5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 1 Common Trajectory Prediction Capability for Decision Support Tools Steven Green NASA Sip Swierstra Eurocontrol HQ 5th USA/Europe ATM 2003 R&D Seminar June Budapest, Hungary

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 2 Common Trajectory Prediction Capability for Decision Support Tools Purpose  Purpose: –Identify key challenges Trajectory Prediction (TP) services for the future –Present design considerations to address these challenges for a range of TP applications

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 3 Motivation  Trajectory Prediction (TP) is cross cutting and critical to ATM.  Good News Much progress towards modernization: >Many parallel and independent developments: Resulting in many Decision Support Tools (DSTs) »MAESTRO, COMPASS, CALM, CTAS, URET, MTCD, FACTS, D2, PARR, EDA, CORA, FAST, TFM applications,….  Future challenges >Greater ATM complexity may require more and/or advanced DSTs.  Goal >A generic approach/architecture >Towards the concept of “Common Trajectory Prediction Services”

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 4 Outline  Operational Context  TP performance  Sources of TP error  TP design considerations  Solutions toward a Common TP capability  Conclusions

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 5 Operational Context Common Flight Data Operational Improvements High Level ATM objectives Trajectory Prediction Operational Concept Decision Support Tools Requirements Top - down

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 6 Operational Context Common Flight Data Operational Improvements High Level ATM objectives Trajectory Prediction Operational Concept Decision Support Tools Requirements Top - down Capabilities Bottom - up Reality Often we adapt DSTs for what they are fit for

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 7 What means: Performance is “good enough”? 0.13 nm/min Practical lower error bound Uncertainty (1 SD) Look-ahead time (min) Longitudinal error (nm) Magill S., 1997, Trajectory predictability and frequency of conflict-avoiding action Assumptions Aircraft speed : 1% Along track Wind speed: 7 kt rms Air temperature: 2 Air temperature: 2 0 C

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 8 Performance: What is “good enough”? 0.28 nm/min Reported Accuracy for URET-TP 0.2 nm/min Design target European MTCDs 0.13 nm/min Practical lower error bound Kauppinnen S. et Al., 2002, European Medium-term conflict detection field trials Look-ahead time (min) Uncertainty (1 SD) Longitudinal error (nm) Mary Lee Cale et al., 2001, A generic sampling technique for measuring aircraft TP accuracy

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 9 Sources of Error Data errors Incomplete, inaccurate, untimely input data Modelling errors Simplification, omissions in mathematical models Intent errors Unpredictability of pilot intent: Pilot discretion Unpredictability of controller intent: Tactical ATC instructions  Aircraft state, meteorological conditions, etc.  A/C performance model, turn modelling, integration time step, etc.  Route, Speed and Altitude profile, etc.

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 10 Route profile example: Lateral Intent Error Maximum Lateral Deviation (nm) Distribution (%) Comparison planned horizontal path and path actually flown Bayraktutar I., 2003, Comparison of vertical and lateral flight profile deviations from planned routes,

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 11 Considerations for future TPs Multi Sector Planning - MSP Continuous Descent Approaches - CDA … New or Enhanced DSTs may be required to support more advanced applications like: Extended look-ahead times may be required by such advanced DSTs

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 12 Example: Cruise – Descent profile management Distance, (nm) Altitude, FL Level (FL) Time (min) Fuel (kg) Planned Profile Source: Aerospaciale PEPC programme Transit time constrained 45 mins The difference: 600 kg Hold at FL 50 Time (min) Fuel (kg) Total Time (min) Fuel (kg)

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 13 How to extend Look-Ahead Times? * Uncertainty still increases with look-ahead time. Look-ahead time Uncertainty Most fielded DSTs use “Open loop” mode * One-shot Trajectory Prediction. * Performance inferencing can reduce error, but * Target uncertainty would be virtually “independent“ of look- ahead time. Advanced DSTs may need “Closed loop” mode * Trajectory Prediction & Control. * Requires generation of active advisories to “close” the loop Look-ahead time Uncertainty

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 14 Example: AMAN with Active Advisories Wind 50 kt AMAN : Arrival Manager Objective: Control flights to to achieve target arrival times Real-time flight tests using B757 flight simulator Nominal range to touch-down: 80 nm Initial altitude: 10,000 ft Wind: 50 kt (270 deg)

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 15 Example: AMAN with Active Advisories (cont’d) AMAN : Arrival Manager Algorithmic approach: Assess perturbations in horizontal, vertical and speed profiles

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 16 Example: AMAN with Active Advisories (cont’d) Final uncertainty for 25 min look-ahead time = 0.3 nm All profiles achieved target time  10 sec AMAN : Arrival Manager Results

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 17 Design considerations for common TP capabilities  So for a “common” TP, we need the flexibility to dynamically control the fidelity of the calculation process.  This requires the isolation of the algorithms from Input data  This would offer:  real-time control of model fidelity and  facilitate optimum balance between accuracy, speed and cost  Every TP has a way to describe the sequence of segments that comprise the output trajectory.  But most TP systems have the fidelity limitations embedded in it for the specific DST that it has been optimized for. –Example: turn model: >instantaneous turn, >curvilinear turns or >full turn dynamics

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 18 Common Flight Data Trajectory Prediction Decision Support Tools Flight Script A/C performance Meteo Flight Script Data container that comprises all flight specific input data Generic architecture for common TP capabilities

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 19 Generic TP architecture Common Flight Data Trajectory Prediction Decision Support Tools Trajectory Prediction Trajectory Engine Flight Script n – Dim. Flight Profile A/C performance Meteo Trajectory Engine Flight Script TP Kernel Flight Script A/C performance Meteo

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 20 Generation of active control advisories Flight script Trajectory Engine nD flight profile Check compliance Common Flight Data Profile constraints TP kernel

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 21 Flight script Trajectory Engine nD flight profile Check compliance Update Flight script Common Flight Data Profile constraints No: iterate TP kernel Generation of active control advisories (cont’d)

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 22 Flight script Trajectory Engine nD flight profile Check compliance Yes: Accept result Common Flight Data Profile constraints Generate ATM advice for Controllers TP kernel Generation of active control advisories (cont’d)

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 23 Conclusions  TP quality is paramount for DST operation  Quality of input data is important  Performance inferencing  Use of Down Linked Aircraft Parameters (DAP)  Mathematical models require work to support common TP capabilities  Aircraft Performance models with good accuracy and calculation efficiency.  Turn modelling adapted to manoeuvre complexity  An efficient isolation of Flight Script and TP Engine functionality facilitates the potential for a common TP capability  Uncertainty in Controller Intent is a major source of error  Potential mitigation through active control advisories generated by advanced DSTs.

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 24 Conclusions (cont’d) What if we do not develop a common TP capability? We continue as today with many “custom” TP systems. This leads to: Duplication, Potential interoperability problems, Unnecessary costs

Trajectory Prediction: Art or Science?

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 26

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 27  Every TP has a way to describe the sequence of segments that comprise the output trajectory.  But most TPs have the fidelity limitations embedded in it for the specific DST that it has been optimized for. (e.g. choice among instanteneous turn, curvilinear turns or full turn dynamics) Generic architecture for common TP capabilities  So for a “common” TP we need the flexibility to dynamically control the fidelity of the calculation process.  This requires the isolation of the algorithms from the Flight Script.  In effect the script is the “recipe” of the trajectory cooking process.

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 28 TP Fidelity Requirements Drivers  Operational Envelope for advising trajectory changes –e.g., speed profile envelope (next slide)  Trajectory modeling fidelity –Lateral Profile - Turn modeling >Instantaneous turn >Curvilinear turn (circular arc) >Dynamic turn (roll rate dynamics) –Vertical Profile >Flight-path angle (  ) = function of: Speed profile Atmosphere »Temp »Wind »Wind Gradient (shear) »Weight »Pilot procedures Many TP Systems simplify their fidelity with assumptions imbedded within their models

5th USA/Europe ATM 2003 R&D Seminar, Budapest, Hungary Paper 59: Common Trajectory Prediction Capability for Decision Support Tools 29 Any special performance requirements?  TP kernel is embedded in the feedback control loop  Trading calculation bandwidth for overall accuracy  Improved Performance from Trajectory engine  Improve calculation speed.  Select a “fast” aircraft performance model.  Minimise the number of integration steps.  Improve calculation accuracy  Select an accurate aircraft performance model.  Select accurate algorithms where required.