Air Transport Demand Research Articles

A review of the surveys on health effect of aircraft noise for Narita International Airport

In this paper, we review two previous projects on health effects of aircraft noise for residents living around Narita International Airport, and introduce a summary of the new project which started since 2019. The airport was equipped with a Runway‐A at the opening, and a Runway‐B was constructed to keep pace with increased demand for air transport in 2002. The Runway‐C will be accomplished at the end of fiscal year 2028. The first project on health effects of aircraft noise was conducted in 1998 when the airport had only Runway‐A. And then, second project on health effects of aircraft noise was conducted in 2014 after beginning of conditional relaxation of curfew. These two projects were based on cross‐sectional study, and implemented using a questionnaire to investigate psychological effects like noise annoyance, night‐time insomnia, mental effects like emotional instability and depressive tendency, and physical effects like high‐blood pressure. Since 2019, third project on health effects have been started because of disestablishment of nighttime flight restrictions for the Runway‐A. In this new project, longitudinal data are also included and we have a plan to investigate using objective method. The progress of this project until 2022 is reported in this paper.

Air route link prediction based on the PSO-CLP model

To optimize the structure of an air route network, accurate forecasting of future new routes is vital given the rapid growth in demand for air transportation. Based on the theory and method of link prediction, considering the joint influence of network endogenous factors and external attributes, we construct a system of network endogenous factors and external attribute indices and explore the prediction effect of each index. We further construct a prediction index system, explore the prediction effect of coupled indices, design a particle swarm algorithm to determine the weights of each index, and propose a coupled link prediction model based on particle swarm optimization (PSO-CLP). A comparison of the prediction accuracy of this model with the dual-indicator coupled link prediction model and the traditional link prediction model is also conducted to test the stability and reasonableness of the PSO-CLP model. In this study, we use the 2015–2020 Chinese air route network as an example. The instance test shows that the PSO-CLP models significantly outperform the traditional link prediction models and dual-indicator coupled link prediction models in terms of prediction accuracy, stability and computational simplicity, among which the PSO-CLP model, which considers both endogenous factors and external attributes, such as the RWR + Sor + Pop and RWR + RA + GDP indices, has the best forecasting effect. The PSO-CLP model is an effective tool for route prediction, providing a new perspective on route link prediction and air route network optimization.

Testing the algebraic modeling of competitive impacts from wage premium shocks: a case study from a booming airline market

Labor relations issues such as collective bargaining and strike threat power have recently gained increased attention in the Brazilian air transportation market due to a short-term shortage of qualified workforce, leading stronger pilot unions to demand higher wages and better working conditions. Exacerbated by the notable expansion of the Brazilian economy between 2020 and 2023, where air travel demand surged by 60%, this pressure has driven labor costs to grow more rapidly than other cost categories. This paper examines exogenous flight crew cost shocks, treating these as increases in wage premiums resulting from stronger unionization and higher labor market rents. Using a differentiated duopoly model, we analyze the impact of these wage increases on competition between a major network carrier and a small low-fare carrier, introducing the hypothesis of non-exhausted economies of density. Our findings indicate that smaller airlines are more adversely affected by labor cost shocks, though differentiated wage increase schemes can mitigate these impacts, reducing market concentration and preserving consumer welfare. This study contributes to the literature by incorporating wage premium shocks into existing models and suggesting that labor regulations in Brazil should be liberalized to address the rapid growth in air transportation demand and the resultant workforce shortage.

Modeling airport choice for a multi-airport area using a random parameter logit model

Recent years have seen a growing demand for air transport which has led to an increase in the number of airports providing a significant number of flights in Spain and throughout the rest of Europe. Choosing which airport to use depends on the competition between the airlines operating from them and how accessible they are to the residents in the surrounding areas. This research introduces an efficient design based on D-error using a stated preferences survey to study user behaviour when choosing between competing airports. The data is modelled using a Multinomial Logit Model and a Random Parameter Error Component Logit (RPECL), a discrete choice model which allows us to study user preferences taking into account their varying systemic and random tastes, as well as find specific correlations which may exist between similar alternatives. This meant the authors were also able to calculate the direct and cross elasticities to examine the weight of the different variables involved in making the choice of airport. The results showed, for a study case in Spain (Bilbao and Seve Ballesteros - Santander airports), that increased access time to the furthest airport caused an important fall in demand (choice) for that airport. It was also shown that access fees had a lower elasticity than the flight cost and that the fewer the transfers required to reach the final destination increased the probability of choosing an airport. Therefore, in order for smaller airports to compete with larger airports they need to invest more in reducing the cost of flights, attracting more low-cost companies and offering more direct complementary destinations than those provided by larger airports.

항공 교통약자의 권리보호에 관한 고찰

The purpose of this paper is to examine the institutional arrangements in place to facilitate air travel for people with disabilities and other transportation disadvantaged populations as air transportation demand begins to recover from the COVID-19 pandemic.
 The paper is organized into three main sections. The first section is a theoretical review of the public nature of air transportation and the mobility rights of people with disabilities. In particular, it discusses how private commercial airlines, which are not operated by the state, are public, and furthermore, why the right to mobility of persons with disabilities should be guaranteed in air transportation.
 In the second paragraph, we reviewed the comparative legal framework for ensuring the right to mobility of persons with disabilities in air transportation. The regulations of ICAO and IATA were examined as international standards, and legislative examples of developed aviation countries such as the United States and the European Union were analyzed. Through this, we explored various ways to ensure the right to mobility of the air traffic disadvantaged by comparing international standards and cases from advanced countries.
 The third paragraph examines the provisions related to the air traffic disadvantaged in Korea's Aviation Business Act and its implementing regulations, and suggests future revisions.
 Air transportation plays an important role as a means of transportation for the entire society. However, the transportation disadvantaged, especially people with disabilities, face many difficulties in using air transportation services. Since it is difficult to solve these problems through legal amendments alone, it is necessary to recognize the public nature of air transportation and make efforts to ensure the rights and interests of the transportation disadvantaged based on this.
 From a theoretical perspective, this paper explored the reasons why air transportation is public and why it is necessary to prevent the exclusion of the disadvantaged. From a practical perspective, it investigated what legal measures developed countries are taking to protect the disadvantaged in the field of air transportation and tried to suggest ways for Korea to realize the rights and interests of the disadvantaged more effectively. This paper is expected to raise awareness of the theoretical and practical solutions for the protection of the disadvantaged in air transportation and contribute to the improvement of domestic policies and systems.

SUPPORT VECTOR MACHINES FOR FORECASTING NON-SCHEDULED PASSENGER AIR TRANSPORTATION

Forecasting non-scheduled passenger air transportation demand is essential for effective operational planning and decision-making. In this abstract, we explore the use of Gaussian Support Vector Machines (SVM) methods in forecasting nonscheduled passenger air transportation processes. SVM is a type of supervised machine learning algorithm that can be applied to various domains, including nonscheduled passenger air transportation. In classification and regression tasks, SVMs are considered especially useful. SVMs can be used to forecast passenger demand for specific routes or flights. By analysing historical data, including factors such as time of day, day of the week, etc., SVMs can help airlines estimate future passenger demand. This method is crucial for optimising ticket pricing and managing seat inventory. This research proposes the implementation of different Gaussian SVM methods for the forecasting of non-scheduled passenger air transportation.

Prospects of development of Ukrainian air transport regulation

The world transport system is unthinkable without aviation. Currently, there is a positive trend in the field of air transportation, and the role of aviation on a global scale cannot be overestimated. Today our state is in a state of war, but we can claim that Ukraine has significant potential for the development of the aviation market. Before the war, the passenger air transportation market had the highest growth rates among all modes of transport in Ukraine. Such prospects are due, first of all, to significant unmet demand for air transportation, as well as a favorable geographical location for the development of transit transportation. At the same time, the development of instruments for regulating the industry at the national level is of particular importance. This article is devoted to finding ways to improve the legislative and regulatory framework of air transport.

Development of a Network of Landing Sites and Heliports: Ensuring Aviation Accessibility of Settlements in the Arctic Regions

While considering the problems of ensuring aviation accessibility of remote settlements in the regions of the Eastern Arctic Area of Russia, the study has concretised the concept of aviation accessibility of a settlement.The analysis of aviation mobility of the population of the Arctic zone of the Republic of Sakha (Yakutia) was carried out. It has been established that the lack of a sufficient number of landing sites is a limiting factor in performance of regular flights between settlements and the centres of the Arctic regions. Under these conditions, local flights are carried out mainly by MI-8 helicopters which are expensive in operation, with extremely low regularity and not full load.To justify the construction of a network of landing sites in the Arctic region, a methodology has been developed for predicting seasonal distribution of passenger flows within local air transportation. This methodology is based on Ward’s hierarchical clustering method, which allows, considering many criteria, to divide all settlements into clusters, in each of which the demand for air transportation is present during a certain number of months.It is substantiated that construction of a landing site in a settlement is necessary if there is a demand for air transportation for more than five months a year. Using the example of 65 Arctic settlements of the Republic of Sakha (Yakutia), five clusters were identified. For three of them the need to build landing sites is proved. The zones of gravity of regional airports with a range of 150 km were determined, considering the possibility to get from the settlement to the airport in 3–4 hours. Based on the results of clustering and identification of gravity areas of regional airports, a predictable network of landing sites in the Republic of Sakha (Yakutia) was built.

Are supply and demand the main key drivers of airport parking prices? The quantitative study

Parking pricing always poses a challenge to airport management due to their impact on revenue. According to previous studies, parking prices have not considered air transportation supply and demand simultaneously until now, which is why challenges have arisen. Also, no model identifies the factors affecting parking prices and expresses their relationship quantitatively. The contribution of the current paper is to develop numeric models for pricing airport parking using supply and demand data. In this regard, the multiple linear regression approach is used due to its simplicity and interoperability. Three time periods for airport parking pricing are considered as dependent variables: 1-h short-term, 24-h short-term, and 24-h long-term. Independent variables also include air transportation supply and demand characteristics along with socioeconomic variables based on previous research. Employing collected data from 60 airports on all continents, the final model for each parking type is selected. The results indicate that parking expense will be affected more by transportation supply than by transportation demand. As a measure of the airport's supply, short-term parking prices have been significantly affected by the average cost of amenities. By affecting passengers' access to the airport, public transportation ticket prices also impact the cost of 24-h long-term parking. These supply-related factors have a more noticeable effect on parking prices than demand variables, such as the number of airport passengers, and this can be taken into account by managers and planners in the future.

Assessment of Aircraft Carbon Emission in Nigerian Airspace towards Climate Change Mitigation

Global effects of climate change due to greenhouse gas emission are increasing this need to be given more attention. Also further improvements required for the global long-term demand of air transportation and reduction the net greenhouse gas emissions as a contributor to climate change mitigation. This study aimed at examining the carbon emission from domestic air traffic operations in Nigerian with view to mitigating climate change impact from aviation sector. in line with International Civil Aviation Organization resolution A38-18 of 2013 targeting annual emission reduction by at least 2% from 2021 to 2050. Carbon Emission Calculation Methodology was adapted in assessing total carbon dioxide emission by some Nigerian domestic airline in 2019. Cluster and purposive sampling methods were used in selecting the aircrafts. Results indicate B737 as the most commonly used aircraft in domestic routes with 44.9% fleet in the period. 71.5% of routes had fuel consumption to Carbon dioxide emission ratio of 1:3. Azman air had the highest percentage (83%) of the commonest aircraft type (B737s). Again it had 5,984 domestic trips on a cumulative distance of 3,521,218 kilometers with total carbon dioxide emission of 55,729,531 kgCO2. Pearson correlation analysis indicated positive relationship between great circle distance, fuel consumption and carbon dioxide emission on each route and collectively with a high level of significance (r = 0.798, P = 0.001). Sustainable improvement of airspace infrastructures to reduce flying time and carbon dioxide emission. Environmental economics market based measures should be applied to aviation operations to ensure environmental sustainability

Aircraft acquisition optimization under demand and cost fluctuations: Before and after leasing standard changes

Given the high-value characteristics of aircraft, fluctuations in air transport demand, and dynamic leasing and purchasing costs, airlines' decisions regarding aircraft acquisition are crucial to their costs and profits. Based on the International Financial Reporting Standard (IFRS) 16 Leases, which has been in effect since 2019, all leases with terms of more than 12 months are on a balance sheet of lessees. This change may impact airlines’ financial performance and aircraft leasing decisions. This paper focuses on optimizing aircraft acquisition before and after applying IFRS 16 under multiple fluctuating factors, including aircraft demand and acquisition costs, by formulating an integer programming model. The results indicate that airlines still have to acquire a few aircraft from a channel similar to operating leases, with short lease terms but high rents. Moreover, a lease type similar to a financial lease in the old standard is increasingly adopted after the enforcement of IFRS 16 in the numerical experiments. Furthermore, aircraft leases and purchases are required for fewer years under IFRS 16 than under IAS 17.

Fatigue risk management in aviation operations

Fatigue accounts for 15% to 20% of human-related accidents. The increasing annual demand for air transportation has raised concerns in this 24/7 activity, as the crew ends up working irregular hours to meet this demand. Current regulations limit crew working hours to minimize fatigue risks, but do not eliminate them. Scientific studies reveal multiple contributing factors leading to crew fatigue, dispelling the old paradigms that fatigue resulted solely from a linear accumulation of working hours. Based on the findings of these scientific studies, a new support tool is created and presented, with the primary goal of mitigating fatigue-related effects and enhancing crew alertness to operate at a satisfactory performance level. This work aims, through a literature review, to expose the risks associated with fatigue in aviation operations and introduce the fatigue risk management system as a competitive lever to achieve a balance between safety and productivity.

Air passenger flow forecasting using nonadditive forecast combination with grey prediction

Air passenger flow forecasting plays a critical role in managing air transportation. It is important to develop accurate methods for forecasting the flow of passengers to formulate relevant operational plans and the schedule of airports. Even though empirical evidence has shown that forecast combination can improve the prediction accuracy of single-model forecasts, no study in the literature on transportation to date has examined the performance of such combinations to predict the demand for air transport. In light of the usefulness of grey prediction, which does not require applying any statistical test to examine the data at hand, and relationships among forecasts to be reckoned with, this study proposes using the nonadditive Choquet fuzzy integral to nonlinearly synthesize forecasts from four commonly used univariate grey prediction models into composite forecasts. The results of experiments confirmed that the risk arising from the incorrect choice of forecasting models can be eliminated by using forecast combinations. The proposed combination method based on grey prediction significantly outperformed the other competitors considered.

Empirical study of air transport demand forecast in Nigeria

The aim of the study is to estimate the air travel demand forecast of domestic passengers’ travels in Nigeria from 2002-2016. Data wesourced from Central Bank of Nigeria, National Bureau of Statistics, and Federal Airport Authority of Nigeria. The study utilized multiple regressions model using Stata-Graphic software solver to analyze the data. From the analysis, the result shows that there is significant relationship between the explanatory variable (passengers) and unexplanatory variables National Disposable Income, Population, Average Airfare, Gross Domestic Product, Exchange Rate, Total Expenditure, and Crude oil price, accounted for adjusted R of 93.624% relationship with demand for domestic air travel of passenger. The R-square statistic shows that the fitted model explains the variability in Number of passenger (NPAX) which is 96.8124%. The Hypothesis testing reveals that National Disposable Income and Air fare have strong statistically significant relationship with the demand for domestic air travel with P-value of 0.0013. Moreso, there is statistically significant relationship for National Disposable Income, Average Airfare, and Crude oil price with P-value of 0.0017 and 0.0445 respectively. Furthermore, forecast of the number of passengers and average airfare was carried out using ARIMA (1,0,0) model which made available the future predicted values for the number of passenger movement and average airfare for the next 10-12 years to come. Thus, the study recommends that stakeholders in the air transport sector should work towards improving the capacity and infrastructure to accommodate the growth of air travel demand for domestic air travel in Nigeria. Policy implications were made on how to regulate the sector by having a good pricing policy to control the air fare for the industry.

CO2 emission change in China’s aviation industry: A fleet-wide index decomposition and scenario analysis

A fleet-wide index decomposition and scenario analysis model is developed to identify the influencing factors of CO2 emission change in China’s aviation industry and predict CO2 emissions in multiple emission reduction scenarios through 2040. It was discovered that all aircraft types experienced operational improvements during 2009 and 2019. The fleet utilization growth resulted in the most of CO2 emission reduction. However, slowdown in fleet updates existed for most of aircraft types, and the decreased fleet fuel intensity only resulted in CO2 emission reduction in specific years. The rising air transport demand continues to be the greatest obstacle to reducing emissions. Under the baseline scenario, aviation carbon intensity can only realize the 60–65% target by 2035. With endeavours on traffic demand control and technology, aviation CO2 emissions will peak at 123 Mt in 2035. The rapid sustainable alternative fuels substitutions will be essential for achieving China’s 35–40% target before 2040.

Havalimanı Hizmet Kalitesinin Değerlendirilmesinde Yeni Bir ÇKKV Yaklaşımı: MEREC Tabanlı MARCOS ve CoCoSo Yöntemleri

Service quality is seen as an important corporate strategy to ensure competitive advantage. With the increase in demand for air transport, airports have become an important venue, which has brought the issue of airport service quality to the forefront. In airports, which are described as the doors of cities opening to the world, the quality of service is determined if the service provided to passengers coincides with the level of meeting the expectations of the passenger. In this context, within the scope of the study, it is aimed to measure the quality of service by evaluating 17 airports that are evaluated as five-star airports by Skytrax in 2021 based on 11 criteria. While the importance of the criteria was determined with MEREC from the MCDM methods, the ranking of 17 airports according to the quality of service was obtained with MARCOS and CoCoSo methods. Criteria used to assess the quality of service of airports; transportation services, security screenings, immigration services, signs, arrival services, departure services, transfer passenger services, terminal comfort level, terminal facilities, shopping facilities and food/beverage services. As a result of the analysis made by the MEREC method, it was determined that the most important criterion affecting the quality of service at airports was the immigration services criterion. According to the joint results of both MARCOS and CoCoSo methods, it was determined that Chubu Centrair Airport was the airport that offered the best service quality to passengers, and Tokyo Haneda Airport was the last airport in terms of service quality.

Policy impacts on the propensity to travel by HSR in the Amsterdam – London market

This paper investigates the impacts of possible measures to enhance HSR market share in the London-Amsterdam market. To this aim, we adopted a two-level aggregate nested logit model able to jointly model trip distribution and trip generation, thus taking into account that the tested policies may increase or decrease the overall demand. The model was applied to an extensive dataset about passenger flows traveling between London airports and HSR station and Amsterdam in the period 2015–2019. The results show that both the reduction of HSR fares and the application of an air ticket tax, albeit with different impacts in terms of stimulus or reduction effect on overall demand, are ineffective in increasing HSR market share, if not adequately supported by improving HSR service. Increasing HSR frequency and reducing HSR travel times constitute the best opportunities to increase HSR ridership by stimulating a higher substitution effect than modifications in relative fares. Lastly, the recent queueing at airports, following staff shortages and strikes, significantly lower air transport demand and potentially has a substantial upward effect on HSR market share.

Analysis and Forecasting of International Airport Traffic Volume

Globalization has resulted in increases in air transportation demand and air passenger traffic. With the increases in air traffic, airports face challenges related to infrastructure, air services, and future development. Air traffic forecasting is essential to ensuring appropriate investment in airports. In this study, we combined fuzzy theory with support vector regression (SVR) to develop a fuzzy SVR (FSVR) model for forecasting international airport traffic. This model was used to predict the air traffic volumes at the world’s 10 busiest airports in terms of air traffic in 2018. The predictions were made for the period from August 2014 to December 2019. For fuzzy time series, the developed FSVR model can consider historical air traffic changes. The FSVR model can suitably divide air traffic changes into appropriate fuzzy sets, generate membership function values, and establish fuzzy relations to produce fuzzy interpolated values with minimal errors. Thus, in the prediction of continuous data, the fuzzy data with the smallest errors can be subjected to SVR to find the optimal hyperplane model with the minimum distance to the appropriate support vector sample points. The performance of the proposed model was compared with those of five other models. Of the compared models, the FSVR model exhibited the lowest mean absolute percentage error (MAPE), mean absolute error, and root mean square error for all types of traffic at all of the airports analyzed; all of the MAPE values were below 2.5. The FSVR model can predict future growth trends in air traffic, air passenger flows, aircraft flows, and logistics. An airport authority can use this model to analyze the existing operational facilities and service capacity, find bottlenecks in airport operations, and create a blueprint for future development. The findings revealed that implementing a hybrid modeling approach, specifically the FSVR model, can significantly enhance the performance of the SVR model. The FSVR model allows airlines to predict traffic growth patterns, identify viable new destinations, optimize their schedules or fleet, make accurate marketing decisions, and plan traffic effectively. The FSVR model can guide the timely construction of appropriate airport facilities with accurate predictions. Rapid, cost-effective, efficient, and balanced transportation planning enables the provision of fast, cost-effective, comfortable, safe, and convenient passenger and cargo services while ensuring the proper planning of the airport’s capacity for land-side transportation connections.

Optimizing Aviation Maintenance through Algorithmic Approach of Real-Life Data

The aviation industry has been undergoing significant changes in recent years, driven by technological advancements and the increasing demand for safe and efficient air transportation. One area that has seen significant advancements is aircraft maintenance, with the implementation of progressive technologies playing a crucial role. The use of digital technologies has revolutionized the way aircraft maintenance is carried out. For instance, the adoption of predictive maintenance algorithms has enabled airlines to predict when their aircraft will require maintenance, reducing downtime and increasing operational efficiency. This has been made possible by the integration of real-time data. Another technology that has transformed aircraft maintenance is the use of augmented reality and virtual reality. These technologies allow maintenance engineers to carry out procedures with greater accuracy and efficiency, as they can see instructions and parts overlaid on their real-world view. This has been particularly useful for complex and critical tasks, where human error could result in significant consequences. The algorithmization and digitalization of processes is one of the many pillars of the Industry 4.0 concept, which, if used correctly, can dramatically enhance the execution of processes. The algorithmic approach presented in this study can be easily adapted to other industries, and could potentially lead to similar optimizations in other fields. Overall, the implementation of progressive technologies in aircraft maintenance has improved safety, reduced downtime, and increased operational efficiency.

The value of time and the impact on the air freight product portfolio - A study of the South Korean market 2017–2021

Traditionally, time was a crucial factor in determining competitiveness in the air cargo market. This study demonstrates that products whose value changes drastically over time could increase the air transport demand. This study represents that the shipper having products with enormous time sensitivity is more willing to endure the higher fare, indicating the possibility that time-sensitive products will drive the air cargo market's growth. This study also provides empirical evidence for the influence of time factors by showing that time-sensitive products are being transferred from sea to air transport based on ICN and South Korean data. The analysis results are as follows. First, an emerging type of cargo, not a traditional type of cargo, has a significant effect on increasing air transport share. The emerging type of items appeared before Covid-19, and Covid-19 has accelerated their growth. Second, even if airfares increase relatively, the proportion of air transportation does not decrease. It seems to be the result that an emerging type of cargo with higher time sensitivity significantly affects the recent increase in air freight volume. Demand increases in an emerging type of cargo, more sensitive to time, can affect the rise in the air transport share, restricting the negative effect from a relative increase in air freight rates. Last, Covid-19 significantly affects the increase in air transport share. In this regard, this study provides practical implications for the recent growth drivers of the air cargo market. Especially in addition to variables traditionally considered to impact air freight volumes, such as income and price, this study suggests changes in transport items as a growth driver for the recent air cargo market.