Using Dominant-Set clustering to characterize vessel pathway in port areas

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dc.contributor.advisor Pelillo, Marcello it_IT
dc.contributor.author Giudice, Lorenzo <1992> it_IT
dc.date.accessioned 2019-06-19 it_IT
dc.date.accessioned 2019-11-20T07:09:26Z
dc.date.issued 2019-07-10 it_IT
dc.identifier.uri http://hdl.handle.net/10579/15406
dc.description.abstract Vessel pattern identification helps traffic management and collision avoidance in port areas. Suggested routes for ships, also called certified paths, can be extracted by performing data mining techniques on Automatic Identification System (AIS) data. The certified paths are fundamental to properly monitor vessels during their journeys in order to study marine traffic in port areas. Extracting these routes represents the main subject of this thesis. This type of analysis is traditionally performed through clustering, providing a method to automatically extract certified paths given a collections of trajectory data. In this project, the Dominant Set clustering algorithm is applied on vessel trajectories to retrieve these routes. After the process of certified path extraction, a set of points (waypoints) describing them is automatically found. These points are then provided to the vessels approaching or leaving the seaport. Each of them contains information about the allowable areas for the vessels and the time intervals in which ships must approach them to ensure a smooth and safe behaviour in port area. The results obtained shown the superiority of the DS over well known alternatives. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Lorenzo Giudice, 2019 it_IT
dc.title Using Dominant-Set clustering to characterize vessel pathway in port areas it_IT
dc.title.alternative Using Dominant-Set clustering to characterize vessel pathway in port areas it_IT
dc.type Bachelor Thesis it_IT
dc.degree.name Informatica - computer science it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Scienze Ambientali, Informatica e Statistica it_IT
dc.description.academicyear 2018/2019_sessione_estiva it_IT
dc.rights.accessrights closedAccess it_IT
dc.thesis.matricno 851032 it_IT
dc.subject.miur INF/01 INFORMATICA it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend 10000-01-01
dc.provenance.upload Lorenzo Giudice (851032@stud.unive.it), 2019-06-19 it_IT
dc.provenance.plagiarycheck Marcello Pelillo (pelillo@unive.it), 2019-07-08 it_IT


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