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Operationalization of Norms in Aircraft Approach/Departure Decision Support

Operationalization of Norms in Aircraft Approach/Departure Decision Support. Laura Savi čienė , Vilnius University. Problem statement.

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Operationalization of Norms in Aircraft Approach/Departure Decision Support

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  1. Operationalization of Norms in Aircraft Approach/Departure Decision Support Laura Savičienė, Vilnius University

  2. Problem statement • To develop a conception for operationalization of the aircraft approach/departure norms in a decision support system, taking into consideration the use of lidar (laser radar) for aircraft tracking • Tasks: • Modeling norm violation risk in the airport traffic zone • Modeling radar and lidar data fusion • Development of a prototype decision support system Vilnius University, Faculty of Mathematics and Informatics

  3. Context • The SKY-Scanner project: using lidar to track aircraft in the airport traffic zone • Part of the SKY-Scanner project was the DSS for the air traffic controller Vilnius University, Faculty of Mathematics and Informatics

  4. 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

  5. Normative rules in aircraft approach/departure • Norms in the following areas are examined: • Air traffic control (ATC) separation rules: horizontal sep. (in nautical miles) and vertical sep. (in feet) • Airport approach/departure procedures: norms are presented as maps, charts, tables, and textual descriptions • Wake turbulence separation rules: time-based separation • Rules for avoiding volcanic ash: zones of restricted operations depending on particle concentration Vilnius University, Faculty of Mathematics and Informatics

  6. Approach/departure procedures • Each airport has a unique set of the approach/departure procedures Vilnius University, Faculty of Mathematics and Informatics

  7. Related works • Current aviation-related systems do not model norms comprehensively, but there is some research in that direction • Conflict detection and resolution process structure is adapted to aircraft separation conflicts, but can be expanded to cover other normative rules • Study in real-time decision making suggests to facilitate the encoding step of the user’s cognitive process, possibly, by providing more intuitive visualizations • 2D visualizations in ATC are no longer sufficient, and 3D visualizations have drawbacks; possible strategy to overcome this is to augment the 3D screens with auxiliary 2D elements Vilnius University, Faculty of Mathematics and Informatics

  8. Decision support process Vilnius University, Faculty of Mathematics and Informatics

  9. Modeling of norms • Geometrical norms, i.e. those concerning aircraft position and speed, are identified • Norms are modeled from the perspective of violating them • Two norm types are identified: limit-based and deviation-based • Each norm is modeled with a factor, a pattern, and a normative value vN 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 definition associates the modeled norm with a set of thresholds and discrete risk levels • Risk evaluation maps the observed value of the norm factor to a discrete risk level: • For L risk levels, L-1 thresholds (or pairs of th.) are needed • A separate indicator can be created for each norm 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: altitude Norm factor: “altitude”; Norm type: “deviation”; Norm pattern: “= vN”; Expected value: 3900 ft. at 6 DME (deviation 0); Thresholds: dn0=-0.5, dp0=2, dn1=-1, dp1=3.5, dn2=-1.5, dp2=5; Vilnius University, Faculty of Mathematics and Informatics

  13. Visualization of the approach procedure: 2D-in-3D example Vilnius University, Faculty of Mathematics and Informatics

  14. Visualization of the approach procedure: pure-3D with “rings” Vilnius University, Faculty of Mathematics and Informatics

  15. Results (1) • A conception for norm operationalization and norm violation risk model • Each norm is modeled with a factor, a pattern, and a normative value vN • Each norm is represented as a risk definition • Risk evaluation maps the observed value of the norm factor to a discrete risk level • The solution combines well known models (piecewise linear risk model and traffic light decision making principle) Vilnius University, Faculty of Mathematics and Informatics

  16. Results (2) • Prototype decision support system • Demonstrates modeling of several norms • Adapts advanced visualization ideas • Provides real-time demonstration of the solution Vilnius University, Faculty of Mathematics and Informatics

  17. Conclusions • The proposed norm operationalization conception 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, and 2D-in-3D visualizations help comprehend conformance to the approach/departure procedure • Phases, needed to operationalize the norms, are identified, but the process cannot be fully automated Vilnius University, Faculty of Mathematics and Informatics

  18. Thank You for Your Attention! Vilnius University, Faculty of Mathematics and Informatics

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