Forecasting short-term air passenger demand using big data from search engine queries

Forecasting air passenger demand is a critical aspect of formulating appropriate operation plans in airport operation. Airport operation not only requires long-term demand forecasting to establish long-term plans, but also short-term demand forecasting for more immediate concerns. Most airports forecast their short-term passenger demand based on experience, which provides limited forecasting accuracy, depending on the level of expertise. For accurate short-term forecasting independent of the level of expertise, it is necessary to create reliable short-term forecasting models and to reflect short-term fluctuations in air passenger demand. This study aims to develop a forecasting model of short-term air passenger demand using big data from search queries to identify these short-term fluctuations. The suggested forecasting model presents an average forecast error of 5.3% and indicates that an increase of approximately 195,000 air passengers is to be expected 8 months later, as the key query frequencies increase by 0.1%.