Analysis of Strategies Implemented by AirAsia to Cater to the Covid-19 Effects

During the global pandemic, AirAsia reported an annual loss of RM5.9 billion ($1.4 billion) in 2020. The uncertainty of the COVID-19 outbreak, travel restrictions, and border controls had led to weaker air travel demand, which had impacted AirAsia’s operation badly. This paper depicts how Covid-19 impacted AirAsia and the strategies implemented by AirAsia during the pandemic in the marketing aspects. This paper also focuses on how AirAsia can anticipate a strong recovery in the airline industry while expanding to other industries. In extreme and under pressure circumstances, AirAsia implemented many strategies to cater to the effects of Covid-19. However, other strategies, such as focusing on their business in the food industry, should be expanded throughout Malaysia.

Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods

Over the past decades, air transportation has expanded and big data for transportation era has emerged. Accurate travel demand information is an important issue for the transportation systems, especially for airline industry. So, “optimal seat capacity problem between origin and destination pairs” which is related to the load factor must be solved. In this study, a method for determining optimal seat capacity that can supply the highest load factor for the flight operation between any two countries has been introduced. The machine learning methods of Artificial Neural Network (ANN), Linear Regression (LR), Gradient Boosting (GB), and Random Forest (RF) have been applied and a software has been developed to solve the problem. The data set generated from The World Bank Database, which consists of thousands of features for all countries, has been used and a case study has been done for the period of 2014-2019 with Turkish Airlines. To the best of our knowledge, this is the first time that 1983 features have been used to forecast air travel demand in the literature within a model that covers all countries while previous studies cover only a few countries using far fewer features. Another valuable point of this study is the usage of the last regular data about the air transportation before COVID-19 pandemic. In other words, since many airline companies have experienced a decline in the air travel operation in 2020 due to COVID-19 pandemic, this study covers the most recent period (2014-2019) when flight operation performed on a regular basis. As a result, it has been observed that the developed model has forecasted the passenger load factor by an average error rate of 6.741% with GB, 6.763% with RF, 8.161% with ANN, and 9.619 % with LR.

The impact of COVID-19 on air travel demand and CO2 emissions in Brazil

The drastic reduction in economic activities caused by the COVID-19 pandemic creates a unique and timely opportunity to examine the environmental impacts of human activity. In several countries, the aviation sector was dramatically affected by the travel restrictions, resulting in a change of trip demand and in a drop of fuel consumption. Nonetheless, little attention has been paid to the impact of the pandemic on air travel demand, one of the fastest-growing sources of emissions globally. This paper estimates the impact of the COVID-19 on air travel demand and emissions in Brazil, the largest aviation market in Latin America. Combining detailed flight data with daily number of passengers and fuel consumption and data on combustion emission factors, we estimate CO2 emissions of domestic flights in Brazil. A Bayesian structural time-series model was used to estimate the impact of COVID-19 on daily trends of air travel demand and emissions. We find that the COVID-19 pandemic caused a reduction of 68% on national passengers and 62% in total CO2 emissions compared to what would have occurred if the pandemic had not happened. It avoided a total of approximately 4.6 megatons of CO2 between March and December 2020 in Brazil, the equivalent of one year of domestic flight emissions in France. Despite such a sharp drop in commercial aviation, passenger demand recovered to 64.2% of pre-pandemic levels by the end of 2020. CO2 emissions had a 52.6% reduction in 2020 and the emissions per capita increased after the COVID-19 outbreak. Although the precise impact of the COVID-19 on this figure is not yet fully understood, the fast recovery in domestic flights by December 2020 indicates that the emissions could soon return to pre-pandemic levels, demonstrating the challenges of reducing emissions in the aviation sector in the short term.

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.