1. Federal Aviation Administration ATO Future Schedule Generation Performance Analysis and Strategy January 27, 2010

2. 2 Federal Aviation Administration Briefing Overview ATO and Future Schedule Generation Inputs to Future Schedule Generation Sample Day Selection and Averaging Core Logic Future Work

3. 3 Federal Aviation Administration ATO and Future Schedules Core ATO Function Since 2006 –ATO Sponsored Multiple Demand Generation Projects –No consistency across planning projects Streamline process for building future scenarios –Demand/Model Issues Worked out in Advance –Representative Sample Days Assumptions coordinated within ATO –Feasible Schedules –Risk Ranges

4. 4 Federal Aviation Administration DATACOM Investment Analysis ERAM Evaluations NextGen Portfolio Analysis Performance Airspace Management High Altitude Trajectory Based Airspace End Users

5. 5 Federal Aviation Administration Summary of Input to Future Scenarios APO –TAF Airport Operations (Annual Levels) –Future Fleet Assumptions (Annual Projections) –ASPM Gate/Runway Times (Daily Traffic) –OPSNET Data Counts (Daily Traffic) –Cancellations ATO AIMLAB –Filed Flight Information (altitude, speed, waypoints) –Center Crossing Data (Not Filed but used for Center Activity) –Cancellations Consistent with Data Sources Used to Assess ATO Performance

6. 6 Federal Aviation Administration TAF 2004 TAF 2006 2001 – 16,164,900 2017 – 16,174,591 TAF 2008 TAF 2009 25% Down from 2004 19% Down from 2006

7. 7 Federal Aviation Administration

8. 8 Federal Aviation Administration Representative Planning Days Sample Days Throughout Fiscal Year –Reflect Seasonality of the NAS –Peak and Off-peak Days each Fiscal Quarter –8 Days per Fiscal Year Target 90% Planning Day by Facility by Season –Originally Based on 20 CONUS Centers –Assess 90% Day for 35 OEP Airports –ASPM Delay, NAS Wx index

9. 9 Federal Aviation Administration Center Count Traffic for ZMA 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 25-Sep-0414-Nov-043-Jan-0522-Feb-0513-Apr-052-Jun-0522-Jul-0510-Sep-05 FY2005 Counts ZMAFY05FALWNTSPRSUMATO_FALATO_WNTATO_SPRATO_SUMPoly. (ZMA) 5130 5974 5353 4380 Almost all 36 Above Target Days Occur in Winter/Spring 5543 Current Target Day - Mar 23

10. 10 Federal Aviation Administration Representative Days

11. 11 Federal Aviation Administration Seasonal Average Performance Metrics

12. 12 Federal Aviation Administration

13. 13 Federal Aviation Administration

14. 14 Federal Aviation Administration Summary Day Selection 8 Day Samples can vary with criteria –Annual vs. Fiscal quarter accuracy –Center, Airport Facility –Weighted Airport (JFK, ATL, etc) –Performance measures other than counts Flight Hours, Delay, NAS On-Time –Optimal Weighting Coefficients 8 Sample Days with Weighting –Provides reasonable annual estimates by facility –Easier to investigate a smaller samples Arithmetic Average (36 Days) vs. 8 Days

15. 15 Federal Aviation Administration ATO Future Schedule – Core Logic Turn Airport Growth Rates into City Pair Growth Rates (Frater) –Builds the traffic network Constrained Demand –Is the Network Feasible? Future Itineraries –Links flights together, propagated delay Future Fleet –How will the fleet evolve? What has Changed?

16. 16 Federal Aviation Administration How Peaky Should Schedules Become? ATO-F discount delay if above a certain threshold –Generic smoothing algorithm – no limit on time 20 Minute Delay Rule – LGA Study –20 minute average open to interpretation –Iterative process with models FY 2000 Delay Rule –Airports Frozen at 2000 Levels –Sensitive to model Historical Demand/Capacity Ratios –Developed by MITRE for OEP 6.0 & 8.0 –Simple, practical to implement

17. 17 Federal Aviation Administration Percent Above Target Capacity in 15 Minute Bin – How Bad Does It Get? 2006 VMC Capacity Data from ATO-F, Summer 90% Peak Day Known Problem Airports Over Capacity More Balanced Operation Most 15 Minute Bin Peaks Less than 30% Over 40% Selected for Representative As Bad As It Gets Arr/Dep Imbalance Special Cases

18. 18 Federal Aviation Administration

19. 19 Federal Aviation Administration 40% 19 Deps 2 Arr At Cap at 15 Ops

20. 20 Federal Aviation Administration Constrained Schedule Methodology Airport VFR Capacities Compared to IFR Schedule for 15 Minute Intervals Flight Times Adjusted to Reduce Peaks Based on Airline Tolerance for Congestion –15 Minute > 40% above Capacity –1 Hour > 20 % above Capacity –2 Consecutive Hours > 14 % above Capacity –3 Consecutive Hours > 6 % above Capacity –Most Congested Airports Considered First

21. 21 Federal Aviation Administration

22. 22 Federal Aviation Administration

23. 23 Federal Aviation Administration

24. 24 Federal Aviation Administration Future Fleet Fleet Forecast File – (2010-2030) –139 Airlines –Mainline (15), Low-cost (12), Regional (21), Other (49) Cargo (42) –Jet Charter Aircraft Fleet Projections for Several Classes of Aircraft –Link to 3-Char Airline, 4-Char Equipment Codes Relation of Future Fleet File (Greenslet) to Rest of APO Process –Future Operations(Air Carrier, Air Taxi, GA, Military) –Future Enplanements(Air Carrier, Commuter) –Load Factor

25. 25 Federal Aviation Administration Rules for Up-gauging Aircraft

26. 26 Federal Aviation Administration Evidence of Up-gauging Based on the number of seats available in the fleet as predicted by the 2008 Greenslet/APO fleet forecast. –Cargo aircraft are assigned zero seats. 26

27. 27 Federal Aviation Administration Evidence of Up-gauging If the data is segregated by airline user class, we observe that the up- gauging is driven by regional and ‘other’ operations, while mainline carriers lower the number of available seats. 27

28. 28 Federal Aviation Administration Airport Up-gauging At the airport level, LGA is an example of an airport that suggests the need for up-gauging. Based on the TAF 2009 forecast, the enplanements at LGA are forecasted to grow much quicker than the operations. 28

29. 29 Federal Aviation Administration

30. 30 Federal Aviation Administration Future Development Improve Accessibility of Schedules –Understand Data Limitations –Web Access Guidance for Uncertainty –Alternative Scenarios –Mont-Carlo System Performance/Airline Behavior –Demand Shifts Keep Analysis “Costs” Down –Remember the Objective –Did the work make a difference?