Digital Twin Applications in Tourism Destination Management: Predictive Modelling for Tourist Flow and Resource Optimization
DOI:
https://doi.org/10.70112/arss-2025.14.2.4333Keywords:
Digital Twin, Tourism Destination Management, Predictive Modelling, Resource Optimization, SustainabilityAbstract
Tourism destinations across the globe are grappling with growing visitor numbers, congestion, and sustainability challenges. Traditional management strategies, which often rely on historical or static data, have proven insufficient in addressing these dynamic issues. Digital Twin (DT) technology offers an innovative pathway by creating real-time, virtual replicas of destinations that combine diverse data streams, simulate ongoing interactions, and generate predictive insights for managers. This study presents a conceptual framework for applying digital twins in tourism management, structured into five layers: the Input Layer, Digital Twin Core, Predictive Analytics Engine, Decision Support System, and Outcomes. Built on secondary data and aligned with Structural Equation Modelling (SEM) principles, the framework illustrates how raw information can be transformed into actionable strategies. Findings suggest that DTs improve the accuracy of tourist flow forecasting, enable smarter decision-making, and enhance sustainability by optimizing the use of resources. The research contributes to tourism management by connecting technological innovation with sustainability goals and lays the groundwork for future empirical testing.
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