Determining Air Traffic Complexity – Challenges and Future Development
Air traffic complexity is one of the main drivers of the air traffic controllers’ workload. With the forecasted increase of air traffic, the impact of complexity on the controllers' workload will be even more pronounced in the coming years. The existing models and methods for determining air traffic complexity have drawbacks and issues which are still an unsolved challenge. In this paper, an overview is given of the most relevant literature on air traffic complexity and improvements that can be done in this field. The existing issues have been tackled and new solutions have been given on how to improve the determination of air traffic complexity. A preliminary communication is given on the future development of a novel method for determining air traffic complexity with the aim of designing a new air traffic complexity model based on air traffic controller tasks. The novel method uses new solutions, such as air traffic controller tasks defined on pre-conflict resolution parameters, experiment design, static images of traffic situations and generic airspace to improve the existing air traffic complexity models.
Performance Review Commission. Performance Review Report 2017. EUROCONTROL; 2018.
Performance Review Unit. European ANS Performance Data Portal n.d. Available form: http://ansperformance.eu/ [Accessed 12th November 2018].
STATFOR. EUROCONTROL Seven-Year Forecast February 2018; 2018.
Meckiff C, Chone R, Nicolaon J-P. The Tactical Load Smoother for Multi-Sector Planning. 2nd USA/Europe Air Traffic Management R&D Seminar, 1-4 December 1998, Orlando, USA; 1998.
Davis CG, Danaher JW, Fischl MA. The influence of selected sector characteristics upon ARTCC controller activities. Arlington: The Matrix Corporation; 1963.
Mogford RH, Guttman JA, Morrow SL, Kopardekar P. The Complexity Construct in Air Traffic Control: A Review and Synthesis of the Literature. McKee City, NJ: CTA Incorporated; 1995.
Hilburn B. Cognitive Complexity in Air Traffic Control: A Literature Review. Center for Human Performance Research; 2004.
Schmidt DK. On Modeling ATC Work Load and Sector Capacity. Journal of Aircraft. 1976: 531-7.
Hurst MW, Rose RM. Objective Job Difficulty Behavioural Response, and Sector Characteristics in Air Route Traffic Control Centres. Ergonomics. 1978;21(9): 697-708.
Stein ES. Air traffic controller workload: An examination of workload probe. FAA; 1985.
Laudeman IV, Shelden SG, Branstrom R, Brasil CL. Dynamic Density: An Air Traffic Management Metric. Moffett Field: Ames Research Center; 1998.
Chatterji G, Sridhar B. Measures for air traffic controller workload prediction. 1st AIAA, Aircraft, Technology Integration, and Operations, 16-18 Oct 2001, Los Angeles, CA, USA; 2001.
Wyndemere. An Evaluation of Air Traffic Control Complexity. Boulder: 1996.
Kopardekar P. Dynamic density: A review of proposed variables. Federal Aviation Administration; 2000.
Kopardekar P, Magyarits S. Dynamic density: Measuring and predicting sector complexity [ATC]. Proceedings of the 21st Digital Avionics Systems Conference, 27-31 Oct. 2002, Irvine, CA, USA. IEEE; 2002. Available from: doi:10.1109/DASC.2002.1067920
Kopardekar P, Magyarits S. Measurement and prediction of dynamic density. Proceedings of the 5th USA/Europe Air Traffic Management R & D Seminar. Vol. 139; 2003.
Kopardekar P, Schwartz A, Magyarits S, Rhodes J. Airspace complexity measurement: An air traffic control simulation analysis. International Journal of Industrial Engineering: Theory, Applications and Practice. 2009;16(1): 61-70.
Masalonis A, Callaham M, Wanke C. Dynamic Density and Complexity Metrics for Real-Time Traffic Flow Management. Budapest, Hungary; 2003.
Klein A, Rodgers M, Leiden K. Simplified dynamic density: A metric for dynamic airspace configuration and NextGen analysis. Proceedings of the 28th Digital Avionics Systems Conference (DASC): Modernization of Avionics and ATM-perspectives from the Air and Ground, 25-29 Oct. 2009, Orlando, FL, USA. IEEE; 2009. Available from: doi:10.1109/DASC.2009.5347539
Bloem M, Brinton C, Hinkey J, Leiden K, Sheth K. A Robust Approach for Predicting Dynamic Density. Proceedings of the 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), 21-23 Sep. 2009, Hilton Head, SC, USA; 2009.
Chaboud T, Hunter R, Hustache J, Mahlich S, Tullett P. Investigating the Air Traffic Complexity: Potential Impacts on Workload and Costs. Belgium: Eurocontrol; 2000.
Performance Review Commission. Complexity Metrics for ANSP Benchmarking Analysis. Eurocontrol; 2006.
Prevot T, Lee P. Trajectory-Based Complexity (TBX): A modified aircraft count to predict sector complexity during trajectory-based operations. 2011 IEEE/AIAA 30th Digital Avionics Systems Conference, 16-20 Oct. 2011, Seattle, WA, USA. IEEE; 2011. Available form: doi:10.1109/DASC.2011.6096045
Lee P, Prevot T. Prediction of Traffic Complexity and Controller Workload in Mixed Equipage NextGen Environments. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2012;56: 100-4.
Radišić T, Novak D, Juričić B. Reduction of Air Traffic Complexity Using Trajectory-Based Operations and
Validation of Novel Complexity Indicators. IEEE Transactions on Intelligent Transportation Systems. 2017;18(11): 3038-48.
Prandini M, Putta V, Hu J. Air traffic complexity in future Air Traffic Management systems. Journal of Aerospace Operations. 2012;1(3): 281-99.
Gianazza D, Guittet K. Selection and evaluation of air traffic complexity metrics. Proceedings of the 2006 IEEE/AIAA 25th Digital Avionics Systems Conference,15-18 Oct. 2006, Portland, OR, USA. IEEE; 2006. p. 1-12. Available form: doi:10.1109/DASC.2006.313710
Gianazza D. Forecasting workload and airspace configuration with neural networks and tree search methods. Artificial Intelligence. 2010; 174(7-8): 530-49. https://doi.org/10.1016/j.artint.2010.03.001.
Gianazza D. Smoothed traffic complexity metrics for airspace configuration schedules. Fairfax, United States; 2008.
Lee K, Feron E, Pritchett A. Describing Airspace Complexity: Airspace Response to Disturbances. Journal of Guidance, Control, and Dynamics. 2009;32: 210-22.
Wee HJ, Lye SW, Pinheiro J-P. A Spatial, Temporal Complexity Metric for Tactical Air Traffic Control. Journal of Navigation. 2018;71(5): 1040-54. Available from: doi:10.1017/S0373463318000255
Rank A, Dervic A. ATC complexity measures: Formulas measuring workload and complexity at Stockholm TMA. Linköping University; 2015.
Wang H, Song Z, Wen R. Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach. Journal of Advanced Transportation. 2018;2018: Article ID 5254289. 15 p. Available from: doi:10.1155/2018/5254289
Xiao M, Zhang J, Cai K, Cao X. ATCEM: A synthetic model for evaluating air traffic complexity. Journal of Advanced Transportation. 2016;50: 315-25. Available from: doi:10.1002/atr.1321
Zhu X, Cao X, Cai K. Measuring air traffic complexity based on small samples. Chinese Journal of Aeronautics. 2017;30: 1493-505.
Andraši P, Radišić T, Novak D, Juričić B. Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks. Promet – Traffic&Transportation. 2019;31(4): 377-86.
Green SB. How many subjects does it take to do a regression analysis? Multivariate Behavioral Research. 1991;26: 499-510.
Van Voorhis CRW, Morgan BL. Understanding Power and Rules of Thumb for Determining Sample Size. Tutorials in Quantitative Methods for Psychology. 2007;3(2): 43-50. Available from: doi:10.20982/tqmp.03.2.p043
Goldstine HH, von Neumann J. Planning and coding of problems for an electronic computing instrument. John von Neumann Collected Work, Theory of Automata and Numerical Analysis. 1963;5: 152-214.
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