U se of Agent-based Modelling to explore new approaches to Air Traffic Management

1. Use of Agent-based Modelling to explore new approaches to Air Traffic Management Prof Peter Lindsay Boeing Professor of Systems Engineering, University of Queensland Director, ARC Centre for Complex Systems (ACCS)

2. Outline of talk • The future of Air Traffic Management (ATM) • Agent-based models of Air Traffic Control • evaluation of a Decision Support Tool for controllers • Modelling aircraft trajectories • uncertainty and airspace design • Developing system requirementsfor future concepts of operation • using a modelling notation from software engineering • Summary & conclusions

3. Airspace controlled by Airservices Australia

4. How ATC is done in most places

5. ATC in Australia

6. ATM as a non-linear system (Reference: Eurocontrol PRC (2004). “Annual Report of Eurocontrol Performance review Committee 2004”, Eurocontrol , 2004) Aviation Accidents Courtesy of Sameer Alam, ACCS, ADFA

7. ATC trends & challenges • Changing nature of Air Traffic Control: • Australian system now entirely computer-based • Datalink will (partially) replace radio communications • ADSB + GPS will enable radar-like surveillance of whole continent • Automated Dependent Surveillance - Broadcast • Massive savings possible if airlines can choose own trajectories • User Preferred Trajectories (UPT) • UPT is a fundamental change to operational concept • Will require new Collaborative Decision Making protocols, tools & procedures

8. Track Variation DXB-MEL Jun 2006

9. Outline of talk • The future of Air Traffic Management (ATM) • Agent-based models of Air Traffic Control • evaluation of a Decision Support Tool for controllers • Modelling aircraft trajectories • uncertainty and airspace design • Developing system requirementsfor future concepts of operation • using a modelling notation from software engineering • Summary & conclusions

10. ATC display

11. ATC Simulation Environment

12. With emulated ATCo agents

13. Example: real vs emulated controllers

14. ABM to test new operational concepts • Use agent-based modelling & simulation to test system-level effects of new ATM concepts • Example: a Decision Support Tool to help controllers decide what intervention to apply • action that has smallest effect on time at arrivals approach fix • Method: • Agents emulate controller decision procedures • look-ahead time & soft separation standard • Investigated affect of higher traffic volumes

15. Example scenario (GUN feeder sector)

16. Ex scenario (with traffic doubled)

17. Results (1): accumulated delay Unaided agent DST-aided agent S=11NM case (risk averse agents)

18. Accumulated delay (2) Low risk aversion (S=5NM) High risk aversion (S=11NM) L=11NM case

19. Number of interventions made Low risk aversion (S=5NM) High risk aversion (S=11NM)

20. Number of attempts to intervene Low risk aversion (S=5NM) High risk aversion (S=11NM)

21. Conclusions of DST experiment • Preliminary conclusions (with lots of caveats): • Delay will increase non-linearly as traffic volumes increase • Use of the Decision Support Tool does not seem to complicate traffic patterns • but can result in significant reductions in delays • Traffic levels don’t need to increase by much before current procedures start to break down • Unfortunately the DST doesn’t help here • Risk averse controllers create problems for themselves • Procedural breakdown occurs sooner

22. Outline of talk • The future of Air Traffic Management (ATM) • Agent-based models of Air Traffic Control • evaluation of a Decision Support Tool for controllers • Modelling aircraft trajectories • collaboration with Boeing Madrid • uncertainty and airspace design • Developing system requirementsfor future concepts of operation • using a modelling notation from software engineering • Summary & conclusions

23. Goal: improved trajectory prediction

24. Aircraft Intent Description Language (AIDL) • A formal language for describing 4D trajectories

25. AIDL instructions (excerpt)

26. AIDL example

27. Uncertainty factors • Main applications to date have been as notation for communicating aircraft intent • High fidelity but succinct • Ground-to-ground, for interoperability • how can stochastic factors be best incorporated into modelling? • Environmental factors: eg wind • Aircraft performance factors: eg aircraft type, navigation accuracy • Operational factors: eg timing of pilot actions • Initial conditions: eg position, velocity, weight

28. META simulation tool • xx Effect of weight on where & when Top of Climb reached

29. Modelling uncertainty • xx Varying weight & speed settings ... plus wind

30. CDA RTA-change study • Continuous Descent Approach (CDA) trajectory • essentially “glide” from cruise level down to the approach fix • following defined path, at constant groundspeed • but anticipate that Required Time of Arrival (RTA) may need to be changed • Question: what’s the minimum advance notice required to avoid having to abandon the CDA? • as a function of ∆RTA • and how do the uncertainty factors affect this?

31. Outline of talk • The future of Air Traffic Management (ATM) • Agent-based models of Air Traffic Control • evaluation of a Decision Support Tool for controllers • Modelling aircraft trajectories • uncertainty and airspace design • Developing system requirementsfor future concepts of operation • using a modelling notation from software engineering • Summary & conclusions

32. ARC UPT project • Collaboration with Airservices Australia, Qantas & Boeing (Australia, Spain, USA) • 3-year ARC Linkage grant project starting 2009 • Goal: to develop a conceptual model of how User Preferred Trajectories would ideally be implemented • plus step-plan for what new CDM protocols, tools & procedures will be required • Collaborative Decision Making • Research challenge: to develop a modelling framework to facilitate this

33. Whole of system, whole of life-cycle

34. ICAO operational concept Internat Civil Aviation Org.

35. SESAR master plan • xx SESAR = Single European Sky Atm Research consortium

36. Excerpt from Service Level 1

37. SESAR ConOps: control flow

38. SESAR ConOps: data flow

39. Boeing ATM concept for 2020 • ATM as an integrated set of core services Weather Services Airlines/Flight Operations Center DoD Homeland Security Public Safety Weather NAS Infrastructure status Information Management Aircraft state (position, velocity, intent) User preferences Historical Schedules New Flight Plans Navigation Special user requests Resource Allocation (airspace, routes, etc.) Proposed Trajectories 4-D Trajectory Proposed Flight Plans Airspace Management Flow Management Traffic Management Separation Management Aircraft Handoff Coordination Approve/ Reject request Loading estimates Approved/Rejected trajectories Approved/Rejected flight plans Resource Request (airspace) Reroute request Resource Request (airspace, routes) Resource Request (airspace, separation criteria) Capacity limits (region, airport, sector) Aircraft state (position, velocity, intent) Surveillance

40. ARC UPT project approach Year 1: Develop a Behavior Tree model of the ICAO Operational Concept a coherent, high-level, traceable model gate-to-gate trajectories, whole life-cycle Year 2: Trade studies detailed models & experiments to identify potential benefits & constraints of concept elements (TBD) Year 3: Identify & prioritize step changes to existing system identify the key protocols, tools & procedures required in staged requirement sets

41. The Behavior Tree methodology Inventor: Geoff Dromey, Griffith University A methodology for requirements analysis using a simple graphical modelling language Bridge the gap from informal to formal Provide reader with a coherent overview of a Requirements Specification plus check its consistency and completeness

42. Requirements translation Functional Requirement: When a car arrives, if the gate is open the car proceeds, otherwise if the gateis closed, when the driver presses thebutton it causes the gate to open.

43. Integrating a requirement into a BT P-03 BT-03 Matching Precondition Px Px Root Node BT-X Behavior Tree so far Every behaviour fragment has a precondition (often not stated) next requirement

44. Requirements integration - Direct Traceability of Reqs. - Original Vocabulary - Easy to validate - Stakeholders understand - Formal semantics

45. Integrated Behavior Tree Satellite Control System 23 Pages Of Text Problems/Issues =>Yellow – implied behavior => Red – missing behavior

46. Example 1000+ requirement system

47. Recent work with industry

48. Tool support for Behavior Trees • Raytheon-developed Eclipse-based development environment • Graphical editing tools, data dictionaries, etc • Translators to SAL, UML, C++ • Methodology for fault injection, for risk analysis • FMEA using model checking (scalable?) • Extension to model stochastic aspects

49. Summary & conclusions (1) • The future of Air Traffic Management (ATM) • Agent-based models of Air Traffic Control • evaluation of a Decision Support Tool for controllers • Modelling aircraft trajectories • uncertainty and airspace design • Developing system requirementsfor future concepts of operation • using a modelling notation from software engineering

50. Acknowledgements • Air Traffic Control simulator: • Colin Ramsay, Ariel Liebman • ATM data & domain expertise: • Greg McDonald (Airservices) • ATCo Workload study: • Andrew Neal & UQ Key Centre for Human Factors • ICAO operational concept: • Adrian Dumsa • Aircraft Intent Description Language & META trajectory modelling tools: • Miguel Vilaplana (Boeing Madrid) Free Flight & Air Traffic Control