Eti ti C t&Estimating Current & FtFuture System-Wide ......2013/05/28 · Eti ti C t&Estimating Current & FtFuture System-Wide Benefits of Airport Surface Congestion Management*

E ti ti C t & F tEstimating Current & FutureSystem-Wide Benefits of Airporty p

Surface Congestion Management*

Alex Nakahara & Tom G. Reynolds

10th USA/Europe Air Traffic Management Research and Development Seminar (ATM2013)

*This work is sponsored by the Federal Aviation Administration under Air Force Contract #FA8721-05-C-0002. Opinions, interpretations,recommendations and conclusions are those of the author and are not necessarily endorsed by the United States Government.

Outline

• Need for benefits assessment in ATC

• Surface congestion management conceptSurface congestion management concept

• Benefits assessment methodology/results– Multi-fidelity modeling approach

– Fuel burn savings estimatesg

– Environmental impacts

Surface Congestion Management Benefits ATM2013, 6/10/13Slide 2

Need for Benefits Assessment

• Benefits assessment processes help:– Identify operational inefficiencies– Identify/develop capabilities which address identified needs

Set requirements for procurement activities– Set requirements for procurement activities– Provides business case for development and deployment

ConOps, etc

Surface Inefficiency

Benefits Assessment

RequirementsSupport

etc. Inefficiency Benefits Pool

CapabilityDevelopment

Investment AnalysisSupport

• Analysis for procurements typically requires assessments of


capability at large numbers of airports over many years

Terminal Flight Data Manager (TFDM) Benefits Assessment Example

Terminal Flight Data ManagerExternal Sources Operational UsersTower controllers

T i l ATC (TRACON)

Terminal and Surface Surveillance

Terminal ATC (TRACON)

En Route ATC

Flight Operations Centers

Ramp Tower

Flight Plan Data

Net-centric infrastructure

Anticipated Benefits

Airport Authority

Enhanced Electronic flight data Traffic Flow Constraints

Operational & Environmental Performance I tsurveillance display

gmanager Improvement

o Reduced delay

o Reduced fuel burn

Reduced emissions

• Departure metering• Sequencing & scheduling

Decision Support

Flight Operations Data o Reduced emissions

Workload Reduction

Safety Improvements

Cost Avoidance

scheduling• Runway assignment• Airport configuration manager

• Departure route assurance


Weather / HazardsTools (DSTs) Cost Avoidanceassurance

TFDM Benefits Assessment Modeling

• Needed to identify potential benefitsacross key NAS-wide airports out 20 Lack of

shared

Display costs6%

Lack of surface

conformance3%

Other (Safety,paper costs,

staffing costs, etc.)years

• Initially indentified surface inefficienciesInefficient push-back

Inefficient airport

resource

shared surface data

7%

0%<1%

Initially indentified surface inefficiencies

• Computer modeling of DST capabilitieshi h dd k f i ffi i i

p39%

Inefficient sequencing

resource planning

12%

which address key surface inefficiencies– Surface congestion management DST– Airport configuration optimization DST

sequencing33%

Surface inefficiency breakdownAirport configuration optimization DST

– Sequence optimization DST

• Results summarized in TFDM benefitsassessment report– MIT/LL Project Report ATC-394


– MIT/LL Project Report ATC-394

Outline







Surface Congestion Management Concept

• Surface congestion increases taxi times, fuel burn & emissionsA ll t j i t i th U it d St t (2010 ASPM)• Annually, at major airports in the United States (2010 ASPM)– Over 48 million minutes taxi-out delay (over unimpeded times)– 200 million gallons excess taxi fuel => $400-600 million@$2-3/gallong @ g

• Surface congestion management can help– Hold aircraft at gate or ramp (with engines off) to reduce surface

ti & f l b hil t d l ff ti th h tcongestion & fuel burn while not adversely affecting throughput

“Excess” flights held untillater time intervals when Excess

# departing aircraftthat airport can

efficiently handle

Maxefficiencylimit

they can be more efficientlyaccommodated

congestion


Time Interval: 1 2 3 4

Surface Congestion Management Concept

• Operational trials show significant benefits potential– Offer opportunity to tune benefits modelsOffer opportunity to tune benefits models– Need to extend models to more airports and to future years

Post-surfacePre-surfacecongestionmanagement:15 i ft i

Post surfacecongestionmanagement:8 aircraft in queue,8 b i h ld


15 aircraft in queue 8 being held

Outline







Surface Congestion ManagementBenefits Assessment Methodology

• CHALLENGE: appropriate modeling fidelity given wide airport and temporal scope (OEP35 airports, out 20 years)

gy

p p ( p , y )

• Multi-scope/Multi-fidelity modeling approach adopted

Increasing airport scope

HIGH FIDELITY2 AIRPORTS• Field trial results

MEDIUM FIDELITY8 AIRPORTS• Simulated results

LOW FIDELITY35 AIRPORTS• Extrapolated resultsValidate Validate

• Current ops only• Actual taxi times• VMC/IMC• Configuration specific

• Current & future ops• Simulated taxi times• VMC only• Aggregate configurations

p• Future ops• Functional relationships

with other airports & forecast data

ValidateCalibrate

ValidateCalibrate

Increasing model fidelity

• Configuration-specific • Aggregate configurations forecast data


High Fidelity Benefits AssessmentJFK Implementation: 2010-2011

• PASSUR live trials at JFK throughout 2010/11, MIT analysis• Allocate push times to specific flights with airline collaboration

4045

es P f ti

152025303540

f Dep

artu

ren

Surf

ace

Pre-surface congestionmanagement

Post-surface congestion

404505

1015

P f tiutN

o. o

fO

n Post surface congestionmanagement

152025303540 Pre-surface congestion

management

age

Taxi

-Ou

me

(min

s)

05

1015

14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 0:00

Post-surface congestionmanagementAv

era

Tim


Local Time


• Analysis approach : Compare taxi times, fuel burn & emissions before/after metering implementation in main airportbefore/after metering implementation in main airport configurations, everything else being as equal as possible

ASPM Sample Days Config n

Config 2Taxi Time(Pre-SCM) Aggregate

Taxi Time S i

Config 2

time

Config 1

T

ASDE-X Sample Days

SavingsScale toall daysand sumTa

xi t

Take-off Queue

T

Post

-SC

M Pre

-SC

M

Sample DaysTaxi Time

(Post-SCM)

Take-off Queue



s)

% operationsusing single

25• Taxi time savings converted to fuel burn savings

mill

ions engine taxi:

0%20%

20

burn savings accounting for:– Single engine

t i

ings

($ m 40%

60%80%

15taxi use

– Fuel priceFu

el S

av 100%10• Over 2010,

estimated 5.0 million gallons

Ann

ual F

5

million gallons =$12.2 million fuel saving

A

01 2 3 4

• Full results published as ATIO


Fuel Price ($/US gallon)p2011 paper*

*Nakahara, A., T. G. Reynolds, T. White, C. Maccarone & R. Dunsky, “Analysis of a Surface Congestion Management Technique at New York JFK Airport”, 11th AIAA Aviation Technology,

Integration, and Operations (ATIO) Conference, Virginia Beach, VA, 2011.

Medium Fidelity Assessment:8 Study Airports Benefits Modeling

• Throughput saturation curves at core of methodology

y ge

Impacts offuturecapacity

Airport X, Configuration Y,Condition Z

ture

rate capacity

increasesCondition Z

Benefits of holdingSaturation

Dep

art

Saturationpoint, N*

Controlpoint, Nctrl

all flights abovecontrol pointTaxi time benefits=N (τ τ )

throughput,T*

p , ctrl

Traffic Metric, e.g. No. of aircraft on surface, Dep queue length, etc.

=NCongestion(τCongestion –τCtrl)

• Current year: curves can be established from operational dataF t re ears c r es estimated from demand/capacit forecasts


• Future years: curves estimated from demand/capacity forecasts

Medium Fidelity Assessment:8 Study Airports Benefits Modelingy g

Simulation ThroughputSaturation Curves

Results Generation & Validation

Field Trials

ar e ra

te Airport X,Configuration Y,Condition Z

Ann

ual F

uel S

avin

gs ($

mill

ions

)

% operationsusing singleengine taxi:0%20%40%60%80%100%

0

5

10

15

20

25

1 1 5 2 2 5 3 3 5 4

Gate-constrained

BenefitsOperational

DataValidation

Data

Cur

rent

Yea

Ana

lysi

s

Current yearD

epar

ture

Traffic Metric

Condition Z

Fuel Price ($/US gallon)1 1.5 2 2.5 3 3.5 4

Future Year Saturation

Curve Prediction

Cea

rs

saturation curves (2010)

rate Airport X,

Configuration Y

Future Schedules Future

Year Traffic Simulations

Gate Constraints

Futu

re Y

eA

naly

sis

Unconstrained Benefits

Future year

Dep

artu

re r

Traffic Metric

Configuration Y,Condition Z

• Process executed for all 8 study airports

Simulations ysaturation curves

(2015, 2020, 2025, 2030)


Process executed for all 8 study airports

Medium Fidelity Assessment:Curve Prediction and Traffic Simulation

• Saturation curves depend on many factors and relationship is not well understoodnot well understood

• Random Forest model used to predict future saturation curvesModel trained on data from 2000 2010• Model trained on data from 2000 – 2010

• Traffic simulation previously developed at MIT used to determine operating point on saturation curve *p g p

• Taxi time modeled as )()( tWtR qunimpeded


*I. Simaiakis and N. Pyrgiotis, “An Analytical Queuing Model of Airport Departure Processes for Taxi Out Time Prediction”, 10th Annual AIAA ATIO Conference, 2010.

Medium Fidelity Assessment:JFK Sample Results

a) Demand/Capacity/Taxi Time b) Unconstrained BenefitsHistorical Demand (ASPM)Future Demand (TAF)Historical Taxi Time (ASPM)××

Historical (ASPM)Future (MIT)JFK Field Trial

×s 000

s )150

s uctio

n) 300

250× Future Taxi Time (MIT)Historical Capacity (ASPM)

Historical Sat Throughput (MIT)×Future Capacity (FACT2)

× Future Sat Throughput (MIT)

JFK Field Trial

ber o

f Flig

htDe

man

d/10

0ac

ity/1

5 min

sTi

me (

min

s)

100

50 ual B

enef

itsxi

Tim

e Re

du

200

150

2000 2005 2010 2015 2020 2025 2030

Num

bYe

arly

DCa

paTa

xi 50

02000 2005 2010 2015 2020 2025 2030

Annu

(kHo

urs

Tax 100

50

0

c) Gate Utilization d) Gate-constrained BenefitsHistorical (ASPM)Future (MIT)JFK Fi ld T i l

×s uctio

n)

80

t Gat

e 400 Available gates203020252020

Year Year

JFK Field Trial

ual B

enef

itsxi

Tim

e Re

du 60

40

of A

ircra

ft at 300

200

202020152010

Gate

2000 2005 2010 2015 2020 2025 2030

Annu

(kHo

urs

Tax

20

06 8 10 12 14 16 18 20 22 24Nu

mbe

r o 100

0

Gate Constraints


YearTime

Medium Fidelity Assessment:8 Study Airports Fuel Savings Estimatesy g

25JFKORD

80JFKORD

Unconstrained Fuel Benefits Gate-constrained Fuel Benefits

15

20

ORDATLPHLLGAIADDFW50

60

70 ORDATLPHLLGAIADDFW Sa

ving

llons

)

Savi

nglo

ns)

10

15BOS

30

40BOS

nnua

l Fue

l m

illio

n ga

l

nnua

l Fue

l Sm

illio

n ga

l

0

5

0

10

20

JFK highfidelity

An (

An (m

• Gate-constrained fuel saving estimate at 8 study airports over

2010 2015 2020 2025 20302010 2015 2020 2025 2030 modelvalidationYear Year

g y p20 yrs: 950 million gallons/$2.4 billion (@ $2.43/gallon)– Approx. 18% taxi-out and 1% block fuel burn

Full results published as AIAA ATIO2012 conference paper*


– Full results published as AIAA ATIO2012 conference paper

*Nakahara, A. & T. G. Reynolds, “An Approach for Estimating Current and Future Benefits ofAirport Surface Congestion Management Techniques”, 12th AIAA Aviation Technology,

Integration, and Operations (ATIO) Conference, Indianapolis, IN, 2012.

Low Fidelity Assessment:OEP35 Benefits Modeling

• Multiple approaches employed to extrapolate medium fidelity results to OEP35 airports to bound benefit estimates

g

results to OEP35 airports to bound benefit estimates– Scaling factors to apply to medium fidelity studies

• Taxi delay scaling factor– Scale medium fidelity benefits to OEP35 benefits in proportion to

amount of total taxi delay in each setamount of total taxi delay in each set

• Linear regression– Relationship between medium fidelity benefits and key indicator e at o s p bet ee ed u de ty be e ts a d ey d cato

variables which can be forecast for all OEP35 airports

• ClusteringA i OEP35 i t t l t b d ti– Assign OEP35 airports to clusters based on operating characteristics

– Benefit level set by medium-fidelity study airports in each cluster


Surface Congestion ManagementBenefits Roll-Up

250OEP358 TOTAL450

500OEP358 TOTAL

Unconstrained Fuel Benefits Gate-constrained Fuel Benefits

OEP35

150

200

8 TOTALJFKORDATLPHL

300

350

400

450 8 TOTALJFKORDATLPHLLGA Sa

ving

ons)

Savi

ngon

s)

OEP35extrapolation

using low fidelityapproaches

100

150 LGAIADDFWBOS200

250

300 LGAIADDFWBOS

nual

Fue

l Sm

illio

n ga

llo

nual

Fue

l Sm

illio

n ga

llo

50

50

100

150

Ann (m

Ann (m

8 airportmedium fidelity

results

02010 2015 2020 2025 2030

02010 2015 2020 2025 2030

JFK/BOShigh fidelity

resultsYear Year

• Gate-constrained fuel saving estimate at OEP35 airports over 20 yrs: 1.8-2.7 billion gallons, $4.4-6.6 billion (@$2.43/gallon)

• Also equates to 18-26 million metric tons CO2 emissions saved


• Also equates to 18-26 million metric tons CO2 emissions saved

Environmental Impacts in Benefits Assessment

• Ability to characterize environmental impacts/benefits now possible using FAA Aviation Environmental Tool Suitep g

• Allows assessment of physical and monetizable impacts• Climate Aviation Operations

– Greenhouse gasconcentrations

– Temperature changes

pScenarios

Climate Impacts

Full flight emissions:CO2, NOx, etc.

Simplified climate modelsTemperature changes– GDP impacts

• Air quality

• Changes in atmospheric concentrations• Changes in global radiative forcing

• Changes in global temperature

Global average T

Simplified climate models,Climate sensitivityparameters

– Pollutantconcentrations

– Health impacts

Climate Impacts Valuation• Changes in %Gross Domestic Product

• Discounting

Damage functions,Discount rates

Health impacts• Noise

– Noise contours Policy Assessment

Climate costs/year


– Property value & health impacts

Environmental Impactsin Benefits Assessment

Surface Congestion Management Monetized Environment Impacts*

Primary Effects Reduced engine-on time

NoiseImpacts

Reduced noise

Property value and health benefitsProperty value and health benefits

Air Quality Reduced emissions

Impacts Health benefits: $0.2-8.8 billion @ $29-1226/tonne fuel*

ClimateImpacts

Reduced emissions

Climate benefits: $0.1-1.4 billion @ $5-65/tonne CO2*


*Dorbian, C., P. Wolfe & I. Waitz, “Estimating the Climate and Air Quality Benefits of Aviation Fuel and Emissions Reductions”, Atmospheric Environment ,Vol. 45, pp. 2750-2759, 2011.

Summary

• Benefits assessment assists with research priorities and investment analysis processesinvestment analysis processes– Surface congestion management a major study area

• Multi-fidelity modeling approach presented for current & future benefits assessment of surface congestionfuture benefits assessment of surface congestion management– Fuel savings estimates $5.5-9.5 billion across OEP35 2010-2030

• Approaches now available to include environmental ppimpacts in benefits assessment– First order estimates suggest climate and air quality monetized

b fi f i il d f i d f l iSurface Congestion Management Benefits ATM2013, 6/10/13Slide 23

benefits are of similar order of magnitude to fuel cost savings

Documents

Eti ti C t&Estimating Current & FtFuture System-Wide ......2013/05/28 · Eti ti C t&Estimating Current & FtFuture System-Wide Benefits of Airport Surface Congestion Management*