In this article, the authors describe a model of the U.S. air transportation network in which air traffic service provider and airline decision making, the impacts of weather on airport capacity and decision making, the effects of stochastic phenomenon, and the movement of aircraft, crews, and passengers are simulated. The details of the underlying discrete event simulation framework and the constituent modules are presented, along with examples of the results that may be obtained.
Rabbani, Fabio.2004. Implementation of an airline recovery model in an event-based simulation. Master's thesis, Massachusetts Institute of Technology, Cambridge.
2.
Massachusetts Institute of Technology.1997. Existing and required modeling capabilities for evaluating ATM systems and concepts: Final report to the NASA Advanced Air Transportation Technologies (AATT) project. http://web.mit.edu/aeroastro/www./labs/AATT/reviews.html
3.
Ilenda, Victor , Nathan Kleinman, and Stacy Hill.1997. SPSA/ SIMMOD optimization of air traffic delay cost. In Proceedings of the American Control Conference, June.
4.
Boesel, Justin , Carla Gladstone, Jonathan Hoffman, Patricia Massimini, Camille Shidtsuki, and Brian Simmons.2001. TAAM best practices guidelines. MITRE Technical Report MTR 01W0000092, Bedford, MA.
5.
Long, Dou, David Lee, Jesse Johnson, Eric Gaier, and Peter Kostiuk.1998. Modeling air traffic management technologies with a queuing network model of the national airspace system. NASA Contractor Report 208988.
6.
Blair, Eric L. , Frederick P. Wieland, and Anthony E. Zukas.1994. The detailed policy assessment tool (DPAT): A parallel simulation of capacities and queueing delays in the national airspace system. MITRE Technical Report MTR 94W200, Bedford, MA.
7.
Pritchett, Amy R., S.M. Lee, Michael H. Abkin, Alexander Z. Gilgur, R.C. Bea, Kevin M. Corker, S. Verma, A. Jadhav, Tom G. Reynolds, Laurence Vigeant-Langlois, and Geoff Gosling.2002. Examining air transportation safety issues through agent-based simulation incorporating human performance models. In Proceedings of the IEEE/AIAA Digital Avionics Systems Conference.
Vanderson, William.2000. Improving aircraft departure time predictability. Master's thesis, Massachusetts Institute of Technology, Cambridge.
10.
Carr, Francis.2002. Method to predict taxi-out time. http://icatserver.mit.edu/fcarr/
11.
Idris, Husni, John-Paul Clarke, Rani Bhuva, and Laura Kang.2002. Queuing model for taxi-out time estimation . Air Traffic Control Quarterly10 (1): 122.
12.
United StatesFederal Aviation Administration. 2001. Airport capacity benchmark report. http://www.faa.gov/events/benchmarks/download.htm
13.
Gilbo, Eugene P.1993. Airport capacity: Representation, estimation, and optimization. IEEE Transactions on Control Systems Technology1 (3): 144—54.
14.
Gilbo, Eugene P.1997. Optimizing airport capacity utilization in air traffic flow management subject to constraints at arrival and departure fixes. IEEE Transactions on Control Systems Technology5 (5): 490—503.
15.
United States Federal Aviation Administration.2002. Controller manual. ATS publication 7110.65. http://www.faa.gov/atpubs/ATC/index.htm
16.
Wambsganss, Michael.1997. Collaborative decision making through dynamic information transfer. Air Traffic Control Quarterly4 (2): 107—23.
17.
Clarke, Michael.1997. Development of heuristic procedures for flight rescheduling in the aftermath of irregular airline operations . ScD thesis, Massachusetts Institute of Technology, Cambridge.
18.
Lettovsky, Latislav.1997. Airline operations recovery: An optimization approach. PhD diss., Georgia Institute of Technology , Atlanta.
19.
Melconian, Terran.2001. Effects of increased nonstop routings on airline cost and profit. Master's thesis, Massachusetts Institute of Technology, Cambridge.
20.
Kang, Laura.2004. Degradable airline scheduling: An approach to improve airline robustness and customer satisfaction. PhD diss., Massachusetts Institute of Technology, Cambridge.