ATO Future Schedule Generation

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

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

3. 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. End Users • DATACOM Investment Analysis • ERAM Evaluations • NextGen Portfolio Analysis • Performance Airspace Management • High Altitude Trajectory Based Airspace

5. 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. 25% Down from 2004 TAF 2004 19% Down from 2006 TAF 2006 2001 – 16,164,900 2017 – 16,174,591 TAF 2008 TAF 2009

8. 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. Almost all 36 Above Target Days Occur in Winter/Spring 5974 Current Target Day - Mar 23 5543 5130 5353 4380 Center Count Traffic for ZMA 7500 7000 6500 6000 5500 Counts 5000 4500 4000 3500 3000 25-Sep-04 14-Nov-04 3-Jan-05 22-Feb-05 13-Apr-05 2-Jun-05 22-Jul-05 10-Sep-05 FY2005 ZMA FY05 FAL WNT SPR SUM ATO_FAL ATO_WNT ATO_SPR ATO_SUM Poly. (ZMA)

10. Representative Days

11. Seasonal Average Performance Metrics

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

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

20. 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

24. 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. Rules for Up-gauging Aircraft

26. 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. 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. 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

30. 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?