ARTIFICIAL INTELLIGENCE IN GLOBAL TOURISM PLANNING: A PLATFORM-BASED MODEL FOR AUTOMATED TRAVEL ITINERARY GENERATION

Abstract

The global tourism industry is undergoing a paradigm shift driven by the rapid advancement of Artificial Intelligence (AI) and Big Data analytics. As travelers face information overload and increasing complexity in planning multi-destination trips, the demand for personalized, automated itinerary generation systems has surged. This paper presents a comprehensive platform-based model that leverages machine learning (ML), natural language processing (NLP), and combinatorial optimization algorithms to create dynamic travel itineraries. The proposed system integrates user preferences, real-time data (weather, traffic, events), and historical tourism patterns to optimize travel routes, accommodation, and activities. Through a series of simulations and user acceptance testing involving 500 participants, the model demonstrated a 35% improvement in planning efficiency and a 28% increase in user satisfaction compared to traditional manual planning methods. The study also addresses ethical considerations regarding data privacy and algorithmic bias in tourism recommendations. The findings suggest that AI-driven platform models can significantly enhance the tourist experience while optimizing resource allocation for destination management organizations.