Big data and Artificial Intelligence (AI) offer unprecedented opportunities to monitor, forecast, and manage tourism activities in real-time, overcoming the time lags associated with traditional survey data. These technologies enable the processing of unstructured data (text, images, video) to extract insights about tourist sentiment, behavior, and preferences. As destinations strive to become “smart,” integrating AI into destination management ecosystems is crucial for enhancing visitor experiences, optimizing operations, and ensuring data-driven governance.

 

Our team has pioneered the use of diverse big data sources for tourism forecasting and analysis. We demonstrated that web traffic data from Destination Marketing Organizations (DMOs) can significantly improve hotel demand forecasting accuracy. We applied spatial-temporal forecasting models (like STARMA) and machine learning techniques (Lasso, Elastic Net) to forecast daily attraction demand using search engine and social media data. In the realm of unstructured data, we utilized deep learning (CNN) on social media posts to monitor tourist experiences and health issues under air pollution. We employed Natural Language Processing (NLP) with Large Language Models (like GPT-4o) to quantify “Government Attention to Tourism” from policy reports. Our team also developed a video analytics framework (ASC) to determine which aesthetic and content features of DMO videos on Facebook best engage viewers. Additionally, we proposed a “Smart Destination Management Model” that integrates AI initiatives into a collaborative ecosystem for urban destinations.