Review Taxi Out Efficiency Perfromance

Reviewing airport performance:
evaluating a methodology to
measure time efficiency in the
taxi-out phase

11st AIAA Aviation Technology, Integration, and Operations
Conference (ATIO)

Virginia Beach, VA
22nd September, 2011
José L Garcia-Chico
CRIDA
jlgchico@crida.es
Tlf. +34 634535561

Acknowledgements

!   PRU: Philippe Enaud, Francesco Pretti, and Holger Hegendoerfer,
Heloise Cote.

!   Thank all ATMAP members. Special thanks are for Madrid, Barcelona,
Palma de Mallorca, London Heathrow, and Brussels airports that
provided the operational data included in this study.

23-24/02/11

Objectives

Taxi-out efficiency methodology

CS1: Estimate of Congestion

CS2: Unimpeded times vs Comercial FTS

CS3: Data impact

CS4: Push back influence

CS5: Queiuing vs Additional time

Conclusions

Assessing main factors influencing a metric of
time efficiency of airport surface operations

!   Objective
  Evaluate the robustness of a method to review time efficiency in
the taxi-out operations at European airports

!   Context
  Methodology developed by Performance Review Unit (Eurocontrol)
  Analysis in consultation with Airport stakeholders (ATMAP project)
  Included in airport Performance Framework of European legislation
(EU No 691/2010)
  Surface operation performance review
  Airport performance will be eventually reviewed against targets

23-24/02/11

Methodology is built around the notion of
unimpeded time

!   Performance Review of
Taxi-out operations
  Time dimension
  Single flight perspective,
then aggregated
  Post-flight operational data
  Limited in cost and
complexity
  Applicable across airports
  Efficiency is the ability to operate as close as possible to a
optimum reference time

Optimum Reference = Unimpeded time

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Additional time as time efficiency metric

Taxi-out = ATOT - AOBT

Unimpeded time = Taxi-out time with no congestion
(grouped by aircraft type-RWY-gate)

Additional time = Taxi-out – unimpeded time

Optimum time
Number of flights

Additional time

time
Distribution of taxi-out durations

Step 1:
Grouping Flights Aircraft Class Stand Runway

Step 2: ATOT ALDT AOBT
Calculating
Aircraft Congestion Index

Step 3: 10%
9%
8%
7%

Calculating Group 6%
5%
4%
Max Throughput Threshold=
0.5 (MaxThroup*20P group)
3%

Congestion Threshold
2%
1%
0%
20P
of Airport
7

10

22

31

34

37
13

16

19

25

28

•  Group: a/c-stand-rwy

Step 4: 12%
Average
10%
•  Group: a/c-stand-rwy
Calculating 8%

6%

4%
•  Unimpeded flights: Unimpeded Time of Group = Average
Group Unimpeded 2% Cong level < Cong Index
0%
1 4 7 10 13 16 19 22 25 28
• Truncated distribution
Time

Step 5: 10%
9%
8%

Calculating Distribution of Taxiout time
7%
6%
5%
4%
Averaged additional time
- Unimpeded of Group of group
Taxi-out additional time
3%
2%
1%
0%
7
10

34
13
16
19
22
25
28
31

37

Step 6:
Calculating Weighted average of
individual groups
Airport Additional Time
Additional Time

Case Study 1: What parameter gives a
reasonable indication of congestion?

  A single traffic parameter that correlates well with taxi-out time
identifies congestion
  The major causing factor for long taxi-out times is queue size at
departure RWY (Idris et al, 2000)
  Number of departures (Idris 2002, FAA 2009, Simaiakis, 2009)

Unimpeded
flights

Flights impacted by congestion
(with queuing time)

Threshold

Best taxi-out congestion parameter is #
departures & arrivals at airport

  Correlation between taxi-out time:
# departures at airport
# departures & arrivals at airport
# departure runway & arrivals at
airport
  Sample:
MAD, FRA
Jan-Mar 08 data
8 groups a/c-gate-rwy

•  Correlation improves inmost cases (15 out of 16 )
•  Proposed parameter to estimate queue size (congestion):

# departures & arrivals at airport

One example of fitting curve of taxi-out time at
Madrid - Barajas

Case Study 2: How close is unimpeded time
compared to results from commercial FTS tools?

Method
  Simulation in TAAM to calculate taxi time of one aircraft A320 for
grouped gates and RWY
  Flight moved unconstrained
  Sample: 3 months of airport data (Jan-Mar 2008) of MAD, BCN, PMI

TAAM results show a good match with
their counterpart unimpeded times

•  No significant difference in MAD and PMI

•  BCN had unimpeded time 14% lower than time estimated by TAAM.
  TAAM uses fixed procedures, while operations may be flexible

Case Study 3: How unimpeded do unimpeded
time vary with data source?

Method
  Analysis of influence of two variables:
1.  Data source (airline vs airport)
2.  Availability of gate-rwy information
  Change one variable at a time
Sample:
  3 airports: Madrid, Barcelona, Palma
  3 months (Jan-Mar 2008) of airport data and CODA data
  Traffic reduced to CODA sample

Gate-RWY information increases unimpeded
times, thus reduce inefficiency metric

No gate-RWY
information:

Biased towards close
gates

unimpeded times 

additional times 

Data source implies different accuracy on data
stamps

  Airlines report earlier off-block time (on average 2 min) and take-
off time (on average within 1 min)
  Differ from airport to airport

Airport data provides smaller unimpeded times

Airport information:

Taxi-out time &

Unimpeded times 

Additional times 

Case Study 4: How does push-back contribute to
inefficiency metric?

Method
  Record clearance of
controller tower (surrogate
for “Aircraft beginning of taxi
under its own power”)
  Taxi-out using clearance &
AOBT
  3 months of data (Jan-Mar
2008) of Brussels airport

•  Push-back manoeuvre seems to have marginal influence on inefficiency
metric
•  unimpeded times 

Case Study 5: Does additional time measure the
queuing time at departure runway?

Sample
  1 airport: London Heathrow
  Airport surface radar data
  2 months of airport data (Nov-Dec 2009)

Additional time seems to underestimate queuing
time

Additional time correlates strongly with queuing
time at departure runway

•  Unimpeded seems to embed part of the queuing at LHR

•  Additional time and queuing time measure the same phenomena, but biased by an
amount of time
•  Additional time was calculated over 40% traffic, while queuing used 100%
•  Sample of traffic small to have enough statistical results of unimpeded flights

Conclusions

!   Methodology to measure time efficiency is evaluated
!   Captures most influencing factors in taxi-time
!   Fair approximation of inefficiency
!   Simple, easy to apply statistical method
!   Applicable across multiple airports
!   Strongly correlates with queuing time at runway

!   Some caveats to take into account
!   Risk of underestimating queuing time at busy airports
!   Correction required for airports with multiple taxiing procedures for same
gate-rwy
!   Sensitive to data quality and need of long series of data

!   Recommendations
!   Group stands by proximity
!   Long series of data (3 to 6 months)
!   Further validation is advisable to generalize conclusions
23-24/02/11

CRIDA: Centro de Referencia I+D+i ATM

José L Garcia-Chico
CRIDA
Pza Cardenal Cisneros 3,
Madrid, 28040, Spain
jlgchico@crida.es
Tlf. +34 634535561

23-24/02/11

References (1)

!  ICAO Doc 9883, “Manual on Global Performance of the Air Navigation System”, Montreal,
2008
!  Gulding, J., Knorr, D., Rose, M., Bonn, J., Enaud, P., Hegendoerfer, H., “US/Europe
Comparison of ATM-Related Operational Performance”, 8th USA/Europe ATM R&D
Seminar, Napa, CA, 2009.
!  Performance Review Commission, “ATMAP Framework (A Framework for Measuring
Airport Airside and Nearby Airspace Performance)”, December, 2009.
!  Simaiakis, I., and Balakrishnan, H. “Analysis and Control of Airport Departure Processes
to Mitigate Congestion Impacts,” Transportation Research Record: Journal of the
Transportation Research Board, 2010, pp. 22–30.
!  Performance Review Commission, “Performance Review Report: An Assessment of Air
Traffic Management in Europe during the Calendar Year 2007”, May, 2008.
!  Performance Review Commission, “Performance Review Report: An Assessment of Air
Traffic Management in Europe during the Calendar Year 2004”, May, 2005.
!  Goldberg, B., and Cheeser, D., “Sitting on the Runway: Current Aircraft Taxi Times Now
Exceed Pre-9/11 Experience.” Bureau of Transportation Statistics Special Report, May
2008.
!  University of Westminster, “Evaluating the true cost to airlines of one minute of airborne
or ground delay”, 2003.
!  Federal Aviation Administration, “Documentation for Aviation System Performance
Metrics,” Office of Aviation Policy and Plans, 2002
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References (2)

!  Commission Regulation (EU) No 691/2010, Official Journal of the European Union, 29th
July, 2009
!  Idris, H., Clarke, J-P., Bhuva, R., & Kang, L (2002), “Queuing Model for Taxi-out Time
Estimation”, ATC Quarterly; 10(1), pp. 1-22.
!  Simaiakis, I., and H. Balakrishnan. “Queuing Models of Airport Departure Processes for
Emissions Reduction.” AIAA Guidance, Navigation and Control Conference and Exhibit,
Chicago, Ill., 2009.
!  De Neufville R. & Odoni A. “Airport Systems. Planning, deign and management.” New
York: Mc Graw Hill, 2003.
!  Idris, H. “Observations and Analysis of Departure Operations at Boston Logan
International Airport,” Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge,
MA, September, 2000.
!  Atkins, S. “Estimating departure queues to study runway efficiency.” Journal of
Guidance, Control, and Dynamics, Vol 25, No 4, pp 651–657, July, 2002
!  Anagnostakis, I., Idris, H., Clarke, J-P, Feron, E., Hansman, R., & Odoni, A “A
Conceptual Design of a Departure Planner Decision Aid”, 3rd USA/Europe ATM R&D
seminar, Naples, Italy, 2000.
!  Idris, H., Delclare, B., Anagnostakis, I., Hall, W., Clarke, J-P, Feron, E., Hansman, R., &
Odoni, A “Observations of Departure Processes at Logan Airport to Support the
Development of Departure Planning Tools”, 2nd USA/Europe ATM R&D seminar, Orlando,
1998.
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