Airspace Optimization Research – FCA Airspace Capacity Estimator (FACE) April 2010

1. Airspace Optimization Research – FCA Airspace Capacity Estimator (FACE) April 2010 Tim Myers Daniel H. Wagner Associates

2. Problem Statement Where are FCAs needed? What rates should be used? FCA locations and rates are typically limited to predefined FCAs FCA rates are based on static lookup tables Predefined FCAs might not align with constraints Suboptimal FCA placement and rates may lead to Flights controlled by an AFP unnecessarily Flights entering congested airspace without being controlled by an AFP Excess delays Capacity underutilization

3. Predefined FCA

4. FCA rates not available for all weather conditions FCAA05 Wx constraint not captured by FCA FCAA05 used on June 13, 2008

5. FACE Concept Overview Provide the flexibility to define customized FCAs More closely match the locations of predicted airspace constraints Recommend FCA rates that account for how traffic will flow around capacity constraints Based on NetFM assessment Provide real time rate validation for customized FCAs based on historical throughput Filters: date range, city pair, center, altitude, user class, physical class

6. FACE-AOR System Integration Wx-Impacted Capacity Baseline Capacity Historic Throughput Network Flow Model (NetFM)

7. The Network Flow Model (NetFM) What is it? Model of the NAS as a continuous, dynamic, multicommodity network High-level approach Aggregate flows Ignore flight-specific details Reasonably optimal routing What does it do? Solve for a minimum-cost set of flows through the NAS, subject to capacity constraints Produce diagnostics of the projected state of the NAS Predicted traffic flows Potentially congested airspace Severity of capacity constraints Impact of proposed TMIs What is it good for? Help develop strategic approaches for dealing with demand/capacity imbalances Provide a test bed in which TFM researchers can experiment with new TMI concepts

8. The NetFM Network Nodes are centers of a grid of hexagonal cells Node capacities estimated from historical usage A “market” is a combination of a source and a sink Market-to-Market (“city-pair”) demand based on scheduled demand

9. Dynamic Networks (...cont) Nodes are replicated for each time step Arcs go from one time step to the next Costs and constraints may vary with time Demand Capacity Cost Evaluate Reroutes vs. Ground Delay Static Dynamic

10. Markets and Demand The vast majority of demand is concentrated near 91 markets Working towards mapping markets to TRACONs

11. Putting it All Together Nodes Centers of hexagonal cells covering the NAS Capacities estimated from historical usage Arcs Connect each node to its neighbors Cost = flight time Commodities Flights to each destination represent a separate commodity Destinations are the sinks for their commodities Origins are sources for all commodities Demand based on scheduled demand Commodity flow = traffic to a given destination Continuous-valued (does not model discrete flights) Measured in flights/time-step

12. A Severe Weather Day (2008-05-02 17:00) NetFM Optimal Solution Predicted Demand Capacity Utilization Cost of Constraints

13. Theoretical Model Reduced capacity due to weather Flow Relative to Maximum

14. Capacity Flights per Cell per Quarter Hour

15. Predicted Traffic Flow Flights per Cell per Quarter Hour

16. Predicted Change in Traffic Flow

17. Estimating the Effect of Weather We can estimate the effect of weather by comparing the NetFM solution with and without accounting for reduced capacity We can’t observe what actual usage would have been without weather We can observe the difference between a weather day and a “similar” non-weather day NetFM 5/2/08 WX vs. no WX

18. FACE Prototype Overview

19. Predicted Traffic FlowSubject to Available Capacity Direction of predicted flow High predicted traffic flow Source Network Flow Model (NetFM) minimum cost solution to satisfy demand subject to available capacity

20. FCA Rate-Setting Guidance Based on Available Capacity and Predicted Traffic Flow Measure predicted traffic flow in both directions across each FCA segment

21. FCA Rate-Setting Guidance Based on Available Capacity and Predicted Traffic Flow 1615

22. FCA Rate-Setting Guidance Based on Available Capacity and Predicted Traffic Flow 55 flights per ¼ hour = 220 flights per hour Hourly Rate Time

23. Rate Validation from Historical Throughput

24. Historical FCA Throughput “Modes” of usage 75th percentile is 440 flights per hour Median historical throughput is 236 flights per hour NetFM suggestion

25. How much data to include? 292 300 Week 1 Weeks 1-3 297 295 294 Weeks 1-5 Weeks 1-8 Weeks 1-12

26. Location Suggestion for FCAs, SAAs, Perf. Based Serv.

27. Location Suggestion Anticipated demand > 70% of capacity High anticipated demand Demand expected to converge and funnel Costliest capacity constraints

28. Suggestions for FCA Capacity Estimation Con Ops Model predicted traffic flow based on current demand and weather-impacted capacity to identify How demand will respond to the capacity constraints Where capacity constraints will cause the most pain What throughput is achievable in various regions of airspace Allow users to dynamically draw FEA’s/FCAs Obtain rate-setting guidance per segment based on predicted traffic flow subject to available capacity Provide rate validation based on historical throughput Maintain a pre-computed database of historical airspace usage (on regular arcs or sector pairs) Identify arcs that cross the user-drawn FCA Enable filters based on origin, destination, center, physical class, altitude etc. Display the distribution of historical throughput across the user-drawn FCA based on selected filter Export FCA location and rates to operational tools

29. Conclusions and Next Steps Conclusions FCA Rate-Setting Guidance Location Suggestion for FCAs, SAAs, and Performance Based Services Validation of Arc Usage Database Results Next Steps Continue automating FCA Location Suggestion User demonstrations and shadow operations FET Team meetings TMs at the ATCSCC SME consultation Updated Operational Concept Testing and validation