FACET:
Future Air Traffic Management
Concepts Evaluation Tool
Banavar Sridhar
Shon Grabbe
First Annual Workshop
NAS-Wide Simulation in Support of NEXTGEN
10 December, 2008
Ames Research Center
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Outline
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FACET Description
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FACET uses in NEXGEN analysis
– Tube Designs
– Optimization
– Network Analysis
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Issues
– Lack of methodology
– Simulation tools
– Integration of existing tools
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Future ATM Concepts Evaluation Tool (FACET)
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Environment for exploring advanced ATM concepts
FACET design balances fidelity and flexibility
– Utilizes less complex models of aircraft performance and terminal
airspace
– Enables zoom from national to regional to single aircraft level
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FACET architecture enables modeling of ~15,000 aircraft
trajectories at the national level in a few seconds
Runs on a desktop computer (Linux, Solaris, Mac OS X, Win
XP)
– Works with existing FAA systems on an enterprise server
– Accessible via Web to users of Flight Explorer®, Matlab®, and Jython
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3 Operational Modes: Playback, Simulation, Hybrid
Used for visualization, off-line analysis and real-time
planning applications
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Animation: A Day in the Life of Air Traffic
• Smithsonian’s National Air & Space Museum is using FACET in “America by
Air” exhibit
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FACET Displays
Traffic
Winds
Convective Weather
3-D
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FACET Software Architecture
National
Weather
Service
FAA
Traffic Data
Winds
Severe
Weather
FACET CORE FEATURES
Air and Space
Traffic Integration
Route Parser &
Trajectory Predictor
Data
Visualization
Historical
Database
User
Interface
Climb
Aircraft
Descent
Performance
Cruise
Data
Adaptation
Data
Airborne
Self-Separation
Tracks
Flight
Plans
Airspace
Airways
APPLICATIONS
Traffic & Route
Analyzer
Direct Routing
Analysis
Controller
Workload
System-Level
Optimization
Traffic Flow
Management
Airports
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Concept of Tube Network
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Dynamic airspace configuration is a key element of
the Next Generation Air Transportation System
– Flexible airspace boundaries that are dynamically configured
– New airspace classes such as tube airspace
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Tube network connects regions with high traffic volume
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–
–
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Network is dynamic: tailored to demand, winds, and weather
Tube airspace segregated from other airspace classes
Tube traffic gets benefits, e.g., better routes and arrival slots
Control mode inside tube may include self spacing/merging
Concept of operations is not well defined at present
Initial study to expose key research issues
– Develop a common analysis method
– Define and evaluate performance metrics
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Design of Tube Structures
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Implemented in Future ATM Concepts Evaluation
Tool by simulating traffic above 12,000 ft
Historical air traffic data from Aug. 24, 2005 used in
four 6-hour blocks
Five designs based on different methods
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–
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Jet routes
Delaunay triangulation (Sridhar, et al.)
Traffic density
Hough transform
(Xue, et al.)
Network cost optimized (Gupta, et al.)
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Hough
Transform
Network Cost
Optimization
50 great circle tubes
Maximize use by ≤ 5%
additional travel distance
• Cost of each node, link and
flight travel time of the network
optimized (67 links)
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Performance Metrics
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Instantaneous occupancy
– Utilization and activation/deactivation trigger
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Volume occupancy
– Capacity and duration
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Number of conflicts
– Communication and workload
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Frequency of tube crossings
– Communication and workload
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Encounter angles of tube crossings
– Communication and workload
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Number of Conflicts
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Number of conflicts with and without tubes
(simulation: 5 nmi, 1000 ft)
Worse
Jet Routes
Nominal
Delaunay Triangles
Delaunay Triangles
Jet Routes
Conflict
count
Cost Optimized
Traffic Density
Traffic Density
Hough Transform
Hough Transform
Better
Center
Cost Optimized
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Optimization-Simulation Environment
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Strategic Departure Control Model
Objective function:
Minimize the total system delay
Inputs:
- Scheduled departure times
and flight plans
- Sector and airport capacities
Outputs: departure delay
assigned to each flight
2-hr Planning Horizon:
• 4,500 flights, 949 airports and 987 sectors
• 600,000 variables and 650,000 constraints
[Bertsimas and Stock-Patterson, 1998]
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Strategic Weather Translation
• Active area of research
• Four reduced capacity
scenarios considered (0%,
20%, 40%, and 60%) if
Convective Weather
Avoidance Model (CWAM)
60% deviation probability
contours existed
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Tactical Weather Translation
Avoided Convective Weather Avoidance Model (CWAM)
60% deviation contours at FL300
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Rerouting vs Ground Holding Delays
Benefits of departure control model limited without
accounting for flow-based weather impacts
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Model Validation
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Additional viewgraphs
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US Air Traffic Network
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From current flight plan structure of one day, Airport
and Airspace Network (AAN) has ~8000 nodes
4
5
3
6
2
7
…
1
251
250G Node
AAN has 225 nodes with > 250 links (250G) and
ten Centers have more than ten 250G nodes each
 There are 22 1000G nodes in the system today
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Future Traffic Scenarios
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Projected growth of tripling of passengers by
2025 along with increased air taxis and UAVs
Terminal Area Forecast (TAF) generated growth
rates used to create 3X current traffic
3X AAN has 1443 250G nodes and all Centers
have more than forty 250G nodes
There are 262 1000G nodes in the future system
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Impact of Weather
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Convective weather related delay days of more
than 200,000 minutes are increasing
Weather is considered a disturbing agent, either
random or selective
The density of 250G nodes is seen much higher in
some regions
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