Airport terminal passenger forecast under the impact of COVID-19 outbreaks: A case study from China

• Published in: Journal of Building Engineering 2023-04, Vol.65, p.105740, Article 105740
• Main Authors: Tang, Hao, Yu, Juan, Lin, Borong, Geng, Yang, Wang, Zhe, Chen, Xi, Yang, Li, Lin, Tianshu, Xiao, Feng
• Format: Article
• Language: English
• Subjects: Airport terminal; COVID-19; Forecasting model; Machine learning; SHAP values
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2022.105740

Passengers significantly affect airport terminal energy consumption and indoor environmental quality. Accurate passenger forecasting provides important insights for airport terminals to optimize their operation and management. However, the COVID-19 pandemic has greatly increased the uncertainty in airport passenger since 2020. There are insufficient studies to investigate which pandemic-related variables should be considered in forecasting airport passenger trends under the impact of COVID-19 outbreaks. In this study, the interrelationship between COVID-19 pandemic trends and passenger traffic at a major airport terminal in China was analyzed on a day-by-day basis. During COVID-19 outbreaks, three stages of passenger change were identified and characterized, i.e., the decline stage, the stabilization stage, and the recovery stage. A typical “sudden drop and slow recovery” pattern of passenger traffic was identified. A LightGBM model including pandemic variables was developed to forecast short-term daily passenger traffic at the airport terminal. The SHapley Additive exPlanations (SHAP) values was used to quantify the contribution of input pandemic variables. Results indicated the inclusion of pandemic variables reduced the model error by 27.7% compared to a baseline model. The cumulative numbers of COVID-19 cases in previous weeks were found to be stronger predictors of future passenger traffic than daily COVID-19 cases in the most recent week. In addition, the impact of pandemic control policies and passengers' travel behavior was discussed. Our empirical findings provide important implications for airport terminal operations in response to the on-going COVID-19 pandemic. [Display omitted] •A strong correlation was found between COVID-19 cases and daily passenger.•A hysteresis effect of pandemic trends on daily passenger traffic was quantified.•A week-ahead forecast model with pandemic variables was developed.•Inclusion of pandemic variables reduced passenger forecast error by 27.7%.•The impact of pandemic control policy and travel behaviors were discussed.