Air Passenger Research Articles

The Regulatory Standards of Extraordinary Circumstances in Carriage of Passengers by Air: An Analysis of EU law and Implications for Korea

Purpose - The purpose of this paper is to review the statutory framework and Court of Justice of the European Union (CJEU) case law concerning the extraordinary circumstances under EC 261/2004 Regulation (the Regulation). This paper also conducts an examination of the compensation liability system of air carriers within the context of Korean aviation transportation laws.
 Design/Methodology/Approach - This paper studies the scopes, application, and requirements of the extraordinary circumstances in EC 261/2004 Regulation by analyzing cases of CJEU. It seeks to conduct a detailed analysis of extraordinary circumstances in the Korean Aviation Business Act, and contains a comparative analysis between the Regulation and the stance of Korean law.
 Findings - This paper confirms that previous CJEU cases have taken a stringent and passive stance in recognizing extraordinary circumstances that exempt carriers from compensation liability. Also, it presents some critical views against CJEU perspectives and offers several implications for Korean law in its conclusion.
 Research Implications - This paper sheds light on legal issues surrounding applications of the extraordinary circumstances in EC 261/2004 Regulation via case law review. In conclusion, it emphasizes the need for amending the Regulation by explicitly specifying the criteria for the application of extraordinary circumstances to better ensure the appropriate fulfillment of air passenger rights protection. This suggests that it is essential to establish uniform legal standards for procedures related to compensation for air passengers in cases of denied boarding, flight cancellation, or flight delays, rather than solely relying on the flight compensation terms and conditions in Korea.

Modeling joint row- and column-wise correlation in air passenger seat selection: A cross-nested logit approach

Advance seat selection (ASS) fees have gradually become an important revenue generator for the carriers worldwide. In this study, we investigate air passenger seat selection behavior in the ASS service while quantifying spatial correlation structure among seats, which is novel to the literature. Specifically, we employ a Cross-Nested Logit (CNL) model that captures the joint row- and column-wise correlation among seats. The CNL model, together with the Multinomial Logit and Nested Logit models involved as benchmarks, are applied to a rich dataset of 321670 ASS records, extracted from the database of a Chinese regional carrier. The empirical results suggest that the correlation among seats arises from their similarities in both row and column dimensions, with a stronger correlation in the column dimension. And modeling such joint correlation can not only enhance the in-sample model fit, but also facilitate more accurate out-of-sample revenue prediction. Moreover, the utility parameter estimates reveal that passengers exhibit non-linear price sensitivity, representable by a power function with a rational exponent of 0.37. Additionally, passengers display heterogeneous willingness to pay influenced by their flight, booking, and personal characteristics. And seat preferences can be affected by participating in loyalty programs.

An integrated vertiport placement model considering vehicle sizing and queuing: A case study in London

The increasing level of congestion and infrastructure costs in cities have created a need for more intelligent and flexible transport systems. Urban Air Mobility (UAM) introduces the third dimension to intra-urban transport at a minimal infrastructure cost, bypassing congestion and providing reliable travel times to users through the provision of air passenger transportation. The performance of UAM systems is highly dependent on vertiport locations, vehicle sizing and infrastructure specifications, which themselves are intrinsically linked. This study takes a holistic approach to UAM network optimisation by considering the inter-relatedness of these decisions in a three-stage algorithm. In the first stage, a linear vertiport placement model with vehicle sizing constraints is developed to determine the optimal vertiport configuration while considering eVTOL performance. The vertiport configuration serves to determine the operational requirements of the aircraft and are incorporated in the vehicle sizing models. The resulting network configuration and vehicle sizing constraints are used as inputs in the infrastructure model, which utilises open network theory to determine the service rate requirements and the allowed loiter and waiting times based on the number of take-off, landing, and charging pads at each vertiport. These stages are executed sequentially through a feedback mechanism, which balances the infrastructure, operational costs as well as passenger waiting times. The algorithm optimises the profitability of the UAM network ensuring all operational constraints are satisfied. A case study based on the hypothetical implementation of UAM in the city of London is presented using Rolling Origin and Destination Survey data to estimate demand patterns. Our results suggest that ignoring vehicle sizing and infrastructure modelling in the network optimisation stage results in infeasible UAM networks. Furthermore, reducing waiting times and loiter times are critical to reduce operational costs of the UAM network, with all optimal configurations yielding waiting times below 5% of the total flight time. The proposed method can be used to plan future UAM developments whilst ensuring operational viability.

A Study on the Changes in Safety Perception of Air Passengers in the Living with COVID-19 Era: The Case of South Korea

Due to changes brought upon by covid in society, the aviation industry has also entered a new phase that is different from the early days of the COVID-19 outbreak. Therefore, this study aims to enhance our understanding of people's response to the COVID-19 pandemic and their perception regarding aviation safety. Specifically, the study investigates whether in-flight services focusing on sanitation and preventative measures against the virus alleviated negative emotions such as fear and anxiety among passengers, and how this affected their risk perception and group efficacy through cognitive value. To achieve the purpose of the study, an online survey was conducted in December 2021 of passengers with experience boarding aircraft after the COVID-19 outbreak, and 211 valid responses out of 308 were used for analysis. The analysis of the data revealed that in-flight services had a positive effect on cognitive value, which in turn had a positive effect on risk perception and collective efficacy among air passengers. The overall evaluation of in-flight services indicated that passengers now place a greater emphasis on hygiene and quarantine measures. Furthermore, there has been a notable promotion of passengers' efforts and attitudes towards not only ensuring their own safety but also the safety of others and society, which has resulted in a significant improvement in responsible behavior. Key Words: cognitive value, group-efficacy, collective efficacy, safety perception, inflight service, airline service quality

Framing your concerns right: an analysis of air passengers' complaints during two time periods

PurposeConsumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from businesses. Hence, in this research the authors aim to explore the effect of the complainant's social characteristics and the complaint's social and content characteristics on the likelihood of receiving a response to a grievance from the business on social media.Design/methodology/approachThe authors build a conceptual model and then empirically test it to explore the effect of the complainant's characteristics and the complaint's characteristics on the likelihood of response from a business on social media. The authors use data of consumer grievances received by an Indian airline operator on Twitter during two time periods – the first corresponding to lockdown during Covid-19 pandemic, and the second corresponding to the resumption of business as usual following these lockdowns. The authors use logistic regression and the hazard rate model to model the likelihood of response and the response delay, respectively, for social media customer grievances.FindingsComplainants with high social influence are not more likely to get a response for their grievances on social media. While tagging other individuals and business accounts in a social media complaint has negative effect on the likelihood of business response in both the time periods, the effect of tagging regulatory bodies on the likelihood of response was negative only in the Covid-19 lockdown period. The readability and valence of a complaint were found to positively affect the likelihood of response to a social media grievance. However, the effect of valence was significant only in lockdown period.Originality/valueThis research offers insights on what elicits responses from a service provider to consumers' grievances on social media platforms. The extant literature is a plenty on how firms should be engaging consumers on online media and how online communities should be built, but scanty on grievance redressal on social media. This research is, therefore, likely to be useful to service providers who are inclined to improve their grievance handling mechanisms, as well as, to regulatory authorities and ombudsmen.

The Balance in Consumer Protection Between Substantive Law and Enforcement

Traditionally, consumer law has suffered from underenforcement in many countries. Reasons are, in particular, insufficient incentives or means for individual enforcement, the lack of effective collective remedies and/or insufficient resources or the lack of interest of public authorities and/or consumer organizations. Thus, even where strict rules on substantive consumer protection exist, authors diagnosed an incentive not to comply with the law. Quite recently, the picture has changed, at least in certain sectors of business. This is mainly due to technological advancement. The use of digital technology, commonly referred to as ‘legal technology’ or ‘legal tech’, allows the legal profession to drastically increase the efficiency in handling claims. Legal tech claims management companies have been particularly successful in the area of air passengers’ rights but the use of legal tech has also facilitated the mass handling of similar claims, for example, resulting from the Volkswagen diesel scandal. This latter development led to a debate about „overenforcement“. It has been argued that a massive increase in enforcement of existing consumer law may necessitate the relaxation of the substantive law standard. This article discusses the relationship between substantive consumer law and its enforcement, thereby using examples from recent case law and from the current political debate.

Yolcuların Havayolu Seçim Kriterlerinin Değerlendirilmesi

Worldwide, with 100,000 to 200,000 flights per day, air traffic and passenger traffic have been increasing every day. As air traffic and passenger volumes increase, competition among airlines have also intensifying, and airlines develop and implement different strategies that take into account the wishes, needs and requirements of passengers to be prefer by passengers. In this study, the criteria affecting the airline preference of the passengers for domestic flights were determined and analyzed, based on the criteria that the airlines, which implement different strategies in order to be prefered, take into account. While determining the criteria, the airline preference criteria of the passengers on domestic flights (Turkey) were taken as basis. As a method, multi-criteria decision making technique, one of the qualitative research designs, was used. The data were obtained using the structured interview technique and the obtained data were analyzed using the Analytical Hierarchy Process (AHP) method, which is one of the widely used multi-criteria decision making techniques. As a result of conducted the analyzes, it has been seen that the price criterion is the criterion that the passengers pay the most attention when choosing an airline

Assessing the economic impact of the single African air transport market: The case of Tanzania

Tanzania is one of the 21 (out of 54) African countries that are yet to ratify the Single African Air Transport Market (SAATM) launched in 2018 as the first project of the African Union's agenda 2063. SAATM aims to create a single unified air transport market for Africa thereby opening skies across the whole continent and aiding the economic integration agenda. Using the two-stage least squares estimator that is robust to endogeneity bias, this study empirically assesses the economic effects of liberalization of air services between Tanzania and other African countries. The results show that removing restrictions on designation, flight frequency, and capacity for air transport between Tanzania and other parts of Africa would lead to a rise in the number of air passengers. Specifically, the results suggest that between 2010 and 2018, routes linking Tanzania and other African countries that were governed by liberal bilateral air services agreements (BASAs) enjoyed slightly lower fares compared to routes governed by restrictive BASAs. The results also suggest that air travel demand is not very sensitive to fare implying that the increase in the flow of passengers by an average of 16.9% between 2010 and 2018 can be attributed to the enhancement in service quality (as measured by flight frequency), and increased competition resulting from the entry of new players or intensified competition among existing ones. The policy implication of this study might be the need for Tanzania to embrace the SAATM initiative.

The impact of COVID-19 pandemic on air passenger travel: a focus on empirical findings

ABSTRACT The COVID-19 pandemic has had a profound impact on the aviation sector. Correspondingly, Extensive research has explored various perspectives on the effects of the pandemic on the aviation sector. With the pandemic gradually coming to an end, it is important to conduct a detailed review of the literature and analyze past events carefully. This paper presents a systematic review of the literature on the impact of COVID-19 on air passenger travel at different stages. It identifies potential direct effects and indirect effects of the COVID-19 pandemic. It also investigates the two stages of the pandemic-aviation relationship, providing insights into how the relationship has evolved over time. Additionally, the research summarises key findings on the effects of COVID-19 on air travel demand and supply. These findings encompass various aspects, including travel demand, airport operations, airline operations, and network operations. The paper concludes by suggesting that ongoing analysis of the pandemic’s effects can inform future policies and measures that can help the aviation industry recover and thrive.

An Approach for Quick Method of Estimating Passenger And Aircraft Demand for Feeder Air Services in India

The recent and the proposed entry of new airlines both in the ‘no-frills’ and in the "full service" categories has completely changed the aviation scenario in India. Obviously this explosive increase in capacity will have a profound effect on the economy of the airlines both in the short term as well as in the long-term period. A growth of over 25% per annum in passenger traffic may not be sustainable for long; thus galloping increase in capacity may result in mergers and strategic alliances of airlines on one side and closing down of some of the airlines on the other side. A point, which is to be noted is that, most of the new entrants also operate in the major trunk routes and serve the metros and the major cities. What appears to have not been tapped is the equally large potential in the regional and short haul sectors, which serve major, and small city pairs and also act as feeders to the major hubs.Realizing the importance of this, the government in its new civil aviation policy has given thrust to regional/ short-haul air travel. In this paper, an attempt is made to review the hierarchy of airline networks in India with respect to trunk, major and feeder routes. The air passenger traffic carried in terms of revenue passenger kilometers (RPK) is compared with upper class rail traffic. The present status of feeder airline services in India is outlined. It is pertinent to study the passenger traffic demand in these feeder routes as compared to upper class rail traffic as some percentage of upper class rail passengers are willing to shift to air travel due to marginal difference between airfare and upper class rail fare and time factor. The future RPKs are forecasted based on the actual RPKs data and the estimated RPKs of earlier market survey. A suitable aircraft size for airline operations in these feeder routes is arrived at based on existing air traffic data by a simplified approach from the basic principles of airline business. A macroscopic measure is used to determine the aircraft demand by assuming average block speed and utilization for each aircraft type. A sensitivity analysis was carried out for various levels of traffic growth keeping in view the sharp increase of air travel in recent years and further growth in air traffic fuelled by feeder sector as the hinterland opens up industry, services and tourism.

Effect of Tourism and Air Travel Restrictions on the Initial International Spread of the Covid-19 Pandemic

The study aims to model the initial international spread of coronavirus disease 2019 (COVID-19) and analyze the risk of early initial spread using data from 105 countries. A generalized linear model (GLM) is developed to analyze the relationship between the initial international spread and its factors. Findings showed that the countries with high tourism activity are affected early by COVID-19, and countries with a better quality healthcare system can adequately detect the spread. Countries that promptly restricted air passengers have experienced a delay in the initial international spread. A regression analysis of fatalities during the initial stage suggested that the countries with an early initial spread of COVID-19 coupled with a high proportion of the elderly population possess a relatively high risk of fatalities. The study provides insights into the factors influencing the initial international spread of contagious diseases and the effect of air travel restrictions on the spread. This study can aid in contingency planning during the initial spread of pandemics, especially for countries with high tourism activity.

Inaplicabilidade da clássica doutrina de serviços públicos à disciplina do transporte aéreo de passageiros mesmo com o enquadramento da atividade como serviço essencial

In numerous actions submitted to the Judiciary, the legal nature of passenger air transport is placed at the center of the discussion. Would it be a public service or a private activity? This is the case, for example, among many others, of the thesis defended by the MPF and the DPU, which tried, in various processes, to extend the gratuity for people with special needs granted by law in land and waterway transport to air transport. From the lessons of Leon Duguit, Gaston Jèze and Maurice Hauriou, it is intended to extract the main characteristics of public services listed by the classical doctrine, as an exercise to verify the adequacy of air passenger transport to a classical conception of public services. Starting from the analysis of the constitutional text, and also exposing a doctrinal and jurisprudential divergence in the acts of granting these services, it is intended to face the questioning of how these activities would also be framed within a more modern conception of Administrative Law. The purpose of this analysis is to demonstrate that even if air passenger transport is removed from an outdated conception of public services, means must be given to protect the provision of an undeniably essential activity

Air traffic controllers' mental fatigue recognition: A multi-sensor information fusion-based deep learning approach

With the growing density of air passenger traffic, accurately recognizing the level of mental fatigue (MF) experienced by air traffic controllers (ATCOs) is crucial for developing intelligent ATCOs' mental state monitoring systems, which can achieve a more effective and safer human–machine cooperative pattern. However, the existing methods for recognizing ATCOs' MF face significant challenges due to pattern variations between ATCOs and sensor artifacts. This study introduces a framework for ATCOs' MF recognition, utilizing a deep neural network called RecMF, which incorporates multi-sensor information fusion to enhance the performance of MF detection. Specifically, the RecMF employs an attention-enabled CNN-LSTM architecture that simultaneously captures time-series feature representations of electroencephalogram (EEG) signals and eye movements. To validate the effectiveness of RecMF, a fatigue-inducing experiment is conducted involving 28 subjects who are tasked with performing a series of air traffic control (ATC) tasks. The model's performance is evaluated across various time horizons and typical cognitive tasks to gain insights into its capabilities. The evaluation results indicate that the proposed model outperforms other existing methods, thereby confirming its feasibility and effectiveness. Additionally, the effects of MF on ATCOs' cognitive performance are analyzed using analysis of variance (ANOVA). The results reveal that higher levels of MF significantly reduce ATCOs' reaction speed and operational accuracy.

Reproducible air passenger demand estimation

The availability of passenger demand estimates for air traffic routes is crucial to a plethora of application and research problems ranging from, e.g., optimization of airline fleet utilization to complex simulations of whole air transport systems. However, somewhat surprisingly, such demand estimates appear hard to come by directly or even to generate by means of published models. This is in large parts due to the widespread use of expensive proprietary data (such as airline-specific ticket prices for certain flight connections) which is typically employed both to calibrate demand estimation models as well as to evaluate such models in order to obtain demand estimates for given origin–destination airport pairs. With this work, we propose building a data set for the European air transport system from given base data and automatically extracted and processed data from external sources, all of which are (made) freely available in the public domain and thus enable reproducibility and facilitate comparability of research involving air passenger transportation. Moreover, we challenge the long-standing tradition of calibrating so-called gravity models for demand estimation by standard linear regression. For the European air transport system, using the aforementioned publicly available data, we demonstrate that machine learning models and techniques like neural networks or the “kernel trick” can significantly improve the estimation quality with respect to ordinary least-squares. In fact, our results—the best of which were obtained using our feed-forward neural network model (four hidden layers with tanh and ReLU activations)—achieve a performance at least comparable to what has been reported in earlier works that utilized non-public data. Computer code to generate air passenger demand estimates is made publicly available online along with our base data and implementation to collect and curate external data.

How to make you spend more in the airport? An investigation towards ECSI model on willingness to spend more in airport

This study intends to uncover the potential drivers that motivate air passengers to spend more in the airport. To address the gaps in the literature, this study proposed a model that integrated the European Customer Satisfaction Index (ECSI) and Experience Economy Theory. Data was collected from 341 passengers at Kuala Lumpur International Airport and analyzed using Partial Least Squares - Structural Equation Modelling (PLS-SEM). The structural model results suggested that willingness to spend more depended on the passengers’ satisfaction and perceived value. Experience economy was found to mediate the path between airport image and passenger expectation on perceived value. Implications and suggestions for further research were discussed.

Modelling of Air Passenger Transportation in Russia

The use of economic and mathematical methods of forecasting the results of activities of civil aviation organisations, and in particular assessment of the volume of air passenger traffic is quite relevant due to the importance of operational planning of air transport processes, development of strategic directions, technological and technical renewal of air enterprises.The objective of the study is to plan the traffic flow of air passengers using a regression model, considering the results of multifactorial selection of determinants, particularly distinguishing fundamental macro indicators are distinguished, as well as significant indicators of the aviation market.The study of passenger air transportation was carried out using methods of system analysis, methods of mathematical statistics and econometrics. Modelling of the process of passenger transportation has identified the main determinants that positively or negatively affect the dynamics of air passenger traffic. The multiple regression of the study of the processes of connectivity and synchronicity of changes in development of passenger traffic and selected macro indicators in a generalised form is the sum of vectors of influencing variables adjusted for the calculated coefficients.Six-, four- and three-factor regression models were developed. The three-factor model turned to be more reliable with values most close to actual data. Nevertheless, while applying regression model for forecasting air traffic it is necessary to consider not only theoretical aspects, data of official forecasts of macro indicators but expert opinions as well.

Investigating the impact of transportation system and economic growth on carbon emissions: Application of GMM System for 33 european countries.

In Europe, there has been a significant shift in the movement of people and things. Nonetheless, despite the fact that transportation is an important component of the supply chain, its environmental consequences pose a severe threat to the ecosystem as a whole. As a result, we intend to explore the relationship between transportation, economy, and CO2 emissions. We used the Static method with Pooled OLS, then tested the Granger causality to validate the use of dynamic approach via the GMM system. The major findings revealed that GDP and trade openness had a considerable impact on CO2 emissions. Although the three modes of transportation have different effects on CO2 emissions, road density has a positive and considerable impact on CO2 emissions. The railway network is inversely connected to CO2 emissions. While the quantity of flight passengers has no substantial effect on emissions. In terms of the impulse response function, there is an initial shock in period 2 for the response of air passengers carried to CO2 emissions, followed by convergence back to zero in period 6, whereas road density has a slight decrease in period 2 with a post shock peak in period 4, followed by convergence back to zero in period 5. The variance decomposition results reveal a little increase until the fifth period for road density, air passengers, and trade openness with coefficients equal to 0.0893, 0.636, and 1.573, respectively, after which these three variables offer decreasing coefficients.

A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR THE PURPOSE OF PREDICTING NORWEGIAN AIR PASSENGER TRAFFIC

This analysis aims to provide an overview of potential machine learning algorithms that may aid the aviation industry in predicting future air passenger traffic flow, which can help increase stakeholder value as well as improve customer experiences. A review and discussion of the aviation industry’s past, current, and future challenges is provided, as well as an overview of machine learning algorithms, neural networks, and learning methods. Further, an overview and discussion of the architecture of the Long Short-Term Memory (LSTM) network, Support Vector Regression Machine (SVRM), and Random Forest (RF) algorithms is provided. The comparative analysis provides an overview and comparison of the performance of the LSTM, SVRM, and RF models based on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The dataset used includes the hourly number of passengers from scheduled flights at Oslo Airport Gardermoen for the period of January 1, 2009, to December 31, 2019, including the datetime features such as Time (hour), day, month, and year, as well as the weather features of air temperature and mean wind speed, with a total of 96185 samples. The Long Short-Term Memory model exhibited the highest generalization ability, with a performance evaluation on the testing dataset of 0.00445/0.06667 MSE/RMSE. Additionally, the performance of the SVRM and RF models on the testing dataset is 0.00511/0.07147 and 0.00543/0.07368 MSE/RMSE respectively. In addition to the performance, each of the models’ complexity, stability, and ability to predict the hourly and daily fluctuations of passengers are discussed.

Pertanggungjawaban Pihak Maskapai Terhadap Kehilangan dan Kerusakan Bagasi Menurut Peraturan Menteri No. 77 Tahun 2011 Tentang Tanggung Jawab Pengangkut Angkutan Udara

This research is motivated by the relatively high level of aircraft and passenger security, but it is not comparable to the security of baggage services. The purpose of this research is to describe the form of airline responsibility at Makassar Sultan Hasanuddin International Airport in resolving disputes over loss and damage to passenger baggage, the factors that hinder airline baggage protection, as well as the views of Islamic law in resolving problems regarding forms of airline protection for goods. is lost. passenger baggage. Based on the type of data in this study the authors used an empirical approach with qualitative research data types. The types of materials in this study are primary data and secondary data. Data analysis techniques use three stages of data reduction, data display, and conclusion drawing/verification. The results of the study show that the form of air transport legal responsibility for the loss of damaged passengers is recorded in air transport operated by airlines, namely the airline must provide compensation of IDR 200 thousand per kilogram. , a maximum of IDR 4 million. The inhibiting factors for this protection are the acceptance factor of officers in the field, the engine factor, the negligence factor of the officers, and even the negligence factor of air passengers. In Islamic law, responsibility is known as Mas'uliyyah or Accountability, and the concept of responsibility in Islam is Ta'widh. Ta'widh in language is compensation or compensation.

Possibilities and prospects for airport revenue recovery by applying international strategies of economic development

The global air transportation market has tremendously changed over the past three years. Worldwide quarantine restrictions complicated the process of aeronautical communication both at the domestic and international levels. Lockdown resulted in a sharp decrease in the volume of world passenger traffic - a vital source of airport revenues. This article studied global airport revenues and classified airport services by type and revenues according to the sources of their receipt. The structure of aeronautical and non-aeronautical revenues was given, and the most common airport charges were characterized. The state and forecasts of world passenger air transportation volume during 2015-2025 and the impact of the COVID-19 pandemic on them were studied in this article. In particular, the way it redistributed shares of non-aeronautical revenues with an increase in the share of rental revenues. Attention to the impact of a full-scale Russian invasion of Ukraine on the global aviation industry was paid. The current forecasts for the recovery of the global air transportation market were studied. Analysis of the current state of aeronautical and non-aeronautical airport revenues allowed to identify possible strategies for increasing their volume under post-pandemic aeronautical market recovery conditions. Among the possible measures for airports, the following were highlighted: implementation of dynamic pricing based on demand and airport workload, using the diversification strategy for organizing income sources with a focus on non-aeronautical services and air cargo transportation. The importance of greening economic activity in the long term was noted to ensure the investment attraction of airports.