Benchmarking the recovery of air travel demands for US airports during the COVID-19 Pandemic

Abstract

The aviation industry has gone through numerous ups and downs in the past decades. Despite the devastating damage caused by the COVID-19 Pandemic, the aviation industry worldwide still manages to bounce back from the abyss of Q2, 2020, though the speed of recovery is less than satisfactory for most regions. Being aware of the existing literature on air travel demands published since March 2020, this study aims to provide US Primary Hub airports with benchmarks that can help airports predict the recovery of air travel demand during the COVID-19 Pandemic. This study uses the passenger numbers going through airport security checkpoints as the input data and the k-shape clustering algorithm to group airports by their travel demand recovery patterns. The clustering analysis results are presented in a circular dendrogram so that any of the 118 subject airports can quickly locate their benchmarking airports. In this process, the geographic location and hub category of an airport are found to play important roles in determining how local outbound traffic recovers during the Pandemic. We also test if state political preference in the 2020 Presidential Election affects local airport traffic but cannot find any convincing results. The method used by this study can be fed with up-to-date data to produce more timely and reliable results to guide airports and other stakeholders through the recovery journey.

Keywords

• COVID-19
• Air travel demands
• Clustering analysis
• Time-series analysis
• Dynamic time warping