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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
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ATM in Fast-Time Simulations
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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!
[[email protected]][https://www.researchgate.net/profile/Emmanuel_Sunil]
