Multivariate hierarchical DBSCAN model for enhanced maritime data analytics

Abstract

Clustering is an important data analytics technique and has numerous use cases. It leads to the determination of insights and knowledge which would not be readily discernible on routine examination of the data. Enhancement of clustering techniques is an active field of research, with various optimisation models being proposed. Such enhancements are also undertaken to address particular issues being faced in specific applications. This paper looks at a particular use case in the maritime domain and how an enhancement of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering results in the apt use of data analytics to solve a real-life issue. Passage of vessels over water is one of the significant utilisations of maritime regions. Trajectory analysis of these vessels helps provide valuable information, thus, maritime movement data and the knowledge extracted from manipulation of this data play an essential role in various applications, viz., assessing traffic densities, identifying traffic routes, reducing collision risks, etc. Optimised trajectory information would help enable safe and energy-efficient green operations at sea and assist autonomous operations of maritime systems and vehicles. Many studies focus on determining trajectory densities but miss out on individual trajectory granularities. Determining trajectories by using unique identities of the vessels may also lead to errors. Using an unsupervised DBSCAN method of identifying trajectories could help overcome these limitations. Further, to enhance outcomes and insights, the inclusion of temporal information along with additional parameters of Automatic Identification System (AIS) data in DBSCAN is proposed. Towards this, a new design and implementation for data analytics called the Multivariate Hierarchical DBSCAN method for better clustering of Maritime movement data, such as AIS, has been developed, which helps determine granular information and individual trajectories in an unsupervised manner. It is seen from the evaluation metrics that the performance of this method is better than other data clustering techniques.