Public Transport

, Volume 5, Issue 1–2, pp 25–40 | Cite as

The trade-off between taxi time and fuel consumption in airport ground movement

  • Stefan Ravizza
  • Jun Chen
  • Jason A. D. Atkin
  • Edmund K. Burke
  • Paul Stewart
Original Paper

Abstract

Environmental issues play an important role across many sectors. This is particularly the case in the air transportation industry. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and a key area of study is whether fuel burn and environmental impact improvements will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used.

Keywords

Airport operations Environmental impact Graph-based approach Ground movement Multi-objective routing 

Notes

Acknowledgements

The authors wish to thank the Engineering and Physical Sciences Research Council (EPSRC) for providing the funding which made this research possible. We would also like to thank Flughafen Zürich AG who provided the real dataset and the trained commercial pilots who provided us with helpful insights into taxi behaviours at large airports, especially those who work for Swiss International Air Lines AG.

References

  1. Atkin JAD, Burke EK, Greenwood JS (2010a) TSAT allocation at London Heathrow: the relationship between slot compliance, throughput and equity. Public Transp 2(3):173–198. doi:10.1007/s12469-010-0029-2 CrossRefGoogle Scholar
  2. Atkin JAD, Burke EK, Ravizza S (2010b) The airport ground movement problem: past and current research and future directions. In: Proceedings of the 4th international conference on research in air transportation (ICRAT 2010), Budapest, Hungary, pp 131–138 Google Scholar
  3. Atkin JAD, Burke EK, Greenwood JS (2011a) A comparison of two methods for reducing take-off delay at London Heathrow airport. J Sched 14(5):409–421. doi:10.1007/s10951-011-0228-y CrossRefGoogle Scholar
  4. Atkin JAD, Burke EK, Ravizza S (2011b) A more realistic approach for airport ground movement optimisation with stand holding. In: Proceedings of the 5th multidisciplinary international scheduling conference (MISTA 2011), Phoenix, Arizona, USA Google Scholar
  5. Balakrishnan H, Jung Y (2007) A framework for coordinated surface operations planning at Dallas-Fort Worth International airport. In: Proceedings of the AIAA guidance, navigation, and control conference, Hilton Head, SC, USA Google Scholar
  6. Binder A, Albrecht T (2012) Predictive energy-efficient running time control for metro lines. In: Proceedings of the conference on advanced systems for public transport (CASPT12), Santiago, Chile Google Scholar
  7. Burgain P, Feron E, Clarke JP (2009) Collaborative virtual queue: benefit analysis of a collaborative decision making concept applied to congested airport departure operations. Air Traffic Control Q. 17(2):195–222 Google Scholar
  8. Cassell R, Evers C (1998) Development of airport surface surveillance performance requirements. In: Proceedings of the AIAA/IEEE/SAE 17th digital avionics systems conference (DASC), vol 2. doi:10.1109/DASC.1998.739821 Google Scholar
  9. Chen J, Mahfouf M (2006) A population adaptive based immune algorithm for solving multi-objective optimization problems. In: Artificial immune systems. Lecture notes in computer science, vol 4163. Springer, Berlin, pp 280–293 CrossRefGoogle Scholar
  10. Chen J, Stewart P (2011) Planning aircraft taxiing trajectories via a multi-ojective immune optimisation. In: Proceedings of the 7th international conference on natural computation (ICNC 2011), Shanghai, China, vol 4, pp 2235–2240. doi:10.1109/ICNC.2011.6022587 CrossRefGoogle Scholar
  11. Climaco JCN, Martins EQV (1982) A bicriterion shortest path algorithm. Eur J Oper Res 11(4):399–404. doi:10.1016/0377-2217(82)90205-3 CrossRefGoogle Scholar
  12. Deau R, Gotteland JB, Durand N (2009) Airport surface management and runways scheduling. In: Proceedings of the 8th USA/Europe air traffic management research and development seminar, Napa, CA, USA Google Scholar
  13. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271. doi:10.1007/BF01386390 CrossRefGoogle Scholar
  14. European Commission (2011) Flightpath 2050, Europe’s vision for aviation. Report of the High Level Group on Aviation Research. doi:10.2777/50266
  15. García J, Berlanga A, Molina JM, Casar JR (2005) Optimization of airport ground operations integrating genetic and dynamic flow management algorithms. AI Commun 18(2):143–164 Google Scholar
  16. Gotteland JB, Durand N, Alliot JM (2003) Handling CFMU slots in busy airports. In: Proceedings of the 5th USA/Europe air traffic management research and development seminar, Budapest, Hungary Google Scholar
  17. Herrero JG, Berlanga A, Molina JM, Casar JR (2005) Methods for operations planning in airport decision support systems. Appl Intell 22(3):183–206. doi:10.1007/s10791-005-6618-z CrossRefGoogle Scholar
  18. ICAO (2008) International standards and recommended practises. Annex 16. Environmental protection: Aircraft engine emissions. International Civil Aviation Organisation Google Scholar
  19. Khadilkar H, Balakrishnan H (2011) Estimation of aircraft taxi-out fuel burn using flight data recorder archives. In: Proceedings of the AIAA guidance, navigation, and control conference Google Scholar
  20. Lawler EL (1972) A procedure for computing the k best solutions to discrete optimization problems and its application to the shortest path problem. Manag Sci 18(7):401–405 CrossRefGoogle Scholar
  21. Lesire C (2010) Iterative planning of airport ground movements. In: Proceedings of the 4th international conference on research in air transportation (ICRAT 2010), Budapest, Hungary, pp 147–154 Google Scholar
  22. Marín Á (2006) Airport management: taxi planning. Ann Oper Res 143(1):191–202. doi:10.1007/s10479-006-7381-2 CrossRefGoogle Scholar
  23. Marín Á, Codina E (2008) Network design: taxi planning. Ann Oper Res 157(1):135–151. doi:10.1007/s10479-007-0194-0 CrossRefGoogle Scholar
  24. Morris KM (2005) Results from a number of surveys of power settings used during taxi operations. Tech. rep., ENV/KMM/1126/14.8, British Airways Google Scholar
  25. Nikoleris T, Gupta G, Kistler M (2011) Detailed estimation of fuel consumption and emissions during aircraft taxi operations at Dallas/Fort worth international airport. Transp Res, Part D, Transp Environ 16(4):302–308. doi:10.1016/j.trd.2011.01.007 CrossRefGoogle Scholar
  26. Pesic B, Durand N, Alliot JM (2001) Aircraft ground traffic optimisation using a genetic algorithm. In: Proceedings of the genetic and evolutionary computation conference (GECCO), San Francisco, California, USA, pp 1397–1404 Google Scholar
  27. Rappaport DB, Yu P, Griffin K, Daviau C (2009) Quantitative analysis of uncertainty in airport surface operations. In: Proceedings of the AIAA aviation technology, integration, and operations conference Google Scholar
  28. Ravizza S, Atkin JAD (2011) Exploration of the ordering for a sequential airport ground movement algorithm. Tech. rep., 1543, University of Nottingham Google Scholar
  29. Roling PC, Visser HG (2008) Optimal airport surface traffic planning using mixed-integer linear programming. Int. J. Aerospace Eng. 28(1):1–11. doi:10.1155/2008/732828 CrossRefGoogle Scholar
  30. Schüpbach K, Zenklusen R (2011) Approximation algorithms for conflict-free vehicle routing. In: Proceedings of the 19th annual European symposium on algorithms (ESA). doi:10.1007/978-3-642-23719-5_54 Google Scholar
  31. Smeltink JW, Soomer MJ, de Waal PR, van der Mei RD (2004) An optimisation model for airport taxi scheduling. In: Proceedings of the INFORMS Annual Meeting, Denver, Colorado, USA Google Scholar
  32. Stettler MEJ, Eastham S, Barrett SRH (2011) Air quality and public health impacts of UK airports. Part I. Emissions. Atmos Environ 45(31):5415–5424. doi:10.1007/s12469-013-0060-1 CrossRefGoogle Scholar
  33. Wey CC, Anderson BE, Hudgins C, Wey C, Li-Jones X, Winstead E, Thornhill LK, Lobo P, Hagen D, Whitefield P, Yelvington PE, Herndon SC, Onasch TB, Miake-Lye RC, Wormhoudt J, Knighton WB, Howard R, Bryant D, Corporan E, Moses C, Holve D, Dodds W (2006) Aircraft Particle Emissions eXperiment (APEX). Tech. rep., NASA Google Scholar
  34. Yen JY (1971) Finding the k shortest loopless paths in a network. Manag Sci 17(11):712–716 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefan Ravizza
    • 1
  • Jun Chen
    • 2
  • Jason A. D. Atkin
    • 1
  • Edmund K. Burke
    • 3
  • Paul Stewart
    • 2
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.School of EngineeringUniversity of LincolnLincolnUK
  3. 3.Department of Computing and MathematicsUniversity of StirlingStirlingUK

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