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Before making travel plans, people often use the Internet to collect relevant information to help themselves make better decisions. Among the numerous information search channels, Internet search engine is used by the vast number of travelers because of its low cost and high efficiency. To a large extent, Internet search behavior is the external manifestation of users' psychological activities, reflecting their concerns, needs and preferences. Therefore, Internet search data can reflect the air passenger demand information to a certain extent. In this manuscript, a novel decomposition ensemble model is proposed to discuss the role of Internet search data in air passenger demand forecasting. In the empirical study, the relevant data of Shanghai Pudong International Airport and Beijing Capital International Airport are taken as samples. The results show that the proposed forecasting model can integrate the advantages of decomposition-ensemble strategy and deep learning algorithm, and achieve more accurate and reliable prediction results than all benchmark models. This further indicates that adding Internet search data into the forecasting model can effectively improve the prediction performance of air passenger demand, and can provide scientific and reliable decision support for air transport management. [ABSTRACT FROM AUTHOR]

Before making travel plans, people often use the Internet to collect relevant information to help themselves make better decisions. Among the numerous information search channels, Internet search engine is used by the vast number of travelers because of its low cost and high efficiency. To a large extent, Internet search behavior is the external manifestation of users’ psychological activities, reflecting their concerns, needs and preferences. Therefore, Internet search data can reflect the air passenger demand information to a certain extent. In this manuscript, a novel decomposition ensemble model is proposed to discuss the role of Internet search data in air passenger demand forecasting. In the empirical study, the relevant data of Shanghai Pudong International Airport and Beijing Capital International Airport are taken as samples. The results show that the proposed forecasting model can integrate the advantages of decomposition-ensemble strategy and deep learning algorithm, and achieve more accurate and reliable prediction results than all benchmark models. This further indicates that adding Internet search data into the forecasting model can effectively improve the prediction performance of air passenger demand, and can provide scientific and reliable decision support for air transport management.

This article analyzes the behavior of the demand of domestic and international air passengers in a context of liberalized air transport market and with a total deregulation of airfares (using as case study the Colombian air transport market), and where it is also contemplated the influence, on this demand, of several factors of diverse origin (economic, market / competition and geographic). And due to the complexity of the air transport system, the use of System Dynamics is considered appropriate as an analysis tool. The main results indicate a convergence (in the study period) to a stabilization status of the load factor, which suggests that the air operators were able to better understand the behavior of the demand and thereby better adjust their supply.

In this research, we examine how socio-economic mobility affects domestic passenger enplanement volumes at U.S. airports. In addition to metrics such as income and population levels, socio-economic mobility has been identified as an important characteristic of the socio-economic fabric of market areas. As such, it is a potentially significant determinant of demand for goods and services, including air travel. Drawing on data from the U.S. domestic airline industry and newly available measures of socio-economic mobility, we empirically discern how the latter affects both yields and passenger counts at U.S. airports. The results offer compelling evidence that greater mobility is associated with lower air fares. In addition, our findings suggest that greater passenger volumes are also lower in areas marked by higher socio-economic mobility, all else equal. Collectively, our results document the significance of socio-economic mobility as a determinant of air travel demand and thereby highlight the importance of considering it in the context of forecasting, demand management and, ultimately, infrastructure planning. [ABSTRACT FROM AUTHOR]

The aim of this paper is to compare passenger (pax) demand between airports based on the arithmetic mean (MPD) and the median pax demand (MePD). A three phases approach is applied. First phase, we use bootstrap procedures to estimate the distribution of the arithmetic MPD and the MePD for each block of routes distance; second phase, we use percentile, standard, bias corrected, and bias corrected accelerated methods to calculate bootstrap confidence bands for the MPD and the MePD; and third phase, we implement Monte Carlo (MC) experiments to analyse the finite sample performance of the applied bootstrap. Our results conclude that it is more meaningful to use the estimation of MePD rather than the estimation of MPD in the air transport industry. By carrying out MC experiments, we demonstrate that the bootstrap methods produce coverages close to the nominal for the MPD and the MePD. [ABSTRACT FROM AUTHOR]

In classic logic, there exists an implication of the form p ⇒ q ≡ n(p) v q (where n(p) is the negation of p and v the maximum). If we consider the fact that the propositions p and q take only the values 0 and 1, then the values of the classic implication are well-defined. In fuzzy logic, where the proposition can take any value in the closed interval [0, 1], there is an infinite number of fuzzy implications which can be used; hence, a method of selecting the most appropriate implication is required. In this paper, we propose a method of evaluation of the different fuzzy implications using available statistical data. The choice of the appropriate implication is based on the deviation of the truth value of the fuzzy implication from the real values, as described by the statistical data. [ABSTRACT FROM AUTHOR]

In recent years, domestic and international air passenger markets have expanded steadily around the world with the rapid growth of low cost carriers and aggressive route expansion; however, the unprecedented crisis caused by the coronavirus disease 2019 (COVID-19) pandemic resulted in greatly decreased air travel and an uncertain future for the aviation industry. The present study examined South Korean passengers, airlines, and government policy responses to the COVID-19 pandemic, and it suggests policy directions for the pandemic and post-pandemic periods. Air passengers respond to internal and external factors, and their demand for travel will increase with the reduction in global COVID cases and vaccine distribution. South Korean airlines have used various means to overcome decreased passenger numbers, such as domestic route transitions, freight transportation expansion, and mergers and acquisitions; Korean Air recorded a profit through its foray into cargo transport in 2020. The Korean government is trying to curb the spread of COVID-19 and help the industry to recover by establishing an airport quarantine system at Incheon international airport. As the COVID-19 pandemic continues, it is necessary to continuously monitor the responses of passengers, industry, and governments and to share relevant information. • Study on the COVID-19 impact on airlines, passengers and government policies. • Air passengers respond very sensitively to internal and external environments. • Airlines need to diversify their business models and strategies. • Monitoring the response of the passengers, industry, and governments and to share relevant information with each other. [ABSTRACT FROM AUTHOR]