39 kwakkel uncertainty in airport master planning

8/14/2019 39 Kwakkel Uncertainty in Airport Master Planning

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Uncertainty in Master PlanningA crucial challenge in ASP is how to deal

with uncertainty about the future. It is nec-

essary to take into account the future world,

which the organization will operate in, in

order to make decisions that shape and

guide what an organization is, what it does,

and why it does it. In case of ASP, uncertain-

ty is even more important, given the fact that

decisions made today can shape and influ-

ence the airport performance for many years

to come. For example, the decision to builda new runway at a specific location will

likely influence the airport more than fifty

years from now. It is therefore necessary to

have a thorough assessment of potential

developments that influence the future in

which the airport will operate, if one wants

to plan effectively. In AMP, however, only

demand uncertainties are considered, which

are assessed through forecasting. Often,

only a single demand forecast is created, and

a Master Plan is designed based on that sin-

gle forecast.

Criticism of Master PlanningAMP and demand forecasting as the

approach for the treatment of uncertainty in

ASP has come under increasing criticism(see for example, de Neufville, 1991a;

Walker, 2000; Flyvbjerg et al., 2003). The

demand forecasts are practically always

wrong, and Master Plans are often nearly

impossible to implement. As such, AMP

often has seriously negative consequences

for the long-term development of an airport,

including an inability to implement the plan,

severe capacity constraints due to unantici-

pated (noise) regulations, an inability to

meet aviation demand, and unnecessary

investments in airside and landside facilities.

Finding ways to deal with the many uncer-

tainties surrounding the future of the air

transport system is especially urgent in light

of the fact that, in the coming years, the con-text in which airports operate is expected to

become even more dynamic. Demand for

air transport is expected to increase signifi-

cantly, but there is uncertainty regarding

numerous aspects, such as the extent of the

AIRPORT RESEARCH

e-zine edition, Issue 39 1

The Problem of Uncertainty in Airport Master Planning

Airport strategic planning (ASP) focuses on the development of plans for the medium-term and long-termdevelopment of an airport. Strategic planning is defined as ‘the managerial activities that produce fun-damental decisions and actions that shape and guide what the organization is, what it does, and why itdoes it’ (Bryson 1995: pp. 4-5, as cited in Bryson 2004). Strategic planning can be done in many differ-ent ways. In airports, the dominant approach is airport Master Planning (AMP), which results in a MasterPlan that ‘presents the planner’s conception of the ultimate development of a specific airport’ (ICAO,1987, pp. 1-2). In the US, the FAA has set up strict guidelines for an AMP study (FAA, 2005 and earlierversions). Internationally, IATA reference manuals as well as books about airport planning by leadingscholars heavily influence AMP practices (e.g. ICAO, 1987; de Neufville and Odoni, 2003; IATA, 2004).

By J.H. Kwakkel

Picture 1: Artists impression of Berlin’s new BBI Airport. Courtesy Berlin Brandenburg Airport



increase, on which routes, by which carriers,

what the noise contours will be, what the

regulatory regimes will be, etc. The United

States, which was the first country to liber-

alize its air transport market, is already fac-

ing these issues. Hub operations can move

from one airport to another, and airports are

increasingly called upon to comply with the

wishes of airlines (Dempsey et al., 1997; de

Neufville and Odoni, 2003). The European

Union started to liberalize its internal market

in the mid 1990s, and this resulted in dra-

matic changes in air traffic demand. In addi-

tion, airports and airlines are being priva-

tized, which introduces new stakeholders

(e.g. within the financial market) and new

requirements for airport performance. These

developments lead to uncertainties about the

future performance of the airport (e.g.

capacity, delays, noise and financial per-

formance), uncertainties about the robust-ness of the policies airport decision-makers

want to implement, and uncertainties

regarding the implementability of these

policies. Together, these uncertainties ham-

per ASP and make the traditional AMP

approach even less appropriate.

The Challenge of uncertainty in ASPA general definition of uncertainty is ‘any

departure from the unachievable ideal of

complete determinism’ (Walker et al.,

2003). Uncertainty is not simply a lack of

knowledge, since an increase in knowledgemight lead to an increase of knowledge

about things we do not know and thus

increase uncertainty ( ). The traditional way

to deal with uncertainty in airport planning

is through AMP. Based upon a limited num-

ber of forecasts of future traffic demand, a

static plan is designed that can accommo-

date the forecasted traffic demand in an ade-

quate way. It is assumed that the airport

authorities are able to independently imple-

ment the plan without any opposition from

other stakeholders (Dempsey et al., 1997;

Burghouwt and Huys, 2003).

Master Planning Airport Master Planning (AMP) is the

process of developing a Master Plan.

According to ICAO, the United Nations

body for civil aviation, ‘an airport Master

Plan presents the planner’s conception of the

ultimate development of a specific airport’

(ICAO, 1987, pp. 1-2). This definition is

also used by the International Air Transport

Association (IATA) (IATA, 2004).

According to the FAA, the United Statesregulator of aviation, ‘an airport Master Plan

is a comprehensive study of the airport and

typically describes short-, medium-, and

long-term plans for airport development’

(FAA, 2005)’, which is almost identical to

the ICAO definition.

The goal of a Master Plan is to provide a

blueprint that will determine future airport

developments (Dempsey et al., 1997;

Burghouwt and Huys, 2003). As such, it

describes the strategy of an airport operator

for the coming years, without specifying

operational concepts or management issues.

A typical Master Plan, according to the

FAA, should contain (i) a technical report

containing the analyses conducted during

the development of the Master Plan; (ii) a

summary report that brings together facts,

conclusions and recommendations relevant

to a wider public; (iii) an airport layout plan

drawing set which contains a graphical rep-

resentation of the proposed developments in

the Master Plan; and (iv) a website and pub-lic information kit for providing information

about the Master Plan to the public (FAA,

2005). The time horizon covered in a Master

Plan can vary, depending on the situation of

the airport for which a Master Plan is devel-

oped. A short-term Master Plan has a time

horizon of roughly five years, a mid-term

Master Plan has a time horizon of six to ten

years, and a long-term Master Plan has a

time horizon of 20 years (FAA, 2005).

AMP follows a strict linear process. Themost commonly used guidelines (e.g. FAA,

2005; ICAO, 1987; IATA, 2004) are funda-

mentally the same, although they differ in

detail (de Neufville and Odoni, 2003). The

key steps in an AMP process are:

> Analyze existing conditions;

> Make an aviation forecast;

> Determine facility requirements;

> Develop and evaluate several alternatives;

> Develop the best alternative into a detailed

Master Plan.

Forecasting The aviation forecast is the main premise for

a new Master Plan. By comparing the fore-

cast with the existing conditions, an assess-

ment can be made whether there is a need

for new or expanded facilities. As such, avi-

ation forecasting is the main way in which

uncertainties about the future context, in

which an airport operates, are handled. A

forecast is a statement, usually in probabilis-

tic terms, about the future state or properties

of a system based on a known past and pres-

ent. The basic concept of forecasting is sim-

ple: past trends, based on time series or the-

ories about underlying mechanisms, areidentified and extrapolated forward. In

mathematical terms, a relationship between

independent variables (X1, X2, …, Xn) and

the dependent variable (Y) is developed:

Y = f(X1, X2, …, Xn)

that correlates well with past performance.

This formula is then extrapolated to obtain a

forecast for the year of interest. According to

the FAA, forecasts should be realistic, based

on the latest available data, reflect the cur-

rent conditions at the airport, supported byinformation in the study, and provide an

adequate justification for the airport plan-

ning and development (FAA, 2001).

How are forecasts made? The first step in

making forecasts is to identify the depend-


Picture 2: Berlin Tempelhof will be closed-down in favour for the new BBI Airport



ent variables (the Y’s) to forecast.

Depending on the relevant issues and poten-

tial problems of a particular airport, the vari-

ables to estimate through forecasting gener-

ally include aircraft operations, passengers,

air carrier enplanements, passenger

enplanements, air carrier and commuter

operations, tons of cargo, and aircraft oper-

ations by type of aircraft. The next step is to

gather and analyze the data on the related

independent variables (the X’s), which are

assumed to be necessary to forecast the vari-

ables of interest. Relevant data sources

include previous forecasts for the airport,

historical aviation data, forecasts of other

airports in the region, forecasts for air travel

in the region, and socio-economic data.

These data should be analyzed to see

whether they are appropriate to be used and

not ‘contaminated’ by unique events, such

as a major sport event that created a tempo-rary major boost in air traffic. The final step

is to select a forecast method, such as regres-

sion and trend analysis or share analysis, and

apply it in order to obtain an aviation fore-

cast of Y for some future year.

Forecasting is based on the idea of identify-

ing trends and underlying mechanisms,

based on the past and the present, and

extrapolating them forward. However, it

might be that the phenomenon you want to

forecast has recently gone through changes,or is expected to undergo changes (e.g. trend

breaks). In such situations, it is unwise to

simply extrapolate based on past trends and

known underlying mechanisms. In such sit-

uations, forecasters sometimes use trend

break scenarios to produce forecasts that

deviate from past trends (de Neufville and

Odoni, 2003).

The inadequacy of airport Master

PlanningAMP, as the main way to treat uncertainty,

has proven to be ineffective. There are manyexamples of Master Plans that turned out to

fail in practice (e,g, Nelkin, 1974; Nelkin,

1975; Szyliowics and Goetz, 1995; Demsey

et al., 1997; de Neufville and Odoni 2003;

Cidell, 2004). One example will be dis-

cussed here.

An illustration: Amsterdam Airport SchipholIn 1995, after a two year process, which is

known as the so-called physical planning

key decision Schiphol (PKB Schiphol), a

number of major decisions regarding thefuture of Schiphol Airport were made. The

main objective was to facilitate the process

of Schiphol becoming a mainport, while at

the same time improve the quality of living

in the area surrounding the airport.

Improving the quality of living should be

measured in terms of noise, emissions, and

third-party risk compared to the 1990 situa-

tion. Until 2003, only noise would be con-

sidered. Emissions and third-party risk

would become relevant after that. The plan-

ning period was 20 years, from 1995 till

2015. Forecasts were created based on three

scenarios in order to come to a decision. It

became clear during the development of the

PKB that in only one of the three scenarios

both objectives could be achieved. The final

PKB was based exclusively on the aviation

forecasts derived from this scenario. The

key decisions of the PKB were (Dutch

Parliament, 1998-1999):

> Schiphol would be allowed to grow into a

small hub airport, with KLM as its hub car-rier that should serve roughly 40 to 45 mil-

lion passengers in 2015;

> A fifth runway would be built;

> Until 2003, which was the expected year

the fifth runway would open, the noise situ-

ation should not get worse than the situation

in 1990, which implies a maximum of

around 15,000 houses in the high noise con-

tour (the so called “stand still” principle);

> After 2003, the maximum number of

houses in the high noise contour would be

lowered to 10,000 houses;> An insulation program for houses would

be implemented within the high noise con-

tour;

> A study into the development of Lelystad

airport to relieve Schiphol;

> A high-speed rail link from the

Netherlands to Belgium and France, and

from the Netherlands to Germany, would be

developed that would pass Schiphol, in

order to reduce the number of short-haul

flights from Schiphol.

As it turned out, the limits of the noise reg-ulations were reached in 1999, leading to a

temporary shutdown of the airport, and the

passenger limit was reached in 2005. The

two-year costly process that aimed at devel-

oping a plan adequate for 20 years turned

out to have a lifetime of less than ten years.

How did this happen? The model used to

create the demand forecast was based upon

a relationship between GDP and traffic

demand that represented past experience

quite well. However, due to a number of

trend breaks that happened after the fore-casts had been made; this relationship no

longer produced good predications, result-

ing in forecasts significantly lower than the

traffic demand actually experienced. The

unexpected high rate of growth of air traffic

demand was due to (i) an unanticipated

rapid growth of the hub network of KLM,

leading to an increase in transfer passengers;

(ii) an alliance between KLM and

Northwest Airlines, which fed passengers

from both airlines through Schiphol; and

(iii) The European Union’s liberalization

process of the air transport industry, which

increased competition among air carriers,

lowering air fares, and paving the way for

low-cost carriers.

The inadequacy of aviation forecasting What is clear from the foregoing illustration

is that AMP does not succeed in reaching its

goals. Plans are quickly obsolete and are not

robust with regard to the future. In other

words, uncertainty (e.g. aviation demand,

regulatory context, technological break-throughs, and stakeholder behavior) is a key

source of problems in ASP.

One reason that AMP does not achieve its

goals is that the only uncertainties that are

considered are demand uncertainties, which

are addressed through forecasting.

However, forecasting has come under

increasing scrutiny. Criticism can be split

into two categories: forecasting failure due

to bias, and forecasting failure due to uncer-

tainty. Forecasters’ bias contributes to fore-cast failure in several ways.

> Forecasters have a tendency to misjudge

the relevance of (recent) data (Porter et al.,

1991);

> Forecasters often have a poor database

that has internal biases caused by the data

collection system (Flyvbjerg et al., 2003);

> Forecasters often integrate political wish-

es into their forecasts (Flyvbjerg et al.,

2003);

> Forecasters use data from their home

countries (instead of the local areas) for cal-

ibrating their models (Flyvbjerg et al.,2003);

> Forecasts by project promoters may be

even more biased, since the promoter has an

interest in presenting the project in as favor-

able a light as possible (Flyvbjerg et al.,

2003).

Forecasting failure due to uncertainty man-

ifests itself in several ways. As pointed out

by Flyvbjerg et al. (2003), discontinuous

behavior of the phenomena we try to fore-

cast, unexpected changes in exogenous fac-tors, unexpected political activities, and

missing realization of complementary poli-

cies are important reasons for forecasting




failure. Ascher (1978) sees faulty core

assumptions as a prime reason for forecast-

ing failure. It refers to the fact that since the

phenomenon we are trying to forecast is not

completely understood, forecasters have to

make assumptions about the data they need,

the formula to be used, etc. (Porter et al.

1991). The use of historical data as a means

of testing the adequacy of a given formula

does not solve this problem, for there are an

infinite number of formulas possible that

can match the given historical data. Related

to this is the fact that, in order to forecast a

dependent variable Y based on a formula Y

= f(X1, X2, …, Xn), we need forecasts for

the future values of the n independent vari-

ables. Instead of forecasting a single vari-

able, we end up forecasting n variables.

Even if we were able to address the prob-

lems identified under the label of forecaster

bias, this category of forecasting failure, dueto uncertainty, implies that forecasting

always can go wrong. By looking at the past

and assuming that past behavior will contin-

ue in the future, we overlook a large part of

the uncertainty that, when it manifests itself,

will lead to trend breaks.

Closing RemarksIn conclusion, until now, a static reactive

approach, in the form of Master Planning, to

Airport Strategic Planning has dominated

the airport planning and design process. Intraditional AMP, the uncertainties are often

ignored, oversimplified, handled probabilis-

tically, or handled through the use of fore-

casts and scenarios. These methods have

proven insufficient for handling the uncer-

tainties airports face, since they assume that

the future is known to a degree that is suffi-

cient for making appropriate decisions.

Hence, finding new ways to deal with the

different uncertainties surrounding the

future is a key issue in air transport research.

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Policy-makers and Planners, Baltimore: Johns

Hopkins University Press.

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Burghouwt G., Huys M., 2003. “Deregulation

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About the Author

J.H. Kwakkel is PhD researcher at the Faculty of Technology, Policy and Management of the

Delft University of Technology.

[email protected]


Picture 3: Computer animation of BBI’s new terminal. Courtesy Berlin Brandenburg Airport.

39 kwakkel uncertainty in airport master planning

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