Key Findings of 2013 ATRS Global Airport Performance ... · RESIDUAL VFP: NORTH AMERICA (YVR=1.0),...
Transcript of Key Findings of 2013 ATRS Global Airport Performance ... · RESIDUAL VFP: NORTH AMERICA (YVR=1.0),...
Key Findings of 2013 ATRS Global Airport Performance Benchmarking project
ww.atrsworld.org
© Air Transport Research Society (ATRS)
Prof. Tae Hoon OumDr. Yapyin ChooProf. Chunyan Yu
ATRS Global Airport Benchmarking Task Force Asia Pacific: P. Forsyth, Xiaowen Fu, Yeong‐Heok Lee,
Yuichiro Yoshida, Japhet Law, Shinya HanaokaEurope: Nicole Adler, Jaap de Wit, Hans‐Martin Niemeier, Eric PelsNorth America: Tae Oum, Bijan Vasigh, Jia Yan, Chunyan YuMiddle East: Paul Hooper
OUTLINE
Objective of the ATRS Benchmarking Study
Airports Included and ATRS Database
Some Characteristics of Sample Airports
Methodology
Key Results on Efficiency and Costs
User Charge Comparisons
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
OBJECTIVE OF THE BENCHMARKING STUDY
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
To provide a comprehensive, unbiased comparison of airport performance focusing on Productivity and Operating/Mgt Efficiency Unit Cost Competitiveness Airport User Charges
Our study does not treat service quality differentials across airports because of ourresearch resource constraints
Airport Database2013 ATRS Global Airport Performance Benchmarking Project
Airports Included in the 2012 Report
© Air Transport Research Society (ATRS)5
Canada (12)+US(65) 77 airports
Europe 77 airports17 airport groups
Asia Pacific 35 Asian airports16 Oceania airports9 airport groups
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐Total 195 airports
26 airport groups
ATRS AIRPORT DATABASE, FY 2002‐2011
The ATRS Database contains historic information (since FY 2002) including financial data, traffic and capacity data for the major airports and airport groups in the following geographic regions: Asia Pacific including Oceania; Europe; North America Limited data on S. America and Africa
The data in each continent is segregated into: Traffic statistics and composition Airport characteristics (runways, terminals, ownership form, etc) Aeronautical Activities and Revenue Non‐Aeronautical Activities and Revenue Labor input and other Operating Expenses Financial info obtained from Balance Sheets
Visit http://www.atrsworld.org/Database.html for more details and to purchase.
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
Data Sources: FY 2002-2011
Airport’s Financial Statements, Annual Reports and direct data requests;
US FAA, DOT statistics;Association of European Airlines (AEA) Statistics ICAO Digest of Statistics: annual and monthly traffic data annual financial data - not for all airports
ACI; IATA annual traffic statistics; capacity information; airport
charges general information surveys (Asia Pacific and Europe)
occasional and not complete IMF and World Bank – various price indices
including GDP deflators for service sectors and PPPUS Census Bureau, Statistics Canada – regionally
based Cost of Living Index© Air Transport Research Society (ATRS) 7
Airport Characteristics2013 ATRS Global Airport Performance Benchmarking Project
PASSENGERS TRAFFIC, FY2011 (IN ’000 PASSENGERS)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
PEK
HND
HKG DXB
CGK
SIN
BKK
CAN
PVG
KUL
DEL
SYD
ICN
NRT
SHA
BOM
MNL
SZX
MEL
TPE
BNE
GMP
XMN KIX
AKL
SUB
MAA PER
HAK
NGO
HKT
ADL
CMB
CHC
OOL
WLG
PEN
MFM CN
S
CNX
GUM
DRW
NAN
PNH
HDY
REP
TSV
NTL
ZQN CEI
DUD
LHR
CDG
FRA
AMS
MAD
FCO
MUC
IST
BCN
LGW
ORY
ZRH
PMI
CPH
VIE
OSL
DUS
MAN MXP
ARN
BRU
DUB
STN
TXL
HEL LIS
ATH
SAW
HAM GVA
TLV
AGP
PRG
LPA
NCE
ALC
CGN
LED
LTN
EDI
WAW LIN
BUD
BHX
LYS
BGY
GLA
OPO CRL
BLQ
NAP
BRS
HAJ RIX
BSL
CIA
TRN
MLA
SOF
BEG
ZAG
KEF
TLL
LUX
SZG
BTS
LJU
ATL
ORD
LAX
DFW
DEN JFK
SFO
IAH
PHX
CLT
LAS
MIA
MCO
EWR
MSP
SEA
YYZ
DTW PHL
BOS
LGA
FLL
IAD
BWI
SLC
MDW DCA
HNL
SAN
YVR
TPA
PDX
YUL
STL
YYC
MCI
MEM MKE
BNA
OAK
HOU
RDU
CLE
AUS
SMF
SNA
MSY PIT
SJC
SAT
DAL
RSW
IND
CVG
CMH
YEG
PBI
ABQ
BDL
JAX
ONT
ANC
YOW
BUR
PVD
RNO
OKC
TUS
YHZ
YWG
SDF
RIC
ALB
YYJ
YQB
YYT
YQR
Asia Pacific Europe North America
PASSENGER TRAFFIC (’000)-TOP 10 AIRPORTS:
FY 2007, 2009, 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
10,000
20,000
30,000
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60,000
70,000
80,000
90,000
100,000
2007 2009 2011
Asia Pacific Europe North America
AIRCRAFT MOVEMENTS, FY 2010 (’000 ATM)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
100
200
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800
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PEK
HND
CAN
CGK
PVG
HKG
SIN
DEL
BKK
SYD
KUL
BOM
SHA
ICN
SZX
MNL
MEL
NRT
BNE
TPE
AKL
XMN
GMP
MAA
KIX
SUB
PER
WLG
HAK
NGO
ADL
CHC
NAN
HKT
PEN
GUM
CMB
CNS
MFM OOL
CNX
TSV
DRW
REP
PNH
NTL
HDY
DUD
ZQN CEI
CDG
FRA
LHR
MAD
AMS
MUC
FCO IST
BCN
ZRH
CPH
LGW VIE
BRU
OSL
ORY
DUS
ARN
MXP
HEL
PMI
ATH TXL
MAN
DUB
GVA
HAM
NCE
PRG
LIS
STN
CGN
WAW LYS
SAW LED
LPA
BUD
AGP
EDI
TLV
LTN LIN
BHX
BSL
CRL
LUX
HAJ
ALC RIX
GLA
BGY
BLQ
BRS
NAP
OPO
CIA SOF
BEG
TRN
ZAG TLL
LJU
MLA BTS
KEF
SZG
ATL
ORD
DFW DEN LAX
CLT
IAH
LAS
DTW PHL
PHX
YYZ JFK
EWR
MSP SFO
MIA
LGA
BOS
SEA
MEM
MCO
SLC
YVR
IAD
DCA
HNL
BWI
FLL
YYC
YUL
PDX
MDW CLE
STL
SAN
TPA
OAK
CVG
MKE
HOU
RDU
IND
SDF
MCI
PIT
YOW YEG
AUS
SAT
DAL
YWG
CMH
SMF
ABQ
MSY
ANC
SJC
BDL
BNA
BUR
SNA
YHZ
JAX
ONT
RSW YQB YYJ
RIC
OKC
PVD
ALB TUS
PBI
RNO
YYT
YQR
Asia Pacific Europe North America
PASSENGERS PER AIRCRAFT MOVEMENTS,FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
20
40
60
80
100
120
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200
NRT
HND
BKK
HKG
SIN
TPE
ICN
HKT
PEK
GMP
OOL
SHA
CMB
CGK
KUL
CEI
MNL
MEL
HDY
SUB
CAN
KIX SZX
PER SYD
HAK
BOM DEL
PVG
BNE
CNX
XMN
NGO
MAA
MFM ADL
ZQN
PEN
CNS
AKL
PNH
DRW NTL
REP
CHC
DUD
WLG TSV
GUM
NAN
LHR
LGW
ALC TLV
STN
PMI
IST
CDG
AGP
ORY
AMS
BGY
FRA
MAD
DUB
SAW
FCO
MAN
BCN
MLA LIS
MXP
OPO
TXL
LTN LIN
GLA LPA
ARN
BHX
MUC
DUS
NAP
OSL
CPH
EDI
CIA
ZRH KEF
BRS
SZG
HAM VIE
BLQ
TRN LED
ATH
BUD
GVA
BRU
PRG
WAW
HEL
SOF
CGN
LYS
RIX
BEG CRL
NCE
HAJ
BTS
BSL
ZAG TLL
LJU
LUX JFK
MCO
SEA
PBI
LAX
BNA
SFO
SNA
RSW
MDW
TPA
SAN FLL
MIA ATL
MSY SJC
PHX
SMF
BWI
DFW
EWR
DEN
MSP
RNO
IAD
BOS
LAS
YYZ
ORD
IAH
CLT
AUS
MCI
DTW JAX
PHL
PDX
SLC
SAT
DCA
LGA
DAL
STL
PVD
PIT
HNL
RDU
BDL
YUL
TUS
ONT
HOU
MKE
YVR
OKC
CMH
ABQ
YYC
OAK
IND
BUR
YEG
ANC
RIC CLE
YHZ
CVG
ALB YYT
YOW
YQR
MEM YWG
SDF
YYJ
YQB
Asia Pacific Europe North America
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
% NON‐AERO REVENUE, FY 2011
0%
10%
20%
30%
40%
50%
60%
70%
80%HKG OOL
TSV
DUD AKL
GMP
PER
CAN
CMB
ICN
NAN BNE
SIN
SYD
CHC
MEL
HAK
GUM ZQN
NRT
PEK
NTL
PVG
WLG ADL
BKK
CEI
HDY
HKT
CNX
SZX
DRW
XMN
NGO
KUL
KIX
CGK
PEN
FRA
OSL BSL
CDG
ORY
NCE
TRN
HAJ
AMS
ATH CPH
VIE
BUD
DUB
CIA
FCO
TLV
RIX
ZRH
TLL
LIN
MXP LTN
BHX
LYS
ZAG
MAN
HAM LJU
GVA GLA
ARN NAP
MUC
SOF
LGW EDI
BGY
STN LIS
OPO
SAW
LHR
SZG
MLA
CGN
DUS
LPA
ALC
AGP
BLQ
TXL
WAW
BCN
BTS
HEL
PMI
MAD
LED
BEG
OKC TPA
BUR
MCI RIC
JAX
PHX
FLL
YYJ
MCO YQR
ATL
CMH
RNO
BNA
RDU
IND
MKE
DAL
MSP YEG
TUS
YQB
SLC
YYC PBI
SMF
DFW
SNA
RSW SJC
CLT
YVR
AUS
ALB
ABQ
YYT
SAN
SAT
SDF
PVD
YHZ
BOS
YUL
YOW LAS
DTW
HOU
CVG
HNL
OAK
DCA
SFO
PDX
ONT BDL
MSY
LAX
SEA
DEN
YWG
BWI
PIT
IAD
MIA
IAH
MDW
ORD PHL
YYZ
CLE
STL
EWR
LGA
JFK
MEM ANC
Asia Pacific Europe North America
Methodology2013 ATRS Global Airport Performance Benchmarking Project
AIRPORT PRODUCTIVITY INDEX
Outputs
• Aircraft movement• Passenger• {Cargo tonnes}• Non‐aeronautical revenue output
Inputs
• Labour• Other non‐capital (soft‐cost) input
• [Runways, terminal size, # of gates]
© Air Transport Research Society (ATRS)Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
METHODOLOGY: EFFICIENCY MEASUREMENT
Variable Factor Productivity (VFP) Index Impossible ‐ Total Factor Productivity (TFP) because of capital input cost accounting problem (comparable across different countries)
Unit Operating Cost Competitiveness Index: Combines VFP and Input Price Index
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
MULTILATERAL AGGREGATION METHOD• This multilateral output (input) index procedure uses the following revenue (cost) shares to aggregate output (inputs)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
GROSS VARIABLE FACTOR PRODUCTIVITY (VFP)NORTH AMERICA LARGE AIRPORTS
(YVR=1.0), FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
ATL
CLT
MSP YVR
LGA
TPA
MCO SLC
PHX
FLL
SFO
LAS
EWR
SEA
MDW DTW PHL
DCA
HNL
BOS
DFW
ORD SAN
DEN JFK
IAD
IAH
BWI
LAX
MIA
POTENTIAL REASONS FOR THE MEASURED PRODUCTIVITY (GROSS VFP) DIFFERENTIALS
Factors Beyond Managerial Control:
• Airport size (Scale of aggregate output)• Average aircraft size using the airport• Share of international traffic• Share of air cargo traffic• Extent of capacity shortage ‐ congestion delay• Connecting/transfer ratio
We compute residual (Net) Variable Factor Productivity (RVFP) after removing effects of these Factors
© Air Transport Research Society (ATRS) 19Objective Data Airport
Characteristics Methodology Efficiency & Cost User Charge
GROSS VARIABLE FACTOR PRODUCTIVITY VS RESIDUAL VFP: NORTH AMERICA
(YVR=1.0), FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
ATL
MSP CLT
TPA
MCO YVR
SFO
FLL
LGA
MDW SL
CSEA
HNL PHX
EWR
BOS
JFK
DTW PHL
LAS
SAN
DCA
BWI
IAD
DFW ORD IAH
DEN MIA
Gross VFP Residual VFP
ALTERNATIVE APPROACHES
We explored Alternative approaches: Data Envelopment Analysis (DEA) Econometric Cost Function Approach including Stochastic Frontier methods (SFA)
The rankings for top and bottom ranked airportsare consistent despite using VFP, DEA or SFA.
Note: Industry acceptance of our report using more advanced/sophisticated methods is one of our major concern
RESIDUAL RANKING COMPARISON OF TOP 15 AIRPORTS IN US
© Air Transport Research Society (ATRS) 22
0510152025303540455055
ATL
RDU
RNO
CLT
PBI
BNA
MSP JAX
LGA
SAT
TPA
SNA
MCO
MKE FLL
Rank
Residual VFP Ranking Residual DEA Ranking Residual SFA Ranking
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
RESIDUAL RANKING COMPARISON OF BOTTOM 15 AIRPORTS IN US
© Air Transport Research Society (ATRS) 23
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BOS
CVG
CLE
SJC
ALB
PHL
DFW STL
ONT
LAX
ORD BWI
PIT
MSY
MIA
Rank
Residual VFP Ranking Residual DEA Ranking Residual SFA Ranking
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
RESIDUAL RANKING COMPARISON OF MID‐RANKED 15 AIRPORTS IN US
© Air Transport Research Society (ATRS) 24
0510152025303540455055
PDX
SAN
SLC
OAK RIC
ABQ
SEA
HNL
EWR
IAD
LAS
MEM
MDW DCA
IAH
JFK
IND
PHX
SMF
AUS
SDF
DEN
DTW
SFO
MCI
Rank
Residual VFP Ranking Residual DEA Ranking Residual SFA Ranking
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
Key Results on Efficiency & Cost2013 ATRS Airport Benchmarking
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): EUROPE LARGE AIRPORTS (CPH=1.0), FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
CPH
ATH
ZRH
OSL
LIS
AMS
CDG
FCO
ARN
MXP
PMI
VIE
MAN
IST
LGW
STN
HEL
ORY
DUB
BCN
TXL
MAD
DUS
FRA
LHR
MUC
ANA
Schiph
olAD
RAD
PSw
edavia
Avinor
SEA
DAA
MAG
Berlin
AENA
Fraport
Finavia
BAA
TAV
PPL
0
0.2
0.4
0.6
0.8
1
1.2
AirportGroups
Copenhagen Kastrup, Athens, Zurich
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): EUROPE SMALL & MEDIUM AIRPORTS (CPH=1.0), FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
GVA
BSL
NCE
CIA
LJU
BHX
BLQ
SAW
HAJ
KEF
TLV
EDI
LPA
LTN
TRN
TLL
ALC
NAP
BGY
MLA
GLA
HAM
RIX
AGP
SZG
BUD
ZAG
SOF
LIN
BTS
WAW
BEG
CGN
LED
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Geneva, Basel, Nice
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): ASIA (HKG=1.0), FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
GMP
ICN
GUM
HAK
HKG
SIN
CGK
PEK
HDY
CAN
CNX
PVG
PEN
XMN
HKT
BKK
SZX
NRT
KUL
CMB
NGO
KIX
KAC
AAI
APII
AOT
MAH
B
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Residual VFP Mean
Airports AirportGroups
Gimpo, Incheon, Guam
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): OCEANIA (SYD=1.0), FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
SYD
AKL
TSV
DRW
MEL
OOL
CHC
DUD
BNE
PER
ZQN
ADL
WLG
NAN
NTL
APA
C
QAL
ADG
0
0.2
0.4
0.6
0.8
1
1.2
Residual VFP Mean
Airports Airport GroupsSydney, Auckland, Townsville
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): NORTH AMERICA LARGE AIRPORTS (YVR=1.0), FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
ATL
MSP
CLT
TPA
MCO
YVR
SFO
FLL
LGA
MDW
SEA
SLC
HNL
EWR
PHX
BOS
JFK
DTW
PHL
SAN
LAS
DCA
BWI
IAD
DFW
ORD
IAH
DEN
MIA
LAX
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Atlanta, Minneapolis St. Paul, Charlotte
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): N. AMERICA SMALL & MEDIUM AIRPORTS (YVR=1.0), FY 2011
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
OKC
RIC
RDU
YYJ
YYC
BNA
YQR
PVD
BDL
PBI
TUS
RNO
YYT
JAX
MKE
YEG
SNA
ABQ
PDX
SJC
SAT
IND
MSY
RSW
DAL
YOW
SMF
YHZ
AUS
MCI
MEM
YWG
OAK
SDF
CMH
BUR
ANC
ALB
CVG
YUL
PIT
ONT
HOU
STL
CLE
YQB
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Oklahoma City, Richmond, Raleigh‐Durham
TOP EFFICIENCY PERFORMERS (2013)(based on Net VFP index=operating/management efficiency)
• Asian Airports:• Gimpo, Incheon, Guam
• Oceania Airports:• Sydney, Auckland, Townsville
Asia Pacific:
• Large Airports (> 15 million pax):• Copenhagen Kastrup, Athens, Zurich
• Small/Medium Airports (< 15 millions Pax):• Geneva, Basel, Nice
Europe:
© Air Transport Research Society (ATRS) 32Objective Data Airport
Characteristics Methodology Efficiency & Cost User Charge
TOP EFFICIENCY PERFORMERS (2013)(based on Net VFP index=operating/management efficiency)
• Large Airports (> 15 million pax):• {Atlanta (Globally Most Efficient Airport)}•Minneapolis St Paul, Charlotte, Tampa
• Small/Medium Airports (< 15 millions Pax):• Oklahoma City, Richmond, Raleigh‐Durham
North America:
• Hartsfield‐Jackson Atlanta International Airport
Global (10th Global Excellence Award)
© Air Transport Research Society (ATRS) 33Objective Data Airport
Characteristics Methodology Efficiency & Cost User Charge
PAST AIRPORT EFFICIENCY EXCELLENCE TOP PERFORMERS, 2008 ‐ 2012
North America
Europe
Asia‐Pacific
2012
Hartsfield‐Jackson Atlanta International
Airport
Large Airport Category: Copenhagen KastrupInternational AirportSmall/Medium Airport
Category:Genève Aéroport
Asian Airport Excellence Award:
Seoul GimpoInternational AirportOceania Excellence
Award:Sydney Airport
2008
Hartsfield‐Jackson Atlanta International
Airport
Copenhagen KastrupInternational Airport
Hong Kong International Airport
2009
Hartsfield‐Jackson Atlanta International
Airport
Copenhagen KastrupInternational Airport
Hong Kong International Airport
2010
Hartsfield‐Jackson Atlanta International
Airport
Large Airport Category: Oslo International
AirportSmall/Medium Airport
Category:Geneva Cointrin
International Airport
Large Airport Category:Hong Kong International
AirportSmall/Medium Airport
Category:Seoul Gimpo
International Airport
2011
Hartsfield‐Jackson Atlanta International
Airport
Large Airport Category: Oslo International
AirportCopenhagen KastrupInternational AirportSmall/Medium Airport
Category:Genève Aéroport
Asian Airport Excellence Award:
Hong Kong International Airport
Oceania Excellence Award:
Sydney Airport
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT EUROPE ‐ LARGE AIRPORTS (CPH=0.0)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
ATH
LIS
CPH
MAN
PMI
FCO
STN
MXP
BCN
AMS
MAD VIE
LGW
ARN
DUB
FRA
LHR
DUS
MUC
CDG
TXL
ZRH
ORY
HEL
OSL
ANA
PPL
TAV
MAG
ADR
Schiph
olFraport
SEA
DAA
Swed
avia
AENA BAA
ADP
Berlin
Finavia
Avinor
‐1
‐0.8
‐0.6
‐0.4
‐0.2
0
0.2
0.4
Airport GroupsAirports
Athens, Lisbon, ANA (Aeroportos de Portugal)
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT EUROPE ‐ SMALL & MEDIUM AIRPORTS (CPH=0.0)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
LJU
BSL
TLL
RIX
SOF
MLA
BTS
CIA
SAW
BHX
BEG
BUD
ZAG
LPA
WAW
ALC
LTN
EDI
HAJ
NCE
KEF
BGY
TRN
AGP
LED
GLA
NAP
HAM
TLV
LIN
SZG
BLQ
CGN
GVA
‐0.7
‐0.6
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0
0.1
0.2
0.3
0.4
Ljubljana(Slovenia), Basel, Tallinn (Estonia)
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT ASIA (HKG=0.0) – THE HIGHER THE BETTER
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
HAK
GMP
CGK
CMB
XMN
HKG
HDY
ICN
CNX
PVG
PEK
PEN
SZX
HKT
BKK
KUL
CAN
GUM
SIN
NGO
KIX
NRT
AAI
APII
AOT
MAH
B
KAC
‐2
‐1.5
‐1
‐0.5
0
0.5
Cost Competitiveness Mean
AirportGroupsAirports
Haikou, Seoul Gimpo, Airport Authority of India
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT OCEANIA (SYD=0.0)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
AKL
DUD
CHC
SYD
ZQN
MEL
WLG
DRW
BNE
PER
ADL
NTL
QAL
APAC
ADG
‐0.6
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0
0.1
0.2
0.3
AirportGroupsAirport s
Queensland Airport Limited (QAL),Auckland, Dunedin (NZ)
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT N. AMERICA ‐ LARGE AIRPORTS (YVR=0.0)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
ATL
CLT
MCO
MSP
FLL
SLC
PHX
YVR
LAS
DTW
SEA
IAH
DFW
BWI
HNL
MDW SAN
ORD
DEN
PHL
BOS
MIA
DCA
IAD
SFO
LAX
EWR
TPA
JFK
LGA
‐2
‐1.5
‐1
‐0.5
0
0.5
1
Atlanta, Charlotte, Orlando
COST COMPETITIVENESS: = NET VFP AND INPUT PRICE EFFECT N. AMERICA ‐ SMALL & MEDIUM AIRPORTS (YVR=0.0)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
OKC
RIC
RDU
BNA
TUS
ABQ
IND
RNO
PBI
YYC
JAX
YQR
SAT
DAL
MSY
YYJ
MKE
SDF
PVD
MEM
RSW
MCI
AUS
YEG
YYT
CMH
PDX
BDL
BUR
ALB
SNA
CVG
HOU
YWG
PIT
STL
SMF
CLE
YHZ
YUL
SJC
YOW
YQB
ONT
ANC
‐0.4
‐0.2
0
0.2
0.4
0.6
0.8
1
Oklahoma City, Richmond (Virginia), Raleigh‐Durham
User Charge Comparison2013 ATRS Airport Benchmarking
LANDING CHARGES FOR BOEING 767‐400, 2012 (IN US$)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
GUM KUL
DXB
CMB
TPE
SUB
BKK
CEI
CNX
HDY
HKT
CGK
MNL
BOM DEL
MAA
PEN
PNH
REP
CAN
HAK
PEK
PVG
SHA
SZX
XMN
MFM DUD
ICN
GMP
SIN
NAN
ZQN
PER
HKG
WLG AKL
NTL TSV
NTL‐Ni ght
OOL SYD
NGO
DRW KIX
NRT
HND NAP BGY
DUS
ARN
STN‐off peak
BRU
LTN
MLA
STN‐peak
LGW‐off‐peak RIX
FRA
BLQ
LUX
MUC TRN
FCO
MXP LIN CIA
BSL (France Sector)
PRG LIS
OPO VIE
ZAG
GLA
HEL
HAM
GVA EDI
KEF
TXL
SOF‐offpeak
ALC
CPH
NCE
CGN
BSL (Sw
iss S
ector)
SAW
MAN
‐peak
IST
MAN
‐offpeak
SOF‐peak OSL LED
AGP
LPA
PMI
LYS
BCN
ZRH
BUD
BEG
HAJ
TLL
MAD CRL
ATH TLV
CDG
ORY
AMS
WAW
LHR
BHX
BTS
LJU
LGW‐peak
SZG
DUB
BRS
CLT
ATL
BUR
RIC
TUS
MEM SAT
PBI
PHX
SLC
SAN
RNO
MCO PHL
MSY
RDU
IND
SDF
TPA
BNA
HOU JAX
ABQ MCI FLL
ONT SNA
*ANC
MSP SJC
MIA
RSW
ALB IAH
LAS
OKC
*YVR SEA
AUS
CMH
DCA
OAK
DTW
PDX
MDW
*YYJ
*YQR
DFW
BDL
IAD
*YOW
DEN
*YEG PIT
*YHZ BWI
HNL
SMF
LAX
*YYC
MKE
*YWG
*YUL BOS
SFO
*YYT
ORD
*JFK
Off Peak STL
*JFK
Peak
*YQB
*EWR Off Peak
*EWR Peak
*LGA
Off Peak
*LGA
Peak
*YYZ
US$
Landing Charges for Boeing 767 Mean
Asia Pacific Europe North America
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
TPE
DUD
DEL
BOM
MAA HKG
KUL (LCCs)
SIN (LCC
s)
GUM
MNL
CMB
SIN
MFM CAN
HAK
PEK
PVG
SHA
SZX
XMN
GMP
SUB
CGK
KUL
ZQN
PEN
ICN
DXB
CEI
CNX
HDY
HKT
BKK
TSV
WLG NTL
CHC
REP
PNH
DRW
OOL
PER
MEL
ADL
NTL‐Night
CNS
SYD
HND
BNE‐Pe
ak
BNE
NGO
AKL
NRT‐1N
NRT‐1S
NRT‐2
KIX
ASIA PACIFIC: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 737‐800, 2012
(IN US$)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
Lowest charges: Taipei Taoyuan, Dunedin (New Zealand)Highest charges: Osaka Kansai, Tokyo Narita
0
2000
4000
6000
8000
10000
12000
RIX
CRL
LUX
NAP
BGY
MAN
‐offp
eak
HEL
CIA
BLQ
ARN
KEF
TLL
CGN
GVA
ZAG
FCO (LCC
s)OSL
CPH(LCCs)
ALC
BSL (France
Sector)
MAN
‐peak
AGP
LPA
PMI
STN‐off p
eak
FCO
BCN
MXP TRN
NCE
STN‐pe
akGL
ALTN
LIN
HAM
CPH
EDI
BUD (LCC
s)MAD HAJ
MLA
BSL (Sw
iss Sector)
LYS
DUS
TXL
BEG (LCC
s)SO
F‐offpeak
SAW
SOF‐pe
akOPO
TLV(LCCs)
LGW‐off‐pe
akBH
XIST
LIS
AMS
VIE
LED
MUC
WAW DUB
BUD
BRU
SZG
BEG
BTS
LJU
ATH
FRA
ZRH
PRG
BRS
ORY
CDG
TLV
LGW‐peak
LHR
EUROPE: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 737‐800, 2012
(IN US$)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
Lowest charges: Riga (Latvia), Brussel South CharleroiHighest charges: London Heathrow, London Gatwick‐ Peak
NORTH AMERICA: COST PER ENPLANED PASSENGER, 2011 (IN US$)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
5
10
15
20
25
30
35
40
CLT
BUR
ATL
SLC
FLL
MCI
TPA
MEM PHX
MKE
MSP
OKC
MCO RIC
RDU
BNA
DFW JAX
RNO
SDF
SAN
TUS
MSY
RSW
CMH
HOU
ALB
PBI
MDW ABQ
AUS
LAS
DTW
BDL
SAT
IAH
PHL
SNA
BWI
SMF
ANC
IND
OAK PVD
HNL
SEA
LAX
DEN SJC
PDX
ONT
ORD STL
DCA
BOS
SFO
LGA
PIT
MIA IAD
EWR
JFK
YYJ
YQR
YEG
YYC
YYT
YQB
YVR
YHZ
YOW
YWG
YUL
YYZ
US$
CPE US Mean Canada Mean
US Airports Canadian Airports
United States:Lowest CPE: Charlotte, California Bob Hope (Burbank,CA)Highest CPE: New York JFK, Newark Liberty
Canada:Lowest CPE: Victoria, ReginaHighest CPE: Toronto, Montreal
ASIA PACIFIC: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 767, 2012 (IN US$)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
TPE
DUD
DEL
BOM
MAA HKG
GUM
KUL (LCCs)
SIN (LCC
s)CM
BMNL
MFM SIN
CAN
HAK
PEK
PVG
SHA
SZX
XMN
KUL
GMP
SUB
CGK
ICN
DXB
ZQN
PEN
WLG CEI
CNX
HDY
HKT
BKK
TSV
PNH
REP
PER
MEL NTL
OOL
NTL‐Night
DRW
ADL
CNS
SYD
BNE
BNE‐Pe
akNG
OAK
LHN
DNR
T‐1N
NRT‐1S
NRT‐2
KIX
US$
Combined Landing and Passenger Charges for Boeing 767 Mean
Lowest charges: Taipei Taoyuan, Dunedin (New Zealand)Highest charges: Osaka Kansai, Tokyo Narita
EUROPE: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 767, 2012 (IN US$)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
RIX
LUX
CRL
NAP
BGY
MAN
‐offp
eak
CIA
ARN
HEL
BLQ
KEF
CGN
OSL TLL
FCO (LCC
s)STN‐off p
eak
ZAG
GVA
ALC
MAN
‐peak
STN‐pe
akCP
H(LCCs)
AGP
LPA
PMI
BSL (France
Sector)
LTN
FCO
BCN
TRN
MXP NCE
BUD (LCC
s) LIN
LGW‐off‐pe
akGL
ADU
SED
ISO
F‐offpeak
CPH
HAM
MAD
SOF‐pe
akMLA IST
HAJ
OPO
BSL (Sw
iss Sector) LIS
TXL
BEG (LCC
s)LYS
SAW
TLV(LCCs)
VIE
BUD
LED
MUC BHX
BRU
AMS
ATH
WAW DUB
BEG
FRA
LGW‐peak
SZG
PRG
BTS
LJU
ZRH
BRS
ORY
CDG
TLV
LHR
US$
Combined Landing and Passenger Charges for Boeing 767 Mean
Lowest charges: Riga (Latvia), LuxembourgHighest charges: London Heathrow, Ben Gurion (Tal Aviv)
NORTH AMERICA: COST PER ENPLANED PASSENGER, 2011 (IN US$)
Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge
0
5
10
15
20
25
30
35
40
CLT
BUR
ATL
SLC
FLL
MCI
TPA
MEM PHX
MKE
MSP
OKC
MCO RIC
RDU
BNA
DFW JAX
RNO
SDF
SAN
TUS
MSY
RSW
CMH
HOU
ALB
PBI
MDW ABQ
AUS
LAS
DTW
BDL
SAT
IAH
PHL
SNA
BWI
SMF
ANC
IND
OAK PVD
HNL
SEA
LAX
DEN SJC
PDX
ONT
ORD STL
DCA
BOS
SFO
LGA
PIT
MIA IAD
EWR
JFK
YYJ
YQR
YEG
YYC
YYT
YQB
YVR
YHZ
YOW
YWG
YUL
YYZ
US$
CPE US Mean Canada Mean
US Airports Canadian Airports
United States:Lowest CPE: Charlotte, California Bob Hope (Burbank,CA)Highest CPE: New York JFK, Newark Liberty
Canada:Lowest CPE: Victoria, ReginaHighest CPE: Toronto, Montreal
ATRS AIRPORT BENCHMARKING REPORT
The ATRS Global Airport Performance Benchmarking Report : 3 volumes, over 600 pages of valuable data and analysis.
Can be purchased by visiting www.atrsworld.org
Report sale finances our annual benchmarking research project
© Air Transport Research Society (ATRS)49
Thank You2014 ATRS World Conference
(Bordeaux, France, end of June, 2014)