Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-27T06:08:32.183Z Has data issue: false hasContentIssue false

A Spatial, Temporal Complexity Metric for Tactical Air Traffic Control

Published online by Cambridge University Press:  16 May 2018

Hong Jie Wee*
Affiliation:
(School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore) (Thales Air System SAS, Rungis, France)
Sun Woh Lye
Affiliation:
(School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore)
Jean-Philippe Pinheiro
Affiliation:
(Thales Air System SAS, Rungis, France)

Abstract

Tactical monitoring and controlling of air traffic is becoming increasingly difficult to manage for Air Traffic Controllers (ATCOs) owing to an increasingly complex traffic flow. A dynamic tactical complexity model, herein known as Conflict Activity Level (CAL), has been developed and is presented in this paper. This can be achieved either by establishing an overall score for an entire region or sub-regions of interest as specified by user's input location and time. This is done by evaluating the likely aircraft flight shape profile based on its current and projected position and trajectory. From the flight shape profile, CAL values are computed based on instantaneous existing traffic numbers in the overall region or sub-regions of interest. The proposed complexity approach shows good agreement with other methods in terms of ranking the order of complexity of various air traffic scenarios and the key influencing factors contributing to conflict.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2018 

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

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, 15081526.Google Scholar
Bowen, D. (2014). The SESAR Concept and i4D. Educational Workshop in ATM global, Beijing, China.Google Scholar
Chatterji, G. and Sridhar, B. (2001). Measures for air traffic controller workload prediction. 1st AIAA, Aircraft, Technology Integration, and Operations Forum. American Institute of Aeronautics and Astronautics.Google Scholar
Delahaye, D. and Puechmorel, S. (2000). Air traffic complexity: towards intrinsic metrics. Proceedings of the third USA/Europe Air Traffic Management R & D Seminar, Napoli, Italy.Google Scholar
Endsley, M.R. (2011). Designing for situation awareness: An approach to user-centered design. CRC Press.Google Scholar
Endsley, M.R. and Garland, D.J. (2000). Situation awareness analysis and measurement. CRC Press.Google Scholar
Enea, G. and Porretta, M. (2012). A comparison of 4D-trajectory operations envisioned for Nextgen and SESAR, some preliminary findings. 28th Congress of the International Council of the Aeronautical Sciences, 2328.Google Scholar
Eurocontrol. (1997). A Consistent Vertical Collision Risk Model for Crossing and Parallel Tracks. Eurocontrol.Google Scholar
ICAO. (2009). Required Navigation Performance Authorisation Required (RNP AR) Procedure Design Manual. First Edition ed. Montreal. https://www.icao.int/Meetings/PBN-Symposium/Documents/9905_cons_en.pdfGoogle Scholar
ICAO. (2014). Forecasts of Scheduled Passenger and Freight Traffic. May 2014 ed. https://www.icao.int/sustainability/pages/eap_fp_forecast_tables.aspxGoogle Scholar
Kang, Z. and Landry, S.J. (2015). An Eye Movement Analysis Algorithm for a Multi-element Target Tracking Task: Maximum Transition-Based Agglomerative Hierarchical Clustering. IEEE Transactions on Human-Machine Systems, 45, 1324.Google Scholar
Kolmogorov, A. (1959) Entropy per unit time as a metric invariant of automorphisms. Doklady Akademii Nauk SSSR, 124, 754755.Google Scholar
Kopardekar, P. and Magyarits, S. (2002). Dynamic density: measuring and predicting sector complexity [ATC]. Proceedings of the 21st Digital Avionics Systems Conference, Irvine, California, USA, 2C4-2C4.Google Scholar
Kopardekar, P. and Magyarits, S. (2003). Measurement and prediction of dynamic density. Proceedings of the 5th USA/Europe Air Traffic Management R & D Seminar, Hungary, Budapest, 139.Google Scholar
Kopardekar, P.H., Schwartz, A., Magyarits, S. and Rhodes, J. (2009). Airspace Complexity Measurement: An Air Traffic Control Simulation Analysis. International Journal of Industrial Engineering, 16, 6170.Google Scholar
Krajček, K., Nikoliæ, D. and Domitroviæ, A. (2015). Aircraft Performance Monitoring from Flight Data. Tehnicki vjesnik/Technical Gazette, 22.Google Scholar
Laudeman, I.V., Shelden, S., Branstrom, R. and Brasil, C. (1998).Dynamic density: An air traffic management metric. In: Center, N. A. R. (ed.). Moffett Field, CA United States.Google Scholar
Lee, K., Feron, E. and Pritchett, A. (2007). Air Traffic Complexity: An Input-Output Approach. American Control Conference, 9–13 July, 474479.Google Scholar
Masalonis, A.J., Callaham, M.B. and Wanke, C.R. (2003). Dynamic density and complexity metrics for realtime traffic flow management. 5th USA/Europe Air Traffic Management R & D Seminar, Budapest, Hungary, 139.Google Scholar
Mogford, R.H., Guttman, J., Morrow, S. and Kopardekar, P. (1995). The Complexity Construct in Air Traffic Control: A Review and Synthesis of the Literature. CTA Inc, McKee City, NJ.Google Scholar
Mutuel, L.H., Neri, P. and Paricaud, E. (2013). Initial 4D Trajectory Management Concept Evaluation. Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013) LOCATION?? Airport.Google Scholar
Puechmorel, S. and Delahaye, D. (2009). New trends in air traffic complexity. EIWAC 2009, ENRI International Workshop on ATM/CNS, Tokyo, Japan. 5560. https://hal-enac.archives-ouvertes.fr/hal-00938199/file/323.pdf.Google Scholar
Radanovic, M., Eroles, M.A.P., Koca, T. and Nieto, F.J.S. (2017). Self-Reorganized Supporting Tools for Conflict Resolution in High-Density Airspace Volumes. Twelfth USA/Europe Air Traffic Management Research and Development Seminar, Seattle, Washington, USA, 10.Google Scholar
Sridhar, B., Sheth, K.S. and Grabbe, S. (1998). Airspace complexity and its application in air traffic management. 2nd USA/Europe Air Traffic Management R&D Seminar, Orlando, USA. 16.Google Scholar
Wee, H.J., Lye, S.W. and Pinheiro, J.-P. (2016). Computing Eye Tracking Metric for a Radar Display Using a Remote Eye Tracker. International Conference on Artificial Intelligence and Computer Engineering. Future City Hotel Wuhan, China: DEStech Publication, Inc.Google Scholar
Wee, H.J., Lye, S.W. and Pinheiro, J.-P. (2017a). Real Time Eye Tracking Interface for Visual Monitoring of Radar Controllers. AIAA Modeling and Simulation Technologies Conference. American Institute of Aeronautics and Astronautics.Google Scholar
Wee, H.J., Trapsilawati, F., Lye, S.W., Chen, C.-H. and Pinheiro, J.-P. (2017b). Real Time Bio Signal Interface for Visual Monitoring of Radar Controllers. Transdisciplinary Engineering: A Paradigm Shift: Proceedings of the 24th ISPE Inc. International Conference on Transdisciplinary Engineering, July 10–14, IOS Press, 394.Google Scholar