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This paper has reviewed the history of EOP (training) development and then illustrated the curriculum design of cabin service English training from the three perspectives of ESP, CLIL and Business Discourse. It takes the cabin crew English training of China Southern Airlines (CZ) as the case and puts forward an operational framework composed of three integrated essential elements: language basic skills, cabin service practices and cabin service communication competence. It argues that on the basis of mastery of language skills and cabin service practices flight attendants should further to improve their communication competence as the course objective of cabin service English training.

Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a novel aviation CO 2 emissions reduction method by introducing a market-based incentive mechanism. An aviation carbon emissions prediction model is proposed to characterize both the microscopic flight dynamics and the macroscopic air traffic demand trend. Based on emissions prediction, a linear climate response model is applied to evaluate the environmental impact of the aviation carbon emissions. Finally, a market-based framework for aviation CO 2 emissions reduction is established based on a two-phase carbon trading model, which provides a piecewise charge method through emission taxation and cap-and-trade. A case study is carried out to demonstrate the effectiveness of the proposed framework using a real-world dataset from the Hefei Air Traffic Control Zone in China. Three scenarios are defined to describe the possible development speed of sustainable aviation fuel application in the future: not applied, medium-speed development, and high-speed development. The results show that the carbon peak will be reached in 2025 in the high-speed development scenario, when the emission amount and associated environmental impact will be reduced by 23.7% and 22.3%, respectively. Thus, the market-based framework proposed in this paper can effectively reduce aviation CO 2 emissions and mitigate the effects of global warming. • An aviation CO 2 emissions prediction model is proposed considering both microscopic flight dynamics and macroscopic traffic demand. • A more efficient market-based CO 2 emission reduction framework is developed based on a two-phase carbon trading model by means of taxation and cap-and-trade. • Case study shows that the proposed method has a more optimal incentive for CO 2 emission reduction. [ABSTRACT FROM AUTHOR]

A volcanic eruption is one of the most critical natural hazards in air transportation. In the European region, the Eyjafjallajökull eruption in 2010 triggered extensive discussions and efforts to adopt a risk-based volcanic contingency management plan in civil aviation. However, there has been relative lack of such efforts in the Asia–Pacific region. In this paper, a hypothetical eruption scenario of Mt. Paektu is studied to evaluate its impact on international air traffic using South Korean airspace. Mt. Paektu is an active volcano, and man-made earthquakes caused by North Korea's recent nuclear weapons tests have elevated concerns about the possibility of an eruption. Based on multiple route closure tolerance criteria, direct and indirect losses including system serviceability, cancellation cost and passenger losses were evaluated, utilizing air route network data set and six-day ash dispersal scenario. Under the zero tolerance, system serviceability ranged between 3 and 60%. System serviceability ranged between 51 and 100% under the most lenient tolerance of 50%. Flight cancellation costs were $231 M and $68 M under the zero and 50% tolerance criteria. More than 80% of flights and 77% of cancellation costs were associated with the Asian region, especially China and Japan. In summary, impact on international air traffic was significant, and the gaps according to variation in tolerance were evident. Decisions on tolerance criteria are critical and must consider trade-offs between aircraft damage and system serviceability. Moreover, airspaces of China and Japan need to be considered in conjunction with Korea to assess the volcanic hazards in the region. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach that utilizes aircraft seat capacity and its route flight time to predict carbon emissions for aircraft manufacturers during the aircraft design stage. To address data fluctuations arising from the human-aircraft-environment-management system throughout the production and operation of the future-designed aircraft, we employ the interval-valued data type, replacing the traditional point data type, and construct an interval-valued regression model. Within the parameterized method framework, our programming model incorporates additional constraints to ensure the intersection of each interval-valued sample. This addresses the challenges significant data fluctuations pose, leading to improved fitting performance. Furthermore, the model is demonstrated to satisfy Kuhn-Tucker conditions for solvability, and the obtained regressed parameters exhibit favorable small-sample properties. An empirical study using airline operating data from China validates the prediction effectiveness of the programming model. Finally, based on these predictions, we provide applications and suggestions for decision-makers aimed at reducing CO 2 emissions. • Estimated aircraft carbon emissions for Chinese routes. • More precise estimations based on an improved parameterized model. • For narrow-body aircraft, increasing seat capacity and controlling flight time are crucial for carbon emissions reduction. • For wide-body aircraft, controlling seat capacity and long-distance flights benefits carbon emission reduction. [ABSTRACT FROM AUTHOR]

In this work, a machine translation framework is proposed to tackle the flight plan generation in the air transport field. Diverging from the traditional human expert‐based way, a novel sequence‐to‐sequence transfer transformer network to automatic flight plan generation with enhanced operational acceptability is presented. It allows the user to translate the departure and arrival airport pairs denoted as test sentences, into the flyable waypoint sequences denoted as the corresponding source sentences. The approach leverages deep neural networks to autonomously learn air transport specialized knowledge and human expert insights from industry legacy data. Moreover, a multi‐head attention mechanism is adopted to model the complex correlation between airport pairs. Besides, we introduce an innovative waypoint embedding layer to learn effective embeddings for waypoint sequences. Additionally, an extensive flight plan dataset is constructed utilizing real‐world data in China spanning from July to September 2019. Employing the proposed model, rigorous training and testing procedures are conducted on this dataset, yielding remarkably favourable outcomes based on automatic evaluation metrics that are BLEU and METEOR, which outperform other popular approaches. More importantly, the proposed approach achieves high performance in the operational validation and visualization, showing its application potential for real‐world air traffic operation. [ABSTRACT FROM AUTHOR]

Due to cope with relying on controllers badly and lacking of quantitative models to assist decision-making in air traffic flow management, this paper studied the delay optimization model and its solving algorithm for route network. Firstly, it established a traffic flow assignment model with the objective function of minimum flight time and capacity constraints. Secondly, considering that classical Hungarian algorithm was only suitable for small and medium-sized operations, it made the improvement of the calculation of efficiency matrix by making the maximum probability of the number of zero elements in the circle equal to the order of the efficiency matrix. Next, it constructed the cost function of the en-route by using the MIMIC queuing theory model. The function, which replaced the constant efficiency value in the efficiency matrix with a variable cost, made the efficiency matrix change with time and flow. Finally,it took part airspaces of China and the actual operational data of 2016 as examples. The results show that the algorithm can increase the access capacity by 8. 372% and the departure capacity by 8. 999%; In terms of delay optimization,it can reduce the average delay of each aircraft. In terms of algorithm performance, it has less iterations and less solution time than the classical Hungarian algorithm, and is more suitable for actual control operation. [ABSTRACT FROM AUTHOR]

The aircraft ferry service is an important link in the transfer of travelers between the remote parking stand and the terminal. By analyzing the process of inbound and outbound ferry service, the relationship between the in-place time of each ferry on the same flight is clarified. A ferry service splitting method considering the difference in in-place time is proposed. Based on this, a dynamic programming scheduling model for ferries is innovatively developed. Considering the impact of flight arrival and departure uncertainty on the in-place time of ferry service, the model is transformed into a dynamic programming stochastic model with opportunity constraints. The optimization objective of this model is to minimize the number of ferries used. To describe the model more easily, a sample average approximation technique is introduced to transform the stochastic model into a deterministic model with confidence. A genetic algorithm based on Monte Carlo random sampling is designed to solve the model. The experimental results based on the actual operation data of an airport in South China show that the dynamic splitting method considering the difference in the in-place time of the ferry can improve the resource utilization efficiency; the scheduling scheme based on the dynamic planning stochastic model has better robustness. [ABSTRACT FROM AUTHOR]

The air route network, which supports all the flight activities of the civil aviation, is the most fundamental infrastructure of air traffic management system. In this paper, we study the Chinese air route network (CARN) within the framework of complex networks. We find that CARN is a geographical network possessing exponential degree distribution, low clustering coefficient, large shortest path length and exponential spatial distance distribution that is obviously different from that of the Chinese airport network (CAN). Besides, via investigating the flight data from 2002 to 2010, we demonstrate that the topology structure of CARN is homogeneous, howbeit the distribution of flight flow on CARN is rather heterogeneous. In addition, the traffic on CARN keeps growing in an exponential form and the increasing speed of west China is remarkably larger than that of east China. Our work will be helpful to better understand Chinese air traffic systems. [ABSTRACT FROM AUTHOR]

Abstract: Because of climate change and the popularity of air travel, the issue of low-carbon economy of air transportation industry is no longer ignorable. However, there is no standard measurable indicator to evaluate low-carbon economy, especially for air transportation industry. To fill in the blank of this area, this paper firstly discusses general indicators of low-carbon economy for air transportation industry. Secondly, it explores measurable indicators to assess the level of low-carbon aviation based on generally published data. Finally, it takes Shenzhen aviation as a case to discuss the evaluation of its low-carbon economy with the established measurable indicators. This paper not only provides indicators for low-carbon economy of air transportation with practical measurement effectiveness, but also opens up a new direction for the study of low-carbon air transportation. [Copyright &y& Elsevier]

The continuous performance evaluation of commercial service airports is an important part of airport operations. Despite its significance, the existing airport ranking approaches by various aviation organizations and agencies does not provide a comprehensive methodology to rank the airports based on multiple criteria. This paper proposes a comprehensive multi-stage multi-criteria data analytics approach that combines data envelopment analysis, best-worst method, and multi-attributive border approximation area comparison method to provide a robust ranking framework. The ranking procedure is based on seven criteria, i.e. passenger throughput, number of direct routes, number of check-in counters, terminal area, number of gates, number of flights, and number of runways. The proposed method ranks the shortlisted airports from the best to the worst and evaluates the strengths and weaknesses of each of the candidate airports. A real-world example involving the ranking of the efficient major commercial service airports in China is demonstrated to validate the applicability of the proposed approach. The insights generated have implications for the benchmarking of airports. • Provides a comprehensive framework and generate insightful implications for benchmarking airports. • A multi-stage multi-criteria data analytics approach that combines DEA, BWM, and MABAC method is proposed. • The proposed method ranks shortlisted airports from the best to the worst. • Evaluates the strengths and weaknesses of each of the candidate airports. • A real-world example involving the ranking of the major commercial service airports in China is demonstrated. [ABSTRACT FROM AUTHOR]

Based on nationwide aircraft performance and scheduling gathered from Variflight.com, the work evaluates how the identified factors, such as aircraft taxi-in and taxi-out time, airborne time, Origin–destination Pair Herfindahl–Hirschman Index (OD-Pair HHI), hub airport, and flight distance, coexisted in order to organize aviation scheduling more effectively and appropriately in the air transportation system. The result indicates that the variables of a hub airport, OD-Pair HHI and throughput have a more significant effect on SBT than the duration of each flight phase does (i.e., aircraft taxi-in and taxi-out time, airborne time, etc.). Fierce competition has prompted airlines to improve their on-time performance (OTP), instead of scheduling shorter block times. Moving forward, if the departure airport is a major one, the SBT will increase by 3.43 min, indicating that airport throughput is a primary determinant in adding or shortening SBT in China's aviation system. [ABSTRACT FROM AUTHOR]

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Direct flights across the Taiwan Strait have been the hottest issue in cross-Strait relations since the 1980s. Although Taiwan's policy toward direct flights is not yet clear, many believe that the entry of both sides into the World Trade Organization (WTO) will speed up the flight schedule process. There are, however, some technical problems to be solved before the airlines at either side can operate nonstop flights across the Strait. One critical issue regards the striking of a bilateral agreement between China and Taiwan. If both sides agree to negotiate such an official document, serious political and economic conflicts would likely occur as soon as negotiations are begun. This paper discusses the history of air transportation services among Macau, Hong Kong, Taiwan, and China. Past and new agreements are examined, their impacts and possible influences on the regional markets are analyzed, and both the business and political considerations related to these agreements are explored. Given the experiences of both past and existing agreements, the format as well as the content of any cross-Strait bilateral agreement would need to be carefully constructed in order to be acceptable to both sides. The possibility of a multilateral agreement within these states and zones, and the resulting impacts on this area will also be examined. [ABSTRACT FROM AUTHOR]

This study investigated passengers' choice between high-speed rail (HSR) and low-cost carriers (LCCs) in China. A survey of 1,080 passengers was performed and a logit regression method was used to identify the key factors in modal choice behaviors. The results indicated that frequency, price, perceived behavioral control (PBC), and attitudes were significant determinants of choice between HSR and LCCs, while service quality, access, and subjective norms were not important factors. Group differences in terms of the factor impact were also identified. Frequency and attitudes had a greater influence on business passengers, compared to nonbusiness passengers, in terms of their mode preference. Price affected the two types of passenger groups in a similar way, while PBC only influenced the choice behavior of the nonbusiness passengers. These findings fill the knowledge gap of the choice between HSR and LCCs and provide useful insights for HSR and LCCs to improve competitiveness in China. [ABSTRACT FROM AUTHOR]