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 [email protected] Tlf. +34 634535561
Acknowledgements
23-24/02/11
! 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.
Objectives
Taxi-out efficiency methodology
CS1: Estimate of Congestion
CS3: Data impact
CS2: Unimpeded times vs Comercial FTS
CS4: Push back influence
CS5: Queiuing vs Additional time
Conclusions
Assessing main factors influencing a metric of time efficiency of airport surface operations
23-24/02/11
! 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
Objectives
Taxi-out efficiency methodology
CS1: Estimate of Congestion
CS3: Data impact
CS2: Unimpeded times vs Comercial FTS
CS4: Push back influence
CS5: Queiuing vs Additional time
Conclusions
Methodology is built around the notion of unimpeded time
23-24/02/11
! 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
time
Num
ber o
f flig
hts
(g
roup
ed b
y ai
rcra
ft ty
pe-R
WY-
gate
)
Optimum time
Additional time
Taxi-out = ATOT - AOBT
Additional time = Taxi-out – unimpeded time
Distribution of taxi-out durations
Additional time as time efficiency metric
Unimpeded time = Taxi-out time with no congestion
Stand Runway Step 1: Grouping Flights Aircraft Class
AOBT ALDT Step 2: Calculating Aircraft Congestion Index
ATOT
Step 3: Calculating Group Congestion Threshold 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
7 10
13
16
19
22
25
28
31
34
37 20P
• Group: a/c-stand-rwy
Max Throughput of Airport
Threshold= 0.5 (MaxThroup*20P group)
Step 4: Calculating Group Unimpeded Time
0% 2% 4% 6% 8% 10% 12%
1 4 7 10 13 16 19 22 25 28
Average • Group: a/c-stand-rwy • Unimpeded flights: Cong level < Cong Index • Truncated distribution
Unimpeded Time of Group = Average
Step 5: Calculating Taxi-out additional time
Distribution of Taxiout time - Unimpeded of Group
Averaged additional time of group
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
7 10
13
16
19
22
25
28
31
34
37
Step 6: Calculating Additional Time
Weighted average of individual groups Airport Additional Time
Objectives
Taxi-out efficiency methodology
CS1: Estimate of Congestion
CS3: Data impact
CS2: Unimpeded times vs Comercial FTS
CS4: Push back influence
CS5: Queiuing vs Additional time
Conclusions
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)
Threshold
Flights impacted by congestion (with queuing time)
Unimpeded flights
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
Objectives
Taxi-out efficiency methodology
CS1: Estimate of Congestion
CS3: Data impact
CS2: Unimpeded times vs Comercial FTS
CS4: Push back influence
CS5: Queiuing vs Additional time
Conclusions
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
Objectives
Taxi-out efficiency methodology
CS1: Estimate of Congestion
CS3: Data impact
CS2: Unimpeded times vs Comercial FTS
CS4: Push back influence
CS5: Queiuing vs Additional time
Conclusions
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
Objectives
Taxi-out efficiency methodology
CS1: Estimate of Congestion
CS3: Data impact
CS2: Unimpeded times vs Comercial FTS
CS4: Push back influence
CS5: Queiuing vs Additional time
Conclusions
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
Objectives
Taxi-out efficiency methodology
CS1: Estimate of Congestion
CS3: Data impact
CS2: Unimpeded times vs Comercial FTS
CS4: Push back influence
CS5: Queiuing vs Additional time
Conclusions
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
Objectives
Taxi-out efficiency methodology
CS1: Estimate of Congestion
CS3: Data impact
CS2: Unimpeded times vs Comercial FTS
CS4: Push back influence
CS5: Queiuing vs Additional time
Conclusions
Conclusions
23-24/02/11
! 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
CRIDA: Centro de Referencia I+D+i ATM
23-24/02/11
José L Garcia-Chico CRIDA
Pza Cardenal Cisneros 3, Madrid, 28040, Spain
[email protected] Tlf. +34 634535561
References (1)
23-24/02/11
! 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
References (2)
23-24/02/11
! 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.
