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June 29, 2018

Emmanuel Sunil, Ólafur Þórðarson, Joost Ellerbroek and Jacco Hoekstra

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Outline

Introduction

Baseline Analytical Model

Fast-Time Simulation Experiments

Results and Numerically Adjusted Models

Conclusions

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1.Introduction

4

ATM in Fast-Time Simulations

5

Metropolis Project

Tubes

4 Constraints

• X Position

• Y Position

• Altitude

• Speed

Zones

2 Constraints

• X Position

• Y Position

Layers

1 Constraint

• Altitude

Unstructured

0 Constraints

Four Airspace Concepts of Increasing Structure

Compared Using Fast-Time Simulations

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Pros and Cons of Simulations

Pros Cons

High-level system dynamics

Can be very realistic

Analyze different conditions

Time consuming

Results can be qualitative

Difficult to generalize results

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Conflict Count Models

Inst. Conflict Count =

Number of combinations of 2 aircraft

Conflict probability between any 2 aircraft

X

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Intrinsic Airspace Safety

Intrinsic safety is the safety that is provided exclusively by the constraints imposed on traffic

motion by an airspace design

Tubes

4 Constraints

• X Position

• Y Position

• Altitude

• Speed

Zones

2 Constraints

• X Position

• Y Position

Layers

1 Constraint

• Altitude

Unstructured

0 Constraints

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Conflict Count Models

Inst. Conflict Count =

Number of combinations of 2 aircraft

Conflict probability between any 2 aircraft

X

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What is a Traffic Scenario?

1. Speed

2. Heading

3. Altitude

4. Density/Spatial

A traffic scenario describes the distributions of aircraft:

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Traffic Scenario Assumptions

1. Speed is equal for all aircraft

2. Heading distribution is uniform

3. Altitude distribution is uniform

4. Density/spatial distribution is uniform

How accurate are these models for more realistic traffic scenarios?

Ideal Traffic Scenario

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Research Goals

1. Study the effect of traffic scenario assumptions on the accuracy of analytical conflict count models for more realistic traffic scenarios

– Unstructured airspace used as a case study

2. Derive and test ‘model adjustments’ to generalize the models for all traffic scenarios

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2.Baseline Analytical Model

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Intrusions vs. Conflicts

2𝑆ℎ

Intrusion Conflict

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Conflict Count Model for

Unstructured Airspace

Inst. Conflict Count =

Number of combinations of 2 aircraft

Conflict probability between any 2 aircraft

X

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Number of Combinations of 2 A/C

1

3

24 5 6

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Average Conflict Probability

𝑝 =𝐵𝑐

𝐵𝑡𝑜𝑡𝑎𝑙

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Average Conflict Probability

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Traffic Scenario Assumptions

Assumes uniform altitude and density/spatial distributions of traffic

𝑝 =𝐵𝑐

𝐵𝑡𝑜𝑡𝑎𝑙

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Traffic Scenario Assumptions

Assumes equal ground speed and uniform heading distribution of traffic

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Traffic Scenario Assumptions

1. Speed is equal for all aircraft

2. Heading distribution is uniform

3. Altitude distribution is uniform

4. Density/spatial distribution is uniform

Ideal Traffic Scenario

For Unstructured Airspace, all 4 assumptions affect the average conflict probability between any two aircraft

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3.Fast-Time Simulation Experiments

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BlueSky Open ATM Simulator

https://github.com/ProfHoekstra/bluesky

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4 Assumptions – 4 Experiments

1. Ground-Speed Experiment

2. Heading Experiment

3. Altitude Experiment

4. Spatial Experiment

Traffic Scenarios

• 5 Densities (5-100 a/c per 10,000 NM2)

• 5 Repetitions

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Ground Speed Experiment

Baseline analytical model

Different mix of aircraft types

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Heading Experiment

Uniform Normal

Bimodal Ranged-Uniform

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Altitude Experiment

Baseline analytical model

Traffic in upper airspace for

fuel efficiency

One preferreddestination

Two preferreddestinations

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Spatial Experiment

Uniform

Hot Spot 1 Hot Spot 2

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4.Results and Numerically Adjusted

Models

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Model Accuracy Measurement

Analytical Model for Ideal Traffic Scenario

No. Inst Conflicts = Model ∙ k

𝑘 = 1 100% accuracy

𝑘 < 1 Overestimation

𝑘 > 1 Underestimation

k = 1.024 (97.6%)

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Ground Speed Experiment

No substantial effect of ground speed distribution on safety for Unstructured Airspace

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Ground Speed Adjustment

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Ground Speed Adjustment

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Ground Speed Experiment

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Heading Experiment

Heading distribution can cause the analytical model to over-estimate conflict counts

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Heading Adjustment

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Heading Adjustment

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Heading Experiment

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Altitude Experiment

Altitude distribution can cause the analytical model to significantly under-estimate conflict counts

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Altitude Adjustment

A/C j in vertical range of A/C i

Altitude spread of all A/C

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Altitude Experiment

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Spatial Experiment

Spatial distribution can cause the analytical model to significantly under-estimate conflict counts

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Spatial Adjustment

𝐶𝑡𝑜𝑡𝑎𝑙 = 𝐶𝑎𝑟𝑒𝑎1 + 𝐶𝑎𝑟𝑒𝑎2 + 𝐶𝑎𝑟𝑒𝑎1,2

Area 2

Area 1

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Spatial Experiment

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5.Conclusions

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Conclusions

• Analytical model very accurate for ideal traffic scenario

• Conflict probability was incorrectly predicted by analytical model for non-uniform heading, altitude and spatial distributions.

• Spatial distribution of traffic led to the largest negative impact on the accuracy of the analytical model

• Ground-speed distribution did not affect analytical model accuracy

• Numerical model adjustments increased model accuracy for non-ideal conditions

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Conclusions

• Adjusted conflict count models:

– Understand the relationships between the factors affecting airspace safety

– Tool for practical airspace design applications

• Future work will extend model adjustments for layered and other airspace designs

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Thank You For Your Attention!

[e.sunil@tudelft.nl][https://www.researchgate.net/profile/Emmanuel_Sunil]