Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies

Andreas Komninos (), Charalampos Kostopoulos () and John Garofalakis ()
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Andreas Komninos: University of Patras
Charalampos Kostopoulos: OptionsNet IT Services and Consulting, Optionsnet
John Garofalakis: University of Patras

Information Technology & Tourism, 2022, vol. 24, issue 2, No 5, 265-298

Abstract: Abstract Sailing holiday activities represent a significant portion of the Blue Economy growth in Europe and across the world. Due to the global financial crisis, yacht ownership has declined, but demand for such holiday products remained steady, therefore shifting the yachters profile towards younger and less experienced consumers who prefer to charter boats, rather than own one. Boat chartering offers more flexibility to explore different regions from year to year, but this means that significantly more time must be spent planning the route, since local experience is absent. The tourists’ experience during the initial contemplation and planning phase, taking place weeks or months before an actual trip, and where a broad range of route options needs to be explored, could thus significantly benefit from support given by automated IT tools. Current literature demonstrates a complete lack of research in the development of itinerary recommendation systems in the context of sailing holidays. In this paper, we describe a methodology for the automatic generation of route recommendations, based on the semantic modelling of spatial data, and the determination of realistic sea route options, based on vessel density maps produced from raw AIS data. We demonstrate the implementation and results from this methodology using one of the most popular sailing regions of Greece, namely the Ionian Sea, as a case study.

Keywords: Maritime trip planning; Vessel routing; Route planning; Itinerary recommendation; Genetic algorithms; Semantic spatial modelling (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40558-022-00224-x

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