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All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Effects of Automation Types on Air Traffic Controller Situation Awareness and Performance

The Joint Planning and Development Office has proposed the introduction of automated systems to help air traffic controllers handle the increasing volume of air traffic in the next two decades (JPDO, 2007). Because fully automated systems leave operators out of the decision-making loop (e.g., Billings, 1991), it is important to determine the right level and type of automation that will keep air traffic controllers in the loop. This study examined the differences in the situation awareness (SA) and collision detection performance of individuals when they worked with information acquisition, information analysis, decision and action selection and action implementation automation to control air traffic (Parasuraman, Sheridan, & Wickens, 2000). When the automation was unreliable, the time taken to detect an upcoming collision was significantly longer for all the automation types compared with the information acquisition automation. This poor performance following automation failure was mediated by SA, with lower SA yielding poor performance. Thus, the costs associated with automation failure are greater when automation is applied to higher order stages of information processing. Results have practical implications for automation design and development of SA training programs.

Sethumadhavan, A.↗
NASA NTRS2011

Breakdowns in Coordination Between Air Traffic Controllers

This talk outlines the complexity of coordination in air traffic control, introduces the NextGen technologies, identifies common causes for coordination breakdowns in air traffic control and examines whether these causes are likely to be reduced with the introduction of NextGen technologies. While some of the common causes of breakdowns will be reduced in a NextGen environment this conclusion should be drawn carefully given the current stage of development of the technologies and the observation that new technologies often shift problems rather than reduce them.

Bearman, Chris↗
NASA NTRS1972

Human factors aspects of air traffic control

An overview of human factors problems associated with the operation of present and future air traffic control systems is presented. A description is included of those activities and tasks performed by air traffic controllers at each operational position within the present system. Judgemental data obtained from controllers concerning psychological dimensions related to these tasks and activities are also presented. The analysis includes consideration of psychophysiological dimensions of human performance. The role of the human controller in present air traffic control systems and his predicted role in future systems is described, particularly as that role changes as the result of the system's evolution towards a more automated configuration. Special attention is directed towards problems of staffing, training, and system operation. A series of ten specific research and development projects are recommended and suggested work plans for their implementation are included.

Older, H. J.↗
NASA NTRS2016

Multifactor Interactions and the Air Traffic Controller: The Interaction of Situation Awareness and Workload in Association with Automation

Air traffic controllers (ATCOs) must maintain a consistently high level of human performance in order to maintain flight safety and efficiency. In current control environments, performance-influencing factors such as workload, fatigue and situation awareness (SA) can co-occur, and interact, to affect performance. However, multifactor influences and the association with performance are under-researched. This study utilized a high fidelity human in the loop enroute air traffic control simulation to investigate the relationship between workload, situation awareness and ATCO performance. The study aimed to replicate and extend Edwards, Sharples, Wilson and Kirwan's (2012) previous study and confirm multifactor interactions with a participant sample of ex-controllers. The study also aimed to extend Edwards et al.'s previous research by comparing multifactor relationships across 4 automation conditions. Results suggest that workload and SA may interact to produce a cumulative impact on controller performance, although the effect of the interaction on performance may be dependent on the context and amount of automation present. Findings have implications for human-automation teaming in air traffic control, and the potential prediction and support of ATCO performance.

Workload↗
NASA NTRS1980

Air traffic control using a microwave landing system

The performance of air traffic controllers using a simulated microwave landing system to control the landing of STOL aircraft is investigated. In a series of two experiments, controllers were asked to achieve a 60-sec separation between targets at the missed approach point when only two targets were on the simulated radar scope, and in the presence of 25 targets, with up to 10 on the screen at any one time. In both experiments, the presence of a ground wind is found to degrade separation performance, and an increased work load results in even greater separation variability, as the controllers did not have time to convert aircraft distances into times. In addition, curved courses are found to have an effect on performance. It is thus recommended that in order to create a practical air traffic control system with separation standards based on time, as in the MLS, controller work loads should be reduced and aircraft positions should be displayed with respect to time rather than distance.

Gershzohn, G.↗
NASA NTRS1985

Space based radar application to air traffic control

Future control of increasing air traffic on international routes will require significant advances in techniques employed. Development is underway, but at this time does not include addition of radar coverage of vast areas of oceanic traffic. This paper addresses the potential for a space-based radar to provide detection and track of aircraft in future traffic scenarios. Factors that must be considered in further tradeoff and concept optimization are discussed. These include: (1) satellite constellation characteristics and associated coverage, (2) efficient use of available energy in track-while-scan strategies, (3) development of tracker approaches to accommodate inherent system constraints, and (4) the application of transponders to assist in dense traffic environments. The analysis assumes the continued development and availability of technology required for high spacecraft power systems (hundreds of kilowatts), and for radar subclutter visibility.

Caime, W. J.↗
NASA NTRS1989

Design of a final approach spacing tool for TRACON air traffic control

This paper describes an automation tool that assists air traffic controllers in the Terminal Radar Approach Control (TRACON) Facilities in providing safe and efficient sequencing and spacing of arrival traffic. The automation tool, referred to as the Final Approach Spacing Tool (FAST), allows the controller to interactively choose various levels of automation and advisory information ranging from predicted time errors to speed and heading advisories for controlling time error. FAST also uses a timeline to display current scheduling and sequencing information for all aircraft in the TRACON airspace. FAST combines accurate predictive algorithms and state-of-the-art mouse and graphical interface technology to present advisory information to the controller. Furthermore, FAST exchanges various types of traffic information and communicates with automation tools being developed for the Air Route Traffic Control Center. Thus it is part of an integrated traffic management system for arrival traffic at major terminal areas.

Davis, Thomas J.↗
NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗
NASA NTRS1990

Automatic speech recognition in air traffic control

Automatic Speech Recognition (ASR) technology and its application to the Air Traffic Control system are described. The advantages of applying ASR to Air Traffic Control, as well as criteria for choosing a suitable ASR system are presented. Results from previous research and directions for future work at the Flight Transportation Laboratory are outlined.

Karlsson, Joakim↗
NASA NTRS1991

Flight tests with a data link used for air traffic control information exchange

Previous studies showed that air traffic control (ATC) message exchange with a data link offers the potential benefits of increased airspace system safety and efficiency. To accomplish these benefits, data link can be used to reduce communication errors and relieve overloaded ATC voice radio frequencies, which hamper efficient message exchange during peak traffic periods. Flight tests with commercial airline pilots as test subjects were conducted in the NASA Transport Systems Research Vehicle Boeing 737 airplane to contrast flight operations that used current voice communications with flight operations that used data link to transmit both strategic and tactical ATC clearances during a typical commercial airflight from takeoff to landing. The results of these tests that used data link as the primary communication source with ATC showed flight crew acceptance, a perceived reduction in crew work load, and a reduction in crew communication errors.

Knox, Charles E.↗
NASA NTRS2004

Use of Structure as a Basis for Abstraction in Air Traffic Control

The safety and efficiency of the air traffic control domain is highly dependent on the capabilities and limitations of its human controllers. Past research has indicated that structure provided by the airspace and procedures could aid in simplifying the controllers cognitive tasks. In this paper, observations, interviews, voice command data analyses, and radar analyses were conducted at the Boston Terminal Route Control (TRACON) facility to determine if there was evidence of controllers using structure to simplify their cognitive processes. The data suggest that controllers do use structure-based abstractions to simplify their cognitive processes, particularly the projection task. How structure simplifies the projection task and the implications of understanding the benefits structure provides to the projection task was discussed.

Davison, Hayley J.↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRS2017

Workload and Performance in Air Traffic Control: Exploring the Influence of Levels of Automation and Variation in Task Demand

In air traffic control, task demand and workload have important implications for the safety and efficiency of air traffic. Task demand is dynamic, however, research on demand transitions and associated controller perception and performance is limited. In addition, there is a comparatively restricted understanding of the influence of task demand transitions on workload and performance, in association with automation. This study used an air traffic control simulation to investigate the influence of task demand transitions and two conditions of varying automation, on workload and efficiency-related performance. Findings showed that a both the direction of the task demand variation, and the amount of automation, influenced the relationship between workload and performance. Further research is needed to enhance understanding of demand transition and workload history effects on operator experience and performance, in both air traffic control and other safety-critical domains.

air traffic control↗
NASA NTRS1972

The optimal control of merging aircraft - Implementation of the hybrid air traffic controller.

The control of merging aircraft is formulated as a finite-time, quadratic optimal control problem of a linear system with state and control constraints. The purpose of this paper is to demonstrate that the Hybrid Air Traffic Controller (HAC), which has been previously developed as a solution to this problem, may be easily implemented. Use is made of both the properties of the algebraic solution to the matrix Riccati equation and the structure of the linear model. This approach results in a real-time synthesis procedure for the HAC which does not rely on iterative numerical integration techniques.

Schatz, J. G.↗
NASA NTRS1991

Design and evaluation of an air traffic control Final Approach Spacing Tool

This paper describes the design and simulator evaluation of an automation tool for assisting terminal radar approach controllers in sequencing and spacing traffic onto the final approach course. The automation tool, referred to as the Final Approach Spacing Tool (FAST), displays speed and heading advisories for arriving aircraft as well as sequencing information on the controller's radar display. The main functional elements of FAST are a scheduler that schedules and sequences the traffic, a four-dimensional trajectory synthesizer that generates the advisories, and a graphical interface that displays the information to the controller. FAST has been implemented on a high-performance workstation. It can be operated as a stand-alone in the terminal radar approach control facility or as an element of a system integrated with automation tools in the air route traffic control center. FAST was evaluated by experienced air traffic controllers in a real-time air traffic control simulation. simulation results summarized in the paper show that the automation tools significantly reduced controller work load and demonstrated a potential for an increase in landing rate.

Davis, Thomas J.↗