Search Results (61)

As an energy-intensive industry, the aviation sector’s carbon emissions have drawn significant attention. Against the backdrop of the “dual carbon” goals, how to enhance the carbon emission efficiency of airlines has become an urgent issue to be addressed for both industry development and low-carbon targets. This paper constructs an evaluation system for the carbon emission efficiency of airlines and uses the SBM-DDF model under the global production possibility set, combined with the bootstrap-DEA method, to calculate the efficiency values. On this basis, the fuzzy-set qualitative comparative analysis method is employed to analyze the synergistic effects of multiple influencing factors in three dimensions: economic benefits, transportation benefits, and energy consumption on improving carbon emission efficiency. The research findings reveal that, first, a single influencing factor does not constitute a necessary condition for achieving high carbon emission efficiency; second, there are four combinations that enhance carbon emission efficiency: “load volume-driven type”, “scale revenue-driven type”, “high ticket price + technology-driven type”, and “passenger and cargo synergy mixed type”. These discoveries are of great significance for promoting the construction of a carbon emission efficiency system by Chinese airlines and achieving high-quality development in the aviation industry. Full article

Oil prices are volatile. How does this affect Japanese and South Korean firms? Since they import almost all of their oil, oil price increases may harm their economies. To investigate these issues, this paper examines how oil prices affect sectoral stock returns. Using Hamilton’s method to decompose oil price changes into portions driven by global demand and by oil supply, the results indicate that many sectors in both countries benefit from increases in global aggregate demand that raise oil prices. Many industrial firms in Japan that produce advanced products also benefit from supply-driven oil price changes. The finding that many firms benefit from higher oil prices indicates that blanket subsidies to compensate for oil price increases are unnecessary. Targeted subsidies would be more economical and eco-friendly. Many sectors in Japan and Korea that produce for the domestic economy are harmed by oil price increases. Large oil price swings will continue due to wars, tariffs, geopolitical events, and climate change. These will whipsaw sectors in both countries. To shield their economies from oil price changes, Japan and Korea should invest in technologies to improve wind, solar, and hydro power and should facilitate intra-regional trade in renewables. They should also encourage individual sectors such as airlines, cosmetics, agriculture, hotels, semiconductors, and automobiles to reduce their exposure to fossil fuels and to choose environmentally friendly production methods. In addition, both countries should expedite their targets for achieving carbon neutrality. This paper considers ways to achieve these goals. Full article

The timely identification of probable causes in aviation incidents is crucial for averting future tragedies and safeguarding passengers. Typically, investigators rely on flight data recorders; however, delays in data retrieval or damage to the devices can impede progress. In such instances, experts resort to supplementary sources like eyewitness testimonies and radar data to construct analytical narratives. Delays in this process have tangible consequences, as evidenced by the Boeing 737 MAX accidents involving Lion Air and Ethiopian Airlines, where the same design flaw resulted in catastrophic outcomes. To streamline investigations, scholars advocate for natural language processing (NLP) and topic modelling methodologies, which organize pertinent aviation terms for rapid analysis. However, existing techniques lack a direct mechanism for deducing probable causes. To bridge this gap, this study trains and evaluates the performance of a transformer-based model in predicting the likely causes of aviation incidents based on long-input raw text analysis narratives. Unlike traditional models that classify incidents into predefined categories such as human error, weather conditions, or maintenance issues, the trained model infers and generates the likely cause in a human-like narrative, providing a more interpretable and contextually rich explanation. By training the model on comprehensive aviation incident investigation reports like those from the National Transportation Safety Board (NTSB), the proposed approach exhibits promising performance across key evaluation metrics, including BERTScore with Precision: (M = 0.749, SD = 0.109), Recall: (M = 0.772, SD = 0.101), F1-score: (M = 0.758, SD = 0.097), Bilingual Evaluation Understudy (BLEU) with (M = 0.727, SD = 0.33), Latent Semantic Analysis (LSA similarity) with (M = 0.696, SD = 0.152), and Recall Oriented Understudy for Gisting Evaluation (ROUGE) with a precision, recall and F-measure scores of (M = 0.666, SD = 0.217), (M = 0.610, SD = 0.211), (M = 0.618, SD = 0.192) for rouge-1, (M = 0.488, SD = 0.264), (M = 0.448, SD = 0.257), M = 0.452, SD = 0.248) for rouge-2 and (M = 0.602, SD = 0.241), (M = 0.553, SD = 0.235), (M = 0.5560, SD = 0.220) for rouge-L, respectively. This demonstrates its potential to expedite investigations by promptly identifying probable causes from analysis narratives, thus bolstering aviation safety protocols. Full article

This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, routine transactions but lean towards human agents for nuanced, emotionally complex issues. Our regression findings indicate that perceived benefits and simplicity of tasks significantly boost satisfaction and trust in AI services. Through fuzzy set analysis, we uncover a gradient of preference rather than a stark dichotomy between AI and human interaction. This insight enables airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency. This research not only enriches the theoretical discourse on human–computer interaction in service delivery but also guides practical implementation with implications for AI-driven services across industries focused on customer experience. Full article

Delays are a global issue affecting both airports and airlines. Departure delays are particularly likely to propagate across airports, rendering the entire flight network susceptible to increased delay durations. The delay network and its duration fluctuate daily or even hourly across airports. This study investigates the association between departure delay duration and delay network properties. Using various network metrics, we apply a fixed-effect Prais–Winsten regression model within a panel data framework covering the period from 2018 to 2021 for two full-service carriers in Japan. The key findings reveal that higher in-degree centrality is associated with longer departure delays. Betweenness centrality suggests that, in addition to hub airports, some spoke airports may function as delay bridges, thereby increasing delay durations. Eigenvector centrality is linked to shorter but more frequent departure delays across the network, which are more likely to result in frequent delay propagations of shorter durations. The results indicate that some airports may form delay clusters among themselves, potentially extending departure delay durations among connected airports. During the COVID-19 pandemic, the state of emergency contributed to varying associations between network properties and departure delay durations. These outcomes are expected to provide valuable insights for airline delay and schedule management policymakers. Full article

Small airlines face significant challenges in maintaining operational efficiency and ensuring compliance with regulatory standards due to limited resources, fragmented technical support ecosystems, and high operational costs. Aviation Technical Support as a Service (ATSaaS) offers an innovative, scalable framework to address these issues by providing centralized or decentralized platforms for technical support. This study examines the advantages, disadvantages, and life cycle costs of centralized and decentralized data conversion architectures within the ATSaaS model. Using mathematical models and a numerical example, the research highlights the trade-offs between initial investments and long-term operational costs, with centralized systems benefiting from economies of scale and decentralized systems offering flexibility. This study also identifies limitations and future research directions. Full article

Airport slot allocation is a key short-term solution to address airport capacity constraints, and it has long been a focus of research in the field of air traffic management. The existing studies primarily consider constraints such as airport capacity and flight operations, optimizing the slot allocation of arrival and departure flights to maximize the utilization of airport resources. This study proposes an airline fairness index based on a demand-side value system and addresses the problem of flight slot allocation by developing a tri-objective model. The model simultaneously considers the maximum slot deviation, total slot deviation, and airline fairness. Additionally, dynamic capacity constraints using rolling time windows and constraints on slot migration during peak periods are incorporated. The $\epsilon$-constraint method is employed in conjunction with a large-neighborhood search heuristic to solve a two-stage optimization process, yielding an efficient allocation scheme. The experimental results show that the introduction of rolling capacity constraints effectively resolves the issue of continuous overcapacity that arises when only a fixed capacity is considered. Additionally, the proposed airline fairness index, based on a demand-side value system, can significantly improve fairness during the slot allocation process. By sacrificing at most 16% of the total displacement, it is possible to reduce the unfairness index by nearly 80%. Full article

Global warming and CO₂ emissions have become increasingly pressing concerns, with the aviation industry contributing significantly to these issues. In response, efforts have been made to develop environmentally sustainable aviation solutions. This paper examines the Direct Operating Costs (DOCs) of the Wright Spirit, one of the options for electric flight, compared to the conventional BAe 146 on which the Wright Spirit is based. Utilising methodologies adapted from previous studies (largely the AEA method), the analysis investigates various factors influencing DOC including battery prices, flight duration, and charging time. The results indicate a 73% increase in overall DOCs from the BAe 146 to the Wright Spirit, largely influenced by battery costs and lifespan. However, an 83% reduction in fuel/energy costs suggests the potential viability of the Wright Spirit, particularly with anticipated reductions in battery prices. For instance, a quartering of battery prices could result in a GBP 5 increase in costs for 1 h flights; compared to the BAe 146. Moreover, the analysis finds the battery lifespan and charging time to be the most important factors to control in order to increase commercial feasibility. Ticket price comparisons suggest that the Wright Spirit’s costs align closely with current market prices, with a flight from Paris to Heathrow predicted to cost the airline GBP 136.10 per passenger. Future research could explore alternative electric aircraft designs to further assess their impact on DOCs and ticket prices Full article

The data envelopment analysis (DEA) models have been widely recognized and applied in various fields. However, these models have limitations, such as their inability to globally rank DMUs, the efficiency values are definite numerical values, they are unable to reflect potential efficiency changes, and they fail to adequately reflect the degree of the decision maker’s preference. In order to address these shortcomings, this paper combines possibility theory with self-interest and non-self-interest principles to improve the DEA model to provide a more detailed reflection of the differences between DMUs. First, the self-interest and non-self-interest principles are employed to establish the DEA evaluation model, and the determined numerical efficiency is transformed into efficiency intervals. Second, an attitude function is added to the common possible-degree formula to reflect the decision maker’s preference, and a more reasonable method for solving the attitude function is presented. Finally, the improved possible-degree formula proposed in this paper is used to rank and compare the interval efficiencies. This improved method not only provides more comprehensive ranking information but also better captures the decision maker’s preferences. This model takes preference issues into account and has improved stability and accuracy compared with existing models. The application of the improved model in airlines shows that the model proposed in this paper effectively achieved a full ranking. From a developmental perspective, the efficiency levels of Chinese airlines were generally comparable. Joyair and One Two Three performed poorly, exhibiting significant gaps compared with other airlines. Full article

One of the major issues facing commercial airlines is the time that it takes to board passengers. Further, most airlines wish to increase the number of trips that an aircraft can make between two or more cities. Thus, reducing the overall boarding times by a few minutes will have a significant impact on the number of trips made by an aircraft, as well as enabling improvements in key measures such as the median and 75th and 95th percentiles. Looking at such measures other than the mean is critical as it is well known that the mean can under- or overestimate the performance of any model. While there is considerable literature on the study of strategies to decrease boarding times, the same cannot be said about the study of the boarding time given a particular strategy for boarding. Thus, the focus of this paper is to study analytically (using suitable stochastic models) and numerically the impact of reducing the average time on the key measures to help the system to plan accordingly. This is achieved using a well-known probability distribution, namely the phase type distribution, to model various events involved in the boarding process. Illustrative numerical results show a reduction in the percentile values when the average boarding times are decreased. Understanding the percentiles of the boarding times, as opposed to relying only on the average boarding times, will help management to adopt a better boarding strategy that in turn will lead to an increase in the number of trips that an aircraft can make. Full article

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety. Full article

In recent years, the rapid growth of air traffic has intensified pressure on the air transport system, leading to congestion problems in airports and airspace. The projected increase in demand exacerbates these issues, necessitating immediate attention. Additionally, there is a growing concern regarding the environmental impact of the aviation sector. To tackle these challenges, the adoption of advanced methods and technologies shows promise in expanding current airspace capacity and improving its management. This paper presents an overview of sustainable aviation, drawing on publications from academia and industry. The emphasis is on optimizing both flight and ground operations. Specifically, the review delves into recent advancements in airline operations, airport operations, flight operations, and disruption management, analyzing their respective research objectives, problem formulations, methodologies, and computational experiments. Furthermore, the review identifies emerging trends, prevailing obstacles, and potential directions for future research. Full article

This study highlights the disproportionate number of fatal and non-fatal accidents in general aviation (GA) compared to airline carriers, emphasizing the need to investigate the contributing factors to these incidents. It identifies poor decision-making and a lack of situational awareness as key issues and presents a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to analyze preflight information used by GA pilots. The findings underscore the significance of operational factors in ensuring a successful flight and suggest modifications to pilot license renewal processes, with an emphasis on the adoption of digital preflight tools. A new theoretical framework based on the operational factors identified is also introduced, which could serve as a foundation for future studies and interventions aimed at enhancing safety in general aviation. Full article

In-flight medical incidents are becoming increasingly critical as passengers with diverse health profiles increase in the skies. In this paper, we reviewed how airlines, aviation authorities, and healthcare professionals respond to such emergencies. The analysis was focused on the strategies developed by the top ten airlines in the world by examining training in basic first aid, collaboration with ground-based medical support, and use of onboard medical equipment. Appropriate training of crew members, availability of adequate medical resources on board airplanes, and improved capabilities of dialogue between a flying plane and medical doctors on the ground will contribute to a positive outcome of the majority of medical issues on board airlines. In this respect, the adoption of advanced telemedicine solutions and the improvement of real-time teleconsultations between aircraft and ground-based professionals can represent the future of aviation medicine, offering more safety and peace of mind to passengers in case of medical problems during a flight. Full article

Investors are one of the primary target audiences for corporate communication; they seek non-financial as well as financial information from the companies they invest in, and they consider the social and environmental sustainability of these companies in addition to their economic sustainability. Because of this, as a tool for investor relations, annual reports now routinely and regularly incorporate non-financial information. This study examined thirty annual reports from six public airline firms issued between 2018 and 2022. A total of 8115 expressions on social, economic, and environmental sustainability issues—divided into 125 themes—were coded and analyzed in Maxqda 2020. Among the conclusions was the fact that, in 2022, all of the examined companies devoted 9% of their pages to sustainability statements and included them as an individual topic in their reports. It was found that 64% of both the sustainability-related pages of the reports and the messages of the company managers included in the report consisted of social sustainability statements, with the theme of governance playing an important role in these explanations. Additionally, it was revealed that the topics of governance issues (22.6%), the natural environment (21.7%), and human resources development (15.5%) are the most frequently discussed social, environmental, and economic sustainability themes in corporate annual reports. Full article