Domestic Air Passenger Research Articles

Can air traffic booms induce innovation and bridge regional innovation gaps?

AbstractThe key role of innovation in long-term economic growth is well-established, but it is unevenly distributed across regions. This paper examines how increased air passenger traffic fosters innovation and whether it reduces innovation disparities. Focusing on regional innovation in Indonesia, measured by patent activity, we utilize the exogenous airline deregulation in the early 2000s, which significantly boosted domestic air passenger traffic. Using a newly geocoded patent dataset for Indonesian municipalities from 1995 to 2016, we find that domestic air passenger traffic positively affects regional patenting. This result is robust across various samples and sensitivity tests. However, increased air passenger traffic alone may not suffice to reduce innovation disparities within the country.

Regular domestic air passenger market dynamics: The Brazilian case

Brazil occupies a prominent position as one of the largest domestic air passenger markets globally. In May 2019, OAG Aviation Worldwide Limited (OAG), a renowned global travel data provider, ranked Brazil as the world’s 6th largest domestic market. This study identifies and meticulously analyses statistical trends in how service levels affect passenger demand on domestic air routes in Brazil. To that end, it employs a panel-data gravity model incorporating service as an instrumental variable. The findings confirm the influence of traditional gravity explanatory variables, while also contributing novel insights into the impact of service levels on domestic routes. The analysis reveals that, while factors such as income and distance play a fundamental role in shaping domestic demand, level of service emerges as a crucial determinant on regional connections. Overall, the statistics suggest growing divergences between Brazilian airlines and regional air transport. Accordingly, substantial changes are necessary in both government policies and the services offered by the airline industry in order to harness the full potential of Brazil’s domestic air transport passenger market and foster regional development.

Research on the Quality Development of Qingdao Tourism Based on GIS

With the transformation and upgrading of industrial structure and the increasing development of service industry, tourism is becoming more and more important for the proportion of regional economic development. In recent years, Qingdao tourism develops rapidly, while promoting regional development, tourism chaos appears. For the high quality development of Qingdao tourism, it is very important to think about the existing problems and solve the problems encountered in the development of tourism. This paper will make a gray correlation analysis on various factors affecting the development of Qingdao tourism: resource advantage, infrastructure and market demand, analyze the impact of different factors on the development of Qingdao tourism, and draw a conclusion: The grey correlation degree of the total number of domestic tourists, air and railway passenger volume ranks the top three, indicating that these three factors play an important role in Qingdao's tourism development. The correlation degree between star-rated hotels and tourism development is the least, indicating that their impact on tourism development is not as great as expected. At the same time, combined with climate, policy and other factors, this paper puts forward suggestions for Qingdao's tourism development.

An artificial neural network for predicting air traffic demand based on socio-economic parameters

Over the past five years, emerging economies have consistently progressed toward achieving greater economic independence. The aviation industry has played a significant role in driving this progress. Accurately forecasting the future demand for air travel is crucial for making long-term commitments to these economies. The findings of this study provide valuable insights into the provision of low-cost passenger carrier demand. They can assist low-cost carriers, airports, airport consultants, and government agencies in their strategic planning and development processes. The primary objective of this study is to identify and evaluate artificial intelligence-based solutions, specifically, Artificial Neural Networks (ANN), to determine the most effective model for projecting domestic demand for low-cost carriers. The study analyzes nine variables that impact the accuracy of predicting domestic air passenger demand in emerging countries using a Multilayer Feed-Forward Backpropagation Neural (MFFBPN) network. The performance of the developed ANN model for predicting air passenger demand is assessed through training, validation, and testing using 240 data sets. The evaluation metrics employed, including mean square error (MSE), root mean square error (RMSE), accuracy, precision, sensitivity, and specificity, indicate the effectiveness of the developed ANN model, with results of 0.0019 for MSE, 0.0440 for RMSE, 0.92 for accuracy, 0.92 for precision, 0.93 for sensitivity, and 0.95 for specificity. We show that modeling based on artificial intelligence approaches and ANN is more effective than conventional linear regression models.

Deep Learning Models for Airport Demand Forecasting With Google Trends

Managers gain new insights into how operational benefits can be achieved. Forecasting problems for passenger flow in airports are gaining interest among marketing researchers, but comparison of stochastic optimisation methods via deep learning forecasts with search query data is not yet available in the aviation field. To fill this gap, the current study predicts the demand of Madrid airport demand with Google search query data using H2O deep learning method. The findings indicate that there is a long-term relationship between search queries and actual passenger demand. Besides, search queries “fly to madrid,” and “flights to madrid spain” were found to be the cause of the actual domestic air passenger demand in Madrid. Also, to determine the best forecasting accuracy, stochastic gradient descent (SGD) optimisers were used. Specifically, findings indicate that Adam is a better optimiser increasing forecasting accuracy for Madrid airports.

Assessing the Determinants of Choice of Airline among Domestic Air Travelers in Lagos, Nigeria

The patronage or choice of an airline is determined by the airline’s ability to offer numerous service qualities toher passengers’ satisfaction. Several studies have identified numerous factors determining passengers’ choice ofairlines. However, there is still a paucity of information on the degree of determinants of these determiningfactors. This paper, therefore, examines the degree of influence these determinant factors have on domestic airpassengers’ choice of an airline in Lagos, Nigeria. Primary data were adopted using the structured questionnairethat focused on the demographic profiles of domestic air passengers, ranking of airline service qualities and thedeterminants of their choice of airline. The primary data were collected through 352 structured questionnaireforms administered by systematic random sampling method to 400 domestic air passengers at both the departureand arrival lounge of the domestic wing of Murtala Muhammed Terminal 2 (MM2) in Lagos. The resultsrevealed that Air Peace and Max Air ranked first and second respectively among the top ten operational airlinesin terms of service quality preference. Furthermore, the results of the hypothesis indicated that the passengerairfare and route availability influence domestic passengers’ choice of airline. The stepwise multiple regressionresults further indicated that passenger airfare exerted more influence at 89.2% (F=27889.785, p<0.05). Thestudy will help the airline’s marketing unit to identify the airline service quality they need to improve on, and itwill help policymakers not only in Nigeria but also all over the sub-continent to think about long-term strategiestowards the provision of sustainable airline services.

Operational performance of Indian passenger airlines using hierarchical categorical DEA approach

The challenges faced by the Indian Aviation market are complex and perplexing for the decision-makers in this sector. This industry has faced debilitating losses during COVID-19 pandemic times. The travel restrictions have been gradually lifted worldwide, and therefore the airlines can expect to witness a growth in domestic and international air passenger traffic during 2021–22. The losses incurred during the pandemic times need to be recovered. Thus, there is an urgent need to strategically plan and improve the operations so that the airlines can perform efficiently and earn revenues. Airlines in India work under heterogeneous environments depending upon their segment of operations. Some of the airlines operate for only domestic passengers and others for both domestic and international passengers. Therefore these airlines must be assessed as per their homogeneous peer group to obtain substantial results. The proposed study has discussed an approach to study airlines' operational performance using hierarchical categorical data envelopment analysis (DEA). The airlines have been categorized based upon their segment of operations. The efficiency of the airlines has been evaluated for the period 2014–19 as per their categorical input value. Window analysis has also been performed for a meaningful analysis of the results. The outcomes of the study will be helpful for policymakers to improvise the present working model of the industry and alleviate their performance.

Effect of flight connectivity on the introduction and evolution of the COVID-19 outbreak in Canadian provinces and territories.

BackgroundThe COVID-19 pandemic has challenged health services and governments in Canada and around the world. Our research aims to evaluate the effect of domestic and international air travel patterns on the COVID-19 pandemic in Canadian provinces and territories.MethodsAir travel data were obtained through licensed access to the ‘BlueDot Intelligence Platform’, BlueDot Inc. Daily provincial and territorial COVID-19 cases for Canada and global figures, including mortality, cases recovered, and population data were downloaded from public datasets. The effects of domestic and international air travel and passenger volume on the number of local and non-local infected people in each Canadian province and territory were evaluated with a semi-Markov model. Provinces and territories are grouped into large (>100 000 confirmed COVID-19 cases and > 1 000 000 inhabitants) and small jurisdictions (≤100 000 confirmed COVID-19 cases and ≤ 1 000 000 inhabitants).ResultsOur results show a clear decline in passenger volumes from March 2020 due to public health policies, interventions, and other measures taken to limit or control the spread of COVID-19. As the measures were eased, some provinces and territories saw small increases in passenger volumes, although travel remained below pre-pandemic levels. During the early phase of disease introduction, the burden of illness is determined by the connectivity of jurisdictions. In provinces with a larger population and greater connectivity, the burden of illness is driven by case importation, although local transmission rapidly replaces imported cases as the most important driver of increasing new infections. In smaller jurisdictions, a steep increase in cases is seen after importation, leading to outbreaks within the community.ConclusionsHistorical travel volumes, combined with data on an emerging infection, are useful to understand the behaviour of an infectious agent in regions of Canada with different connectivity and population size. Historical travel information is important for public health planning and pandemic resource allocation.

Mapping carbon emissions of China's domestic air passenger transport: From individual cities to intercity networks

As a significant carbon emission source with high growth potential, the air transport sector plays a crucial role in China's decarbonisation efforts. However, the spatial pattern and evolutionary dynamics of aviation carbon emissions in China have not been thoroughly studied. This study proposed a framework to reveal the spatial characteristics and influencing factors of aviation carbon emissions at the city level. Using data from 2019 to construct the aviation carbon emissions network of China (ACENC), the novelty of the study lies in the subdivision of carbon emissions of air passenger transport into cities and intercity lines in China, which helps to reveal the spatial characteristics of individual cities in the intercity network. Beijing, Shanghai, Shenzhen, Chengdu, and Guangzhou were the cities with the highest carbon emissions, and the routes between these cities caused a significant amount of carbon emissions. >80 % of the total carbon emissions can be attributed to two communities in the network, owing to their large size and strong connections. Correlation analysis indicates that a city's carbon emissions are significantly related to its demographic and economic attributes as well as its connection with other cities, while a city's carbon emission intensity may be influenced by its centrality in the whole network and the structure of the community to which it belongs. Overall, the presented results provide directions for stakeholders and policymakers to regulate carbon emissions from air transportation.

Differentiated pricing for airline ancillary services considering passenger choice behavior heterogeneity and willingness to pay

Under the differentiated service reform of Chinese airlines in 2021, it is an inevitable trend to design and develop differentiated ancillary products, which is the optimization scheme of the supply-side for airlines starting from user demands. The trend of domestic air passengers paying for seat selection is becoming increasingly prevalent. By taking the seat selection fee as an example, this study examines the formulation of ancillary service fees based on passengers' willingness to pay. Using a stated preference (SP) experiment method, this study investigates passengers' seat selection behavior and uses the latent class conditional logit model (LCL) to classify passengers based on seat selection preference. Then, considering the passengers' socioeconomic characteristics, travel characteristics, flight characteristics, seat categories, and passenger categories, this study constructs a model of willingness to pay for seat selection through logit regression. Finally, this study further explores the influencing factors that affect passengers' willingness to pay by using the model and conducting a confirmatory analysis between the model results and the investigation data. The research results show that: (1) passengers' cabin seat selection preferences are heterogeneous, and passengers are divided into three categories; (2) the passenger's personal characteristics (gender, age, etc.) have no significant influence on the willingness to pay for the three categories of passengers; however, the flight purpose for business and leisure travel, day flight, flight duration, the number of luggage, person accompanied, person unaccompanied, and preferred seat have a significant impact on it; (3) the model of willingness to pay has strong correctness and practicability, so in practice, with this method, airlines can predict the willingness to pay for seat selection according to passenger categories and then provide differentiated services and charge fees. It provides theory and decision support for airlines to conduct seat management and design differentiated ancillary service products.

A Regional View of Passenger Air Link Evolution in Brazil

Passenger flows among cities, at both the domestic and international levels and in different countries and regions, have been studied for forecasting purposes. This paper seeks not a forecasting mechanism, but to understand, by analyzing passenger origins and destinations by Brazilian sub-region, how Brazil’s domestic air passenger network links have evolved. Using income, population, and fare price as inputs, and seats sold as output, air link performance is examined by data envelopment analysis to discuss the regional link of domestic passenger traffic in Brazil and its dynamics, considering two specific years. The findings indicate that, although the highest passenger flow density is concentrated in Brazil’s Southeast region, performance by emerging origins and destinations (O-Ds), such as those connecting the Northeast, display more substantial strength indices and advances (Malmquist analysis). The analysis of specific links was also important, which showed that important Brazilian airports are not necessarily more competent in generating trips. The Catch-Up indicator for innovation reveals the weak point in Brazil’s air transport network. Although some airports enjoy strong networkability, they do not correspond in passenger origin or destination strength.

Investigating Gamification Effect of Frequent Flyer Program on Brand Loyalty in the Aviation Industry

The frequent-flyer program is a loyalty program offered by airlines, also credited as the pioneer of gamification in services. This study empirically investigated the gamification effect of frequent-flyer programs on air passengers’ brand loyalty. A conceptual model based on the gamification theory was suggested. A questionnaire-based survey method was used for data collection from domestic air passengers in India. Data were collected from 418 respondents selected using convenient purposive sampling, and 356 filled questionnaires were used for analysis. Covariance-based structural equation modeling was used for data analysis. The path analysis and mediation test supported that the FFP game-element was positively associated with the air passengers’ loyalty. The psychological and behavioral outcomes of the game element fully mediated the relationship between the game element and the loyalty of air passengers towards aviation companies. This research would be helpful to the aviation managers to plan policies for enhancing loyalty.

Passenger, airline, and policy responses to the COVID-19 crisis: The case of South Korea

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.

Predicting airline passengers’ loyalty using artificial neural network theory

The study explores a model for predicting airline loyalty using the antecedents indicated in previous studies. Data was collected using a questionnaire distributed to 614 domestic air passengers using the snowball sampling method. The measurement tool had 16 scale items constructed on the recommendations of previous studies. Passenger satisfaction, airline service quality, passenger perceived value, and airline image are identified as determinants for airline loyalty. The predictive analytical approach of Artificial Neural Network theory and covariance-based Structural Equation Modelling for determining causality is employed in the study. The artificial neural network model predicts airline loyalty with 89% accuracy. Sensitivity analysis suggests passenger satisfaction as the most significant predictor of airline loyalty. The causal study supports that passenger satisfaction mediates the relationship between airline service quality and airline loyalty.

Air Transportation, Economy and Causality: Remote Towns in Brazil’s Amazon Region

There is a vast amount of literature on relations between air transport activities and economic growth. The recognition that these are important relationships has aroused the interest of researchers, who have studied them with regard to remote towns. The empirical studies have concentrated more on developed areas, such as Europe, North America, and Australia. No empirical studies were found for Brazil, which holds most of the Amazon region. In this study, the Granger causality method was used to investigate for causal relationships between regular domestic air passenger transport links and the gross domestic product of small municipalities in this region. The results indicate short- and long-term, two-way causality in which gross domestic product has a stronger impact on air transport than vice versa.

An Empirical Study on the Demand of Air Passenger Transport and Regional Economic Development in Inner Mongolia

As an important part of the comprehensive transportation system, air transportation is the key link connecting regional exchanges. And air transportation can actively promote the development of regional economy. In this paper, the airport operation and regional economy of the leagues and cities in Inner Mongolia are taken as the research object, the co-integration and Granger causality between the air passenger throughput (TQ) and regional gross domestic product (GDP) of the leagues and cities are analyzed, and the correlation between air passenger throughput and regional gross domestic product is discussed. It is found that there is co-integration relationship between the air passenger throughput and regional gross domestic product in seven cities. The League cities with co-integration relationship are Hohhot City, Ordos City, Baotou City, Tongliao City, Xilin Gol League, Hulunbuir City and Bayannur City. In Granger causality analysis, regional gross domestic product of Hohhot City, Baotou City, Hulun Buir City, Tongliao City, Chifeng City and Bayannur City, Xilin Gol League and Hinggan League is the Granger cause of air passenger throughput, and vice versa. regional gross domestic product and air passenger throughput of Ordos City are mutually Granger causality. In general, regional economic development plays a more significant role in promoting the growth of air transport. Through comparative study, it is found that regional economic development can better promote the growth of air transport in Tongliao City.

Behavior of Air Passenger Demand in a Liberalized Market

Abstract 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.

Energy Effectiveness of Jet Fuel Utilization in Brazilian Air Transport

Since World War I, the commercial aviation industry has seen many improvements that now allow people and goods to reach the other side of the world in a few hours, consuming much less fuel than in recent decades. Improvements in cargo capacity and energy efficiency were significant, and in this scenario, commercial airlines were able to thrive and bring great benefits to the world economy. However, this sector is facing environmental challenges due to the intensive use of jet fuel. Brazil is one of the largest domestic air passenger markets in the world and still has great growth potential, considering its economic potential and territorial dimensions, which are roughly the same size as the US and twice the size of the European Union. This paper discusses the partial productivity of jet fuel in Brazilian domestic aviation and proposes an econometric method to support public regulators and airline decisions. The proposed model uses variables, such as aircraft size, route characteristics, and idle flight capacity, in a panel data analysis. The results show that reducing idle capacity is one of the best ways to achieve better short-term fuel efficiency, and therefore will reduce the environmental impacts and have positive economic effects on commercial air transport activities. This paper brings a new approach to the discussion of airline performance, focusing on the use of jet fuel, with economic and environmental consequences.

Competition in the Domestic Air Passenger Transport Market in China

Competition in the Domestic Air Passenger Transport Market in China

SPATIAL PATTERNS OF DOMESTIC AIR PASSENGER TRAFFIC GENERATION IN NIGERIA

This study aims at analyzing the spatial patterns of domestic air passenger traffic generated by the interacting city-pairs in Nigeria’s air transportation system at five points in time – 2003, 2006, 2010, 2014 and 2018 to establish the spatial and temporal changes that have taken place over time. It also examined the relationships between the populations of the cities and the volumes of domestic air passenger traffic generated by the cities to establish the impact of population on the traffic generating capacity of the cities. The volumes of domestic air passengers handled by each city, the percentage share of total traffic, the populations of the cities, the Pearson’s Product Moment Correlation Coefficient and the student’s ‘t’ test were used for the analyses. The study revealed that Lagos was the dominant domestic air passenger traffic generation centre. It established that few centres generated most of the domestic air passenger traffic in Nigeria and that the spatial pattern of traffic generation tended more towards concentration than dispersal. The study also found that there were statistically positive relationships between the populations of the cities and the volumes of traffic generated by the cities. Balanced regional development is recommended, among others, to help redistribute population among the cities in the air transportation system in Nigeria so as to increase their air passenger traffic generating capacity.