Author, as appears in the article.: Ayebakuro Orama, Jonathan; Huertas, Assumpcio; Borras, Joan; Moreno, Antonio; Anton Clave, Salvador
Department: Geografia
URV's Author/s: Anton Clavé, Salvador / Borràs Nogués, Joan / Huertas Roig, Maria Asuncion / Moreno Ribas, Antonio / Orama, Ayebakuro Jonathan
Keywords: Tourist flows Tourist clusters Tourism destinations Social media data Services Search Networks Mobility patterns Location Information-technology Hospitality Experience Big-data analytics Behavior Artificial intelligence
Abstract: In order to enhance tourists' experiences, Destination Management Organizations need to know who their tourists are, their travel preferences, and their flows around the destination. The study develops a methodology that, through the application of Artificial Intelligence techniques to social media data, creates clusters of tourists according to their mobility and visiting preferences at the destination. The applied method improves the knowledge about the different mobility patterns of tourists (the most visited points and the main flows between them within a destination) depending on who they are and what their preferences are. Clustering tourists by their travel mobility permits uncovering much more information about them and their preferences than previous studies. This knowledge will allow DMOs and tourism service providers to offer personalized services and information, to attract specific types of tourists to certain points of interest, to create new routes, or to enhance public transport services.
Research group: Grup de Recerca d'Anàlisi Territorial i Estudis Turístics (GRATET)
Thematic Areas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: ayebakurojonathan.orama@estudiants.urv.cat antonio.moreno@urv.cat sunsi.huertas@urv.cat salvador.anton@urv.cat salvador.anton@urv.cat
Author identifier: 0000-0002-2622-3224 0000-0003-3945-2314 0000-0001-6684-4220 0000-0001-9818-2778 0000-0001-9818-2778
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/2076-3417/12/12/5834
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Applied Sciences-Basel. 12 (12): 5834-
APA: Ayebakuro Orama, Jonathan; Huertas, Assumpcio; Borras, Joan; Moreno, Antonio; Anton Clave, Salvador (2022). Identification of Mobility Patterns of Clusters of City Visitors: An Application of Artificial Intelligence Techniques to Social Media Data. Applied Sciences-Basel, 12(12), 5834-. DOI: 10.3390/app12125834
Article's DOI: 10.3390/app12125834
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications
Repositori URV