Modeling Normative Rules in Decision Support for Aircraft Approach/Departure

1. Modeling Operationalization of Normative Rules in Decision Support for Aircraft Approach/Departure Laura Savičienė, Vilnius University

2. The subject domain • Air traffic control (ATC) • Providing aircraft separation • Maintaining orderly flow of air traffic • Providing information Mission 123, do you have problems? I think, I have lost my compass. Judging the way you are flying, you lost the whole instrument panel! Vilnius University, Faculty of Mathematics and Informatics

3. Outline • Statement of the research task and context • Description of the proposed solution • Wake turbulence separation rule modeling example • Conclusions Vilnius University, Faculty of Mathematics and Informatics

4. Context • This work continues the research done in the EU SKY-Scanner project (2007 – 2010) • Project aim was to demonstrate tracking of aircraft with eye-safe laser radar (lidar): • Rotating laser array • Hardware and software for lidar control • Decision support system (DSS) for the air traffic controllers Vilnius University, Faculty of Mathematics and Informatics

5. The approach/departure DSS • The decision support is based on the normative rules for the aircraft Vilnius University, Faculty of Mathematics and Informatics

6. Assumptions • Assumption 1: lidar, used together with the primary radar, provides aircraft position with a high degree of accuracy • Assumption 2: the DSS simply informs the controller, who takes the decision on actions Vilnius University, Faculty of Mathematics and Informatics

7. Problem statement • Operationalization of norms and visualization (presenting for visual cognition) of normative behavior in a decision support system Vilnius University, Faculty of Mathematics and Informatics

8. Normative rules in aircraft approach/departure Vilnius University, Faculty of Mathematics and Informatics

9. Modeling of norms • We identify “geometrical norms”, i.e. those concerning aircraft position and speed • Norms are modeled from the perspective of violating them Vilnius University, Faculty of Mathematics and Informatics

10. Modeling of risk • Each normative rule is represented as a risk definition in the decision support system • Risk evaluation maps the observed value of the norm factor to a discrete risk level Vilnius University, Faculty of Mathematics and Informatics

11. Risk definition example: indicated airspeed Norm factor: ‘indicated airspeed’; Norm type: ‘limit’; Norm patter: ‘<= vN’; Expected value: 210 kt.; Thresholds: v0 = 202 kt., v1 = 206 kt., v2 = 214 kt.; Vilnius University, Faculty of Mathematics and Informatics

12. Risk definition example: glide path Norm factor: ‘glide path’; Norm type: ‘deviation’; Norm pattern: ‘= vN’; Expected value: 3.33⁰ (deviation 0); Thresholds: dn0 = -0.01, dp0 = 0.01, dn1 = -0.1, dp1 = 0.1, dn2 = -0.25, dp2 = 0.25 Vilnius University, Faculty of Mathematics and Informatics

13. Norm operationalization steps • Set up risk representation structure (risk levels associated with traffic-light colors) • Create risk definitions (define factor, expected value, pattern, and thresholds) • Set up risk indicators for each risk definition Vilnius University, Faculty of Mathematics and Informatics

14. Wake turbulence • Vortices generated by the flying aircraft • Persist between 1 and 3 minutes • Descend 500 to 900 feet at distances of up to five miles behind the aircraft • Wind can cause vortices to drift or to break up Vilnius University, Faculty of Mathematics and Informatics

15. Wake turbulence separation rules Vilnius University, Faculty of Mathematics and Informatics

16. Wake turbulence modeling • Existing models of wake turbulence: • Behavior of vortices • Affected wake area Vilnius University, Faculty of Mathematics and Informatics

17. Wake area definition in the decision support system • Wake area is composed of polyhedrons • Leading aircraft’s past positions for the time interval defined in the norm (i.e. 120 seconds) are used • The risk evaluation estimates the time Δt it takes the following aircraft to reach the wake area Vilnius University, Faculty of Mathematics and Informatics

18. Wake turbulence risk definition Norm factor: ‘time-based turbulence separation’; Norm pattern: ‘≥vN’; Expected value: 120 s; Norm type: ‘limit’; Thresholds: v7 = vN = 120 s, v6 = 122 s, v5 = 124 s, v4 = 126 s, v3 = 128 s, v2 = 130 s, v1 = 132 s, v0 =134 s; Vilnius University, Faculty of Mathematics and Informatics

19. The DSS 2D-in-3D prototype: wake turbulence risk Vilnius University, Faculty of Mathematics and Informatics

20. Conclusions • The proposed norm operationalization conception (method) enables to represent a subset of aircraft approach/departure normative rules (geometrical norms) in a decision support system for the air traffic controller • The prototype decision support system provides an integrated solution to facilitating the controller: risk indicators automate detection of possible norm violations • Phases, needed to operationalize the norms, are identified, but the process cannot be fully automated Vilnius University, Faculty of Mathematics and Informatics

21. Thank You!