17th atrs world conference paper -...

MAGALHÃES, L; REIS, V.; MACÁRIO, R.

1

17TH ATRS WORLD CONFERENCE PAPER

FACTOR AND CLUSTER ANALYSIS APPLIED TO FLEXIBLE AIRPORTS’

PERFORMANCE DATA

Liliana Magalhães, Vasco Reis, Rosário Macário

Instituto Superior Técnico – Universidade de Lisboa, DECivil, CESUR

Avenida Rovisco Pais, 1049-001 Lisboa, Portugal

+351-218418424

[email protected]

[email protected]

[email protected]

Presenter name: Liliana Magalhães

ABSTRACT

Airport operators have to anticipate future possible scenarios which might occur so that

their airport can face the changes. Unpredictable changes on the circumstances lead

forecasts often to fail as the future is not foreseeable. Flexibility helps to maintain or

increase airport’s performance levels by improving the adaptability of the airport’s

functions to external changes. The objective of this work is to understand if flexible airports

are at least able to keep their perform results towards external changes. Flexible airports

were identified based on the literature review and a survey launched to worldwide airports.

To achieve this, the ATRS Airport Benchmarking Report from 2004 until 2011 (except

2010) was used as data sample, for the following performance categories: productivity and

efficiency, costs and financial. A factorial analysis was performed to reduce the variables,

as most of them were highly correlated, to observe how airport evolved regarding the

obtained factors. Using this data, a cluster analysis was conducted to explore whether

flexible airport are similar among them or not during that time interval. Flexible airports

performance seems to be similar to the other airports. However, there are some evidences

supporting the advantages of flexibility but more studies are required.

KEYWORDS: Airport Flexibility, Factor Analysis, Cluster Analysis

mailto:[email protected]




2

CLASSIFICATION: Airport Strategy, Management and Operations; Airport and Airline

Performance

INTRODUCTION

The current economic turmoil has generated a high level of uncertainty in the expected

evolution of air transport markets. Airlines are able to change their network structures

overnight. The oil price, flu epidemics, and financial and economic woes further add to the

volatility of aviation demand development. Combined with tensions between economic and

environmental impacts, this makes airport strategic planning a challenging task. As de

Neufville (2008, p.36) points out “airport planning paradigm is shifting from the traditional

pattern, which is determined by high standards, established customers and long-term

forecast, to that of recognizing great uncertainty at forecasts, broad range standards and

potential for a rapidly changing customer’s base.” Airports’ expansion became increasingly

uncertain and risky, which has led many authors to advocate the need of airports being

increasingly flexible: that it is, able to adjust towards necessity.

Additionally, the uncertainty regarding the composition of demand is highly important

nowadays. Low-cost carriers have been growing and contribute to increase the dynamic of

aviation market due to their strategy of minimizing costs. Their routes change with high

frequency, creating new ones and promoting the development of regional airports.

Moreover, they require specific features at airports in order to fulfil their goals (e.g. quick

turnaround times and simple passenger terminals). Therefore, airports must be able to adapt

to their clients’ needs – airlines requirements – without high investments on it.

The theoretical roots of flexibility can be traced back to the late 1950s (Shuchi et al., 2012).

Flexibility has been applied in other fields besides airport infrastructures: manufacturing

engineering (Suarez & Cusumano, 1991; Taylor, 1991; Schulz et al., 2000; Ross et al.

2008); engineering design, e.g. bridges, oil platforms (Neufville and Scholtes, 2011); and

building design (Till and Schneider, 2005; Schneider and Till, 2007) to name a few.

However, the need for flexibility in airport design is a recent recognition (de Neufville and

Belin, 2002; Edwards, 2005; de Neufville, 2008). Only few authors have been studied the


3

concept of flexibility and no universal definition was accepted so far (Morlok and Chang,

2004; Edwards, 2005; Burghouwt, 2007; de Neufville, 2008; Gil & Tether, 2011; Shuchi et

al., 2012).

Only few authors present their own definition of this concept. For Morlok and Chang

(2004) flexibility is described as system’s ability to adapt to external changes but keeping

the system’s performance levels satisfactory. This definition is similar to our understating

of flexibility and it matches with the advantage of flexibility that we are analysing in this

work. de Neufville (2008, p.53) defines flexibility from a design perspective, as a “group of

technical features that enable the owners to change, easily and inexpensively, the

configuration of their facility to meet new needs”. Edwards (2005) states that flexible

design is used to reply to particular changing requirements. This second definition differs

from the previous one since it does not mention expenses or the easiness of changing. It

does not consider the way of doing the change. It is just concerned with the ability to

change. For Burghouwt (2007), flexibility is the same as re-adaptability and is defined as an

ability to perform constant adjustments towards changing circumstances. For Shuchi et al.

(2012, p. 350) flexibility is also consider as the same as adaptability, and they define it as

“the ability to adapt to the environment without making any permanent change to the

environment”. Gil and Tether (2011) associate flexibility with design and risk management.

This approach differs from all the others founded for airport flexibility.

Some similarities can be found among these definitions. One can conclude that for the four

authors, flexibility is associated with some sort of change, as all of them use the word

“change” or a variation of it. Moreover, all of the authors consider flexibility as an ability

of the airport, directly or indirectly. For two of them this is explicitly mentioned in the

definition. Edwards (2005) defines flexibility as a response and de Neufville (2008) as a

feature, so our understanding is that these words can be considered as synonyms of ability.

Our understanding of flexibility is that it is the ability of having an infrastructure as

mutable as possible to adapt to future requirements and able to, at least, keep its

performance results. Moreover, from our point of view flexibility is closely linked with

optimizing the investment and the infrastructure’s performance by reducing its idleness.

Moreover, the application of flexibility can be performed at strategic or operational levels


4

and for each level different flexible options are required. For example, at operational level

it is common to use moveable walls and at strategic level the most common options is the

land saving for future expansions (Magalhães et al., 2013)

Airports have been embedded with flexibility to deal with volatile market environment and

uncertainty about future traffic demand and composition. Magalhães et al. (2013) present

several examples of flexible airports all over the world: Dublin International Airport,

Niagara Falls International Airport, Vancouver International Airport, Amsterdam Schiphol

International Airport, Southampton Airport and Bangkok Suvarnabhumi Airport.

Flexibility is very important for infrastructures with long life-cycle such as airports, so that

they can be able to change their functions and processes to respond to external necessities

with minimum costs. Moreover, as de Neufville (2008, p. 54) states, “flexible designs

incorporate capabilities to adjust easily to different scenarios. They create ‘real options’,

similar to financial options (such as puts and calls) that give their owners the right, but not

an obligation, to take an action, now or in the future”. This means that the infrastructure is

embedded with the ability to adapt or change its function but only if necessary managers

“activate” this feature. More than keeping the costs at their minimum, flexibility has the

advantage of keeping the system’s performance levels satisfactory whenever it is necessary

to adapt to external changes (Morlok and Chang, 2004), which is for us the key advantage.

Building an airport so large that it can deal with higher future demand does not mean to be

flexible. Being flexible is related with exploring the infrastructure until the operation

performance reaches its maximum that is when we reach the maximum efficiency.

The objective of this work is to understand how the so-called flexible airports’ performance

differs from the other airports. We want to assess whether flexible airport are at least able

to keep their performance results through time, when compared with the other airports. We

want to explore how flexible airports performance evolved over time. Since flexible

airports are by definition better prepared to deal with the uncertain, we expect that these

airports are more resilient and as such, their performances should be almost stable over

time. To achieve this, we performed a factorial analysis on the ATRS Airport

Benchmarking Report of 2004 in order to reduce the provided variables which are highly


5

correlated. The obtained factorial structure was applied to other years (2011 to 2005 except

2010) in order to analyse how these configuration of factors evolved through this time

interval. As the factors represent different typologies configuration of variables, the

analysis over this time period will allow us to identify changes through time. Then, a

cluster analysis was performed for each year of the time period in order to observe how

flexibility has been influencing the airports development for each obtained factor.

Moreover, we also wanted to observe whether flexible airports are similar among them or

not for the period under analysis.

DATA SAMPLE AND FLEXIBLE AIRPORTS CONSIDERED

The base data sample used is the ATRS Airport Benchmarking Report of 2004 that

considers airports from North America, Europe and Asia. Then, we also used the ATRS

Airport benchmarking Reports of 2011, 2009, 2008, 2007, 2006, 2005 but we only

considered the information related with those 140 airports. The data provided at the reports

is two years lagged from the year of publication. For instance, the report of 2011 contains

data from 2009 and so on. Our designation for the years will be hereinafter related with the

data presented at the report to avoid misperceptions.

Deciding if an airport is flexible or not is not a trivial task since so far, there is no common

definition as explained above. We considered as flexible airports the ones pointed by

Magalhães et al. (2013). This classification result from a literature review on airport

flexibility and also a survey released on 2012 to worldwide airports. The goals of this

survey were: to understand whether an airport is flexible, and also which are the flexible

options that the airport has. Flexible options can be applied to different levels of

development: strategic, tactical and operational. Examples of flexible options at the three

levels are: land saving for future expansion ate strategic level, moveable partition walls at

tactical level and, for instance, moving systems such as check-in counters at operational

level. The airports that were considered as flexible are the following:


6

Table 1 - Airports considered as being flexible

North America Europe Asia

AUS AMS BKK

BNA ARN WLG

DEN ATH

DTW BRU

JAX CDG

MEM DUB

PDX LIS

RNO

YEG

YUL

YVR

The Airport Benchmarking Reports provided by ATRS present performance indicators for

the following aspects of airport operation: productivity and efficiency, unit costs and cost

competitiveness, financial results and airport charges. The airport charges have no

implications for our objective so we decided to not consider this type of indicators.

Regarding the other three groups of data we consider only the following indicators:

Productivity and Efficiency

o Passenger per employee;

o Aircraft movements per employee;

o Passengers per gate;

o Passengers per square meter of terminal space;

o Aircraft movements per runway;

Unit Costs and Cost Competitiveness

o Labour cost per passenger;

o Labour cost per movement;


7

o Variable cost per passenger;

o Variable cost per aircraft movement;

Financial results

o Aeronautical revenue per aircraft movement;

o Concession revenue per passenger;

o Operational revenue per passenger;

o Operational revenue per movement;

o Operational revenue per employee.

These indicators were chosen based on the following rational: (1) to have indicators from

the three performance groups; (2) to have indicators which are not repeated and that

characterize the group to avoid overlap of information. These fourteen indicators will

hereinafter be designated as variables.

METHDOLOGY

To achieve the objectives of this work we conducted a factorial analysis on the ATRS

Airport Benchmarking data of 2002 to reduce the provided variables which are highly

correlated. The obtained factorial structure was applied to the data for 2009 to 2003 except

2008, in order to analyse how these configuration of factors evolved through this time

interval. After that, a cluster analysis was performed for each year in order to observe how

the airports have been developing for each obtained factor. Methodology will be explained

with more details below. The software used to perform these analyses were the SPSS

Statistics and MS Excel.

Firstly, we conducted a correlation analysis over the base data sample (2002) and

concluded that most of the variables are significantly correlated. The obtained significance

values were higher than 0.25 for most of the compared pairs of variables. As such, we

decided to perform a Factorial Analysis to reduce the number of variables to take into

account. The goal with this reduction is to be more efficient and parsimonious.

Factorial Analysis

Factor analysis or principal components analysis is used to reduce a multivariate data set

and to interpret data. As Washington et al. (2003) explains, this method provides a more


8

parsimonious description of the data and uses linear combinations of the original variables

to clarify the variance-covariance structure. The method builds factors which are not

directly observed in the sample by grouping the variables provided with a certain

“explanation” score for each of them (Marôco, 2011).

To measure the adequacy of the extracted factors from the sample we focus on the Kayser-

Meyer-Olkin (KMO) value and also on the percentage of the total variance explained by the

factors. According to Marôco (2011) the KMO value should be equal or higher than 0.8 to

consider the factorial structure as a good one. However, we considered a KMO value equal

or higher than 0.7 already a good value. We used the principal components method and the

number of factors retained are based on the Kaiser criteria, which only retains factors with

an eigenvalue higher than 1.The rotation method used was the Varimax whose outcome is a

factorial structure where one or more variables are highly related with only one factor and

little associated with the others. The scores estimation was based on the Regression method

and the analysis was performed over the correlation matrix. This analysis was conducted on

SPSS Statistics software. To apply the factorial structure obtained for 2002 to the other

years it was necessary to determine the standardize variables for each year.

Cluster Analysis

Cluster analysis is an exploratory technique that groups variables into homogeneous groups

with one or more common characteristics. Each observation from a certain cluster is similar

to the others which belong to that same cluster but different from the observations which

belong to other clusters (Marôco, 2011). The clusters identification is based on the measure

of the similarity which is usually based on a metric distance. We used as hierarchical

method the Ward’s method, which groups observations in order to minimize the sum of the

squared errors. For the interval we used the Square Euclidean Distance. This analysis was

conducted on SPSS Statistics software.

The clusters analysis is based on the values obtained for each airport and each factor for

each year, which result from the application of the factorial structure obtained for 2002 to

the other years. Based on this, we were able to obtain a cluster analysis for each airport

considering the outcomes of the factorial analysis.


9

RESULTS AND DISCUSSION

The correlation matrix is presented in Appendix 1. It possible to observe that only two of

the variables are not correlated with the other ones: passengers per square meter of terminal

space and aircraft movements per runway. The majority of the variables are highly

correlated as the significance values are higher than 0.25. We highlighted those cells in red

to help their identification.

Factorial Analysis

The rotated component matrix obtained for the factorial analysis is presented in Table 2.

Four factors were obtained. By looking at the colours in the table is possible to observe

which variables are more relevant for each factor and they are in green. The obtained KMO

was 0.708 and the total variance explained was 83.499%.

Table 2 - Rotated Component Matrix

Factor 1 Factor 2 Factor 3 Factor 4

Passengers per employee (2002) (thousands) -,062 -,690 -,119 ,558

Aircraft movement per employee (2002) -,272 -,679 -,468 ,291

Passengers per Gate (2002) ,113 -,049 ,887 -,022

Passengers per M2 of Terminal Space (2002) -,244 ,062 ,598 ,107

Aircraft Movement per Runway (2002) -,041 ,034 ,089 ,913

Labour Cost per Passenger (2002) (US$) ,161 ,911 -,153 ,122

Labour Cost per Aircraft Movement (2002) (US$) ,557 ,751 ,075 ,152

Variable Cost per Passenger (2002) (US$) ,868 ,391 -,125 -,055

Variable Cost per Aircraft Movement (2002) (US$) ,968 ,174 ,023 -,030

Aeronautical Revenue per Aircraft Movement (2002) ,940 ,144 -,040 -,033

Concession Revenue per Passenger (2002) ,846 ,188 ,103 -,027

Operational Revenue per Passenger (2002) (US$) ,870 ,338 -,128 -,132

Operational Revenue per Aircraft Movement (2002) (US$) ,969 ,131 ,042 -,066

Operational Revenue per Employee (2002) (US$) ,729 -,277 -,337 ,126

Factor 1 can be designated as the financial factor as this factor is mainly explained by

costs and revenues: variable cost per passenger, variable cost per aircraft movement,

aeronautical revenue per aircraft movement, concession revenue per passenger, operational


10

revenue per passenger and per movement. Factor 2 can be designated as the labour cost

factor since the more relevant variables to characterize this factor are the labour cost per

passenger and the labour cost per aircraft movement. Factor 3 is the terminal’s

performance factor as the more relevant variables for this factor are passengers per gate

and passengers per square meter of terminal space. Lastly, Factor 4 can be designated as the

airport performance factor as the more relevant variables are aircraft movements per

runway and passengers per employee.

Flexibility has been directly associated with performance as flexible airports are more able

to adapt to changes and remain competitive. Moreover, definitions like the one provided by

de Neufville (2008) lead us to associate flexibility with the terminal’s performance since it

mentions a group of technical features to change the facility’s configuration. Our

understanding is that being flexible is also a synonymous of being resilient – a flexible

airport should be able to, at least, keep is performance results towards new external

changes. From the four previous factors, our understanding is that Factor 3 is the one which

better characterizes its gains since most interventions are performed at the terminal.

However, Factors 1 and 4 can also characterize the performance results of flexible airports

either.

Appendix 2 presents for each factor and airport, the differences between each year and

2002. Comparing 2004 with 2002 for Factors 3 and 4 one can conclude that most airports

had a negative evolution and this situation kept for 2005. However, for the further years

they have better results. The year 2004 seems to be a difficult year for most of the airports

when compared with 2002 regarding Factor 1 as their results got worse especially for Asian

airports. This situation slightly changes for the further years of analysis. Factor 2 is the one

for which more airport presents bad results during more years, except for 2002.

Focusing on the airports, on Factor 1 FRA constantly appears as one of the airports with

lower results, except for 2004-2002, 2007-2002 and 2009-2002. This led us to believe that

this airport probably was able to invert its financial situation. In opposition, ICN airport

constantly appears with the highest results for this factor. For 2007-2002 and 2009-2002

CGD, which is a flexible airport, appears right next to ICN with better financial results.


11

Factor 2 is headed by ARN, which is a flexible airport, except for 2007-2002 and 2009-

2002 where FRA starts to lead. Despite the increase in the labour costs per passenger and

aircraft movement, FRA was able to increase its revenues (Factor 1) during the same

period. Except for 2003-2002 and 2004-2002, JFK is the airport which presents lower

values for Factor 2 which means lower labour costs per passenger and aircraft movement.

Most of the leaders of Factor 3 are North American airports. SAN and OAK appear on the

top for all the intervals in analysis, but others like CLT, SNA and SJC appears most of the

times. This is the factor where more airports present better results. Nevertheless, MDW

constantly present the worst results.

Factor 4 is headed mostly by the same airports as Factor 3 except for 2004-2002, where

only NRT is the leader. For 2009-2002 one flexible airport presents the highest results:

ATH. For this factor, ATL present the lowest results for 2004-2002, 2005-2002 and 2006-

2002. Despite the good results of few of the airports considered as being flexible, the

majority do not present significant higher results for one particular factor.

Magalhães et al. (2013) concluded from the survey results that most of the flexible

solutions used nowadays are applied at the terminal. Since Factor 3 is the one which

describes the terminal’s performance, we decided to plot only the results for this factor.

Figure 1 presents the average results obtained by flexible and non-flexible airports through

the years. The objective of this figure is to help to analyse if flexible airports are able to

keep their performance results through time. Observing the figure two aspects can be

highlighted: non-flexible airports present better average performance results, and on

average the flexible airports were not able to keep their performance results. This general

overview of this factor is not favourable to our hypothesis for flexible airports. However,

we analysed the situation for each flexible airport in North America (Figure 2), Europe

(Figure 3) and Asia (Figure 4). The averages, the maximums and minimums for each year

were calculated considering only the airports from each continent.


12

Figure 1 - Average results for Factor 3 for Flexible and Non-Flexible Airports

For North America is possible to observe that YEG is always above the average for this

factor and since 2004, so has been AUS. YUL was above the average until 2007 but in

2009 it dropped. It is important to notice that airports like MEM and RNO present almost

constant results since 2005. This is consistent with the perspective that a flexible airport

should be more resilient to external changes and at least, able to keep its performance.

In Europe, CDG, AMS and DUB are always above the average except in 2005 for the last

two airports. Also here, the behaviour of BRU, ARN and CDG can be considered as quite

stable. DUB is the only flexible airport which has been close to the maximum values since

2006. Moreover, its increase from 2005 to 2006 is quite high. BRU, ARN and LIS are the

only airports that present negative values for 2009.

In Asia we only have two airports. However, we can observe that WLG is increasing its

results since 2004 and possibly soon will leave the negative zone. As for BKK, despite the

slightly decrease in 2007 it seems to be growing and possibly it is already above the

average.


13

Figure 2 - Results for Factor 3 for Flexible Airports in North America

Figure 3 - Results for Factor 3 for Flexible Airports in Europe


14

Figure 4 - Results for Factor 3 for Flexible Airports in Asia

The behaviour of each flexible airport is quite specific and no common performance results

on a particular factor were found. However, this study launches a new research topic as

some of the flexible airport present a few evidences of being more resilient than the non-

flexible airports. This is based on their constant performance results but requires further

analysis.

Clusters Analysis

The cluster membership is presented on Appendix 3. We obtained the analysis from five to

twelve clusters, but we decided to base our conclusions for the eight clusters grouping as

the results are more balance for this case. One of the aspects that capture our attention was

the fact that apart from 2006, Cluster 8 is constantly almost filled only by Japanese airports.

This means that these two airports (NRT and KIX) are very similar but different from the

others. Moreover, ATL and the airports in New York area (JFK, EWR and LGA) are

constantly in a specific cluster with no more than 3 or 4 other airports in some years, expect

for 2004 and 2005. So, these airports must have common performance characteristics that

differentiate them from the others.


15

There is no specific cluster that gathers all flexible airports. However, CDG and DUB are

usually on the same cluster or in different clusters but with few members. This is not the

case of LIS which only appears more or less isolated for 2002. The Canadian flexible

airports always appear in the same cluster but sometimes, like for 2002 and 2009, in a

cluster that is different from the one where non-flexible Canadian airports are. Apart from

2004, the flexible airports MEM and PDX are also appearing together.

We estimated the average for each cluster regarding each factor and built graphics that are

presented on Figure 5, Figure 6 and Figure 7 for Factors 1, 3 and 4 only as we consider

Factor 2 as not appropriate to analyse flexibility.

For Factor 1 is possible to observe that Cluster 8 presents the highest results. This cluster is

composed only by Japanese airports. Only for 2006 the Japanese airports are substitute by

CDG, MUC and VIE, and as we can observe, the results dropped significantly. Cluster 1,

despite being constant, presents the lowest results. This cluster in 2002 contains airports

from all continents, including the flexible airports AUS, BNA, JAX, RNO and ATH, but

this scenario changes and in 2009 it has only three European airports (MXP, PRG and

RIX). The others are from North America and Asia.

Figure 5 - Annual Average of Factor 1 for each Cluster


16

For Factor 3, three clusters present positive results for all years: 4, 5 and 7. However, only

cluster 7 presents a tendency to increase its results. This cluster is composed only by

European airports for 2009, including CDG and DUB. Cluster 4 does not present any

flexible airport for 2009 and it is composed by two American airports (MDW and CLT)

and eight Asian airports. Regarding Cluster 5, it has 32 members for 2009 from which two

of the airports are flexible: DEN and BKK.


Regarding Factor 4, the clusters 2, 4 and 6 always present positive results. However, cluster

6 is more constant than the other ones and it is composed by fourteen European airports and

one airport from North American for 2009. None of them are flexible. Cluster 2 is

composed by several airports until 2005 but since 2006 this clusters is mainly taken by

ATL and the three airports in New York (JFK, EWR and LGA), and in 2009 they are the

only members of the cluster. Cluster 4 is mainly a North American and Asian cluster but is

possible to find a few European airports in some years (2002, 2003, 2004 and 2006). And

here, several flexible airports can be found in different years but in 2009 there is none.


17


No major conclusion can be drawn for all the flexible airports in terms of cluster

membership. However, some flexible airports constantly appear in the same cluster. For

instance, AUS, BNA, JAX and RNO are constantly in Cluster 1. YEG, YUL and YVR are

also usually on Cluster 2. Lastly, AMS, ARN and ATH constantly appear together in

Cluster 5 or Cluster 3.

CONCLUSIONS

This work represents an adding contribute to increase the knowledge on the advantages of

flexibility applied to airports. Our goal was to understand if the so-called flexible airports

were able to at least keep its performance results through time, comparing with the non-

flexible airports. The used data did not allow us to validate our hypothesis. This might be

related with two aspects: the performance variables that we choose may not be the most

adequate to analyse the advantages of flexibility or; the advantages of flexibility may not be

related with our hypothesis, which is having a resilient performance towards change. More

studies on this topic are needed.

It is also important to notice that the results obtained leads to conclude that flexible airports

do not present higher performance results when compared to the other ones. This is

consistent with Morlok and Chang (2004) perspective that flexibility is related with


18

keeping satisfactory performance results towards external changes. This does not

necessarily mean higher performance results when compared to the non-flexible airports,

but being resilient. However, we were not able to validate this hypothesis with the data

used. Still, some of the flexible airports present higher results on variables related with

financial results and terminal’s performance when compared with the non-flexible airports.

But these results cannot be generalized to all the flexible airports in the data sample.

We also wanted to explore how flexible airport have been developing through time.

Regarding this aspect, we were able to notice that some of the flexible airports present a

stable behaviour in terms of performance results. This is consistent with our view of

flexible airport’s ability of keeping its performance results through time. This result is a

valuable and new insight on the study of airport flexibility that should be explored with

more detail for each flexible airport.

The classification of which airports are flexible is based on a previous work that identified

20 airports as being flexible. However, we are convinced that there are more than 20

airports that can be considered as flexible but it was not possible to identify them so far.

With a higher sample of flexible airports, especially for Europe and Asia, more conclusions

can be drawn regarding their performance results. It is our understanding that the next step

on the topic of airport flexibility is to develop a new tool to classify an airport as flexible or

not.

ACKNOWLEDEGMENTS

We would like to acknowledge to our colleague Luís Martinez, Ph.D. in Transportation,

who kindly provide valuable insights related with the factorial and clusters analysis to our

work.

REFERENCES

Air Transport Research Society (2004), Airport Benchmarking Report – Global Standards

for Airport Excellence, Parts I, II and III, Centre for Transportation Studies, Vancouver,

Canada


19


for Airport Excellence Parts I, II and III, Centre for Transportation Studies, Vancouver,

Canada



Canada



Canada



Canada

Air Transport Research Society, (2009), Airport Benchmarking Report – Global Standards


Canada



Canada

Burghouwt, G. (2007), Airline network development in Europe and its implications for

Airport Planning, Ashgate, Hampshire, England

de Neufville, R. and Belin, S. (2002), “Airport passenger buildings: efficiency through

shared use of facilities”, Journal of Transportation Engineering, May/June

de Neufville, R. (2008), “Low-cost Airports for low-cost airlines: flexible design to manage

the risks”, Transportation Planning and Technology. Vol. 31, No. 1, pp. 35-68

de Neufville, R. and Scholtes, S. (2011), Flexibility in Engineering Design, Engineering

Systems, The MIT Press, Cambridge, Massachusetts, EUA


20

Edwards, B. (2005), The Modern Airport Terminal. Second ed. London and and New

York: Spon Press. 1-276

Gil, N. and Tether, B., (2011), “Project risk management and design flexibility: analysing a

case and conditions of complementarity”, Research Policy, 40, pp. 415-428

Magalhães, L., Reis, V. and Macário, R. (2013), “Airport Flexibility: a First Glimpse on the

External Factors”. 13th WCTR Conference, Rio de Janeiro, Brazil, July

Marôco, J. (2011), Análise Estatística com o SPSS Statistics, 5ª Edição, Report Number

Morlok, E.K. and Chang, D.J. (2004), “Measuring capacity flexibility of a Transportation

System”. Transportation Research Part A: Policy and Pratice, 38 (6), pp. 405-420

Ross, A.M., Rhodes, D.H. and Hastings, D.E. (2008), “Defining Changeability:

Reconciling Flexibility, Adaptability, Scalability, Modifiability, and Robustness for

Maintaining System Lifecycle Value”. Systems Engineering, Vol. 11, No. 3, pp. 246-262

Schneider, T. and Till, J. (2007), Flexible Housing. Oxford, UK: Architectural Press

Schulz, A.P, Fricke, E. and Igenbergs, E. (2000), “Enabling Changes in Systems

throughout the Entire Life-Cycle – Key to Success?”, Published in: Proceedings of the 10th

annual INCOSE conference, July 2000, Minneapolis, USA

Shuchi, S., Drogemuller, R. and Kleinschmidt, T. (2012), “Flexible Airport Terminal

Design: Towards a Framework”, Proceedings of the IIE Asian Conference 2012, Furama

Riverfront Hotel, Singapore, pp. 348-356

Suarez, F., Cusumano, M. and Fine, C. (1991), Flexibility and Performance: A Literature

Critique and Strategic Framework, Sloan School WP 3298-91-BPS, Massachusetts

Institute of Technology

Taylor, T. (1991), Evaluating and Selecting Manufacturing Flexibility, S.M. Thesis,

Department of Mechanical Engineering, Massachusetts Institute of Technology


21

Till, J. and Schneider, T. (2005), “Flexible housing: the means to the end”, Architectural

Research Quarterly, Vol. 9, No. 3/4. pp. 287-296

Washington, S., Karlaftis, M. and Mannering, F. (2003), Statistical and Econometric

Methods for Transportation Data Analysis, Chapman & Hall/CRC


22

APPENDIX 1 – Correlation matrix for the 14 variables

Passengers per

employee (2002)

(thousands)

Aircraft

movement per

employee (2002)

Passengers

per Gate

(2002)

Passengers per

M2 of Terminal

Space (2002)

Aircraft

Movement

per Runway

(2002)

Labour Cost

per Passenger

(2002) (US$)

Labour Cost per

Aircraft

Movement

(2002) (US$)

Variable

Cost per

Passenger

(2002)

Variable Cost per

Aircraft Movement

(2002) (US$)

Aeronautical

Revenue per

Aircraft

Movement (2002)

Concession

Revenue per

Passenger

(2002)

Total

Revenue per

Passenger

(2002) (US$)

Total Revenue

per Aircraft

Movement

(2002) (US$)

Total

Revenue per

Employee

(2002) (US$)

Passengers per employee (2002)

(thousands) 1

Aircraft movement per

employee (2002) 0,74468545 1

Passengers per Gate (2002) 0,166086211 -0,156519034 1

Passengers per M2 of Terminal

Space (2002) 0,076580412 0,079753953 0,315223928 1

Aircraft Movement per Runway

(2002) 0,423589696 0,291199427 0,162425583 0,097480519 1

Labour Cost per Passenger (2002)

(US$) -0,44911902 -0,420234573 -0,12029893 0,048248702 -0,04990617 1

Labour Cost per Aircraft

Movement (2002) (US$) -0,39083947 -0,550419357 0,080458866 0,023645085 0,07862574 0,81470685 1

Variable Cost per Passenger

(2002) (US$) -0,327778007 -0,378638032 -0,070043 -0,060426654 -0,036260867 0,73040652 0,783584028 1

Variable Cost per Aircraft

Movement (2002) (US$) -0,16759974 -0,355793171 0,097724186 -0,098817228 0,034205491 0,343176406 0,66457071 0,83382791 1

Aeronautical Revenue per

Aircraft Movement (2002) -0,184113186 -0,341044521 0,041864844 -0,200543938 -0,043860683 0,233438319 0,569022394 0,77935245 0,963269999 1

Concession Revenue per

Passenger (2002) -0,237766125 -0,442378068 0,08623718 -0,152187583 0,02710992 0,230506857 0,57035312 0,70848097 0,844178695 0,835449172 1

Total Revenue per Passenger

(2002) (US$) -0,41838214 -0,512476688 -0,01324494 -0,226714574 -0,115874363 0,625788375 0,735760343 0,91311245 0,79908281 0,767302649 0,769063785 1

Total Revenue per Aircraft

Movement (2002) (US$) -0,222320538 -0,42039739 0,151181422 -0,204423304 0,001174542 0,218061552 0,580344336 0,76723126 0,955355468 0,908810485 0,865388078 0,847916688 1

Total Revenue per Employee

(2002) (US$) 0,319963109 0,285685698 -0,04693745 -0,189608705 0,067911174 -0,124461888 0,101496488 0,41670989 0,572407476 0,5192045 0,384154458 0,368438119 0,542179853 1


23

APPENDIX 2 – Differences for each year and 2002 for airport and factor (North America)

dF1 03-02 dF2 03-02 dF3 03-02 dF4 03-02 dF1 04-02 dF2 04-02 dF3 04-02 dF4 04-02 dF1 05-02 dF2 05-02 dF3 05-02 dF4 05-02 dF1 06-02 dF2 06-02 dF3 06-02 dF4 06-02 dF1 07-02 dF2 07-02 dF3 07-02 dF4 07-02 dF1 09-02 dF2 09-02 dF3 09-02 dF4 09-02

ABQ -0,02936 -0,03878 0,306614 0,034927 -1,08196 -0,17633 1,648847 1,329488 0,514913 -0,20327 1,744634 1,141413 0,495162 -0,40707 1,505567 1,297221 0,382578 -0,40041 1,456632 1,294182 0,437556 -0,22349 1,75065 0,857085

ALB -0,01723 0,026198 0,303137 0,025124 -1,08996 -0,07586 1,144118 1,288216 0,429046 0,080171 1,053489 1,256998 0,452035 0,212034 1,002426 0,658588 0,430143 0,038628 1,042018 0,540917 0,268502 -0,11035 1,022222 0,950682

ATL -0,23388 0,679067 -0,20375 -1,06244 -0,01949 0,601239 -0,13791 -2,77232 -0,28697 0,574466 0,070764 -2,47522 -0,37454 0,071933 -0,18813 -1,13212 -0,30662 0,296245 -0,23091 -1,09345 -0,2486 0,234477 -0,05281 -0,94903

AUS -0,03574 -0,05707 0,267927 -0,00011 -1,01848 -0,1782 1,810091 1,352962 0,421676 -0,3472 1,730272 1,619066 0,461164 -0,42851 2,018828 1,114422 0,393105 -0,54239 2,078911 1,203188 0,395354 -0,37295 2,213101 1,128866

BNA 0,398129 -0,16895 1,172697 1,028521 -1,00541 -0,06028 1,040154 1,313653 0,289052 -0,15698 0,989167 1,211393 0,364148 0,033908 1,276398 0,4491 0,336539 -0,08619 1,314562 0,460219 0,350144 0,0567 1,530868 0,5329

BOS 0,260406 -0,30007 1,151353 -0,06392 -0,43801 -0,37788 1,703734 0,074093 0,23373 -0,32559 1,325253 -0,09897 0,265433 -0,28571 1,209416 -0,18792 0,209692 -0,51139 1,123589 -0,28844 0,280339 -0,47761 1,155912 -0,3428

BWI -0,03544 0,058608 0,094458 -0,08545 -0,50289 0,142247 0,188115 0,041035 -0,05231 0,165876 -0,09056 -0,04397 -0,00866 0,129218 -0,13592 -0,18801 -0,02379 0,048589 -0,15888 -0,22402 -0,01627 -0,03753 0,085745 -0,2123

CLE 0,025288 0,088368 1,35598 1,200385 -0,65134 0,061782 1,166493 1,485944 0,045007 0,100294 1,133617 1,315066 -0,02884 0,110047 1,146062 1,176665 -0,10171 0,145313 1,236204 1,472444 -0,12275 0,254029 1,392582 1,334844

CLT 0,053949 0,167711 2,052017 2,694205 -0,5992 0,294159 2,125772 1,825149 0,087928 -0,17854 1,972782 2,366468 -0,01583 0,178913 2,491804 2,780207 -0,05782 0,332501 2,575311 2,925361 0,015824 -0,12542 2,823186 2,663912

CVG -0,10658 -0,01238 -0,01919 0,280677 -0,66011 -0,0976 -0,12487 -0,67497 -0,13344 -0,08468 -0,35878 -0,59855 -0,23125 0,413613 -0,46847 -0,96625 -0,25238 0,433419 -0,45338 -1,08831 -0,25059 0,607317 -0,8086 -2,07108

DCA 0,011135 -0,08901 0,376216 0,24916 -0,32324 -0,1612 0,62579 0,264401 -0,01487 -0,29712 0,837124 0,238985 -0,07005 -0,29784 1,151852 0,452852 -0,09109 -0,40864 0,835031 0,522173 -0,19355 -0,27583 1,110077 0,430747

DEN -0,12094 -0,11702 0,028238 -0,31066 0,080028 -0,29094 0,232653 -0,30534 -0,15284 -0,31768 0,742175 -0,29843 -0,20385 -0,35057 0,464374 -0,10011 -0,39264 -0,19841 -0,21464 -0,06181 -0,18272 -0,54201 0,796383 0,102495

DFW -0,02653 -0,08746 0,062663 -0,05401 -0,67639 -0,33637 0,089012 -0,21656 0,176427 -0,35131 0,41376 -0,40728 0,232381 -0,18612 0,388551 -0,11921 0,155646 -0,17285 0,224777 -0,22923 0,146531 -0,21731 0,52436 -0,27792

DTW 0,098973 -0,14978 0,107747 0,177458 -0,62691 -0,27778 0,290129 0,439353 0,070509 -0,24288 -1,04863 -0,07957 0,023812 -0,18459 -0,86948 -0,0373 -0,05627 -0,20469 -1,01118 -0,12484 0,035959 -0,36057 -1,22646 0,002602

EWR 0,175556 -0,56978 0,126687 0,55227 1,255692 -0,84725 0,165468 -0,12983 0,044959 -0,62822 0,552292 -0,00671 -0,07688 -0,74874 0,576737 0,718048 -0,42816 -0,4277 0,667931 0,519887 -0,23264 -0,54163 0,560816 0,66423

FLL -0,01023 0,010183 -0,13919 0,025076 -0,42942 -0,03079 0,076584 -0,07015 0,145816 -0,33228 0,061274 -0,19744 0,167558 -0,22333 0,315735 -0,14577 0,062345 -0,21451 0,290846 -0,06133 0,006757 -0,09525 0,085563 -0,19749

HNL 0,173291 -0,04496 0,219415 -0,04246 -0,47261 -0,07854 0,439817 0,01459 8,43E-05 -0,06677 0,675992 0,008783 0,009718 -0,02327 0,323546 -0,09551 -0,10523 -0,0028 0,266874 -0,066 -0,00187 -0,00692 0,242036 -0,00807

IAD 0,284667 -0,06395 -0,3106 -0,47075 -0,46608 -0,41007 -0,2916 -0,68179 0,125942 -0,85531 -0,43328 0,014217 0,230629 -0,50494 -0,3721 -0,37936 0,093906 -0,37788 -0,18996 -0,5355 0,078736 -0,51914 -0,44488 -0,19568

IAH 0,114634 0,144502 1,809757 1,689298 -0,58967 0,371973 1,848335 1,508547 0,113609 0,483461 1,716234 1,608486 0,156118 0,006454 2,367068 2,151055 0,08774 0,160585 1,861278 2,108265 0,066163 0,143905 1,58181 2,015511

IND 0,141425 0,094101 0,153238 0,01053 -0,81761 0,005111 0,159025 0,247068 0,158985 -0,16896 0,241074 0,18784 0,138857 -0,03782 0,136709 0,115981 0,13521 -0,09665 0,173613 -0,12118 0,165152 0,103326 -0,17133 -0,17981

JAX -0,01747 0,048889 0,290212 0,065393 -1,07543 0,065536 1,178608 1,374671 0,552644 0,12436 1,493829 1,2504 0,527045 0,000539 1,532234 0,948261 0,478649 -0,18182 1,590377 0,931844 0,403781 -0,27037 2,089794 0,943691

JFK 0,359519 -0,76129 0,552284 0,45893 1,669798 -1,17611 0,63565 0,650404 0,201981 -1,39514 1,456223 1,008559 -0,06233 -1,48855 1,202764 1,162945 -0,44669 -1,75856 0,888651 1,791092 0,00523 -2,15468 0,880151 1,975656

LAS -0,08796 -0,02794 -0,36587 -0,23377 -0,34159 -0,1708 -0,07871 -0,51117 -0,06095 -0,33646 -0,20209 -0,44375 -0,11521 -0,08142 0,188782 -0,1222 -0,14269 -0,04162 0,058955 -0,20415 -0,15492 0,13968 -0,25695 -0,27615

LAX -0,14902 0,064815 0,07067 -0,14674 -0,33998 -0,15552 0,252734 -1,00288 0,076367 -0,24638 0,539761 -0,80315 0,084809 0,063397 0,647735 0,000645 -0,059 0,133956 -0,01304 -0,00433 0,135658 0,150667 0,128682 -0,25712

LGA 0,09134 -0,55433 0,307005 -0,26993 0,283903 -0,95084 -0,43802 -0,97171 0,224262 -1,23541 -0,31582 -0,14381 -0,00689 -0,9959 -0,10881 0,865908 -0,1376 -0,81147 -0,31746 0,702236 -0,05986 -0,66306 -0,35953 0,655059

MCI -0,01017 0,109316 0,019703 -0,09395 -0,66741 0,215213 -0,13418 0,122098 -0,10115 0,271095 -0,18404 0,020197 0,031144 0,18706 -0,28933 -0,1788 -0,08044 0,159342 -0,04672 -0,15484 -0,0804 0,183162 -0,02492 -0,21719

MCO 0,059873 -0,01795 0,017996 -0,42223 -0,1472 0,042161 0,130172 -0,28808 -0,04102 -0,12489 0,661052 -0,13724 -0,04791 -0,09371 0,346393 -0,17581 -0,22681 0,038486 -0,07069 -0,1726 -0,04246 -0,20621 0,385575 -0,17893

MDW 0,086021 -0,32394 -2,16375 0,241774 -0,44397 -0,68248 -2,59104 0,92785 1,012426 -0,77782 -2,43337 0,436683 0,139164 -0,47147 -2,55227 0,184553 0,052289 -0,41538 -2,5965 0,11259 0,220928 -0,8343 -2,80863 0,362362

MEM -0,02264 -0,25228 0,486298 -0,00518 -0,60919 -0,25617 0,363562 -0,05518 -0,07024 -0,27182 0,164209 -0,07897 -0,0754 -0,22188 0,241346 -0,18558 -0,13577 -0,18955 0,209128 -0,22655 -0,20451 -0,12609 0,273238 -0,25968

MIA 0,107933 -0,22348 -0,07544 -1,0966 -0,00205 -0,35898 0,110099 -1,17638 0,048116 -0,40981 0,072951 -1,14832 0,095129 -0,67938 0,164941 -1,17971 0,016857 -0,92429 0,361458 -1,15203 -0,10569 -1,00058 0,507399 -1,22444

MKE -0,03298 0,000674 0,384492 0,01805 -1,01115 -0,13107 0,833148 1,370648 0,32917 -0,26932 0,812621 1,454913 0,347926 -0,26196 0,800545 0,997616 0,269874 -0,25746 0,901648 1,008372 0,23173 -0,12831 1,202669 0,978743

MSP -0,07627 0,205663 0,232021 -0,31872 -0,4338 0,04807 -0,01819 -1,36548 -0,04615 0,071274 0,201118 -1,47904 -0,09499 0,18458 0,305937 -1,40512 -0,15987 0,247764 0,070053 -1,57194 -0,1597 0,252361 0,162581 -1,58365

MSY 0,521708 -0,28339 1,361951 0,831547 -0,90805 -0,0888 1,439577 1,397089 0,207789 0,2152 1,188118 0,925479 0,413289 0,074703 0,958412 0,204913 0,478567 -0,63151 0,774814 0,93784 0,481962 -0,86914 0,774118 1,35

OAK 0,702465 -0,38424 3,579543 2,469051 -0,7909 -0,45271 2,923898 1,515221 0,809474 -0,53013 3,270131 1,347873 0,67947 -0,50747 4,222811 1,57719 0,448541 -0,07841 3,24567 1,149718 0,684745 0,145888 2,992421 1,014536

ONT -0,01707 0,247772 0,214983 0,107402 -1,01153 0,126224 2,612493 1,795844 0,560665 -0,00027 1,550858 1,470152 0,576938 0,182597 1,26936 1,29475 0,444644 0,131527 1,178745 1,254299 0,480657 0,370186 1,021621 1,144543

ORD -0,08171 -0,05433 -0,098 0,000103 -0,27175 -0,26785 -0,07573 -0,52227 -0,07492 -0,2918 0,200331 -0,37741 -0,13234 -0,13173 0,289168 0,317236 -0,13327 -0,11138 0,101504 0,227153 -0,06617 -0,29802 -0,11899 0,297898

PBI -0,04907 -0,07136 0,299199 -0,03191 -0,81991 -0,32625 1,675132 1,318418 0,504971 -0,65396 1,309524 1,67444 0,579978 -0,34919 1,454599 0,672 0,447948 -0,43727 1,548097 0,709081 0,474111 -0,2686 1,481543 0,588443

PDX 0,000843 0,075258 0,359065 0,034689 -0,22507 0,249008 0,552422 -0,1757 -0,09048 0,163601 0,583274 -0,24696 -0,11889 0,177056 0,481204 -0,35448 -0,25361 0,230051 0,634934 -0,48031 -0,23072 0,317743 0,547777 -0,52791

PHL 0,001379 -0,06802 0,302823 -0,07936 -0,53143 -0,15479 0,134624 -0,38656 -0,0384 -0,25515 0,404965 -0,134 -0,04739 -0,18495 0,492482 0,224295 -0,10257 -0,15127 0,44821 0,186812 -0,15993 0,098892 0,537318 0,326973

PHX -0,02225 -0,03383 0,577504 0,112356 -0,4666 -0,19956 0,268883 -1,16295 -0,03797 -0,06167 0,429823 -1,04562 -0,03358 0,089569 0,35344 -0,35979 -0,19187 0,078646 0,133073 -0,24703 -0,09743 0,084323 0,418876 -0,29246

PIT 0,149515 0,260019 1,179812 1,567562 -0,62776 0,361661 1,724197 1,531952 0,044321 0,823069 1,313928 1,020154 0,093396 0,885012 1,0939 0,763982 0,087687 0,901604 0,765373 0,543636 0,199638 0,953849 1,13382 0,370961

RDU -0,02515 0,119211 0,177352 -0,02667 -0,60686 0,100959 -0,55735 0,019055 0,130483 0,024842 0,075804 0,008743 -0,07151 0,223825 0,005629 -0,41353 -0,12605 0,168884 0,163412 -0,32889 -0,08941 0,198521 0,470553 -0,4835

RIC -0,03974 -0,13878 0,361342 -0,0781 -1,0962 -0,17775 1,185414 1,218338 0,355765 -0,28877 0,89578 1,113639 0,344086 -0,41742 0,844555 0,625583 0,287597 -0,49179 0,99809 0,551595 0,296277 -0,53601 1,128991 0,612569

RNO -0,01721 0,004246 0,288601 0,024392 -1,0991 -0,14888 1,197199 1,179238 0,333797 -0,22623 1,318074 1,206931 0,385798 -0,0824 1,396117 0,656018 0,328691 -0,22509 1,360445 0,549431 0,404961 0,023757 1,298054 0,644283

SAN 0,505868 0,216647 2,438608 3,723538 -0,76033 1,579285 2,795503 9,968269 0,426642 0,38243 2,019959 2,45731 0,406474 0,616668 2,492683 3,650005 0,346842 0,614315 2,773068 3,491221 0,274888 0,734041 3,068312 3,530672

SAT -0,02623 -0,0486 0,277648 0,0138 -1,09867 -0,07248 2,069446 1,370615 0,257395 -0,08634 1,608957 1,619798 0,30167 -0,10118 1,902963 1,06506 0,264484 -0,11177 1,787905 1,102767 0,364663 -0,05627 1,939596 1,203119

SDF -0,02054 0,029275 0,279191 0,02941 -1,12458 0,095556 1,08492 1,365447 0,431996 0,042368 0,977789 1,257365 0,521867 -0,04131 0,956086 0,996182 0,435883 -0,06833 0,970015 0,973836 0,460892 -0,0792 1,001889 1,052025

SEA 0,010194 -0,02856 -0,03657 -0,12068 -0,35229 -0,34476 -0,32805 -1,28008 0,153826 -0,39016 -0,04096 -1,06131 0,176874 -0,28501 -0,12466 -0,2894 0,049607 -0,23189 -0,14475 -0,22252 -0,00328 -0,48478 0,066775 -1,14639

SFO 0,19543 0,053321 0,145358 -0,2925 0,1427 -0,36318 0,698648 -0,37685 0,040644 -0,57367 0,797731 -0,39005 -0,01097 -0,40075 0,611259 -0,38395 -0,08786 -0,57266 0,514812 -0,46778 -0,14361 -0,66484 0,659205 -0,24495

SJC 0,548136 -0,03149 2,547983 1,844595 -0,79237 -0,04098 1,777869 1,185324 0,506316 -0,12864 1,988604 1,401346 0,497627 0,045092 2,435135 1,148065 0,373803 -0,05508 2,294313 0,715736 0,583173 -0,04876 2,502905 0,983404

SLC -0,0447 0,056365 -0,67746 -0,23165 -0,70553 0,104664 -0,54883 -0,36215 -0,08437 -0,15519 -1,01086 -0,34983 -0,13863 -0,12481 -0,66825 -0,07813 -0,15942 -0,0835 -0,72116 -0,37204 -0,18185 0,054426 -0,55093 -0,03824

SMF 0,324109 -0,13632 4,224934 2,08524 -1,00678 -0,1926 2,110637 1,605028 0,472963 -0,29378 2,194767 1,416762 0,924608 -0,18415 1,627985 1,305954 0,443485 -0,49916 1,681939 1,600895 0,627688 -0,43945 1,803729 1,243408

SNA 0,011407 -0,14657 0,285136 0,223122 -0,80683 -0,7811 2,270983 1,803812 0,867182 -1,09436 2,895003 3,797841 0,930823 -0,60472 3,884964 2,03347 0,808758 -0,47528 3,738755 2,009651 0,800161 -0,35096 3,632872 1,768323

STL 0,057271 0,236919 -0,45859 -0,135 -0,6641 0,459759 -0,26145 0,094523 -0,04195 0,440325 -1,06924 -0,32261 -0,07186 0,50043 -1,0342 -0,59091 -0,04432 0,397127 -1,08908 -0,63694 -0,04383 0,574283 -1,08981 -0,81543

TPA 0,069013 0,096745 0,160902 -0,03663 -0,46435 0,235545 0,00951 -0,06877 -0,03767 0,175557 0,369221 -0,08192 -0,02779 0,271228 0,594412 -0,16855 -0,11449 0,265849 0,539438 -0,18905 -0,12636 0,319838 0,569735 -0,33672

YEG 0,217 -0,06711 -0,1878 -0,09076 -0,88662 0,027498 -0,03179 0,576918 0,184888 -0,06607 -0,06344 0,481443 0,270032 -0,2939 -0,43028 0,55162 0,241125 -0,23158 -0,15738 0,405779 0,107068 -0,19785 -0,1239 0,179356

YHZ 0,713155 -0,94934 4,458021 0,743323 -0,85614 0,172699 0,258248 1,189595 0,13735 0,146041 0,305923 1,202888 0,220965 0,152417 -0,0409 0,774136 0,142554 0,160064 0,036837 0,751318 0,106862 0,160587 0,110643 0,919761

YOW 0,028595 0,027892 -0,2678 0,206268 -0,60723 0,121813 -0,32767 0,96326 0,115552 0,160253 -0,16572 0,781452 0,179263 0,179993 -0,54919 0,277421 0,065597 0,150977 -0,41437 0,195309 -0,00016 -0,06292 -0,54431 0,410533

YUL 0,117308 0,071517 -0,18426 0,172531 -0,73512 -0,04372 -0,2935 0,176453 0,371067 -0,1741 -0,02865 0,308627 0,144756 0,134927 -0,46299 0,051443 0,183331 0,029185 -0,32093 0,080057 0,563354 -0,15439 0,025662 0,268581

YVR 0,063408 0,020303 0,275036 -0,30122 -0,27608 0,082649 0,595455 -0,09052 0,067469 0,196239 0,62386 -0,24156 0,203529 0,169295 0,322764 -0,01039 -0,00437 0,383516 0,224422 -0,06723 -0,01338 0,44188 0,257634 -0,49098

YWG -0,03311 0,112431 0,29747 0,050229 -1,1016 0,212873 0,961801 1,321003 0,271869 0,280602 1,326028 1,188349 0,371832 0,314489 0,999913 1,388314 0,304033 0,284349 1,066444 1,436089 0,322621 0,264864 1,096769 1,526153

YYC 0,157357 -0,42654 -0,16035 0,35662 -0,45518 -0,21597 0,159748 0,585042 0,181852 -0,24394 0,564212 0,448897 0,208973 -0,29493 -0,19609 0,567112 0,161072 -0,37264 -0,10763 0,745345 0,246912 -0,49219 0,083091 0,760826

YYZ 0,016039 0,133118 -0,10999 -0,10043 -0,50325 0,035614 -0,19453 -0,10549 0,378797 0,296945 0,110942 5,443893 -0,19004 0,183982 -2,04664 -1,49628 0,215028 0,185301 -1,83417 -1,62091 0,636122 -0,38851 -0,30569 0,364119


24

APPENDIX 2 – Differences for each year and 2002 for airport and factor (Europe)


AMS -0,98274 -0,12113 1,542164 1,353069 -0,03574 -0,29014 1,462345 1,619174 0,496903 -0,37145 1,750901 1,11453 0,428844 -0,48532 1,810984 1,203295 0,431094 -0,31588 1,945174 1,128974 0,035739 0,057066 2005,732 0,000108

ARN -1,40354 0,108676 -0,13254 0,285132 0,398129 0,011975 -0,18353 0,182872 -0,03398 0,202861 0,103701 -0,57942 -0,06159 0,082765 0,141866 -0,5683 -0,04799 0,225653 0,358172 -0,49562 -0,39813 0,168953 2005,827 -1,02852

ATH -0,69842 -0,07781 0,552381 0,138015 0,260406 -0,02552 0,1739 -0,03504 0,005027 0,014362 0,058063 -0,12399 -0,05071 -0,21132 -0,02776 -0,22452 0,019933 -0,17754 0,004559 -0,27888 -0,26041 0,30007 2007,849 0,063922

BCN -0,46744 0,083639 0,093657 0,126489 -0,03544 0,107269 -0,18501 0,041488 0,026784 0,07061 -0,23038 -0,10256 0,011652 -0,01002 -0,25334 -0,13856 0,019172 -0,09613 -0,00871 -0,12685 0,035445 -0,05861 -0,09446 0,085454

BHX -0,67663 -0,02659 -0,18949 0,285559 0,025288 0,011926 -0,22236 0,114681 -0,05413 0,021679 -0,20992 -0,02372 -0,127 0,056945 -0,11978 0,272059 -0,14804 0,165661 0,036602 0,134459 -0,02529 -0,08837 -1,35598 -1,20039

BRU -0,65315 0,126448 0,073755 -0,86906 0,053949 -0,34625 -0,07923 -0,32774 -0,06978 0,011202 0,439787 0,086002 -0,11177 0,16479 0,523294 0,231157 -0,03813 -0,29313 0,771168 -0,03029 -0,05395 -0,16771 -2,05202 -2,6942

BUD -0,55353 -0,08523 -0,10569 -0,95564 -0,10658 -0,07231 -0,33959 -0,87922 -0,12468 0,425988 -0,44929 -1,24693 -0,1458 0,445795 -0,43419 -1,36898 -0,14401 0,619692 -0,78941 -2,35176 0,106577 0,012375 0,019189 -0,28068

BTS -0,33437 -0,07219 0,249573 0,015241 0,011135 -0,20811 0,460908 -0,01017 -0,08118 -0,20883 0,775635 0,203691 -0,10222 -0,31963 0,458815 0,273013 -0,20469 -0,18681 0,733861 0,181587 -0,01114 0,089012 -0,37622 -0,24916

CDG 0,200968 -0,17392 0,204415 0,005324 -0,12094 -0,20067 0,713937 0,01223 -0,08291 -0,23356 0,436136 0,210549 -0,2717 -0,08139 -0,24288 0,248848 -0,06178 -0,42499 0,768146 0,413156 0,12094 0,117017 -0,02824 0,310662

CGN -0,64986 -0,24891 0,026349 -0,16255 -0,02653 -0,26385 0,351097 -0,35327 0,258911 -0,09865 0,325888 -0,0652 0,182176 -0,08538 0,162114 -0,17522 0,173061 -0,12985 0,461697 -0,22392 0,026529 0,087465 -0,06266 0,054007

CIA -0,72588 -0,128 0,182383 0,261895 0,098973 -0,0931 -1,15638 -0,25703 -0,07516 -0,03481 -0,97723 -0,21476 -0,15525 -0,05492 -1,11893 -0,3023 -0,06301 -0,2108 -1,33421 -0,17486 -0,09897 0,149778 -0,10775 -0,17746

CPH 1,080137 -0,27747 0,038781 -0,6821 0,175556 -0,05844 0,425605 -0,55898 -0,25243 -0,17896 0,45005 0,165777 -0,60371 0,142072 0,541244 -0,03238 -0,4082 0,028145 0,434129 0,11196 -0,17556 0,569776 -0,12669 -0,55227

DUB -0,41919 -0,04097 0,215776 -0,09523 -0,01023 -0,34247 0,200466 -0,22251 0,177791 -0,23351 0,454927 -0,17084 0,072578 -0,22469 0,430038 -0,08641 0,01699 -0,10543 0,224755 -0,22256 0,010233 -0,01018 0,139192 -0,02508

DUS -0,6459 -0,03359 0,220402 0,057046 0,173291 -0,02181 0,456578 0,051239 -0,16357 0,021689 0,104131 -0,05306 -0,27852 0,042156 0,047459 -0,02355 -0,17516 0,038037 0,022622 0,034386 -0,17329 0,044956 -0,21941 0,042455

EDI -0,75074 -0,34612 0,018999 -0,21104 0,284667 -0,79136 -0,12267 0,484966 -0,05404 -0,44099 -0,0615 0,091394 -0,19076 -0,31393 0,120645 -0,06475 -0,20593 -0,45519 -0,13428 0,275066 -0,28467 0,06395 0,310603 0,47075

FCO -0,70431 0,227471 0,038578 -0,18075 0,114634 0,338959 -0,09352 -0,08081 0,041484 -0,13805 0,557311 0,461757 -0,02689 0,016083 0,051521 0,418967 -0,04847 -0,0006 -0,22795 0,326214 -0,11463 -0,1445 -1,80976 -1,6893

FRA -0,95903 -0,08899 0,005787 0,236538 0,141425 -0,26306 0,087837 0,17731 -0,00257 -0,13192 -0,01653 0,105451 -0,00622 -0,19075 0,020375 -0,13171 0,023727 0,009225 -0,32456 -0,19034 -0,14142 -0,0941 -0,15324 -0,01053

GVA -1,05796 0,016647 0,888397 1,309278 -0,01747 0,075471 1,203617 1,185008 0,54451 -0,04835 1,242022 0,882868 0,496114 -0,23071 1,300166 0,866452 0,421246 -0,31926 1,799582 0,878299 0,017465 -0,04889 -0,29021 -0,06539

HAM 1,310279 -0,41481 0,083366 0,191474 0,359519 -0,63385 0,903939 0,549629 -0,42185 -0,72726 0,65048 0,704015 -0,80621 -0,99727 0,336368 1,332161 -0,35429 -1,39339 0,327868 1,516726 -0,35952 0,76129 -0,55228 -0,45893

HEL -0,25363 -0,14286 0,287162 -0,27741 -0,08796 -0,30851 0,16378 -0,20998 -0,02725 -0,05348 0,554652 0,111569 -0,05473 -0,01367 0,424826 0,029612 -0,06696 0,167622 0,108922 -0,04239 0,087962 0,027942 0,36587 0,233765

IST -0,19096 -0,22034 0,182064 -0,85614 -0,14902 -0,3112 0,469091 -0,65641 0,233834 -0,00142 0,577065 0,147383 0,090027 0,069141 -0,08371 0,142407 0,284683 0,085852 0,058013 -0,11038 0,149025 -0,06482 -0,07067 0,146738

KEF 0,192563 -0,39651 -0,74502 -0,70178 0,09134 -0,68108 -0,62282 0,126116 -0,09823 -0,44157 -0,41582 1,135839 -0,22894 -0,25714 -0,62447 0,972166 -0,1512 -0,10873 -0,66653 0,924989 -0,09134 0,554327 -0,30701 0,26993

LGW -0,65724 0,105897 -0,15388 0,216051 -0,01017 0,161779 -0,20374 0,11415 0,041318 0,077744 -0,30903 -0,08485 -0,07027 0,050026 -0,06642 -0,06089 -0,07023 0,073847 -0,04463 -0,12324 0,010174 -0,10932 -0,0197 0,093953

LHR -0,20708 0,060111 0,112176 0,134152 0,059873 -0,10694 0,643057 0,284989 -0,10778 -0,07576 0,328398 0,246417 -0,28668 0,056436 -0,08868 0,24963 -0,10234 -0,18826 0,367579 0,243297 -0,05987 0,01795 -0,018 0,422229

LIS -0,52999 -0,35853 -0,4273 0,686076 0,086021 -0,45388 -0,26962 0,194909 0,053143 -0,14753 -0,38853 -0,05722 -0,03373 -0,09144 -0,43276 -0,12918 0,134907 -0,51036 -0,64489 0,120587 -0,08602 0,323942 2,163746 -0,24177

LJU -0,58655 -0,00389 -0,12274 -0,05 -0,02264 -0,01954 -0,32209 -0,07379 -0,05276 0,030396 -0,24495 -0,1804 -0,11313 0,062727 -0,27717 -0,22137 -0,18187 0,126193 -0,21306 -0,2545 0,022641 0,252278 -0,4863 0,00518

MAD -0,10998 -0,1355 0,185543 -0,07978 0,107933 -0,18633 0,148395 -0,05171 -0,0128 -0,4559 0,240385 -0,08311 -0,09108 -0,70082 0,436902 -0,05543 -0,21362 -0,77711 0,582843 -0,12783 -0,10793 0,223478 0,075444 1,096601

MAN -0,97817 -0,13174 0,448656 1,352598 -0,03298 -0,27 0,428129 1,436863 0,380909 -0,26264 0,416054 0,979566 0,302857 -0,25813 0,517156 0,990322 0,264713 -0,12898 0,818177 0,960693 0,032983 -0,00067 -0,38449 -0,01805

MLA -0,35753 -0,15759 -0,25021 -1,04675 -0,07627 -0,13439 -0,0309 -1,16032 -0,01872 -0,02108 0,073916 -1,0864 -0,0836 0,042101 -0,16197 -1,25322 -0,08343 0,046697 -0,06944 -1,26492 0,07627 -0,20566 -0,23202 0,318723

MUC -1,42976 0,194584 0,077626 0,565542 0,521708 0,498585 -0,17383 0,093932 -0,10842 0,358088 -0,40354 -0,62663 -0,04314 -0,34812 -0,58714 0,106293 -0,03975 -0,58575 -0,58783 0,518453 -0,52171 0,283385 -1,36195 -0,83155

MXP -1,49336 -0,06847 -0,65564 -0,95383 0,702465 -0,14589 -0,30941 -1,12118 -0,02299 -0,12323 0,643269 -0,89186 -0,25392 0,305831 -0,33387 -1,31933 -0,01772 0,530128 -0,58712 -1,45451 -0,70246 0,38424 -3,57954 -2,46905

ORY -0,99445 -0,12155 2,397511 1,688442 -0,01707 -0,24804 1,335875 1,36275 0,59401 -0,06518 1,054378 1,187348 0,461715 -0,11625 0,963763 1,146897 0,497729 0,122414 0,806638 1,037141 0,017072 -0,24777 -0,21498 -0,1074

OSL -0,19003 -0,21352 0,022273 -0,52237 -0,08171 -0,23747 0,298333 -0,37752 -0,05063 -0,07741 0,387171 0,317134 -0,05156 -0,05705 0,199507 0,227051 0,015541 -0,24369 -0,02099 0,297795 0,081712 0,054328 0,098003 -0,0001

PRG -0,77084 -0,25489 1,375933 1,350326 -0,04907 -0,5826 1,010325 1,706348 0,62905 -0,27783 1,155399 0,703908 0,497021 -0,36591 1,248897 0,740989 0,523184 -0,19724 1,182343 0,62035 0,049073 0,071359 -0,2992 0,031908

RIX -0,22592 0,17375 0,193356 -0,21039 0,000843 0,088343 0,224209 -0,28164 -0,11973 0,101798 0,122139 -0,38917 -0,25446 0,154792 0,275868 -0,515 -0,23156 0,242485 0,188712 -0,5626 -0,00084 -0,07526 -0,35907 -0,03469

SOF -0,53281 -0,08678 -0,1682 -0,3072 0,001379 -0,18714 0,102143 -0,05464 -0,04876 -0,11693 0,189659 0,303654 -0,10395 -0,08326 0,145387 0,26617 -0,16131 0,166908 0,234495 0,406332 -0,00138 0,068016 -0,30282 0,079358

STN -0,44435 -0,16572 -0,30862 -1,27531 -0,02225 -0,02784 -0,14768 -1,15797 -0,01134 0,123401 -0,22406 -0,47214 -0,16962 0,112478 -0,44443 -0,35939 -0,07518 0,118155 -0,15863 -0,40481 0,022246 0,033832 -0,5775 -0,11236

TLL -0,77727 0,101642 0,544385 -0,03561 0,149515 0,56305 0,134117 -0,54741 -0,05612 0,624993 -0,08591 -0,80358 -0,06183 0,641585 -0,41444 -1,02393 0,050123 0,69383 -0,04599 -1,1966 -0,14952 -0,26002 -1,17981 -1,56756

TXL -0,58171 -0,01825 -0,7347 0,045728 -0,02515 -0,09437 -0,10155 0,035415 -0,04636 0,104614 -0,17172 -0,38686 -0,1009 0,049674 -0,01394 -0,30222 -0,06425 0,07931 0,293201 -0,45682 0,025154 -0,11921 -0,17735 0,026672

VIE -1,05646 -0,03897 0,824071 1,296442 -0,03974 -0,14999 0,534438 1,191743 0,383827 -0,27864 0,483213 0,703687 0,327338 -0,35301 0,636747 0,629698 0,336019 -0,39724 0,767649 0,690673 0,039742 0,138779 -0,36134 0,078104

WAW -1,08189 -0,15313 0,908598 1,154846 -0,01721 -0,23047 1,029474 1,182539 0,403007 -0,08664 1,107516 0,631626 0,3459 -0,22933 1,071845 0,525039 0,42217 0,019511 1,009453 0,619891 0,017209 -0,00425 -0,2886 -0,02439

ZRH -1,26619 1,362638 0,356895 6,244732 0,505868 0,165783 -0,41865 -1,26623 -0,09939 0,400021 0,054076 -0,07353 -0,15903 0,397668 0,33446 -0,23232 -0,23098 0,517394 0,629704 -0,19287 -0,50587 -0,21665 -2,43861 -3,72354


25

APPENDIX 2 – Differences for each year and 2002 for airport and factor (Asia)


ADL -1,07244 -0,02388 1,791798 1,356815 -0,02623 -0,03774 1,331309 1,605998 0,327897 -0,05258 1,625315 1,05126 0,290712 -0,06317 1,510257 1,088967 0,39089 -0,00767 1,661948 1,189319 0,026228 0,0486 -0,27765 -0,0138

AKL -1,10404 0,066282 0,80573 1,336037 -0,02054 0,013093 0,698599 1,227955 0,542409 -0,07058 0,676895 0,966773 0,456426 -0,09761 0,690824 0,944426 0,481435 -0,10847 0,722698 1,022615 0,020542 -0,02927 -0,27919 -0,02941

BKK -0,36248 -0,3162 -0,29148 -1,15941 0,010194 -0,3616 -0,00439 -0,94063 0,16668 -0,25645 -0,08809 -0,16873 0,039413 -0,20333 -0,10818 -0,10184 -0,01348 -0,45622 0,103347 -1,02571 -0,01019 0,02856 0,036571 0,120677

BNE -0,05273 -0,4165 0,55329 -0,08436 0,19543 -0,62699 0,652373 -0,09755 -0,2064 -0,45407 0,465901 -0,09146 -0,28329 -0,62598 0,369454 -0,17528 -0,33904 -0,71817 0,513847 0,047546 -0,19543 -0,05332 -0,14536 0,292496

BOM -1,34051 -0,00948 -0,77011 -0,65927 0,548136 -0,09714 -0,55938 -0,44325 -0,05051 0,076586 -0,11285 -0,69653 -0,17433 -0,02358 -0,25367 -1,12886 0,035037 -0,01726 -0,04508 -0,86119 -0,54814 0,031494 -2,54798 -1,8446

CAN -0,66083 0,048299 0,128623 -0,13049 -0,0447 -0,21155 -0,3334 -0,11818 -0,09394 -0,18118 0,009208 0,153522 -0,11473 -0,13986 -0,0437 -0,14038 -0,13715 -0,00194 0,126525 0,193417 0,044695 -0,05636 0,677456 0,231652

CGK -1,33088 -0,05629 -2,1143 -0,48021 0,324109 -0,15746 -2,03017 -0,66848 0,600499 -0,04784 -2,59695 -0,77929 0,119377 -0,36285 -2,54299 -0,48435 0,303579 -0,30313 -2,42121 -0,84183 -0,32411 0,136317 -4,22493 -2,08524

CHC -0,81824 -0,63453 1,985846 1,58069 0,011407 -0,9478 2,609867 3,574719 0,919416 -0,45815 3,599827 1,810348 0,797351 -0,32871 3,453618 1,786529 0,788754 -0,2044 3,347736 1,5452 -0,01141 0,146567 -0,28514 -0,22312

CNS -0,72137 0,22284 0,197138 0,229524 0,057271 0,203406 -0,61064 -0,18761 -0,12913 0,263511 -0,57561 -0,45591 -0,10159 0,160208 -0,63049 -0,50194 -0,1011 0,337364 -0,63122 -0,68043 -0,05727 -0,23692 0,458591 0,135001

CNX -0,53336 0,1388 -0,15139 -0,03214 0,069013 0,078812 0,208319 -0,04529 -0,0968 0,174483 0,43351 -0,13192 -0,18351 0,169104 0,378536 -0,15242 -0,19538 0,223093 0,408833 -0,30009 -0,06901 -0,09674 -0,1609 0,03663

DEL -1,10362 0,094612 0,156011 0,667679 0,217 0,00104 0,124365 0,572203 0,053032 -0,22679 -0,24248 0,64238 0,024124 -0,16447 0,030426 0,49654 -0,10993 -0,13074 0,063904 0,270116 -0,217 0,067113 0,187801 0,09076

DXB -1,5693 1,12204 -4,19977 0,446272 0,713155 1,095382 -4,1521 0,459565 -0,49219 1,101758 -4,49892 0,030814 -0,5706 1,109406 -4,42118 0,007996 -0,60629 1,109929 -4,34738 0,176439 -0,71316 0,949342 -4,45802 -0,74332

HAK -0,63583 0,09392 -0,05987 0,756992 0,028595 0,132361 0,102085 0,575184 0,150668 0,152101 -0,28139 0,071153 0,037001 0,123084 -0,14657 -0,01096 -0,02875 -0,09081 -0,27651 0,204265 -0,0286 -0,02789 0,267804 -0,20627

HDY -0,85243 -0,11523 -0,10924 0,003922 0,117308 -0,24562 0,155615 0,136096 0,027448 0,06341 -0,27873 -0,12109 0,066023 -0,04233 -0,13667 -0,09247 0,446046 -0,22591 0,209923 0,096051 -0,11731 -0,07152 0,18426 -0,17253

HKG -0,33949 0,062346 0,32042 0,210701 0,063408 0,175936 0,348824 0,059661 0,140121 0,148992 0,047729 0,290833 -0,06778 0,363213 -0,05061 0,233996 -0,07679 0,421578 -0,0174 -0,18976 -0,06341 -0,0203 -0,27504 0,301222

HKT -1,06849 0,100442 0,664331 1,270774 -0,03311 0,168172 1,028557 1,13812 0,404941 0,202059 0,702443 1,338085 0,337143 0,171919 0,768973 1,38586 0,355731 0,152434 0,799299 1,475924 0,03311 -0,11243 -0,29747 -0,05023

ICN -0,61254 0,21057 0,320102 0,228422 0,157357 0,182604 0,724566 0,092276 0,051616 0,131607 -0,03574 0,210492 0,003716 0,053905 0,052727 0,388725 0,089555 -0,06564 0,243445 0,404206 -0,15736 0,426542 0,160354 -0,35662

KIX -0,51929 -0,0975 -0,08454 -0,00505 0,016039 0,163828 0,220933 5,544324 -0,20608 0,050865 -1,93665 -1,39585 0,198989 0,052183 -1,72418 -1,52048 0,620084 -0,52163 -0,1957 0,464549 -0,01604 -0,13312 0,109991 0,100431

KUL 0,07755 -0,13701 0,318169 0,221965 0,504737 -0,17489 -0,50436 -0,09581 -0,00367 -0,25313 0,265856 0,242669 0,149584 -0,31299 0,21609 -0,20004 0,101336 -0,25042 0,107016 -0,05802 -0,50474 -0,17817 0,289006 0,013435

MEL -1,40562 -0,13717 0,159615 -0,07804 0,345834 -1,47915 0,474033 -0,71004 0,165209 -1,0577 0,507896 -0,4517 0,835545 -2,69681 0,231083 -1,51755 1,203941 -2,60919 0,162996 -1,44739 -0,34583 -3,36672 1,145225 -0,8801

MFM -1,06257 -0,24298 1,003854 1,463889 -0,10971 0,161326 0,984522 2,334356 1,827003 0,212859 0,694573 2,520616 1,838749 -0,19111 0,91166 2,391011 1,792214 0,066636 0,913929 2,827025 0,10971 -0,3572 -0,4318 0,329527

MNL -0,67458 -0,02018 -0,04804 -0,58645 -0,02168 -0,05624 -0,24363 -0,88976 0,341326 -0,04278 0,355351 -0,26975 1,071642 -0,54052 0,094595 -0,18585 0,651153 0,539299 -1,68914 -1,25376 0,021677 -0,35109 0,386093 -0,25453

NRT 0,662461 -0,12136 0,017588 0,310508 -0,6915 -0,30386 -0,2156 0,007294 0,961823 0,103506 -0,44044 0,030138 0,630949 -0,20775 -2,13292 -1,31483 0,3546 -0,55066 0,233781 0,499196 0,691502 -0,00148 -0,46884 0,028982

PEK -0,04004 -0,32644 0,16936 0,020184 -0,01967 -0,18655 -0,00027 0,058159 0,439476 -0,20189 -0,16288 0,092555 0,206138 -0,38892 -0,07358 0,022859 0,532834 -0,71393 0,594341 -0,06474 0,019671 -0,14296 0,050054 -0,07558

PEN -1,19039 0,266923 -0,2949 1,109301 -0,0458 -0,11096 1,165327 1,312355 -0,00427 -0,04194 1,214889 1,3175 1,290446 0,073775 -0,11234 1,203096 1,651687 0,820024 0,674825 1,756036 0,045798 -0,05295 -0,28056 -0,01344

PER -1,19039 0,075083 0,79191 1,236075 -0,0458 -0,1486 1,587417 1,122325 0,137381 -0,39304 2,076786 0,58839 0,788852 1,082331 0,331597 1,17383 0,853903 1,305407 0,23483 1,361802 0,045798 -0,05295 -0,28056 -0,01344

PVG 0,348682 -0,15186 -0,04486 -0,46795 -0,89041 -0,18765 0,143987 -0,5602 1,422728 0,017433 0,333589 0,210169 2,630641 -0,78253 0,104565 -0,18023 3,221328 -0,68249 0,286752 -0,00021 0,890411 -0,66512 0,181347 -0,33417

SEL 0,138583 3,685072 -1,3853 2,303089 -0,16651 3,317991 -0,84212 2,385439 0,605294 3,284449 -1,43018 1,85035 0,736124 2,704058 -1,17662 1,557593 0,643648 2,714036 -1,04125 1,63269 0,166513 4,290046 -1,79049 2,048485

SHA -1,08812 0,015533 0,797503 0,752508 0,08392 -0,58412 1,475337 0,708458 0,39712 -0,71928 3,507463 0,890245 1,297098 -1,41665 3,705828 0,575558 0,53843 -0,93706 1,832589 0,504645 -0,08392 -0,0209 -0,94366 -0,54326

SIN -0,06082 0,03842 -0,00862 0,094749 -0,03032 0,208104 -0,47811 0,011272 0,441193 0,314315 0,039677 0,467904 0,280454 0,261289 0,032419 0,332844 0,157076 0,358999 0,006966 0,17792 0,030319 -0,28453 0,517093 0,030028

SYD -0,62776 0,245139 -0,55481 0,45793 0,121966 -0,22878 -0,67866 0,391114 0,005827 -0,26369 1,452569 0,63337 0,819923 -1,11981 1,389826 1,050503 1,0183 -0,64432 1,502813 1,138488 -0,12197 -0,13319 0,895041 0,279742

SZX 1,017457 -1,56102 0,424518 -0,52117 -0,96519 -0,55003 0,300828 0,030132 1,198561 -0,73312 0,029209 -0,15654 1,779814 -1,39438 0,420622 -0,37457 1,622314 -1,32211 0,649632 0,27873 0,965189 -0,59224 0,226566 -0,32981

TPE -0,85231 -0,13252 0,414398 -0,1139 0,406515 -0,36768 -0,33012 -0,66588 0,025198 -0,60947 0,177285 -0,15054 0,215086 -0,15397 -1,28408 -0,91018 -0,05284 -0,54403 -0,89942 -0,93871 -0,40651 -0,35415 -0,09181 -1,25028

WLG -0,56437 -0,23265 0,545406 0,165027 -0,08986 -0,44005 0,502158 0,034479 0,173086 -0,61803 0,538499 -0,14855 1,039836 -1,35981 0,855151 -0,48035 0,622992 -1,31091 1,064484 0,030572 0,089861 -0,08736 0,280569 -0,08663

XMN 2,037575 -0,11411 -0,24833 -0,74339 -1,5999 -0,11508 -0,07989 -0,44843 -0,08928 0,002786 -0,2931 0,202013 1,590669 4,784065 -1,49289 2,951937 2,031405 4,42294 -1,29117 2,749742 1,599897 -0,44217 -0,0457 -0,33134


26

APPENDIX 3 – Cluster membership (2002, 2003, 2004, 2005)

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

ABQ ATL BOS LAX MDW MIA IST KIX ABQ ATL BNA DFW LAX MIA YHZ KIX ABQ ALB ATL LAX SAN ARN EDI KIX ABQ ALB ATL BWI MIA YYZ CDG NRT

ALB CVG BWI SEA EDI SFO LIS NRT ALB CLT BOS DTW SEA SFO TXL NRT AUS BNA CLT MIA CDG BOM NRT DCA AUS EWR CLT SFO MAD CGN

AUS EWR DCA BCN PEK CDG MXP AUS CVG BWI FLL BCN ARN SEL BOS CLE CVG ONT CGN MEL DEN BNA JFK CVG AMS DUB

BNA JFK DEN LGW SEL CGN CNX JAX EWR CLE IAH GVA BHX BWI IAD DEN SFO DUB PEK DFW BOS LGA DTW ARN DUS

CLE LGA DFW LHR SHA CPH HKT MKE JFK DCA LAS LGW BRU DCA IND DTW AMS HEL PVG FLL CLE MDW IAD ATH HAM

CLT MSP DTW MAD DUB ICN ONT LGA DEN MDW LHR CDG DFW JAX EWR BCN LJU SEL HNL IND OAK IAH BCN MUC

IAH PHX FLL DUS MEL PBI MSP HNL OAK MAD CIA HNL MCI FLL BHX ORY SHA LAS JAX SNA MEM BHX ORY

JAX HNL FCO MFM RIC PHX IAD ORD STN CPH IAH MEM JFK BRU RIX LAX MCI LHR MKE BRU VIE

MKE IAD GVA PEN RNO SAN IND SJC DUB MCO MKE LAS CIA VIE MCO MSY HKG PBI CIA

MSY IND HEL PER SAT MCI SLC DUS MSY RDU LGA CPH MSP ONT ICN PDX CPH

OAK LAS MAN PVG SDF MCO SMF FCO PBI RIC MDW DUS ORD PIT KIX PHL FCO

ONT MCI MUC TPE SNA MEM AMS HAM PDX RNO MSP FCO PHX RIC MEL RDU GVA

PBI MCO ORY YWG MSY EDI HEL PHL SDF OAK GVA SAN RNO MFM SLC HEL

PIT MEM VIE ATH PDX FRA MAN PIT YEG ORD HAM SEA SAT SIN YVR LGW

RIC ORD WAW BUD PHL MUC ORY SAT YHZ PHX LGW SJC SDF SYD YYC LIS

RNO PDX ZRH BTS PIT AKL VIE SEA YOW SNA LHR SMF STL TPE BNE LJU

SAN PHL KEF RDU BKK WAW SJC YUL YVR MAD TPA YEG WLG MAN

SAT RDU LJU STL BOM ZRH SLC YWG YYC MAN EDI YHZ MLA

SDF SLC MLA TPA HKG SMF BUD ADL OSL FRA YOW OSL

SJC STL RIX YEG ICN STL KEF AKL STN IST YUL PRG

SMF TPA SOF YOW PEK TPA MUC BNE WAW ADL YWG RIX

SNA YEG TLL YUL PVG YYZ SOF HKG ZRH AKL BUD STN

YHZ YUL DXB YVR SHA ATH TXL ICN BKK BTS WAW

YOW YVR HAK YYC SIN BTS CNS PER CAN KEF ZRH

YWG YYC HDY YYZ FRA DXB SYD CHC MXP

ATH YYZ SZX CGN IST HAK TPE DEL SOF

BUD AMS XMN IST LIS XMN WLG KUL TLL

BTS ARN LIS MLA MNL TXL

CIA BHX MXP MXP PEK BOM

KEF BRU OSL PRG PER CGK

LJU FRA PRG TLL PVG CNS

MLA HAM ADL BKK SEL CNX

RIX OSL BNE CAN SHA DXB

SOF PRG CAN CGK SZX HAK

STN AKL CGK CHC HDY

TLL BKK CHC CNX HKT

TXL BNE CNS DEL PEN

ADL CHC CNX HDY XMN

BOM HKG DEL HKT

CAN KUL HKT KUL

CGK SIN KUL MFM

CNS SYD MEL MNL

DEL WLG MFM PEN

DXB MNL SIN

HAK PEN SZX

HDY PER

MNL SYD

SZX TPE

XMN WLG

2002 2003 2004 2005


27

APPENDIX 3 – Cluster membership (2006, 2007, 2009)

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

ABQ ATL CLT LAX MIA OAK AMS CDG ABQ ATL BOS CLT FLL BCN CDG KIX ABQ ATL BOS CLT DCA ONT CDG KIX

ALB EWR DCA BCN SFO SNA HKG MUC ALB EWR IAD DEN MDW DUB FRA NRT ALB EWR BWI MDW DEN BCN DUB NRT

AUS JFK DEN DUB ARN CIA ICN VIE AUS JFK LAX IAH OAK LGW MUC PER AUS JFK DTW ADL DFW BTS FRA

BNA LGA DFW LGW ATH EDI KIX BNA LGA MIA LAS SNA LHR VIE BNA LGA HNL BNE FLL CGN LGW

BOS BNE DTW LHR BHX TXL MFM BWI ONT MCO CIA MAD CLE IAD HKG LAS CPH LHR

BWI MEL FLL MAD BRU CGK NRT CLE SFO MSP IST STN CVG IAH ICN LAX DUS MUC

CLE SYD IAH CGN CNX SIN CVG AMS ORD CGK IND MEM MEL MCO GVA VIE

CVG LAS CPH DEL DCA ARN PHL CNX JAX MSP PER MIA KEF

HNL MCO DUS DXB DFW ATH PHX DEL MCI MSY SYD OAK LJU

IAD MDW FCO MNL DTW BRU SAN DXB MKE PDX TPE ORD MAD

IND MSP GVA SEL HNL CGN SEA HKT PBI PHL PHX MAN

JAX ORD HAM IND CPH YVR MNL PIT RDU SAN ORY

MCI PHL HEL JAX DUS YYC SEL RIC SLC SEA SOF

MEM PHX MAN MCI EDI AKL SHA RNO YUL SFO TLL

MKE SAN ORY MEM FCO BNE SAT YVR SJC WAW

MSY SEA OSL MKE GVA HKG SDF YWG SNA

ONT SJC WAW MSY HAM ICN SMF YYC BHX

PBI SLC ZRH PBI HEL MEL STL YYZ CIA

PDX FRA PDX LIS PEK TPA AMS EDI

PIT IST PIT LJU SYD YEG ARN FCO

RDU LIS RDU MAN TPE YHZ ATH IST

RIC STN RIC ORY WLG YOW BRU TXL

RNO AKL RNO OSL MXP BUD AKL

SAT BKK SAT PRG PRG HAM BKK

SDF CAN SDF RIX RIX HEL CAN

SMF PEK SJC TXL BOM LIS CGK

STL PVG SLC WAW CHC MLA CNX

TPA SHA SMF ZRH CNS OSL HKT

YEG SZX STL BKK DEL STN PEK

YHZ TPE TPA CAN HAK ZRH PVG

YOW YEG PVG HDY DXB SEL

YUL YHZ SIN MFM KUL SZX

YVR YOW SZX MNL SIN

YWG YUL PEN WLG

YYC YWG SHA

YYZ YYZ XMN

BUD BHX

BTS BUD

KEF BTS

LJU KEF

MLA MLA

MXP MXP

PRG SOF

RIX TLL

SOF ADL

TLL BOM

ADL CHC

BOM CNS

CHC HAK

CNS HDY

HAK KUL

HDY MFM

HKT PEN

KUL XMN

PEN

PER

WLG

XMN

20092006 2007

17th atrs world conference paper -...

Documents