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Estimating the impact of COVID-19 on air travel in the medium and long term using neural network and Monte Carlo simulation

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  • Truong, Dothang

Abstract

The COVID-19 pandemic has had a substantial impact on the airline industry. Air travel in the United States declined in 2020 with significantly lower domestic and international flights. The dynamic change and uncertainty in the trend of COVID-19 have made it difficult to predict future air travel. This paper aims at developing and testing neural network models that predict domestic and international air travel in the medium and long term based on residents' daily trips by distance, economic condition, COVID-19 severity, and travel restrictions. Data in the United States from various sources were used to train and validate the neural network models, and Monte Carlo simulations were constructed to predict air travel under uncertainty of the pandemic and economic growth. The results show that weekly economic index (WEI) is the most important predictor for air travel. Additionally, daily trips by distance play a more important role in the prediction of domestic air travel than the international one, while travel restrictions seem to have an impact on both. Sensitivity analysis results for four different scenarios indicate that air travel in the future is more sensitive to the change in WEI than the changes in COVID-19 variables. Additionally, even in the best-case scenario, when the pandemic is over and the economy is back to normal, it still takes several years for air travel to return to normal, as before the pandemic. The findings have significant contributions to the literature in COVID-19's impact on air transportation and air travel prediction.

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  • Truong, Dothang, 2021. "Estimating the impact of COVID-19 on air travel in the medium and long term using neural network and Monte Carlo simulation," Journal of Air Transport Management, Elsevier, vol. 96(C).
    • Handle: RePEc:eee:jaitra:v:96:y:2021:i:c:s0969699721001083
    DOI: 10.1016/j.jairtraman.2021.102126
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    References listed on IDEAS

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    Cited by:

    1. Elif Bozkaya & Levent Eriskin & Mumtaz Karatas, 2023. "Data analytics during pandemics: a transportation and location planning perspective," Annals of Operations Research, Springer, vol. 328(1), pages 193-244, September.
    2. Li, Siping & Xu, Gangyan & Zhou, Yaoming, 2023. "How air transport networks respond to long-lasting disruptive events like COVID-19: The first step toward long-term resilience," Transportation Research Part A: Policy and Practice, Elsevier, vol. 177(C).
    3. Hanson, Daniel & Toru Delibasi, Tuba & Gatti, Matteo & Cohen, Shamai, 2022. "How do changes in economic activity affect air passenger traffic? The use of state-dependent income elasticities to improve aviation forecasts," Journal of Air Transport Management, Elsevier, vol. 98(C).
    4. Hu, Rong & Feng, Huilin & Witlox, Frank & Zhang, Junfeng & Connor, Kevin O., 2022. "Airport capacity constraints and air traffic demand in China," Journal of Air Transport Management, Elsevier, vol. 103(C).
    5. Truong, Dothang & Truong, My D., 2022. "How do customers change their purchasing behaviors during the COVID-19 pandemic?," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).

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