Statistical trajectory-distance metric for nautical route clustering analysis using cross-track distance. Issue 2 (13th April 2022)

• Record Type: Journal Article
• Authors: Yoo, Wonchul; Kim, Tae-wan
• Is Part Of: Journal of computational design and engineering. Volume 9:Issue 2(2022)
• Page Start: 731
• Page End: 754
• Publication Date: 2022-04-13
• Subjects: trajectory clustering -- trajectory-distance metric -- clustering analysis -- dynamic time warping -- statistical distance -- automatic identification system; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwac024
• Languages: English
• ISSNs: 2288-4300

Abstract

ABSTRACT: This study presents a novel statistical trajectory-distance metric specialized for nautical route clustering analysis. Based on the dynamic time warping (DTW) metric, one of the most used metrics for trajectory-distance, the statistical trajectory-distance metric was defined by replacing the distance term in DTW with a linear combination of the Jensen–Shannon divergence and Wasserstein distance. Each waypoint from a nautical route was modelled as a discrete and asymmetric binomial normal distribution defined by the cross-track distance (XTD) of the waypoint. The model was then used to compute the statistical distance between waypoints. Nautical route clustering was performed using density-based spatial clustering of applications with noise and the statistical trajectory-distance metric. The nautical route for the clustering analysis, including the XTD information, was extracted from automatic identification system data from the southern sea of the Korean Peninsula. The clustering results were evaluated by comparing them with the results of other popular trajectory-distance metrics. The proposed method was more effective compared to other trajectory-distance when the trajectories pass on both sides of a small island, which is frequent case in coastal route clustering. Graphical Abstract: