impact of eobt uncertainty on airport surface congestion

Sandeep Badrinath & Hamsa BalakrishnanMIT Department of Aeronautics & Astronautics

Emily Joback & Tom Reynolds ([email protected])MIT Lincoln Laboratory

NASA ATD-2 Industry Workshop4-5 September 2019

Impact of EOBT Uncertainty on Airport Surface Congestion Management

Sponsor: Chris Dorbian, FAA Office of Environment & Energy

EOBT Analysis - 2TGR 09/05/19

• Airport surface congestion leads to increased taxi time, fuel burn & environmental impacts

• Advanced automation systems are under development to reduce surface congestion– FAA Terminal Flight Data Manager (TFDM)– NASA Airspace Technology Demonstrator-2

(ATD-2)

Motivation

• Effectiveness of systems depend on algorithm design and accuracy of key input data, e.g., Earliest Off-Block Time (EOBT)

Need for analysis to understand relationship between automation system benefits and EOBT accuracy to inform future algorithm design and airline investments


Motivation

Congestion management

algorithm

AirportRunway Configuration

Earliest Off Block Time (EOBT)

Target Off Block

Time (TOBT)

…Taxi-out Time Prediction

EOBT: 1205Z

EOBT: 1210Z

EOBT: 1215Z

EOBT: 1203Z

EOBT: 1220Z

Large queue

Default procedures: aircraft push when ready

Congestion management procedure: aircraft publish EOBTs

09 27

09 27


09 27Motivation

Smaller queue


algorithm



Target Off Block

Time (TOBT)


Large queue


EOBT: 1215Z

EOBT: 1220Z


09 27


Smaller queue


algorithm



Target Off Block

Time (TOBT)



Motivation

Aircraft held at gate with engines off

Aircraft in large queuewith engines on


09 27

09 27


• Development and validation of queuing network models for surface operations at Charlotte (CLT)

• Evaluation of a candidate congestion management algorithm using queuing model (NASA’s ATD-2) at CLT

• Estimation of levels of EOBT uncertainty in currently reported data at Charlotte (CLT), Dallas (DFW) & Newark (EWR)

• Assessment of the impact of EOBT uncertainty on the performance of congestion management algorithm

Outline



• Need queuing model to allow with/without surface congestion management comparisons

Queuing Network Model of CLT

Charlotte airport layout Queuing network representation

36L 36C 36R

Taxi-in ramp Taxi-out ramp

Runway crossing

Runway queue

GateArrivals

Departures


• Comparison between the model and data for a typical day (06/25/16)

Model Validation: Typical Day

Runway queue: 36C

Local time (hr) Local time (hr)Taxi

-out

runw

ay q

ueue

36C

Local time (hr)

Taxi

-out

ram

p qu

eue

Ramp queue

Taxi

-out

runw

ay q

ueue

36R Runway queue: 36R


• Comparison between the model and data for a typical day (06/25/16)

Model Validation: Typical DayAv

erag

e ta

xi-o

ut

time

(min

)

Local time (hr)

15-min average taxi-out time


• Error statistics on an independent test set: 7,484 departures, May/June 2016, CLT northflow

• Queuing network model can be adapted to other airports– Extended to DFW and EWR for this phase of the analysis

Model Validation: Aggregate Statistics

Statistics (min)

Gate to spot

Spot to runway Taxi-out

Mean values 9.68 10.40 20.09 Mean error -0.87 -0.58 -1.45 Mean |error| 3.08 2.7 4.35 % flights with |error| < 5 mins

82% 86% 69%

Taxi-out time (min)

Rel

ativ

e fr

eque

ncy model

data






Outline



Congestion Management Algorithm: Ideal Case

(a) Default scenario (no congestion management)

Take-off time

Taxi-out time

Unimpeded time Wait time

Push ready time


Congestion Management Algorithm: Ideal Case


Take-off time

Taxi-out time


(b) Congestion management(ideal case)

Gate hold time = predicted wait

time

Taxi-out time = unimpeded time

TOBT (new gate release time)

Push ready time

TOBT = Target Off Block Time

EOBT


• Buffer parameter accounts for errors in taxi-out time prediction, EOBT and other sources, in order to avoid losing runway utilization

• ATD-2 logic: TOBT = EOBT + max(0, Predicted wait time – Buffer)

Congestion Management Algorithm: ATD-2 logic


Push ready time Take-off time

Taxi-out time


(b) Congestion management (ATD-2 algorithm logic)

Gate hold time = predicted wait time -

buffer Unimpeded time


BufferTaxi-out time

EOBT

EOBT = Earliest Off Block TimeTOBT = Target Off Block Time


Congestion Management Algorithm: ATD-2 logic


Push ready time Take-off time

Taxi-out time


(b) Congestion management (ATD-2 algorithm logic)

Gate hold time = predicted wait time -

buffer Unimpeded time


BufferTaxi-out time

EOBT

EOBT = Earliest Off Block TimeTOBT = Target Off Block Time

• Buffer parameter accounts for errors in taxi-out time prediction, EOBT and other sources, in order to avoid losing runway utilization

• ATD-2 logic: TOBT = EOBT + max(0, Predicted wait time – Buffer)


• Departure metering logic tested using stochastic simulations– 6,447 departures over 15 day period at CLT

• Taxi-out time reduction depends on the choice of excess queue buffer (larger the buffer, lower the benefits)

• Optimal buffer is lowest value that ensures no loss in runway utilization

• Results with a planning horizon of 20 min

Congestion Management: Perfect EOBT information

Optimal buffer

20 minute planning horizon

Tim

e (m

in)

Buffer time (min)






Outline



• Many airlines publish EOBT data through TFMS feed– EOBT messages

from a major airline shown here

• EOBT error(t) = EOBT(t) – AOBT

• EOBT error varies for different look-ahead times

Evaluation of Empirical EOBT Uncertainty

EOBT error (min)

Prob

abili

ty d

ensi

ty

CLT: 12/13/2017 –02/12/2018

Lookahead time

AOBT = Actual Off Block Time


Evaluation of Empirical EOBT Uncertainty

EOBT error (min)

Prob

abili

ty d

ensi

ty

DFW: 05/01/2018 –07/31/2018

Lookahead time

Lookahead time

EWR: 01/01/2018 –11/11/2018

• Many airlines publish EOBT data through TFMS feed– EOBT messages

from a major airline shown here

• EOBT error(t) = EOBT(t) – AOBT

• EOBT error varies for different look-ahead times

AOBT = Actual Off Block Time


Evaluation of EOBT Uncertainty Summary Results

Stan

dard

dev

iatio

n of

EO

BT

erro

r (m

in)

Lookahead time (min)

• CLT: 12/13/2017 –02/12/2018

• DFW: 05/01/2018 –07/31/2018

• EWR: 01/01/2018 –11/11/2018






Outline



• EOBT uncertainty impacts congestion management because of– Reduced prediction accuracy of taxi-out times– Non-conformance to the target pushback time

• Need to increase excess queue buffer parameter to account for uncertainties and to maintain runway utilization

Congestion Management in the Presence of EOBT Uncertainty

20 minute planning horizon

Std. dev. of EOBT error (min)

Opt

imal

buf

fer t

ime

(min

)


Taxi

-out

tim

e re

duct

ion

(min

)


Congestion Management in the Presence of EOBT Uncertainty

Ann

ual f

uel b

urn

savi

ngs

(Mkg

)


Taxi-out time & fuel benefits decrease as EOBT error increases


• Surface congestion management automation systems will enable fuel and emissions reductions

• Analysis presented to understand relationship between automation system benefits and input data (esp. EOBT) accuracy

• Informs future algorithm design and airline investment decisions

• Recommended next steps– Extend analysis to broader range of airports and operating conditions– Analyze incentives for airlines to improve the accuracy of EOBT data– Develop and evaluate surface congestion algorithms that can

• Explicitly handle uncertainties

• Account for uncertainties in arrival times, in addition to EOBT uncertainties

Summary


Disclaimer

DISTRIBUTION STATEMENT A. Approved for public release: distributionunlimited. This material is based upon work supported by the FederalAviation Administration under Air Force Contract No. FA8702-15-D-0001.Any opinions, findings, conclusions or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect theviews of the Federal Aviation Administration.

© 2019 Massachusetts Institute of Technology.

Delivered to the U.S. Government with Unlimited Rights, as defined inDFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding anycopyright notice, U.S. Government rights in this work are defined byDFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use ofthis work other than as specifically authorized by the U.S. Governmentmay violate any copyrights that exist in this work.

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