33
Benchmarking Airport Productivity and the Role of Capacity Utilization – A Study of selected European Airports Branko Bubalo GAP Project Berlin, July 2009
Benchmarking Airport Productivity
and the Role of Capacity Utilization –
A Study of selected European
Airports
Branko Bubalo GAP Project
Berlin, July 2009
Contents
• Introduction
• Why Airport „Benchmarking“?
• Understanding the System
• Models and Guidelines
• Static Analysis
• Dynamic Analysis
• Conclusion
• “Airport capacity cannot be conjured up overnight. This is about looking at the long term, beyond the present crisis. Given the seriousness of the capacity crunch we are facing, this means not only looking into optimizing existing infrastructure, but also allowing the development of new infrastructure where needed.” Olivier Jankovec, Director General ACI EUROPE November 2008
Introduction• Despite „financial crisis“ and other
„threats“ to air transportation, air traffic is expected to grow continiously globally and in Europe
• Demand grows stronger than airport capacities
• „Capacity Crunch“ possible in the next couple of years – gridlocking parts of the European air system
• European Air Traffic is projected to
grow at 4% average per year over
the next decade (2010-2020) and
is about to double by 2025.
• “unaccommodated demand in
2025 […] could cost the European
economy €90bn/year.” ACI-Europe
Oktober 2008
Introduction• European Hubs are among the most important connections for the
global air transport system, but also among the most congested
airports.
LHR
CDG
FRA
Why Airport Benchmarking?
• Finding the „best-in-class“ based on performance and
efficiency
• Giving answers to private owners, stock market,
governments, users and regulators
• Dilemma: How to compare „like with like“ in cross
country comparison?
• Problematic for prices, accounting standards, services
provided, labor contracted out, future or realized
investment plans, service quality. (Forsyth 2004)
Understanding the SystemIdea:
• Develop a productivity benchmark indicator based on operations/flights taking capacity utilization into account.
• ->Delay per aircraft seems adequate, but hard to predict
• Throughput measures like arrivals, departures or total flights per hour per runway or airport, which are generated from real world daily flight schedules, should be used.
• Aircraft mix and runway configuration should be taken into account.
• Reduction of complexity to a minimum and development of peer groups or categories.
• Find basis for evaluating (peak) charges schemes, PAX numbers, forecasts, ground handling staff planning, investments and slot trading.
Understanding the System
Drawbacks of Productivity
Benchmarking Studies
• Econometric productivity models widely used – DEA,
SFA, TFP
• Also key performance indicators (KPI) - Partial
Productivity, TQM, Balanced Scorecard
• Lack of standardisation of Input/Output combination for
econometric and linear programming calculations
• Underestimation of dynamics at an airport, therefore lack
of „systematic“ approach
• more „microscopic“ details needed on a day-to-day or
even hourly basis
Relationship between hourly
Capacity, Demand and Delay• Delays per aircraft increase
disproportionally, the closer demand approaches Ultimate or Practical (hourly) Capacity
• A Practical Capacity, lower than the Ultimate Capacity, with regard to an acceptable level of delay (e.g. 4 Minutes per aircraft), is used in practice.
• Delay per aircraft would fit as a reasonable indicator for level of congestion of an airport, but not easy to calculate ->Simulation
• Rule-of-Thumb: Practical Capacity=80% of Ultimate Capacity
Figure 4. ASV and Hourly Capacity by Runway-use Configuration and Groups (Source: Bubalo 2009 from FAA (1983)).
MI=81-120 MI=>121
Group Subgroup Runway Config No ASV IFR Hourly Capacity ASV IFR Hourly Capacity Best-in-class MDRC Airport
I a 1 210,000 53 240,000 50 50 STN
I b 9 225,000 59 265,000 60 52 CGN
I b 14 225,000 69 265,000 60 66 VIE
I b 15 225,000 69 265,000 60
II a 2 285,000 59 340,000 60 61 MAN
II a 10 285,000 59 340,000 60 66 ZRH
II a 17 285,000 59 340,000 60
II b 13 295,000 59 350,000 60
II c 16 300,000 59 355,000 60 82 FRA
II c 18 300,000 59 355,000 60
II c 19 300,000 59 355,000 60
II d 3 300,000 70 365,000 75 70 MXP
II d 11 300,000 70 365,000 75
II e 4 315,000 105 370,000 99 88 LHR
II e 12 315,000 105 370,000 99 90 FCO
III a 5 310,000 70 375,000 75
III b 6 315,000 70 385,000 75
III c 7 510,000 117 645,000 120
III d 8 565,000 117 675,000 120 106 CDG
Runway Capacity Envelope for FRA Airport
on Busy Day June 26, 2008
0
5
10
15
20
25
30
35
40
45
50
0 5 10 15 20 25 30 35 40 45 50
Arrivals in Ops per hour
Dep
art
ure
s i
n O
ps p
er
ho
ur
FRA Capacity Envelope per Hour on PDTHUW26 2009
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Arrivals in Ops per hour
Dep
art
ure
s i
n O
ps p
er
ho
ur
Scheduled Flights Actual flights
Main Workpackages for Thesis
• Static Benchmarking of 60 European airports, of different size, chosen randomly, but partly based on interconnectivity and importance for the Air Transport Network representing 50% of overall European traffic.
• Dynamic Benchmarking using simulation software to gain insight into runway, gate, taxiway and apron operations at development of queues and delays at 18 single runway airports.
• At a later stage, simulation of more complex airports, with BRU, LHR, Berlin-Brandenburg International (BBI) and FRA.
Demand Profiles for Estimation of
Daily and hourly Productivity• Which airport is most
„productive“?
• Most important output is „flights per hour“!
• PAX not directly under managerial control, depends on aircraft size chosen by airline
Weekdays Operations Pattern and Capacities
(Sample Week Mon 03/16/2009 - Fri 03/20/2009)
0
20
40
60
80
100
120
00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24
Time
Op
s p
er
ho
ur
LHR_2009-03-16_TOT LHR_2009-03-17_TOT LHR_2009-03-18_TOT
LHR_2009-03-19_TOT LHR_2009-03-20_TOT maximum declared capacity of all sources
LHR technical capacity vfr LHR technical capacity ifr peak_day_2008_total
Weekdays Operations Pattern and Capacities
(Sample Week Mon 03/16/2009 - Fri 03/20/2009)
0
20
40
60
80
100
120
140
160
180
200
00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24
Time
Op
s p
er
ho
ur
CDG_2009-03-16_TOT CDG_2009-03-17_TOT CDG_2009-03-18_TOT
CDG_2009-03-19_TOT CDG_2009-03-20_TOT maximum declared capacity of all sources
CDG technical capacity vfr CDG technical capacity ifr peak_day_2008_total
tot_slots
Weekdays Operations Pattern and Capacities
(Sample Week Mon 03/16/2009 - Fri 03/20/2009)
0
20
40
60
80
100
120
140
00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24
Time
Op
s p
er
ho
ur
FRA_2009-03-16_TOT FRA_2009-03-17_TOT FRA_2009-03-18_TOT
FRA_2009-03-19_TOT FRA_2009-03-20_TOT maximum declared capacity of all sources
FRA technical capacity vfr FRA technical capacity ifr peak_day_2008_total
Slot coordination total min Slot coordination total max
Models and Guidelines
• IATA „Airport Capacity/Demand Management“ 1981
• IATA „Airport Development Reference Manual“ 1995 & 2004
• ICAO Annex 14 „Aerodromes“ 2004
• ICAO „Aerodrome Design Manual“ 1985 and „Airport Planning Manual“ 1987
• FAA AC 150/5060-5 „Airport Capacity and Delay“ 1983/1995
• FAA SIMMOD airport and airspace SIMulation and MODeller, around 1990
• Eugene .P. Gilbo: "Optimizing airport capacity utilization in air traffic flow management […]“ 1993 & “Airport capacity: representation, estimation, optimization” 1997
• Milan Janic: “The Sustainability of Air Transportation” 2007
• Adib Kanafani: “The Consistency of Traffic Forecasts for Airport Master Planning” 1981
Static Analysis: Data Sources
• OAG flight schedule data, for daily demand profile at airports and aircraft mix on typical busy day and later SIMMOD
• Flightstats flight schedule data, for daily demand on peak days and actual flight times
• Airport charts for runway, taxiway and apron system configuration and number of parking positions, needed for ultimate capacity of airfield
• Google Earth (GE) for airport coordinates
• IATA Airport Capacity and Demand Profiles 2003 for maximum declared runway and terminal capacities
• National Airport Coordinators for most recent slots per hour or maximum declared capacity, respectively, though slot distribution may vary over the day.
BEG Nikola Tesla
Airport Diagram
BEG Demand Diagram
Weekdays Operations Pattern and Capacities
(Sample Week Mon 03/16/2009 - Fri 03/20/2009)
0
10
20
30
40
50
60
00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24
Time
Op
s p
er
ho
ur
BEG_2009-03-16_TOT BEG_2009-03-17_TOT BEG_2009-03-18_TOT
BEG_2009-03-19_TOT BEG_2009-03-20_TOT maximum declared capacity of all sources
BEG technical capacity vfr BEG technical capacity ifr
BEG Assumption Rectangle
Assumption Rectangle and Capacities of BEG Airport Traffic for the year 2008
Annual Operations (AO)= 44454 n= 60 PAX/Ops Annual Passengers (AP)= 2650048
y= 0.02025% x= 0.04653%
Hourly Operations (HO)= 9 m= 137 PAX/Ops Hourly Passengers (HP)= 1233
hourly annually
Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 2327 5000000
(AP/MCTC)
Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)= 53% 53%
Runway Capacity
0
1
2
3
4
5
6
7
8
9
10
Op
era
tio
ns
pe
r h
ou
r
y=HO/AO Max decl terminal cap PAX/hr
Terminal Capacity
0
500
1000
1500
2000
2500
PA
X p
er
ho
ur
Max decl terminal cap PAX/hr HP in PAX/hr
Where does BEG fit into?Rank Airport Group
Rwy
config.
no.
no. of
rwy Mix Index %
VFR
ops/hr IFR ops/hr
Annual Service
Volume (ASV)
Annual Demand
Ops *2007
EUROSTAT
Annual Demand/
ASV
1 VIE 1 14 2 109 77 59 225,000 251,216 111.7%
2 STN 1 1 1 102 55 53 210,000 191,520 91.2%
3 DUB 1 14 3 108 77 59 225,000 200,891 89.3%
4 PRG 1 9 2 102 76 59 225,000 164,055 72.9%
5 LIS 1 1 2 117 55 53 210,000 141,905 67.6%
6 HAM 1 9 2 106 76 59 225,000 151,752 67.4%
7 STR 1 1 1 101 55 53 210,000 139,757 66.6%
8 WAW 1 9 2 103 76 59 225,000 147,985 65.8%
9 CGN 1 9 2.5 104 76 59 225,000 138,528 61.6%
10 EDI 1 14 2 100 77 59 225,000 115,177 51.2%
11 BHX 1 1 1 104 55 53 210,000 104,480 49.8%
12 GLA 1 1 1.5 101 55 53 210,000 93,654 44.6%
13 LTN 1 1 1 102 55 53 210,000 83,318 39.7%
14 LCY 1 1 1 100 55 53 210,000 77,274 36.8%
15 NUE 1 1 1 108 55 53 210,000 57,922 27.6%
16 SXF 1 1 1 100 55 53 210,000 55,114 26.2%
17 CIA 1 1 1 100 55 53 210,000 54,870 26.1%
18 BEG 1 1 1 100 55 53 210,000 44,454 21.2%
19 LBA 1 1 1 97 55 53 210,000 39,603 18.9%
20 RHO 1 1 1 100 55 53 210,000 32,776 15.6%
21 HHN 1 1 1 128 51 50 240,000 34,311 14.3%
22 DRS 1 1 1 100 55 53 210,000 28,257 13.5%
23 BSL 1 9 2 102 76 59 225,000 27,879 12.4%
24 FMO 1 1 0.5 100 55 53 210,000 21,968 10.5%
25 SZG 1 1 1 100 55 53 210,000 21,166 10.1%
26 ZAG 1 1 1 100 55 53 210,000 20,442 9.7%
27 RTM 1 1 1 100 55 53 210,000 18,517 8.8%
28 WRO 1 1 1 100 55 53 210,000 17,861 8.5%
29 GRZ 1 1 1 100 55 53 210,000 17,286 8.2%
30 SCN 1 1 1 100 55 53 210,000 9,731 4.6%
Groups of Runway Systems
Rank Airport Group
Rwy
config.
no.
no. of
rwy Mix Index %
VFR
ops/hr IFR ops/hr
Annual Service
Volume (ASV)
Annual Demand
Ops *2007
EUROSTAT
Annual Demand/
ASV
1 CDG 3 8 4 140 189 120 675,000 569,281 84.3%
2 MAD 3 8 4 118 210 117 565,000 470,315 83.2%
3 AMS 3 4 + 9 5.5 136 175 159 635,000 443,677 69.9%
1 FRA 2 16 3 149 129 60 355,000 486,195 137.0%
2 MUC 2 4 2 112 111 105 315,000 409,654 130.0%
3 LHR 2 4 2 170 103 99 370,000 475,786 128.6%
4 BCN 2 12 3 103 111 105 315,000 339,020 107.6%
5 FCO 2 12 3 114 111 105 315,000 328,213 104.2%
6 LGW 2 2 2 118 105 59 285,000 258,917 90.8%
7 CPH 2 12 2.5 109 111 105 315,000 250,170 79.4%
8 DUS 2 2 2 107 105 59 285,000 223,410 78.4%
9 ORY 2 12 2.5 112 111 105 315,000 238,384 75.7%
10 MAN 2 2 2 116 105 59 285,000 206,498 72.5%
11 OSL 2 4 2 101 111 105 315,000 226,221 71.8%
12 MXP 2 3 2 122 103 75 365,000 257,361 70.5%
13 IST 2 16 3 117 146 59 300,000 206,188 68.7%
14 NCE 2 2 2 55 121 56 260,000 173,584 66.8%
15 ZRH 2 10 3 121 94 60 340,000 223,707 65.8%
16 ARN 2 12 3 106 111 105 315,000 205,251 65.2%
17 BRU 2 12 3 123 103 99 370,000 240,341 65.0%
18 BBI 2 4 2 105 111 105 315,000 200,565 63.7%
19 ATH 2 4 2 110 111 105 315,000 193,123 61.3%
20 PMI 2 4 2 100 111 105 315,000 184,605 58.6%
21 HEL 2 12 3 107 111 105 315,000 174,751 55.5%
22 TXL 2 2 2 107 105 59 285,000 145,451 51.0%
23 LYS 2 2 2 102 105 59 285,000 132,076 46.3%
24 HAJ 2 4 2.5 100 111 105 315,000 70,481 22.4%
25 PSA 2 2 2 103 105 59 285,000 38,525 13.5%
26 LEJ 2 4 2 121 103 99 370,000 41,370 11.2%
27 LGG 2 2 2 237 94 60 340,000 26,815 7.9%
BEG Results from SIMMODBEG Flights and Delays per Flight from SIMMOD
(Flightplan OAG Thu 03/19/2009)
0
1
2
3
4
5
0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24
Time
Dela
y in
min
per
flig
ht
0
10
20
30
40
50
60
Op
s p
er
ho
ur
DEP delay min per flight BEG DEP delay min per flight BEG 100 DEP delay min per flight BEG 200 Total Ops BEG
Total Ops BEG 100 Total Ops BEG 200 Tech. IFR Cap. Max. Delay per Flight
Static Analysis Results
• Having more insight into airport activities will bring better answers and will deliver better indicators for productivity benchmarks.
• Throuput measures seem adequate since they cover productivity, efficiency and service quality (e.g. PAX/hr per check-in-counter, Aircraft/hr per parking position)
-> more research needed!
CDG Capacity and DemandDemand Diagram for CDG airport on PDTHUW26 2009
0
10
20
30
40
50
60
70
80
90
100
110
120
130
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time of Day
Op
s p
er h
ou
r
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Avg
. D
ela
ys p
er S
ch
ed
ule
d F
lig
ht
in M
inu
tes
Average Delays in Minutes per Scheduled Flight Scheduled Arrivals CDG 2009-06-25
Scheduled Departures CDG 2009-06-25 Total Scheduled Flights
Actual Arrivals CDG 2009-06-25 Actual Departures CDG 2009-06-25
Total Actual Flights Maximum Declared Runway Capacity
Maximum IFR Capacity
CDG Capacity Envelope per
Hour on PDTHUW26 2009
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Arrivals in Ops per hour
Dep
art
ure
s in
Op
s p
er
ho
ur
Scheduled FlightsActual flights
LHR Capacity and DemandDemand Diagram for LHR airport on PDTHUW26 2009
0
10
20
30
40
50
60
70
80
90
100
110
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time of Day
Op
s p
er
ho
ur
0
5
10
15
20
25
Avg
. D
ela
ys p
er
Sch
ed
ule
d F
lig
ht
in
Min
ute
s
Average Delays in Minutes per Scheduled Flight Scheduled Arrivals LHR 2009-06-25
Scheduled Departures LHR 2009-06-25 Total Scheduled Flights
Actual Arrivals LHR 2009-06-25 Actual Departures LHR 2009-06-25
Total Actual Flights Maximum Declared Runway Capacity
Maximum IFR Capacity
LHR Capacity Envelope per Hour on
PDTHUW26 2009
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Arrivals in Ops per hour
Dep
art
ure
s i
n O
ps p
er
ho
ur
Scheduled Flights
Actual flights
FRA Capacity and DemandDemand Diagram for FRA airport on PDTHUW26 2009
0
10
20
30
40
50
60
70
80
90
100
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time of Day
Op
s p
er h
ou
r
0
5
10
15
20
25
30
Avg
. D
ela
ys p
er S
ch
ed
ule
d F
lig
ht
Average Delays in Minutes per Scheduled Flight Scheduled Arrivals FRA 2009-06-25
Scheduled Departures FRA 2009-06-25 Total Scheduled Flights
Actual Arrivals FRA 2009-06-25 Actual Departures FRA 2009-06-25
Total Actual Flights Maximum Declared Runway Capacity
Maximum IFR Capacity
FRA Capacity Envelope per
Hour on PDTHUW26 2009
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Arrivals in Ops per hour
Dep
art
ure
s i
n O
ps p
er
ho
ur
Scheduled Flights
Actual flights
Conversion to approximate Seat and
PAX Distribution for Terminal Utilization
• Operations per hour are converted with average seat number of aircraft type.
• SLF of .85 is assumed, to convert further into Design Peak Hour (boarded) PAX.
FRA Demand Diagram for Seat Distribution on PDTHUW26 2009
0
2000
4000
6000
8000
10000
12000
14000
16000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time of Day
Seats
per h
ou
r
Total Seats per Hour Arrivals Seats per Hour Departure Seats per Hour
Dynamic Analysis
• Using FAA„s SIMMOD engine with Visual SIMMOD (VS)
for estimating runway capacity and DELAY per flight.
• SIMMOD uses airport design data, flight plan data,
aircraft data, flight path data, distances and coordinates
as inputs.
• Configuration of holding points in airspace and departure
queues (DQ) on the ground is needed
• Gate, link, DQ and holding capacities must be defined
• Flights are „fed“ into the simulated airport system
continiously, random factors are applied.
Dynamic Analyis Results
• Isolation of potential „bottlenecks“ at airports which cause
delay & reduce productivity. -> VS animation
Dynamic Analysis ResultsLGW Flights and Delays per Flight from SIMMOD
(Flightplan OAG Thu 03/19/2009)
0
5
10
15
20
25
30
35
0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24
Time
Dela
y in
min
per
flig
ht
0
20
40
60
80
100
120
Op
s p
er
ho
ur
DEP delay min per flight clone000 DEP delay min per flight clone005 DEP delay min per flight clone010 DEP delay min per flight clone015
DEP delay min per flight clone020 DEP delay min per flight clone030 DEP delay min per flight clone050 DEP delay min per flight clone100
Total Ops clone000 Total Ops clone005 Total Ops clone010 Total Ops clone015
Total Ops clone020 Total Ops clone030 Total Ops clone050 Total Ops clone100
Max. Decl. Cap. Tech. IFR Cap. Max. Delay per Flight
Conclusion• Hopefully „one step closer“ to a „valid“ productivity benchmark.
• More indicators at hand for econometric and other benchmarking analysis -> further research needed
• Application of revenue factors, airport charges, and investment plans is interesting, challenging and promising.
• Cross checking needed against similar studies (e.g. EUROCONTROL Performance Review Report (PRR 2007) and airport master plans.
• Biggest Problems:
• Is slot coordination really effective? Is „grandfathering“ a utilization & performance limitor with regard to LCC expansion?
• Coordination among stakeholders, environmental debates, funding and politics -> collaborative descision making (CDM)
• Lack of technology, concerning aircraft separation standards (ADS-B, SESAR, NextGEN)
