Over Mental Health and Performance in Air Traffic Controllers

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Over Mental Health and Performance in Air Traffic Controllers

Due to the increase in uncrewed aerial vehicle (UAV) usage, it is crucial to understand its impact on air traffic controllers overall mental health and performance. The goal of this study is to see how unmanned aerial vehicles (UAVs) flying in restricted airspace affects the mental health and performance of air traffic controllers. Twenty-four air traffic management (ATM) program participants executed three different air traffic control (ATC) scenarios in a within-subject experimental research design using an en-route ATC simulation system. Each scenario had an extra level of UAV automated systems. The participants mental health and performance were evaluated with subjective and objective measurements. When UAVs are brought into circumstances, ANOVA tests within individuals will be undertaken to indicate substantial effects on participants overall mental health and performance. When controlled UAVs are brought into the current system, participants should expect additional workload, mental issues like higher stress levels, and poor performance. These expected outcomes show that UAV integration in the existing airspace will significantly affect controllers mental health and performance.

Introduction

The national aviation system (NAS) is usually dominated by human-crewed aircraft, but currently, uncrewed aircraft are starting to gain ground. Due to the rapid development of unmanned aerial vehicles (UAVs), their usage has increased within NAS because they have more flexible launching locations, shortened flight hours, and minimum operating costs (Rath, 2018). Since the demand for UAV operation is growing, numerous applications in civilian and military sectors are using it due to its operational and economic benefits. Although UAVs have multiple advantages in the aviation industry, their continued technological advancements and increased practical capabilities pose problems to the current airspace structure (Belmonte et al., 2019). The proliferation of UAVs has led to the development of safety issues. UAVs have been involved in numerous mishaps due to crashes with other aircraft, and most regions have had multiple near-misses.

Furthermore, if the amount of UAV automation rises, ATC will lose control over air traffic structures. Additionally, there is the possibility of controllers experiencing mental issues such as stress. Initially, ATC work has always stressed that they are responsible for the movement and movement and direction of thousands of lives onboard general aviation aircraft daily. Therefore, with the introduction of UAVs, the ATC work will be extremely high-stressing as there will be an increased need for airspace safety (Boukoberine et al., 2019). Due to this, the current proposal is meant for the study that investigates if air traffic controllers overall mental health and performance will be affected by the implementation UAV national aviation system.

Statement of the Problem

According to Gong & Wu (2021), the federal aviation administration (FAA) has restricted uncontrolled airspaces from operating below 400 ft. However, in the future, the federal aviation administration is planning to grant unmanned aircraft licenses to be controlled airspace, which will give them access to human-crewed aircraft operations (Doarn et al., 2019). Since overseeing UAVs is an additional obligation to the controllers primary tasks, this will be a new element of ATC operations. It is desirable to implement automated uncrewed aircraft to some extent, but there is a likelihood of adverse effects occurring. Previously conducted studies have revealed that automation can aggravate the operational workload, leading to additional responsibilities that can increase stress levels and reduce the controllers performance. Considering that stress and poor performance are likely to endanger aviation safety, its critical to assess how UAVs affect ATCs general mental health and job performance.

Historical Antecedents

Air traffic control is among the most complex occupations in the world. For individuals to qualify for ATC work, they should have advanced cognitive skills and knowledge to complete various activities that will culminate in safe and reliable air traffic. Stressing factors in controllers workplaces can affect their well-being and performance. Numerous previous studies have revealed that automation is one of the stressing factors becoming more common in the aviation industry. The level of automation determines the operators control of the system, and it can eliminate or minimize them because most tasks will be executed automatically. Correspondingly, UAVs use a higher automation degree, always leading to minimal control by the controllers (Gaffey & Bhardwaj, 2020). Overall, mental illness and performance have been shown to negatively impact increasing UAV automation levels and reduce air traffic controller control levels, according to Gong & Wus (2021) study. Integrating UAVs into present airspace may, in general, escalate air traffic complexity and controller effort by requiring them to manage more aircraft.

UAV refers to an aircraft that operates without having a pilot on board. They are a type of unmanned aircraft system (UAS) that comprises UAVs and related items. Governmental agencies such as the national aeronautics and space and space administration (NASA) use UAS in their projects to ensure they have significance in sciences, national defense and security, commercial usage, and emergencies (Gong & Wu, 2021). Although UAV integration is challenging, progress has been made in the aviation sector. According to Rabinovich et al. (2018), the national aviation system has changed its focus to developing unmanned vehicle technology to ensure that UAVs are monitored and regulated to improve airspace safety. Advances in UAV technology in controlled airspace will necessitate NAS changes to reduce possible conflicts with existing aviation traffic.

Currently, air traffic management is evolving to ensure that it accommodates uncrewed aircraft. NASA plans to inspect infrastructure at an altitude above 10,000 ft. in its UAS management; the company wants to make sure that the public and commercial benefits of UAV operations are realized. This indicates that high-altitude UAVs operations will happen in the future (Kuru, 2021). However, the impact of UAVs on the ATM system must be considered. Airspace traffic becomes more complicated when UAVs are employed, making it difficult for controllers to manage both human-piloted and unmanned aircraft. The study by Kuru (2021only deals with how uncrewed aircraft are likely to affect air traffic controllers overall mental health and performance.

When UAVs are deployed in the airspace, they add to controllers burden by requiring different procedures and requiring them to make other decisions. This indicates that increased traffic is likely to affect controllers workload, leading to their mental health conditions. Gabrielli and Lund (2020) study provide differences between chronic and chronic mental health conditions. Chronic mental health condition happens regularly, while acute ones happen momentarily. If a chronic illness remains untreated for an extended period, it can harm physically and mentally (Lundberg & Johansson, 2020). Mental health conditions will be examined by evaluating controller stress levels to determine the effects of UAV interconnection in the NAS. Furthermore, performance is primarily affected in stressful situations, and it has the capability of endangering general airspace safety.

UAVs operations depend on automation, whereby pilots are not involved in most cases. This indicates that using UAVs increases traffic complexity and the use of automation. Due to additional UAVs, regulations have been changed in the NAS (Lundberg et al., 2018). There is a need for air traffic controllers to adapt to communicating and managing the new system. When automation is implemented, it can change how controllers perceive and respond to unavoidable circumstances, particularly when the structure designer did not intend changes. A study conducted by Ruskin et al. (2021) revealed that changes in the usual routine due to automation likely affect controllers cognitive processes as they will face difficulties adjusting to changes. Additionally, another study conducted by Vascik et al. (2018) has a primary goal of automation in the aviation industry to reduce human work and mistakes to improve efficiency and safety. However, automation adds operative procedures and cognitive processes that can lead to increased job demands and complexity. Argues that although automation is beneficial to NAS, it can cause mental health conditions that affect controllers performance.

Most research looks at automation and unmanned aerial vehicles (UAVs) and links them to stress and performance. However, there is little discussion of how UAVs in todays airspace affect air traffic controllers overall mental health and performance (Mahmoodi & Kazemi, 2020). Additionally, the studies are not looking into a particular type of stress resulting from automation and UAVs. This research proposal will complete all the existing gaps in the available literature.

Study Purpose and Goals

There has been no research on how unmanned aerial vehicles affect air traffic controllers general mental health and performance. Due to this gap in the literature, this study aims to evaluate the results of UAV integration in controlled air space on air traffic controllers overall mental health and performance (Rodrigues et al., 2018). Additionally, the studys outcomes will provide a precise understanding of the UAVs impacts from ATCs point of view in ensuring efficient and safe airspaces.

The study investigates if controllers experience more stress in particular ATC circumstances when there is an increase in uncrewed aircraft and reduced flight control. Additionally, the study will show how the implementation of UAVs in the current NAS affects the controllers mental health conditions and performance (Shrestha et al., 2021). Understanding how a reduction in management affects controllers can lead to solutions to safety issues, including poor performance and excessive stress.

Research Questions

Is there a significant impact on an air traffic controllers overall mental health and performance when air traffic is under different levels of control?

This is an impact research question because it aims to determine if UAV integration affects the controllers mental health and performance in current air traffic circumstances. A study revealed that the controllers mental health and performance are usually affected by job complexity and workload. When airborne traffic automation is applied, it increases work and job complexity leading to mental health issues and lowering controller effectiveness. Generally, if a controller has a minor control degree of airspace due to UAV, it will affect their mental health and performance.

Participants

Students who are currently enrolled in the air traffic management (ATM) program to perform duties as controllers will participate in this study. For any participant to qualify for the study, they should have sufficient knowledge of ATC processes and capabilities to operate the I-SIM simulator. Generally, the sample used is limited to students that have completed all ATM classes and are currently air traffic control radar in the form of attachment or internship.

Twenty-four voluntary participants will be recruited to complete this study. Further on, G*Power statistical tool will perform numerous sample size calculations such as t-tests (Sándor, 2019). Recruiting 24 participants is that it is divisible by three and will deal with counterbalancing within-subject design. Due to this, the sample size will be based on three groups (within-subjects) whereby alpha=.05 and moderate effect size (Cohens d=.05) to ensure the power of results is adequate.

Apparatus and Materials: NeXus-10 MKII

This device will measure the participants psychophysiological responses during the research. The device has the capability of detecting peripheral signals such as body temperature, skin conductance, and heart rate, and physiological responses like electrocardiogram (ECG), electroencephalogram (EEG), electrooculography (EOG), and electromyography (EMG) (Tahir et al., 2019). Due to the nature of this study, NeXus-10 MKII will be used to measure heart variability rate (HRV) and galvanic skin response (GSR) to identify participants mental health and performance during each circumstance.

Participants will be required to wear a fingertip monitor on their left-hand ring finger. This process counts the number of beats per minute (BPM) and the variation between low and high BPM in milliseconds [ms]. The electrical signals will measure skin conductance through the skin in units of microelements [µS] (Wallace et al., 2020). GSR responses will be recorded using Ag/AgCl silver-chloride strips wrapped around participants middle and little fingers on their left hands.

NASA Task Load Index (NASA-TLX)

This is a multifunctional rating-scale questionnaire that NASA developed used to quantify perceived mental issues while at the workplace subjectively. It is broken down into six different subjective subscales:

Frustration.
Temporal demands.
Performance.
Mental demand.
Effort.
Physical demand.

The participants will be given all of the subscale explanations to guarantee that they understand the purpose of the questionnaire and that their answers are accurate.

I-SIM®

This ATC modeling and simulation system provides high-fidelity en-route air traffic and space training circumstances. Additionally, the system supports the integration of UAVs in the airspace. The system is similar to the en-route used in FAA academy training in environments such as display system replacement (DSR) and en-route automation modernization (ERAM). In the pre-configured scenarios, study participants will be instructed to apply their phraseology and coordination methods to ensure aircraft vertical, lateral, and longitudinal separation (Wilson et al., 2018). At the same time, the participants will use compatible keyboard instructions and maintain adequate communication with the fake pilots via a headset. In terms of missed handoffs, the I-SIM system will objectively assess performance. A higher number of failed handoffs will indicate poor participant performance.

Demographic Form

The participants age and gender were collected using an explicitly-created self-report questionnaire for this study. The samples general characteristics are revealed via demographic data.

Certified ATC Specialist Subjective Rating Form

Sollenberger, Stein, and Gromelski designed this form in 1997 to evaluate the effectiveness of air traffic controllers. Certified ATC experts who are skilled controllers in operating ATC procedures and available ATC situations on the I-SIM typically use the form. The evaluation form includes questions that indicate a subjective estimate of participants overall performance and other ATC-related criteria such as context-awareness (Tahir et al., 2019). The ATC specialist for this study was the SME. The rating form was utilized to assess the task efficacy of the participants who completed the ATC situations in the simulated environment.

Procedures

The effects of UAVs on controllers overall mental health and productivity will be studied using a within-subjects experimental approach. The study will have two dependent variables: mental health and performance. Independent variables will include various forms of traffic conditions, such as human-crewed and uncrewed aircraft, as well as manipulations that entail increasing the number of UAVs and deciding whether the participant will be able to control them in a case. In manned circumstances, the participant will have complete control, and there will be 12 manned and no uncrewed aircraft. Participants will completely control six human-crewed and uncrewed aircraft in mixed circumstances (Rodrigues et al., 2018). Participants in the UC situation will have partial control of six aircraft, while the remaining six will be uncontrolled due to the fact that they are unmanned and have pre-determined fight plans that cannot be adjusted.

To handle the en-route air traffic model and satisfy the traffic situations, two positions will be required: virtual pilot and sensor controller. Participants will be in charge of radar controllers, while co-researchers will act the part of a remote pilot. Since ATC lab assistants have expertise and experience working with the I-SIM system, they will be used to recruit remote pilots. (Shrestha et al., 2021). There will be continuous communication between remote pilots and controllers (participants), and their instructions will be converted into actual commands on the simulator to maneuver the aircraft in the situations. The participants principal role is to control aircraft movement and maintain efficient communication with remote pilots to ensure a smooth traffic flow.

Before starting the process, all the participants will be informed of the importance and procedures of the study and be given a consent form. The number of planes in each scenario and the level of control over the plane were explained to the participants ahead of time. Additionally, they will be informed of the 30 seconds delay each time the participant-controller talked with the remote UAV pilot in the mixed scenario (Gaffey & Bhardwaj, 2020). Then, for secrecy, each participant was allocated a random participant number. Each participant filled out a demographic questionnaire that included their age and gender. The results from these procedures will be recorded to determine the mental health and performance of the controllers.

Data Analysis

The researcher will enter all the data scores in IBM SPSS, and the within-subject ANOVA test will be performed. All procedures will be followed strictly to avoid data recording issues and minimize experimental biases. An in-person survey will be presented to NASA-TLX participants to guarantee that their response rate is 100%, resulting in bias reduction. During experiments, appropriate and unbiased language will be used to prevent provoking participants. Generally, NASA-TLX will evaluate how controllers workload affects their mental health and performance.

Anticipated Outcomes

The participants overall anxiety levels and productivity will differ substantially in all three ATC situations except for the psychological reaction measure. In mixed conditions, participants will experience more significant stress and worst performance because they will be controlling UAVs and human-crewed aircraft. On the other hand, players are more likely to miss handoffs than in the manned position in the UC circumstance. The most excellent workload will be experienced in the mixed scenario. Generally, the effect sizes for these differences in the experiment will validate the populations medium to significant observation effect.

References

Belmonte, L., Morales, R., & Fernández-Caballero, A. (2019). Computer vision in autonomous unmanned aerial Vehiclesa systematic mapping study. Applied Sciences, 9(15), 3196.

Boukoberine, M., Zhou, Z., & Benbouzid, M. (2019). A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects. Applied Energy, 255, 113823.

Doarn, C., Polk, J., & Shepanek, M. (2019). Health challenges including behavioral problems in long-duration spaceflight. Neurology India, 67(8), 190.

Gabrielli, J., & Lund, E. (2020). Acute-on-chronic stress in the time of COVID-19: assessment considerations for vulnerable youth populations. Pediatric Research, 88(6), 829-831.

Gaffey, C., & Bhardwaj, A. (2020). Applications of unmanned aerial vehicles in cryosphere: latest advances and prospects. Remote Sensing, 12(6), 948.

Gong, Y., & Wu, X. (2021). Research on orbit optimization of manned spacecraft based on dynamics. Journal of Physics: Conference Series, 1985(1), 012067.

Kuru, K. (2021). Planning the future of smart cities with swarms of fully autonomous unmanned aerial vehicles using a novel framework. IEEE Access, 9, 6571-6595.

Lundberg, J., & Johansson, B. (2020). A framework for describing interaction between human operators and autonomous, automated, and manual control systems. Cognition, Technology & Work, 23(3), 381-401.

Lundberg, J., Arvola, M., Westin, C., Holmlid, S., Nordvall, M., & Josefsson, B. (2018). Cognitive work analysis in the conceptual design of first-of-a-kind systems designing urban air traffic management. Behaviour & Information Technology, 37(9), 904-925.

Mahmood, A., & Kazemi I. (2020). Optimal motion cueing algorithm for accelerating phase of manned spacecraft in human centrifuge. Chinese Journal of Aeronautics, 33(7), 1991-2001.

Rabinovich, S., Curry, R., & Elkaim, G. (2018). Toward dynamic monitoring and suppressing uncertainty in wildfire by multiple unmanned air vehicle system. Journal of Robotics, 2018, 1-12.

Rath, M. (2018). Smart traffic management system for traffic control using automated mechanical and electronic devices. IOP Conference Series: Materials Science and Engineering, 377, 012201. Web.

Rodrigues, S., Paiva, J., Dias, D., Aleixo, M., Filipe, R., & Cunha, J. (2018). Cognitive impact and psychophysiological effects of stress using a biomonitoring platform. International Journal of Environmental Research and Public Health, 15(6), 1080.

Ruskin, K., Corvin, C., Rice, S., Richards, G., Winter, S., & Clebone Ruskin, A. (2021). Alarms, alerts, and warnings in air traffic control: An analysis of reports from the Aviation Safety Reporting System. Transportation Research Interdisciplinary Perspectives, 12, 100502.

Sándor, Z. (2019). Challenges caused by the unmanned aerial vehicle in the air traffic management. Periodica Polytechnica Transportation Engineering, 47(2), 96-105.

Shrestha, R., Oh, I., & Kim, S. (2021). A Survey on operation concept, advancements, and challenging issues of urban air traffic management. Frontiers In Future Transportation, 2.

Tahir, A., Böling, J., Haghbayan, M., Toivonen, H., & Plosila, J. (2019). Swarms of unmanned aerial vehicles A survey. Journal of Industrial Information Integration, 16, 100106.

Vascik, P., Hansman, R., & Dunn, N. (2018). Analysis of urban air mobility operational constraints. Journal of Air Transportation, 26(4), 133-146.

Wallace, R., Loffi, J., Holliman, J., Metscher, D., & Rogers, T. (2020). Evaluating LAANC utilization & compliance for small unmanned aircraft systems in controlled airspace. International Journal of Aviation, Aeronautics, and Aerospace.

Wilson, M., Farrell, S., Visser, T., & Loft, S. (2018). Remembering to execute deferred tasks in simulated air traffic control: The impact of interruptions. Journal of Experimental Psychology: Applied, 24(3), 360-379.

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