modeling operationalization of normative rules in decision support for aircraft approach/departure...
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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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The subject domain
• Air traffic control (ATC)– Providing aircraft separation– Maintaining orderly flow of air traffic– Providing information
July 11, 2012 Vilnius 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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Outline
• Statement of the research task and context• Description of the proposed solution• Wake turbulence separation rule modeling
example• Conclusions
July 11, 2012 Vilnius University, Faculty of Mathematics and Informatics
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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, 2012 Vilnius University, Faculty of Mathematics and Informatics
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The approach/departure DSS
• The decision support is based on the normative rules for the aircraft
July 11, 2012 Vilnius University, Faculty of Mathematics and Informatics
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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, 2012 Vilnius University, Faculty of Mathematics and Informatics
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Problem statement
• Operationalization of norms and visualization (presenting for visual cognition) of normative behavior in a decision support system
July 11, 2012 Vilnius University, Faculty of Mathematics and Informatics
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Normative rules in aircraft approach/departure
July 11, 2012 Vilnius University, Faculty of Mathematics and Informatics
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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, 2012 Vilnius University, Faculty of Mathematics and Informatics
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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, 2012 Vilnius University, Faculty of Mathematics and Informatics
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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.;July 11, 2012 Vilnius University, Faculty of Mathematics and
Informatics
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Risk definition example: glide path
Norm factor: ‘glide path’;
Norm type: ‘deviation’;
Norm pattern: ‘= vN’;
Expected value: 3.33 (deviation 0);⁰Thresholds: dn0 = -0.01, dp0 = 0.01, dn1 = -0.1, dp1 = 0.1, dn2 = -0.25, dp2 = 0.25July 11, 2012 Vilnius University, Faculty of Mathematics and
Informatics
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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, 2012 Vilnius University, Faculty of Mathematics and Informatics
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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, 2012 Vilnius University, Faculty of Mathematics and Informatics
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Wake turbulence separation rules
July 11, 2012 Vilnius University, Faculty of Mathematics and Informatics
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Wake turbulence modeling
• Existing models of wake turbulence:– Behavior of vortices– Affected wake area
July 11, 2012 Vilnius University, Faculty of Mathematics and Informatics
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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, 2012 Vilnius University, Faculty of Mathematics and Informatics
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Wake turbulence risk definition
July 11, 2012 Vilnius University, Faculty of Mathematics and Informatics
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;
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The DSS 2D-in-3D prototype: wake turbulence risk
July 11, 2012 Vilnius University, Faculty of Mathematics and Informatics
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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 automatedJuly 11, 2012 Vilnius University, Faculty of Mathematics and
Informatics
Thank You!
July 11, 2012