Dr. Banavar Sridhar NASA Ames Research Center Moffett Field, CA 94035 Banavar.Sridhar@nasa

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Ames Research CenterAmes Research Center

Incredible Challenges of the Air Traffic Control System

Modeling, Control and Optimization in the

National Airspace System

Dr. Banavar Sridhar

NASA Ames Research Center

Moffett Field, CA 94035

[email protected]

UCSC SeminarMay 27, 2004

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Outline What is the National Airspace System (NAS)?

– Scope

– Influence on the economy

– Transformation

– Comparison with other networks

Technology: Research in Traffic Flow Management (TFM)

– Strategic flow models

– FACET simulation and modeling capability

Transformation of the NAS Questions?

3


Visualization of Air Traffic DataVisualization of Air Traffic Data

QuickTime™ and aAnimation decompressor

are needed to see this picture.



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5


Hierarchy in TFM

Centralized command and control structure Command Center, Herndon, VA 20 Centers 830 high and low-altitude sectors

Sector 1 Sector 2

Center 1 Center 2

Sector XXX

Center 20

Command Center

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Time-Scales in Air Traffic Management

Ref: Boeing/Aslaug Haraldsdottir

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Inter-Center Traffic Flow

ZSE

ZLC

ZMP

ZAU

ZOB

ZNY

ZBW

ZOA

ZDVZKC

ZME

ZID ZDC

ZLAZAB ZFW

ZTL

ZHUZJX

ZMA

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TFM problem

Capacity– Theoretical maximum flow rate supported by the separation

standard

Throughput– Rate of flow realized in operation

Efficiency– How close is throughput to capacity?

Objective– Maximize flow rate to meet traffic demand

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Types of Control (TFM actions)

Ground Delay Program– Controlling aircraft departure time to manage aircraft arrival

rates

Metering (Miles-in-Trail)– Controlling flow of aircraft into a center by imposing flow

restrictions on aircraft one or more centers away

Reroutes– Congested En-route area

– Weather

– Special Use Airspace

Playbook– Effort to provide a common understanding of re-routing

strategy under previously defined situations

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Transforming the NAS

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September 11, 2001 Chronology of events

8:45 a.m. A large plane crashes into World Trade Center north tower.

9:03 a.m. A second plane crashes into World Trade Center south tower.

9:17 a.m. FAA shuts down all New York City area airports. 9:40 a.m. FAA grounds civilian flights 10:24 a.m. FAA reports that all inbound transatlantic aircraft flying

into the United States are being diverted to Canada. 12:30 p.m.: The FAA says 50 flights are in U.S. airspace, but none

are reporting any problems.

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13


Commercial Transport Enplanements

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

05 06 07 08 09 10 11 12 13 1495 96 97 98 99 00 01 02 03 04

Large Air Carrier

Passenger Enplanements

(Millions)

Calendar Year

ForecastActual

Source: 1990-2002: U.S. Air Carriers, Form 41, U.S. DOT; 2003-2014 FAA Forecasts

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System Reaching Saturation

Target Year* Source: LMI, Alternatives for Improving Transportation Throughput and Performance, March 2002

0

20

40

60

80

100

120

140

160

180

RevenuePassengerMiles Lost

(Billions)

2005 2010 2015

Baseline

New Hubs

Nighttime Operations

Schedule Smoothing

Direct Service

Larger Aircraft

Aggressive NewTechnology

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* Source: LMI, Alternatives for Improving Transportation Throughput and Performance, March 2002

What is at stake in air transportation?

Lost growth and output from air transportation due to demand outstripping capacity*

– Unserved demand of 180 billion Revenue Passenger Miles (RPMs) resulting lost annual economic output of $23 billion by 2015 ($23B does not include additional impact of lost user productivity)

– Major policy / operational alternatives within the current air transportation architecture recaptures only a small fraction of unserved demand and economic output

Large, continuing security costs to protect the system from acts of terrorism

– Difficult to measure efficacy

Rising costs, rising frustrations, lost opportunities

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What makes NAS different?

Safety is paramount Human-in-the-loop decision making at all levels System capacity limits established by human performance Changes need to be done while the system is in operation Difficulty in modeling user reaction to events Availability/absence/uncertainty of information Need to get consensus among various parties: FAA, unions,

airlines, aircraft manufacturers, etc. Status of automation/decision support tools

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Strategic Flow Models

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Outline

Strategic Flow Models Linear Time-variant Dynamic System

representation Flow Matrix Forcing Function Example Bounds on the Model Concluding remarks

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Traffic Flow Models

Detailed models – Useful for developing algorithms affecting individual aircraft

– Controller/Traffic Manager decision support tools Aggregate models

– Useful for understanding the general behavior of the system

– Effectively address system uncertainties and long term behavior

Detailed Aggregate

Deterministic CTAS, FACET, CRCT Flow models

Stochastic Sector Congestion Queuing models

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Traffic Flow

Different Centers Atlanta Center on different days

0

50

100

150

200

250

300

0:00 2:00 4:00 6:00 8:00 10:0012:0014:0016:0018:0020:0022:000:00

Time (UTC)

Traffic Density

ZTL

ZLA

ZMP

0

100

200

300

400

0:00 2:00 4:00 6:00 8:00 10:0012:0014:0016:0018:0020:0022:000:00

Time (UTC)

Traffic Density

8-May-03

6-May-03 7-May-03

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Linear Time-Varying Dynamic Traffic Flow Model

€

ix (k+1)=xi(k)− βijxij=1

N

∑ (k)+ β jij=1j≠i

N

∑ xj(k)+di(k)

€

x(k+1)=A(k)x(k)+B(k)u(k)+C(k)w(k)

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A Matrix (May 6, 2003: 6 hour average, 5-11P.M, PST)

€

zla zoa zse zlc zdv zab zmp zkc zfw zhu zau zme ztl zob zid zny zbw zdc zjx zma

zla .91 .05 0 .03 .02 .05 0 0 0 0 0 0 0 0 0 0 0 0 0 0

zoa .04 .91 .03 .04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

zse 0 .02 .93 .03 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

zlc .01 .01 .02 .86 .06 0 .01 0 0 0 0 0 0 0 0 0 0 0 0 0

zdv .01 0 0 .03 .86 .03 .05 .02 0 0 0 0 0 0 0 0 0 0 0 0

zab .03 0 0 0 .02 .87 0 .01 .03 .01 0 0 0 0 0 0 0 0 0 0

zmp 0 0 0 0 .03 0 .85 .01 0 0 .05 0 0 0 0 0 0 0 0 0

zkc 0 0 0 0 .01 .02 .02 .84 .02 0 .02 .03 0 0 .02 0 0 0 0 0

zfw 0 0 0 0 0 .03 0 .01 .85 .06 0 .05 0 0 0 0 0 0 0 0

zhu 0 0 0 0 0 0 0 0 .05 .88 0 .01 0 0 0 0 0 0 .01 0

zau 0 0 0 0 0 0 .06 .03 0 0 .84 0 0 .03 .03 0 0 0 0 0

zme 0 0 0 0 0 0 0 .04 .06 .01 0 .84 .04 0 .03 0 0 0 0 0

ztl 0 0 0 0 0 0 0 0 0 .01 0 .04 .84 0 .05 0 0 .03 .06 0

zob 0 0 0 0 0 0 .01 0 0 0 .05 0 0 .75 .05 .06 .01 .01 0 0

zid 0 0 0 0 0 0 0 .05 0 0 .04 .03 .04 .07 .81 0 0 .02 0 0

zny 0 0 0 0 0 0 0 0 0 0 0 0 0 .07 0 .79 .05 .07 0 0

zbw 0 0 0 0 0 0 0 0 0 0 0 0 0 .02 0 .08 .82 .01 0 0

zdc 0 0 0 0 0 0 0 0 0 0 0 0 .03 .03 .01 .06 .01 .83 .03 0

zjx 0 0 0 0 0 0 0 0 0 .01 0 0 .05 0 0 0 0 .02 .86 .07

zma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .04 .88

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Variation of A Matrix

€

.91 .05 0 .03 .02

.04 .91 .03 .04 0

0 .02 .93 .03 0

.01 .01 .02 .86 .06

.01 0 0 .03 .86

€

.92 .05 0 .03 .02

.04 .91 .03 .04 0

0 .02 .91 .03 0

.01 .01 .02 .86 .06

.01 0 0 .03 .83

€

.92 .05 0 .03 .02

.04 .91 .03 .03 0

0 .02 .93 .03 0

.01 .02 .02 .87 .06

.01 0 0 .03 .86

€

.82 .04 0 .01 .01

.04 .87 .01 .03 0

0 .04 .88 .02 0

.01 .04 .06 .84 .03

.03 0 0 .08 .85

€

.89 .05 0 .02 .01

.04 .89 .02 .02 0

0 .02 .92 .02 0

.01 .03 .04 .87 .04

.01 0 0 .05 .89

€

.89 .05 0 .03 .01

.04 .91 .03 .03 0

0 .02 .92 .02 0

.01 .02 .03 .84 .04

.01 0 0 .06 .87

Daily Variation: May 6,7,8 2003 5-11 P.M

Variation during May 6: 11P.M- 5A.M, 5-11 A.M, 11A.M-5 P.M

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Variation of A Matrix During May 6, 2003 5-11 P.M

€

.96 .03 0 .01 .01

.01 .96 .01 .00 0

0 .01 .97 .01 0

.01 .01 .01 .96 .02

.01 0 0 .01 .93

€

.90 .05 0 .03 .02

.04 .92 .03 .03 0

0 .03 .94 .03 0

.01 .01 .01 .87 .04

.01 0 0 .03 .87

€

.90 .05 0 .04 .02

.04 .91 .03 .03 0

0 .03 .95 .03 0

.01 .01 .02 .87 .06

.01 0 0 .03 .87

Hourly Variation

€

.90 .06 0 .03 .02

.04 .89 .02 .04 0

0 .02 .93 .03 0

.01 .03 .03 .85 .08

.01 0 0 .05 .84

€

.91 .07 0 .03 .03

.05 .89 .04 .05 0

0 .03 .91 .04 0

.01 .01 .02 .83 .07

.00 0 0 .04 .81

€

.90 .06 0 .05 .03

.05 .91 .05 .07 0

0 .02 .90 .04 0

.00 .01 .01 .79 .06

.00 0 0 .02 .83

€

.93 .04 0 .02 .01

.02 .94 .02 .02 0

0 .02 .95 .02 0

.01 .01 .01 .91 .03

.01 0 0 .02 .90

€

.90 .05 0 .03 .02

.04 .90 .02 .03 0

0 .02 .94 .03 0

.01 .02 .02 .86 .07

.01 0 0 .04 .85

€

.90 .06 0 .04 .03

.05 .90 .04 .06 0

0 .03 .90 .04 0

.01 .01 .02 .81 .07

.01 0 0 .03 .82

Two-Hour Variation

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Modeling A(k) Constant for different time intervals

0

100

200

300

0:00 2:00 4:00 6:00 8:00 10:0012:0014:0016:0018:0020:0022:000:00

Time (UTC)

Traffic Density

Actual

1 Hour Averaging

6 Hour Averaging

24 Hour Averaging

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Normalized mean and standard deviation of Error

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Modeling of the forcing function: (Bu+Cw)

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 zla 41 38 43 48 46 41 39 41 36 27 33 37 30 31 31 28 27 26 24 24 22 19 19 16 zoa 35 30 36 39 39 35 30 32 27 20 23 23 21 21 19 16 19 21 22 18 12 10 8 7 zse 31 27 22 22 21 19 16 13 12 15 16 17 16 12 13 15 18 14 8 9 11 10 8 10 zlc 23 20 19 20 19 11 5 7 7 6 5 9 11 8 8 10 6 3 3 3 4 3 5 7 zdv 8 13 22 23 22 17 8 7 9 6 7 14 17 15 10 6 7 6 6 9 8 6 4 2 zab 7 19 18 19 24 26 19 10 8 11 19 21 18 16 14 17 14 12 16 13 12 9 7 6 zmp 27 27 28 26 20 15 12 8 7 8 12 17 18 18 12 9 8 9 12 13 10 6 2 3 zkc 20 17 17 20 19 16 17 13 7 6 7 9 12 15 15 13 8 5 8 7 4 3 4 4 zfw 26 27 26 31 34 29 22 24 24 21 18 17 21 24 19 14 17 17 15 14 11 7 3 3 zhu 37 31 27 21 16 14 11 13 16 25 29 23 18 14 12 13 11 10 8 6 7 6 3 5 zau 36 38 34 27 29 33 29 30 22 14 17 24 26 24 28 27 19 13 16 15 11 11 7 8 zme 9 6 7 6 6 5 6 7 6 5 6 10 10 10 14 14 14 16 15 8 6 6 5 4 ztl 32 31 26 22 22 26 26 26 22 19 21 23 23 27 30 25 20 18 15 11 12 17 19 15 zob 28 33 24 22 17 9 10 15 18 16 20 26 22 21 17 11 9 7 4 3 4 6 5 3 zid 15 14 15 20 24 22 15 13 14 11 12 11 7 7 6 7 13 13 14 15 12 6 4 5 zny 23 23 22 21 25 21 16 20 19 16 14 11 11 11 9 11 10 8 10 9 4 3 3 7 zbw 17 14 12 13 15 12 13 15 10 7 5 6 11 11 7 4 5 4 2 2 1 2 3 2 zdc 29 28 25 21 21 24 22 26 29 30 32 24 20 18 17 15 11 9 6 6 7 4 3 2 zjx 16 15 20 21 15 15 14 8 11 15 12 9 7 8 7 7 6 5 4 2 2 3 2 0 zma 16 12 15 17 14 11 8 9 9 8 12 11 6 10 10 6 7 5 4 5 4 4 3 3

Departure Counts (May 6, 2003: Every 10 Minutes)

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Effect of using A from previous days

0

100

200

300

400

0:00 2:00 4:00 6:00 8:00 10:0012:0014:0016:0018:0020:0022:000:00

Time (UTC)

Traffic Density

Actual

May 7, 24 Hour averaged




Atlanta Center (ZTL) Traffic Counts for May 8, 2003 predicted using May 7 and May 6 flow matrices

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Departure Counts (May 6-8, 2003: Every 10 Minutes)

10 10 10 20 20 20 30 30 30 40 40 40 50 50 50 60 60 60 zla 41 50 48 38 46 35 43 50 37 48 44 40 46 47 44 41 49 40 zoa 35 27 31 30 30 29 36 37 35 39 31 39 39 28 34 35 27 30 zse 31 30 28 27 28 27 22 24 25 22 23 24 21 24 21 19 22 15 zlc 23 16 19 20 17 17 19 26 15 20 20 16 19 18 17 11 15 11 zdv 8 11 10 13 15 12 22 19 18 23 22 19 22 23 17 17 19 16 zab 7 21 14 19 20 20 18 21 20 19 18 14 24 20 21 26 27 27 zmp 27 30 28 27 27 31 28 29 26 26 25 23 20 18 24 15 12 22 zkc 20 14 18 17 14 19 17 13 17 20 13 22 19 14 24 16 14 18 zfw 26 22 30 27 24 27 26 26 26 31 24 36 34 31 36 29 28 23 zhu 37 37 34 31 31 37 27 23 33 21 15 25 16 14 26 14 13 18 zau 36 36 38 38 33 33 34 29 31 27 28 28 29 32 29 33 36 28 zme 9 8 10 6 7 10 7 11 16 6 10 12 6 6 8 5 7 8 ztl 32 34 29 31 30 26 26 26 26 22 27 24 22 27 25 26 22 19 zob 28 20 23 33 25 30 24 28 25 22 28 16 17 27 16 9 25 19 zid 15 16 14 14 13 16 15 15 17 20 19 22 24 18 30 22 19 22 zny 23 18 26 23 19 17 22 19 20 21 18 24 25 22 27 21 25 29 zbw 17 14 19 14 17 23 12 16 24 13 12 19 15 10 16 12 11 10 zdc 29 27 29 28 27 27 25 26 32 21 24 34 21 26 34 24 31 26 zjx 16 16 18 15 21 15 20 24 20 21 19 19 15 17 23 15 16 20 zma 16 14 15 12 10 8 15 10 10 17 14 13 14 12 10 11 15 8

May 6 May 7 May 8

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Modeling departures using mean value

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Error Bounds for Model

€

P(k+1)=A(k)P(k) TA k( )+C(k)Q(k) TC k( )

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Modeling departure errors as gaussian

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Concluding Remarks Described linear time varying models to represent traffic flow for

developing strategic TFM decisions. Linear dynamic traffic flow system model with a slowly varying

transition matrix and Gaussian departure representation adequately represents traffic behavior at the Center-level.

Error bounds around nominal traffic counts in the Centers was described.

Numerical examples presented using actual traffic data from four different days to demonstrate the model characteristics.

Advantages – Unlike trajectory-based models, these models are less susceptible to

uncertainties in the system,– The model order is reduced by several orders of magnitude from 5000

aircraft trajectories to 23 states at any given time – Tools and techniques of modern system theory can be applied to this model

because of its form. Capabilities of this class of models for strategic traffic flow

management will be explored in the future.

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Future ATM Concepts Evaluation Tool (FACET)

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Future ATM Concepts Evaluation Tool (FACET)

Environment for exploring advanced ATM concepts

Balance between fidelity and flexibility– Model airspace operations at U.S. national level (~10,000

aircraft)– Modular architecture for flexibility– Software written in “C” and “Java” programming languages

» Easily adaptable to different computer platforms» Runs on Sun, PC and Macintosh computers

3 Operational Modes: Playback, Simulation, Hybrid

Used for visualization, off-line analysis and real-time planning applications

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FACET Architecture

Applications

Air and Space Traffic Integration

Airborne Self-Separation

Data Visualization

Direct-To Analysis

Dynamic Density

System-Level Optimization

Traffic Flow Management

ETMS/ASDI

NOAAWinds

Flight plans & Positions

ClimbCruiseDescent

CentersSectorsAirwaysAirports

Aircraft Performance

Data

Adaptation Data

Traffic & Route Analyser

User Interface

Route Parser &Trajectory Predictor

Weather

HistoricalDatabase

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FACET DisplaysTraffic

3-DConvective Weather

Winds

38


ATL Arrivals (Purple) and Departures (Green)

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FACET Display

16

17

40


Severe Weather Playbook Reroutes(Eastbound Traffic over Watertown)

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Alternative effects of TFM actions

NominalLocal

Reroute

MIT Local Reroute

PlaybookA

D

B

C

B

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Integrated traffic counts in ZMP Sector 16

00:00 16- 17- 16- 16-00:05 17- 19 +A 16- 16-00:10 19+G 20 +G 18+G 16-00:15 16- 18 +G 16- 14-00:20 11- 11- 11- 9-00:25 10- 10- 10- 11-00:30 8- 11- 11- 11-00:35 9- 14- 15- 15-00:40 8- 13- 13- 13-00:45 8- 14- 11- 11-00:50 10- 14- 16- 16-00:55 9- 12- 13- 13-

Time [A] [B] [C] [D]

[A] Nominal Counts, [B] Playbook Reroute, [C] Playbook + MIT, [D] Playbook + MIT+Local Reroute.

ImpactedAircraftCount

TotalDelay(min)

AverageDelay(min)

Playbook 48 1448 30.16MIT 18 48 2.69Local Reroute 4 8.0 2.0

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EWR and LGA Delay Contours

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FACET for AOC Applications

March 2001: request by Aircraft Dispatcher’s Federation (ADF) team to increase NASA research

FACET modified to work with Aircraft Situation Display to Industry (ASDI) data

Developed a version of FACET for AOC use

Enable efficient operations planning by AOC–Risk analysis–Departure planning and congestion assessment–Integration with weather

Commercial Technology Office to license the software to Flight Explorer (FACET release in FE 6.0, October 2004)

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Comments from Airline Dispatchers

“I usually (almost always) plan for the worst case scenario. The ability to tailor fuel uplift to individual flights with a very high degree of confidence in the probability of en route delay is worth tens of millions of dollars to the airlines. It costs me about $400,000 a year to carry one additional minute of fuel on each flight. If I am carrying an average of 35 minutes, and I really only need a system-wide average of 15 minutes, that would be worth $8 million per year to my airline alone.”

“I would find the predictive data very helpful in planning routing and fuel load.” “The concept of alerting a dispatcher regarding ATC sector overload and inbound

ATC reroutes is an excellent idea.” “To the dispatcher at the desk, I think it would give him a huge advantage to see,

understand, plan, fuel and brief the crews on possible ATC initiatives based on volume issues.”

“FACET would be great because when the Command Center says, or the ATC community says “These are your three options,” we could say: “You know, you might want to consider a fourth option here that we could game or model on FACET.”

“We’ve been asking for a common situation display for a long time. This may be the basis for it.”

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Transformation of the NAS

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Commission on the Future of the United States

Aerospace IndustryRecommendations:

#2: “The Commission recommends transformation of the U.S. air transportation system as a national priority.”

• Rapidly deploy a new highly automated ATM system

#3 “The Commission recommends that the U.S. create a space imperative.

#9 “The Commission recommends that the federal government significantly increase its investment in basic aerospace research, which enhances U.S. national security, enables breakthrough capabilities, and fosters an efficient, secure and safe aerospace transportation system.”

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Strategic Plan& Perf. Goals






DoDDoDFAAFAA



OEPOEP CIPCIP

Infra.PlanInfra.Plan

R&DPlanR&DPlan

NASANASA DHSDHS

Aviation SystemJoint Program

Office

Aviation SystemJoint Program

Office

As Required As Required As Required

Executive Board

R&DPlanR&DPlan

R&DPlanR&DPlan

NationalPlan

NationalPlan

R&DPlanR&DPlan

Pgm.PlanPgm.Plan

JPO develops and maintains National Transformational Plan which includes:

– Associated policies, technology, processes

– Overall operational concepts

– Supporting research– Implementation strategies– Policy and

implementation commitments

Partners in Development ofNational Plan for the Future

NAS

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Time

Cap

abili

tyDefining a Transformational System

Future NAS(initial design

space)

Current NAS

Transition-1 NAS

Transition-2 NAS

Transition space

Future NAS

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Issues in the transformation of NAS

Automation– Need– Impact

Human Factors Policy

– Regulations– Certification

Equity– Allocation of scarce resources– Sharing of information

Cost of equipment Integration with existing systems Software verification and validation

Documents

Dr. Banavar Sridhar NASA Ames Research Center Moffett Field, CA 94035 Banavar.Sridhar@nasa