Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks

  • Petar Andraši
  • Tomislav Radišić FPZ
  • Doris Novak
  • Biljana Juričić
Keywords: air traffic complexity, complexity estimation, artificial neural networks, genetic algorithm, human-in-the-loop simulation, air traffic management

Abstract

Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controllers. However, there is a need to make a method for complexity estimation which can be used without constant controller input. So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajectory-based).

References

Meckiff C, Chone R, Nicolaon J-P. The tactical load smoother for multi-sector planning. Proceedings of the 2nd usa/europe air traffic management research and development seminar; 1998.

Christien R, Benkouar A, Chaboud T, Loubieres P. Air traffic complexity indicators & ATC sectors classification. Proceedings of the 21st Digital Avionics Systems Conference, 27-31 Oct. 2002, Irvine, CA, USA, vol. 1. IEEE; 2002. p. 2D3-2D3.

Majumdar A, Ochieng W. Factors affecting air traffic controller workload: Multivariate analysis based on simulation modeling of controller workload. Transportation Research Record: Journal of the Transportation Research Board. 2002;1788(1): 58-69.

Mogford RH, Guttman J, Morrow S, Kopardekar P. The Complexity Construct in Air Traffic Control: A Review and Synthesis of the Literature. CTA INC MCKEE CITY NJ; 1995.

Schmidt DK. On modeling ATC work load and sector capacity. Journal of Aircraft. 1976;13: 531-537.

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 E. Air traffic controller workload: An examination of workload probe. Atlantic City, New Jersey: FAA; 1985.

Laudeman IV, Shelden S, Branstrom R, Brasil C. Dynamic density: An air traffic management metric. Ames Research Center. Report No. A-99-10366, 1998.

Chatterji G, Sridhar B. Measures for air traffic controller workload prediction. 1st AIAA, Aircraft, Technology Integration, and Operations Forum; 2001, p. 5242.

Pawlak WS, Brinton CR, Crouch K, Lancaster KM. A framework for the evaluation of air traffic control complexity. AAIA Guidance, Navigation, and Control Conference, 29-31 July 1996, San Diego, CA, USA; 1996. p. 3856.

Kopardekar P. Dynamic density: A review of proposed variables. FAA WJHTC Internal Document Overall Conclusions and Recommendations. Federal Aviation Administration, 2000.

Kopardekar P, Magyarits S. Dynamic density: measuring and predicting sector complexity. Proceedings of the 21st Digital Avionics Systems Conference, 27-31 Oct. 2002, Irvine, CA, USA, vol. 1. IEEE; 2002. p. 2C4–2C4.

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.

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: 3038-3048.

Radišić T, Novak D, Juričić B. Development and validation of an ATC research simulator. Proceedings of the INAIR 2015 International Conference on Air Transport, 12-13 Nov 2015, Amsterdam, The Netherlands; 2016.

Kopardekar PH, 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: 61-70.

Gianazza D, Guittet K. Evaluation of air traffic complexity metrics using neural networks and sector status. Proceedings of the 2nd International Conference on Research in Air Transportation, ICRAT 2006, 24-28 June 2006, Belgrade, Serbia and Montenegro; 2006. p. 113-122.

Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems. 1989;2: 303-314.

Levenberg K. A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics. 1944;2: 164-168.

Published
2019-08-10
How to Cite
1.
Andraši P, Radišić T, Novak D, Juričić B. Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks. PROMET [Internet]. 2019Aug.10 [cited 2019Oct.15];31(4):377-86. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3018
Section
Articles