The Jet:Wise Model of National Air Space System Evolution

Abstract

Jet:Wise is an agent-based model that explores the evolution of the airline industry and its interactions with the National Airspace System (NAS). It models competitive decisions airlines make, such as setting fleet mix, itineraries, schedules, and fares and responding to delays and missed connections. The model addresses questions of the form, “How would a specific, quantifiable change in one aspect of the NAS affect the entire NAS?” Examples include changes in operational costs, passenger demand, and flight times; removal of certain Federal Aviation Administration restrictions; improvements in technology; new facilities; congestion pricing; mergers; startup airlines; and the expansion of low-cost carriers. Understanding their complex interaction is beyond the ability of traditional modeling tools and has not been attempted until now. Validation evidence is presented: Jet:Wise is shown to produce a close correlation between simulated and observed leisure passenger counts both by origin-destination city pairs and for individual flights.

Get full access to this article

View all access options for this article.

References

[1] Niedringhaus, W. 2001. A simulation tool to analyze the future interaction of airlines and the U.S. National Airspace System. In Handbook of airline strategy: Challenges and solutions, regulatory issues, and public policy, edited by G. F. Butler and M. R. Keller. New York: Aviation Week/McGraw-Hill .

[2] Niedringhaus, W. 2000. An agent-based model of the airline industry . In International Conference on Complex Systems, sponsored by the New England Complex Systems Institute, Nashua, NH.

[3] Niedringhaus, W., F. Wanke, F. Wieland, and L. Wojcik. 2002. Modeling the NAS: A grand challenge for modeling and simulation . In First International Conference on Grand Challenges for Modeling and Simulation, January, San Antonio, TX.

[4] Hargrove-Gray, B. 1994. SIMMOD . In Simulation Conference 20, CACI, Inc., Washington, DC.

[5] CACI Products Company. 1991. SIMMOD release 1.2 user’s manual. Washington, DC: CACI, Inc.

[6] Preston Aviation Solutions, Inc. 2002. TAAM solutions. Marietta, GA: Preston Aviation Solutions, Inc.

[7] Long, D., D. A. Lee, J. P. Johnson, E. M. Gaier, and P. F. Kostiuk. 1998. Modeling air traffic management technologies with a queueing network of the National Airspace System. NASA Contractor Rep. 208988, Washington, DC .

[8] Long, D., V. Stouffer-Coston, P.F. Kostuk, R. Kula, and B. Fernandez. 2001. Integrating LMINET with TAAM and SIMMOD: A feasibility study. NASA Tech. Rep. CR-2001-210875, Langley Research Center .

[9] Wieland, F. 1998. Parallel simulation for aviation applications . In Proceedings of the 1998 Winter Simulation Conference, edited by D. J. Medeiros, E. F. Watson, J. S. Carson, and M. S. Manivannan, 1191-1198 . Washington, DC: IEEE.

10.

[10] Corning, Peter A. 2002. The re-emergence of “emergence”: A venerable concept in search of a theory . Complexity 7 (6): 000-000 .

11.

[11] Mitchell, Melanie. 1998. Introduction to genetic algorithms. Cambridge, MA: MIT Press .

12.

[12] Congress on Evolutionary Computation. 2000. Sponsored by IEEE Neural Networks Council, the Evolutionary Programming Society, and the Institution of Electrical Engineers, San Diego, CA . Available: http://pcgipseca.cee.hw.ac.uk/cec2000/main.html

13.

[13] Casti, J. 1997. Would-be worlds. New York: John Wiley .

14.

[14] Gaylord, R. J., and L. J. D’Andria. 1998. Simulating Society: A Mathematica toolkit for modeling socioeconomic behavior. New York: Springer-Verlag .

15.

[15] Tesfatsion, L. 2002. Agent-based computational economics: Growing economies from the bottom up . Artificial Life 8 (1): 000-000 .

16.

[16] Epstein, J. M., and R. Axtell. 1996. Growing artificial societies: Social science from the bottom up. Cambridge, MA: MIT Press .

17.

[17] Jenkins, D., ed. 1995. Handbook of airline economics. New York: McGraw-Hill .