Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T16:07:25.443Z Has data issue: false hasContentIssue false
  • English
  • Français

EEG-Based Analysis of Air Traffic Conflict: Investigating Controllers’ Situation Awareness, Stress Level and Brain Activity during Conflict Resolution

Published online by Cambridge University Press:  20 November 2019

Fitri Trapsilawati ,
Muhammad Kusumawan Herliansyah ,
Agustyandini Sekar Asih Nur Sari Nugraheni ,
Mifta Priani Fatikasari  and
Gharsina Tissamodie
Fitri Trapsilawati*
Affiliation:
(Department of Mechanical and Industrial Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jalan Grafika no. 2, Yogyakarta55281, Indonesia)
Muhammad Kusumawan Herliansyah
Affiliation:
(Department of Mechanical and Industrial Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jalan Grafika no. 2, Yogyakarta55281, Indonesia)
Agustyandini Sekar Asih Nur Sari Nugraheni
Affiliation:
(Department of Mechanical and Industrial Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jalan Grafika no. 2, Yogyakarta55281, Indonesia)
Mifta Priani Fatikasari
Affiliation:
(Department of Mechanical and Industrial Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jalan Grafika no. 2, Yogyakarta55281, Indonesia)
Gharsina Tissamodie
Affiliation:
(Department of Mechanical and Industrial Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jalan Grafika no. 2, Yogyakarta55281, Indonesia)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The effects of air traffic conflict geometry have been well investigated in prior studies, particularly in the context of the pilot, though little in the context of the air traffic control officer (ATCO). No study to date has investigated the effects of conflict geometry on human factors variables of ATCOs through objective and physiological approaches. This study examines the effects of conflict geometries on ATCO situation awareness, stress level and brain activity during conflict resolution. Fifteen participants were instructed to resolve six different conflict geometries: crossing level, crossing non-level, converging level, converging non-level, overtaking level, and overtaking non-level. The results indicate that converging and crossing conflicts led to lower situation awareness (SA), higher stress level, and higher theta activation at the temporal and parietal lobes. Level conflict led to lower SA. The findings offer two implications, providing insights for the formal guidelines in ATC conflict resolution training and provision of inputs for the conflict resolution aid development.

Keywords

Conflict geometryAir traffic controlSituation awarenessEEG, brain activity
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 3 , May 2020 , pp. 678 - 696
Copyright
Copyright © The Royal Institute of Navigation 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abbass, H. A., Tang, J., Amin, R., Ellejmi, M., and Kirby, S. (2014). Augmented Cognition Using Real-Time EEG-Based Adaptive Strategies for Air Traffic Control. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 58). Los Angeles, CA: SAGE Publications Sage CA, 230–234.CrossRefGoogle Scholar
Astolfi, L., Toppi, J., Borghini, G., Vecchiato, G., He, E. J., Roy, A., Cincotti, F., Salinari, S., Mattia, D., He, B., and Babiloni, F (2012). Cortical Activity and Functional Hyperconnectivity by Simultaneous EEG Recordings from Interacting Couples of Professional Pilots. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 4752–4755.CrossRefGoogle Scholar
Bernardi, G., Betta, M., Ricciardi, E., Pietrini, P., Tononi, G., and Siclari, F. (2019). Regional delta waves in human rapid eye movement sleep. Journal of Neuroscience, 39(14), 2686–2697.CrossRefGoogle ScholarPubMed
Boag, C., Neal, A., Loft, S., and Halford, G. S. (2006). An analysis of relational complexity in an air traffic control conflict detection task. Ergonomics, 49(14), 1508–1526.Google Scholar
Borghini, G., Aricò, P., Di Flumeri, G., Salinari, S., Colosimo, A., Bonelli, S., Napoletano, L., Ferreira, A., and Babiloni, F. (2015). Avionic Technology Testing by Using a Cognitive Neurometric Index: A Study with Professional Helicopter Pilots. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 6182–6185.CrossRefGoogle Scholar
CANSO. (2012). Accelerating Air Traffic Management Efficiency: A Call to Industry. Paper of ATM Global Environment Efficiency Goals for 2050. Civil Air Navigation Service Organization.Google Scholar
Chatterji, G., and Sridhar, B. (2001). Measures for Air Traffic Controller Workload Prediction. Paper Presented at the 1st AIAA Aircraft, Technology Integration, and Operations Forum.CrossRefGoogle Scholar
Christien, R., Benkouar, A., Chaboud, T., and Loubieres, P. (2002). Air Traffic Complexity Indicators & ATC Sectors Classification. In Proceedings of the 21st Digital Avionics Systems Conference. IEEE, 2D3-2D3.CrossRefGoogle Scholar
CNS. (2017). Parietal Lobes. From www.neuroskills.com/brain-injury/parietal-lobes.php.Google Scholar
Dharmawan, Z. (2007). Analysis of Computer Games Player Stress Level Using EEG Data. Master of Science Thesis Report, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Netherlands.Google Scholar
Di Stasi, L. L., Diaz-Piedra, C., Suárez, J., McCamy, M. B., Martinez-Conde, S., Roca-Dorda, J., and Catena, A. (2015). Task complexity modulates pilot electroencephalographic activity during real flights. Psychophysiology, 52(7), 951–956.CrossRefGoogle ScholarPubMed
Durso, F. T., Hackworth, C. A., Truitt, T. R., Crutchfield, J., and Nikolic, D. (1999). Situation Awareness as a Predictor of Performance in En Route Air Traffic Controllers: DTIC Document.CrossRefGoogle Scholar
Durso, F. T., Dattel, A. R., Banbury, S. and Tremblay, S. (2004). SPAM: The real-time assessment of SA. A Cognitive Approach to Situation Awareness: Theory and Application, 1, 137–154.Google Scholar
Emotiv. (2019). From www.emotiv.com/epoc/.Google Scholar
Endsley, M., Sollenberger, R., and Stein, E. (1999). The Use of Predictive Displays for Aiding Controller Situation Awareness. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 43). Los Angeles, CA: SAGE Publications Sage CA, 51–55.CrossRefGoogle Scholar
Erzberger, H. (2006). Automated Conflict Resolution for Air Traffic Control. Proceedings of the 25th International Congress of Aeronautical Sciences. Hamburg, Germany, 1–27.Google Scholar
Eyferth, K., Niessen, C. and Spaeth, O. (2003). A model of air traffic controllers’ conflict detection and conflict resolution. Aerospace Science and Technology, 7(6), 409–416.CrossRefGoogle Scholar
Falkland, E. C., and Wiggins, M. W. (2019). Cross-task cue utilisation and situational awareness in simulated air traffic control. Applied Ergonomics, 74, 24–30.CrossRefGoogle ScholarPubMed
Frackowiak, R. S. J. (1998). The functional architecture of the brain. Daedalus (Vol. 127). Cambridge, MA: The MIT Press, 105–130.Google Scholar
Freitas, Â. M., Portuguez, M. W., Russomano, T., de Freitas, M., Silvello, S. L. D. S., and Costa, J. C. D. (2017). Effects of an alternating work shift on air traffic controllers and the relationship with excessive daytime sleepiness and stress. Arquivos de Neuro-Psiquiatria, 75(10), 711–717.CrossRefGoogle ScholarPubMed
Gamon, D. (2016). Your Brain and What It Does. From www.brainwaves.com/.Google Scholar
Giraudet, L., Imbert, J.-P., Bérenger, M., Tremblay, S., and Causse, M. (2015). The neuroergonomic evaluation of human machine interface design in air traffic control using behavioral and EEG/ERP measures. Behavioural Brain Research, 294, 246–253.CrossRefGoogle Scholar
Gomez-Beldarrain, M., Harries, C., Garcia-Monco, J. C., Ballus, E., and Grafman, J. (2004). Patients with right frontal lesions are unable to assess and use advice to make predictive judgments. Journal of Cognitive Neuroscience, 16(1), 74–89.CrossRefGoogle ScholarPubMed
Goritzlehner, R., Borst, C., Ellerbroek, J., Westin, C., van Paassen, M., and Mulder, M. (2014). Effects of Transparency on the Acceptance of Automated Resolution Advisories. Paper Presented at the Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on.CrossRefGoogle Scholar
Grandjean, E. P., Wotzka, G., Schaad, R., and Gilgen, A. (1971). Fatigue and stress in air traffic controllers. Ergonomics, 14(1), 159–165.CrossRefGoogle ScholarPubMed
Groome, D., and Eysenck, M. (2016). An Introduction to Applied Cognitive Psychology. Psychology Press, Oxon, UK.Google Scholar
Hilburn, B. (2004). Cognitive complexity in air traffic control: a literature review. (EEC Note No. 04/04). Brétigny-sur-Orge. Center of Human Performance Research, France.Google Scholar
Histon, J. M., Hansman, R. J., Gottlieb, B., Kleinwaks, H., Yenson, S., Delahaye, D., and Puechmorel, S. (2002). Structural Considerations and Cognitive Complexity in Air Traffic Control. Proceedings. The 21st Digital Avionics Systems Conference (Vol. 1). IEEE, 1C2–1C2.CrossRefGoogle Scholar
Hou, X., Trapsilawati, F., Liu, Y., Sourina, O., Chen, C.-H., Mueller-Wittig, W., and Ang, W. T. (2017). EEG-based human factors evaluation of conflict resolution aid and tactile user interface in future air traffic control systems. In: Stanton, NA., Steven Landry, S., Di Bucchianico, Giuseppe and Vallicelli, Andrea (EDs), Advances in Human Aspects of Transportation. Springer International Publishing, Switzerland, 885–897.CrossRefGoogle Scholar
ICAO. (2013). Identify Specific Training for Pilots and Air Traffic Controllers. Bangkok, Thailand: International Civil Aviation Organization.Google Scholar
Isaac, A. R., and Ruitenberg, B. (2017). Air Traffic Control: Human Performance Factors. Routledge, Oxon, UK.CrossRefGoogle Scholar
Jimenez-Molina, A., Retamal, C., and Lira, H. (2018). Using psychophysiological sensors to assess mental workload during web browsing. Sensors, 18(2), 458.CrossRefGoogle ScholarPubMed
Jou, R.-C., Kuo, C.-W., and Tang, M.-L. (2013). A study of job stress and turnover tendency among air traffic controllers: the mediating effects of job satisfaction. Transportation Research Part E: Logistics and Transportation Review, 57, 95–104.CrossRefGoogle Scholar
Kimura, D. (1963). Right temporal-lobe damage: perception of unfamiliar stimuli after damage. Archives of Neurology, 8(3), 264–271.CrossRefGoogle ScholarPubMed
Kiroi, V. N., Aslanyan, E. V., Bakhtin, O. M., Minyaeva, N. R., and Lazurenko, D. M. (2016). EEG correlates of the functional state of pilots during simulated flights. Neuroscience and Behavioral Physiology, 46(4), 375–381.CrossRefGoogle Scholar
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2–3), 169–195.CrossRefGoogle ScholarPubMed
Klimesch, W., Schimke, H. and Schwaiger, J. (1994). Episodic and semantic memory: An analysis in the EEG theta and alpha band. Electroencephalography and Clinical Neurophysiology, 91(6), 428–441.CrossRefGoogle ScholarPubMed
Krozel, J., and Peters, M. (1997). Conflict detection and resolution for free flight. Air Traffic Control Quarterly, 5(3), 181–212.Google Scholar
Lagopoulos, J., Xu, J., Rasmussen, I., Vik, A., Malhi, G. S., Eliassen, C. F., and Holen, A. (2009). Increased theta and alpha EEG activity during nondirective meditation. The Journal of Alternative and Complementary Medicine, 15(11), 1187–1192.CrossRefGoogle ScholarPubMed
Li, F., Lee, C. H., Xu, G., Chen, C. H., and Khoo, L. P. (2017). A QFD-enabled conceptualization for reducing alarm fatigue in vessel traffic service centre. Transdisciplinary Engineering: A Paradigm Shift, 821–828.Google Scholar
Li, J., Zhang, Z., and He, H. (2018). Hierarchical convolutional neural networks for EEG-based emotion recognition. Cognitive Computation, 10(2), 368–380.CrossRefGoogle Scholar
Li, F., Chen, C. H., Lee, C. H., and Khoo, L. P. (2019a). A user requirement-driven approach incorporating TRIZ and QFD for designing a smart vessel alarm system to reduce alarm fatigue. The Journal of Navigation, 1–21, DOI: https://doi.org/10.1017/S0373463319000547.Google Scholar
Li, F., Chen, C. H., Lee, C. H., and Khoo, L. P. (2019b). Hybrid data-driven vigilance model in traffic control center using eye-tracking data and context data. Advanced Engineering Informatics, 42, 100940.CrossRefGoogle Scholar
Murphy, E. D., Albert, H. A., Chen, J. M., and Anderson, G. G. (2012). The Role of Mental Computations in Current and Future En Route Air Traffic Control. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 56). Los Angeles, CA: Sage Publications Sage CA, 110–114.CrossRefGoogle Scholar
Nolan, M. (1999). Fundamentals of air traffic control. New York: Cengage Learning.Google Scholar
Nunes, A. (2003). The Impact of Automation Use on the Mental Model: Findings from the Air Traffic Control Domain. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 47). Los Angeles, CA: SAGE Publications Sage CA, 66–70.CrossRefGoogle Scholar
Pierce, L. G., Bleckley, M. K., and Crayton, L. (2013). The Utility of the Air Traffic Selection and Training Test Battery in Hiring Graduates of an Air Traffic-Collegiate Training Initiative Program. Federal Aaviation Administration Oklahoma City Civil Aerospace Medical Inst.Google Scholar
Prevot, T., Homola, J. R., Martin, L. H., Mercer, J. S., and Cabrall, C. D. (2012). Toward automated air traffic control—investigating a fundamental paradigm shift in human/systems interaction. International Journal of Human-Computer Interaction, 28(2), 77–98.CrossRefGoogle Scholar
Rantanen, E. M., and Nunes, A. (2005). Hierarchical conflict detection in air traffic control. The International Journal of Aviation Psychology, 15(4), 339–362.CrossRefGoogle Scholar
Rantanen, E. M., and Wickens, C. D. (2012). Conflict resolution maneuvers in air traffic control: investigation of operational data. The International Journal of Aviation Psychology, 22(3), 266–281.CrossRefGoogle Scholar
Renata, V., Li, F., Lee, C. H., and Chen, C. H. (2018). Investigation on the Correlation between Eye Movement and Reaction Time under Mental Fatigue Influence. 2018 International Conference on Cyberworlds (CW). IEEE, 207–213.CrossRefGoogle Scholar
Rihs, T. A., Michel, C. M., and Thut, G. (2007). Mechanisms of selective inhibition in visual spatial attention are indexed by α -band EEG synchronization. European Journal of Neuroscience, 25(2), 603–610.CrossRefGoogle ScholarPubMed
Sarter, N. B., and Woods, D. D. (1991). Situation awareness: a critical but ill-defined phenomenon. The International Journal of Aviation Psychology, 1(1), 45–57.CrossRefGoogle Scholar
Scallen, S., Smith, K., and Hancock, P. (1996). Pilot Actions During Traffic Situations in a Free-Flight Airspace Structure. Paper Presented at the Proceedings of the Human Factors and Ergonomics Society Annual Meeting.CrossRefGoogle Scholar
Shou, G., and Ding, L. (2013). Frontal Theta EEG Dynamics in a Real-World Air Traffic Control Task. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 5594–5597.Google Scholar
Siddiqee, W. (1973). A mathematical model for predicting the number of potential conflict situations at intersecting air routes. Transportation Science, 7(2), 158–167.CrossRefGoogle Scholar
Smelser, N. J., and Baltes, P. B. (2001). International Encyclopedia of the Social & Behavioral Sciences (Vol. 12). Elsevier Amsterdam, Oxford, UK.Google Scholar
Sperandio, J.-C. (1978). The regulation of working methods as a function of work-load among air traffic controllers. Ergonomics, 21(3), 195–202.CrossRefGoogle ScholarPubMed
Suijkerbuijk, H. C. H., Borst, C., Mulder, M., and Van Paassen, M. M. (2005). Development and Experimental Evaluation of a Performance-Based Vertical Situation Display. AIAA Guidance, Navigation and Control Conference and Exhibit (August), 1–23. https://doi.org/10.2514/6.2005-5961CrossRefGoogle Scholar
Tan, D., and Nijholt, A. (2010). Brain-computer interfaces and human-computer interaction. In: Brain–Computer Interfaces. Springer, London, 3–19.CrossRefGoogle Scholar
Thomas, L. C., and Rantanen, E. M. (2006). Human factors issues in implementation of advanced aviation technologies: A case of false alerts and cockpit displays of traffic information. Theoretical Issues in Ergonomics Science, 7(5), 501–523.CrossRefGoogle Scholar
Thomas, L. C., and Wickens, C. D. (2005). Effects of Display Dimensionality, Conflict Geometry, and Time Pressure on Conflict Detection and Resolution Performance Using a Cockpit Display of Traffic Information (A. H. F. Division, Trans.). Savoy, Illinois: Institute of Aviation, University of Illinois at Urbana-Champaign.Google Scholar
Trapsilawati, F., Chen, C. H., and Khoo, L. P. (2016). An Investigation into Conflict Resolution and Trajectory Prediction Aids for Future Air Traffic Control. ISPE TE, 503–512.Google Scholar
Trapsilawati, F., Wickens, C., Chen, C. H., and Qu, X. (2017). Transparency and Conflict Resolution Automation Reliability in Air Traffic Control. 19th International Symposium on Aviation Psychology, 419.Google Scholar
Trapsilawati, F., Nugraheni, A. S. A. N. S., and Herliansyah, M. K. (2018). Analysis of Air Traffic Conflict Geometry on Brain Activity. 2018 4th International Conference on Science and Technology (ICST) (Vol. 1). IEEE, 1–5.CrossRefGoogle Scholar
*USDOT. (2016). Number of Pilot-Reported Near Midair Collisions (NMAC) by Degree of Hazard. From US Department of Transportation. www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_statistics/html/table_02_15.html.Google Scholar
Wee, H. J., Lye, S. W., and Pinheiro, J. P. (2018). A spatial, temporal complexity metric for tactical air traffic control. The Journal of Navigation, 71(5), 1040–1054.CrossRefGoogle Scholar
Wee, H. J., Lye, S. W., and Pinheiro, J. P. (2019). An integrated highly synchronous, high-resolution, real-time eye tracking system for dynamic flight movement. Advanced Engineering Informatics, 41, 100919.CrossRefGoogle Scholar
Weiland, M. Z., Roberts, D. M., Fine, M. S., and Caywood, M. S. (2013). Real-Time Research Methods: Monitoring Air Traffic Controller Workload During Simulation Studies Using Electroencephalography (EEG). Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 57). Los Angeles, CA: SAGE Publications Sage CA, 1615–1619.Google Scholar
Westin, C. A., Borst, C., and Hilburn, B. (2013). Mismatches between automation and human strategies: An investigation into future air traffic management decision aiding. 17th International Symposium on Aviation Psychology. Wright University, Dayton, OH, 530–535.Google Scholar
Wilson, G. F. (2002). An analysis of mental workload in pilots during flight using multiple psychophysiological measures. The International Journal of Aviation Psychology, 12(1), 3–18.CrossRefGoogle Scholar
Yang, Q., and Dattel, A. R. (2017). Task Complexity and Time Pressure Affect Air Traffic Controller's Performance and Workload. 19th International Symposium on Aviation Psychology, 359.Google Scholar
Zeier, H. (1994). Workload and psychophysiological stress reactions in air traffic controllers. Ergonomics, 37(3), 525–539.CrossRefGoogle ScholarPubMed