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Using forecasting to evaluate the impact of COVID-19 on passenger air transport demand

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Using forecasting to evaluate the impact of COVID-19 on passenger air transport demand. / Li, Xishu; de Groot, Maurits; Bäck, Thomas.

In: Decision Sciences, 17.10.2021.

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Li, Xishu ; de Groot, Maurits ; Bäck, Thomas. / Using forecasting to evaluate the impact of COVID-19 on passenger air transport demand. In: Decision Sciences. 2021.

Bibtex

@article{5b3b95b772574fc29e9b2fdf53d85a16,
title = "Using forecasting to evaluate the impact of COVID-19 on passenger air transport demand",
abstract = "The COVID-19 pandemic caused a drastic drop in passenger air transport demand due to two forces: supply restriction and demand depression. In order for airlines to recover, the key is to identify which force they are fighting against. We propose a method for separating the two forces of COVID-19 and evaluating the respective impact on demand. Our method involves dividing passengers into different segments based on passenger characteristics, simulating different scenarios, and predicting demand for each passenger segment in each scenario. Comparing the predictions with each other and with the real situation, we quantify the impact of COVID-19 associated with the two forces, respectively. We apply our method to a dataset from Air France–KLM and show that from March 1st to May 31st 2020, the pandemic caused demand at the airline to drop 40.3% on average for passengers segmented based on age and purpose of travel. The 57.4% of this decline is due to demand depression, whereas the other 42.6% is due to supply restriction. In addition, we find that the impact of COVID-19 associated with each force varies between passenger segments. The demand depression force impacted business passengers between age 41 and 60 the most, and it impacted leisure passengers between age 20 and 40 the least. The opposite result holds for the supply restriction force. We give suggestions on how airlines can plan their recovery using our results and how other industries can use our evaluation method.",
keywords = "COVID-19, passenger air transport, airline recovery, demand forecasting, simulation",
author = "Xishu Li and {de Groot}, Maurits and Thomas B{\"a}ck",
year = "2021",
month = oct,
day = "17",
doi = "10.1111/deci.12549",
language = "English",
journal = "Decision Sciences",
issn = "0011-7315",
publisher = "Wiley-Blackwell",

}

RIS

TY - JOUR

T1 - Using forecasting to evaluate the impact of COVID-19 on passenger air transport demand

AU - Li, Xishu

AU - de Groot, Maurits

AU - Bäck, Thomas

PY - 2021/10/17

Y1 - 2021/10/17

N2 - The COVID-19 pandemic caused a drastic drop in passenger air transport demand due to two forces: supply restriction and demand depression. In order for airlines to recover, the key is to identify which force they are fighting against. We propose a method for separating the two forces of COVID-19 and evaluating the respective impact on demand. Our method involves dividing passengers into different segments based on passenger characteristics, simulating different scenarios, and predicting demand for each passenger segment in each scenario. Comparing the predictions with each other and with the real situation, we quantify the impact of COVID-19 associated with the two forces, respectively. We apply our method to a dataset from Air France–KLM and show that from March 1st to May 31st 2020, the pandemic caused demand at the airline to drop 40.3% on average for passengers segmented based on age and purpose of travel. The 57.4% of this decline is due to demand depression, whereas the other 42.6% is due to supply restriction. In addition, we find that the impact of COVID-19 associated with each force varies between passenger segments. The demand depression force impacted business passengers between age 41 and 60 the most, and it impacted leisure passengers between age 20 and 40 the least. The opposite result holds for the supply restriction force. We give suggestions on how airlines can plan their recovery using our results and how other industries can use our evaluation method.

AB - The COVID-19 pandemic caused a drastic drop in passenger air transport demand due to two forces: supply restriction and demand depression. In order for airlines to recover, the key is to identify which force they are fighting against. We propose a method for separating the two forces of COVID-19 and evaluating the respective impact on demand. Our method involves dividing passengers into different segments based on passenger characteristics, simulating different scenarios, and predicting demand for each passenger segment in each scenario. Comparing the predictions with each other and with the real situation, we quantify the impact of COVID-19 associated with the two forces, respectively. We apply our method to a dataset from Air France–KLM and show that from March 1st to May 31st 2020, the pandemic caused demand at the airline to drop 40.3% on average for passengers segmented based on age and purpose of travel. The 57.4% of this decline is due to demand depression, whereas the other 42.6% is due to supply restriction. In addition, we find that the impact of COVID-19 associated with each force varies between passenger segments. The demand depression force impacted business passengers between age 41 and 60 the most, and it impacted leisure passengers between age 20 and 40 the least. The opposite result holds for the supply restriction force. We give suggestions on how airlines can plan their recovery using our results and how other industries can use our evaluation method.

KW - COVID-19

KW - passenger air transport

KW - airline recovery

KW - demand forecasting

KW - simulation

U2 - 10.1111/deci.12549

DO - 10.1111/deci.12549

M3 - Journal article

JO - Decision Sciences

JF - Decision Sciences

SN - 0011-7315

ER -