A literature review and citation analyses of air travel demand studies published between 2010 and 2020

Abstract Accurate forecasting of air travel demand is vital for the resource planning of the air transportation industry. Therefore, identifying contributing factors and understanding the effect of these factors in causing the variation of air travel demand have been one of the key focus areas in air transportation research. This article reviews 87 air travel demand studies published from 2010 to 2020 and summarizes these studies using their input data and primary analytical methods. We also devise and conduct three citation analyses to further explore the relationships among the reviewed studies. Our review finds that a typical empirical study of air travel demand analysis would focus on the demand at the national level, employ time-series data concerning socio-economic and airline operational factors and use time-series based methods to estimate the relationship among the selected time-series. These studies are mostly applying existing analytical frameworks to specific problems rather than developing original methods, therefore their relationship to each other is parallel rather than sequential. A small number of references are frequently cited by the reviewed studies primarily because of their methodological contribution to time-series analysis. A common limitation of existing literature is that very few reviewed studies provide validation of their analyses. In addition, methods that are not regression or time-series based have very limited application in this area so far, so are the non-convention data such as mobility data or social media data. Besides providing a systematic summary of recent publications in a specific field, this review uses a relatively objective and replicable framework to compare and link studies by their references, which can be visualized by the figures included in this review. This review is expected to benefit future researchers that are interested in either air transportation or the application of time-series forecasting in an applied domain.