics Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada

Sustainability, 2021, vol. 13, issue 15, 1-22

Abstract: Today, maritime transportation represents a substantial portion of international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this paper, we present an enhanced density-based spatial clustering of applications with noise (DBSCAN) method to model vessel behaviours based on trajectory point data. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis distance metric, which considers the correlation between the points representing vessel locations. This research proposes applying the clustering method to historical Automatic Identification System (AIS) data using an algorithm to generate 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. Further, an automatic and data-driven approach is proposed to select the initial parameters for the enhanced DBSCAN approach. Results are presented from two case studies using an openly available Gulf of Mexico AIS dataset as well as a Saint Lawrence Seaway and Great Lakes AIS licensed dataset acquired from ORBCOMM (a maritime AIS data provider). These research 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 behaviour anomalies for auto-vessel development towards the sustainability of marine transportation.

Keywords: DBSCAN; vessel trajectory clustering; Mahalanobis metric; machine learning; marine transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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