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The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions. [ABSTRACT FROM AUTHOR]

When measuring the electromagnetic parameters of flexible material with an air coaxial line, the specimen is prone to bend and deform in the fixture, which results in erroneous results. In order to solve this problem, this paper proposes a new measurement method. Firstly, a rigid material is selected and loaded into the air coaxial line for two-port S parameters measurement. Then, the flexible material is attached to one end of the rigid material and loaded into the air coaxial line together for repeated two-port S parameter measurement. According to the S parameters measured and the two-port network cascading theory, the S parameters of the flexible material separately loaded in the fixture can be deduced. Finally, the electromagnetic parameters of the flexible material can be calculated by the Nicolson−Ross−Weir (NRW) method. Experimental results show that the method proposed is effective and reliable. [ABSTRACT FROM AUTHOR]

To be different from the traditional concept of congestion, congestion propagation based on the correlation between aircraft is given. And the main resource shared and competed for in airspace is the air route network, especially the intersection linking the multiroute. The system composed of congestion propagation units operates in airspace network, which is limited by the network geometry and the correlation between aircraft. This paper presents models based on the congestion and propagation characteristics in complex network, predicting the trend of congestion propagation and the peak of congestion size. By analyzing the relationships between system parameters and congestion propagation and accounting for the effects of propagation across networks, this paper enhances the current dynamics models of congestion propagation in airspace. Firstly, a heterogeneous network model is introduced to reveal the propagation process of aircraft with different degrees of correlation. This is followed by the specification of two simplified models for short-term prediction, just taking the sector capacity, propagation rate, and dissipation rate into account. And the propagation rate and dissipation rate depend on the sector geometry and aircraft distribution. Using them (sector capacity, propagation rate, and dissipation rate), the prediction models are accurate in predicting the evolution of congestion peak and propagation trend in comparison with the sample data of intersections in the sector. Of them, the model with capacity limitation is more accurate on busy hour. And on non-busy hour, capacity is insensitive in predicting congestion clusters. Furthermore, the computing method of propagation rate and dissipation rate is given in our paper. Finally, a numerical analysis is performed, in which it is demonstrated that system capacity, propagation rate, and dissipation rate have different effects on congestion propagation in airspace. The results show that low propagation and high dissipation rates not only are nonlinear but also decrease the level of congestion in the propagation of congestion. In particular, of the three parameters, system capacity affects the rate of convergence, with a low-capacity system reaching a stable state quickly and therefore providing a basis for sector partitioning. The method proposed in this paper should enable air traffic controllers to better understand the characteristics of congestion and its propagation for the benefits of both congestion management and improvement of efficiency. Significantly, airspace designers can take congestion propagation into consideration for optimizing the airspace structure in the future. [ABSTRACT FROM AUTHOR]

The introduction of new Air Traffic Management (ATM) concepts such as Trajectory Based Operations (TBO) may produce a significant impact in all performance areas, that is, safety, capacity, flight efficiency, and others. The performance framework in use today has been tailored to the operational needs of the current ATM system and must evolve to fulfill the new needs and challenges brought by the TBO content. This paper presents a novel performance assessment framework and methodology adapted to the TBO concept. This framework can assess the key performance areas (KPAs) of safety, capacity, and flight efficiency; equity and fairness are also considered in this research, in line with recent ATM trends. A case study is presented to show the applicability of the framework and to illustrate how some of the complex interdependencies among KPAs can be captured with the proposed approach. This case study explores the TBO concept of “strategic 4D trajectory deconfliction,” where the early separation tasks of 4D trajectories at multisector level are assessed. The framework presented in this paper could potentially support the target-setting and performance requirements identification that should be fulfilled in the future ATM system to ensure determined levels of performance. [ABSTRACT FROM AUTHOR]

This paper develops an optimisation model to address the dynamic multileg capacity control problem for the airline, taking into account the competition from the high-speed rail (HSR). Conventional capacity control models assume that the passenger demand is independent and that airlines control capacity independently. The proposed model considers the semi-independent demand, i.e., a customer of certain fare class arrives and makes choices from available alternatives provided by both airline and HSR. If the customer's first option is rejected, their booking request will be diverted both horizontally to the other operator and vertically to parallel flights within the same operator. Making use of this assumption, the customer's second and third choices are captured. With this model, airlines can control the sales process to determine whether or not to accept an initial booking request or diverted booking request to maximise their revenue. The deterministic linear programming (DLP) method is proposed as a model solution, enabling the computation of partitioned allocation and the bid price of each product. The optimal capacity control strategy is then obtained based on bid price control. To validate the model, numerical experiments are conducted to simulate customers' actual arrival and to show that the model is capable of producing better revenue, especially in the event that demand exceeds supply. Moreover, the demand diversion effect brings more revenue for both airline and HSR, and this potential revenue source has been ignored by previous research studies. [ABSTRACT FROM AUTHOR]

This paper considers an integrated hub location and revenue management problem in which a set of capacities is available from which one can be chosen for each hub and the disruption is considered in a star-star shaped airline network. We propose a two-stage stochastic programming model to maximize the profit of the network in which the cost of installing the hubs at different levels of capacities, the transportation cost, and the revenue obtained by selling airline tickets are considered. To provide flexible solutions, a hybrid two-stage stochastic programming-robust optimization model is developed by putting relative emphasis on a weighted sum of profit maximization. Furthermore, a sample average approximation approach is used for solving the stochastic programming formulation and a genetic algorithm approach is applied for both formulations. Numerical experiments are conducted to verify the mathematical formulations and compare the performance of the used approaches. [ABSTRACT FROM AUTHOR]

The impact of freight transport in cities is significant, and as such correct planning and management thereof help reduce their enormous negative impact. Above all, the special large vehicles have a greater impact than the remainder of freight vehicles, so a special attention should be paid to them. The vehicles which supply or pick up large amounts of goods at specific points throughout the city are an example of this type of vehicles. The aim of this paper is to minimize the cost of this freight transport type from a social, economic, and environmental viewpoint. To this effect, an optimization model has been proposed based on bilevel mathematical programming which minimizes the total system costs. City network model data are obtained on the lower level such as vehicle flow and travelling times, which are then used on the upper level to calculate total system costs. The model has been applied to a real case in Santander (Spain), whose final result shows the size and typology of the fleet of vehicles necessary to have the least impact on the city. The greater the vehicles size is (i.e., using fewer trucks), the less the cost of the freight transport is. [ABSTRACT FROM AUTHOR]

This paper studies the effects of Southwest Airlines, the largest low-cost carrier (LCC) in the U.S., on other carriers' payoff functions and entry probabilities. A static entry game model is developed and estimated by viewing entry as an indicator of underlying profitability and making use of Nash Equilibrium. Results indicate that Southwest has a remarkable and negative impact on the payoffs of other carriers. This impact is firm-specific, with LCCs being more affected than full-service carriers (FSCs). Comparing the two service types, the results show that Southwest's nonstop presence apparently imposes more downward pressure on opponents' profits than its connecting presence. A counterfactual experiment is then conducted. Once Southwest is counterfactually removed, the probability of each carrier entering a market significantly changes. This paper examines Southwest's impacts from a new perspective and extends literature on entry game estimation. [ABSTRACT FROM AUTHOR]

Summary: The recent pandemic caused by COVID‐19 has globally affected air transport mobility as well as the airline industry in general. Numerous restrictions have been implemented in airline transport, which is potentially leading toward severe long‐term impacts on the global airline industry. In this paper, air transport mobility was analyzed regarding Europe (EU) based on the available data from the relevant sources associated with the airline industry. Data were analyzed in specific periods from January to April of 2020, which corresponded with the initialization of the pandemic in the EU and later in its full development. Specifically, two airports were selected in Croatia as case studies to analyze the impact of COVID‐19 more thoroughly on mobility together with the estimation of carbon footprint during the pandemic and the year before the pandemic state. The results revealed that COVID‐19 gradually affected air transport mobility in the EU where a peak was reached in April with a reduction in the number of flights in the EU region reaching more than 89%. Cargo traffic was not significantly affected by the pandemic, and was even increased in some cases due to the supply of medical equipment in the fight against the disease. The analyzed case study revealed the reduction in air transport mobility for selected airports to be more than 96%, which directly affected the reduction of CO2 emissions to factor 1.81 for the commercial airport of Zagreb and 3.49 for the seasonal airport of Split. A normalization of air transport mobility is expected to be reached through a 1‐year period with a continued reduction in the number of flights ranging from 15% to 25%, based on the projections and expectations from relevant EU associations. [ABSTRACT FROM AUTHOR]

In this paper, we develop a route-traffic-based method for detecting community structures in airline networks. Our model is both an application and an extension of the Clauset-Newman-Moore (CNM) modularity maximization algorithm, in that we apply the CNM algorithm to large airline networks, and take both route distance and passenger volumes into account. Therefore, the relationships between airports are defined not only based on the topological structure of the network but also by a traffic-driven indicator. To illustrate our model, two case studies are presented: American Airlines and Southwest Airlines. Results show that the model is effective in exploring the characteristics of the network connections, including the detection of the most influential nodes and communities on the formation of different network structures. This information is important from an airline operation pattern perspective to identify the vulnerability of networks. [ABSTRACT FROM AUTHOR]

The HITRAN database (High-Resolution Transmission molecular absorption database) is an extremely helpful reference for selecting lines of molecular species. In the case of water vapor, a particular strong absorption line around λ ≈ 1392.5 nm is suitable for the detection of molecules, not only because of its high strength but also because it is well separated from the neighboring transitions, thus avoiding any overlap. First, we present the optical system that uses a distributed feedback (DFB) laser diode, emitting around λ ≈ 1392.5 nm with a power ≈ 3 mW and linewidth ≤ 10 MHz. For metrological needs, we are looking for a means to control the water vapor concentration in ambient air in near real time and especially when an optical chilled mirror hygrometer is used. The latter instrument is widely used due to its performance with regard to both accuracy and repeatability of measurements. Here, using the molecular absorption device the use of such an instrument is examined from the point of view of its impact on the measurement of relative humidity. This paper reports the measured frequency positions of the observed line at λ = 1392.5337 nm for different air pressures and compares them with the values given in the HITRAN database. In addition, we discuss the possibility of using water vapor detection by spectroscopy to observe the change of the shape and the position of the absorption line produced during measurements of the dew point temperature by the optical chilled mirror hygrometer. [ABSTRACT FROM AUTHOR]

In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management. [ABSTRACT FROM AUTHOR]

In 2008, European Union (EU) announced that from 2012, each international flight taking off and landing in EU would be given an emission permit. Therefore, the period of 2008-2012 can be regarded as a buffer period for global airlines. Although EU formally decides to exclude non-EU airlines from the EU Emission Trading System on March 4, 2014, it is necessary to investigate the impacts of the policy on airline energy efficiency in this period. Airline energy efficiency is divided into three stages-operations stage, service stage, and sales stage-and Greenhouse gas emission is treated as an undesirable output of service stage. Two models, network range-adjusted measure model with weak disposability and network range-adjusted measure model with strong disposability, are established to evaluate the efficiencies of 22 international airlines from 2008 to 2012. The results show that (i) most airlines' efficiencies have decreased in the period, and the EU Emission Trading System is not effective for the efficiency improvement; (ii) the average efficiency of European airlines is almost the same as that of non-European airlines; and (iii) the model with weak disposability is more reasonable in distinguishing efficiency differences, while strong disposability is a more reasonable way in treating undesirable outputs. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The coexistence of high-speed rail (HSR) and airline in a busy transportation corridor generates competition between the two transportation modes. An unfair competition between HSR and airline not only reduces both revenues, but also triggers a series of social problems. Based on generalized costs, this paper proposes combining an improved gray prediction model, modified gravity model, and Logit model to predict the average passenger flow, induced passenger flow, and transfer passenger flow. According to the predicted results, we establish a game model that considers different stages of the HSR development. For demonstrative purposes, the approach is applied to an empirical study in China, that is, the competition between Beijing-Shenyang HSR and airline. Malignant fare war will make both parties lose out. Either mode that improves service quality will generate more revenue. If both parties improve the level of service, all incomes of the HSR, airline, and community increase. Results show the HSR contribution is greater than the airline in the case study. [ABSTRACT FROM AUTHOR]

Essentially, strategic fleet planning is vital for airlines to yield a higher profit margin while providing a desired service frequency to meet stochastic demand. In contrast to most studies that did not consider slot purchase which would affect the service frequency determination of airlines, this paper proposes a novel approach to solve the fleet planning problem subject to various operational constraints. A two-stage fleet planning model is formulated in which the first stage selects the individual operating route that requires slot purchase for network expansions while the second stage, in the form of probabilistic dynamic programming model, determines the quantity and type of aircraft (with the corresponding service frequency) to meet the demand profitably. By analyzing an illustrative case study (with 38 international routes), the results show that the incorporation of slot purchase in fleet planning is beneficial to airlines in achieving economic and social sustainability. The developed model is practically viable for airlines not only to provide a better service quality (via a higher service frequency) to meet more demand but also to obtain a higher revenue and profit margin, by making an optimal slot purchase and fleet planning decision throughout the long-term planning horizon. [ABSTRACT FROM AUTHOR]

This study is devoted to a new research topic in real-time airline operations, the redispatching of unit load devices (ULDs) under demand variations. We develop a new hybrid framework to solve the problem of ULD redispatch following the time-sequence decision-making required by airlines. The hybrid framework is developed by integrating techniques including the probability distribution technique to simulate different types of operational demand, the adjustable number of stages which is needed to meet the requirements of a decision-making process following a time sequence and the time pressure characteristic of real operations, and the scenario tree and probability rule approaches which are aimed and representing all possible demand scenarios for a stage, while the network flow technique is applied to represent the movement and location of ULDs at each airport over time and is used for the development of the associated mathematical model and the simulation. We performed a simulation of 2,000 cases based on different operational days and types of operational demand. The results show that this hybrid framework is able to achieve stability and also a small variability of both ULD operating costs and solution times, which could allow the airline to save on ULD operating costs, under demand variations in real-time operations. [ABSTRACT FROM AUTHOR]

Air transportation direct share is the ratio of direct passengers to total passengers on a directional origin and destination (O&D) pair. Direct share is an essential factor of passenger flow distribution and shows passengers' general preference for direct flight services on a certain O&D. A better understanding and a more accurate forecast of direct share can benefit air transportation planners, airlines, and airports in multiple ways. In most of the previous research and applications, it is commonly assumed that direct share is a fixed ratio, which contradicts the air transportation practice. In the Federal Aviation Administration (FAA) Terminal Area Forecast (TAF), the O&D direct share is forecasted as a constant based on the latest observation of direct share on the O&D. To find factors which have significant impacts on O&D direct share and to build an accurate model for O&D direct share forecasting, both parametric and nonparametric machine learning models are investigated in this research. We propose a novel category-based learning method which can provide better forecasting performance compared to employing the single modeling method for O&D direct share forecasting. Based on the comparison, the developed category-based learning model is a promising replacement for the model used for O&D direct share forecasting by the FAA TAF. [ABSTRACT FROM AUTHOR]

Understanding passengers' responses to fare changes is the basis to design reasonable price policies. This work aims to explore retiming responses of travelers changing departure times due to a prepeak discount pricing strategy in the Beijing subway in China, using smart card records from an automatic fare collection (AFC) system. First, a new set of classification indicators is established to segment passengers through a two-step clustering approach. Then, the potentially influenced passengers for the fare policy are identified, and the shifted passengers who changed their departure time are detected by tracing changes in passengers' expected departure times before and after the policy. Lastly, the fare elasticity of departure time is defined to measure the retiming responses of passengers. Two scenarios are studied of one month (short term) and six months (middle term) after the policy. The retiming elasticity of different passenger groups, retiming elasticity over time, and retiming elasticity functions of shifted time are measured. The results show that there are considerable differences in the retiming elasticities of different passenger groups; low-frequency passengers are more sensitive to discount fares than high-frequency passengers. The retiming elasticity decreases greatly with increasing shifted time, and 30 minutes is almost the maximum acceptable shifted time for passengers. Moreover, the retiming elasticity of passengers in the middle term is approximately twice that in the short term. Applications of fare optimization are also executed, and the results suggest that optimizing the valid time window of the discount fares is a feasible way to improve the congestion relief effect of the policy, while policy makers should be cautious to change fare structures and increase discounts. [ABSTRACT FROM AUTHOR]

Analysis of flight delay and causal factors is crucial in maintaining airspace efficiency and safety. However, delay samples are not independent since they always show a certain aggregation pattern. Therefore, this study develops a novel spatial analysis approach to explore the delay and causal factors which is able to take dependence and the possible problem involved including error correlation and variable lag effect of causal factors on delay into account. The study first explores the delay aggregation pattern by measuring and quantifying the spatial dependence of delay. The spatial error model (SEM) and spatial lag model (SLM) are then established to solve the error correlation and the variable lag effect, respectively. Results show that the SEM and SLM achieve better fit than ordinary least square (OLS) regression, which indicates the effectiveness of considering dependence by employing spatial analysis. Moreover, the outcomes suggest that, aside from the well-known weather and flow control factors, delay-reduction strategies also need to pay more attention to reducing the impact of delay at the previous airport. [ABSTRACT FROM AUTHOR]

The increase in air traffic worldwide requires improvement of flight operational efficiency. This study aims to reveal the potential benefits, namely, savings on fuel consumption and flight time, which are expected for Japanese airspace, by statistically evaluating the operational efficiency defined by average differences of fuel consumption, flight time, and flight distance between the original and the optimized flight of domestic flights in Japan. The aircraft position and time data used in this study were obtained from Collaborative Actions for Renovation of Air Traffic Systems Open Data—the radar data released by the Japan Civil Aviation Bureau. Flight information, such as air data and fuel flow, is estimated by applying meteorological data and aircraft performance model to the position information of radar data. Each reconstructed trajectory is optimized in terms of flight fuel consumption and flight time with an assumed cost index (CI). Dynamic programming is used as the trajectory optimization method. The flight fuel consumption and flight time of the optimized flight are compared with the original values to evaluate the operational efficiency. Herein, approximately one-third of 1-day data, i.e., 1087 cases of four aircraft types, are analyzed with reasonable CI settings. Our research findings suggest that flight fuel consumption and flight distance can be saved by 312 kg and 19.7 km, respectively, on average for the object flights. Following a statistical comparison between the original and the optimized flights, it was observed that two types of features, namely, flying on a detoured path and flying with nonoptimal altitude and speed in the cruise phase, are major factors which deteriorate the total operational efficiency in terms of fuel consumption, flight time, and flight distance. [ABSTRACT FROM AUTHOR]

In this research, we consider a flight scheduling problem for oligopolistic competition with direct flights and a point to point network. In this type of market situation, passengers are sensitive to the departure time of a flight rather than the transfer time. The airline needs to carefully consider the departure times of their competitors when determining their own. Therefore, unlike past approaches which have only considered one departure time for a competitor's flight, a flight scheduling framework is developed which takes into consideration possible competitor departure times. The framework includes two dependent stages which are repeatedly solved during the solution process. In addition, an upper bound model is also designed to evaluate the solution quality. Numerical tests are performed using data for Taiwan's outlying island route which is characterized by the above market situation. Satisfactory results are obtained, showing the good performance of the framework. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study develops a new performance evaluation model, called the meta dynamic network slack-based measure (MDN-SBM). This model incorporates the concept of meta-frontiers to facilitate comparisons of performance of decision making units, while at the same time it generalizes the slack-based measure (SBM), network SBM, and dynamic SBM. This MDN-SBM model is capable of dealing with two important special features of the transportation industry: unfavorable accidents and non-storable goods, i.e. available seat-kilometers that are wasting assets that lose value completely if unsold before departure. Hence, this generalized model contains higher differentiable capability than all its SBM-related submodels in the literature. To demonstrate, 35 international airlines with two divisions (production and consumption) in three terms (one-year time periods from 2007 to 2009) have been analyzed using meta-performance efficiency measures (ME) for all decision making units and have also been compared by geographical area (Asia-Pacific, N. America/Europe). The numerical example and comparison validate the proposed MDN-SBM model and suggest the airlines should put more focus on input resources reduction for productivity improvement. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]