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Improving the management of air traffic congestion during the approach phase

Published online by Cambridge University Press:  12 April 2023

O. Idrissi Open the ORCID record for O. Idrissi [Opens in a new window] ,
A. Bikir Open the ORCID record for A. Bikir [Opens in a new window]  and
K. Mansouri Open the ORCID record for K. Mansouri [Opens in a new window]
O. Idrissi*
Affiliation:
University Hassan II Casablanca, Laboratory Signals, distributed systems and artificial intelligence, ENSET, Mohammedia, Morocco
A. Bikir
Affiliation:
University Hassan II Casablanca, Laboratory Signals, distributed systems and artificial intelligence, ENSET, Mohammedia, Morocco
K. Mansouri
Affiliation:
University Hassan II Casablanca, Laboratory Signals, distributed systems and artificial intelligence, ENSET, Mohammedia, Morocco
*
*Corresponding author. Email: iodrimane@gmail.com
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Abstract

Nowadays most busy international airports and their corresponding terminal areas are suffering from huge congestion issues due to the simultaneity of their arrival aircraft. The aim of this paper is to establish a new separation method using time- based-separation, speed modification during approach phases and Point Merge System (PMS) so as to ensure efficiently the traffic flow. This work took as a case study the busiest airport of Morocco, The Mohammed V International airport of Casablanca. The proposed management model offers very good results when compared with other models such as the first-come first-served (FCFS) model.

Keywords

Air traffic flow ManagementMerge Point SystemParticle Swarm Optimization (PSO)Metaheuristic OptimizationAircraft Sequencing ProblemContinuous Descent Operations

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 127 , Issue 1316 , October 2023 , pp. 1752 - 1773
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

IATA. World passenger traffic of the year 2019, 2020. https://www.iata.org/contentassets/12851812b6e6455eb8363726eb326fef/2020-02-06-01-fr.pdfGoogle Scholar
ICAO. CARATS, 2010. https://www.icao.int/APAC/Meetings/2010/atfm_sg1/3CARATS%20presentation.pdfGoogle Scholar
Eurocontrol. Traffic growth 2035, 2013. https://www.eurocontrol.int/publication/challenges-growth-2013Google Scholar
Errico, A. and Di Vito, V. Study of point merge technique for efficient continuous descent operations in tma, IFAC-PapersOnLine, 2018, 51, (9), pp 193–199.CrossRefGoogle Scholar
Xu, Y., Zhang, H., Liao, Z. and Yang, L. A dynamic air traffic model for analyzing relationship patterns of traffic flow parameters in terminal airspace, Aerosp. Sci. Technol., 2016, 55, pp 10–23.CrossRefGoogle Scholar
Ruiz, S., Piera, M.A. and Del Pozo, I. A medium term conflict detection and resolution system for terminal maneuvering area based on spatial data structures and 4d trajectories, Transp. Res. Part C Emerg. Technol., 2013, 26, pp 396–417.CrossRefGoogle Scholar
Caccavale, M.V., Iovanella, A., Lancia, C., Lulli, G. and Scoppola, B. A model of inbound air traffic: The application to heathrow airport, J. Air Transp. Manag., 2014, 34, pp 116–122.CrossRefGoogle Scholar
Diao, X. and Chen, C.-H. A sequence model for air traffic flow management rerouting problem, Transp. Res. E Logist. Transp. Rev., 2018, 110, pp 15–30.CrossRefGoogle Scholar
Velasco, G.M., Mulder, M. and van Paassen, M.M. Air traffic controller decision-making support using the solution space diagram, IFAC Proc. Vol., 2010, 43, (13), pp 227–232.CrossRefGoogle Scholar
Man, L. An agent-based approach to automated merge 4d arrival trajectories in busy terminal maneuvering area, Procedia Eng., 2015, 99, pp 233–243.CrossRefGoogle Scholar
Bongo, M.F., Alimpangog, K.M.S., Loar, J.F., Montefalcon, J.A. and Ocampo, L.A. An application of dematel-anp and promethee ii approach for air traffic controllers’ workload stress problem: A case of mactan civil aviation authority of the philippines, J. Air Transp. Manag., 2018, 68, pp 198–213.CrossRefGoogle Scholar
Kistan, T., Gardi, A., Sabatini, R., Ramasamy, S. and Batuwangala, E. An evolutionary outlook of air traffic flow management techniques, Prog. Aerosp. Sci., 2017, 88, pp 15–42.CrossRefGoogle Scholar
Prakash, R., Piplani, R. and Desai, J. An optimal data-splitting algorithm for aircraft scheduling on a single runway to maximize throughput, Transp. Res. Part C Emerg. Technol., 2018, 95, pp 570–581.CrossRefGoogle Scholar
Janić, M. Analysing and modelling some effects of solutions for matching the airport runway system capacity to demand, J. Air Transp. Manag., 2017, 65, pp 166–180.CrossRefGoogle Scholar
Toratani, D. Application of merging optimization to an arrival manager algorithm considering trajectory-based operations, Transp. Res. Part C Emerg. Technol., 2019, 109, pp 40–59.CrossRefGoogle Scholar
Rodríguez-Sanz, Á., Comendador, F.G., Valdés, R.A. and Pérez-Castán, J.A. Characterization and prediction of the airport operational saturation, J. Air Transp. Manag., 2018, 69, pp 147–172.CrossRefGoogle Scholar
Liang, M., Delahaye, D. and Maréchal, P. Integrated sequencing and merging aircraft to parallel runways with automated conflict resolution and advanced avionics capabilities, Transp. Res. Part C Emerg. Technol., 2017, 85, pp 268–291.CrossRefGoogle Scholar
Riahi, V., Newton, M.H., Polash, M., Su, K. and Sattar, A. Constraint guided search for aircraft sequencing, Expert Syst. Appl., 2019, 118, pp 440–458.CrossRefGoogle Scholar
Murça, M.C.R. and Müller, C. Control-based optimization approach for aircraft scheduling in a terminal area with alternative arrival routes, Transp. Res. Part E Logist. Transp. Rev., 2015, 73, pp 96–113.CrossRefGoogle Scholar
Dalmau, R. and Prats, X. Controlled time of arrival windows for already initiated energy-neutral continuous descent operations, Transp. Res. Part C Emerg. Technol., 2017, 85, pp 334–347.CrossRefGoogle Scholar
Pandian, P.P. and Rout, I.S. Parametric investigation of machining parameters in determining the machinability of inconel 718 using taguchi technique and grey relational analysis, Procedia Comput. Sci., 2018, 133, pp 786–792.CrossRefGoogle Scholar
Bennell, J.A., Mesgarpour, M. and Potts, C.N. Dynamic scheduling of aircraft landings, Eur. J. Oper. Res., 2017, 258, (1), pp 315–327.CrossRefGoogle Scholar
Gatsinzi, D., Nieto, F.J.S. and Madani, I. Ecac use case of optimised pre-tactical time of arrival adjustments to reduce probability of separation infringements, IFAC-PapersOnLine, 2018, 51, (9), pp 186–192.CrossRefGoogle Scholar
Doc, I. 9854: Global air traffic management operational concept. International Civil Aviation Organization, 2005.Google Scholar
ICAO, D. Continuous descent operations (cdo) manual, ICAO, 2010, Montreal.Google Scholar
Clerc, M. and Siarry, P. Une nouvelle métaheuristique pour l’optimisation difficile: la méthode des essaims particulaires, J3eA, 2004, 3, p 007.CrossRefGoogle Scholar