Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network

Emre Koyuncu; Barış Başpınar

doi:10.18038/aubtda.268872

EN

Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network

Abstract

In this paper, we investigate the effect of local disturbances in European airports over the global delay characteristics of the air traffic network with and without ground holding program. First, the historical air traffic data is used for analyzing the busiest European airports. Then, network models are constructed in order to simulate balancing the demand and capacity and delay propagation across the network under disruptive events. These models, which are stochastic Queuing Network Models (QNM), are used to run in different scenarios where the capacities of airports are reduced to simulate local disturbances (e.g. heavy rain in the airport areas, air traffic controller strikes, etc.). The impact of a local capacity reduction in the airports to the European network are analyzed, and performances of these models, with and without ground holding implementation (i.e. QNM and QNM-GH), are compared.

Keywords

Air Transportation,Air Traffic Modelling and Simulation,Demand and Capacity Balancing

References

Aguiar, B., Torres, J., and Castro, A.J. (2011). Operational problems recovery in airlines–a specialized methodologies approach. In Progress in Artiﬁcial Intelligence, 83–97. Springer.
Airbus (2015). Global market forecast flying by numbers 2015 - 2034. Technical Report D14029463, Airbus.
Arias, P., Guimarans, D., and M´ujica, M. (2013). A new methodology to solve the stochastic aircraft recovery problem using optimization and simulation. In International Conference on Interdisciplinary Science for Innovative Air Traffic Management (ISIATM). Toulouse, France.
Bayen, A.M., Raffard, R.L., and Tomlin, C.J. (2006). Adjoint-based control of a new eulerian network model of air traffic ﬂow. IEEE transactions on Control systems technology, 14(5), 804–818.
Bertsimas, D., Lulli, G., and Odoni, A. (2011). An integer optimization approach to large-scale air traffic ﬂow management. Operations Research, 59(1), 211–227.
Bilimoria, K.D., Sridhar, B., Chatterji, G., Sheth, K., and Grabbe, S. (2001). Facet: Future atm concepts evaluation tool. Air Traffic Control Quarterly, 9(1).
Castelli, L., Pellegrini, P., and Pesenti, R. (2011). Airport slot allocation in europe: economic efficiency and fairness. International journal of revenue management, 6(1-2), 28–44.
Cook, A. J., Tanner, G., & Anderson, S. (2004). Evaluating the true cost to airlines of one minute of airborne or ground delay: final report.
Daganzo, C.F. (1994). The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B: Methodological, 28(4), 269–287.
Daganzo, C.F. (1995). The cell transmission model, part ii: network traffic. Transportation Research Part B: Methodological, 29(2), 79–93.

Hong, S. and Harker, P.T. (1992). Air traffic network equilibrium: toward frequency, price and slot priority analysis. Transportation Research Part B: Methodological, 26(4), 307–323.
ICAO (2011). Flightpath 2050 Europe’s vision for aviation maintaining global leadership & serving society’s needs. Technical report, High Level Group (HLG) on Aviation and Aeronautics Research.
Lighthill, M.J. and Whitham, G.B. (1955). On kinematic waves. ii. a theory of traffic ﬂow on long crowded roads. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, volume 229, 317–345. The Royal Society.
Long, D. and Hasan, S. (2009). Improved prediction of ﬂight delays using the lminet2 system-wide simulation model. In 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Hilton Head, SC.
Menon, P.K., Sweriduk, G.D., Lam, T., Diaz, G., and Bilimoria, K.D. (2006). Computer-aided eulerian air traffic ﬂow modelling and predictive control. Journal of Guidance, Control, and Dynamics, 29(1), 12–19.
Menon, P.K., Sweriduk, G.D., and Bilimoria, K.D. (2004). New approach for modelling, analysis, and control of air traffic ﬂow. Journal of guidance, control, and dynamics, 27(5), 737–744.
Pyrgiotis, N. (2012). A stochastic and dynamic model of delay propagation within an airport network for policy analysis. Ph.D. thesis, Massachusetts Institute of Technology.
Pyrgiotis, N., Malone, K.M., and Odoni, A. (2013). Modelling delay propagation within an airport network. Transportation Research Part C: Emerging Technologies, 27, 60–75.
Rebollo, J.J. and Balakrishnan, H. (2012). A network-based model for predicting air traffic delays. In 5th International Conference on Research in Air Transportation.
Rebollo, J.J. and Balakrishnan, H. (2014). Characterization and prediction of air traffic delays. Transportation Research Part C: Emerging Technologies, 44, 231–241.
Richards, P.I. (1956). Shock waves on the highway. Operations research, 4(1), 42–51.
Sun, D. and Bayen, A.M. (2008). Multicommodity eulerian-lagrangian large-capacity cell transmission model for en route traffic. Journal of guidance, control, and dynamics, 31(3), 616–628.
Sun, D., Strub, I.S., and Bayen, A.M. (2007). Comparison of the performance of four eulerian network ﬂow models for strategic air traffic management. Networks and Heterogeneous Media, 2(4), 569.
Tu, Y., Ball, M.O., and Jank, W.S. (2008). Estimating ﬂight departure delay distributions a statistical approach with long-term trend and short-term pattern. Journal of the American Statistical Association, 103(481), 112–125.
Wieland, F. (1997). Limits to growth: results from the detailed policy assessment tool [air traffic congestion]. In Digital Avionics Systems Conference, 1997. 16th DASC., AIAA / IEEE, volume 2, 9–2. IEEE.
Work, D.B. and Bayen, A.M. (2008). Convex formulations of air traffic ﬂow optimization problems. Proceedings of the IEEE, 96(12), 2096–2112.

Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Emre Koyuncu
İSTANBUL TEKNİK ÜNİVERSİTESİ
0000-0002-0726-4979
Türkiye

Barış Başpınar This is me
İSTANBUL TEKNİK ÜNİVERSİTESİ
Türkiye

Publication Date

June 30, 2017

Submission Date

November 25, 2016

Acceptance Date

-

Published in Issue

Year 2017 Volume: 18 Number: 2

DOI

https://doi.org/10.18038/aubtda.268872

IZ

https://izlik.org/JA95KM52YT

APA

Koyuncu, E., & Başpınar, B. (2017). Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, 18(2), 360-374. https://doi.org/10.18038/aubtda.268872

AMA

1.Koyuncu E, Başpınar B. Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network. AUJST-A. 2017;18(2):360-374. doi:10.18038/aubtda.268872

Chicago

Koyuncu, Emre, and Barış Başpınar. 2017. “Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18 (2): 360-74. https://doi.org/10.18038/aubtda.268872.

EndNote

Koyuncu E, Başpınar B (June 1, 2017) Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18 2 360–374.

IEEE

[1]E. Koyuncu and B. Başpınar, “Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network”, AUJST-A, vol. 18, no. 2, pp. 360–374, June 2017, doi: 10.18038/aubtda.268872.

ISNAD

Koyuncu, Emre - Başpınar, Barış. “Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 18/2 (June 1, 2017): 360-374. https://doi.org/10.18038/aubtda.268872.

JAMA

1.Koyuncu E, Başpınar B. Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network. AUJST-A. 2017;18:360–374.

MLA

Koyuncu, Emre, and Barış Başpınar. “Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 18, no. 2, June 2017, pp. 360-74, doi:10.18038/aubtda.268872.

Vancouver

1.Emre Koyuncu, Barış Başpınar. Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network. AUJST-A. 2017 Jun. 1;18(2):360-74. doi:10.18038/aubtda.268872

Abstract

Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

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Acceptance Date

Published in Issue

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