ames research center 1 facet: future air traffic management concepts evaluation tool banavar sridhar...
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FACET:Future Air Traffic Management
Concepts Evaluation Tool
Banavar SridharShon Grabbe
First Annual WorkshopNAS-Wide Simulation in Support of NEXTGEN
10 December, 2008
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Outline
FACET Description
FACET uses in NEXGEN analysis– Tube Designs– Optimization– Network Analysis
Issues– Lack of methodology– Simulation tools– Integration of existing tools
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Future ATM Concepts Evaluation Tool (FACET)
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 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
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
3-DConvective Weather
Traffic Winds
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FACET Software Architecture
NationalWeatherService
Winds
SevereWeather
FAATraffic Data
Tracks
Flight Plans
Aircraft Performance
Data
ClimbDescent
Cruise
AirspaceAirways
Airports
Adaptation Data
HistoricalDatabase
Traffic & Route Analyzer
User Interface
Route Parser &Trajectory Predictor
FACET CORE FEATURESAPPLICATIONS
Air and Space Traffic Integration
AirborneSelf-Separation
DataVisualization
Direct Routing Analysis
Controller Workload
System-Level Optimization
Traffic Flow Management
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Concept of Tube NetworkConcept of Tube Network
Dynamic airspace configuration is a key element ofthe Next Generation Air Transportation System
– Flexible airspace boundaries that are dynamically configured
– New airspace classes such as tube airspace
Tube network connects regions with high traffic volume– 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
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– Jet routes– Delaunay triangulation (Sridhar, et al.)– Traffic density– Hough transform (Xue, et al.)– Network cost optimized (Gupta, et al.)
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50 great circle tubes Maximize use by ≤ 5%
additional travel distance
Hough Transform
• Cost of each node, link and flight travel time of the network optimized (67 links)
Network Cost Optimization
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Instantaneous occupancy– Utilization and activation/deactivation trigger
Volume occupancy– Capacity and duration
Number of conflicts– Communication and workload
Frequency of tube crossings– Communication and workload
Encounter angles of tube crossings– Communication and workload
Performance Metrics
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Number of Conflicts
Number of conflicts with and without tubes (simulation: 5 nmi, 1000 ft)
Conflictcount
Center
Jet Routes
Delaunay Triangles
Traffic Density
Hough Transform
Cost Optimized
Delaunay Triangles
Cost Optimized
Hough Transform
Traffic Density
Jet Routes
Nominal
Worse
Better
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Optimization-Simulation Environment
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Strategic Departure Control Model
2-hr Planning Horizon:
• 4,500 flights, 949 airports and 987 sectors
• 600,000 variables and 650,000 constraints
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
[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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From current flight plan structure of one day, Airport and Airspace Network (AAN) has ~8000 nodes
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
1
2
3
45
6
7
251
…
250G Node
US Air Traffic Network
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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
Future Traffic Scenarios
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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
Impact of Weather
The density of 250G nodes is seen much higher in some regions