Air Traffic Control Operations Research Articles

Virtual/augmented reality-based human–machine interface and interaction modes in airport control towers

The concept of an innovative human–machine interface and interaction modes based on virtual and augmented reality technologies for airport control towers has been developed with the aim of increasing the human performances and situational awareness of air traffic control operators. By presenting digital information through see-through head-mounted displays superimposed over the out-of-the-tower view, the proposed interface should stimulate controllers to operate in a head-up position and, therefore, reduce the number of switches between a head-up and a head-down position even in low visibility conditions. This paper introduces the developed interface and describes the exercises conducted to validate the technical solutions developed, focusing on the simulation platform and exploited technologies, to demonstrate how virtual and augmented reality, along with additional features such as adaptive human–machine interface, multimodal interaction and attention guidance, enable a more natural and effective interaction in the control tower. The results of the human-in-the-loop real-time validation exercises show that the prototype concept is feasible from both an operational and technical perspective, the solution proves to support the air traffic controllers in working in a head-up position more than head-down even with low-visibility operational scenarios, and to lower the time to react in critical or alerting situations with a positive impact on the human performances of the user. While showcasing promising results, this study also identifies certain limitations and opportunities for refinement, aimed at further optimising the efficacy and usability of the proposed interface.

Agri-voltaics near airport facilities. Reconciling the risk of solar glare with power generation

The installation of photovoltaic systems on cultivated land enables both agricultural and green energy production simultaneously. The result is an efficient and inclusive use of land that promotes the rural economy, enables the recovery and maintenance of land and prevents its abandonment when it is considered not profitable. This intent is facilitated in semi-rural areas where there is already the presence of infrastructure connected to the national power grid, but at the same time is limited by other human activities. Runway approach and air traffic control operations impose very stringent constraints on the possibility that photovoltaic panels, reflecting solar radiation, may glare airport operators.This article provides a methodology for verifying such phenomena (control function) and mitigating them by maximizing solar power generation (objective function). Through a case study located in central Italy, positioned near an airport, it was possible to investigate the design variables to protect the landing air routes and the airport control tower.In this case study, with an azimuth angle of 47° and tilt angle of 25°, and thanks to a 5 m high tree screen placed on the edge of the land on which the agri-voltaic installation stands, it is possible to secure airport operations by eliminating all forms of solar glare. At the same time, 21.9 GWh of photovoltaic energy is produced annually, while avoiding the release of 8,773 tons of CO2 into the atmosphere. Compared with the optimal, unconstrained case, photovoltaic panels arranged in this way decrease their annual energy production by only 6.8 %.

Harnessing Automation and IIoT in Air Traffic Control for Advanced Efficiency and Safety

The combination of automation and Industrial Internet of Things (IIoT) technology has transformed air traffic control (ATC), improving efficiency and safety in aviation. This article investigates the varied influence of automation and IIoT on ATC operations, emphasizing their importance in maximizing airspace use, boosting situational awareness, and limiting operational hazards. The advent of automatic flight data processing, radar surveillance, and automated decision-support technologies has reduced controller workload and increased operational efficiency in ATC. IIoT sensors and devices enable ATC systems to integrate real-time data on aircraft positions, weather, and runway status, allowing controllers to make more informed decisions and manage air traffic flow. Finally, automation and IIoT have evolved into critical tools for upgrading air traffic control, assisting controllers in managing airspace congestion, optimizing operational performance and safely and quick, efficient traffic movement. This study examines how automation and IIoT alter ATC operations, resulting in a more efficient, robust, and sustainable aviation environment. Keywords— Industrial Internet of Things (IIoT), Air Traffic Control (ATC), Automation, Operational Hazards, Radar surveillance, IoT sensors, Aviation environment.

Research on assessment of air traffic control operation quality based on track data

AbstractEvaluating the quality of air traffic control operations is crucial for enhancing airspace management. Thus, this paper proposes a data mining approach for conducting a comprehensive assessment of control operation quality (COQ) in increasingly complex operation environments. First, the authors establish a COQ evaluation index system that combines both subjective and objective measures. Key index parameters are determined using wavelet filtering and interval estimation techniques on the basis of data mining results. Second, the authors apply an entropy‐weighted cloud model to label data samples and classify COQ into ‘excellent’, ‘good’, and ‘fair’ levels. Finally, the authors establish an support vector machine‐based COQ assessment model using XGBoost feature combinations to verify the practical feasibility and scientific validity of their approach.

Ensuring Safety for Artificial-Intelligence-Based Automatic Speech Recognition in Air Traffic Control Environment

This paper describes the safety assessment conducted in SESAR2020 project PJ.10-W2-96 ASR on automatic speech recognition (ASR) technology implemented for air traffic control (ATC) centers. ASR already now enables the automatic recognition of aircraft callsigns and various ATC commands including command types based on controller–pilot voice communications for presentation at the controller working position. The presented safety assessment process consists of defining design requirements for ASR technology application in normal, abnormal, and degraded modes of ATC operations. A total of eight functional hazards were identified based on the analysis of four use cases. The safety assessment was supported by top-down and bottom-up modelling and analysis of the causes of hazards to derive system design requirements for the purposes of mitigating the hazards. Assessment of achieving the specified design requirements was supported by evidence generated from two real-time simulations with pre-industrial ASR prototypes in approach and en-route operational environments. The simulations, focusing especially on the safety aspects of ASR application, also validated the hypotheses that ASR reduces controllers’ workload and increases situational awareness. The missing validation element, i.e., an analysis of the safety effects of ASR in ATC, is the focus of this paper. As a result of the safety assessment activities, mitigations were derived for each hazard, demonstrating that the use of ASR does not increase safety risks and is, therefore, ready for industrialization.

Risk Topics Discovery and Trend Analysis in Air Traffic Control Operations—Air Traffic Control Incident Reports from 2000 to 2022

The safety of air traffic control (ATC) operations is an important cornerstone for the sustainable development of the civil aviation industry. In order to clarify the risk factors in the control operation process and to achieve digital representation of the safety risks of civil aviation control operations, starting from the ATC incident reports, we fully mine the safety risk information and unspoken rules of ATC operations. A risk perception model for air traffic control operations safety based on the Latent Dirichlet Allocation (LDA) topic model and the Semantic Network Based on BERT (BSN) model is suggested. First, 17 risk topics and keywords were found in the incident reports collected using the LDA topic model. These topics included those pertaining to the stage of aircraft operation, human factors in control operation, and the sector or airspace operation status and structure. The findings indicate that while most risk subjects have not changed significantly, they do show an upward tendency. Human factors and operational rules and procedures account for the highest share of all key causes, and they also have a significant impact on how risk topics evolve over time. Finally, the BSN model in the air traffic control field was built based on the keywords of each risk issue in order to highlight any potential correlations between distinct risk topics. The results show that some risk topics have interrelated risk characteristics, and there are regularities of mutual evolution between these risk topics. The relevant research results can better mine air traffic control unsafe information and lay a foundation for accurately perceiving air traffic control operations risks.

AKAASH: A realizable authentication, key agreement, and secure handover approach for controller-pilot data link communications

Controller-Pilot Data Link Communications (CPDLC) are rapidly replacing voice-based Air Traffic Control (ATC) communications worldwide. Being digital, CPDLC is highly resilient and bandwidth efficient, which makes it the best choice for traffic-congested airports. Although CPDLC initially seems to be a perfect solution for modern-day ATC operations, it suffers from serious security issues. For instance, eavesdropping, spoofing, man-in-the-middle, message replay, impersonation attacks, etc. Cyber attacks on the aviation communication network could be hazardous, leading to fatal aircraft incidents and causing damage to individuals, service providers, and the aviation industry. Therefore, we propose a new security model called AKAASH, enabling several paramount security services, such as efficient and robust mutual authentication, key establishment, and a secure handover approach for the CPDLC-enabled aviation communication network. We implement the approach on hardware to examine the practicality of the proposed approach and verify its computational and communication efficiency and efficacy. We investigate the robustness of AKAASH through formal (proverif) and informal security analysis. The analysis reveals that the AKAASH adheres to the CPDLC standards and can easily integrate into the CPDLC framework.

Most influential factors in the detection of potential conflicts between en-route aircraft by ATCO

The objective of this methodology is to create a model capable of analysing and predicting the identification of potential conflicts between aircraft during en-route flights by air traffic control operators (ATCOs). This aspect is crucial to prevent Separation Minima Infractions (SMIs) and ensure safety. Initially, a Bayesian Network Model (BNM) is developed to define the safety event and its key dimensions. Using descriptive analysis and statistical knowledge, appropriate models are selected to represent the frequency of safety events. In addition, a data-driven approach is used to enhance model development. The BNM is calibrated, adjusted and subjected to sensitivity analysis using real data. The explanatory power and mixed effects of models and independent variables are assessed. By considering mixed effects in the sensitivity analysis, safety performance is characterised not only in terms of individual dimensions, but also in terms of their combinations. This allows the identification of threshold values that would minimise the occurrence of safety incidents.

The role of human factors in aviation ground operation-related accidents/incidents: A human error analysis approach

Aviation is a complex socio-technical system with interconnected and interdependent subsystems, such as flight operations, air traffic control, aircraft maintenance and ground operations. However, safety and risk research has not paid, thus far, adequate attention to all subsystems, resulting in possibly undetected or underestimated risks. This study focuses on Ground Operations (GO) as a subsystem and analyses the role of human factors in ground operations related accidents and incidents. 87 accident and incident reports (from 2000 to 2020) were analysed in three stages, using the Human Factors Dirty Dozen (HF DD) Model and the Human Factors Analysis and Classification Scheme (HFACS) as a basis for the third stage, a systematic thematic analysis. The findings indicate that lack of situational awareness and failure to follow prescribed procedures are the main causal and contributing factors in GO-related accidents and incidents. Three operational actions were identified as most critical: aircraft pushback/towing, aircraft arrival and departure, and aircraft weight and balance. An agenda for future research and recommendations for industry corrective action are proposed.

Learning for Air Traffic Management: guidelines for future AI systems

The SESAR-funded Modern ATM via Human / Automation Learning Optimisation (MAHALO) project recently completed two years of technical work exploring the human performance impacts of AI and Machine Learning (ML), as applied to enroute ATC conflict detection and resolution (CD&R). It first developed a hybrid ML CD&R capability, along with a realtime simulation platform and experimental User Interface. After a series of development trials, the project culminated in a pair of field studies (i.e., human-in-the-loop trials) across two EU countries, with a total of 35 operational air traffic controllers. In each of these two field studies, controller behaviour was first captured in a pre-test phase, and used to train the ML system. Subsequent main experiment trials then experimentally manipulated within controllers both Conformance (as either a personalised-, group average-, or optimized model) and Transparency (as ether a baseline vector depiction, an enhanced graphical diagram, or a diagram-plus-text presentation). The proposed paper presents guidelines on the design and implementation of ML systems in Air Traffic Control, derived from the results and lesson learned from the Simulations, as well as the qualitative feedback received from the controllers themselves.

An Air Traffic Control Wind Compression Forecasting Tool for the TRACON Environment

Strong wind in the lower levels of the atmosphere near major airports can impact normal air traffic control (ATC) operations with respect to the horizontal spacing of aircraft. This is known in the ATC community as wind compression. Wind compression occurs when a strong wind from a specific direction and speed (at critical altitudes) impact normal ATC operations. The impact of wind compression becomes most noticeable when the spacing between aircraft decreases near or below minimal acceptable limits. Previous attempts to forecast wind compression have been unsuccessful. Wind shear calculations, time, height wind forecasts, and maximum winds below a certain altitude, such as 3050 m (10 000 ft), do not adequately convey the impact of wind compression. These methods are insufficient because they do not account for the flight profile of the numerous arrival routes that aircraft must travel to land at major airports. The Terminal Radar Approach Control (TRACON) Wind Compression Tool assists aviation forecasters in determining which arrival routes are impacted and the specific layers of the approach that are susceptible to wind compression. This program (1) diagnoses five different layers on an arrival route that may have potential wind compression impacts and (2) forecasts the onset and end of a compression event, the altitudes impacted, and the relative strength of the wind compression. Key information using the wind forecast from three National Weather Service models is condensed for each model hour and is placed into a timeline forecast.

Creating an ATC knowledge graph in support of the artificial situational awareness system

Automation has been recognized as a possible solution for increasing air traffic controller workload trends. This paper presents a methodology for creating an air traffic control knowledge graph, which is used as part of a hybrid artificial intelligence system for air traffic control operations. The system combines machine learning and symbolic reasoning with the purpose of achieving artificial situational awareness in a narrow domain of en-route air traffic control operations. This approach allows the use of user-defined knowledge alongside existing knowledge repositories. The novel knowledge graph development methodology is universal for any area of air traffic management which relies on the aeronautical information exchange models. In this paper we also present the open-source tools which were developed to make this approach possible and system performance evaluations. Future work should address achieving real-time operation and additional task automation, accompanied by appropriate ontology and graph expansion.

Unraveling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks.

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.

A Digital Twin-Based Platform towards Intelligent Automation with Virtual Counterparts of Flight and Air Traffic Control Operations

Automation technologies have been deployed widely to boost the efficiency of production and operations, to trim the complicated process, and to reduce the human error involved. Nevertheless, aviation remains human-centred and requires collaboration between different parties. Given the lack of a collaborative decision-making training platform for air traffic operations in the industry, this study utilises the concept of cyber-physical systems (CPS) to formulate a system architecture for pilots and air traffic control officers training in collaborative decision making by linking and integrating the virtual counterparts of flights and air traffic control operations. Collaborative decision-making training and the corresponding intelligent automation aids could be realised and supported. A performance analysis via a flight task undertaken with different computational load settings was prepared to evaluate the platform’s latency and integrity. The latency is presented using its 95% confidence interval, and integrity is presented using the percentage of data loss during wireless transmission. The results demonstrated convincing performance and a promising system robustness in both domains.

Effects of increased cognitive load on field of view in multi-task operations involving surveillance

Many multi-task operations require human operators to simultaneously perform visual surveillance and a secondary task. As the trend in the technological advancement of surveillance systems indicates high likelihood of heavy interdependence between human and technology in the foreseeable future, it is paramount to venture beyond the context of driving, an area where similar research is saturated in, and investigate the functional field of view (FFoV) of multi-tasking human operators in other operations involving surveillance. In the present study, we investigated the effects of multi-tasking and stimulus parameters (size and contrast) on the response accuracy and response time for identifying visual targets appearing within one’s visual field. Mixed analyses of variance revealed that only response time was negatively affected by multi-tasking, while both response accuracy and response time deteriorated as either the size or contrast of the visual targets decreased. In addition, a significant interaction effect between the investigated parameters of the visual target on response accuracy implies the need for multi-task operations involving surveillance to consider such and future findings critically as they evolve their technology and systems to aid the human operators. The current findings provide insights for surveillance and air traffic control (ATC) operations in future studies.

Explanation of Machine-Learning Solutions in Air-Traffic Management

Advances in the trusted autonomy of air-traffic management (ATM) systems are currently being pursued to cope with the predicted growth in air-traffic densities in all classes of airspace. Highly automated ATM systems relying on artificial intelligence (AI) algorithms for anomaly detection, pattern identification, accurate inference, and optimal conflict resolution are technically feasible and demonstrably able to take on a wide variety of tasks currently accomplished by humans. However, the opaqueness and inexplicability of most intelligent algorithms restrict the usability of such technology. Consequently, AI-based ATM decision-support systems (DSS) are foreseen to integrate eXplainable AI (XAI) in order to increase interpretability and transparency of the system reasoning and, consequently, build the human operators’ trust in these systems. This research presents a viable solution to implement XAI in ATM DSS, providing explanations that can be appraised and analysed by the human air-traffic control operator (ATCO). The maturity of XAI approaches and their application in ATM operational risk prediction is investigated in this paper, which can support both existing ATM advisory services in uncontrolled airspace (Classes E and F) and also drive the inflation of avoidance volumes in emerging performance-driven autonomy concepts. In particular, aviation occurrences and meteorological databases are exploited to train a machine learning (ML)-based risk-prediction tool capable of real-time situation analysis and operational risk monitoring. The proposed approach is based on the XGBoost library, which is a gradient-boost decision tree algorithm for which post-hoc explanations are produced by SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Results are presented and discussed, and considerations are made on the most promising strategies for evolving the human–machine interactions (HMI) to strengthen the mutual trust between ATCO and systems. The presented approach is not limited only to conventional applications but also suitable for UAS-traffic management (UTM) and other emerging applications.

Integration of conflict resolution automation and vertical situation display for on-ground air traffic control operations

AbstractBoth conflict resolution aid (CRA) and vertical situation display (VSD) systems may contribute to air traffic control (ATC) operations. However, their effectiveness still needs to be examined before being widely adopted in ATC facilities. This study aims to examine empirically the use of CRA and VSD as well as the systems’ interaction in ATC operations. It was found that CRA benefited conflict resolution performance by 13⋅7% and lowered workload by 46⋅4% compared with manually performing the task. The VSD could also reduce the air traffic controllers’ (ATCOs) workload and improve their situation awareness. Ultimately, when the first CRA failure occurred, the situation awareness supported by VSD offset the performance decrements by 30%. The findings from this study demonstrate that integrating VSD with CRA would benefit ATC operations, regardless of the CRA's imperfection.

Analysis of Terminal Area Airspace Operation Status Based on Trajectory Characteristic Point Clustering

The terminal control area airspace system is one of the most complicated subsystems in the entire air traffic control system. With the continuous increase of air traffic volume, the utilization of its airspace resources has become a bottleneck restricting the sustainable development of the system. Monitor and obtain its operating status information and formulating corresponding mitigation measures are essential to improve the overall performance of the system. In order to achieve that, a new system analysis method based on clustering of trajectory characteristic point is proposed. The flight trajectories from radar position data are reconstructed at first. Followed by the method of extracting flight trajectory characteristic points including turning points, altitude transition points and speed adjustment points, all characteristic points are extracted. Then, K-means is applied for clustering according to their 3D space position, while the results are analyzed by introducing evaluation indexes based on air traffic control operation strategies. Numerical experiments show significant clustering characteristics and revealing the operation status of the terminal area airspace, which can help air traffic manager to identify defects in airspace operation and providing guidance for future airspace planning and flight procedure adjustment.

The impact of heterogeneous arrival and departure rates of flights on runway configuration optimization

ABSTRACT 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.

Human-in-the-loop evaluation of an augmented reality based interface for the airport control tower

An innovative airport control tower concept based on the use of modern augmented reality technologies has been developed and validated by means of human-in-the-loop experiments in a simulated environment. An optical-based augmented reality interface underpins the proposed concept that consists in providing air traffic control operators in the airport control tower with complete head-up information, as opposed to the current mix of information retrieval through both head-up real view and head-down interfaces. Specific measurement of the time spent by the operator working in either head-up or head-down position, show that the proposal has a clear effect in stimulating the air traffic control operator to work in a head-up position more than in a head-down position, with positive effects on his/her situational awareness and perceived workload, especially when dealing with low visibility conditions operational scenarios.