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

Yi Gao

31 citations0/0 files storedTransportation Research Interdisciplinary Perspectives

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Aviation Industry Analysis and Trends Air Traffic Management and Optimization COVID-19 Pandemic Impacts

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 31 citations exist upstream.

References

2021Forecasting tourism recovery amid COVID-19 2021COVID-19 pandemic and prospects for recovery of the global aviation industry 2020Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact 2020The impacts of COVID-19 pandemic on public transit demand in the United States 2020How did COVID-19 impact air transportation? A first peek through the lens of complex networks 2020Changes in air passenger demand as a result of the COVID-19 crisis: using Big Data to inform tourism policy 2020Tourism Demand and the COVID-19 Pandemic: An LSTM Approach 2020Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19

Cited by

2024COVID-19 and aviation, lessons learned from the trenches: A survey among participants at the 26th ATRS World Conference 2022Airport terminal passenger forecast under the impact of COVID-19 outbreaks: A case study from China 2023The impact of COVID-19 pandemic on air passenger travel: a focus on empirical findings 2023How air transport networks respond to long-lasting disruptive events like COVID-19: The first step toward long-term resilience

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.