1. Terminal Decision Support Tool Aiman Al Gingihy Danielle Murray Sara Ataya OR/SYST 699 Fall 2013 Faculty Presentation December 13, 2013

2. Agenda Introduction o Why are we here today? o Who cares? o Solving the problem Analysis o Gap Analysis o Alternatives Analysis Recommendations Next Steps Acknowledgements Questions? 2

3. Introduction 3

4. Why Are We Here Today? The Federal Aviation Administration (FAA) is transforming the national airspace Next Generation Air Transportation System (NextGen) What is NextGen? o Agency initiate to move from a ground based system to a satellite system Why? o Shorter routes o Less time and fuel o Less traffic delays o More capacity How is team TDST going to look into solving this? o By enabling the use of Performance Based Navigation in the Terminal Environment 4

5. Defines performance requirements o More flexibility through NAS o Dynamic Management of Aircraft How? o Through advanced procedures; RNAV/RNP Procedures o Optimize use of airspace!! What is Performance Based Navigation? 5 Example of Complex Merge in Terminal Environment

6. Where is the Terminal Environment?? TRACON Boundary 30 to 50 miles from airport 6

7. Who Cares?? Stakeholders; that’s who! 7 Terminal Controller

8. So What is the Problem? Terminal Controllers; the primary stakeholder do not have a tool to allow aircraft to use these advanced procedures 8

9. How We Will Solve This Problem 9 Methodology Approach!!

10. Gap Analysis 10

11. Current StateFuture State Transition from conventional routes to advanced routes Current aircraft equipage to fly these procedures is about 60% Current utilization of advanced procedures NAS wide is 10% Enhance safety aspects Reduce carbon emissions Reduce flight delays Reduce noise impacts Deliver a more efficient, consistent flow of traffic down to the runway 11

12. What is the “Formal” Gap?? 1.Inability to continue efficient arrival operations into terminal airspace 2.Lack of automation for Terminal controllers that can support mixed equipage operations 12 RESULT: Pulling Aircraft off approaches….. Losing benefits from procedures … WHICH, Jeopardizes the investment the agency has made

13. Remedies?? 13

14. Actual Aircraft 10 nm 2 nm 13 nm Merge Point 25 NM 2 nm 8 nm 6 nm 7 nm Projected Aircraft Indicator 25 NM A Near-term Solution: Relative Position Indicator (RPI) © 2009 The MITRE Corporation. All rights reserved. Approved for Public Release; Distribution Unlimited. Case # 09-0127

15. TSS Video 15

16. RemediesRemediesRemediesRemedies 16 Relative Position IndicatorTSS Lite + RPI Terminal Sequencing and Spacing Type of Display Aid RelativeAbsolute Methodology behind Display Aid Calculated Relative Position Schedule Based Position Number of System Dependencies 1 (STARS)2 (STAR, ERAM, and TBFM)3 (STARS, ERAM, and TBFM) Equipage Mixed Equipage Environment Aids in Complex Merges within Terminal Environment Connection with TBFM Complement TBFM System One piece of information inherent in TBFM Developed within TBFM System User Benefits may vary based on controller experience (inexperienced can gain greater benefit) Benefits may incrementally improve with additional information for controller Precision of tools allows for inexperienced/ experienced controller to see benefits Incorporation of Winds for Solution? NoYes Provide Trajectory Solution? NoYes via sequenceYes via speed and sequence Benefits  Reduces controller workload  Early Application of Speed Delay/Reduce Delay Vectoring  Enables OPD operations  Provides two more pieces of information to controllers to help sequence a/c  Further reduces controller Workload  Allow 95% a/c to stay on RNP curved path approach  Provide streamlined arrival solution; increasing predictability

17. Alternatives Analysis 17

18. Methodology 18

19. Utility Analysis System Level Analysis Purpose o Help the decision maker identify which alternative will best meet the expectations of NextGen Alternatives 1.TSS 2.TSS Lite & RPI 3.RPI 19 Attributes 1.Time to Mature Capability 2.Time to Adapt/Train Capability 3.Maintain/Increase Throughput 4.RNP Utilization/Predictability 5.Fuel/Mission Saving 6.Reliability 7.Controller Acceptability 8.Systems Use 9.Target Accuracy 10.Collision

20. TSS TSS Lite + RPI (Runway assignments and sequence numbers plus RPI) RPI Time to Mature Capability 1 = TRL 1, 2, 5=TRL 4, 10= TRL 9 557 Time to Adapt/Train 1= 1 year or more, 5 = five months, and 10 = 1 month. 789 Maintain/Increase Throughput 1= 0% increase, 5= 5% increase, 10= 10% increase 765 RNP Utilization/Predictability 1 = 50% of a/c stay on approach, 5 = 75%, 10= 100% 976 Fuel/Emissions 1 = 5% savings on fuel/emissions, 5 = 10%, 10 = 15% 865 Reliability 1= reliable 10% of the time, 5= reliable 75% of the time, 10= 100% of the time 678 Controller Acceptability 1= no buy in, 5 = somewhat buy in, 10 = greatly buy in 986 System Use 1= 0 facilities able to use capability, 5 = 35 facilities able to use capability, 10 = 70 facilities or more able to use capability 5510 Target Accuracy 1 = not accurate, 5 = somewhat accurate, 10 = very accurate 976 Collision Risk 1 =.001% risk 5 =.0001%, 10 =.00001% risk 910 20

21. Hierarchy Decision Tree Best System to Enable Use of PBN Time 0.36 Maturity 0.56 Adapt/Train 0.44 Benefits 0.20 Throughput 0.34 Utilization/Predicta bility 0.43 Fuel/ Emissions 0.23 Op Suitability 0.44 Reliability 0.22 Acceptability 0.16 System Use 0.14 Target Accuracy 0.23 Collision 0.25 21 What is needed to address the current gap and meet NextGen expectations Weights generated elicitation – swing weights Are attributes independent?

22. Value Function 22 Attribute Value Function Maturity0.20 Adapt/Train0.16 Collision0.11 Target Accuracy0.10 Reliability0.10 RNP Utilization/Predictability0.09 Acceptability0.07 Throughput0.07 System Use0.06 Fuel/Emissions0.05

23. Alternative Ranking 23 Results of MAVT

24. Cost vs. Utility 24 Fixed Cost TSS$70M TSS Lite/RPI$12M RPI$10M Reoccurring Cost TSS$450K TSS Lite/RPI$350K RPI$200K

25. Scenario Analysis AlternativesUser Scenario Agency Scenario SE Scenario Benefits > Time Scenario Equal Weights for Level 1 Attributes TSS0.690.730.710.720.69 TSS Lite + RPI0.660.650.680.660.64 RPI0.720.660.720.670.68 25

26. Sensitivity Analysis 26 Ran k Attribute Steepness/Slo pe 1 Maturity0.198 2 Adapt/Train0.158 3 Collision0.112 4 Target Accuracy0.101 5 Reliability0.098 6 RNP Utilization/Predictability 0.085 7 Acceptability0.073 8 Throughput0.068 9 System Use0.061 10 Fuel/Emissions0.046 Performed “what if” analysis to study the behavior of the attribute change as the score changes

27. Final Conclusions/Recommendations The scores differ considerably between the ATC perspective of values and FAA headquarters' perspective. Recommend a meeting at the decision maker level to set clear priorities on what is MOST important If cost is not an issue, one potential recommendation is a phased approach of RPI followed by TSS o Allow agency to realize some sort of benefits in near term 27 If Decision Maker Priority Is…. Capability TimeRPI BenefitsTSS CostRPI

28. Next Steps Deliver final report Place emphasis that decision makers need to determine clear priorities for defined attributes Provide alternatives analysis spreadsheet formulas to key decision makers so they can see test different scenarios 28

29. Acknowledgements The Terminal Decision Support team would like to thank our FAA Sponsor as well as team of Subject Matter Experts Subject Matter ExpertOrganization En Route Controller/TBFM SME Federal Aviation Administration Former Airline Pilot/Current FAA Manager Federal Aviation Administration Terminal Automation SME MITRE Corporation Terminal/PBN Automation SME Federal Aviation Administration Dr Lance Sherry, Executive Director of the Center for Air Transportation Systems Research George Mason University Paula Lewis, PA FAA - Assistant Administrator for Regions and Center Operations George Mason University Dr. Andrew Loerch, Associate Professor/Associate Chair SEOR Department George Mason University 29

30. Questions? Feedback Aiman: aalgingi@gmail.com Danielle: danielle.murray3@gmail.com Sara: ataya.sara@gmail.com aalgingi@gmail.comdanielle.murray3@gmail.comataya.sara@gmail.comaalgingi@gmail.comdanielle.murray3@gmail.comataya.sara@gmail.com 30 Full List of References Available in Final Report

31. Backup Slides 31

32. Description of Criteria Time to Mature Capability : This metric represents how mature the actual capability is at this point in time. This is a quantitative metric as both tools have undergone a maturity assessment as recently as September 30, 2013. In terms of the analysis, 1 = TRL 1, 2, 5=TRL 4, 10= TRL 9. TRL speaks to the Technical Readiness Level of the Capability. We are assuming that each capability would be brought to a max level of a TRL 9 before the next stage in the lifecycle. The figure below describes each level in the TRL framework [27]. Time to Adapt/Train : This metric is based upon research and development performed to date. As RPI is incrementally more mature, this capability requires a much shorter timeframe than TSS. As such, it will take a longer time for site adaptation and training. For the purpose of this analysis, we recognize this time to be a reoccurring measure as this step will need to take place at each site. This number is quantitative based upon analysis. In terms of the analysis, 1= year or more, 5 = five months, and 10 = 1 month. Maintain/Increase Throughput: Throughput is a measure of number of landings per hour on a given runway. This metric is a qualitative relationship based upon individual data derived from both TSS and RPI simulations. In terms of the analysis, 1= 0% increase to throughput, 5= 5% increase, 10= 10% increase or more to throughput. RNP Utilization/Predictability: This metric represents a key objective – making arrivals as efficient as possible using PBN procedures. TSS provides a toolset which makes things as efficient as possible being that it is based upon an absolute schedule. RPI does provide greater efficiency compared to baseline operations but is not as efficient as TSS being that it is a relative tool. Included in this metric, is the ability of controllers to keep aircraft on RNP approaches. TSS has proved to be extremely efficient in keeping airplans on their RNP curved path approaches. While RPI has also proven effectiveness with allowing controllers to keep aircraft on PBN procedures, an evaluation of how many aircraft have been taken off their RNP curved path approach has not been conducted. Nonetheless, TSS demonstrates a clear gain in efficiency with controllers keeping aircraft on 95% of the time. In terms of the analysis, 1 = 50% of a/c stay on approach, 5 = 75% of a/c stay on approach, 10= 100% of a/c stay on approach. 32

33. Description of Criteria Fuel/Emissions: This metric is based on both qualitative and quantitative data. While an apples to apples comparison of the two capabilities cannot be performed, data and subject matter expertise opinion demonstrates that TSS will provide more fuel and emissions savings than RPI. In terms of the analysis, 1 = 5% savings on fuel/emissions, 5 = 10% savings on fuel/emissions, 10 = 15% savings on fuel/emissions. Reliability: This is the ability of the system to perform and maintain its functions in routine circumstances, as well as unexpected circumstances. This includes off nominal situations where controllers are being faced with difficult situations where the system is being tested in terms of sensitivity and flexibility. This is a qualitative assessment based upon subject matter expertise. In terms of the analysis, 1= reliable 10% of the time, 5= reliable 75% of the time, 10= 100% of the time.. Controller Acceptability : This metric represents the amount of buy in controllers have provided in regards to both capabilities. Human factors element (reduce workload, etc)This metric is based upon controller involvement in both RPI and TSS simulations and their subsequent feedback which has been documented in simulation result reports. In terms of the analysis, 1= no buy in, 5 = somewhat buy in, 10 = greatly buy in. System Use : This metric represents how many facilities will be able to use the capability. TSS is dependent on the facility having TBFM whereas RPI does not have a similar constraint. Both capabilities have a dependency on STARS. The factor of what facilities will gain benefit from either/or is also taken into account. The weights associated with this metric are qualitative based upon subject matter expertise of all factors listed above. In terms of the analysis, 1= 0 facilities able to use capability, 5 = 35 facilities able to use capability, 10 = 70 facilities or more able to use capability. Target Accuracy: In specific terms, accuracy is a degree of closeness to the actual value. For this analysis, we focus on the level of accuracy the system gives in terms to the information it displays to the controllers. The more accurate the information, the more precisely they can deliver aircraft to the runway. This is also a qualitative assessment based upon subject matter expertise. In terms of the analysis, 1 = not accurate, 5 = somewhat accurate, 10 = very accurate. Collision Risk : This metric was included to show that none of these capabilities truly have a collision risk. All of these tools are decision support tools to the controllers and controllers are ultimately responsible for separation of aircraft. In terms of the analysis, 1 =.001% risk 5 =.0001%, 10 =.00001% risk. 33