Journal of Air Transport Management最新文献

Metroplex terminal areas is involved with numerous air weaving sections, where various types of arrival and departure traffic flows intersect or converge, leading to high-risk aircraft conflicts and inefficient Metroplex operation. The primary objective of this study is to explore the conflict risk characteristics of operational interactions between airports in Metroplex terminal areas with large-scale trajectory data, including risk evaluation, risk propagation and risk prediction. One complete month of ADS-B trajectory data is collected from a representative multi-airport system in Central and South China to illustrate the procedure. Specifically, flight trajectories are firstly clustered within Metroplex terminal areas, and a trajectory-tube model is then built to characterize the airport flows within the same cluster. Then, a clustering-based collision probability calculation method is proposed to evaluate the risk of air weaving sections. The spatial patterns of risks in air weaving sections reveal that operational interactions among Metroplex airports are quite different under various runway configurations. Furthermore, an adaptive graph spatial-temporal transformer (ASTT) network is developed to predict the future risk of each air weaving section in Metroplex terminal areas. The results indicate that the developed ASTT network can collaboratively encode the spatiotemporal characteristics in the air weaving section risk data, which achieves more accurate and robust prediction performance than other compared models during multiple look-ahead time steps. Moreover, the adaptive spatial graph technique designed in the ASTT can also reveal the hidden risk propagation effects among air weaving sections. The results of this study could provide insightful suggestions to air traffic authorities for developing effective coordination and conflict mitigation strategies, such as redesigning flight procedures, reallocating routes, and optimizing aircraft sequences to enhance the efficiency and safety of Metroplex operations.

To optimize the structure of an air route network, accurate forecasting of future new routes is vital given the rapid growth in demand for air transportation. Based on the theory and method of link prediction, considering the joint influence of network endogenous factors and external attributes, we construct a system of network endogenous factors and external attribute indices and explore the prediction effect of each index. We further construct a prediction index system, explore the prediction effect of coupled indices, design a particle swarm algorithm to determine the weights of each index, and propose a coupled link prediction model based on particle swarm optimization (PSO-CLP). A comparison of the prediction accuracy of this model with the dual-indicator coupled link prediction model and the traditional link prediction model is also conducted to test the stability and reasonableness of the PSO-CLP model. In this study, we use the 2015–2020 Chinese air route network as an example. The instance test shows that the PSO-CLP models significantly outperform the traditional link prediction models and dual-indicator coupled link prediction models in terms of prediction accuracy, stability and computational simplicity, among which the PSO-CLP model, which considers both endogenous factors and external attributes, such as the RWR + Sor + Pop and RWR + RA + GDP indices, has the best forecasting effect. The PSO-CLP model is an effective tool for route prediction, providing a new perspective on route link prediction and air route network optimization.

Net cash flow is extremely important in capital-intensive industries. In industries operating on a global scale, such as the airline industry, situations such as a terrorist attack, financial crises, pandemic, or war can make cash flow uncertain and unreliable. The airline industry has faced significant cash flow problems during the COVID-19 pandemic. The interruption of net cash flow, especially during crises (e.g., Covid-19), causes some industries to experience financial stress. In this study, we aim to reveal the financial determinants of corporate cash holdings in the airline industry, based on the airline business model. In this context, we empirically analyzed airlines employing low-cost and traditional business models. Unlike previous research, we analyze the financial determinants of airlines' net cash flows from financing activities, investing activities and operating activities. This paper makes a substantial contribution to the literature by leveraging a comprehensive analysis of cash flow dynamics, shedding new light on financial performance. We devised a comprehensive approach by formulating five distinct models to scrutinize the determinants that influence corporate cash holdings. The proposed model demonstrates versatility within various segments of the aviation industry, making it applicable to both traditional and low-cost airline business models. The findings of the study indicate that there are differences between models regarding the financial determinants of corporate cash holdings. The analysis reveals interesting insights contrary to the conventional wisdom on corporate cash holdings in the airline industry. For instance, one of the most interesting findings of the study is that the financial structure of airlines is significantly determined by the source of net cash flow (from financing, investment and operational activities). Furthermore, the findings of the study provide a multidimensional understanding of the factors affecting airlines' corporate cash holdings.

Flight operations over the North Pacific Ocean are affected by strong westerly jet streams, which have day-to-day weather variations as well as seasonal trends. Fast-time simulation is used to evaluate the effect of proposed airspace and procedure changes on flight operations, and the results must reflect seasonal trends while not being overly influenced by conditions on any given day. Aggregating the results of air traffic flow simulations using the winds on a large number of days spread over a year will achieve this but requires considerable time and effort, and a method to obtain a reasonable evaluation using a small set of representative wind conditions and a minimum number of simulations is desired. This paper proposes using clustering to achieve this. To avoid having to cluster large meteorological data sets and to reduce the dimensionality of the data, we used Pacific Organised Track System (PACOTS) tracks as a surrogate for wind conditions since these are calculated considering the daily winds aloft. For a schedule of ten major trans-Pacific flight services, we compared statistical trends over a full year of routes and those on wind days chosen by five candidate methods, which consisted of clustering and non-clustering methods. The constant-interval selection of days from a dendrogram produced via Ward's clustering captured the seasonal variation of winds over the studied year with the highest fidelity. Airspaces in which winds aloft dominate flight planning and for which daily wind-optimal tracks are published exist in other oceanic areas, and the proposed method is also applicable to their simulation studies.

In recent decades, substantial efforts for liberalization and deregulation of the aviation sector have facilitated the entry of new firms into markets and led to a significant reduction in airfares. How competition in the airline sector has evolved at the global scale since the 2000s receives little attention in the literature, presumably due to the unavailability of appropriate datasets. This study investigates the airline market concentration across worldwide markets for the years 2010 to 2022; to the best of our knowledge this is the first such effort with longitudinal data for more than 10 years at a global scale. We find heterogeneities in the extent, trend, and stability of market concentration and aim to identify potential drivers behind. We believe that our study contributes to the rich existing literature on airline competition by providing an empirical reference baseline for the global aviation system in the first part of the 21st century.

Urban air mobility (UAM) is a new concept in urban transportation systems; UAM employs an electric vertical takeoff and landing aircraft for operation in low-altitude urban areas. It complements the saturated conventional ground transportation systems in congested urban areas. The successful implementation of UAM as an urban transportation option necessitates the creation of appropriate policies and implementation strategies tailored to the UAM target market. This study aimed to provide policymakers with insights and policy implications based on a market analysis of the UAM. A stated preference survey was conducted with potential UAM users, and logit-based discrete choice models were developed. Subsequently, the models and actual traffic data were used to estimate the UAM demand in the Seoul metropolitan area in Korea, and the policy implications were derived, accounting for economic, social, and demographic factors. It was concluded that UAM policies should be tailored to the potential market for UAM, promote integrated mobility as a service, and enhance the social acceptance of UAM. Policymakers can use the results of this study to formulate necessary regulations and infrastructure for facilitating efficient and effective UAM integration.

Air travelers are often hurried and at risk of missing their flights. Many of them are quite sensitive to service quality and overall travel times. In larger airports the demand for parking spaces forces many users to park quite far from passenger terminals. Thus, it is desirable to allocate available parking spaces at any given time in ways that minimize the users' access distances. Although the major layout and allocation decisions for airport parking are highly constrained by available land and physical parking infrastructure, a well-designed parking clustering scheme based on expected parking durations could assist airport operators in effectively allocating limited space, which minimizes the travelers' average access distances between parking facilities and an airport terminal by employing grouping concepts based on the expected parking time durations of vehicles. In subdividing the available parking facilities, the boundaries between different user groups (e.g., short, medium, and long term) are optimized based on the distributions of (1) expected vehicle dwell times and (2) available parking capacity at various access distances from airport terminals. Even with only two clusters (e.g., short-term and long-term parking), results show that average access distances from parking spaces to terminals can be reduced by 25–44%. Although additional subdivisions of the available parking areas can yield additional savings of travelers’ time and access distances, the marginal benefits decrease rapidly as the number of subdivisions grows.

Service quality is of paramount importance for the long-term sustainability of the airline industry, which is characterized by intense competition. However, previous research in this field has frequently been limited by shortcomings in sample size, efficiency, and dependability. This study addresses these deficiencies by introducing refined insights and establishing a comprehensive yet highly elucidative ranking framework. Initially, we employ Latent Semantic Analysis (LSA) to distill principal themes and sentiments from online reviews of 80 airlines. Subsequently, we employ the SentiWordNet lexicon and the TextBlob package for sentiment analysis based on the aforementioned reviews. Following this, we construct a hierarchical structure using the computation of compromise solutions, employing an integrated Technique for Order Preference by Similarity to Ideal Solution, vis-à-vis Kriterijumska Optimizacija I Kompromisno Resenje-Adversarial Interpretive Structural Model (TOPSIS-VIKOR-AISM) methodology. Finally, the ranking of airlines from best to worst based on perceptions gained from online reviews provides an immediate visualization solution. This study not only assists consumers in making informed decisions but also provides airlines with insights that can be used to enhance their service offerings. The study presents novel insights into the assessment of service quality, with potential applicability to the airline industry and beyond.

Thunderstorms can cause a decrease in airports' dynamic capacity, and taxiway runway capacity is a key element of airport airfield operations. In this paper, based on the cell transmission model (CTM), the meteorological operation parameters of thunderstorms are set according to the airport aircraft taxiing rules, and a CTM is proposed for evaluating the resilience of the airport aircraft area operation network under thunderstorm weather. Based on the CTM, the network performance parameters are obtained, and the number of aircraft waiting for the network and the network operation efficiency are used as the basic performance indicators to establish a resilience index that reflects the dynamic change in the resilience of the thunderstorm meteorological interference network. Finally, this paper collects and analyzes the operation data of Tianjin Binhai International Airport in 2019 to build a CTM for aircraft operation in the flight area. The results show that the model's simulated airport departure operation data is almost consistent with the trend of the actual departure operation data, which verifies that the model can be used to simulate traffic for the airport airfield operation network. In addition, based on the resilience index, the sensitivity analysis of different parameters to network performance under thunderstorm weather was also studied. Based on the evaluation results of different parameter scenarios and indicators, we drew the resilience map of the airport ground operation network, and found that the network shows different resilience levels in different thunderstorm level scenarios. This study provides a great method and evaluation index for modeling and resilience evaluation of aircraft operation networks in the airfield, which is expected to improve airport operations, reduce flight delays, and improve service quality.

The growing global air traffic has necessitated more efficient ground management in airports, especially during peak hours. Current approaches mainly consist of radar-based systems and radio communications, which provide limited visual assistance. Hence, Decision Support Systems (DSS) are designed to assist human Air Traffic Controllers (ATCos) by providing reliable recommendations based on current data and situations. The Airport Ground Optimizer (AGO) is a DSS designed for ATCos to manage the complexities of ground traffic in airports. It is based on advanced optimization algorithms and intuitive user interfaces, offering an enhanced operational experience. AGO uses mixed-integer programming (AGO-MIP) and stochastic programming (AGO-STC) to detect conflicts based on expected gate release and taxi times, and then optimizes the entire schedule to minimize hold fuels, resolve conflicts, and reduce fuel consumption, emissions, and delay times. Its key strength lies in translating complex mathematical solutions into user-friendly visual representations. AGO's core functionality includes comprehensive visualizations such as position charts, delay graphs, and potential conflict maps, providing a clear, real-time picture of ground operations for informed decision-making. AGO's queue and gate occupancy analysis calculates and visualizes the maximum queue length and concurrent number of aircraft at gates, enhancing airport capacity, reducing aircraft waiting times, and streamlining ground traffic flow. Simulated in a high-traffic Turkish airport layout, AGO-MIP demonstrated significant improvement in operational efficiency compared to traditional First Come First Served (FCFS) approach. AGO outperformed the traditional FCFS approach, reducing hold fuel by 27.9%, cutting delay time by 21.6%, lowering HC, CO, and NOx emissions by 16.4%, 22.5%, and 29.3% respectively, and decreasing the maximum queue length and its duration by 15.3% and 26.6%, as well as decreasing the number of delayed aircraft by 4.8%. The AGO-STC is also tested using pushback release uncertainties in the case airport, providing robust and feasible sequences, clear feedback for all possible scenarios, and detailed outcomes for each scenario. The study concludes by discussing potential developments and current issues of the tool in detail.