A research team led by Prof. Haiyan Song, Principal Investigator, Associate Dean of the School of Hotel and Tourism Management, Chair Professor, and Mr and Mrs Chan Chak Fu Professor in International Tourism, has launched a new forecasting platform “The Development of an Automated and Self-Adaptive Tourism Demand Forecasting Platform for the Greater Bay Area (GBA-TDFP)”.


With key functions including big data visualisation, market sensitivity analysis, short-, medium- and long-term forecasting, sentiment analysis and interactive scenario forecasting, the platform serves as a tool for industry professionals, policy makers and academics to adapt and generate forecasts of visitor arrivals to the GBA in different economic scenarios. Hypothetical values for determinant variables (such as GDP and price levels) inputted through web browsers are incorporated into the estimated econometric models to generate scenario forecasts.


To facilitate accurate tourism demand forecasting and actively respond to the ongoing challenges for developing sustainable tourism strategies that foster long-term economic growth in the region, the project has collected macroeconomic data that include Gross Domestic Product, Consumer Price Index and exchange rates of the GBA cities and their key source markets from statistical departments and international organisations such as the International Monetary Fund. The project also leverages big data collected from popular online and social media platforms such as Google, Ctrip and Baidu.


By integrating cloud computing, big data and artificial intelligence techniques with advanced forecasting methods, the GBA-TDFP provides innovative insights and invaluable guidance for industry professionals and academics, effectively transforming vast amounts of data into actionable information, enabling stakeholders to make better informed decisions and maximise the value derived from it.