1. Ames Research Center 1 FACET: Future Air Traffic Management Concepts Evaluation Tool Banavar Sridhar Shon Grabbe First Annual Workshop NAS-Wide Simulation in Support of NEXTGEN 10 December, 2008

2. Ames Research Center 2 Outline FACET Description FACET uses in NEXGEN analysis –Tube Designs –Optimization –Network Analysis Issues –Lack of methodology –Simulation tools –Integration of existing tools

3. Ames Research Center 3 Future ATM Concepts Evaluation Tool (FACET) Environment for exploring advanced ATM concepts FACET design balances fidelity and flexibility –Utilizes less complex models of aircraft performance and terminal airspace –Enables zoom from national to regional to single aircraft level FACET architecture enables modeling of ~15,000 aircraft trajectories at the national level in a few seconds Runs on a desktop computer (Linux, Solaris, Mac OS X, Win XP) –Works with existing FAA systems on an enterprise server –Accessible via Web to users of Flight Explorer ®, Matlab ®, and Jython 3 Operational Modes: Playback, Simulation, Hybrid Used for visualization, off-line analysis and real-time planning applications

4. Ames Research Center 4 Animation: A Day in the Life of Air Traffic Smithsonian’s National Air & Space Museum is using FACET in “America by Air” exhibit

5. Ames Research Center 5 FACET Displays 3-DConvective Weather TrafficWinds

6. Ames Research Center 6 FACET Software Architecture National Weather Service Winds Severe Weather FAA Traffic Data Tracks Flight Plans Aircraft Performance Data Climb Descent Cruise Airspace Airways Airports Adaptation Data Historical Database Traffic & Route Analyzer User Interface Route Parser & Trajectory Predictor FACET CORE FEATURES APPLICATIONS Air and Space Traffic Integration Airborne Self-Separation Data Visualization Direct Routing Analysis Controller Workload System-Level Optimization Traffic Flow Management

7. Ames Research Center 7 Concept of Tube Network Dynamic airspace configuration is a key element of the Next Generation Air Transportation System –Flexible airspace boundaries that are dynamically configured –New airspace classes such as tube airspace Tube network connects regions with high traffic volume –Network is dynamic: tailored to demand, winds, and weather –Tube airspace segregated from other airspace classes –Tube traffic gets benefits, e.g., better routes and arrival slots –Control mode inside tube may include self spacing/merging –Concept of operations is not well defined at present Initial study to expose key research issues –Develop a common analysis method –Define and evaluate performance metrics

8. Ames Research Center 8 Design of Tube Structures Implemented in Future ATM Concepts Evaluation Tool by simulating traffic above 12,000 ft Historical air traffic data from Aug. 24, 2005 used in four 6-hour blocks Five designs based on different methods –Jet routes –Delaunay triangulation(Sridhar, et al.) –Traffic density –Hough transform(Xue, et al.) –Network cost optimized(Gupta, et al.)

9. Ames Research Center 9 50 great circle tubes Maximize use by ≤ 5% additional travel distance Hough Transform Cost of each node, link and flight travel time of the network optimized (67 links) Network Cost Optimization

10. Ames Research Center 10 Instantaneous occupancy –Utilization and activation/deactivation trigger Volume occupancy –Capacity and duration Number of conflicts –Communication and workload Frequency of tube crossings –Communication and workload Encounter angles of tube crossings –Communication and workload Performance Metrics

11. Ames Research Center 11 Number of Conflicts Number of conflicts with and without tubes (simulation: 5 nmi, 1000 ft) Conflict count Center Jet Routes Delaunay Triangles Traffic Density Hough Transform Cost Optimized Delaunay Triangles Cost Optimized Hough Transform Traffic Density Jet Routes Nominal Worse Better

12. Ames Research Center 12 Optimization-Simulation Environment

13. Ames Research Center 13 Strategic Departure Control Model 2-hr Planning Horizon: 4,500 flights, 949 airports and 987 sectors 600,000 variables and 650,000 constraints Objective function: Minimize the total system delay Inputs: - Scheduled departure times and flight plans - Sector and airport capacities Outputs: departure delay assigned to each flight [Bertsimas and Stock-Patterson, 1998]

14. Ames Research Center 14 Strategic Weather Translation Active area of research Four reduced capacity scenarios considered (0%, 20%, 40%, and 60%) if Convective Weather Avoidance Model (CWAM) 60% deviation probability contours existed

15. Ames Research Center 15 Tactical Weather Translation Avoided Convective Weather Avoidance Model (CWAM) 60% deviation contours at FL300

16. Ames Research Center 16 Rerouting vs Ground Holding Delays Benefits of departure control model limited without accounting for flow-based weather impacts

17. Ames Research Center 17 Model Validation

18. Ames Research Center 18 Additional viewgraphs

19. Ames Research Center 19 From current flight plan structure of one day, Airport and Airspace Network (AAN) has ~8000 nodes AAN has 225 nodes with > 250 links (250G) and ten Centers have more than ten 250G nodes each There are 22 1000G nodes in the system today 1 2 3 4 5 6 7 251 … 250G Node US Air Traffic Network

20. Ames Research Center 20 Projected growth of tripling of passengers by 2025 along with increased air taxis and UAVs Terminal Area Forecast (TAF) generated growth rates used to create 3X current traffic 3X AAN has 1443 250G nodes and all Centers have more than forty 250G nodes There are 262 1000G nodes in the future system Future Traffic Scenarios

21. Ames Research Center 21 Convective weather related delay days of more than 200,000 minutes are increasing Weather is considered a disturbing agent, either random or selective Impact of Weather The density of 250G nodes is seen much higher in some regions