A panel data model to predict airline passenger volume

Abstract

Airline passenger volume is an important reference for the implementation of aviation capacity and route adjustment plans. This paper explores the determinants of airline passenger volume and proposes a comprehensive panel data model for predicting volume. First, potential factors influencing airline passenger volume are analyzed from Geo-economic and service-related aspects. Second, the principal component analysis (PCA) is applied to identify key factors that impact the airline passenger volume of city pairs. Then the panel data model is estimated using 120 sets of data, which are a collection of observations for multiple subjects at multiple instances. Finally, the airline data from Chongqing to Shanghai, from 2003 to 2012, was used as a test case to verify the validity of the prediction model. Results show that railway and highway transportation assumed a certain proportion of passenger volumes, and total retail sales of consumer goods in the departure and arrival cities are significantly associated with airline passenger volume. According to the validity test results, the prediction accuracies of the model for 10 sets of data are all greater than 90%. The model performs better than a multivariate regression model, thus assisting airport operators decide which routes to adjust and which new routes to introduce.

Graphical abstract

Keywords

Airline passenger volume / Traffic prediction / Panel data model / Airline route decision / Transportation engineering