AI and Big Data

Why is this topic important? 
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.
 
What has our team done so far? 
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.

Recommendation

Zhang, Ziqiong; Yang, Yang; Wang, Xueyan; Wang, Chuxin; Zhang, Zili

2026

INFORMATION & MANAGEMENT

Yang, Lisi; Yang, Yang; Huang, Xijia; Yan, Kai

2026

TOURISM MANAGEMENT

Zhang, Xiaowei; Huang, Xingyu; Yang, Yang; Liu, Wei

2026

TOURISM MANAGEMENT

Related Presentations

Machine Learning and Artificial Intelligence Research in Tourism and Hospitality

University of Macau

Macau (Online)

Tourist behavior analysis using online user generated data

Kyung Hee University

Seoul, Korea (Online)

Spatial Analytics

Workshop on Informatics, Data Science, and Economics in Hospitality and Tourism Research

University of Houston, Houston, TX

COVID19tourism Index and its application in tourism management

University of Perpignan

Perpignan, France

Related Resources

Pulse of American Domestic Tourism

“The ‘Pulse of American Domestic Tourism’ project serves as a digital monitor for the nation’s internal mobility. By mining transportation-derived mobility data, we develop a comprehensive matrix of tourism flows connecting American MSAs. This data-driven approach unveils the rhythmic shifts in visitor demand and regional connectivity. Crucially, we ground these digital insights through extensive cross-validation with household survey data, creating a verified, high-resolution framework for understanding the evolving landscape of domestic travel.”

Key Vocabulary Used (Why it works):

  • Inter-MSA travel flows: Specific and accurate to your methodology.
  • Arterial circulation / Rhythmic shifts: Reinforces the “Pulse” metaphor without being cheesy.
  • High-granularity / Spatiotemporal precision: Highlights the “Big Data” advantage.
  • Rigorously cross-validated: Emphasizes the reliability of your model (crucial for academic trust).
  • Ground-truth metrics: A professional way to refer to the survey data as the standard of truth.

Restaurant Resilience Index

The Restaurant Resilience Index was developed to characterize the regional restaurant industry’s resilience to the COVID-19 pandemic across U.S. counties. Estimated from econometric results regarding daily restaurant demand, this index incorporates key moderating variables—specifically ethnicity, political ideology, dining habits (eat-in vs. off-premise), and restaurant diversity—that were found to influence the magnitude of demand decline caused by the pandemic and stay-at-home orders. By visualizing these data, potentially through tools like an ArcGIS dashboard, the index enables government entities and stakeholders to pinpoint geographically vulnerable areas and effectively allocate support resources, such as consumer voucher programs, to the hardest-hit local businesses.

 

Link to the Restaurant Resilience Index dashboard.

COVID19tourism Index

The COVID19tourism index was developed to monitor the pandemic’s multifaceted impact on the global tourism industry. This index comprises five distinct sub-indices designed to track the specific effects of COVID-19 across various aspects of tourism activities. By utilizing this tool, destinations are enabled to assess their recovery status, generate rigorous forecasts, and benchmark their performance against potential competitors. Sub-indices The COVID19tourism index is comprised of five distinct sub-indices. These sub-indices were designed to track the specific effects of the pandemic across different aspects of tourism activities.

Dashboard Utility The index functions as a tool that enables destinations to perform three primary functions:
• Evaluate Recovery: Destinations can use the tool to assess their current recovery status.
• Forecast: The tool allows users to produce rigorous forecasts regarding tourism trends.
• Benchmark: Destinations can use the index to benchmark their performance against potential competitors

Link to the COVID19tourism Index Dashboard

Link to download the data