Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T21:59:37.662Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysis of aircraft ground traffic flow and gate utilisation using a hybrid dynamic gate and taxiway assignment algorithm

Published online by Cambridge University Press:  10 April 2017

Orhan Ertugrul Guclu  and
Cem Cetek
Orhan Ertugrul Guclu
Affiliation:
Faculty of Aeronautics and Astronautics, Department of Air Traffic Control, Anadolu University, Eskişehir, Turkey
Cem Cetek*
Affiliation:
Faculty of Aeronautics and Astronautics, Department of Air Traffic Control, Anadolu University, Eskişehir, Turkey
*
ccetek@anadolu.edu.tr
Get access Rights & Permissions [Opens in a new window]

Abstract

The rapid increase in the demand for air transportation over the last four decades has led to serious capacity problems for both the airside and landside components of major airports. The efficient management of existing airside resources seems to be the most effective and practical approach to overcome these capacity and traffic flow problems. Although integrated management of aircraft parking position assignments and ground movement planning processes are vital for the effective use of resources and for efficient operations, the current practice is that these processes are handled separately by different agents. This study proposes a hybrid dynamic system, an integrated methodology of taxi path and gate assignment using a knowledge-based decision-making approach to model effectively time-variant and realistic operational features of aircraft gate management and route planning. The model assigns the most suitable parking positions with minimum taxi time and taxi delay among a reduced solution set, satisfying pre-defined decision criteria as well as monitoring ground movements and, if necessary, reassigning new taxi paths and parking positions in real time. Both the proposed integrated methodology and the separate gate assignment and ground management operations currently in use were implemented, analysed and compared in a fast-time simulation model of Istanbul Ataturk Airport (LTBA). The hybrid dynamic assignment model provided significant improvements in taxi times, ground delays and gate utilisation.

Keywords

airport capacity managementaircraft parking position assignmentaircraft ground movement planningfast-time simulation modellinghybrid dynamic system
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1240 , June 2017 , pp. 721 - 745
Copyright
Copyright © Royal Aeronautical Society 2017 

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

1. Ashford, N.J. and Wright, P.H. Airport Engineering, 3rd ed, Wiley, 1992, New York, New York, US.Google Scholar
2. Janic, M. Air Transport System Analysis and Modelling, Gordon Breach Science Publishers, 2000, Philadelphia, Pennsylvania, US.Google Scholar
3. Cetek, C., Cavcar, A., Cinar, E. and Aybek, F. Capacity and delay analysis for airport manoeuvring areas using simulation, Aircraft Engineering and Aerospace Technology: An Int J, January 2014, 86, (1), pp 43-55.Google Scholar
4. Atkin, J.A.D., Burke, E.K. and Ravizza, S. The airport ground movement problem: Past and current research and future directions, 4th International Conference on Research in Air Transportation, June 2010, Budapest, Hungary, pp 131-138.Google Scholar
5. Guépet, J., Briant, O., Gayon, J.P. and Acuna-Agost, R. The aircraft ground routing problem: Analysis of industry punctuality indicators in a sustainable perspective, European J Operational Research, February 2016, 248, (3), pp 827-839.Google Scholar
6. ICAO, Global Air Navigation Plan 2013-2028, Doc 9750-AN/963, ICAO Publications, 4th ed, 2013, Montreal, Canada.Google Scholar
7. SESAR, European ATM master plan: The roadmap for delivering high performing aviation for Europe, Executive View, Edition 2015, EU Publication Office, Luxembourg.Google Scholar
8. Marks, A. and Rietsema, K. Airport information systems—Airside management information systems, Intelligent Information Management, May 2014, 6, pp 149-156.Google Scholar
9. Kjenstad, D., Mannino, C., Nordlander, T.E., Schittekat, P. and Smedsrud, M. Optimizing AMAN-SMAN-DMAN at Hamburg and Arlanda airport, 3rd SESAR Innovation Days, November 2013, Stockholm, Sweden.Google Scholar
10. Babic, O., Teodorovic, D. and Tošic, V. Aircraft stand assignment to minimize walking, J Transportation Engineering, January 1984, 110, (1), pp 55-66.Google Scholar
11. Bihr, J. A conceptual solution to the aircraft gate assignment problem using 0,1 linear programming, Computers and Industrial Engineering, January 1990, 19, (1–4), pp 280-284.Google Scholar
12. Yan, S. and Huo, C.M. Optimization of multiple objective gate assignments, Transportation Research Part A, June 2001, 35, (5), pp 413-442.Google Scholar
13. Mangoubi, R.S. and Mathaisel, D.F. Optimizing gate assignment at airport terminals, Transportation Science, May 1985, 19, (2), pp 173-178.Google Scholar
14. Haghani, A. and Chen, M.C. Optimizing gate assignments at airport terminals, Transportation Research Part A, August 1998, 32, (6), pp 437-454.Google Scholar
15. B.A.C.o.E.B TEAM and A.I.C.o.E. TEAM, Gate assignment solution (GAM) using hybrid heuristic algorithm, Draft technical white paper on gate assignment solution, 2009, Wipro Technologies (WIT), Bangalore, India.Google Scholar
16. Ding, H., Lim, A., Rodrigues, B. and Zhu, Y. Aircraft and gate scheduling optimization at airports, Proceedings of the 37th Annual Hawaii International Conference on System Sciences, January 2004, pp 1185-1192, Hawaii, US.Google Scholar
17. Xu, J. and Bailey, G. The airport gate assignment problem: Mathematical model and a tabu search algorithm, Proceedings of the 34th Hawaii International Conference on System Sciences, January 2001, Hawaii, US.Google Scholar
18. Drexl, A. and Nikulin, Y. Multicriteria airport gate assignment and Pareto stimulated annealing, IIE Transactions, February 2008, 40, (4), pp 385-397.Google Scholar
19. Cheng, C.H., Ho, S. and Kwan, C.L., The use of meta-heuristics for airport gate assignment, Expert Systems with Applications, November 2012, 39, (16), pp 12430-12437.Google Scholar
20. KIM, S.H., Feron, E. and Clarke, J.P. Gate assignment to minimize passenger transit time and aircraft taxi time, J Guidance, Control, and Dynamics, 2013, 36, (2), pp 467-475.Google Scholar
21. Kim, S.H., Feron, E., Clarke, J.P., Marzuoli, A. and Delahaye, D. Airport gate scheduling for passengers, aircraft, and operation, 10th USA/Europe ATM Research and Development 2013 Seminar, January 2013, Chicago, Illinois, US.Google Scholar
22. Neuman, U.M. and Atkin, J.A.D. Airport gate assignment considering ground movement, 4th International Conference on Computational Logistics, September 2013, Volume 8197, Copenhagen, Denmark, pp 184–198.Google Scholar
23. Gerdes, I. and Temme, A. Taxi routing for aircraft: Creation and controlling, Proceedings of the 2nd SESAR Innovation Days, November 2012, Braunschweig, Germany.Google Scholar
24. Chua, Z.K., Cousy, M., Andre, F. and Causse, M. Simulating air traffic control ground operations: Preliminary results from project modern taxiing, 4th SESAR Innovation Days, November 2014, Madrid, Spain.Google Scholar
25. Ravizza, S., Atkin, J.A. and Burke, E.K. A more realistic approach for airport ground movement optimisation with stand holding. J Scheduling, April 2013, 17, (5), pp 507-520.Google Scholar
26. Stergianos, C., Atkin, J., Schittekat, P., Nordlander, T.E., Gerada, C. and Morvan, H. The importance of considering pushback time and arrivals when routing departures on the ground at airports, Lecture Notes in Management Science, 2016, 8, pp 41-46.Google Scholar
27. Bolat, A. Procedures for providing robust gate assignments for arriving aircrafts, European J Operational Research, 2000, 120, (1), pp 63-80.Google Scholar
28. Gu, Y. and Chung, C.A., Genetic algorithm approach to aircraft reassignment problem, J Transportation Engineering, September–October 1999, 125, (5), pp 384-389.Google Scholar
29. Yan, S., Shieh, C.Y. and Chen, M. A simulation framework for evaluating airport gate assignments, Transportation Research Part A, December 2002, 36, (10), pp 885-898.Google Scholar
30. Brazile, R.P. and Swigger, K.M. GATES: An airline gate assignment and tracking expert system, IEEE Expert, Summer 1988, 4, (2), pp 33-39.CrossRefGoogle Scholar
31. Gossling, G. Design of an expert system for aircraft gate assignment, Transportation Research Part A, January 1990, 24, (1), pp 59-69.Google Scholar
32. Cheng, Y. Network-based simulation of aircraft at gates in airport terminals, J Transportation Engineering, March-April 1998, 124, (2), pp 188-196.Google Scholar
33. Cheng, Y. A rule-based reactive model for simulation of aircraft on airport gates, Knowledge-Based Systems, January 1998, 10, (4), pp 225-236.Google Scholar
34. ICAO, Rules of the air and air traffic services doc 4444 ATM 501, Air Traffic Management, 15th ed, 2007, ICAO Publications, Montreal, Canada.Google Scholar
35. ICAO, Aircraft type designators—DOC 8643, ICAO Publications, 40th ed, 2012, Montreal, Canada.Google Scholar
36. NUIC, A. User Manual for the Base of Aircraft Data (BADA) Revision 3.5, Eurocontrol Experimental Centre, ECC Note No. 11/03, 2003, Brétigny-sur-Orge Cedex.Google Scholar
37. Kazda, A. and Caves, R.E. Airport Design and Operation, 2nd ed, 2007, Elsevier, 2007, Oxford, England, p 130.Google Scholar
38. Eurocontrol, Central Office for Delay Analysis Publications: 2014 Summer Taxi-in Times, Available at http://www.eurocontrol.int/articles/coda-publications [cited 01 December 2015].Google Scholar
39. EUROCONTROL, Market Segments in European Air Traffic, STATFOR Doc 534 v1, 2014.Google Scholar
40. DHMI, Statistics, Available at http://www.dhmi.gov.tr/istatistik.aspx [cited 25 December 2014].Google Scholar
41. Guclu, O.E. Optimization of Aircraft Taxi Movements and Gate Allocation in the Airports, M.Sc. Thesis, 2015, Anadolu University (in Turkish).Google Scholar
42. Genc, H.M., Erol, O.K., Eksin, I., Berber, M.F. and Guleryuz, B.O. A stochastic neighbourhood search approach for airport gate assignment problem, Expert Systems with Applications, 2012, 39, (1), pp 316-327.Google Scholar
43. DHMI, AIP Turkey Aerodromes, General Directorate of State Airports Authority, Ankara, Turkey, 2014.Google Scholar