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A regression model for terminal airspace delays

Published online by Cambridge University Press:  08 May 2017

F. Aybek Çetek ,
Y.M. Kantar  and
A. Cavcar
F. Aybek Çetek*
Affiliation:
Faculty of Aeronautics and Astronautics, Department of Air Traffic Control, Anadolu University, Eskişehir, Turkey
Y.M. Kantar
Affiliation:
Faculty of Science, Department of Statistics, Anadolu University, Eskişehir, Turkey
A. Cavcar
Affiliation:
Faculty of Aeronautics and Astronautics, Department of Air Traffic Control, Anadolu University, Eskişehir, Turkey
*
[email protected]
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Abstract

Air Traffic Management (ATM) research generally focuses on achieving a safer, more effective and economical air traffic system. The current airspace system has become increasingly strained as the demand for air travel has steadily grown. Innovative, proactive and multi-disciplinary approaches to research are needed to solve flight congestion and delays as a consequence of this rapid growth. As a result of this growth, air traffic flow becomes more complex, especially in Terminal Airspaces (TMA) where climb and descent manoeuvres of departing and arriving flights take place around airports. As air traffic demand exceeds the capacity in a TMA, the resultant congestion leads to delays that spread all over the system. Therefore, the reduction of delays is critical for airspace designers to increase customer satisfaction and the perception of service quality. Numerous studies have been conducted to reduce delays within TMAs. This research focuses on defining the causes of delays quantitatively through statistical analysis. The first step was to create a fast-time simulation model of sample airspace for collecting delay data. After building up this model using the SIMMOD fast-time ATM simulation tool, simulation experiments were run to produce various traffic scenarios and to generate traffic delay data. The number of airports, entry points, fixes and flight operations in airspace and the probability of wide-body aircraft were considered as independent variables. The correlations between the considered variables were analysed, and the total delay data was modelled using a linear regression model. The findings of regression model present a statistical approach for airspace designers and air traffic flow planners.

Keywords

Air traffic managementregression modelfast-time simulationTMA capacity
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1239 , May 2017 , pp. 680 - 692
Copyright
Copyright © Royal Aeronautical Society 2017 

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References

REFERENCES

1. ICAO, Rules of the Air Traffic Services, Doc 4444 ATM 501 Air Traffic Management, 2011, ICAO Publications, Montreal, Canada.Google Scholar
2. Wei, G. and Chaowei, H. Application of computer simulation research on Chongqing TMA airspace structure, Computer Science and Information Technology (ICCSIT), 3rd IEEE International Conference, 2010, pp 386–390.CrossRefGoogle Scholar
3. Janic, M. and Tosic, V. Terminal airspace capacity model, Transportation Research Part A: General, 1982, 16, (4), pp 253–260. Google Scholar
4. Horonjeff, R. and McKelvey, F.X. Airport Airside Capacity and Delay, Planning and Design of Airports, 1994, McGraw-Hill, Boston, Massachusetts, US, pp 293–361.Google Scholar
5. TOSCA-II Work Package 7 Airport Capacity Enhancement Interim Report, 1997, Eurocontrol Reports.Google Scholar
6. Kelton, D.W. and Law, M.A. Simulation Modelling and Analysis, 2nd ed, 1991, McGraw-Hill, New York, US, pp 1–7.Google Scholar
7. Majumdar, A. and Ochieng, W.Y. The factors affecting airspace capacity in Europe: a cross-sectional time-series analysis using simulated controller workload data, J Navigation, Royal Institute of Navigation, 2004, pp 385–405, DOI:10.1017/S0373463304002863 Google Scholar
8. Rydin, A. Stockholm TMA capacity: a study of the landing rate and its effects on arrival outcome, Linköping University Department of Science and Technology, 2013, Linköping, Sweden.Google Scholar
9. Kleinman, N.L., Hill, S.D. and Ilenda, V.A. SPSA/SIMMOD optimization of air traffic delay cost, American Control Conference, 1997.CrossRefGoogle Scholar
10. Gao, W., Xu, X., Diao, L. and Ding, H. SIMMOD based simulation optimization of flight delay cost for multi-airport system, International Conference on Intelligent Computation Technology and Automation, 2008, pp 698–702.CrossRefGoogle Scholar
11. Odoni, A.R., Bowman, J., Delahaye, D., Deyst, J.J., Feron, E., Hansman, R.J., Khan, K., Kuchar, K.J., Pujet, N. and Simpson, R.W. Existing and required modeling capabilities for evaluating ATM systems and concepts, Final Report to National Aeronautics and Space Administration Ames Research Center, International Center for Air Transportation, 1997.Google Scholar
12. SIMMOD Reference Manual, AOR-200. FAA, U.S. Department of Transportation, 1989.Google Scholar
13. RAMS Plus Simulation Solutions: Welcome to RAMS Plus www.ramsplus.com February 2012.Google Scholar
14. ATAC, http://www.atac.com June 2012.Google Scholar
15. Aybek, F. and Cavcar, A. Delay analysis for the RNAV approach procedure at Istanbul Yesilkoy TMA using SIMMOD, 5th International Conference on Research in Air Transportation, May 2012, Berkeley, California, US.Google Scholar
16. Aeronautical information publication, Aerodrome, December 2011, Turkey.Google Scholar