AI-Enabled Big Data Analytics for Digital Tourism Management
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Abstract
Smart Tourism constitutes a response to the growth of visitation in urban agglomerations and heritage sites, focusing on enhancing the visitor experience through innovation and technology. AI-driven predictive analytics are applied to Big Data sources, delivering explorative and prescriptive insights for tourism management. Drivers of success include data-sharing culture, trust among stakeholders, hardware infrastructure, model accuracy, and system maturity level. AI-driven Big Data Analytics empower users to harness vast data sources and extract meaningful information, enabling discovery and actionable recommendations. Four supporting mechanisms—User-Centric Design, Data-Driven Decision Making, Service-Dominant Logic, Data Science—guide the development of AI-driven predictive analytics methods
Mechanisms that enhance the predictive capability of visitor demand capture data from different domains and undergo data preparation and modelling. Demand prediction generates historical, real-time, and anticipated demand at multiple levels of granularity and time scales. These inputs help Personalisation and Recommender Analytics understand visitor behaviours and preferences, providing context-sensitive personal recommendations. AI-driven Big Data tourism Predictive Analytics are deployed in urban destination management and cultural tourism settings. Urban destinations use Big Data sources to analyse crowd dynamics, stakeholder interactions, and tourism flows. Cultural tourism Demand-Personalisation Analytics improve visitor experience, conservation of resources, and effectiveness of interpretation
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