Air Traffic Control Environment Research Articles

Overview Of Approach Control Unit Cabin Facilities Related To Noise

As an ATC, we should be able to give our full focus when providing air traffic services. If we cannot focus when working due to noise in the cabin, then the safety of the aircraft can also be threatened. Therefore, an ATC should get a work environment that can support his performance so that he can work optimally. This article delves into the challenges encountered by Air Traffic Controllers (ATCs) during their duties at the Tarakan Approach Control Unit, a crucial component of Juwata International Airport in Tarakan, North Kalimantan, Indonesia. The study revolves around the adverse effects of excessive noise levels within the ATC cabin on controller performance and aviation safety. Through qualitative descriptive methodology, this study explores the factors contributing to the noise problem within the Tarakan APP cabin. It identifies two major sources of noise: gaps in windows and doors, permitting external sounds to infiltrate the cabin, and the lack of sound insulation in the flooring. The results section introduces alternative solutions proposed by the author to mitigate the noise issue and enhance the ATC work environment. These solutions encompass the use of headsets, transitioning to a Voice Communication Control System (VCCS), installing sound-absorbing materials on the cabin floor, and incorporating UPVC to seal gaps in windows and doors. In conclusion, this study highlights the critical need for an enabling work environment that supports ATCs in ensuring aviation safety. The author encourages relevant authorities to consider and implement the proposed solutions to establish a more efficient, effective, safe, and comfortable air traffic control environment at Tarakan APP, ultimately benefiting air navigation services.

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.

Validation of a new method for designing air traffic control alarms

Alarms, alerts, and warnings are critical to maintaining safety in the National Airspace System and should be designed to support aircraft separation as well as supplementary tasks such as weather avoidance. The purpose of this study is to validate a novel alarm design framework by asking air traffic controllers to evaluate an existing alarm. MethodsWe invited four air traffic controllers to participate in a structured interview that is part of a novel Signal Design Framework. Controllers were asked a series of scripted questions about 15 specific alarm properties. They were then asked to choose the three properties most important to the design of the conflict alert. Lastly, controllers were asked a series of questions about the overall quality of the taxonomy and its potential for impacting aviation safety. ResultsAll participants agreed that the taxonomy captured all the important characteristics of an alarm and that no gaps or failures existed in the alarm framework. They also agreed that the framework was easy to understand, that the structured interview was easy to understand, and that applying the framework to alarm design and revision would improve alarm ease of use, reduce confusion, and improve overall safety. ConclusionsThe structured interview encouraged controllers to think about the Conflict Alert and helped them to develop novel solutions that could potentially improve this alarm in the Air Traffic Control environment.

Validating Automatic Speech Recognition and Understanding for Pre-Filling Radar Labels—Increasing Safety While Reducing Air Traffic Controllers’ Workload

Automatic speech recognition and understanding (ASRU) for air traffic control (ATC) has been investigated in different ATC environments and applications. The objective of this study was to quantify the effect of ASRU support for air traffic controllers (ATCos) radar label maintenance in terms of safety and human performance. Therefore, an implemented ASRU system was validated within a human-in-the-loop environment by ATCos in different traffic-density scenarios. In the baseline condition, ATCos performed radar label maintenance by entering verbally instructed ATC commands with a mouse and keyboard. In the proposed solution, ATCos were supported by ASRU, which achieved a command recognition rate of 92.5% with a command error rate of 2.4%. ASRU support reduced the number of wrong or missing inputs from ATCos into the radar label by a factor of two, which contemporaneously improved their situational awareness. Furthermore, ATCos where able to perform more successful secondary tasks when using ASRU support, indicating a greater capacity to handle unexpected events. The results from NASA TLX showed that the perceived workload decreased with a statistical significance of 4.3% across all scenarios. In conclusion, this study provides evidence that using ASRU for radar label maintenance can significantly reduce workload and improve flight safety.

A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches

Automatic spoken instruction understanding (SIU) of the controller-pilot conversations in the air traffic control (ATC) requires not only recognizing the words and semantics of the speech but also determining the role of the speaker. However, few of the published works on the automatic understanding systems in air traffic communication focus on speaker role identification (SRI). In this article, we formulate the SRI task of controller-pilot communication as a binary classification problem. Furthermore, the text-based, speech-based, and speech-and-text-based multi-modal methods are proposed to achieve a comprehensive comparison of the SRI task. To ablate the impacts of the comparative approaches, various advanced neural network architectures are applied to optimize the implementation of text-based and speech-based methods. Most importantly, a multi-modal speaker role identification network (MMSRINet) is designed to achieve the SRI task by considering both the speech and textual modality features. To aggregate modality features, the modal fusion module is proposed to fuse and squeeze acoustic and textual representations by modal attention mechanism and self-attention pooling layer, respectively. Finally, the comparative approaches are validated on the ATCSpeech corpus collected from a real-world ATC environment. The experimental results demonstrate that all the comparative approaches worked for the SRI task, and the proposed MMSRINet shows competitive performance and robustness compared with the other methods on both seen and unseen data, achieving 98.56% and 98.08% accuracy, respectively.

Evaluation of a Decision-Based Invocation Strategy for Adaptive Support for Air Traffic Control

Air traffic controller workload is a limiting factor in the current air traffic management system. Adaptive support systems have the potential to balance controller workload and gain acceptance as they provide support during times of need. Challenges in the design of adaptive support systems are to decide when and how to trigger support. The goal of this study is to gain empirical insights into these challenges through a human-in-the-loop experiment, featuring a simplified air traffic control environment in which a novel triggering mechanism uses the quality of the controller's decisions to determine when support is needed. The designed system seeks to prevent high workload conditions by providing resolution advisories when the controller exceeds a threshold of “self-complicating” decisions. Results indicate that the new system is indeed capable of increasing the efficiency and safety compared to full manual control without intervention. More adaptive support, however, increased the frustration of participants, decreased acceptance, and did not result in improved workload ratings. These findings suggest that, unless we can better infer human intent in complex work environments, adaptive support at the level of decision-making is problematic. A potentially more fruitful direction is to provide support at the level of information integration, with full decision-making authority with the human.

Development of a Methodology for Assessing Workload within the Air Traffic Control Environment in the Czech Republic

The increase in civil aviation traffic and, in general, in aviation traffic going through airspace or a military terminal control area, and the increase in military operations in temporarily reserved areas bring higher requirements for airspace throughput and for the workload of military air traffic controllers. For an objective assessment of the military air traffic controllers’ workload, it is desirable to set the maximum level of workload that can be required of such personnel. This assessment is also important for planning staffing and training. In the civil air traffic control environment, the workload of air traffic controllers is clearly determined by the complexity and density of air traffic, i.e., the throughput capacity of sectors. However, this method is not suitable for measuring the workload of military air traffic controllers, because the nature of military flight activities requires solving different situations in the airspace and thus generates a different workload. One way of obtaining more objective data on the actual workload of military air traffic controllers is to accurately determine the difficulty of individual air traffic control activities, i.e., the most common activities carried out by military air traffic controllers in the course of their duty. The difficulty of a selected air traffic control activity will be represented by a weight. A method for determining this weight is presented, including the proposal of specific weights for the calculation of the military air traffic controllers’ workload during simulation training, using the functionality “Workload”.

A machine learned go-around prediction model using pilot-in-the-loop simulations

Go-around manoeuvers are challenging for the management of arriving air traffic due to their complexity and unpredictability. In the present work, we propose to augment the air traffic control (ATC) environment with a predictive tool that assists Air traffic controllers (ATCOs) in the detection and prediction of the likelihood of go-around events. First, a safe and cost-effective flight approach data collection mechanism is introduced via a pilot-operated flight simulator under varying visibility conditions. Then, data are processed and a go-around probability assignment mechanism is introduced. Finally, a go-around probability prediction model is proposed. The proposed model updates the go-around probability for each new flight record, namely every second, and converges by detecting either a go-around or a successful landing. Experiments are conducted for two airports, namely Philadelphia International Airport (PHL) and Van Nuys Airport (KVNY), which feature different go-around procedures and are operated by different types of aircraft. Findings demonstrate that the trajectories of predicted go-around probabilities follow closely the computed probabilities regardless of the dissimilarities in the trajectory patterns. Furthermore, results suggest that the likelihood of go-around exceeds 93% when the go-around is initiated, for almost 50% of the flights in the test set. Moreover, the proposed model is able to detect the successful landing of aircraft. In fact, the system indicates low chances of go-around when the aircraft landed. For instance, the likelihood of a go-around at the touchdown is below 35% and 10% for KPHL and KVNY, respectively. The findings underscore the ability of the proposed model in providing accurate and timely go-around probabilities. This will help the ATCO to be prepared for re-sequencing arrival flights and clearing the go-around path, prior to the actual initiation of a go-around.

FlightBERT: Binary Encoding Representation for Flight Trajectory Prediction

Flight Trajectory Prediction (TP) is an essential task in Air Traffic Control (ATC). Currently, the TP task is usually achieved by regression approaches, which concatenates several scalar attributes of the observation into a low-dimensional vector as the inputs. However, it is difficult to accurately model aircraft motion patterns using low-dimensional features in complex and time-varying ATC environments. To improve the performance of the TP task, in this paper, a novel framework, called FlightBERT, is proposed based on Binary Encoding (BE) representation, which enables us to tackle the TP task as a multi binary classification problem. Specifically, the scalar attributes of the flight trajectory are encoded into binary codes and transformed into a high-dimensional representation by the attribute embedding module. Considering the prior knowledge among flight attributes, an Attribute Correlation Attention (ACoAtt) block is designed to explicitly capture the correlations among the specific attributes. A stacked Transformer block is applied to serve as the backbone network, which is followed by the predictor to generate the outputs. Considering the nature of flight trajectory, a hybrid constrained loss, i.e., combining the mean square error loss with the binary cross-entropy loss, is innovatively designed to optimize the proposed framework. The proposed method is validated on a large-scale dataset, which is collected from the real-world ATC environment. The experimental results demonstrate that the proposed method outperforms other baselines by quantitative and qualitative evaluations.

A human-centred approach based on functional near-infrared spectroscopy for adaptive decision-making in the air traffic control environment: A case study

Safety-critical systems like air traffic control (ATC) are usually less automated than might be expected by the public, so human intelligence will remain at the core in the decision-making (DM) process. Meanwhile, human factors (HFs) need to be fully considered in the DM process, which can design the ATC system to be more intelligent and more adaptive to the behaviour of the user. However, the existing DM research lacks the systematic methods that fully consider human performance in a smart manner. This study proposed a human-centred adaptive DM methodology that combines subjective and objective measurements made by functional near-infrared spectroscopy (fNIRS) via intelligent automation (IA). Moreover, this paper also described a case study of radar display map operation, including descriptive and optimised maps, to illustrate the proposed approach and verify its feasibility and effectiveness. The results were determined by jointly considering the user-generated and system-generated data and suggested that the proposed approach could capture subjective and objective data, take into consideration the HFs information to provide real-time online feedback and adjust the decision support system to HFs. It is hoped that this study can promote the methodology of human-centred subjective and objective data-driven applications in the future ATC environment adaptive decision research.

Design implications for teamwork in ATC

In air traffic control (ATC), teamwork is a key component among air traffic control operators (ATCOs) to safely direct the aircraft through the sky and on the ground. To be able to design for future ATC systems, we must understand how ATCOs work together, their teamwork, and how they experience and perceive teamwork, in different ATC environments. We conducted interviews with 16 ATCOs working in four different ATC environments (en-route control, terminal area control, tower control for a small airport and tower control for a large airport in Sweden) and analysed the results in the light of the “Big Five” model of teamwork. The main contributions of this paper are to show: (1) how eight teamwork factors are differently manifested by the ATCOs in the different ATC environments, (2) that teamwork in ATC is important during routine operations, during stressful work, and during abnormal situations, and (3) that the design of the organisation, the environment, and the tools, affects teamwork and the importance of different teamwork factors.

The effects of dynamic workload and experience on commercially available EEG cognitive state metrics in a high-fidelity air traffic control environment

The current study evaluated the validity of commercially available electroencephalography (EEG) cognitive state metrics of workload and engagement in differentially experienced air traffic control (ATC) students. EEG and pupil diameter recordings were collected from 47 ATC students (27 more experienced and 20 less experienced) during a high-fidelity, variable workload approach-control scenario. Scenario workload was manipulated by increasing the number of aircraft released and the presence of a divided attention task. Results showed that scenario performance significantly degraded with increased aircraft and the presence of the divided attention task. No scenario performance differences were found between experience groups. The EEG engagement metric significantly differed between experience groups, with less experienced controllers exhibiting higher engagement than more experienced controllers. The EEG workload metric and pupil diameter were sensitive to workload manipulations but did not differentiate experience groups. Commercially available EEG cognitive state metrics may be a viable tool for enhancing ATC training.

Cleared for takeoff? A snap-shot of climate for change in a civil aviation context

High risk industries are facing increasingly volatile, uncertain, complex and ambiguous (VUCA) environments requiring continual innovation and change. And this is particularly true within the civil aviation industry where increasing focus on economic performance, increased competition, and growing safety threats, require continual adjustments. In this paper, we present the findings of a case study conducted within the Norwegian national air traffic management organization – Avinor, in preparation for a major reorganization initiative. In this study, we mapped the aggregated positioning for innovation and change in the three main Air Traffic Control Centers in Norway using a multi- method approach that was taken to assess the climate for creativity and change at each ATCC from both quantitative and qualitative perspectives, and a separate assessment of individual preferences for change to help organizational leaders better understand each unit’s positioning for change. The results suggest that participants at the different ATCCs had developed distinctly different problem-solving preferences at both the group and individual levels despite the fact that each facility was manned by Norwegian air traffic controllers having similar education and training backgrounds, performed the same high-altitude air traffic control functions, used the same type of equipment, and operated within the same air traffic control environment. Yet, each unit proved to be distinctly different from each other in their positioning and readiness for change.

The influence of task load on situation awareness and control strategy in the ATC tower environment

The safe and efficient operation of air traffic is highly dependent on the performance of the Air Traffic Control Officer (ATCO). The ATCOs control the traffic within defined areas by monitoring the traffic and granting clearances. A key element in analyzing the ATCOs is their interaction with the environment through their workplace. Especially the influence of task load on their situation awareness (SA) and applied control strategy provides information on the quality of the workplace. As task load increases, controllers are able to maintain performance by using different management or compensation strategies. This article supports the evaluation of ATCO’s workplaces by focusing on whether probe techniques for assessing SA are applicable for tower control operation and for measuring the influences of increased task load on the control strategy. An experiment with nine ATCOs was conducted in a simulated real-time air traffic control environment. Different measurements for SA were applied and compared regarding their efficiency and validity. The manipulation of task load and visibility influenced the SA and control strategy at the same time. Performance metrics were selected in advance to evaluate the participant’s efficiency. SA was measured with a probe technique and an offline self-assessment method. Findings suggest that probe techniques increase the insight into the understanding of SA in comparison to self-assessment and that they are applicable to the air traffic control environment. Control strategies were derived from the information-gathering process via the eye-movement behavior and connected to task load. The results imply that SA is part of the individual performance and that increasing demand through task load is handled with an adaptation of the control strategy.

Real-Time Assessment of Operator State in Air Traffic Controllers Using Ocular Metrics

This study examined the utility of continuous operator state monitoring in predicting air traffic control officer (ATCO) workload and fatigue. Participants (N=8) were observed in live operational air traffic control environments for 60-minute periods. ATCO state was assessed using a real-time, computer vision-based system which tracked operator gaze and pupil diameter. Workload and fatigue were also assessed via the adapted Bedford Workload Scale and Samn-Perelli Fatigue Scale, respectively. Standard deviation of gaze was a significant predictor of both max and mean workload, showing a strong negative relationship with both subjective measures. Pupil diameter showed a significant positive relationship with operator fatigue. Our findings demonstrate the utility of continuous ocular metrics of workload and fatigue in operational environments.

Theoretical Aspects of Erroneous Actions During the Process of Decision Making by Air Traffic Control

Abstract The Theoretical Aspects of Erroneous Actions During the Process of Decision Making by Air Traffic Control evaluates the factors affecting the operational decision-making of a human air traffic controller, interacting in a dynamic environment with the flight crew, surrounding aircraft traffic and environmental conditions of the airspace. This article reviews the challenges of air traffic control in different conditions, ranging from normal and complex to emergency and catastrophic. Workload factors and operating conditions make an impact on air traffic controllers’ decision-making. The proposed model compares various operating conditions within an assumed air traffic control environment subsequently comparing them against a theoretically “perfect” air traffic control system. A mathematical model of flight safety assessment has been proposed for the quantitative assessment of various hazards arising during the process of Air Traffic Control. The model assumes events of various severity and probability ranging from high frequency and low severity up to less likely and catastrophic ones. Certain limitations of the model have been recognised and further improvements for effective hazard evaluation have been suggested.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

The neuroergonomic evaluation of human machine interface design in air traffic control using behavioral and EEG/ERP measures

The Air Traffic Control (ATC) environment is complex and safety-critical. Whilst exchanging information with pilots, controllers must also be alert to visual notifications displayed on the radar screen (e.g., warning which indicates a loss of minimum separation between aircraft). Under the assumption that attentional resources are shared between vision and hearing, the visual interface design may also impact the ability to process these auditory stimuli. Using a simulated ATC task, we compared the behavioral and neural responses to two different visual notification designs—the operational alarm that involves blinking colored “ALRT” displayed around the label of the notified plane (“Color-Blink”), and the more salient alarm involving the same blinking text plus four moving yellow chevrons (“Box-Animation”). Participants performed a concurrent auditory task with the requirement to react to rare pitch tones. P300 from the occurrence of the tones was taken as an indicator of remaining attentional resources. Participants who were presented with the more salient visual design showed better accuracy than the group with the suboptimal operational design. On a physiological level, auditory P300 amplitude in the former group was greater than that observed in the latter group. One potential explanation is that the enhanced visual design freed up attentional resources which, in turn, improved the cerebral processing of the auditory stimuli. These results suggest that P300 amplitude can be used as a valid estimation of the efficiency of interface designs, and of cognitive load more generally.

Estimation fusion with radar and ADS-B for air traffic surveillance

The surveillance sensor that has been mainly used for target tracking in air traffic control (ATC) environment is radar. The automatic dependent surveillance - broadcasting (ADS-B), which is based on the technologies of global navigation satellite systems (GNSS), is recently participating in ATC systems for more accurate and reliable surveillance. Although ADS-B provides more accurate and a wider variety of measurements than does radar, it needs careful considerations for the application of the ATC systems. This is due to the fact that the reliability of ADS-B measurements is dependent upon each aircraft whereas that of radar is not. This study proposes a practical system for the estimation fusion of multiple heterogeneous sensors, which includes radar and ADS-B, whose measurements and sensor characteristics are different from one another. A centralized fusion architecture for the data that are received asynchronously from multiple sensors is adopted for the optimum estimation fusion. The three-dimensional earth-centered earth-fixed (ECEF) coordinate system is used for the common coordinates for preprocessing and filtering in order to avoid the registration errors, which can occur in two-dimensional coordinates with a stereographic projection at the system center. In case of the ADS-B, the validity of the sensor data for each aircraft is primarily checked using the accuracy and integrity information of the aircraft. Another important validation for the ADS-B is performed using a comparison of the ADS-B data with the radar data because the ADS-B validation information from each aircraft may not be fully reliable. Furthermore, additional measurements from the downlinked aircraft parameters (DAP), which are available in mode-S radar and ADS-B, are incorporated into state estimation. A variable-sized measurement vector and matrix are used for the tracking filter in order to dynamically reflect the availability of the additional measurements. The simulation results indicate that the proposed fusion system can improve the tracking performance with the advantages of different types of surveillance sensors.

Situation Awareness for Mid-air Detect-and-avoid System for Remotely Piloted Aircraft

As part of the European Detect and Avoid project MIDCAS three human-in-the-loop simulation campaigns were conducted with remotely piloted aircraft system equipped with a Detect and Avoid system flying according to instrument flight rules in an air traffic control environment. Situation awareness for remotely piloted aircraft system pilots and air traffic controllers was studied as part of an evaluation of the operational concept. Experienced pilots and air traffic controllers participated. Data was collected for qualitative analysis including observer logs, questionnaires, and notes from debriefings/workshops. In general the situation awareness was good however there were also examples of lost situation awareness during the simulations, reported in this paper. Lessons learned include how the system could further support remotely piloted aircraft system pilots situation awareness by highlighting if intruder is transponder equipped or not and semi-static or frozen information regarding traffic avoidance and standardized phraseology should be used to further support shared situation awareness between remotely piloted aircraft system pilot and air traffic controller. Given that remotely piloted aircraft systems are new in this kind of setting the result from this simulations are promising in that there were no major concerns found concerning situation awareness that would risk significantly affect the operational concept.