Federal Aviation Administration ATO Future Schedule Generation Performance Analysis and Strategy...

Federal AviationAdministration

ATO Future Schedule Generation

Performance Analysis and Strategy

January 27, 2010

2Federal AviationAdministration

Briefing Overview

• ATO and Future Schedule Generation

• Inputs to Future Schedule Generation

• Sample Day Selection and Averaging

• Core Logic

• Future Work


ATO and Future Schedules

• Core ATO Function Since 2006– ATO Sponsored Multiple Demand Generation Projects– No consistency across planning projects

• Streamline process for building future scenarios– Demand/Model Issues Worked out in Advance– Representative Sample Days

• Assumptions coordinated within ATO– Feasible Schedules – Risk Ranges


• DATACOM Investment Analysis

• ERAM Evaluations

• NextGen Portfolio Analysis

• Performance Airspace Management

• High Altitude Trajectory Based Airspace

End Users


Summary of Input to Future Scenarios

• APO– TAF Airport Operations (Annual Levels)– Future Fleet Assumptions (Annual Projections)– ASPM Gate/Runway Times (Daily Traffic)– OPSNET Data Counts (Daily Traffic)– Cancellations

• ATO AIMLAB – Filed Flight Information (altitude, speed, waypoints)– Center Crossing Data (Not Filed but used for Center Activity)– Cancellations

• Consistent with Data Sources Used to Assess ATO Performance


TAF 2004

TAF 2006

2001 – 16,164,9002017 – 16,174,591

TAF 2008

TAF 2009

25% Down from 2004

19% Down from 2006

OEP35 TAF Total Operations FY2001-2025

12,000,000

14,000,000

16,000,000

18,000,000

20,000,000

22,000,000

24,000,000

26,000,000

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

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

Op

era

tio

ns

TAF 2009 Forecast TAF 2008 Forecast TAF 2006 TAF 2004 OPSNET Actuals Track


2006 TAF v. 2009 TAF Projections

-30.0%

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

Forecast Fiscal Year

% C

han

ge

Ttl

Op

s F

Y09

v.

FY

06

ATL JFK PHL LAS


Representative Planning Days

• Sample Days Throughout Fiscal Year– Reflect Seasonality of the NAS– Peak and Off-peak Days each Fiscal Quarter– 8 Days per Fiscal Year

• Target 90% Planning Day by Facility by Season– Originally Based on 20 CONUS Centers– Assess 90% Day for 35 OEP Airports– ASPM Delay, NAS Wx index


Center Count Traffic for ZMA

3000

3500

4000

4500

5000

5500

6000

6500

7000

7500

25-Sep-04 14-Nov-04 3-Jan-05 22-Feb-05 13-Apr-05 2-Jun-05 22-Jul-05 10-Sep-05

FY2005

Co

un

ts

ZMA FY05 FAL WNT SPR SUM ATO_FAL ATO_WNT ATO_SPR ATO_SUM Poly. (ZMA)

5130

5974

5353

4380

Almost all 36 Above Target Days Occur in Winter/Spring

5543 Current Target Day - Mar 23


Representative Days

-10

-8

-6

-4

-2

0

2

4

6

8

10

ZAB ZAU ZBW ZDC ZDV ZFW ZHU ZID ZJX ZKC ZLA ZLC ZMA ZME ZMP ZNY ZOA ZOB ZSE ZTL

CONUS Center

Per

cen

t D

iffe

ren

ce f

rom

90%

Day

23-Jul L1 L2


Seasonal Average Performance Metrics

-10

-8

-6

-4

-2

0

2

4

6

8

10


CONUS Center

Perc

en

t D

iffe

ren

ce f

rom

Seas

on

al A

ve

rag

e

23-Jul 15 Aug L1 L2


FY2009 True Average vs 8 Day Weighting

-5

-4

-3

-2

-1

0

1

2

3

4

5


CONUS Center

% D

iffe

ren

ce f

rom

Tru

e A

vera

ge


FY2009 True Average vs 8 Day Weighting

-5

-4

-3

-2

-1

0

1

2

3

4

5


CONUS Center

% D

iffe

ren

ce f

rom

Tru

e A

vera

ge

Series1 L L


Summary Day Selection

• 8 Day Samples can vary with criteria– Annual vs. Fiscal quarter accuracy– Center, Airport Facility– Weighted Airport (JFK, ATL, etc)– Performance measures other than counts

• Flight Hours, Delay, NAS On-Time

– Optimal Weighting Coefficients

• 8 Sample Days with Weighting– Provides reasonable annual estimates by facility– Easier to investigate a smaller samples

• Arithmetic Average (36 Days) vs. 8 Days


ATO Future Schedule – Core Logic

• Turn Airport Growth Rates into City Pair Growth Rates (Frater)

– Builds the traffic network

• Constrained Demand

– Is the Network Feasible?

• Future Itineraries

– Links flights together, propagated delay

• Future Fleet

– How will the fleet evolve?

• What has Changed?


How Peaky Should Schedules Become?

• ATO-F discount delay if above a certain threshold– Generic smoothing algorithm – no limit on time

• 20 Minute Delay Rule – LGA Study– 20 minute average open to interpretation– Iterative process with models

• FY 2000 Delay Rule– Airports Frozen at 2000 Levels– Sensitive to model

• Historical Demand/Capacity Ratios– Developed by MITRE for OEP 6.0 & 8.0– Simple, practical to implement


0

10

20

30

40

50

60

0%-10% 10%-20% 20%-30% 30%-40% 40%-50% 50%-60% 60%-70% 70%-80%

Pct Above Capacity

# o

f 1

5 M

inu

te B

ins

Ab

ov

e

Pct Above Capacity

Percent Above Target Capacity in 15 Minute Bin – How Bad Does It Get?2006 VMC Capacity Data from ATO-F, Summer 90% Peak Day

Known Problem AirportsOver Capacity More Balanced Operation

Most 15 Minute Bin PeaksLess than 30% Over

40% Selected for Representative As Bad As It Gets

Arr/Dep ImbalanceSpecial Cases


Newark Airport FY 2006Ratio of Filed Operations to Optimal VFR Capacity

0

500

1000

1500

2000

2500

3000

3500

4000

0.6-0.7 0.7-0.8 0.8-0.9 0.9-1.0 1.0-1.1 1.1-1.2 1.2-1.3 1.3-1.4 1.4-1.5 1.5-1.6 1.6-1.7 1.7-1.8 1.8-1.9 1.9+

Filed Operations/VFR Capacity

# o

f Q

uar

ter

Ho

ur

Bin

s

0.6-0.7 0.7-0.8 0.8-0.9 0.9-1.0 1.0-1.1 1.1-1.2 1.2-1.3 1.3-1.4 1.4-1.5 1.5-1.6 1.6-1.7 1.7-1.8 1.8-1.9 1.9+

Upper Bound for Qtr Operations Growth


Newark EWR Traffic Flow - Summer Quarter FY2006NAS Model Day: 4:00am to 4:00am

0

5

10

15

20

25

30

35

40

44.

75 5.5

6.25 7

7.75 8.

59.

25 1010

.75

11.5

12.2

5 1313

.75

14.5

15.2

5 1616

.75

17.5

18.2

5 1919

.75

20.5

21.2

5 2222

.75

23.5

24.2

5 2525

.75

26.5

27.2

5 2828

.75

29.5

Local Time

Tra

ffic

Flo

w

ATO Sched Cap Cap40

Results in about 85 Operations per hour 21.25/qtr

62% AboveATO-F Curve43 42 35 50

48% Above43 % Above

38% Above33%32%

29%40%19 Deps 2 ArrAt Cap at 15 Ops


Constrained Schedule Methodology

• Airport VFR Capacities Compared to IFR Schedule for 15 Minute Intervals

• Flight Times Adjusted to Reduce Peaks

• Based on Airline Tolerance for Congestion – 15 Minute > 40% above Capacity– 1 Hour > 20 % above Capacity– 2 Consecutive Hours > 14 % above Capacity– 3 Consecutive Hours > 6 % above Capacity– Most Congested Airports Considered First


Cleveland CLE Traffic Flow - Summer Quarter FY2017NAS Model Day: 4:00am to 4:00am

0

5

10

15

20

25

30

35

40

Zulu Prime + Hour

Tra

ffic

Flo

w

ATO Sched ATO_Sched_Base ATO_Con_Rev Cap Cap40

Results in about 86 Operations per hour 21.5/qtr

All Departures

All Arrivals


ORD - Avg Minutes Delay

0

10

20

30

40

50

60

70

80

90

FY07 FY08 FY09 FY10 FY11 FY12 FY13 FY14 FY15 FY16 FY17 FY18

Fiscal Year

Ave

rag

e M

inu

tes

of

Del

ay

0

500

1000

1500

2000

2500

3000

3500

4000

Unconstrained TrimmedU_Ops T_Ops


TAF Projections and Constrained Airports - 2025 Projection

0

100

200

300

400

500

600

PHL LAS ATL MDW LAX EWR JFK SNA PHX

Airport

Nu

mb

er o

f T

rim

med

Flig

hts

2006 Projection 2009 Projection


Future Fleet

• Fleet Forecast File – (2010-2030)– 139 Airlines

– Mainline (15), Low-cost (12), Regional (21), Other (49) Cargo (42)

– Jet Charter

• Aircraft Fleet Projections for Several Classes of Aircraft– Link to 3-Char Airline, 4-Char Equipment Codes

• Relation of Future Fleet File (Greenslet) to Rest of APO Process– Future Operations (Air Carrier, Air Taxi, GA, Military)

– Future Enplanements (Air Carrier, Commuter)

– Load Factor


21500000

22000000

22500000

23000000

23500000

24000000

24500000

25000000

25500000

26000000

2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

En

pla

nem

ents

350000

355000

360000

365000

370000

375000

380000

385000

390000

Op

eration

s

AC Enplanements AC Operations

JFK TAF Projections - Enplanments and Operations

Rules for Up-gauging Aircraft


Evidence of Up-gauging

• Based on the number of seats available in the fleet as predicted by the 2008 Greenslet/APO fleet forecast.

– Cargo aircraft are assigned zero seats.

26


Evidence of Up-gauging

• If the data is segregated by airline user class, we observe that the up-gauging is driven by regional and ‘other’ operations, while mainline carriers lower the number of available seats.

27


Airport Up-gauging

• At the airport level, LGA is an example of an airport that suggests the need for up-gauging. Based on the TAF 2009 forecast, the enplanements at LGA are forecasted to grow much quicker than the operations.

28


90

95

100

105

110

115

120

2000

2001

2002

2003

2004

2005

2006

2007

2008

avg

. nu

mb

er o

f se

ats

per

flig

ht

Scheduled services (main 34 airports)

All scheduled services

90

95

100

105

110

115

120

2000

2001

2002

2003

2004

2005

2006

2007

2008

Scheduled services (OEP 34 airports)

All scheduled services

Intra-European flights US domestic flights (CONUS)

Source: FAA/ PRC analysis


Future Development

• Improve Accessibility of Schedules– Understand Data Limitations– Web Access

• Guidance for Uncertainty– Alternative Scenarios– Mont-Carlo

• System Performance/Airline Behavior– Demand Shifts

• Keep Analysis “Costs” Down– Remember the Objective– Did the work make a difference?

Federal Aviation Administration ATO Future Schedule Generation Performance Analysis and Strategy...

Documents

Transcript of Federal Aviation Administration ATO Future Schedule Generation Performance Analysis and Strategy...