Hava Trafik Kontrolörü Adaylarının Gerçek Zamanlı Simülasyon Senaryolarındaki Hata Sayıları ve Performans Skorlarının İncelenmesi

Year 2022, Volume: 14 Issue: 3, 87 - 97, 31.12.2022

Ebru Yazgan Fulya Aybek Çetek Deniz Şimşek Nazım Ata Nihal Erginel Seçkin Tuncer Barış Karabayrak Ahmet Aydın Kadir Dönmez Çağla Yıldızlar Tuğçe Toktay

https://doi.org/10.29137/umagd.1201368

Abstract

Bu çalışmada gerçek zamanlı simülasyonlarda uçak sayısına ve hava sahası yapısına bağlı olarak tasarlanan dört farklı düzeydeki görev zorluğuna (kolay, orta, zor, çok zor) sahip senaryolardaki egzersizler boyunca, simülasyon eğitimini almış ve almamış (deneyimli-deneyimsiz) iki farklı grup üzerinde zihinsel iş yüklerinin çok boyutlu olarak incelenmesi için iki aşamalı bir deneysel süreç yürütülmüştür. Çalışmanın ilk aşamasında simülasyon öncesi katılımcıların reaksiyon süreleri FitLight Trainer sistemi kullanılarak üç farklı protokol üzerinden değerlendirilmiştir. Deneyimli ve deneyimsiz gruplar arasında reaksiyon süreleri bakımından anlamlı bir farklılık gözlenmemiştir. İkinci aşamada ise reaksiyon süreleri homojen olan bu grupların gerçek zamanlı simülasyon senaryolarında yaptıkları hata sayıları ve elde ettikleri performans skorları birbirleri ile kıyaslanmıştır. %90 güven seviyesinde deneyimli grup daha az hata yapmıştır. Ayrıca senaryoların farklı zorluk derecesinde olması hata sayılarında istatistiksel olarak farklılık yaratmıştır. Kolay-orta senaryolarda öğrenciler zor-çok zor senaryolara göre istatistiksel olarak anlamlı derecede daha az hata yapmışlardır. Bununla birlikte performans skorlarının senaryo zorluk derecelerine göre değişimleri incelenmiş ve performans skorları arasında anlamlı farklılık olduğu ortaya çıkmıştır (p-value=0,00 < 0,05). Kolay-orta senaryolarda zor-çok zor senaryolara göre öğrencilerin performans skorları anlamlı derecede yüksek çıkmıştır. Çalışma sonuçlarına göre hata sayıları ve performans skorlarının birbirini desteklediği görülmüştür. Senaryoların zorluk derecelerinin bilişsel iş yüküne (hata sayıları, performans skorları) etkisi olduğu söylenebilir.

Keywords

İnsan faktörleri, Hava trafik kontrolörü, Bilişsel iş yükü, Reaksiyon süresi

Supporting Institution

TÜBİTAK

Project Number

221M224

Thanks

Bu çalışma Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 221M224 numaralı proje kapsamında desteklenmiştir. Bu çalışma 28. Ulusal Ergonomi Kongresi’nde sunulmuştur.

Investigation of Error Numbers and Performance Scores of Air Traffic Controller Candidates in Real-time Simulation Scenarios

Abstract

In this study, during the exercises in scenarios with four different levels of task difficulty (easy, medium, difficult, very difficult) designed depending on the number of aircraft and airspace structure in real-time simulations, mental workload was carried out on two different groups, who received simulation training and did not receive simulation training (experienced and inexperienced). A two-stage experimental process was carried out for multidimensional examination of mental workloads. In the first stage of the study, the reaction times of the participants before the simulation were evaluated over three different protocols using the FitLight Trainer system. No significant difference was observed between the experienced and inexperienced groups in terms of reaction times. In the second stage, these groups with homogeneous reaction times were compared with each other for the error numbers and performance scores of in real-time simulation scenarios. At the 90% confidence level, the experienced group made fewer mistakes. In addition, the different difficulty levels of the scenarios created a statistical difference in the error numbers. In easy-medium scenarios, students made statistically significantly fewer mistakes than in difficult-very difficult scenarios. Also, the changes of performance scores according to scenario difficulty levels were examined and it was revealed that there was a significant difference between performance scores (p-value=0,00 < 0,05). Students' performance scores were significantly higher in easy-medium scenarios compared to difficult-very difficult scenarios. According to the results of the study, it was seen that the error numbers and performance scores supported each other. It can be said that the difficulty levels of the scenarios have an effect on the cognitive workload (error numbers, performance scores).

Keywords

Human factors, Air traffic controller, Cognitive workload, Reaction time

Project Number

221M224

References

• Abbass, H. A., Tang, J., Ellejmi, M., & Kirby, S. (2014). Visual and auditory reaction time for air traffic controllers using quantitative electroencephalograph (QEEG) data. Brain Inform, 1(1-4):39-45. doi: 10.1007/s40708-014-0005-8
• Ackerman, P. L., & Cianciolo, A. T. (1999). Psychomotor abilities via touch‐panel testing: Measurement innovations, construct, and criterion validity. Human Performance, 12:3-4, 231-273. doi: 10.1080/08959289909539871
• Ahlstrom, U., & Friedman-Berg, F. J. (2006). Using eye movement activity as a correlate of cognitive workload. International journal of industrial ergonomics, 36(7), 623-636.
• Antoško, M., Piľa, J., Korba, P., & Lipovský, P. (2014). Psychological readiness of air traffic controllers for their job. In: Our Sea 61(1-2): 5-8. - ISSN 0469-6255
• Antoško, M., Sabo, J., Hovanec, M., Korba, P., & Sekelová, M. (2017). How to Evaluate the Actual Psychological Readiness of Atco. Proceedings of 20th International Scientific Conference, Transport Means 2017.
• Das, S., Gandhi, A., & Mondal, S. (1997). Effect of premenstrual stress on audiovisual reaction time and audiogram. Indian J Physiol Pharmacol, 41(1):67-70.
• Djokic, J., Lorenz, B., & Fricke, H. (2010). Air traffic control complexity as workload driver. Transp. Res. Part C Emerg. Technol., vol. 18, no. 6, pp. 930-936. doi: 10.1016/j.trc.2010.03.005
• Eißfeldt, H., Heil, M. C., & Broach, D. (2002). Staffing the ATM System: The Selection of Air Traffic Controllers (1st ed.). Routledge. https://doi.org/10.4324/9781315242538
• Federal Aviation Administration. (2021a). Air traffic organization policy. ORDER JO 7110.65Z.
• Federal Aviation Administration. (2021b). Forecasts of IFR aircraft handled by FAA air route traffic control centers. FY 2021–2040.
• Finkelman, J. M. (1994). A large database study of the factors associated with work-induced fatigue. Human Factors, 36(2), 232-243.
• Katanić, B., Ilić, P., Stojmenović, A., & Vitasović, M. (2020). The application of Fitlight trainer system in sports. Fizička kultura, 74(2), 115-126. https://doi.org/10.5937/fizkul74-27189
• Lesiuk, T. (2008). The effect of preferred music listening on stress levels of air traffic controllers. The Arts in Psychotherapy, 35(1), 1-10.
• Li, W. C., Moore, P., Zhang, J., Lin, J., & Kearney, P. (2022). The impact of out-the-window size on air traffic controllers’ visual behaviours and response time on digital tower operations. International Journal of Human-Computer Studies, Volume 166, 102880, ISSN 1071-5819. https://doi.org/10.1016/j.ijhcs.2022.102880
• Némethová, H., Balint, J., & Vagner, J. (2019). The Education and Training Methodology of the Air Traffic Controllers in Training, 556-563. 10.1109/ICETA48886.2019.9040125
• Nocera, F. D., Fabrizi, R., Terenzi, M., & Ferlazzo, F. (2006). Procedural errors in air traffic control: effects of traffic density, expertise, and automation. Aviation, space, and environmental medicine, 77(6), 639-643.
• Örs, B. S., Cantas, F., Onarıcı Gungor, E., & Sımsek, D. (2019). ASSESSMENT AND COMPARISON OF VISUAL SKILLS AMONG ATHLETES. Spor ve Performans Araştırmaları Dergisi, 10 (3), 231-241. doi: 10.17155/omuspd.522342
• Reynolds, A., & Miller, J. (2009). Display Size Effects in Visual Search: Analyses of Reaction Time Distributions as Mixtures. Quarterly Journal of Experimental Psychology, 62(5):988-1009. doi:10.1080/17470210802373027
• Rogošić, T., Juričić, B., Aybek Çetek, F., & Kaplan, Z. (2021). ATCO radar training assessment and flight efficiency: The correlation between trainees’ scores and fuel consumption in real-time simulations. The Aeronautical Journal, 125(1287), 949-965. doi:10.1017/aer.2020.142
• Shenvi, D., & Balasubramanian, P. A. (1994). Comparative study of visual and auditory reaction times in males and females. Indian J Physiol Pharmacol, 38:229–229.
• Simsek, D., Ozboke, C., & Gultekin, E. A. (2021). Evaluation of the Use of Postural Control Strategies during Dual-Tasks of Hearing-Impaired Athletes. Montenegrin Journal of Sports Science and Medicine, 10(1), 11-17. doi: 10.26773/mjssm.210302
• Stager, P., Hameluck, D., & Jubis, R. (1989). Underlying factors in air traffic control incidents. In Proceedings of the Human Factors Society Annual Meeting, Vol. 33, No. 2, pp. 43-46. Sage CA: Los Angeles, CA: SAGE Publications.
• Vogt, J., Hagemann, T., & Kastner, M. (2006). The impact of workload on heart rate and blood pressure in en-route and tower air traffic control. Journal of Psychophysiology, 20(4), 297-314. https://doi.org/10.1027/0269-8803.20.4.297
• Zhang, X., Yuan, L., Zhao, M., & Bai, P. (2019). Effect of Fatigue and Stress on Air Traffic Control Performance. 5th International Conference on Transportation Information and Safety (ICTIS), pp. 977-983. doi: 10.1109/ICTIS.2019.8883823