Multivariate hierarchical DBSCAN model for enhanced maritime data analytics

Newaliya, Nitin; Singh, Yudhvir

doi:10.1016/j.datak.2024.102282

Data & Knowledge Engineering

2024

DOI: 10.1016/j.datak.2024.102282

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Multivariate hierarchical DBSCAN model for enhanced maritime data analytics

Nitin Newaliya,

Yudhvir Singh

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2024

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Cited by 2 publications

References 30 publications

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DBSCAN Speedup for Time-Serpentine Datasets

Romanuke

2024

Applied Computer Systems

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An approach to speed up the DBSCAN algorithm is suggested. The planar clusters to be revealed are assumed to be tightly packed and correlated constituting, thus, a serpentine dataset developing rightwards or leftwards as time goes on. The dataset is initially divided into a few sub-datasets along the time axis, whereupon the best neighbourhood radius is determined over the first sub-dataset and the standard DBSCAN algorithm is run over all the sub-datasets by the best neighbourhood radius. To find the best neighbourhood radius, it is necessary to know ground truth cluster labels of points within a region. The factual speedup registered in a series of 80 000 dataset computational simulations ranges from 5.0365 to 724.7633 having a trend to increase as the dataset size increases.

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DBSCAN Speedup for Time-Serpentine Datasets

Romanuke

2024

Applied Computer Systems

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Research on the Optimization of Ship Trajectory Clustering Based on the OD–Hausdorff Distance

Liu,

Yang,

Xiong

et al. 2024

JMSE

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With the growth of global trade, port shipping is becoming more and more important. In this paper, an analysis of a ship’s inbound and outbound track characteristics is conducted using the OD–Hausdorff distance. The accuracy and efficiency of trajectory data analysis have been enhanced through clustering analysis. Trajectories are arranged in a time sequence, and representative port segments are selected. An improved OD–Hausdorff distance method is employed to capture the dynamic characteristics of a ship’s movements, such as speed and heading. Additionally, the DBSCAN algorithm is utilized for clustering, allowing for the processing of multidimensional AIS data. Data cleaning and preprocessing have ensured the reliability of the AIS data, and the Douglas–Peucker algorithm is used for trajectory simplification. Significant improvements in the accuracy and efficiency of trajectory clustering have been observed. Therefore, the main channel of the Guan River and the right side of Yanwei Port are usually followed by ships greater than 60 m in length, with a lateral Relative Mean Deviation (RMD) of 7.06%. Vessels shorter than 60 m have been shown to have greater path variability, with a lateral RMD of 7.94%. Additionally, a crossing pattern at Xiangshui Port is exhibited by ships shorter than 60 m due to the extension of berths and their positions at turns. Enhanced clustering accuracy has provided more precise trajectory patterns, which aids in better channel management.

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Multivariate hierarchical DBSCAN model for enhanced maritime data analytics

Cited by 2 publications

References 30 publications

DBSCAN Speedup for Time-Serpentine Datasets

DBSCAN Speedup for Time-Serpentine Datasets

Research on the Optimization of Ship Trajectory Clustering Based on the OD–Hausdorff Distance

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