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A COMPETITIVENESS ANALYSIS OF M A J O R AIRPORTS IN ASIA USING FUZZY LINGUISTIC APPROACH

Yonghwa Park The Korea Transport Institute, Seoul Korea

1. INTRODUCTION

Demand for air transport in the Asia region is growing faster than in any other region in the world. Statistics complied by the ICAO shows that it grew over 10 per cent per annum during the last decade. There are no signs of this sharp increasing rate from abating and all the indications are that it will be sustained well into the 21st century. The potential for air traffic growth in this region is enormous. Positive sources such as the density of population, powerful economical growth, political stability in many countries, deregulation of air transport, and existing competitive airlines, have shed light on some illuminating statistics.

According to the Airports Council International statistics (ACI, 1995), five of the top 30 busiest airports in the world, in terms of numbers of passenger handled in 1994, are situated in the Asia Region particularly North and South-East Asia. When it comes to the numbers of aircraft movements, however, not a single Asian airport features in the top 30 worldwide. This means that a greater number of passengers are being moved with fewer aircraft movements. In this case, air traffic on long-hanl routes has clearly been and will remain dominant although traffic on short-haul routes will grow at a faster rate than on long-hanl routes. Indeed, it boasts the highest ratio of wide-bodied aircraft in its airline fleets than that of any other region.

Many Asian nations are undertaking the expansion project of existing airport's facilities and construction of new airports to meet its dramatically increasing demand. The ambition of almost every government in the region is to build and enhance an airport infrastructure with sufficient capacity and sophistication to become the main international hub airport. It can not be achieved to the status of air hub by not only aspiration and sudden revolution, but the complements of careful strategies and continuous efforts.

In a highly competitive market of North and South-East Asia, almost all major airports are getting enormous competition to take the market-powered airport, now and in the future. It would be valuable to analyse the potential competitiveness of them in the near future when every new constructed airports will operate during the initial phase. To analyse their competitiveness, this study has adopted several factors, from the various literature reviews, which are recognised as an important elements at the airport system. Also, the fuzzy linguistic approach used and the evaluation is based on the airport experts' points of view. 2. OVERVIEW OF AIRPORT INFRASTRUCTURE IN ASIA

2.1 Constraints and Remedies

The high growth rate of air demand in the Asia Pacific Economic Co-operation(APEC) region is surging more air transport infrastructure to serve with high quality levels to the users. Since late 1980s, the North and South-East Asia region has had the largest number of new airports' construction in the world. Almost every major airport in the region is currently involved in or planning expansion, renovation, or upgrading project. Therefore, many existing airports are serving very closed to capacity. Activities to stretch every available inch of capacity out of congested existing airports until new infrastructure will come on line is unprecedented, but crucial.

Airspace congestion is an existing and growing problem in the Asia-Pacific Rim, especially in the Northeast Asian region. Airspace congestion issue has emerged to be the most serious threat to the growth of air traffic movement. It will bring more delays to passengers and higher fuel consumption for aircraft, which are assigned by air traffic control under optimal flight levels and flight paths. (Park et al., 1996) The existing international airspace system is unable to accommodate the growing demands placed on it by aviation operations. As the region is faced with a highly fragmented airspace network and a very wide variance in the sophistication of air traffic control systems, there must be a regional approach to the problem

An outstanding research, APEC congestion points study (Maunsell, 1995), observed the constraints of airport infrastructure in the APEC region and also suggested some useful remedies to improve its system. The congestion problems and causes in the major airport in North and South-East Asia are summarised in Table 1.

To improve the current and near future air transport congestion at the region's airports, there are suggested such practical solutions as:

achievement of a smooth and timely transition to satellite based communications, navigation, surveillance, and air traffic management (CNS/ATM) systems in the region. achievement of fair and equitable opportunity for carriers in the market under liberalised circumstances. development of airport infrastructure without environmental obstacles. co-operation and co-ordination between the users, operators, regulators and providers of transport infrastructure and services. introduction of new technology and facilities to save the service processing time for passengers, aircraft, and freight. improvement of the productivity and efficiency of airport operations by introducing advanced equipment. the role of pricing in demand management. stimulation private sector investment at airport infrastructure and efforts toward privatisation. Table 1 : Summary of Airport Congestion Problems and Causes AIRPORT INFRASTRUCTURE OPERATIONS& REGULATORY& TECHNOLOGY INSTITUTIONAL HONGKONG/KAITAK , apron, and terminal at capacity /NARITA Single runway severely Environmental and constraints capacity ATC restriction Terminal at capacityat on nmway peak time movement SEOUL/KIMPO Runway, aircraft parking, Passenger and baggage Environmental gates and temtinal close processing restriction and to capacity Inefficient airlines ATC limitation allocation at terminals by DMZ ]DONM UANO Runwayand terminal close to capacitya t peak hours SINOAPORE/ Lack of gate capacity at peak hours TAIPEffCHIANOKaI SHEK Lack of apron capacity for overnight parking BEIJ1NG/CAPITAL Terminal at capacity Severe passenger processing delays Source : Maunsell, A P E C Congestion Points Study - Phase 11, draft final report, 1995.

2.2 Development Plans

Over the last few years, the major airports in North and South-East Asia has been operating at or close to their capacity. As a result, Korea, Japan, China, , Thailand, Malaysia, and Indonesia took the decision to expedite the development of new airports. In September 1994, the new Kansai Airport in Japan opened and other new airports in the region will operate within the end of the 20th century. In particular, New Seoul Metropolitan Airport which has been renamed as Inchon , Maiaysia's new international gateway at Sepang serving , and Nong Ngu Hao, the so-called Cobra Swamp, as the second Bangkok International Airport, will be built in phases, with an ultimate planned capacity of 100 million passengers per annum. Other new airport projects such as Chek Lap Kok in Hong Kong, Shanghai's Pudong in East China, and Kansai in Japan were launched with passenger handling capacities ranging from 40 to 80 million per year. Kansai Airport had already completed the first phase in September 1994.

In Korea, the air transport industry took a major leap forward in the late 1980s with the liberalisation of overseas travel and deregulation of air transport market. In Seoul metropolitan area alone, air traffic has increased by an average of 12.9% a year between 1985 and 1994 and is expected to continually grow by an annual average of approximately 9% by year 2000. The current national gateway airport, Kimpo Airport, is situated close to the capital city of Seoul and currently operates at limit capacity. Therefore, the Korean government has decided to built a new off-shore airport in Youngjong island. The first phase of the project is scheduled for completion in 2000. It is expected to cost US$ 4.98 billion and will have the capacity to handled 27 million passengers and 1.7 million tonnes cargo a year. With the completion of the final phase of development, the new airport will be able to handle up to 100 million passengers, 7 million tonnes cargo, and 530,000 aircraft operations per annum (KOACA, 1994).

China is the fastest growing economy and largest numbers of population and scale of territory. However, the aviation market in China remains hugely under-developed and only a tiny percentage of the country's enormous population travels by air. As progress towards much more open market economy continues, air traffic can be expected to continue its unprecedented levels of growth as the population becomes increasingly affluent and air travel becomes an utility accessible to a much larger percentage of the populace. As the cost factor becomes less of a constraint to growth, the spotlight is being focused on airport infrastructure as the major hurdle and there is growing awareness that demand will all too rapidly outstrip available capacity(Paylor, 1994). The major programmes of the existing airport's modernisation, expansion, and improvement of facilities and new airport development are being implemented across the country, especially in capital area, South and East China. Shanghai's Pudong New Airport will be a top grade international airport which is scheduled for completion in 2005.

Japan is one of the largest air transport markets in the world even though it has a compact population. Also, Japan has extraordinary geographical characteristic of only 30 per cent of the land area being fiat. Hence, it is strongly restricted from developing airports in suitable land area. The major international airports in Tokyo and Osaka had faced environmental considerations, including noise pollution. As a result, Kansai has been constructed on a man-made island 5 kilometres off-shore in Osaka Bay. This is the first sea-reclaimed airport in the world. The first phase of development gives a capacity of 30 million passengers a year and 160,000 aircraft movements. Final phase will see the completion of three runways with handling capacity of 50 million passengers and 260,000 aircraft movements a year (KIX, 1995). The other improvement and expansion plans include a feasibility study on a new airport development to serve Tokyo, the on-going construction for the next phase of Kansai, the second runway construction at Tokyo Narita Airport, and continual extension of international facilities at major regional airports.

New airport in Hong Kong, Chek Lap Kok, has been scheduled to open in late 1997. However, it will be slightly delayed few months until 1998. The Airport Core Programme is one of the most ambitious infrastructure development programmes in the world which involves 10 interdependent projects. With eight of the ten airport core programme projects nearly fully funded by government, or in a case by the private sector, nothing should stop their swift and efficient completion. (Blake, 1994) The first phase of Chek Lap Kok project has been planned to build only one runway and operate with a capacity of 27 million passengers and 160,000 aircraft movements a year. The final stage of construction will be to build two runways with handling capacity of 87 million passengers and 320,000 aircraft movements per annum. After opening the new airport, the existing in city centre will be closed.

Singapore Changi Airport is one of the region's - and indeed the world's - most highly acclaimed airports. It is consistently highly rated by passengers and has managed to maintain standards despite the passage of time. It could claim to be the region's first real international hub, being strategically located at the crossroads between Europe and the Far East, and the Far East and Australasia. Almost all traffic between Australia ' and Europe passes through , and national flag carrier offers an extensive network of regional services to provide good connections. (Paylor, 1994) Changi Airport was opened in 1981 with a design capacity of 12 million passengers a year and the second terminal opened in 1991 With annual capacity of 12 million passengers. With traffic increasing at its present rate, an additional passenger terminal and cargo building, aircraft stands, and fuel storage are expected to be needed by the year 2002.

In Southeast Asia, the development of massive new airport projects has been launched or are ambitiously ready to start. They will be a threat to the current dominant hub of Singapore's Changi Airport. There are two mega-port on-going developments which are Sepang New Airport serving for Kuala Lumpur and Bangkok's Nong Ngu Hao, with an ultimate design capacity of up to 100 million passengers a year. They have been planned to shift the region's hub from Changi to them. in Philippines also has its sights set on a major regional hub role in terms of passengers as well as freight. Taiwan has set Up airport upgrading strategies in preparation for the introduction of direct airlinks with China.

In summary, the airport's expansion and construction plans in the North and South- East Asia region are shown in Table 2.

Table 2 : Overview of Major Airport's Expansion and New Development Plans in North and South-East Asia COUNTRY AREA CAPACITY / REGION AIRPORT (ha) RUNWAY (rail. Pax./yr) PLANS 700 4.0Kan×i 22 JAPAN NARITA* 1,065 3.5Km×l 38 Completion in 1999 2.5Kmxl KANS~d* 510 3.5Km×l 25 Opened in Sep. 1994 ...... b ~ o ~ . . . . . ~ : S K m ~ 2 ...... 4 0 ...... L q n g . : t ~ . p l a ~ ...... HONGKONO CHEKLAPKOK 1,248 3.8Krn×l 35 Completion in April 1998 ...... 3:8Km×l . . 87 .... Cgraplefign !n 2040 ...... HONGQIAO* n/a 3.2Km×l 12 Expansion of Pax. Terminal by CHINA...... 1996 mrd.fl~e2 nd runway ...... ~ o ~ 9 ...... 3 , 0 0 0 . . 4 : S K ~ . r ~ ...... ~ 9 ...... Q ~ e n . i n . . 1 . 9 ~ ...... TAIWAN CnIANGKAISHEK* 1,200 3.4Km×l 22 Expansion of Pax. Terminal by 3.1Kmxl 1999 PHILIPPINES CLARK* rga ida 0.5 - 0.7 Expansion of terminals by 1998 ...... 1 , 6 9 o ...... ' g a ...... r ~ a ...... 0 3 ~ : y e ~ . ~ 9 o o ...... THAILAND NONGNOUHAO 3,200 3.7Kmx2 30 Completion in 2000 ...... 4-0Kinx2...... 100. . . . C0mplefipn in.2020 . .. ' MALAYSIA SEPANG 1,250 4.0Kmx2 25 Open in 1997 ...... ! o 9 q q 4 . 9 K ~ 2 ...... ~ 9 ...... ~ o m ~ ! e ~ g n ~n 2og(~ ...... KOREA INCHON 1,098 3.8Km×l 27 Open in 2000 4,700 4.0Kanx3 100 Completion in 2020 Note : Mark * indicates existing airport. Source: KOACA(1996) 3. METHODOLOGY

3.1 The Fuzzy Approach

Fuzzy set theory was developed by Zadeh (1965) as an area of research in mathematical system theory. Since the early 1960s, it has been used as a suitable mathematical tool for deafing with systems of organised complexity. The application of fuzzy set theory can be found in a wide variety of fields, such as artificial intelligence, computer science, meteorology, human factors engineering, interpersonal communication, medicine, pattern recognition, robotics, and transport. It has also applied to an evaluation methodology (Park, 1994). Fuzzy set theory has attempted to deal with the decision processing that involves subjective judgment. Subjective judgment of an evaluation has typically faced the problem of building a mathematical framework, because it can not deal effectively with the decision maker's feeling of ambiguity, uncertainty and vagueness.

A linguistic variable is defined as a variable, the values of which are words, phrases, or sentences in a given language where such a language can either be natural or artificial (Schmucker, 1983). Zadeh (1973) presented in a nutshell the motivation for fuzzy logic and approximate reasoning as "in retreating from precision in the face of overpowering complexity, it is natural to explore the use of what might be called linguistic variable, that is, variable whose values are not numbers but words or sentences in a natural or artificial language and the motivation for the use of words or sentences rather than numbers is that linguistic characterisations are, in general, less specific than numerical ones."

In applying a fuzzy linguistic variable approach to the measurement of competitiveness of the major airports in North and South-East Asia, this analysis has adopted the methodology ofKarwowski & Mital (1986) and Wilhelm & Parsaei (1991). In order to analyse airport's competitiveness, we define two fuzzy linguistic variables: importance(X) and competitiveness(Y). The linguistic variable X = importance associated with each of the competitive factors, and the other variable Y = competitiveness of each airport to represent its potential market power in East Asia.

The general terms, importance and competitiveness, are still imprecise and can be further modified using a linguistic hedge or a modifier, which is an operation that modifies the meaning of a term or, more generally, of a fuzzy set (Zimmerman, 1991). The concept of linguistic hedges or modifiers is very important and useful for using linguistic variables in fuzzy logic. A hedge acts as a modifier in order to determine the meaning of an arbitrary term of the term set using natural language statements such as very, fairly, highly, indeed, more or less, low, etc.

The primary values of the two variable X and Y are defined on the universe of discourse [0, 1] and the operators' form, using a linguistic hedge, can be given new variable values. Suppose R(x) and RLv) are defined as a semantic rule for associating a meaning with each variable name and it is also a fuzzy subset on the universe of discourse for linguistic variable X and Y. Therefore, we assume that the primary values o f X = importance of variables and new variable values using hedges are as follows: R(x)={importantance}={x, ,UR,(Ux), for all ux ~U~}, i = 1 , 2, ..,/, I = 6 , where, /~Rj(U.,) is the compatibility or membership function of u, in R(x). Therefore, new linguistic variable values are defined such as:

Rl(x) = { most important (A1)} =/&4 ~ U~, RE(X)= { more important (A2)} =/t~ 2 ~ Ux, R3(x) = { important (A3)} = ~ ~ U~, R4(x) = { less important (A4)} =/&0.5 ~ U~, Rs(x) = { unimportant (A5)} = 1-/& ~ U~, and R6(x) = { Least important (A6)} = 1-/& z ~ U~.

The other values of variable Y= competitiveness can be modified by below:

R ( y ) = {competitiveness} = {y, gRj(uy), for all uy ~Uy}, j = 1, 2,., J, J = 7 , where, ~tRj(Ue) is the compatibility or membership function of u e in R(v). The new values of linguistic modified variables are defined such as:

Rl(y) = { indeed superior 031)} =/+6 ~ Uy, R2(v) = { more superior 032)} = / z f ~ Uy, R30~) = { superior 033)} = flyLs ~ Ue , Ra(v) = { average 034)} = px E Uy, Rs(v) = { inferior 035)} = 1-/~ L5 ~ Ur , Rdy) = { more inferior 036)} = l-p~ s a Uy, and RT(y)= { most inferior (B7)} = 1-/+ 6 ~ Uy.

To solve the fuzzy relation of two linguistic variable X and Y, some methodologies can be provided using in the heuristic algorithm. In this analysis, Sanchez's (1976) methodology is considered for solving the basic linguistic equation below hold:

x, =to o y ~ (Eq. i) where, x~ is value of importance of a competitive factor i, Y1 is value of competitiveness of an efirportj, r 0. is a fuzzy relation of factor i and airport j, and o is a composition operator which defined such that the compatibility function of r~j o y / i s determined from

ItR<,oy)(Ux,Uy) = Max [ Min {/~R(x)(U,,Uy), /~(r)(Uy)}], for each u x. uy

Thus, Sanchez's procedure is used for finding the fuzzy relation, r0; in the equation, given xi andy/, as presented in equation below: r,i = x S ® y~ (Eq. 2) where, x f is the transpose ofx~, ® is a compositional operator defined such that the membership function of x r ® yj which is determined from /z (~®y~ = Min LuR(~)(x)T // oc/ZR(r)(Uy)] , and oc is a compositional operator defined such that the elements of its compatibility fimction are determined as follow:

1 if /ffR(~)(U~)

Another useful concept is the intersection of fuzzy relations. This intersection is defined as a fuzzy relation 9~i =f-I~r~ having a compatibility function given by Kaufinann(1975): ' '

p~(ux, u )= Min [pR(,)(U~, Uy)]. Y x , y 0Eq. 4)

Finally, the competitiveness of each airport, ,uRjcy~, ~(Ue), can be described by a fuzzy relation as the rule of compositional inference. In order to determine the competitiveness of each airport, it is needed to calculated such fuzzy relation as:

(Eq. 5)

3.2 Heuristic Algorithm for Analysis of Airport's Competitiveness

In the methodology presented in this paper, the linguistic assessments of x, and y~ are provided by an expert survey. The expert survey was carried out by interviews and mail-back questionnaires. There were 33 useful respondents whose nationality consisted of 18.2% Korean, 15.2% Japanese, 12.1% Chinese, 9.1% Hong Kong residents and also Singaporean, and American, Indonesian, Thai, Filipino, and others were 6.1% respectively. All respondents have worked in the airport-related field and 82% of them had over 10 years of experience.

The factors influencing the airport competitiveness was selected by comprehensive literature reviews. Initially, the expert questionnaire used 10 factors to determine the degree of importance to airport competitiveness, but this attempt was analysed for the potential competitiveness rather than current conditions so that few factors can not be accepted. Therefore, this analysis assumes that all selected airports will operate with the high level of services and with highly advanced facilities in the future. The factors are briefly described such as: Geographical characteristics of airport (GEO) : this involves such elements as each airport's site selection, size of airport-related community to generate sufficient air demand, and the possibility of international airline networks etc. Airport accessibility (ACS) : this is concerned with access time, availability of transport modes, and transport modal split for minimising congestion, and connecting networks. Environmental effects (ENV) : regarding such factors as aircraft noise, air and water pollution to an airport vicinity community. Business and operational conditions of airlines (BOC) : which will in turn help customers apply successful operational strategies and improve the level of services such as multiple air routes and high frequencies. Socio-economic effects' (SEC) : which are influencing effects on the airport- related industries, regional community development, job creation, investment opportunity to the airport vicinity and others. Airport regional development (ARD) : this is the potential possibility of airport regional development considering international trading zones, free banking centres, sports and leisure complexes, logistic and convention centers, airport- related industrial zones, etc. Level of airport charges" (LAC) : this mainly consist of landing, passenger, and aero-handling charges, and aircraft parking fee which can estimate at newly developing airports based on the current levels. Availability of expansion planning implementation (AEP) : this is concemed about the level of availability of planning implementation for expansion facilities at the existing airports and construction of new airports in order to increase capacity.

Originally, 18 airports in the East Asia region were selected to analyse their competitiveness. Here, however, 9 major airports which are currently operating or under constructing were concerned. Table 3 shows these airports.

Table 3. Selected Major Airport in East Asia to Analyse the Competitiveness NATION / AIRPORT NATION / AIRPORT REGION REGION J~AN TokyoNarita (NRT) TAIWAN TaipeiChiang Kai Shek (CKS) Osaka Kansai (KIX) SINGAPORE Changi( SIN) HONGK ONG ChekL ap Kok (CLK) CHINA ShanghaiPudong (PDG) THAILAND BangkokNong Ngu Hao (NNH) KOREA SeoulInchon (NSI) MALAYSIA KualaL umpur Sepang( SEP)

® Step 1

In the process of analysing a competitiveness of airport, the degree of importance of influencing factors must be set up and will be assessed linguistically by selecting values of the variable X = importance. Through the expert survey, it has assessed the importance of each of the influencing factors to the airport competitiveness as shown in Table 4. Table 4. Linguistic Assessment of the Importance of each Factor

RELATIVE DEGREE VALUE OF FACTORS OF IMPORTANCE IMPORTANCE CRITERIA Geographic (GEO) t.oo w = ~ J A1 = [ 0 . 9 0 , 1 . 0 0 ] Accessibility (ACS) 0.95 w = ~,x2 A2 = [ 0.80,0.89 ] Environment (ENV) 0.64 IM = M° '5 Operational conditions (BOC) 0.63 ~ = M ° s A3 = [ 0 . 7 0 , 0 . 7 9 ] Socio-economic (SEC) 0.43 LI = 1- Mx A4 = [ 0.60,0.69 ] Regional development (ARD) 0.46 LI = 1-/zx A5 = [ 0.50,0.59 ] Airport charges (LAC) 0.48 LI = I-/2x A6 = l 0.00,0.49 ] Planning implication (AEP) 0.53 I M = ~,os

By mail-back surveys and interviews with airport experts, the relative degrees of competitiveness of each airport are calculated and then the set of each airport competitiveness is assessed by use of values of the linguistic variable Y = c o m p e t i t i v e n e s s as shown in Table 5. The values competitiveness of each airport are divided into seven linguistic criteria which are:

B1 = [ 0.96,1.00 ], B2 = [ 0.91,0.95 ], B3 = [ 0.86,0.90 ], B4= [ 0.81,0.85 ], B5 = [ 0.76,0.80 ], B6 = [ 0.71,0.75 ], and B7= [ 0.00,0.70 1. where, Uis the set of universe of discourse [ 0 , 1 ] = {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}.

Table 5. Linguistic Assessment of the Comp~itiveness of each Airport

AIRPORT FACTORS NRT KIX CLK PDG NSI CKS SIN NNI-I SEP GEO = A1 B5 B3 B4 B5 B2 B4 B1 B3 B4 ACS= A1 B6 B4 B2 B4 B4 B5 B1 B4 B3 E N V = A4 B5 B1 B1 B5 B1 B5 B3 B2 B2 BOC = A4 B4 B4 BI B5 B2 B4 B1 B3 B5 SEC = A6 B4 B2 B5 BI B1 B5 BI B2 B3 ARD= A6 B6 B3 B1 B1 B1 B3 B2 B1 B1 LAC = A6 B7 B7 B5 B4 B5 B1 B1 B6 B4 AEP = A5 B6 B5 BI B4 B2 B3 B1 B3 B4

Step 2

For each airport and each influencing factor, the linguistic assessment, r~, in equation (Eq. 2) uses the compatibility functions of the fuzzy linguistic variables of value X = importance and Y = competitiveness. For instance, let us assess the competitiveness of Kansai Airport (KIX) using the factor of environmental effects(ENV), i = 3. ENV is assessed as important(IM) in terms of importance and KIX's competitiveness considering this factor, j = 2 , which is assessed as indeed superior(IS). This relationship is expressed by the fuzzy relation r32, which is shown below: r32 = xr ® Y2 : p r (x) ® R~(y)

0.000 0.320 0.450 0.550 0.630 0.710 ®[0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.531 1.000] 0.780 0.840 0.890 0.950 1.000

-0.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000" 0.000 '0.000 0.000 0.00I 0.004 0.016 0.047 0.118 0.262 1.000 1.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 1.0000 1.000 0.000 0.000 0.000 0.00I 0.004 0.016 0.047 0.118 0.262 0.531 1.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 10.531 1.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 10.531 1.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.53I 1.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.531 1.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.531 1.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.I18 0.262 0.531 1.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.531 1.000

• Step 3

This step is formed across each of the 9 selected airports for each of the 8 influencing factors using the intersection of fuzzy relations in equation (3). It is based on step 2 which computed the fuzzy relations representing the 72 entries (8 factors x 9 airports). For example, the intersection 9~j of the fuzzy relations associated with j = 2, Kansai Airport across the 8 influencing factors to the competitiveness.

"0.000 0.001 0.008 0.027 0.064 0.I25 0.216 0.343 0.284 0.146 0.000' 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.26 0.146 0.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.146 0.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.146 0.000 0.000 0.000 0.000 0.001 0.004 0016 0.047 0.118 0.262 0.146 0 . 0 0 0 ~2 ~- 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.146 0.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.146 0.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.146 0.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.146 0 . 0 0 0 0.000 0.000 0.000 0.00I 0.004 0.016 0.047 0.118 0.262 0.531 0.000 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.I18 0.262 0.531 0.000 • S t e p 4

Using the equation (5), the maximum competitiveness of each airport can be calculated as follow:

N R T ,UR,(y,~o(Uy ) = [0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.657 0.488 0.271 0.000] KIX : ,UR~(y~(Uy ) = [0.000 0.001 0.008 0.027 0.064 0.125 0.216 0.343 0.284 0.531 0.000] CLK t.ta~(y,~l(Uy) = [0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.531 1.000] PDG ,Un4(y,~,)(Uy ) = [0.000 0.100 0.200 0.300 0.400 0.500 0.535 0.414 0.284 0.531 0.000] NSI : ¢tR,(y.~l(Uy) = [0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.531 1.000] C K S : pR0(y,,=)(uy)= [0.000 0.100 0.200 0.300 0.400 0.500 0.535 0.414 0.284 0.146 0.000] S I N : = [0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.531 1.000] N N H : = [0.000 0.001 0.008 0.027 0.064 0.125 0.216 0.343 0.512 0.729 0.000] SEP : llno(y,~o(Uy ) = [0.000 0.001 0.008 0.027 0.064 0.125 0.216 0.343 0.284 0.531 0.000]

4. RESULTS

Each airport competitiveness can be calculated by the relative Euclidean distance (Kaufmann, 1975). The relative Euclidean distance definesf(A) in terms of a metric distance of A from any of the nearest crisp sets. In here, we can apply this distance to measure each airport's competitiveness and the shortest distance has been shown to be the most competitive airport against any others in the North and South-East Asia Region. The relative Euclidean distance(Sj) can be defined as:

1 2 6 j = n [,UR(y,~(Uy)--,UR(y,,,~,~(Uy)] f o r j = 1,2, ....,J, J = 9,

where: n is the number of elements in the universe &discourse ue, ¢tRj(r,~(Uy) is the ideal compatibility function in terms of the linguistic variable of indeed superior for an airport j , and tZR/y,~,,~(Uy ) is the maximum compatibility function for the competitiveness of an airportj. Finally, the competitiveness of major airports in East Asia are represented in terms of a metric distance (Euclidean distance), which can be chosen as the best competitive airport by the minimum relative distance. The competitiveness of all selected airport are shown in Table 6.

Table 6. Relative Euclidean Distances for All Selected Airport

RANKING SELECTED AIRPORT RELATIVE DISTANCE 1 Chek Lap Kok Airport (Japan) 0.0869 1 New Seoul (Inchon) Airport (Korea) 0.0869 1 Singapore Changi Airport (Singapore) 0.0869 4 Nong Ngu Hao Airport (Indonesia) 0.1636 5 Kansai Airport (Japan) 0.1687 5 Sepang Airport (Malaysia) 0.1687 7 Pudong Airport (China) 0.1842 8 Tokyo Narita (Japan) 0.1939 9 Clfiang Kai Shek (Taiwan) 0.1953

Thus, we find Chek Lap Kok, Changi, and Inchon International Airport will be the most competitive airport in the target year 2010 in the Southeast and the Northeast Asia region since they have the minimum relative Euclidean distance from the ideal linguistic assessment of competitiveness. The next are Nong Ngu Hao and Kansai Airport.

A degree of competitive relationship described a numeric index between two airports (see Figure A-1 in Appendix). The highest competitive relationship will be between Inchon Airport in Korea and Narita Airport in Japan. Inchon vs. Kansai and Sepang vs. Changi are also expected to have highly competitive relationship after their final developing phase around year 2020.

5. CONCLUSION

The methodology proposed in this paper can provide a practical and applicable assessment of the airport competitiveness in the East Asia region. In particular, it has dealt with how it can convert the influencing factors to the competitiveness of finite scales using linguistic variables. This application of fuzzy linguistic approach has been investigated as even more flexible and adaptable to deal with competitiveness associated with designated influencing factors. In this analysis, the factors affecting the competitiveness of airports have adopted a broad range and have different weighting values.

The findings were obtained from a relatively small numbers of expert, but all respondents had long experience within the airport-related field of Asia and worldwide. They assessed the importance of the influencing factors to the airport competitiveness. The most important factor was designated as the airport geographical characteristics, and the next was airport accessibility. Other factors such as environmental effects to an airport vicinity community, business and operational conditions of airlines, and availability of expansion planning implementation were evaluated as the moderate levels. The most competitive airports in the North and South-East Asian region have been analysed as Chek Lap Kok Airport in Hong Kong, Inchon Airport in Korea, and Changi Airport in Singapore. Among them, only Changi Airport is an operating airport and the others are undertaking construction to be the new mega-airport. All of them have aspiration of being the hub airport in the region. After all new airports become operational, the airport competitiveness gets more in depth. Therefore, the airports must develop sufficient infrastructure and simplify the array of various limitations, for example, regulations and institutional regulatory. The reinforcement of international co-operations is also very crucial.

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Figure A-1. Competitive Degree of Major Airports in East Asia i'~ii%!iili171;7':?' 7,i : ~7! ?~::': ': i'??! i;'i !:ii !i:; i ~~ i :ili,i ~;~:;iii~:i i~i~: :i!:i~: ::~:!~:Y'Y:~': :!ii;lii iil li ,i ~~ ~::!7'? ,%

Table A-1. Compatibility Functions for X = Importance

VARIABLE IYNIVERSEOF DISCOURSE VALUEx 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Mostlmportantux4(A1) 0.000 0.000 0.002 0.008 0.026 0.063 0.130 0.240 0.410 0.656 1.000 More Important ux~(A2) 0.000 0.010 0.040 0.090 0.160 0.250 0.360 0.490 0.640 0.810 1.000 Important ux(A3) 0.000 0.I00 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Less Important Ux°'5(A4) 0.000 0.316 0.447 0.548 0.632 0.707 0.775 0.837 0.894 0.949 1.000 Unimportant 1-ux(A5) 1.000 0.900 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Leastlmportantl-ux2(A6) 1.000 0.990 0.960 0.910 0.840 0.750 0.640 0.510 0.360 0.190 0.000

Table A-2. Compatibility Functions for Y= Competitiveness

VARIABLE UNIVERSE OF DISCOURSE VALUE y 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Indeed Superior u6 031 ) 0.000 0.000 0.000 0.001 0.004 0.016 0.047 0.118 0.262 0.531 1.000 More Superior u~ (132) 0.000 0.001 0.008 0.027 0.064 0.I25 0.216 0.343 0.512 0.729 1.000 Superior url5 (B3) 0.000 0.032 0.089 0.164 0.253 0.354 0.465 0.586 0.716 0.854 1.000 Average u~(B4) 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Inferior 1-uvl 5(B5) 1.000 0.968 0.911 0.836 0.747 0.646 0.535 0.414 0.284 0.146 0.000 More Inferior l-u~ (B6) 1.000 0.999 0.992 0.973 0.936 0.875 0.784 0.657 0.488 0.271 0.000 Most Inferior 1-u6(B7) 1.000 1.000 1.000 0.999 0.996 0.984 0.953 0.882 0.738 0.469 0.000 (New Abstract Form)

A COMPETITIVENESS ANALYSIS OF MAJOR AIRPORTS IN ASIA USING FUZZY LINGUISTIC APPROACH

Yonghwa Park The Korea Transport Institute, Seoul Korea

Asia is one of the fastest growing regions in the world. Over the last decade, air traffic growth in the Asian Rim has been the greatest on earth and the potential for further growth is enormous. In order to meet its dramatically increasing demand, many Asian nations are planning and/or constructing of new airports. The ambition of almost every government in the region is to build and enhance an airport infrastructure with sufficient capacity and sophistication to become the main international hub airport. It would be valuable to analyse the potential competitiveness of these airports. They consist of nine major airports: (1) China - Shanghai New Pudong Airport, (2) Hong Kong - Chek Lap Kok Airport, (3) Japan - Tokyo Narita, and Osaka Kansal Airport, (4) Korea - Inchon (New Seoul) Airport, (5) Malaysia - Sepang New Airport, (6) Singapore - Changi Airport, (7) Taiwan - Chiang Kai Shek Airport, and (8) Thailand - Bangkok Nong Ngu Hao Airport. This analysis has adopted eight main factors for assessment: airport geographical characteristics, access system to an airport, environmental effects, airlines' business and operational conditions, airport regional development, availability of planning implementation, socio-economic effects, and airport charges to the users. To analyse the competitiveness of the selected airports, this study has adopted a fuzzy linguistic approach and is based on the airport experts' points of view.