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Big Data in Real Time for the Management of Tourist Destinations: The TOURETHOS Platform Technological Model

In: Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability

Author

Listed:
  • José Juan Hernández-Cabrera

    (University of Las Palmas de Gran Canaria)

  • Ana María Plácido-Castro

    (University of Las Palmas de Gran Canaria)

  • Jacques Bulchand-Gidumal

    (University of Las Palmas de Gran Canaria)

Abstract

Big data is one of the main existing promises for improving the management of tourist destinations. The acquisition of large amounts of data from different sources, their consolidation and exploitation by means of artificial intelligence algorithms will allow the achievement of various objectives for destination management, such as understanding tourist flows, an increase and better distribution of tourist spending, improving the quality of life of residents and achieving better sustainability. Additional benefits could even be obtained if this big data were to be managed in real time. To achieve these objectives, it is necessary to have high quality and reliable data sources. This article describes a technological platform called Tourethos, which allows active collaboration between different stakeholders to collect data on the movements of tourists in the territory based on their connections to Wi-Fi networks in the area. This data source has interesting and valuable characteristics: it is relatively simple to collect, it can be easily anonymized and it offers a sufficient level of precision to draw valuable conclusions for the management of tourist destinations in real time.

Suggested Citation

  • José Juan Hernández-Cabrera & Ana María Plácido-Castro & Jacques Bulchand-Gidumal, 2024. "Big Data in Real Time for the Management of Tourist Destinations: The TOURETHOS Platform Technological Model," Springer Proceedings in Business and Economics, in: Antonio J. Guevara Plaza & Alfonso Cerezo Medina & Enrique Navarro Jurado (ed.), Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability, pages 137-147, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-52607-7_13
    DOI: 10.1007/978-3-031-52607-7_13
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