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