Elevating Urban Tourism: Data-Driven Insights and AI-Powered Personalization with Large Language Models Brilliance

The thriving urban tourism industry fueled by rapid technological advancements and urbanization trends, faces a complex array of challenges. Thus, this study presents a comprehensive approach to address these challenges, encompassing city-wide data-driven analysis and personalized AI-powered solutions. At the city-wide level, the potential of data-driven methodologies was presented by collecting, integrating, and analyzing tourism data taking Shanghai as an illustrative case. Through web scraping and Geographic Information System (GIS) analysis, the layout of tourist attractions and transportation systems was optimized. The novel concept of “average accessibility of tourist attractions” was introduced to evaluate ease of access, considering factors such as tourist population and traffic congestion. On an individual level, ChatGPT was used as a powerful large language model for an interactive tourist guide as it was capable of providing personalized travel recommendations based on the Shanghai tourism dataset. The result of this study spans data-driven urban tourism insights to AI-driven tailored tourism guidance, enhances urban tourism's accessibility, enriches visitor experiences, and promotes sustainable urban development.