Testing the differentiated impact of the COVID-19 pandemic on air travel demand considering social inclusion

U.S. network and low-cost carriers' performance in response to COVID-19: Strictness of government policies and passengers' panic

Coronavirus outbreak has been highly disruptive for aviation sector. There is strong correlation between COVID-19 related news, volatility in transportation, low confidence in travel safety, and uncertainty in this era. In this research, we study and distinguish the COVID-19's impact on U.S. airlines' performance. The network and low-cost carriers responded differently to it in terms of capacity reduction, market share reduction, scheduled flights reduction, flight cancellations, and service quality in the year 2020. We illustrate low-cost carrier had higher efficiency compared to network ones during pandemic by applying Network Data Envelopment Analysis. Furthermore, the effects of two key factors that emerge from COVID-19, the government's stringency actions and passengers' panic, on U.S. airlines efficiency are studied. Our analysis demonstrate that the negative effect is more significant for passengers' panic than it is for governments' stringency measures. In addition, we show that passengers' panic has more impact on the efficiency of network carriers compared to low-cost carrier.

Impact of perceptions and attitudes on air travel choices in the post-COVID-19 era: A cross-national analysis of stated preference data

The COVID-19 pandemic and the consequent travel restrictions have had an unprecedented impact on the air travel market. However, a rigorous analysis of the potential role of safety perceptions and attitudes towards COVID-19 interventions on future air passenger choices has been lacking to date. To investigate this matter, 1469 individuals were interviewed between April and September 2020 in four multi-airport cities (London, New York City, Sao Paulo, Shanghai). The core analysis draws upon data from a set of stated preference (SP) experiments in which respondents were asked to reflect on a hypothetical air travel journey taking place when travel restrictions are lifted but there is still a risk of infection. The hybrid choice model results show that alongside traditional attributes, such as fare, duration and transfer, attitudinal and safety perception factors matter to air passengers when making future air travel choices. The cross-national analysis points towards differences in responses across the cities to stem from culturally-driven attitudes towards interpersonal distance and personal space. We also report the willingness to pay for travel attributes under the expected future conditions and discuss post-pandemic implications for the air travel sector, including video-conferencing as a substitute for air travel.

The Long-Term effects of COVID-19 on travel behavior in the United States: A panel study on work from home, mode choice, online shopping, and air travel

A critical challenge facing transportation planners is to identify the type and the extent of changes in people's activity-travel behavior in the post-Covid-19 pandemic world. In this study, we investigate the travel behavior evolution by analyzing a longitudinal two-wave panel survey data conducted in the United States from April 2020 to May 2021. Encompassing nearly 3,000 respondents across different states, we explored the effects of the pandemic on four major categories of work from home, travel mode choice, online shopping, and air travel. We utilized descriptive and econometric measures, including random effects ordered probit models, to shed light on the pandemic-induced changes and the underlying factors affecting the future of mobility in the post-pandemic world. Upon concrete evidence, our findings substantiate significant observed (i.e., during the pandemic) and expected (i.e., after the pandemic) changes in people's habits and preferences. According to our results, 48% of the respondents anticipate having the option to WFH after the pandemic, which indicates an approximately 30% increase compared to the pre-pandemic period. In the post-pandemic period, auto and transit commuters are expected to be 9% and 31% less than pre-pandemic, respectively. A considerable rise in hybrid work and grocery online shopping is expected. Moreover, 41% of pre-covid business travelers expect to have fewer flights (after the pandemic) while only 8% anticipate more, compared to the pre-pandemic.

Exploring the co-movements between COVID-19 pandemic and international air traffic: A global perspective based on wavelet analysis

The travel and tourism industry was one of the fastest-growing industries before the onset of the COVID-19 pandemic. However, to avoid COVID-19 spread, the government authorities imposed strict lockdown and international border restrictions except for some emergency international flights that badly hit the travel and tourism industry. The study explores the nexus between international air departures and the COVID-19 pandemic in this strain. We use a novel wavelet coherence approach to dissect the lead and lag relationships between international flight departures and COVID-19 deaths from January 2020 to September 2020 (COVID-19 first wave period). The results reveal that international flights cause the spread of COVID-19 spread during May 2020 to June 2020 worldwide. The overall findings suggest asymmetries between daily international flight departures and COVID-19 deaths globally at different time-frequency periods due to uncertainty surrounding the COVID-19 pandemic. The study will be conducive for the policymakers to control the upsurge of COVID-19 spread worldwide.

ANN-Based traffic volume prediction models in response to COVID-19 imposed measures

Many countries around the globe have imposed several response measures to suppress the rapid spread of the COVID-19 pandemic since the beginning of 2020. These measures have impacted routine daily activities, along with their impact on economy, education, social and recreational activities, and domestic and international travels. Intuitively, the different imposed policies and measures have indirect impacts on urban traffic mobility. As a result of those imposed measures and policies, urban traffic flows have changed. However, those impacts are neither measured nor quantified. Therefore, estimating the impact of these combined yet different policies and measures on urban traffic flows is a challenging task. This paper demonstrates the development of an artificial neural networks (ANN) model which correlates the impact of the imposed response measure and other factors on urban traffic flows. The results show that the adopted ANN model is capable of mapping the complex relationship between traffic flows and the response measures with a high level of accuracy and good performance. The predicted values are closed to the observed ones. They are clustered around the regression line, with a coefficient of determination ( R 2 ) of 0.9761. Furthermore, the developed model can be generalized to determine the anticipated demand levels resulted from imposing any of the response measures in the post-pandemic era. This model can be used to manage traffic during mega-events. It can be also utilized for disaster or emergency situations, where traffic flow estimates are highly required for operational and planning purposes.

The Impact of the COVID-19 Pandemic on China's Airline Industry

Background

The COVID-19 pandemic has posed a great challenge to the development of China's airline industry. Although the existing literature has analyzed the economic impact of the pandemic on the airline industry from different perspectives, it remains to be further studied given the operating characteristics of different types of airlines in China.

Methods

Using a new perspective of heterogeneous airline service models, this study collects high-frequency data on stock prices on six sample airline companies (including both full-service airlines and low-cost airlines) in China over 519 trading days, from August 1, 2019 to September 15, 2021, and identifies structural change points for each company's stock price using the Quandt-Andrews test. The outcome is used to construct an econometric model to quantify the economic impact of the pandemic on different airlines' stock prices under different structural changes.

Results

All results have passed the Quandt-Andrews test. The impact coefficient of full-service airlines is negative, while that of low-cost airlines is positive. Most of them have passed the test at the significance level of 10%.

Conclusions

All Chinese airlines experienced significant sudden changes in stock prices due to the pandemic, but there are sectoral differences in the order of the sudden changes, with full-service airlines experiencing structural changes much earlier than low-cost airlines. In addition, the impact of the pandemic on stock prices varies across airline types, with a negative impact on full-service airlines and a significant positive effect on most low-cost airlines.

STARTUPS: Founding airlines during COVID-19 - A hopeless endeavor or an ample opportunity for a better aviation system?

The devastating impact of COVID-19 on aviation is unprecedented and undoubted in the recent sci-entific literature, with many studies having dissected different facets of COVID-19-induced changes to the industry. A few studies have stepped further and highlighted that the COVID-19 pandemic could have positive long-term impacts on aviation. Given that traditional air carriers are known to be reluctant for performing high-risk experiments outside their business-as-usual, parts of hope for a better aviation future rests on novel players entering the industry. The pandemic - against common perception and odds - might have created a rare opportunity for airline startups to enter the market. In this study, we first dissect the impact of the COVID-19 pandemic on aviation and how it possibly created a breeding ground for new airlines. We propose a framework of eight facets, STARTUPS, covering flight Suspensions, Talents, Aircraft, Recovery, Travel demand, Uniquity, Policy making, and Strategy. Moreover, we analyze the business model and markets of 46 airline startups, established or becoming active during the pandemic. Our study is concluded with a dis-cussion on the risk factors for airline startups during the COVID-19 pandemic and induced policy challenges. Our analysis, we believe, is complementary to existing studies on COVID-19, leveraging a novel perspective on the pandemic and the aviation industry.

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

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.