Key Findings of 2013 ATRS Global Airport Performance Benchmarking project ww.atrsworld.org Prof. Tae Hoon Oum Dr. Yapyin Choo Prof. Chunyan Yu ATRS Global Airport Benchmarking Task Force Asia Pacific: P. Forsyth, Xiaowen Fu, Yeong‐Heok Lee, Yuichiro Yoshida, Japhet Law, Shinya Hanaoka Europe: Nicole Adler, Jaap de Wit, Hans‐Martin Niemeier, Eric Pels North America: Tae Oum, Bijan Vasigh, Jia Yan, Chunyan Yu Middle East: Paul Hooper © Air Transport Research Society (ATRS) 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 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 our research resource constraints Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge 2013 ATRS Global Airport Performance Benchmarking Project Airport Database Airports Included in the 2012 Report Canada (12)+US(65) Europe 77 airports 77 airports 17 airport groups Asia Pacific 35 Asian airports 16 Oceania airports 9 airport groups ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Total 195 airports 26 airport groups © Air Transport Research Society (ATRS) 5 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 PPP US Census Bureau, Statistics Canada – regionally based Cost of Living Index © Air Transport Research Society (ATRS) 7 2013 ATRS Global Airport Performance Benchmarking Project Airport Characteristics PEK 0 Objective Data Airport Characteristics Methodology IAD FLL SLC BW I Efficiency & Cost User Charge YQ R YYT YQ B YYJ ALB RIC SDF YW G YH Z TU S O KC RN O PVD BU R YOW AN C ONT JAX BDL ABQ PBI YEG CMH CVG IN D RSW DAL SAT SJC PIT MSY SN A SMF AU S CLE RDU H OU O AK BN A MKE MEM MCI YYC STL YU L PDX TPA YVR SAN H NL DCA MDW Europe LG A BOS PH L DTW YYZ SEA MSP EW R MCO MIA LAS CLT PH X IAH SFO JFK DEN DFW LAX O RD ATL LJU BTS SZG LU X TLL KEF ZAG BEG SOF MLA TRN CIA BSL RIX H AJ BRS N AP BLQ CRL OPO G LA BG Y LYS BH X BU D LIN W AW EDI LTN LED CG N ALC N CE LPA PRG AG P TLV G VA H AM SAW ATH LIS H EL TXL STN DU B BRU ARN MXP MAN DU S Asia Pacific OSL VIE CPH PMI ZRH ORY LG W BCN IST MU C FCO MAD AMS FRA CDG LH R DU D CEI ZQ N N TL TSV REP H DY PN H N AN DRW GUM CN X CN S MFM PEN W LG O OL CH C CMB ADL H KT NGO H AK PER MAA SU B AKL KIX XMN G MP BN E TPE MEL SZX MN L BOM SH A N RT ICN SYD DEL KU L PVG CAN BKK SIN CG K DXB H KG HND PASSENGERS TRAFFIC, FY2011 (IN ’000 PASSENGERS) 100,000 90,000 North America 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 PASSENGER TRAFFIC (’000)TOP 10 AIRPORTS: FY 2007, 2009, 2011 100,000 90,000 Asia Pacific Europe North America 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 2007 Objective Data Airport Characteristics 2009 2011 Methodology Efficiency & Cost User Charge 0 Objective Data Europe Airport Characteristics Methodology ATL OR D DFW DEN LAX C LT IAH LAS DTW P HL P HX YYZ JFK EW R M SP SFO M IA LGA B OS SEA M EM MCO SLC YV R IAD DC A HNL BWI FLL YYC YUL P DX M DW C LE STL SAN TP A OAK CVG MKE HOU R DU IND SDF MCI P IT YOW YEG AUS SAT DAL YW G CMH SM F AB Q M SY ANC SJC B DL B NA B UR SNA YHZ JAX ONT R SW YQB YYJ R IC OK C P VD ALB TUS P BI R NO YYT YQR Asia Pacific C DG FR A LHR M AD AM S M UC FC O IST BCN ZR H CPH LGW V IE BRU OSL OR Y DUS AR N MXP HEL PMI ATH TX L M AN DUB GV A HAM NC E PRG LIS STN C GN W AW LYS SAW LED LP A B UD AGP EDI TLV LTN LIN B HX B SL C RL LUX HAJ ALC R IX GLA B GY B LQ BRS NAP OP O C IA SOF B EG TR N ZAG TLL LJU M LA B TS K EF SZG P EK HND C AN C GK P VG HK G SIN DEL BKK SYD K UL B OM SHA IC N SZX M NL M EL NR T B NE TP E AK L XMN GM P M AA K IX SUB P ER W LG HAK NGO ADL C HC NAN HK T P EN GUM CMB C NS M FM OOL C NX TSV DR W R EP P NH NTL HDY DUD ZQN C EI AIRCRAFT MOVEMENTS, FY 2010 (’000 ATM) 1000 North America 900 800 700 600 500 400 300 200 100 Efficiency & Cost User Charge 0 Objective Data Europe Airport Characteristics Methodology JFK MCO SEA PBI LAX B NA SFO SNA R SW M DW TP A SAN FLL M IA ATL M SY SJC P HX SM F BWI DFW EW R DEN M SP R NO IAD B OS LAS YYZ OR D IAH C LT AUS MCI DTW JAX P HL P DX SLC SAT DC A LGA DAL STL P VD P IT HNL R DU B DL YUL TUS ONT HOU MKE YVR OK C CMH ABQ YYC OAK IND B UR YEG ANC R IC C LE YHZ C VG ALB YYT YOW YQR M EM YW G SDF YYJ YQB Asia Pacific LHR LGW ALC TLV STN PMI IST C DG AGP OR Y AM S B GY FR A M AD DUB SAW FC O M AN BCN M LA LIS MXP OP O TX L LTN LIN GLA LP A AR N B HX M UC DUS NAP OSL CPH EDI C IA ZR H K EF BRS SZG HAM VIE B LQ TR N LED ATH B UD GVA BRU PRG W AW HEL SOF C GN LYS R IX B EG CRL NC E HAJ B TS B SL ZAG TLL LJU LUX NR T HND BKK HK G SIN TP E IC N HK T P EK GM P OOL SHA CMB C GK K UL C EI M NL M EL HDY SUB C AN K IX SZX P ER SYD HAK B OM DEL P VG B NE C NX XMN NGO M AA M FM ADL ZQN P EN C NS AK L P NH DR W NTL R EP C HC DUD W LG TSV GUM NAN PASSENGERS PER AIRCRAFT MOVEMENTS, FY 2011 200 North America 180 160 140 120 100 80 60 40 20 Efficiency & Cost User Charge Objective TSV OOL HKG 0% Data Airport Characteristics Methodology Efficiency & Cost User Charge ANC MEM JFK LGA EWR STL CLE YYZ PHL ORD MDW IAH MIA IAD PIT BWI YWG DEN SEA LAX MSY BDL ONT PDX SFO DCA OAK HNL CVG HOU DTW LAS YOW YUL BOS YHZ PVD SDF SAT SAN YYT ABQ ALB AUS YVR CLT SJC RSW SNA DFW SMF PBI YYC SLC YQB TUS YEG MSP DAL MKE IND RDU BNA RNO Europe CMH ATL YQR MCO YYJ FLL PHX JAX RIC MCI BUR TPA OKC BEG LED MAD PMI HEL BTS BCN WAW TXL BLQ AGP ALC LPA DUS CGN MLA SZG LHR SAW OPO LIS STN BGY EDI LGW SOF MUC NAP ARN GLA GVA LJU HAM MAN ZAG LYS BHX LTN MXP LIN TLL ZRH RIX TLV FCO CIA DUB Asia Pacific BUD VIE CPH ATH AMS HAJ TRN NCE ORY CDG BSL OSL FRA PEN CGK KIX KUL NGO XMN DRW SZX CNX HKT HDY CEI BKK ADL WLG PVG NTL PEK NRT ZQN GUM HAK MEL CHC SYD SIN BNE NAN ICN CMB CAN PER GMP AKL DUD % NON‐AERO REVENUE, FY 2011 80% North America 70% 60% 50% 40% 30% 20% 10% 2013 ATRS Global Airport Performance Benchmarking Project Methodology AIRPORT PRODUCTIVITY INDEX • • • • Outputs Inputs Aircraft movement Passenger {Cargo tonnes} Non‐aeronautical revenue output • Labour • Other non‐capital (soft‐cost) input • [Runways, terminal size, # of gates] Objective Data Airport Methodology Efficiency & Cost © Air Transport Research Society (ATRS) Characteristics 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 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 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 0 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge 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 Objective Data Airport Methodology Efficiency & Cost Characteristics © Air Transport Research Society (ATRS) User Charge 19 GROSS VARIABLE FACTOR PRODUCTIVITY VS RESIDUAL VFP: NORTH AMERICA (YVR=1.0), FY 2011 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 ATL MSP CLT TPA MCO YVR SFO FLL LGA MDW SLC SEA HNL PHX EWR BOS JFK DTW PHL LAS SAN DCA BWI IAD DFW ORD IAH DEN MIA 0 Gross VFP Objective Data Airport Characteristics Residual VFP Methodology Efficiency & Cost User Charge 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 airports are 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 VFP Ranking Objective Data Residual DEA Ranking FLL MKE MCO SNA TPA SAT LGA JAX MSP BNA PBI CLT RNO RDU 55 50 45 40 35 30 25 20 15 10 5 0 ATL Rank RESIDUAL RANKING COMPARISON OF TOP 15 AIRPORTS IN US Residual SFA Ranking Airport Methodology Efficiency & Cost Characteristics © Air Transport Research Society (ATRS) User Charge 22 Residual VFP Ranking Objective Data Residual DEA Ranking MIA MSY PIT BWI ORD LAX ONT STL DFW PHL ALB SJC CLE CVG 55 50 45 40 35 30 25 20 15 10 5 0 BOS Rank RESIDUAL RANKING COMPARISON OF BOTTOM 15 AIRPORTS IN US Residual SFA Ranking Airport Methodology Efficiency & Cost Characteristics © Air Transport Research Society (ATRS) User Charge 23 55 50 45 40 35 30 25 20 15 10 5 0 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 RANKING COMPARISON OF MID‐RANKED 15 AIRPORTS IN US Residual VFP Ranking Objective Data Residual DEA Ranking Residual SFA Ranking Airport Methodology Efficiency & Cost Characteristics © Air Transport Research Society (ATRS) User Charge 24 2013 ATRS Airport Benchmarking Key Results on Efficiency & Cost RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): EUROPE LARGE AIRPORTS (CPH=1.0), FY 2011 Copenhagen Kastrup, Athens, Zurich ZRH 1 CPH ATH 1.2 0.4 ADR ADP Swedavia Avinor SEA DAA MAG Berlin AENA Fraport Finavia BAA TAV PPL FCO ARN MXP PMI VIE MAN IST LGW STN HEL ORY DUB BCN TXL MAD DUS FRA LHR MUC 0.6 AMS CDG 0.8 ANA Schiphol OSL LIS Airport Groups 0.2 0 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): EUROPE SMALL & MEDIUM AIRPORTS (CPH=1.0), FY 2011 1 CGN BTS LIN WAW BEG 0.4 SOF ZAG BUD SZG RIX AGP 0.5 HAM GLA MLA NAP ALC TLL BGY 0.6 TRN LTN LPA EDI KEF HAJ TLV 0.7 SAW BLQ LJU BHX 0.8 CIA NCE BSL 0.9 GVA Geneva, Basel, Nice LED 0.3 0.2 0.1 0 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge Airports HKG HAK GUM 1.2 Gimpo, Incheon, Guam ICN 1.4 GMP RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): ASIA (HKG=1.0), FY 2011 Airport Groups MAHB AOT APII AAI KAC KIX NGO CMB 0.4 KUL NRT SZX BKK HKT XMN PEN 0.6 PVG CNX CAN HDY PEK 0.8 CGK SIN 1 0.2 0 Residual VFP Objective Data Airport Characteristics Mean Methodology Efficiency & Cost User Charge RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): OCEANIA (SYD=1.0), FY 2011 1.2 0.4 0.2 0 Residual VFP Objective Data Airport Characteristics Mean Methodology Efficiency & Cost User Charge ADG APAC NTL NAN 0.6 WLG ADL ZQN PER BNE DUD CHC OOL MEL DRW 0.8 QAL Airport Groups Airports TSV 1 AKL SYD Sydney, Auckland, Townsville RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): NORTH AMERICA LARGE AIRPORTS (YVR=1.0), FY 2011 1.6 ATL 1.8 Atlanta, Minneapolis St. Paul, Charlotte LAX MIA DEN IAH 0.6 ORD DFW IAD BWI DCA LAS SAN PHL DTW JFK BOS PHX EWR 0.8 HNL SLC SEA MDW LGA SFO FLL 1 YVR MCO TPA 1.2 CLT MSP 1.4 0.4 0.2 0 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): N. AMERICA SMALL & MEDIUM AIRPORTS (YVR=1.0), FY 2011 OKC 1.6 Oklahoma City, Richmond, Raleigh‐Durham 1.2 1 0.8 0.6 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 RIC RDU 1.4 0.4 0.2 0 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge TOP EFFICIENCY PERFORMERS (2013) (based on Net VFP index=operating/management efficiency) Asia Pacific: • Asian Airports: • Gimpo, Incheon, Guam • Oceania Airports: • Sydney, Auckland, Townsville Europe: • Large Airports (> 15 million pax): • Copenhagen Kastrup, Athens, Zurich • Small/Medium Airports (< 15 millions Pax): • Geneva, Basel, Nice Objective Data Airport Methodology Efficiency & Cost Characteristics © Air Transport Research Society (ATRS) User Charge 32 TOP EFFICIENCY PERFORMERS (2013) (based on Net VFP index=operating/management efficiency) North America: • 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 Global (10th Global Excellence Award) • Hartsfield‐Jackson Atlanta International Airport Objective Data Airport Methodology Efficiency & Cost Characteristics © Air Transport Research Society (ATRS) User Charge 33 PAST AIRPORT EFFICIENCY EXCELLENCE TOP PERFORMERS, 2008 ‐ 2012 2008 2009 2010 2011 2012 North America Europe Asia‐Pacific Objective Hartsfield‐Jackson Atlanta International Airport Copenhagen Kastrup International Airport Hong Kong International Airport Data Hartsfield‐Jackson Atlanta International Airport Hartsfield‐Jackson Atlanta International Airport Hartsfield‐Jackson Atlanta International Airport Hartsfield‐Jackson Atlanta International Airport Copenhagen Kastrup International Airport Large Airport Category: Oslo International Airport Small/Medium Airport Category: Geneva Cointrin International Airport Large Airport Category: Oslo International Airport Copenhagen Kastrup International Airport Small/Medium Airport Category: Genève Aéroport Large Airport Category: Copenhagen Kastrup International Airport Small/Medium Airport Category: Genève Aéroport Hong Kong International Airport Large Airport Category: Hong Kong International Airport Small/Medium Airport Category: Seoul Gimpo International Airport Asian Airport Excellence Award: Hong Kong International Airport Oceania Excellence Award: Sydney Airport Asian Airport Excellence Award: Seoul Gimpo International Airport Oceania Excellence Award: Sydney Airport Airport Characteristics Methodology Efficiency & Cost User Charge COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT EUROPE ‐ LARGE AIRPORTS (CPH=0.0) 0.4 CPH ANA LIS ATH 0.2 Airport Groups Airports ‐0.6 Finavia ‐0.4 PPL TAV MAG ADR Schiphol Fraport SEA DAA Swedavia AENA BAA ADP Berlin ‐0.2 MAN PMI FCO STN MXP BCN AMS MAD VIE LGW ARN DUB FRA LHR DUS MUC CDG TXL ZRH ORY HEL OSL 0 Athens, Lisbon, ANA (Aeroportos de Portugal) Avinor ‐0.8 ‐1 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT EUROPE ‐ SMALL & MEDIUM AIRPORTS (CPH=0.0) ZAG BUD BEG BHX SAW 0.1 CIA BTS MLA SOF RIX 0.2 TLL 0.3 BSL LJU 0.4 CGN BLQ ‐0.5 SZG TLV LIN ‐0.4 HAM NAP GLA LED Ljubljana(Slovenia), Basel, Tallinn (Estonia) AGP ‐0.3 TRN BGY KEF NCE EDI LTN HAJ ‐0.2 ALC ‐0.1 WAW LPA 0 GVA ‐0.6 ‐0.7 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge AAI Airport Groups HKG XMN Airports CMB CGK GMP 0.5 HAK COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT ASIA (HKG=0.0) – THE HIGHER THE BETTER KAC MAHB AOT APII SIN GUM ‐0.5 CAN KUL BKK HKT SZX PEN PEK PVG CNX ICN HDY 0 Haikou, Seoul Gimpo, Airport Authority of India NRT NGO ‐1.5 KIX ‐1 ‐2 Cost Competitiveness Objective Data Airport Characteristics Methodology Mean Efficiency & Cost User Charge COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT OCEANIA (SYD=0.0) QAL Airport Groups CHC Airport s DUD 0.2 AKL 0.3 SYD 0.1 ZQN 0 ‐0.4 ADG APAC Queensland Airport Limited (QAL), Auckland, Dunedin (NZ) ADL ‐0.3 PER BNE DRW ‐0.2 WLG MEL ‐0.1 NTL ‐0.5 ‐0.6 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT N. AMERICA ‐ LARGE AIRPORTS (YVR=0.0) YVR PHX SLC FLL MSP MCO 0.5 CLT ATL 1 LAX SFO IAD DCA MIA BOS PHL DEN ORD SAN HNL MDW ‐0.5 BWI DFW IAH SEA DTW LAS 0 EWR ‐1 TPA Atlanta, Charlotte, Orlando LGA JFK ‐1.5 ‐2 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge COST COMPETITIVENESS: = NET VFP AND INPUT PRICE EFFECT N. AMERICA ‐ SMALL & MEDIUM AIRPORTS (YVR=0.0) RDU 0.6 Oklahoma City, Richmond (Virginia), Raleigh‐Durham RIC 0.8 OKC 1 YEG AUS RSW MCI PVD MEM SDF YYJ MKE DAL MSY JAX YQR PBI SAT 0.2 YYC RNO ABQ IND BNA TUS 0.4 Objective Data Airport Characteristics Methodology Efficiency & Cost ONT User Charge ANC YQB SJC YOW YHZ YUL CLE STL ‐0.4 SMF PIT YWG HOU ALB PDX BUR SNA CVG ‐0.2 BDL CMH YYT 0 2013 ATRS Airport Benchmarking User Charge Comparison 0 Objective Europe Landing Charges for Boeing 767 Data Airport Characteristics Methodology 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 Asia Pacific 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 (Swiss Sector) 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 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‐Night OOL SYD NGO DRW KIX NRT HND US$ LANDING CHARGES FOR BOEING 767‐400, 2012 (IN US$) 8,000 7,000 6,000 5,000 4,000 3,000 North America 2,000 1,000 Mean Efficiency & Cost User Charge ASIA PACIFIC: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 737‐800, 2012 (IN US$) 9000 Lowest charges: Taipei Taoyuan, Dunedin (New Zealand) Highest charges: Osaka Kansai, Tokyo Narita 8000 7000 6000 5000 4000 3000 2000 1000 Airport Characteristics Methodology Efficiency & Cost KIX NRT‐2 NRT‐1S AKL User Charge NRT‐1N BNE NGO BNE‐Peak SYD HND CNS ADL NTL‐Night PER MEL OOL DRW REP PNH NTL CHC TSV WLG BKK HKT HDY CEI CNX ICN DXB PEN KUL ZQN SUB CGK GMP SZX XMN SHA PEK PVG HAK CAN SIN Data MFM MNL CMB GUM SIN (LCCs) HKG Objective KUL (LCCs) MAA DEL BOM TPE DUD 0 RIX CRL LUX NAP BGY MAN‐offpeak HEL CIA BLQ ARN KEF TLL CGN GVA ZAG FCO (LCCs) OSL CPH(LCCs) ALC BSL (France Sector) MAN‐peak AGP LPA PMI STN‐off peak FCO BCN MXP TRN NCE STN‐peak GLA LTN LIN HAM CPH EDI BUD (LCCs) MAD HAJ MLA BSL (Swiss Sector) LYS DUS TXL BEG (LCCs) SOF‐offpeak SAW SOF‐peak OPO TLV(LCCs) LGW‐off‐peak BHX IST LIS AMS VIE LED MUC WAW DUB BUD BRU SZG BEG BTS LJU ATH FRA ZRH PRG BRS ORY CDG TLV LGW‐peak LHR 12000 EUROPE: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 737‐800, 2012 (IN US$) 10000 Lowest charges: Riga (Latvia), Brussel South Charleroi Highest charges: London Heathrow, London Gatwick‐ Peak 8000 6000 4000 2000 0 Objective Data Airport Characteristics Methodology Efficiency & Cost User Charge NORTH AMERICA: COST PER ENPLANED PASSENGER, 2011 (IN US$) 40 Canada: Lowest CPE: Victoria, Regina Highest CPE: Toronto, Montreal 35 30 US$ 25 20 Canadian Airports US Airports United States: Lowest CPE: Charlotte, California Bob Hope (Burbank,CA) Highest CPE: New York JFK, Newark Liberty 15 10 CPE Objective Data Airport Characteristics US Mean YYJ YQR YEG YYC YYT YQB YVR YHZ YOW YWG YUL YYZ 0 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 5 Canada Mean Methodology Efficiency & Cost User Charge ASIA PACIFIC: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 767, 2012 (IN US$) 16,000 Lowest charges: Taipei Taoyuan, Dunedin (New Zealand) Highest charges: Osaka Kansai, Tokyo Narita 14,000 12,000 US$ 10,000 8,000 6,000 4,000 2,000 Combined Landing and Passenger Charges for Boeing 767 Objective Data Airport Characteristics Methodology KIX NRT‐2 NRT‐1S NRT‐1N AKL HND NGO BNE‐Peak SYD BNE ADL CNS DRW NTL‐Night NTL OOL PER MEL REP TSV PNH BKK HKT HDY CEI CNX PEN WLG ZQN ICN DXB SUB CGK KUL GMP SZX XMN SHA PEK PVG HAK SIN CAN MNL MFM CMB SIN (LCCs) KUL (LCCs) HKG GUM MAA DEL BOM TPE DUD 0 Mean Efficiency & Cost User Charge EUROPE: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 767, 2012 (IN US$) 18,000 Lowest charges: Riga (Latvia), Luxembourg Highest charges: London Heathrow, Ben Gurion (Tal Aviv) 16,000 14,000 12,000 US$ 10,000 8,000 6,000 4,000 0 RIX LUX CRL NAP BGY MAN‐offpeak CIA ARN HEL BLQ KEF CGN OSL TLL FCO (LCCs) STN‐off peak ZAG GVA ALC MAN‐peak STN‐peak CPH(LCCs) AGP LPA PMI BSL (France Sector) LTN FCO BCN TRN MXP NCE BUD (LCCs) LIN LGW‐off‐peak GLA DUS EDI SOF‐offpeak CPH HAM MAD SOF‐peak MLA IST HAJ OPO BSL (Swiss Sector) LIS TXL BEG (LCCs) 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 2,000 Combined Landing and Passenger Charges for Boeing 767 Objective Data Airport Characteristics Methodology Mean Efficiency & Cost User Charge NORTH AMERICA: COST PER ENPLANED PASSENGER, 2011 (IN US$) 40 Canada: Lowest CPE: Victoria, Regina Highest CPE: Toronto, Montreal 35 30 US$ 25 20 Canadian Airports US Airports United States: Lowest CPE: Charlotte, California Bob Hope (Burbank,CA) Highest CPE: New York JFK, Newark Liberty 15 10 CPE Objective Data Airport Characteristics US Mean YYJ YQR YEG YYC YYT YQB YVR YHZ YOW YWG YUL YYZ 0 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 5 Canada Mean Methodology Efficiency & Cost User Charge 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 You 2014 ATRS World Conference (Bordeaux, France, end of June, 2014)