AI-BASED RESERVATION SYSTEMS FOR TOURIST ATTRACTIONS: A PREDICTIVE APPROACH TO MANAGING SEASONAL FLUCTUATIONS AND VISITOR FLOW

Abstract

Tourism is one of the most dynamic industries globally, yet it faces significant challenges, particularly in managing visitor flow during peak seasons. Overcrowding not only negatively impacts the visitor experience but also places immense pressure on infrastructure, resources, and service quality. This paper investigates the potential of AI-based reservation systems as a predictive approach to managing seasonal fluctuations in visitor numbers at tourist attractions. The system utilizes machine learning algorithms and predictive analytics to forecast visitor demand, taking into account various factors such as historical attendance data, weather conditions, special events, and other relevant variables. By analyzing these factors, the system can predict the number of visitors for a given period, allowing for more accurate and dynamic adjustments to reservation availability. The AI-based system enables real-time monitoring of visitor capacity, helping to optimize ticketing, scheduling, and crowd management strategies. It allows for adaptive reservations, where entry times can be adjusted to avoid congestion, ensuring a smoother and more enjoyable experience for visitors. Furthermore, the integration of solar-powered infrastructure ensures that the system operates sustainably, reducing the carbon footprint of tourist operations while maintaining high energy efficiency. The use of renewable energy sources supports eco-friendly tourism practices, aligning with growing sustainability goals in the industry. Through case studies and simulation models, this research demonstrates the effectiveness of AI-powered reservation systems in managing fluctuating visitor attendance. The findings highlight not only the operational benefits, such as reduced overcrowding and resource optimization, but also the enhancement of customer satisfaction. By ensuring that attractions can handle peak periods efficiently, the proposed system contributes to the long-term sustainability and success of tourist destinations.