Contemporary Airport Demand
Forecasting
Choice Models and Air Transport Forecasting07

Discussion Paper 2014 • 07
Benedikt Mandel
MKmetric GmbH,
Karlsruhe, Germany

Contemporary Airport Demand
Forecasting
Choice models and air transport forecasting

Discussion Paper No. 2014-07

Benedikt MANDEL
MKmetric GmbH, Karlsruhe, Germany

April 2014

THE INTERNATIONAL TRANSPORT FORUM
The International Transport Forum at the OECD is an intergovernmental organisation
with 54 member countries. It acts as a strategic think-tank, with the objective of helping
shape the transport policy agenda on a global level and ensuring that it contributes toeconomic growth, environmental protection, social inclusion and the preservation ofhuman life and well-being. The International Transport Forum organises an annual
summit of Ministers along with leading representatives from industry, civil society andacademia.
The International Transport Forum was created under a Declaration issued by the Councilof Ministers of the ECMT (European Conference of Ministers of Transport) at its Ministerial
Session in May 2006 under the legal authority of the Protocol of the ECMT, signed in
Brussels on 17 October 1953, and legal instruments of the OECD.
The Members of the Forum are: Albania, Armenia, Australia, Austria, Azerbaijan, Belarus,
Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Chile, People’s Republic of China,
Croatia, Czech Republic, Denmark, Estonia, Finland, France, Former Yugoslav Republic ofMacedonia, Georgia, Germany, Greece, Hungary, Iceland, India, Ireland, Italy, Japan,
Korea, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Mexico, Republic of Moldova,
Montenegro, the Netherlands, New Zealand, Norway, Poland, Portugal, Romania, RussianFederation, Serbia, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey,
Ukraine, United Kingdom and United States.
The International Transport Forum’s Research Centre gathers statistics and conducts
co-operative research programmes addressing all modes of transport. Its findings arewidely disseminated and support policymaking in Member countries as well as
contributing to the annual summit.

Discussion Papers
The International Transport Forum’s Discussion Paper Series makes economic research,commissioned or carried out at its Research Centre, available to researchers andpractitioners. The aim is to contribute to the understanding of the transport sector and toprovide inputs to transport policy design.
ITF Discussion Papers should not be reported as representing the official views of the ITF
or of its member countries. The opinions expressed and arguments employed are thoseof the authors.
Discussion Papers describe preliminary results or research in progress by the author(s)and are published to stimulate discussion on a broad range of issues on which the ITF
works. Comments on Discussion Papers are welcomed, and may be sent to: International
Transport Forum/OECD, 2 rue André-Pascal, 75775 Paris Cedex 16, France.
For further information on the Discussion Papers and other JTRC activities, please email:itf.contact@oecd.org
The Discussion Papers can be downloaded from:www.internationaltransportforum.org/jtrc/DiscussionPapers/jtrcpapers.html
The International Transport Forum’s website is at: www.internationaltransportforum.org
This document and any map included herein are without prejudice to the status of or sovereignty over any
territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or
area.

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTINGTABLE OF CONTENTS

ABSTRACT..................................................................................................... 3
KEYWORDS ................................................................................................... 4
1. INTRODUCTION ........................................................................................ 5
1.1. The aviation world changed ................................................................... 5
1.2. Model capabilities................................................................................. 7
1.3. Data implications ................................................................................. 9
2. MODEL ................................................................................................... 122.1. Theoretical framework ........................................................................ 122.2. Reflection of the status quo as reference point & scenario simulation ........ 20
2.3. Complexity and coverage .................................................................... 23
3. APPLICATION .......................................................................................... 24
3.1. Airport choice .................................................................................... 24
3.2. Route competitors .............................................................................. 25
3.3. Flight segments ................................................................................. 26
3.4. Demand sensitivity to slots ................................................................. 27
3.5. Dynamic catchment ........................................................................... 29
3.6. Long-term forecast ............................................................................ 32
SUMMARY ................................................................................................... 34
ACKNOWLEDGEMENT ................................................................................... 35

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 20143

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

ABSTRACTThis paper describes the econometric system approach developed by MKmetric toperform short and long-term air transport demand forecasts while considering various
determinants such as socio-economy, policy, infrastructure and land use. The necessities
for modelling air transport evoking from a transport system point of view and thechanges of the aviation world occurred during the last decade are investigated. Based onthese findings the mathematical framework is outlined considering shortfalls of traditionalmodels used in aviation forecasting and restrictions caused by classical functional forms.
The increasing gap of information for modelling is described and alternative data sources
used for the development of the system approach are listed. As all models are imperfectdescribing just a part of real life, it sheds a light on the necessity to validate models andthe prerequisite of complexity needed to cope with multi-sector scenario simulations forstrategic, tactical and operational developments as well as political decisions. Finallysome analysis examples demonstrate the power of the approach used focusing on the
choice modelling reflecting consumers’ behaviour.

KEYWORDS

System approach, passenger demand, short-term, long-term, forecast, choice models,non-linearity, consumer behaviour, elasticity, spatial competition, BSP, MIDT, data gaps,
network, access-egress choice, airport choice, route choice, dynamic catchment,
multimodal, inter-modal, intra-modal.

4

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

1. INTRODUCTION

There are various situations where airport demand forecasting is requested to provide
facts for decision makers either on route development, airline marketing, infrastructure
investment, competition analyses, risk assessment and policy or entrepreneurial tacticaland strategic simulations of scenarios. These questions can be addressed to a short,medium or long term horizon for passenger market potentials. Thus there are different
user groups with different targets but all focus on change of demand, effects on the
supply side and the consumers respectively market behaviour under changing conditions,which is the core of consumer choice modelling in context of demand forecasting.
While politicians might want to know the effects of taxation, bilateral agreements or thenecessity of infrastructural investments for upgrading an existing or building a new
airport, the regional authorities as airport owners might concentrate on the overall
question of an airports’ passenger potential or on PSO (public service obligation) routes
to increase the regional accessibility, the airports may address questions concerning newroutes to develop the destination portfolio and airlines are concerned with the enrichment
and rearrangement of their network. There are plenty of questions to which the approachdescribed in this document can supply added value for the stakeholders, but all of themin common require to forecast demand under changing supply conditions, thus a
passenger market potential analysis based upon a consumer behaviour sensitive model
reflecting the competitive transport situation is necessary.
The model itself needs to be flexible so that long term forecasts can be done (ex ante) as
well as short term simulations (ex post) are possible to enable consistency. The longterm forecast covers all effects starting from the generation of traffic based on
socioeconomic attractors while the short term simulation concentrates on the existing
traffic checking the consumers’ reaction on changes in the supply side. The latter is themost interesting for airlines and airports working at development perspectives of the airservices taking into account the offers available at competing airports and theircatchment areas encountering interdependencies between new and existing services in
the air transport system as a whole.
1.1. The aviation world changed
Since decades procedures have been designed to predict the air transport market
developments and models have been capable to forecast securely air passenger demandpotentials based on historic data (statistics) and full market samples (BSP, MIDT). Thesestandard approaches are uni-modal focusing solely on air transport and a specific airport.
For the determination of connectivity measures network variables are used, which
derived from commercial available schedule sources such as OAG.
But since the 90’s the market situation changed strongly as liberalisation, deregulationand globalisation have been enormous drivers with strong influence on the aviation
market. Thus the aviation industry has been faced with a differentiation inside the airline

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

5

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

world so that beside network, regional and charter carriers the new low cost / low fareairlines and hybrid carriers combining all kind of services evoked. The intensified liberal
air service agreement policy tore down barriers and opened new markets while
competition increased. The latter leading to necessary consolidation in the airline world
by mergers and acquisitions all around the world (in Europe IAG, AF/KLM, LufthansaGroup, and Air Berlin), which is still ongoing. One consequence has been the rise of
global airline alliances to defend markets, leverage synergies and to influence consumers’
behaviour by customer loyalty programmes.
Furthermore military airports have been converted to civil ones or have been upgraded
respectively build. And airlines have been newly founded or dramatically boosted theirbusiness, especially when looking to the Gulf region. These Gulf airlines placed huge
aircraft orders with focus on the new developed aircrafts allowing to draw from
economies of scales, operating with lower costs and to offer multiple attractive
destinations due to the aircrafts’ range. In consequence new hub facilities have been
established enriching the portfolio of consumers’ choice set while enhancing thecompetition among airlines. In the meantime all airlines react and upgrade their fleet by
new aircrafts such as A380, B787, A350, A320 neo, B797, CS-Series, etc. which allow ahigher efficiency, productivity and more flexible operation. Also new products on board
have been developed and a higher differentiation in the fare structure with tariff
decomposition driven by aggressive yield management came up. Especially the hub &spoke services are marked very sensitively to supply and demand changes, so thatspecial offers make detours attractive.
With the globalised economy and the free settlement across the European Union member
states as well as the new service products new markets raised as consumers undertake
more short trips. Also migration loops back allowing to serve new destinations, VFR andmultiple leisure trips by air became more affordable. Last but not least the consumer has
been educated to be more sensitive to price and other product attributes, which was
supported by the IT-systems with web based platforms making offers easily transparent.
In consequence the airlines and airports are faced with new type of consumers withdifferent behaviour, requesting modern communication (e.g. internet, Facebook, Twitter,YouTube) and distribution channels (internet, kiosk, mobiles, social media). Together
with the cost pressure due to competition airlines boost direct sales channels avoiding
global distribution system (GDS) costs whenever possible although such internet based
GDS increased their sales power (Expedia, Opodo, etc.). But the competition is not justbetween airlines or air alliances respectively on flight segments and routes, it takes place
as well between airports irrespectively whether these are hub or spoke airports. On top it
has to be mentioned that there is a kind of symbiotic relationship between the air and railmode as the high speed rail services compete with air services (e.g. Lyon-Paris,Frankfurt/Stuttgart/Strasbourg-Paris, Barcelona-Madrid, all around Germany) and at thesame time airlines with no air feeder system use these rail services to link their products
to a wider spread market by offering rail & fly products. The competitive situation is as
well inspired by new regulations such as strengthened passenger rights and emission
trading scheme or more local environmental fees respectively national taxes on airservices.
Finally the formerly stable markets became unstable as schedules change faster,
especially the low cost carriers often follow the try & error principle, and economic
drivers like GDP, kerosene price, or bank crises are peaking with high variance.
Unpredictable are global disease effects (e.g. SARS, bird flu) and environmental
catastrophes (e.g. volcano eruption, hurricanes, earth quakes, flooding) as well as terror
acts, all leading to increased safety and security efforts.

6

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

The section could be easily extended but it should just show that too much has changed
so that the classical forecasting approaches based on trends of the past or service ofquality prolongations (SQI) neither reflect the radical market development nor cope with
the increasing data leakage as direct sales remain proprietary information. To respect thechallenges outlined before and to incorporate functionalities to deal with the new market
developments, models must mirror the point of view of consumers more detailed. Usuallypeople neither live nor work or make holidays at airports. For the majority the origin anddestination of a trip are locations in the vicinity of airports respectively the region.
Therefore the models’ point of view has to turn from the airport to the region as starting
point of the trip decision so that the traditional static view becomes a consumer oriented
dynamic one. Thus airport catchments are no longer defined by fixed isochrones todetermine the airports’ passenger potential; they are flexible reflecting the competitive
situation route by route.
1.2. Model capabilities
The challenges outlined a demand forecast reflecting the passenger market potential isfacing need to be treated by an adequate forecasting method. With focus on air transport
forecasting effects of future actions any market changes have to be measured orconsidered within a consistent modelling approach. Irrespective which mathematical form
is used at least the following instances need to be reflected:
 competition of destinations, e.g. complementary or substitutionoRegions are competing with each other for inbound tourism andinvestments to increase employment and finally welfare of the population.
Thus the accessibility by air of a region is an important asset for its
development perspectives. Parallel decreasing travel costs allow consumers
to use the money saved for additional trips or to combine several
destinations.

 competition of modes (multimodality), e.g. air, rail and roado

The surface networks are extended all around the world and especiallyhigh speed rail services offer alternative travel options which can easily
compete with air services due to attractive travel times and prices.

 co-operation of modes (inter-modality), e.g. air-rail, air-roado

The extensions of surface networks allow an intensive co-operation ofmodes as well. Beside the classical highway access especially airport
railway stations embedded in a high speed rail network qualify best to
increase the catchment area of an airport to substitute feeder flights and tooffer rail & fly services.

 competition within the aviation mode (intra-modality), e.g. airports, airlinesoA high density of airports with overlapping destination portfolios fromdifferent type of carriers favours competition. Starting the trip from a
region there is the opportunity to choose among different originating
airports and one could arrive at different airports at the destination area
which is not only the case at multi-airport cities: One also has to consider
how intensively those airports are embedded in the surface transport
networks. Looking at each originating airport, the different choices for air
services has to be considered too. Beside nonstop flights there are often
offered plenty of different transfer routes, in some cases even from theBenedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 20147

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

same airline / alliance. In case of transfer connections the hub airports /airlines are competing as well which enhances the necessity to consider
various route alternatives. All alternative air services together reflect theconsumers’ choice set and the competitive situation where catchments
overlap and airports / airlines highlight the strengths of their product (e.g.
time, price, frequency, operation days, slots, capacity, ASA) to attract
passengers.
 interdependency of the different decision levels of the consumers, e.g. slot choice,airport choice, access/egress choice, mode choice and travel demandoAs a trip does not start at the airport door one has to consider the whole
travel chain starting from access/egress of airports, the time slice the trip
takes place, and the route taken. Moreover the attractiveness of an air trip
versus other modes or even whether a trip can be undertaken (budget oftime, financial welfare) determine the consumers decision or choicerespectively.

 sensitivity of the consumers to supply changes, e.g. elasticity of level of service
variables, thresholds, captivitiesoDifferent types of consumers show different sensitivities to productcharacteristics. While some travellers are very time sensitive and request ahigh frequent service during the day and at any day of the week, othertravellers are more cost sensitive and first look for low prices and find justone flight per week sufficient to be carried just in time to their holiday
location. The consumers behave non-linear and have different thresholds
where they reconsider their decision; they might be captive to producttypes and inelastic to pricing while travel time is dominating their choicemost. The models need to reflect the consumers’ utility ideas byembedding the travel alternatives’ characteristics (e.g. time, cost,frequency, operation days, alliance or online connection) in the functionalform.

 synergetic power of networks, e.g. air alliances, hub & spoke, high-speed railoThere are different air service products on the market and beside point topoint services large networks are operated evoking massive hub & spoke
systems around the world. Instead of just analysing and forecasting thedemand potential of one airport today thinking in networks to cover local
and global effects in parallel is a necessity. Consumers with a low value oftime accept huge detours and there are airports serving more transfer
passengers than local traffic. Routes are developed based on the scaling
effects a hub system allows benefiting as well from alliances among
airlines. The place an airport occupies in the market reflects the relative
position as competitor, whereby the networks (of air services as well as ofaccess/egress surface modes) define the impedances and theattractiveness. Without considering the synergies of networks at the
airport in focus and as well the ones of its competitors it is neither possible
to locate the market position today nor in future.

 economic valence and ecological balance8

For a stable forecast it is essential that the air services are of economic valence as
beside some PSO routes reflecting cohesion policy no subsidised network can be

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

maintained in long-term as sustainable transport system. Moreover the system
must be in line with the international competition rules and able to bear its
external costs such as the emission scheme imposed by the EU.
The following figure depicts the emphasis outlined above in a striking way.
Figure 1. Consumers' complex alternative travel opportunities

Source: MKmetric, 2014

Furthermore air transport is determined by external factors. They concern the socioeconomy (e.g. population, GDP, ex-/import), technology (e.g. infrastructure,
motorization, vehicle / propulsion types), urbanisation (land use, population / economy
density), and policy (national / international constraints, regulations, agreements).
Considering these market circumstances the model will be able to quantify, analyse andevaluate effects on travel demand and consumer reactions to planned actions for shortand long-term investigations. To satisfy all criteria mentioned above a consistent systemapproach with various econometric models dealing with the different requirements,
implications and dynamic networks have to be used allowing to explain all
interdependencies in parallel so that no transport activity remains unexplained or will be
lost. Neither an airport focused trend forecast nor a QSI model allows capturing thesedifferent effects and interdependencies.
1.3. Data implications
Following the argumentation above one need to shed a light on the data implications
necessary to establish, validate and run such a model approach.
The historic workhorse of MIDT / BSP databases, which have been a splendid source of
information in the past, do neither cover all necessary data aspects nor ensure therequested quality due to dramatic reductions in sample size and the exclusion of markets
such as LCC and Charter. Even IATA stated this fact (Symposium Istanbul 2010) andmentioned that in consequence products like PaxIS are no longer of value added for
airports and airlines (Symposium Singapore 2012). It was stated by Bryan Wilson1 that
PaxIS has lost 35% of total data (70% in Europe), and that it is no longer useful formany clients. (Please note that the BSP/MIDT data sources never covered Charter and
LCC data, thus the total market coverage is even much smaller.)1.

http://www.worldtek.com/wp-content/uploads/2011/10/Direct-Data-Services-Wilson.pdf

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 20149

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

In parallel to the shrinking data sample the Global Distribution Systems (GDS) like
Abacus, Amadeus, Galileo, Infinity, Sabre, Travelport, Travelsky, Worldspan etc. arefighting to defend their Marketing Information Data Tape (MIDT) dominance and there
are heavy legal challenges ongoing about the use of the BSP data. GDS provider want toprotect and invent own products with customized solutions. In 2011 IATA reacted to theinsufficient data situation by offering an alternative product, the Direct Data Service2(DDS). The product idea is not revolutionary and the challenge is with the data provider.
IATA invites travel agencies, airlines, and airports to share data on different granularity
levels by signing a Contribution and License Data Agreement (CLDA). Just the ones who
signed in have access to the collected data according to the agreement signed. But given
the increased competitive situation and the strong engagement of carriers into direct
sales techniques, why should airlines and airports provide these data to IATA-DDS sothat competitors gain knowledge about markets to explore? Airlines already hardly share
all market respective sales data within an alliance.
According to SITA, who regularly undertakes IT trend surveys3 covering the 200 largest
airlines worldwide the tendency to direct sales will even be intensified and diversified.
The survey launched in 2013 shows that until 2016:-

97% of the airlines will strongly invest into personalized and mobile customer
services-

90% of the airlines will offer tickets via mobile phones-

Share of boarding passes by apps on mobile phones will increase from 53% to80%-

The share of direct sales by airlines will increase from 54% to 67%-

65% of airlines offer mobile schedules already today (2013)-

61% of airlines offer mobile check-in already today (2013)-

60% of airlines will offer service on lost baggage-

63% of airlines will offer services to change reservations-

57% of airlines will offer services to allow for feed back-The value of GDS ticket sales will decrease from 46% to 33% of the turnover ofclassic travel agents-

14% of all ticket sales will be executed by mobile phones, social media and fullautomatic sale stations

These survey results of SITA are reconfirmed by airline managers like Lionel Guérin 4,
Chairman and CEO of HOP (Brit Air, Regional and Airlinair) monitor as well. He stated in2.

http://www.directdataservice.com/

3.

http://www.sita.aero/surveys-reports/industry-surveys-reports/airline-it-trends-survey-20134.

http://corporate.airfrance.com/en/press/news/article/item/3-questions-for-lionel-guerinchairman-and-ceo-of-hop/

10

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

an interview on 29.01.2013: “The other main new trend concerns the explosion of online
sales: over 60% of Europeans systematically buy their airline tickets on the internet.”
The direct sales channel does not just help airlines to become more cost effective, theyalso can optimise the data usage for personalized services to customers maximising thesale of add on services. The SITA study outlines that the sales of additional services will
become much more important as airlines generate nearly nine times higher sales whenpurchasing the tickets to customers directly. Referring to the published business reportsof the LCCs this argument holds. In consequence, the airlines are forced to intensify theirdirect sales efforts due to the multiple beneficial effects and the competitive advantage
they can materialize. Due to globalisation and the strong competition airlines in all areasof the world diversify their business model as a logical step forward. As side effect theairlines reduce their dependence on distributors and save related costs when increasing
mobile sales and other direct sale services.
Thus the traditional commercial data sources (BSP / MIDT) and the new products offeredby IATA and the GDS provider are faced with declining samples and therefore depend on
data gap-handling procedures to forecast the total market based on fragments of
information as if any, often just the indirect sales data are available in the databases
reflecting a small share of all sales and still LCC and Charter are missing. Beside theaforementioned data problem the classical forecasting tools have to cope in parallel withthe market and consumer behaviour changes rising the risk of the forecasts’ quality. This
concerns not only the long-term investigations, also the classical Service of Quality Index(SQI) approaches struggle with a rapidly declining base of information resulting into alack of quality.
In consequence for the forecasting task the fragmented commercial available MIDT / BSP
data are not sufficient, neither in coverage and quality nor in dealing with the outlined
challenges. Other data sources are needed to compensate the shortfalls and to cope withthe model requirements. In the following the main data types are listed enabling a model
development based on public and mostly free data sources 5:Volume observations to cover the whole transport market, e.g.
 detailed Eurostat and national statistics (on flight, on stage)
 publications of airports and airlines, IATA samples* publications of airport and airline associations
 link counts for road
 transport figures for railConsumer elasticity to identify the consumers’ sensitivity, e.g.
 air transport passenger surveys at airports across Europe6*
 mobility and travel surveys across Europe panel data of households

5.*

Indication for commercial data source

6.

Having access to the 10% ticket sample in the US as non-US citizen and combining this witha 10% ticket sample for Europe, which is technically possible with minor effort and costs forairlines, would open a new dimension for researchers and modelling.

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201411

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Mode networks to mirror the consumers’ choice set, e.g.
 air schedules (OAG, Cedion, Innovata, ...)7*
 air traffic management data (EUROCONTROL, national representation)
 information from the air transport industry (destination portfolios, loadfactors, …)
 surface networks (road, bus)
 railway, ferry schedules*



Regional indicators to diversify regional attractiveness, e.g.
 socio-economy (UN, IWF, OECD, EZB, Eurostat, ...)*partly
 satellite data (Corine aggregates, ...)
 tourist information
 land use data meteorological and geological data

These data allow building up, validating and running a forecasting model. The
methodology to be applied is explained in the following section, whereby the basics of theeconometric part are outlined while for the details it is referred to the publications
available on our web site www.mkm.de.

2. MODEL

Within this section the principle method and models are described which are used by
MKmetric to deal with the requests of forecasting transport demand under considerationof the global transport market in parallel. The basic mathematical form of the models is
displayed and publications are listed to study in depth the mathematical details of theeconometric models. In addition the description provides the essential characteristics andfeatures a model must reflect when dealing successfully with the challenges andrequirements for forecasting as outlined above.
2.1. Theoretical framework
The forecasting procedure and the resulting analyses are based on a consequentsystematic view of transportation. It is therefore necessary to embed air transport
forecasting and simulation in a framework of relevant relationships that take into accountthe whole transport market as well as demographic, economic, political, spatial andtechnical components. A modelling process based on these interrelationships explains the
7.

EUROCONTROL refuses sharing schedule related data with the argument the information
belong to the airlines, which is disconcerting as airlines publish schedules but unfortunately
not at one place consistently.

12

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

transport market by multimodal and multi-sector determinants. This approach ensuresthe consistency of the whole model system in every step of the simulation process as themodels always process balanced figures of all endogenous measures. Hence, no transport
activity appears or disappears unexplained within the system. Changes in the system's
state are substitutive or complementary and synergetic effects lead to new situations
concerning diversion, accessibility or attractiveness. These effects can be analysed withrespect to modes (e.g. road, rail, sea, and air) and/or trip purposes (e.g. business,
vacation, private).
In the light of the complexity outlined in the previous chapters it is obvious that there
must be a sequence of models dealing with all the interrelationships and dependencies tocatch the consumers’ behaviour. The system approach developed is based on theclassical 4 step procedure, namely generation, distribution, modal split and assignment.
There are some differences in combining steps, modelling techniques, functional form
and additional modelling steps to cope with specific effects within air transport. The
following figure shows the principle structure.
Figure 2. Sequence of models for air transport forecasting

Source: MKmetric, 2014

The basis of all models contains the determinants of transport. It is the data base where
all information is stored to run the models, e.g. socio-economy, regional attractors, networks. The second block defines the scenarios to be tested. Short-term scenarios areoften used to execute ex post8 forecasts investigating a specific question, e.g. changes in
network, competition, air service agreements or taxation. Long-term scenarios are mainly used for ex ante forecasts and contain a bunch of assumptions such as socio-economic
development, infrastructure investments, capacity constraints, airline strategies or policy
measures. In principle the scenario definitions can encompass all input data for themodels and any measures with quantitative influence on the model variables. The richness of a scenario definition depends on the level of granulation the models determine.
The first modelling block concerns the generation-distribution model. It computes thetotal traffic volumes, where they emerge and whereto they are directed. There aremodels splitting the demand and the distribution of traffic, whereby first the mobility
8.

What effects appear in case a certain activity would have taken place in the reference year.

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201413

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

pattern and then the demand distribution is computed. Here we follow the combined
generation-distribution approach to incorporate in parallel the substitution andcomplementarities of destinations and to deal with the requirement of spatialcompetition. In the following the principle mathematical form is displayed, for moredetails see the papers listed below9.
The flow T from region i to region j is computed by a non-linear function gd with socioeconomic factors A (a= 1, ..., A) and the modes utility, e.g. network impedances, U(m=1, ..., M) as follows:

Tij  g d ({Aija },{U ijm }) with

1
1
Aija  S ia 2 S ja2 whereby S reflects variables like GDP, population, purchase power, income andU ijm  e ij mvwhereby v is an additive non-linear function of network impedances like time anddistance under consideration of all modes and consumer related data like age andgender.
As regression problem one can formulate the above into a simple multiplicative form,
such as:Tij   0 Aij11 .... AijAA U ijU uij
To detect and gain the non-linear effects Box-Cox transformations (BCT) of the strictly
positive variables are applied. To guarantee for a constant variance a variable correcting
heteroscedasticity is imposed. The error term is analysed under consideration of nonlinearity (BCT) to extract functional information so that a pure stochastic term is left.
The functional form is enriched by a vector describing the dependencies of neighbouring
regions as destination of a trip so that one can cope with the problem of irrelevance of
spatial competition which is inherent due to Luce’s IIA axiom.
Given the following situation we assume region 2 and region 7 to be close substitutes.

9.

Entwicklung eines gekoppelten Verkehrserzeugungs- und verteilungsmodells für den
Personenfernverkehr - Development of a linked trip generation and distribution model for
long distance passenger traffic, on behalf of the German Ministry of Transport FENr.:
60307/92; Université de Montréal Centre du Recherche sur les Transports (C.R.T.), MKmetricGmbH, University of Karlsruhe (TH) Institute of Economic Policy and Research (IWW);
Gaudry M., Mandel B., Rothengatter W.
Introducing Spatial Competition through an Autoregressive Contiguous Distributed (AR-C-D)
Process in Intercity Generation-Distribution Models within a Quasi-Direct Format (QDF),
Université de Montréal C.R.T., MKmetric GmbH Karlsruhe, Universität Karlsruhe (TH)Institute of Economic Policy and Research (IWW); Gaudry M., Mandel B., Rothengatter W.;
CRT-97114

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Figure 3. Spatial competition

Source: MKmetric GmbH, 2014

Under consideration of this spatial competition Tij looks like:

T12  f ( A12 , A17 ,U12 ,U17 )  12
T17  f ( A12 , A17 ,U12 ,U17 )  17
Now all competing regions (below denoted with index k) influence the attractiveness ofthe flow between i and j, hence competition among regions is introduced which can leadto substitutive but as well to complementary effects in the choice of destination.

Tij  Tik   (U ij  U ik )  ij
For more details about the spatial competition it is recommended to read the article‘Introducing Spatial Competition through an Autoregressive Contiguous Distributed (ARC-D) Process in Intercity Generation-Distribution Models within a Quasi-Direct Format(QDF)’ available on www.mkm.de .
The next modelling steps concern the consumer’s choice on transport mode, access /egress to / from the airport, airport and route as well as time slice in which the trip takes
part. A mode choice model10 is used to identify the consumer’s elasticities with respect tothe alternative transport modes air, rail and road. Especially for countries / areas with adense highway network and available high speed rail services the competition amongmodes is to be considered essentially. But this leads directly to the next choice problemof the consumer: How to approach the airport? Excellent high-speed rail connections
from and to an airport increases the catchment area enormously allowing convenient
access or egress. This influences the slot, airport and route choice as well which all
together explain the consumers’ selection of services and the competitive situation 11.
While the first modelling step concerns the generation – distribution calculation allowing
the construction of a matrix of traffic flows between the regions in focus, the second
model level splits the amount of travellers detected between modes, access / egress
options, airports, routes and time slices. Depending on the underlying data one can apply
share or probabilistic models whereby the estimation algorithms differ but the principle

10.

Schnellverkehr und Modal Split - High Speed Transport and Modal Split, Baden Baden:Nomos
Verlag; Mandel

11.

Airport Choice & Competition - a Strategic Approach, Mandel B.; 3rd Air Transport ResearchGroup (ATRG) Conference; Hong Kong

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201415

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

remains the same. In the following the principle is outlined by using a share model,
which is based on aggregated observations for the mode choice problem.
The share of travellers using mode m on relation i j (denoted as t=1, ...,i , ..., I) is equal
to the attractiveness v of mode m on relation t in context of the sum attractiveness of allmodes p (p=1, ..., M) on relation t. So it is just the relative attractiveness of one mode in
respect to the one of all potential available modes. This is expressed by:v

e mt

sh(m) t  M

e

with

v pt

vmi   0m    n N ni xn    a S ai    p Ppi
(

)

n

a

p

p 1

whereby N are network characteristics, e.g. time, cost, frequency (where λ indicates nonlinearity which is discussed later), S are socio-economic characteristics, e.g. age-class,gender, employment, household size, income and P are characteristics of the trip
purpose, e.g. trip duration, business.
Alternatively probability models based on disaggregate observations can be formulated in
the same way. The probability of an individual traveller n choosing a mode i is expressed
as follows:
K

P(i)n U ini, jCn U jn( x    x )with U in  e

i i

k 1

ki kinjwhere U is expressing the utility of the mode n experienced by individual i or j (denotedas m) and X refers to the N, S and P characteristics stated above. Both forms are of
simple logistic form reflecting the proportion of one alternative to all others, whereby thedependent variable is given by an observed share of demand an alternative attracted oralternatively the dependent variable is an individual decision of a consumer for analternative out of a set of alternatives. The latter usually is extracted from an individual
survey.
Assuming observations, samples and all other necessary input data are perfect so thatthe models fulfil all criteria mentioned before, it is essential to know what type offunctional form the models use. Analysis based upon observations drawn from travel
surveys and traffic counting show that consumer do not behave linear. First they tend toreverse their decision according to changes in the attractiveness of an alternative (e.g.
the reduction of a trip duration by 30 minutes will have larger effects on short haulroutes than of long haul routes as it makes a proportional higher difference to travel one
hour instead of 1:30 h in comparison to travel 7 h instead of 7:30 h) and second they are
as well inelastic to a certain extent (e.g. one day return trip above 600 km distancerequires air mode and the reverse some people fear to fly).
To encounter these behavioural issues the mathematical form of a model has to allow foradjustments of the curvature measured on the base of observations. Such additional
degrees of freedom must be estimated simultaneously so that each characteristic
describing an alternative is weighted correctly in relationship to all other characteristics
to reflect the balance of its influence upon the traveller properly. The degrees of freedom

16

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

mentioned concern the non-linearity and the captivity of the functional form. To explain
their importance two simple interpretations out of daily live are provided in the following.
People with a certain low monthly budget could not afford to travel by air 10 years ago,
but today with low cost offers from LCC’s and legacy carriers they can, so a certain
threshold was passed and those people started to travel by air. Another example
concerns the saturation effect captured as well by captivity as people have a certain timebudget available for trips which cannot be extended, e.g. limitation of vacation time,even if interesting offers are available at the market. Thus consumer’s elasticity is a key
issue to understand when forecasting.
The models shown above imply a linear functional form. In consequence the effect of agiven difference in transport conditions is independent of the service level characteristics
so that the response curve to changes in service characteristics is symmetric with respect
to its inflection point. But as outlined the traveller’s behaviour is non-linear. Thresholds
influence the decision and activate changes in product choice.
Therefore the functional form must be flexible so that one can circumvent linearity invariables and consider the level of the variable. To introduce the non-linearity different
possibilities exist. E.g. the Box-Cox transformation can be applied for strictly positive
variables:( x k  1) kk  0BC : x (  )  fork  0 ln( x)The BC transformation applied on the probability model leads to the following form:K( x    x
i i

k 1

e

P(i)n K( )( x    x
e

j

j

k 1

ki

ki kin)( )kj kjnkj)j

The following figure shows the different curvatures of the linear Logit function and theones with transformations applied on a positive variable12. The range of thresholds is withthe asymmetric tail of the function and indicates the change of travel time where people
start to reconsider their behaviour over-proportionally.
When looking at the linear and BC-Logit curvature it has to be noted that the choiceprobabilities go to zero (one) when the representative utility Vi goes to - (+) so that(see figure above) one cannot model thick tails due to specification error, modeller
ignorance, compulsive consumption or captivity to alternatives. To bypass this constraint,
the captivity of consumers, the Box-Tukey transformation can be used:12.

Linear or Non-linear Utility Functions in Logit Models? The Impact on German High Speed
Rail Demand Forecasts; Transportation Research Journal Vol. 28B; Elsevier Science Ltd;
Mandel B., Gaudry M., Rothengatter W.; 1994

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201417

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTINGk  0, ( xk   k )  0
(( x   k )k  1) kwithBT : x (  ,  )   kk  0, ( xk   k )  0ln( xk   k )
The BT transformation applied on the probability model leads to the following form:K( x    x
i i

ki

K

( x    x
j

e

j

ki

ki kink 1eP(i)n ( ,  ))( ,  )kj

kj)

kj kjnk 1j

In the figure below it can be seen that there are about 20% inelastic captive riders who
are forced to use the displayed alternative irrespective of any changes in the variable,
e.g. long-haul business travellers. The μ coefficient allows the function to converge to acertain level at both sides of the function.
Figure 4. Functional forms
1P
0.8
Linear-Logit
0.6

BC-Logit
BT-Logit

0.4
0.2
0

decreasing travel time
Source: MKmetric GmbH, 2014

A linear functional form which considers captivities is available when choosing the Dogit
model. But also a Box-Cox transformation can be applied in parallel. This allows catching
captive riders in combination with non-linearity of variables. This form looks as follows:K( x    x
i i

e

( )
ki

ki kink 1K( x    x

)

j

 e

j

kj kjn

k 1

kj

)

in j

P(i)n K

( x    x(1    )  e
j

18

( )

jn

j

j

k 1

( )

kj kjnkj)

j

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Within the system approach outlined the technique of choice modelling described is used
for all the choice problems mentioned.
Up to now the models are independent from each other and to encounter the effects fromone decision level respectively model level to the other one has to link the modelling
steps by the quasi-direct format (QDF)13 using the representative utility function of thelower level models in the upper ones as an additional independent explanatory variable,
which is called modal utility index. This link ensures consistency of behaviour across thesystem approach.
As example the quasi direct format is explained for the generation-distribution model
which includes the modal utility index, respectively denominator, of the modal split model
as an independent characteristic in the mathematical expression of the problem in focus.
K( x    x

QDF : T  Tij  S  T   e
m
ij

m
ij

ij

j

j

k 1

( )

kj kjnkj)j

The linkage of the model levels is as well a prerequisite for the reflection of theinterdependencies within the decision process of a consumer 14. This may concern therelationship of the consumer’s choice and travel demand as well as the one concerning
the competition of modes (multi-modality – rail-road-air-sea), the co-operation of modes(inter-modality – transport chains across modes like rail & fly, sea-road) and thecompetition within a transport mode system (intra-modality – e.g. selection of different
routes including various transfer hubs and airports at the origin and destination). As
example it is referred to the establishment of high speed rail services like the TGV which
reduced the air traffic significantly between Paris and Lyon, Brussels or Strasbourg(multi-modal effect).
On the other side the establishment of a new high speed rail service station at an airport
like in Frankfurt resulted in a huge extension of the catchment area (intra-modal effect)and substituted short haul flights (inter-modal effects). In the light that consumers
always try to maximise their utility and do not follow a static behaviour such effects haveto be considered to draw a realistic picture of the transport system and the effects which
occur when taking strategic, tactic and operational decisions.
Another aspect addressed concerns the traveller’s elasticity. For all expressions one canderive this measure such as the point elasticity: (S(i)n , xkin ) S(i)n xkin
x
S(i)
kin
n

13.

Methodological Developments within the Quasi-Direct Format Demand Structure: theMulticountry Application for Passengers MAP-1; Strategic European Multi-Modal Modelling
Gaudry M., Heinitz F., Last J., Mandel B.; Working Paper BETA n°9815

14.

The Interdependency of Airport Choice and Travel Demand; Taking stock of air liberalization,
Proceedings of the International Symposium at the ICAO; Kluwer Academic Press; Mandel B.

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

19

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

It is just the derivation of the function for a certain variable. As a result a ceteris paribus
variation of the demand for alternative i by a 1% change of the characteristic value ofalternative i can be computed. So market effects can be analysed when changing supply
characteristics, e.g. reducing the price of an alternative, increasing the service frequency
or introducing a new service.
In principle the whole system approach can be expressed as a function of elasticities,
such as: (T , xk )   (T , xk )   ( P(mod e) , x )   ( P(airport ) , x )   ( P(access / egress) , x )   ( P(time slice ) , x )   ( P(airline ) , x )
i
n k
n k
n k
n k
n k

or in simplified notification:

 (alternati ve)   (total flow)   (mod e)   (airport )   (access / egress)   (time slice )   (airline )With this set of elasticities all kind of competition within the transport system ismeasurable15 as the model set is interpreted as a cascade of elasticities.
Last but not least, procedures are required to compute impedances which reflect theattractiveness of each alternative based on the infrastructure networks of all modes. The
different algorithms used cope with the specifics of the modes. Assuming the granulationlevel of schedules for air transport connection builder have to be used which consider
flights, airlines, alliances, code shares, time zones and connecting times at airports aswell as access/egress time. It is recommended to develop a tailor made procedure to
optimise the algorithm so that it can cope with the problem addressed in harmony withthe available computer processing power, whereby parallel processing should be kept inmind to minimize run time. The impedances computed need to contain the network pathinformation to allow an assignment of the traffic demand.
Please note that the methodological description provided just sheds a light upon theprinciples of the system approach and it is recommended to have a look at the
publications mentioned which can be withdrawn from the following web site:www.mkm.de section publications/papers.
To complete the picture it is obvious that once the demand forecast is executed aircraft
movements differentiated by e.g. 8 MTOW classes and schedule or peak day analyses
can be derived. For air cargo an equivalent approach is developed.
2.2. Reflection of the status quo as reference point & scenario simulation
The analysis of the forecast can base on an ex post or either an ex ante scenario. Whilethe basic framework data of a reference year for an ex post analysis are available fromnational statistical offices or published commercial sources like schedules respectively,
the data for an ex ante scenario have to be worked out for the specific year to beforecasted.
The ex post forecasts can be used in two ways. First of all the representation of a givenreference year is important to validate the systems output in front of the reality, saymeasured observations like statistics. This exercise is envisaged by a matrix bounding
15.

Measuring Competition in Air Transport; Airports and Air Traffic - Regulation, Privatisation
and Competition Hamburg, Germany, HWWA; Peter Lang Press; Mandel B.

20

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

approach and has to be executed always at first when setting up the system for a projectto ensure a high quality of results. The principle procedure is depicted in the following
figure.
The calibration is an iterative process where data from various sources and model results
are assessed to detect irregularities, inconsistencies and errors. The arrows symbolise
the principle process flow, while the recursive parts and their nesting are not displayed
due to their complexity.
Let’s assume a model set is available as well as all the necessary input data to complete
a run (see the blue coloured boxes in the following figure). As result of a ceteris paribus
model run for the reference year one receives an ex post forecast. These model results
are validated by independent data sources like national statistics, link counts, surveys,
etc. to identify irregularities, inconsistencies and errors. They are investigated bychecking input data, procedures, programmes and models whereby errors are corrected– in the worst case even by setting up new model estimations. This step is essential to
make sure that model results are reflecting reality at an objective reference case or abase year respectively. This allows enhancing market transparency and displaying a fullmarket picture of a previous year, to benchmark demand effects which can beinvestigated as ex post forecast as it is crucial to understand the competitive situation
stakeholders are in.
Figure 5. Validation, calibration and simulation

Source: MKmetric GmbH, 2014

The second possibility of ex post forecasting can be used to answer the question: ‘What
would have been the effects, if a certain determinant of transport would have beenchanged?’ So without any bios, due to uncertainties every assumption of future
developments have, one can investigate transport policy actions or strategic respectively
tactical changes of the market participants.
In addition market potentials of new routes can be investigated under ceteris paribus
conditions of the existing market. A successive scan of new air services (SONAR)

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201421

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

identifies, evaluates, and ranks all technically possible routes from a defined airport. It
takes into account the offers of all competing airports and their catchment areas. The
procedure SONAR implements the best identified routes in the simulation system andintegrates them into an actual flight schedule. Finally, the passenger potential iscalculated. The system allows optimising the existing flight services, if passenger
potential is not yet being fully exploited. Interdependencies between new and existing
services in the air transport system as a whole are fully considered. As a result, one getsthe most profitable routes for a certain airport. In case a network shall be optimised or ifroutes shall be identified for a specific fleet of aircrafts the same approach can be used,just the internal routines need to be arranged differently as the principle remains valid.
The ex ante forecasts allow a wide range of scenario simulations. Any change of thetransport determinants described within the framework conditions can be applied and willlead to effects. Whether one or several of determinants change does not matter for thesystem approach as all influencing factors will interact consistently according to thelinkage of all econometric models engaged. Therefore a scenario can consider the effectsof e.g. implementation of kerosene tax, the establishment of a new airport, theinstallation of new high speed rail services and a change of the home base carrier’s
network strategy at the same time. Obviously network dynamics as well as more
complex socio-economic interrelations are covered across all modes, such as the effects
of regional differences in purchase power or geo-metrics and the interrelationship ofmobility and oil price.
For the different model stages below some examples of scenario elements are listed:Generation – distribution of traffic:
 Socio-economic framework data (e.g. population, trade, GDP, oil)
 Political decisions (e.g. taxes, security, noise charges)
 Regional attractiveness (e.g. type of region, temperature, sea, mountains) Political decisions (e.g. traffic rights, subsidies)Mode choice: Network/Service characteristics
operation days)(e.g.

 Political decisions
investments)

service(e.g.

public

time,

cost,

obligation

frequency,(PSO),

transfer,

infrastructure

Airport / Route choice: Service-Structure (e.g. new route / closure, minimum connecting times(MCT), airline base, transfer node) Infrastructure (e.g. access / egress by surface modes, inter-modal routes)

Assignment:
 Infrastructure networks (e.g. new airports, new high speed rail (HSR)
services) and/or changes of network attributes (e.g. capacity, speed,
charges, tolls, fees)

22

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Finally it has to be noted that the approach outlined is not just focused on one airport, inprinciple scenarios can cover multiple airports and in consequence considers theinterdependencies evoking.
2.3. Complexity and coverage
To reflect a realistic picture of the transport activities it is necessary to mirror thesituation a consumer is facing to a maximum extent. Therefore the starting point
concerns the location of the travellers, respectively the place they live and the place theywant to go to as usually people neither life nor make vacation at an airport, they just use
transport systems to overcome the distance between two locations e.g. their home oroffice as origin and the destination beach or a business place in focus. In respect of thisprinciple the transport flows have to be modelled from door to door respectively region toregion.
The system approach installed uses the NUTS 3 level 16 (nomenclature of territorial units
for statistics) for the European member states as representation of the travellers
location, while the rest of the world is reflected by larger zones based on administrative
boundaries on NUTS 2, 1 or 0 level (in total about 2300 regions). The implemented
system approach concentrates therefore upon all traffic within, to, from and acrossEurope.
For the example of the air mode figure 1 in chapter 1.2 shows the difference of theconventional static approach and the more complex dynamic network approach wefollow. In consequence of the regionalisation all the mode specific networks have to becompatible with this level of detail. So the road network consists of links down to thethird order of streets (ca. 2.8 Mio. links) and the rail network considers rail services
according to the published schedules reflecting different service types. The air modeconsiders within Europe all airports with published services based on HAFAS 17 plus morethan 200 representative airports for the rest of the world. Of course the air services arereflected as published in the schedules but need to be enriched by charter services or
LCC services which are not covered by the commercial schedule providers. The
underlying demand data framework must be representative and highly valuable for allcommercial traffic.
To cope with intra-modality especially when considering competing hubs respectively
alliances the route and airport choice models must consider at least 16 intercontinental
and 10 continental alternatives. Only in combination of detailed regionalisation anddetailed network representation a model is flexible enough to deal with the network
dynamics of the transport market. Thus results of the system approach are dynamic, e.g.
the catchment of an airport depends on the air services offered at all competing airports
and can be different for each route and passenger segment (resp. trip purpose).

16.

http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction

17.

http://www.hacon.de/hafas-en

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201423

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

3. APPLICATIONIn this section some analyses are highlighted which point out the capabilities of theexisting tool of MKmetric using the modal approach as described in the previous sections,with special focus on airport and route choice. They all base on the status quo situationof air transport as existing in the year 2012.
3.1. Airport choice
The nucleus of the model is the airport choice of the consumer reflecting consumers’
behaviour. For the trip from one distinct region to another the passenger has to decide
which travel alternatives he chooses. This decision bases on individual weighting of theattributes applying for each travel alternative. In the model this weighting results in thecalculation of choice probabilities assigned to the distinct route alternatives.
The following figure displays the sixteen travel options available within the simulation
model between the city of Bremen, Germany, and the centre region of Thailand, around
Bangkok and the choice probabilities on them for non-business-travellers.
Seven of the route alternatives start from the nearest airport Bremen (BRE) to the regionof origin. As there are no direct flights between BRE and BKK, they all consist of flight
options including a plane transfer at one hub, from which flights to BRE as well as to BKK
are provided, namely Amsterdam (AMS), Paris (CDG), Frankfurt (FRA), Istanbul (IST),
Munich (MUC) and Zurich (ZRH), of which the one via FRA is the mostly chosen (17.2%).
In addition there are also travel options available from other airports: Düsseldorf (DUS),
Hamburg (HAM) and Hannover (HAJ), from which a flight to Bangkok is possible nonstop(from DUS and FRA) or via Hubs to which no flights from BRE are offered, e.g. like thosefrom the Gulf carriers Etihad at Abu Dhabi (AUH) or Emirates at Dubai (DXB). From allthese options the share of the one to go by surface transport to FRA and take a nonstop
flight to BKK from there has the second largest share in this market (14%), a result of
the excellent accessibility of Frankfurt airport by high speed rail.

24

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Figure 6. Airport choice for a trip from Bremen to Bangkok

Source: MKmetric GmbH, x-via web, 2014

3.2. Route competitors
Similar to the airport choice analysis as mentioned before, this example displays thedifferent routes which compete for a distinct OD-market. As input for the analysis wehave chosen the airports of Sao Paulo (GRU), Brazil and Tokyo Narita (NRT), Japan and alimitation of the catchment area of both airports by 120 minutes of access / egress time.
The figure displays the most relevant route chosen between those regions, from which
the airports mentioned can be reached within 2 hours by surface transport. The routes
are coloured in line with the number of passengers travelling on each of them. The routes
considered not necessarily do all link the airports of GRU and NRT.
As GRU and NRT are more or less antipodes to each other (greater circle distance is at
about 18500 km), there are eastbound as well as westbound travel options.
The westbound travel-options displayed, which in this case take the core of demand are
all with a plane change at an US-airport. The route via Dallas Fort Worth (DFW) is themost important of them, attracting more than 40,000 passengers, while the others, e.g.
via New York’s John F. Kennedy Airport (JFK), Chicago (ORD), Newark (EWR) or
Washington (IAD), have smaller shares on the total demand (≈100,000 passengers as
sum of both directions). Minor passenger flows (not displayed) on this OD-pair occur via
San Francisco, Houston and Toronto, Canada.
In the eastbound direction, the routes from GRU via the European airports Paris (CDG),
Frankfurt (FRA), Munich (MUC) and Rome (FCO) to Tokyo are the most relevant travel

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201425

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

options. While most of them are heading to Tokyo’s Narita airport (NRT), there is also asecond option when travelling via CDG: instead of flying to Narita, via Paris it has also
been possible to travel to/from Tokyo’s Haneda Airport (HND), from which in 2012 directflights to Paris existed.
Figure 7. Route competitors for the route GRU - NRT

Source: MKmetric GmbH, x-via web, 2014

3.3. Flight segments
After calculating all routes choices for every pair of origin/destination regions andenriching them with the underlying passenger flows on regional base it is possible
assigning them either to distinct flight segments or in case of transfer connections tosequences of them. One possible analysis of such an assigned air network is to depict a
specific flight segment and point out how many travellers use it, either as nonstop flightor also as part of a flight connection. Just imagine you are sitting in a plane and interview
all passengers on board from where they come and to where they are travelling. The
result of such an “interview” on a flight between Hamburg (HAM) and Dubai (DXB) isdisplayed in the following picture. While the number of passengers, which entered at
HAM from another flight, is minimal, a vast majority of passengers originally starting to
fly from HAM, do not have their final destination at DXB, but transfer there for otherdestinations, either in the Middle East, in South-East Asia, Oceania or in Eastern andSouthern part of Africa. Also those passengers, travelling between the city of Bremen andthe Thailand, centre, we had identified in the airport choice analysis, are part of thepassenger volumes displayed below.
It should be mentioned that this type of analysis is not only feasible for existing routes,
furthermore it can be used to identify the acquirable demand for new routes to be

26

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

implemented into an existing air network and point out the routes’ passenger potential
concerning local traffic as well as the number of transfer passengers who would use a
new route.
Figure 8. Passenger diversity on the flight segment HAM – DXB

Source: MKmetric GmbH, x-via web, 2014

3.4. Demand sensitivity to slots
Routes to or from a hub or also connecting to hubs provide multiple transfer option on
one or both of the airports connected. The amount of transfer options available and their
attractiveness for the consumer depends on the transfer time between the flights and thefrequency such transfer options are provided. Long layovers or transfer options only
rarely provided make competing routes more attractive. In addition, on an airlines pointof view not only the absolute number of transfer passengers is in focus, but also their
total travel distance is of importance, as the revenues received from an intercontinental
passenger are higher than those from a short-haul passenger.
To cope with this needs an analysis has been developed, the ‘weighted connectivity’. The
weighted connectivity for a specific route takes into account all transfer options available
for each distinct slot at one of the airports connected by the route, weighted by the
duration between an arrival at this slot and the next available departure time for eachdifferent transfer destination. In addition each of these transfer options are weighted by
the passenger potential from the considered routes’ origin to each transfer destination. In
the following pictures the analysis for the existing route between Seoul (ICN) and Prague(PRG), either for PRG and in the second picture for ICN is shown.

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201427

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Figure 9. Passenger demand sensitivity to the air service Seoul – Prague

Arrival/departure from ICN at PRG versus demand

Arrival/departure from PRG at ICN versus demand
Source: MKmetric GmbH, x-via web, 201428

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

This connectivity analysis neglects transfer options requiring a relevant detour relative toan existing nonstop flight option. For example when doing a weighted connectivity
analysis for the route PRG-ICN, transfer options for Abu Dhabi (AUH) are neglected, as
AUH is available nonstop from PRG as well as from ICN and flying from ICN via PRG tothe Middle East (or even worse from PRG via ICN to AUH) would mean an enormous
detour compared to the existing nonstop flights.
Also the minimum connecting times for the airports connected were taken into account.
The values calculated are on an index base with a value of 100 for that arrival /departure slot offering a maximum of connectivity. These graphs shows vertically theweighted connectivity results of the pre-analysis for an air service for a distinct route
whereby the red line depicts the arrival index at the mentioned airport while the blue linereflects the departure index along the horizontal timeline.
So when marking the current arrival / departure times for the route considered in PRG
and as well in ICN (black spots), one can see that, the slots chosen do not provide themaximum connectivity possible at both airports (index values of 20/30 at ICN and 60/35
at PRG). But when operating the flights about four to five hours earlier, i.e. stop at PRG
around lunch time instead of late afternoon and arrive/depart at ICN in the morninginstead of midday, would lead to a significant higher connectivity for these flights,
resulting in index values of 60/90 at PRG and 75/60 at ICN.
This analysis can also be used, as a pre-step procedure to identify an optimal schedule
for a route to be examined in the simulation model.
3.5. Dynamic catchment
It was outlined in the airport choice section, that consumers may chose a starting airport
which is quite distant form their region, if its accessibility is good and the flight options
offered there (e.g. nonstop flights) are not available from the airport which is closest to
their region with a significant probability. For the question of the size of an airports’catchment this means, that such a catchment can be defined neither by just drawing acircle in a distinct distance around this airport, nor that all regions within a specificisochrone “belong” to an airport exclusively, while other regions strictly can be assigned
to other airports. To point this out, the catchment of an airport for two different
destination markets is displayed and all regions from which an airport can attract
passengers are coloured.

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

29

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Figure 10. Catchment of Frankfurt for destinations in the Far East

Source: MKmetric GmbH, x-via web, 2014

The different colours from yellow to red reflect the market share the airport considered
has in the specific region. Market share in this context means: if an airport can attract 24
out of 100 passengers from a region, while the 76 others chose different airports to start
their trip, the market share of the airport considered in this region is 24%. Regions fromwhich the airport can attract less than 5% of all passengers are displayed in grey.
In the figure above the catchment by market share for the airport of Frankfurt
concerning destinations in the Far East is depicted. On base of regions where Frankfurt
can achieve a market share of at least 5%, the area stretches from the very North ofGermany to Switzerland and the most Western part of Austria in the South, covering a
distance of about 1,000 kilometres. In the East-West direction parts of the Netherlands,
Belgium, France and Luxembourg belong to this catchment, which reaches in the east
Saxony and even that part of the Czech Republic bordering to Germany – in total a
distance of more than 600 kilometres is covered in this direction. The provision of
nonstop flights to numerous destinations in the market considered, of which the most are
not available from other airports in the close or larger vicinity of Frankfurt, with some ofthem even exclusively provided at Frankfurt for the European continent and the excellent
connection of FRA with high speed rail services, are the reason for this huge catchment
concerning the Far East market.

30

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Figure 11. Catchment of Frankfurt for destinations on the British Isles

Source: MKmetric GmbH, x-via web, 2014

Different to that, the catchment of Frankfurt concerning destinations on the British Isles:
By far smaller, just covering the centre regions of Germany plus some neighbouring
départements in France, with a total extension between 300 and less than 500
kilometres. The reason for this in comparison quite small catchment is that thedestination portfolio of most airports surrounding Frankfurt at least covers one of the
London airports and in many cases also quite a few other destinations in the UK or in
Ireland.
Such analyses allow an airport to identify quite well, where its core markets are or where
it might be worthwhile to put more marketing efforts, but it gives no answers in which
regions a distinct other airport is a serious competitor. To answer such type of questions
it is useful to display simultaneously the catchment areas of two different (competing)
airports in one graph. As example this analysis is displayed for the two airports ofGeneva (GVA), Switzerland and Lyon (LYS), France, both with quite a few internationaland even a limited number of intercontinental flights in their destination portfolios.
Regions where LYS has a strong market position are coloured in red, those where GVA
dominates come in green and all regions where other airports can acquire the core ofdemand, but from which at least minimal passenger numbers use one of the two
considered airports are in blue. Regions where market domination is with none of theconsidered airports or their competitors are with mixed colours. For example the RhôneAlpes area directly between GVA and LYS which is in yellow and heavily competed
between the two airports considered.

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201431

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

The figure shows the market domination of GVA, which not only covers the Northern partof the Lac Léman area, but also the French regions in South of Geneva, which are famous
Ski resorts, while the focus of LYS, beside the city of Lyon, more lies in the South of LYS
down to Avignon, from where the influence of the Marseille airport (MRS) dominates themarket.
Figure 12. Competition between Lyon and Geneva for originating passengers

Source: MKmetric GmbH, x-via web, 2014

3.6. Long-term forecast
Finally it should be mentioned that modelling air transport as described in the previous
chapters is not limited for status quo analyses or short term estimations on passenger
potentials for new routes, but also can be used for long term forecasts, as executed forexample in the early 90’s for the airport system of Berlin with a long term forecast for
the year 2010.
Within this long-term forecast- the political framework,- the planned development of surface transport infrastructure- the network development according to demand for the Berlin airports as well as fortheir competitors
had been considered.

32

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

Covering all those aspects led – from today’s’ point of view – to a quite realistic estimation of the passenger demand until 2010, although it should be mentioned that at thetime of forecasting (1993) the identified demand for Berlin of 23 million passengers in
2010, was criticised, as several other forecasts with standard models promoted muchhigher passenger volumes for Berlin, reaching in maximum up to 60 million passengers.
These optimistic forecasts resulted out of enthusiastic assumptions blinded by thedevelopment of the first booming years after German reunification concerning the trends
in passenger figures at the Berlin airports and economic development of the city, whileneglecting any long term effects on modal split by new built or upgraded railway lines,
the strategies of leading European network carriers, the development in air transport inneighbouring Poland and many other components of serious forecasting which were notconsidered adequately.
Figure 13. Long term forecast Berlin (1993 to 2010)

BER - Forecast and Development
25

23
BER-MKM (1993)

21

BER-actual amount

Mill. Passenger

19

17

15

13

11

9
1993
1995
Source: MKmetric GmbH, 2014

1997

1999

2001

2003

2005

2007

2009

It has to be noted that the executed forecast just concerned a point forecast for the year
2010 and the values depicted in the figure are based on an interpolation from 1993 to2010 for visualisation purposes.

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201433

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTINGSUMMARY

Since the 90’s the market conditions for air transport have undergone multiple changes
in different sectors. Global drivers have been the liberalisation and deregulation policy aswell as the globalisation of economy with new emerging markets. Stable markets havebecome unstable as economic drivers gain power and unpredictable incidents have direct
effect on air transport while in parallel new regulations are imposed locally. In parallel
the competitive situation within the aviation world is facing an ongoing consolidation withbankruptcy and mergers but as well raising powerful airlines and airports. New aircrafts
change network strategies and new air service products emerged which are marketed
differently using new electronic distribution channels. It is obvious that all thesedevelopments severely influenced the consumer behaviour.
To stay competitive and to develop air transport further all aviation stakeholders need tounderstand the consumer behaviour and the related elasticity to service characteristics indetail, but the available traditional data source declines rapidly so that the classic
aviation forecasting tools become risky and are of rapidly declining use as IATA stated.
In this paper an alternative way for air transport demand forecasting is outlinedreflecting the consumers’ decision process. This system approach considers thedeterminants of transport and airports as well as air services are considered as a part ofa competitive transport system linked with surface mode networks. Modelling theconsumers’ perspective while starting the trip where consumers live and ending a trip
where they work or make vacation, vice versa. In consequence mobility is a matter ofregional attractiveness and the distribution is influenced by spatial divergence as regions
compete for investments and tourists. The accessibility of the regions is determined by achoice set of travel options which concerns a portfolio of alternatives where for airtransport originating and destination airport as well as transfer possibilities vary.
The decision taken by the consumers for the transport alternatives from the region oforigin to the destination region is modelled in a consistent way of interdependent choicemodels which reflect the mode choice, airport and route choice, access/egress and slot
choice. These models mirror the consumer behaviour using logistic functions whereby themathematical form is flexible such that threshold and captivities can be captured. The
elasticities of consumers to characteristics of the alternatives (e.g. time, cost, frequency,
alliance) allow to measure demand effects on changes of the transport system, e.g.
services, infrastructure.
This complex modelling approach allows considering local and global competition, andinstead of fixed catchments the approach deals with dynamic ones determined by theattractiveness of the alternative transport opportunities available for each trip. Thus
overlapping catchments and route competitors for short and long haul services
individually can be analysed as the short- or long-term demand forecast for an airport ora route always considers the competitors endogenously as explained by some examples.

34

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 2014

CONTEMPORARY AIRPORT DEMAND FORECASTING: CHOICE MODELS AND AIR TRANSPORT FORECASTING

The price to run such a system approach is complexity in modelling, extensive computing
power and intensive data collection although the latter are mostly public data but often
just available with a delay due to slow publication processes. Other barriers tocircumvent are the aviation industry sensitivity to market information preventing accessto purchase data, consumer surveys or even aircraft movement data, and the still
existing large differences in publication rules for air transport statistics across Europe andthroughout the world making the data harmonisation process intensive and the validation
process irreplaceable.

ACKNOWLEDGEMENT

I am grateful to Oliver Schnell for his contributions to the application chapter and hiscomments on drafts of the paper. The approach outlined builds on a variety of research
projects partially supported by the European Commission and the German Federal
Ministry of Transport and Digital Infrastructure in combination with airports and airlines.
The responsibility for any remaining shortcomings remains the author’s.

Benedikt Mandel — Discussion Paper 2014-07 — © OECD/ITF 201435

International Transport Forum
2 rue André Pascal
75775 Paris Cedex 16
itf.contact@oecd.org
www.internationaltransportforum.org