Dynamic Flow Scheduling in Air Traffic Network Based on Game Theory

Abstract:

Recently, many works have employed deterministic strategies in dealt with flow scheduling problem in Air traffic flow management (ATFM). In practice, however, they are proved unreasonable for omitting airline’s preference in ATFM. In this paper, we develop a novel air traffic flow control model based on game theory to solve this problem in air traffic network. In the flow model, a resource (link/time pair in air traffic network) allocation strategy that uses sequential game method to predict traffic allocation is proposed in resource sharing environment. The problem of multiple airlines competes for an optimal route is formulated as a multi-player dynamic game. Through finding the Nash equilibrium solution of the multi-player dynamic game, the optimal flow scheduling is also formed at the same time. The flow model also incorporates many key characteristics of ATFM, such as competitive airlines, allowing multiply flow control strategies and so on. Numerical simulation results show the feasibility of solving the air traffic flow control problem using game theory on the air traffic network.

• Periodical: Key Engineering Materials (Volumes 439-440)
• Pages: 977-982
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.439-440.977
• Online since: June 2010
• Authors: Si Liang Wang
• Keywords: Air Traffic Control, Flow Scheduling, Game Theory, Nonlinear Programming

References

[1] D. Bertsimas and S. Stock Patterson, The air traffic flow management problem with en route capacities, Operations Research, vol. 46, no. 3, pp.406-422, May-Jun (1998).

DOI: 10.1287/opre.46.3.406

[2] P. Menon, G. Sweriduk, and K. Bilimoria, A new approach for modeling, analysis and control of air traffic flow, in AIAA Guidance, Navigation and Control, Monterey, CA, August (2002).

DOI: 10.2514/6.2002-5012

[3] A. M. Bayen, R. L. Raffard, and C. J. Tomlin, Eulerian network model of air traffic flow in congested areas, in American Control Conference 2004, June (2004).

DOI: 10.23919/acc.2004.1384733

[4] A. M. Bayen, R. L. Raffard, and C. J. Tomlin. Adjoint-based constrained control of Eulerian transportation networks: Application to air traffic control, in American Control Conference 2004, June (2004).

DOI: 10.23919/acc.2004.1384736

[5] Jimmy Krozel. Terminal area guidance incorporating heavy weather. AIAA -97-3541, (1997).

[6] Nilim and L. El Ghoul. Air traffic control under stochastic environments. American Control Conference, April (2003).

[7] Maheswaran RT, Basar T. Nash equilibrium and decentralized negotiation in auctioning divisible resources. Group Decision and Negotiation, 2003, 12(5): 361-395.

DOI: 10.1023/b:grup.0000003745.98183.8d

[8] Yeo C S, Buyya R. Pricing for utility-driven resource management and allocation in clusters. International Journal of High Performance Computing Applications, 2007, 21(4): 405-418.

DOI: 10.1177/1094342007083776

[9] Weng C L, Lu X D. A pricing algorithm for market-based resource management on grid computing systems. Journal of Computer Research and Development, 2004, 41(7): 1151-1156.

[10] Iosup A, Jan M, Sonmez O, et al. On the dynamic resource availability in grids. 8th IEEE/ACM International Conference on Grid Computing, 2007: 26-33.

DOI: 10.1109/grid.2007.4354112

[11] Cao HQ, Xiao N, Lu XC, Liu Y. A market-based approach to allocate resources for computational grids. Journal of Computer Research and Development, 2002, 39(8): 913-916.