Major Airlines Research Articles

Understanding Airline Passengers during Covid-19 Outbreak to Improve Service Quality: Topic Modeling Approach to Complaints with Latent Dirichlet Allocation Algorithm.

The COVID-19 pandemic has deeply affected the airline industry, as it has many sectors, and has created tremendous financial pressure on companies. Flight bans, new regulations, and restrictions increase consumer complaints and are emerging as a big problem for airline companies. Understanding the main reasons triggering complaints and eliminating service failures in the airline industry will be a vital strategic priority for businesses, while reviewing the dimensions of service quality during the COVID-19 pandemic provides an excellent opportunity for academic literature. In this study, 10,594 complaints against two major airlines that offer full-service and low-cost options were analyzed with the Latent Dirichlet Allocation algorithm to categorize them by essential topics. Results provide valuable information for both. Furthermore, this study fills the gap in the existing literature by proposing a decision support system to identify significant service failures through passenger complaints in the airline industry utilizing e-complaints during an unusual situation such as the COVID-19 pandemic.

An Aero-Engine Damage Detection Method with Low-Energy Consumption Based on Multi-Layer Contrastive Learning

The health of aero-engines is pivotal to the safe operation of aircraft. With increasing service time, the internal components of the engine will be damaged by threats from different sources, so it is necessary to regularly detect the damage inside the engine. At present, most of the detection methods of major airlines rely on the internal images of the engine obtained by manual use of a borescope to detect damage or traditional machine learning methods, which consume high levels of human and computational resources but have low efficiency. Artificial intelligence in various fields can achieve better performance than traditional methods, but to achieve the industrialization standard of Green AI, we need further research. Accordingly, we introduce a multi-layer contrastive learning method to a lightweight target detection model design, which is applied to real aero-engine borescope images of complex components to accomplish real-time damage detection. We intensively conduct comparative experiments to evaluate the effectiveness of our method. The verification results demonstrate that the method can help our model perform excellently compared with other available baseline models.

Voice in Words: A Mixed-Method Approach for Decoding Digital Footprints Using Online Reviews

ABSTRACT Using a mixed-method technique, the present study investigates the causal relationship between consumer judgments about airline service quality and their online recommendations. It also examines the essential attributes that determine key airline service quality dimensions. The current research was conducted on passenger reviews, ratings, and recommendations data (1270 business class and 2464 economy class passengers) from Skytraxratings.com of three major airlines using logistic regression and sentiment analysis. The empirical investigation of service ratings depicts that the fitted logit model adequately explains airline service quality–airline recommendation relationship for business and economy class travelers. Furthermore, this study identifies the crucial service attributes and their sentiment strengths toward key service dimensions. The study results enable a comprehensive understanding of airline service quality based on real-time consumer insights, which can help strategize and develop customized interventions for airline service designs and realignments.

항공사 객실승무원의 서번트리더십 지각이 상사신뢰와 직무성과에 미치는 영향: FSC를 중심으로

The purpose of this study was to investigate the relationship between servant leadership, supervisor trust, and job performance of airline crews working for large domestic airlines. This study collected data through 293 surveys prepared by cabin crew through a total of 370 surveys distributed to the two major full-service airlines in Korea based on structural equation modeling using AMOS. We also used a quota sampling scheme within the non-probability sampling model. The main results derived through this study are as follows. First, it was found that servant leadership had a significant effect on supervisor

An examination of the potential impact of 5G on air travel in the U.S.

The rollout of 5G wireless service has been much anticipated by consumers across the U.S. since it will bring faster data download speeds and expanded service; however, the impact to aviation and air travel are as a major issue and the potential impact to aviation has not been estimated or assessed. This paper fills that gap and provides the first analysis of the potential number of flights impacted by 5G at ten of the busiest airports in the U.S. Using detailed historical weather and flight data in 2019 at 10 large hub airports, we find that nearly 15,700 flight arrivals could be directly impacted annually, at the ten airports considered. The geographical and seasonal impact varies significantly with more flights affected by the potential for 5G interference at airports in Seattle and Dallas Fort-Worth and relatively few flights affected at airports in Phoenix and Las Vegas. A case study examining ten years of data at Chicago O’Hare International, a hub for two major airlines, suggests that over 2,400 flights every year could be impacted by 5G interference. Fortunately, there has been increasing cooperation between the cell phone industry and aviation stakeholders to ensure a safe rollout of 5G C-band; this paper provides a significant contribution by documenting the issues and context for aviation operations.

Remaining Useful Life Estimation of Cooling Units via Time-Frequency Health Indicators with Machine Learning

Predictive Maintenance (PM) strategies have gained interest in the aviation industry to reduce maintenance costs and Aircraft On Ground (AOG) time. Taking advantage of condition monitoring data from aircraft systems, Prognostics and Health Maintenance (PHM) practitioners have been predicting the life span of aircraft components by applying Remaining Useful Life (RUL) concepts. Additionally, in prognostics, the construction of Health Indicators (HIs) plays a significant role when failure advent patterns are strenuous to be discovered directly from data. HIs are typically supported by data-driven models dealing with non-stationary signals, e.g., aircraft sensor time-series, in which data transformations from time and frequency domains are required. In this paper, we build time-frequency HIs based on the construction of the Hilbert spectrum and propose the integration of a physics-based model with a data-driven model to predict the RUL of aircraft cooling units. Using data from a major airline, and considering two health degradation stages, the advent of failures on aircraft systems can be estimated with data-driven Machine Learning models (ML). Specifically, our results reveal that the analyzed cooling units experience a normal degradation stage before an abnormal degradation that emerges within the last flight hours of useful life.

DYNAMIC MODEL OF PERFORMANCE MEASUREMENT OF MIDDLE EAST AIRLINES

Over the past decades, the rapid development of the airline industry has occurred in the Middle East and particularly in the United Arab Emirates. The need for the development of the aviation sector has emerged due to the strategically important position that Middle East occupies as the central point between the eastern and western hemispheres. Its major airlines, so called Middle East Big Three (MEB3) - Emirates, Etihad and Qatar Airways, have become dominant in the region, as well as across the globe, primarily thanks to the high quality of their services and globally recognized brand. The focus of the assessment are the airlines’ non-financial performance indicators, such as: the number of carried passengers and cargo, number of employees, serving destinations, fleet size, etc. The analysis covered a period of twelve years, starting from 2005 until 2016, which was characterized by the expansion of these airlines. For each observed company, the efficiency was first separately measured using the Data Envelopment Analysis (DEA) method, followed by the dynamic analysis and measurement of their efficiencies using the DEA Window analysis. In both cases, Emirates Airlines had the best performance.

Assessment of in-cabin noise of wide-body aircrafts

The aviation industry has seen dramatic growth over the decades till the recent disruption due to the COVID-19 pandemic. Moreover, long-haul routes with a distance of more than 4000 km are common for major airlines worldwide. Therefore, aircraft cabin noise assessment is essential, especially in long-haul flights, for passenger and flight crew health wellness. In this paper, the cabin noise of five wide-body aircraft, namely Airbus A330-300ER, A350-900, A380–800, and Boeing B777-200ER and B787-900, was recorded using a calibrated in-house developed smartphone application. The sound pressure levels of in-cabin noise have been measured on two different decibel scales, namely, A-weighted [dB(A)] and C-weighted scales [dB(C)]. The sound pressure levels of Airbus A380–800 were lowest among selected models, while the in-cabin pressure level values of Airbus A350-900 were maximum. However, the difference in decibel levels between the aircraft is minimal as it is within 3 dB.

Internet penetration and airline pricing: Evidence from China

This paper investigates the impacts of increasing Internet penetration on airfares and price dispersion in the Chinese airline market. It is found that an increase in Internet penetration is associated with higher average airfares and lower price dispersion between the major Chinese carriers. It appears that the increase in Internet penetration seems to have strengthened the major airlines’ ability to maintain price stability, which is an indication of the existence mutual forbearance among the major carriers confirmed in other studies. Higher prices and lower price dispersion are mostly to occur in the most heavily markets. This research also finds that if carriers possess similar degree of market power, the price dispersion between the airline pair is smaller. The findings can generate important policy insights, and inform anti-trust policies in the post-Covid period, when more consumers use Internet for search and inquiries, and when big data and artificial intelligence technologies mature.

Affability: The Secret Sauce to Ensuring Success

Features| May 2022 Affability: The Secret Sauce to Ensuring Success Shena J. Scott, MBA, FACMPE; Shena J. Scott, MBA, FACMPE Search for other works by this author on: This Site PubMed Google Scholar Phillip J. Richardson, MD, MBA, FASA, FACHE, FAACD, CPE Phillip J. Richardson, MD, MBA, FASA, FACHE, FAACD, CPE Search for other works by this author on: This Site PubMed Google Scholar ASA Monitor May 2022, Vol. 86, 32–33. https://doi.org/10.1097/01.ASM.0000830848.15474.3c Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Cite Icon Cite Get Permissions Search Site Citation Shena J. Scott, Phillip J. Richardson; Affability: The Secret Sauce to Ensuring Success. ASA Monitor 2022; 86:32–33 doi: https://doi.org/10.1097/01.ASM.0000830848.15474.3c Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsASA Monitor Search Advanced Search Topics: anxiety, compassion, creativity, listening, nurses, practice management, surgical procedures, operative, burnout, disruptive behavior, medical errors A classic adage for ensuring success as an anesthesiologist are the three A's – being Able, Available, and Affable. In short, it suggests that success in one's career can be ensured if one is capable, takes time, and is friendly. Yet are they all equal? Some would suggest “ability” is mission critical, but it is assumed in our industry, just like safely flying on major airline carriers. Patients (consumers), surgeons, and administrators similarly take for granted that their physicians are competent. Likewise, “availability” is just the starting point. If there aren't enough people to “cover the shop,” the “shop” will find a team that can be available. However, being affable is an often-overlooked key element in physician-patient relationships as well as in business encounters. Affability is defined as the quality of having a friendly and good-natured manner. While that may sound simple, there is a lot to explore beneath the... You do not currently have access to this content.

Major court and tribunal decisions in Australia in 2021

This 2021 annual survey of Australia's significant court and tribunal decisions spans five key areas. First, the focus is on the High Court's clarification around how employment status is to be determined. Secondly, it examines decisions that have continued to arise out of the Coronavirus pandemic with respect to dismissals stemming from mandatory vaccination policies, an employer's liability in respect of an employee's death caused by Coronavirus, as well as a series of cases involving Australia's major airline, Qantas Airways Ltd (Qantas). Thirdly, consideration turns to developments in general protections and the first application for a stop sexual harassment order made to the Fair Work Commission. Fourthly, it reviews recent decisions concerning out-of-hours conduct and connection to employment. Finally, decisions giving rise to record penalties for contraventions of the Fair Work Act 2009 (Cth) (FW Act) are evaluated.

Rethinking the China-U.S. Bilateral Air Services Agreement from China’s Perspective: Approaches to Fair Competition

Bilateral relations between the People’s Republic of China (PRC) and the United States (U.S.) have never attracted the world’s attention more than today. This article analyses how two countries that are totally different in societies, economic systems, cultures, and values compete in the China-U.S. bilateral air services. With the trend of the liberalization as well as privatization of international air transport, the U.S. and China show two development models that influence their aviation sectors: complete liberalization vs. Socialist Market Economy (SME). The article focuses on the competition issues embodied in the China-U.S. bilateral air services and provides approaches to fair competition between the U.S. airlines and three major Chinese airlines impacted by China’s SME and the ‘Reform and Opening-up’ policy. The article further discusses the opportunity for deeper cooperation through a China-U.S. Open Skies agreement with convergent legal, economic, and institutional regimes. China, Fair Competition, Air Services Agreement, Open Skies

Airline Disruption Management: A Naturalistic Decision-Making Perspective in an Operational Control Centre

Operations Control Centres (OCCs) are critical structures used by airlines to oversee the execution of all planned flights, managing punctuality, regularity and customer support. In this study, we investigated the decision-making during flight disruptions inside an OCC from the naturalistic decision-making perspective. We conducted a mini-ethnography case study in a major South American airline, focussing on how functions critical to the flight disruption management cope with variability. Data collection included document analyses, field notes, direct observations and interviews. The functional description of work-as-done revealed how the OCC constantly and actively looks for signs of disruption while monitoring the normal operation and rebalancing resources. The decision-making process is distributed and decentralised across multiple functions, where experts from each function rely on a repertoire of strategies to deploy innovative solutions to dynamic scenarios. Five different mechanisms were identified that converge functions to disarm potential disruptions before they compromise the flight network, and continuously create and reinforce system buffers.

A multilayered block network model to forecast large dynamic transportation graphs: An application to US air transport

Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art probabilistic latent network model to forecast multilayer dynamic graphs that are increasingly common in transportation and proposes a community-based extension to reduce the computational burden. Flexible time series analysis is obtained by modeling the probability of edges between vertices through latent Gaussian processes. The models and Bayesian inference are illustrated on a sample of 10-year data from four major airlines within the US air transportation system. Results show how the estimated latent parameters from the models are related to the airlines’ connectivity dynamics, and their ability to project the multilayer graph into the future for out-of-sample full network forecasts, while stochastic blockmodeling allows for the identification of relevant communities. Reliable network predictions would allow policy-makers to better understand the dynamics of the transport system, and help in their planning on e.g. route development, or the deployment of new regulations.

Revenue efficiency across airline business models: A bootstrap non-convex meta-frontier approach

The intense price competition has hurt the revenue performance of the U.S. airline industry. In this context, network-legacy carriers (NLCs), low-cost carriers (LCCs), and ultra-low-cost carriers (ULCCs) in the U.S rely on different competitive advantages to create and promote their revenue efficiency. This study investigates the revenue efficiency differentials of the three airline business models in the U.S – NLC, LCC, and ULCC, using a two-stage data envelopment analysis (DEA) approach. In the first stage, a bootstrap non-convex meta-frontier framework is developed to estimate the DEA-based revenue efficiency of the airlines adopting different business models. In the second stage, a bootstrap truncated regression is employed to explain the differences in the airlines' revenue efficiency. Based on a data sample of the major airlines in the U.S. from 2015 to 2019, the study reveals the following findings: (1) The revenue efficiency varied significantly across the three airline business models. In which, ULCC type exhibited the lowest revenue efficiency; (2) Due to technological constraints, ULCCs averagely collected less than 17.6% of their potential revenue during the study period. Both LCC and NLC types demonstrated the most advanced technologies in generating revenue; however, NLCs generally outperformed LCCs; (3) Service quality, operational size, and airfare significantly impacted the airlines’ revenue performance.

Galactic Air Improves Ancillary Revenues with Dynamic Personalized Pricing

Ancillaries are a rapidly growing source of revenue for airlines, yet their prices are currently statically determined using rules of thumb and are matched only to the average customer or to customer groups. Offering ancillaries at dynamic and personalized prices based on flight characteristics and customer needs could greatly improve airline revenue and customer satisfaction. Through a start-up (Deepair) that builds and deploys novel machine learning techniques to introduce such dynamically priced ancillaries to airlines, we partnered with a major European airline, Galactic Air (pseudonym), to build models and algorithms for improved pricing. These algorithms recommend dynamic personalized ancillary prices for a stream of features (called context) relating to each shopping session. Our recommended prices are restricted to be lower than the human-curated prices for each customer group. We designed and compared multiple machine learning models and deployed the best-performing ones live on the airline’s booking system in an online A/B testing framework. Over a six-month live implementation period, our dynamic pricing system increased the ancillary revenue per offer by 25% and conversion rate by 15% compared with the industry standard of human-curated rule-based prices.

Robust long-term aircraft heavy maintenance check scheduling optimization under uncertainty

Long-term heavy maintenance check schedules are crucial in the aviation industry since airlines need them to prepare the required maintenance tools, workforce, and aircraft spare parts. However, most airlines adopt a manual approach to plan the heavy maintenance check schedules in current practice. This manual process relies on the experience of their maintenance planners, and the resulting heavy maintenance schedules need frequent adjustment because of uncertainty. This paper applies a genetic algorithm (GA) to generate robust aircraft heavy maintenance check schedules. It aims to reduce the workload and the frequency of revising heavy maintenance schedules considering uncertainties associated with heavy maintenance check duration and aircraft daily utilization. A major European airline case study shows that the GA finds robust and efficient multi-year aircraft heavy maintenance schedules for a fleet of 45 aircraft in 30 min. Compared with the current approach followed by the airline, the algorithm reduces the total number of heavy maintenance checks by 7% while increasing utilization by 4.4%, which could potentially lead to a reduction of direct annual maintenance costs between $122.5K and $612.5K. Furthermore, when testing the robustness of the 4-years maintenance check schedules produced, a Monte Carlo analysis has shown that all aircraft could be maintained before their check due date for 41% of the episodes simulated, compared to 0.27% of the episodes for the single deterministic scenario approach.

Why Do Unsuccessful Companies Survive? U.S. Airlines, Aircraft Leasing, and GE, 2000–2008

Warren Buffett famously commented that the U.S. airline industry had made zero profit in its first nine decades. Subsequently, between the millennium and the Great Financial Crisis the airlines in total lost almost $60 billion. Yet no major airline was liquidated or taken over in those nine years. Financial support was repeatedly provided by GE, the conglomerate supplier of leasing finance, engines, and servicing. The article offers a historical perspective on the factors behind this relationship between GE and airlines. It outlines the benefits or costs to GE, airline shareholders, and passengers; the relevance of the model for other industries; and implications for different notions of efficiency.

A Data-drive Approach for Robust Cockpit Crew Training Scheduling

This work addresses the cockpit crew training scheduling problem. The objective is to produce a robust cockpit crew training schedule, including the assignment of trainees, instructors and simulators. To attain this objective, we propose a scheduling framework composed of four modules: a Training Scheduling & Assignment Model (TS&AM), a Disruption Generator (DG), a Rule-Based Recovery (RBR) algorithm, and a Neural Network (NN). The TS&AM is an integer programming model that integrates the scheduling of courses and the assignment of resources. The output roster serves as input for a data-driven DG based on Monte-Carlo Simulation. The disruptions are then solved using the RBR algorithm. Finally, The NN feedback algorithm learns the recovery costs experienced in the disruption impact simulator and updates these costs in the TS&AM to generate more robust rosters. The proposed modelling framework was calibrated, tested, and demonstrated in a simulation environment developed using four years of historical crew training data from a major European airline. The experiment showed that our approach outperformed the roster produced by the airline. The approach proposed produces rosters that reduce recovery costs by 21 percent, while still decreasing total training costs by 3 percent.

A Panel Study of Outsourced Maintenance Impact on Major U.S. Passenger Airlines' Profitability (1995-2019)

This study investigates eight viable United States major passenger airlines' outsourced maintenance impact on profitability between 1995 and 2019 by using panel data analysis. The results demonstrate that the percentage of airline outsourced maintenance and inhouse maintenance labor pay have no statistically significant impact on profitability. The researchers call for the further research with a larger sample, and more time periods to explore airlines' outsourced maintenance impact on profitability.