Forecasting Air Travel Demand

Yafei Zheng received her PhD in management sciences and engineering from both University of Chinese Academy of Sciences and City University of Hong Kong in 2016. Her research interests include economic forecasting and risk management. She is now working at the postdoctoral research station of Shenwan Hongyuan Securities Co., Ltd. in Shanghai, China. Kin Keung Lai received his PhD from Michigan State University in the United States. He is currently Director of the Research Center of Technology Management, College of Innovation and Entrepreneurial, University of Chinese Academy of Sciences, Beijing, China. He is also Honorary Professor at the Department of Industrial and Manufacturing Systems Engineering, Hong Kong University, Hong Kong. Prior to his post, he was Chair Professor of Management Science at the City University of Hong Kong. His main areas of research interests are operations and supply chain management, financial and business risk analysis, and modeling using computational intelligence. Currently, he is President of the Asia Association on Risk and Crises Management. He was the recipient of the Joon S. Moon Distinguished International Alumni Award of the Michigan State University and appointed as the Chang Jiang Scholar Chair Professor by the Ministry of Education, China. He was also elected as the Academician of the International Academy of Systems and Cybernetic Sciences. Shouyang Wang received his PhD in operations research from Institute of Systems Science, Chinese Academy of Sciences, in 1986. He is currently Bairen Distinguished Professor of Management Science at Academy of Mathematics and Systems Science of Chinese Academy of Sciences and the Lotus Distinguished Professor of Management Science of Hunan University at Changsha. He is also Adjunct Professor at over 30 universities around the world. His current research interests include risk management, economic forecasting and supply chain management.

1. Introduction 2. Existing Research 3. Theoretical Basis – TEI@I Methodology 4. A Scientometric Analysis of Demand Forecasting (1975-2015): A Visual Description 5. An Integrated Short-term Forecasting Framework with Empirical Mode Decomposition Method 6. A Novel Seasonal Decomposition-based Short-term Forecasting Framework with Google Trends Data 7. A Medium-term Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average 8. Long-term Air Travel Demand Forecasting: An Integrated Method with ARDL Bounds Testing Approach and Scenario Planning 9. Conclusions and Future Research