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

Yıl 2017, Cilt: 18 Sayı: 2, 360 - 374, 30.06.2017

Emre Koyuncu Barış Başpınar

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• 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.