FAA Air Traffic Organization NAS Operational Analyses and Challenges to Modeling the Future

FAAAir Traffic Organization

NAS Operational Analyses andChallenges to Modeling the Future


Operations Planning

OperationalEvolution

PlanOffice

SystemsEngineering

Office

William J. Hughes

Technical Center

Vice PresidentVicki Cox - Acting

StrategyOffice

Aviation Research

& DevelopmentOffice

NAS Weather

Office

Forecasts & Operations Analysis

Business Outlook Strategy Development

& Analysis

PerformanceAnalysis

Office

InternationalOffice

TechnologyDevelopment

Office


ATO ATO Strategy and NAS PerformanceStrategy and NAS Performance

ATO-P Forecast Tool

ACES/NASPAC/etcATO NAS Performance Tools

NAS Strategy Simulator

ATO-P Strategic AnalysisCapability


Why have Trajectory Based Forecast?

• Keeps lots of O.R. analysts in business!

•Drives focus on flows and en route constraints

•Helps revenue estimates


Annual NAS Traffic Change– Traffic increased 5.9% from CY00 to CY05.

Total Center Traffic - ETMS

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

40000000

45000000

50000000

2000 2001 2002 2003 2004 2005

Bo

un

da

ry C

ros

sin

gs


Center Level Changes

Center % Traffic Change CY00-CY0518%

13% 12%12%

11%9%

8% 8% 8% 8% 8%

6% 6%5%

1% 0% 0%

0%-1%

-2% -3%-5%

0%

5%

10%

15%

20%Z

AB

ZA

N

ZA

U

ZB

W

ZD

C

ZD

V

ZF

W

ZH

U

ZID

ZJX

ZK

C

ZL

A

ZL

C

ZM

A

ZM

E

ZM

P

ZN

Y

ZO

A

ZO

B

ZS

E

ZT

L

% C

ha

ng

e


User Class Changes

User Class % Change CY00-CY05

9.73%

1.79%3.40%

9.06%

-8.91%

-12%

-7%

-2%

3%

8%

13%

GA JET GA Piston GA Turbo Total GA Commercial

%C

ha

ng

e


Regional Carrier Changes

Regional vs. Other Commercial Traffic % Change CY00-CY05

46.91%

-1.93%

-5%

5%

15%

25%

35%

45%

55%

Regional Carriers Other Commercial

% C

han

ge


Mainline vs. LCC % Change CY00-CY05

-20%

26%

-35%

-25%

-15%

-5%

5%

15%

25%

35%

Mainline Low Cost

% C

ha

ng

e

Commercial Mainline vs. LCC


Independence Air at ZDC

Total ZDC Operations

170000

190000

210000

230000

250000

270000

290000

310000

330000

350000Ja

n-0

0

Ap

r-00

Jul-

00

Oct

-00

Jan

-01

Ap

r-01

Jul-

01

Oct

-01

Jan

-02

Ap

r-02

Jul-

02

Oct

-02

Jan

-03

Ap

r-03

Jul-

03

Oct

-03

Jan

-04

Ap

r-04

Jul-

04

Oct

-04

Jan

-05

Ap

r-05

Jul-

05

Oct

-05

Jan

-06

Op

erat

ion

s

IndependenceAir


Flight Level Workload Changes

Low: <280 High: >= 280 and <= 390 Super High: >390

Traffic By Altitude (CY00-CY05)

-1%

15%

110%

20%

-20%

0%

20%

40%

60%

80%

100%

120%

LOW_ATL HIGH_ALT SUPER_HIGH TOTAL HIGH

% C

ha

ng

e


-20 -15 -10 -5 0 5 10 15 20

% Change CY’00-CY’05

Low Altitude Workload Changes(By Center)


-20 -15 -10 -5 0 5 10 15 20

% Change CY’00-CY’05

High Altitude Workload Changes(By Center)


Why have Seasonal Forecasting?

• Demand– Seasonal schedules– Peak vs. Low traffic months– Seasonal impacts on workload

• Operational Constraints– Seasonal constraints– NAS delay procedures


Seasonal Schedules

12500

13000

13500

14000

14500

15000

Jan-

03

Mar

-03

May

-03

Jul-0

3

Sep

-03

Nov

-03

Jan-

04

Mar

-04

May

-04

Jul-0

4

Sep

-04

Nov

-04

Sch

edu

led

Arr

ival

s

Spring Break

Spring BreakSummer

Summer

Winter

Data: MIA (ASPM)


Peak vs. Low Traffic Volume

% Difference in Traffic Volume CY05(Peak Month vs. Low Month)

60%56%

39%

33% 31%

27% 25%22% 21% 21% 20% 19% 17% 16% 16% 16% 16% 15% 15% 14% 13%

0%

10%

20%

30%

40%

50%

60%

70%

ZM

A

ZA

N

ZB

W

ZJX

ZS

E

ZM

P

ZLC

ZD

V

ZN

Y

ZO

B

ZA

U

ZK

C

ZF

W

ZO

A

ZM

E

ZT

L

ZID

ZD

C

ZH

U

ZA

B

ZLA

% D

iffe

ren

ce


Seasonal Impacts on Workload

ZMA vs. ZMP Operations

60000

80000

100000

120000

140000

160000

180000

200000

Jan

-00

Ma

y-0

0

Se

p-0

0

Jan

-01

Ma

y-0

1

Se

p-0

1

Jan

-02

Ma

y-0

2

Se

p-0

2

Jan

-03

Ma

y-0

3

Se

p-0

3

Jan

-04

Ma

y-0

4

Se

p-0

4

Jan

-05

Ma

y-0

5

Se

p-0

5

Jan

-06

Op

era

tio

ns


Importance of Seasonal Forecasting

• Operational Constraints– Seasonal constraints– NAS delay procedures

• Demand– Seasonal schedules– Peak vs. Low traffic months– Seasonal impacts on workload


Seasonal Change in Constraints

0%

5%

10%

15%

20%

25%

30%

35%

Jan-

00

May

-01

Oct

-02

Feb

-04

Jul-0

5

Lat

e F

lig

hts

(%

)

% Late Flights OEP35


Seasonal Constraints

IMC Operations and Conditions By Season

26%

15% 15% 15%

27%

16% 16%17%

0%

5%

10%

15%

20%

25%

30%

WINTER SPRING SUMMER FALL

%IMC FLIGHTS

%IMC PERIODS


Seasonal Constraints

Convective Weather Index

0

50

100

150

200

250

300


CW

I


Modeling Challenges

• Where delay is taken vs. cause of delay - En route congestion

• Convective weather case

• Annualizing results

• Interconnectivity of delay

•Uncertainty in demand


Where Delay is Taken

Gate Delay vs. Airborne Delay By Season

12.50

8.55

13.32

8.04

1.60 1.62 1.20 1.44

0

2

4

6

8

10

12

14

16


Dela

y (M

inut

es)

Gate Dep. DelayAirborne Delay


- How much is driven by en route/convective congestion?

Gate vs. Total Delay

OEP35 Gate. Dep. Delay vs. OEP35 Gate Arrival Delay

-2

0

2

4

6

8

10

12

14

16

18

Jan

-00

May

-00

Sep

-00

Jan

-01

May

-01

Sep

-01

Jan

-02

May

-02

Sep

-02

Jan

-03

May

-03

Sep

-03

Jan

-04

May

-04

Sep

-04

Jan

-05

May

-05

Sep

-05

Jan

-06

De

lay

(M

inu

tes

)

Avg. Gate Dep. Delay

Avg. Gate Arrival Delay


MIA Arrivals vs. Gate Delay

MIA Arrivals vs. Avg.Gate Dep. Delay

0

2000

4000

6000

8000

10000

12000

14000

16000

Ja

n-0

0

Ap

r-0

0

Ju

l-0

0

Oc

t-0

0

Ja

n-0

1

Ap

r-0

1

Ju

l-0

1

Oc

t-0

1

Ja

n-0

2

Ap

r-0

2

Ju

l-0

2

Oc

t-0

2

Ja

n-0

3

Ap

r-0

3

Ju

l-0

3

Oc

t-0

3

Ja

n-0

4

Ap

r-0

4

Ju

l-0

4

Oc

t-0

4

Ja

n-0

5

Ap

r-0

5

Ju

l-0

5

Arr

iva

ls

0

5

10

15

20

25

De

lay

(M

in.)

Arrivals

GateDepDelay


Interconnectivity of Delay

MIA Gate Arrival Delay vs. OEP 35 Gate Arrival Delay

-2

0

2

4

6

8

10

12

14

16

18

Jan-

00

Apr

-00

Jul-0

0

Oct

-00

Jan-

01

Apr

-01

Jul-0

1

Oct

-01

Jan-

02

Apr

-02

Jul-0

2

Oct

-02

Jan-

03

Apr

-03

Jul-0

3

Oct

-03

Jan-

04

Apr

-04

Jul-0

4

Oct

-04

Jan-

05

Apr

-05

Jul-0

5

Del

ay (

Min

.)

MIA Gate Arr Delay

OEP35 Gate Arrival Delay


M IA G ate Arr iv al D e lay v s. O E P 35 G ate D e p artu re D e lay

-2

0

2

4

6

8

10

12

14

16

18

Ja

n-0

0

Ap

r-0

0

Ju

l-0

0

Oc

t-0

0

Ja

n-0

1

Ap

r-0

1

Ju

l-0

1

Oc

t-0

1

Ja

n-0

2

Ap

r-0

2

Ju

l-0

2

Oc

t-0

2

Ja

n-0

3

Ap

r-0

3

Ju

l-0

3

Oc

t-0

3

Ja

n-0

4

Ap

r-0

4

Ju

l-0

4

Oc

t-0

4

Ja

n-0

5

Ap

r-0

5

Ju

l-0

5

De

lay

(M

in.)

M IA G ateA rrD e lay

O E P 35 G ateD epD elay

Interconnectivity of Delay


Forecast Uncertainties


• Current FAA forecasts provide annual activity levels

• Current FAA forecasts assume aviation business models stay the same

• Actual demand varies seasonally, daily, and hourly

• Aviation industry is fundamentally changing

• Understanding these changes is essential to preparing for the future

Summary


Current Demand Forecasting

Forecasted IFR Aircraft Handled(2006 Forecasting Conference)

0

10000

20000

30000

40000

50000

60000

70000

800002

00

0

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

Air

cra

ft(T

ho

us

an

ds

)

Military

General Aviation

Air Taxi/Commuter

Air Carrier


Mainline Legacy Carriers

Main Line % Change CY00-CY0584%

56%

29%

8%0%

-6%-12%

-19%-26%

-33%-40%

-20%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%A

LO

HA

HA

WA

IIA

N

SP

IRIT

AL

AS

KA

MID

WE

ST

AM

ER

ICA

N

NO

RT

HW

ES

T

CO

NT

INE

NT

AL

DE

LT

A

Un

ite

d

U.S

. A

irw

ay

s

To

tal

Ma

inli

ne

% C

ha

ng

e


Low Cost Carriers

– Independence Air began flying in 2004.

Low Cost Carrier % Change CY00-CY05

112%90%

13%

-4% -7%

26%

993%

-50%

0%

50%

100%

150%

200%J

et

Blu

e

Fro

nti

er

So

uth

we

st

Air

tra

n

Am

eri

ca

We

st

Am

eri

ca

nT

ran

s

To

tal

LC

C

% C

ha

ng

e


6/20/2005 FAAAir Traffic OrganizationDecember 2005

ADJUSTED OPERATIONAL AVAILABILITY AT THE 35 OEP AIRPORTS

99.77

99.5

98.9

99.0

99.1

99.2

99.3

99.4

99.5

99.6

99.7

99.8

99.9

100.0

Oct Nov Dec Jan Feb Mar Apr May June July Aug Sept YTD Targ

Perc

en

t A

vailab

ility

FY 2005 FY 2006 YTD Target

6/20/2005 FAAAir Traffic OrganizationFebruary 2005

Airport Weather by Category (OEP 35)

74.3777.87

73.52

80.0976.36 76.26

65.16

71.85 70.0375.67

11.210.59

10.37

10.18

9.45 10.15

12.49

12.8612.31

10.82

10.517.85

10.21

6.579.03 8.57

13.02

9.7810.13

8.13

3.91 3.69 5.893.15 5.16

9.335.51 7.53 5.395.02

50%

60%

70%

80%

90%

100%

FY04 FY05 FY04 FY05 FY04 FY05 FY05 FY 06 FY05 FY06

October November December January February

Pe

rce

nt

Sc

he

du

led

Op

era

tio

ns

None Minor Moderate Severe


FY 2006 Category A&B Operational Errors(Counts & Rates)

* FY 2006 Rates may contain estimated activity counts. The Rate is cumulative from month to month

6158

43 45

34

0 0 0 0 0 0 0

4.61

4.294.14

3.93

4.27

4.62

0.00

0.75

1.50

2.25

3.00

3.75

4.50

5.25

OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

Rat

e p

er M

illio

n A

ctiv

ity

0

20

40

60

80

100

120

FY 06 Cat A&B Count Performance Limit

FY 06 Estimated Rate FY 05 Actual Rate

FY 2006 Performance Limit4.27 per million activity

6158

43 45

34

0 0 0 0 0 0 0

4.61

4.294.14

3.93

4.27

4.62

0.00

0.75

1.50

2.25

3.00

3.75

4.50

5.25


Rat

e p

er M

illio

n A

ctiv

ity

0

20

40

60

80

100

120



FY 2006 Performance Limit4.27 per million activity


AVERAGE DAILY CAPACITY AT THE EIGHT METROPOLITAN AREAS

70424

68,750

60000

62000

64000

66000

68000

70000

72000

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep YTD Targ

AR

RIV

AL

+ D

EP

AR

TU

RE

CA

PA

CIT

Y

FY 2006 YTD Target


NAS ON-TIME ARRIVALS at 35 OEP

88.35

87.4

80.0

82.0

84.0

86.0

88.0

90.0

92.0

94.0

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep YT Tar

FY 2005 FY2006 YTD Target


FY 2006 Category A&B Runway Incursions (Counts & Rates)

* FY 2006 Rates may contain estimated operation counts. The Rate is cumulative from month to month

4

1

0

3

1

0 0 0 0 0 0 0

0.493

0.340

0.4120.377

0.551

0.766

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80


Rat

e p

er M

illio

n O

per

atio

ns

0

1

2

3

4

5

6

7

8



FY 2006 Performance Limit.551 per million operations

4

1

0

3

1

0 0 0 0 0 0 0

0.493

0.340

0.4120.377

0.551

0.766

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80


Rat

e p

er M

illio

n O

per

atio

ns

0

1

2

3

4

5

6

7

8



FY 2006 Performance Limit.551 per million operations


AVERAGE DAILY CAPACITY AT THE 35 OEP AIRPORTS

101296

101191

97000

98000

99000

100000

101000

102000

103000

104000

105000

106000

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep YTD Tar

AR

RIV

AL

+ D

EP

AR

TU

RE

CA

PA

CIT

Y

FY 2005 FY 2006 YTD Target


OE: FY06 Critical Acquisitions on Schedule and Budget

• FY06 85% Acquisition Target– Cost Target: Ensure 85% of major baseline Capital Investment programs are within

10% of budget.

– Schedule Target: Ensure 85% of major Capital Investment programs meet established activity milestone schedule dates

– Status

• Current FY06 Cost targets are within their 10% budget thresholds.

• 100 % of the 85% Schedule milestones are on schedule.

– Nine (9) of the 39 Final milestones (23%) have been completed on or ahead of schedule through the December reporting period.

– Eight Interim milestones (57%) have also been completed through December

• 39 milestones are being tracked against 31 programs for the FY06 85% Acquisition Goal

• Fourteen (14) interim milestones are also tracked and not included as part of the 85% Acquisition Performance Goal Metric

• A total of 53 milestones will be statused each month (the 39 + the 14)


OE: Critical Acquisitions on Schedule and Budget

– Four Final milestones are due in January.

Documents

FAA Air Traffic Organization NAS Operational Analyses and Challenges to Modeling the Future