Modeling Vessel Behaviours By Clustering Ais Data Using Optimized DBSCAN
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Today, maritime transportation represents substantial international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this research thesis, we present an enhanced density-based spatial clustering (DBSCAN) method to model vessel behaviors. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis Distance metric that considers the correlations of the points representing the locations of the vessels. The clustering method is applied to historical Automatic Identification System (AIS) data and generates an action recommendation tool and a model for detecting vessel trajectory anomalies. Two case studies present outcomes from the openly available Gulf of Mexico AIS data, and Saint Lawrence Seaway and Great Lakes AIS licensed data acquired from ORBCOMM (a maritime AIS data provider). This research proposes a framework for modeling AIS data, an algorithm for generating 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. This work's 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 behavior anomalies for auto-vessels development.