The Hong Kong Polytechnic University (PolyU) has unveiled a new forecasting platform that will serve as a valuable tool for industry professionals, policy makers and academics to adapt and generate forecasts of visitor arrivals to the Greater Bay Area (GBA) in different economic scenarios.
Titled The Development of an Automated and Self-Adaptive Tourism Demand Forecasting Platform for the Greater Bay Area (GBA-TDFP), the project adopts an interdisciplinary approach to integrate well-established theories in economics, tourism management and computer science. It features key functions such as big data visualisation, market sensitivity analysis, short-, medium- and long-term forecasting, sentiment analysis, and interactive scenario forecasting.
While the economy has now largely recovered from the shocks caused by travel restrictions and public health measures taken during the pandemic, there are still challenges to overcome, including labour shortage, supply constraints, changing economic conditions, and shifts in consumer behaviour.
To facilitate accurate forecasting, the project has collected macroeconomic data such as GDPs, CPIs and exchange rates of the GBA cities and their key source markets from statistical departments and international organisations such as the International Monetary Fund. For short-term tourism demand forecasting, the project has leveraged big data collected from popular online and social media platforms such as Google, Ctrip and Baidu.
The GBA-TDFP serves to simplify the process for policy makers and industry leaders to conduct what-if scenario analyses on tourism demand forecasts. Users can input hypothetical values for determinant variables (such as GDP and price levels) through web browsers, which are then incorporated into the estimated econometric models to generate scenario forecasts.
With advances in technology, destinations and visitors are increasingly dependent on information and communications technologies. By integrating cloud computing, big data and artificial intelligence techniques with advanced forecasting methods, the GBA-TDFP offers innovative insights and valuable guidance for both industry professionals and academics, effectively transforming vast amounts of data into actionable information, enabling stakeholders to make informed decisions and maximise the value derived from it.
“It all points to the fact that in order to sustain ongoing recovery, accurate forecasts of tourism demand recovery are crucial for policy makers and practitioners to be able to develop sustainable tourism strategies that foster long-term economic growth in the region,” remarked Haiyan Song, principal investigator and associate dean of the School of Hotel and Tourism Management at PolyU.
