Spatial Clustering Approach for Vessel Path Identification

Abuella, Mohamed, Postdoktor, 1980- (author): Högskolan i Halmstad,Akademin för informationsteknologi

Atoui, M. Amine, 1989- (author): Högskolan i Halmstad,Centrum för forskning om tillämpade intelligenta system (CAISR)

Nowaczyk, Sławomir, 1978- (author): Högskolan i Halmstad,Centrum för forskning om tillämpade intelligenta system (CAISR)

(creator_code:org_t)

Piscataway, NJ : IEEE, 2024
2024
English.
In: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 12, s. 66248-66258

Related links:: https://doi.org/10.1...; show more...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five clusters achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation. © 2013 IEEE.

By the author/editor: Abuella, Mohamed ...; Atoui, M. Amine, ...; Nowaczyk, Sławom ...; Johansson, Simon; Faghani, Ethan

About the subject

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.