Journal of Air Transport Management最新文献

The rapid progression of technology has enabled the transformation of flying cars from a theoretical concept to a practical reality, aimed at alleviating urban traffic congestion and meeting the demand for efficient transportation. Key challenges currently revolve around legal frameworks, technological advancements, and financial resources. Additionally, societal concerns, particularly regarding user acceptance of flying cars, play a crucial role in their development. Therefore, investigating user acceptance behavior and identifying influencing factors for flying cars is essential for informing policy decisions, adjusting market strategies, assessing commercial feasibility, and ensuring successful integration into urban landscapes.

This study offers a synthesis, evaluation, and critique of relevant literature on technology acceptance models, user acceptance in the transportation sector, trust, and perceived risk. User behavioral intention is established as the primary criterion for assessing acceptance, with six core latent variables identified: performance expectancy, effort expectancy, social influence, hedonic motivation, trust, and perceived risk. Theoretical hypotheses are formulated based on these variables, leading to the creation of a theoretical model, followed by questionnaire design. A structural equation model is developed using sample data to validate research hypotheses and explore moderating effects, including the impact of demographic characteristics.

The research outcomes reveal that various factors influence user behavioral intention towards flying cars, with perceived risk emerging as the most significant factor. Trust not only directly affects user intention but also indirectly influences it through other variables. Moreover, age is identified as a significant moderating factor in this context.

The study draws several key conclusions: safety is a primary concern for users, emphasizing the need to enhance safety features of flying cars; trust plays a dual role, warranting further investigation into strategies for building user trust; catering to users over 30 requires emphasizing simplicity and safety, while younger users are more influenced by peers, necessitating tailored promotional strategies by companies.

This paper expands the utility of the UTAUT2 model, offering valuable insights for future research on flying cars and proposing policy recommendations for the commercial advancement of flying cars based on the research findings.

Climate change is affecting the stability of all types of transportation systems, including air transport. Ensuring the disaster preparedness capability of an airport has become a crucial issue. Prior studies on assessing airport resilience and risk analysis have failed to consider the impact of the disaster at various stages and the interdependence between the factors or criteria. This study proposes airport resilience model that considers the various stages of disaster response. First, we collect criteria affecting airport resilience through literature review and experts' survey to develop a novel evaluation framework. Second, this study applies the modified DEMATEL (Decision Making Trial and Evaluation Laboratory) which was modified by introducing the concept of heterogeneous impact analysis and combined with the concept of centrality weighting. Then, the modified DEMATEL is further combined with the Dombi Weighted Aggregator method to explore the interdependence relationships among the criteria and effectively integrate expert opinions. Finally, modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) analysis is applied to assess the resilience performance of Taiwan's major international airports and provide suggestions for improvement. The results indicate that pre-disaster preparation, with a weight of 0.338 among four stages, is the most crucial stage for enhancing airport resilience. This can be achieved through the implementation of effective emergency response procedures and evacuation plans in the event of a disaster. Additionally, this study suggests various improvement strategies based on the assessment results to enhance disaster resistance and resilience.

Introduction

Organizational changes, such as downsizing, can have profound implications for organizations, working conditions, and individual well-being. Similarly, rapid expansion also carries potential risks to individual health. During the Covid-19 pandemic, airlines experienced substantial organizational changes, such as downsizing and furloughs, followed by rapid expansion during the ramp-up phase of flying, posing risks to the health and safety of aviation personnel in the new post-pandemic aviation landscape.

Method

This cross-sectional and mixed-method survey study aimed to identify what post-pandemic challenges pilots (N = 6379) and cabin crew (N = 2679) face regarding working conditions, health, and flight safety.

Results

The results indicate deteriorated working conditions, health, and perceived safety among crew in the new aviation landscape. One in two cabin crew and one in three pilots report a decline in mental health. Whilst most pilots and cabin crew report no change in overall safety, 29% of cabin crew and 36% of surveyed pilots state that safety has deteriorated since the onset of the pandemic. This development is connected to an increased sense of industry instability, job insecurity, imbalanced job design, and management distrust among aviation crew. Furthermore, the uncertainties surrounding the industry have not only impacted job security and induced job-related worry but have also intensified operational pressures, with perceived impacts on flight and passenger safety.

Conclusion

The organizational framework, e.g., financial pressures, may have an effect on safety, either directly or indirectly by financial worry impeding crew performance. Hence, safety cannot be examined in isolation from employee health but must be understood in relation to the complex dynamics and competing objectives within aviation. Further, crew experiences across Europe are largely homogeneous, suggesting that identified risks may not be airline specific. Therefore, it is important to further examine the industry framework for inherent risk factors that could impact employee health and flight safety.

The future development of aeronautical navigation foresees an infrastructure rationalization of radionavigation aids with the aim of maintaining only the Minimum Operational Network, which brings benefits in terms of operational cost savings, promotes sustainability and optimal use of the radio spectrum. To ensure that the necessary navigation performance is preserved, the Distance Measuring Equipment (DME) plays a significant role, as DME/DME navigation is a short-term contingency solution when Global Navigation Satellite System is unavailable. Therefore, new DME ground stations are put into operation even though other navigation aids are being decommissioned at the same time. This paper addresses a question of possible DME network rationalization by developing a software model using a combination of a rule-based model, approximating of airborne DME interrogators interacting with DME ground transponders, with the implementation of the Gradient Boosting Regression to predict load of DME ground stations. The model is validated by comparing the results with the real load data obtained from an Air Navigation Service Provider. Several test cases are performed to evaluate the capability of the European DME network, simulating a reduction in the number of en-route DME stations and increases in air traffic using clustering methods. The results show that the ground station load limit was rarely reached, demonstrating the robustness and the potential for rationalization of the DME infrastructure.

The COVID-19 pandemic has significantly impacted the civil aviation exchange between China and foreign countries. But few studies have focused on the effects of COVID-19 on aviation emissions. This paper seeks to calculate the emissions of six pollution (CO₂, CO, HC, NOx, SO₂, and PM2.5) from the international routes from and to China during 2019–2021 and discusses the impacts of COVID-19 on the emission change. The Modified BFFM2-FOA-FPM method is proposed to unify the CO₂ and non-CO₂ calculations. The error rate between the calculated results and the official data from Civil Aviation Administration of China is about 2.74%. The results show that the COVID-19 has resulted in a reduction in overall emissions of the international routes from and to China, but the intensity of the unit passenger turnover has increased, and the overall emissions of some airlines have increased. Moreover, compared with 2020, there is no apparent recovery of international routes from and to China in 2021.

The COVID-19 pandemic dealt a significantly heavy blow to the already competitive air travel industry. As air travel demand is recovering post-pandemic, both airports and airlines are seeking to establish a steady stream of non-aeronautical/ancillary revenues, especially retail, to aid financial recovery. However, a rigorous analysis of passengers' purchasing behaviour during the whole air travel process has been lacking to date. To investigate this matter, we analyse data from a cross-national survey administered in four multi-airport cities (London in the UK, New York City in the US, Shanghai in China, and Sao Paulo in Brazil) in 2020, asking about the respondent's most recent air trip before the pandemic. A Multiple Discrete-Continuous Extreme Value (MDCEV) model was estimated to jointly analyse the discrete decision (to purchase or not) and continuous aspect (of how much is spent) during three stages of travel: at the departure airport, in flight, and at the transfer airport (if applicable). Six hypotheses, about the stage of travel, product and service type, passenger attributes, context of travel, and presence of companions are postulated and examined empirically. The results demonstrate how such factors shape shopping decisions during different stages of travel. The modelling results point towards the need for better pre-flight product information and more tailored offers. Airport retail requires more flexibility in terms of locations, operating hours and channels (in-store, deliveries) to improve shopping convenience. Use of digital tools and passenger data can aid in realising retail revenue improvement. Proposed future research directions include a focus on the transfer airport (where most spending is observed), post-pandemic behaviour changes, the role of product and service attributes, use of non-survey data and environmental impacts of the evolution in air travel retail.

This study investigates the effect of Wild Your Weekends, an “all-you-can-fly” flight pass program launched by China Eastern Airlines during the COVID-19 pandemic in 2020–2021. We leverage the Propensity Score Matching (PSM) method to estimate the causal effect of the flight pass on travelers’ spending behavior based on observational data. To reduce confounding bias, the PSM method pairs flight pass holders with individuals who are very similar to them but did not purchase the flight pass and compares the expenditures of the two groups. This study is one of the few studies that directly addresses flight passes and lays the groundwork for understanding the socioeconomic effects of the flight pass. The results could not only help the airlines to understand the utility of flight passes for travelers, but also provide policymakers with guidance to make policies that sustainably promote travels to underdeveloped regions with the help of refined flight pass programs.

Despite the impact of the COVID-19 pandemic, airline managers are still expected to look beyond financial and operational metrics to include sustainability goals that address key environmental, social, and governance aspects of the airline business. While there is a considerable body of literature addressing airline efficiency in terms of environmental performance, studies that integrate the various dimensions of sustainability are notably scarce. This paper aims to fill this gap by estimating technical efficiency using a sample of 34 airline groups between 2019 and 2022, with a Malmquist-Data Envelopment Analysis (DEA) methodology that includes financial, operational, and sustainability scores (Environmental and Social). Our results show a productivity loss between 10% and 14% during the COVID-19 period, with a better performance once the carriers are credited for their sustainability achievements. Using a second-stage bootstrapped truncated regression, we confirm the positive impact of hubbing operations and also find significant differences across geographical regions.

Rapid and on-time baggage transportation plays a crucial role in ensuring customer satisfaction and is an important source of greenhouse gas (GHG) emissions in the aviation sector. Modular Vehicle (MV) is an emerging transportation technology that allows vehicles to adjust their capacity flexibly by assembling or disassembling identical detachable units. This innovative technology offers a new perspective to decarbonize the aviation sector, as it holds promise for reducing GHG emissions in flight baggage transportation. To investigate this possibility, this study proposes an MV operation paradigm and a corresponding “greenest” MV scheduling problem that aims to minimize MV-relevant GHG emissions while transporting baggage from the terminal to the aircraft without delay. To solve the problem efficiently, a fast construction-merging heuristic is proposed based on the theoretical properties of the problem. A series of case studies at the Tampa International Airport were conducted to evaluate the performance of the proposed MV operation and the construction-merging heuristic. The results indicate that the proposed MV operation effectively reduces GHG emissions, and the heuristic solves near-optimal solutions to the investigated problem much faster than Gurobi, a state-of-the-art commercial solver for integer programs, without much loss of the optimality of the solutions. Results from this study provide important managerial and operational insights into decarbonizing baggage transportation for airport operators.

In recent years, there has been an increase in traffic demand. This means that the balance between the capacity of the Air Traffic Control system and traffic demand is affected. As demand exceeds capacity, measures such as the Air Traffic Flow and Capacity Management regulations have emerged to reduce the number of flights in the airspace. Complexity is a topic widely studied by researchers all over the world. For this reason, the objective of this paper is to develop a complexity indicator that can be used to predict complexity of Air Traffic Control sectors with help of Machine Learning models. The structure of complexity prediction is based on different machine learning models predicting operational variables using Random Forest Algorithms, and then predicting the complexity combining the results of the Machine Learning models. With this artificial intelligence application, the objective is to predict a complex variable by structuring the problem and dividing it in simpler models. Thanks to the application of the methodology, the Air Traffic Control service can see which possible flows or sectors will be congested and thus allocate resources optimally, but also simulations of different scenarios can be made to analyse how the operation changes, and thus structure the traffic prior to the operation.