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Modeling Operationalization of Normative Rules in Decision Support for Aircraft Approach/Departure Laura Savičienė, Vilnius University July 11, 2012

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2 The subject domain Air traffic control (ATC) –Providing aircraft separation –Maintaining orderly flow of air traffic –Providing information July 11, 2012Vilnius University, Faculty of Mathematics and Informatics Mission 123, do you have problems? Judging the way you are flying, you lost the whole instrument panel! I think, I have lost my compass.

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3 Outline Statement of the research task and context Description of the proposed solution Wake turbulence separation rule modeling example Conclusions July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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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 July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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5 The approach/departure DSS The decision support is based on the normative rules for the aircraft July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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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 July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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7 Problem statement Operationalization of norms and visualization (presenting for visual cognition) of normative behavior in a decision support system July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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8 Normative rules in aircraft approach/departure July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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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 July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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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 July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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11 Risk definition example: indicated airspeed Norm factor: ‘indicated airspeed’; Norm type: ‘limit’; Norm patter: ‘<= v N ’; Expected value: 210 kt.; Thresholds: v 0 = 202 kt., v 1 = 206 kt., v 2 = 214 kt.; July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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12 Risk definition example: glide path Norm factor: ‘glide path’; Norm type: ‘deviation’; Norm pattern: ‘= v N ’; Expected value: 3.33 ⁰ (deviation 0); Thresholds: d n0 = -0.01, d p0 = 0.01, d n1 = -0.1, d p1 = 0.1, d n2 = -0.25, d p2 = 0.25 July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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13 Norm operationalization steps 1.Set up risk representation structure (risk levels associated with traffic-light colors) 2.Create risk definitions (define factor, expected value, pattern, and thresholds) 3.Set up risk indicators for each risk definition July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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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 July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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15 Wake turbulence separation rules July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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16 Wake turbulence modeling Existing models of wake turbulence: –Behavior of vortices –Affected wake area July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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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 July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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18 Wake turbulence risk definition July 11, 2012Vilnius University, Faculty of Mathematics and Informatics Norm factor: ‘time-based turbulence separation’; Norm pattern: ‘≥v N ’; Expected value: 120 s; Norm type: ‘limit’; Thresholds: v 7 = v N = 120 s, v 6 = 122 s, v 5 = 124 s, v 4 = 126 s, v 3 = 128 s, v 2 = 130 s, v 1 = 132 s, v 0 =134 s;

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19 The DSS 2D-in-3D prototype: wake turbulence risk July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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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 July 11, 2012Vilnius University, Faculty of Mathematics and Informatics

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Thank You! July 11, 2012

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