Airspace Complexity and Other Research Topics

Airspace Complexity and Other Research Topics

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 1 Outline FACET Description

FACET uses in NEXGEN analysis Tube Designs Optimization Network Analysis Issues Lack of methodology Simulation tools Integration of existing tools Ames Research Center 2 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

Ames Research Center 3 Animation: A Day in the Life of Air Traffic Smithsonians National Air & Space Museum is using FACET in America by Air exhibit Ames Research Center 4 FACET Displays Traffic Winds Convective Weather 3-D

Ames Research Center 5 FACET Software Architecture National Weather Service FAA Traffic Data Winds Severe Weather Tracks Flight Plans FACET CORE FEATURES Air and Space

Traffic Integration Data Visualization User Interface Climb Adaptation Data Airborne Self-Separation Route Parser & Trajectory Predictor Historical Database Aircraft Descent

Performance Cruise Data Airspace Airways Airports APPLICATIONS Traffic & Route Analyzer Direct Routing Analysis Controller Workload System-Level Optimization Traffic Flow Management Ames Research Center

6 Concept of Tube Network 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 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 Ames Research Center 7 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.) Ames Research Center 8

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) Ames Research Center 9 Performance Metrics 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 Ames Research Center

10 Number of Conflicts 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 Ames Research Center 11 Optimization-Simulation Environment Ames Research Center 12 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] Ames Research Center 13 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 Ames Research Center 14 Tactical Weather Translation Avoided Convective Weather Avoidance Model (CWAM) 60% deviation contours at FL300 Ames Research Center 15 Rerouting vs Ground Holding Delays

Benefits of departure control model limited without accounting for flow-based weather impacts Ames Research Center 16 Model Validation Ames Research Center 17 Additional viewgraphs Ames Research Center 18 US Air Traffic Network

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 Ames Research Center 19 Future Traffic Scenarios 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 Ames Research Center 20 Impact of Weather 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 Ames Research Center 21

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