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Safety is the lifeline of civil aviation. With the advancement of technology, human factors have become the primary factor affecting aviation safety. Among these, human errors in air traffic control (ATC) account for a significant proportion of aviation accidents. In order to investigate human errors in ATC, this paper introduces two common human factors conceptual models – the Reason model and the HFACS model, and analyzes the specific application of the HFACS model in aviation. It is found that the HFACS model can effectively establish a classification system for human errors in ATC. Moreover, this paper combines the HFACS model with AHP (Analytic Hierarchy Process) and uses the HFACS model to establish a first and second level indicator system for human errors in ATC. We calculated the weights of each specific factor using the Analytic Hierarchy Process. The results show that the most significant influencing factor in the first level indicators is organizational factors, while the most significant influencing factor in the second level indicators is Air Traffic Resource Management. Among the 13 second level indicators, the smallest weight is the controller's violation. Based on the calculated weight results, in order to improve the safety level of ATC, the first step should be to improve the level of ATC organizational management. This is also of great significance for improving the safety level of the entire aviation system. The findings of this study suggest that the HFACS model, combined with AHP, can be an effective tool for identifying and analyzing human factors in ATC and ultimately improving the safety of the aviation industry. [ABSTRACT FROM AUTHOR]

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology—Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B's signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft. [ABSTRACT FROM AUTHOR]

With the steady increase of air traffic column, an auxiliary decision tool is required to compensate the operation redundancy deficiency of more sectors of air traffic control. To solve the problem of nonconflict high-density departure and arrival traffic flow, this method is expected to rapidly establish and maintain safe separation with more flexible changing strategies for aircraft heading and speed. This paper proposes an improved reinforcement learning framework to achieve conflict detection and resolution. The proposed framework includes the first development of an air traffic flow model based on a multiagent Markov decision process. The goal reward function was then maximized by improved Monte-Carlo tree search combined with an upper confidence bound tree. Three simulation scenarios were designed for illustrating the improvements of the proposed algorithm, with the results indicating that the algorithm could establish and maintain safe separation between 20 agents in the simplified hexagon-shaped airspace of Huadong, China. Furthermore, the proposed method was demonstrated to reduce the number of conflicts between aircraft agents by up to 26.32% compared to previous research. [ABSTRACT FROM AUTHOR]

Monitoring and analyzing radio interference sources play a crucial role in ensuring the safe operation of civil aviation navigation, communication, airport management, and air traffic control. Traditional ground monitoring methods are slow and inadequate for tracking aerial and mobile interference sources effectively. Although flight methods such as helicopters and airships can effectively monitor aerial interference, the flight approval process is time-consuming and expensive. This paper investigates a novel approach to locating civil aviation radio interference sources using four unmanned aerial vehicles (UAVs) to address this issue. It establishes a model for aerial positioning of radio interference sources with the four UAVs and proposes a method for time synchronization and data communication among them. The paper conducts simulations of the four-UAV time–frequency difference positioning method, analyzing the geometric accuracy dilution with different deployment configurations of the UAVs, positioning biases, and root mean square errors (RMSEs) under varying interference source movement speeds. The simulation results provide crucial data to support subsequent experiments. [ABSTRACT FROM AUTHOR]

Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios. [ABSTRACT FROM AUTHOR]

The Authors investigated the Aviation English proficiency levels of Vilnius Tech Aircraft Piloting and Air Traffic Control (ATC) students by considering general English examination scores, marks for Aviation English modules and proficiency levels of a specialised speaking task. The aim was to assess how well students acquire the skills and abilities not only to pass an Aviation English proficiency examination (level 4), but also to obtain a higher level 5 necessary for a longer endorsement of their language certificate. Finally, it was determined which areas of language are the easiest and most difficult to master with regards to either a level 4 or 5 according to the ICAO (International Civil Aviation Organisation) Rating scale. It was found that four out of five students have the background skills to reach at least an ICAO level 4 upon graduating, however only a fifth are able to achieve a higher level 5. The proportion of level 5 students does not match the proportion of students with good initial general English results, so there should be a greater focus on improving the language training programme by making it more challenging and broadening its scope in terms of vocabulary and grammar in particular. [ABSTRACT FROM AUTHOR]

With the continuous increase in flight flows, the flight conflict risk in the airspace has increased. Aiming at the problem of conflict resolution in actual operation, this paper proposes a tactical conflict resolution strategy based on Deep Reinforcement Learning. The process of the controllers resolving conflicts is modelled as the Markov Decision Process. The Deep Q Network algorithm trains the agent and obtains the resolution strategy. The agent uses the command of altitude adjustment, speed adjustment, or heading adjustment to resolve a conflict, and the design of the reward function fully considers the air traffic control regulations. Finally, simulation experiments were performed to verify the feasibility of the strategy given by the conflict resolution model, and the experimental results were statistically analyzed. The results show that the conflict resolution strategy based on Deep Reinforcement Learning closely reflected actual operations regarding flight safety and conflict resolution rules. [ABSTRACT FROM AUTHOR]

The primary concern of an air traffic controller is to ensure the safety and fluidity of ever-increasing air traffic. This requires effective training through practical work supervised by instructors. Based on certain rules called separation rules, the trainee must find a solution to a traffic configuration defined by flight plans (FPL) initially containing a number of conflicts. This solution will then be compared to the one proposed by the instructor. The purpose of this article is to replace the instructor with a Geographical Information System (GIS) solution combined with a genetic algorithm which, from a set of FPLs, will find the best solution to ensure on the one hand the safety of the aircraft but also minimizing the distance and the changes to be made. The application will use the GAMA platform, very suitable for this and a set of tests composed of actual exercises will be performed to validate the work. [ABSTRACT FROM AUTHOR]

Minimization of approaching time and maximization of the utilization of air route and airport resources are the two ultimate goals of air traffic control. This research considers arrival sequence and the uncertain arrival times at entry waypoints of flights and investigates the impact of air traffic controller specific parameters in the model. The two-stage optimization framework will first determine a deterministic schedule and optimize the approaching time while considering additional longitudinal separation, efficiency loss from deterministic schedule, and cruise speed adjustment in the second-stage optimization model via sample average approximation. The numerical experiments suggested an efficiency loss of 20%, which can absorb the empirical probabilistic lateness at entry waypoints with the real-world data. The proposed method could determine the estimated average delay time at runways with different settings of additional buffer for longitudinal separation and trade-off parameters between the estimated average delay time and estimated penalty cost of cruise speed adjustment. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to analyse the air traffic control (ATC)-related accidents and incidents. The paper aims to determine technical, operational and environmental factors that influence ATC-related accidents and incidents. This is important to reduce the number of these accidents. Design/methodology/approach: This predictive and dipendence study investigated situational factors indicated in the data sets of National Transportation Safety Board (NTSB) and European Union Aviation Safety Agency (EASA) (for the period 2008–2018). The specific factors were time of day in which accident occurred, location, air traffic management (ATM) contribution, flight rules, ATC unit and outside factors. Logistic regression was used to differentiate factors between fatal ATC-related accidents versus ATC-related incidents. Further, by using Pearson's chi-squared test, significant relationships between all factors in ATC-related accidents and ATC-related incidents were identified. Findings: The results showed that five factors of total six factors – ATM contribution, flight rules, ATC unit, outside factors and aircraft location – influence to ATC-related accidents and incidents. Further, results showed significant relationships between each all factors in ATC-related accidents and incidents. According to that, differences and similitudes are presented. Originality/value: After more than 20 years, study about ATC-related accidents and incidents was necessary to establish changements in thıs type of accidents. Belong of that, this is the first study that used data sets of both, NTSB and EASA, to determine the factors that affect ATC-related accidents and incidents. [ABSTRACT FROM AUTHOR]

This paper analyzes the application of Controller Pilot Data Link Communication (CPDLC) technology to different air traffic control sectors. It also presents the analysis of the performed research and tests. A CPDLC program is being developed that is adapted for aerodrome flight control. The program takes into account the advantages and disadvantages of other software, as well as the recommendations of other authors. The program is developed using JAVASCRIPT and HTML programming languages. The tests of the developed CPDLC program are performed in the Expert NITA flight control simulator. The CAPAN method is used for data analysis. Analysis of changes in workload, language errors, and time saved using different communication methods during simulations is also performed. [ABSTRACT FROM AUTHOR]

In air traffic management, the controller uses the control instruction to adjust the aircraft status, and the pilot confirms by repeating the control instruction. The correct understanding of control instruction is of great significance to flight safety. This paper proposes a new method of air traffic control information extraction, which is based on pre-training and fine tuning of the pre-training language model. It uses transfer learning to extract regulatory information under the condition of small samples. This method can not only reduce the cost of training data annotation, but also improve the accuracy of information extraction. The simulation results show that the accuracy of the new model is not less than 98%, and the key information in the control instructions can be extracted effectively. This method can improve the intelligence of air traffic control system, assist controllers to understand the contents of control instructions, support flight conflict detection, and ensure air transport safety. [ABSTRACT FROM AUTHOR]

The popularity of air transportation, both for personal and commercial uses, has been growing in recent years. As a result, the traffic volume in the airspace is constantly increasing, which leads to the higher chances of conflicts among aircraft. In air traffic management, conflict detection and resolution are challenging and stressful tasks due to the highly dynamic nature of aircraft flight plans as well as the interdependency among pilots' decisions. Therefore, reliable and comprehensive decision-making techniques are necessary to deal with such conflicts in the airspace in a timely manner. In this regard, innovative technological developments are essential to assist decision-makers. In this paper, we propose a semi-decentralized, three-stage algorithm based on game theory to resolve potential conflicts among multiple aircraft traveling in a 3D shared airspace. The result from the algorithm shows that deviation costs are highly sensitive to the level of congestion in the airspace. The proposed algorithm provides useful information for air traffic control and pilots to enhance their coordination and facilitate decision-making procedures in scenarios with single and multiple conflicts during both nominal and deviated-from-nominal situations. [ABSTRACT FROM AUTHOR]

Today, aircraft demand is exceeding the capacity of the Air Traffic Control (ATC) system. As a result, airspace is becoming a very complex environment to control. The complexity of airspace is thus closely related to the workload of controllers and is a topic of great interest. The major concern is that variables that are related to complexity are currently recognised, but there is still a debate about how to define complexity. This paper attempts to define which variables determine airspace complexity. To do so, a novel methodology based on the use of machine learning models is used. In this way, it tries to overcome one of the main disadvantages of the current complexity models: the subjectivity of the models based on expert opinion. This study has determined that the main indicator that defines complexity is the number of aircraft in the sector, together with the occupancy of the traffic flows and the vertical distribution of aircraft. This research can help numerous studies on both air traffic complexity assessment and Air Traffic Controller (ATCO) workload studies. This model can also help to study the behaviour of air traffic and to verify that there is symmetry in structure and the origin of the complexity in the different ATC sectors. This would have a great benefit on ATM, as it would allow progress to be made in solving the existing capacity problem. [ABSTRACT FROM AUTHOR]

When aircraft are flying along the routes, their states present diversity and complexity. To clarify the control or monitoring relationship between air sectors and aircraft in the airspace and to promote reliability and efficiency of air traffic control (ATC), in this paper, an aircraft control multilayer network (ACMN) model is built by considering the air sector network (ASCN) and the aircraft state network (ASTN). The characteristics of ACMN are studied based on complex network theory. Simultaneously, according to the multilayer characteristics of ACMN and the relative entropy theory, a robustness analysis method is proposed. Results show that the proposed method is applicable for evaluating the structural stability and robustness of ACMN and is effective in improving the efficiency of ATC. It can also provide a new idea for the robustness research of multilayer networks. [ABSTRACT FROM AUTHOR]

Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means of compliance for future AI-based regulation. Designers and developers must understand how the learning assurance process of any machine learning (ML) model impacts trust. ML is a narrow branch of AI that uses statistical models to perform predictions. This work deals with the learning assurance process for ML-based systems in the field of air traffic control. A conflict detection tool has been developed to identify separation infringements among aircraft pairs, and the ML algorithm used for classification and regression was extreme gradient boosting. This paper analyses the validity and adaptability of EASA W-shaped methodology for ML-based systems. The results have identified the lack of the EASA W-shaped methodology in time-dependent analysis, by showing how time can impact ML algorithms designed in the case where no time requirements are considered. Another meaningful conclusion is, for systems that depend highly on when the prediction is made, classification and regression metrics cannot be one-size-fits-all because they vary over time. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to set out a methodology for characterising the complexity of air traffic control (ATC) sectors based on individual operations. This machine learning methodology also learns from the data on which the model is based. Design/methodology/approach: The methodology comprises three steps. Firstly, a statistical analysis of individual operations is carried out using elementary or initial variables, and these are combined using machine learning. Secondly, based on the initial statistical analysis and using machine learning techniques, the impact of air traffic flows on an ATC sector are determined. The last step is to calculate the complexity of the ATC sector based on the impact of its air traffic flows. Findings: The results obtained are logical from an operational point of view and are easy to interpret. The classification of ATC sectors based on complexity is quite accurate. Research limitations/implications: The methodology is in its preliminary phase and has been tested with very little data. Further refinement is required. Originality/value: The methodology can be of significant value to ATC in that when applied to real cases, ATC will be able to anticipate the complexity of the airspace and optimise its resources accordingly. [ABSTRACT FROM AUTHOR]

This study investigates the views of Australian Air Traffic Controllers (ATCOs) on increasing levels of automation in Air Traffic Managements Systems (ATMS). An online survey of current and former Australian ATCOs was conducted to identify what tools are considered useful and to what extent automation should be used within ATMS, as well as how increasing automation is expected to impact ATCOs situational awareness. The results highlighted that automation tools that supported and assisted the controller were more likely to be accepted by ATCOs. Automation tools that assumed/replaced the decision making and authority of the ATCO, had lower levels of acceptance by controllers. Increased automation (electronic strips) was considered to have both a positive and negative effect on ATCO situational awareness dependant on the type of role performed by the controller. When transitioning to systems with increased automation, the lack of system understanding/training, skill degradation and the changing role of controllers were considered the three biggest safety risks. By considering this research, the aviation industry can focus on the introduction of automation tools that can best assist ATCOs. Additionally, this study provides invaluable research from ATCOs to inform Air Navigation Service Providers (ANSPs) when implementing more automated ATMS. • Automation tools that supported the ATCO were more likely to be accepted by ATCOs. • Automation that replaced decision making of the ATCO had lower levels of acceptance. • Kinaesthetic reinforcement of strip placement was considered crucial by controllers. • Training, skill loss and changing role of ATCOs considered greatest safety risks. [ABSTRACT FROM AUTHOR]

Today's air traffic management (ATM) system evolves around the air traffic controllers and pilots. This human-centered design made air traffic remarkably safe in the past. However, with the increase in flights and the variety of aircraft using European airspace, it is reaching its limits. It poses significant problems such as congestion, deterioration of flight safety, greater costs, more delays, and higher emissions. Transforming the ATM into the "next generation" requires complex human-integrated systems that provide better abstraction of airspace and create situational awareness, as described in the literature for this problem. This paper makes the following contributions: (a) It outlines the complexity of the problem. (b) It introduces a digital assistance system to detect conflicts in air traffic by systematically analyzing aircraft surveillance data to provide air traffic controllers with better situational awareness. For this purpose, long short-term memory (LSTMs) networks, which are a popular version of recurrent neural networks (RNNs) are used to determine whether their temporal dynamic behavior is capable of reliably monitoring air traffic and classifying error patterns. (c) Large-scale, realistic air traffic models with several thousand flights containing air traffic conflicts are used to create a parameterized airspace abstraction to train several variations of LSTM networks. The applied networks are based on a 20–10–1 architecture while using leaky ReLU and sigmoid activation function. For the learning process, the binary cross-entropy loss function and the adaptive moment estimation (ADAM) optimizer are applied with different learning rates and batch sizes over ten epochs. (d) Numerical results and achievements by using LSTM networks to predict various weather events, cyberattacks, emergency situations and human factors are presented. [ABSTRACT FROM AUTHOR]

With the development of global air transport， air traffic services， including air traffic control， aeronautical operational control，etc， have entailed higher performance of aviation communication systems. As one of the new generation aviation communication system schemes， L‑band digital aeronautical communication system （L‑DACS） could achieve a better data transmission rate， spectrum utilization efficiency and capacity， solving scarcity of spectrum resources. This paper summarizes L‑DACS’s state‑of‑the‑art progresses and studies， specifically its technology evolution and standardization development. Then， a draft of layered system framework is introduced， covering network， datalink and physics levels. Finally， challenges that L‑DACS faces and its corresponding developing proposals are given. This review provides a reference for the research of aviation broadband communication. [ABSTRACT FROM AUTHOR]

Fast simulation technology is very important to explore the consequences of air traffic management decision. Although a variety of simulation tools have been developed to make decisions for air traffic controllers, the cognitive process of air traffic controllers is not effectively integrated into these tools. This article studies a probabilistic cellular automata (CA) model for air traffic flow simulation. By introducing the safety distance parameter into the CA model, the equilibrium property of air traffic system is studiedusing the statistical physics method. The simulation results show that the data are in good agreement with the measured results. [ABSTRACT FROM AUTHOR]

Commercial air travel in the United States has grown significantly in the past decade. While the reasons for air traffic delays can vary, the weather is the largest cause of flight cancellations and delays in the United States. Air Traffic Control centers utilize Traffic Management Initiatives such as Ground Stops and Expect Departure Clearance Times (EDCT) to manage traffic into and out of affected airports. Airline dispatchers and pilots monitor EDCTs to adjust flight blocks and flight schedules to reduce the impact on the airline's operating network. The use of time-series data mining can be used to assess and quantify the impact of surface weather variables on EDCTs. A major hub airport in the United States, Charlotte Douglas International Airport, was chosen for the model development and assessment, and Vector Autoregression and Recurrent Neural Network models were developed. While both models were assessed to have demonstrated acceptable performance for the assessment, the Vector Autoregression outperformed the Recurrent Neural Network model. Weather variables up to six hours before the prediction time period were used to develop the proposed lasso regularized Vector Autoregression equation. Precipitation values were assessed to be the most significant predictors for EDCT values by the Vector Autoregression and Recurrent Neural Network models. [ABSTRACT FROM AUTHOR]

This paper analyzes the feasibility of time-based metering operations for domestic flights in Japan, which have shorter flight times than international flights. For metering air traffic, aircraft are required to adjust their fly-over times at specified fixes. Speed control is typically used to adjust the fly-over time without stretching the lateral path. This study applies flight-level change in addition to speed control to increase the achievable airborne delay. The achievable airborne delay using speed control and flight-level change is evaluated statistically based on past actual radar tracking data. Through illustrative numerical simulations for the domestic flights to Tokyo International Airport, the achievable delays can be more than doubled and higher compliance rates are expected when changing the flight level in addition to speed control. Moreover, the potential benefit of saving fuel using speed control is evaluated, and a fuel savings of 2-4% is expected on average. Based on the analysis in this study, the maximum potential achievable delays and feasibility of time-based metering for domestic flights in Japan can be evaluated. [ABSTRACT FROM AUTHOR]

Fault tolerance is a key requirement for En Route Automation Modernization (ERAM), the FAA's system that manages En Route air traffic over the USA. A system failure could lead to hundreds of flights being delayed or cancelled. Using experience from earlier systems a set of techniques were built into ERAM at inception, including a hot standby copy of each executable and the latest state checkpointed in disk files. As the system matured through formal testing and operational experience at the first sites (2010 - 2015), the goal of 100% availability was not achieved so additional techniques were added. These included exception safety, runaway process protection, and proactive monitoring of the system to detect defects and often resolve them without the air traffic controllers being aware. With the implementation of these additional techniques the FAA has measured ERAM as 100% available from October 2016 at all 20 operational sites. Software fault tolerance techniques have been well documented [2]; this extended abstract describes the specific techniques that have led to ERAM achieving continuous 24x7 availability for 6 years. [ABSTRACT FROM AUTHOR]

Operational demands in safety‐critical systems impose a risk of failure to the operators especially during urgent situations. Operators of safety‐critical systems learn to make decisions effectively throughout extensive training programs and many years of experience. In the domain of air traffic control, expensive training with high dropout rates calls for research to enhance novices' ability to detect and resolve conflicts in the airspace. While previous researchers have mostly focused on redesigning training instructions and programs, the current paper explores possible benefits of novel visual representations to improve novices' understanding of the situations as well as their decision‐making process. We conduct an experimental evaluation study testing two ecological visual analytics interfaces, developed in a previous study, as support systems to facilitate novice decision‐making. The main contribution of this paper is threefold. First, we describe the application of an ecological interface design approach to the development of two visual analytics interfaces. Second, we perform a human‐in‐the‐loop experiment with forty‐five novices within a simplified air traffic control simulation environment. Third, by performing an expert‐novice comparison we investigate the extent to which effects of the proposed interfaces can be attributed to the subjects' expertise. The results show that the proposed ecological visual analytics interfaces improved novices' understanding of the information about conflicts as well as their problem‐solving performance. Further, the results show that the beneficial effects of the proposed interfaces were more attributable to the visual representations than the users' expertise. [ABSTRACT FROM AUTHOR]

We provide evidence for a power law relationship between the subjective one-dimensional Instantaneous Self Assessment workload measure (five-level ISA-WL scale) and the radio communication of air traffic controllers (ATCOs) as an objective task load variable. It corresponds to Stevens' classical psychophysics relationship between physical stimulus and subjective response, with characteristic power law exponent γ of the order of 1. The theoretical model was validated in a human-in-the loop air traffic control simulation experiment with traffic flow as environmental stimulus that correlates positively with ATCOs frequency and duration of radio calls (task load, RC-TL) and their reported ISA-WL. The theoretical predictions together with nonlinear regression-based model parameter estimates expand previously published results that quantified the formal logistic relationship between the subjective ISA measure and simulated air traffic flow (Fürstenau et al. in Theor Issues Ergon Sci 21(6): 684–708, 2020). The present analysis refers to a psychophysics approach to mental workload suggested by (Gopher and Braune in Hum Factors 26(5): 519–532, 1984) that was recently used by (Bachelder and Godfroy-Cooper in Pilot workload esimation: synthesis of spectral requirements analysis and Weber's law, SCL Tech, San Diego, 2019) for pilot workload estimation, with a corresponding power law exponent in the typical range of Stevens' exponents. Based on the hypothesis of cognitive resource limitation, we derived the power law by combination of the two logistic models for ISA-WL and communication TL characteristics, respectively. Despite large inter-individual variance, the theoretically predicted logistic and power law parameter values exhibit surprisingly close agreement with the regression-based estimates (for averages across participants). Significant differences between logistic ISA-WL and RC-TL scaling parameters and the corresponding Stevens exponents as ratio of these parameters quantify the TL/WL dissociation with regard to traffic flow. The sensitivity with regard to work conditions of the logistic WL-scaling parameter as well as the power law exponent was revealed by traffic scenarios with a non-nominal event: WL sensitivity increased significantly for traffic flow larger than a critical value. Initial analysis of a simultaneously measured new neurophysiological (EEG) load index (dual frequency head maps, DFHM, (Radüntz in Front Physiol 8: 1–15, 2017)) provided evidence for the power law to be applicable to the DFHM load measure as well. [ABSTRACT FROM AUTHOR]

The rapid growth of the airline industry has caused an enormous demand in the context of air transport and air traffic congestion in several hub airports. In order to alleviate this situation and resolve the imbalance between the arrival and departure rate, efficient runway usage in airport capacity management is an immediate and feasible solution as compared to airport expansion and runway construction. Air Traffic Control (ATC) operators could optimize their runway capacity by operating dynamic runway configuration in switch-mode runways based on the air and airport traffic conditions. A semi-mixed mode runway is considered in this paper, wherein some runways are configured for either landing or take-off operations, while others are operated in switch mode. The demand for arrival and departure is subject to the passenger's demand, flight availability, and timings, preferred flight schedule, and frequency of flight schedule service, and usually vary in different hours (peak and nonpeak hours). Given this feature, ATC can reconfigure the runway mode responding to the current demand for arrival and departure and further seize the runway capacity via a systematic approach. Under the semi-mixed mode situation, formulating the coordination of dynamic runway configuration planning and the Aircraft Sequencing and Scheduling Problem is proposed. The air traffic pattern in Hong Kong International Airport (HKIA) is used as a test case to evaluate the performance of this proposed model. Based on the test results, it was found that this dynamic runway configuration planning and semi-mixed runway design can utilize runway capacity more efficiently. In the numerical study, the dynamic runway configuration planning achieved 71.6%% and 37.08% reduction of flight tardiness than the two segregated runway systems (two landings and one take-off runways and one landing and two take-off runways) in HKIA. [ABSTRACT FROM AUTHOR]

An accurate recognition method of the air traffic flow pattern is proposed based on historical flight trajectory data to understand the spatial distribution of air traffic flow and improve the airspace utilization in the terminal area. Due to the high dimensionality of flight trajectory data, we establish a trajectory similarity model based on the geodesic distance and use an improved spectral clustering algorithm to classify the flight trajectory sample data. An improved algorithm based on the minimum spanning tree is presented to extract the skeleton similarity between the tracks and obtain the model of the prevalent traffic flow. The experimental results show that the method can accurately divide 1070 flight paths into 5 categories and extract 7 prevalent traffic flows, exhibiting strong robustness to abnormal trajectories and noise. [ABSTRACT FROM AUTHOR]

Nowadays, air transportation is known as the most safety type of transportation. However, there have been a lot of airplane crashes in which a lot of people lost their lives alongside with a huge material damage and a high cost for air industry. The causes of airplane crashes are different and various. One of the causes is air traffic control errors. Research shows that 6% - 10% of all aircraft crash accidents are caused by air traffic control errors. However, this type of research is very important for the sustainability of traffic management. In this research data of airplane accidents and incidents for the period between 2008-2018 in the US caused by air traffic control errors taken from NTSB and data of airplane accidents and incidents for the same period in Europe taken from EASA were used. Both data were analyzed according to various factors such as time of the day of the occurrence, place of the occurrence, type of flight rules, the ATC unit where the error was made, weather conditions, and traffic frequency. All occurrences (accidents and incidents) were analyzed according to these factors and results have been shown in graphs for the US airspace and European airspace compared to each other. The results of the study show differences in the percentage of occurrences with ATC contribution and occurrences affected by traffic density as external factors and VFR accidents related to ATC. According to other factors, results are compatible with each other. [ABSTRACT FROM AUTHOR]

Small-scale aviation has been driven extensively by recent technological developments. Distinct micro/small scale mobility modes are being interlined, where automated Vertical Take-Off and Landing Aircraft (VTOLs) are being conceptualized for Urban Air Mobility (UAM) in the form of air taxi, cargo, disaster relief, or medical help. This implicates many simultaneous flights over cities, which is a significant challenge. Traditional air traffic control is customized for commercial aviation, and it is not suitable for the dynamic variation in the flight routes of UAM. Consequently, a literature review is conducted firstly for air traffic management subject to UAM. Then, as a critical finding, a self-organizing model integrating particularly micro/small scale UAM is proposed utilizing the swarm concept to leverage the autonomous behavior of VTOLs. Rules for self-organization are set, which are then discussed in conjunction with available technologies such as Global Positioning System (GPS) and Traffic Alert and Collision Avoidance System (TCAS). Finally, the basic concept definition is elaborated to determine challenges and future research. [ABSTRACT FROM AUTHOR]

Air traffic control (ATC) is a safety-critical, cooperative work domain, which faces usability challenges due to technology driven development in the past. In this work, we followed a user-centered design process to explore how novel interaction concepts increase user experience in ATC. Based on controllers' needs we envisioned one unified interface together with three possible interaction concepts (the mouse interface, flight-dial and tangible interface) addressing different aspects of ATC. We prototypically implemented the interaction concepts and iterated each prototype based on feedback from 24 controllers. Qualitative data from these iterative formative evaluations indicated that controllers prefer interfaces that are efficient to use, minimalistic, customizable and context sensitive. A summative evaluation (N  = 12) showed that the hedonic quality of all three concepts were higher compared to the system currently in use. Our results and insights can provide guidance and inspiration for the future design of ATC interfaces. [ABSTRACT FROM AUTHOR]

In recent years, due to the close coupling between airports, airport risk propagation has become a huge challenge. However, it has not been fully understood on the network level. Airport risk can be transferred through other airports owing to connected resources. In this study, we consider two risk factors including airport delay and saturation and propose a risk coupling model based on a clustering algorithm to fit the index and form risk series. To understand the risk propagation mechanism, we build risk propagation networks based on the Granger Causality test, and we apply complex network theory to analyze the evolution of the risk propagation network. We study the regular pattern of risk propagation from perspectives of time and space. Through network analysis, we find four time stages in the risk propagation process and the participation of airports in risk propagation has a positive correlation with airport sizes. In addition, more large airports tend to prevent risk propagation in unoccupied and normal situations, while small airports perform better than large airports in busy situations. Via the conclusion, our work can assist airlines or air traffic managers in controlling the scale of risk propagation before its key time turning point. By identifying the critical airport level and related factors in risk propagation, they can also reduce single airport risk and risk participation through corresponding risk control measures, finally avoiding the large-scale spread of risk and reducing delay or cancellation of more flights. [ABSTRACT FROM AUTHOR]

The air transport market has a strategic character, promotes economic and social development, and has a strong correlation with the level of economic activity. The Air Traffic Management (ATM) system plays an essential role in air transportation and can be characterized as a sociotechnical system that is too complex to research through classical approaches such as systems engineering. In this sense, the application of Complex Network Theory (CNT) analysis and modeling paradigms is driven by the need to accommodate the growth of air traffic within an already saturated ATM infrastructure. The present work describes the development of a resilience evaluation model of ATM systems based on CNT, its metrics and analysis tools. The model was applied to the Brazilian ATM system, demonstrating its usefulness in identifying the air traffic control (ATC) agencies that have the greatest influence on the network. The results also showed that the Brazilian ATM network is resilient to random failures; however, it is particularly vulnerable to targeted attacks to the ATC agencies with the highest centrality. The research findings can be applied to prioritize the deployment of systems and equipment that enhance the resilience and operability of the Brazilian ATM system. [ABSTRACT FROM AUTHOR]

Computer-aided air traffic management has increasingly attracted the interest of the operations research community. This includes, among other tasks, the design of decision support tools for the detection and resolution of conflict situations during flight. Even if numerous optimization approaches have been proposed, there has been little debate toward homogenization. We synthesize the efforts made by the operations research community in the past few decades to provide mathematical models to aid conflict detection and resolution at a tactical level. Different mathematical representations of aircraft separation conditions are presented in a unifying analysis. The models, which hinge on these conditions, are then revisited, providing insight into their computational performance. [ABSTRACT FROM AUTHOR]

Dense and complex air traffic requires higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use today: AI tools can act on their own initiative, increasing the capacity of ATCOs to control higher volumes of traffic. However, given that the air traffic control (ATC) domain is safety critical, requires AI systems to which ATCOs are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions and operational transparency. ResoLver, the system that this article presents, addresses these challenges using an enhanced graph convolutional reinforcement learning method operating in a multiagent setting where each agent – representing a flight – performs a CD&R task, jointly with other agents. We show that ResoLver can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing also operational transparency issues, which have been validated by ATCOs in simulated real-world settings. • Formulates the multi-agent conflicts detection and resolution problem. • Proposes ResoLver whose deep reinforcement learning engine resolves conflicts. • Addresses operational transparency issues. • Provides evaluation and validation results in simulated real-world cases. [ABSTRACT FROM AUTHOR]

In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of intelligence about military and government movements. Typically, these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology, the R&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than 2 million aircraft. We show that we can obtain the correct aircraft category with an accuracy of greater than 88%. In cases, where no metadata is available, this approach can be used to create the data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect particular sensitive aircraft such as police and surveillance aircraft using this method. [ABSTRACT FROM AUTHOR]

One of the most important issues facing both the American and European air traffic management systems will be to provide enough capacity to meet increasing air traffic demand while maintaining a high level of safety. In this work, we focus on the final approach flight phase and, in particular, on the missed approach procedure, which is conducted when unexpected occurrences force the aircrew to abort the landing. The current missed approach consists in recommencing the approach from the initial approach fix and, in some cases, it is just an outbound heading to wait for air traffic control instructions. In this article, we propose and evaluate the performance of a new missed approach procedure supported by current state-of-the-art communication, navigation, and surveillance technology. Our proposal, referred to as aircraft reinjection system, basically consists in identifying a gap in the inbound traffic flow, in accordance with the International Civil Aviation Organization standards, to reinject the affected aircraft and providing a new route to redirect the aircraft to that mentioned gap. Its evaluation proves that this proposal can achieve important time savings, helping to reduce the use of active runways, with the corresponding positive impact on the environment, noise, and pollution. Furthermore, and more importantly, it may enhance safety levels when the aircrew performs an unstable approach on final since they will be more willing and trained to perform an up-to-date and time-sensitive procedure rather than current old-fashioned ones. [ABSTRACT FROM AUTHOR]

Air traffic control's aim is to optimise airport capacity, that is, to increase the number of aircraft movements per hour maintaining a limited delay. There are several definitions of capacity, which depend on the considered airport element. This study focused on the development of a method that allows evaluating the impact of tower air traffic controllers' workload on airport capacity. It adapts a model for the workload of sector controllers designed by Eurocontrol to tower controllers and tests it on a heavily busy international airport. In order to collect controllers' working times, a campaign on data research has been carried out from which it was possible to extrapolate the hourly percentage of work of the various tower controllers using a fast-time simulation software. By imposing an hourly working threshold on tower air traffic controllers, it was possible to obtain a maximum number of manageable aircraft, which was compared with the airside capacity of the airport. The results show that the maximum traffic manageable from the airside would produce unacceptable workload for tower controllers, highlighting the link between airport capacity and human component. [ABSTRACT FROM AUTHOR]

In the document of the Radio Technical Commission for Aeronautics (RTCA) [1], a methodology has been developed for the flight safety level assessment when using a promising automatic dependent surveillance system in broadcast mode (ADS-B) intended for certification of an air traffic control system. As the analysis presented in the article has shown, this technique is based on an erroneous hypothesis, the use of which leads to underestimated requirements for the ADS-B certified system. The example considered in the article shows that the rejection of this erroneous hypothesis can lead to tightening the requirements by an order of magnitude. To obtain correct results, the article proposes an easy-to-compute modification of the technique that is devoid of this, generally speaking, serious drawback. The use of the proposed technique is illustrated by an example. [ABSTRACT FROM AUTHOR]

In this article, the system model of the terminal control area is built considering the constraints between different departure routes. The balance for the departure routes in the terminal control area is achieved and the optimal control inputs for the departure flights are also acquired. In particular, the dioid algebra is employed to describe all kinds of concurrent events in the terminal control area and define the interactions between different flight trajectories. In this way, the new optimization model and solution procedure is introduced in order to improve the operation effect of air traffic flow. Finally, different scenarios such as deterministic and stochastic cases are given to demonstrate the performance of the system model. The essence of the system model is allocating optimal time slots to flights coming from different airports. [ABSTRACT FROM AUTHOR]

The OpenSky Network is a non-profit association that crowdsources the global collection of live air traffic control data broadcast by aircraft and makes them available to researchers. OpenSky's data have been used by over 100 academic groups in the past 5 years, with popular research applications ranging from improved weather forecasting to climate analysis. With the COVID-19 outbreak, the demand for live and historic aircraft flight data has surged. Researchers around the world use air traffic data to comprehend the spread of the pandemic and analyse the effects of the global containment measures on economies, climate and other systems. With this work, we present a comprehensive air traffic dataset, derived and enriched from the full OpenSky data and made publicly available for the first time (; https://doi.org/10.5281/zenodo.3931948 , last access: 9 February 2021). It spans all flights seen by the network's more than 3500 members between 1 January 2019 and 1 July 2020. The archive is being updated every month and for the first 18 months includes 41 900 660 flights, from 160 737 aircraft, which were seen to frequent 13 934 airports in 127 countries. [ABSTRACT FROM AUTHOR]

Weather events affect air traffic control (ATC) in many ways, for there are many situations that need to be reported in pilot-controller communication. This paper attempts to analyze the language used to express the impact of meteorological phenomena to air traffic operations, particularly in regard to aeronautical English, that is, the communication used during radiotelephony by air traffic controllers in training situations. For that, two types of analyses will be carried out: one regarding the formulaic structure of lexical units using 11 Aeronautical Meteorology terms within the ATC context (phase 1); and another one concerning the use of these terms by students in three ATC courses (for TWR, ACC and APP facilities) and how it affects their performance during communication activities in a learning environment (phase 2). These analyses will be based on rationales of lexical semantics for terminology; corpus linguistics (CL), comprising English for Specific Purposes (ESP) and learner corpora; and considerations about vocabulary assessment on aeronautical English exams. Results suggest that terminological patterns discussed in this paper show how meaning is dependent on context, and how lexical semantic analysis of terms may contribute to reveal nuances of language used in a specialized context. In this way, it indicates courses have been efficient in teaching and practicing the use of the main meteorological terms related to aeronautical English and that, despite some mistakes students make, evidence points out that they are able to report weather conditions to pilots and to understand pilots' requests in a proficient level concerning vocabulary. [ABSTRACT FROM AUTHOR]

U.S. National Airspace System modernization began with the publication of the Next Generation Air Transportation System Integrated Plan (NextGen) in 2004 to accommodate forecasted air travel demand increases in the United States. This framework proposed an integrated approach to safety, environmental sustainability, reduced fuel burn, and increased airspace and airport capacity by using automated capabilities. One of these capabilities, the Optimized Profile Descent (OPD) is an automated procedure created to link the en route phase of flight with the terminal area within the context of NextGen goals. This type of automated procedure was developed during the NextGen short phase (2004-2012) for both air traffic control and aircraft but continue to be used in a non-integrated manner. It is the confluence of incompatible automated and manual air traffic management techniques that produce a favorable location for an altitude deviation. The purpose of this study is to determine the effect of air traffic control intervention on altitude deviations reported during optimized profile descent arrival procedures in the U.S. National Airspace System from January 1, 2012 to January 1, 2018. Examination of aviation safety reports from this time period showed that air traffic control intervention did affect altitude deviations, specifically in the areas of aircrew error, communication error, and equipment malfunction or limitation. This analysis also demonstrated the failure of the altitude deviation rate to return to normal historic levels after the introduction of NextGen procedures, making altitude deviation a leading safety indicator for the U.S. National Airspace System. [ABSTRACT FROM AUTHOR]