Airport capacity vs. demand: Mismatch or mismanagement?

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Transportation Research Part A 42 (2008) 203–226

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Airport capacity vs. demand: Mismatch or mismanagement?

Michael A. Madas, Konstantinos G. Zografos *

TRANsportation Systems and LOGistics Laboratory (TRANSLOG), Department of Management Science and Technology,

Athens University of Economics and Business, Evelpidon 47A and Lefkados 33, Athens 113 62, Greece

Received 13 September 2006; received in revised form 26 June 2007; accepted 23 August 2007

Abstract

Since well-publicized congestion and delay problems encountered by European and US airports entered the politicalarena, there is an unprecedented pressure experienced by policy makers upon investigating and adopting strategies formanaging demand and allocating scarce airport capacity. During the last few years, there is an ongoing policy debatewithin the European Community to undertake further work for a drastically revised regulatory framework aiming to dealwith the scarcity of airport capacity through the efficient allocation of airport slots. One of the primary policy concerns lieson the compatibility of alternative slot allocation strategies in different airport settings. The objective of this paper is three-fold: (i) to develop and apply a methodological framework for the multi-criteria evaluation and selection of the most com-patible slot allocation strategy with respect to policy criteria and priorities in various airport settings, (ii) to examine theapplicability of policy compatibility results in various airport settings and their potential acceptability from different indus-try stakeholders, and (iii) to provide policy recommendations for European airport policy making and planning.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Policy compatibility analysis; Airport slot allocation; Airport capacity management; Multi-criteria evaluation

1. Introduction

Recent air transport forecasts speak for an increase in international air passenger traffic in Europe by anaverage of 3.6% per year between 2002 and 2020 with some analysts anticipating double traffic increase figuresby 2020 (ACI Europe, 2004). The rapid traffic growth has resulted in severe congestion and delay problems,which, in turn, have self-constrained air transport growth and become a major transport policy issue on bothsides of the Atlantic. The congestion and delay problems are expected to further deteriorate when consideringthat over half of Europe’s 50 largest airports have already reached or are close to reaching their saturationpoints in terms of declared ground capacity with only few planned major developments/expansions (DotEconLtd., 2001). Similar findings (NERA, 2004) indicate that European airports suffering from excess demand

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* Corresponding author. Tel.: +30 210 82 03 673/674/675; fax: +30 210 82 03 684.E-mail address: [email protected] (K.G. Zografos).

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204 M.A. Madas, K.G. Zografos / Transportation Research Part A 42 (2008) 203–226

throughout peak times of the day will experience an average increase of traffic movements of 20% vis-a-vis anaverage capacity increase of only 5% by 2007.

The increasing imbalance between capacity and traffic has resulted in congestion and delay figures that havedrawn the attention of aviation policy makers investigating alternative means of coping with the mismatchbetween aviation capacity and demand. Options towards increasing airport capacity through the expansionof existing or building of new airports address only the supply-side of the airport congestion problem. Therather bleak prospects in providing in the short-term sufficient capacity to satisfy all anticipated futuredemand, in conjunction with physical constraints and political opposition to airport capacity expansiondue to land use planning and environmental concerns, stress the need for a thorough examination of demandmanagement instruments as a potential vehicle for bridging the gap between supply (i.e., capacity) anddemand (i.e., traffic) for scarce airport slots.

Demand management has been long recognized as a principal instrument to deal with capacity shortfallsand delay phenomena in the transportation system. Transportation researchers have extensively examinedduring the past few decades the potential for demand management in different transport modes1 mainly inthe form of administrative measures: (i) port demand service reallocation (Zografos and Martinez, 1990),(ii) diversion of general aviation traffic in ‘‘reliever’’ airports (Fisher, 1989) and air traffic perimeter rules(Morrison, 2001), and (iii) road traffic restrictions, cordon-based and other urban highway pricing throughthe popular forms of travel demand management (TDM) measures (Saleh, 2007). More recently, a policyand research shift towards pure economic instruments with emphasis on road congestion charging seems tobe the prevailing view with many researchers arguing that ‘‘it is not a matter of if but of when’’ (Hensherand Puckett, 2007) and how (Bonsall et al., 2007). In the air transport context, there is a long and comprehen-sive body of knowledge on the application of pure economic or hybrid instruments such as the implementationof congestion-based pricing schemes in airport and en route traffic (Levine, 1969; Carlin and Park, 1970; Fan,2001; Odoni, 2001; Brueckner, 2002), as well as secondary slot trading and slot auctions (DotEcon Ltd., 2001;TUB, 2001; NERA, 2004; Mott MacDonald, 2006).

Despite the substantial literature and research initiatives/proposals on airport demand management,attempts to bring forward such measures were very few and fragmented (Madas and Zografos, 2006). As aresult, the identified gap between demand and supply still persists and becomes even more acute due to thefact that the already scarce airport capacity is not efficiently managed and allocated as attested by the ‘‘latehand-backs’’, ‘‘no shows’’, and ‘‘slot babysitting’’ figures observed in most of the busiest European airports(ACI Europe, 2004).2 These imply a hard misuse of the allocated slots mainly due to late return (or evenno use at all) of slots that cannot be reallocated and, therefore, jeopardise the ability of the airport operatorto manage scarce capacity efficiently and make the best (i.e., maximum) use of available airport infrastructure.The ever-tighter mismatch between airport capacity and demand has already triggered policy discussions thatbring into the forefront two challenging policy questions: (i) which are the driving forces and reasons behindthe mismanagement of capacity?, and (ii) how demand management measures and capacity allocation schemescould be jointly deployed to bridge the gap between airport demand and capacity?

During the last few years, the European Commission pursues a radical revision of the existing slot alloca-tion regime towards the adoption of market-driven allocation mechanisms aiming to deal with the scarcity ofairport capacity. In response to this policy orientation, a substantial amount of research work has been doc-umented in the literature (DotEcon Ltd., 2001; Federal Aviation Administration (FAA), 2001; TUB, 2001;Fan and Odoni, 2002; Zografos and Madas, 2003; NERA, 2004; Madas and Zografos, 2006) with the aimto review and critically assess the alternative slot allocation options in the form of administrative measuresand rules accompanied or adjusted by market-based instruments aiming to allocate scarce airport capacity(i.e., slots) more efficiently. In this effort, one of the primary policy concerns lies on the compatibility of alter-native slot allocation strategies in different airport settings, as well as the selection and implementation of aslot allocation strategy that will fulfil certain policy criteria and priorities for these airport settings.

1 Demand management in the air transport context covers a wide range of decisions including waste disposal, stand and gate allocation,concessions etc. Nevertheless, the major airport demand management ‘‘version’’ pertains to the strategic level of allocation of scarcerunway capacity as reflected on slot allocation.

2 See glossary of terms in the end of the paper.

M.A. Madas, K.G. Zografos / Transportation Research Part A 42 (2008) 203–226 205

The ultimate objective of this paper is to develop and apply a methodological framework for the assessmentand selection of the most compatible slot allocation strategy for each different type of airport on the basis ofmultiple policy criteria and priorities (i.e., policy compatibility analysis). In addition, the policy compatibilityanalysis results will be examined and discussed in terms of their applicability in various airport settings andtheir acceptance from the various industry stakeholders so that some policy recommendations and a roadmapfor policy implementation can be proposed. It is important to note here, however, that the paper does notdirectly deal with the long-term sustainability requirement for any joint capacity–demand management solu-tion. As a matter of fact, the selection of a slot allocation strategy cannot be seen independently of parallelefforts in building new or expanding the existing capacity in the long run. There should be an industry con-sensus that the slot allocation strategy as a demand-side effort should complement rather than replace supply-side efforts towards building new capacity.

The remainder of this paper consists of seven major thematic sections. Section 2 provides some empiricalevidence and policy background information on slot allocation. Section 3 presents the methodological frame-work that was developed in order to perform the policy compatibility assessment. Section 4 briefly discussesthe identified airport slot allocation strategies. Section 5 presents the results of the airport typology develop-ment process and briefly discusses the characteristics of the resulted airport clusters. Section 6 elaborates onthe policy compatibility assessment framework and Section 7 presents the results and the final synthesis of thepolicy compatibility analysis, while Section 8 provides some policy conclusions and research recommenda-tions. Finally, the paper is complemented by a glossary of relevant terms and nomenclature, as well as the listof references.

2. Background

Access to airports is typically managed – if any – through a landing fee that is proportional to the maximumtake-off weight (MTOW) of the aircraft irrespectively of the actual infrastructure utilization and traffic/con-gestion levels. It is noteworthy that, based on international comparisons, aeronautical charges related to air-port/ground operations amount to about 7% of airlines’ costs with en route charges adding some 5% (Nilsson,2003). Currently, at most airports, aircraft are charged a small, relatively to the total operating costs of theairlines, fee that is uniform throughout the day. This fee is based on the weight of the aircraft and it amountsto $5 per passenger on average at some highly congested airports. This obviously cannot effectively manipulatetraffic and subsequently alleviate the resulted congestion and delay problems. As a result, the latest experiencesof some of the busiest airports and the exceptionally high delay figures suggest that ‘‘weight-based fees haverather turned into wait-based landing fees’’ (Morrison, 2001).

The historical justification for the weight-based landing fees was the ability-to-pay principle (i.e., highercharge for large aircraft carrying more passengers as compared to smaller aircraft and general aviation), aswell as the notion of cost-relatedness (TUB, 2001). Typically, charges are designed and set in order for theairport authority to break even, but, on the other hand, these charges are not at a level capable of automat-ically clearing the market. With the failure of governments around the world to invest adequately and on atimely basis in their airports and the documented failure of the ‘‘do nothing’’ alternative (De Neufville andOdoni, 2003) (let delay be an invisible hand controlling access to congested airports), a set of administrativemechanisms have been put into place in order to handle traffic more efficiently, or at least to establish a clearand concrete slot allocation mechanism (with hidden and explicit inefficiencies though). In particular, theindustry has developed alternative means and procedures (dating back to a system created by IATA in1947 and gradually updated over the years) to handle the conflicts of interest involved in the allocation ofscarce airport capacity. Traditionally, capacity at congested airports is expressed in slots (i.e., an expressionof capacity representing the permission given to a carrier to operate an air service at a slot-controlled airporton a specific date and time for the purpose of landing or take-off – see glossary of terms) and allocated withinthe framework of voluntary guidelines developed and evolved over the years under the auspices of IATA.

Slot allocation in European Union’s airports falls within the scope of the European Union Single (Air)Market, thus being subject to a common regulatory framework under European Council Regulation. TheEuropean Community introduced Regulation 95/93 ‘‘on common rules for the allocation of slots at Commu-nity airports’’ on 18 January 1993 (European Commission, 1993). The regulation was enacted in order to give


legal force to existing custom and practice in slot allocation in Europe, since it mainly retains key features ofthe IATA system and does not seek any radical departure from this. In general terms, it reaffirms the historicslot usage (i.e., ‘‘grandfather rights’’ – see glossary of terms) principle, it allows slots to be freely exchanged ortransferred on a strict and transparent one-to-one (non-monetary) basis subject to confirmation of feasibilityby the slot coordinator.

The role of the traditional IATA slot allocation system along with the enhanced version introduced by theRegulation 95/93 was crucial and effective in airports operating under capacity or experiencing only temporalcapacity shortages. However, this is not the case for airports already operating close to or at practical capac-ity, thus being currently under severe congestion. On the other hand, a number of voids and gaps have beenexperienced from the current implementation of the Regulation (e.g., airport designation process, capacityassessment studies, role and responsibilities of slot coordinator) (PricewaterhouseCoopers, 2000). In responseto these problems, the European Commission commissioned several studies with the aim to assess the state ofimplementation and identify required modifications or improvements of the Regulation 95/93 on the alloca-tion of slots at European Community’s airports (Coopers and Lybrand, 1995; PricewaterhouseCoopers,2000). On the basis of the aforementioned studies, the European Commission proposed (European Commis-sion, 2001) and eventually enacted (European Commission, 2004) a Regulation amending Regulation 95/93 onthe allocation of slots at European Community airports.

In parallel with the policy formulation and legislation process, there is an ongoing policy debate within theEuropean Community to undertake further work for a drastically revised regulation on the mechanisms andinstruments for the allocation of slots (CAA House, 2001; DotEcon Ltd., 2001; TUB, 2001). The EuropeanCommission should, by 2003 (European Commission, 2001), and after consultation with the interested partiesand relevant airport stakeholders, have proposed a reform of the slot regime that will mostly fit the definitionof market and/or hybrid slot allocation approaches. This policy reform should inter alia address the basicthorny matters of introduction of market-driven slot allocation mechanisms/instruments (e.g., trading, auc-tions, congestion pricing), the elimination or reduction of historic slot holdings (i.e., grandfather rights),the definition of rights and obligations that go with slots, as well as the debatable argument of slot ownership(i.e., perpetual usage right vs. property right of airlines) (Starkie, 1998). In addition, it should also pronounceits position with respect to the distribution of slot allocation proceeds, the financing of airport capacity expan-sion, and the role and responsibilities of slot coordination committees. Unfortunately, this long-awaited dras-tic revision of the existing slot allocation regime has not been materialised yet (by the time of writing, onlyminor regulatory amendments of the existing Regulation 95/93 have been constituted). In effect, the busiestairports around the world have been explicitly denied the use of market-based instruments to allocate capacityand, as a result, have more or less complied with the IATA guidelines or the amended Regulation 95/93 inEurope with local interpretations and modifications (Fan and Odoni, 2002).

To deal with the aforementioned expectations for fundamental policy amendments, the European Commis-sion commissioned a study (i.e., consultation paper) to assess the effects of different slot allocation schemes(NERA, 2004) aiming to bring market-based approaches in the forefront. The consultation paper (NERA,2004) proposed a drastic revision of the current status and practice by introducing market-driven slot alloca-tion instruments in various contexts: (i) secondary trading, (ii) posted prices, (iii) auction of pool slots com-plemented by secondary trading, and (iv) auction of 10% of slots complemented by secondary trading. Theseslot allocation measures and instruments have been extensively examined and critically assessed in the litera-ture (DotEcon Ltd., 2001; TUB, 2001; Zografos and Madas, 2003; NERA, 2004).

Despite the comprehensive amount of research in this field, there seems to exist a noteworthy gap as to howthese instruments can be integrated and operationalized in order to constitute a set of principal building com-ponents for the development and implementation of an overall slot allocation strategy. This paper capitaliseson existing state-of-the-art and state-of-practice review work in the field of airport slot allocation instrumentsin order to define a series of distinct and integrated strategies as the candidate implementation options for allo-cating scarce airport capacity. The presented research work addresses the basic policy requirement by formu-lating strategies introducing market-driven allocation instruments in different extent and contexts (Madas andZografos, 2006) and explicitly dealing with the open policy issues stated above. Moreover, it goes beyond thestated policy objectives by developing and implementing a multi-criteria assessment framework for the selec-tion of the most compatible slot allocation strategy for each different airport setting on the basis of stated


policy criteria and priorities and their potential acceptability from the various industry stakeholders (i.e., pol-icy compatibility analysis).

3. Methodological framework

The policy compatibility analysis aims to formulate an evaluation framework that will provide guidance forthe selection of the most compatible (with respect to specific policy criteria) slot allocation strategy for thevarious types of airports. In order to perform the policy compatibility analysis, three basic methodologicalsteps should be followed (Fig. 1): (i) identification of the alternative slot allocation strategies and options

Fig. 1. Overall methodological framework of the policy compatibility analysis.


(Step 1, i.e., what should be evaluated), (ii) development of a typology of airports (i.e., airport cluster analysis)(Step 2, i.e., where/in which airport context the identified strategies should be evaluated), and (iii) assessment ofthe policy compatibility of the identified strategies in the elicited airport clusters (Step 3, i.e., how the identifiedstrategies should be evaluated for each airport cluster).

In the initial step (i.e., Step 1), the slot allocation strategies were identified. However, different types of air-ports exhibit different congestion patterns and traffic characteristics, have different objectives and constraints,and therefore, require different congestion or demand management approaches for the allocation of slots(NERA, 2004). In effect, an airport classification scheme (i.e., airport cluster analysis) was required in orderto cope with the peculiarities and characteristics of different airports (i.e., Step 2). This step enables to inves-tigate whether the compatibility of alternative slot allocation strategies varies with the identified airportclusters.

Several families of techniques for the development of typologies/ clusters can be found in the literature withthe most popular being the hierarchical agglomerative, the iterative partitioning, and the factor analytic (Ald-enderfer and Blashfield, 1984). The hierarchical agglomerative clustering was applied for the purposes of thecluster analysis under consideration due to a number of characteristics, advantages, and properties exhibitedby these methods: (i) they are the most frequently used in such problems and conceptually simple to imple-ment, (ii) they produce, by definition, tight and non-overlapping clusters of entities, (iii) they can be efficientlyapplied for classifying a reasonable number (e.g., less than 400) of cases (i.e., airports) into groups that arehomogeneous within themselves and heterogeneous between each other, and (iv) they do not necessitate a pri-

ori (user-specified) determination of the number of resulted clusters (as in the case of iterative partitioningmethods). The airport cluster analysis and its underlying methodological aspects are further discussed in Sec-tion 5.

Finally, Step 3 operationalizes the assessment framework by examining the compatibility of the strategies(from Step 1) in the various airport settings/clusters (from Step 2) with respect to specific policy criteria andindicators. The evaluation problem at hand exhibits the following characteristics that should be captured fromthe methodology applied for the compatibility assessment (Zografos et al., 1997; Zografos and Giannouli,2001):

• a substantial number of stakeholders and different policy actors (e.g., policy makers, slot coordinators, air-port operators, airlines, associations/bodies representing the collective interests of the industry) involved inand affected by the adoption of slot allocation strategies,

• multiple (and often conflicting) policy criteria and indicators,• different policy priorities (relative importance of policy criteria) in the various types of airports

based on the airport’s intrinsic characteristics and the severity of the experienced congestion prob-lems,

• difficulties encountered in expressing in quantitative terms certain criteria or comparing qualitative criteriabased on absolute values/measurements, and

• required involvement of policy makers/experts in the assessment of the policy compatibility (i.e., expertjudgement) and the assignment of importance weights into the policy criteria and indicators in different air-port clusters.

In order to deal with the particular assessment problem, a multi-criteria technique should be considered.Among the available techniques, the Analytical Hierarchy Process (AHP) developed by Saaty (Saaty, 1990)was found to appropriately capture the aspects of the multi-criteria assessment problem under consideration.Basically, the Analytical Hierarchy Process is a multi-criteria assessment technique decomposing a complexevaluation problem into its components, arranging these components or variables into a hierarchical order,and assigning numerical values to subjective (individual or group) judgments on the relative importance ofeach variable (Saaty, 1990). The judgments are then synthesized to determine which variables/alternativeshave the highest priority, and thus should be acted upon to influence the solution of the problem (i.e., per-formance and ranking of each strategy for each airport cluster with respect to multiple policy criteria for theparticular problem at hand). The multi-criteria assessment approach is further elaborated in a followingsection.


4. Slot allocation strategies

Based on the state-of-the-art and state-of-practice review of slot allocation instruments presented in the lit-erature (DotEcon Ltd., 2001; Federal Aviation Administration (FAA), 2001; TUB, 2001; Zografos andMadas, 2003; NERA, 2004; Madas and Zografos, 2006), a number of distinct airport slot allocation strategieshave been identified: (i) status quo with recycling and centralized trading of the pool (Enhanced Status Quo –Strat.1), (ii) grandfather rights with recycling, auctioning of the pool, and secondary trading (Gradual –Strat.2), (iii) grandfather rights with full trading of all slots (Controlled Trading – Strat.3), (iv) pure, conges-tion-based pricing strategy (Congestion Pricing – Strat.4), and (v) removal of grandfather rights accompaniedby decentralized auctions and secondary trading (Big Bang with auctions and secondary trading – Strat.5).Table 1 sketches the identified airport slot allocation strategies along with their key features, rules, and com-ponents. A more elaborated description of the strategies and their functional principles and rules can be foundin (Madas and Zografos, 2006).

4.1. The ‘‘Enhanced Status Quo’’ Strategy (Strat.1)

The ‘‘Enhanced Status Quo’’ strategy involves the minimum departure from the existing system on thegrounds that it fully maintains the overriding principle of historic slot holdings based on grandfather rights.It retains the rationale of administrative coordination of slot allocation in conjunction with primary slot trad-ing (coordinated trading). In particular:

• Grandfathered slots will remain with current holders with no change envisaged as compared to their cur-rent status except for recycling. Non-grandfathered slots will be seasonally accumulated in a slot pool.

• A portion of the pooled slots will be set aside in order to serve policy objectives such as the promotion ofcompetition, the facilitation of access by new entrants, and the safeguarding of regional routes/airports andsmall communities.

• Coordinated trading stands for the establishment of a trading environment that will be centrally adminis-tered by a mutually trusted allocation entity as compared to a fully open, market-driven trading directlybetween airlines. In the coordinated trading, the slot pool (with the exemption of policy-driven allocatedslots) will be subject to a slot trading process in the form of an informal, one-by-one auction with directnegotiations of the airlines with the central allocation entity. Slot trading will be applied merely as a pri-mary allocation mechanism (secondary trading will not be allowed) complementary to the existing historicslot holdings.

• The duration of usage rights for all slots (apart from policy-designated) will be governed by two mecha-nisms: (i) the adoption of a tighter ‘‘use it or lose it’’ rule that will be specifically defined to leave no roomfor interpretation and subjective judgment by the monitoring entity (e.g., slot coordinator), and (ii) theestablishment of a slot recycling mechanism in the form of a ‘‘x% rule of used slots with slot classes’’.

Table 1Alternative airport slot allocation strategies

Instruments and rules Strategies

Enhanced Status Quo Gradual Controlled Trading Congestion Pricing Big Bang

Grandfather rightsp p p

Removed RemovedCentralized trading with policy criteria

p

Primary tradingp

Secondary tradingp p p

Auctionsp p

Congestion Feesp

Recyclingp p p p

Use it or lose it rulep p

Policy-designated slotsp p p p p

All slots? (Only pool) (Only pool)p p p


• Sales proceeds from coordinated slot trading will go directly to the airports as the actual provider of capac-ity and the physical entity that will undertake the capacity expansion projects to deal with scarcity of slots.

4.2. The ‘‘Gradual’’ Strategy (Strat.2)

The ‘‘Gradual’’ strategy also involves a conservative approach albeit with a more clear orientation to mar-ket mechanisms and a slightly more drastic revision of the status quo especially with regards to secondary allo-cation. In principle, it also retains the grandfather rights in the primary allocation process, however, itattempts an application of market mechanisms in two parallel directions. Besides grandfather rights, theremaining slots will be auctioned at the airport level with monetary trading between airlines being also allowedon a secondary level. In particular:

• Grandfather rights will be still valid and recognized as historic slot holdings but they will not be tradable ona monetary basis (non-monetary slot exchanges will still be allowed). The non-grandfathered and not pol-icy-designated slots will be subject to an auction-based allocation accompanied by secondary trading. Atthe primary allocation level, the pool slots will be allocated by means of decentralized auctions organizedon an airport-specific basis by a mutually trusted entity. At the secondary allocation level, monetary tradingof auctioned slots will be allowed through bilateral airlines’ negotiations.

• The usage rights for all slots (except for policy-designated) will be governed by the application of a tighter‘‘use it or lose it’’ rule accompanied by a slot recycling mechanism in the form of earmarking with slotclasses.

• The anticipated revenues from the primary allocation (i.e., auctions) and grandfather rents will be capital-ized by airports, while the revenues from secondary allocation/trading will be available to the correspond-ing slot holders.

4.3. The ‘‘Controlled Trading’’ Strategy (Strat.3)

The ‘‘Controlled Trading’’ strategy essentially combines conservative and innovative elements in one strat-egy. In particular, it retains with slight modifications/adaptations the principle of grandfather rights, butsimultaneously allows full (primary and secondary) monetary trading based on bilateral negotiations eitherbetween the airport and airlines (primary trading) or between airlines (secondary trading). The characteriza-tion ‘‘controlled’’ trading stems from the principle that although full (i.e., primary and secondary) trading isallowed, primary allocation is self-controlled by the historic slot holdings, which could be also subject to mon-etary trading. In particular:

• Grandfather rights will be temporarily revoked so that a temporary pool of slots will be built with: (i) slotsthat will be (eventually) retained by their owners during the previous scheduling season (x%), (ii) slots thatwill be redistributed on the basis of performance criteria among existing carriers operating in this airport(y%), and (iii) slots that will be recycled and returned to the pool (z%).

• Slots that are not grandfathered and not allocated based on policy criteria will be subject to primary andsecondary trading. At the primary trading level, the pool slots will be owned by the airport and will betraded to airlines through a centralized trading environment based on one-to-one negotiations with airlines.At a secondary level, airlines having acquired some particular slots through primary trading will have theoption to secondarily trade these slots.

• The deployment of a slightly modified (in terms of varying validity periods), two-phase earmarking processis proposed for this strategy. Airlines enjoying the renewed/updated grandfather rights (i.e., {x + y}%) willbe temporarily recognized as the current owner of slots at time zero. For these slots, a short validity period(between one and three years) will be set in the initial phase. On the other hand, slots acquired throughprimary trading from the airport (i.e., z% except for policy-driven allocated slots) will be assigned witha longer validity period (3–5 years), with slots acquired through secondary trading being assigned with their


earmarked validity period, respectively. By the end of their validity period, all slots will be returned to thepool and traded through bilateral negotiations of the airport with airlines (with secondary trading alsoallowed).

• The revenues generated from primary trading will go to airports, while revenues from secondary tradingwill be enjoyed by the airlines.

4.4. The ‘‘Congestion Pricing’’ Strategy (Strat.4)

The ‘‘Congestion Pricing’’ strategy represents the most direct pricing method for addressing the real causesof the mismatch between capacity and demand for airport operations (Odoni, 2001). Under the congestionpricing strategy, grandfather rights will be abandoned and a congestion-based scheme with fees varying withcongestion throughout the day will be set by an administrative authority. In particular:

• Grandfather rights will be totally removed and each carrier could operate at any time or slot by paying thecorresponding scarcity rent (i.e., congestion fee).

• The Congestion Pricing strategy can be structured to include different components/types of fees addressingvarious policy or operational objectives. Some of the most widely discussed (TUB, 2001; De Neufville andOdoni, 2003) pricing components will be integrated to produce a three-part tariff that will combine: (i) thetraditional weight-based fees and passenger surcharges, (ii) a flat reservation fee applied per movement inthe form of membership dues or ‘‘no-show’’ penalties, and (iii) a congestion-based fee. The congestion-based fee will be the newly introduced scarcity surcharge applied during congested period(s) per movementand will vary within the day based on congestion levels and patterns. More specifically, it is proposed toapply a flat fee (only during congested periods) that will be entirely independent of the aircraft weight,but will vary within the day based on congestion.

• A portion of slots will be exempted from congestion fees in order to promote policy objectives. Newentrants will not be excluded from the congestion fees to avoid anti-competitive behavior or discriminatorypractices. The policy-dictated slot allocation will be based on a lottery among users requesting slots/routesthat are eligible for this category.

• The revenues derived from the congestion fees will be capitalized by the airports as the actual providers ofthis capacity.

4.5. The ‘‘Big Bang’’ Strategy (Strat.5)

The ‘‘Big Bang’’ strategy represents the opposite extreme vis-a-vis the ‘‘Enhanced Status Quo’’ and the‘‘Gradual’’ strategy on the continuum of the proposed strategies. Grandfather rights will be abandoned withthe entire slot pool being allocated by means of market-based instruments (i.e., decentralized auctions accom-panied by secondary trading). In particular:

• The size of the pool (maximum number of available slots) will be administratively declared by the slot coor-dinator. Grandfather rights will be removed so that the entire slot portfolio will be directly available for(re)allocation. This removal will be thorough (for all grandfather rights) and immediate (not gradual)(TUB, 2001).

• A portion of the pool slots will be set aside for policy-driven slot allocation purposes. These will be againallocated without any economic rent involved on a lottery basis and will not be tradable (exchangeable on anon-monetary basis).

• The remaining (not policy-designated) slots will be thereafter subject to decentralized airport auctions.These auctions will be conducted with a standard frequency (every 3–5 years) and will be coordinatedby a mutually trusted authority. Auctioned slots will be exempted from landing fees and their duration willdepend on the standard auction frequency (3 or 5 years). In a secondary level, auctioned slots will be trad-able between airlines on a monetary basis.


• A ‘‘use it or lose it’’ rule will be used to confiscate slots that have not been in compliance with minimum slotusage during season. Slots that will be made available in the meantime will be traded by the airport in nego-tiation with airlines.

• Airports will be entitled to receive the revenues derived from the primary auction process. On the otherhand, revenues from secondary trading will go to current holders, respectively.

5. Airport typology/clusters

Having defined the slot allocation strategies, an airport typology should be developed in order to identifythe various airport environments/settings (i.e., airport clusters) within which the strategies will be evaluated.As a matter of fact, different airport environments/settings may exhibit different congestion patterns, delayfigures, and traffic characteristics, while they most probably have different objectives and constraints andshould comply with different policy priorities. This, in turn, means that they may require different congestionor demand management approaches for the allocation of slots (NERA, 2004). As a result, it should be exam-ined whether different or a common slot allocation regime should be established and applied to the airportnetwork. To that end, an airport classification scheme (i.e., airport cluster analysis) was developed in orderto cope with the peculiarities and characteristics of different airports, and thereafter investigate whether thecompatibility of alternative slot allocation strategies varies with the identified airport clusters.

Special attention has been given to select airport clustering variables reflecting the theoretical reasoning andaddressing the actual root of the congestion problem, namely the mismatch between demand (traffic) and sup-ply (capacity). These variables should be capable of capturing the real essence and motivation of the conges-tion problem, whose major determinants are capacity, traffic, and the way that capacity is managed andallocated to accommodate traffic. The following categories and specific types of variables were considered

Table 2Cluster analysis properties and methodological decisions

Cluster analysis properties and methodological decisions

Cluster analysis method(Hierarchical vs. K-means)

Hierarchical method (i.e., the resulted classification has an increasing number of nested clustersand the result resembles a phylogenetic classification). It is the most widely used technique, fitsbetter to the specific conceptual problem, and it is simple, fast, and reliable. Moreover, it can beefficiently applied for classifying a reasonable number (e.g., less than 400) of cases (i.e., airports)into groups that are homogeneous within themselves and heterogeneous between each other,while it does not necessitate the a priori determination of the number of clusters (required byiterative partitioning such as K-means clustering) (Aldenderfer and Blashfield, 1984)

Agglomerative vs. divisive Agglomerative method: all cases/airports are initially considered as single-member clusters whichare gradually fused to form larger, but still homogeneous clusters

Monothetic vs. polythetic method Polythetic agglomerative (i.e., based on multiple clustering variables employed in the specificanalysis)

Type of clustering variables/data Interval data (e.g., number of passengers, number of aircraft movements, declared capacity inhourly aircraft movements, percentages/number of slots utilized or grandfathered)

Clustering objects (cases vs. variables) Cases (i.e., airports)Clustering sample ‘‘Fully Coordinated’’ and ‘‘Schedule Coordination Request’’ EU airportsDistance vs. similarity measures Distance measures (due to the type of selected variables, i.e., interval)Type of distance measures Squared Euclidean distanceClustering algorithm The Complete Linkage (‘‘Maximum or Furthest Neighbor Method’’) algorithm has been used as

the mechanism and rule of measuring distances. The complete linkage method is clearlyappropriate for Euclidean distance metrics (Lance and Williams, 1967) and normally producesvery tight clusters of similar cases, which conceptually fits with the purposes of the airporttypology development process

Software used The (Hierarchical) Cluster Analysis module of SPSS (version 12.0) has been used as the mostwidely used, reliable, and extensively validated statistical software package

Data sources Data for the identified clustering variables have been collected from: (i) ACI Europe, (ii)European Union – Airport Coordinators’ Association (EUACA), (iii) national slotcoordinators, and (iv) EUROCONTROL Centre for Delay Analysis (CODA)


to be the most appropriate for airport clustering purposes in the context of airport slot allocation (Williamson,2000; CONNEKT, 2003): (i) capacity metrics (i.e., declared capacity), (ii) traffic/congestion metrics (i.e., air-craft and passenger movements, average delay per movement), and (iii) measures of effectiveness of slot allo-cation and utilization including competition/entry barriers (i.e., misused slots, unsatisfied demand, slotmobility). Finally, one additional variable (i.e., grandfathered over total number of slots) has been includedto provide a measure of the competition and the ease of market entry for newcomers.

The sample of airports acting as clustering entities included European Union airports designated both as‘‘Level 3 (Schedule Coordination Request – SCR)’’ according to IATA designation and ‘‘Fully Coordinated’’as envisaged by European Commission designation guidelines in 2002–2003. The airport clustering sample didnot include airports that did not fully meet both criteria of ‘‘Level 3’’ and ‘‘Fully Coordinated’’, as well asairports (mostly non-Category 1 airports in Greece and Spain) that did not merit a year-round designationas ‘‘fully coordinated’’. The selection of the particular sample of airports stems from the fact that these arethe largest and practically the busiest airports playing a strategic role in European air transport. In effect, acoordinated effort to manage demand in relation to capacity through slot allocation is or will be soon neces-sary as a result of the experienced or anticipated congestion problems and the documented insufficiency ofcapacity investments to meet the forecasted traffic volumes. In order to develop the airport typology, the fol-lowing cluster analysis properties have been established (Table 2).

According to the aforementioned cluster analysis properties, four clusters emerged from the clustering sam-ple of airports based on the selected clustering variables. Each cluster is highlighted in Table 3 by means ofdifferent colour codes.

5.1. Cluster 4 – ‘‘Small National Spokes’’

The airports included in this cluster are small, satellite or regional airports acting as the spokes oftheir national airport network, respectively. Typical examples of airports included in Cluster 4 are Berlin

Table 3Resulted airport clusters

Airport IATA code Cluster Airport IATA code Cluster

Helsinki/Vantaa HEL 4 Brussels BRU 2Berlin/Schonefeld SXF 4 Copenhagen CPH 2Berlin/Ternpelhof THF 4 Dusseldorf DUS 2Berlin/Tegel TXL 4 Munich MUC 2Dublin DUB 4 Stuttgart STR 2Turin TRN 4 Milan/Malpensa MXP 2Milan/Linate LIN 4 Rome/Fiumicino FCO 2Milan/Bergamo BGY 4 Barcelona BCN 2Venice VCE 4 Madrid/Barajas MAD 2Florence FLR 4 Arlanda ARN 2Naples NAP 4 Paris/Charles de Gaulle CDG 1Rome/Ciampino CIA 4 Paris/Orly ORY 1Palermo PMO 4 Frankfurt FRA 1London/Stansted STN 4 Amsterdam/Schiphol AMS 1Manchester MAN 4 London/Heathrow LHR 1Vienna VIE 3 London/Gatwick LGW 1Athens ATH 3 Lyon LYS UnclassifiedThessaloniki SKG 3 Catania CTA UnclassifiedLisbon LIS 3 Faro FAO UnclassifiedPorto OPO 3 Funchal FNC UnclassifiedAlicante ALC 3 Bilbao BIO UnclassifiedFuerteventura FUE 3 Tenerife Norte TFN UnclassifiedGran Canaria LPA 3 Bromma BMA UnclassifiedLanzarote ACE 3 Eindhoven EIN UnclassifiedMalaga AGP 3 Rotterdam RTM UnclassifiedPalma de Mallorca PMI 3Tenerife Sur TFS 3


Tempelhof, Berlin Tegel, Dublin, Turin, Milan Bergamo, Milan Linate, Venice, London Stansted, and Man-chester. The average number of aircraft and passenger movements amounts at 98,500 and 7,200,000 move-ments, respectively. The demand is sufficiently covered through the existing, relatively small capacity (i.e.,average hourly declared capacity of 30 runway movements) on the grounds that the initially requested slotsare even exceeded by the slots allocated at the IATA conference. On the other hand, a considerable misuseof slots is observed since there is a substantial portion (i.e., 15%) of slots that were initially allocated butnot eventually operated. Furthermore, most of the operated slots (i.e., 80%) represent historic usage rights.

5.2. Cluster 3 – ‘‘Large National Spokes and Small National Hubs’’

This cluster contains small and medium-sized airports acting mostly as larger (as compared to Cluster 4)spokes of the national airport network or small national hubs channelling traffic from the national spokesto international hubs and vice versa. Typical examples of airports included in Cluster 3 are Malaga, Thessa-loniki, Palma de Mallorca, and Porto (‘‘large national spokes’’), as well as Vienna, Athens, and Lisbon (‘‘smallnational hubs’’). The average traffic figures amount at 93500 aircraft and 8500000 passenger movements,respectively. No substantial differences in traffic volumes are observed as compared to Cluster 4 airports.Besides, the slightly higher passenger traffic volume and lower aircraft movements seems to account for thenational hubbing role for some of these airports (larger average aircraft sizes and load factors). The demandis not sufficiently covered with the use of existing capacity (i.e., average hourly declared capacity of 36 runwaymovements) since the initially requested slots slightly exceed (i.e., 4.5%) the slots allocated at the IATA con-ference. On the other hand, a misuse of slots is also observed since there is a substantial number (i.e., 20%) ofslots allocated but not operated. Finally, only a small portion (i.e., 32%) of slots are grandfathered, a fact thatindicates a rather open market with a promising growth potential. The latter is further supported by the unsat-isfied demand especially for those airports (i.e., ‘‘small national hubs’’) aiming to take a hand in the interna-tional airport market (shifting to ‘‘large international hubs’’).

5.3. Cluster 2 – ‘‘Large International Hubs’’

Cluster 2 contains major, metropolitan airports of the European airport network acting mostly as large inter-national hubs (at least for certain national carriers) with focus on intra-European routes and a growth potentialto establish one of the major European hubs included in Cluster 1. Practically, the airports included in Cluster 2are primary and secondary large hubs of some of the major European airlines, which operate these airports asservers of traffic both among international destinations, as well as between domestic and international destina-tions. Typical examples of airports included in Cluster 2 are Munich, Brussels, Copenhagen, Malpensa, Fiumi-cino, Madrid, and Barcelona. The average traffic figures are substantially higher than the ones observed inCluster 3 airports mainly due to their international hubbing role, the strategic positioning in both the interna-tional and national airport market, as well as the ‘‘hosting’’ of some of the major European carriers such as Luf-thansa, Iberia, and Alitalia. The slots allocated at the IATA conferences practically accommodate the entiretyof existing demand expressed by the initially requested slots, which probably accounts for the large airportcapacity, as well as the supporting or reliever service that some of these airports provide to Cluster 1 airports.Nevertheless, the same does not hold true for the slot usage, where the highest figure (i.e., 26%) of slot misuse isobserved. This could be probably explained by the fact that these airports mainly represent ‘‘captive’’ marketsof certain (national/‘‘flag’’) carriers who pursue to ensure their market share and foothold on their primary orreliever/supporting hubs. In effect, they ‘‘overbid’’ in their slot requests, while simultaneously maintaining theirhistoric slots some of which are not eventually operated. Finally, the presence of dominant carriers is furtherexplained by the quite low slot mobility (i.e., 71% of slots are grandfathered), a fact that indicates a rather closeand ‘‘captive’’ market with substantial entry barriers and well-established incumbent airlines.

5.4. Cluster 1 – ‘‘Super Hubs’’

Cluster 1 airports represent the largest, busiest, and most congested European airports with a worldwidepresence and a strategic role in the European airport network. Practically, the airports included in Cluster


1 are the primary hubs of the major European airlines (i.e., Lufthansa, Air France/KLM, British Airways)operating these airports as the major intra and extra-EU hubs by accommodating traffic mostly between inter-national airport destinations. Paris/Charles de Gaulle, Frankfurt, Amsterdam/Schiphol, and London Hea-throw are typical examples of airports included in Cluster 1. The average traffic figures are an order ofmagnitude higher than the ones observed in Cluster 2 airports. They are the most severely congested airports,an observation that can be also deduced by the notable unsatisfied demand figures. More specifically, there is asubstantial gap (i.e., 15%) – the largest among all clusters – between the slots initially requested and the slotsallocated at the IATA conferences. This mismatch reveals the congestion levels suffered by these airports, aswell as the insufficiency of existing large capacity to accommodate the experienced traffic mainly throughoutthe entire day and not only during specific peak periods. Furthermore, it provides evidence of the marketattractiveness of those airports for new entrants and incumbent carriers for economies of scale/efficiencyand traffic purposes. On the other hand, despite the experienced lack of capacity, available slots are not effi-ciently used as reflected on the 20% of slot initially allocated but not eventually operated. This might accountfor slot complementarity reasons, i.e., airlines acquiring slots but not succeeding to match these slots with thecorresponding slots at the origin or destination airport. Finally, Cluster 1 airports are the most ‘‘captive’’ air-port markets on the grounds that the vast majority of slots (i.e., 90%) are subject to grandfather rights.

6. Policy compatibility assessment framework

The objective of the policy compatibility analysis is the identification of the most compatible slot allocationstrategy for each airport cluster on the basis of multiple policy criteria and indicators, as well as their prioritiesassigned in each airport setting/cluster. According to the particular problem characteristics discussed in a pre-vious section, the multi-criteria evaluation problem at hand has been dealt with the use of the Analytical Hier-archy Process (AHP) (Saaty, 1990). AHP has some notable advantages as a multi-criteria assessment methodand fulfils the following methodological properties that are absolutely in alignment with the characteristicsand methodological requirements of the evaluation problem under consideration (Zografos et al., 1997; Zog-rafos and Giannouli, 2001). In particular, the selected evaluation technique:

• Considers multiple (even conflicting) criteria, priorities, and trade-off’s (e.g., different priorities of policyobjectives in the different airport clusters). AHP takes into account the relative priorities of criteria/indica-tors in a specific evaluation problem and enables evaluators to select the best alternative based on their sub-jective goals and priorities.

• Expresses and quantifies the relative importance of the various criteria and indicators in non-homogeneouspanels of experts/judges.

• Enables multiple judgments and group decision making but does not insist on consensus; instead, it synthe-sizes the outcome of diverse judgments.

• Compiles the assessments and expert judgments of various decision makers/experts and identifies ‘‘compro-mising’’ solutions. Eventually, it leads to an overall estimate of the desirability – with respect to perfor-mance, compatibility, importance, etc. – of each alternative (e.g., the most compatible strategy for eachairport typology based on the different priorities of the policy criteria and the relative compatibility of strat-egies in each airport typology).

• Tracks the logical consistency of judgments used in determining priorities and provides an easy quantitativeway in order to improve logical consistency.

• Enables sensitivity analysis.• Enables the use of intangible, qualitative criteria by providing a scale for measuring and establishing their

priorities.• Provides a hierarchical structuring of the evaluation problem by decomposing it from the evaluation goal to

evaluation criteria/ indicators, and alternatives that are applicable to each airport typology.

AHP provides a practical way to deal quantitatively with complex decision making problems and an effec-tive framework for group decision-making. It is based on three fundamental principles (Saaty, 2004) which arefurther explained in what follows: (i) the principle of constructing hierarchies, (ii) the principle of establishing


priorities, and (iii) the principle of logical consistency. For the purposes of this assessment problem, the Ana-lytic Hierarchy Process model involves the development of n hierarchies (where n signifies the number ofresulted airport clusters) consisted of the evaluation goal (i.e., policy compatibility assessment), the relevantpolicy criteria and indicators, as well as the evaluation alternatives (airport slot allocation strategies) for eachairport cluster (Fig. 2).

Based on the above presented AHP model, the assessment levels are structured as follows:

• Level 0 (‘‘Ultimate Evaluation Goal’’): it represents the multi-criteria assessment goal, namely the rankingof alternative slot allocation strategies in terms of their compatibility with the identified policy criteria andindicators.

• Level 1 (‘‘Evaluation Criteria’’): it assesses the priorities/weights of the policy criteria (‘‘what is the relativeimportance of policy criterion m as compared to policy criterion y in airport cluster n?’’).

• Level 2 (‘‘Evaluation Indicators’’): it assesses the priorities/weights of the policy indicators (‘‘what is therelative importance of policy indicator k as compared to policy indicator l in airport cluster n?’’).

• Level 3 (‘‘Evaluation Alternatives’’): it represents the lowest level of assessment in which the strategies arecomparatively assessed with respect to their compatibility with each policy indicator (‘‘what is the relativecompatibility of strategy i as compared to strategy j with respect to policy indicator k in airport cluster n?’’).

The policy criteria and indicators involved in the selection of the most compatible strategy in each airportcluster were elicited from the literature and other relevant policy studies (PricewaterhouseCoopers, 2000;TUB, 2001; NERA, 2004). They generally fall into one of the following broad categories: (i) efficiency crite-rion, (ii) cost criterion, (iii) implementation criterion, and (iv) acceptability criterion. The efficiency criterionaddresses the capability of a strategy to deal with the scarcity of slots and produce a rationalised slot alloca-tion outcome. The cost criterion assesses the cost aspects associated with the implementation of a certain strat-egy. The implementation criterion examines the ease and pace of implementation, while the acceptabilitycriterion assesses the ease of acceptance and adoption potential of a certain strategy. The identified policy cri-teria and their associated indicators are presented and defined in Table 4.

Fig. 2. Hierarchical decomposition of the policy compatibility assessment problem.

Table 4Policy compatibility criteria and indicators

Criterion Indicator Definition

Efficiency Allocativeefficiency

It measures the capability of the strategy to allocate slots to those with the greatest willingnessto pay (TUB, 2001; DotEcon Ltd., 2001; European Commission, 2001)

Competitiveefficiency

It measures the strategy’s capability of promoting competition through the elimination of entrybarriers to newcomers and discriminatory practices in favour of established carriers (EuropeanCommission, 2001; TUB, 2001)

Infrastructuralefficiency

It measures the extent to which the slot allocation proceeds are efficiently distributed.‘‘Efficiency’’ provides for the distribution of slot allocation revenues to those having the meansat their disposal to eliminate the scarcity in the short or long run (Airports CouncilInternational (ACI) Europe, 2002)

Cost Cost-relatedness It measures the extent to which airport charges are representative of actual costs. The structureand level of airport charges should reflect the externalities and real costs for providing thecorresponding airport services, as well as the demand (and congestion) levels (EuropeanCommission, 2001; Airports Council International (ACI) Europe, 2002)

Implementationcost

It measures the costs (e.g., transaction, organisation/coordination, technology) envisaged forthe implementation of the various strategies (TUB, 2001)

Implementation Complexity It measures the degree of implementation complexity of a strategy in the form ofadministration, preparatory time required, horizon of the strategy, as well as the necessaryorganizational arrangements and coordination efforts (TUB, 2001)

Flexibility It measures the degree of flexibility of strategy’s implementation in the form of: (i) flexibility tochange in the short/medium run (temporal flexibility), and (ii) flexibility of simultaneousimplementation of different strategies in the various airports (spatial flexibility)

Fiducial protection It measures the phasing and graduality of a strategy’s implementation. A phased approach willallow for an early notification and gradual adaptation of airlines, and thereby will respect theprinciple of fiducial protection so as to avoid paying compensations to airlines for the abolitionof grandfather rights (TUB, 2001)

Acceptability Stakeholders’inertia

It measures the degree of expected resistance of the affected stakeholders (e.g., carriers,airports) to the changes introduced by the different strategies

Transparency It measures the degree of transparency of the strategy in that it will not be vulnerable to legalchallenge


The AHP technique provides also a measuring scale that efficiently deals with subjective judgments(through relative as compared to absolute measurements) in order to derive priorities. In particular, the ExpertChoice software calculates priorities through judgments on intangible criteria/indicators for which no scalehas been developed and no absolute measurement can be obtained. Two basic ways are available: (i) throughpairwise comparisons, and (ii) through ratings (Saaty, 2006). The former (i.e., pairwise comparison assessmentmode) introduces a series of matrices of pairwise comparisons expressing the relative importance of the ele-ments in a given level of the hierarchy with respect to the elements in the level immediately above. This assess-ment mode has been used for deriving priorities up to Level 2 (Evaluation Indicators). The pairwisecomparisons are made on the basis of a 9-unit scale on the grounds that experience has shown that it is rea-sonable and reflects the degree to which evaluators can discriminate the intensity of relationships between ele-ments (Saaty, 1990).

The selection of the most compatible alternative in Level 3 (Evaluation Alternatives) has been madethrough the ratings assessment mode. In practice, the ratings assessment mode provides an assessment formenabling respondents to shift the emphasis of the analysis from one where alternatives are compared againsteach other (i.e., pairwise comparisons) with respect to specific criteria/indicators to another where standardsare established for the criteria/indicators and then alternatives are compared against these standards. Thisshift of focus enables a less complicated and less time-consuming evaluation of a large number of alternatives.In particular, the following question is formulated for the compatibility assessment purposes in the lowestlevel (Level 3): ‘‘what is the degree of compatibility of strategy i with respect to policy indicator k for airportcluster n?’’.


Finally, one of the major advantages of the Analytic Hierarchy Process is its capability to track the logicalconsistency of judgments used in determining priorities, identify inconsistent judgements, and evaluate theconsistency of the experts’ judgments by calculating an index called consistency ratio (CR). Generally,the value of the consistency ratio should be at maximum 0.10 (i.e., 10% inconsistency), which constitutesthe threshold of tolerable inconsistency in judgements (Saaty, 1990). Consistency ratio (CR) values greaterthan 0.10 might be somewhat random, and thus should be further investigated and possibly revised.

7. Policy compatibility assessment results

Having selected the assessment technique, a targeted panel of experts and key stakeholders has beenselected and surveyed with the use of a detailed survey instrument. Given the policy magnitude and researchinterest of the slot allocation problem, special emphasis has been placed in obtaining the perspectives andjudgments of academic experts and researchers, as well as the relevant aviation policy makers in Europe.On the other hand, considering the involvement and interaction of several interest groups, the viewpointsof major industry players and stakeholding groups in the allocation of airport slots, namely the airportsand airlines, were also pursued. The selection of specific individual experts has been made with view to thefollowing: (i) substantial expertise in existing slot allocation procedures, (ii) familiarity with the aviation indus-try developments in general and demand-capacity mismatch in particular, and (iii) representation of variousindustry roles and viewpoints (through the expert groups discussed below) with respect to slot allocation. Forthe purposes of this analysis, the targeted survey experts were grouped as follows:

• Expert Group 1: 19 academic/research experts with substantial expertise in slot allocation in Europe and/orinvolvement in relevant policy studies, i.e., (TUB, 2001; NERA, 2004).

• Expert Group 2: 10 experts from European airports or their associations/bodies expressing and promotingthe airports’ perspectives and interests.

• Expert Group 3: seven experts from European airlines with substantial presence in the European and/ortheir national airport networks.

• Expert Group 4: five senior (European) policy makers in the slot allocation domain who are responsibleeither for the formulation of the slot allocation policy or the implementation and monitoring of the slotallocation process at national level.

The data collection process has been conducted with the use of a detailed survey instrument which wasaccompanied by a brief report summarizing the background material of the survey along with some guidelinesfor the completion of the questionnaire. The survey instrument consisted of four main parts. Part 1 presentedthe survey objectives, while Part 2 provided a summary of the major background information (i.e., slot allo-cation strategies, policy criteria and indicators, airport clusters) that the respondent should be familiar with inorder to properly fill in the questionnaire. Part 3 presented the questionnaire and included three separategroups of tables: (i) the 1st Group of Tables included the pairwise comparisons between the various policycriteria and aimed to derive priorities for these policy criteria in each airport cluster, (ii) the 2nd Group ofTables included the pairwise comparisons between the various policy indicators and aimed to derive prioritiesfor these policy indicators in each airport cluster, and (iii) the 3rd Group of Tables included the rating of theslot allocation strategies with respect to the policy indicators of the 2nd Group. Part 3 was complemented by aseries of tables asking respondents to self-rate their field expertise in each group of tables. Finally, Part 4 askedfor some personal details of the respondents.

The survey instrument was initially subject to a trial testing by a small panel of experts with the aim tovalidate its content, as well as the ease of completeness and interpretation of terms and definitions (e.g., strat-egies, criteria, indicators). Based on this initial trial testing of the survey instrument, it was thereafter circu-lated to the targeted stakeholders and experts. In parallel, a number of interviews were conducted in orderto familiarize the experts with the method and its associated properties (e.g., AHP pairwise comparison tables,measurement scales) and provide clarifications on the policy criteria and indicators or the details of the slotallocation strategies. The time required for an expert to get familiar with the survey content and methodand fill in the entire questionnaire was between 2 and 3 h. The survey and data collection took place during


the period September 2005–March 2006. The initially collected survey instruments were checked for their log-ical consistency, while in cases where major inconsistencies were detected, the respondents were contacted inorder to discuss and propose certain changes or revisions that might be necessary. Finally, the respondentsprovided their feedback on the suggested changes along with their final revisions which were eventually subjectto the data analysis process. For the sake of clarity, the following description of views and arguments has beenderived either from the synthesis of direct responses provided by experts through the questionnaire or addi-tional qualitative feedback provided complementarily to structured questionnaire responses.

The data analysis of the collected survey instruments was split in two parallel streams of work: (i) per groupassessment for each of the identified groups of experts, and (ii) overall assessment for the entire sample ofexperts. The analysis was concluded with the comparative assessment and the identification of differencesand/or similarities between the overall assessment and the group-based assessment, while a final synthesiscompiles the results for all airport clusters and expert groups and elicits some converging or compromisingsolutions of the policy compatibility assessment.

In what follows, the synthesis of judgments of all expert groups for all airport clusters is presented in termsof the performance and ranking of: (i) various policy criteria (Fig. 3), and (ii) competing strategies (Fig. 4).The following observations and conclusions can be drawn from the comparative analysis among airport clus-ters and synthesis of judgments among expert groups:

• The ranking of the policy criteria remains unaffected, while their relative performance does not vary sub-stantially with the airport clusters (Fig. 3). The expert groups have recognized the currently experiencedcongestion problems and severe market distortion especially in busiest airports dominated by well-estab-lished carriers, thus placing their higher importance valuations towards promoting the efficiency of the allo-cation process and eliminating market entry barriers. In effect, the primary emphasis was placed on theefficiency of the slot allocation process that will potentially remedy the congestion problem and eliminatethe imbalance between demand and supply along with their externalities (e.g., delays, noise, environmentalconcerns). Moreover, it is important to stress the fact that the efficiency criterion constitutes by far the basicpriority for all airport clusters that are currently severely congested or might become so in the near future.In addition, the expert groups have basically converged to the finding that the implementation feasibilityand complexity represent quite important aspects for the selection of a slot allocation strategy that willnot compromise feasibility and realistic implementation in favour of increased efficiency. Furthermore,the respondents have considered that the acceptability aspects of the airport community should be consid-ered with a lower priority as compared to efficiency and implementation, albeit with careful view to poten-tial effects on industry dynamics. Finally, the cost criterion has obtained the lowest, by far, importancerating in all airport clusters. This can be attributed to the fact that cost and complexity aspects are not

Cross-cluster Ranking of Policy Criteria (All Expert Groups)

0.1000.1200.1400.1600.1800.2000.2200.2400.2600.2800.3000.3200.3400.3600.3800.400

Cluster 1 Cluster 2 Cluster 3 Cluster 4

Airport Clusters

Per

form

ance

of S

trat

egie

s

Efficiency Cost Implementation Acceptability

Fig. 3. Performance and ranking of policy criteria per airport cluster (all expert groups).

Cross-cluster Ranking of Strategies ( All Expert Groups)

0.177

0.182

0.187

0.192

0.197

0.202

0.207

0.212

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Airport Clusters

Per

form

ance

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Strat.4 Strat.3 Strat.5 Strat.2 Strat.1

Fig. 4. Performance and ranking of strategies per airport cluster (all expert groups).


really an issue for the largest, busiest, and severely congested European airports, which most probably havealready both the resources and the in-house expertise to deal with more complicated and costly slot allo-cation options bringing promises for higher efficiency. On the other hand, the recognition of severe conges-tion problems, either currently suffered or anticipated for the future, may render the implementation costcriterion of lower importance and has led experts to the conclusion that all airports will be able to accom-modate the cost of a more heavy-duty solution in return of a strategy being feasible and able to promotecompetition and new entrants, establish transparent allocation processes, and eventually boost the effi-ciency of congestion management.

• The efficiency criterion was assigned with the highest importance rating in all airport clusters, but it grad-ually weakens by yielding ground to the implementation and the acceptability criteria while moving fromlarger, international (i.e., Clusters 1 and 2) to smaller, national/regional (i.e., Clusters 3 and 4) airports.This is consistent with the empirical assumption that smaller national or regional airports mainly urgefor the feasibility of implementation and the potential acceptance of the airport community even in theexpense of efficiency. On the other hand, the cost, implementation, and acceptability aspects gain increasingimportance in smaller airports (i.e., Clusters 3 and 4), which may not have the financial resources or the in-house expertise to deal with more complicated or costly solutions.

• It is important to highlight the fact that the academic/research group of experts (i.e., Expert Group 1) rep-resent the only group that assigned varying relative importance weights into the policy criteria among theairport clusters, where as all other expert groups assigned the same importance ratings in policy criteria forall airport clusters. In particular, the Expert Group 1 respondents placed the highest – but graduallydecreasing – importance rating on the efficiency criterion in conjunction with a low – but gradually increas-ing – importance weight on the cost criterion while moving from larger, international (i.e., Clusters 1 and 2)to smaller, national/ regional (i.e., Clusters 3 and 4) airports. This behaviour can be justified by the reason-ing discussed in the previous paragraph and seems to substantially ‘‘explain’’ the variance in the relativeimportance of policy criteria among airport clusters for the entire group of experts.

• The previous finding (i.e., different importance weights assigned by Expert Group 1 on policy criteria forthe different airport clusters) may provide some evidence for a more profound and system-wide perspectiveof the academic/research experts (i.e., Expert Group 1) for the entire industry with special emphasis on its‘‘local’’ requirements and peculiarities that may dictate the need for a differentiation in policy priorities andstrategies for implementation in various airport settings, respectively. On the other hand, the counterpartfinding (i.e., invariant importance weights placed by all other Expert Groups on policy criteria for all air-port clusters) could be simultaneously interpreted as a binding constraint posed by the actually affectedstakeholders (represented by all other Expert Groups) who basically urge for a non-discriminatory, harmo-nized, and commonly applied regime for the entire airport network.


• More progressive strategies (in terms of their radical departure from the status quo) with emphasis onStrat.5 (‘‘Big Bang’’) and Strat.3 (‘‘Controlled Trading’’) draw strength from the efficiency criterion, butfall considerably short of more conservative strategies in all other criteria. On the other hand, the most con-servative strategies (i.e., ‘‘Enhanced Status Quo’’ – Strat.1, ‘‘Gradual’’ – Strat.2) mainly receive high scoreson the cost and implementation criteria and moderate scores on the acceptability criterion. As a result, pro-gressive strategies (i.e., ‘‘Big Bang’’ – Strat.5, ‘‘Controlled Trading’’ – Strat.3) give more way to conserva-tive strategies (i.e., Strat.2, Strat.1) while moving to smaller airports (i.e., Clusters 3 and 4) that placerelatively less emphasis on the efficiency criterion and more emphasis on the implementation and accept-ability criteria.

• Strat.4 (‘‘Congestion Pricing’’) has been generally considered as the most appropriate alternative for all air-port clusters with respect to its compatibility with the identified policy criteria and their associated indica-tors (Fig. 4). Strat.4 introduces a very open and adaptive slot allocation regime, which can be easilycustomized with the needs and characteristics of the local airport context through the appropriate conges-tion fee schemes and levels. Besides, this allows and favours the horizontal implementation of this strategyacross all airport clusters. On the other hand, Strat.5 (‘‘Big Bang’’) introduces the most radical departurefrom status quo on the grounds that it totally eliminates historic slot holdings, it involves several drasticregulatory amendments, it is quite cumbersome in terms of organizational and institutional arrangements,and it introduces quite controversial allocation mechanisms and rules. Nevertheless, these radical/ progres-sive reforms were considered quite difficult or unrealistic to implement in large airports (i.e., Clusters 1 and2) or even impossible or utopian for smaller national airports (i.e., Clusters 3 and 4). This seems to be theprime reason behind the low average ranking of Strat.5 in all airport clusters. For similar reasons, moreconservative strategies obtain better ranking in smaller airports (i.e., Clusters 3 and 4).

• Strat.4 (‘‘Congestion Pricing’’) is clearly superior to all other strategies in all airport clusters, while moreconservative solutions (i.e., Strat.2, Strat.1) are brought into the forefront and gain ground in smaller air-port clusters (i.e., Clusters 3 and 4) that do not and/or cannot accommodate radical changes in the existingslot allocation regime. In certain expert groups (i.e., Airlines of Expert Group 3), the most conservativestrategies (i.e., Strat.2, Strat.1) have obtained the highest ranking, which can be also attributed to their res-ervations and opposition against any new slot allocation strategy placing their existing competitive positioninto question.

• In general, some slight alignment can be observed in the assessments of the academic/research group ofexperts (i.e., Expert Group 1) and the airport respondents (i.e., Expert Group 2) in that they prefer Strat.4as the most compatible slot allocation strategy. This is in line with the reasonable assumption that aca-demic/research experts of Expert Group 1 are more ‘‘neutral’’ respondents without any conflict of interestor inherent inertia that could be affected by the drastic solutions suggested by progressive strategies. Inaddition, due to their research profile and forward thinking, Expert Group 1 respondents were most prob-ably expected to applaud more progressive or pioneering alternatives at least for large international air-ports, a fact that has been affirmed by the assessment results. The airport respondents (i.e., ExpertGroup 2) have also applauded more progressive strategies and particularly Strat.4. As a matter of fact,more progressive strategies bring promises for a more prominent role for the airports and ensure that air-ports, as the actual providers of capacity, will have a say in the allocation of slots and the resulted proceeds.

• Airlines (i.e., Expert Group 3) and policy makers (i.e., Expert Group 4) agree that more progressive strat-egies (i.e., Strat.5, Strat.4) are not preferable. But airlines provide the highest ranking to the most conser-vative strategies (i.e., Strat.2, Strat.1), while policy makers give the highest compatibility score to a moreprogressive alternative (i.e., ‘‘Controlled Trading’’ – Strat.3). It should be mentioned here that the surveyedairlines are basically well established ‘‘players’’ in their local and/or international networks, and conse-quently ‘‘favoured’’ in different extent by the existing status and regime. The assessments of the airlinegroup would be most probably significantly different, should other airlines (e.g., low-fares carriers, new air-lines) had participated in the survey process.3 As far as the participating/surveyed (established) airlines are

3 A number of regional, low-fares, and new airlines have been invited to participate in the survey, but they have not eventuallyresponded.


concerned, it was reasonable to expect their clear preference towards more conservative strategies thatwould maintain or incrementally improve the existing slot allocation regime. Strategies introducing sub-stantial reforms and aggressive (re)allocation mechanisms might affect the competitive balances and marketdynamics of the airline industry and therefore have been condemned. In that respect, airlines approve ofsmall, mostly operational, improvements or even small and smooth transition to a new slot allocation sys-tem, but resist vigorously to reforms that do not fully respect (or even abolish) their historic slot usagerights (i.e., Strat.3, Strat.4, Strat.5).

• Finally, policy makers (i.e., Expert Group 4) have chosen the most forward-looking and drastic solution. Inparticular, they represent the sole expert group that gave the first-preference rating to a strategy involvingslot trading in both primary and secondary allocation level. This is in line with the EU policy considerationsand industry pressures (CAA House, 2001) towards the adoption of market-based mechanisms in slot allo-cation with emphasis on slot trading.

8. Policy conclusions and research recommendations

This paper has examined the compatibility of alternative slot allocation strategies for the various airportsettings based on their profile characteristics and the embedded decision making and policy prioritiesexpressed in the form of importance weights on policy criteria and indicators. The policy criteria and indica-tors are not necessarily of the same priority/importance in the different airport settings. This justified the needfor developing an airport typology (i.e., airport clusters) by assessing then the policy compatibility of the iden-tified slot allocation strategies in the different airport clusters. Therefore, the starting working assumption –subject to confirmation or rejection – was that the policy compatibility of a strategy varies with the airportclusters. Based on the results of the compatibility analysis, it can be safely concluded that the weights assignedto policy indicators in each different airport cluster do not result in a substantially differing compatibility ofthe various strategies in the different airport clusters. This finding is further supported by the empiricalevidence brought by several policy makers contemplating that the implementation of a common (i.e.,airport-wide) strategy seems to be more realistic; albeit with certain flexibility and room for local adaptations.In particular, it has been stressed that a common slot allocation regime should be established and accompa-nied by rules and guidelines aiming to adapt this regime in the particular airport context.

The expert panel has acknowledged the long standing debate in the aviation industry and the policy makingbodies of the European Union concerning the formal introduction of market-driven mechanisms and rules inthe slot allocation process with a dual policy role: (i) bridging the gap between growing traffic/demand andscarce capacity (i.e., mismatch management), and (ii) striving against the currently experienced misuse ofscarce capacity (i.e., misuse management). This debate has run between airlines, airport operators, and theregulatory bodies for more than a decade without having resulted in any clear consensus among and in-between the parties involved. The basic thorny matters, besides the practical complexities and sometimesnot workable procedures, rest with the conflicting evaluations of the possible outcomes in the real economicand operational sphere of the industry, should such drastic changes be applied. As a matter of fact, industrystakeholders have, in many cases, conflicting objectives and interests, while the application of a new slot allo-cation regime may disorder the balances and market forces of different stakeholding groups or even specificactors (e.g., established carriers vs. new entrants, low-cost carriers) within a stakeholder group (e.g., airlines).Therefore, there is an imperative need for a commonly agreed, realistic, and quantitative impact assessmentfrom the potential application of each alternative scheme. A continuation of the presented research work isfurther elaborating on this research topic. Policy makers should actively pursue and communicate the resultsof such an impact assessment by explicitly or implicitly considering the viewpoints and impacts ‘‘imposed’’ oneach industry stakeholder.

A number of policy attempts motivated by relevant research work have been documented with the purpose ofintroducing market-driven slot allocation schemes through regulatory amendments but they have not materia-lised until now. On the other hand, there is an industry consensus that the currently experienced (and mostimportantly those anticipated for the future) congestion and delays of the major European airports undoubtedlypoint to such a direction in one way or another. Definitely, the adoption of market-driven mechanisms is notrisk-free or without complications. There have been some reasonable concerns about the legal definition of a


slot (and which rights or obligations go with), the monetary valuation of slots, the possible monopolistic behav-iours of incumbent airlines, the (neutral) role and responsibilities of the slot coordinators, or even the possibilitythat airports will consider it as a ‘‘golden’’ opportunity to increase their revenues through slot trading withoutthe necessary capacity expansions or probably without being able to make such expansions due to physical con-straints. On the other hand, these should not condemn, by default, the use of market-driven mechanisms,because they can be complemented by certain instruments or rules that can efficiently address these concernssuch as: (i) the designation of slots for particular uses and with prescribed recycling priorities and possessionthresholds, (ii) the establishment of recycling mechanisms aiming to increase slot mobility and recycle slotsfor redistribution among new entrants, (iii) the application of revenue neutrality schemes under which revenuesgenerated by the market-driven slot allocation are put back into the system as a means of constantly reducingairport charges (e.g., off peak), (iv) the earmarking of slot allocation proceeds for capacity expansion/improve-ments projects, and (v) regulatory adjustments for a thorough definition of slot rights and obligations.

As a matter of fact, the existing system cannot (and will not definitely in the future) cope with the currentand mainly forecasted traffic volumes, hence urging for a more radical departure from the status quo. Basedon the congestion and delay evidence, it comes that the current slot allocation regime needs to be drasticallyimproved, while small, periodical adjustments or local adaptations of the status quo may not suffice. Never-theless, the deployment of market-driven mechanisms and rules does not presuppose the total elimination ofprovisions stemming from the current slot allocation regime on the grounds that certain rules and mechanisms(e.g., ‘‘use it or lose it’’ rule, designation of slots, historic usage rights) are maintained by certain strategies andare further complemented by a more market-driven allocation approach. In that respect, the total and simul-taneous elimination of grandfather rights and the simultaneous auctioning of all slots in one go (e.g., ‘‘BigBang’’ strategy) might not be a realistic option for a number of reasons (e.g., practical implementation, slotcomplementarity, fiducial protection). In that respect, a congestion pricing scheme confronts directly with thesevere congestion problems of the busiest European airports by means of varying congestion fees and enjoyshigh acceptability albeit without compromising the implementability of the strategy. It allows the horizontalimplementation of a common slot allocation regime across all airports and introduces a very open and adap-tive regime, which can be easily customized with the needs and characteristics of the local airport contextthrough the appropriate congestion fee schemes and levels. Despite the fact that a number of open issues stillneed to be formalised (e.g., structure, components, and levels of fees, revenue neutrality of fees, traffic super-vision in terms of safety restrictions, role of slot coordinators and historic usage rights), the congestion pricingcan be clearly seen as a ‘‘middle-of-the-road’’ market-driven solution that can potentially act as a transitionroadmap to a more radical slot allocation regime in the future.

The empirical evidence should be definitely accompanied by a detailed, quantitative impact assessment builton the basis of an active industry dialogue and careful consideration of the practicality of implementation andthe potential impacts of such schemes on each stakeholding group. Such an impact assessment exercise (whichis currently ongoing by the authors as a follow-up of this research work) is expected to be the next hot policydebate on slot allocation for the coming years. Finally, it is commonly agreed that the selection of a slot allo-cation strategy cannot be seen independently of parallel efforts in building new or expanding the existingcapacity. There should be an industry consensus that the slot allocation strategy as a demand-side effortshould complement rather than replace supply-side efforts towards building new capacity.

Glossary of terms

Term Description/definition

Airport designation The process of designating an airport as ‘‘fully coordinated’’ or ‘‘coordinated’’ afterdeeply exploring, in consultation with all airport users, operators and regulators, theopportunities for increasing capacity. The airport designation exhibits certaindifferences between the relevant EU regulation and the IATA guidelines

(continued on next page)


Babysitting of slots A practice whereby air carriers use a slot differently from the way it was intended at thetime of allocation; usually by operating a slot with a small aircraft instead of a largeaircraft. The air carrier can thus reach the 80% threshold for slot usage, as specified bythe ‘‘use-it-or-lose-it’’ rule, and thus benefit from ‘‘grandfather rights’’ over these slotsfor the next scheduling season. ‘‘Babysitting’’ slots at congested airports prevents scarcecapacity being efficiently allocated and used (ACI Europe, 2004)

Declared capacity According to EU Regulation 95/93, ‘‘at an airport where slot allocation takes place, thecompetent authorities shall determine the capacity available for slot allocation [mostfrequently in terms of hourly runway movements] twice yearly in cooperation withrepresentatives of air traffic control, customs and immigration authorities and aircarriers using the airport and/or their representative organizations and the airportcoordinator according to commonly recognized methods’’

Fiducial protection Fiducial protections stands ‘‘where a person has enjoyed established ownership rightsover property that he will lose, [therefore] the person will be entitled to protection of hislegitimate interests. Fiducial protection is intended to mitigate the damage potential tothe former enjoyer of established ownership rights. Protection can come in the form ofchanges to be made in a gradual way or the payment of compensation’’ (TUB, 2001)

Fully coordinated/coordinatedairport

According to EU Regulation 95/93, a ‘‘fully coordinated airport shall mean acoordinated airport where, in order to land or take-off, during the periods for which it isfully coordinated, it is necessary for an air carrier to have a slot allocated by acoordinator. A coordinated airport shall mean an airport where a coordinator has beenappointed to facilitate the operations of air carriers operating or intending to operate atthat airport’’. ‘‘Fully coordinated’’ becomes ‘‘coordinated’’, whereas ‘‘coordinated’’becomes ‘‘schedules facilitated’’ according to EU Regulation No. 793/2004

Grandfather rights The grandfather rights stand for historic slot holdings and signify the right to dispose ofa slot of the coming flight schedule period, if it has been already used by the respectiveairline in the current period’’

Late hand-backs Slots that were initially allocated during the worldwide scheduling conferences, but notused during the scheduling season and not returned to the slot coordinator by the slotreturn deadline. Air carriers may retain slots for the purpose of trading, hoarding, oruncertainty in the scheduling of their operations. Late return of unwanted slots severelyrestricts the possibility of finding alternative users (ACI Europe, 2004)

Maximum Take-OffWeight (MTOW)

A measure of the aircraft weight that is used to calculate (proportionally) the landingfees for a given flight

No shows A number of slots initially allocated but not eventually operated (actualized in terms ofnumber of movements) at the end of the scheduling season (ACI Europe, 2004)

ScheduleCoordinationRequest (SCR)

SCR represents the former airport designation status according to IATA and it providesfor a coordinator appointed to allocate slots on a voluntary basis. All ‘‘fullycoordinated’’ (based on EU Regulation) airports are also classified as SCR, but on theother hand, not all SCR airports are designated as ‘‘fully coordinated’’. The majority ofairports use the terms ‘‘fully coordinated’’ and SCR synonymously. However, the newterminology and classifications in the revised IATA Worldwide Scheduling Guidelines(March 2000) suggest three levels of coordination (Level 1: ‘‘non-coordinated’’, Level 2:‘‘schedules facilitated’’, Level 3: ‘‘fully coordinated’’) based on different degrees ofutilization of airport capacity

Slot Slots were introduced as an expression of capacity in late 1960’s to deal with congestionand delay problems and represent (according to the European Commission Regulation95/93 and its recent amendments) ‘‘the permission given to a carrier to use the full rangeof airport infrastructure necessary to operate an air service at a slot-controlled airporton a specific date and time for the purpose of landing or take-off’’



Slot coordinator According to EU Regulation 95/93, the EU Member State responsible for a coordinatedor fully coordinated airport shall ensure the appointment of a natural or legal personwith detailed knowledge of air carrier scheduling coordination as airport (slot)coordinator after having consulted the air carriers using the airport regularly, theirrepresentative organizations, and the airport authorities. The same coordinator may beappointed for more than one airport. The slot coordinator is responsible for theallocation of slots in accordance with the EU Regulation and shall act in a neutral, non-discriminatory, and transparent way

Slot coordinationcommittee

According to EU Regulation 95/93, the EU Member State responsible for a coordinatedor fully coordinated airport shall ensure that in an airport that has been designated asfully coordinated, a coordination committee is set up to assist, in a consultativecapacity, the slot coordinator

Slot pool According to EU Regulation 95/93, ‘‘. . . a [slot] pool shall be set up for eachcoordinated period and shall contain newly created slots, unused slots, and slots whichhave been given up by a carrier during, or by the end of, the season or which otherwisebecome available’’

Use-it-or-lose-itRule

It represents a minimum slot usage rule based on which (according to EU Regulation95/93), ‘‘. . . if the 80% usage of the series of slots cannot be demonstrated, all the slotsconstituting that series shall be placed in the slot pool, unless the non-utilization can bejustified on the basis of [a number of . . .] reasons


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