Objective Measures to Support Airspace Management
Air traffic management consists of adjusting flows of aircraft through the National Airspace System. A delicate balance is achieved between allowing more aircraft to fly and take the chance of overcrowding the sky, thereby creating delays, higher operating costs and increased emissions, and allowing fewer aircraft in the sky, thereby under-utilizing available resources and missing economic opportunities. This balance is traditionally achieved by evaluating sector capacities on an hourly basis and by adjusting aircraft flows so that no sector undergoes traffic exceeding its capacity. Sector capacity evaluation depends on several factors, including weather and neighboring sector activity, and is usually obtained by relying on human expertise. This expertise has shown itself to provide safe solutions over time. However, it is thought that excessive human involvement with the evaluation of sector capacities may lead to overly subjective capacity estimates.
Project 22 will determine whether recently developed objective traffic complexity metrics may be used to evaluate airspace capacity. The complexity metrics will be used as oracles to probe sectors and sample traffic and determine if they are “overloaded” or not. An iterative process can then be set up to determine the highest traffic densities with acceptable levels of complexity, that is, sector capacities. Many environmental factors can affect sector capacity because they can lead to increased traffic complexity: These include weather and neighboring sector closures; accounting for them in complexity calculations will constitute an important component of the research effort of this project.
Project 22’s anticipated outcome is a fully functional sector capacity map that will serve as an important input for air traffic management purposes. This map will be built entirely from objective measures of complexity and will be used as a complement to human expertise to optimize air traffic flow through the National Airspace.
Georgia Institute of Technology
John-Paul Clarke, associate professor, School of Aerospace Engineering, Georgia Institute of Technology email@example.com
Eric Feron, professor, School of Aerospace Engineering, Georgia Institute of Technology firstname.lastname@example.org
Steve Bradford, email@example.com
A two-stage stochastic program for air traffic conflict resolution under wind uncertainty. A. Vela and E. Salaün, Proceedings of the 28th Digital Avionics Systems Conference, October 2009.
Graceful degradation of air traffic operations: airspace sensitivity to degraded surveillance systems. M. Gariel and E. Feron, IEEE, vol. 96, no. 12 , pp. 2028-2039, December 2008.
Trajectory Clustering and an Application to Airspace Monitoring. M. Gariel, A. Srivastava and E. Feron, Submitted to IEEE Intelligent Transportation Systems.
Trajectory Clustering and an Application to Airspace Monitoring. M. Gariel, A. Srivastava and E. Feron, Proceedings of the Conference on Intelligent Data Understanding, October 2009.
Airspace statistical proximity maps based on data-driven flow modeling. E. Salaün, M. Gariel, A. Vela, E. Feron and J-P. Clarke, Proceedings of the AIAA Infotech@Aerospace Conference, April 2010.
Predicting Controller Communication Time for Capacity Estimation. A. Vela, E. Salaün, P. Burgain, J-P. Clarke, K. Feigh, E. Feron, W. Singhose and S. Solak, Proceedings of 4th International Conference on Research in Air Transportation, June 2010.
Determining bounds on controller workload rates at an intersection. A. Vela, E. Salaün, M. Gariel, E. Feron, J-P. Clarke and W. Singhose, Proceedings of the American Control Conference, June 2010.
Data Driven Approaches for Analytical Predictive Measures of Airspace Complexity. M. Gariel and A. Vela, MIT International Center for Air Transportation Seminar, March 2010.