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Aviation transport is an integral part of the global infrastructure that ensures fast and efficient movement of people and cargo around the world. However, international armed conflicts create serious challenges for the aviation industry, affecting the safety of flights, the economic stability of airlines, as well as the international aviation community as a whole. This article is devoted to the analysis of the impact of armed conflicts on air traffic, taking into account real examples from different regions of the world. One key aspect of the research is examining the effects of airspace closures over conflict zones, which force airlines to change routes, increasing fuel and operating costs. In addition, the issue of flight security in conflict zones, where commercial aircraft are at increased risk of accidental or deliberate attacks, is analyzed. An important aspect of the article is also the assessment of economic losses suffered by airlines and airports due to conflicts. In particular, cases of flight cancellations, a decrease in passenger traffic, as well as an increase in insurance premiums, which significantly affect the financial performance of companies, are considered. The article also touches on issues of international cooperation in the field of aviation security, which is critically important for minimizing risks and ensuring the stability of air traffic in armed conflicts. In particular, the role of international organizations such as ICAO and the effectiveness of existing international agreements and treaties aimed at coordinating actions between states during crisis situations are analyzed. The article also focuses on developing practical recommendations for airlines and governments to improve their conflict preparedness, including route planning, risk management and improved information sharing systems. Given the complexity and multifaceted nature of the issue, the authors emphasize the need for further scientific research in this area in order to develop new strategies and tools to ensure the safety and stability of the global aviation industry in the face of armed conflicts. Thus, the article makes a significant contribution to the understanding of the impact of international armed conflicts on air traffic and offers a comprehensive approach to solving this problem. [ABSTRACT FROM AUTHOR]

Quality testing and monitoring advancements have allowed modern production processes to achieve extremely low failure rates, especially in the era of Industry 4.0. Such processes are known as high‐yield processes, and their data set consists of an excess number of zeros. Count models such as Poisson, Negative Binomial (NB), and Conway‐Maxwell‐Poisson (COM‐Poisson) are usually considered good candidates to model such data, but the excess zeros are larger than the number of zeros, which these models fit inherently. Hence, the zero‐inflated version of these count models provides better fitness of high‐quality data. Usually, linearly/non‐linearly related variables are also associated with failure rate data; hence, regression models based on zero‐inflated count models are used for model fitting. This study is designed to propose deep learning (DL) based control charts when the failure rate variables follow the zero‐inflated COM‐Poisson (ZICOM‐Poisson) distribution because DL models can detect complicated non‐linear patterns and relationships in data. Further, the proposed methods are compared with existing control charts based on neural networks, principal component analysis designed based on Poisson, NB, and zero‐inflated Poisson (ZIP) and non‐linear principal component analysis designed based on Poisson, NB, and ZIP. Using run length properties, the simulation study evaluates monitoring approaches, and a flight delay application illustrates the implementation of the research. The findings revealed that the proposed methods have outperformed all existing control charts. [ABSTRACT FROM AUTHOR]

This paper proposes a new bi-objective aircraft and crew recovery model. Unlike previous researches, the proposed model considers all individual flight legs in the recovery process. The objective functions minimize the recovery cost regarding crew swapping, flight cancellation, flight delay, and crew deadhead costs. Besides, the crew sit time is reduced considering the maximum legal working hours and the minimum essential rest time. Moreover, maintenance operations can be done during the ground time, not necessarily at the end of duty day. The model is never infeasible because flight cancelation is an alternative solution for recovering a schedule. A modified Tabu Search (TS) approach has been developed to solve the model. The performance of the model and solution approach is examined by several test problems in small, medium, and large scales. The results indicate that TS reaches optimal and close-optimal in a short computational time, especially in large scale problems. [ABSTRACT FROM AUTHOR]

Motivated by a critical issue of airline planning process, this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions. Following the route network scheme and generated flight timetables, aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management. This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints, rules, and regulations. Considering multiple locations for airline maintenance and crew bases, we solve the problem of integrated Aircraft Maintenance Routing and Crew Rostering (AMRCR) to achieve the minimum airline cost. One real challenge to the efficiency of the planning results is the possible disruptions in the initial scheduled flights. Due to the fact that disruption scenarios are expressed discretely with a specified probability, and we provide adjustable decisions under disruption to deal with this disruption risk, we provide a Two-Stage Scenario-Based Robust Optimization (TSRO) model. In this model, here-and-now or first-stage variables are the initial resource assignment. Furthermore, to adapt itself to different disruption scenarios, the model considers some adjustable variables, such as the decision to cancel the flight in case of disruption, as wait-and-see or second-stage variables. Considering the complexity of integrated models, and the scenario-based decomposable structure of the TRSO model to solve it with better computational performance, we apply the column and row generation (CRG) method that iteratively considers the disruption scenarios. The numerical results confirm the applicability of the proposed TSRO model in providing the AMRCR problem with an integrated and robust solution with an acceptable level of computational tractability. To evaluate the proposed TSRO model, which solves the AMRCR problem in an integrated and robust manner, five Key Performance Indicators (KPIs) like Number of delayed/canceled flights, Average delay time, and Average profit are taken into account. As key results driven by conducting a case study, we show the proposed TSRO model has substantially improved the solutions at all indicators compared with those of the sequential/non-integrated and nominal/non-robust models. The simulated instances used to assess the performance of the proposed model and CRG method reveal that both CPLEX and the CRG method exhibit comparable and nearly optimal performance for small-scale problems. However, for large-scale instances the proposed TSRO model falls short in terms of computational efficiency. Conversely, the proposed CRG method is capable of significantly reducing computational time and the optimality gap to an acceptable level. [ABSTRACT FROM AUTHOR]

The winter storm of late 2022 caused difficulties for many US-based airlines, but none more so than Southwest Airlines. Like other airlines, Southwest canceled flights in the early stages of the storm. Their attempted recoveries failed due to procedural and technology issues. The airline was unable to cope with the magnitude of canceled flights, aircraft and crews in the wrong places, disrupted passenger plans, and luggage separated from its owners. Over the next several days, Southwest canceled tens of thousands of flights, called in thousands of additional staff to support manual operations, and flew a reduced schedule for a time before returning to normal operations. The case provides numerous scenarios for you to analyze and research, to strengthen your understanding of the underlying concepts. [ABSTRACT FROM AUTHOR]

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models' average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%. [ABSTRACT FROM AUTHOR]

In response to the rapid growth of air passenger and cargo transportation services and the sharp increase in congestion at various airports, it is necessary to optimize the allocation of flight schedules. On the basis of reducing the total airport delay time and ensuring the total deviation of flight schedules applied by airlines, it is necessary to consider finely configuring flight schedules with slot coordination parameters, introducing a 5 min slot coordination parameter, and optimizing airport flight schedules in different time periods. This article considers factors such as flight schedule uniqueness, corridor flow restrictions, and time adjustment range limitations to establish a three-objective flight-schedule refinement configuration model, which is solved using the NSGA-II algorithm based on the entropy weight method. Taking Beijing Capital International Airport as an example, the optimized results show that the total flight delay was reduced from 4130 min to 1142 min, and the original delay of 389 flights was reduced to 283 flights. Therefore, flight schedule optimization considering the fine-grained configuration of slot coordination parameters can effectively reduce airport delays, fully utilize time resources, and reduce waste of time slot resources. [ABSTRACT FROM AUTHOR]

Slot structure is the equilibrium result of market demand side and slot resource supply side, while slot parameters reflect the operational support capacity of the aviation system. Time parameters reflect the operational support capability of the aviation system. Time structure should not only reflect changes in market demand, but also meet the constraints of operational efficiency. Constructing a reasonable 18–24 h timetable profile for busy airports that meets normal expectations for declared capacity and seasonal scheduling is a challenge in civil aviation slot management. This study utilizes historical data on airport flights and weather conditions to establish a regression prediction model for the time structure using K-means clustering and partial least squares regression. Additionally, ensemble learning is employed to forecast flight delay levels. The findings demonstrate that random forest yields favorable results in regression and prediction tasks, allowing for the integration of upper (good weather) and lower (severse weather) limits of the time profile with delay predictions as time parameter intervals. Consequently, the flights falling within these intervals achieve an average delay level of less than 15 min which meets the expectations of normal flight. [ABSTRACT FROM AUTHOR]

Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account. [ABSTRACT FROM AUTHOR]

Efficient specialized vehicle cooperative scheduling is significant for airport operations, particularly during times of high traffic, which reduces the risk of flight delays and increases customer satisfaction. In this paper,we construct a multi-type vehicles collaborative scheduling model with the objectives of minimizing vehicle travel distance and vehicle waiting time. Additionally, a three-layer genetic algorithm is designed, and the crossover and mutation operations are enhanced to address the scheduling model. Due to the numerous uncertainties and stochastic interferences in airport operations, conventional scheduling methods unable to effectively address these challenges, this paper combines improved genetic algorithm, simulation algorithm, and digital twins technology, proposing a multi-strategy scheduling framework for specialized vehicles based on digital twins. The scheduling framework utilises digital twins to capture dynamic data from the airport and continuously adjusts the scheduling plan through the scheduling strategy to ensure robust scheduling for specialized vehicles. In the event of severe delays at the airport, fast and efficient re-scheduling can be achieved. Finally, the effectiveness of the proposed scheduling framework is validated using domestic flight data, and extensive experiments and analyses are conducted in different scenarios. This research contributes to addressing the optimization problem of cooperative scheduling for multi-type vehicles at airports. [ABSTRACT FROM AUTHOR]

Accurate and timely flight delay prediction cannot be overemphasized because of the ever-increasing demand for air travel and its importance in deploying intelligent transportation systems. Nonetheless, there has not been a universal solution to the problem, as more intelligent flight decision systems are required for the aviation industry's future growth. Existing flight delay classification and prediction approaches are mainly shallow traffic models and do not satisfy many applications in the real world. Our motivation to rethink the deep architecture model for predicting flight delays emanates from the problem. In this research, we proposed a technique that modified stacked autoencoder architecture parameters for training the network and understanding the link between space, time and information gained from the flight on-time data. We developed three different types of autoencoders based on the architecture of the modified stacked autoencoder. The models learn the generic flight delay features, and it's trained greedily in a layer-wise fashion. To the best of our knowledge, this is the first time these performances of vanilla autoencoder, logistic regression autoencoder and Multilayer perceptron for classification were evaluated based on the developed modified stacked autoencoder architecture. Moreover, our experiment demonstrates that the models achieved varying levels of accuracy in the flight delay classifications task. The deep vanilla autoencoder shows superior accuracy, recall and precision performance compared to logistic regression autoencoder and Multilayer perceptron autoencoders at different parameter settings. [ABSTRACT FROM AUTHOR]

For addressing the challenges of decreased attitude and trajectory tracking accuracy and a delayed response in the flight control operations of quadcopter unmanned aerial vehicles (UAVs) under the uncertainties of model parameters and external disturbances, this study leverages the advantages of the non-causal declarative modeling language Modelica in system modeling and simulation. In addition, it incorporates the nonlinear Active Disturbance Rejection Control (ADRC) framework for disturbance observation, estimation, and compensation. A state observer is designed to mitigate the impact of external disturbances and model uncertainties through feed-forward compensation, and stability analysis is conducted. Numerical simulations for hover resistance demonstrate that, compared to the cascade proportional integral differential (PID) control strategy, PID-NLADRC reduces the maximum deviation induced by wind disturbances by ∼50% and shortens the disturbance influence time by around 40%. Simulations for different trajectories, such as planar or spatial, smooth or abrupt changes, indicate that under the PID-NLADRC control strategy, the real-time spatial distance deviation mean is reduced by 69.5%, and the peak time is shortened by 75.7%. Validation through multi-objective applications and physical experiments highlights the advantages of PID-NLADRC in terms of positioning accuracy, rapid tracking, and disturbance suppression, aligning well with the fast, precise, and robust flight control requirements of quadcopter UAVs. [ABSTRACT FROM AUTHOR]

Two key features of airline departure delays are that they cascade and that there can be exceptional peaks. We model these features using an intensity‐based Hawkes process. Our application to all KLM departure delays at Amsterdam Schiphol airport in January 2015 shows that volatility in departure delays is endogenous. We correlate the key parameters of the estimated Hawkes process with daily weather conditions and find that these conditions amplify the self‐exciting feature of departure delays. [ABSTRACT FROM AUTHOR]

Airport networks are interconnected through flight routes, with delays at upstream airports leading to delays at downstream airports, thus causing delay propagation. Exploring the mechanisms of delay propagation in airport networks provides scientific insights for managing and controlling delays in aviation systems. Existing methods, such as Granger causality tests and transfer entropy, must be revised to address the nonlinear causal relationships of delays in airport networks. So, this paper proposes a causality mining method for delay propagation in airport networks based on partial correlation-based multivariate conditional independence (PCMCI). This method comprehensively considers all airports and causality mining in two stages. The first stage uses conditional independence tests to obtain the parent node set of the target airport, which includes both true and false causal relationships. The second stage employs instantaneous conditional independence tests to eliminate false causal relationships and obtain test statistics representing the strength of causality. Based on historical delay data from US airports over a year, the experimental results show that multiple factors cause delay propagation in airport networks rather than a single causal relationship. The scope of delay propagation is limited, mainly affecting a few airports closely connected to it. Delays at airports with small flight volumes are more likely to propagate. Few airport pairs in the network mutually propagate delays and, often, delays at airports affected by a particular airport's delay also exhibit causal relationships with each other. This method provides a new perspective for deepening the understanding of delay propagation mechanisms in airport networks. [ABSTRACT FROM AUTHOR]

There is an interdependence between operational-based flight delay aircraft maintenance routing problem (AMRPD) and maintenance staffing problem (MSP). Furthermore, MSP and maintenance rostering problem (MRP) are interdependent. In the literature, most of the studies only consider a single interdependence and neglect the other one. This results in difficulties in implementing the optimal plan constructed by each problem, leading finally to an increase in the operational cost of airlines and maintenance providers. Therefore, the main objective of this study is to investigate these interdependences simultaneously. This is accomplished by developing a Stackelberg game model (LFSGM), in which leader and follower are the airlines and maintenance providers, respectively. This LFSGM is modelled as a bi-level mixed integer linear programming (MILP) model. The upper-level is represented by the AMRPD, while the integrated maintenance staffing and rostering (IMSR) functions as the lower-level. The solution of the LFSGM is obtained from an iterative algorithm. The viability and superiority of the LFSGM are demonstrated by presenting a case study of a leading airline in the Middle East serviced by four maintenance providers. The results reveal a significant cost saving for airline and maintenance providers. [ABSTRACT FROM AUTHOR]

This article investigates the causes of occasional flight instability observed in Unmanned Aerial Vehicles (UAVs). The issue manifests as unexpected oscillations that can lead to emergency landings. The analysis focuses on delays in the Extended Kalman Filter (EKF) algorithm used to estimate the drone's attitude, position, and velocity. These delays disrupt the flight stabilization process. The research identifies two potential causes for the delays. First cause is magnetic field distrurbances created by UAV motors and external magnetic fields (e.g., power lines) that can interfere with magnetometer readings, leading to extended EKF calculations. Second cause is EKF fusion step implementation of the PX4-ECL library combining magnetometer data with other sensor measurements, which can become computionally expensive, especially when dealing with inconsistent magnetic field readings. This can significantly increase EKF processing time. The authors propose a solution of moving the magnetic field estimation calculations to a separate, lower-priority thread. This would prevent them from blocking the main EKF loop and causing delays. The implemented monitoring techniques allow for continuous observation of the real-time operating system's behavior. Since addressing the identified issues, no significant problems have been encountered during flights. However, ongoing monitoring is crucial due to the infrequent and unpredictable nature of the disturbances. [ABSTRACT FROM AUTHOR]

This paper analyzes approaches to developing models for the technical exploitation (TE) of aircraft within operational management framework. The operational management contour of the TE of the airline's AC fleet is considered as a TE process consisting of a sequence of flight routes, which, in turn, comprise a sequence of flights between airports in the airline network during the calendar interval when the aircraft are in this contour. We present a model that evaluates the capacity for aircraft recovery at the network airports of a hypothetical airline (dubbed "RAF") if the need so arises, utilizing the probabilistic and temporal characteristics of the airports used for aircraft recovery. [ABSTRACT FROM AUTHOR]

Flight delay identification is an important way to coordinate the operation time of airport ground service providers and improve the efficiency of airport operations. By analyzing the flight turnaround operation process, considering the randomness and synchronization of the turnaround process, and using Colored Petri Nets and Python (4.0.1), we explore the correlation between various links in the flight turnaround process and the take-off delay at the next station. This paper is committed to improving the service performance of airports and airlines, dynamically predicting flight delays, and providing guidance for avoiding excessive time in the actual operation of bad combinations. The results show that there are six kinds of bad combinations in the departure slip-out link, which is the most likely to affect the transit time. The maximum lifting degree in the bad combination is 2.043, and the maximum average delay time in the bad combination is 22.5 min. When the combination of passenger boarding and departure slip-out time is too long, it has a great positive correlation with delay. When the other links are in a state of being able to pass the station on time, the departure time and baggage loading and unloading are the two links that most affect the flight delay value. [ABSTRACT FROM AUTHOR]

The short-term traffic flow prediction can help to reduce flight delays and optimize resource allocation. Using chaos dynamics theory to analyze the chaotic characteristics of en-route traffic flow is the basis of short-term en-route traffic flow prediction and ensuring the orderly and smooth state of the en-route. This paper takes the time series of en-route traffic flow extracted from Automatic-Dependent Surveillance Broadcast (ADS-B) measured data as the research object, uses the improved C–C method to reconstruct the phase space, and uses the improved small data volume method to calculate the Lyapunov index to identify the chaos phenomenon of en-route traffic flow. In order to avoid the interference of chaos phenomenon on traffic prediction, the Wavelet Neural Network (WNN) model is established to predict the traffic flow at en-route points. The experimental shows that when the number of iterations is 10,000, the average accuracy of WNN prediction is 0.87173, and the average running time is 6.9335334 s. According to the experimental results, it can be seen that the smaller number of iterations has more advantages in running time, which greatly reduces the overall running time. At the same time, it indicates that appropriately increasing or reducing the number of iterations in this experiment has little effect on the results. [ABSTRACT FROM AUTHOR]

Slot allocation in a single airport aims to maximize the utilization of airport-declared capacity under operational and regulation constraints, while that in a multi-airport system (MAS) has to take airspace capacity into account. This is due to the fact that the conflict of using the limited capacity of certain departure/arrival fixes in the terminal airspace could induce unnecessary flight delays. The uncertainty of flying times between the airport and congested fixes makes it even more complicated for slot allocation in a MAS. Traffic flow may exceed capacity when the flying times of flights change. In this paper, the authors propose an uncertainty slot allocation model for a MAS (USAM). The objective of the model is to minimize the total displacement of slot requests in the MAS while considering all of the capacity constraints, as well as the uncertainty of flying time. The constraints of departure/arrival fixes are formulated as chance constraints, and then the Lyapunov theorem is applied for reformulation. The USAM is applied in the MAS of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Specifically, the impact of the uncertainty of flying times from five airports to airspace fix YIN is investigated. Results show that the total displacement would increase if the uncertainty of flying time was considered. The optimized schedule using the USAM, however, is more robust and can satisfy capacity constraints under various scenarios. [ABSTRACT FROM AUTHOR]

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft. [ABSTRACT FROM AUTHOR]

Aiming at the disrupted flight recovery problem, this paper established a mixed-integer programming model based on the resource assignment model to minimize the recovery cost. To deal with the large-scale examples, the Logic-Based Benders decomposition algorithm is designed to divide the problem into a master problem and sub-problems. The algorithm uses MIP in the master problem to determine flight cancellations and aircraft replacements. In the sub-problems, MIP or CP is used to determine the departure time of delayed flights. Later, incorporating sectional constraints into the main problem and iterating until an optimal solution is obtained. Furthermore, the added cutting plane constraint in the iterations of the Benders decomposition algorithm are strengthened to eliminate more inferior solutions. By comparing the results of CPLEX, the Logic-Based Benders decomposition algorithm, and the enhanced Benders decomposition algorithm, it is verified that the improved Benders decomposition algorithm can solve large-scale examples more efficiently with a faster time and fewer iterations. [ABSTRACT FROM AUTHOR]

Flight delays are a major concern for both travelers and airlines, with significant financial and reputational consequences. Accurately predicting flight delays is crucial for enhancing customer satisfaction and airline revenues. In this paper, we leverage the power of artificial intelligence and machine learning techniques to build a framework for accurately predicting flight delays. To achieve this, we collected flight information from September 2017 to April 2023, along with weather data, and performed extensive feature engineering to extract informative features to train our model. We conduct a comparative analysis of various popular machine learning architectures with distinctive characteristics, aiming to determine their efficacy in achieving optimal accuracy on our newly proposed dataset. Based on our evaluation of various architectures, our findings demonstrate that CatBoost outperformed the others by achieving the highest test accuracy and the lowest error rate in the challenging use case of Saudi Arabia. Moreover, to simulate real-world scenarios, our framework evaluates the best-performing model that has been selected for deployment in a web application, which provides users with the ability to accurately forecast flight delays and offers a user-friendly dashboard with valuable insights and analysis capabilities. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users' trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible four-dimensional trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from the Eurocontrol data repository. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions to the problem facilitates a consensus between the network manager and airspace users. In view of the level of accuracy of the solutions and the excellent computational performance, we are optimistic that the proposed approach can make a significant contribution to the development of the next generation of air traffic flow management tools. [ABSTRACT FROM AUTHOR]

The prediction of flight delays is one of the important and challenging issues in the field of scheduling and planning flights by airports and airlines. Therefore, in recent years, we have witnessed various methods to solve this problem using machine learning techniques. In this article, a new method is proposed to address these issues. In the proposed method, a group of potential indicators related to flight delay is introduced, and a combination of ANOVA and the Forward Sequential Feature Selection (FSFS) algorithm is used to determine the most influential indicators on flight delays. To overcome the challenges related to large flight data volumes, a clustering strategy based on the DBSCAN algorithm is employed. In this approach, samples are clustered into similar groups, and a separate learning model is used to predict flight delays for each group. This strategy allows the problem to be decomposed into smaller sub-problems, leading to improved prediction system performance in terms of accuracy (by 2.49%) and processing speed (by 39.17%). The learning model used in each cluster is a novel structure based on a random forest, where each tree component is optimized and weighted using the Coyote Optimization Algorithm (COA). Optimizing the structure of each tree component and assigning weighted values to them results in a minimum 5.3% increase in accuracy compared to the conventional random forest model. The performance of the proposed method in predicting flight delays is tested and compared with previous research. The findings demonstrate that the proposed approach achieves an average accuracy of 97.2% which indicates a 4.7% improvement compared to previous efforts. [ABSTRACT FROM AUTHOR]

Today, aviation has grown significantly in importance. However, the challenge of flight delays has become increasingly severe due to the need for safe separation between aircraft to mitigate wake turbulence effects. The primary emphasis of this investigation resides in elucidating the evolutionary attributes of wake vortices in homogeneous isotropy turbulence. The large eddy simulation (LES) method is used to scrutinize the dynamic evolution of wake vortices engendered by an A333 aircraft in the atmospheric milieu and assess its ramifications on the ARJ21 aircraft. The research endeavor commences by formulating an LES methodology for the evolution of aircraft wake vortices, integrating adaptive grid technology to reduce the necessary grid volume significantly. This approach enables the implementation of axial and non-axial grid adaptive refinement, leading to more accurate simulations of both axial and non-axial vortices. Numerical simulations are conducted using the LES approach to scrutinize three distinct rates of turbulence dissipation amidst the ambient atmospheric turbulence, and the results are juxtaposed with Lidar measurements (Wind3D 6000 LiDAR) of wake vortices acquired at Chengdu Shuangliu International Airport (CTU). Subsequently, the rolling moment of the following aircraft is calculated, and three-dimensional hazard zones are determined for the A333. It is found that during the approach phase, the wake turbulence separation minima for an ARJ21 (CAT-F) following an A333 (CAT-B) is 3.35 NM, which represents a reduction of approximately 33% compared to ICAO RECAT (Wake Turbulence Re-categorization). The findings validate the dependability of the fine-grained mesh used in the vortex core region, engendered through the adaptive grid method, which proficiently captures the Crow instability and the interconnected phenomena of vortices in the numerical examination of aircraft wake. The safety of wake encounters primarily depends on the magnitude of environmental turbulence and the development of structural instability in wake vortices. [ABSTRACT FROM AUTHOR]

The coronavirus disease has had a devastating impact on the airline industry worldwide. However, the cancellation of flights has also enormously reduced CO2 emissions in the air. The present study empirically explores the environmental impact of the sectoral lockdown within the EU28 and Turkey by leveraging the wavelet coherence transform (WCT) technique. The results revealed that there is a positive correlation between the number of passengers carried and CO2 emissions. In the short run, during the pandemic, there is a strong positive linkage between the number of passengers carried and CO2 emissions, while in the long run, there are also positive and meaningful relationships between the number of passengers carried and CO2 emissions between 2014 and 2022. The result also indicates that, on a quarterly basis, there was a sharp decline in CO2 emissions caused by the airline industry during the period of 2020–2021. The reverse effect was also detected, which has empirically validated the hypothesis that, during the pandemic, pollution in the atmosphere has declined because of the cancellation of flights. This confirms the hypothesis that there is positive feedback causality between passengers carried and CO2 emissions. This finding has also been supported in the existing energy literature. These outcomes have implications for environmental sustainability targets in the EU and Turkey, as the recovery of international passenger traffic will proceed at a very close pace. More policy caveats are elucidated in the conclusion section. [ABSTRACT FROM AUTHOR]

In this study, we utilize data-driven approaches to predict flight departure delays. The growing demand for air travel is outpacing the capacity and infrastructure available to support it. In addition, abnormal weather patterns caused by climate change contribute to the frequent occurrence of flight delays. In light of the extensive network of international flights covering vast distances across continents and oceans, the importance of forecasting flight delays over extended time periods becomes increasingly evident. Existing research has predominantly concentrated on short-term predictions, prompting our study to specifically address this aspect. We collected datasets spanning over 10 years from three different airports such as ICN airport in South Korea, JFK and MDW airport in the United States, capturing flight information at six different time intervals (2, 4, 8, 16, 24, and 48 h) prior to flight departure. The datasets comprise 1,569,879 instances for ICN, 773,347 for JFK, and 404,507 for MDW, respectively. We employed a range of machine learning and deep learning approaches, including Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, Logistic Regression, Extreme Gradient Boosting, and Long Short-Term Memory, to predict flight delays. Our models achieved accuracy rates of 0.749 for ICN airport, 0.852 for JFK airport, and 0.785 for MDW airport in 2-h predictions. Furthermore, for 48-h predictions, our models achieved accuracy rates of 0.748 for ICN airport, 0.846 for JFK airport, and 0.772 for MDW airport based on our experimental results. Consequently, we have successfully validated the accuracy of flight delay predictions for longer time frames. The implications and future research directions derived from these findings are also discussed. [ABSTRACT FROM AUTHOR]

Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach's efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved. [ABSTRACT FROM AUTHOR]

This study is motivated by the absence of similar research that investigates the link between terrorism level and social and environmental performance measured by the ESG score. This study adopts cross‐country panel data represented by 5082 worldwide companies during the period 2011 to 2020. The terrorism level has been measured using the Global Terrorism Index as reported by The Institute for Economics and Peace Limited. Our findings indicate that the occurrence of terrorist events has a significant negative impact on ESG performance, both collectively and individually, due to the various consequences. Many businesses may suspend operations in countries where the threat of terrorism is increasing, resulting in a flight of investors and the disruption of development projects, which may weaken overall economic performance and voluntary projects. Thus, it is likely to result in undermining companies' environmental and social practices. In addition, as fears of terrorist attacks grow, the general system of corporate governance may deteriorate, halting sustainable development initiatives in these countries. Terrorist attacks introduce complex moral dilemmas, challenge stakeholder priorities, affect a company's legitimacy, and reshape the institutional landscape. Companies must navigate these multifaceted challenges, balancing immediate stakeholder concerns, reputation restoration, compliance obligations, and evolving societal expectations. Our findings are significant, with numerous implications for different stakeholders such as regulators, policymakers, investors, managers, and government agencies. [ABSTRACT FROM AUTHOR]

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Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport and airline performance. Thus this paper proposed an effective algorithm called Flight Delay Path Previous-based Machine Learning (FDPP-ML) capable of improved prediction of individual flight delay minutes using regression models to an up level of accuracy. As aviation system connectivity presents complex spatial–temporal correlations, machine learning approaches have addressed flight delay prediction by using complex flight or weather features, or private information for specific airports and airlines that are hard to obtain, In contrast, the proposed FDPP-ML improved prediction based only on basic flight schedule features even with wide flight networks. The FDPP-ML consists of a novel algorithm with a supervised learning model, which works on reshaping datasets and creates two new features the main feature is previous flight delay (PFD) for flight paths, there is a strong relationship between departure and arrival delay, and vice versa for the same flight path, which increases the strength of the training model based on historical data. For target future flights, the algorithm works on inheriting the predicted flight delay to the next flight on the same flight path and repeats this process to end the prediction forecast horizon. The proving of approach effectiveness by using a wide network of US flight arrival and departure flights containing 366 airports and 10 airlines with various metrics accuracies of regression, and explanatory the impacts on various forecast horizons 2, 6, and 12 h for future flights. The FDPP-ML outperforms traditional training models by using machine and deep learning models and improving model accuracy in 10 models with an average of up to 39% in MAE, and 42% in MSE in a forecast horizon of 2 h. Finally, providing airport and airline analysis further reveals that can improve prediction than traditional training models for the individual busiest airports "Core 30" with an average of 35% in MAE and 42% in MSE respectively, and for the busiest 10 airlines with an average of 36% in MAE and 47% in MSE respectively. The findings of this study may offer informative recommendations to airport regulators and aviation authorities for developing successful air traffic control systems for enhanced flight delay prediction to flight operational effectiveness, not only over the US flight network but with wide worldwide flight networks if a dataset of flights existed. [ABSTRACT FROM AUTHOR]

In trajectory-based operations, trajectory negotiation and verification are conducive to using airspace resources fairly, reducing flight delay, and ensuring flight safety. However, most of the current methods are based on route negotiation, making it difficult to accommodate airspace user-initiated trajectory requests and dynamic flight environments. Therefore, this paper develops a framework for trajectory negotiation and verification and describes the trajectory prediction, negotiation, and verification processes based on a four-dimensional trajectory. Secondly, users predict flight trajectories based on aircraft performance and flight plans and submit them as requested flight trajectories to the air traffic management (ATM) system for negotiation in the airspace. Then, a spatiotemporal weighted pattern mining algorithm is proposed, which accurately identifies flight combinations that violate the minimum flight separation constraint from four-dimensional flight trajectories proposed by users, as well as flight combinations with close flight intervals and long flight delays in the airspace. Finally, the experimental results demonstrate that the algorithm efficiently verifies the user-proposed flight trajectory and promptly identifies flight conflicts during the trajectory negotiation and verification processes. The algorithm then analyzes the flight trajectories of aircrafts by applying various constraints based on the specific traffic environment; the flight combinations which satisfy constraints can be identified. Then, based on the results identified by the algorithm, the air traffic management system can negotiate with users to adjust the flight trajectory, so as to reduce flight delay and ensure flight safety. [ABSTRACT FROM AUTHOR]

Over the past several years, media reports of airline passenger misconduct have climbed sharply. Research has shown that while industry-level factors such as delayed and canceled flights, poor customer service, and overcrowded seating contribute to increased unruly behavior, additional contextual elements related to personal use of alcohol (prohibited and served) have also played a role in this shift. Drawing on data from more than 915 incidents voluntarily reported to the Aviation Safety Reporting System (ASRS) over a 21-year time period, we use a multi-level case classification coding process to examine three broad types of passenger misconduct - physical, verbal, and other general types. Our results highlight the diverse range of behaviors associated with unruly airline passengers and offer insights into how environmental factors, spatial context, weak enforcement of existing federal legislation, as well as industry structures and practices, create pathways for offender behavior. [ABSTRACT FROM AUTHOR]

Challenges in assessing build quality is one the significant hurdles that limits widespread adoption of additive manufacturing (AM) for critical applications. Collecting acoustic process emissions is a compelling yet cost effective strategy for monitoring AM processes. However, several technical challenges need to be overcome initially in order to fully leverage the information content of the captured acoustic emissions and to potentially correlate specific acoustic sensor footprints to the process anomalies that can often associated with build defects and a reduction in part quality or performance. This paper aims to address some of these challenges, provide potential technical solutions, and offer guidelines in order to enable process monitoring of powder bed fusion (PBF) AM processes using acoustic sensors in the near future. Experimental data collected during a PBF-AM process is utilized to characterize the acoustic footprint of the interaction between the laser and the powder bed. Differences between active and inactive periods of the laser are highlighted in the frequency domain. A significant initial acoustic transient was observed, associated with laser turn-on. Automated data alignment strategies are explored that aim to compensate for the time of flight delay as a function of location on the build plate. Proper data alignment may be utilized to triangulate laser location using an array of microphones, which could open up potential for use on multi-laser PBF-AM systems or systems in which the laser position data is not available to the end-user. Triangulation is demonstrated using experimental data. [ABSTRACT FROM AUTHOR]

Purpose: Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost. Design/methodology/approach: A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders' decomposition approach is used to solve the proposed model. Findings: The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited. Originality/value: The authors confirm that this is an original paper and has not been published or under consideration in any other journal. [ABSTRACT FROM AUTHOR]

Problem definition: In many resource scheduling problems for services with scheduled starting and completion times (e.g., airport gate assignment), a common approach is to maintain appropriate buffer between successive services assigned to a common resource. With a large buffer, the chances of a "crossing" (i.e., a flight arriving later than the succeeding one at the gate) will be significantly reduced. This approach is often preferred over more sophisticated stochastic mixed-integer programming methods that track the arrival of all the flights to infer the number of "conflicts" (i.e., a flight arriving at a time when the assigned gate becomes unavailable). We provide a theoretical explanation, from the perspective of robust optimization for the good performance of the buffering approach in minimizing not only the number of crossings but also the number of conflicts in the operations. Methodology/results: We show that the buffering method inherently minimizes the worst-case number of "conflicts" under both robust and distributionally robust optimization models using down-monotone uncertainty sets. Interestingly, under down-monotone properties, the worst-case number of crossings is identical to the worst-case number of conflicts. Using this equivalence, we demonstrate how feature information from flight and historical delay information can be used to enhance the effectiveness of the buffering method. Managerial implications: The paper provides the first theoretical justification on the use of buffering method to control for the number of conflicts in resource assignment problem. Funding: This work was supported by the 2019 Academic Research Fund Tier 3 of the Ministry of Education-Singapore [Grant MOE-2019-T3-1-010] and the Research Grants Council of Hong Kong SAR, China [Grant PolyU 152240/17E]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0572. [ABSTRACT FROM AUTHOR]

The recent coronavirus pandemic was a black swan event, and the airline industry was one of the most stricken. The crisis that airlines had to face during the pandemic was not just a health-related crisis but a consequential one within their own canceled flights, rebooking concerns, and a surge in refund requests they lacked the liquidity to address. Communication with stakeholders is even more crucial in times of distress. How airlines respond to the crisis shapes their reputation and future engagements. This study aims to discuss what the pandemic prompted in the Twitter communication of the three leading airlines in the Philippines, providing an insight into how they behaved before and during the pandemic, focusing on their message content and delivery strategy. Utilizing a descriptive-qualitative research design, the study thematically analyzes airlines' tweets and customer responses. The study contributes communication strategies that can help airlines instill confidence among passengers during and after a crisis, with an emphasis on instructing information, human connection, empathy, and responsiveness, and demonstrates how airlines can strategically optimize the microblogging platform in crisis communication and customer service support. [ABSTRACT FROM AUTHOR]