Benchmarking Airport Productivity and the Role of Capacity Utilization – a Study of Selected European Airports

Benchmarking Airport Productivity and the Role of Capacity Utilization – A Study of Selected European Airports

Branko Bubalo

Thesis submitted to the faculty of the University of Applied Sciences Berlin In partial fulfillment of the requirements for the degree of

Diplom-Wirtschaftsingenieur Umweltmanagement

Dr. Jürgen Müller Dr. Anne Graham

Berlin, Germany, February 15, 2009

To my family, my best friend and partner Claudia and our lovely son Richard Alexius.

Statutory Declaration

I hereby confirm, that I have independently composed this Diploma Thesis and that no other than the indicated aid and sources have been used. This work has not been presented to any other examination board.

______(Branko Bubalo), Berlin, February 15, 2009

Table of Contents

Table of Contents Table of Contents ...... 4 Abbreviations ...... 6 Sample Airports ...... 11 Basic Annual Indicators for Sample Airports ...... 12 Preface ...... 15 1 Introduction ...... 18 1.1 Recent Developments and Future Outlook in Air Transportation ...... 19 1.2 Fuel Prices Affecting Air Transportation ...... 24 1.3 New Airspace Surveillance Technologies ...... 25 1.4 Capacity ...... 26 1.5 Ultimate Capacity ...... 30 2 Methodologies and Models ...... 31 2.1 Data Collection ...... 32 2.2 Peak Period Estimation and Data ...... 33 2.3 Considerations Concerning the Data ...... 37 2.4 The Benchmarking Concept ...... 38 2.5 Criticism and Findings of Technical, Operational and Infrastructural Inputs used in Previous Benchmarking Studies ...... 40 3 Airport Capacity Assessment...... 44 3.1 The “Kanafani Model” and Assessment Results ...... 46 3.2 Analytical Model for Calculating Ultimate Capacity ...... 51 3.3 Simulation Setup ...... 54 3.3.1 Available Simulation Models ...... 54 3.3.2 Single Runway Airports ...... 58 3.3.3 Airport Charts ...... 59 3.3.4 Airport Coordinates ...... 60 3.3.5 Separation Minima and Wake Vortex Classification...... 61 3.3.6 Weather Data ...... 63 3.3.7 Wind Direction...... 64 3.3.8 Preferential Runway System ...... 66 3.3.9 Apron, Runway Exits and Taxiway layout ...... 67 3.3.10 Gate, Departure Queue and Airspace ...... 68 3.4 Simulation Run and Scenarios ...... 69

Benchmarking Airport Productivity and the Role of Capacity Utilization 4 Table of Contents

4 Results of the Simulation Benchmarking ...... 70 4.1 Results for the LHR and BBI Simulations ...... 74 4.2 Traffic Flows at the Simulated Airports ...... 77 5 Summary of Results and Conclusions ...... 80 6 Outlook...... 83 References and further readings ...... 85 Electronic and Internet Sources ...... 96 Appendix ...... 99 Figures 23...... 102 Figures 26 and 27 ...... 112 Figures 16...... 117 Figures 17...... 147 Figures 18...... 168 Figures 19...... 178 Figures 28...... 185 Contacts...... Error! Bookmark not defined.

Benchmarking Airport Productivity and the Role of Capacity Utilization 5 Abbreviations

Abbreviations

ABS Airport Business Suite ACD Airport Capacity And Delay ACI Airports Council International ADRM Airport Development Reference Manual ADS-B Automatic Dependent Surveillance Broadcast ADV Arbeitsgemeinschaft Deutscher Verkehrsflughäfen AO Annual Operations AP Annual Passengers ARC Aachen Research Center ARR Arrival ASV Annual Service Volume ATA Air Transport Association ATAC The Air Transport Association of Canada ATC Air Traffic Control ATM Air Traffic Management ATS Air Traffic System ATSL Air Transportation Systems Laboratory BAA British Airport Authority BRIC Brazil, Russia, India, China (emerging Markets) BSE Berlin School of Economics CAA Civial Aviation Authority CAMACA Commonly Agreed Methodology for Airport Airside Capacity Assessment CDM Collaborative Decision Making CEO Chief Executive Officer CFMU Central Flow Management Unit DEP Departure DFS Deutsche Flugsicherung DfT Department for Transport DOT U.S. Department of Transportation DPH Design Peak Hours FAA Federal Aviation Administration FSX Flight Simulator® (Microsoft) GAP German Airport Performance GE Google Earth® (Software) GPS Global Positioning System GUI Grafical User Interface Software H Aircraft Class Heavy (> 136,000 kg) Ha Hectar HO Design Peak Hour Operation HP Design Peak Hour Passengers IATA International Air Transport Association ICAO International Civial Aviation Organization IFR Instrument Flight Rules INM Integrated Noise Model IMC Instrument Meteorological Conditions IVAO International Virtual Aviation Association L Aircraft Class Large (7,000 to 136,000 kg) Benchmarking Airport Productivity and the Role of Capacity Utilization 6 Abbreviations

LCC Low Cost Carriers, Low Fares Airline M refer to L MIT Massachuchettes Institute of Technology MTOW Maximum Take-Off weight Mvts Movements (Ops and Flights) NAS National Airspace System NASA National Aeronautics ans Space Administration NGATS Next Generation Air Transportation System NextGen Next Generation Air Transportation System NLR National Aerospace Laboratory of the Netherlands nmi Nautical Miles OAG Official Airline Guide Ops Operations PACS Pan-European Airport Capacity and Delay Analysis Support PAX Passengers PD Peak Days PDTHUW26 Design Peak Day, Thursday of Week 26 PH Peak Hour PHW122009 Peak Hour Of Week 12, 2009 PPP Public Private Partnership RAMS Reorganized ATC Mathematical Simulator RNP Required Navigation Performance ROT Runway Occupancy Time Rwy Runway S Aircraft Class Small (<7000 kg) SES Single European Sky SESAR Single European Sky ATM Research Programme SIMMOD Airport and Airspace Simulation Model SLF Seat Load Factor TAAM Total Airspace and Airport Modeler THENA Thematic Network on Airport Activities TRB Transportation Research Board UK United Kingdom USD United-States Dollar VFR Visual Flight Rules Virginia Tech Virginia Polytechnic Institute and State University VMC Visual Meteorological VS Visual SIMMOD

Benchmarking Airport Productivity and the Role of Capacity Utilization 7

List of Figures

Main Text

Figure 1 Annual Passengers at Sample European Airports with above 10 million PAX from 2003 to 2007 (Bubalo 2009, EUROSTAT 2008) page 11

Figure 2 Annual Passengers at Sample European Airports with below 10 million PAX from 2003 to 2007 (Bubalo 2009, EUROSTAT 2008) page 11

Figure 3 Annual Flights at Sample European Airports with above 100,000 flights from 2003 to 2007 (Bubalo 2009, EUROSTAT 2008) page 12

Figure 4 Annual Flights below 100,000 Operations at Sample European Airports from 2003 to 2007 (Source: Bubalo 2009, EUROSTAT 2008) page 12

Figure 5 Passengers and Operations Change from 2003 to 2007 (Source: Bubalo 2009, EUROSTAT 2008) page 13

Figure 6 Most Constraining Points in European Air Traffic in 2007 page 13

Figure 7 Annual monthly traffic and variations (PRC 2007) page 19

Figure 8 The long-term view of growth is of a stable long-termed trend page 29

Figure 9 Ranking of the main global hubs (Ambrosetti 2008) page 21

Figure 10 Limiting Factors for Capacity and Interdependencies at Airports page 26

Figure 11 Target Groups and Offerings at Airports (Bubalo 2009 adapted from Leutenecker & Fraport 2008) page 28

Figure 12 Relationship between delay-related and ultimate capacity page 29

Figure 13 Traffic and total delays comparison with equivalent weeks of the previous year (CFMU 2008) page 34

Figure 14 Capacity and ASV for long-range planning (FAA 1995) page 39

Figure 15 Groupings of runway configurations in relation to Annual Service Volume (ASV), (Bubalo 2009 derived from FAA 1995) page 40

Figure 16a STN Weekdays Operations Pattern and Capacities page 44

Figure 17a Assumption Rectangle and Capacities of FRA Airport for the year 2007/2008 (Bubalo 2009) page 48

Figure 18a Typical Capacity Envelope for LHR airport page 51

Benchmarking Airport Productivity and the Role of Capacity Utilization 8

Figure 18b Typical Capacity Envelope for IST airport page 51

Figure 19a STR Airport Layout for SIMMOD page 59

Figure 20 Vortex generation on take-off and landing (CAA 1999) page 60

Figure 21 Ceiling and Visibility Routines (De Neufville 2003) page 61

Figure 22 Example printout of windrose (two bi-directional runways) page 63

Figure 23a Example BHX Flights and Delays per Flight from SIMMOD page 69

Figure 24 LHR airport SIMMOD simulation for 20% growth scenario page 74

Figure 25 BBI airport SIMMOD simulation for 100% growth scenario page 74

Figure 26 Flows of Airport Traffic at LHR: SIMMOD Base and 20% growth scenario, (Bubalo 2009) page 75

Figure 27 Flows of Airport Traffic at BBI Airport: SIMMOD Base, 20% and 100% growth scenario, (Bubalo 2009) page 77

Appendix

Figures 16 Weekday Operations Pattern and Capacities (Demand Diagrams) for Sample Airports pp. 117

Figures 17 Assumption Rectangles for Sample Airports pp. 147

Figures 18 Typical Capacity Envelopes for Selected Airports pp. 169

Figures 19 Airport Layout for SIMMOD Sample Airports pp. 179

Figures 23 Flights and Delays per Flight from SIMMOD pp. 102

Figures 28 Airport Charts for Sample Airports pp. 186

Benchmarking Airport Productivity and the Role of Capacity Utilization 9

List of Tables

Table 1 Sample Airports and Codes, (Bubalo 2009) page 10

Table 2 Freedom of Action for Airlines before and after Deregulation (De Neufville 2008) page 20

Table 3 Top Five Traffic Days 2005-08 (EUROCONTROL CFMU 2008) page 33

Table 4 Basic Indicators and Airport Configurations Groups for sample airports (Bubalo 2009) pp. 41/42

Table 5 'Static' Capacity Assessment (Bubalo 2009) pp. 46/47

Table 6 Weight parameters (CAA 1999) page 61

Table 7 Average Seatload Factors for Sample Airports page 116

Table 8 SIMMOD arrivals injection time adjustment for flight schedule data, (Bubalo 2009) page 178

Benchmarking Airport Productivity and the Role of Capacity Utilization 10 Sample Airports

Sample Airports

Index IATA ICAO Name Country 1 AMS EHAM Amsterdam NL 2 ARN ESSA Arlanda SE 3 ATH LGAT Athen GR 4 BBI EDDB* Berlin Brandenburg Int DE 5 BCN LEBL Barcelona ES 6 BHX EGBB Birmingham UK 7 BRU EBBR Bruessel BE 8 BSL LFSB Basel CH/FR 9 CDG LFPG Paris Charles de Gaule FR 10 CGN EDDK Köln Bonn DE 11 CIA LIRA Rom Ciampino IT 12 CPH EKCH Kopenhagen DK 13 DRS EDDC Dresden DE 14 DUB EIDW Dublin IE 15 DUS EDDL Duesseldorf DE 16 EDI EGPH Edinburgh UK 17 FCO LIRF Rom Fiumicino IT 18 FMO EDDG Muenster Osnabrueck DE 19 FRA EDDF Frankfurt Main DE 20 GLA EGPF Glasgow UK 21 GRZ LOWG Graz AT 22 HAJ EDDV Hannover DE 23 HAM EDDH Hamburg DE 24 HEL EFHK Helsinki FI 25 HHN EDFH Hahn DE 26 IST LTBA Istanbul TK 27 LBA EGNM Leeds Bradford UK 28 LCY EGLC London City UK 29 LEJ EDDP Leipzig DE 30 LGG EBLG Liege FR 31 LGW EGKK London Gatwick UK 32 LHR EGLL London Heathrow UK 33 LIS LPPT Lisbon PT 34 LTN EGGW London Luton UK 35 LYS LFLL Lyon FR 36 MAD LEMD Madrid ES 37 MAN EGCC Manchester UK 38 MUC EDDM Muenchen DE 39 MXP LIMC Mailand Malpensa IT 40 NCE LFMN Nizza FR 41 NUE EDDN Nuernberg DE 42 ORY LFPD Paris Orly FR 43 OSL ENGM Oslo NO 44 PMI LEPA Palma Mallorca ES 45 PRG LKPR Prag CZ 46 PSA LIRP Pisa IT 47 RHO LGRP Rhodos GR 48 RTM EHRD Rotterdam NL 49 SCN EDDR Saarbruecken DE 50 STN EGSS London Stansted UK 51 STR EDDS Stuttgart DE 52 SXF EDDB Berlin Schoenefeld DE 53 SZG LOWS Salzburg AT 54 TXL EDDT Berlin Tegel DE 55 VIE LOWW Wien AT 56 WAW EPWA Warschau PL 57 WRO EPWR Wroclaw PL 58 ZAG LDZA Zagreb HR 59 ZRH LSZH Zuerich CH

Table 1. Sample Airports and Codes. (Source: Bubalo 2009)

Benchmarking Airport Productivity and the Role of Capacity Utilization 11 Basic Annual Indicators for Sample Airports

Basic Annual Indicators for Sample Airports

Annual Passangers at Sample European Airports above 10 million PAX 2003-2007

80,000,000

70,000,000

60,000,000

50,000,000

40,000,000 AnnualPAX 30,000,000

20,000,000

10,000,000

LIS

VIE

PMI

TXL

HEL

LHR FRA OSL

STN ATH STR

ZRH

BCN DUB CPH ARN BRU DUS NCE

FCO

AMS ORY

MXP PRG

MAD MAN HAM

CDG CGN

MUC LGW 2003 2004 2005 2006 2007 Fig. 1. Annual Passengers at Sample European Airports with above 10 million PAX from 2003 to 2007 (Bubalo 2009, EUROSTAT 2008)

Annual Passangers at Sample European Airports below 10 million PAX 2003-2007

10,000,000

9,000,000

8,000,000

7,000,000

6,000,000

5,000,000

AnnualPAX 4,000,000

3,000,000

2,000,000

1,000,000

EDI CIA

LEJ

LYS

LBA BSL

HAJ

LTN

LCY

SXF

PSA

GLA

BHX SZG

NUE DRS SCN

GRZ LGG

HHN

RTM

MRS

RHO FMO

WRO WAW 2003 2004 2005 2006 2007 Fig. 2. Annual Passengers at Sample European Airports with below 10 million PAX from 2003 to 2007 (Bubalo 2009, EUROSTAT 2008)

Benchmarking Airport Productivity and the Role of Capacity Utilization 12 Basic Annual Indicators for Sample Airports

Annual Flights at Sample European Airports from 2003 to 2007 above 100,000 Flights

600,000

500,000

400,000

300,000 AnnualOps

200,000

100,000

LIS

VIE

EDI

PMI

TXL

LYS

HEL

LHR

FRA OSL ATH STN STR

BHX

ZRH

BCN FCO CPH BRU DUS ARN DUB NCE

MXP

AMS ORY PRG

CDG MAD MAN HAM CGN

MUC

LGW WAW

2003 2004 2005 2006 2007

Fig. 3. Annual Flights at Sample European Airports with above 100,000 flights from 2003 to 2007 (Bubalo 2009, EUROSTAT 2008)

Annual Flights at Sample European Airports from 2003 to 2007 below 100,000 Flights

100,000

90,000

80,000

70,000

60,000

50,000 AnnualOps 40,000

30,000

20,000

10,000

CIA

LEJ

LTN

HAJ LBA BSL

SXF

LCY

GLA

PSA

SZG

NUE DRS LGG GRZ SCN

HHN RTM

MRS RHO FMO WRO

2003 2004 2005 2006 2007

Fig. 4. Annual Flights below 100,000 Operations at Sample European Airports from 2003 to 2007 (Source: Bubalo 2009, EUROSTAT 2008)

Benchmarking Airport Productivity and the Role of Capacity Utilization 13 Basic Annual Indicators for Sample Airports

PAX and Ops Change in Percent from 2003 to 2007* *LGG & WRO change to 2006; WAW change to 2004

300%

250%

200%

150% Change 100%

50%

LIS

VIE

CIA EDI

PMI

LEJ

TXL

LTN

LYS HAJ

SXF

LCY ATH STR STN

LBA HEL FRA BSL

PSA

LHR ZRH

OSL SZG GLA

DUS NUE DRS FCO NCE SCN CPH GRZ ARN BHX

HHN BCN DUB ORY BRU

PRG RTM LGG

MXP AMS

RHO CGN CDG

MAD HAM MRS MAN FMO

MUC

LGW

WRO WAW -50%

% change Ops % change PAX Fig. 5. Passengers and Operations Change from 2003 to 2007*. (Source: Bubalo 2009, EURO- STAT 2008)

Fig. 6. Most Constraining Points in European Air Traffic in 2007. (Source: PRC 2007)

Benchmarking Airport Productivity and the Role of Capacity Utilization 14 Preface

Preface

The last four months have been the most interesting, educational and demand- ing in my life. After struggling to find an appealing topic for my diploma thesis and having a background in process and industrial engineering and environmental management, I do not exaggerate by saying I found a new passion: Ana- lysing the air transport system. Once the system is generally understood it becomes even more interesting and every new piece of information falls into its place.

I want to thank everybody, who helped me along the way to make my diploma thesis happen. Of course first of all I want to thank my supervisors, Dr. Anne Graham from Westminster University, London and Dr. Jürgen Müller from Berlin School of Economics (BSE) for their motivation, support and trust in me to create this peace of work, which I‟m very proud of. Both being busy in their fields of work, they gave me complete freedom to seek the goals needed for my thesis.

Also I want to thank my family for having patience with me through all the years, giving me the freedom to work the way I work, for motivation and for giving financial support for last minute trips or any needed investment. My son Richard is my main spring for inspiration, discipline and aspirations, even without the need of verbal communication.

It‟s amazing how helpful the aviation community was to me. I usually received immediate responses to my requests. Mr. Gregory Bradford of Airport Tools Inc. and Mory Camara of Official Airline Guide (OAG) clearly stand out with providing the tools needed for my study, being the Visual SIMMOD (VS) simulation software and detailed flight schedule data for all European air traffic. I do not know where I would stand had I not had the chance of using Gregory Bradford‟s modelling software VS. Initially I thought this would be a nice addition to my study, which commonly involves reading, data collection, application of models, analysis and writing.

Benchmarking Airport Productivity and the Role of Capacity Utilization 15 Preface

The common way had to be done, too, with enormous amounts of data analysis involved, but the adoption of the software and the simulation I wanted to realise simultaneously. I was indeed aware of the large learning curve for SIMMOD and the challenges involved in getting accustomed to the software, but Gregory Bradford has unbelievably good documentation and tutorials for his software, and he even provided immediate support. This proved to be vitally necessary, and I can‟t thank him enough for his assistance.

For a pilot‟s experience at many European airports I deeply thank Daniel Lam- berg of Air Berlin for explaining flight and airport operations-related questions to me. He also gave hints for fine-tuning the simulation. Another new friend of mine is Charles “Chuck” Eypper, an English teacher from Berlitz School Berlin and an American native, who saw over my writing on very short notice. I deeply want to thank him for help and motivation.

Unfortunately I met Prof. Dr. Joachim Daduna of Berlin School of Economics only at a late stage of my thesis preparation. He was the first and only person I could share and discuss my simulation and data analysis outcome with. Since he has had years of experience as a logistics professor, has consulted companies and governments worldwide on the efficient operation of various transport systems and knows about the complexity of a capacity assessment of such a sophisticated system as the air transport system, he gave me invaluable immediate feedback on my results.

I also want to thank my mother Gabriele Bubalo, having over 35 years of experience as a travel agent and tourist guide, for giving me answers to some basic flight schedule related questions, like code sharing and the importance of only considering non-stop flights, for providing useful information and data on the development of new routes, the demise of airlines, the development of information technology over the years, major aircraft accidents, minimum con- necting times, modal split at airports, distances from city centres, and so on. My familiarity with different IATA (International Air Transport Association) codes proved to be very useful as well. That knowledge dates back to my childhood years, when I loved to study flight schedules and travel operators‟

Benchmarking Airport Productivity and the Role of Capacity Utilization 16 Preface catalogues to dream myself away to different countries and destinations. And as a child I got around a lot, too, and visited many international destinations.

From my colleagues on the German Airport Performance (GAP) student research project, I want to thank Tolga Ülkü of the airport finance team for doing preliminary work on capacity utilization and productivity and for helping me set the basic approach and structure for my diploma thesis about “Benchmark- ing Airport Productivity and the Role of Capacity Utilization – A Study of Se- lected European Airports” and Marius Barbu of the airport charges team for fruitful conversations.

It became clear to me upon seeing the full picture of the air transport system that the key to an evaluation and comparison lies in the knowledge and experience of many different parties. I therefore completely support so called “Col- laborative Decision Making” (CDM) among all stakeholders of the industry, decision-makers, managers as well as politicians, environmental or other non- governmental organizations, to collectively work on problems together, share data and information, and focus on the efficiency of the air transport system and the true needs and welfare of the users of this system and the general public.

Branko Bubalo, Berlin, February 2009

Benchmarking Airport Productivity and the Role of Capacity Utilization 17 Introduction

“The difficult we do immediately, the impossible takes a little longer” Motto of the Navy Seabees in World War II

1 Introduction

We live in adventurous times where everything in the future seems to be possible. As most of the first decade in the new millennium lies behind us, it is time to reflect. We saw ups and downs in almost every global market or industry, the banking industry, the information technology industry, the real estate industry, the automobile industry, the tourism industry, commodities market, and of course the aviation industry. We saw political challenges and military conflicts; we fully realized the damaging potential of climate change by experiencing surprising extremes in global weather and realized the new threats to our life- style resulting from religious extremism. Surely this is nothing new. These have, in one way or another, always been with us, but in addition to those events we saw also major catastrophic natural and climatic events, like the flooding of New Orleans by hurricane “Katrina” and the tsunami in South-East Asia. These events were rather unique.

In the world today with its communication, global transportation and globaliza- tion, all people seem to sit in the same boat. Every major event has affected our lives, directly or indirectly. As we watched the news on television, we felt in- stantly connected with victims, survivors and helpers independent of country, belief or political agenda. This is the positive part of reflecting on the current decade; the world has never been more united.

It is becoming ever more important for the global community to exchange knowledge, information, ideas, opinions, products, capital and culture among its constituent countries. We want to know more about each other; we want to explore; and we want to taste each others‟ cuisine, listen to each others' music, and share each others‟ experiences.

Benchmarking Airport Productivity and the Role of Capacity Utilization 18 Introduction

With the explosive expansion of the Internet over the last 15 years, the speed of globalisation has accelerated, flows of information and products now cover the whole world, and this has became the natural state of affairs for the people and the economy worldwide. China and India are in the throes of a huge transformation process which will almost certainly lead to two new economic “super powers” among a whole group of super powers being the BRIC countries, Brazil, Russia, India and China. This brings challenges to developed regions like Europe as well.

To keep up with the increasing needs of the global community, mass transportation must meet the demand – especially in the area of air transportation. Since time nowadays is such a valuable and precious resource and we already can communicate and share information globally in real time, it is specifically the duty of air transportation to provide timely traffic on a global scale. We physically and virtually want to be anywhere at anytime.

1.1 Recent Developments and Future Outlook in Air Transportation

Air transportation is nowadays so important to the global economy, for both the transportation of passengers and goods, that I doubt it will be permanently affected by the current “financial crisis”. Demand for travel and air transportation will, despite the current financial crisis, continue to grow in the future, not only globally, but also regionally within Europe. Basil Borim, vice president for operations and safety of the Air Transport As- sociation (ATA), claims that “if carriers can develop a business case that makes sense, they‟ll have access to capital to make those investments” (Aviation To- day January 1, 2009). Same will be true for airports, because both industries are tightly connected.

All sources to my knowledge( like the “Challenges of Growth” report of EUROCONTROL) forecast a strong demand for air transportation in the future (Fig. 7). Business and consumption will continue to grow on a global scale (EUROCONTROL 2008).

Benchmarking Airport Productivity and the Role of Capacity Utilization 19 Introduction

Fig. 7. Annual monthly traffic and variations (2007). (Source: EUROCON- TROL 2007)

Also from immediate knowledge I know that people are still seeking to travel and will continue doing so in the future. From earlier crises it is also known that a decline in demand for air transportation is usually absorbed in the following one or two years. In the long run the demand for air transportation will grow 4% annually on average (Fig. 8).

Fig. 8. The long-term view of growth is of a stable long-term trend. (Source: PRC 2007)

What also plays a significant role in the continuous growth of air traffic is the much cited deregulation and liberalization of the air transport market since the mid-seventies in the U.S and the mid-nineties in Europe. Airports formerly

Benchmarking Airport Productivity and the Role of Capacity Utilization 20 Introduction being under federal authority, must behave increasingly like business-driven and profit-oriented entities (Graham 2005). Regulations considering prices, routes and the scheduling process for air travel were gradually abandoned, allowing more freedom and more competition among airlines (Table 2). This led to the emergence of the “no-frills” low cost carriers (LCC) or low fare airlines in the late nineties (e.g. Ryanair and easy- Jet), which have different and more flexible business models than the more established airlines have. This development brought more competition to various portions of the entire air transport system. Airport charges for example, which are weight- or passenger-based and which airlines have to pay for each aircraft takeoff and landing and for the infrastructure used at an airport, are a significant cost factor for airlines. Therefore less developed airports are seeking opportunities to provide services tailored for those LCC‟s (e.g. low charges, basic infrastructure and short turn-around times, like Hahn or Stansted airports). It is plausible to believe that a secondary network of these LCC airports will develop. LCC airports will try to serve a catchment area comparable to the established hubs or international airports; others will see their opportunity in spe- cialized services (e.g. cargo, business flights or general aviation, like Liège (LGG) airport for cargo).

Table 2. Freedom of Action for Airlines before and after Deregulation. (Source: De Neufville 2008)

We will also see more competition on comparable routes. It will not make much difference in time when flying from Rome Ciampino airport to London

Benchmarking Airport Productivity and the Role of Capacity Utilization 21 Introduction

Stansted airport or flying from Rome Fiumicino airport to London Heathrow airport. But the ticket price will make a difference. In metropolitan areas we will usually find more than one airport. The greater London area has five airports (LHR, STN, LGW, LCY, LTN), which are, with the exception of LTN, all operated by the company British Airport Authority (BAA), until 2008 Berlin had three airports (THF, SXF, TXL), and from 2011 on Berlin will most likely only have one newly constructed airport (Berlin- Brandenburg International (BBI)), Rome and Paris have two each FCO, CIA and ORY, CDG, respectively. In addition to these airports, there are airfields and airstrips scattered all across Europe and also others near the large urban areas, which could serve as reliever airports in the future.

Although reliever airports will grow strong, it is still the main European hubs that will dominate the air transport system. A hub is a main international airport which links the regional and national routes, the spokes, with international connections. We therefore speak of a hub-and-spoke network in Europe. Over time, with new routes and airports, this might transform more into a point-to- point network. In the top ten major global hub ranking of 2007, there are three European airports in the top five, first being London Heathrow airport (LHR), third being Paris Charles-de-Gaule (CDG) airport and fifth being Frankfurt am Main airport (FRA) (Fig. 9).

Fig. 9. Ranking of the main global hubs. (Source: the European House Am- brosetti 2008)

Benchmarking Airport Productivity and the Role of Capacity Utilization 22 Introduction

As the simulation of current and future air traffic has shown, LHR is the main bottleneck of the European air traffic system (ATS). LHR faces delays for every departure from early morning on and is highly congested. The problem is, that LHR is the main hub for global air traffic as well (Fig. 3) and creates delays for many destination airports and will affect any connection flight.

Through air traffic procedures during the flights, this can only marginally be reduced. As my simulation has shown, the last flight on each day at LHR airport could already face up a few hours delay. Since LHR operates at a calculated level much over its maximum capacity (ultimate/technical capacity), at around 30% over its ultimate capacity, this is clear. Recently the government and the British Airport Authority (BAA) resolved a long-lasting battle and saw the urgency of the matter to expand London Heath- row as quickly as possible. It is proposed to build a third runway and a sixth terminal by 2020 (BBC November 22, 2008).

Paris Charles-de-Gaulle (CDG) airport is the third global hub and my analysis has shown, that CDG has enough spare capacity for some more years, since it operates at approximately 84% of its technical or ultimate capacity.

Frankfurt (FRA) ranks fifth in the ranking of main global hubs and faces similar problems. FRA operates at a level of 40% over its technical capacity, which means over a years time the demand is 40% higher, than the airport can possibly handle. In this case the fourth runway is already planned and construction began. The new runway is expected to go into service by 2015.

One may ask how operations can work every day under the circumstance of operating over the technical capacity. Well, firstly the technical capacity is an estimate of the annual manageable flights of an airport and secondly, the observations to come up with these estimates for each runway configuration were done several years ago and do not take into account the technical development in air traffic control equipment, which allows less separation between aircrafts

Benchmarking Airport Productivity and the Role of Capacity Utilization 23 Introduction while taking-off or landing and therefore allows more operations per hour or per year (FAA 1995). Still, the estimates of the so called annual service volume (ASV) are approved by the FAA and make the assumption, that only this amount of annual flights should be operated at an airport with the defined configuration and with regard to current ATC rules and practices (FAA 1995, p.5). As a result we see these everyday delays at airports, which either operate close or over the ASV. With the forecasted growth in European air traffic these delays will multiply at over-utilized airports and will affect other airports in the network.

1.2 Fuel Prices Affecting Air Transportation

The decline in demand for air travel during the last three to fourth months during the financial crisis and the high fuel prices over the past year, were a tough to handle combination of events for airlines, airports and travel operators together. The fuel prices have dropped and the oil barrel price is expected to stabilize at around $80, so some financial pressure has been released. At the same time oil price shifts do occur frequently. In IATA‟s view, the industry lost $5.2 billion in 2008, and will see losses of $4.1 billion in 2009. The earnings of 2009 will be important to many airlines and to the whole aerospace industry, and some will have to adjust their business models, routes or services, because the damage of 2008 will be “difficult to overcome” (Aviation Today January 1, 2009).

In October 2008 Calyon Securities published a research report which points out that the U.S. air transport industry is expected to lose $3 billion in 2008 “before posting earnings of $5 billion in 2009”. Calyon also points out that “carriers have positioned themselves such that they should be able to survive the credit challenges until spring, when traffic demand is expected to increase and we forecast the industry returning to profitability”. IATA Director General and CEO Giovanni Bisignani is more gloomy; he ex- pects the situation to remain “bleak” and “the toxic combination of high oil

Benchmarking Airport Productivity and the Role of Capacity Utilization 24 Introduction prices and falling demand continues to poison the industry‟s profitability” (Aviation Today January 1, 2009). The combination of efficient business models, a stabilisation of fuel prices and the global economic environment will bring relief to the aerospace industry. There is still a lot of uncertainty, which pushes even more towards an efficient air transport system, with lower fuel consumption and lower costs for airlines and therefore lower ticket prices for the end consumer.

Speaking of the operational side of air transportation, Basil Baromo indicates that if “you can trim a minute or two off every approach in and out of an airport, the fuel savings are significant. […] They‟re more significant at $140-a- barrel oil than they are at $80-a-barrel oil, but they still are very significant. We‟re talking industry-wide, potentially hundreds of millions of dollars [saved] annually.” Barimo of the ATA further suggests that “introducing some changes to procedures [...] allows [the air transport industry] to squeeze more capacity out of the system and at the same time makes it even safer. […] And once we do this, that capacity translates into reduced delay, and that translates into real savings of [turnaround] time of aircraft, crew, fuel burn and all of those things.” (Aviation Today January 1, 2009)

This means that by efficient airport operation and the smart use of airport capacities, which will result in fewer delays and queuing or ground times, huge amounts of money can be saved by airlines. Cutting costs might be another way to increase profitability, but this has already been done over the past few years due to the new competitive dynamics created by the LCCs. Cutting more costs will call the reputation and business models of main carriers deeply into question, but a saving potential through efficiency is there.

1.3 New Airspace Surveillance Technologies

For airlines it would require new investments in technical equipment, such as communication and non-radar GPS-based surveillance systems, which would in general allow aircraft to use, runways, airports and airspace more precisely and

Benchmarking Airport Productivity and the Role of Capacity Utilization 25 Introduction in higher frequencies. Same is true for the various air traffic control (ATC) stations at airports, which would need more staff and would also need state-of-the- art communication and surveillance equipment. There are developments in that direction all over the world, like the U.S. Next Generation Air Transportation System (NextGen) or the European Single European Sky Programme (SES II, SESAR). Airlines or alliances of airlines even independently plan to implement new technologies to operate their fleets more efficiently. Because the situation is not changing fast enough, airlines are willing to pay millions of dollars for more efficiency. What is missing is the will and speed of most airports and federal governments to also invest in new technologies or capacity expansion. This imbalance has to be evened out. One example: Dallas-based Southwest Airlines wants to invest $175 million in their fleet for Required Navigation Performance (RNP) procedures,, which are expected to “yield $25 million in fuel savings annually” (Aviation Today 1/1/2009). As a comparison a new runway at an airport costs about $300 million (AMS and MAD airports for example in IATA 2003) and would bring benefits to all airlines. Since every party involved would benefit from those investments, costs could be divided between airlines and airports and would in some cases require public private partnership (PPP) (Interview: Daduna January 15, 2009).

In Europe we see a similar development with the installation of Automatic De- pendent Surveillance Broadcast (ADS-B) which Air Berlin is already installing in its fleet (ACSS 2009; Interview: Lamberg). Air Berlin is far ahead in its implementation of the European SES programme, which mandates all fleets be equipped with ADS-B by 2015.

1.4 Capacity

I was overwhelmed to discover the fact that there is not only one capacity, or capacity utilization, at airports, there are many, maybe hundreds of capacities involved.

Benchmarking Airport Productivity and the Role of Capacity Utilization 26 Introduction

Actually every process involved in serving passengers and/or aircraft and/or cargo has its own capacity. On the landside, think of a security capacity, which is the maximum throughput of passengers per unit of time through airport security, or a gate area capacity, which could serve a certain maximum number of passengers per unit of time and allow them to spend their waiting time com- fortably before boarding. The same is true with parking position capacity, air traffic control staff capacity and runway capacity, all of which define the maximum number of aircraft that can be processed per given unit of time. As can be seen, the airport system is highly dynamic, and the situation at an airport can change significantly in a matter of minutes. This is especially true for many secondary airports that have strong peaks in the morning for two or three hours and then experience a sharp decline of traffic after this period (e.g. HAM and ARN). With arriving and departing aircraft carrying as many as 400 passengers at a time (Boeing 747) and airports processing up to 180,000 passengers per day or around 10.000 passengers per hour (LHR), there is an enormous amount of traffic activity at an airport over the course of a day. So there is no simple answer to “capacity” (Fig. 10).

Fig. 10. Limiting Factors for Capacity and Interdependencies at Airports. (Source: Bubalo 2009)

Benchmarking Airport Productivity and the Role of Capacity Utilization 27 Introduction

Due to time and data factors, I have almost exclusively concentrated on the most crucial capacity of an airport system, the runway capacity. One could ar- gue that terminal capacity might be more important, but I think that is not the case. The fundamental function of an airport is to provide an interface between air traffic and ground traffic. So therefore the concept is that a runway is the most significant construction at an airport. You need a runway or a system of runways to meet your local demand in air traffic services. Everything else is rather an additional “service” for the customers. You can see this at the first airstrips built in Australia and Europe or as a matter of fact at any of the original airstrips. Almost immediately there was a need for hangars for aircraft repair, sufficient parking stands and fuel stations, as is the case at military air bases. With the emergence of commercial air traffic and the establishment of regular routes or “lines”, the need for passenger facilities arose at developed airports. Since flying was very costly in the early days, passengers expected some convenient services at airports and from the airlines. With larger aircraft and more air travellers, airports needed passenger waiting areas, luggage arrival and pick up areas.1. At large airports nowadays around 50% of the total airport revenue is generated by non-aviation activities. This means that revenue generated through charges and service fees for processing aircraft and passengers account for only half of the overall revenues, the other half comes from commercial activities like rents, leases, concessions and marketing, by providing room for shops, restaurants, offices, conference rooms and hotels.. Airports have become places where passengers and consumers like to spend time and money. The close proximity to air transport proves to be rather beneficial to many enterprises and constantly attracts more businesses (TRB 1975, p.1)(Fig. 11). In fact for main hubs and large airports it is an obvious fact that terminals are strong revenue generators. Internationally we find many other examples of still

1 I strongly recommend the “Air Australia” documentary by Alan Lindsay about the adventurous times of aviator Charles Kingsford Smith and the development of Qantas Airline in Australia. It gives an insight into the transformation of an air traffic service over the years from its early adventurious beginnings. http://www.guba.com/watch/3000056128

Benchmarking Airport Productivity and the Role of Capacity Utilization 28 Introduction developing airports which provide only basic service. Duty-free and souvenir shops or restaurants are very popular among all airports. Additionally the amount of passenger traffic, tourists and level of privatization play a significant role in revenue generation. Thus, incentives are in place to get more revenue from the passengers, but nonetheless after all has been said and done, it is still the runway which provides the needed traffic.

In a discussion with Klaus Knoepfle, a former ground handling operation manager at Stuttgart airport (STR), he gave an example of a quick adaptation to the unanticipated need for additional terminal capacity at STR. As the number of passengers, especially in the no-frills sector grew strongly, an old terminal was demolished, but, as it would take some time to rebuild, the only solution was to transform a maintenance hangar into a passenger terminal, which now is Ter- minal 4. And that worked perfectly well for the low-fare and charter airlines (Interview: Knoepfle 2008).

This kind of temporary solution is of course not possible with runways.

Fig. 11. Target Groups and Offerings at Airports. (Source: Bubalo 2009 adapted from Leutenecker & Fraport 2008)

Benchmarking Airport Productivity and the Role of Capacity Utilization 29 Introduction

1.5 Ultimate Capacity

The ultimate capacity expresses the maximum physical capability of a runway system to process aircraft [ed. Demand] for specific conditions (Horonjeff 1994, p. 310). This type of capacity is also refered to as the technical capacity. Beyond this capacity, which is expressed as total flights per time (usually one hour), the acceptable level of average delays per flights is too high to be able to operate the airport, or the runway, any further. It is suggested in various literature (De Neufville 2003; ADRM 2004; ACD 1995 and others) that the acceptable average level of delay per flight of four minutes should never be exceeded. This therefore is a strong criterion to estimate the practical capacity. The relationship between average delays per flight and practical capacity is shown in Fig. 12.

Fig. 12. Relationship between delay-related and ultimate capacity. (Source: Horonjeff 1994)

Benchmarking Airport Productivity and the Role of Capacity Utilization 30 Methodologies and Models

The correlation of delay and capacity also shows that the delay reacts very sen- sitive to an increase in operations per hour when surpassing the practical capacity. It makes a huge difference if an airport operates at 60% to 65% or if it operates between 85% and 90% of it ultimate capacity (de Neufville 2003, p. 448). And it is because airport inhancement programmes, especially for constructing a runway, take many years to plan (usually more than 10 years, due to long lasting approval procedures concerning environmental concerns), that a timely planning process should be started as soon as capacity shortages are foresee- able. We will see the relationship between demand, delay and capacities further in the „static‟ airport analysis and later in the „dynamic‟ airport system analysis when running different airport simulations.

2 Methodologies and Models

The flood of information on airport benchmarking, management, engineering, efficiency and operations is interesting, but also never-ending. It is difficult to point out one specific model which could be applied to measure airport productivity or to generate a benchmark. The research on airport benchmarking is still ongoing and the obstacles involved have been pointed out in many academic papers. That is why this study will only cover a selection of models, the idea being more to produce a workbook which will give an overview of what is going on at airports on a day-to-day basis and to identify a greater choice of inputs for statistical and econometric calculations than has thus far been done.

With the collection of very recent data, I wanted to create a snapshot of the airports today. This very transparent approach should answer questions like “What is the annual/hourly capacity of an airport?”, “Is the airport congested, and when?”, “Is there enough spare capacity for development in the years to come with respect to growth in traffic and the continuous emergence of new (LCC) airlines and routes?” and “How do I operate airports efficiently to meet

Benchmarking Airport Productivity and the Role of Capacity Utilization 31 Methodologies and Models modern environmental challenges and to help airlines reduce delays and the costs of fuel consumption?”.

Regarding the literature, it was handled quite differently, as it spans the last four decades. In this case I wanted to try to get to the roots of models and thoughts about airport capacity, like the wonderful “Airport Landside Capac- ity” Special Report 159 of the Transportation Research Board (TRB) from 1975 which could be published today without loosing any of its topicality. In short, there was a need felt to understand the capacity problem in the air transport system.

I started collecting relevant books, and did some data collection. From within the GAP project I had access to a great deal of airport-related literature and data. Unfortunately much of the data had been modified by different people, was outdated or simply didn‟t have the scope of what I needed. Also much of the literature was related to economics and econometrics and had little rele- vance to airport infrastructure and operations. But still it provided me with useful information. The “Airport Capacity and Delay” (ACD) guideline from the U.S. Federal Aviation Administration (FAA 1995, p. 14) generally suggests what kind of data would be needed for a capacity and delay assessment of airports, and, while referring to different components like taxiways, exits and gates, it mainly concentrates on runway capacity. Another valuable source of information is the IATA “Airport Development Reference Manual” (ADRM) (IATA 2004) which also gives hints on how to do capacity measurements on land- and airside, covering passenger and aircraft facilities, and where to look for data. The main assessment data requires information on flight schedules, aircraft, airspace, airport configuration and weather data.

2.1 Data Collection

An often cited source of reliable flight schedule data is the Official Airline Guide (OAG) database. A substantial amount of this analysis is completely

Benchmarking Airport Productivity and the Role of Capacity Utilization 32 Methodologies and Models based on available OAG data. OAG provides for a 5000 USD subscription fee for access to recent and 12 month forward flight schedules data for over 1000 airlines and 3500 airports. The 14-day trial access to their database was fully functional, but limited to one week of data between 16th and 22nd March 2009. Access to their database is via an online Java application which provides the ability to choose from among 130 different indicators, filtering and putting these together in tabular form. OAG provided many conversation and decoding tables, this being aircraft information, maximum take-off weight (MTOW), seat numbers, ranges, speeds, etc., and airline or aircraft coding and decoding. Flight schedules data for all European airports for the whole week period was collected. This resulted in about 20-25,000 daily European flight entries.

The result is a main operational database with information on each flight, such as carrier, departure time, arrival time, aircraft type, origin, destination, distance, flying time, seat configuration, seat number, service type (passenger, cargo or mixed) and flight number. It was possible to choose any European airport for this analysis. A sample of 58 airports was selected because of the strong interconnectivity between the airports and because of the importance of the data for the GAP research project.

Unfortunately, using the OAG trial access, the flight data of peak periods was neither accessable nor calculatable. So the next step was to obtain the peak period information for European airports and traffic elsewhere.

2.2 Peak Period Estimation and Data

From the EUROCONTROL Pan-European Airport Capacity and Delay Analy- sis Support (PACS) and OneSky Central Flow Management Unit (CFMU) online sources and databases I collected the relevant data, such as daily, weekly and monthly reports of overall European traffic and delays for the years 2000 to 2008.

Benchmarking Airport Productivity and the Role of Capacity Utilization 33 Methodologies and Models

Top 5 Traffic Days

2005 Flights 2006 Flights 2007 Flights 2008 Flights Fri 17/06/2005 30663 Fri 15/09/2006 31914 Fri 31/08/2007 33506 Fri 27/06/2008 34476 Fri 01/07/2005 30569 Fri 01/09/2006 31841 Fri 29/06/2007 33480 Thu 26/06/2008 33895 Fri 02/09/2005 30469 Fri 30/06/2006 31686 Fri 14/09/2007 33371 Fri 13/06/2008 33833 Fri 16/09/2005 30338 Fri 08/09/2006 31553 Fri 07/09/2007 33279 Thu 19/06/2008 33383 Fri 09/09/2005 30169 Fri 22/09/2006 31550 Fri 21/09/2007 32971 Fri 04/0//2008 33342 Table 3. Top Five Traffic Days 2005-08. (Source: EUROCONTROL CFMU 2008)

Each weekly report provides a diagram of the previous week‟s traffic and delays, so in the report of week 52 or 53 I would be able to find a yearly diagram which includes the traffic development over the past year (Fig. 13). When studying these graphs for the development of European air traffic during the year a repeating pattern is observable. It became obvious that in weeks 25, 26, 35 and 36 the traffic will always have its peak for the whole year. The average delays per week are also the highest during these four weeks. From the literature, especially chapter 24 of de Neufville‟s “Airport Systems – Planning, Design and Management” (de Neufville 2003, p.851) I knew what to consider in isolating the “peak days” (PD), the “design peak hours” (DPH), and the peak periods in general. From observations it is known that Thursdays and Fridays are almost always the busiest days of the whole week at airports. So I figured that on the mentioned four weeks on Thursdays and Fridays must be the busiest days of the whole year (Table 3). What can also be seen from the diagram (Fig. 13) is that the peak days of the previous year are exceeded by the traffic on peak days in the current year, at least as long as there is growth, which might not be true in the year 2009.

The isolation of peak weeks must have been made due to the lack of precise schedule data of a whole year for each airport. Depending on the source, per definition: “The design peak hour (DPH) is a busy hour, but not the busiest hour - the peak hour (PH), of the year, maybe the 20th, 30th, 40th PH, or the 95th percentile of the busiest day [ed. (PD]), or the PH of the average day of the peak month of

Benchmarking Airport Productivity and the Role of Capacity Utilization 34 Methodologies and Models the year, or the PH of the average day of the two peak months of the year” and so on (de Neufville 2003, p.853).

Fig. 13. Traffic and total delays comparison with equivalent weeks of the previous year; PC Objective set to 1.7 min of delay per flight (Source: Weekly report 52/2008 EUROCONTROL CFMU 2008)

IATA (1981) has a more general definition for the peak period: “A period that is representative of a normal busy period, and not one related to peak time, such as religious festivals or some other short holiday period.”

I found it not very useful to use any of the definitions for DPH as a reference, since I observe a conciderable sample of overall European traffic with many airports and had to find a simple solution. Since doing these DPH calculations would require more or less daily traffic data from each airport for a whole year for each of the 58 sample airports, the following simple method was applied.

Most sources point out that it largely depends on the study and availability of data, which definition for estimating the DPH will be the best. Therefore the Thursday of week 26 is suggested to be used as design peak day (PDTHUW26) of all airports. Overall air traffic in Europe on this day is so high, that through interconnections it will have an effect on all analysed airports.

Benchmarking Airport Productivity and the Role of Capacity Utilization 35 Methodologies and Models

The peak week 26 always falls into the top 5 busiest weeks of the year. The peak hour of the second busiest day of week 26, the Thursday (PDTHUW26), is equivalent to roughly the 1st to 30th 2 busiest hour during the whole year at each airport. The PH of PDTHUW26 is then the design peak hour (DPH).

For peak day information the website FlightStats.com has been used, which has information about all recent flights and gives one the ability to track flights in real time with scheduled and actual times and delays. Flightstats.com also allows viewing scheduled and actual times at each airport for recent and past flights, with origin and destination, flight number, gate, delayed status and actual delays in minutes. With some tricks it is possible to extract the flight schedules for different airports for the last five years. The outcome is flight schedule data for another 25 airports for the PDTHUW26 from the last four years. Unfortunately, for the resulting tables major formatting would have been needed, so only peak data for the PDTHUW26 of 2008 is included in the diagrams for this study.

As mentioned above peak day data could only be obtained for 25 airports. This means that for most of the sample airports another peak period had to be selected. In the cases without PDTHUW26 data the peak hour (PH) of week 12 of 2009 (PHW122009), where I have OAG flight schedules data for, was chosen. This was usually the Monday, Thursday or the Friday. Since the data is from a week in March, the resulting PH or DPH is lower than the DPH for PDTHUW26.

Another step was to obtain annual data to have an overview of the different annual throughputs at airports, namely the number of passengers per year and the number of take-offs and landings per year.

2 This largely depends on the individual peak hours over the peak days. Usually airport have one to five peak hours on those days, when considering arrivals and departures separately, they have even more. So there is variation which can be finetuned. Benchmarking Airport Productivity and the Role of Capacity Utilization 36 Methodologies and Models

EUROSTAT is a one-stop resource for various statistics of the European Un- ion. From here annual figures for all analysed airports (except for IST) was collected. The data is categorized into cargo traffic and passenger traffic, available seats, and boarded passengers. This allowed the calculation of the annual average seat load factors (SLF) at each airport.

To sum up the panel data collection, there remains to mention the data from the slot coordinators of each involved country on the maximum declared capacities or slots, which represent the maximum number of operations allowed at airports in any given one hour period and usually divided into departures and landings and for different seasons into arrival and departure patterns. This data is included in many diagrams to give an overview of the limit set by the slot coordinators on the number of possible operations at an airport. The limitation of slots per hour could have various reasons, for example lack of ATC equipment or local noise restrictions.

2.3 Considerations Concerning the Data

The reader of this study should be aware of the quality of the data. Even with the maximum care in editing the data it is possible that certain information might not be correctly displayed or calculated. Since accurate data is the back- bone of this study, as little as possible of the data was inserted manually. Most of it was processed through links and scripts from the original sources. If there were errors in the input tables at the point of origin, then these errors probably have persisted throughout all calculations. If the reader recognizes certain errors, please do not hesitate to contact the author. I will be glad to follow up on identified errors and correct them as necessary.

Benchmarking Airport Productivity and the Role of Capacity Utilization 37 Methodologies and Models

2.4 The Benchmarking Concept

From the point of view of the literature, it almost seems as though the only widely accepted way of doing performance (productivity) benchmarking among airports is to use econometric calculations like the Data Envelopment Analysis or the Stochastic Frontier Analysis. Both methods are very appealing either for their simplicity or their usability in data analysis. At the same time each have major drawbacks.

In reality there is no universally adopted benchmarking method. There are instead many different benchmarking methodologies emerging. The concept of benchmarking was first invented and introduced by copy- machine manufacturer Rank Xerox in the 1970s. The most prominent benchmarking method in use today is the 12-stage methodology by Robert Camp, which consists of the following stages: 1) Select subject ahead 2) Define the process 3) Identify potential partners 4) Identify data sources 5) Collect data and select partners 6) Determine the gap 7) Establish process differences 8) Target future performance 9) Communicate 10) Adjust goal 11) Implement 12) Review/recalibrate. (http://en.wikipedia.org/wiki/Benchmarking, January 12, 2009)

These are rather basic steps and do not reveal which indicators should be used and how it is possible to collect confidential data from your competitors. But these are the core problems, the solutions to which could cost huge amounts of time and money.

Benchmarking Airport Productivity and the Role of Capacity Utilization 38 Methodologies and Models

The goal of a benchmarking study should be the identification and isolation of a Best Practice, which is more efficient in a process, technique or method and which provides more output for a given input by comparison among similar entities.

During the preparation of this study, I came across many benchmarking studies comparing different airports across regions or globally and I quickly learned the difficulties involved in making these airports comparable in multiple aspects. There is a wide range of financial models, regulatory systems and operational policies which must be considered when doing a cross regional benchmarking study (Pilling 2002). As Graham (2005) points out it is indeed very difficult, if not impossible, to establish methodologies to benchmark airports, especially when making com- parisons across different national borders. This lies partly in the diversity of accounting procedures for economic indicators. Depreciation of assets is but one such example, where one will find huge differences among countries or enterprises. This will result in totally misleading financial productivity indicators. It is therefore necessary to carefully predefine any input and output measure for any productivity analysis, whether financial, operational or environmental.

There has been some thought given to developing a methodology for a stripped-down airport, where accounts and physical measures are universally adjusted to a “common set of rules”, to make airports internationally comparable. So far this attempt has foundered due to a lack of the timely receipt of resources, or a simple lack of financial or personnel resources (Graham 2005).

The attempt in this study for benchmarking must be seen in the larger context of performance and productivity benchmarking studies. This in no way is the “final” product of an airport productivity benchmarking, but should be seen as a definite step in that direction. This thesis is based around the questions: Which operational and infrastructural input data is needed for productivity benchmarking? How can one reasonably

Benchmarking Airport Productivity and the Role of Capacity Utilization 39 Methodologies and Models include that data into further calculations? And how can one obtain that data in a rapid and sustained way?

2.5 Criticism and Findings of Technical, Operational and Infrastruc- tural Inputs used in Previous Benchmarking Studies

Number of runways: Using the number of runways as an input for productivity analysis does not provide reliable output. The efficiency of a runway system, which is most critical to airport operations, and therefore to the productivity of the whole airport system, is not solely based upon the actual number of runways. By studying the FAA and IATA guidelines, it is clear that the efficiency and capability of a runway system is also based largely upon the configuration of the runways, i.e., the physical location, width, length; orientation and type of pavement of the runway(s). Even such additional factors as altitude, humid- ity, temperature, obstacles in the flight paths (mountains, skyscrapers, towers, bridges, poles, large trees, etc.), location of residential areas, and the slope of the runway have an effect on the throughput of a runway.

Total length of runways: The overall length of the runway system of an airport is another example of the difficulty researchers have so far encountered in evaluating the efficiency of an airport system. This again is not an adequate indicator. The total length of all runways at an airport is of little importance, as it does not take into consideration the number of runways actually used, the primary types of aircraft the airport serves, and the spacing between runways. A Boeing 737 needs as little as 1400 meters or about 4600 feet for landing and 1800 meters or about 5900 feet for taking-off. So a runway system comprisisng a total of 6000 metres could have many possible runway configurations, and therefore could serve a wide range of aircraft types, resulting in the number of passengers served and number of operations to differ tremendously.

The problem could be narrowed down to three groups of runway configurations for which within each group the opportunities to serve aircrafts are about equal. The runway configurations 1 to 19 are suggested by the FAA (FAA 1995)(Fig. 14).

Benchmarking Airport Productivity and the Role of Capacity Utilization 40 Methodologies and Models

Fig. 15. Groups of runway configurations in relation to Annual Service Volume (ASV). (Source: Bubalo 2009 derived from FAA 1995)

As it can be seen in figure 15, the suggested groups for the most common Mix Indices of airports between 81% and 180% are: . Group 1 includes configurations 1, 9, 14 and 15. . Group 2 includes runway configurations 2, 3, 4, 5, 6, 10, 11, 12, 13, 16, 17, 18 and 19. . And Group 3 would include the more complex and productive configurations 7 and 8.

I suggest considering groups 1 to 3 separately for a productivity analysis. Annual numbers, capacity utilization and basic indicators are displayed in Ta- ble 3 for all 58 sample airports.

Number of gates: Using the number of airport gates does not tell the whole story concerning the productivity of an airport. Gates can be reserved for spe-

Benchmarking Airport Productivity and the Role of Capacity Utilization 41 Methodologies and Models cific airlines and therefore are not made available to other airlines. It is also questionable whether the number of gates includes all available parking positions, which is the preferred measurement for available stand capacity at an airport. Also one must consider the amount of time an aircraft actually blocks a gate or a parking position. Hence average turn-around times should be factored in as well.

Number of check-in-counters: What is true for the number of gates vis-à-vis aircraft is also true for the number of check-in-counters, in relation to passengers. One does not know for how long a gate is kept open or is being reserved for an airline. The category check-in-counters might be used in relation to the capacity measure “passenger throughput”, which in this case could mean passengers per hour per check-in-counter (Marine Board 1986).

Area of an airport: The total airport area is sometimes used in productivity analysis as an input measure. This category does not allow one to see the availability and costs of available property around an airport. The location of an airport therefore plays an important role, as well. Also the efficient usage of already acquired property should be considered as well as the share of property used for the landside, like terminal area or car access and parking area. Passen- gers, movements or runways per area could give a hint regarding the productivity of such property. It is difficult to use “area of an airport” for capital productivity analysis without considering the above-mentioned factors. For this reason, I would suggest using the apron area for analysis instead, since it is more important to operations. Sufficient apron area will provide space for parking, maintenance, and manoeuvring aircraft. It is even more important for the diverse streams of traffic that pass through its area, such as passenger transport, freight/cargo transport, general aviation, and military flights.

Benchmarking Airport Productivity and the Role of Capacity Utilization 42 Methodologies and Models Capacity Saturated MI=%(C+3*D) (ops/hour) >75% Annual Annual Rwy no. Service Demand Ops config. Of VFR IFR Volume *2007 Annual Rank Airport Group no. rwy Mix Index % ops/hr ops/hr (ASV) EUROSTAT 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 LHR 2 4 2 170 103 99 370,000 475,786 128.6% 3 MUC 2 4 2 112 111 105 315,000 409,654 130.0% 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 MXP 2 3 2 122 103 75 365,000 257,361 70.5% 8 CPH 2 12 2.5 109 111 105 315,000 250,170 79.4% 9 BRU 2 12 3 123 103 99 370,000 240,341 65.0% 10 ORY 2 12 2.5 112 111 105 315,000 238,384 75.7% 11 OSL 2 4 2 101 111 105 315,000 226,221 71.8% 12 ZRH 2 10 3 121 94 60 340,000 223,707 65.8% 13 DUS 2 2 2 107 105 59 285,000 223,410 78.4% 14 MAN 2 2 2 116 105 59 285,000 206,498 72.5% 15 IST 2 16 3 117 146 59 300,000 206,188 68.7% 16 ARN 2 12 3 106 111 105 315,000 205,251 65.2% 17 BBI 2 4 2 105 111 105 315,000 200,565 63.7% 18 ATH 2 4 2 110 111 105 315,000 193,123 61.3% 19 PMI 2 4 2 100 111 105 315,000 184,605 58.6% 20 HEL 2 12 3 107 111 105 315,000 174,751 55.5% 21 NCE 2 2 2 55 121 56 260,000 173,584 66.8% 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 LEJ 2 4 2 121 103 99 370,000 41,370 11.2% 26 PSA 2 2 2 103 105 59 285,000 38,525 13.5% 27 LGG 2 2 2 237 94 60 340,000 26,815 7.9% 1 VIE 1 14 2 109 77 59 225,000 251,216 111.7% 2 DUB 1 14 3 108 77 59 225,000 200,891 89.3% 3 STN 1 1 1 102 55 53 210,000 191,520 91.2% 4 PRG 1 9 2 102 76 59 225,000 164,055 72.9% 5 HAM 1 9 2 106 76 59 225,000 151,752 67.4% 6 WAW 1 9 2 103 76 59 225,000 147,985 65.8% 7 LIS 1 1 2 117 55 53 210,000 141,905 67.6% 8 STR 1 1 1 101 55 53 210,000 139,757 66.6% 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 LBA 1 1 1 97 55 53 210,000 39,603 18.9% 19 HHN 1 1 1 128 51 50 240,000 34,311 14.3% 20 RHO 1 1 1 100 55 53 210,000 32,776 15.6% 21 DRS 1 1 1 100 55 53 210,000 28,257 13.5% 22 BSL 1 9 2 102 76 59 225,000 27,879 12.4% 23 FMO 1 1 0.5 100 55 53 210,000 21,968 10.5% 24 SZG 1 1 1 100 55 53 210,000 21,166 10.1% 25 ZAG 1 1 1 100 55 53 210,000 20,442 9.7% 26 RTM 1 1 1 100 55 53 210,000 18,517 8.8% 27 WRO 1 1 1 100 55 53 210,000 17,861 8.5% 28 GRZ 1 1 1 100 55 53 210,000 17,286 8.2% 29 SCN 1 1 1 100 55 53 210,000 9,731 4.6% Table 4. Basic Indicators and Airport Configuration Groups for Sample Air- ports. (Source: Bubalo 2009)

Benchmarking Airport Productivity and the Role of Capacity Utilization 43 Airport Capacity Assessment

Number of passengers or number of movements (Mvts, ATM, Ops): I see difficulties in using number of passengers or movements processed per year. When comparing data from EUROSTAT for annually boarded passengers or movements at each airport tothe number of movements or passengers displayed on the individual airport websites, there are differences, from 2% to 10%. In a productivity analysis this could be significant. One does not know what kinds of passengers are counted (transit, domestic, international or even simply visi- tors to the terminal). The same is true of operations; it is usually unclear from aggregated numbers, whether general aviation, cargo, or military operations are included. So it is advisable to try to use only one trusted source for this kind of data which ideally includes different user groups.

3 Airport Capacity Assessment

IATA (1981) categorizes three basic measurements “to assess the capacity of an airport”. One is the “Direct Observation” method, which would require traffic observations, during peak and off-peak periods. “By analysing this information it is possible to determine a measure of utilization of the airport and its various sub- systems and the total airport system” (IATA 1981).

Another method is the “Comparison” method which I understand nowadays as a benchmarking method. This method compares airports of the same size, traffic demand characteristics and configuration.

The third method mentioned in connection with airport capacity measurement is the “Mathematical Modelling” method, which by computer simulations would “predict the impact of projected schedules on the various airport facilities”. “Such models, when calibrated with actual data of passenger behaviour characteristics and traffic profiles, can serve as an effective tool for assessing airport capacity” (IATA 1981).

Benchmarking Airport Productivity and the Role of Capacity Utilization 44 Airport Capacity Assessment

For this thesis all three methods have been used for at least 20 of my sample of 58 airports. From my perspective the simulation method is really the only method which allows a “dynamic” view of an airport system. Using this method, it can therefore be observed how, where, and when “bottlenecks” can occur at airports. It is also the only method which allows simulations of the future growth of air traffic. IATA points out that “regardless which method is used, two principle factors must prevail: (i) the comfort and convenience of airport users is directly related to the capacity and level of service provided by the facility, and (ii) capacity and level of service [ed. e.g. Delay] are interrelated and must always be considered together” (IATA 1981). Exactly that is considered throughout my analysis. Due to time limitations I was only able to do a capacity assessment for runway capacities. Terminal capacities are only implemented into the “Kanafani Model” (Kanafani 1981) with 2003 data. Any recent changes in terminal capacity are going to be included in the assumption rectangles for future publications.

The first most obvious assessment is done by plotting the demand curves for each airport for the 12th week of 2009 with OAG flight schedule data (fig. 16a). The individual explanation of each diagram is beyond the scope of this study, but for different purposeses the collection of these capacity and demand diagrams will be very useful.

The collection of the operations or demand diagrams would most likely refer to the observation method mentioned by IATA (1981). Table 5 incorporates the main observed and calculated values for the airport capacity assessment including mix index (a derived percentage value for the airports‟ aircraft mix of per- centages of aircraft category Heavy (here D) and Large (here C); percentage C (or M/L) plus 3 times D (H))) and design factors for the “Kanafani Model” (Kanafani 1981).

Benchmarking Airport Productivity and the Role of Capacity Utilization 45 Airport Capacity Assessment

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

STN_2009-03-16_TOT STN_2009-03-17_TOT STN_2009-03-18_TOT STN_2009-03-19_TOT STN_2009-03-20_TOT maximum declared capacity of all sources STN technical capacity vfr STN technical capacity ifr peak_day_2008_total tot_slots Fig. 16a. STN Weekdays Operations Pattern and Capacities. (Source: Bubalo derived from OAG 2009)

3.1 The “Kanafani Model” and Assessment Results

What I refer to as the “Kanafani Model” is a very simple yet informative relationship model, which connects different capacities of an airport with certain factors. By analysing the demand profiles of each airport, with maximum capacities and peak day data integrated, it is possible to isolate the existing peak hour (PH), which we use as design peak hour (DPH). To clear this up again, on figures 16 in the appendix the found maximum operations per hour of each diagram, the PH during PDTHUW26 or PHW122009, is used as a DPH measure. The PH passenger (PAX) or operations (Ops) numbers3 for either PHW122009 or PDTHUW26 are taken into consideration to be able to do DPH calculations within this study. To complete the assumption rectangle with the values for HO

3 Hourly Passengers (HP) and Hourly Operations (HO) and in the “Kanafani Model” Benchmarking Airport Productivity and the Role of Capacity Utilization 46 Airport Capacity Assessment

(DPH) PAX, HO (DPH) Ops, annual PAX and annual Ops it is also necessary to know the average seat load factors (SLF) and the aircraft mix by number of seats over the year and during the DPH separately (Kanafani 1981). In the calculations a DPH SLF of 90% was chosen, assuming the factor of boarded PAX to available seats per aircraft. This SLF was also assumed to be the average among all aircraft classes. By multiplying the average seat number of the aircrafts during the DPH, ACH, and the SLF of 0.9, LFH, you receive the value m. Without detailed flight schedule data which states aircraft types and corresponding average seat numbers the calculation of ACH will not be easy to calculate. The „static‟ capacity assessment in table 5 provides basic numbers on peak hour values and capacity.

The correspondend value to m (DPH Passengers divided by DPH Operations) is n (the annual number of PAX divided by the annual number of Operations). The condition of m being larger than n usually holds true.

When actually looking at an assumption rectangle we realize that the connection between annual and DPH values are the design hour factors x and y. These values are calculated by DPH value divided by annual value (x=HO/AO; y=HP/AP). The condition of x being larger than y usually also holds true. Consequently the following relationship must always hold true (x/y)=(m/n), which has been calculated to check the assumption triangle relationships and is incorporated in table 5.

In the model description, Kanafani (1981) states that the passengers at an airport should be considered separately for domestic, international and international transit. Due to the lack of data this kind of detail cannot be integrated.

Benchmarking Airport Productivity and the Role of Capacity Utilization 47 Airport Capacity Assessment airport ICAO Name Country Area in Runways Capacity limit dictated by Mix Index AO Annual AP Annual IFR Slot CAP Decl Terminal Annual Ter- HO Design ha Ops in Pax in CAP Ops/hr CAP PAX/hr minal CAP Peak Hour (000s) million Ops/hr million Pax/yr (DPH) Ops AMS EHAM Amsterdam NL 2678 5.5 noise, atc, runway 136 443.68 47.85 169 108 110 ARN ESSA Arlanda SE 3100 3 Noise 106 205.25 18.01 105 80 68 ATH LGAT Athen GR 1700 2 110 193.12 16.53 105 52 35 BBI EDDB* Berlin Brandenburg Int DE 2 105 200.57 19.72 105 BCN LEBL Barcelona ES 3 103 339.02 32.81 105 60 60 BHX EGBB Birmingham GB 1 104 104.48 9.32 53 40 30 BRU EBBR Bruessel BE 1245 3 Atc 123 240.34 17.93 99 74 55.0 70 BSL LFSB Basel CH 536 1.5 noise, atc 102 27.88 0.92 59 3,500 14 CDG LFPG Paris Charles de Gaule FR 3238 4 noise, atc 140 569.28 59.55 120 106 20,300 110 CGN EDDK Köln Bonn DE 1000 2.5 Atc 104 138.53 10.55 59 52 4,000 26 CIA LIRA Rom Ciampino IT 1 100 54.87 5.35 53 35 14 CPH EKCH Kopenhagen DK 1180 2.5 109 250.17 21.40 105 83 66 DRS EDDC Dresden DE 280 1 100 28.26 1.89 53 30 1,500 12 DUB EIDW Dublin IE 3 108 200.89 23.31 59 44 40 DUS EDDL Duesseldorf DE 613 2 Noise 107 223.41 17.85 59 38 52 EDI EGPH Edinburgh GB 2 100 115.18 9.06 59 47 34 FCO LIRF Rom Fiumicino IT 1600 3 atc, runway, apron 114 328.21 33.62 105 90 80 FMO EDDG Muenster Osnabrueck DE 0.5 100 21.97 1.61 53 24 2,680 7 FRA EDDF Frankfurt Main DE 1900 3 Runway 149 486.20 54.50 82 82 14,000 88 GLA EGPF Glasgow GB 1.5 101 93.65 8.86 53 26 GRZ LOWG Graz AT 1 100 17.29 0.97 53 14 5 HAJ EDDV Hannover DE 2.5 100 70.48 5.67 105 40 17 HAM EDDH Hamburg DE 563 2 Runway 106 151.75 12.85 59 48 46 HEL EFHK Helsinki FI 3 107 174.75 13.10 105 50 48 HHN EDFH Hahn DE 1 128 34.31 4.11 50 8 IST LTBA Istanbul TK 940 3 Atc 117 206.19 25.49 59 40 1,619 42 LBA EGNM Leeds Bradford GB 1 97 39.60 2.90 53 13 LCY EGLC London City GB 39 1 noise, atc, runway,apron 100 77.27 2.91 53 24 3,600 37 LEJ EDDP Leipzig DE 2 121 41.37 3.04 99 20 7 LGG EBLG Liege BE 2 237 26.82 0.33 60 4 LGW EGKK London Gatwick GB 683 2 Runway 118 258.92 35.27 59 50 12,000 51 LHR EGLL London Heathrow GB 1117 2 atc, runway, apron 170 475.79 68.28 99 88 100 LIS LPPT Lisbon PT 503 2 runway, apron, terminal 117 141.91 13.52 53 32 38 LTN EGGW London Luton GB 1 102 83.32 9.94 53 24 LYS LFLL Lyon FR 2000 2 Runway 102 132.08 7.19 59 51 4,918 43 MAD LEMD Madrid ES 4 noise, atc, runway 118 470.32 51.40 117 100 110 MAN EGCC Manchester GB 883 2 116 206.50 22.33 59 61 46 MUC EDDM Muenchen DE 1500 2 Noise 112 409.65 34.07 105 90 16,000 93 MXP LIMC Mailand Malpensa IT 2 122 257.36 23.97 75 70 42 NCE LFMN Nizza FR 400 2 noise, runway 55 173.58 10.38 56 50 7,400 52 NUE EDDN Nuernberg DE 1 Atc 108 57.92 4.29 53 30 3.2 19 ORY LFPD Paris Orly FR 1530 2.5 Noise 112 238.38 26.42 105 76 24.0 62 OSL ENGM Oslo NO 1300 2 101 226.22 19.04 105 80 7,300 68 PMI LEPA Palma Mallorca ES 2 terminal 100 184.61 23.10 105 60 12,000 42 PRG LKPR Prag CZ 2 102 164.06 12.40 59 38 44 PSA LIRP Pisa IT 2 103 38.53 3.71 59 14 13 RHO LGRP Rhodos GR 1 atc, apron 100 32.78 3.63 53 13 6 RTM EHRD Rotterdam NL 1 100 18.52 1.13 53 8 SCN EDDR Saarbruecken DE 1 100 9.73 0.39 53 20 6 STN EGSS London Stansted GB 1 102 191.52 23.80 53 50 46 STR EDDS Stuttgart DE 400 1 terminal 101 139.76 10.35 53 40 12.5 40 SXF EDDB Berlin Schoenefeld DE 1 100 55.11 6.35 53 17 SZG LOWS Salzburg AT 1 100 21.17 1.98 53 20 8 TXL EDDT Berlin Tegel DE 2 107 145.45 13.37 59 41 47 VIE LOWW Wien AT 2 runway 109 251.22 18.77 59 66 4,400 67 WAW EPWA Warschau PL 506 2 atc, runway 103 147.99 9.29 59 34 3,000 32 WRO EPWR Wroclaw PL 1 100 17.86 1.27 53 11 ZAG LDZA Zagreb HR 1 100 20.44 1.99 53 18 ZRH LSZH Zuerich CH 783 3 atc, runway 121 223.71 20.81 60 66 9,200 56 Benchmarking Airport Productivity and the Role of Capacity Utilization 48 Airport Capacity Assessment HP Design IFR CAP Slot CAP Terminal m=HP/HO in n=AP/AO in Design Hour y=HO/AO x/y m/n PAX per Ops per PAX in Max IFR Ops/hr per Max timeframe for Main Peak IATA Peak Hour PAX Utilization Utilization Utilization DPH PAX/ Ops PAX/Ops Factor Area (ha) Area million/ runway ARR or DEP per Time SLF 0.9 x=HP/AP (ha) Runway runway in mm:ss 13530 65% 102% 123 108 0.02828% 0.02479% 1.1405 1.1405 17,868 166 8.70 32 01:53 12-13 AMS 7344 65% 85% 108 88 0.04077% 0.03313% 1.2306 1.2306 5,811 66 6.00 37 01:37 8-9 ARN 4270 33% 67% 122 86 0.02584% 0.01812% 1.4257 1.4257 9,721 114 8.26 56 01:05 15-16 ATH 0 98 9.86 53 01:09 BBI 7380 57% 100% 123 97 0.02249% 0.01770% 1.2708 1.2708 10.94 35 01:43 19-20 BCN 3240 57% 75% 108 89 0.03477% 0.02871% 1.2109 1.2109 9.32 53 01:08 18-19 BHX 8050 71% 95% 33% 115 75 0.04489% 0.02913% 1.5411 1.5411 14,405 193 5.98 34 01:45 19-20 BRU 1120 24% 32% 80 33 0.12179% 0.05022% 2.4252 2.4252 1,716 52 0.61 51 01:11 19-20 BSL 16280 92% 104% 80% 148 105 0.02734% 0.01932% 1.4148 1.4148 18,391 176 14.89 47 01:16 10-11 CDG 3198 44% 50% 80% 123 76 0.03031% 0.01877% 1.6151 1.6151 10,550 139 4.22 30 01:58 10-11 CGN 2310 26% 40% 165 98 0.04316% 0.02551% 1.6917 1.6917 5.35 53 01:08 20-21 CIA 6534 63% 80% 99 86 0.03054% 0.02638% 1.1574 1.1574 18,134 212 8.56 44 01:21 8-9 CPH 1032 23% 40% 69% 86 67 0.05466% 0.04247% 1.2871 1.2871 6,743 101 1.89 55 01:05 8-9 DRS 5880 68% 91% 147 116 0.02523% 0.01991% 1.2670 1.2670 7.77 20 03:03 7-8 DUB 5720 88% 137% 110 80 0.03204% 0.02328% 1.3767 1.3767 29,120 364 8.93 53 01:09 10-11 DUS 3910 58% 72% 115 79 0.04317% 0.02952% 1.4624 1.4624 4.53 30 02:02 8-9 EDI 11120 76% 89% 139 102 0.03308% 0.02437% 1.3572 1.3572 21,010 205 11.21 37 01:37 9-10 FCO 679 13% 29% 25% 97 73 0.04209% 0.03186% 1.3209 1.3209 3.23 106 00:34 6-7 FMO 13552 107% 107% 97% 154 112 0.02487% 0.01810% 1.3738 1.3738 28,685 256 18.17 43 01:24 12-13 FRA 2704 49% 104 95 0.03050% 0.02776% 1.0988 1.0988 5.91 35 01:42 8-9 GLA 370 9% 36% 74 56 0.03802% 0.02893% 1.3143 1.3143 0.97 53 01:08 8-9 GRZ 1547 16% 43% 91 81 0.02726% 0.02412% 1.1303 1.1303 2.27 42 01:26 18-19 HAJ 4738 78% 96% 103 85 0.03687% 0.03031% 1.2163 1.2163 22,826 270 6.43 38 01:35 8-9 HAM 4704 46% 96% 98 75 0.03592% 0.02747% 1.3078 1.3078 4.37 35 01:43 16-17 HEL 1224 16% 153 120 0.02980% 0.02332% 1.2781 1.2781 4.11 50 01:12 19-20 HHN 6174 71% 105% 381% 147 124 0.02422% 0.02037% 1.1892 1.1892 27,113 219 8.50 49 01:14 17-18 IST 1144 25% 88 73 0.03941% 0.03283% 1.2005 1.2005 2.90 53 01:08 17-18 LBA 2442 70% 154% 68% 66 38 0.08386% 0.04788% 1.7513 1.7513 74,670 1,981 2.91 55 01:05 8-9 LCY 854 7% 35% 122 73 0.02813% 0.01692% 1.6623 1.6623 1.52 50 01:13 20-21 LEJ 0 7% 0 12 0.00000% 0.01492% 0.0000 0.0000 0.16 30 02:00 19-20 LGG 7140 86% 102% 60% 140 136 0.02025% 0.01970% 1.0278 1.0278 51,634 379 17.63 53 01:09 8-9 LGW 18700 101% 114% 187 144 0.02739% 0.02102% 1.3031 1.3031 61,127 426 34.14 52 01:10 15-16 LHR 4750 72% 119% 125 95 0.03513% 0.02678% 1.3119 1.3119 26,882 282 6.76 28 02:11 8-9 LIS 3576 45% 149 119 0.03599% 0.02881% 1.2495 1.2495 9.94 53 01:08 8-9 LTN 3655 73% 84% 74% 85 54 0.05082% 0.03256% 1.5608 1.5608 3,596 66 3.60 53 01:09 8-9 LYS 14630 94% 110% 133 109 0.02846% 0.02339% 1.2169 1.2169 12.85 29 02:03 10-11 MAD 4278 78% 75% 93 108 0.01916% 0.02228% 0.8600 0.8600 25,291 234 11.17 53 01:09 8-9 MAN 10044 89% 103% 63% 108 83 0.02948% 0.02270% 1.2987 1.2987 22,711 273 17.03 56 01:05 8-9 MUC 5040 56% 60% 120 93 0.02102% 0.01632% 1.2883 1.2883 11.99 38 01:36 10-11 MXP 3692 93% 104% 50% 71 60 0.03556% 0.02996% 1.1872 1.1872 25,953 434 5.19 61 01:00 10-11 NCE 1558 36% 63% 134% 82 74 0.03635% 0.03280% 1.1082 1.1082 4.29 53 01:08 7-8 NUE 8618 59% 82% 110% 139 111 0.03262% 0.02601% 1.2544 1.2544 17,265 156 10.57 44 01:21 19-20 ORY 8364 65% 85% 115% 123 84 0.04392% 0.03006% 1.4611 1.4611 14,649 174 9.52 56 01:05 8-9 OSL 5544 40% 70% 46% 132 125 0.02400% 0.02275% 1.0547 1.0547 11.55 53 01:09 8-9 PMI 4884 75% 116% 111 76 0.03940% 0.02682% 1.4691 1.4691 6.20 30 02:02 10-11 PRG 1820 22% 93% 140 96 0.04901% 0.03374% 1.4525 1.4525 1.86 30 02:02 12-13 PSA 636 11% 46% 106 111 0.01754% 0.01831% 0.9582 0.9582 3.63 53 01:08 21-22 RHO 664 15% 83 61 0.05855% 0.04320% 1.3553 1.3553 1.13 53 01:08 7-8 RTM 318 11% 30% 53 40 0.08248% 0.06166% 1.3377 1.3377 0.39 53 01:08 7-8 SCN 7360 87% 92% 160 124 0.03092% 0.02402% 1.2875 1.2875 23.80 53 01:08 18-19 STN 4280 75% 100% 83% 107 74 0.04137% 0.02862% 1.4455 1.4455 25,863 349 10.35 55 01:05 10-11 STR 2329 32% 137 115 0.03669% 0.03085% 1.1894 1.1894 6.35 53 01:08 21-22 SXF 904 15% 40% 113 93 0.04574% 0.03780% 1.2103 1.2103 1.98 53 01:08 21-22 SZG 5311 80% 115% 113 92 0.03971% 0.03231% 1.2289 1.2289 6.69 30 02:02 9-10 TXL 7169 114% 102% 163% 107 75 0.03819% 0.02667% 1.4319 1.4319 9.39 30 02:02 10-11 VIE 2848 54% 94% 95% 89 63 0.03066% 0.02162% 1.4180 1.4180 18,355 292 4.64 38 01:35 15-16 WAW 1353 21% 123 71 0.10649% 0.06159% 1.7291 1.7291 1.27 53 01:08 10-11 WRO 1800 34% 100 97 0.09034% 0.08805% 1.0260 1.0260 1.99 53 01:08 14-15 ZAG 7000 93% 85% 76% 125 93 0.03363% 0.02503% 1.3435 1.3435 26,582 286 6.94 31 01:55 12-13 ZRH Table 5. „Static‟ Capacity and Productivity AssessmBenchmarkingent. (Source: Airport Bubalo Productivity 2009 with and data the from Role IATA of Capacity 2003, UtilizationEUROSTAT 2007, OAG 2008) 49

Airport Capacity Assessment

As we can see above in example fig. 17a it is now possible to directly apply the maximum (declared) capacities for the runway and the terminal to the assumption rectangle. The quotient of the DPH values, HO and HP, and the maximum capacity values lead us to a capacity utilization value. In the case of FRA this means a capacity utilization of 107% for the runway and a capacity utilization of 97% for the terminal. Since the terminal data is from 2003, these values will be corrected in the future.

Assumption Rectangle and Capacities of FRA Airport Traffic for the year 2007/8

Annual Operations (AO)= 486195 n= 112 PAX/Ops Annual Passengers (AP)= 54501001

y= 0.01810% x= 0.02487%

Hourly Operations (HO)= 88 m= 154 PAX/Ops Hourly Passengers (HP)= 13552 hourly annually Maximum Declared Capacity= 82 Max Decl. Terminal Capacity= 14000 0 (AP/MCTC) Runway Utilization (HO/MCD)= 107% Terminal Utilzation (HP/MCTC)= 97%

Runway Capacity Terminal Capacity

100 16000 90 14000 80 12000 70 60 10000 50 8000 40 6000

30 per PAX hour 4000

20 Operationsper hour 10 2000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Fig. 17a. Assumption Rectangle and Capacities of FRA Airport for the year 2007/2008. (Source: Bubalo 2009 derived from IATA 2003, Flightstats.com 2008 and OAG 2009 data)

The design hour factors are specifically useful for converting annual numbers for movements and passengers to DPH values. As long as the airport configuration does not change and the SLFs and DPH average seat numbers stay in limit, the design hour factors will also not change. Therfore it is possible to estimate the DPH number of passengers from the annual numbers.

Benchmarking Airport Productivity and the Role of Capacity Utilization 50 Airport Capacity Assessment

For example in the case of FRA (fig. 17a) the annual number of Passengers is AP=54,501,001 PAX, the DPH Ops have been looked up from the corresponding demand diagram (fig. 10 in the appendix), which is HO=88 Ops per hour, and the ACH value of m=154 PAX per Ops has been calculated from the flight schedule data. By multiplying HO with m we get the value for DPH PAX of HP=13552 PAX per hour. The design peak hour factor for the passenger conversion is then x=HP/AP=0.02487%. If terminal planners forecast 70 million annual PAX five years into the future (assuming an annual growth rate of 5%) this will lead to a DPH PAX value of 70*0.02487%=17409 PAX per hour in the terminal. Under current conditions this would mean a terminal capacity utilization of 124%, which will most likely create unacceptable service quality conditions for PAX in the terminal. For each extension of capacity new design hour factors must be calculated. For a collection of assumption rectangles please refer to all other figures 17 in the appendix.

3.2 Analytical Model for Calculating Ultimate Capacity

Janic (2000) and de Neufville (2003) both describes a very practical desci- sion making model of Eugene P. Gilbo (2001) for estimating the ultimate capacity of a runway or a runway system under current conditions. With the traffic data for each airport from the direct observation method, it is possible to apply the model to my sample airports. Firstly, it is necessary to plot the points for departures per hour (or by 15 minutes) over the corresponding arrivals per hour for each airports‟ runway system. Different sets of flight schedule or traffic data, for various time periods, on and off-seasonal, could be used. For the following model, OAG flight schedule data for the 12 week of 2009 (March 16 until March 22, 2009) has been used on the airport sample. Secondly, it is nescessary to construct an envelope over the maximum points as indicated as a red line, in fig. 18a and 18b and in figures 18 in the Appen- dix. The resulting typical capacity envelope (de Neufville 2003) is the visu-

Benchmarking Airport Productivity and the Role of Capacity Utilization 51 Airport Capacity Assessment alization of the maximum throughput capacity of the runway layout of the airport under current conditions and distinguishes beween a feasible region below the envelope, in which operations are possible, and an infeasible reagion beyond the envelope, in which operations are not possible (De Neufville 2003, p. 419)(Janic 2000, p. 282)(fig. 17a). Thirdly, in the shown diagrams on typical capacity envelope (Fig. 17) it is expect the departures and arrivals in the “capacity envelope” model to be scattered along a 45° line (for my scaling) from the origin, which would mean that arrivals and departures are evenly divided per hour (or 15 minutes). The further the crossing point, from the envelope and the 45° line, is away from the origin, the more efficient is the runway system. As we can see in fig. 18a, LHR airport almost represents such an ideal case of maximum runway efficiency. Each runway is almost fully utilized, during any operating time. LHR operates at a high utilization rate and is able to serve 44 arrivals and 45 departures all day long. Of course we do not see the actual delays occurring during the operation at such high utilisation rates, but that would also be interesting to compare.

Fig. 18a. Typical Capacity Envelope for LHR airport. (Source: Bubalo 2009)

Benchmarking Airport Productivity and the Role of Capacity Utilization 52 Airport Capacity Assessment

Fig. 18b. Typical Capacity Envelope for IST airport. (Source: Bubalo 2009)

IST airport, to the contrary, represent an airport, which is ineffectively operating its runways. Although IST can serve up to 28 departing or arriving aircrafts per hour, resulting in about 56 operations per hour, this is never achieved at the same time. The typical capacity envelope of IST airport shows that a maximum of 20 arrivals and 20 departures, resulting in 40 operations, per hour could be reached under present conditions. It would be interesting to know, maybe through simulation, how far more efficiency from procedure changes could increase the present maximum of 40 Ops per hour towards the achievable maximum or ultimate capacity of 56 operations per hour.

This model could easily be used for strategic planning and to isolate runway inefficiencies, as it is “effective and efficient” (Janic 2000, p.283).

Please refere to figures 17 in the appendix for more examples of the applied “Capacity Envelope” model.

Benchmarking Airport Productivity and the Role of Capacity Utilization 53 Airport Capacity Assessment

3.3 Simulation Setup

Since financial resources were very limited, many potential software options could not be used for this study. It was virtually impossible to convince people to allow me to have a copy of their software for educational research purposes. And on top to that there was not much time to learn the programs anyway. Some field search on available simulation software is shortly reviewed.

3.3.1 Available Simulation Models

In 2001 the Thematic Network on Airport Activities (THENA), a collabora- tion of Aena, Deutsche Flugsicherung (DFS), National Aerospace Labora- tory of the Netherlands (NLR), and Transavia the European Commission, among others dealing with airport activities, provided an overview of “state of the art” airport simulation and modelling solutions in Europe. This collaborative group pointed out the need for a pan-European attempt to develop more or less standardized techniques for analysing and modelling the air transport system. The three focal points for such a standardized analytical technique that were mentioned are: the theoretical models for performing analytical and statistical analysis, the fast time simulation which allows “what if?” scenarios with real world parameters, and the real time simulation which involves human “in-the-loop” techniques on real world techno- logical equipment to test systems under as realistic conditions as possible with live data feeds sometimes, like for example aircraft flight simulators for pilot training or air traffic control simulators (AENA 2002). One could imagine this to be the most expansive solution. In 2003 the Air Transportation Systems Laboratory (ATSL) of the Virginia Polytechnic Institute and State University (Virginia Tech) published another overview of simulation models in the “Descriptions of Airport and Airspace Simulation Models” presentation of Dr. Antonio A. Trani and Dr. Hojong Baik (Virginia Tech 2003). They primarily compared the three programs

Benchmarking Airport Productivity and the Role of Capacity Utilization 54 Airport Capacity Assessment

TAAM, SIMMOD and RAMS (Reorganized ATC Mathematical Simulator). The latter, being an airspace-only simulation tool supported and developed by EUROCONTROL, will not be further discussed in this context.

There was some discussion about using the CAST Software from Aachen Research Center (ARC). They developed a simulation software called “To- tal Airport Simulation” which would include a runway capacity simulation. Currently ARC provides simulation software for terminal planning, ground handling processes, and aircraft movements and operations. They are also involved in various master planning projects such as for the terminals of the new Berlin-Brandenburg International Airport.

Another simulation software package which has become quite popular in the past few years is the Total Airport Airspace Modeller (TAAM), which provides a 4-D simulation (three dimensions plus time) of airspace and airport traffic. This software is now distributed by Jeppensen, a subdivision of Boe- ing. Jeppensen is also the main distributor for airport diagrams, airspace maps, flight paths, and pilot information under the JeppView brand. With TAAM and JeppView, Boeing has some powerful software products at hand, that are invaluable for the aviation community. TAAM unfortunately is a very costly simulation environment ( >$300,000 per licence) which as a result is rarely used for academic research. Although it is probably the most advanced fast-time simulation tool, as it covers “gate- to-gate” operations through airports and 3-dimensional airspace and time. To learn more about the TAAM simulation software, I strongly recommend the “TAAM Best Practice Guidelines” of the MITRE Corporation, a divi- sion of the Center for Advanced Aviation System Development, which not only specifically covers the TAAM software, but also gives valuable in- sights and instructions on fast-time simulation in general (MITRE 2001).

In Europe the modelling and simulation tool CAMACA (Commonly Agreed Methodology for Airport Airside Capacity Assessment) is often cited. This has however morphed into Pan-European Airport Capacity and Delay Analysis Support (PACS), which is also accessible through the OneSky web

Benchmarking Airport Productivity and the Role of Capacity Utilization 55 Airport Capacity Assessment application of EUROCONTROL. PACS is a collaborative information- sharing platform which enables airports and stakeholders to share information and thereby maintain full control over their data distribution. This information is then used by EUROCONTROL for air transport system capacity planning purposes. One drawback of PACS is that they are not especially enthusiastic about sharing information for educational research purposes. At least PACS did provide aggregate annual data from 2005 and 2006 for maximum declared capacities, annual delays, busy hour values, and annual movements at a predefined selection of airports. Unfortunately, due to poor documentation and a lack of contemporaneity and support, this information had to be checked against other sources, did not cover all of the sample airports, or simply could not be used at all.

The Delft University of Technology developed the Airport Business Suite (ABS) which is a collection of analysis and modelling tools for decision- support and strategy improvement on airside capacities and delays, scheduling, peak day and future scenario analysis, terminal capacities, and the implementation of the Integrated Noise Model (INM) (Roling 2007). This tool is used by master students in airport planning courses. Unfortunately ABS could not be obtained for this study (Interview: Visser 2008).

During my research I came across many studies that used the Airport and Airspace Simulation Model (SIMMOD) engine, which is a reliable and af- fordable way of modelling and simulating airport operations, from the Fed- eral Aviation Administration (FAA), a subdivision of the U.S. Department of Transportation (DOT). The roots of this modelling software go back at least 15 years. It is an extension of the “Airport Capacity and Delay” (ACD) (FAA 1995) document, the first edition of which dates back to 1983. SIM- MOD is frequently compared to the before-mentioned TAAM software. Since the SIMMOD engine is freely available and has been proven over many years, the choice was clear. I just needed GUI software to use this modelling engine effectively and intuitively.

Benchmarking Airport Productivity and the Role of Capacity Utilization 56 Airport Capacity Assessment

I had read about the AirportTools VisualSIMMOD (VS) software on the internet. This is a competitor of the similar SIMMOD Plus! software of ATAC corporation. I was delighted to obtain a copy directly from the author of this fine piece of software, Gregory Bradford. He offers a free copy of VS for educational purposes, which I promptly obtained from his website,. From then on it took only some practice to be able to set up the first functional airport simulation run.

For a very detailed microscopic view of airport operations, and especially ground operations,, a much longer setup time would have been required. For a rough estimation of ultimate capacity and for isolating bottlenecks around the runway, I found this software well suited to my needs. It was much more convenient to use in fact than having to break down annual data into daily or even hourly figures on the basis of the typical peak hour, delay or capacity analysis, and vague assumptions concerning daily operating times. And again, the logic behind the VS program, SIMMOD, is from official sources and has been proven dozens of times. The only alternative to using this free engine is to use flat files, which means using text files full of code, as inputs for the simulation. This is the way it had been done in the past for traffic or system related calculations before computer technology, memory, speed and graphic display were as developed as they are today (Interview: Daduna).

Such flat files, which the program and/or engine put out, are still an invaluable resource for the isolation of potential problems during the run of the simulation. For example, so called gridlocks can occur, which means some capacities or connections are badly configured, so the simulation comes to a halt. This occurred very frequently during different runs of the traffic growth scenarios, where either gate, link or departure queue capacities were at their maximum. Doing the airport creation for a total of 21 airports meant a lot of searching and correcting, but at the same time it speeded up my learning process and got me used to the user interface, data requirements, program and simulation.

Benchmarking Airport Productivity and the Role of Capacity Utilization 57 Airport Capacity Assessment

My first desire was to try to do the simulation for all 58 of my sample airports, to have a fast time simulation for a good portion of overall European air traffic. This had to be limited early on, because, given the struggle with airport complexities and the amount of time needed to configure a really complex airport, it simply could not be done with that level of detail in the time available.

3.3.2 Single Runway Airports

The literature covers “single runway airports” as a basic introduction to the field of modelling or analysing airport systems. The concepts are much clearer when starting simple and then evolving into more complex configurations of airports, with many runways and different take-off/landing patterns. In Europe the Amsterdam Schiphol airport (AMS) is a good example of an extremely complex airport design. Internationally, especially in the U.S., we find much more complex designs than we have here in Europe. Dallas Fort Worth (DFW) and Chicago O‟Hare (ORD) airports, with 7 runways each, are typicalexamples.

So 18 airports of the total sample of 58 were chosen to be included in the simulation. This drastically reduced the amount of time needed to set up the airport simulation, since the departure/arrival system is much simpler for the kinds of airports selected. At a later stage in the project I began creating two more airports with parallel runway systems, the planned Berlin-Brandenburg International (BBI) airport and London Heathrow (LHR) airport.

Let me briefly explain the reason for a simulation in the first place. The simulation is fed with the real future scheduled traffic data for Thurs- day, March 19th of 2009. All other relevant data about the layout, dimensions and specific operations at each simulated airport is also “real world data”. A properly prepared airport simulation is run to show a possible reality of the dynamics of this complex and unpredictable system. Any random factors, like weather or gate wait times for example, can also be considered.

Benchmarking Airport Productivity and the Role of Capacity Utilization 58 Airport Capacity Assessment

For the simulation the most recent SIMMOD Engine (Version: 3.1), which includes the logic for airport and airspace simulation, was obtained directly from the Federal Aviation Administration; for which I am greatly indebted to Mr. John Zinna for his assistance.

3.3.3 Airport Charts

The main set up requires geographical data for each airport, with runways lengths, names and coordinates of the initial points, also the location of runway exits and main taxiways, location of departure queues, and so on. The flight simulation enthusiasts‟ community, the International Virtual Aviation Association (IVAO), shares such information on flights, ATC, aircraft and airports for real time flight simulations. Many of the community members use a network version of Microsoft Flight Simulator® (FSX) to fly online under real world weather conditions or to be in the position of an air traffic controller, who is directing those flights.

These enthusiasts share their information through online forums on the internet. It was therefore possible to obtain the most recent Jeppensen JeppView airport charts, with relevant data for preferential runway systems for time of day, length of runway, number and position of runways, descriptions of the taxiways to and from the runways, threshold positions, number of parking positions, airspace arrival, departure and noise abatement paths, time restrictions, aircraft restrictions, airport maps, terminal dimensions, and tower location and altitudes. This is a lot of data, and I was pleased to find in JeppView a one-stop source for this crucial information to use in the setup of my simulation. For Euro- pean Airports airport charts are available free of charge from the European AIS Database (www.ead.eurocontrol.com). There is a considerable amount of configuration time needed when the airport structure with its links, paths and connections is created. So the practical goal was to reduce the complexity of each individual airport to a minimum. To make the simulation run accurately however, it was also necessary

Benchmarking Airport Productivity and the Role of Capacity Utilization 59 Airport Capacity Assessment to define and enter flight plans, travel routes, link and gate capacities, holding and departure queue points, separation minima, aircraft types, random factors, taxi paths, displaced thresholds, and so on. However anybody involved in airport master planning should give any fast- time simulation software a try.

3.3.4 Airport Coordinates

Another fine invention of our time is the greatly and truly appreciated Google Earth® (GE) software, which provided the coordinates for each runway end and satellite imagery as a background layer for the simulation. With the right positioning and scaling of the image it was possible to later draw and place all airport links and connections correctly on the “map” (fig. 19a).

Fig. 19a. STR Airport Layout for SIMMOD. (Source: Bubalo 2009, Google Earth)

I‟m not aware that this has been done in other studies in the past. The only downside would be if there were some kind of inaccuracy in the coordinates,

Benchmarking Airport Productivity and the Role of Capacity Utilization 60 Airport Capacity Assessment but a comparison with other sources indicated only minimal differences that were not statistically significant. An accuracy of about plus or minus five meters was perfectly accurate enough for simulation purposes. So the coordinates for runway initial points of all 18 single runway airports were entered into the program.

3.3.5 Separation Minima and Wake Vortex Turbulence Classification

The most central limitation in airspace operations are the separation minima in airspace, especially during arrival and departure approach on the same runway. These separation minima define the minimum distance, in either time or distance, between successive aircraft. During high altitude cruising, this minimum separation is required to maintain accurate aircraft radar surveillance by the ATC.

Fig. 20. Vortex generation on take-off and landing. (Source: CAA 1999)

During landing and departures approaches this distance is critical, as it en- sures that turbulence, caused by the wingtips of an aircraft, the so-called wake turbulance or wake vortex (fig. 20), will not harm the following aircraft, causing it to roll or pitch. This wake turbulence is stronger the heavier an aircraft is. This led to the development of the wake turbulance classification, based on maximum take-off weight (MTOW) in the categories Heavy (H) for aircrafts above 136 tons, Large (L) for aircrafts between 7 and 136 tons and Small (S) for aircrafts below 7 tons (FAA 1995). Abbreviations HVY, LRG, SML are used in SIMMOD. Categories Heavy (H), Medium (M) and Light (L) (CAA 1999) (Table 6) or Heavy (H), Medium (M) and Small (S) (ICAO ADRM 2004) are also used. It is strongly advised to stick to one set of abbreviations and definitions. As several source documents are

Benchmarking Airport Productivity and the Role of Capacity Utilization 61 Airport Capacity Assessment used, it is possible that more than one set are used in this study; the reader will find they vary with the source.

Table 6. Weight parameters (maximum take-off weight in kg). (Source: CAA 1999)

De Neufville lists in his book “Airport Systems” the “single-runway IFR separation requirements in the United States in 2000” (p.380; 2003), which, where used, was only slightly modified for my simulation. These kinds of matrices define the required distances between each aircraft category depending on the type of operation of the preceding aircraft (arrival or departure). A medium (M)/large (L) class aircraft following another medium/large class aircraft thus would have to have 3 nautical miles (nmi) of horizontal separation during arrival and 60 seconds of separation during departure. For each case, arrival followed by arrival (A-A), arrival followed by departure (A-D), departure followed by departure (D-D) and departure followed by arrival (D-A), different separation minima must be applied. This is basically the most difficult part of the set up and fine tuning of the simulation.

For the simulation the separation minima are assumed to be identical at each simulated airport. They could differ from airport to airport, however, depending on the technical equipment of the ATC. Any future developments concerning changes in separation minima, like for example the new GPS based surveillance technology Automatic Dependent Surveillance Broadcast (ADS-B) as part of FAA‟s Next Generation Air Transportation System (NextGen) and EUROCONTROL‟s Single European Sky ATM Research Programme (SESAR), can therefore be integrated into the simulation.

Benchmarking Airport Productivity and the Role of Capacity Utilization 62 Airport Capacity Assessment

Fig. 21. Ceiling (ft) and Visibility (mi) Routines. (Source: de Neufville 2003)

3.3.6 Weather Data

There are five major flight routines based on ATC equipment and weather (good visibility, cloud ceiling, and precipitation): Visual flight rules (VFR) are in force for a visibility of 5 miles and a cloud ceiling of 2500ft/760 meters, which allows maximum operations per hour on a runway. Instrument flight rules (IFR) are required for a visibility of 1 mile and a cloud ceiling of 800ft/240 meters, which allows for only reduced operations per hour on a runway. The CAT I routine for a visibility of ½ mile and a ceiling of 200ft/60 meters, The CAT II routine for a visibility of 0.223 miles and a ceiling of 100ft/ 30 meters and The CAT III routine for zero visibility and ceiling, which requires automated landings such as in severe fog and weather conditions, as often experienced in Great Britain and the Netherlands (Fig. 21).

Benchmarking Airport Productivity and the Role of Capacity Utilization 63 Airport Capacity Assessment

So each weather condition dictates its own flight rules or routine. Which routine can be flown, however, also depends on the technical equipment at airports and on the type of aircraft. General aviation aircraft, with little sophisticated equipment on board, can usually only take off, fly, and land under VFR conditions. All modern commercial aircraft are normally able to fly and land in CAT III conditions, e.g. in case of severe rain, fog, or snow. Due to the regional weather conditions, IFR is the flight routine most used in Europe and is used about “99%” of the time (Interview: Lamberg 2009).

3.3.7 Wind Direction

Airports nowadays have up to seven runways to operate on, though the ma- jority of international airports operate normally with only one or two,. Even the airports with as many as seven runways can rarely use all of them at the same time. AMS has six runways but rarely uses more than three. The reason for this is again the weather. For the initial planning of an airport, the master planning, weather data has to be collected and analysed for the chosen site. Wind data is essential for planning runways and must be obtained for many previous years from nearby weather stations. Wind data is then plotted by percentage of overall time, direction and strength in a so called “wind rose diagram” or “wind data plot” (Horonjeff 1994, pp. 268).

A square, representing the future runway and the maximum allowed lateral wind speed, is aligned over this wind rose in the direction of the strongest and most persistent wind direction. For the planned runway, the wind direction and opposite wind direction together must represent roughly 95% of the overall wind conditions. The reason for this is the sensitivity of aircraft to lateral winds. Aircraft are therefore much safer to land and take-off when these operations are done into the wind. In Germany most airport runways are oriented in an East- West direction.

Benchmarking Airport Productivity and the Role of Capacity Utilization 64 Airport Capacity Assessment

Fig. 22. Example printout of windrose (two bi-directional runways). (FAA 1989)

If the 95% condition cannot be met, another crosswind runway for the second strongest condition must be built (Fig. 22). For airport master planning or runway planning this preliminary work is mandatory. This explains why some airports have multiple runways, but use only one at a time. In regions with frequently changing wind directions (Netherlands, AMS) this is often the case. On the other hand, if an airport has a parallel runway system and experiences strong crosswinds (> 15 miles per hour, Horonjeff 1994), it must eventually stop operations and close down the airport.

Benchmarking Airport Productivity and the Role of Capacity Utilization 65 Airport Capacity Assessment

3.3.8 Preferential Runway System

For all airports with more than one runway, a preferential runway system exists. This means that 95% of the time the wind conditions will correspond to the system. The preferential runway system information is given as part of the material containing airport information for the pilot, including airport diagrams. This material also explains the flight path and runway name (e.g. 9R or 27L) for arrivals and departures at that airport. Most of the time alternative runways or additional information concerning curfews or noise related procedures are also stated. With these documents a pilot can prepare himself to configure the aircraft for landing or departure at airports, without undue risk of facing surprises. When taking-off from an airport or a couple of minutes before reaching an airport, the appropriate runway-in-use is communicated to the pilot from ATC.

For airports with a complex system of runways, different combinations of runways for different times of the day or during peak arrival and departure periods, result in different preferential runways being announced for each condition. This could mean that the departure and landing patterns change over the day switching among all the runways.

That is the reason why the simulation of complex airport systems is so much more sophisticated. An airport having only one runway is a simple case, aircraft can land or take-off in either direction, which does not affect the total number of operations. Airports with parallel runways mainly use one runway for departures and one runway for arrivals, the segregated mode, this is also the assumption that governs the simulation of the parallel runway airports BBI and LHR. The operation and simulation in mixed mode, which would allow departures and landings on each of the parallel runways, is also difficult to simulate. It is doubted that there is any advantage for air traffic controllers in mixed mode runways for operational efficiency and hourly capacity with regard to runway safety and stress, at least, at LHR. For Heathrow‟s airport expansion

Benchmarking Airport Productivity and the Role of Capacity Utilization 66 Airport Capacity Assessment plan, which includes the construction of a third runway, the government explicitly ruled out the possibility of using mixed mode at LHR in the in- terim until the new runway is built (www.heathrowairport.com on January 23, 2009). When the preferential runway system of an airport is known, e.g. which runways are utilized most of the time, that information is integrated into the simulation by designating arriving or departing aircrafts to the according runways and routes of the simulated airport.

3.3.9 Apron, Runway Exits and Taxiway layout

The next step in the creation of the layout of an airport in the simulation is the design of the apron, the runway exits, and the taxiways. SIMMOD allows one to assign certain runway exits and taxiways to specific categories of aircraft (see above). Heavy aircraft need a longer touch-down or take-off distance than large or small aircraft and therefore different exits and taxi paths for each category of aircraft are required. To minimise the actual runway occupancy time (ROT), the time which an aircraft actually spends on the runway, so called high-speed exits are constructed. These exits have a 45° angle from the runway and allow the aircraft a rapid exit at a higher speed from the runway. The layout of the taxiways and exits are taken from the individual airport diagrams of an airport and are also entered into the VS program. Using the aircraft data, the SIMMOD logic then assigns the corresponding exit and taxiway to each flight. This can test the efficiency of the current exit and taxiway layout of an airport in serving the current aircraft mix, which means the mix of heavy, large and small aircraft using the airport.

For the present study, the apron area was kept fairly simple. The apron was assumed to have unlimited capacity and was represented by shortcut links in the direction of the taxiways and the ends of the runways. The focus was on displaying an average distance from the taxiway interfaces to the gate areas by as few links as possible.. Aircraft were also allowed to pass each other in the same or opposite direction on these apron links.

Benchmarking Airport Productivity and the Role of Capacity Utilization 67 Airport Capacity Assessment

3.3.10 Gate, Departure Queue and Airspace

The gates for the simulation are representative gates, which means there is one gate having the capacity of all available parking positions for aircraft at each single airport. This information is also taken from the airport diagrams. No differentiation between remote parking positions or gate stands has been made. Since remote parking would require additional ground handling equipment, like shuttle busses and stair vehicles, and would take more time for passengers to reach the terminal, it can be integrated into the simulation at a later time. All aircraft move with low push-back speed a short distance away from the gate until they reach the apron link. All speed limits on the ground links are realistic. The speed limit for the apron area is between 5 and 10 knots (10 to 20 kilometres per hour), for the taxiways between 15 and 20 knots (30 to 40 kilometres per hour), for the push-back 5 knots, and for the high speed exits 35 knots (65 kilometres per hour) (Interview: Lamberg 2009).

For departing aircraft the last point before turning onto the runway to take off is the runway hold position. Beginning from this point and stretching rearwards, a departure queue will form composed of the following aircraft waiting to take-off. Under ideal circumstances a departure queue will never form, since the number of arriving and departing aircraft should never be higher than the number of aircraft that can be processed by the runway. Unfortunately it does not work this way in reality. As we know from the daily demand diagrams of the airports, the arriving and departing aircraft come and go in waves or patterns, because different airlines have scheduled their flights at similar times. So we can observe these peaks at certain hours of the day. Departure queues will develop usually only during these peak periods. It is also here at this point that most flight delays occur. For this reason we will closely look at the situation of departure queues during peak hours. This is actually one of the main reasons for running the simulation.

Benchmarking Airport Productivity and the Role of Capacity Utilization 68 Airport Capacity Assessment

One more element is missing for the simulation setup, that is the definition of approach and departure paths in the airspace. For this I hand defined only basic airspace routes for the simulation. These paths, represented by links, reach maybe 15 to 20 nautical miles (30 to 40 kilometres) beyond an airport. Arriving aircraft that enter the simulation at the beginning of these links have to meet the separation standards between succeeding aircraft, otherwise they will have to wait in a holding airspace or holding pattern until the preceding aircraft has reached its proper separation. The holding airspace is represented by the entry point of the arriving aircraft into the simulation.

3.4 Simulation Run and Scenarios

Please refer to the figures 19 in the Appendix for the final setup for the simulated airports. With this configuration and the definition of separation minima, gates, speed limits and so on, the simulation could be started.

Following the documentation for the VS software, I created a base scenario, which represents the status quo at each airport (www.airporttools.com). Af- ter a successful run more scenarios were set up. With the VS‟s functionality of cloning flights, it is possible to simulate growth scenarios or future scenarios by applying a probability factor to each flight. A cloning probability of 0.3, for example, would represent a 30% growth rate of traffic at each airport. Therefore every flight would have a 30% chance of creating a copy of itself to simulate additional scheduled flights in the future. This leads to the demand and delay diagrams, figures 16 in the Appendix. The following scenarios were created: A base scenario and scenrios with 5%, 10%, 15%, 20%, 30%, 50%, 100% and 150% cloning probability or growth rate.. The scenarios should test each airport‟s current and future conditions, its operational performance, and its ultimate capacity through occurring delays. It is also remarkably useful for isolating potential bottlenecks in the airport layout which disturb the operational flow of aircraft.

Benchmarking Airport Productivity and the Role of Capacity Utilization 69 Results of the Simulation Benchmarking

Another probability factor is implemented into the gate departure times, which are the scheduled departure times, to simulate the probability of late- ness or delay of each flight from 0 to 4 minutes, which could result from late passengers or other airline-related delays. Short delays of this sort are considered acceptable by most airlines.

4 Results of the Simulation Benchmarking

The comparison of similar-sized airports, each having only one effective runway, is a great advantage, since the similar preconditions make the simulation operator‟s task simpler, as he can use the same input for operational productivity and process efficiency for each. The input for productivity at single runway airports includes its length, the location of its exits, its gate and departure queue capacities, and the length of its taxiways (Duran 2005). The maximum throughput and ultimate capacity in flight operations per hour is basically the same for each simulated airport runway, with around 55 operations per hour. Now each flight is directed by the SIMMOD logic from the initial or injection point, where the flight enters the simulation, to the termination point, where the flight exits the simulation. All movements and procedures of each flight are recorded and reported. The changing traffic over the simulated day represents the changing demand for air transportation, which is given by the flight schedule. The pattern of airport demand, its related maximum declared capacity, and its ultimate capacity under IFR conditions is exemplarily shown in fig. 23a.

Benchmarking Airport Productivity and the Role of Capacity Utilization 70 Results of the Simulation Benchmarking

BHX Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 40 80

35 70

30 60

25 50

20 40

15 30 Opshour per Delay in min per flightmin in per Delay

10 20

5 10

0 0 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

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 DEP delay min per flight clone150 Total Ops clone000 Total Ops clone005 Total Ops clone010 Total Ops clone015 Total Ops clone020 Total Ops clone030 Total Ops clone050 Total Ops clone100 Total Ops clone150 Max. Decl. Cap. Tech. IFR Cap. Max. Delay per Flight Fig. 23a. Example BHX Flights and Delays per Flight from SIMMOD. (Source: Bubalo 2009)

VS and SIMMOD report each change of movement of aircraft at each airport. The most important measurements are the individual times needed to get from one node over the link to the next node in the simulation. This results in taxiing times, ROT, approach times, push-back times- and waiting times at gates, on runways, and in theairspace.. The waiting times are the delays. Consequently the reports allow analysis of gate wait delays, departure queue delays, and airspace delays.

The delays per flight per hour of the day are also displayed in the demand and delay diagrams in the Appendix (Figures 23). The yellow straight horizontal line in each diagram suggests the maximum tolerated delay of 4 minutes per flight as recommended by the FAA and EUROCONTROL (de Neufville 2003, p. 448) (A maximum tolerated delay of 5 minutes has actually been graphed to add another buffer minute. This will be corrected for subsequent publication to avoid confusion). Just as in all other demand diagrams, the dotted red horizontal line indicates the maximum declared capac-

Benchmarking Airport Productivity and the Role of Capacity Utilization 71 Results of the Simulation Benchmarking ity, and the light blue horizontal line indicates the ultimate capacity of IFR flights.

When considering the various growth scenarios for each airport, one can observe just when the ultimate capacity, in operations per hour, or the maximum acceptable delay will be reached. This of course assumes that the daily pattern in demand will not dramati- cally change in the future. From observations it is known that the characteristics of the demand patterns do not change much over the years. Airports with strong seasonal variability might exhibit different characteristics in the demand pattern for in season and off-season periods, but this is truer for airports in the Mediterranian region.

Generally it can be said that, with a few exceptions, the observed single runway airports have enough spare runway capacity for future development. BHX airport will reach its current maximum declared capacity with a growth of 50% over today‟s traffic, the ultimate capacity will be reached at somewhere around 100% growth. CIA, DRS and FMO airports have by far sufficient spare capacity for future development and from a runway operational view do not actually require a maximum declared capacity. GLA airport will be able to meet a growth of 150% over today‟s traffic, so the possibility of future expansion is definitely there. GRZ and HHN airports do not require any restrictions and have sufficient capacity to develop freely. LBA airport will reach its ultimate capacity measured in acceptable delays at a growth rate of 100-150% over current traffic, and thus will be able to expand freely without any restrictions. LCY airport already has immense problems serving its current demand in traffic. During the morning and evening periods, the maximum declared capacity is exceeded. This will only of course get worse in the future. Al- though when considering the ultimate capacity of the runway, the limit will be reached with a growth of as little as 50% over the current level. It is highly advisable to implement peak-hour charges or other instruments to

Benchmarking Airport Productivity and the Role of Capacity Utilization 72 Results of the Simulation Benchmarking spread the traffic more evenly throughout the day. Since LCY airport has a sharp decline in demand between 10 and 16 o‟clock, much otherwise wasted spare capacity can be profitable utilized in this manner. LGW airport is the most congested of all in the entire simulated airport sample. Like LCY airport, LGW already faces a huge current congestion problem and actual saturation. LGW airport still operates below its maximum declared capacity, but this limit will likely be reached at a growth rate of only 10% over today‟s traffic. Because the limits for maximum declared capacity and ultimate capacity are so close to each other, the ultimate capacity of LGW‟s current airport runway configuration will be reached at a growth rate of 30-50%. This does not give LGW much room for further development without major investment in new airport infrastructure, mainly in increasing runway capacity. LTN is the reliever airport for the London area, since this is the only airport in the whole region that is not yet saturated. Though the current layout of LTN looks very poor and could use some major investment. LTN is expected to be able to grow freely until a growth of above 150% (but below 200%) of current traffic is reached. Concerning their runways, PSA and SCN airports do not need any restric- itions on maximum declared capacity. There is ample spare capacity to allow for free future development. These two airports would benefit from an increase in their attractiveness. STN airport is another example of a London regional airport that is already hugely oversaturated and will be at its absolute limit in only a very few years. Like LGW‟s maximum capacity, STN‟s maximum declared capacity and its ultimate capacity are very close together. This means that STN will reach its maximum declared capacity at a growth rate of between 10% and 20% over today‟s traffic level and will reach its ultimate capacity at around 50%. A schedule change or peak hour charge is required to free up more spare runway capacity. STR is perhaps the only single runway airport in Germany that will face saturation in the near future. The traffic especially in the morning periods between 6 and 10 o‟clock is responsible for severe peaking at STR and will therefore limit the growth of the airport. Under current conditions a future

Benchmarking Airport Productivity and the Role of Capacity Utilization 73 Results of the Simulation Benchmarking traffic growth of 50% will be the maximum growth rate until the ultimate runway capacity is reached. Even growth scenarios with smaller growth rates (as little as 10%) will lead to unacceptable average delays of over four minutes per flight in the morning periods. Airport expansion programmes, schedule changes, and peak hour charges are highly recommended for STR. SXF airport as it is today, in 2009, will easily be able to process any future air traffic demand. It will not be before a growth of 150% over current traffic is reached that any limits in either delay per flight or ultimate capacity are reached. But in 2011 the new Berlin-Brandenburg International Airport (BBI) will replace the current Airports TXL and SXF, therefore this scenario projection is rather academic. Therefore the BBI airport will have to process the demand of both airports. A simulation scenario has been pro- duced for that case as well and will be discussed below. The remaining two airports, SZG and ZAG, will not have their expansion restricted in the near future. Although SZG has a maximum declared capacity of 20 flights per hour, the reasons for this limitation are clearly not runway-related, since the ultimate capacity is calculated as 53 operations per hour. ZAG airport might face some delays beyond a 150% growth in traffic.

4.1 Results for the LHR and BBI Simulations

To provide a starting point for possible future simulations of more complex airport runway systems, I tried to create at least two parallel runway airports, namely LHR and BBI. Both airports are modelled in segregated mode for simplicity reasons and because segregated mode is believed to be a safer operational mode than is mixed mode. The creation of a parallel runway airport is somewhat similar to that of a single runway airport. You need some additional taxiways for the second runway, but only the one gate is needed. Then, in the flight schedule, you need to assign all arrivals to the arrival runway and all departures to the departure runway.

Benchmarking Airport Productivity and the Role of Capacity Utilization 74 Results of the Simulation Benchmarking

The traffic demand for the BBI airport is the combined flight schedule of SXF and TXL. This is an oversimplification, but still it provides a practical traffic scheme. The simulation run went so smoothly, that additional information could be processed. For LHR airport a base scenario and a 20% growth scenario were simulated. Beyond that growth rate the delays experienced were simply absurd, and, because exceptionally long departure queues blocked all the apron and taxiway links, the whole simulation gridlocked and stopped.

In Fig. 24 it can be seen that as early as 8:10 in the morning at LHR already 29 aircraft are waiting in the departure queue for take-off. This departure queue will persist for the entire day and will dissolve only late at night. And just to remind the reader, this demand is derived from a daily sample of off- season traffic on March 19th, 2009!

For BBI a base, a 20% growth, and a 100% growth scenario were created. With a forecasted doubling of traffic over current levels, it was analysed that BBI will reach its ultimate capacity. With a steady growth in LCC traffic at SXF or BBI, the projected maximum capacity level will be approached in 14 years (estimating 5% average growth; 1.05^14=1.98 -> 98% growth after 14 years). In the 100% scenario, BBI airport faces problems similar to LHR given a 20% growth scenario (Fig. 25 & Fig. 27).

Benchmarking Airport Productivity and the Role of Capacity Utilization 75 Results of the Simulation Benchmarking

Fig. 24: LHR airport SIMMOD simulation for 20% growth scenario (ARR are indicated in red, DEP are indicated in blue)(Source: Bubalo 2009)

Fig. 25. BBI airport SIMMOD simulation for 100% growth scenario. (without arrivals and gate occupation; Source: Bubalo 2009)

Benchmarking Airport Productivity and the Role of Capacity Utilization 76 Results of the Simulation Benchmarking

4.2 Traffic Flows at the Simulated Airports

To indicate the flow of operations at LHR, a flow chart, which is a diagram of the cumulative operations at the airport, has been graphed (Fig. 26). This kind of graph is also known from queuing theory. This theory cannot be discussed here as any examination would fall beyond the parameters of my thesis (Hansen 2002; de Neufville 2003, 838; Janic 2000, p.48).

Flows of Airport Traffic at LHR: SIMMOD Base Scenario

1500 1400 1300 1200 1100 1000 900 800 700 600

500 Operations 400 300 200 100 0 -100 -200

-300

00:00 - 01:00 - 00:00 02:00 - 01:00 03:00 - 02:00 04:00 - 03:00 05:00 - 04:00 06:00 - 05:00 07:00 - 06:00 08:00 - 07:00 09:00 - 08:00 10:00 - 09:00 11:00 - 10:00 12:00 - 11:00 13:00 - 12:00 14:00 - 13:00 15:00 - 14:00 16:00 - 15:00 17:00 - 16:00 18:00 - 17:00 19:00 - 18:00 20:00 - 19:00 21:00 - 20:00 22:00 - 21:00 23:00 - 22:00 24:00 - 23:00 01:00 - 00:00 02:00 - 01:00 03:00 - 02:00 04:00 - 03:00 05:00 - 04:00 06:00 - 05:00 Time

cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand Total Difference DEP Difference ARR Difference

Flows of Airport Traffic at LHR: SIMMOD 20% Growth Scenario

1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 600

500 Operations 400 300 200 100 0 -100 -200 -300

-400

00:00 - 01:00 - 00:00 02:00 - 01:00 03:00 - 02:00 04:00 - 03:00 05:00 - 04:00 06:00 - 05:00 07:00 - 06:00 08:00 - 07:00 09:00 - 08:00 10:00 - 09:00 11:00 - 10:00 12:00 - 11:00 13:00 - 12:00 14:00 - 13:00 15:00 - 14:00 16:00 - 15:00 17:00 - 16:00 18:00 - 17:00 19:00 - 18:00 20:00 - 19:00 21:00 - 20:00 22:00 - 21:00 23:00 - 22:00 24:00 - 23:00 25:00 - 24:00 26:00 - 25:00 27:00 - 26:00 28:00 - 27:00 29:00 - 28:00 30:00 - 29:00 -500 Time

cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand Total Difference DEP Difference ARR Difference Fig. 26. Flows of Airport Traffic at LHR: SIMMOD Base and 20% Growth scenario. (Source: Bubalo 2009)

Benchmarking Airport Productivity and the Role of Capacity Utilization 77 Results of the Simulation Benchmarking

The flow, service and queuing diagrams are particularly interesting for the actual and maximum amount of delayed flights per hour. In these flow diagrams demand curves for arrivals, departures and total operations are drawn. Additionally the service curves and the difference of both flows is shown. Generally the demand curve represents the demand for a service at a specific time. If the airport or runway is congested, the demand cannot be met at that specific moment and is delayed. This „delayed demand‟ is represented by the service curve. It is interesting to note that arrival demand is usually not significantly delayed in any scenario. It is virtually always delays in departure flows and delays occurring in the departure queue that are observed. From the queuing diagrams we see that under current conditions, LHR has as many as 200 aircraft waiting at about 9:00 pm to depart from the runway. These 200 airplanes will have to wait as long as 6 ½ hours to take-off. Even with a margin of error, this still gives an indication of the current saturation level of LHR as the world‟s largest hub. The government-approved expansion of LHR with a third runway and a new 6th terminal is therefore vital for the airport‟s future development (BBC January 15, 2008)

Flows of Airport Traffic at BBI Airport: SIMMOD Base Scenario

700

600

500

400

300 Operations 200

100

-100

ARR Difference DEP Difference Total Difference cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand

Benchmarking Airport Productivity and the Role of Capacity Utilization 78 Results of the Simulation Benchmarking

Flows of Airport Traffic at BBI Airport: SIMMOD 20% Growth Scenario

800

700

600

500

400

300 Operations

200

100

-100

ARR Difference DEP Difference Total Difference cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand

Flows of Airport Traffic at BBI Airport: SIMMOD 100% Growth Scenario

1100

1000

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800

700

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500

400

Operations 300

200

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-100

-200

-300 Time

ARR Difference DEP Difference Total Difference cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand Fig. 27. Flows of Airport Traffic at BBI Airport: SIMMOD Base, 20% and 100% growth scenario. (Source: 2009).

It would be interesting to compare the results of the base scenarios with delay analysis to actual real-world values. At a future stage it might be possible to collect actual data for the 12th week of 2009. This could be used to further fine-tune the simulation.

Benchmarking Airport Productivity and the Role of Capacity Utilization 79 Summary of Results and Conclusions

5 Summary of Results and Conclusions

The outcome of the data analysis is very promising. With basically only detailed flight schedule data and some basic annual statistics on airports, many mathematical models can be applied to airport capacity analysis. With additional airport configuration and infrastructure data, airport simulations can be programmed. Some models like the “Kanafani” model, for calculating the relationship between the annual numbers of PAX and Ops to their referring design hour values (Figures 17), the analytical model to estimate the ultimate capacity of the runway system (Figures 18), part of the queing model with flow diagrams (Fig. 26 & 27) and the SIMMOD simulation have been applied to the analysed airports. Since airport managers, planners and regulators want to have accurate capacity data at their finger tips, I think they have good choices in operational models, that could increase the productivity and efficiency of an airport or could give estimates of the current and future capacity utilization. If some preliminary work has been done in collecting data, setting up a base scenario in the simulation and creating dynamic working tables in a spreadsheet program like Microsoft Excel, different models can be tried out. The free online data sources (Flightstats.com, OAG, EUROSTAT, ACI, EUROCONTROL and others) and computer capacities make it nowadays simple to estimate runway capacities and ultimate capacities at airports very quickly. The work that has been put into this study makes it even realistic to build an instant calculator for runway capacity utilization. As soon as recent flight schedules data, with informations on aircraft type, is available, basic models like the ones presented in this paper can be repro- duced. The first step, however, should be plotting the daily demand diagrams like shown in figures 16. With recent data and informations on maximum declared capacity from the national slot coordination, the demand diagrams give a good indication of the situation at the studied airport.

Benchmarking Airport Productivity and the Role of Capacity Utilization 80 Summary of Results and Conclusions

It should be mentioned that for a thorough terminal capacity analysis much more detailed data must be known about each passenger facility at airports, like number of check-in-counters, number of people precessed over time, number of security lanes, number of baggage belts, streams of people going in and out the entrance and so on. Therefore terminal capacity and utilization is only analyised roughly in the assumption rectangles presented in the Appendix (figures 17).

Since Benchmarking involves the comparison of similar processes among competitors, it depends on the objective of the benchmarking you want to realise. The basic indicators in table 4 and the (static) Airport Capacity As- sessment, listed in table 5, give an overview over annual numbers and throughput and productivity indicators. Benchmarking airports by these indicators can sometimes lead to contrary results. The productivity indicator annual Ops per area of airport property is maybe a good measure for capital productivity and the LCY airport for example has with 1,981Ops per area the highest score. NCE ranks second, 434 Ops/ha, and LHR ranks third, 426 Ops/ha. When looking at PAX per area we see a different picture, with LCY being first again with 74,670 PAX per area, LHR is second with 61,127 PAX/ha and LGW is third with 51,634 PAX per area per year. This is just to show how difficult it is to complete a partial productivity benchmarking study based on operational indicators.

Airports are so highly dynamic systems, that just looking at averaged annual numbers just does not give credible results. Looking at daily and hourly pattern of airport traffic gives a much better indication. However simulation of single runway airports was chosen as another technique to analyse the capacity utilization of airports and their runway system. As the word is, simulation of airports is believed to be very complicated and time-consuming, which is true for the first set ups. Unfortunately in practice of planners, you would need to simulate an airport maybe only every few years. With some practice and regular tasks, simulations can actually be set up in no time. I actually created the BBI and LHR airport simulations on one

Benchmarking Airport Productivity and the Role of Capacity Utilization 81 Summary of Results and Conclusions day each, pasting all flight related data into the simulation and having it run during a 3 hour train ride from Berlin to Bremen on one occasion. A senior who sat face to face to me got very interested in my work. It turned out that he was a former aircraft engineer of Messerschmitt and he was deeply im- pressed that simulations can run on a laptop. So simulation is also a tool which seems to appeal to people and makes them curious for the subject „traffic‟. Maybe its more like watching a movie, than looking at a complex mathematical model, what makes the difference.

As soon as there is some basic framework from preparing a previous simulation study, it will always be possible to set up any new airport scenario. I can again strongly recommend the AirportTool VS software, which delivers a user friendly interface and a thorough online documentation and tutorial (www.AirportTools.com). Working with a simulation also give a “feel” for the whole airport system. You almost suffer with those passengers being delayed at congested virtual airport, like at LGW, STN or LHR, and you feel it is your duty to provide relief for that specific airport by fine-tuning the operations. Many technical aspects of the simulation and an airport is now logical, since mostly physical. Even highly sophisticated mathematical models like the queuing theory in operations research are well understood, when working with airport flow charts from the simulation reports (Fig. 26 & 27). The reports put out by the SIMMOD engine and the VS software also de- liver detailed delay data for each processed flight. With simple spreadsheet applications all necessary capacity related informations from the simulation can be put together as it is shown in figures 23 in the Appendix.

Benchmarking Airport Productivity and the Role of Capacity Utilization 82 Outlook

6 Outlook

It would be very interesting to know how the results for this study would change over time. A Benchmarking study like this should be made on a regular basis, like annually or even quarterly, to furthermore understand the air traffic system over longer periods. Growth of traffic, economic changes, terrorist threats, jumping fuel prices, new LCC airlines, new airports, expanding airports, new routes, bigger airport operating and management companies, higher efficiencies and so on, will bring frequent challenges to the air transport system. Having a regular detailed report about airport developments over many years, like this or the IATA Capacity and Demand Profiles (IATA 2003), the Performance Re- view Report of EUROCONTROL (2007) or the ICCSAI Factbook (2008), will give much deeper results for airport evaluation. Unfortunately, the IATA Capacity and Demand Profiles are not only very expensive, they are also only published once every few years.

To make some final comments it should be pointed out that the role of capacity utilization is very important in measuring airport productivity. From simulation we have seen that the closer an airport is to its ultimate capacity, the more delays will occur at an airport. The actual capacity utilization of the runway component, the apron, the gates and the terminal or any other potential congestion point in the system, is mandatory for operators to know at any time. For airport planners and managers it is also necessary to know the utilization of airport components to make predictions for future developments and investment descissions.

If only the circumstance of strong delays will have a strong impact on cus- tomer satisfaction and might result in declining passenger numbers must be assessed by questionaires of airport passengers. Most likely it depends, how an airport manages its delays. Delays should never directly occur on the runways or the apron. If delays are predictable it is more useful to let passengers stay in the terminal area as long as possible. If the environment of

Benchmarking Airport Productivity and the Role of Capacity Utilization 83 Outlook an airport terminal is friendly to the consumer, he or she will most likely tolerate some delay. Every minute waiting in an airplane, waiting in the departure queue for taking-off will be much more frustrating. So there is also a psychological component to benchmarking, which is hard to measure.

The next research objective should be to extend this study with financial data, on revenues through charges and non-aviation activities, to possibly develop methods for estimating total revenues of an airport per any time period. Furthermore a DEA study should be developed trying to overcome the previous obstacles related to this model. Researchers should be invited for comments and support on the present outcome and on further steps regarding econometric or other analysis‟. The combination of benchmarking and simulation of airport has been tried for this study for the first time to my knowledge and should be continued in the future. I will be very glad to be able to teach people the use and useful- ness of airport simulation. With a hand full of tools many airport capacity related questions could be answered.

All in all, I had a very good time doing the work for this thesis and I don‟t regret a minute. It is still almost unbelievable that I can finally say I am fin- ished. I thank everybody, who supported me during the last months and helped me to create something, which I can be proud of. I‟m very positive with the results and I think I accomplished my mission.

Benchmarking Airport Productivity and the Role of Capacity Utilization 84 References and further readings

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Kralicek, P., Böhmdorfer, F., Kralicek, G. (2008), “Kennzahlen für Geschäftsführer”, 5. Auflage, mi-Fachverlag, FinanzBuch Verlag GmbH, München.

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Benchmarking Airport Productivity and the Role of Capacity Utilization 94 References and further readings

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Benchmarking Airport Productivity and the Role of Capacity Utilization 98 Appendix

Appendix

Benchmarking Airport Productivity and the Role of Capacity Utilization 99 Appendix

Benchmarking Airport Productivity and the Role of Capacity Utilization 100 Appendix

Fig. 8. Capacity and ASV for long range planning. (Source: FAA 1995

Benchmarking Airport Productivity and the Role of Capacity Utilization 101 Appendix

Figures 23

Flights and Delays per Flight for Sample Airports from SIMMOD

Benchmarking Airport Productivity and the Role of Capacity Utilization 102 Appendix

BHX Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 40 80

35 70

30 60

25 50

20 40

15 30 Opshour per Delay in min per flightmin in per Delay

10 20

5 10

0 0 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

CIA Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

Benchmarking Airport Productivity and the Role of Capacity Utilization 103 Appendix

DRS Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

FMO Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

Benchmarking Airport Productivity and the Role of Capacity Utilization 104 Appendix

GLA Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 7 60

6 50

5 40

4 30

3 Opshour per

20 Delay in min per flightmin in per Delay 2

10 1

0 0 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

GRZ Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

Benchmarking Airport Productivity and the Role of Capacity Utilization 105 Appendix

HHN Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

LBA Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 7 60

6 50

5 40

4 30

3 Opshour per

20 Delay in min per flightmin in per Delay 2

10 1

0 0 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

Benchmarking Airport Productivity and the Role of Capacity Utilization 106 Appendix

LCY Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 60 100

90 50 80

70 40 60

30 50

40 Opshour per 20 Delay in min per flightmin in per Delay 30

20 10 10

0 0 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

LGW Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 35 120

30 100

25 80

20 60

15 Opshour per

40 Delay in min per flightmin in per Delay 10

20 5

0 0 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

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

Benchmarking Airport Productivity and the Role of Capacity Utilization 107 Appendix

LTN Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

PSA Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 70

60 5

50 4

40 3

30 Opshour per

2 Delay in min per flightmin in per Delay 20

1 10

0 0 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

Benchmarking Airport Productivity and the Role of Capacity Utilization 108 Appendix

SCN Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

STN Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 100 140

90 120 80

70 100

60 80 50 60

40 Opshour per Delay in min per flightmin in per Delay 30 40

20 20 10

0 0 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

Benchmarking Airport Productivity and the Role of Capacity Utilization 109 Appendix

STR Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 60 100

90 50 80

70 40 60

30 50

40 Opshour per 20 Delay in min per flightmin in per Delay 30

20 10 10

0 0 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

SXF Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 7 60

6 50

5 40

4 30

3 Opshour per

20 Delay in min per flightmin in per Delay 2

10 1

0 0 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

Benchmarking Airport Productivity and the Role of Capacity Utilization 110 Appendix

SZG Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

ZAG Flights and Delays per Flight from SIMMOD (Flightplan OAG Thu 03/19/2009) 6 60

5 50

4 40

3 30 Opshour per

2 20 Delay in min per flightmin in per Delay

1 10

0 0 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

Benchmarking Airport Productivity and the Role of Capacity Utilization 111 Appendix

Figures 26 and 27

Flows of Airport Traffic from SIMMOD

Benchmarking Airport Productivity and the Role of Capacity Utilization 112 Appendix

Flows of Airport Traffic at LHR: SIMMOD Base Scenario

1500 1400 1300 1200 1100 1000 900 800 700 600

500 Operations 400 300 200 100 0 -100 -200

-300

cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand Total Difference DEP Difference ARR Difference

Flows of Airport Traffic at LHR: SIMMOD 20% Growth Scenario

1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 600

500 Operations 400 300 200 100 0 -100 -200 -300

-400

00:00 - 01:00 - 00:00 02:00 - 01:00 03:00 - 02:00 04:00 - 03:00 05:00 - 04:00 06:00 - 05:00 07:00 - 06:00 08:00 - 07:00 09:00 - 08:00 10:00 - 09:00 11:00 - 10:00 12:00 - 11:00 13:00 - 12:00 14:00 - 13:00 15:00 - 14:00 16:00 - 15:00 17:00 - 16:00 18:00 - 17:00 19:00 - 18:00 20:00 - 19:00 21:00 - 20:00 22:00 - 21:00 23:00 - 22:00 24:00 - 23:00 25:00 - 24:00 26:00 - 25:00 27:00 - 26:00 28:00 - 27:00 29:00 - 28:00 30:00 - 29:00 -500 Time

cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand Total Difference DEP Difference ARR Difference

Benchmarking Airport Productivity and the Role of Capacity Utilization 113 Appendix

Flows of Airport Traffic at BBI Airport: SIMMOD Base Scenario

700

600

500

400

300 Operations 200

100

-100

ARR Difference DEP Difference Total Difference cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand

Flows of Airport Traffic at BBI Airport: SIMMOD 20% Growth Scenario

800

700

600

500

400

300 Operations

200

100

-100

ARR Difference DEP Difference Total Difference cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand

Benchmarking Airport Productivity and the Role of Capacity Utilization 114 Appendix

Flows of Airport Traffic at BBI Airport: SIMMOD 100% Growth Scenario

1100

1000

900

800

700

600

500

400

Operations 300

200

100

-100

-200

-300 Time

ARR Difference DEP Difference Total Difference cumulative ARR Flow cumulative ARR Demand cumulative DEP Flow cumulative DEP Demand cumulative Total Flow cumulative Total Demand

Benchmarking Airport Productivity and the Role of Capacity Utilization 115 Appendix

Sum of Annual SLF time iata_code 2003 2004 2005 2006 2007 % change AMS 70.0% 70.2% 71.1% 65.2% 75.4% 7.6% ARN 65.1% 63.4% 66.4% 69.2% 69.6% 6.9% ATH 70.0% 70.0% 70.0% 70.0% 70.0% 0.0% BCN 66.7% 60.5% 67.8% 69.9% 69.6% 4.4% BHX 72.4% 71.6% 71.2% 72.2% 72.6% 0.2% BRU 62.5% 64.0% 66.2% 67.3% 68.3% 9.3% BSL 39.0% 70.0% 70.0% 70.0% 70.0% 79.6% CDG 65.4% 70.0% 70.0% 70.0% 70.0% 7.0% CGN 63.4% 68.1% 70.6% 72.1% 72.1% 13.8% CIA 70.3% 72.1% 76.3% 77.6% 78.3% 11.3% CPH 56.4% 57.1% 60.3% 70.7% 70.9% 25.7% DRS 70.5% 70.9% 69.0% 70.1% 71.1% 0.8% DUB 69.5% 71.1% 76.1% 74.7% 74.5% 7.1% DUS 68.1% 69.6% 69.3% 69.1% 70.2% 3.1% EDI 73.9% 67.4% 66.7% 68.8% 69.8% -5.5% FCO 61.8% 63.8% 65.0% 66.1% 68.5% 11.0% FMO 67.3% 71.9% 70.3% 71.5% 70.5% 4.8% FRA 71.6% 73.3% 73.5% 73.8% 75.3% 5.2% GLA 75.4% 75.0% 73.7% 73.8% 73.4% -2.8% GRZ 64.0% 64.3% 64.8% 69.2% 68.0% 6.4% HAJ 67.7% 69.6% 69.4% 70.1% 71.5% 5.5% HAM 67.8% 69.8% 70.9% 70.6% 72.0% 6.1% HEL 56.2% 58.6% 61.8% 66.4% 68.6% 22.1% HHN 66.4% 73.7% 68.8% 64.2% 67.6% 1.7% LBA 73.7% 73.6% 72.7% 69.9% 67.3% -8.7% LCY 53.2% 51.9% 52.9% 56.6% 53.0% -0.5% LEJ 71.3% 74.0% 73.7% 72.9% 71.4% 0.2% LGG 70.0% 70.0% 70.0% 87.0% 32.5% -53.6% LGW 76.0% 76.3% 75.0% 76.8% 77.4% 1.8% LHR 72.1% 72.9% 72.8% 72.8% 73.6% 2.1% LIS 56.8% 70.0% 70.0% 59.4% 61.0% 7.5% LTN 78.6% 77.0% 78.0% 78.3% 78.6% 0.0% LYS 61.0% 70.0% 70.0% 70.0% 70.0% 14.7% MAD 66.9% 58.1% 68.2% 70.2% 71.2% 6.5% MAN 76.9% 74.1% 73.1% 74.8% 75.1% -2.3% MRS 65.8% 70.0% 70.0% 70.0% 70.0% 6.5% MUC 67.9% 69.7% 71.0% 71.6% 73.3% 7.9% MXP 65.8% 68.6% 69.2% 70.8% 72.0% 9.4% NCE 67.7% 70.0% 70.0% 70.0% 70.0% 3.3% NUE 68.0% 71.4% 71.8% 71.0% 71.1% 4.6% ORY 70.6% 70.0% 70.0% 70.0% 70.0% -0.8% OSL 62.3% 62.0% 65.5% 66.2% 68.7% 10.2% PMI 80.2% 71.1% 79.4% 80.9% 81.0% 0.9% PRG 64.7% 64.0% 65.0% 67.0% 66.4% 2.6% PSA 63.7% 67.0% 70.4% 73.9% 71.7% 12.6% RHO 70.0% 70.0% 70.0% 70.0% 70.0% 0.0% RTM 70.0% 70.0% 70.0% 61.0% 56.2% -19.8% SCN 65.6% 68.5% 68.2% 70.1% 66.3% 1.1% STN 75.3% 74.0% 74.4% 75.4% 74.5% -1.1% STR 66.3% 67.6% 69.0% 70.4% 70.0% 5.5% SXF 68.4% 70.7% 73.7% 75.0% 75.7% 10.7% SZG 71.0% 72.6% 70.8% 73.8% 72.1% 1.6% TXL 66.7% 69.4% 69.4% 70.9% 70.7% 6.0% VIE 65.5% 64.3% 66.4% 68.9% 69.5% 6.1% WAW 70.0% 59.9% 62.8% 63.9% 64.7% -7.6% WRO 70.0% 70.0% 70.0% 70.1% 75.0% 7.1% ZRH 64.6% 65.1% 67.3% 71.0% 70.9% 9.7% Mean 67.3% 68.6% 69.6% 70.6% 70.0% Table 7. Average Seat Load Factors for Sample Airport from 2003 to 2007. (Source: Bubalo from EUROSTAT 2008)

Benchmarking Airport Productivity and the Role of Capacity Utilization 116 Appendix

Figures 16

Weekdays Operations Pattern and Capacities for Sample Airports

Benchmarking Airport Productivity and the Role of Capacity Utilization 117 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Sun 03/22/2009)

200

180

160

140

120

100

80 Ops per hour

0 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

AMS_2009-03-16_TOT AMS_2009-03-17_TOT AMS_2009-03-18_TOT AMS_2009-03-19_TOT AMS_2009-03-20_TOT maximum declared capacity of all sources AMS technical capacity vfr AMS technical capacity ifr peak_day_2008_total tot_slots_arr_peak tot_slots_dep_peak

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour 40

0 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

ARN_2009-03-16_TOT ARN_2009-03-17_TOT ARN_2009-03-18_TOT ARN_2009-03-19_TOT ARN_2009-03-20_TOT maximum declared capacity of all sources ARN technical capacity vfr ARN technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 118 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour 40

0 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

ATH_2009-03-16_TOT ATH_2009-03-17_TOT ATH_2009-03-18_TOT ATH_2009-03-19_TOT ATH_2009-03-20_TOT maximum declared capacity of all sources ATH technical capacity vfr ATH technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour 40

0 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

BCN_2009-03-16_TOT BCN_2009-03-17_TOT BCN_2009-03-18_TOT BCN_2009-03-19_TOT BCN_2009-03-20_TOT maximum declared capacity of all sources BCN technical capacity vfr BCN technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 119 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour 20

0 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

BHX_2009-03-16_TOT BHX_2009-03-17_TOT BHX_2009-03-18_TOT BHX_2009-03-19_TOT BHX_2009-03-20_TOT maximum declared capacity of all sources BHX technical capacity vfr BHX technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

BRU_2009-03-16_TOT BRU_2009-03-17_TOT BRU_2009-03-18_TOT

BRU_2009-03-19_TOT BRU_2009-03-20_TOT maximum declared capacity of all sources

BRU technical capacity vfr BRU technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 120 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

Ops per hour Opsper 30

0 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

BSL_2009-03-16_TOT BSL_2009-03-17_TOT BSL_2009-03-18_TOT

BSL_2009-03-19_TOT BSL_2009-03-20_TOT maximum declared capacity of all sources

BSL technical capacity vfr BSL technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

200

180

160

140

120

100

80 Ops per hour Opsper

0 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

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

Benchmarking Airport Productivity and the Role of Capacity Utilization 121 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

Ops per hour Opsper 30

0 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

CGN_2009-03-16_TOT CGN_2009-03-17_TOT CGN_2009-03-18_TOT CGN_2009-03-19_TOT CGN_2009-03-20_TOT maximum declared capacity of all sources CGN technical capacity vfr CGN technical capacity ifr peak_day_2008_total Slot coordination total min Slot coordination total max

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009; Slot limitation to 15 Ops/hr due to noise impact analysis)

30 Ops per hour Opsper 20

0 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

CIA_2009-03-16_TOT CIA_2009-03-17_TOT CIA_2009-03-18_TOT CIA_2009-03-19_TOT CIA_2009-03-20_TOT maximum declared capacity of all sources CIA technical capacity vfr CIA technical capacity ifr peak_day_2008_total Slot coordination total max

Benchmarking Airport Productivity and the Role of Capacity Utilization 122 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

CPH_2009-03-16_TOT CPH_2009-03-17_TOT CPH_2009-03-18_TOT

CPH_2009-03-19_TOT CPH_2009-03-20_TOT maximum declared capacity of all sources

CPH technical capacity vfr CPH technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

DRS_2009-03-16_TOT DRS_2009-03-17_TOT DRS_2009-03-18_TOT DRS_2009-03-19_TOT DRS_2009-03-20_TOT maximum declared capacity of all sources DRS technical capacity vfr DRS technical capacity ifr peak_day_2008_total Slot coordination total max

Benchmarking Airport Productivity and the Role of Capacity Utilization 123 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

40 Ops per hour Opsper 30

0 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

DUB_2009-03-16_TOT DUB_2009-03-17_TOT DUB_2009-03-18_TOT DUB_2009-03-19_TOT DUB_2009-03-20_TOT maximum declared capacity of all sources DUB technical capacity vfr DUB technical capacity ifr peak_day_2008_total tot_slots

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

DUS_2009-03-16_TOT DUS_2009-03-17_TOT DUS_2009-03-18_TOT DUS_2009-03-19_TOT DUS_2009-03-20_TOT maximum declared capacity of all sources DUS technical capacity vfr DUS technical capacity ifr peak_day_2008_total Slot coordination total min Slot coordination total max

Benchmarking Airport Productivity and the Role of Capacity Utilization 124 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

40 Ops per hour Opsper 30

0 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

EDI_2009-03-16_TOT EDI_2009-03-17_TOT EDI_2009-03-18_TOT

EDI_2009-03-19_TOT EDI_2009-03-20_TOT maximum declared capacity of all sources

EDI technical capacity vfr EDI technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

FCO_2009-03-16_TOT FCO_2009-03-17_TOT FCO_2009-03-18_TOT

FCO_2009-03-19_TOT FCO_2009-03-20_TOT maximum declared capacity of all sources

FCO technical capacity vfr FCO technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 125 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

FMO_2009-03-16_TOT FMO_2009-03-17_TOT FMO_2009-03-18_TOT

FMO_2009-03-19_TOT FMO_2009-03-20_TOT maximum declared capacity of all sources

FMO technical capacity vfr FMO technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

140

120

100

60 Ops per hour Opsper

0 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

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

Benchmarking Airport Productivity and the Role of Capacity Utilization 126 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

GLA_2009-03-16_TOT GLA_2009-03-17_TOT GLA_2009-03-18_TOT

GLA_2009-03-19_TOT GLA_2009-03-20_TOT maximum declared capacity of all sources

GLA technical capacity vfr GLA technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

GRZ_2009-03-16_TOT GRZ_2009-03-17_TOT GRZ_2009-03-18_TOT

GRZ_2009-03-19_TOT GRZ_2009-03-20_TOT maximum declared capacity of all sources

GRZ technical capacity vfr GRZ technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 127 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

HAJ_2009-03-16_TOT HAJ_2009-03-17_TOT HAJ_2009-03-18_TOT

HAJ_2009-03-19_TOT HAJ_2009-03-20_TOT maximum declared capacity of all sources

HAJ technical capacity vfr HAJ technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

Ops per hour Opsper 30

0 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

HAM_2009-03-16_TOT HAM_2009-03-17_TOT HAM_2009-03-18_TOT HAM_2009-03-19_TOT HAM_2009-03-20_TOT maximum declared capacity of all sources HAM technical capacity vfr HAM technical capacity ifr peak_day_2008_total Slot coordination total max

Benchmarking Airport Productivity and the Role of Capacity Utilization 128 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

HEL_2009-03-16_TOT HEL_2009-03-17_TOT HEL_2009-03-18_TOT

HEL_2009-03-19_TOT HEL_2009-03-20_TOT maximum declared capacity of all sources

HEL technical capacity vfr HEL technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

HHN_2009-03-16_TOT HHN_2009-03-17_TOT HHN_2009-03-18_TOT

HHN_2009-03-19_TOT HHN_2009-03-20_TOT maximum declared capacity of all sources

HHN technical capacity vfr HHN technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 129 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

160

140

120

100

Ops per hour Opsper 60

0 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

IST_2009-03-16_TOT IST_2009-03-17_TOT IST_2009-03-18_TOT

IST_2009-03-19_TOT IST_2009-03-20_TOT maximum declared capacity of all sources

IST technical capacity vfr IST technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

LBA_2009-03-16_TOT LBA_2009-03-17_TOT LBA_2009-03-18_TOT

LBA_2009-03-19_TOT LBA_2009-03-20_TOT maximum declared capacity of all sources

LBA technical capacity vfr LBA technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 130 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

LCY_2009-03-16_TOT LCY_2009-03-17_TOT LCY_2009-03-18_TOT

LCY_2009-03-19_TOT LCY_2009-03-20_TOT maximum declared capacity of all sources

LCY technical capacity vfr LCY technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

LEJ_2009-03-16_TOT LEJ_2009-03-17_TOT LEJ_2009-03-18_TOT

LEJ_2009-03-19_TOT LEJ_2009-03-20_TOT maximum declared capacity of all sources

LEJ technical capacity vfr LEJ technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 131 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

100

40 Ops per hour Opsper

0 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

LGG_2009-03-16_TOT LGG_2009-03-17_TOT LGG_2009-03-18_TOT

LGG_2009-03-19_TOT LGG_2009-03-20_TOT maximum declared capacity of all sources

LGG technical capacity vfr LGG technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

LGW_2009-03-16_TOT LGW_2009-03-17_TOT LGW_2009-03-18_TOT LGW_2009-03-19_TOT LGW_2009-03-20_TOT maximum declared capacity of all sources LGW technical capacity vfr LGW technical capacity ifr peak_day_2008_total tot_slots

Benchmarking Airport Productivity and the Role of Capacity Utilization 132 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

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)

30 Ops per hour Opsper 20

0 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

LIS_2009-03-16_TOT LIS_2009-03-17_TOT LIS_2009-03-18_TOT

LIS_2009-03-19_TOT LIS_2009-03-20_TOT maximum declared capacity of all sources

LIS technical capacity vfr LIS technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 133 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

LTN_2009-03-16_TOT LTN_2009-03-17_TOT LTN_2009-03-18_TOT

LTN_2009-03-19_TOT LTN_2009-03-20_TOT maximum declared capacity of all sources

LTN technical capacity vfr LTN technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

LYS_2009-03-16_TOT LYS_2009-03-17_TOT LYS_2009-03-18_TOT

LYS_2009-03-19_TOT LYS_2009-03-20_TOT maximum declared capacity of all sources

LYS technical capacity vfr LYS technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 134 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

250

200

150

100 Ops per hour Opsper

0 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

MAD_2009-03-16_TOT MAD_2009-03-17_TOT MAD_2009-03-18_TOT

MAD_2009-03-19_TOT MAD_2009-03-20_TOT maximum declared capacity of all sources

MAD technical capacity vfr MAD technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

MAN_2009-03-16_TOT MAN_2009-03-17_TOT MAN_2009-03-18_TOT MAN_2009-03-19_TOT MAN_2009-03-20_TOT maximum declared capacity of all sources MAN technical capacity vfr MAN technical capacity ifr peak_day_2008_total tot_slots

Benchmarking Airport Productivity and the Role of Capacity Utilization 135 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

MUC_2009-03-16_TOT MUC_2009-03-17_TOT MUC_2009-03-18_TOT

MUC_2009-03-19_TOT MUC_2009-03-20_TOT maximum declared capacity of all sources

MUC technical capacity vfr MUC technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

MXP_2009-03-16_TOT MXP_2009-03-17_TOT MXP_2009-03-18_TOT

MXP_2009-03-19_TOT MXP_2009-03-20_TOT maximum declared capacity of all sources

MXP technical capacity vfr MXP technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 136 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

140

120

100

60 Ops per hour Opsper

0 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

NCE_2009-03-16_TOT NCE_2009-03-17_TOT NCE_2009-03-18_TOT NCE_2009-03-19_TOT NCE_2009-03-20_TOT maximum declared capacity of all sources NCE technical capacity vfr NCE technical capacity ifr peak_day_2008_total tot_slots

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

NUE_2009-03-16_TOT NUE_2009-03-17_TOT NUE_2009-03-18_TOT

NUE_2009-03-19_TOT NUE_2009-03-20_TOT maximum declared capacity of all sources

NUE technical capacity vfr NUE technical capacity ifr

Benchmarking Airport Productivity and the Role of Capacity Utilization 137 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

ORY_2009-03-16_TOT ORY_2009-03-17_TOT ORY_2009-03-18_TOT

ORY_2009-03-19_TOT ORY_2009-03-20_TOT maximum declared capacity of all sources

ORY technical capacity vfr ORY technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

OSL_2009-03-16_TOT OSL_2009-03-17_TOT OSL_2009-03-18_TOT

OSL_2009-03-19_TOT OSL_2009-03-20_TOT maximum declared capacity of all sources

OSL technical capacity vfr OSL technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 138 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

PMI_2009-03-16_TOT PMI_2009-03-17_TOT PMI_2009-03-18_TOT

PMI_2009-03-19_TOT PMI_2009-03-20_TOT maximum declared capacity of all sources

PMI technical capacity vfr PMI technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

Ops per hour Opsper 30

0 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

PRG_2009-03-16_TOT PRG_2009-03-17_TOT PRG_2009-03-18_TOT

PRG_2009-03-19_TOT PRG_2009-03-20_TOT maximum declared capacity of all sources

PRG technical capacity vfr PRG technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 139 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

PSA_2009-03-16_TOT PSA_2009-03-17_TOT PSA_2009-03-18_TOT

PSA_2009-03-19_TOT PSA_2009-03-20_TOT maximum declared capacity of all sources

PSA technical capacity vfr PSA technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

RHO_2009-03-16_TOT RHO_2009-03-17_TOT RHO_2009-03-18_TOT

RHO_2009-03-19_TOT RHO_2009-03-20_TOT maximum declared capacity of all sources

RHO technical capacity vfr RHO technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 140 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

RTM_2009-03-16_TOT RTM_2009-03-17_TOT RTM_2009-03-18_TOT

RTM_2009-03-19_TOT RTM_2009-03-20_TOT maximum declared capacity of all sources

RTM technical capacity vfr RTM technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

SCN_2009-03-16_TOT SCN_2009-03-17_TOT SCN_2009-03-18_TOT

SCN_2009-03-19_TOT SCN_2009-03-20_TOT maximum declared capacity of all sources

SCN technical capacity vfr SCN technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 141 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

STR_2009-03-16_TOT STR_2009-03-17_TOT STR_2009-03-18_TOT

STR_2009-03-19_TOT STR_2009-03-20_TOT maximum declared capacity of all sources

STR technical capacity vfr STR technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 142 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

SXF_2009-03-16_TOT SXF_2009-03-17_TOT SXF_2009-03-18_TOT

SXF_2009-03-19_TOT SXF_2009-03-20_TOT maximum declared capacity estimate

SXF technical capacity vfr SXF technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

SZG_2009-03-16_TOT SZG_2009-03-17_TOT SZG_2009-03-18_TOT

SZG_2009-03-19_TOT SZG_2009-03-20_TOT maximum declared capacity of all sources

SZG technical capacity vfr SZG technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 143 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

120

100

60 Ops per hour Opsper 40

0 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

TXL_2009-03-16_TOT TXL_2009-03-17_TOT TXL_2009-03-18_TOT TXL_2009-03-19_TOT TXL_2009-03-20_TOT maximum declared capacity of all sources TXL technical capacity vfr TXL technical capacity ifr peak_day_2008_total Slot coordination total max

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

40 Ops per hour Opsper 30

0 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

VIE_2009-03-16_TOT VIE_2009-03-17_TOT VIE_2009-03-18_TOT

VIE_2009-03-19_TOT VIE_2009-03-20_TOT maximum declared capacity of all sources

VIE technical capacity vfr VIE technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 144 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

Ops per hour Opsper 30

0 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

WAW_2009-03-16_TOT WAW_2009-03-17_TOT WAW_2009-03-18_TOT

WAW_2009-03-19_TOT WAW_2009-03-20_TOT maximum declared capacity of all sources

WAW technical capacity vfr WAW technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

WRO_2009-03-16_TOT WRO_2009-03-17_TOT WRO_2009-03-18_TOT

WRO_2009-03-19_TOT WRO_2009-03-20_TOT maximum declared capacity of all sources

WRO technical capacity vfr WRO technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 145 Appendix

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

30 Ops per hour Opsper 20

0 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

ZAG_2009-03-16_TOT ZAG_2009-03-17_TOT ZAG_2009-03-18_TOT

ZAG_2009-03-19_TOT ZAG_2009-03-20_TOT maximum declared capacity of all sources

ZAG technical capacity vfr ZAG technical capacity ifr peak_day_2008_total

Weekdays Operations Pattern and Capacities (Sample Week Mon 03/16/2009 - Fri 03/20/2009)

100

40 Ops per hour Opsper

0 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

ZRH_2009-03-16_TOT ZRH_2009-03-17_TOT ZRH_2009-03-18_TOT

ZRH_2009-03-19_TOT ZRH_2009-03-20_TOT maximum declared capacity of all sources

ZRH technical capacity vfr ZRH technical capacity ifr peak_day_2008_total

Benchmarking Airport Productivity and the Role of Capacity Utilization 146 Appendix

Figures 17

Assumption Rectangles for Sample Airports

Benchmarking Airport Productivity and the Role of Capacity Utilization 147 Appendix Assumption Rectangle and Capacities of AMS Airport Traffic for the year 2007/8

Annual Operations (AO)= 443677 n= 108 PAX/Ops Annual Passengers (AP)= 47849426

y= 0.02479% x= 0.02828%

Hourly Operations (HO)= 110 m= 123 PAX/Ops Hourly Passengers (HP)= 13530 hourly annually Maximum Declared Capacity= 108 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 102% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

120 16000 14000 100 12000 80 10000 60 8000 6000 40 PAX per PAX hour 4000

Operationsper hour 20 2000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of ARN Airport Traffic for the year 2007/8

Annual Operations (AO)= 205251 n= 88 PAX/Ops Annual Passengers (AP)= 18013660

y= 0.03313% x= 0.04077%

Hourly Operations (HO)= 68 m= 108 PAX/Ops Hourly Passengers (HP)= 7344 hourly annually Maximum Declared Capacity= 80 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 85% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

90 8000 80 7000 70 6000 60 5000 50 4000 40 3000

30 PAX per PAX hour 20 2000 Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of ATH Airport Traffic for the year 2007/8

Annual Operations (AO)= 193123 n= 86 PAX/Ops Annual Passengers (AP)= 16525385

y= 0.01812% x= 0.02584%

Hourly Operations (HO)= 35 m= 122 PAX/Ops Hourly Passengers (HP)= 4270 hourly annually Maximum Declared Capacity= 52 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 67% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

60 4500 4000 50 3500 40 3000 2500 30 2000 20 1500 PAX per PAX hour 1000

Operationsper hour 10 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 148 Appendix Assumption Rectangle and Capacities of BCN Airport Traffic for the year 2007/8

Annual Operations (AO)= 339020 n= 97 PAX/Ops Annual Passengers (AP)= 32814023

y= 0.01770% x= 0.02249%

Hourly Operations (HO)= 60 m= 123 PAX/Ops Hourly Passengers (HP)= 7380 hourly annually Maximum Declared Capacity= 60 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 100% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

70 8000 60 7000 50 6000 5000 40 4000 30 3000

20 per PAX hour 2000

Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of BHX Airport Traffic for the year 2007/8

Annual Operations (AO)= 104480 n= 89 PAX/Ops Annual Passengers (AP)= 9318566

y= 0.02871% x= 0.03477%

Hourly Operations (HO)= 30 m= 108 PAX/Ops Hourly Passengers (HP)= 3240 hourly annually Maximum Declared Capacity= 40 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 75% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

45 3500 40 3000 35 2500 30 25 2000 20 1500 15

PAX per PAX hour 1000 10

Operationsper hour 500 5 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of BRU Airport Traffic for the year 2007/8

Annual Operations (AO)= 240341 n= 75 PAX/Ops Annual Passengers (AP)= 17934323

y= 0.02913% x= 0.04489%

Hourly Operations (HO)= 70 m= 115 PAX/Ops Hourly Passengers (HP)= 8050 hourly annually Maximum Declared Capacity= 74 Max Decl. Terminal Capacity= 0 55000000 (AP/MCTC) Runway Utilization (HO/MCD)= 95% Terminal Utilzation (HP/MCTC)= 33%

Runway Capacity Terminal Capacity

80 9000 70 8000 60 7000 6000 50 5000 40 4000 30 3000 20 per PAX hour 2000 Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 149 Appendix Assumption Rectangle and Capacities of BSL Airport Traffic for the year 2007/8

Annual Operations (AO)= 27879 n= 33 PAX/Ops Annual Passengers (AP)= 919648

y= 0.05022% x= 0.12179%

Hourly Operations (HO)= 14 m= 80 PAX/Ops Hourly Passengers (HP)= 1120 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 3500 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)= 32%

Runway Capacity Terminal Capacity

16 4000 14 3500 12 3000 10 2500 8 2000 6 1500

4 per PAX hour 1000

Operationsper hour 2 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of CDG Airport Traffic for the year 2007/8

Annual Operations (AO)= 569281 n= 105 PAX/Ops Annual Passengers (AP)= 59549883

y= 0.01932% x= 0.02734%

Hourly Operations (HO)= 110 m= 148 PAX/Ops Hourly Passengers (HP)= 16280 hourly annually Maximum Declared Capacity= 106 Max Decl. Terminal Capacity= 20300 0 (AP/MCTC) Runway Utilization (HO/MCD)= 104% Terminal Utilzation (HP/MCTC)= 80%

Runway Capacity Terminal Capacity

120 25000

100 20000 80 15000 60 10000

40 PAX per PAX hour 5000

Operationsper hour 20

0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of CGN Airport Traffic for the year 2007/8

Annual Operations (AO)= 138528 n= 76 PAX/Ops Annual Passengers (AP)= 10549875

y= 0.01877% x= 0.03031%

Hourly Operations (HO)= 26 m= 123 PAX/Ops Hourly Passengers (HP)= 3198 hourly annually Maximum Declared Capacity= 52 Max Decl. Terminal Capacity= 4000 0 (AP/MCTC) Runway Utilization (HO/MCD)= 50% Terminal Utilzation (HP/MCTC)= 80%

Runway Capacity Terminal Capacity

60 4500 4000 50 3500 40 3000 2500 30 2000 20 1500 PAX per PAX hour 1000

Operationsper hour 10 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 150 Appendix Assumption Rectangle and Capacities of CIA Airport Traffic for the year 2007/8

Annual Operations (AO)= 54870 n= 98 PAX/Ops Annual Passengers (AP)= 5351861

y= 0.02551% x= 0.04316%

Hourly Operations (HO)= 14 m= 165 PAX/Ops Hourly Passengers (HP)= 2310 hourly annually Maximum Declared Capacity= 35 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 40% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

40 2500 35 2000 30 25 1500 20 15 1000

10 per PAX hour 500 Operationsper hour 5 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of CPH Airport Traffic for the year 2007/8

Annual Operations (AO)= 250170 n= 86 PAX/Ops Annual Passengers (AP)= 21397874

y= 0.02638% x= 0.03054%

Hourly Operations (HO)= 66 m= 99 PAX/Ops Hourly Passengers (HP)= 6534 hourly annually Maximum Declared Capacity= 83 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 80% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

90 7000 80 6000 70 5000 60 50 4000 40 3000 30

PAX per PAX hour 2000 20

Operationsper hour 1000 10 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of DRS Airport Traffic for the year 2007/8

Annual Operations (AO)= 28257 n= 67 PAX/Ops Annual Passengers (AP)= 1888091

y= 0.04247% x= 0.05466%

Hourly Operations (HO)= 12 m= 86 PAX/Ops Hourly Passengers (HP)= 1032 hourly annually Maximum Declared Capacity= 30 Max Decl. Terminal Capacity= 1500 0 (AP/MCTC) Runway Utilization (HO/MCD)= 40% Terminal Utilzation (HP/MCTC)= 69%

Runway Capacity Terminal Capacity

35 1600 30 1400 25 1200 1000 20 800 15 600

10 per PAX hour 400

Operationsper hour 5 200 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 151 Appendix Assumption Rectangle and Capacities of DUB Airport Traffic for the year 2007/8

Annual Operations (AO)= 200891 n= 116 PAX/Ops Annual Passengers (AP)= 23307302

y= 0.01991% x= 0.02523%

Hourly Operations (HO)= 40 m= 147 PAX/Ops Hourly Passengers (HP)= 5880 hourly annually Maximum Declared Capacity= 44 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 91% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

50 7000 45 6000 40 35 5000 30 4000 25 20 3000

15 per PAX hour 2000 10 Operationsper hour 1000 5 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of DUS Airport Traffic for the year 2007/8

Annual Operations (AO)= 223410 n= 80 PAX/Ops Annual Passengers (AP)= 17850852

y= 0.02328% x= 0.03204%

Hourly Operations (HO)= 52 m= 110 PAX/Ops Hourly Passengers (HP)= 5720 hourly annually Maximum Declared Capacity= 38 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 137% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

60 7000

50 6000 5000 40 4000 30 3000 20

PAX per PAX hour 2000

Operationsper hour 10 1000

0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of EDI Airport Traffic for the year 2007/8

Annual Operations (AO)= 115177 n= 79 PAX/Ops Annual Passengers (AP)= 9057505

y= 0.02952% x= 0.04317%

Hourly Operations (HO)= 34 m= 115 PAX/Ops Hourly Passengers (HP)= 3910 hourly annually Maximum Declared Capacity= 47 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 72% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

50 4500 45 4000 40 3500 35 3000 30 2500 25 2000 20 1500

15 per PAX hour

10 1000 Operationsper hour 5 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 152 Appendix Assumption Rectangle and Capacities of FCO Airport Traffic for the year 2007/8

Annual Operations (AO)= 328213 n= 102 PAX/Ops Annual Passengers (AP)= 33615219

y= 0.02437% x= 0.03308%

Hourly Operations (HO)= 80 m= 139 PAX/Ops Hourly Passengers (HP)= 11120 hourly annually Maximum Declared Capacity= 90 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 89% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

100 12000 90 80 10000 70 8000 60 50 6000 40 4000

30 per PAX hour 20

Operationsper hour 2000 10 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of FMO Airport Traffic for the year 2007/8

Annual Operations (AO)= 21968 n= 73 PAX/Ops Annual Passengers (AP)= 1613249

y= 0.03186% x= 0.04209%

Hourly Operations (HO)= 7 m= 97 PAX/Ops Hourly Passengers (HP)= 679 hourly annually Maximum Declared Capacity= 24 Max Decl. Terminal Capacity= 2680 0 (AP/MCTC) Runway Utilization (HO/MCD)= 29% Terminal Utilzation (HP/MCTC)= 25%

Runway Capacity Terminal Capacity

30 3000

25 2500

20 2000

15 1500

10 1000 PAX per PAX hour

Operationsper hour 5 500

0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of FRA Airport Traffic for the year 2007/8

Annual Operations (AO)= 486195 n= 112 PAX/Ops Annual Passengers (AP)= 54501001

y= 0.01810% x= 0.02487%

Runway Capacity Terminal Capacity

100 16000 90 14000 80 12000 70 60 10000 50 8000 40 6000

30 per PAX hour 4000

20 Operationsper hour 10 2000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 153 Appendix Assumption Rectangle and Capacities of GLA Airport Traffic for the year 2007/8

Annual Operations (AO)= 93654 n= 95 PAX/Ops Annual Passengers (AP)= 8864561

y= 0.02776% x= 0.03050%

Hourly Operations (HO)= 26 m= 104 PAX/Ops Hourly Passengers (HP)= 2704 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

30 3000

25 2500

20 2000

15 1500

10 1000 PAX per PAX hour

Operationsper hour 5 500

0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of GRZ Airport Traffic for the year 2007/8

Annual Operations (AO)= 17286 n= 56 PAX/Ops Annual Passengers (AP)= 973283

y= 0.02893% x= 0.03802%

Hourly Operations (HO)= 5 m= 74 PAX/Ops Hourly Passengers (HP)= 370 hourly annually Maximum Declared Capacity= 14 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 36% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

16 400 14 350 12 300 10 250 8 200 6 150

4 per PAX hour 100

Operationsper hour 2 50 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of HAJ Airport Traffic for the year 2007/8

Annual Operations (AO)= 70481 n= 81 PAX/Ops Annual Passengers (AP)= 5674524

y= 0.02412% x= 0.02726%

Hourly Operations (HO)= 17 m= 91 PAX/Ops Hourly Passengers (HP)= 1547 hourly annually Maximum Declared Capacity= 40 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 43% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

45 1800 40 1600 35 1400 30 1200 25 1000 20 800

15 600 PAX per PAX hour 10 400 Operationsper hour 5 200 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 154 Appendix Assumption Rectangle and Capacities of HAM Airport Traffic for the year 2007/8

Annual Operations (AO)= 151752 n= 85 PAX/Ops Annual Passengers (AP)= 12851171

y= 0.03031% x= 0.03687%

Hourly Operations (HO)= 46 m= 103 PAX/Ops Hourly Passengers (HP)= 4738 hourly annually Maximum Declared Capacity= 48 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 96% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

60 5000 4500 50 4000 40 3500 3000 30 2500 2000

20 1500 PAX per PAX hour 1000

Operationsper hour 10 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of HEL Airport Traffic for the year 2007/8

Annual Operations (AO)= 174751 n= 75 PAX/Ops Annual Passengers (AP)= 13095008

y= 0.02747% x= 0.03592%

Hourly Operations (HO)= 48 m= 98 PAX/Ops Hourly Passengers (HP)= 4704 hourly annually Maximum Declared Capacity= 50 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 96% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

60 5000 4500 50 4000 40 3500 3000 30 2500 2000

20 1500 PAX per PAX hour 1000

Operationsper hour 10 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of HHN Airport Traffic for the year 2007/8

Annual Operations (AO)= 34311 n= 120 PAX/Ops Annual Passengers (AP)= 4107351

y= 0.02332% x= 0.02980%

Hourly Operations (HO)= 8 m= 153 PAX/Ops Hourly Passengers (HP)= 1224 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

9 1400 8 1200 7 1000 6 5 800 4 600 3

PAX per PAX hour 400 2

Operationsper hour 200 1 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 155 Appendix Assumption Rectangle and Capacities of IST Airport Traffic for the year 2007/8

Annual Operations (AO)= 206188 n= 124 PAX/Ops Annual Passengers (AP)= 25486578

y= 0.02037% x= 0.02422%

Hourly Operations (HO)= 42 m= 147 PAX/Ops Hourly Passengers (HP)= 6174 hourly annually Maximum Declared Capacity= 40 Max Decl. Terminal Capacity= 1619 0 (AP/MCTC) Runway Utilization (HO/MCD)= 105% Terminal Utilzation (HP/MCTC)= 381%

Runway Capacity Terminal Capacity

45 7000 40 6000 35 5000 30 25 4000 20 3000 15

PAX per PAX hour 2000 10

Operationsper hour 1000 5 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of LBA Airport Traffic for the year 2007/8

Annual Operations (AO)= 39603 n= 73 PAX/Ops Annual Passengers (AP)= 2903101

y= 0.03283% x= 0.03941%

Hourly Operations (HO)= 13 m= 88 PAX/Ops Hourly Passengers (HP)= 1144 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

14 1400 12 1200 10 1000 8 800 6 600

4 per PAX hour 400

Operationsper hour 2 200 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of LCY Airport Traffic for the year 2007/8

Annual Operations (AO)= 77274 n= 38 PAX/Ops Annual Passengers (AP)= 2912123

y= 0.04788% x= 0.08386%

Hourly Operations (HO)= 37 m= 66 PAX/Ops Hourly Passengers (HP)= 2442 hourly annually Maximum Declared Capacity= 24 Max Decl. Terminal Capacity= 3600 0 (AP/MCTC) Runway Utilization (HO/MCD)= 154% Terminal Utilzation (HP/MCTC)= 68%

Runway Capacity Terminal Capacity

40 4000 35 3500 30 3000 25 2500 20 2000 15 1500

10 per PAX hour 1000

Operationsper hour 5 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 156 Appendix Assumption Rectangle and Capacities of LEJ Airport Traffic for the year 2007/8

Annual Operations (AO)= 41370 n= 73 PAX/Ops Annual Passengers (AP)= 3036175

y= 0.01692% x= 0.02813%

Hourly Operations (HO)= 7 m= 122 PAX/Ops Hourly Passengers (HP)= 854 hourly annually Maximum Declared Capacity= 20 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 35% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

25 900 800 20 700 600 15 500 400 10

300 PAX per PAX hour 5 200 Operationsper hour 100 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of LGG Airport Traffic for the year 2007/8

Annual Operations (AO)= 26815 n= 12 PAX/Ops Annual Passengers (AP)= 328571

y= 0.01492% x= 0.00000%

Hourly Operations (HO)= 4 m= 0 PAX/Ops Hourly Passengers (HP)= 0 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

4.5 1 4 0.9 3.5 0.8 3 0.7 0.6 2.5 0.5 2 0.4

1.5 0.3 PAX per PAX hour 1 0.2 Operationsper hour 0.5 0.1 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of LGW Airport Traffic for the year 2007/8

Annual Operations (AO)= 258917 n= 136 PAX/Ops Annual Passengers (AP)= 35266312

y= 0.01970% x= 0.02025%

Hourly Operations (HO)= 51 m= 140 PAX/Ops Hourly Passengers (HP)= 7140 hourly annually Maximum Declared Capacity= 50 Max Decl. Terminal Capacity= 12000 0 (AP/MCTC) Runway Utilization (HO/MCD)= 102% Terminal Utilzation (HP/MCTC)= 60%

Runway Capacity Terminal Capacity

60 14000

50 12000 10000 40 8000 30 6000 20

PAX per PAX hour 4000

Operationsper hour 10 2000

0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 157 Appendix Assumption Rectangle and Capacities of LHR Airport Traffic for the year 2007/8

Annual Operations (AO)= 475786 n= 144 PAX/Ops Annual Passengers (AP)= 68279364

y= 0.02102% x= 0.02739%

Hourly Operations (HO)= 100 m= 187 PAX/Ops Hourly Passengers (HP)= 18700 hourly annually Maximum Declared Capacity= 88 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 114% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

120 20000 18000 100 16000 80 14000 12000 60 10000 8000

40 6000 PAX per PAX hour 4000

Operationsper hour 20 2000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of LIS Airport Traffic for the year 2007/8

Annual Operations (AO)= 141905 n= 95 PAX/Ops Annual Passengers (AP)= 13521399

y= 0.02678% x= 0.03513%

Hourly Operations (HO)= 38 m= 125 PAX/Ops Hourly Passengers (HP)= 4750 hourly annually Maximum Declared Capacity= 32 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 119% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

40 5000 35 4500 4000 30 3500 25 3000 20 2500 15 2000 1500 10 per PAX hour 1000 Operationsper hour 5 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of LTN Airport Traffic for the year 2007/8

Annual Operations (AO)= 83318 n= 119 PAX/Ops Annual Passengers (AP)= 9935650

y= 0.02881% x= 0.03599%

Hourly Operations (HO)= 24 m= 149 PAX/Ops Hourly Passengers (HP)= 3576 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

30 4000 3500 25 3000 20 2500 15 2000 1500 10 PAX per PAX hour 1000

Operationsper hour 5 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 158 Appendix Assumption Rectangle and Capacities of LYS Airport Traffic for the year 2007/8

Annual Operations (AO)= 132076 n= 54 PAX/Ops Annual Passengers (AP)= 7192586

y= 0.03256% x= 0.05082%

Hourly Operations (HO)= 43 m= 85 PAX/Ops Hourly Passengers (HP)= 3655 hourly annually Maximum Declared Capacity= 51 Max Decl. Terminal Capacity= 4918 0 (AP/MCTC) Runway Utilization (HO/MCD)= 84% Terminal Utilzation (HP/MCTC)= 74%

Runway Capacity Terminal Capacity

60 6000

50 5000

40 4000

30 3000

20 2000 PAX per PAX hour

Operationsper hour 10 1000

0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of MAD Airport Traffic for the year 2007/8

Annual Operations (AO)= 470315 n= 109 PAX/Ops Annual Passengers (AP)= 51401399

y= 0.02339% x= 0.02846%

Hourly Operations (HO)= 110 m= 133 PAX/Ops Hourly Passengers (HP)= 14630 hourly annually Maximum Declared Capacity= 78 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 141% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

120 16000 14000 100 12000 80 10000 60 8000 6000 40 PAX per PAX hour 4000

Operationsper hour 20 2000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of MAN Airport Traffic for the year 2007/8

Annual Operations (AO)= 206498 n= 108 PAX/Ops Annual Passengers (AP)= 22331760

y= 0.02228% x= 0.01916%

Hourly Operations (HO)= 46 m= 93 PAX/Ops Hourly Passengers (HP)= 4278 hourly annually Maximum Declared Capacity= 61 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 75% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

70 4500 60 4000 3500 50 3000 40 2500 30 2000 1500

20 per PAX hour 1000 Operationsper hour 10 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 159 Appendix Assumption Rectangle and Capacities of MUC Airport Traffic for the year 2007/8

Annual Operations (AO)= 409654 n= 83 PAX/Ops Annual Passengers (AP)= 34067138

y= 0.02270% x= 0.02948%

Hourly Operations (HO)= 93 m= 108 PAX/Ops Hourly Passengers (HP)= 10044 hourly annually Maximum Declared Capacity= 90 Max Decl. Terminal Capacity= 16000 0 (AP/MCTC) Runway Utilization (HO/MCD)= 103% Terminal Utilzation (HP/MCTC)= 63%

Runway Capacity Terminal Capacity

100 18000 90 16000 80 14000 70 12000 60 10000 50 8000 40 6000

30 per PAX hour

20 4000 Operationsper hour 10 2000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of MXP Airport Traffic for the year 2007/8

Annual Operations (AO)= 257361 n= 93 PAX/Ops Annual Passengers (AP)= 23972609

y= 0.01632% x= 0.02102%

Hourly Operations (HO)= 42 m= 120 PAX/Ops Hourly Passengers (HP)= 5040 hourly annually Maximum Declared Capacity= 70 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 60% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

80 6000 70 5000 60 4000 50 40 3000

30 2000 20 per PAX hour

Operationsper hour 1000 10 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of NCE Airport Traffic for the year 2007/8

Annual Operations (AO)= 173584 n= 60 PAX/Ops Annual Passengers (AP)= 10381225

y= 0.02996% x= 0.03556%

Hourly Operations (HO)= 52 m= 71 PAX/Ops Hourly Passengers (HP)= 3692 hourly annually Maximum Declared Capacity= 50 Max Decl. Terminal Capacity= 7400 0 (AP/MCTC) Runway Utilization (HO/MCD)= 104% Terminal Utilzation (HP/MCTC)= 50%

Runway Capacity Terminal Capacity

60 8000 7000 50 6000 40 5000 30 4000 3000 20 PAX per PAX hour 2000

Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 160 Appendix

Assumption Rectangle and Capacities of NUE Airport Traffic for the year 2007/8

Annual Operations (AO)= 57922 n= 74 PAX/Ops Annual Passengers (AP)= 4285819

y= 0.03280% x= 0.03635%

Hourly Operations (HO)= 19 m= 82 PAX/Ops Hourly Passengers (HP)= 1558 hourly annually Maximum Declared Capacity= 30 Max Decl. Terminal Capacity= 0 3200000 (AP/MCTC) Runway Utilization (HO/MCD)= 63% Terminal Utilzation (HP/MCTC)= 134%

Runway Capacity Terminal Capacity

35 1800 30 1600 1400 25 1200 20 1000 15 800 600

10 per PAX hour 400 Operationsper hour 5 200 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of ORY Airport Traffic for the year 2007/8

Annual Operations (AO)= 238384 n= 111 PAX/Ops Annual Passengers (AP)= 26415520

y= 0.02601% x= 0.03262%

Hourly Operations (HO)= 62 m= 139 PAX/Ops Hourly Passengers (HP)= 8618 hourly annually Maximum Declared Capacity= 76 Max Decl. Terminal Capacity= 0 24000000 (AP/MCTC) Runway Utilization (HO/MCD)= 82% Terminal Utilzation (HP/MCTC)= 110%

Runway Capacity Terminal Capacity

80 10000 70 9000 8000 60 7000 50 6000 40 5000 30 4000 3000 20 per PAX hour 2000 Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of OSL Airport Traffic for the year 2007/8

Annual Operations (AO)= 226221 n= 84 PAX/Ops Annual Passengers (AP)= 19043914

y= 0.03006% x= 0.04392%

Hourly Operations (HO)= 68 m= 123 PAX/Ops Hourly Passengers (HP)= 8364 hourly annually Maximum Declared Capacity= 80 Max Decl. Terminal Capacity= 7300 0 (AP/MCTC) Runway Utilization (HO/MCD)= 85% Terminal Utilzation (HP/MCTC)= 115%

Runway Capacity Terminal Capacity

90 9000 80 8000 70 7000 60 6000 50 5000 40 4000

30 3000 PAX per PAX hour 20 2000 Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 161 Appendix

Assumption Rectangle and Capacities of PMI Airport Traffic for the year 2007/8

Annual Operations (AO)= 184605 n= 125 PAX/Ops Annual Passengers (AP)= 23103107

y= 0.02275% x= 0.02400%

Hourly Operations (HO)= 42 m= 132 PAX/Ops Hourly Passengers (HP)= 5544 hourly annually Maximum Declared Capacity= 60 Max Decl. Terminal Capacity= 12000 0 (AP/MCTC) Runway Utilization (HO/MCD)= 70% Terminal Utilzation (HP/MCTC)= 46%

Runway Capacity Terminal Capacity

70 14000 60 12000 50 10000 40 8000 30 6000

20 per PAX hour 4000

Operationsper hour 10 2000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of PRG Airport Traffic for the year 2007/8

Annual Operations (AO)= 164055 n= 76 PAX/Ops Annual Passengers (AP)= 12395484

y= 0.02682% x= 0.03940%

Hourly Operations (HO)= 44 m= 111 PAX/Ops Hourly Passengers (HP)= 4884 hourly annually Maximum Declared Capacity= 38 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 116% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

50 6000 45 40 5000 35 4000 30 25 3000 20 2000

15 per PAX hour 10

Operationsper hour 1000 5 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of PSA Airport Traffic for the year 2007/8

Annual Operations (AO)= 38525 n= 96 PAX/Ops Annual Passengers (AP)= 3713247

y= 0.03374% x= 0.04901%

Hourly Operations (HO)= 13 m= 140 PAX/Ops Hourly Passengers (HP)= 1820 hourly annually Maximum Declared Capacity= 14 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 93% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

16 2000 14 1800 1600 12 1400 10 1200 8 1000 6 800 600 4 per PAX hour 400 Operationsper hour 2 200 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 162 Appendix

Assumption Rectangle and Capacities of RHO Airport Traffic for the year 2007/8

Annual Operations (AO)= 32776 n= 111 PAX/Ops Annual Passengers (AP)= 3625962

y= 0.01831% x= 0.01754%

Hourly Operations (HO)= 6 m= 106 PAX/Ops Hourly Passengers (HP)= 636 hourly annually Maximum Declared Capacity= 13 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 46% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

14 700 12 600 10 500 8 400 6 300

4 per PAX hour 200

Operationsper hour 2 100 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of RTM Airport Traffic for the year 2007/8

Annual Operations (AO)= 18517 n= 61 PAX/Ops Annual Passengers (AP)= 1133999

y= 0.04320% x= 0.05855%

Hourly Operations (HO)= 8 m= 83 PAX/Ops Hourly Passengers (HP)= 664 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

9 700 8 600 7 500 6 5 400 4 300 3

PAX per PAX hour 200 2

Operationsper hour 100 1 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of SCN Airport Traffic for the year 2007/8

Annual Operations (AO)= 9731 n= 40 PAX/Ops Annual Passengers (AP)= 385538

y= 0.06166% x= 0.08248%

Hourly Operations (HO)= 6 m= 53 PAX/Ops Hourly Passengers (HP)= 318 hourly annually Maximum Declared Capacity= 20 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 30% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

25 350 300 20 250 15 200

10 150

PAX per PAX hour 100 5 Operationsper hour 50

0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 163 Appendix

Assumption Rectangle and Capacities of STN Airport Traffic for the year 2007/8

Annual Operations (AO)= 191520 n= 124 PAX/Ops Annual Passengers (AP)= 23800028

y= 0.02402% x= 0.03092%

Hourly Operations (HO)= 46 m= 160 PAX/Ops Hourly Passengers (HP)= 7360 hourly annually Maximum Declared Capacity= 50 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 92% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

60 8000 7000 50 6000 40 5000 30 4000 3000 20 PAX per PAX hour 2000

Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of STR Airport Traffic for the year 2007/8

Annual Operations (AO)= 139757 n= 74 PAX/Ops Annual Passengers (AP)= 10345148

y= 0.02862% x= 0.04137%

Hourly Operations (HO)= 40 m= 107 PAX/Ops Hourly Passengers (HP)= 4280 hourly annually Maximum Declared Capacity= 40 Max Decl. Terminal Capacity= 0 12500000 (AP/MCTC) Runway Utilization (HO/MCD)= 100% Terminal Utilzation (HP/MCTC)= 83%

Runway Capacity Terminal Capacity

45 4500 40 4000 35 3500 30 3000 25 2500 20 2000

15 1500 PAX per PAX hour 10 1000 Operationsper hour 5 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of SXF Airport Traffic for the year 2007/8

Annual Operations (AO)= 55114 n= 115 PAX/Ops Annual Passengers (AP)= 6348233

y= 0.03085% x= 0.03669%

Hourly Operations (HO)= 17 m= 137 PAX/Ops Hourly Passengers (HP)= 2329 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

18 2500 16 14 2000 12 1500 10 8 1000

6 PAX per PAX hour 4 500 Operationsper hour 2 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 164 Appendix

Assumption Rectangle and Capacities of SZG Airport Traffic for the year 2007/8

Annual Operations (AO)= 21166 n= 93 PAX/Ops Annual Passengers (AP)= 1976216

y= 0.03780% x= 0.04574%

Hourly Operations (HO)= 8 m= 113 PAX/Ops Hourly Passengers (HP)= 904 hourly annually Maximum Declared Capacity= 20 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 40% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

25 1000 900 20 800 700 15 600 500 10 400

300 PAX per PAX hour 5 200 Operationsper hour 100 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of TXL Airport Traffic for the year 2007/8

Annual Operations (AO)= 145451 n= 92 PAX/Ops Annual Passengers (AP)= 13374172

y= 0.03231% x= 0.03971%

Hourly Operations (HO)= 47 m= 113 PAX/Ops Hourly Passengers (HP)= 5311 hourly annually Maximum Declared Capacity= 41 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= 115% Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

50 6000 45 40 5000 35 4000 30 25 3000 20 2000

15 per PAX hour 10

Operationsper hour 1000 5 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of VIE Airport Traffic for the year 2007/8

Annual Operations (AO)= 251216 n= 75 PAX/Ops Annual Passengers (AP)= 18772289

y= 0.02667% x= 0.03819%

Hourly Operations (HO)= 67 m= 107 PAX/Ops Hourly Passengers (HP)= 7169 hourly annually Maximum Declared Capacity= 66 Max Decl. Terminal Capacity= 4400 0 (AP/MCTC) Runway Utilization (HO/MCD)= 102% Terminal Utilzation (HP/MCTC)= 163%

Runway Capacity Terminal Capacity

80 8000 70 7000 60 6000 50 5000 40 4000 30 3000

20 per PAX hour 2000

Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 165 Appendix

Assumption Rectangle and Capacities of WAW Airport Traffic for the year 2007/8

Annual Operations (AO)= 147985 n= 63 PAX/Ops Annual Passengers (AP)= 9287882

y= 0.02162% x= 0.03066%

Hourly Operations (HO)= 32 m= 89 PAX/Ops Hourly Passengers (HP)= 2848 hourly annually Maximum Declared Capacity= 34 Max Decl. Terminal Capacity= 3000 0 (AP/MCTC) Runway Utilization (HO/MCD)= 94% Terminal Utilzation (HP/MCTC)= 95%

Runway Capacity Terminal Capacity

40 3500 35 3000 30 2500 25 2000 20 1500 15

PAX per PAX hour 1000 10

Operationsper hour 5 500 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of WRO Airport Traffic for the year 2007/8

Annual Operations (AO)= 17861 n= 71 PAX/Ops Annual Passengers (AP)= 1270560

y= 0.06159% x= 0.10649%

Hourly Operations (HO)= 11 m= 123 PAX/Ops Hourly Passengers (HP)= 1353 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

12 1600 1400 10 1200 8 1000 6 800 600 4 PAX per PAX hour 400

Operationsper hour 2 200 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Assumption Rectangle and Capacities of ZAG Airport Traffic for the year 2007/8

Annual Operations (AO)= 20442 n= 97 PAX/Ops Annual Passengers (AP)= 1992455

y= 0.08805% x= 0.09034%

Hourly Operations (HO)= 18 m= 100 PAX/Ops Hourly Passengers (HP)= 1800 hourly annually Maximum Declared Capacity= 0 Max Decl. Terminal Capacity= 0 0 (AP/MCTC) Runway Utilization (HO/MCD)= Terminal Utilzation (HP/MCTC)=

Runway Capacity Terminal Capacity

20 2000 18 1800 16 1600 14 1400 12 1200 10 1000 8 800 600

6 per PAX hour

4 400 Operationsper hour 2 200 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 166 Appendix

Assumption Rectangle and Capacities of ZRH Airport Traffic for the year 2007/8

Annual Operations (AO)= 223707 n= 93 PAX/Ops Annual Passengers (AP)= 20813870

y= 0.02503% x= 0.03363%

Hourly Operations (HO)= 56 m= 125 PAX/Ops Hourly Passengers (HP)= 7000 hourly annually Maximum Declared Capacity= 66 Max Decl. Terminal Capacity= 9200 0 (AP/MCTC) Runway Utilization (HO/MCD)= 85% Terminal Utilzation (HP/MCTC)= 76%

Runway Capacity Terminal Capacity

70 10000 9000 60 8000 50 7000 40 6000 5000 30 4000 3000 20 per PAX hour 2000 Operationsper hour 10 1000 0 0

Max decl rwy cap Ops/hr HO in Ops/hr Max decl terminal cap PAX/hr HP in PAX/hr

Benchmarking Airport Productivity and the Role of Capacity Utilization 167 Appendix

Figures 18

Typical Capacity Envelope for Selected airports

Benchmarking Airport Productivity and the Role of Capacity Utilization 168 Appendix

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Origin ARR dec hour ARR hour:min:sec BHX 0.050898286 00:03:03 CIA 0.047690539 00:02:52 DRS 0.045427225 00:02:44 FMO 0.046495328 00:02:47 GLA 0.049584016 00:02:59 GRZ 0.045793516 00:02:45 HHN 0.046062356 00:02:46 LBA 0.045504241 00:02:44 LCY 0.052258602 00:03:08 LGW 0.054734039 00:03:17 LTN 0.048612769 00:02:55 PSA 0.04931992 00:02:58 SCN 0.045496808 00:02:44 STN 0.052042623 00:03:07 STR 0.054858154 00:03:17 SXF 0.047341488 00:02:50 SZG 0.046007198 00:02:46 ZAG 0.050506007 00:03:02 Table 8. SIMMOD arrivals injection time adjustment for flight schedule data. (Source: Bubalo 2009) 177

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Figures 19

Airport Layout for SIMMOD Sample Airports

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BHX Airport Layout for SIMMOD

CIA Airport Layout for SIMMOD

DRS Airport Layout for SIMMOD 179

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FMO Airport Layout for SIMMOD

GLA Airport Layout for SIMMOD

GRZ Airport Layout for SIMMOD

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HHN Airport Layout for SIMMOD

LBA Airport Layout for SIMMOD

LCY Airport Layout for SIMMOD

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LGW Airport Layout for SIMMOD

LTN Airport Layout for SIMMOD

PSA Airport Layout for SIMMOD

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SCN Airport Layout for SIMMOD

STN Airport Layout for SIMMOD

STR Airport Layout for SIMMOD

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SXF Airport Layout for SIMMOD

SZG Airport Layout for SIMMOD

ZAG Airport Layout for SIMMOD 184

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Figures 28

Airport Charts for Sample Airports

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Appendix II: Supplemental Information - OAG Traffic Schedule Data Analysis for Sample Airports

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Bubalo B., Benchmarking Airport Productivity and the Role of Capacity Utilization, Diploma Thesis, Berlin, 2009: Appendix II

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Top 15 Carriers AMS Weekly Flights Avg Day Share 4090 584 54.11% KLM-ROYAL DUTCH AIRLINES

256 37 3.39% TRANSAVIA.COM 230 33 3.04% LUFTHANSA GERMAN AIRLINES 226 32 2.99% EASYJET 214 31 2.83% AIR FRANCE

186 27 2.46% BRITISH AIRWAYS

184 26 2.43% NORTHWEST AIRLINES 128 18 1.69% MARTINAIR HOLLAND 117 17 1.55% VLM AIRLINES 110 16 1.46% AER LINGUS

106 15 1.40% SAS SCANDINAVIAN AIRLINES

94 13 1.24% BMI BRITISH MIDLAND 70 10 0.93% SWISS 70 10 0.93% MALEV HUNGARIAN AIRLINES 64 9 0.85% TAP AIR PORTUGAL

6145 878 81.3% Summe 7558 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins AMS Weekly flights Avg Day Share 167 24 2.21% London Heathrow 120 17 1.59% London City 100 14 1.32% Paris Charles de Gaulle 83 12 1.10% Frankfurt 78 11 1.03% München 76 11 1.01% Barcelona 69 10 0.91% Wien 67 10 0.89% Madrid 63 9 0.83% Kopenhagen 62 9 0.82% Zürich 59 8 0.78% Hamburg 54 8 0.71% Birmingham 53 8 0.70% Genf 52 7 0.69% Stockholm Arlanda 51 7 0.67% London Gatwick 1154 165 15.3% Summe 7558 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations AMS Weekly flights Avg Day Share 168 24 2.22% London Heathrow 119 17 1.57% London City 100 14 1.32% Paris Charles de Gaulle 85 12 1.12% Frankfurt 78 11 1.03% München 77 11 1.02% Barcelona 68 10 0.90% Wien 67 10 0.89% Madrid 64 9 0.85% Kopenhagen 62 9 0.82% Zürich 59 8 0.78% Hamburg 56 8 0.74% Stockholm Arlanda 55 8 0.73% Mailand Malpensa 54 8 0.71% Birmingham 53 8 0.70% Genf 1165 166 15.4% Summe 7558 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Distances AMS Weekly Share Distance in kilometers flights 2167 28.67% 401-800 1699 22.48% 1-400 1016 13.44% 801-1200 651 8.61% 1201-1600 247 3.27% 1601-2000 235 3.11% 2001-2400 137 1.81% 9201-9600 130 1.72% 7601-8000 128 1.69% 8801-9200 124 1.64% 4801-5200 119 1.57% 5601-6000 114 1.51% 6001-6400 100 1.32% 3201-3600 97 1.28% 6401-6800 72 0.95% 2401-2800 7036 93.1% Summe 7558 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types AMS Weekly Share Aircraft Type Avg Range MTOW Cruise WTC flights Seats in km in t Speed in km/h 1843 24.38% Boeing737Passenger 143 4180 63 888 M 916 12.12% Fokker70 76 3732 42 856 M 537 7.11% Fokker50 48 2842 22 531 M 526 6.96% AirbusIndustrieA319 133 6800 76 930 M 463 6.13% AirbusIndustrieA320 156 5500 77 930 M 396 5.24% Fokker100 105 3111 46 919 M 209 2.77% Boeing777Passenger 291 14250 298 896 H 183 2.42% Boeing747(MixedConfiguration) 270 13330 397 938 H 170 2.25% AirbusIndustrieA330-200 260 11866 230 978 H 142 1.88% AirbusIndustrieA321 184 5500 93 930 M 132 1.75% Embraer190 99 4260 50 869 M 129 1.71% Boeing737-300Passenger 133 4180 63 888 M 121 1.60% Boeing737-500Passenger 111 4400 52 888 M 114 1.51% Boeing747-400F(Freighter) 0 8245 397 938 H 103 1.36% Boeing747(Freighter) 0 8245 397 938 H 5984 79.2% Summe 7558 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Carriers ARN Weekly Flights Avg Day Share 1576 225 40.99% SAS SCANDINAVIAN AIRLINES 473 68 12.30% SKYWAYS 222 32 5.77% NORWEGIAN AIR SHUTTLE 202 29 5.25% BLUE1 182 26 4.73% LUFTHANSA GERMAN AIRLINES 136 19 3.54% NEXTJET 110 16 2.86% FINNAIR 80 11 2.08% AIR BALTIC CORPORATION 76 11 1.98% BRITISH AIRWAYS 70 10 1.82% KLM-ROYAL DUTCH AIRLINES 62 9 1.61% AVITRANS NORDIC AB 56 8 1.46% ESTONIAN AIR 54 8 1.40% AUSTRIAN AIRLINES AG 42 6 1.09% AIR FRANCE 38 5 0.99% LOT - POLISH AIRLINES 3379 483 87.9% Summe 3845 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins ARN Weekly flights Avg Day Share 117 17 3.04% Helsinki 116 17 3.02% Oslo 82 12 2.13% Lulea 78 11 2.03% Kopenhagen 76 11 1.98% London Heathrow 58 8 1.51% Goeteborg 56 8 1.46% Amsterdam 52 7 1.35% Umea 48 7 1.25% Frankfurt 47 7 1.22% Vaxjo 47 7 1.22% Malmö 40 6 1.04% München 39 6 1.01% Paris Charles de Gaulle 38 5 0.99% Skelleftea 34 5 0.88% Ostersund 928 133 24.1% Summe 3845 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations ARN Weekly flights Avg Day Share 116 17 3.02% Helsinki 114 16 2.96% Oslo 82 12 2.13% Lulea 78 11 2.03% Kopenhagen 77 11 2.00% London Heathrow 57 8 1.48% Goeteborg 52 7 1.35% Umea 52 7 1.35% Amsterdam 48 7 1.25% Frankfurt 47 7 1.22% Vaxjo 46 7 1.20% Malmö 40 6 1.04% München 39 6 1.01% Paris Charles de Gaulle 38 5 0.99% Skelleftea 34 5 0.88% Ostersund 920 131 23.9% Summe

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Appendix 3845 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances ARN Weekly Share Distance in kilometers flights 1295 33.68% 1-400 1142 29.70% 401-800 684 17.79% 1201-1600 417 10.85% 801-1200 74 1.92% 1601-2000 66 1.72% 2001-2400 37 0.96% 2401-2800 28 0.73% 6001-6400 26 0.68% 2801-3200 22 0.57% 8001-8400 14 0.36% 6401-6800 12 0.31% 4401-4800 11 0.29% 6801-7200 9 0.23% 7201-7600 4 0.10% 9201-9600 3841 99.9% Summe 3845 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types ARN Weekly Share Aircraft Type Avg Range MTOW Cruise WTC flights Seats in km in t Speed in km/h 762 19.82% Boeing736-700Passenger 115 5840 65 933 M 410 10.66% Fokker50 48 2842 22 531 M 369 9.60% Boeing(Douglas)MD-82 155 4925 68 906 M 274 7.13% Boeing738-700Passenger 161 5420 71 937 M 223 5.80% AirbusIndustrieA320 156 5500 77 930 M 163 4.24% AirbusIndustrieA319 133 6800 76 930 M 126 3.28% Saab340 34 1667 13 523 M 119 3.09% BritishAerospaceJetstream31 19 1978 7 491 M 112 2.91% AvroRJ85 83 2796 44 763 M 112 2.91% AirbusIndustrieA321 184 5500 93 930 M 112 2.91% Boeing(Douglas)MD-83 152 4925 73 906 M 110 2.86% Saab2000 50 2907 23 681 M 101 2.63% Boeing(Douglas)MD-81 148 4925 64 906 M 95 2.47% Boeing737-500Passenger 111 4400 52 888 M 81 2.11% Boeing737-700Passenger 127 6110 69 932 M 3169 82.4% Summe 3845 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Carriers ATH Weekly Flights Avg Day Share 1292 185 41.98% OLYMPIC AIRLINES 920 131 29.89% AEGEAN AIRLINES 84 12 2.73% LUFTHANSA GERMAN AIRLINES 68 10 2.21% EASYJET 56 8 1.82% SWISS 46 7 1.49% CYPRUS AIRWAYS 42 6 1.36% BRITISH AIRWAYS 42 6 1.36% AIR FRANCE 40 6 1.30% ALITALIA 36 5 1.17% AIR ONE 28 4 0.91% EMIRATES 28 4 0.91% TURKISH AIRLINES 28 4 0.91% IBERIA 28 4 0.91% KLM-ROYAL DUTCH AIRLINES 24 3 0.78% CZECH AIRLINES 2762 395 89.7% Summe 3078 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins ATH Weekly flights Avg Day Share 140 20 4.55% Thessaloniki 96 14 3.12% Heraklion 65 9 2.11% Larnaca 63 9 2.05% Rhodos 58 8 1.88% Chania 50 7 1.62% Paris Charles de Gaulle 49 7 1.59% Rom Fiumicino 42 6 1.36% Mytilene 42 6 1.36% Mailand Malpensa 39 6 1.27% München 38 5 1.23% London Heathrow 35 5 1.14% Kos 35 5 1.14% Frankfurt 32 5 1.04% Samos 30 4 0.97% Chios 814 116 26.4% Summe 3078 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations ATH Weekly flights Avg Day Share 140 20 4.55% Thessaloniki 96 14 3.12% Heraklion 63 9 2.05% Rhodos 60 9 1.95% Larnaca 58 8 1.88% Chania 50 7 1.62% Paris Charles de Gaulle 49 7 1.59% Rom Fiumicino 42 6 1.36% Mytilene 42 6 1.36% Mailand Malpensa 39 6 1.27% München 38 5 1.23% London Heathrow 35 5 1.14% Frankfurt 35 5 1.14% Kos 32 5 1.04% Samos 30 4 0.97% Chios 809 116 26.3% Summe 3078 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Distances ATH Weekly Share Distance in kilometers flights 1346 43.73% 1-400 376 12.22% 801-1200 330 10.72% 401-800 316 10.27% 2001-2400 284 9.23% 1201-1600 178 5.78% 1601-2000 144 4.68% 2401-2800 32 1.04% 7601-8000 32 1.04% 3201-3600 28 0.91% 2801-3200 6 0.19% 6801-7200 4 0.13% 8801-9200 2 0.06% 3601-4000 0 0.00% 0 0 0.00% 0 3078 100.0% Summe 3078 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types ATH Weekly Share Aircraft Type Avg Range MTOW Cruise WTC flights Seats in km in t Speed in km/h 758 24.63% AirbusIndustrieA320 156 5500 77 930 M 637 20.70% Boeing737-400Passenger 148 3810 68 888 M 278 9.03% AvroRJ100 102 2554 46 763 M 274 8.90% ATR72 68 2222 22 526 M 236 7.67% AirbusIndustrieA321 184 5500 93 930 M 168 5.46% ATR42-300/320 46 1944 17 491 M 118 3.83% DeHavillandDHC-8Dash8 37 2037 16 501 M 115 3.74% AirbusIndustrieA319 133 6800 76 930 M 112 3.64% Boeing737-300Passenger 133 4180 63 888 M 42 1.36% Boeing737Passenger 143 4180 63 888 M 40 1.30% CanadairRegionalJet900 88 3207 37 829 M 38 1.23% Boeing767Passenger 224 11390 187 953 H 36 1.17% AirbusIndustrieA340 268 14800 275 978 H 28 0.91% AirbusIndustrieA300- 232 7700 175 941 H 600Passenger 24 0.78% Boeing737-700Passenger 127 6110 69 932 M 2904 94.3% Summe 3078 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Carriers BCN Weekly Flights Avg Day Share 1211 173 23.16% IBERIA 736 105 14.08% SPANAIR 612 87 11.71% CLICKAIR 480 69 9.18% VUELING AIRLINES 270 39 5.16% LUFTHANSA GERMAN AIRLINES 261 37 4.99% AIR EUROPA 252 36 4.82% EASYJET 174 25 3.33% AIR FRANCE 122 17 2.33% TAP AIR PORTUGAL 112 16 2.14% BRITISH AIRWAYS 84 12 1.61% KLM-ROYAL DUTCH AIRLINES 80 11 1.53% SWISS 70 10 1.34% AIR BERLIN 54 8 1.03% ALITALIA 42 6 0.80% EASYJET SWITZERLAND SA 4560 651 87.2% Summe 5228 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins BCN Weekly flights Avg Day Share 379 54 7.25% Madrid 136 19 2.60% Palma Mallorca 106 15 2.03% Paris Charles de Gaulle 79 11 1.51% Sevilla 77 11 1.47% Amsterdam 69 10 1.32% Mailand Malpensa 64 9 1.22% Bilbao 63 9 1.21% London Heathrow 55 8 1.05% Lissabon 54 8 1.03% Rom Fiumicino 48 7 0.92% München 48 7 0.92% Malaga 47 7 0.90% Frankfurt 45 6 0.86% Paris ORY 39 6 0.75% Ibiza 1309 187 25.0% Summe 5228 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations BCN Weekly flights Avg Day Share 381 54 7.29% Madrid 143 20 2.74% Palma Mallorca 106 15 2.03% Paris Charles de Gaulle 79 11 1.51% Sevilla 76 11 1.45% Amsterdam 68 10 1.30% Mailand Malpensa 64 9 1.22% Bilbao 63 9 1.21% London Heathrow 55 8 1.05% Lissabon 54 8 1.03% Rom Fiumicino 48 7 0.92% München 48 7 0.92% Malaga 47 7 0.90% Frankfurt 45 6 0.86% Paris ORY 43 6 0.82% Brüssel 1320 189 25.2% Summe 5228 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Distances BCN Weekly Share Distance in kilometers flights 1898 36.30% 401-800 1765 33.76% 801-1200 663 12.68% 1-400 449 8.59% 1201-1600 166 3.18% 1601-2000 146 2.79% 2001-2400 49 0.94% 2801-3200 40 0.77% 6001-6400 22 0.42% 2401-2800 8 0.15% 8401-8800 8 0.15% 7201-7600 5 0.10% 3601-4000 4 0.08% 9201-9600 4 0.08% 10401-10800 1 0.02% 3201-3600 5228 100.0% Summe 5228 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types BCN Weekly Share Aircraft Type Avg Range MTOW Cruise WTC flights Seats in km in t Speed in km/h 1892 36.19% AirbusIndustrieA320 156 5500 77 930 M 730 13.96% AirbusIndustrieA319 133 6800 76 930 M 508 9.72% CanadairRegionalJet200 50 3713 23 859 M 302 5.78% Boeing738-700Passenger 161 5420 71 937 M 253 4.84% AirbusIndustrieA321 184 5500 93 930 M 193 3.69% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M 180 3.44% Boeing717-200 116 3350 55 906 M 180 3.44% Boeing(Douglas)MD-87 123 5248 64 906 M 124 2.37% Boeing737Passenger 143 4180 63 888 M 82 1.57% Boeing757(Passenger) 159 7315 116 935 M 80 1.53% Boeing(Douglas)MD-82 155 4925 68 906 M 76 1.45% Boeing737-700Passenger 127 6110 69 932 M 66 1.26% Boeing737-300Passenger 133 4180 63 888 M 56 1.07% EmbraerRJ145 49 2460 21 834 M 51 0.98% Boeing737-400Passenger 148 3810 68 888 M 4773 91.3% Summe 5228 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Carriers BHX Weekly Flights Avg Day Share 538 77 30.24% FLYBE 250 36 14.05% RYANAIR 218 31 12.25% BMIBABY 142 20 7.98% LUFTHANSA GERMAN AIRLINES 100 14 5.62% AIR FRANCE 82 12 4.61% KLM-ROYAL DUTCH AIRLINES 72 10 4.05% EASTERN AIRWAYS 64 9 3.60% AER LINGUS 56 8 3.15% BRUSSELS AIRLINES 50 7 2.81% MONARCH AIRLINES 42 6 2.36% SWISS 28 4 1.57% EMIRATES 22 3 1.24% EASYJET SWITZERLAND SA 22 3 1.24% SAS SCANDINAVIAN AIRLINES 22 3 1.24% CITY AIRLINE 1708 244 96.0% Summe 1779 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins BHX Weekly flights Avg Day Share 62 9 3.49% Paris Charles de Gaulle 56 8 3.15% Edinburgh 54 8 3.04% Amsterdam 54 8 3.04% Dublin 52 7 2.92% Glasgow 45 6 2.53% Düsseldorf 45 6 2.53% Frankfurt 32 5 1.80% Belfast 28 4 1.57% Brüssel 27 4 1.52% Isle of Man 22 3 1.24% Aberdeen 21 3 1.18% München 21 3 1.18% Zürich 19 3 1.07% Genf 19 3 1.07% Newcastle 557 80 31.3% Summe 1779 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations BHX Weekly flights Avg Day Share 62 9 3.49% Paris Charles de Gaulle 56 8 3.15% Edinburgh 54 8 3.04% Amsterdam 54 8 3.04% Dublin 52 7 2.92% Glasgow 45 6 2.53% Düsseldorf 45 6 2.53% Frankfurt 32 5 1.80% Belfast 28 4 1.57% Brüssel 27 4 1.52% Isle of Man 22 3 1.24% Aberdeen 21 3 1.18% München 21 3 1.18% Zürich 19 3 1.07% Newcastle 19 3 1.07% Genf 557 80 31.3% Summe 1779 Total Flights Week (03/16/ - 03/22/2009

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Appendix Top 15 Distances BHX Weekly Share Distance in kilometers flights 874 49.13% 401-800 334 18.77% 1-400 304 17.09% 801-1200 114 6.41% 1201-1600 54 3.04% 1601-2000 28 1.57% 5601-6000 20 1.12% 2801-3200 15 0.84% 5201-5600 14 0.79% 2401-2800 8 0.45% 4801-5200 6 0.34% 6001-6400 6 0.34% 2001-2400 2 0.11% 3201-3600 1779 100.0% Summe 1779 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types BHX Weekly Share Aircraft Type Avg Range MTOW Cruise WTC flights Seats in km in t Speed in km/h 340 19.11% DeHavillandDHC-8Dash8- 73 2401 29 649 M 400Dash8q 245 13.77% Boeing738-700Passenger 161 5420 71 937 M 186 10.46% Boeing737-300Passenger 133 4180 63 888 M 150 8.43% AvroRJ85 83 2796 44 763 M 118 6.63% Embraer195 107 3334 48 869 M 102 5.73% AirbusIndustrieA320 156 5500 77 930 M 88 4.95% Boeing737-500Passenger 111 4400 52 888 M 76 4.27% EmbraerRJ145 49 2460 21 834 M 72 4.05% BritishAerospaceJetstream41 29 2759 11 546 M 57 3.20% Boeing737Passenger 143 4180 63 888 M 50 2.81% Fokker100 105 3111 46 919 M 44 2.47% CanadairRegionalJet700 70 3674 34 859 M 28 1.57% AirbusIndustrieA319 133 6800 76 930 M 26 1.46% AirbusIndustrieA321 184 5500 93 930 M 26 1.46% Saab340 34 1667 13 523 M 1608 90.4% Summe 1779 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Carriers BRU Weekly Flights Avg Day Share 1384 198 33.43% BRUSSELS AIRLINES 424 61 10.24% LUFTHANSA GERMAN AIRLINES 182 26 4.40% BRITISH AIRWAYS 156 22 3.77% BMI BRITISH MIDLAND 110 16 2.66% JETAIRFLY 106 15 2.56% SAS SCANDINAVIAN AIRLINES 106 15 2.56% SWISS 92 13 2.22% JET AIRWAYS INDIA 84 12 2.03% KLM-ROYAL DUTCH AIRLINES 78 11 1.88% IBERIA 68 10 1.64% AIR FRANCE 64 9 1.55% AUSTRIAN AIRLINES AG 62 9 1.50% ALITALIA 58 8 1.40% TAP AIR PORTUGAL 53 8 1.28% FLYBE 3027 432 73.1% Summe 4140 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins BRU Weekly flights Avg Day Share 90 13 2.17% London Heathrow 70 10 1.69% München 69 10 1.67% Mailand Malpensa 67 10 1.62% Frankfurt 64 9 1.55% Wien 63 9 1.52% Madrid 58 8 1.40% Rom Fiumicino 53 8 1.28% Berlin Tegel 52 7 1.26% Genf 49 7 1.18% Kopenhagen 46 7 1.11% Prag 45 6 1.09% Amsterdam 43 6 1.04% Barcelona 42 6 1.01% Lissabon 42 6 1.01% Zürich 853 122 20.6% Summe 4140 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations BRU Weekly flights Avg Day Share 95 14 2.29% London Heathrow 69 10 1.67% Mailand Malpensa 68 10 1.64% München 66 9 1.59% Frankfurt 62 9 1.50% Wien 59 8 1.43% Madrid 58 8 1.40% Rom Fiumicino 53 8 1.28% Berlin Tegel 52 7 1.26% Genf 50 7 1.21% Kopenhagen 46 7 1.11% Amsterdam 46 7 1.11% Prag 42 6 1.01% Zürich 40 6 0.97% Lyon 40 6 0.97% Manchester 846 121 20.4% Summe 4140 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Distances BRU Weekly Share Distance in kilometers flights 1487 35.92% 401-800 826 19.95% 801-1200 556 13.43% 1-400 311 7.51% 1201-1600 292 7.05% 1601-2000 159 3.84% 2001-2400 100 2.42% 5601-6000 75 1.81% 3201-3600 66 1.59% 6001-6400 54 1.30% 4401-4800 38 0.92% 7601-8000 33 0.80% 2801-3200 31 0.75% 4801-5200 29 0.70% 6801-7200 24 0.58% 6401-6800 4081 98.6% Summe 4140 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types BRU Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC 514 12.42% AvroRJ85 83 2796 44 763 M 502 12.13% AirbusIndustrieA319 133 6800 76 930 M 432 10.43% AvroRJ100 102 2554 46 763 M 335 8.09% AirbusIndustrieA320 156 5500 77 930 M 257 6.21% Boeing737-400Passenger 148 3810 68 888 M 230 5.56% Boeing737-300Passenger 133 4180 63 888 M 164 3.96% CanadairRegionalJet200 50 3713 23 859 M 135 3.26% AirbusIndustrieA321 184 5500 93 930 M 120 2.90% EmbraerRJ145 49 2460 21 834 M 118 2.85% Boeing737-500Passenger 111 4400 52 888 M 115 2.78% Boeing738-700Passenger 161 5420 71 937 M 104 2.51% Boeing747(Freighter) 0 8245 397 938 H 78 1.88% CanadairRegionalJet900 88 3207 37 829 M 72 1.74% AirbusIndustrieA330-200 260 11866 230 978 H 70 1.69% Fokker50 48 2842 22 531 M 3246 78.4% Summe 4140 Total Flights Week (03/16/ - 03/22/2009

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Top 15 Carriers BSL Weekly Flights Avg Day Share 180 26 20.64% AIR FRANCE 162 23 18.58% EASYJET SWITZERLAND SA 150 21 17.20% LUFTHANSA GERMAN AIRLINES 122 17 13.99% SWISS 66 9 7.57% EASYJET 42 6 4.82% BRITISH AIRWAYS 40 6 4.59% TWIN JET 38 5 4.36% AUSTRIAN AIRLINES AG 18 3 2.06% TUIFLY 16 2 1.83% RYANAIR 12 2 1.38% CIMBER AIR 8 1 0.92% TURKISH AIRLINES 6 1 0.69% SUNEXPRESS 4 1 0.46% KOREAN AIR 4 1 0.46% MALAYSIA AIRLINES 868 124 99.5% Summe 872 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins BSL Weekly flights Avg Day Share 65 9 7.45% Mulhouse 38 5 4.36% München 28 4 3.21% Frankfurt 28 4 3.21% Paris Charles de Gaulle 27 4 3.10% Berlin Schönefeld 22 3 2.52% Amsterdam 21 3 2.41% London Heathrow 19 3 2.18% Wien 17 2 1.95% Lyon 12 2 1.38% London City 12 2 1.38% Barcelona 12 2 1.38% Hamburg 12 2 1.38% London Gatwick 11 2 1.26% Brüssel 10 1 1.15% Düsseldorf 334 48 38.3% Summe 872 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances BSL Weekly Share Distance in kilometers flights 387 44.38% 401-800 309 35.44% 1-400 94 10.78% 801-1200 28 3.21% 1201-1600 24 2.75% 1601-2000 13 1.49% 2801-3200 12 1.38% 2001-2400 2 0.23% 8401-8800 2 0.23% 4801-5200 1 0.11% 2401-2800 872 100.0% Summe 872 Total Flights Week (03/16/ - 03/22/2009

230

Appendix

Top 15 Aircraft Types BSL Weekly Share Aircraft Type Avg Range MTOW Cruise Speed WTC flights Seats in km in t in km/h 250 28.67% AirbusIndustrieA319 133 6800 76 930 M 122 13.99% AvroRJ100 102 2554 46 763 M 88 10.09% Embraer190 99 4260 50 869 M 68 7.80% EmbraerRJ145 49 2460 21 834 M 54 6.19% CanadairRegionalJet700 70 3674 34 859 M 52 5.96% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M 50 5.73% CanadairRegionalJet 50 3713 23 859 M 40 4.59% Beechcraft1900Dairliner 19 1384 8 512 M 34 3.90% CanadairRegionalJet200 50 3713 23 859 M 30 3.44% Boeing738-700Passenger 161 5420 71 937 M 24 2.75% Embraer170 73 3889 36 869 M 22 2.52% AirbusIndustrieA320 156 5500 77 930 M 18 2.06% Boeing738-700(Winglets)Passenger 118 5662 71 937 M 10 1.15% ATR72 68 2222 22 526 M 4 0.46% Boeing747-200(Freighter) 0 9075 378 938 H 866 99.3% Summe 872 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers MLH Weekly Flights Avg Day Share 172 25 60.56% AIR FRANCE 80 11 28.17% TWIN JET 18 3 6.34% AIRLINAIR 8 1 2.82% AIGLE AZUR 4 1 1.41% ATLAS BLUE 2 0 0.70% AIR ALGERIE 284 41 100.0% Summe 284 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins MLH Weekly flights Avg Day Share 65 9 22.89% Basel 41 6 14.44% Paris ORY 10 1 3.52% Toulouse 10 1 3.52% Marseille 9 1 3.17% Rennes 3 0 1.06% Constantine 2 0 0.70% Marrakech 1 0 0.35% Algier 142 20 50.0% Summe 284 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations MLH Weekly flights Avg Day Share 65 9 22.89% Basel 41 6 14.44% Paris ORY 10 1 3.52% Marseille 10 1 3.52% Toulouse 9 1 3.17% Rennes 3 0 1.06% Constantine 2 0 0.70% Marrakech 1 0 0.35% Algier 142 20 50.0% Summe 284 Total Flights Week (03/16/ - 03/22/2009

231

Appendix

Top 15 Distances MLH Weekly Share Distance in kilometers flights 140 49.30% 401-800 130 45.77% 1-400 10 3.52% 1201-1600 4 1.41% 2001-2400 284 100.0% Summe 284 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types MLH Weekly Share Aircraft Type Avg Range MTOW Cruise WTC flights Seats in km in t Speed in km/h 98 34.51% Beechcraft1900Dairliner 19 1384 8 512 M 68 23.94% AirbusIndustrieA319 133 6800 76 930 M 44 15.49% Embraer190 99 4260 50 869 M 34 11.97% EmbraerRJ145 49 2460 21 834 M 14 4.93% Fokker100 105 3111 46 919 M 12 4.23% Embraer170 73 3889 36 869 L 8 2.82% AirbusIndustrieA320 156 5500 77 930 M 4 1.41% Boeing737-400Passenger 148 3810 68 888 M 2 0.70% Boeing738-700Passenger 161 5420 71 937 M 284 100.0% Summe 284 Total Flights Week (03/16/ - 03/22/2009

232

Appendix

Top 15 Carriers CDG Weekly Flights Avg Day Share 5665 809 58.20% AIR FRANCE 560 80 5.75% LUFTHANSA GERMAN AIRLINES 440 63 4.52% EASYJET 179 26 1.84% ALITALIA 170 24 1.75% FLYBE 156 22 1.60% BRITISH AIRWAYS 144 21 1.48% VUELING AIRLINES 126 18 1.29% SAS SCANDINAVIAN AIRLINES 98 14 1.01% KLM-ROYAL DUTCH AIRLINES 86 12 0.88% AER LINGUS 84 12 0.86% SWISS 77 11 0.79% AIR EUROPA 70 10 0.72% FINNAIR 66 9 0.68% AMERICAN AIRLINES 66 9 0.68% LOT - POLISH AIRLINES 7987 1141 82.1% Summe 9733 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins CDG Weekly flights Avg Day Share 126 18 1.29% Frankfurt 125 18 1.28% London Heathrow 122 17 1.25% Rom Fiumicino 120 17 1.23% Mailand Malpensa 109 16 1.12% Madrid 106 15 1.09% Barcelona 104 15 1.07% München 100 14 1.03% Amsterdam 83 12 0.85% Zürich 76 11 0.78% Genf 75 11 0.77% Dublin 74 11 0.76% Düsseldorf 66 9 0.68% Kopenhagen 64 9 0.66% Moskau SVO 62 9 0.64% Birmingham 1412 202 14.5% Summe 9733 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations CDG Weekly flights Avg Day Share 124 18 1.27% Frankfurt 124 18 1.27% London Heathrow 122 17 1.25% Rom Fiumicino 122 17 1.25% Mailand Malpensa 109 16 1.12% Madrid 106 15 1.09% Barcelona 104 15 1.07% München 100 14 1.03% Amsterdam 83 12 0.85% Zürich 82 12 0.84% Dublin 76 11 0.78% Genf 74 11 0.76% Düsseldorf 63 9 0.65% Moskau SVO 62 9 0.64% Birmingham 60 9 0.62% Kopenhagen 1411 202 14.5% Summe 9733 Total Flights Week (03/16/ - 03/22/2009

233

Appendix

Weekly Share Distance in kilometers flights 3192 32.80% 401-800 1910 19.62% 801-1200 1074 11.03% 1-400 737 7.57% 1201-1600 339 3.48% 1601-2000 305 3.13% 2001-2400 247 2.54% 9201-9600 186 1.91% 8801-9200 183 1.88% 5201-5600 179 1.84% 5601-6000 166 1.71% 3201-3600 129 1.33% 2401-2800 119 1.22% 6001-6400 113 1.16% 6401-6800 110 1.13% 8001-8400 8989 92.4% Summe 9733 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types CDG

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1821 18.71% AirbusIndustrieA320 156 5500 77 930 M

1326 13.62% AirbusIndustrieA319 133 6800 76 930 M

816 8.38% AirbusIndustrieA321 184 5500 93 930 M

614 6.31% AirbusIndustrieA318 115 3705 62 930 M

467 4.80% AvroRJ85 83 2796 44 763 M

459 4.72% EmbraerRJ145 49 2460 21 834 M

310 3.19% Boeing777-200Passenger 315 14250 298 896 H

261 2.68% AirbusIndustrieA330-200 260 11866 230 978 H

250 2.57% Boeing777-300ERPassenger 353 14594 352 896 H

215 2.21% AirbusIndustrieA340-300 181 13500 260 978 H

212 2.18% Embraer190 99 4260 50 869 M

208 2.14% CanadairRegionalJet700 70 3674 34 859 M

208 2.14% CanadairRegionalJet200 50 3713 23 859 M

188 1.93% Boeing747-400(Passenger) 435 13480 397 938 H

186 1.91% Fokker100 105 3111 46 919 M

7541 77.5% Summe

9733 Total Flights Week (03/16/ - 03/22/2009

234

Appendix

Top 15 Carriers CGN

Weekly Flights Avg Day Share

496 71 30.52% GERMANWINGS 368 53 22.65% LUFTHANSA GERMAN AIRLINES

266 38 16.37% TUIFLY

232 33 14.28% AIR BERLIN

42 6 2.58% KLM-ROYAL DUTCH AIRLINES

36 5 2.22% AUSTRIAN AIRLINES AG 30 4 1.85% INTERSKY

22 3 1.35% TURKISH AIRLINES

14 2 0.86% WIZZ AIR 14 2 0.86% SUNEXPRESS

14 2 0.86% EASYJET 12 2 0.74% CZECH AIRLINES

12 2 0.74% BMI BRITISH MIDLAND

10 1 0.62% BLUEBIRD CARGO 10 1 0.62% CONDOR FLUGDIENST

1578 225 97.1% Summe

1625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins CGN Weekly flights Avg Day Share 132 19 8.12% München 125 18 7.69% Berlin Tegel 66 9 4.06% Hamburg 38 5 2.34% Wien 34 5 2.09% Berlin Schönefeld 21 3 1.29% Amsterdam 21 3 1.29% Zürich 21 3 1.29% London Stansted 20 3 1.23% London Heathrow 20 3 1.23% Paris Charles de Gaulle 19 3 1.17% Dresden 17 2 1.05% Palma Mallorca 15 2 0.92% Friedrichshafen 13 2 0.80% Prag 13 2 0.80% Istanbul 575 82 35.4% Summe 1625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations CGN Weekly flights Avg Day Share 132 19 8.12% München 125 18 7.69% Berlin Tegel 66 9 4.06% Hamburg 38 5 2.34% Wien 34 5 2.09% Berlin Schönefeld 235

Appendix

21 3 1.29% Amsterdam 21 3 1.29% Zürich 21 3 1.29% Istanbul 20 3 1.23% London Heathrow 19 3 1.17% Dresden 18 3 1.11% London Stansted 17 2 1.05% Palma Mallorca 17 2 1.05% Paris Charles de Gaulle 15 2 0.92% Friedrichshafen 13 2 0.80% Prag 577 82 35.5% Summe 1625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances CGN

Weekly flights Share Distance in kilometers

946 58.22% 401-800

325 20.00% 1-400

125 7.69% 801-1200

88 5.42% 1201-1600

78 4.80% 1601-2000

25 1.54% 2801-3200

23 1.42% 2001-2400

6 0.37% 3201-3600

4 0.25% 3601-4000

4 0.25% 2401-2800

1 0.06% 8801-9200

1625 100.0% Summe

1625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types CGN

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

534 32.86% AirbusIndustrieA319 133 6800 76 930 M

292 17.97% Boeing737-700Passenger 127 6110 69 932 M

176 10.83% Boeing737-300Passenger 133 4180 63 888 M

160 9.85% AirbusIndustrieA320 156 5500 77 930 M

76 4.68% Boeing738-700Passenger 161 5420 71 937 M

58 3.57% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

56 3.45% Boeing737-500Passenger 111 4400 52 888 M

46 2.83% CanadairRegionalJet200 50 3713 23 859 M

40 2.46% Fokker50 48 2842 22 531 M

36 2.22% CanadairRegionalJet 50 3713 23 859 M

26 1.60% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M 236

Appendix

20 1.23% AirbusIndustrieA321 184 5500 93 930 M

14 0.86% ATR42-300/320 46 1944 17 491 M

12 0.74% EmbraerRJ145 49 2460 21 834 M

10 0.62% Boeing757-300Passenger 265 6425 122 935 M

1556 95.8% Summe

1625 Total Flights Week (03/16/ - 03/22/2009

237

Appendix

Top 15 Carriers CIA Weekly Flights Avg Day Share 440 63 74.07% RYANAIR 90 13 15.15% EASYJET 42 6 7.07% WIZZ AIR 22 3 3.70% EASYJET SWITZERLAND SA

594 85 100.0% Summe

594 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins CIA Weekly flights Avg Day Share 35 5 5.89% London Stansted 28 4 4.71% Bergamo 21 3 3.54% Gerona 14 2 2.36% Paris ORY 14 2 2.36% Hahn 14 2 2.36% Charleroi 14 2 2.36% Madrid

14 2 2.36% Beauvais

14 2 2.36% Bukarest BBU

14 2 2.36% Treviso

13 2 2.19% Berlin Schönefeld

7 1 1.18% Valencia

7 1 1.18% Genf

7 1 1.18% Santander

7 1 1.18% Nykoping

223 32 37.5% Summe

594 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations CIA

Weekly flights Avg Day Share

35 5 5.89% London Stansted

28 4 4.71% Bergamo

21 3 3.54% Gerona

14 2 2.36% Paris ORY

14 2 2.36% Charleroi

14 2 2.36% Beauvais

14 2 2.36% Bukarest BBU

14 2 2.36% Madrid

14 2 2.36% Hahn

14 2 2.36% Treviso

13 2 2.19% Berlin Schönefeld

7 1 1.18% Valencia 238

Appendix

7 1 1.18% Genf

7 1 1.18% East Midlands

7 1 1.18% Santander

223 32 37.5% Summe

594 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances CIA

Weekly flights Share Distance in kilometers

262 44.11% 801-1200

134 22.56% 1201-1600

120 20.20% 401-800

70 11.78% 1601-2000

8 1.35% 1-400

594 100.0% Summe

594 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types CIA

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

440 74.07% Boeing738-700Passenger 161 5420 71 937 M

112 18.86% AirbusIndustrieA319 133 6800 76 930 M

42 7.07% AirbusIndustrieA320 156 5500 77 930 M

594 100.0% Summe

594 Total Flights Week (03/16/ - 03/22/2009

239

Appendix

Top 15 Carriers CPH Weekly Flights Avg Day Share 2154 308 50.30% SAS SCANDINAVIAN AIRLINES 496 71 11.58% CIMBER AIR 96 14 2.24% WIDEROE'S FLYVESELSKAP 92 13 2.15% AIR FRANCE 88 13 2.06% AIR BALTIC CORPORATION 82 12 1.91% LUFTHANSA GERMAN AIRLINES 80 11 1.87% KLM-ROYAL DUTCH AIRLINES 80 11 1.87% BRITISH AIRWAYS 78 11 1.82% NORWEGIAN AIR SHUTTLE 68 10 1.59% EASYJET 64 9 1.49% FINNAIR 60 9 1.40% AIR BERLIN 56 8 1.31% BMI BRITISH MIDLAND 56 8 1.31% AUSTRIAN AIRLINES AG 54 8 1.26% BLUE1 3604 515 84.2% Summe 4282 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins CPH Weekly flights Avg Day Share 129 18 3.01% Oslo 85 12 1.99% Helsinki 82 12 1.91% Aalborg 82 12 1.91% London Heathrow 78 11 1.82% Stockholm Arlanda 77 11 1.80% Aarhus 64 9 1.49% Amsterdam 60 9 1.40% Karup 60 9 1.40% Paris Charles de Gaulle 56 8 1.31% Frankfurt 50 7 1.17% Brüssel 49 7 1.14% Goeteborg 46 7 1.07% München 43 6 1.00% Billund 41 6 0.96% Zürich 1002 143 23.4% Summe 4282 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations CPH Weekly flights Avg Day Share 129 18 3.01% Oslo 86 12 2.01% Aalborg 85 12 1.99% Helsinki 79 11 1.84% London Heathrow 78 11 1.82% Stockholm Arlanda

240

Appendix

77 11 1.80% Aarhus 66 9 1.54% Paris Charles de Gaulle 63 9 1.47% Amsterdam 56 8 1.31% Karup 56 8 1.31% Frankfurt 49 7 1.14% Goeteborg 49 7 1.14% Brüssel 46 7 1.07% München 43 6 1.00% Billund 41 6 0.96% Zürich 1003 143 23.4% Summe 4282 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances CPH

Weekly flights Share Distance in kilometers

1366 31.90% 401-800

1297 30.29% 801-1200

1090 25.46% 1-400

154 3.60% 1201-1600

124 2.90% 2001-2400

45 1.05% 1601-2000

37 0.86% 8401-8800

33 0.77% 2401-2800

30 0.70% 6801-7200

26 0.61% 6001-6400

13 0.30% 4801-5200

13 0.30% 7601-8000

12 0.28% 7201-7600

10 0.23% 6401-6800

10 0.23% 3201-3600

4260 99.5% Summe

4282 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types CPH

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

542 12.66% CanadairRegionalJet 50 3713 23 859 M

346 8.08% AirbusIndustrieA319 133 6800 76 930 M

330 7.71% Boeing(Douglas)MD-82 155 4925 68 906 M

291 6.80% AirbusIndustrieA321 184 5500 93 930 M

250 5.84% Boeing(Douglas)MD-81 148 4925 64 906 M

214 5.00% ATR72 68 2222 22 526 M

212 4.95% AirbusIndustrieA320 156 5500 77 930 M 241

Appendix

209 4.88% Boeing(Douglas)MD-87 123 5248 64 906 M

202 4.72% CanadairRegionalJet900 88 3207 37 829 M

196 4.58% ATR42-500 47 1630 19 563 M

140 3.27% Boeing737-300Passenger 133 4180 63 888 M

132 3.08% Boeing738-700Passenger 161 5420 71 937 M

104 2.43% BritishAerospace146Passenger 98 2909 42 776 M

100 2.34% Boeing737-500Passenger 111 4400 52 888 M

90 2.10% Boeing737Passenger 143 4180 63 888 M

3358 78.4% Summe

4282 Total Flights Week (03/16/ - 03/22/2009

242

Appendix

Top 15 Carriers DRS Weekly Flights Avg Day Share 240 34 48.58% LUFTHANSA GERMAN AIRLINES 70 10 14.17% AIR BERLIN 64 9 12.96% CIRRUS AIRLINES 64 9 12.96% GERMANWINGS 48 7 9.72% AUSTRIAN AIRLINES AG 6 1 1.21% INTERSKY 2 0 0.40% BLUE WINGS

494 71 100.0% Summe

494 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins DRS

Weekly flights Avg Day Share

50 7 10.12% Düsseldorf

47 7 9.51% Frankfurt

45 6 9.11% München

19 3 3.85% Köln-Bonn

18 3 3.64% Wien

16 2 3.24% Zürich

16 2 3.24% Hamburg

13 2 2.63% Stuttgart

6 1 1.21% Leipzig

4 1 0.81% Palma Mallorca

4 1 0.81% Nürnberg

3 0 0.61% Friedrichshafen

2 0 0.40% Teneriffa

1 0 0.20% Antalya

1 0 0.20% Fuerteventura

245 35 49.6% Summe

494 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations DRS

Weekly flights Avg Day Share

50 7 10.12% Düsseldorf

47 7 9.51% Frankfurt

45 6 9.11% München

19 3 3.85% Köln-Bonn

18 3 3.64% Leipzig

16 2 3.24% Hamburg

16 2 3.24% Zürich

13 2 2.63% Stuttgart

243

Appendix

6 1 1.21% Wien

4 1 0.81% Palma Mallorca

4 1 0.81% Nürnberg

3 0 0.61% Friedrichshafen

2 0 0.40% Las Palmas

1 0 0.20% Antalya

1 0 0.20% Fuerteventura

245 35 49.6% Summe

494 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances DRS

Weekly flights Share Distance in kilometers

272 55.06% 1-400

202 40.89% 401-800

8 1.62% 3201-3600

7 1.42% 1201-1600

3 0.61% 3601-4000

2 0.40% 2001-2400

494 100.0% Summe

494 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types DRS

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

102 20.65% Boeing737-300Passenger 133 4180 63 888 M

68 13.77% AirbusIndustrieA319 133 6800 76 930 M

66 13.36% CanadairRegionalJet200 50 3713 23 859 M

64 12.96% FairchildDornier328-100 31 1852 14 624 M

54 10.93% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

36 7.29% AvroRJ85 83 2796 44 763 M

30 6.07% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

28 5.67% Boeing738-700Passenger 161 5420 71 937 M

24 4.86% CanadairRegionalJet900 88 3207 37 829 M

12 2.43% Boeing737-500Passenger 111 4400 52 888 M

8 1.62% AirbusIndustrieA320 156 5500 77 930 M

2 0.40% BritishAerospace146-300Passenger 100 2817 44 776 M

494 100.0% Summe

494 Total Flights Week (03/16/ - 03/22/2009

244

Appendix

Top 15 Carriers DUB Weekly Flights Avg Day Share 1374 196 40.81% RYANAIR 1058 151 31.42% AER LINGUS 268 38 7.96% AER ARANN 152 22 4.51% AIR FRANCE 94 13 2.79% BMI BRITISH MIDLAND 86 12 2.55% BRITISH AIRWAYS 56 8 1.66% FLYBE 42 6 1.25% SAS SCANDINAVIAN AIRLINES 42 6 1.25% LUFTHANSA GERMAN AIRLINES 28 4 0.83% DELTA AIR LINES 28 4 0.83% CONTINENTAL AIRLINES 14 2 0.42% AIR SOUTHWEST 14 2 0.42% MALEV HUNGARIAN AIRLINES 14 2 0.42% ETIHAD AIRWAYS 14 2 0.42% SWISS 3284 469 97.5% Summe 3367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins DUB Weekly flights Avg Day Share 135 19 4.01% London Heathrow 96 14 2.85% London Gatwick 82 12 2.44% Paris Charles de Gaulle 65 9 1.93% Cork 61 9 1.81% London Stansted 58 8 1.72% Manchester 54 8 1.60% Birmingham 44 6 1.31% London City 37 5 1.10% Edinburgh 35 5 1.04% Amsterdam 34 5 1.01% Frankfurt 27 4 0.80% Bristol 26 4 0.77% Galway 26 4 0.77% London Luton 26 4 0.77% Madrid 806 115 23.9% Summe 3367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations DUB Weekly flights Avg Day Share 135 19 4.01% London Heathrow 96 14 2.85% London Gatwick 75 11 2.23% Paris Charles de Gaulle 65 9 1.93% Cork 61 9 1.81% London Stansted 245

Appendix

58 8 1.72% Manchester 54 8 1.60% Birmingham 45 6 1.34% London City 37 5 1.10% Edinburgh 35 5 1.04% Amsterdam 34 5 1.01% Frankfurt 27 4 0.80% Bristol 26 4 0.77% Galway 26 4 0.77% London Luton 26 4 0.77% Madrid 800 114 23.8% Summe 3367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances DUB

Weekly flights Share Distance in kilometers

1096 32.55% 401-800

1050 31.19% 1-400

408 12.12% 1201-1600

330 9.80% 1601-2000

252 7.48% 801-1200

78 2.32% 4801-5200

40 1.19% 2801-3200

39 1.16% 5601-6000

24 0.71% 2401-2800

14 0.42% 5201-5600

14 0.42% 6001-6400

8 0.24% 8001-8400

8 0.24% 2001-2400

6 0.18% 6401-6800

3367 100.0% Summe

3367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types DUB

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1377 40.90% Boeing738-700Passenger 161 5420 71 937 M

962 28.57% AirbusIndustrieA320 156 5500 77 930 M

164 4.87% ATR42-300/320 46 1944 17 491 M

157 4.66% AvroRJ85 83 2796 44 763 M

138 4.10% AirbusIndustrieA321 184 5500 93 930 M

104 3.09% ATR72 68 2222 22 526 M

68 2.02% AirbusIndustrieA330 356 12000 230 978 H

50 1.49% AirbusIndustrieA319 133 6800 76 930 M

44 1.31% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M 246

Appendix

42 1.25% AirbusIndustrieA330-200 260 11866 230 978 H

40 1.19% Boeing737-400Passenger 148 3810 68 888 M

30 0.89% BritishAerospace146-200Passenger 90 2909 42 776 M

25 0.74% Boeing757(Passenger) 159 7315 116 935 M

22 0.65% Boeing736-700Passenger 115 5840 65 933 M

22 0.65% Boeing757-200Passenger 235 7315 116 935 M

3245 96.4% Summe

3367 Total Flights Week (03/16/ - 03/22/2009

247

Appendix

Top 15 Carriers DUS Weekly Flights Avg Day Share 1712 245 43.66% LUFTHANSA GERMAN AIRLINES 912 130 23.26% AIR BERLIN 114 16 2.91% AIR FRANCE 98 14 2.50% BRITISH AIRWAYS 94 13 2.40% FLYBE 84 12 2.14% SAS SCANDINAVIAN AIRLINES 82 12 2.09% AUSTRIAN AIRLINES AG 80 11 2.04% KLM-ROYAL DUTCH AIRLINES 72 10 1.84% TUIFLY 66 9 1.68% BLUE WINGS 56 8 1.43% TURKISH AIRLINES 56 8 1.43% SWISS 44 6 1.12% L.T.U. INTERNATIONAL AIRWAYS 42 6 1.07% IBERIA 34 5 0.87% CONDOR FLUGDIENST 3546 507 90.4% Summe 3921 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins DUS Weekly flights Avg Day Share 149 21 3.80% München 113 16 2.88% Berlin Tegel 86 12 2.19% Hamburg 80 11 2.04% Zürich 74 11 1.89% Paris Charles de Gaulle 74 11 1.89% London Heathrow 72 10 1.84% Stuttgart 65 9 1.66% Wien 60 9 1.53% Mailand Malpensa 50 7 1.28% Dresden 50 7 1.28% Nürnberg 49 7 1.25% Frankfurt 45 6 1.15% Birmingham 41 6 1.05% Manchester 40 6 1.02% Amsterdam 1048 150 26.7% Summe 3921 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations DUS Weekly flights Avg Day Share 149 21 3.80% München 113 16 2.88% Berlin Tegel 86 12 2.19% Hamburg 80 11 2.04% Zürich 74 11 1.89% Paris Charles de Gaulle

248

Appendix

74 11 1.89% London Heathrow 72 10 1.84% Stuttgart 65 9 1.66% Wien 61 9 1.56% Mailand Malpensa 50 7 1.28% Dresden 50 7 1.28% Nürnberg 49 7 1.25% Frankfurt 45 6 1.15% Birmingham 41 6 1.05% Manchester 40 6 1.02% Amsterdam 1049 150 26.8% Summe 3921 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances DUS

Weekly flights Share Distance in kilometers

1935 49.35% 401-800

788 20.10% 1-400

354 9.03% 801-1200

269 6.86% 1201-1600

175 4.46% 2001-2400

130 3.32% 1601-2000

79 2.01% 2801-3200

36 0.92% 7201-7600

34 0.87% 3201-3600

31 0.79% 4801-5200

26 0.66% 2401-2800

14 0.36% 6001-6400

12 0.31% 6401-6800

10 0.26% 8801-9200

8 0.20% 3601-4000

3901 99.5% Summe

3921 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types DUS

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

626 15.97% AirbusIndustrieA320 156 5500 77 930 M

572 14.59% CanadairRegionalJet200 50 3713 23 859 M

474 12.09% Boeing737-300Passenger 133 4180 63 888 M

412 10.51% AirbusIndustrieA319 133 6800 76 930 M

342 8.72% CanadairRegionalJet700 70 3674 34 859 M

208 5.30% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

143 3.65% Boeing737-700Passenger 127 6110 69 932 M 249

Appendix

134 3.42% Boeing738-700Passenger 161 5420 71 937 M

108 2.75% AirbusIndustrieA321 184 5500 93 930 M

90 2.30% CanadairRegionalJet 50 3713 23 859 M

90 2.30% Boeing737-500Passenger 111 4400 52 888 M

74 1.89% AirbusIndustrieA330-200 260 11866 230 978 H

72 1.84% BritishAerospace146-200Passenger 90 2909 42 776 M

66 1.68% Fokker50 48 2842 22 531 M

66 1.68% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

3477 88.7% Summe

3921 Total Flights Week (03/16/ - 03/22/2009

250

Appendix

Top 15 Carriers EDI Weekly Flights Avg Day Share 430 61 21.38% FLYBE 322 46 16.01% EASYJET 299 43 14.87% BRITISH AIRWAYS 276 39 13.72% BMI BRITISH MIDLAND 182 26 9.05% RYANAIR 130 19 6.46% AIR FRANCE 98 14 4.87% BMIBABY 80 11 3.98% FLYGLOBESPAN 68 10 3.38% KLM-ROYAL DUTCH AIRLINES 28 4 1.39% LUFTHANSA GERMAN AIRLINES 26 4 1.29% AER LINGUS 22 3 1.09% AER ARANN 14 2 0.70% CONTINENTAL AIRLINES 10 1 0.50% BLUEBIRD CARGO 10 1 0.50% DELTA AIR LINES 1995 285 99.2% Summe 2011 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins EDI Weekly flights Avg Day Share 127 18 6.32% London Heathrow 80 11 3.98% London City 63 9 3.13% London Gatwick 56 8 2.78% Manchester 56 8 2.78% Birmingham 41 6 2.04% Amsterdam 37 5 1.84% Dublin 35 5 1.74% Paris Charles de Gaulle 30 4 1.49% Southampton 29 4 1.44% London Stansted 28 4 1.39% Cardiff 25 4 1.24% London Luton 23 3 1.14% Belfast 18 3 0.90% East Midlands 18 3 0.90% Belfast 666 95 33.1% Summe 2011 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations EDI Weekly flights Avg Day Share 126 18 6.27% London Heathrow 77 11 3.83% London City 63 9 3.13% London Gatwick 57 8 2.83% Manchester 56 8 2.78% Birmingham

251

Appendix

41 6 2.04% Amsterdam 37 5 1.84% Dublin 35 5 1.74% Paris Charles de Gaulle 30 4 1.49% Southampton 29 4 1.44% London Stansted 28 4 1.39% Cardiff 25 4 1.24% London Luton 23 3 1.14% Belfast 18 3 0.90% East Midlands 18 3 0.90% Belfast 663 95 33.0% Summe 2011 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances EDI

Weekly flights Share Distance in kilometers

1173 58.33% 401-800

427 21.23% 1-400

165 8.20% 801-1200

90 4.48% 1201-1600

86 4.28% 1601-2000

34 1.69% 2001-2400

24 1.19% 5201-5600

6 0.30% 3201-3600

2 0.10% 4001-4400

2 0.10% 2801-3200

2 0.10% 2401-2800

2011 100.0% Summe

2011 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types EDI

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

462 22.97% AirbusIndustrieA319 133 6800 76 930 M

232 11.54% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

184 9.15% Boeing738-700Passenger 161 5420 71 937 M

168 8.35% EmbraerRJ145 49 2460 21 834 M

134 6.66% AirbusIndustrieA320 156 5500 77 930 M

126 6.27% AvroRJ85 83 2796 44 763 M

120 5.97% Saab340 34 1667 13 523 M

98 4.87% Boeing737-300Passenger 133 4180 63 888 M

78 3.88% Embraer195 107 3334 48 869 M

60 2.98% AvroRJ100 102 2554 46 763 M

56 2.78% Boeing737-500Passenger 111 4400 52 888 M 252

Appendix

54 2.69% Boeing737Passenger 143 4180 63 888 M

42 2.09% Boeing737-400Passenger 148 3810 68 888 M

32 1.59% Boeing737-700Passenger 127 6110 69 932 M

24 1.19% Boeing736-700Passenger 115 5840 65 933 M

1870 93.0% Summe

2011 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers FCO Weekly Flights Avg Day Share 2521 360 39.18% ALITALIA 990 141 15.38% AIR ONE 306 44 4.76% MERIDIANA 180 26 2.80% AIR FRANCE 164 23 2.55% LUFTHANSA GERMAN AIRLINES 155 22 2.41% EUROFLY 142 20 2.21% EASYJET 124 18 1.93% BLUE PANORAMA AIRLINES 106 15 1.65% VUELING AIRLINES 97 14 1.51% BRITISH AIRWAYS 77 11 1.20% IBERIA 69 10 1.07% TAP AIR PORTUGAL 62 9 0.96% WIND JET 56 8 0.87% WIZZ AIR 56 8 0.87% SWISS 5105 729 79.3% Summe 6435 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins FCO Weekly flights Avg Day Share 284 41 4.41% Mailand LIN 190 27 2.95% Cagliari 129 18 2.00% Palermo 122 17 1.90% Paris Charles de Gaulle 114 16 1.77% Catania 112 16 1.74% Turin 89 13 1.38% Mailand Malpensa 81 12 1.26% Bari 79 11 1.23% Madrid 72 10 1.12% Venedig 71 10 1.10% London Heathrow 60 9 0.93% Genua 58 8 0.90% Brüssel 57 8 0.89% Frankfurt 54 8 0.84% Barcelona 1572 225 24.4% Summe 6435 Total Flights Week (03/16/ - 03/22/2009

253

Appendix

Top 15 Destinations FCO Weekly flights Avg Day Share 310 44 4.82% Mailand LIN 190 27 2.95% Cagliari 131 19 2.04% Palermo 122 17 1.90% Paris Charles de Gaulle 115 16 1.79% Catania 112 16 1.74% Turin 107 15 1.66% Mailand Malpensa 79 11 1.23% Madrid 76 11 1.18% London Heathrow 74 11 1.15% Bari 72 10 1.12% Venedig 60 9 0.93% Genua 58 8 0.90% Brüssel 57 8 0.89% Frankfurt 54 8 0.84% Barcelona 1617 231 25.1% Summe 6435 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances FCO

Weekly flights Share Distance in kilometers

2749 42.72% 401-800

1102 17.13% 801-1200

1045 16.24% 1-400

643 9.99% 1201-1600

254 3.95% 2001-2400

216 3.36% 1601-2000

108 1.68% 6801-7200

55 0.85% 8001-8400

42 0.65% 9601-10000

37 0.57% 4001-4400

29 0.45% 7201-7600

26 0.40% 8801-9200

26 0.40% 9201-9600

23 0.36% 4401-4800

19 0.30% 10801-11200

6374 99.1% Summe

6435 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types FCO

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

254

Appendix

1616 25.11% AirbusIndustrieA320 156 5500 77 930 M

853 13.26% Boeing(Douglas)MD-82 155 4925 68 906 M

749 11.64% AirbusIndustrieA321 184 5500 93 930 M

732 11.38% Boeing(Douglas)MD-80 148 4925 64 906 M

602 9.36% AirbusIndustrieA319 133 6800 76 930 M

430 6.68% Boeing737Passenger 143 4180 63 888 M

174 2.70% Embraer170 73 3889 36 869 M

136 2.11% CanadairRegionalJet900 88 3207 37 829 M

94 1.46% Fokker100 105 3111 46 919 M

94 1.46% Boeing737-300Passenger 133 4180 63 888 M

88 1.37% Boeing767Passenger 224 11390 187 953 H

87 1.35% Boeing737-500Passenger 111 4400 52 888 M

82 1.27% Boeing777-200Passenger 315 14250 298 896 H

80 1.24% Boeing738-700Passenger 161 5420 71 937 M

65 1.01% Boeing737-400Passenger 148 3810 68 888 M

5882 91.4% Summe

6435 Total Flights Week (03/16/ - 03/22/2009

255

Appendix

Top 15 Carriers FMO Weekly Flights Avg Day Share 144 21 40.45% AIR BERLIN 132 19 37.08% LUFTHANSA GERMAN AIRLINES 32 5 8.99% CIRRUS AIRLINES 26 4 7.30% AIR FRANCE 10 1 2.81% TUIFLY 10 1 2.81% INTERSKY 2 0 0.56% BLUE WINGS

356 51 100.0% Summe

356 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins FMO

Weekly flights Avg Day Share

60 9 16.85% München

28 4 7.87% Frankfurt

16 2 4.49% Stuttgart

13 2 3.65% Palma Mallorca

13 2 3.65% London Stansted

13 2 3.65% Paris Charles de Gaulle

12 2 3.37% Berlin Tegel

7 1 1.97% Wien

5 1 1.40% Friedrichshafen

3 0 0.84% Nürnberg

2 0 0.56% Las Palmas

2 0 0.56% Teneriffa

2 0 0.56% Fuerteventura

1 0 0.28% Lanzarote

1 0 0.28% Antalya

178 25 50.0% Summe

356 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations FMO

Weekly flights Avg Day Share

60 9 16.85% München

28 4 7.87% Frankfurt

16 2 4.49% Stuttgart

13 2 3.65% Palma Mallorca

13 2 3.65% London Stansted

13 2 3.65% Paris Charles de Gaulle

12 2 3.37% Berlin Tegel

7 1 1.97% Wien

256

Appendix

5 1 1.40% Friedrichshafen

3 0 0.84% Nürnberg

3 0 0.84% Las Palmas

2 0 0.56% Fuerteventura

1 0 0.28% Teneriffa

1 0 0.28% Lanzarote

1 0 0.28% Antalya

178 25 50.0% Summe

356 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances FMO

Weekly flights Share Distance in kilometers

196 55.06% 401-800

118 33.15% 1-400

26 7.30% 1201-1600

8 2.25% 3201-3600

6 1.69% 2801-3200

2 0.56% 2401-2800

356 100.0% Summe

356 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types FMO

Weekly Range in MTOW in Cruise Speed in flights Share Aircraft Type Avg Seats km t km/h WTC

90 25.28% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

50 14.04% CanadairRegionalJet900 88 3207 37 829 M

42 11.80% Embraer170 73 3889 36 869 M

40 11.24% Boeing738-700Passenger 161 5420 71 937 M

32 8.99% FairchildDornier328-100 31 1852 14 624 M

26 7.30% EmbraerRJ135 37 2500 19 930 M

16 4.49% CanadairRegionalJet200 50 3713 23 859 M

14 3.93% CanadairRegionalJet700 70 3674 34 859 M

12 3.37% AirbusIndustrieA320 156 5500 77 930 M

10 2.81% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

10 2.81% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

10 2.81% Boeing737-300Passenger 133 4180 63 888 M

2 0.56% Boeing737-700Passenger 127 6110 69 932 M

2 0.56% AirbusIndustrieA319 133 6800 76 930 M

356 100.0% Summe

356 Total Flights Week (03/16/ - 03/22/2009

257

Appendix

Top 15 Carriers FRA Weekly Flights Avg Day Share 5499 786 62.55% LUFTHANSA GERMAN AIRLINES 182 26 2.07% AUSTRIAN AIRLINES AG 176 25 2.00% CONDOR FLUGDIENST 174 25 1.98% SAS SCANDINAVIAN AIRLINES 146 21 1.66% AIR FRANCE 133 19 1.51% BRITISH AIRWAYS 126 18 1.43% LOT - POLISH AIRLINES 86 12 0.98% AIR BERLIN 84 12 0.96% UNITED AIRLINES 82 12 0.93% ADRIA AIRWAYS 80 11 0.91% FLYBE 78 11 0.89% TUIFLY 72 10 0.82% IBERIA 70 10 0.80% SWISS 68 10 0.77% KLM-ROYAL DUTCH AIRLINES 7056 1008 80.3% Summe 8791 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins FRA Weekly flights Avg Day Share 124 18 1.41% Paris Charles de Gaulle 113 16 1.29% London Heathrow 111 16 1.26% Berlin Tegel 104 15 1.18% Wien 89 13 1.01% Hamburg 86 12 0.98% München 85 12 0.97% Amsterdam 80 11 0.91% Madrid 77 11 0.88% Zürich 66 9 0.75% Brüssel 57 8 0.65% Rom Fiumicino 56 8 0.64% Kopenhagen 56 8 0.64% Istanbul 53 8 0.60% Genf 51 7 0.58% Dubai 1208 173 13.7% Summe 8791 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations FRA Weekly flights Avg Day Share 126 18 1.43% Paris Charles de Gaulle 113 16 1.29% London Heathrow 111 16 1.26% Berlin Tegel 97 14 1.10% Wien 89 13 1.01% Hamburg

258

Appendix

84 12 0.96% München 83 12 0.94% Amsterdam 80 11 0.91% Madrid 77 11 0.88% Zürich 67 10 0.76% Brüssel 57 8 0.65% Rom Fiumicino 56 8 0.64% Kopenhagen 55 8 0.63% Istanbul 53 8 0.60% Genf 49 7 0.56% Düsseldorf 1197 171 13.6% Summe 8791 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances FRA

Weekly flights Share Distance in kilometers

2667 30.34% 401-800

1555 17.69% 1-400

1074 12.22% 801-1200

731 8.32% 1201-1600

423 4.81% 1601-2000

226 2.57% 2001-2400

210 2.39% 2801-3200

198 2.25% 6001-6400

186 2.12% 4801-5200

168 1.91% 8801-9200

145 1.65% 6401-6800

135 1.54% 9201-9600

133 1.51% 8001-8400

127 1.44% 6801-7200

122 1.39% 8401-8800

8100 92.1% Summe

8791 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types FRA

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1083 12.32% AirbusIndustrieA320 156 5500 77 930 M

1064 12.10% AirbusIndustrieA321 184 5500 93 930 M

867 9.86% Boeing737-300Passenger 133 4180 63 888 M

685 7.79% AirbusIndustrieA319 133 6800 76 930 M

607 6.90% Boeing737-500Passenger 111 4400 52 888 M

464 5.28% Boeing747-400(Passenger) 435 13480 397 938 H 259

Appendix

412 4.69% BritishAerospace146-300Passenger 100 2817 44 776 M

248 2.82% AirbusIndustrieA300-600Passenger 232 7700 175 941 H

198 2.25% AirbusIndustrieA330-300 181 8900 230 978 H

188 2.14% Boeing(Douglas)MD-11(Freighter) 0 6770 286 946 H

174 1.98% CanadairRegionalJet200 50 3713 23 859 M

167 1.90% AirbusIndustrieA340-300 181 13500 260 978 H

160 1.82% Boeing767-300Passenger 238 11390 187 953 H

158 1.80% AvroRJ85 83 2796 44 763 M

142 1.62% AirbusIndustrieA340-600 323 13900 365 990 H

6617 75.3% Summe

8791 Total Flights Week (03/16/ - 03/22/2009

260

Appendix

Top 15 Carriers GLA Weekly Flights Avg Day Share 430 61 30.74% FLYBE 272 39 19.44% EASYJET 207 30 14.80% BRITISH AIRWAYS 184 26 13.15% BMI BRITISH MIDLAND 98 14 7.01% FLYGLOBESPAN 82 12 5.86% BMIBABY 52 7 3.72% KLM-ROYAL DUTCH AIRLINES 28 4 2.00% AER LINGUS 14 2 1.00% CONTINENTAL AIRLINES 14 2 1.00% EMIRATES 12 2 0.86% AIR SOUTHWEST 4 1 0.29% PAKISTAN INTERNATIONAL AIRLINES 2 0 0.14% AIR TRANSAT A.T.INC.

1399 200 100.0% Summe

1399 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins GLA Weekly flights Avg Day Share 103 15 7.36% London Heathrow 52 7 3.72% Birmingham 45 6 3.22% London Gatwick 41 6 2.93% Manchester 29 4 2.07% London Luton 28 4 2.00% London Stansted 27 4 1.93% Southampton 26 4 1.86% Amsterdam 25 4 1.79% London City 23 3 1.64% Stornoway 22 3 1.57% Belfast 21 3 1.50% Belfast 18 3 1.29% East Midlands 18 3 1.29% Cardiff 18 3 1.29% Bristol 496 71 35.5% Summe 1399 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations GLA Weekly flights Avg Day Share 108 15 7.72% London Heathrow 52 7 3.72% Birmingham 45 6 3.22% London Gatwick 41 6 2.93% Manchester

29 4 2.07% London Luton

28 4 2.00% London Stansted

27 4 1.93% Southampton 261

Appendix

26 4 1.86% Amsterdam

25 4 1.79% London City

23 3 1.64% Stornoway

22 3 1.57% Belfast

21 3 1.50% Belfast

18 3 1.29% East Midlands

18 3 1.29% Cardiff

18 3 1.29% Bristol

501 72 35.8% Summe

1399 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances GLA

Weekly flights Share Distance in kilometers

787 56.25% 401-800

430 30.74% 1-400

46 3.29% 1601-2000

32 2.29% 2001-2400

22 1.57% 801-1200

18 1.29% 3201-3600

16 1.14% 1201-1600

14 1.00% 4801-5200

14 1.00% 5601-6000

10 0.71% 6401-6800

4 0.29% 3601-4000

4 0.29% 2801-3200

2 0.14% 5201-5600

1399 100.0% Summe

1399 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types GLA

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

362 25.88% AirbusIndustrieA319 133 6800 76 930 M

178 12.72% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

138 9.86% Saab340 34 1667 13 523 M

126 9.01% AirbusIndustrieA320 156 5500 77 930 M

76 5.43% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

74 5.29% Boeing737-300Passenger 133 4180 63 888 M

68 4.86% EmbraerRJ145 49 2460 21 834 M

46 3.29% Boeing737-400Passenger 148 3810 68 888 M

44 3.15% DeHavillandDHC-6TwinOtter 19 1704 6 338 L

262

Appendix

42 3.00% Boeing737Passenger 143 4180 63 888 M

36 2.57% AvroRJ100 102 2554 46 763 M

34 2.43% ATR72 68 2222 22 526 M

28 2.00% Embraer195 107 3334 48 869 M

24 1.72% Boeing737-500Passenger 111 4400 52 888 M

24 1.72% EmbraerRJ135 37 2500 19 930 M

1300 92.9% Summe

1399 Total Flights Week (03/16/ - 03/22/2009

263

Appendix

Top 15 Carriers GRZ Weekly Flights Avg Day Share 142 20 44.38% LUFTHANSA GERMAN AIRLINES 96 14 30.00% AUSTRIAN AIRLINES AG 26 4 8.13% ROBIN HOOD AVIATION 20 3 6.25% WELCOME AIR 20 3 6.25% INTERSKY 8 1 2.50% RYANAIR 8 1 2.50% TUIFLY

320 46 100.0% Summe

320 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins GRZ

Weekly flights Avg Day Share

29 4 9.06% Wien

28 4 8.75% Frankfurt

27 4 8.44% München

20 3 6.25% Stuttgart

11 2 3.44% Düsseldorf

10 1 3.13% Innsbruck

9 1 2.81% Zürich

5 1 1.56% Linz

5 1 1.56% Friedrichshafen

5 1 1.56% Berlin Tegel

4 1 1.25% London Stansted

4 1 1.25% Köln-Bonn

1 0 0.31% Las Palmas

1 0 0.31% Teneriffa

1 0 0.31% Hurghada

160 23 50.0% Summe

320 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations GRZ

Weekly flights Avg Day Share

29 4 9.06% Wien

28 4 8.75% Frankfurt

27 4 8.44% München

20 3 6.25% Stuttgart

11 2 3.44% Düsseldorf

10 1 3.13% Innsbruck

9 1 2.81% Zürich

5 1 1.56% Linz

264

Appendix

5 1 1.56% Friedrichshafen

5 1 1.56% Berlin Tegel

4 1 1.25% Köln-Bonn

4 1 1.25% London Stansted

1 0 0.31% Las Palmas

1 0 0.31% Luxor

1 0 0.31% Teneriffa

160 23 50.0% Summe

320 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances GRZ

Weekly flights Share Distance in kilometers

164 51.25% 401-800

142 44.38% 1-400

8 2.50% 1201-1600

4 1.25% 3201-3600

2 0.63% 2801-3200

320 100.0% Summe

320 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types GRZ

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

75 23.44% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

54 16.88% BritishAerospace146-300Passenger 100 2817 44 776 M

43 13.44% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

32 10.00% ATR42-500 47 1630 19 563 M

32 10.00% CanadairRegionalJet 50 3713 23 859 M

26 8.13% Saab340 34 1667 13 523 M

20 6.25% FairchildDornier328-100 31 1852 14 624 M

9 2.81% DeHavillandDHC-8Dash8 37 2037 16 501 M

9 2.81% Fokker70 76 3732 42 856 M

8 2.50% Boeing738-700Passenger 161 5420 71 937 M

7 2.19% Boeing737-700Passenger 127 6110 69 932 M

4 1.25% AirbusIndustrieA321 184 5500 93 930 M

1 0.31% Boeing737-500Passenger 111 4400 52 888 M

320 100.0% Summe

320 Total Flights Week (03/16/ - 03/22/2009

265

Appendix

Top 15 Carriers HAJ Weekly Flights Avg Day Share 296 42 27.16% LUFTHANSA GERMAN AIRLINES 198 28 18.17% AIR BERLIN 128 18 11.74% TUIFLY 76 11 6.97% AIR FRANCE 56 8 5.14% SWISS 54 8 4.95% KLM-ROYAL DUTCH AIRLINES 38 5 3.49% BMI BRITISH MIDLAND 38 5 3.49% AUSTRIAN AIRLINES AG 36 5 3.30% SAS SCANDINAVIAN AIRLINES 34 5 3.12% FLYBE 24 3 2.20% CZECH AIRLINES 20 3 1.83% WELCOME AIR 18 3 1.65% CONDOR FLUGDIENST 14 2 1.28% AEROFLOT RUSSIAN AIRLINES 14 2 1.28% TURKISH AIRLINES 1044 149 95.8% Summe 1090 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins HAJ Weekly flights Avg Day Share 82 12 7.52% München 54 8 4.95% Paris Charles de Gaulle 42 6 3.85% Frankfurt 40 6 3.67% Zürich 37 5 3.39% Wien 33 5 3.03% Stuttgart 27 4 2.48% Amsterdam 23 3 2.11% Palma Mallorca 19 3 1.74% London Heathrow 18 3 1.65% Kopenhagen 15 2 1.38% Brüssel 13 2 1.19% London Stansted 12 2 1.10% Prag 11 2 1.01% Istanbul 8 1 0.73% Las Palmas 434 62 39.8% Summe 1090 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations HAJ Weekly flights Avg Day Share 82 12 7.52% München 54 8 4.95% Paris Charles de Gaulle 42 6 3.85% Frankfurt 40 6 3.67% Zürich 37 5 3.39% Wien

266

Appendix

33 5 3.03% Stuttgart 27 4 2.48% Amsterdam

23 3 2.11% Palma Mallorca

19 3 1.74% London Heathrow

18 3 1.65% Kopenhagen

15 2 1.38% Brüssel

13 2 1.19% London Stansted

12 2 1.10% Prag

11 2 1.01% Istanbul

8 1 0.73% Las Palmas

434 62 39.8% Summe

1090 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances HAJ

Weekly flights Share Distance in kilometers

708 64.95% 401-800

178 16.33% 1-400

54 4.95% 1201-1600

52 4.77% 3201-3600

44 4.04% 1601-2000

26 2.39% 2001-2400

20 1.83% 801-1200

6 0.55% 2401-2800

2 0.18% 2801-3200

1090 100.0% Summe

1090 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types HAJ

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

226 20.73% AirbusIndustrieA319 133 6800 76 930 M

114 10.46% EmbraerRJ145 49 2460 21 834 M

104 9.54% AirbusIndustrieA320 156 5500 77 930 M

72 6.61% ATR72 68 2222 22 526 M

72 6.61% CanadairRegionalJet 50 3713 23 859 M

72 6.61% Boeing737-700Passenger 127 6110 69 932 M

64 5.87% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

52 4.77% Fokker50 48 2842 22 531 M

46 4.22% Boeing737-300Passenger 133 4180 63 888 M

267

Appendix

44 4.04% AvroRJ100 102 2554 46 763 M

36 3.30% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

34 3.12% CanadairRegionalJet200 50 3713 23 859 M

30 2.75% AirbusIndustrieA321 184 5500 93 930 M

24 2.20% ATR42-300/320 46 1944 17 491 M

20 1.83% FairchildDornier328-100 31 1852 14 624 M

1010 92.7% Summe

1090 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers HAM Weekly Flights Avg Day Share 1166 167 42.11% LUFTHANSA GERMAN AIRLINES 462 66 16.68% AIR BERLIN 146 21 5.27% TUIFLY 88 13 3.18% CIRRUS AIRLINES 76 11 2.74% AIR FRANCE 76 11 2.74% KLM-ROYAL DUTCH AIRLINES 72 10 2.60% GERMANWINGS 62 9 2.24% SAS SCANDINAVIAN AIRLINES 56 8 2.02% BRITISH AIRWAYS 42 6 1.52% OLT OSTFRIESISCHE LUFTTRANSPORT GMBH 42 6 1.52% SWISS 36 5 1.30% CZECH AIRLINES 34 5 1.23% BRUSSELS AIRLINES 32 5 1.16% CONDOR FLUGDIENST 28 4 1.01% AUSTRIAN AIRLINES AG 2418 345 87.3% Summe 2769 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins HAM Weekly flights Avg Day Share 146 21 5.27% München 97 14 3.50% Stuttgart 89 13 3.21% Frankfurt 86 12 3.11% Düsseldorf 66 9 2.38% Köln-Bonn 59 8 2.13% Amsterdam 59 8 2.13% Zürich 52 7 1.88% Wien 49 7 1.77% London Heathrow 44 6 1.59% Paris Charles de Gaulle 39 6 1.41% Nürnberg 38 5 1.37% Brüssel

268

Appendix

31 4 1.12% Kopenhagen 29 4 1.05% Saarbrücken 27 4 0.98% Palma Mallorca 911 130 32.9% Summe 2769 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations HAM Weekly flights Avg Day Share 146 21 5.27% München 97 14 3.50% Stuttgart 89 13 3.21% Frankfurt 86 12 3.11% Düsseldorf 66 9 2.38% Köln-Bonn 59 8 2.13% Amsterdam 59 8 2.13% Zürich 52 7 1.88% Wien 49 7 1.77% London Heathrow 44 6 1.59% Paris Charles de Gaulle 39 6 1.41% Nürnberg 38 5 1.37% Brüssel 31 4 1.12% Kopenhagen 29 4 1.05% Saarbrücken 27 4 0.98% Palma Mallorca 911 130 32.9% Summe 2769 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances HAM

Weekly flights Share Distance in kilometers

1640 59.23% 401-800

554 20.01% 1-400

242 8.74% 801-1200

128 4.62% 1601-2000

62 2.24% 3201-3600

51 1.84% 2001-2400

45 1.63% 1201-1600

14 0.51% 4801-5200

14 0.51% 6001-6400

9 0.33% 2401-2800

6 0.22% 2801-3200

4 0.14% 3601-4000

2769 100.0% Summe 269

Appendix

2769 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types HAM

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

392 14.16% AirbusIndustrieA319 133 6800 76 930 M

295 10.65% AirbusIndustrieA320 156 5500 77 930 M

276 9.97% CanadairRegionalJet200 50 3713 23 859 M

271 9.79% Boeing737-300Passenger 133 4180 63 888 M

254 9.17% AirbusIndustrieA321 184 5500 93 930 M

192 6.93% Boeing737-500Passenger 111 4400 52 888 M

97 3.50% Boeing737-700Passenger 127 6110 69 932 M

94 3.39% CanadairRegionalJet700 70 3674 34 859 M

88 3.18% FairchildDornier328-100 31 1852 14 624 M

78 2.82% Boeing738-700Passenger 161 5420 71 937 M

68 2.46% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

62 2.24% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

56 2.02% AirbusIndustrieA300-600Passenger 232 7700 175 941 H

56 2.02% Embraer170 73 3889 36 869 M

52 1.88% CanadairRegionalJet 50 3713 23 859 M

2331 84.2% Summe

2769 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers HEL Weekly Flights Avg Day Share 1942 277 54.06% FINNAIR 458 65 12.75% FINNCOMM AIRLINES 408 58 11.36% BLUE1 110 16 3.06% LUFTHANSA GERMAN AIRLINES 106 15 2.95% SAS SCANDINAVIAN AIRLINES 90 13 2.51% AIR BALTIC CORPORATION 56 8 1.56% NORDIC SOLUTIONS AIR SERVICES 48 7 1.34% AVITRANS NORDIC AB 42 6 1.17% KLM-ROYAL DUTCH AIRLINES 40 6 1.11% BRITISH AIRWAYS 40 6 1.11% JOB AIR - CENTRAL CONNECT AIRLINES 38 5 1.06% AIR BERLIN 28 4 0.78% AUSTRIAN AIRLINES AG 22 3 0.61% BRUSSELS AIRLINES 20 3 0.56% AEROFLOT RUSSIAN AIRLINES 3448 493 96.0% Summe

270

Appendix

3592 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins HEL Weekly flights Avg Day Share 116 17 3.23% Stockholm Arlanda 99 14 2.76% Oulo 85 12 2.37% Kopenhagen 65 9 1.81% Vaasa 56 8 1.56% Kuopio 50 7 1.39% Tallinn 49 7 1.36% Jyvaskyla 49 7 1.36% Oslo 49 7 1.36% London Heathrow 48 7 1.34% Mariehamn 47 7 1.31% Frankfurt 46 7 1.28% München 45 6 1.25% Riga 43 6 1.20% Tampere 41 6 1.14% Amsterdam 888 127 24.7% Summe 3592 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations HEL Weekly flights Avg Day Share 117 17 3.26% Stockholm Arlanda 97 14 2.70% Oulo 85 12 2.37% Kopenhagen 63 9 1.75% Vaasa 55 8 1.53% Kuopio 50 7 1.39% Tallinn 49 7 1.36% Jyvaskyla 49 7 1.36% Oslo 49 7 1.36% London Heathrow 48 7 1.34% Mariehamn 47 7 1.31% Frankfurt 46 7 1.28% München 45 6 1.25% Riga 41 6 1.14% Amsterdam 40 6 1.11% Tampere 881 126 24.5% Summe 3592 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances HEL 271

Appendix

Weekly flights Share Distance in kilometers

1340 37.31% 1-400

646 17.98% 401-800

488 13.59% 801-1200

438 12.19% 1201-1600

429 11.94% 1601-2000

52 1.45% 7601-8000

44 1.22% 2001-2400

22 0.61% 4401-4800

20 0.56% 3201-3600

19 0.53% 2401-2800

18 0.50% 2801-3200

14 0.39% 6001-6400

14 0.39% 7201-7600

10 0.28% 5201-5600

10 0.28% 5601-6000

3564 99.2% Summe

3592 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types HEL

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

458 12.75% AirbusIndustrieA319 133 6800 76 930 M

401 11.16% AirbusIndustrieA320 156 5500 77 930 M

399 11.11% Embraer170 73 3889 36 869 M

350 9.74% ATR72 68 2222 22 526 M

287 7.99% Embraer190 99 4260 50 869 M

228 6.35% ATR42-500 47 1630 19 563 M

220 6.12% AvroRJ85 83 2796 44 763 M

197 5.48% AirbusIndustrieA321 184 5500 93 930 M

166 4.62% Boeing(Douglas)MD-90 157 5003 76 810 M

164 4.57% Saab340 34 1667 13 523 M

86 2.39% EmbraerRJ145 49 2460 21 834 M

66 1.84% Fokker50 48 2842 22 531 M

64 1.78% AirbusIndustrieA340 268 14800 275 978 H

56 1.56% Boeing(Douglas)MD-82 155 4925 68 906 M

54 1.50% Boeing737-500Passenger 111 4400 52 888 M

3196 89.0% Summe

3592 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers HHN Weekly Flights Avg Day Share

272

Appendix

490 70 83.62% RYANAIR 50 7 8.53% AEROFLOT RUSSIAN AIRLINES 12 2 2.05% EMIRATES 8 1 1.37% WIZZ AIR 6 1 1.02% ETIHAD AIRWAYS 5 1 0.85% BRITISH AIRWAYS 4 1 0.68% ASIA WINGS 4 1 0.68% QANTAS AIRWAYS 4 1 0.68% AIR ARMENIA 3 0 0.51% VLADIVOSTOK AIR

586 84 100.0% Summe

586 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins HHN Weekly flights Avg Day Share 31 4 5.29% London Stansted 21 3 3.58% Berlin Schönefeld 15 2 2.56% Moskau SVO 14 2 2.39% Rom Ciampino 13 2 2.22% Nykoping 12 2 2.05% Bergamo 11 2 1.88% Dublin 10 1 1.71% Novosibirsk 10 1 1.71% Gerona 7 1 1.19% Lübeck 7 1 1.19% Goeteborg 7 1 1.19% Treviso 7 1 1.19% Pisa 7 1 1.19% Porto 7 1 1.19% Torp 179 26 30.5% Summe 586 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations HHN Weekly flights Avg Day Share 28 4 4.78% London Stansted 22 3 3.75% Moskau SVO 21 3 3.58% Berlin Schönefeld 14 2 2.39% Rom Ciampino 13 2 2.22% Nykoping 12 2 2.05% Bergamo 11 2 1.88% Dublin 10 1 1.71% Gerona 7 1 1.19% Lübeck 7 1 1.19% Goeteborg 7 1 1.19% Treviso 7 1 1.19% Pisa 7 1 1.19% Porto 273

Appendix

7 1 1.19% Torp

4 1 0.68% Tampere

177 25 30.2% Summe

586 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances HHN

Weekly flights Share Distance in kilometers

222 37.88% 401-800

188 32.08% 801-1200

58 9.90% 1201-1600

45 7.68% 2001-2400

31 5.29% 4801-5200

16 2.73% 1601-2000

12 2.05% 2801-3200

6 1.02% 2401-2800

4 0.68% 6401-6800

2 0.34% 9201-9600

2 0.34% 8801-9200

586 100.0% Summe

586 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types HHN

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

490 83.62% Boeing738-700Passenger 161 5420 71 937 M

56 9.56% Boeing(Douglas)MD-11(Freighter) 0 6770 286 946 H

16 2.73% Boeing747-400F(Freighter) 0 8245 397 938 H

12 2.05% AirbusIndustrieA320 156 5500 77 930 M

5 0.85% Boeing(Douglas)DC10(Freighter) 0 6115 264 908 H

4 0.68% AntonovAn-12 0 3600 61 669 M

3 0.51% Boeing747(Freighter) 0 8245 397 938 H

586 100.0% Summe

586 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers IST Weekly Flights Avg Day Share 3052 436 71.46% TURKISH AIRLINES 174 25 4.07% ATLASJET AIRLINES 96 14 2.25% LUFTHANSA GERMAN AIRLINES 57 8 1.33% BLUE WINGS 52 7 1.22% AIR FRANCE 42 6 0.98% ALITALIA

274

Appendix

42 6 0.98% AEROFLOT RUSSIAN AIRLINES 42 6 0.98% SWISS 36 5 0.84% KIBRIS TURKISH AIRLINES 33 5 0.77% OLYMPIC AIRLINES 32 5 0.75% KLM-ROYAL DUTCH AIRLINES 30 4 0.70% BRITISH AIRWAYS 26 4 0.61% TAROM 26 4 0.61% AIR ASTANA 22 3 0.52% EMIRATES 3762 537 88.1% Summe 4271 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins IST Weekly flights Avg Day Share 121 17 2.83% Ankara 121 17 2.83% Izmir 92 13 2.15% Antalya 57 8 1.33% Adana 55 8 1.29% Frankfurt 49 7 1.15% Ercan 47 7 1.10% Paris Charles de Gaulle 43 6 1.01% Moskau SVO 39 6 0.91% Amsterdam 37 5 0.87% München 35 5 0.82% London Heathrow 34 5 0.80% Dubai 30 4 0.70% Zürich 30 4 0.70% Mailand Malpensa 28 4 0.66% Gaziantep 818 117 19.2% Summe 4271 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations IST Weekly flights Avg Day Share 121 17 2.83% Ankara 121 17 2.83% Izmir 92 13 2.15% Antalya 57 8 1.33% Adana 56 8 1.31% Frankfurt 55 8 1.29% Paris Charles de Gaulle 49 7 1.15% Ercan 42 6 0.98% Moskau SVO 37 5 0.87% Amsterdam 37 5 0.87% München 35 5 0.82% London Heathrow 34 5 0.80% Dubai 29 4 0.68% Zürich 28 4 0.66% Gaziantep 275

Appendix

28 4 0.66% Kayseri 821 117 19.2% Summe 4271 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances IST

Weekly flights Share Distance in kilometers

900 21.07% 401-800

681 15.94% 1601-2000

557 13.04% 801-1200

550 12.88% 1-400

502 11.75% 2001-2400

446 10.44% 1201-1600

257 6.02% 2401-2800

108 2.53% 2801-3200

69 1.62% 3601-4000

66 1.55% 3201-3600

34 0.80% 8001-8400

26 0.61% 7201-7600

15 0.35% 6801-7200

14 0.33% 4401-4800

14 0.33% 7601-8000

4239 99.3% Summe

4271 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types IST

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1404 32.87% Boeing738-700Passenger 161 5420 71 937 M

1030 24.12% AirbusIndustrieA320 156 5500 77 930 M

650 15.22% AirbusIndustrieA321 184 5500 93 930 M

224 5.24% AirbusIndustrieA319 133 6800 76 930 M

170 3.98% Boeing737-400Passenger 148 3810 68 888 M

116 2.72% CanadairRegionalJet900 88 3207 37 829 M

86 2.01% Boeing737Passenger 143 4180 63 888 M

61 1.43% AirbusIndustrieA310Freighter 0 9600 164 954 H

60 1.40% AirbusIndustrieA310-300Passenger 217 9600 164 954 H

54 1.26% AirbusIndustrieA340-300 181 13500 260 978 H

50 1.17% AirbusIndustrieA330 356 12000 230 978 H

276

Appendix

38 0.89% Boeing737-300Passenger 133 4180 63 888 M

28 0.66% AirbusIndustrieA300(Freighter) 0 4850 171 941 H

27 0.63% Boeing757(Passenger) 159 7315 116 935 M

24 0.56% Boeing767Passenger 224 11390 187 953 H

4022 94.2% Summe

4271 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers LBA Weekly Flights Avg Day Share 178 25 28.48% JET2.COM 145 21 23.20% BMI BRITISH MIDLAND 84 12 13.44% FLYBE 72 10 11.52% EASTERN AIRWAYS 52 7 8.32% RYANAIR 42 6 6.72% KLM-ROYAL DUTCH AIRLINES 24 3 3.84% MANX2 24 3 3.84% AIR SOUTHWEST 4 1 0.64% PAKISTAN INTERNATIONAL AIRLINES

625 89 100.0% Summe

625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LBA Weekly flights Avg Day Share 33 5 5.28% Amsterdam 31 4 4.96% Southampton 26 4 4.16% London Heathrow 26 4 4.16% Aberdeen 19 3 3.04% Dublin 16 2 2.56% Belfast 16 2 2.56% Brüssel 14 2 2.24% Belfast 13 2 2.08% Edinburgh

13 2 2.08% Glasgow

12 2 1.92% Isle of Man

12 2 1.92% Bristol

10 1 1.60% Paris Charles de Gaulle

8 1 1.28% Alicante

8 1 1.28% Genf

257 37 41.1% Summe

625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LBA

Weekly flights Avg Day Share

33 5 5.28% Amsterdam

31 4 4.96% Southampton 277

Appendix

26 4 4.16% Aberdeen

25 4 4.00% London Heathrow

19 3 3.04% Dublin

16 2 2.56% Belfast

16 2 2.56% Brüssel

14 2 2.24% Belfast

13 2 2.08% Edinburgh

13 2 2.08% Glasgow

12 2 1.92% Isle of Man

12 2 1.92% Bristol

10 1 1.60% Paris Charles de Gaulle

8 1 1.28% Alicante

8 1 1.28% Genf

256 37 41.0% Summe

625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances LBA

Weekly flights Share Distance in kilometers

373 59.68% 1-400

130 20.80% 401-800

42 6.72% 1601-2000

42 6.72% 801-1200

24 3.84% 1201-1600

8 1.28% 2801-3200

4 0.64% 6001-6400

2 0.32% 4001-4400

0 0.00% 0

625 100.0% Summe

625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types LBA

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

162 25.92% Boeing737-300Passenger 133 4180 63 888 M

82 13.12% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

78 12.48% EmbraerRJ145 49 2460 21 834 M 278

Appendix

72 11.52% BritishAerospaceJetstream41 29 2759 11 546 M

66 10.56% EmbraerRJ135 37 2500 19 930 M

52 8.32% Boeing738-700Passenger 161 5420 71 937 M

28 4.48% Fokker100 105 3111 46 919 M

24 3.84% Let410 21 1040 6 365 L

24 3.84% DeHavillandDHC-8Dash8 37 2037 16 501 M

14 2.24% Fokker70 76 3732 42 856 M

10 1.60% Boeing757-200Passenger 235 7315 116 935 M

6 0.96% Boeing757-200(Winglets)Passenger 204 7686 122 935 M

4 0.64% AirbusIndustrieA310Passenger 222 9600 142 954 H

2 0.32% Embraer195 107 3334 48 869 M

1 0.16% AirbusIndustrieA321 184 5500 93 930 M

625 100.0% Summe

625 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers LCY Weekly Flights Avg Day Share 403 58 23.05% VLM AIRLINES 377 54 21.57% BRITISH AIRWAYS 360 51 20.59% AIR FRANCE 216 31 12.36% SWISS 174 25 9.95% LUFTHANSA GERMAN AIRLINES 80 11 4.58% KLM-ROYAL DUTCH AIRLINES 66 9 3.78% SAS SCANDINAVIAN AIRLINES 48 7 2.75% LUXAIR 24 3 1.37% AIR ONE

1748 250 100.0% Summe

1748 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LCY Weekly flights Avg Day Share 119 17 6.81% Amsterdam 80 11 4.58% Zürich 77 11 4.41% Edinburgh 70 10 4.00% Genf 45 6 2.57% Dublin 43 6 2.46% Rotterdam 42 6 2.40% Luxembourg 41 6 2.35% Frankfurt 31 4 1.77% Antwerpen 31 4 1.77% Manchester 30 4 1.72% Paris ORY

25 4 1.43% Glasgow

23 3 1.32% Dundee

22 3 1.26% München 279

Appendix

18 3 1.03% Berlin Tegel

697 100 39.9% Summe

1748 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LCY

Weekly flights Avg Day Share

120 17 6.86% Amsterdam

80 11 4.58% Edinburgh

80 11 4.58% Zürich

76 11 4.35% Genf

44 6 2.52% Dublin

43 6 2.46% Rotterdam

42 6 2.40% Luxembourg

41 6 2.35% Frankfurt

31 4 1.77% Paris ORY

31 4 1.77% Antwerpen

31 4 1.77% Manchester

25 4 1.43% Glasgow

23 3 1.32% Dundee

22 3 1.26% München

18 3 1.03% Berlin Tegel

707 101 40.4% Summe

1748 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances LCY

Weekly flights Share Distance in kilometers

930 53.20% 401-800

584 33.41% 1-400

178 10.18% 801-1200

56 3.20% 1201-1600

1748 100.0% Summe

1748 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types LCY

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

482 27.57% AvroRJ100 102 2554 46 763 M

471 26.95% Fokker50 48 2842 22 531 M

446 25.51% AvroRJ85 83 2796 44 763 M

102 5.84% FairchildDornier328-100 31 1852 14 624 M

54 3.09% ATR42-500 47 1630 19 563 M

46 2.63% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

280

Appendix

46 2.63% AvroRJ70 69 2998 43 763 M

34 1.95% BritishAerospace146Passenger 98 2909 42 776 M

26 1.49% EmbraerRJ135 37 2500 19 930 M

21 1.20% BritishAerospace146-200Passenger 90 2909 42 776 M

20 1.14% BritishAerospace146-300Passenger 100 2817 44 776 M

1748 100.0% Summe

1748 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers LEJ Weekly Flights Avg Day Share 216 31 54.55% LUFTHANSA GERMAN AIRLINES 48 7 12.12% GERMANWINGS 36 5 9.09% AUSTRIAN AIRLINES AG 34 5 8.59% AIR BERLIN 28 4 7.07% AIR FRANCE 10 1 2.53% EUROPEAN AIR TRANSPORT 8 1 2.02% CONDOR FLUGDIENST 4 1 1.01% AIR MALTA 4 1 1.01% TUIFLY 2 0 0.51% L.T.U. INTERNATIONAL AIRWAYS 2 0 0.51% BLUE WINGS 2 0 0.51% HAMBURG INTERNATIONAL 2 0 0.51% POLAR AIR CARGO

396 57 100.0% Summe

396 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LEJ Weekly flights Avg Day Share

34 5 8.59% Frankfurt

33 5 8.33% München

23 3 5.81% Düsseldorf

18 3 4.55% Dresden

14 2 3.54% Paris Charles de Gaulle

12 2 3.03% Stuttgart

12 2 3.03% Köln-Bonn

10 1 2.53% Palma Mallorca

5 1 1.26% New York JFK

5 1 1.26% Lyon

5 1 1.26% Sharjah

5 1 1.26% Delhi

4 1 1.01% Nürnberg

4 1 1.01% East Midlands

4 1 1.01% Las Palmas

188 27 47.5% Summe 281

Appendix

396 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LEJ

Weekly flights Avg Day Share

33 5 8.33% München

33 5 8.33% Frankfurt

23 3 5.81% Düsseldorf

14 2 3.54% Paris Charles de Gaulle

12 2 3.03% Köln-Bonn

12 2 3.03% Stuttgart

12 2 3.03% Wien

10 1 2.53% Palma Mallorca

6 1 1.52% Dresden

6 1 1.52% Bahrain

5 1 1.26% New York JFK

5 1 1.26% Lyon

4 1 1.01% Nürnberg

4 1 1.01% Las Palmas

3 0 0.76% Hong Kong

182 26 46.0% Summe

396 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances LEJ

Weekly flights Share Distance in kilometers

260 65.66% 1-400

40 10.10% 401-800

20 5.05% 1201-1600

17 4.29% 3201-3600

16 4.04% 801-1200

11 2.78% 4001-4400

10 2.53% 6001-6400

8 2.02% 8801-9200

6 1.52% 4401-4800

3 0.76% 1601-2000

2 0.51% 2001-2400

2 0.51% 7601-8000

1 0.25% 2401-2800

396 100.0% Summe

396 Total Flights Week (03/16/ - 03/22/2009

282

Appendix

Top 15 Aircraft Types LEJ

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

62 15.66% CanadairRegionalJet700 70 3674 34 859 M

50 12.63% AirbusIndustrieA319 133 6800 76 930 M

38 9.60% Boeing(Douglas)MD-11(Freighter) 0 6770 286 946 H

36 9.09% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

34 8.59% Boeing738-700Passenger 161 5420 71 937 M

34 8.59% Boeing737-300Passenger 133 4180 63 888 M

32 8.08% BritishAerospace146-300Passenger 100 2817 44 776 M

28 7.07% EmbraerRJ145 49 2460 21 834 M

24 6.06% CanadairRegionalJet900 88 3207 37 829 M

12 3.03% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

12 3.03% AirbusIndustrieA320 156 5500 77 930 M

10 2.53% AvroRJ85 83 2796 44 763 M

10 2.53% Boeing757-200PF(Freighter) 0 5675 116 935 M

4 1.01% CanadairRegionalJet200 50 3713 23 859 M

4 1.01% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

390 98.5% Summe

396 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers LGG Weekly Flights Avg Day Share 24 3 21.82% C.A.L. CARGO AIRLINES 22 3 20.00% KALITTA AIR 22 3 20.00% EL AL ISRAEL AIRLINES 19 3 17.27% JETAIRFLY 13 2 11.82% ICELANDAIR 6 1 5.45% TNT AIRWAYS S.A. 4 1 3.64% BELLE AIR

110 16 100.0% Summe

110 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LGG

Weekly flights Avg Day Share

18 3 16.36% Tel Aviv

10 1 9.09% Newark

7 1 6.36% New York JFK

4 1 3.64% Reykjavik

3 0 2.73% Shanghai Pu Dong

2 0 1.82% Pristina

2 0 1.82% Teneriffa

2 0 1.82% Hurghada

283

Appendix

1 0 0.91% Humberside

1 0 0.91% Alicante

1 0 0.91% Chicago ORD

1 0 0.91% East Midlands

1 0 0.91% Monastir

1 0 0.91% Ostend

54 8 49.1% Summe

110 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LGG

Weekly flights Avg Day Share

20 3 18.18% Tel Aviv

11 2 10.00% Bahrain

7 1 6.36% Reykjavik

5 1 4.55% New York JFK

3 0 2.73% Ostend

3 0 2.73% Shanghai Pu Dong

2 0 1.82% Pristina

2 0 1.82% Las Palmas

2 0 1.82% Hurghada

1 0 0.91% Djerba

56 8 50.9% Summe

110 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances LGG

Weekly flights Share Distance in kilometers

42 38.18% 2801-3200

22 20.00% 5601-6000

11 10.00% 4401-4800

11 10.00% 2001-2400

6 5.45% 8801-9200

5 4.55% 1201-1600

4 3.64% 1-400

4 3.64% 3201-3600

2 1.82% 1601-2000

2 1.82% 401-800

1 0.91% 6401-6800

110 100.0% Summe

110 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types LGG 284

Appendix

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

68 61.82% Boeing747(Freighter) 0 8245 397 938 H

19 17.27% Boeing737-700Passenger 127 6110 69 932 M

13 11.82% Boeing757-200PF(Freighter) 0 5675 116 935 M

6 5.45% Boeing747-400F(Freighter) 0 8245 397 938 H

4 3.64% BritishAerospace146-300Passenger 100 2817 44 776 M

110 100.0% Summe

110 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers LGW Weekly Flights Avg Day Share 1154 165 30.23% EASYJET 1124 161 29.44% BRITISH AIRWAYS 374 53 9.80% FLYBE 126 18 3.30% RYANAIR 84 12 2.20% MONARCH AIRLINES 82 12 2.15% VIRGIN ATLANTIC AIRWAYS 72 10 1.89% EASYJET SWITZERLAND SA 56 8 1.47% AIR SOUTHWEST 56 8 1.47% AURIGNY AIR SERVICES 54 8 1.41% AER LINGUS 48 7 1.26% TAP AIR PORTUGAL 42 6 1.10% EMIRATES 42 6 1.10% DELTA AIR LINES 32 5 0.84% THOMSONFLY 28 4 0.73% AIR COMET 3374 482 88.4% Summe 3818 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LGW Weekly flights Avg Day Share 96 14 2.51% Dublin 74 11 1.94% Jersey 72 10 1.89% Genf 63 9 1.65% Edinburgh 61 9 1.60% Guernsey 59 8 1.55% Malaga 51 7 1.34% Amsterdam 47 7 1.23% Madrid 46 7 1.20% Manchester 45 6 1.18% Glasgow 35 5 0.92% Barcelona 35 5 0.92% Venedig 33 5 0.86% Rom Fiumicino 30 4 0.79% Alicante

285

Appendix

28 4 0.73% Newquay Cornwall 775 111 20.3% Summe 3818 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LGW Weekly flights Avg Day Share 96 14 2.51% Dublin 74 11 1.94% Jersey 72 10 1.89% Genf 63 9 1.65% Edinburgh 61 9 1.60% Guernsey 59 8 1.55% Malaga 51 7 1.34% Amsterdam 47 7 1.23% Madrid 45 6 1.18% Glasgow 45 6 1.18% Manchester 35 5 0.92% Barcelona 35 5 0.92% Venedig 33 5 0.86% Rom Fiumicino 30 4 0.79% Alicante 27 4 0.71% Teneriffa 773 110 20.2% Summe 3818 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances LGW

Weekly flights Share Distance in kilometers

1039 27.21% 401-800

627 16.42% 801-1200

550 14.41% 1-400

472 12.36% 1201-1600

326 8.54% 1601-2000

210 5.50% 2001-2400

120 3.14% 6401-6800

110 2.88% 2801-3200

82 2.15% 5201-5600

67 1.75% 2401-2800

58 1.52% 6801-7200

34 0.89% 5601-6000

26 0.68% 6001-6400

20 0.52% 3601-4000

18 0.47% 4801-5200

3759 98.5% Summe

3818 Total Flights Week (03/16/ - 03/22/2009

286

Appendix

Top 15 Aircraft Types LGW

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1566 41.02% AirbusIndustrieA319 133 6800 76 930 M

570 14.93% Boeing737-400Passenger 148 3810 68 888 M

248 6.50% AirbusIndustrieA320 156 5500 77 930 M

202 5.29% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

172 4.50% Embraer195 107 3334 48 869 M

154 4.03% Boeing738-700Passenger 161 5420 71 937 M

106 2.78% Boeing777Passenger 291 14250 298 896 H

104 2.72% AirbusIndustrieA321 184 5500 93 930 M

104 2.72% Boeing737-500Passenger 111 4400 52 888 M

86 2.25% Boeing737-300Passenger 133 4180 63 888 M

82 2.15% Boeing747-400(Passenger) 435 13480 397 938 H

56 1.47% DeHavillandDHC-8Dash8 37 2037 16 501 M

56 1.47% ATR72 68 2222 22 526 M

40 1.05% Boeing757-200Passenger 235 7315 116 935 M

28 0.73% Boeing777-300ERPassenger 353 14594 352 896 H

3574 93.6% Summe

3818 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers LHR Weekly Flights Avg Day Share 3723 532 41.05% BRITISH AIRWAYS 1126 161 12.42% BMI BRITISH MIDLAND 430 61 4.74% LUFTHANSA GERMAN AIRLINES 316 45 3.48% VIRGIN ATLANTIC AIRWAYS 274 39 3.02% AER LINGUS 273 39 3.01% SAS SCANDINAVIAN AIRLINES 212 30 2.34% AMERICAN AIRLINES 168 24 1.85% IBERIA 144 21 1.59% KLM-ROYAL DUTCH AIRLINES 126 18 1.39% AIR CANADA 126 18 1.39% UNITED AIRLINES 112 16 1.23% AIR FRANCE 107 15 1.18% ALITALIA 90 13 0.99% TAP AIR PORTUGAL 88 13 0.97% SWISS 7315 1045 80.7% Summe 9069 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LHR Weekly flights Avg Day Share 287

Appendix

168 24 1.85% Amsterdam 135 19 1.49% Dublin 126 18 1.39% Edinburgh 124 18 1.37% Paris Charles de Gaulle 120 17 1.32% New York JFK 113 16 1.25% Frankfurt 108 15 1.19% Glasgow 104 15 1.15% Manchester 95 14 1.05% Brüssel 93 13 1.03% München 91 13 1.00% Madrid 83 12 0.92% Zürich 79 11 0.87% Dubai 79 11 0.87% Kopenhagen 77 11 0.85% Hong Kong 1595 228 17.6% Summe 9069 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LHR Weekly flights Avg Day Share 167 24 1.84% Amsterdam 135 19 1.49% Dublin 127 18 1.40% Edinburgh 125 18 1.38% Paris Charles de Gaulle 120 17 1.32% New York JFK 113 16 1.25% Frankfurt 104 15 1.15% Manchester 103 15 1.14% Glasgow 93 13 1.03% München 91 13 1.00% Madrid 90 13 0.99% Brüssel 83 12 0.92% Zürich 82 12 0.90% Kopenhagen 80 11 0.88% Aberdeen 78 11 0.86% Dubai 1591 227 17.5% Summe 9069 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances LHR

Weekly flights Share Distance in kilometers

2177 24.00% 401-800

1096 12.09% 1-400

1003 11.06% 801-1200

992 10.94% 1201-1600

764 8.42% 5201-5600

282 3.11% 2401-2800 288

Appendix

254 2.80% 5601-6000

223 2.46% 9601-10000

205 2.26% 9201-9600

204 2.25% 6001-6400

203 2.24% 8401-8800

202 2.23% 3201-3600

192 2.12% 1601-2000

156 1.72% 2001-2400

153 1.69% 6801-7200

8106 89.4% Summe

9069 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types LHR

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1884 20.77% AirbusIndustrieA320 156 5500 77 930 M

1407 15.51% AirbusIndustrieA319 133 6800 76 930 M

1182 13.03% AirbusIndustrieA321 184 5500 93 930 M

802 8.84% Boeing747-400(Passenger) 435 13480 397 938 H

737 8.13% Boeing777Passenger 291 14250 298 896 H

344 3.79% Boeing767Passenger 224 11390 187 953 H

330 3.64% Boeing757(Passenger) 159 7315 116 935 M

282 3.11% AirbusIndustrieA340-600 323 13900 365 990 H

203 2.24% EmbraerRJ145 49 2460 21 834 M

186 2.05% Boeing767-300Passenger 238 11390 187 953 H

145 1.60% Boeing777-300ERPassenger 353 14594 352 896 H

134 1.48% Boeing737Passenger 143 4180 63 888 M

129 1.42% Boeing(Douglas)MD-82 155 4925 68 906 M

121 1.33% Boeing737-300Passenger 133 4180 63 888 M

114 1.26% AirbusIndustrieA340-300 181 13500 260 978 H

8000 88.2% Summe

9069 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers LIS Weekly Flights Avg Day Share 1546 221 66.44% TAP AIR PORTUGAL 124 18 5.33% EASYJET 98 14 4.21% IBERIA 70 10 3.01% LUFTHANSA GERMAN AIRLINES 64 9 2.75% AIR FRANCE 63 9 2.71% SATA INTERNATIONAL 54 8 2.32% VUELING AIRLINES 47 7 2.02% BRITISH AIRWAYS 36 5 1.55% EASYJET SWITZERLAND SA 289

Appendix

28 4 1.20% KLM-ROYAL DUTCH AIRLINES 26 4 1.12% BRUSSELS AIRLINES 24 3 1.03% AIGLE AZUR 16 2 0.69% AER LINGUS 14 2 0.60% SWISS 14 2 0.60% CONTINENTAL AIRLINES 2224 318 95.6% Summe 2327 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LIS Weekly flights Avg Day Share 109 16 4.68% Madrid 84 12 3.61% Funchal 77 11 3.31% Porto 57 8 2.45% London Heathrow 55 8 2.36% Barcelona 46 7 1.98% Paris Charles de Gaulle 41 6 1.76% Frankfurt 40 6 1.72% Paris ORY 37 5 1.59% Brüssel 34 5 1.46% Amsterdam 32 5 1.38% Genf 27 4 1.16% München 26 4 1.12% Zürich 25 4 1.07% Rom Fiumicino 23 3 0.99% Lyon 713 102 30.6% Summe 2327 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LIS Weekly flights Avg Day Share 108 15 4.64% Madrid 86 12 3.70% Funchal 76 11 3.27% Porto 57 8 2.45% London Heathrow 55 8 2.36% Barcelona 46 7 1.98% Paris Charles de Gaulle 42 6 1.80% Brüssel 41 6 1.76% Frankfurt 40 6 1.72% Paris ORY 34 5 1.46% Amsterdam 32 5 1.38% Genf 27 4 1.16% München 26 4 1.12% Zürich 25 4 1.07% Rom Fiumicino 23 3 0.99% Lyon 718 103 30.9% Summe 2327 Total Flights Week (03/16/ - 03/22/2009 290

Appendix

Top 15 Distances LIS

Weekly flights Share Distance in kilometers

535 22.99% 1201-1600

526 22.60% 1601-2000

336 14.44% 801-1200

331 14.22% 401-800

217 9.33% 1-400

87 3.74% 2401-2800

61 2.62% 5601-6000

45 1.93% 2001-2400

42 1.80% 7601-8000

39 1.68% 2801-3200

28 1.20% 5201-5600

24 1.03% 7201-7600

20 0.86% 6401-6800

14 0.60% 3201-3600

14 0.60% 8001-8400

2319 99.7% Summe

2327 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types LIS

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

730 31.37% AirbusIndustrieA319 133 6800 76 930 M

705 30.30% AirbusIndustrieA320 156 5500 77 930 M

178 7.65% AirbusIndustrieA321 184 5500 93 930 M

150 6.45% Fokker100 105 3111 46 919 M

136 5.84% AirbusIndustrieA330-200 260 11866 230 978 H

106 4.56% EmbraerRJ145 49 2460 21 834 M

70 3.01% Beechcraft1900Dairliner 19 1384 8 512 M

56 2.41% Boeing757(Passenger) 159 7315 116 935 M

34 1.46% AirbusIndustrieA340-300 181 13500 260 978 H

32 1.38% Boeing737Passenger 143 4180 63 888 M

20 0.86% AirbusIndustrieA310Passenger 222 9600 142 954 H

20 0.86% CanadairRegionalJet200 50 3713 23 859 M

13 0.56% Boeing747(Passenger) 395 13480 397 938 H

11 0.47% ATR42-300/320 46 1944 17 491 M

10 0.43% Boeing737-300Passenger 133 4180 63 888 M

2271 97.6% Summe

291

Appendix

2327 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers LTN Weekly Flights Avg Day Share 628 90 49.22% EASYJET 206 29 16.14% RYANAIR 176 25 13.79% WIZZ AIR 70 10 5.49% MONARCH AIRLINES 70 10 5.49% SKYEUROPE 64 9 5.02% AER ARANN 24 3 1.88% TRANSAVIA.COM 12 2 0.94% FLYBE 8 1 0.63% WIZZ AIR UKRAINE 8 1 0.63% MNG AIRLINES CARGO 6 1 0.47% THOMSONFLY 4 1 0.31% AIR FRANCE

1276 182 100.0% Summe

1276 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LTN Weekly flights Avg Day Share 36 5 2.82% Genf 29 4 2.27% Glasgow 28 4 2.19% Paris Charles de Gaulle 26 4 2.04% Dublin 25 4 1.96% Belfast 25 4 1.96% Warschau 25 4 1.96% Edinburgh 18 3 1.41% Galway 18 3 1.41% Budapest 15 2 1.18% Bratislava 15 2 1.18% Malaga 14 2 1.10% Waterford 13 2 1.02% Katowice 13 2 1.02% Madrid 13 2 1.02% Dortmund 313 45 24.5% Summe 1276 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LTN Weekly flights Avg Day Share 36 5 2.82% Genf 29 4 2.27% Glasgow 26 4 2.04% Dublin 26 4 2.04% Paris Charles de Gaulle 25 4 1.96% Belfast 25 4 1.96% Warschau 25 4 1.96% Edinburgh 292

Appendix

18 3 1.41% Galway 18 3 1.41% Budapest 15 2 1.18% Bratislava 15 2 1.18% Malaga 14 2 1.10% Waterford 13 2 1.02% Katowice 13 2 1.02% Dortmund 13 2 1.02% Amsterdam 311 44 24.4% Summe 1276 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances LTN

Weekly flights Share Distance in kilometers

465 36.44% 401-800

304 23.82% 1201-1600

162 12.70% 801-1200

116 9.09% 1-400

104 8.15% 1601-2000

60 4.70% 2001-2400

39 3.06% 2401-2800

14 1.10% 2801-3200

12 0.94% 3201-3600

1276 100.0% Summe

1276 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types LTN

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

428 33.54% Boeing737-700Passenger 127 6110 69 932 M

212 16.61% Boeing738-700Passenger 161 5420 71 937 M

208 16.30% AirbusIndustrieA320 156 5500 77 930 M

200 15.67% AirbusIndustrieA319 133 6800 76 930 M

70 5.49% Boeing737-700(Winglets)Passenger 99 6352 69 932 M

46 3.61% AirbusIndustrieA321 184 5500 93 930 M

36 2.82% ATR72 68 2222 22 526 M

28 2.19% ATR42/ATR72 46 1944 17 491 M

24 1.88% Boeing737Passenger 143 4180 63 888 M

12 0.94% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

8 0.63% AirbusIndustrieA300(Freighter) 0 4850 171 941 H

4 0.31% AirbusIndustrieA300B4/A300C4/A300F4 0 4850 165 941 H

1276 100.0% Summe

1276 Total Flights Week (03/16/ - 03/22/2009

293

Appendix

Top 15 Carriers LYS Weekly Flights Avg Day Share 1488 213 64.19% AIR FRANCE 182 26 7.85% EASYJET 152 22 6.56% LUFTHANSA GERMAN AIRLINES 46 7 1.98% BRUSSELS AIRLINES 46 7 1.98% BRITISH AIRWAYS 42 6 1.81% IBERIA 38 5 1.64% AUSTRIAN AIRLINES AG 38 5 1.64% TAP AIR PORTUGAL 32 5 1.38% AIR ALGERIE 28 4 1.21% AIGLE AZUR 28 4 1.21% AIRLINAIR 26 4 1.12% CITY AIRLINE 24 3 1.04% TUNIS AIR 20 3 0.86% HEX'AIR 14 2 0.60% TURKISH AIRLINES 2204 315 95.1% Summe 2318 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins LYS Weekly flights Avg Day Share 50 7 2.16% Bordeaux 50 7 2.16% Toulouse 48 7 2.07% Paris Charles de Gaulle 46 7 1.98% Madrid 45 6 1.94% Frankfurt 44 6 1.90% München 40 6 1.73% Düsseldorf 40 6 1.73% Brüssel 30 4 1.29% Nantes 29 4 1.25% Strasbourg 26 4 1.12% Amsterdam 25 4 1.08% Nizza 25 4 1.08% Barcelona 24 3 1.04% Pau 23 3 0.99% Venedig 545 78 23.5% Summe 2318 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations LYS Weekly flights Avg Day Share 51 7 2.20% Toulouse 50 7 2.16% Bordeaux 48 7 2.07% Paris Charles de Gaulle 46 7 1.98% Madrid 45 6 1.94% Frankfurt

294

Appendix

44 6 1.90% München 40 6 1.73% Düsseldorf 40 6 1.73% Brüssel 30 4 1.29% Nantes 29 4 1.25% Strasbourg 26 4 1.12% Amsterdam 25 4 1.08% Nizza 25 4 1.08% Barcelona 24 3 1.04% Pau 23 3 0.99% Venedig 546 78 23.6% Summe 2318 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances LYS

Weekly flights Share Distance in kilometers

1328 57.29% 401-800

492 21.23% 1-400

308 13.29% 801-1200

100 4.31% 1201-1600

70 3.02% 1601-2000

8 0.35% 6001-6400

4 0.17% 8801-9200

2 0.09% 4001-4400

2 0.09% 7201-7600

2 0.09% 6801-7200

2 0.09% 2401-2800

2318 100.0% Summe

2318 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types LYS

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

410 17.69% AirbusIndustrieA319 133 6800 76 930 M

280 12.08% Fokker100 105 3111 46 919 M

256 11.04% EmbraerRJ145 49 2460 21 834 M

228 9.84% CanadairRegionalJet 50 3713 23 859 M

136 5.87% CanadairRegionalJet700 70 3674 34 859 M

118 5.09% ATR72 68 2222 22 526 M

114 4.92% AirbusIndustrieA320 156 5500 77 930 M

88 3.80% ATR42-500 47 1630 19 563 M

82 3.54% CanadairRegionalJet200 50 3713 23 859 M

72 3.11% AirbusIndustrieA318 115 3705 62 930 M

68 2.93% Embraer170 73 3889 36 869 M 295

Appendix

66 2.85% ATR42/ATR72 46 1944 17 491 M

64 2.76% AvroRJ85 83 2796 44 763 M

50 2.16% EmbraerRJ135 37 2500 19 930 M

42 1.81% Boeing737-500Passenger 111 4400 52 888 M

2074 89.5% Summe

2318 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers MAD Weekly Flights Avg Day Share 4454 636 53.80% IBERIA 801 114 9.68% SPANAIR 537 77 6.49% AIR EUROPA 362 52 4.37% EASYJET 266 38 3.21% VUELING AIRLINES 238 34 2.87% RYANAIR 166 24 2.01% LUFTHANSA GERMAN AIRLINES 158 23 1.91% AIR FRANCE 134 19 1.62% TAP AIR PORTUGAL 114 16 1.38% AIR COMET 110 16 1.33% BRITISH AIRWAYS 70 10 0.85% KLM-ROYAL DUTCH AIRLINES 56 8 0.68% ALITALIA 48 7 0.58% LAN AIRLINES 48 7 0.58% BRUSSELS AIRLINES 7562 1080 91.3% Summe 8279 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins MAD Weekly flights Avg Day Share 381 54 4.60% Barcelona 137 20 1.65% Valencia 123 18 1.49% Palma Mallorca 109 16 1.32% Paris Charles de Gaulle 108 15 1.30% Lissabon 107 15 1.29% Las Palmas 101 14 1.22% Bilbao 95 14 1.15% Paris ORY 91 13 1.10% London Heathrow 89 13 1.08% Teneriffa 83 12 1.00% Malaga 80 11 0.97% Frankfurt 79 11 0.95% Rom Fiumicino 67 10 0.81% Amsterdam 66 9 0.80% Vigo 1716 245 20.7% Summe 8279 Total Flights Week (03/16/ - 03/22/2009

296

Appendix

Top 15 Destinations MAD Weekly flights Avg Day Share 379 54 4.58% Barcelona 137 20 1.65% Valencia 124 18 1.50% Palma Mallorca 109 16 1.32% Paris Charles de Gaulle 109 16 1.32% Lissabon 106 15 1.28% Las Palmas 101 14 1.22% Bilbao 95 14 1.15% Paris ORY 91 13 1.10% London Heathrow 88 13 1.06% Teneriffa 83 12 1.00% Malaga 80 11 0.97% Frankfurt 79 11 0.95% Rom Fiumicino 67 10 0.81% Amsterdam 66 9 0.80% Vigo 1714 245 20.7% Summe 8279 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances MAD

Weekly flights Share Distance in kilometers

2490 30.08% 401-800

1628 19.66% 1201-1600

1342 16.21% 1-400

969 11.70% 801-1200

702 8.48% 1601-2000

145 1.75% 2401-2800

139 1.68% 2001-2400

127 1.53% 8001-8400

111 1.34% 3201-3600

88 1.06% 6801-7200

82 0.99% 5601-6000

77 0.93% 10001-10400

71 0.86% 8801-9200

49 0.59% 8401-8800

43 0.52% 6401-6800

8063 97.4% Summe

8279 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types MAD

Weekly Range in MTOW in Cruise Speed in flights Share Aircraft Type Avg Seats km t km/h WTC

2483 29.99% AirbusIndustrieA320 156 5500 77 930 M 297

Appendix

1383 16.70% AirbusIndustrieA319 133 6800 76 930 M

768 9.28% Boeing738-700Passenger 161 5420 71 937 M

761 9.19% AirbusIndustrieA321 184 5500 93 930 M

508 6.14% CanadairRegionalJet200 50 3713 23 859 M

384 4.64% CanadairRegionalJet900 88 3207 37 829 M

260 3.14% AirbusIndustrieA340 268 14800 275 978 H

236 2.85% Boeing(Douglas)MD-87 123 5248 64 906 M

190 2.29% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

124 1.50% AirbusIndustrieA340-600 323 13900 365 990 H

111 1.34% ATR72 68 2222 22 526 M

99 1.20% Boeing757(Passenger) 159 7315 116 935 M

96 1.16% Boeing737Passenger 143 4180 63 888 M

85 1.03% AirbusIndustrieA330 356 12000 230 978 H

76 0.92% Boeing(Douglas)MD-82 155 4925 68 906 M

7564 91.4% Summe

8279 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers MAN Weekly Flights Avg Day Share 548 78 19.64% FLYBE 341 49 12.22% BMI BRITISH MIDLAND 204 29 7.31% BRITISH AIRWAYS 168 24 6.02% LUFTHANSA GERMAN AIRLINES 120 17 4.30% RYANAIR 116 17 4.16% MONARCH AIRLINES 108 15 3.87% BMIBABY 100 14 3.58% VLM AIRLINES 82 12 2.94% KLM-ROYAL DUTCH AIRLINES 70 10 2.51% AIR FRANCE 68 10 2.44% EASYJET 64 9 2.29% AER LINGUS 56 8 2.01% SAS SCANDINAVIAN AIRLINES 52 7 1.86% SWISS 48 7 1.72% THOMSONFLY 2145 306 76.9% Summe 2790 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins MAN Weekly flights Avg Day Share 104 15 3.73% London Heathrow 58 8 2.08% Dublin 58 8 2.08% Paris Charles de Gaulle 57 8 2.04% Edinburgh 45 6 1.61% Frankfurt 45 6 1.61% London Gatwick 41 6 1.47% Amsterdam 298

Appendix

41 6 1.47% Glasgow 41 6 1.47% Düsseldorf 40 6 1.43% Aberdeen 40 6 1.43% Brüssel 38 5 1.36% Belfast 31 4 1.11% Southampton 31 4 1.11% London City 29 4 1.04% Isle of Man 699 100 25.1% Summe 2790 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations MAN Weekly flights Avg Day Share 104 15 3.73% London Heathrow 59 8 2.11% Paris Charles de Gaulle 58 8 2.08% Dublin 56 8 2.01% Edinburgh 49 7 1.76% Amsterdam 46 7 1.65% Frankfurt 46 7 1.65% London Gatwick 42 6 1.51% Brüssel 41 6 1.47% Glasgow 41 6 1.47% Düsseldorf 40 6 1.43% Aberdeen 38 5 1.36% Belfast 31 4 1.11% Southampton 31 4 1.11% London City 29 4 1.04% Isle of Man 711 102 25.5% Summe 2790 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances MAN

Weekly flights Share Distance in kilometers

1016 36.42% 1-400

715 25.63% 401-800

415 14.87% 801-1200

174 6.24% 1601-2000

81 2.90% 1201-1600

77 2.76% 5201-5600

64 2.29% 2801-3200

48 1.72% 5601-6000

40 1.43% 6001-6400

37 1.33% 2401-2800

29 1.04% 6401-6800

26 0.93% 2001-2400 299

Appendix

24 0.86% 3201-3600

19 0.68% 6801-7200

10 0.36% 10801-11200

2775 99.5% Summe

2790 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types MAN

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

436 15.63% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

310 11.11% AirbusIndustrieA319 133 6800 76 930 M

295 10.57% EmbraerRJ145 49 2460 21 834 M

264 9.46% AirbusIndustrieA320 156 5500 77 930 M

160 5.73% Boeing738-700Passenger 161 5420 71 937 M

142 5.09% Boeing737-300Passenger 133 4180 63 888 M

132 4.73% AirbusIndustrieA321 184 5500 93 930 M

100 3.58% Fokker50 48 2842 22 531 M

84 3.01% Boeing737Passenger 143 4180 63 888 M

73 2.62% Boeing737-400Passenger 148 3810 68 888 M

56 2.01% Embraer195 107 3334 48 869 M

55 1.97% ATR72 68 2222 22 526 M

48 1.72% AvroRJ85 83 2796 44 763 M

44 1.58% AirbusIndustrieA330-200 260 11866 230 978 H

38 1.36% BritishAerospaceJetstream41 29 2759 11 546 M

2237 80.2% Summe

2790 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers MUC Weekly Flights Avg Day Share 4941 706 64.87% LUFTHANSA GERMAN AIRLINES 592 85 7.77% AIR BERLIN 146 21 1.92% GERMANWINGS 140 20 1.84% AIR FRANCE 128 18 1.68% LOT - POLISH AIRLINES 88 13 1.16% TUIFLY 88 13 1.16% BRITISH AIRWAYS 74 11 0.97% SAS SCANDINAVIAN AIRLINES 70 10 0.92% CONDOR FLUGDIENST 66 9 0.87% KLM-ROYAL DUTCH AIRLINES 64 9 0.84% SPANAIR 60 9 0.79% CIRRUS AIRLINES 60 9 0.79% CIMBER AIR 56 8 0.74% AEGEAN AIRLINES 300

Appendix

56 8 0.74% SWISS 6629 947 87.0% Summe 7617 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins MUC Weekly flights Avg Day Share 149 21 1.96% Düsseldorf 146 21 1.92% Hamburg 142 20 1.86% Berlin Tegel 132 19 1.73% Köln-Bonn 104 15 1.37% Paris Charles de Gaulle 93 13 1.22% London Heathrow 84 12 1.10% Frankfurt 82 12 1.08% Hannover 78 11 1.02% Amsterdam 78 11 1.02% Wien 69 10 0.91% Zürich 68 10 0.89% Brüssel 60 9 0.79% Münster 52 7 0.68% Mailand Malpensa 49 7 0.64% Madrid 1386 198 18.2% Summe 7617 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations MUC Weekly flights Avg Day Share 149 21 1.96% Düsseldorf 146 21 1.92% Hamburg 142 20 1.86% Berlin Tegel 132 19 1.73% Köln-Bonn 104 15 1.37% Paris Charles de Gaulle 93 13 1.22% London Heathrow 86 12 1.13% Frankfurt 82 12 1.08% Hannover 78 11 1.02% Amsterdam 78 11 1.02% Wien 70 10 0.92% Brüssel 69 10 0.91% Zürich 60 9 0.79% Münster 51 7 0.67% Mailand Malpensa 49 7 0.64% Madrid 1389 198 18.2% Summe 7617 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances MUC

Weekly flights Share Distance in kilometers

301

Appendix

3478 45.66% 401-800

1517 19.92% 1-400

1022 13.42% 801-1200

756 9.93% 1201-1600

291 3.82% 1601-2000

66 0.87% 8401-8800

58 0.76% 4401-4800

50 0.66% 6401-6800

50 0.66% 2401-2800

48 0.63% 2801-3200

42 0.55% 3201-3600

42 0.55% 7201-7600

40 0.53% 7601-8000

29 0.38% 8801-9200

28 0.37% 9201-9600

7517 98.7% Summe

7617 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types MUC

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1203 15.79% AirbusIndustrieA320 156 5500 77 930 M

889 11.67% AirbusIndustrieA319 133 6800 76 930 M

574 7.54% CanadairRegionalJet900 88 3207 37 829 M

558 7.33% AvroRJ85 83 2796 44 763 M

472 6.20% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

466 6.12% AirbusIndustrieA321 184 5500 93 930 M

368 4.83% ATR72 68 2222 22 526 M

338 4.44% Boeing737-500Passenger 111 4400 52 888 M

288 3.78% ATR42-500 47 1630 19 563 M

274 3.60% CanadairRegionalJet200 50 3713 23 859 M

250 3.28% CanadairRegionalJet700 70 3674 34 859 M

212 2.78% Boeing738-700Passenger 161 5420 71 937 M

158 2.07% Boeing737-700Passenger 127 6110 69 932 M

110 1.44% CanadairRegionalJet 50 3713 23 859 M

108 1.42% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

6268 82.3% Summe

7617 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers MXP Weekly Flights Avg Day Share 676 97 19.28% EASYJET 470 67 13.41% LUFTHANSA GERMAN AIRLINES 302

Appendix

312 45 8.90% ALITALIA 200 29 5.70% AIR ONE 144 21 4.11% AIR FRANCE 125 18 3.57% VOLARE S.P.A. 84 12 2.40% SWISS 70 10 2.00% AIR BERLIN 70 10 2.00% KLM-ROYAL DUTCH AIRLINES 58 8 1.65% BRUSSELS AIRLINES 58 8 1.65% IBERIA 56 8 1.60% BRITISH AIRWAYS 54 8 1.54% AUSTRIAN AIRLINES AG 54 8 1.54% TAP AIR PORTUGAL 48 7 1.37% FLYBE 2479 354 70.7% Summe 3506 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins MXP Weekly flights Avg Day Share 122 17 3.48% Paris Charles de Gaulle 107 15 3.05% Rom Fiumicino 69 10 1.97% Brüssel 68 10 1.94% Barcelona 63 9 1.80% Madrid 61 9 1.74% Düsseldorf 55 8 1.57% Amsterdam 51 7 1.45% München 42 6 1.20% Neapel 42 6 1.20% Zürich 42 6 1.20% Athen 38 5 1.08% Wien 35 5 1.00% Palermo 35 5 1.00% Frankfurt 31 4 0.88% Bari 861 123 24.6% Summe 3506 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations MXP Weekly flights Avg Day Share 120 17 3.42% Paris Charles de Gaulle 89 13 2.54% Rom Fiumicino 69 10 1.97% Brüssel 69 10 1.97% Barcelona 63 9 1.80% Madrid 60 9 1.71% Düsseldorf 52 7 1.48% München 49 7 1.40% Amsterdam 42 6 1.20% Neapel 42 6 1.20% Zürich 303

Appendix

42 6 1.20% Athen 38 5 1.08% Wien 35 5 1.00% Palermo 35 5 1.00% Frankfurt 31 4 0.88% Bari 836 119 23.8% Summe 3506 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances MXP

Weekly flights Share Distance in kilometers

1405 40.07% 401-800

880 25.10% 801-1200

268 7.64% 1-400

247 7.05% 1201-1600

226 6.45% 1601-2000

80 2.28% 2001-2400

65 1.85% 6401-6800

63 1.80% 4401-4800

59 1.68% 2401-2800

37 1.06% 8801-9200

34 0.97% 7601-8000

29 0.83% 9201-9600

25 0.71% 9601-10000

21 0.60% 7201-7600

14 0.40% 10001-10400

3453 98.5% Summe

3506 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types MXP

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1071 30.55% AirbusIndustrieA319 133 6800 76 930 M

612 17.46% AirbusIndustrieA320 156 5500 77 930 M

132 3.76% AirbusIndustrieA321 184 5500 93 930 M

122 3.48% Embraer170 73 3889 36 869 M

118 3.37% Boeing737Passenger 143 4180 63 888 M

112 3.19% CanadairRegionalJet900 88 3207 37 829 M

98 2.80% AvroRJ100 102 2554 46 763 M

96 2.74% EmbraerRJ145 49 2460 21 834 M

78 2.22% Boeing738-700Passenger 161 5420 71 937 M

78 2.22% ATR72 68 2222 22 526 M

68 1.94% AirbusIndustrieA330-200 260 11866 230 978 H 304

Appendix

66 1.88% Boeing(Douglas)MD-80 148 4925 64 906 M

66 1.88% CanadairRegionalJet200 50 3713 23 859 M

58 1.65% Boeing737-700Passenger 127 6110 69 932 M

56 1.60% BritishAerospace146-300Passenger 100 2817 44 776 M

2831 80.7% Summe

3506 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers NCE Weekly Flights Avg Day Share 1297 185 40.49% HELI AIR MONACO 574 82 17.92% AIR FRANCE 174 25 5.43% EASYJET 160 23 5.00% HELI SECURITE 120 17 3.75% LUFTHANSA GERMAN AIRLINES 112 16 3.50% CCM AIRLINES 100 14 3.12% BRITISH AIRWAYS 82 12 2.56% IBERIA 62 9 1.94% EASYJET SWITZERLAND SA 56 8 1.75% SWISS 40 6 1.25% KLM-ROYAL DUTCH AIRLINES 40 6 1.25% BRUSSELS AIRLINES 38 5 1.19% FLYBABOO 32 5 1.00% TAP AIR PORTUGAL 28 4 0.87% AUSTRIAN AIRLINES AG 2915 416 91.0% Summe 3203 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins NCE Weekly flights Avg Day Share 761 109 23.76% Monaco Heliport 158 23 4.93% Paris ORY 57 8 1.78% Paris Charles de Gaulle 42 6 1.31% London Heathrow 29 4 0.91% Genf 28 4 0.87% Frankfurt 28 4 0.87% Zürich 27 4 0.84% Amsterdam 27 4 0.84% Brüssel 27 4 0.84% München 25 4 0.78% Lyon 23 3 0.72% Barcelona 22 3 0.69% Madrid 21 3 0.66% Bastia 21 3 0.66% Ajaccio 1296 185 40.5% Summe 3203 Total Flights Week (03/16/ - 03/22/2009

305

Appendix

Top 15 Destinations NCE Weekly flights Avg Day Share 696 99 21.73% Monaco Heliport 158 23 4.93% Paris ORY 57 8 1.78% Paris Charles de Gaulle 42 6 1.31% London Heathrow 29 4 0.91% Genf 28 4 0.87% Frankfurt 28 4 0.87% Zürich 27 4 0.84% Amsterdam 27 4 0.84% Brüssel 27 4 0.84% München 25 4 0.78% Lyon 23 3 0.72% Barcelona 22 3 0.69% Madrid 21 3 0.66% Bastia 21 3 0.66% Ajaccio 1231 176 38.4% Summe 3203 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances NCE

Weekly flights Share Distance in kilometers

1689 52.73% 1-400

872 27.22% 401-800

454 14.17% 801-1200

108 3.37% 1201-1600

44 1.37% 1601-2000

14 0.44% 2401-2800

10 0.31% 4401-4800

8 0.25% 6401-6800

4 0.12% 2001-2400

3203 100.0% Summe

3203 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types NCE

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1457 45.49% EurocopterEcureuilAS350/AS3552 5 730 2 246 L

398 12.43% AirbusIndustrieA319 133 6800 76 930 M

212 6.62% AirbusIndustrieA320 156 5500 77 930 M

148 4.62% ATR72 68 2222 22 526 M

98 3.06% AirbusIndustrieA321 184 5500 93 930 M

82 2.56% Boeing757(Passenger) 159 7315 116 935 M

76 2.37% Fokker70 76 3732 42 856 M 306

Appendix

76 2.37% Fokker100 105 3111 46 919 M

68 2.12% CanadairRegionalJet200 50 3713 23 859 M

64 2.00% CanadairRegionalJet700 70 3674 34 859 M

64 2.00% Boeing737-500Passenger 111 4400 52 888 M

54 1.69% EmbraerRJ145 49 2460 21 834 M

44 1.37% Boeing737-300Passenger 133 4180 63 888 M

38 1.19% AvroRJ100 102 2554 46 763 M

36 1.12% CanadairRegionalJet900 88 3207 37 829 M

2915 91.0% Summe

3203 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers NUE Weekly Flights Avg Day Share 354 51 36.49% LUFTHANSA GERMAN AIRLINES 350 50 36.08% AIR BERLIN 64 9 6.60% AIR FRANCE 54 8 5.57% SWISS 50 7 5.15% KLM-ROYAL DUTCH AIRLINES 32 5 3.30% AUSTRIAN AIRLINES AG 20 3 2.06% OLT OSTFRIESISCHE LUFTTRANSPORT GMBH 14 2 1.44% TURKISH AIRLINES 12 2 1.24% NIKI 10 1 1.03% SAS SCANDINAVIAN AIRLINES 6 1 0.62% TUIFLY 2 0 0.21% BLUE WINGS 2 0 0.21% SUNEXPRESS

970 139 100.0% Summe

970 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins NUE Weekly flights Avg Day Share 50 7 5.15% Düsseldorf 48 7 4.95% Paris Charles de Gaulle 46 7 4.74% Berlin Tegel 44 6 4.54% Frankfurt 39 6 4.02% Hamburg 34 5 3.51% München 28 4 2.89% Zürich 27 4 2.78% Wien 25 4 2.58% Amsterdam 12 2 1.24% Bremen 11 2 1.13% Palma Mallorca 11 2 1.13% Brüssel 307

Appendix

11 2 1.13% London Stansted 8 1 0.82% Istanbul 8 1 0.82% Teneriffa 402 57 41.4% Summe 970 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations NUE

Weekly flights Avg Day Share

50 7 5.15% Düsseldorf

48 7 4.95% Paris Charles de Gaulle

46 7 4.74% Berlin Tegel

44 6 4.54% Frankfurt

39 6 4.02% Hamburg

34 5 3.51% München

28 4 2.89% Zürich

27 4 2.78% Wien

25 4 2.58% Amsterdam

12 2 1.24% Bremen

11 2 1.13% Palma Mallorca

11 2 1.13% Brüssel

11 2 1.13% London Stansted

8 1 0.82% Istanbul

8 1 0.82% Teneriffa

402 57 41.4% Summe

970 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances NUE

Weekly flights Share Distance in kilometers

472 48.66% 1-400

358 36.91% 401-800

36 3.71% 3201-3600

30 3.09% 1201-1600

28 2.89% 2801-3200

22 2.27% 801-1200

18 1.86% 1601-2000

4 0.41% 2401-2800

2 0.21% 2001-2400

970 100.0% Summe

970 Total Flights Week (03/16/ - 03/22/2009

308

Appendix

Top 15 Aircraft Types NUE

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

122 12.58% Boeing738-700Passenger 161 5420 71 937 M

114 11.75% AirbusIndustrieA319 133 6800 76 930 M

100 10.31% CanadairRegionalJet700 70 3674 34 859 M

64 6.60% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

64 6.60% EmbraerRJ145 49 2460 21 834 M

58 5.98% AirbusIndustrieA320 156 5500 77 930 M

58 5.98% CanadairRegionalJet200 50 3713 23 859 M

56 5.77% BritishAerospace146-300Passenger 100 2817 44 776 M

54 5.57% AvroRJ100 102 2554 46 763 M

50 5.15% Fokker70 76 3732 42 856 M

38 3.92% ATR72 68 2222 22 526 M

36 3.71% AirbusIndustrieA330-300 181 8900 230 978 H

30 3.09% BritishAerospace146-200Passenger 90 2909 42 776 M

28 2.89% Boeing737-700Passenger 127 6110 69 932 M

26 2.68% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

898 92.6% Summe

970 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers ORY Weekly Flights Avg Day Share 2295 328 52.55% AIR FRANCE 272 39 6.23% EASYJET 250 36 5.72% IBERIA 162 23 3.71% ROYAL AIR MAROC 158 23 3.62% AIRLINAIR 138 20 3.16% AIGLE AZUR 112 16 2.56% TRANSAVIA.COM FRANCE 98 14 2.24% TAP AIR PORTUGAL 92 13 2.11% SKYEUROPE 88 13 2.02% TUNIS AIR 84 12 1.92% AIR ALGERIE 72 10 1.65% AIR EUROPA 58 8 1.33% AIR BERLIN 52 7 1.19% CORSAIR 52 7 1.19% EASYJET SWITZERLAND SA 3983 569 91.2% Summe 4367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins ORY 309

Appendix

Weekly flights Avg Day Share 214 31 4.90% Toulouse 158 23 3.62% Nizza 110 16 2.52% Marseille 95 14 2.18% Madrid 87 12 1.99% Bordeaux 57 8 1.31% Montpellier 49 7 1.12% Casablanca 46 7 1.05% Biarritz 45 6 1.03% Barcelona 43 6 0.98% Strasbourg 41 6 0.94% Mulhouse 40 6 0.92% Porto 40 6 0.92% Lissabon 39 6 0.89% Pau 38 5 0.87% Toulon 1102 157 25.2% Summe 4367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations ORY Weekly flights Avg Day Share 214 31 4.90% Toulouse 158 23 3.62% Nizza 110 16 2.52% Marseille 95 14 2.18% Madrid 87 12 1.99% Bordeaux 57 8 1.31% Montpellier 49 7 1.12% Casablanca 46 7 1.05% Biarritz 45 6 1.03% Barcelona 43 6 0.98% Strasbourg 41 6 0.94% Mulhouse 40 6 0.92% Porto 40 6 0.92% Lissabon 39 6 0.89% Pau 38 5 0.87% Toulon 1102 157 25.2% Summe 4367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances ORY

Weekly flights Share Distance in kilometers

2182 49.97% 401-800

700 16.03% 801-1200

500 11.45% 1201-1600

353 8.08% 1-400

194 4.44% 1601-2000 310

Appendix

142 3.25% 2001-2400

82 1.88% 6801-7200

66 1.51% 6401-6800

46 1.05% 4001-4400

38 0.87% 5601-6000

28 0.64% 9201-9600

17 0.39% 2801-3200

5 0.11% 7601-8000

4 0.09% 3601-4000

3 0.07% 7201-7600

4360 99.8% Summe

4367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types ORY

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1208 27.66% AirbusIndustrieA319 133 6800 76 930 M

1046 23.95% AirbusIndustrieA320 156 5500 77 930 M

396 9.07% AirbusIndustrieA321 184 5500 93 930 M

358 8.20% Boeing738-700Passenger 161 5420 71 937 M

142 3.25% ATR42-500 47 1630 19 563 M

142 3.25% Fokker100 105 3111 46 919 M

134 3.07% CanadairRegionalJet700 70 3674 34 859 M

92 2.11% Boeing737-700(Winglets)Passenger 99 6352 69 932 M

90 2.06% Beechcraft1900Dairliner 19 1384 8 512 M

86 1.97% ATR42/ATR72 46 1944 17 491 M

76 1.74% Boeing737-700Passenger 127 6110 69 932 M

74 1.69% Boeing777-300ERPassenger 353 14594 352 896 H

61 1.40% AvroRJ85 83 2796 44 763 M

50 1.14% Boeing737Passenger 143 4180 63 888 M

50 1.14% CanadairRegionalJet 50 3713 23 859 M

4005 91.7% Summe

4367 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers OSL Weekly Flights Avg Day Share 1993 285 48.33% SAS SCANDINAVIAN AIRLINES 1065 152 25.82% NORWEGIAN AIR SHUTTLE 226 32 5.48% WIDEROE'S FLYVESELSKAP 158 23 3.83% LUFTHANSA GERMAN AIRLINES 118 17 2.86% BRITISH AIRWAYS 70 10 1.70% KLM-ROYAL DUTCH AIRLINES 62 9 1.50% FINNAIR 50 7 1.21% DANISH AIR TRANSPORT 311

Appendix

44 6 1.07% CIMBER AIR 40 6 0.97% AIR NORWAY/NORTH FLYING 38 5 0.92% BRUSSELS AIRLINES 36 5 0.87% BLUE1 30 4 0.73% CZECH AIRLINES 28 4 0.68% AUSTRIAN AIRLINES AG 28 4 0.68% AIR FRANCE 3986 569 96.7% Summe 4124 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins OSL Weekly flights Avg Day Share 176 25 4.27% Trondheim 175 25 4.24% Bergen 153 22 3.71% Stavanger 129 18 3.13% Kopenhagen 114 16 2.76% Stockholm Arlanda 77 11 1.87% Tromsoe 62 9 1.50% Aalesund 62 9 1.50% London Heathrow 61 9 1.48% Bodo 57 8 1.38% Kristiansand 49 7 1.19% Helsinki 48 7 1.16% Frankfurt 48 7 1.16% Amsterdam 45 6 1.09% Evenes 43 6 1.04% Haugesund 1299 186 31.5% Summe 4124 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations OSL Weekly flights Avg Day Share 176 25 4.27% Trondheim 174 25 4.22% Bergen 156 22 3.78% Stavanger 129 18 3.13% Kopenhagen 116 17 2.81% Stockholm Arlanda 77 11 1.87% Tromsoe 62 9 1.50% Aalesund 62 9 1.50% London Heathrow 61 9 1.48% Bodo 57 8 1.38% Kristiansand 49 7 1.19% Helsinki 48 7 1.16% Frankfurt 48 7 1.16% Amsterdam 45 6 1.09% Evenes 43 6 1.04% Haugesund

312

Appendix

1303 186 31.6% Summe 4124 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances OSL

Weekly flights Share Distance in kilometers

2052 49.76% 1-400

894 21.68% 801-1200

504 12.22% 401-800

454 11.01% 1201-1600

58 1.41% 1601-2000

50 1.21% 2401-2800

30 0.73% 4001-4400

28 0.68% 2001-2400

22 0.53% 2801-3200

14 0.34% 5601-6000

7 0.17% 7601-8000

5 0.12% 3601-4000

3 0.07% 4801-5200

2 0.05% 5201-5600

1 0.02% 3201-3600

4124 100.0% Summe

4124 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types OSL

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

927 22.48% Boeing737-300Passenger 133 4180 63 888 M

500 12.12% Boeing737-400Passenger 148 3810 68 888 M

393 9.53% Boeing737-700Passenger 127 6110 69 932 M

383 9.29% Boeing738-700Passenger 161 5420 71 937 M

324 7.86% Boeing736-700Passenger 115 5840 65 933 M

218 5.29% Boeing737-700(Winglets)Passenger 99 6352 69 932 M

170 4.12% DeHavillandDHC-8-100Dash/88Q 39 2037 16 501 M

157 3.81% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

112 2.72% AirbusIndustrieA320 156 5500 77 930 M

90 2.18% AirbusIndustrieA319 133 6800 76 930 M

72 1.75% Boeing(Douglas)MD-81 148 4925 64 906 M

62 1.50% FairchildDornier328-100 31 1852 14 624 M

62 1.50% Boeing737-500Passenger 111 4400 52 888 M

56 1.36% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

313

Appendix

56 1.36% Boeing737Passenger 143 4180 63 888 M

3582 86.9% Summe

4124 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers PMI Weekly Flights Avg Day Share 800 114 35.96% AIR BERLIN 443 63 19.91% IBERIA 286 41 12.85% AIR EUROPA 168 24 7.55% SPANAIR 108 15 4.85% TUIFLY 80 11 3.60% EASYJET 74 11 3.33% RYANAIR 64 9 2.88% CLICKAIR 44 6 1.98% CONDOR FLUGDIENST 38 5 1.71% FLYGLOBESPAN 38 5 1.71% NIKI 16 2 0.72% BMIBABY 14 2 0.63% SWISS 12 2 0.54% LAGUN AIR 10 1 0.45% BMI BRITISH MIDLAND 2195 314 98.7% Summe 2225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins PMI Weekly flights Avg Day Share 143 20 6.43% Barcelona 124 18 5.57% Madrid 77 11 3.46% Ibiza 75 11 3.37% Valencia 73 10 3.28% Mahon 43 6 1.93% Alicante 31 4 1.39% Düsseldorf 29 4 1.30% Frankfurt 27 4 1.21% Hamburg 26 4 1.17% Sevilla 24 3 1.08% Berlin Tegel 23 3 1.03% München 23 3 1.03% Hannover 23 3 1.03% Stuttgart 21 3 0.94% Malaga 762 109 34.2% Summe 2225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations PMI Weekly flights Avg Day Share

314

Appendix

136 19 6.11% Barcelona 123 18 5.53% Madrid 77 11 3.46% Ibiza 75 11 3.37% Valencia 73 10 3.28% Mahon 43 6 1.93% Alicante 31 4 1.39% Düsseldorf 29 4 1.30% Frankfurt 27 4 1.21% Sevilla 27 4 1.21% Hamburg 24 3 1.08% Berlin Tegel 23 3 1.03% München 23 3 1.03% Hannover 23 3 1.03% Stuttgart 21 3 0.94% Malaga 755 108 33.9% Summe 2225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances PMI

Weekly flights Share Distance in kilometers

853 38.34% 1-400

546 24.54% 1201-1600

422 18.97% 401-800

222 9.98% 801-1200

182 8.18% 1601-2000

2225 100.0% Summe

2225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types PMI

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

771 34.65% Boeing738-700Passenger 161 5420 71 937 M

370 16.63% AirbusIndustrieA320 156 5500 77 930 M

208 9.35% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

199 8.94% Boeing737-700Passenger 127 6110 69 932 M

150 6.74% AirbusIndustrieA319 133 6800 76 930 M

114 5.12% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

96 4.31% ATR72 68 2222 22 526 M

86 3.87% Boeing(Douglas)MD-87 123 5248 64 906 M

82 3.69% AirbusIndustrieA321 184 5500 93 930 M

46 2.07% CanadairRegionalJet200 50 3713 23 859 M

31 1.39% Boeing737-300Passenger 133 4180 63 888 M

22 0.99% Boeing717-200 116 3350 55 906 M

315

Appendix

14 0.63% Boeing(Douglas)MD-88 146 4925 68 906 M

12 0.54% EmbraerRJ135/140/145 50 2460 21 834 M

12 0.54% Boeing757-300Passenger 265 6425 122 935 M

2213 99.5% Summe

2225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers PRG Weekly Flights Avg Day Share 773 110 29.32% CZECH AIRLINES 139 20 5.27% SKYEUROPE 132 19 5.01% LUFTHANSA GERMAN AIRLINES 86 12 3.26% EASYJET 76 11 2.88% SWISS 71 10 2.69% AIR FRANCE 71 10 2.69% AUSTRIAN AIRLINES AG 66 9 2.50% AEROFLOT RUSSIAN AIRLINES 58 8 2.20% BRUSSELS AIRLINES 54 8 2.05% RYANAIR 53 8 2.01% BRITISH AIRWAYS 45 6 1.71% KLM-ROYAL DUTCH AIRLINES 44 6 1.67% NORWEGIAN AIR SHUTTLE 40 6 1.52% URAL AIRLINES 38 5 1.44% MALEV HUNGARIAN AIRLINES 1746 249 66.2% Summe 2636 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins PRG Weekly flights Avg Day Share 48 7 1.82% Amsterdam 48 7 1.82% Frankfurt 46 7 1.75% Brüssel 43 6 1.63% Paris Charles de Gaulle 41 6 1.56% München 39 6 1.48% London Heathrow 39 6 1.48% Moskau SVO 37 5 1.40% Ostrava 36 5 1.37% Budapest 33 5 1.25% Zürich 32 5 1.21% Wien 31 4 1.18% Warschau 31 4 1.18% Düsseldorf 27 4 1.02% Madrid 26 4 0.99% Bratislava 557 80 21.1% Summe 2636 Total Flights Week (03/16/ - 03/22/2009

316

Appendix

Top 15 Destinations PRG Weekly flights Avg Day Share 48 7 1.82% Amsterdam 48 7 1.82% Frankfurt 46 7 1.75% Brüssel 43 6 1.63% Paris Charles de Gaulle 41 6 1.56% München 39 6 1.48% London Heathrow 39 6 1.48% Moskau SVO 38 5 1.44% Ostrava 37 5 1.40% Budapest 33 5 1.25% Zürich 32 5 1.21% Wien 31 4 1.18% Warschau 31 4 1.18% Düsseldorf 28 4 1.06% Madrid 26 4 0.99% Bratislava 560 80 21.2% Summe 2636 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances PRG

Weekly flights Share Distance in kilometers

961 36.46% 401-800

731 27.73% 801-1200

421 15.97% 1-400

227 8.61% 1201-1600

143 5.42% 1601-2000

67 2.54% 2401-2800

25 0.95% 2001-2400

19 0.72% 2801-3200

11 0.42% 6401-6800

9 0.34% 7601-8000

8 0.30% 4401-4800

8 0.30% 3201-3600

6 0.23% 8001-8400

2636 100.0% Summe

2636 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types PRG

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

431 16.35% AirbusIndustrieA320 156 5500 77 930 M

371 14.07% AirbusIndustrieA319 133 6800 76 930 M

308 11.68% ATR42-300/320 46 1944 17 491 M 317

Appendix

270 10.24% Boeing737-500Passenger 111 4400 52 888 M

185 7.02% Boeing737-700(Winglets)Passenger 99 6352 69 932 M

175 6.64% Boeing737-400Passenger 148 3810 68 888 M

171 6.49% ATR72 68 2222 22 526 M

89 3.38% Saab340 34 1667 13 523 M

85 3.22% Boeing738-700Passenger 161 5420 71 937 M

80 3.03% Boeing737-300Passenger 133 4180 63 888 M

76 2.88% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

48 1.82% Fokker70 76 3732 42 856 M

46 1.75% Fokker100 105 3111 46 919 M

40 1.52% AirbusIndustrieA321 184 5500 93 930 M

36 1.37% AvroRJ100 102 2554 46 763 M

2411 91.5% Summe

2636 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers PSA Weekly Flights Avg Day Share 256 37 47.15% RYANAIR 57 8 10.50% ALITALIA 42 6 7.73% LUFTHANSA GERMAN AIRLINES 42 6 7.73% AIR FRANCE 36 5 6.63% EASYJET 28 4 5.16% BRITISH AIRWAYS 20 3 3.68% WIND JET 14 2 2.58% AIR ONE 10 1 1.84% IBERIA 8 1 1.47% TRANSAVIA.COM 8 1 1.47% DELTA AIR LINES 8 1 1.47% CLICKAIR 6 1 1.10% BELLE AIR 6 1 1.10% TUIFLY 2 0 0.37% EUROFLY

543 78 100.0% Summe

543 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins PSA Weekly flights Avg Day Share 35 5 6.45% Rom Fiumicino 21 3 3.87% Paris Charles de Gaulle 21 3 3.87% München 21 3 3.87% London Gatwick 14 2 2.58% London Stansted 12 2 2.21% Palermo 9 1 1.66% Paris ORY 9 1 1.66% Alghero 318

Appendix

8 1 1.47% Cagliari 7 1 1.29% Trapani 7 1 1.29% Hahn 7 1 1.29% Gerona 7 1 1.29% Eindhoven 7 1 1.29% Charleroi 7 1 1.29% Beauvais 192 27 35.4% Summe

543 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations PSA

Weekly flights Avg Day Share

35 5 6.45% Rom Fiumicino

21 3 3.87% Paris Charles de Gaulle

21 3 3.87% München

21 3 3.87% London Gatwick

14 2 2.58% London Stansted

12 2 2.21% Palermo

9 1 1.66% Paris ORY

9 1 1.66% Alghero

8 1 1.47% Cagliari

7 1 1.29% Trapani

7 1 1.29% Gerona

7 1 1.29% Eindhoven

7 1 1.29% Charleroi

7 1 1.29% Beauvais

7 1 1.29% Hahn

192 27 35.4% Summe

543 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances PSA

Weekly flights Share Distance in kilometers

218 40.15% 801-1200

164 30.20% 401-800

88 16.21% 1-400

46 8.47% 1201-1600

16 2.95% 1601-2000

9 1.66% 6401-6800

2 0.37% 2401-2800

543 100.0% Summe

543 Total Flights Week (03/16/ - 03/22/2009

319

Appendix

Top 15 Aircraft Types PSA

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

256 47.15% Boeing738-700Passenger 161 5420 71 937 M

52 9.58% AirbusIndustrieA319 133 6800 76 930 M

42 7.73% ATR42-500 47 1630 19 563 M

36 6.63% CanadairRegionalJet 50 3713 23 859 M

30 5.52% AirbusIndustrieA320 156 5500 77 930 M

28 5.16% ATR72 68 2222 22 526 M

28 5.16% Embraer170 73 3889 36 869 M

22 4.05% Boeing737Passenger 143 4180 63 888 M

12 2.21% Boeing737-400Passenger 148 3810 68 888 M

8 1.47% Boeing767Passenger 224 11390 187 953 H

6 1.10% Boeing737-700Passenger 127 6110 69 932 M

6 1.10% Boeing(Douglas)MD-82 155 4925 68 906 M

6 1.10% Fokker100 105 3111 46 919 M

6 1.10% CanadairRegionalJet200 50 3713 23 859 M

4 0.74% CanadairRegionalJet900 88 3207 37 829 M

542 99.8% Summe

543 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers RHO Weekly Flights Avg Day Share 134 19 66.34% OLYMPIC AIRLINES 62 9 30.69% AEGEAN AIRLINES 6 1 2.97% CYPRUS AIRWAYS

202 29 100.0% Summe

202 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins RHO

Weekly flights Avg Day Share

63 9 31.19% Athen

10 1 4.95% Heraklion

10 1 4.95% Karpathos

7 1 3.47% Thessaloniki

4 1 1.98% Kastelorizo

2 0 0.99% Samos

2 0 0.99% Kos 320

Appendix

2 0 0.99% Mytilene

1 0 0.50% Chios

101 14 50.0% Summe

202 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations RHO

Weekly flights Avg Day Share

63 9 31.19% Athen

10 1 4.95% Heraklion

10 1 4.95% Karpathos

7 1 3.47% Thessaloniki

4 1 1.98% Kastelorizo

2 0 0.99% Samos

2 0 0.99% Kos

2 0 0.99% Mytilene

1 0 0.50% Chios

101 14 50.0% Summe

202 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances RHO

Weekly flights Share Distance in kilometers

140 69.31% 401-800

62 30.69% 1-400

202 100.0% Summe

202 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types RHO

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

64 31.68% AirbusIndustrieA320 156 5500 77 930 M

42 20.79% DeHavillandDHC-8Dash8 37 2037 16 501 M

42 20.79% Boeing737-400Passenger 148 3810 68 888 M

34 16.83% ATR42-300/320 46 1944 17 491 M

16 7.92% ATR72 68 2222 22 526 M

4 1.98% AirbusIndustrieA319 133 6800 76 930 M

202 100.0% Summe

202 Total Flights Week (03/16/ - 03/22/2009

321

Appendix

Top 15 Carriers RTM Weekly Flights Avg Day Share 138 20 53.08% TRANSAVIA.COM 122 17 46.92% VLM AIRLINES

260 37 100.0% Summe

260 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins RTM

Weekly flights Avg Day Share

43 6 16.54% London City

12 2 4.62% London Luton

9 1 3.46% Hamburg

9 1 3.46% Manchester

7 1 2.69% Salzburg

7 1 2.69% Rom Fiumicino

7 1 2.69% Faro

7 1 2.69% Malaga

7 1 2.69% Innsbruck

7 1 2.69% Alicante

5 1 1.92% Nizza

4 1 1.54% Genf

4 1 1.54% Grenoble

2 0 0.77% Friedrichshafen

130 19 50.0% Summe

260 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations RTM

Weekly flights Avg Day Share

43 6 16.54% London City

12 2 4.62% London Luton

9 1 3.46% Hamburg

9 1 3.46% Manchester

7 1 2.69% Salzburg

7 1 2.69% Rom Fiumicino

7 1 2.69% Faro

7 1 2.69% Malaga

7 1 2.69% Innsbruck

7 1 2.69% Alicante

5 1 1.92% Nizza

4 1 1.54% Genf

4 1 1.54% Grenoble

322

Appendix

2 0 0.77% Friedrichshafen

130 19 50.0% Summe

260 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances RTM

Weekly flights Share Distance in kilometers

110 42.31% 1-400

84 32.31% 401-800

28 10.77% 1601-2000

28 10.77% 1201-1600

10 3.85% 801-1200

260 100.0% Summe

260 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types RTM

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

138 53.08% Boeing737Passenger 143 4180 63 888 M

122 46.92% Fokker50 48 2842 22 531 M

260 100.0% Summe

260 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers SCN Weekly Flights Avg Day Share 132 19 58.67% LUXAIR 52 7 23.11% AIR BERLIN 32 5 14.22% CIRRUS AIRLINES 9 1 4.00% HAMBURG INTERNATIONAL

225 32 100.0% Summe

225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins SCN

Weekly flights Avg Day Share

33 5 14.67% Luxembourg

29 4 12.89% Hamburg

21 3 9.33% Berlin Tegel

17 2 7.56% München

7 1 3.11% Palma Mallorca

1 0 0.44% Zürich

1 0 0.44% Prag

1 0 0.44% Las Palmas 323

Appendix

1 0 0.44% Hurghada

1 0 0.44% Teneriffa

112 16 49.8% Summe

225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations SCN

Weekly flights Avg Day Share

33 5 14.67% Luxembourg

29 4 12.89% Hamburg

21 3 9.33% Berlin Tegel

17 2 7.56% München

8 1 3.56% Palma Mallorca

1 0 0.44% Zürich

1 0 0.44% Prag

1 0 0.44% Lanzarote

1 0 0.44% Fuerteventura

1 0 0.44% Sharm El Sheik

113 16 50.2% Summe

225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances SCN

Weekly flights Share Distance in kilometers

102 45.33% 401-800

102 45.33% 1-400

15 6.67% 801-1200

4 1.78% 2801-3200

2 0.89% 3201-3600

225 100.0% Summe

225 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types SCN

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

76 33.78% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

46 20.44% EmbraerRJ145 49 2460 21 834 M

35 15.56% Boeing737-700Passenger 127 6110 69 932 M

32 14.22% FairchildDornier328-100 31 1852 14 624 M

22 9.78% EmbraerRJ135 37 2500 19 930 M

12 5.33% AirbusIndustrieA320 156 5500 77 930 M

2 0.89% Boeing738-700Passenger 161 5420 71 937 M

225 100.0% Summe

225 Total Flights Week (03/16/ - 03/22/2009 324

Appendix

Top 15 Carriers STN Weekly Flights Avg Day Share 1923 275 66.89% RYANAIR 516 74 17.95% EASYJET 134 19 4.66% AIR BERLIN 68 10 2.37% GERMANWINGS 42 6 1.46% AEGEAN AIRLINES 38 5 1.32% EASTERN AIRWAYS 36 5 1.25% NORWEGIAN AIR SHUTTLE 26 4 0.90% BRITISH AIRWAYS 16 2 0.56% HELLO 14 2 0.49% AURIGNY AIR SERVICES 14 2 0.49% TURKISH AIRLINES 14 2 0.49% ASTRAEUS 12 2 0.42% KIBRIS TURKISH AIRLINES 6 1 0.21% ASIANA AIRLINES 5 1 0.17% CYPRUS AIRWAYS 2864 409 99.6% Summe 2875 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins STN Weekly flights Avg Day Share 61 9 2.12% Dublin 35 5 1.22% Rom Ciampino 29 4 1.01% Edinburgh 28 4 0.97% Nykoping 28 4 0.97% Hahn 28 4 0.97% Belfast 28 4 0.97% Torp 28 4 0.97% Glasgow 27 4 0.94% Glasgow 27 4 0.94% Belfast 25 4 0.87% Shannon 23 3 0.80% Düsseldorf 22 3 0.77% Salzburg 22 3 0.77% Alicante 21 3 0.73% Berlin Schönefeld 432 62 15.0% Summe 2875 Total Flights Week (03/16/ - 03/22/2009

325

Appendix

Top 15 Destinations STN Weekly flights Avg Day Share 61 9 2.12% Dublin 35 5 1.22% Rom Ciampino 31 4 1.08% Hahn 29 4 1.01% Edinburgh 28 4 0.97% Nykoping 28 4 0.97% Belfast 28 4 0.97% Torp 28 4 0.97% Glasgow 27 4 0.94% Glasgow 27 4 0.94% Belfast 25 4 0.87% Shannon 25 4 0.87% Amsterdam 23 3 0.80% Düsseldorf 22 3 0.77% Salzburg 22 3 0.77% Alicante 439 63 15.3% Summe 2875 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances STN

Weekly flights Share Distance in kilometers

1077 37.51% 401-800

816 28.42% 801-1200

512 17.83% 1201-1600

196 6.83% 1601-2000

149 5.19% 1-400

71 2.47% 2401-2800

24 0.83% 2801-3200

9 0.31% 2001-2400

5 0.24% 6401-6800

4 0.17% 3201-3600

3 0.11% 9201-9600

2 0.07% 8401-8800

1 0.04% 8801-9200

1 0.04% 4801-5200

1 0.04% 4401-4800

2871 99.9% Summe

2875 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types STN

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

1872 65.11% Boeing738-700Passenger 161 5420 71 937 M 326

Appendix

596 20.73% AirbusIndustrieA319 133 6800 76 930 M

89 3.10% Boeing737Passenger 143 4180 63 888 M

68 2.37% Boeing737-700Passenger 127 6110 69 932 M

66 2.30% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

58 2.02% AirbusIndustrieA320 156 5500 77 930 M

38 1.32% BritishAerospaceJetstream41 29 2759 11 546 M

32 1.11% Boeing747(Freighter) 0 8245 397 938 H

30 1.04% Boeing737-300Passenger 133 4180 63 888 M

14 0.49% ATR72 68 2222 22 526 M

6 0.21% AirbusIndustrieA310Passenger 222 9600 142 954 H

3 0.10% Boeing(Douglas)MD-11(Freighter) 0 6770 286 946 H

2 0.07% Boeing767-200Passenger 192 12315 179 953 H

1 0.03% Boeing747-400F(Freighter) 0 8245 397 938 H

2875 100.0% Summe

2875 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers STR Weekly Flights Avg Day Share 808 115 35.77% LUFTHANSA GERMAN AIRLINES 328 47 14.52% GERMANWINGS 220 31 9.74% AIR BERLIN 184 26 8.15% TUIFLY 96 14 4.25% AIR FRANCE 82 12 3.63% SWISS 68 10 3.01% KLM-ROYAL DUTCH AIRLINES 42 6 1.86% BRITISH AIRWAYS 40 6 1.77% CONDOR FLUGDIENST 38 5 1.68% CZECH AIRLINES 38 5 1.68% AUSTRIAN AIRLINES AG 38 5 1.68% SAS SCANDINAVIAN AIRLINES 32 5 1.42% CIRRUS AIRLINES 32 5 1.42% FLYBE 28 4 1.24% TURKISH AIRLINES 2074 296 91.8% Summe 2259 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins STR Weekly flights Avg Day Share 98 14 4.34% Berlin Tegel 97 14 4.29% Hamburg 72 10 3.19% Düsseldorf 57 8 2.52% Wien 48 7 2.12% Paris Charles de Gaulle 41 6 1.81% London Heathrow 41 6 1.81% München 41 6 1.81% Frankfurt 327

Appendix

41 6 1.81% Zürich 34 5 1.51% Amsterdam 33 5 1.46% Hannover 26 4 1.15% Barcelona 23 3 1.02% Palma Mallorca 22 3 0.97% Berlin Schönefeld 21 3 0.93% Bremen 695 99 30.8% Summe 2259 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations STR Weekly flights Avg Day Share 98 14 4.34% Berlin Tegel 97 14 4.29% Hamburg 72 10 3.19% Düsseldorf 57 8 2.52% Wien 48 7 2.12% London Heathrow 48 7 2.12% Paris Charles de Gaulle 41 6 1.81% Frankfurt 41 6 1.81% München 41 6 1.81% Zürich 34 5 1.51% Amsterdam 33 5 1.46% Hannover 26 4 1.15% Barcelona 23 3 1.02% Palma Mallorca 22 3 0.97% Berlin Schönefeld 21 3 0.93% Bremen 702 100 31.1% Summe 2259 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances STR

Weekly flights Share Distance in kilometers

1167 51.66% 401-800

526 23.28% 1-400

304 13.46% 801-1200

86 3.81% 1601-2000

60 2.66% 2801-3200

58 2.57% 1201-1600

26 1.15% 2001-2400

14 0.62% 7201-7600

12 0.53% 2401-2800

6 0.27% 3201-3600

2259 100.0% Summe

2259 Total Flights Week (03/16/ - 03/22/2009

328

Appendix

Top 15 Aircraft Types STR

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

501 22.18% AirbusIndustrieA319 133 6800 76 930 M

248 10.98% CanadairRegionalJet200 50 3713 23 859 M

210 9.30% Boeing737-500Passenger 111 4400 52 888 M

166 7.35% AirbusIndustrieA320 156 5500 77 930 M

146 6.46% ATR72 68 2222 22 526 M

128 5.67% Boeing737-700Passenger 127 6110 69 932 M

106 4.69% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

78 3.45% CanadairRegionalJet700 70 3674 34 859 M

70 3.10% AvroRJ100 102 2554 46 763 M

68 3.01% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

62 2.74% ATR42-500 47 1630 19 563 M

56 2.48% EmbraerRJ145 49 2460 21 834 M

52 2.30% Fokker70 76 3732 42 856 M

36 1.59% Saab2000 50 2907 23 681 M

32 1.42% FairchildDornier328-100 31 1852 14 624 M

1959 86.7% Summe

2259 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers SXF Weekly Flights Avg Day Share 396 57 40.49% EASYJET 224 32 22.90% GERMANWINGS 198 28 20.25% RYANAIR 42 6 4.29% AEROFLOT RUSSIAN AIRLINES 32 5 3.27% NORWEGIAN AIR SHUTTLE 20 3 2.04% AER LINGUS 18 3 1.84% CONDOR FLUGDIENST 8 1 0.82% EGYPTAIR 8 1 0.82% SUNEXPRESS 6 1 0.61% BELAVIA 6 1 0.61% EL AL ISRAEL AIRLINES 4 1 0.41% ROSSIYA - RUSSIAN AIRLINES 4 1 0.41% ASTRAEUS 4 1 0.41% SYRIAN ARAB AIRLINES 4 1 0.41% HELLO 974 139 99.6% Summe 978 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins SXF Weekly flights Avg Day Share

329

Appendix

34 5 3.48% Köln-Bonn 27 4 2.76% Basel 26 4 2.66% München 22 3 2.25% Stuttgart 21 3 2.15% Hahn 21 3 2.15% London Stansted 21 3 2.15% Moskau SVO 19 3 1.94% Paris ORY 17 2 1.74% Dublin 14 2 1.43% Nykoping 13 2 1.33% Zweibrücken 13 2 1.33% Rom Ciampino 13 2 1.33% Barcelona 13 2 1.33% Madrid 13 2 1.33% Mailand Malpensa 287 41 29.3% Summe 978 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations SXF Weekly flights Avg Day Share 34 5 3.48% Köln-Bonn 27 4 2.76% Basel 26 4 2.66% München 22 3 2.25% Stuttgart 21 3 2.15% Hahn 21 3 2.15% London Stansted 21 3 2.15% Moskau SVO 19 3 1.94% Paris ORY 17 2 1.74% Dublin 14 2 1.43% Nykoping 13 2 1.33% Zweibrücken 13 2 1.33% Mailand Malpensa 13 2 1.33% Rom Ciampino 13 2 1.33% Barcelona 13 2 1.33% Madrid 287 41 29.3% Summe 978 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances SXF

Weekly flights Share Distance in kilometers

412 42.13% 401-800

278 28.43% 801-1200

163 16.67% 1201-1600

64 6.54% 1601-2000

15 1.53% 1-400

14 1.43% 2801-3200 330

Appendix

12 1.23% 2001-2400

8 0.82% 3601-4000

8 0.82% 2401-2800

4 0.41% 3201-3600

978 100.0% Summe

978 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types SXF

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

636 65.03% AirbusIndustrieA319 133 6800 76 930 M

214 21.88% Boeing738-700Passenger 161 5420 71 937 M

60 6.13% AirbusIndustrieA320 156 5500 77 930 M

30 3.07% Boeing737-300Passenger 133 4180 63 888 M

22 2.25% Boeing737-700Passenger 127 6110 69 932 M

6 0.61% CanadairRegionalJet 50 3713 23 859 M

6 0.61% AirbusIndustrieA321 184 5500 93 930 M

4 0.41% Boeing737-500Passenger 111 4400 52 888 M

978 100.0% Summe

978 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers SZG Weekly Flights Avg Day Share 134 19 34.01% AUSTRIAN AIRLINES AG 52 7 13.20% RYANAIR 48 7 12.18% TUIFLY 32 5 8.12% CIRRUS AIRLINES 24 3 6.09% THOMSONFLY 18 3 4.57% TRANSAVIA.COM 16 2 4.06% AUSTROJET 16 2 4.06% NIKI 14 2 3.55% BRITISH AIRWAYS 12 2 3.05% NORWEGIAN AIR SHUTTLE 8 1 2.03% EASYJET 4 1 1.02% KRASNOYARSK AIRLINES 4 1 1.02% SAS SCANDINAVIAN AIRLINES 4 1 1.02% FLYBE 4 1 1.02% AER LINGUS 390 56 99.0% Summe 394 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins SZG Weekly flights Avg Day Share 28 4 7.11% Frankfurt 26 4 6.60% Wien 331

Appendix

22 3 5.58% London Stansted 16 2 4.06% Zürich 11 2 2.79% Linz 11 2 2.79% London Gatwick 7 1 1.78% Rotterdam 7 1 1.78% Köln-Bonn 7 1 1.78% Hamburg 6 1 1.52% Berlin Tegel

6 1 1.52% Dublin

5 1 1.27% Oslo

5 1 1.27% Stuttgart

5 1 1.27% Palma Mallorca

4 1 1.02% Hannover

166 24 42.1% Summe

394 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations SZG

Weekly flights Avg Day Share

28 4 7.11% Frankfurt

26 4 6.60% Wien

22 3 5.58% London Stansted

16 2 4.06% Zürich

11 2 2.79% Linz

11 2 2.79% London Gatwick

7 1 1.78% Rotterdam

7 1 1.78% Hamburg

7 1 1.78% Köln-Bonn

6 1 1.52% Berlin Tegel

6 1 1.52% Dublin

5 1 1.27% Palma Mallorca

5 1 1.27% Stuttgart

5 1 1.27% Oslo

4 1 1.02% Hannover

166 24 42.1% Summe

394 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances SZG

Weekly flights Share Distance in kilometers

130 32.99% 401-800

118 29.95% 1-400

84 21.32% 801-1200

46 11.68% 1201-1600 332

Appendix

12 3.05% 1601-2000

2 0.51% 3201-3600

2 0.51% 2801-3200

394 100.0% Summe

394 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types SZG

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

84 21.32% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

60 15.23% Boeing738-700Passenger 161 5420 71 937 M

38 9.64% CanadairRegionalJet 50 3713 23 859 M

36 9.14% Boeing738-700(Winglets)Passenger 118 5662 71 937 M

32 8.12% FairchildDornier328-100 31 1852 14 624 M

32 8.12% Boeing737-300Passenger 133 4180 63 888 M

20 5.08% AirbusIndustrieA320 156 5500 77 930 M

20 5.08% Boeing737-300(Winglets)Passenger 142 4365 63 888 M

18 4.57% Boeing737Passenger 143 4180 63 888 M

16 4.06% DeHavillandDHC-8Dash8 37 2037 16 501 M

12 3.05% Boeing737-700Passenger 127 6110 69 932 M

10 2.54% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

10 2.54% AirbusIndustrieA319 133 6800 76 930 M

4 1.02% Boeing737-700(Winglets)Passenger 99 6352 69 932 M

2 0.51% Embraer195 107 3334 48 869 M

394 100.0% Summe

394 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers TXL Weekly Flights Avg Day Share 966 138 32.16% AIR BERLIN 922 132 30.69% LUFTHANSA GERMAN AIRLINES 160 23 5.33% TUIFLY 80 11 2.66% SAS SCANDINAVIAN AIRLINES 80 11 2.66% BRITISH AIRWAYS 70 10 2.33% AIR FRANCE 68 10 2.26% KLM-ROYAL DUTCH AIRLINES 62 9 2.06% BRUSSELS AIRLINES 56 8 1.86% SWISS 48 7 1.60% AIR BALTIC CORPORATION 46 7 1.53% CIRRUS AIRLINES 42 6 1.40% IBERIA 40 6 1.33% INTERSKY 30 4 1.00% LUXAIR 28 4 0.93% AUSTRIAN AIRLINES AG

333

Appendix

2698 385 89.8% Summe 3004 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins TXL Weekly flights Avg Day Share 142 20 4.73% München 125 18 4.16% Köln-Bonn 113 16 3.76% Düsseldorf 111 16 3.70% Frankfurt 98 14 3.26% Stuttgart 75 11 2.50% Zürich 70 10 2.33% Wien 53 8 1.76% Brüssel 51 7 1.70% Paris Charles de Gaulle 46 7 1.53% Nürnberg 40 6 1.33% Amsterdam 40 6 1.33% London Heathrow 37 5 1.23% Kopenhagen 24 3 0.80% Palma Mallorca 23 3 0.77% Mannheim 1048 150 34.9% Summe 3004 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations TXL Weekly flights Avg Day Share 142 20 4.73% München 125 18 4.16% Köln-Bonn 113 16 3.76% Düsseldorf 111 16 3.70% Frankfurt 98 14 3.26% Stuttgart 75 11 2.50% Zürich 70 10 2.33% Wien 53 8 1.76% Brüssel 51 7 1.70% Paris Charles de Gaulle 46 7 1.53% Nürnberg 40 6 1.33% Amsterdam 40 6 1.33% London Heathrow 37 5 1.23% Kopenhagen 24 3 0.80% Palma Mallorca 23 3 0.77% Mannheim 1048 150 34.9% Summe 3004 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances TXL

Weekly flights Share Distance in kilometers

1996 66.44% 401-800

334

Appendix

436 14.51% 801-1200

232 7.72% 1-400

178 5.93% 1601-2000

60 2.00% 1201-1600

24 0.80% 6001-6400

20 0.67% 3601-4000

16 0.53% 3201-3600

14 0.47% 2001-2400

14 0.47% 4401-4800

4 0.13% 8401-8800

4 0.13% 7201-7600

2 0.07% 2401-2800

2 0.07% 7601-8000

2 0.07% 8001-8400

3004 100.0% Summe

3004 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types TXL

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

462 15.38% AirbusIndustrieA320 156 5500 77 930 M

340 11.32% AirbusIndustrieA319 133 6800 76 930 M

326 10.85% Boeing737-700Passenger 127 6110 69 932 M

297 9.89% Boeing737-300Passenger 133 4180 63 888 M

226 7.52% Boeing738-700Passenger 161 5420 71 937 M

222 7.39% AirbusIndustrieA321 184 5500 93 930 M

142 4.73% Boeing737-500Passenger 111 4400 52 888 M

93 3.10% Boeing737Passenger 143 4180 63 888 M

86 2.86% BritishAerospace146-200Passenger 90 2909 42 776 M

82 2.73% CanadairRegionalJet200 50 3713 23 859 M

76 2.53% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

74 2.46% CanadairRegionalJet700 70 3674 34 859 M

60 2.00% AirbusIndustrieA300-600Passenger 232 7700 175 941 H

50 1.66% AvroRJ100 102 2554 46 763 M

46 1.53% FairchildDornier328-100 31 1852 14 624 M

2582 86.0% Summe

3004 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers VIE Weekly Flights Avg Day Share 2660 380 55.65% AUSTRIAN AIRLINES AG 306 44 6.40% LUFTHANSA GERMAN AIRLINES 244 35 5.10% NIKI 335

Appendix

226 32 4.73% AIR BERLIN 160 23 3.35% SKYEUROPE 84 12 1.76% ADRIA AIRWAYS 72 10 1.51% GERMANWINGS 56 8 1.17% SWISS 54 8 1.13% BRITISH AIRWAYS 54 8 1.13% KLM-ROYAL DUTCH AIRLINES 52 7 1.09% UKRAINE INTERNATIONAL AIRLINES 52 7 1.09% LOT - POLISH AIRLINES 46 7 0.96% BRUSSELS AIRLINES 42 6 0.88% AIR FRANCE 36 5 0.75% TAROM 4144 592 86.7% Summe 4780 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins VIE Weekly flights Avg Day Share 97 14 2.03% Frankfurt 78 11 1.63% München 76 11 1.59% Zürich 70 10 1.46% Berlin Tegel 68 10 1.42% Amsterdam 65 9 1.36% Düsseldorf 62 9 1.30% Brüssel 62 9 1.30% London Heathrow 61 9 1.28% Innsbruck 57 8 1.19% Stuttgart 53 8 1.11% Paris Charles de Gaulle 52 7 1.09% Hamburg 39 6 0.82% Genf 38 5 0.79% Köln-Bonn 38 5 0.79% Mailand Malpensa 916 131 19.2% Summe 4780 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations VIE Weekly flights Avg Day Share 104 15 2.18% Frankfurt 78 11 1.63% München 76 11 1.59% Zürich 70 10 1.46% Berlin Tegel 69 10 1.44% Amsterdam 65 9 1.36% Düsseldorf 64 9 1.34% Brüssel 62 9 1.30% London Heathrow 336

Appendix

59 8 1.23% Innsbruck 57 8 1.19% Stuttgart 53 8 1.11% Paris Charles de Gaulle 52 7 1.09% Hamburg 39 6 0.82% Genf 38 5 0.79% Köln-Bonn 38 5 0.79% Mailand Malpensa 924 132 19.3% Summe 4780 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances VIE

Weekly flights Share Distance in kilometers

1924 40.25% 401-800

1029 21.53% 801-1200

713 14.92% 1-400

530 11.09% 1201-1600

178 3.72% 1601-2000

126 2.64% 2001-2400

47 0.98% 4001-4400

33 0.69% 3601-4000

30 0.67% 6801-7200

30 0.63% 2401-2800

27 0.56% 3201-3600

24 0.50% 8001-8400

22 0.46% 2801-3200

20 0.42% 8401-8800

14 0.29% 5201-5600

4747 99.3% Summe

4780 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types VIE

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

610 12.76% AirbusIndustrieA319 133 6800 76 930 M

522 10.92% AirbusIndustrieA320 156 5500 77 930 M

467 9.77% Fokker100 105 3111 46 919 M

460 9.62% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

397 8.31% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

397 8.31% CanadairRegionalJet 50 3713 23 859 M

335 7.01% Fokker70 76 3732 42 856 M

202 4.23% AirbusIndustrieA321 184 5500 93 930 M

337

Appendix

161 3.37% Boeing737-700(Winglets)Passenger 99 6352 69 932 M

160 3.35% Boeing737-700Passenger 127 6110 69 932 M

142 2.97% CanadairRegionalJet200 50 3713 23 859 M

92 1.92% Boeing738-700Passenger 161 5420 71 937 M

90 1.88% Boeing737-500Passenger 111 4400 52 888 M

58 1.21% Boeing737Passenger 143 4180 63 888 M

50 1.05% BritishAerospace146-200Passenger 90 2909 42 776 M

4143 86.7% Summe

4780 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers WAW Weekly Flights Avg Day Share 1278 183 58.49% LOT - POLISH AIRLINES 130 19 5.95% LUFTHANSA GERMAN AIRLINES 126 18 5.77% WIZZ AIR 54 8 2.47% AIR FRANCE 49 7 2.24% SWISS 47 7 2.15% NORWEGIAN AIR SHUTTLE 44 6 2.01% AUSTRIAN AIRLINES AG 41 6 1.88% BRITISH AIRWAYS 40 6 1.83% FINNAIR 38 5 1.74% JET AIR 38 5 1.74% KLM-ROYAL DUTCH AIRLINES 38 5 1.74% CZECH AIRLINES 36 5 1.65% SAS SCANDINAVIAN AIRLINES 26 4 1.19% MALEV HUNGARIAN AIRLINES 24 3 1.10% EASYJET 2009 287 91.9% Summe 2185 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins WAW Weekly flights Avg Day Share 53 8 2.43% Paris Charles de Gaulle 51 7 2.33% Gdansk 50 7 2.29% Wroclaw 43 6 1.97% Krakow 42 6 1.92% Frankfurt 41 6 1.88% München 39 6 1.78% Amsterdam 35 5 1.60% Wien 34 5 1.56% London Heathrow 34 5 1.56% Zürich 33 5 1.51% Poznan 31 4 1.42% Prag 29 4 1.33% Düsseldorf 338

Appendix

29 4 1.33% Brüssel 27 4 1.24% Helsinki 571 82 26.1% Summe 2185 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations WAW Weekly flights Avg Day Share 53 8 2.43% Paris Charles de Gaulle 51 7 2.33% Gdansk 50 7 2.29% Wroclaw 43 6 1.97% Krakow 42 6 1.92% Frankfurt 41 6 1.88% München 39 6 1.78% Amsterdam 35 5 1.60% Wien 34 5 1.56% London Heathrow 34 5 1.56% Brüssel 34 5 1.56% Zürich 33 5 1.51% Poznan 31 4 1.42% Prag 29 4 1.33% Düsseldorf 27 4 1.24% Helsinki 576 82 26.4% Summe 2185 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances WAW

Weekly flights Share Distance in kilometers

735 33.64% 801-1200

484 22.15% 401-800

470 21.51% 1-400

368 16.84% 1201-1600

40 1.83% 1601-2000

34 1.56% 2001-2400

22 1.01% 6801-7200

16 0.73% 2401-2800

12 0.55% 7201-7600

4 0.18% 2801-3200

2185 100.0% Summe

2185 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types WAW 339

Appendix

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

331 15.15% Embraer170 73 3889 36 869 M

278 12.72% ATR72 68 2222 22 526 M

206 9.43% AirbusIndustrieA320 156 5500 77 930 M

180 8.24% Boeing737-500Passenger 111 4400 52 888 M

167 7.64% Embraer175 82 3334 39 869 M

150 6.86% EmbraerRJ145 49 2460 21 834 M

128 5.86% ATR42-500 47 1630 19 563 M

90 4.12% AirbusIndustrieA319 133 6800 76 930 M

60 2.75% AvroRJ85 83 2796 44 763 M

60 2.75% Boeing737-300Passenger 133 4180 63 888 M

58 2.65% Boeing737-400Passenger 148 3810 68 888 M

56 2.56% CanadairRegionalJet200 50 3713 23 859 M

52 2.38% Fokker100 105 3111 46 919 M

38 1.74% BritishAerospaceJetstream32 19 1978 7 491 M

38 1.74% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

1892 86.6% Summe

2185 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers WRO Weekly Flights Avg Day Share 140 20 41.42% LOT - POLISH AIRLINES 100 14 29.59% RYANAIR 68 10 20.12% LUFTHANSA GERMAN AIRLINES 14 2 4.14% WIZZ AIR 12 2 3.55% CIMBER AIR 4 1 1.18% NORWEGIAN AIR SHUTTLE

338 48 100.0% Summe

338 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins WRO Weekly flights Avg Day Share

50 7 14.79% Warschau

34 5 10.06% München

14 2 4.14% Frankfurt

10 1 2.96% London Stansted

7 1 2.07% Dublin

6 1 1.78% Kopenhagen

6 1 1.78% Düsseldorf

4 1 1.18% London Luton

340

Appendix

4 1 1.18% East Midlands

3 0 0.89% Shannon

3 0 0.89% Nykoping

3 0 0.89% Liverpool

3 0 0.89% Niederrhein-Weeze

3 0 0.89% Hahn

3 0 0.89% Glasgow

153 22 45.3% Summe

338 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations WRO

Weekly flights Avg Day Share

50 7 14.79% Warschau

34 5 10.06% München

14 2 4.14% Frankfurt

10 1 2.96% London Stansted

7 1 2.07% Dublin

6 1 1.78% Kopenhagen

6 1 1.78% Düsseldorf

4 1 1.18% London Luton

4 1 1.18% East Midlands

3 0 0.89% Shannon

3 0 0.89% Glasgow

3 0 0.89% Liverpool

3 0 0.89% Nykoping

3 0 0.89% Niederrhein-Weeze

3 0 0.89% Hahn

153 22 45.3% Summe

338 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances WRO

Weekly flights Share Distance in kilometers

138 40.83% 401-800

100 29.59% 1-400

341

Appendix

50 14.79% 1201-1600

44 13.02% 801-1200

6 1.78% 1601-2000

338 100.0% Summe

338 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types WRO

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

100 29.59% Boeing738-700Passenger 161 5420 71 937 M

90 26.63% ATR72 68 2222 22 526 M

52 15.38% ATR42-500 47 1630 19 563 M

40 11.83% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

14 4.14% BritishAerospace146-300Passenger 100 2817 44 776 M

14 4.14% AirbusIndustrieA320 156 5500 77 930 M

12 3.55% CanadairRegionalJet200 50 3713 23 859 M

8 2.37% Boeing737-500Passenger 111 4400 52 888 M

4 1.18% Boeing737-300Passenger 133 4180 63 888 M

2 0.59% CanadairRegionalJet900 88 3207 37 829 M

2 0.59% Embraer170 73 3889 36 869 M

338 100.0% Summe

338 Total Flights Week (03/16/ - 03/22/2009

Top 15 Carriers ZAG Weekly Flights Avg Day Share 386 55 60.69% CROATIA AIRLINES 56 8 8.81% LUFTHANSA GERMAN AIRLINES 34 5 5.35% AUSTRIAN AIRLINES AG 28 4 4.40% BH AIRLINES 28 4 4.40% AIR FRANCE 24 3 3.77% GERMANWINGS 24 3 3.77% MALEV HUNGARIAN AIRLINES 14 2 2.20% CZECH AIRLINES 10 1 1.57% TURKISH AIRLINES 10 1 1.57% AEROFLOT RUSSIAN AIRLINES 6 1 0.94% BELLE AIR 6 1 0.94% TAP AIR PORTUGAL 6 1 0.94% WIZZ AIR 342

Appendix

4 1 0.63% FLYBABOO

636 91 100.0% Summe

636 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins ZAG Weekly flights Avg Day Share 35 5 5.50% München 31 4 4.87% Wien 29 4 4.56% Split 28 4 4.40% Frankfurt 28 4 4.40% Sarajevo 25 4 3.93% Dubrovnik 21 3 3.30% Paris Charles de Gaulle 14 2 2.20% Zürich

12 2 1.89% Budapest

9 1 1.42% Zadar

9 1 1.42% London Heathrow

7 1 1.10% Prag

7 1 1.10% Skopje

7 1 1.10% Amsterdam

6 1 0.94% Brüssel

268 38 42.1% Summe

636 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations ZAG

Weekly flights Avg Day Share

35 5 5.50% München

31 4 4.87% Wien

30 4 4.72% Split

28 4 4.40% Frankfurt

28 4 4.40% Sarajevo

25 4 3.93% Dubrovnik

21 3 3.30% Paris Charles de Gaulle

14 2 2.20% Zürich

12 2 1.89% Budapest

10 1 1.57% Zadar

9 1 1.42% London Heathrow

7 1 1.10% Prag

7 1 1.10% Skopje 343

Appendix

7 1 1.10% Amsterdam

5 1 0.79% Brüssel

269 38 42.3% Summe

636 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances ZAG

Weekly flights Share Distance in kilometers

287 45.13% 1-400

220 34.59% 401-800

87 13.68% 801-1200

32 5.03% 1201-1600

10 1.57% 1601-2000

636 100.0% Summe

636 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types ZAG

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

204 32.08% AirbusIndustrieA319 133 6800 76 930 M

130 20.44% DeHavillandDHC-8Dash8-400Dash8q 73 2401 29 649 M

128 20.13% AirbusIndustrieA320 156 5500 77 930 M

38 5.97% CanadairRegionalJet200 50 3713 23 859 M

30 4.72% CanadairRegionalJet700 70 3674 34 859 M

28 4.40% ATR42-300/320 46 1944 17 491 M

24 3.77% Embraer120Brasilia 30 1390 12 463 M

16 2.52% DeHavillandDHC-8-300Dash/88Q 52 2177 20 528 M

14 2.20% Boeing737-500Passenger 111 4400 52 888 M

8 1.26% Fokker70 76 3732 42 856 M

8 1.26% Boeing738-700Passenger 161 5420 71 937 M

6 0.94% CanadairRegionalJet 50 3713 23 859 M

2 0.31% AvroRJ85 83 2796 44 763 M

636 100.0% Summe

636 Total Flights Week (03/16/ - 03/22/2009

344

Appendix

Top 15 Carriers ZRH Weekly Flights Avg Day Share 2352 336 55.43% SWISS 286 41 6.74% LUFTHANSA GERMAN AIRLINES 216 31 5.09% AIR BERLIN 166 24 3.91% BRITISH AIRWAYS 106 15 2.50% AIR FRANCE 86 12 2.03% SAS SCANDINAVIAN AIRLINES 68 10 1.60% KLM-ROYAL DUTCH AIRLINES 64 9 1.51% CIRRUS AIRLINES 54 8 1.27% EDELWEISS AIR 53 8 1.25% AUSTRIAN AIRLINES AG 52 7 1.23% TAP AIR PORTUGAL 42 6 0.99% GERMANWINGS 42 6 0.99% IBERIA 40 6 0.94% ADRIA AIRWAYS 36 5 0.85% NIKI 3663 523 86.3% Summe 4243 Total Flights Week (03/16/ - 03/22/2009

Top 15 Origins ZRH Weekly flights Avg Day Share 83 12 1.96% Paris Charles de Gaulle 83 12 1.96% London Heathrow 80 11 1.89% Düsseldorf 80 11 1.89% London City 77 11 1.81% Frankfurt 76 11 1.79% Wien 75 11 1.77% Berlin Tegel 69 10 1.63% München 65 9 1.53% Genf 62 9 1.46% Amsterdam 59 8 1.39% Hamburg 42 6 0.99% Madrid 42 6 0.99% Brüssel 42 6 0.99% Mailand Malpensa 41 6 0.97% Stuttgart 976 139 23.0% Summe 4243 Total Flights Week (03/16/ - 03/22/2009

Top 15 Destinations ZRH Weekly flights Avg Day Share 83 12 1.96% Paris Charles de Gaulle 83 12 1.96% London Heathrow 345

Appendix

80 11 1.89% Düsseldorf 80 11 1.89% London City 77 11 1.81% Frankfurt 76 11 1.79% Wien 75 11 1.77% Berlin Tegel 69 10 1.63% München 65 9 1.53% Genf 62 9 1.46% Amsterdam 59 8 1.39% Hamburg 42 6 0.99% Madrid 42 6 0.99% Mailand Malpensa 42 6 0.99% Brüssel 41 6 0.97% Stuttgart 976 139 23.0% Summe 4243 Total Flights Week (03/16/ - 03/22/2009

Top 15 Distances ZRH

Weekly flights Share Distance in kilometers

1740 41.01% 401-800

884 20.83% 1-400

505 11.90% 801-1200

279 6.58% 1201-1600

245 5.77% 1601-2000

106 2.50% 6001-6400

95 2.24% 2801-3200

55 1.30% 2001-2400

50 1.18% 4401-4800

44 1.04% 9201-9600

40 0.94% 6401-6800

38 0.90% 8801-9200

28 0.66% 10001-10400

23 0.54% 2401-2800

17 0.40% 7201-7600

4149 97.8% Summe

4243 Total Flights Week (03/16/ - 03/22/2009

Top 15 Aircraft Types ZRH

Weekly Avg Range MTOW Cruise Speed flights Share Aircraft Type Seats in km in t in km/h WTC

903 21.28% AirbusIndustrieA320 156 5500 77 930 M

652 15.37% AirbusIndustrieA319 133 6800 76 930 M

642 15.13% AvroRJ100 102 2554 46 763 M

346

Appendix

268 6.32% Fokker100 105 3111 46 919 M

244 5.75% AirbusIndustrieA321 184 5500 93 930 M

164 3.87% AirbusIndustrieA340-300 181 13500 260 978 H

146 3.44% AirbusIndustrieA330-200 260 11866 230 978 H

136 3.21% AvroRJ85 83 2796 44 763 M

101 2.38% Boeing737-500Passenger 111 4400 52 888 M

76 1.79% FairchildDornier328-100 31 1852 14 624 M

70 1.65% Boeing738-700Passenger 161 5420 71 937 M

70 1.65% Boeing737-700Passenger 127 6110 69 932 M

66 1.56% CanadairRegionalJet900 88 3207 37 829 M

58 1.37% BritishAerospace146-200Passenger 90 2909 42 776 M

56 1.32% Boeing737Passenger 143 4180 63 888 M

3652 86.1% Summe

4243 Total Flights Week (03/16/ - 03/22/2009

347

Appendix Minimum Connection Times for the Sample Airports

Int/Int Dom/Dom Dom/Int Int/Dom in Connection From Terminal To Terminal in HMM in HMM in HMM HMM BCN-BCN - 30 45 45 45 CDG-CDG TN- TN 45 115 130 130 CDG-CDG 1- TN 115 200 200 200 CDG-CDG 2A- TN 115 200 200 200 CDG-CDG 2B- TN 115 200 200 200 CDG-CDG 2D- TN 115 200 200 200 CDG-CDG 2E- TN 115 200 200 200 CDG-CDG 3- TN 115 200 200 200 CDG-CDG 2C- TN 115 200 200 200 CDG-CDG 2G- TN 115 200 200 200 CDG-CDG 2F- TN 115 200 200 200 CDG-CDG TN- 1 115 200 200 200 CDG-CDG 1- 1 130 130 130 130 CDG-CDG 2A- 1 200 200 200 200 CDG-CDG 2B- 1 200 200 200 200 CDG-CDG 2D- 1 200 200 200 200 CDG-CDG 2E- 1 200 200 200 200 CDG-CDG 3- 1 200 200 200 200 ORY-CDG - 300 300 300 300 ORY-ORY - 50 100 100 100 ORY-ORY S- S 20 100 100 100 ORY-ORY W- S 20 115 115 100 ORY-ORY S- W 20 115 115 100 ORY-ORY W- W 20 115 100 100 ORY-XPG - --- 400 400 400 AMS-AMS - 25 50 50 50 AMS-AMS TRN - --- 120 --- 120 AMS-AMS EU ------40 --- AMS-AMS EU TRN ------110 AMS-AMS - TRN ------100 100 AMS-AMS EU - TRN ------100 --- AMS-AMS - EU --- 40 ------AMS-AMS TRN - EU --- 50 ------AMS-AMS EU - EU ------40 AMS-AMS EU TRN - EU ------110 AMS-AMS - EU TRN ------110 AMS-AMS EU - EU TRN ------100 FRA-FRA - 45 45 45 45 LGW-LCY - 330 330 330 330 LGW-LGW N- N 45 45 45 45 LGW-LGW S- N 115 115 115 115 LGW-LGW N- S 115 115 115 115 LGW-LGW S- S 40 45 100 55 LGW-LGW ACI S- S ------50 --- LGW-LGW GCI S- S ------50 --- LGW-LGW JER S- S ------50 --- LGW-LHR - 230 230 230 230 LGW-LTN LHR-LCY - 330 330 330 330 LHR-LGW - 230 230 230 230 LHR-LHR 1- TN 130 130 130 130 LHR-LHR 2- TN 130 130 130 130 LHR-LHR 3- TN 130 130 130 130 LHR-LHR 4- TN 130 130 130 130 LHR-LHR TN- 1 130 130 130 130 348

Appendix Int/Int Dom/Dom Dom/Int Int/Dom in Connection From Terminal To Terminal in HMM in HMM in HMM HMM

LHR-LHR 1- 1 45 45 45 45 LHR-LHR 2- 1 115 115 115 115 LHR-LHR 3- 1 115 115 115 115 LHR-LHR 4- 1 100 100 115 100 LHR-LHR 5- 1 200 200 200 200 LHR-LHR TN- 2 130 130 130 130 LHR-LHR 1- 2 115 110 115 115 LHR-LHR 2- 2 ------100 LHR-LHR 3- 2 --- 115 115 115 LHR-LHR 4- 2 130 130 130 130 LHR-LHR 5- 2 200 200 200 200 LHR-LHR TN- 3 130 130 130 130 LHR-LHR 1- 3 115 115 115 115 LHR-LHR 2- 3 --- 115 115 115 LHR-LHR 3- 3 100 100 100 100 LHR-LHR 4- 3 130 130 130 130 LHR-LHR 5- 3 200 200 200 200 LHR-LHR TN- 4 130 130 130 130 LHR-LHR 1- 4 130 100 100 100 LHR-LHR 2- 4 130 130 130 130 LHR-LHR 3- 4 130 130 130 130 LHR-LHR 4- 4 130 45 45 45 LHR-LHR 5- 4 200 200 200 200 LHR-LHR 1- 5 200 200 200 200 LHR-LHR 2- 5 200 200 200 200 LHR-LHR 4- 4 130 45 45 45 LHR-LHR 5- 4 200 200 200 200 LHR-LHR 1- 5 200 200 200 200 LHR-LHR 2- 5 200 200 200 200 LHR-LHR 3- 5 200 200 200 200 LHR-LHR 4- 5 200 200 200 200 LHR-LHR 5- 5 100 100 100 100 LHR-LHR IE 1- 4 ------115 115 LHR-LHR ACI 1- 4 ------115 115 LHR-LHR GCI 1- 4 ------115 115 LHR-LHR JER 1- 4 ------115 115 LHR-LTN - 325 325 325 325 LHR-STN - 320 320 320 320 LCY-LCY - 30 30 30 30 LCY-LGW - 330 330 330 330 LCY-LHR - 300 300 300 300 LCY-LTN - 400 400 400 400 LCY-STN - 400 400 400 400 STN-LCY - 400 400 400 400 STN-LGW - 300 300 300 300 STN-LHR - 320 320 320 320 STN-LTN - 400 400 400 400 STN-STN - 45 45 45 45 STN-LCY - 400 400 400 400 STN-LGW - 300 300 300 300 STN-LHR - 320 320 320 320 STN-LTN - 400 400 400 400 STN-STN - 45 45 45 45 PMI-PMI - 30 45 45 45 MUC-MUC - 45 45 45 45 MUC-MUC 1- 1 35 35 35 35 MUC-MUC 2- 1 45 45 45 45 349

Appendix Int/Int Dom/Dom Dom/Int Int/Dom in Connection From Terminal To Terminal in HMM in HMM in HMM HMM

MUC-MUC 1- 2 45 45 45 45 MUC-MUC 2- 2 30 30 30 30 VIE-VIE - 30 30 30 30 VIE-VIE DE ------30 30 VIE-VIE - DE --- 30 --- 30 ATH-ATH - 45 45 55 45 ANR-ANR - 20 100 100 100 BRU-BRU - 20 50 50 50 BRU-BRU SC ------50 BRU-BRU - SC ------50 BRU-BRU SC - SC ------50 BRU-BRU GB - GB ------40 BRU-BRU - AMS --- 45 ------BRU-BRU - DUS --- 45 ------BRU-ZYR - --- 400 --- 400 BRU-ZYR - 20 100 100 100 BRU-BRU - 20 50 50 50 BRU-BRU SC ------50 BRU-BRU - SC ------50 BRU-BRU SC - SC ------50 BRU-BRU GB - GB ------40 BRU-BRU - AMS --- 45 ------BRU-BRU - DUS --- 45 ------BRU-ZYR - --- 400 --- 400 ZYR-BRU ------400 400 CPH-CPH - 30 45 45 45 DUS-DUS - 35 35 35 35 DUS-QDU - 300 300 300 300 MGL-MGL - 20 100 100 100 QDU-QDU - 35 35 35 35 HEL-HEL - 20 30 40 35 IST-IST - 30 130 115 100 SAW-SAW - 20 100 100 100 LIS-LIS - 45 100 100 100 TXL-SXF - 300 300 300 300 TXL-THF - 300 300 300 300 TXL-TXL - 30 45 45 45 TXL-TXL AT ------30 --- TXL-TXL GB ------30 --- TXL-TXL - AT --- 30 ------TXL-TXL - GB --- 30 ------SXF-SXF - 30 100 100 100 SXF-THF - 200 200 200 200 SXF-TXL - 300 300 300 300 PRG-PRG - 40 55 55 55 PRG-PRG EU - EU ------45 ARN-ARN TN- TN 20 ------ARN-ARN 2- TN 20 100 100 100 ARN-ARN 3- TN 20 100 100 100 ARN-ARN 4- TN 20 100 100 100 ARN-ARN 5- TN 20 100 100 100 ARN-ARN TN- 2 20 100 100 100 ARN-ARN 2- 2 15 45 50 30 ARN-ARN 3- 2 25 45 ------ARN-ARN 4- 2 25 45 ------ARN-ARN 5- 2 ------50 100 ARN-ARN TX- 2 20 100 100 100 350

Appendix Int/Int Dom/Dom Dom/Int Int/Dom in Connection From Terminal To Terminal in HMM in HMM in HMM HMM

ARN-ARN TN- 3 20 100 100 100 ARN-ARN 2- 3 25 --- 50 --- ARN-ARN 3- 3 15 ------ARN-ARN 4- 3 25 ------ARN-ARN 5- 3 ------50 --- ARN-ARN TX- 3 20 100 100 100 BHX-BHX - 30 45 45 45 BSL-BSL - 30 30 30 30 BSL-MLH - 30 30 30 30 CGN-CGN - 30 30 30 30 CGN-QKL - 300 300 300 300 QKL-CGN - 300 300 300 300 QKL-QKL - 30 ------CIA-CIA - 25 100 100 100 CIA-FCO - 230 230 230 230 FCO-CIA - 230 230 230 230 FCO-FCO - 45 100 100 45 DRS-DRS - 45 100 100 100 DUB-DUB - 45 45 45 45 EDI-EDI - 30 45 45 100 FMO-FMO - 20 20 20 20 GLA-GLA - 30 45 45 45 GLA-PIK - 200 200 200 200 PIK-GLA - 200 200 200 200 PIK-PIK - --- 45 110 45 GRZ-GRZ - 30 30 30 30 HAJ-HAJ - 25 30 30 30 HAM-HAM - 35 35 35 35 LBC-LBC - 20 100 100 100 HHN-HHN - 20 100 100 100 LBA-LBA - 30 45 45 100 LEJ-LEJ - 20 100 100 100 LGG-LGG - --- 100 100 --- GNB-GNB - 15 ------LYS-LYS - 35 45 45 45 MAD-MAD 1- 1 45 45 45 45 MAD-MAD 2- 1 100 100 100 100 MAD-MAD 3- 1 100 100 100 100 MAD-MAD 4- 1 230 230 230 230 MAD-MAD 4S- 1 245 245 245 245 MAD-MAD 1- 2 100 100 100 100 MAD-MAD 2- 2 45 45 45 45 MAD-MAD 3- 2 45 45 45 45 MAD-MAD 4- 2 230 230 230 230 MAD-MAD 4S- 2 245 245 245 245 MAD-MAD 1- 3 100 100 100 100 MAD-MAD 2- 3 45 45 45 45 MAD-MAD 3- 3 45 45 45 45 MAD-MAD 4- 3 230 230 230 230 MAD-MAD 4S- 3 245 245 245 245 MAD-MAD 1- 4 230 230 230 230 MAD-MAD 2- 4 230 230 230 230 MAN-MAN - 30 40 45 40 MAN-MAN CA ------50 50 MAN-MAN US ------50 50 MAN-MAN - CA --- 50 --- 50

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Appendix Int/Int Dom/Dom Dom/Int Int/Dom in Connection From Terminal To Terminal in HMM in HMM in HMM HMM

MAN-MAN - US --- 50 --- 50 MXP-BGY - --- 300 300 300 MXP-LIN - 315 315 315 315 MXP-MXP 1- 1 45 50 50 45 MXP-MXP 2- 1 200 200 200 200 MXP-MXP 1- 2 200 200 200 200 MXP-MXP 2- 2 130 130 130 130 MXP-MXP SC 1- 1 ------45 50 MXP-MXP SC 2- 1 ------100 105 MXP-MXP SC 1- 2 ------100 105 MXP-MXP SC 2- 2 ------40 50 MXP-MXP 1- SC 1 --- 45 --- 50 NCE-NCE 1- 1 35 45 45 45 NCE-NCE 2- 1 100 100 100 100 NCE-NCE 1- 2 100 100 100 100 NCE-NCE 2- 2 35 45 45 45 NUE-NUE - 30 30 30 30 OSL-OSL - 35 40 50 40 OSL-RYG - 400 400 400 400 OSL-TRF - 400 400 400 400 RYG-OSL - 400 400 400 400 RYG-RYG - 20 100 100 100 PSA-PSA - 40 45 45 45 RHO-RHO - 20 100 100 100 RTM-RTM - 20 100 100 100 RTM-RTM EU ------20 --- RTM-RTM EU - TRN ------100 --- RTM-RTM - EU --- 20 ------RTM-RTM TRN - EU --- 50 ------RTM-RTM EU - EU ------20 SCN-SCN - 20 100 100 100 STR-STR - 30 30 30 30 ZWS-ZWS - 30 ------SZG-SZG - 30 30 30 30 WAW- WAW - 35 50 100 40 WRO-WRO - 25 40 40 40 ZAG-ZAG - 30 100 100 100 ZAG-ZAG EU ------40 100 ZAG-ZAG - EU --- 40 --- 100 ZAG-ZAG EU - EU ------40 ZRH-ZRH - 40 40 40 40

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Appendix

Interview- No. Name Vorname Institution Country Position Type of Contact E-Mail URL 1 Graham Prof. Dr. Anne University of Westminster, London UK Senior Lecturer Visit, Interview, E-Mail, Phone [email protected] www.westminster.ac.uk/transport/ 2 Müller Prof. Dr. Jürgen Berlin School of Economics Germany Senior Lecturer E-Mail, Phone [email protected] www.fhw-berlin.de 3 Daduna Prof. Dr. Joachim Berlin School of Economics Germany Senior Lecturer Interview, E-Mail, Phone [email protected] www.fhw-berlin.de 4 Niemeier Prof. Dr. Hans-Martin Hochschule Bremen Germany Senior Lecturer Visit, Interview [email protected] www.hs-bremen.de 5 Starkie David Case Associates UK Senior Associate Visit, Interview [email protected] www.casecon.com 6 Dennis Nigel University of Westminster, London UK Senior Lecturer Visit, E-Mail [email protected] www.westminster.ac.uk/transport/ 7 Visser Prof. Dr. Delft University of Technology Netherlands Senior Lecturer E-Mail [email protected] www.tudelft.nl 8 Knöpfle Klaus kkconsult Germany Dipl. Ing. Interview, E-Mail [email protected] www.t-online.de www.avc-aachen.de; 9 Laubrock Michael Airport Research Center GmbH Germany Managing Partner Interview, E-Mail [email protected] www.airport-consultants.com 10 Crimmin Darien Harvard University, Cambridge USA Manager Harvard Green Campus Initiative Interview, E-Mail [email protected] www.greencampus.harvard.edu 11 Weersma Philip A.B. Hogeschool van Amsterdam Netherlands Program Manager Interview [email protected] www.hva.nl 12 Haynes Chris Airport Charges UK Account Executive Interview [email protected] www.airportcharges.com European Organisation for the Safety of Air Navigation 13 Melrose Alan (EUROCONTROL) Belgium Airport Environmental Specialist Interview [email protected] www.eurocontrol.int European Organisation for the Safety of Air Navigation 14 Fantauzzi Marco (EUROCONTROL) Belgium Airport Data Analyst E-Mail [email protected] www.eurocontrol.int 15 Green Stuart Flightstats.com USA Manager E-Mail [email protected] www.flightstats.com 16 Lamberg Daniel Air Berlin Germany Pilot Interview, E-Mail, Phone [email protected] www.airberlin.com 17 Persch Bernhard Arbeitsgemeinschaft Deutscher Flughäfen (ADV) Germany Economic Affairs E-Mail [email protected] www.adv-net.org International Center for Competitiveness Studies 18 Paleari Stefano in the Aviation Industry (ICCSAI) Italy Scientific Director Interview, E-Mail [email protected] www.iccsai.eu 19 Fritz Jörg-Stefan Lufthansa German Airlines Germany Referent Regulatory Affairs & Strategy Interview [email protected] www.dlh.de 20 Natchia-Kouao Jacques SIMCORE SARL France Director Commercial Interview [email protected] www.simcore.fr 21 Camara Mory OAG Travel Solutions USA International Sales Manager E-Mail [email protected] www.oag.com IVAO Deutschland Membership 22 Schmitz Alexander International Virtual Aviation Organization (IVAO) Germany Coordinator E-Mail [email protected] www.ivao.de 23 Zinna John Federal Aviation Administration (FAA) USA FAA Technical Center E-Mail [email protected] www.faa.gov 24 Bradford Gregory Airport Tools USA Manager E-Mail [email protected] www.yahoo.com 25 Eypper Charles H. Berlitz School of Languages Berlin USA, Germany English Teacher at Berlitz School Berlin E-Mail, Phone [email protected] www.berlitz.de

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