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14-025805-02 | Version 1 | Internal / Client Use Only | This work was carried out in accordance with the requirements of the international quality standard for Market Research, ISO

20252:2012, and with the Ipsos MORI Terms and Conditions which can be found at http://www.ipsos-mori.com/terms. © Ipsos MORI 2015.

14-025805-02 | Version 1 | Internal / Client Use Only | This work was carried out in accordance with the requirements of the international quality standard for Market

Research, ISO 20252:2012, and with the Ipsos MORI Terms and Conditions which can be found at http://www.ipsos-mori.com/terms. © Ipsos MORI 2015.

14-025805-02 | Version 1 | Internal / Client Use Only | This work was carried out in accordance with the requirements of the international quality standard for Market Research,

ISO 20252:2012, and with the Ipsos MORI Terms and Conditions which can be found at http://www.ipsos-mori.com/terms. © Ipsos MORI 2015.



This document details the full Ipsos Connect technical methodology used for the development of the

environment model covering airports. Airports share some of the characteristics of indoor shopping centres -

GPS is not sufficiently accurate to measure movements through either of these enclosed areas. However,

the behaviour of visitors inside each of these environments will be somewhat different. Airport users may be

travelling from the airport or they may be there to wave farewell or to meet and greet friends or relatives. In

both cases the time available will be dictated by departure and arrival times and, for those who are flying,

the location of the departure gate. This means that a separate study was needed to investigate the

behaviour of visitors to airports. This was carried out using recall interviews at selected sites and this work is

described in detail in section 3.

Airport exterior frames (i.e. advertising on approach roads and in the vicinity of the terminal buildings) are

considered in the final chapter of this report. These frames are somewhat different; some will have much in

common with roadside sites while others may be next to the airport entrance so similar to frames inside the

terminal.

The principles followed in developing the audience measurement model are similar to those for other

location based out-of-home media and involve:

Population counts, modelled to provide individual frame audiences.

Demographics and information on general behaviour with regard to visiting airports provided by the

Route travel survey.

Travel survey visits to airports by both travellers and visitors.

Imputation of additional respondent trip records on the travel survey to ensure that airports are

representative of weekly travellers.

The creation of virtual travellers to allow longer time periods to be analysed, more demographic

groups to be represented and more representative sample frames within and across airports to be

profiled.

Visibility adjustments that take into account the quality of individual frame contacts.

A probability model to produce reach and frequency distributions.

The following topic areas are detailed in this document:

General principles of the methodology.

The top 10 airports (by volume of passenger traffic) measured1

.

Visitor counts and sources.

Defining airports – floors, zones, specific locations.

Respondent visits on the Route travel survey with definitions of visits, repeat visits, near-by visits,

working populations.

Recall survey looking at behaviour in airports (carried out in Manchester and Gatwick Airports).

A sub-model to describe how visitors move through airports.

The probability model describing airport use, including virtual respondents.

The final ‘File 1’ frame audience estimates and ‘File 4’ respondent matches to frames with probability

output.

1 In the event there were no maps for Birmingham airport so this could not be fully modelled.



Airport exteriors.

Worked examples have been used to describe many of the processes involved.

The Route travel survey covering 34,165 respondents in years 1-5 and weighted to be representative of the

GB adult (15+) population is referenced throughout this document.

There is an extensive list of material published whilst developing the indoor shopping centre models. A full list

of previously circulated reports that relate directly to this environment is presented in Table 1.1.

Submission

Number Title Date Description Author

Observation surveys

1.1

Airports - audience and

respondent modelling -

R7 plan - client-internal

use - 130119 V1

19 Jan

2013 Describes the behavioural study Mark Flood

1.2

Route – airport research

wave 2 questionnaire -

client-internal use -

130510 - V10 – SC

10 May

2013 Behavioural study questionnaire Sapna Culkin

1.3

Route - airports -

Manchester behavioural

study wave 1&2 results –


131119-V1-PC

15 Jul

2013

Outputs from observational work at

Manchester Airport Paola Codini

1.4

Route - airports - Gatwick

behavioural study results –


131119-V1-PC

19 Nov

2013

Outputs from observational work at

Gatwick Airport Paola Codini

Population counts

2.1

Route - Airport - Population

Counts CAA Data -

internal use - 140924 V1-

PC

27 Jul

2010 Population counts for airport users Paola Codini

Travel survey

3.1

Route - Airport - Travel

Survey Users Year 1-4 -

client-internal use - 1...

3 Mar

2013

Overview of Year 1 - 4 airport users on

travel survey

Amanda

Ayling

3.2

Route - airport CAPI

frequency profile - Client

Internal - 130429nf

29 Apr

2013 Frequency of airport visitors on CAPI Neil Farrer



Submission

Number Title Date Description Author

Behaviour model/probability model

4.1

Route - Airports – Progress

update for MC - Client-

Internal 131122NF V1

22 Nov

2013

Sub-model update and airport traveller

model review Neil Farrer

4.2

Route - Airports – Model

Overview – Client-internal

use - 140603 V2-NF

3 Jun

2013

Population statistics, including Year 5

respondents/Imputations, introduction

to probability model

Neil Farrer

4.3

Route - Airports – Progress

Update 2 – Client-internal

use - 140708 final NF

8 Jul

2014

Update including distribution of

contacts, probability model for airport

visitors, map digitisation

Neil Farrer

4.4

Route Airports - R12

Probability Model

Developments – Client-

internal use - 140908 final

-NF

8 Sep

2014

Updated model for R12 - outlines the

shape of the curve in the reach &

frequency model

Neil Farrer

Modelling airport exteriors

5.1 Modelling airport exteriors -

27/01/15

27 Jan

2015 Methodology for airport exteriors Neil Farrer

The general principles behind the development of the model are as follows:

Preparation

The ten measured airports have been supplied to Ipsos in map format for digitising and placing in

the overall Geographic Infrastructure covering all environments.

Multiple maps representing different floors or larger terminal plans have been provided.

Entrances and exits and other important locations (passport control and departure gates) have

been identified.

Frames are inserted, adjusted and verified so that they can be used in the model.

Airport terminal map infrastructure

All airport terminals have been defined as a series of zones that define the layout. These zones are

overall parts of airports that passengers and visitors may travel through depending on the type and

stage of journey they are taking.

These zones have specific start and end location types that are dependent on the stage of journey

that is being undertaken. So a passenger in the departure hall for example will go from passport

control to departure gate.

Within these zones sets of ‘attractor’ points are located and defined. These are areas that a

passenger or visitor may pass through as part of their visit – check-in, departure boards, security

area etc.

Population count data from the Civil Aviation Authority (CAA) has been calculated. This includes

weekly passenger counts with factors applied to exclude non-eligible passengers for the Route

Travel Survey.

Behaviour count locations have been placed within the zones that define a specific attraction

within the zone.



Geographical links have been created between pairs of count locations to allow movement to be

modelled within and between zones. These are the equivalent of the Navteq road links used in the

roadside environment and a similar procedure was used for the indoor shopping centre analysis.

These links are integrated with other environments to create a complete multi-modal infrastructure.

Data collection

Visits to airports have been identified and coded manually from the GPS respondent records

matched against the entrance points and the full location plan.

Repeat visits within the 9-day survey period are identified and coded separately.

Where necessary return visits are allocated that occur outside the standard survey period. When this

happens the return trip is identified to ensure usage profiles in 9 days are preserved.

The output from this matching is a set of unique respondent days/visits to airports. The start and end

time of a visit are calculated.

External population count data is used to calculate the weekly visitor flow and the audiences in

contact with frames. This has been supplemented by data from a separate study that locates the

movement of visitors in airports.

Additional bespoke fieldwork (a recall study) was carried out at Manchester and Gatwick Airports.

This involved interviewing passengers and visitors, collecting information on behaviour as well as

usage. The study was designed to create the inputs covering how people move around within this

environment.

A separate behaviour model has been developed to simulate how passengers/ visitors move

around the airport.

Airport passenger imputation

The GPS travel survey does not fully represent airport usage despite a number of alternative

recruitment strategies.

Airport travellers have been imputed to bring the survey profile up to that expected by the external

population source.

Claimed past year users of the airport who have not made an airport trip are eligible for imputation.

Targets for imputation are derived from external CAA population data. This also allows targeted

selection of different demographic groups to select in line with population and demographic

targets.

The end outputs are a data-set of actual plus imputed airport passengers that are close to the

target population.

Developing a probability model

The full weighted respondent dataset contains additional questionnaire information collected as

part of the interview process relating to past year visits to specific airports and frequency.

Separate information is collected for both passengers and visitors who are not making a trip. This

is used to help identify respondents visiting airports and to add a likelihood measure.

Distance from home to the airport is also calculated.

Separate models are developed for passengers and visitors who enter the airport terminal

building.

Each airport has a unique probability model created.



East Midlands Airport is alone in not collecting information regarding usage. Here we have used

‘other’ airport usage to help establish a data-set of past year users to model.

Travel zone clusters are used to segment location contacts in the probability model. The standard

list of 1341 zone clusters created on year 1-5 and in use on most environments has been used to

build up the geographical clustering that is used within the probability model. Here, travel zones

have been collapsed to create the optimum definition for each airport.

The negative binomial distribution (NBD) has been fitted around respondent visits to airports

using the collapsed travel zone clusters to create a geographical infrastructure.

The use of virtual traveller records is important in spreading contact records over longer time

periods. Non-visitors to airports within the travel zone cluster are important for deriving these

virtual visits.

Virtual visits need to match actual visits on day and time of visit.

Frames matched on virtual visits should be based on distributions of contacts for actual visits.



The initial agreement with Route was to define a universe of the top 10 airports (by passenger volume). This

information is available from the Civil Aviation Authority (CAA). The starting point was a list of 35 airports. In the

event the top eight were selected together with East Midlands and Aberdeen Airports (eleventh and

thirteenth by passenger volume). The full list is shown in Table 2.1.

The CAA data also provides passenger demographics. Tables 2.2 and 2.3 (below) show the distribution of

passengers by sex, age, social grade and journey purpose (business versus leisure) for each of the selected

airports. Note that the tables are percentaged horizontally so that, for example, Aberdeen has 61.3% male

passengers and 38.8% female passengers. In addition 54.1% said they were travelling on business.

In fact, the passenger profile at Aberdeen is more male and business oriented than at any of the other

airports. In contrast, East Midlands has a passenger ratio of 46.5% men to 53.5% women and as many as

92% say that they are travelling for leisure purposes. Clearly the demographic profile of audiences will be

expected to vary by the type of passenger using each airport. In this context it would be expected that

Aberdeen should cater for a relatively large proportion of business passengers while East Midlands would

have a larger proportion of holidaymakers.



Airport Name Route

10 2012 UK Passengers

2012 7 day

passengers

LONDON HEATHROW Yes 29,893,087 573,292

LONDON GATWICK Yes 22,934,035 439,830

MANCHESTER Yes 15,820,744 303,411

STANSTED Yes 9,859,311 189,082

BIRMINGHAM Yes 6,910,026 132,521

LUTON Yes 6,518,574 125,013

EDINBURGH Yes 6,397,739 122,696

GLASGOW Yes 5,762,404 110,511

BRISTOL 4,816,721 92,375

NEWCASTLE 3,794,709 72,775

EAST MIDLANDS Yes 3,489,538 66,922

LIVERPOOL 3,361,637 64,469

ABERDEEN Yes 2,740,547 52,558

LEEDS BRADFORD 2,493,292 47,816

SOUTHAMPTON 1,533,660 29,412

LONDON CITY 1,485,728 28,493

JERSEY 1,299,631 24,924

CARDIFF 878,100 16,840

GUERNSEY 777,369 14,908

PRESTWICK (Glasgow) 673,011 12,907

EXETER 626,492 12,014

DONCASTER SHEFFIELD 550,403 10,555

INVERNESS 544,821 10,448

LONDON SOUTHEND 537,641 10,310

NORWICH 345,643 6,628

BOURNEMOUTH 268,565 5,150

BLACKPOOL 218,570 4,191

HUMBERSIDE 217,081 4,163

SUMBURGH 134,822 2,585

DURHAM TEES VALLEY 130,407 2,500

KIRKWALL 119,764 2,296

NEWQUAY 64,740 1,241

DUNDEE 43,231 829

WICK 22,569 432

GLOUCESTERSHIRE 12,411 238



Airport Name Male Female 16-34 35-54 55+

ABERDEEN 61.3% 38.8% 31.3% 46.9% 21.9%

BIRMINGHAM 48.1% 51.9% 24.6% 33.5% 41.9%

EDINBURGH 46.8% 53.2% 30.3% 39.9% 29.9%

GLASGOW 51.1% 48.9% 30.0% 47.0% 22.9%

LONDON GATWICK 55.4% 44.6% 34.5% 37.8% 27.7%

LONDON HEATHROW 56.2% 43.8% 37.6% 40.2% 22.2%

LUTON 49.2% 50.8% 36.6% 35.0% 28.4%

MANCHESTER 53.4% 46.6% 27.1% 42.9% 29.9%

EAST MIDLANDS 46.5% 53.5% 23.4% 35.3% 41.3%

STANSTED 53.2% 46.8% 45.0% 33.5% 21.5%

Total 53.5% 46.5% 32.3% 39.5% 28.3%

Airport Name AB C1 C2 DE Business Leisure

ABERDEEN 36.5% 35.8% 23.3% 4.4% 54.1% 45.9%

BIRMINGHAM 33.3% 37.1% 18.3% 11.3% 16.9% 83.1%

EDINBURGH 47.6% 33.1% 13.6% 5.6% 35.6% 64.4%

GLASGOW 29.7% 41.1% 21.0% 8.2% 30.2% 69.8%

LONDON GATWICK 39.0% 42.3% 11.0% 7.6% 14.1% 85.9%

LONDON HEATHROW 49.3% 37.1% 8.7% 4.8% 31.2% 68.8%

LUTON 37.7% 34.4% 17.2% 10.7% 16.3% 83.7%

MANCHESTER 25.0% 37.1% 23.7% 14.2% 15.7% 84.3%

EAST MIDLANDS 27.4% 30.4% 19.9% 22.4% 8.4% 91.6%

STANSTED 29.4% 46.6% 15.2% 8.8% 16.1% 83.9%

Total 37.0% 38.3% 15.6% 9.2% 21.8% 78.2%

Table 2.2 shows that passenger profiles also vary by social grade with the percentage of ABs at Heathrow

(49.3%) and Edinburgh (47.6%) being higher than at any of the other airports. This is likely to reflect the social

grade profile of the catchment area for each airport – some 61.6% of passengers traveling through

Heathrow are from London and the South East as are 73.3% of those travelling through Gatwick (table 2.3).

These data appear to be quite logical and consistent with the type of flights departing from each airport

suggesting that this is a solid foundation on which to build a passenger model.



Airport Name East

Anglia East Mids North West North East Scotland South East

ABERDEEN 0.0% 0.0% 0.0% 0.3% 99.7% 0.0%

BIRMINGHAM 1.2% 20.0% 1.2% 0.2% 0.1% 5.3%

EDINBURGH 0.0% 0.0% 0.1% 0.6% 99.2% 0.0%

GLASGOW 0.0% 0.0% 0.4% 0.2% 99.2% 0.0%

LONDON GATWICK 12.1% 2.6% 0.8% 0.2% 0.1% 40.7%

LONDON HEATHROW 12.5% 4.7% 1.1% 0.5% 0.4% 24.0%

LUTON 35.7% 9.4% 0.5% 0.3% 0.3% 16.2%

MANCHESTER 0.1% 4.8% 55.9% 2.5% 1.8% 0.3%

EAST MIDLANDS 1.4% 62.2% 1.6% 0.3% 0.1% 1.0%

STANSTED 44.4% 4.7% 0.7% 0.3% 0.2% 8.3%

Total 10.1% 5.6% 7.3% 3.8% 13.7% 14.1%

Airport Name London South West Wales West Mids Yrks/Humb

ABERDEEN 0.0% 0.0% 0.0% 0.0% 0.0%

BIRMINGHAM 0.0% 6.1% 2.8% 61.4% 1.8%

EDINBURGH 0.0% 0.0% 0.0% 0.1% 0.0%

GLASGOW 0.0% 0.0% 0.0% 0.0% 0.1%

LONDON GATWICK 32.6% 6.6% 1.3% 2.1% 0.8%

LONDON HEATHROW 37.6% 10.9% 2.7% 3.6% 1.9%

LUTON 28.0% 2.9% 0.6% 4.9% 1.2%

MANCHESTER 0.0% 0.3% 4.7% 6.6% 23.1%

EAST MIDLANDS 0.0% 0.6% 0.4% 16.3% 16.0%

STANSTED 34.8% 2.7% 0.7% 1.9% 1.3%

Total 18.6% 9.4% 2.7% 6.0% 6.3%

CAA does not currently provide passenger counts by day or day part. This information is necessary in order

to develop an audience model. The solution was to use data from the travel survey, which collected

information on actual journeys through airports, including day of week and time of day.

Table 2.4 shows the distribution of arrivals and departures for each airport by day of week. The table is

percentaged horizontally and shows, as would be expected, that the number of passengers leaving and

arriving at each airport is roughly the same each week. Across the ten airports, 18.4% of all departures are

on a Monday. For the three Scottish airports as many as a quarter of all departures are on a Monday.

Sunday is the quietest day with only 8.4% of all departures on that day. Arrivals are fairly well spread across

the week, although Saturday, with 9.9% of all arrivals, is the quietest day.



Departures Day number

Airport 1 (Sat) 2 3 4 5 6 7 (Fri) Total

ABERDEEN 11.6% 12.0% 26.3% 5.6% 12.0% 27.1% 5.6% 131

BIRMINGHAM 8.4% 10.6% 18.5% 17.6% 14.0% 13.9% 17.1% 1243

EDINBURGH 10.9% 10.3% 24.4% 10.7% 16.0% 23.5% 4.2% 590

GLASGOW 9.6% 12.7% 25.6% 7.2% 16.8% 22.4% 5.8% 779

GATWICK 9.0% 5.2% 19.9% 15.6% 19.0% 13.5% 17.8% 4075

HEATHROW 10.8% 11.9% 15.7% 15.0% 17.4% 15.1% 14.1% 3832

LUTON 10.1% 10.8% 18.3% 15.8% 14.5% 13.5% 16.9% 1036

MANCHESTER 11.6% 5.9% 16.4% 14.9% 17.3% 15.3% 18.5% 2631

EAST MIDLANDS 9.9% 10.9% 19.6% 17.0% 13.3% 12.9% 16.3% 925

STANSTED 11.4% 5.5% 17.6% 14.7% 18.7% 16.2% 15.9% 1643

Total 10.2% 8.4% 18.4% 14.9% 17.2% 15.3% 15.6% 16885

Arrivals Day number


ABERDEEN 7.4% 13.2% 12.5% 8.6% 18.7% 24.1% 15.6% 136

BIRMINGHAM 8.9% 10.2% 15.8% 7.5% 16.0% 20.1% 21.5% 1259

EDINBURGH 7.5% 10.9% 14.3% 12.3% 20.9% 18.0% 16.1% 605

GLASGOW 5.5% 9.8% 16.3% 14.2% 23.4% 16.4% 14.5% 799

GATWICK 9.2% 18.2% 24.7% 10.7% 9.2% 16.3% 11.7% 4160

HEATHROW 9.0% 17.3% 9.2% 12.4% 13.3% 21.5% 17.4% 3991

LUTON 8.3% 8.9% 17.0% 8.6% 17.4% 18.4% 21.4% 1063

MANCHESTER 13.0% 13.2% 11.6% 13.1% 21.9% 7.6% 19.6% 2686

EAST MIDLANDS 10.3% 10.3% 17.3% 9.9% 13.3% 20.0% 18.8% 920

STANSTED 14.0% 12.9% 12.0% 13.9% 20.6% 7.3% 19.3% 1665

Total 9.9% 14.3% 15.5% 11.5% 15.7% 16.1% 17.0% 17284

Tables 2.5 and 2.6 show the distribution of arrivals and departures for each airport by eight day parts. These

are defined as follows:

Weekdays Weekends

Day Part 1 06.00-09.59 Day Part 5 06.00-09.59

Day Part 2 10.00-15.59 Day Part 6 10.00-15.59

Day Part 3 16.00-18.59 Day Part 7 16.00-18.59

Day Part 4 19.00-05.59 Day Part 8 19.00-05.59

It is apparent that most departures are at the beginning of the day (day parts 1 and 2) while arrivals are

spread more evenly across the day. Late night arrivals (day parts 7 and 8) at weekends account for 16.1%

of the total compared with 7.4% of departures. This is consistent with passengers returning from holiday.



Departures Day part

Airport 1 2 3 4 5 6 7 8 Total

ABERDEEN 24.7% 26.3% 15.9% 9.6% 3.2% 6.0% 9.2% 5.2% 131

BIRMINGHAM 18.4% 27.8% 13.1% 21.7% 3.4% 6.1% 3.3% 6.1% 1243

EDINBURGH 28.3% 27.5% 12.5% 10.5% 3.0% 6.6% 6.1% 5.5% 590

GLASGOW 27.6% 28.8% 12.2% 9.2% 3.9% 6.6% 7.1% 4.6% 779

GATWICK 19.5% 32.3% 11.7% 22.4% 6.4% 3.1% 1.8% 2.9% 4075

HEATHROW 20.5% 30.2% 12.8% 13.7% 9.9% 5.0% 1.7% 6.2% 3832

LUTON 14.4% 28.6% 14.3% 21.8% 3.6% 7.3% 3.5% 6.5% 1036

MANCHESTER 24.1% 20.2% 4.6% 33.5% 4.7% 6.5% 2.5% 4.0% 2631

EAST MIDLANDS 17.1% 25.9% 16.3% 19.9% 4.0% 5.8% 3.5% 7.5% 925

STANSTED 21.4% 21.5% 4.3% 35.9% 4.6% 6.2% 2.0% 4.1% 1643

Total 20.9% 27.6% 10.7% 22.2% 6.0% 5.3% 2.6% 4.8% 16885

Arrivals Day part


ABERDEEN 14.8% 17.1% 16.3% 31.1% 2.3% 2.7% 1.9% 13.6% 136

BIRMINGHAM 3.0% 22.5% 24.2% 31.2% 0.0% 3.1% 5.1% 10.8% 1259

EDINBURGH 17.2% 25.2% 17.6% 21.6% 4.8% 2.0% 1.2% 10.4% 605

GLASGOW 17.6% 26.0% 18.7% 22.4% 3.8% 2.3% 0.8% 8.4% 799

GATWICK 11.5% 12.5% 22.5% 26.2% 4.7% 4.9% 4.1% 13.6% 4160

HEATHROW 14.5% 10.8% 26.4% 22.0% 1.1% 9.9% 5.2% 10.1% 3991

LUTON 4.0% 24.0% 24.6% 30.2% 0.0% 3.1% 5.2% 8.9% 1063

MANCHESTER 3.7% 16.4% 13.5% 40.2% 1.8% 7.5% 6.6% 10.4% 2686

EAST MIDLANDS 4.4% 23.0% 21.9% 30.2% 0.0% 3.1% 5.9% 11.6% 920

STANSTED 3.2% 15.5% 13.4% 41.0% 1.2% 7.5% 6.3% 11.9% 1665

Total 9.3% 16.3% 21.0% 29.3% 2.1% 6.1% 4.9% 11.2% 17284

It should be noted that the CAA data does not take into account airport visitors who are not travelling from

the airport. This will include, for example, friends and family who come to come to see off or to meet and

greet passengers. While these individuals will not go airside they may spend time in the terminal building.

It was apparent from comparisons with the CAA data that the travel survey under-estimated airport visits. This

was not unexpected – people who were about to go on holiday or to travel abroad would be less inclined

to carry a GPS meter. In order to correct for this an imputation procedure was carried out to attach ‘virtual’

passages through airports to respondents who were identified as being likely to have flown. Passages were

only imputed to those who had departed from the same airport within the last year. Eligible past year visitors

were drawn from:

those visiting in the survey week identified in the travel survey;

non-flyers who claim to have visited an airport;



flyers from an airport who are within a certain distance of the airport (GPS visitor distribution is used to

set a limit of distribution).

Visitors in each of ten selected airports were modelled individually. Travel zone clusters were collapsed to

create suitable areas that could be used for the imputation. The calculated universe data is presented in

Tables 2.7 – 2.11.

Table 2.7 shows the time spent at each airport by departures and arrivals. This shows both actual and

imputed data. In addition a number of ‘virtual’ travellers were created in each environment. Virtual

passengers do not, of course, increase (or decrease) the accuracy of the data. This is a statistical artefact

used to ensure that there are enough cases to use in the analysis. In Table 2.7 this group are shown

separately. The important thing to note is that the time spent using actual, imputed and virtual data is

consistent.

The mean time spent for those departing the airport is around 1.7 hours. This is fairly consistent across all ten

airports, although it does vary from c1.5 hours at East Midlands airport to just under two hours at Stansted. For

those arriving at the airport the mean time was around 0.75 hours.

Departures Arrivals

Duration (hours) Actual Imputed Virtual

contacts

Actual Imputed Virtual

contacts

ABERDEEN 1.89 1.66 1.59 0.53 0.61 0.60

BIRMINGHAM 1.72 1.78 1.75 0.65 0.72 0.71

EDINBURGH 1.62 1.58 1.59 0.70 0.62 0.68

GLASGOW 1.61 1.46 1.49 0.45 0.45 0.43

GATWICK 1.77 1.73 1.75 0.64 0.58 0.60

HEATHROW 1.71 1.78 1.78 0.95 0.94 0.93

LUTON 1.52 1.69 1.62 0.69 0.76 0.71

MANCHESTER 1.66 1.60 1.61 0.91 0.80 0.78

EAST MIDLANDS 1.46 1.51 1.46 0.97 0.75 0.80

STANSTED 1.87 2.00 1.92 0.64 0.64 0.64

Total 1.70 1.72 1.71 0.76 0.75 0.73

Table 2.8 shows the universe statistics for visitors to the terminal building in each airport. As discussed above

some of these visitors will not actually fly from the airport. It can be seen that most of the GPS data relates to

individuals who remain outside the terminal building.



GPS visitors Past year

allocated

visits

Weighted population Travel zone

clusters in

NBD

Airport Inside

terminal

building

Outside only Weekly (000) Annual (000)

ABERDEEN 2 9 86 2 109 2 BIRMINGHAM 13 34 843 14 1002 9 EDINBURGH 18 15 533 19 643 11 GLASGOW 21 47 434 20 523 13 GATWICK 39 53 2617 39 2568 30 HEATHROW 177 185 3439 181 4036 97 LUTON 9 57 578 13 636 8 MANCHESTER 47 68 1723 60 2038 34 EAST MIDLANDS 7 16 342 7 430 5 STANSTED 37 48 1313 32 1410 20 Total 370 532 11908 386 13397

Table 2.9 shows the distribution of airport visitors by day of week. It can be seen that there are differences

between airports in regard to the numbers at weekends or on weekdays. For Gatwick as many as two thirds

of the terminal visitors are on Friday/Saturday/Sunday. The equivalent for Stansted is just over a half while for

the other airports it is around 40%.

Day number


ABERDEEN 14.4% 12.2% 21.6% 11.5% 20.1% 14.4% 5.8% 88 BIRMINGHAM 6.4% 24.6% 22.6% 12.2% 12.9% 9.3% 11.9% 856 EDINBURGH 14.4% 17.6% 18.7% 10.5% 15.6% 16.8% 6.5% 551 GLASGOW 11.5% 18.1% 21.4% 11.4% 14.0% 18.2% 5.4% 455 GATWICK 22.4% 21.9% 9.8% 0.0% 17.8% 7.6% 20.4% 2656 HEATHROW 13.6% 13.0% 14.6% 18.4% 14.6% 10.3% 15.5% 3616 LUTON 6.8% 23.5% 23.5% 11.1% 13.3% 9.8% 12.0% 587 MANCHESTER 18.5% 13.6% 12.6% 9.5% 13.8% 14.9% 17.2% 1770 EAST MIDLANDS 6.5% 19.1% 26.3% 10.8% 14.4% 9.0% 14.0% 349 STANSTED 20.9% 14.9% 12.5% 10.3% 11.2% 14.4% 15.8% 1350 Total 15.7% 17.0% 15.0% 10.9% 14.6% 11.4% 15.4% 12278

Table 2.10 shows the same information analysed by day part. The categories are as follows:

Weekdays Weekends

Day Part 1 06.00-09.59 Day Part 5 06.00-09.59

Day Part 2 10.00-15.59 Day Part 6 10.00-15.59

Day Part 3 16.00-18.59 Day Part 7 16.00-18.59

Day Part 4 19.00-05.59 Day Part 8 19.00-05.59

For Glasgow, Edinburgh and Aberdeen the busiest day part for airport visitors is weekdays from 06.00 -

09.59. For Birmingham, Luton and East Midlands it is weekdays 10.00 - 15.59.



Day part


ABERDEEN 30.2% 20.1% 21.6% 1.4% 0.0% 7.9% 7.2% 11.5% 88 BIRMINGHAM 0.0% 44.7% 18.1% 6.3% 4.3% 18.0% 0.0% 8.7% 856 EDINBURGH 28.0% 17.2% 16.8% 6.0% 4.0% 7.2% 10.9% 9.9% 551 GLASGOW 28.8% 21.4% 14.5% 5.7% 3.0% 8.1% 9.5% 9.0% 455 GATWICK 19.7% 8.7% 7.2% 20.1% 3.8% 14.8% 9.5% 16.2% 2656 HEATHROW 16.8% 24.3% 14.6% 17.8% 6.0% 7.7% 5.0% 7.9% 3616 LUTON 0.0% 46.2% 13.8% 9.8% 5.2% 16.3% 0.0% 8.8% 587 MANCHESTER 10.1% 22.4% 18.6% 16.8% 3.0% 9.8% 8.1% 11.2% 1770 EAST MIDLANDS 0.0% 44.6% 23.4% 6.5% 4.1% 13.8% 0.0% 7.6% 349 STANSTED 9.5% 21.9% 17.7% 15.0% 3.2% 10.9% 9.8% 12.1% 1350 Total 14.1% 23.4% 14.6% 15.1% 4.4% 11.1% 6.6% 10.7% 12278

Table 2.11 shows the average visit duration for both actual and virtual terminal visitors at each airport. For

actual visitors this is shortest at Glasgow (0.18 hours) and Aberdeen (0.26 hours), while the longest is

Birmingham and Gatwick (1.08 hours).

Terminal visitors

Duration (hours) Actual Virtual

contacts

ABERDEEN 0.26 0.36 BIRMINGHAM 1.08 0.85 EDINBURGH 0.60 0.37 GLASGOW 0.18 0.37 GATWICK 1.08 1.08 HEATHROW 0.85 0.85 LUTON 0.79 0.85 MANCHESTER 0.67 0.70 EAST MIDLANDS 0.56 0.81 STANSTED 0.74 0.69 Total 0.79 0.82



The Route travel survey and the GPS methodology work well in plotting travel patterns in most environments.

However, there are specific challenges when applying this methodology in this environment.

GPS works well outdoors but does not always work when inside a building.

Airports have specific geographical structures that are of interest.

Individual locations where behaviour is to be modelled will have limited sample sizes.

There are some parallels here with indoor shopping centres but, clearly, the characteristics of visitors and the

attractor points are very different. The basic requirements for these bespoke surveys are:

Defining an appropriate methodology to collect the information required.

The selection of an appropriate representative sample of locations.

Within each location, the selection of a representative sample of visitors to collect information

pertaining to their visit.

The objective is to understand how passengers and other visitors move through airports and, by extension,

the advertising they are likely to be exposed to. The key aims were to understand the:

Types and frequency of attractor points visited.

Time spent at these attractor points.

Sequencing in which people visited these points.

The survey method used was a ‘recall study’ where information in relation to each visit was collected from a

representative sample of visitors. This included data on their movements that was used to identify key

attributes. These studies sought to understand the behaviour of passengers and visitors through three

different areas of Manchester and Gatwick Airports:

Check-In Hall

Departure Hall

Arrival Hall

Similar bespoke surveys have also been carried out in other environments that could not be measured

directly using GPS (train stations, indoor shopping centres).

This research took place at Manchester Airport (Nov 2012 and May 2013) and Gatwick (Sep 2013). The aim

was to conduct interviews with airport users in order to identify patterns of behaviour whilst in the airport. These

can then be applied to other airports. Respondents were interviewed in check-in, the departure hall and the

arrival hall. The overall length of visit and time spent in different areas were collected. Other variables

collected included passenger type (business/leisure), travel mode to/from airport, distance travelled and

standard demographics (age, gender, working stage, social grade, party size).



Manchester Airport has three terminals with a mixture of international/domestic and business/leisure travel.

Data were collected in two waves by KGS interviewers, across three airport zones, during two main shifts

(6am to 12 noon, and 3pm to 9pm).

Wave 1 (12th

– 26th

November, 2012) gathered insights from:

1. Check-In Hall Passengers (interviewed about their behaviour in the zone before entering

security/passport control)

2. Departure Hall Passengers (interviewed at the gate before boarding the aeroplane)

3. Arrival Hall Passengers (interviewed at the exit to the airport). A small number of arrival hall visitors were

also interviewed.

Wave 2 (21st

May to 14th

June, 2013) gathered insights from:

1. Check-In Hall Visitors (interviewed at the check-In hall exit to the airport)

2. Departure Hall Passengers (interviewed at the gate before boarding the aeroplane)

3. Arrival Hall Visitors (interviewed at the exit to the airport).

A minimum quota of respondents per shift and airport zone was set. Equivalent data were collected at

Gatwick Airport, which has two terminals. For both airports a questionnaire was designed that was relevant to

the respondent type in each of the three airport zones. Typical questions across the three questionnaires

included:

Journey purpose

Main mode of transport to the airport

Distance travelled from home to the airport

Number of return flights taken from this airport in the last 12 months

Gender and age

Postal district

Flight number and time (if passenger)

Arrival and departure time from airport

Time spent in zone

Other questions were specific to the airport zone that they were used in:

Departure Hall respondents were also asked about the amount of time that they spent in the

Check-In Hall and in Security/Passport Control.

Arrival Hall respondents were asked about the amount of time that they spent in Passport Control

and Baggage Claim.

Table 3.1 shows the location types within each airport. Some services/facilities were available in most or all

areas. These include food outlets, retail, seating and toilets. Others were specific to the area in which they



were located – for example Passport Control. Clearly the path taken by a passenger will be influenced by

the facilities that he/she is looking for.

Gatwick Manchester

Check-In

Hall

Departure

Hall Arrival Hall

Check-In

Hall

Departure

Hall Arrival Hall

Check-In Desk X

X

Check-In,

Departure or Arrival

Board

X X X X X X

Food (Dine In),

Food (Dine Out) X X X X X X

Retail X X X X X X

Duty Free

X

X

Information Kiosk

X X X

X

Seating Area X X X X X X

Toilet X X X X X X

Cashpoint/BDC X X X X X X

Airline Lounges

X

X

Internet Points

X

Car Rental

X

X

Security/Passport

Control X

X

Waiting Area and

Gate X

X

Airport Entry/Exit X

X X

X

Waiting inside the

Terminal X

X X

X

Smoking Outside X

X

Transit Shuttle X

X

Met Someone

X

Car Park Pay

Station X X

X

Table 3.2 shows the number of interviews achieved at each location.



Wave Zone Manchester Gatwick

1 Check-In Hall Passengers 141 99

1 Departure Hall Passengers 137 293

1 Arrival Hall Passengers & Visitors 114

1 Arrival Hall Visitors 13

2 Check-In Hall Visitors 66 47

2 Departure Hall Passengers 200 100

2 Arrival Hall Visitors 56 47

Total Interviews 727 586

The data in Table 3.3 suggest that the sample may be over-estimating the percentage of business

travellers. CAA data show that 14% of Gatwick and 16% of Manchester passengers are travelling on

business. This compares with the survey data where the equivalent percentages are 27% and 38%. The

profile is brought into line with universe data as part of the analysis and weighting.

Gatwick Manchester

Check In 99 141

-Business 27 27.3% 53 37.6%

-Leisure 72 72.7% 88 62.4%

Departure 293 337

-Business 80 27.3% 124 36.8%

-Leisure 213 72.7% 213 63.2%

Arrival 100 114

-Business 27 27.0% 39 34.2%

-Leisure 73 73.0% 75 65.8%

Table 3.4 shows that the average distance travelled to get to Gatwick was higher than it was for

Manchester. This seems reasonable taking into account the catchment area and the wide range of flights

available from Gatwick.

Miles Gatwick Manchester

0 13 2% 13 2%



0.1 - 5 21 4% 56 8% 5.1 - 10 24 4% 81 11%

10.1 - 15 23 4% 73 10% 15.1 - 20 38 6% 53 7% 20.1 - 25 57 10% 36 5% 25.1 - 30 61 10% 37 5% 30.1 - 35 53 9% 32 4% 35.1 - 40 37 6% 37 5% 40.1 - 45 27 5% 49 7% 45.1 - 50 42 7% 47 6%

> 50 183 31% 176 24% Don't know 7 1% 34 5%

586 100% 724 100%

The mode of transport used reflects the connections available from each airport. Table 3.5 shows that 75%

of those passing through Gatwick either drive or come by train. This compares with 52% of those travelling to

Manchester. Conversely, as many as 29% of passengers interviewed in Manchester said they had used a

hotel shuttle compared with only 7% at Gatwick.

Gatwick Manchester

Bus 25 4% 17 2%

Car 312 53% 292 40%

Hotel Shuttle 39 7% 211 29%

Taxi 68 12% 112 15%

Train 131 22% 88 12%

Tube 11 2% 7 1%

586 100% 727 100%

Table 3.6 shows the percentage who take more than one return trip from the airport each year. This

indicates that those travelling through Manchester airport are more likely to be frequent flyers.

Gatwick Manchester

1 183 34% 174 29%

2 156 29% 120 20%

3+ 206 38% 298 50%

545 100% 592 100%

Chart 3.1 shows the time spent in the check-in area at each airport for passengers travelling for business or

leisure. It is apparent that those travelling on business spend less time at check-in. At Gatwick the average

time taken by business passengers was 17.9 minutes compared with 34.3 minutes for those travelling for



0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Business Leisure

< 5 mins 6-15 mins

16-29 mins 30+ mins

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Business Leisure

< 5 mins 6-15 mins

16-29 mins 30+ mins

leisure purposes. Around two thirds of business passengers spend less than fifteen minutes in this area

compared with just over a third of leisure travellers.

The equivalent times taken at Manchester Airport were 14.4 minutes for business passengers and 18.8

minutes for leisure travellers. Again, two thirds of business travellers spent less than fifteen minutes at check-in

while this rises to just under 50% of leisure travellers.

Manchester Gatwick

Chart 3.2 shows the different parts of the check-in area that were visited by business and leisure passengers.

The locations vary between the two airports, which reflect their different layouts. In general passengers at

Gatwick spend longer at each of the locations. This is consistent with the fact that both business and leisure

passengers at Gatwick spend more time in total in the departure area.

Not surprisingly, the most visited areas are the check-in desks and the departure boards, followed by toilets

which were visited by around 20% of those in Manchester and 30% of those in Gatwick. As many as 20% of

business passengers visit retail outlets at Gatwick compared with around 10% at Manchester.

Manchester Gatwick



0% 20% 40% 60% 80%

Check-In Desk

Check-In Board

Food (Dine In)

Food (Dine Out)

Retail Store

Information Kiosk

Seating Area

Toilet

Cashpoint/BDC

Entry/Exit

Waiting

Total Leisure Business

0% 20% 40% 60% 80%

Check_ In Desk

Check-In Board

Food (Dine In)

Food (Dine Out)

Retail

Seating Area

Toilet

Cashpoint/BDC

Waiting

Smoke Outside

Transit Shuttle


Chart 3.3 shows the time spent in the departure hall. It should be noted that the time spent in this area is

very similar at both airports. In general terms business travellers are spending slightly less time in departures

than leisure travellers. At Manchester around 15% of business travellers spent less than twenty minutes in

departures. The average time spent by business travellers was 58.7 minutes compared with 76.1 minutes for

leisure travellers. The equivalent times at Gatwick were 54.2 minutes and 84.5 minutes.



0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Business Leisure

< 20 mins 20-40 mins 40-60 mins

1-1.5 hrs 1.5 hrs+

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Business Leisure

< 20 mins 20-40 mins 40-60 mins

1-1.5 hours 1.5 hours+

Manchester Gatwick

Chart 3.4 and Table 3.7 show the amount of time spent and locations visited in Arrivals at each airport.



0% 20% 40% 60% 80% 100%

Food (Dine In)

Food (Dine Out)

Retail Store

Information Kiosk

Seating Area

Toilet

Cashpoint/BDC

Car Rental

Waiting

Car Park Paystation

Baggage Claim


0% 20% 40% 60% 80% 100%

Food (Dine )In

Food (Dine Out)

Retail

Information Kiosk

Seating Area

Toilet

BDC

Car Rental

Waiting

Smoking Outside

Met Someone

Baggage Claim


Manchester Gatwick

Miles Gatwick Manchester

Arrival Board 1 3 Food In 29 22

Food Out 6 5 Retail 5 4

Information 3 Seating Area 18 17

Toilet 4 4 BDC 3

Waiting 19 27 Car Park Station 1 2 Met Someone 3 Average Time Spent in Zone

35 38

The average time spent in Arrivals was 35 minutes at Gatwick and 38 minutes at Manchester. Not surprisingly

the place where most time was spent was the baggage claim area – leisure travellers spent more than

80% of the time they were in Departures here. Business travellers spent less time in baggage claim. This

probably reflects the fact that they were more likely to be travelling with hand luggage alone.

Clearly the time spent in each airport and the locations visited were likely to be somewhat different for visitors

who were not flying from the airport, perhaps because they were seeing off or meeting and greeting friends

or relatives. Chart 3.5 shows the areas visited by those who had come with departing passengers.



0% 20% 40% 60% 80% 100%

Check-In Desk

Check-In Board

Food (Dine In)

Food (Dine Out)

Retail

Seating Area

Toilet

Cashpoint/BDC

Security Area…

Smoking Outside

Transit Shuttle

Manchester Gatwick

For these visitors most time was spent in in the check-in area. At Gatwick some 80% of visitors who were with

departing passengers spent some time in the departure board area. This can be compared with less than

40% for those at Manchester. This is likely to be as a result of the differing layout of the two airports.

Other locations attracting 20% or more of these visitors at both airports included the toilets, food and security

areas. At Manchester more than three quarters of those who were accompanying departures visited the

security area. This is logical as the security area is the location where accompanying friends/relatives have

their last chance to say goodbye.

It is apparent that visitors who were at the airport to meet arriving passengers would have a somewhat

different experience. In particular their time would be concentrated in the arrivals area. The areas visited are

detailed in Chart 3.6, which shows that most time was spent in waiting areas.

0% 20% 40% 60% 80% 100%

Check-In Desk

Check-In Board

Food (Dine In)

Food (Dine Out)

Retail Store

Information

Seating Area

Toilet

Cashpoint/BDC

Security Area

Waiting

Smoking Outside

Car Park Paystation



0% 20% 40% 60% 80% 100%

Arrival Board

Food (Dine In)

Food (Dine Out)

Retail Store

Seating Area

Toilet

Waiting

Car Park Paystation

0% 20% 40% 60% 80% 100%

Arrival Board

Food (Dine In)

Food (Dine Out)

Retail

Information Desk

Seating Area

Toilet

BDC

Waiting

Car Park Paystation

Met Someone

Manchester Gatwick

This section outlines the way in which passages through airports can be broken down into finer detail. This is

based on the work carried out across the two waves of the Manchester Airport study.

Tables 3.8 and 3.9 show the most frequent sequences for passing through the departure area.

o Table 3.8 relates to passengers

o Table 3.9 relates to non-travellers (seeing off friends/relatives).

Table 3.10 shows the top attractors for passengers as they pass through the departure hall (not

accessed by non-travellers).

Table 3.11 shows the equivalent data for arriving passengers.

Table 3.12 shows non-travellers who are meeting and greeting arrivals.

Table 3.8 shows the main attractors for a passenger passing through the check-in hall. A total of 42 different

sequences were noted by respondents and the top eight are shown here. These accounted for 75.9% of

all visits in the check-in hall. The simplest path was from the airport entry straight to security (number two in the

Total Rank column). Business and leisure travelers are compared and it can be seen that business travelers

are more likely to proceed directly to the departure area.

There were multiple different sequences that involved visiting four attractor points before entering the security

area.



Total (n=141) Business (n=53) Leisure (n=88)

Once entering the Check-In Hall, the main

attractor sequences were…

% Rank % Rank % Rank

Check-In Desk, then to Security 21.3 1 28.3 1 17.0 3

Check-In Board, then Check-In Desk, then to

Security

19.1 2 17.0 =2 20.5 1

Straight to Security 17.7 3 17.0 =2 18.2 2

Toilet, then to Security 8.5 4 7.5 4 9.1 4

Check-In Board, then to Security 4.3 5 3.8 5 4.5 5

Toilet, Check-In Board, Check-In Desk, then to

Security

2.1 6 1.9 =6 2.3 6

Toilet, Check-In Desk, then to Security 1.4 =7 1.9 =6 1.1 =7

Check-In Board, Check-In Desk, Retail Store,

then to Security

1.4 =7 1.9 =6 1.1 =7

Table 3.9 shows the equivalent information for airport visitors who were not flying. These seven sequences

account for 54.6% of all visits to the check-in hall by non-passengers. A total of 37 different sequences were

noted by respondents. The simplest were either from the check-in desk to the airport exit (number six in the

Rank column) or from visitors who were seeing off passengers at the security point straight to the airport exit

(1.5%). There were multiple different sequences that involved visiting six attractor points before leaving the

airport.



% Total (n=66) Rank

After entering the Airport, visitors either…

Went to the Check-In Desk, fare-welled

passenger/s at Security Point, then to Airport

Exit.

22.7 1

Went to Check-In Board, Check-In Desk, fare-

welled passenger/s at Security Point, then to

Airport Exit.

7.6 =2

Went to Check-In Board, Check-In Desk, Car

Park Paystation, then to Airport Exit. 7.6 =2

Went to Check-In Desk, Food (Dine In), fare-

welled passenger/s at Security Point, then to

Airport Exit.

6.1 4

Went to Check-In Board, Check-In Desk, Food

(Dine In), fare-welled passenger/s at Security

Point, then to Airport Exit.

4.6 5

Went to Check-In Desk, Toilet, fare-welled

passenger/s at Security Point, then to Airport

Exit.

3.0 =6

Went to Check-In Desk, then to Airport Exit. 3.0 =6

Table 3.10 shows the top attractor points as passengers pass through the Departure Hall. These seven top

sequences account for 14.1% of all passenger visits to the Departure Hall. A total of 243 different

sequences were undertaken by respondents. The simplest was going from the security/passport control area

to the departure gate (number six in the Total Rank column below). The most complex pattern was as follows

(0.3%): Departure Board, Seating Area, Retail, Seating Area, Retail, Duty Free, Seating Area, Departure

Board, Seating Area, Departure Board, then to Gate.




Once respondents passed through the

Security/Passport Control, they were more likely

to go …


To Food (Dine In), then to Gate 2.7 1 3.2 =1 2.4 2

To Departure Board, Retail, Seating Area, then

to Gate 2.4 =2 1.6 7 2.8 1

To Departure Board, Seating Area, then to

Gate 2.4 =2 3.2 =1 1.9 3

To Departure Board, Food (Dine In), then to

Gate 2.1 4 3.2 =1 1.4 4

To Departure Board, Retail, Food (Dine In), then

to Gate 1.8 5 3.2 =1

To Seating Area, then to Gate 1.5 6 3.2 =1

Straight To Gate 1.2 7 3.2 =1

Table 3.11 shows the top attractor points as passengers pass through the arrival hall. These seven top

sequences accounted for 77.2% of all visits to the Arrival Hall by passengers. A total of 30 different

sequences were undertaken by respondents. The simplest was going from the baggage claim area to the

airport exit (No.1 in the Total Rank column below).

The most complex pattern was as follows (0.9%): Retail store to Food (Dine In), to Toilet, to Airport Exit (then

outside airport terminal), to Toilet, to Seating Area, to Information Kiosk, to Retail Store, to Seating Area, to

General Waiting inside Terminal, to Airport Exit.


Once respondents passed through the

Security/Passport Control, they were more likely

to go …


Straight to Airport Exit 40.4 1 43.6 1 38.7 1

Toilet, then to Airport Exit 16.7 2 12.8 2 18.7 2

Waiting inside the Terminal, then to Airport Exit 6.1 3 7.7 4 5.3 =3

Toilet, then Waiting inside the Terminal, then to

Airport Exit

4.4 4 5.1 5 4.0 5

Car Park Paystation, then to Airport Exit 3.5 =5 10.3 3

Seating Area, then to Airport Exit 3.5 =5 5.3 =3

Table 3.12 shows the top attractor points as meet & greet visitors pass through the arrival hall. These seven

sequences account for 43.5% of visits to the Arrival Hall by people waiting to meet and greet passengers.

Some 46 different sequences were undertaken by respondents. The simplest pattern was waiting inside the



terminal then exiting the airport (No. 1 in the Total Rank column below). There were numerous sequences

that involved visitors going to six attractor points in the arrival hall before exiting the airport.

% Total (n=69) Rank

After entering the Airport, visitors either…

Waited inside the Terminal, Car Park Paystation,

then to Airport Exit. 8.7 =1

Went to the Arrival Board, Waited inside the

Terminal, Car Park Paystation, then to Airport

Exit.

8.7 =1

Waited inside the Terminal, then to Airport Exit. 8.7 =1

Waited inside the Terminal, Seating Area, Car

Park Paystation, then to Airport Exit. 7.3 4

Waited inside the Terminal, Toilet, Waited inside

the Terminal, Car Park Paystation, then to

Airport Exit.

4.4 5

Food (Dine In), Toilet, Seating Area, Waited

Inside the Terminal, Car Park Paystation, then to

Airport Exit.

2.9 =6

Waited in Terminal, Car Park Paystation, Waited

in Terminal, then to Airport Exit. 2.9 =6



In order to identify passages through airports maps were required for each of the ten airports with the

location of panels marked. These were provided by the media owners and, as they were received, they

were validated to ensure that passenger and visitor flows could be identified. Where there were multiple

maps for an airport, part of the validation process was to ensure that the flow between maps was

understood. The following procedures were then carried out:

• Key count points for entries, exits, passport control, baggage control, departure and arrival gates

were placed on the map within the OMC Inventory Mapping System (IMS).

• Zones were created to define different areas/sections within the map.

• Entry/exit count points were created to identify the start and end of all visits.

• Behaviour points were created to define different parts of an airport where activities are taking place

(for example shopping areas, food in).

• Intersection points were identified for movement between areas.

With this information added, the maps, including count points and zones, were ready to be digitised.

Digitised Links (the equivalent of Navteq roadside links) were created to join together all pairs of count points

within a zone and to ensure that all parts of the airport area were covered. All the count points within a zone

had to be routable via the Digitised Links

Table 4.1 shows the number of maps and frames to be digitised at each airport.

Airport Map sheets

digitised Frames

Aberdeen 1 53 Birmingham 2 0

East Midlands 1 35 Edinburgh 3 106 Gatwick 13 166 Glasgow 3 76 Heathrow 25 1029

Luton 3 48 Manchester 6 262

Stansted 7 93

Chart 4.1 shows the plan for the arrivals area in Terminal 1 at Manchester. The colour coding identifies the

arrival gate, passport control, baggage claim and arrivals hall.



Blue – Arrival Gate Pink – Passport Control

Green – Baggage Claim Yellow – Arrival Hall

The following behaviour locations (shown in Table 4.2) were identified:



Arrival Gate – 2 entries, 1 Connector to next zone (PC), 2 routing points, no behaviour points

Passport Control – 1 Connector from (AG), 1 Connector to next zone (BC), 2 routing points (passport

control) of which one must be contacted

Baggage Claim – 1 Connector from (PC), 1 Connector to next zone (AH), 9 behaviour points of

which 7 are baggage

Arrival Hall – 1 Connector from (BC), 7 exits, 22 behaviour points. Distribution of behaviour:

Behaviour type Locations Behaviour type Locations

Arrival board 11 Toilet 2 Food In 5 Bureau de change 4

Food Out 6 Internet points 3 Retail 5 Rent a car desks 4

Information 5 Car Park Pay 5 Seating 14

In some cases digitisation of the maps provided was extremely complex. For example, larger airports were

likely to have several maps for each location. Multiple behaviour points needed to be identified, often with

reference to online maps or other sources. It was also necessary to ensure that all parts of the airport were

covered by the links and flows identified. Chart 4.2 shows the maps for Heathrow Terminal 1.



Finally, a series of start and end nodes needed to be created to define all possible journey segments in the

airport. All possible routes were calculated in terms of links passed and total distance. In the analysis a

weight is applied to each route to allocate a higher ratio to shorter distances. Frame contacts are allocated

for each route. Visibility for the contact is calculated using the standard definition that is applied in all

environments. Table 4.3 shows the behaviour points identified for each airport.

Airport Check-

In Desk

Departure

and

Check-In

Board

Arrival

Board

Food

In

Food

Out Retail

Duty

Free

Inform-ation/

Pro-motional

Stand

Seating Toilet

Aberdeen 3 18 2 10 9 15 2 2 13 11

East

Midlands

5 13 3 16 5 20 2 4 14 13

Edinburgh 2 22 1 12 17 21 7 8 20 10

Glasgow 4 25 9 17 4 31 13 11 23 20

Manchester 15 57 23 35 31 65 27 28 55 26

Luton 7 24 4 17 12 26 8 4 19 14

Stansted 6 12 3 18 18 14 0 4 9 13

Gatwick N 4 18 6 16 5 23 6 10 7 30

Gatwick S 5 11 1 8 8 17 7 4 12 6

Heathrow T1 14 32 5 11 13 27 7 12 19 20

Heathrow T3 2 14 3 12 12 20 4 7 10 11

Heathrow T4 6 18 4 10 10 21 9 11 17 16

Heathrow T5 15 48 9 31 28 47 7 13 78 33

Airport

Bureau

de Cha-

nge/Ca

shpnt/Pa

yph-one

Airline

Lounge

Inter-net

Points

Rent a

Car

Desk

Pass-

port

Control

Waiting

Area

(Gate

Zone

Only) +

Gates

Bagg-

age

Claim

Conc-

ourse

Airport

Entry /

Exit

Wait-ing

Inside

Termin-

al

Car Park

Pay-

station

Mach.

Aberdeen 4 6 7 3 9 4 5 1 2 6

East Midlands 10 4 5 3 0 9 10 0 0 4

Edinburgh 6 6 2 1 12 4 9 1 1 5

Glasgow 12 3 9 1 19 3 9 0 2 9

Manchester 29 7 41 12 25 14 18 5 12 26

Luton 7 5 1 1 17 6 15 0 0 3

Stansted 12 0 7 1 0 5 12 2 1 5

Gatwick N 10 3 3 2 2 8 17 1 0 7

Gatwick S 5 3 0 0 0 8 13 0 0 5

Heathrow T1 10 5 3 3 0 15 19 0 2 5

Heathrow T3 6 4 4 1 0 8 13 0 1 4

Heathrow T4 9 4 6 1 0 7 12 0 0 4

Heathrow T5 23 3 10 2 0 12 29 0 0 11



Chapter 3 described the behaviour of airport visitors in some detail, including the locations visited by

different types of visitor. Chapter 4 outlined how maps were digitised with journey segments and nodes

created to define the environment at each of the airports studied. Using this information, the development

of a model for describing how airport visitors move through the environment is described here. The basic

procedure is as follows:

Airport visitor types

Airport visitors can be classified as follows:

Journey type A – those departing on a trip by air

Journey type E – those arriving on a trip by air

Journey type I – interchange passengers

Journey type V – those visiting the airport for other reasons

Journey type W – airport workers

Start and end points

All the start and end points for use within an airport are defined. The definition of a ‘journey’ through the

airport is from start point to end point.

Origins-Intermediate-Destination routes (O-I-D)

These are the intermediate points or areas the passenger passes through while en-route to the journey end

point. They are generated for each airport. All possible journey combinations are identified from Origin to

Intermediate point to Destination (O-I-D) in terms of key location information that all trips must follow.

For individuals taking a flight (visit type A)

1. Entrance in one of the entry points (O)

2. Through passport control (I)

3. End at one of the departure gates (D)

If, for example, there are 3 entry points, 4 passport control, 6 departure gates

= 3 x 4 x 6 = 72 possible journeys

Populations are then assigned based on all O-I-D combinations. An overall unique journey ID references this

route. Other behaviour count point information is allocated later.

Clearly, the longer a visitor is at the airport the greater the number of different behaviour locations might be

visited and longer time may also be spent in these locations. A two-way definition split allows time to be

segmented within the behaviour model calculations.

Modelling Visitor types

For those taking an air trip (journey type A), the overall visit is split into two parts and modelled separately (as

they were surveyed separately in the behaviour study):



In the Check-in area up to Passport Control

o Further segment on time <= 15 mins, > 15 mins

In the Departure Hall up to the Gates

o Further segment on time <= 2 hrs, > 2 hrs but based on total visit duration

For those returning from a trip (type E), then no further split is applied:

Segment on time <= 30 mins, > 30 mins

For those visiting (type V), then no further split is required:

Segment on time <= 30 mins, > 30 mins

For each O-I-D route, a randomised Behaviour Route Record (BRR) is generated to represent a journey

through the airport.

Behaviour Route Records (BRR)

Behaviour Route Records are generated for each random journey to simulate movement through the

areas. Each Behaviour Route Record Pair (BRRP) has several routes created from digitised links for different

possible contacts. The purpose of these BRRs is that they form a large pool of journeys that are then used as

follows:

Actual or virtual journeys generated within the travel survey will have an O-I-D route allocated. One

BRR is selected at random for this journey to form the selected visit and contacts with frames

matched. These contacts then feed into the file 4 outputs.

All O-I-D routes have a weekly population allocated. The 25 x 2 journeys are used against these

populations to build up a file 1 weekly frame population output.

The BRRs are generated by matching at random a behaviour study record, based on the

Manchester/Gatwick surveys carried out in 2012 and 2013. This outputs a list of activities for each BRR (eg

check-in desk, food in, toilet) that need to be matched to equivalent locations. Specific count points are

then randomly selected to match to the behaviour from all the possible routes. For example, if the

behaviour required is visiting a shop and there are 5 count locations with this activity then one of the five is

selected using a random number process.

For the BRR, a list of count locations in sequence is now available to represent the total visit. Each pair of

locations in sequence is labelled a Behaviour Route Record Pair (BRRP). We also need to check each BRRP

to ensure that any movement between zones or map sheets is also allocated a Connector Count Location.

This is important to ensure that a journey flows through the Digitised Links.

Behaviour Route Record Pairs (BRRP)

For the BRR, a sequential list of count locations is available to represent the total visit. Each pair of locations

in sequence is labelled a Behaviour Route Record Pair (BRRP). Each BRRP needs to be checked to ensure

that any movement between zones or map sheets is also allocated a Count Location. This is important to

ensure that a journey flows through the digitised links that have been created.



Full routing is then applied between each set of BRRPs. This follows the same methodology as for other

indoor environments.

Where required multiple routes are generated for a BRRP using a route simulation procedure – each

is called a Link Route.

The total distance for each Link Route is used to generate a distance weight.

The shorter the distance the more likelihood of the routing being taken – the higher the distance

weight.

The total of all distance weights for the Link Routes adds to 1.000.

For actual contact records, one Link Route is selected at random using the distance weight. For virtual

contact records, all Link Routes are selected and the distance weight is used to spread out the contacts.

Table 5.1 shows the number of O-D routes calculated for each airport and the number of journeys for

departing and arriving passengers together with visitors who were not flying. This latter group were divided

into those who were seeing off passengers and those who were meeting & greeting.

Departing

passengers Domestic arrivals International

arrivals Check in visitors Arrival visitors

O-D

Routes

Jour-

neys

O-D

Routes

Jour-

neys

O-D

Routes

Jour-

neys

O-D

Route

s

Jour-

neys

O-D

Routes

Jour-

neys

Aberdeen 48 2400 24 1200 9 450 4 200



East Midlands 192 9600 1 50 5 250 16 800 1 50

Edinburgh 120 6000 45 2250 15 750 4 200 18 900

Gatwick North 560 28000 105 5250 700 35000 16 800 25 1250

Gatwick South 408 20400 45 2250 680 34000 9 450 25 1250

Glasgow 360 18000 18 900 12 600 25 1250 5 250

Heathrow

Terminal 1 3136 156800 80 4000 1435 71750 64 3200 25 1250

Heathrow

Terminal 3 720 36000 240 12000 36 1800 4 200

Heathrow

Terminal 4 324 16200 384 19200 36 1800 36 1800

Heathrow

Terminal 5 920 46000 88 4400 2016 100800 25 1250 36 1800

Luton 168 8400 6 300 18 900 4 200 9 450

Manchester 1164 58200 90 4500 130 6500 97 4850 105 5250

Stansted 738 36900 12 600 140 7000 36 1800 4 200

BRRs are generated by matching at random a behaviour study record (this is based on the Manchester/

Gatwick behaviour studies run in 2012 and 2013). This outputs a list of activities for each BRR (eg check-in

desk, food in, toilet) that needs to be matched to equivalent locations. Each Behaviour count location has

one or more behaviours associated with it. Specific point locations are then randomly selected based on a

match to the behaviour from all those that are possible. For example, if the behaviour required is using an

ATM and there are five locations with this activity then one of the five is selected using a random number

process.

For the BRR, a list of locations in sequence is available to represent the total visit. Each pair of locations in

sequence is labelled a Behaviour Route Record Pair (BRRP). RRPs need to be checked to ensure that any

movement between zones or map sheets is also allocated a Connector Count Location. This is important to

ensure that all journeys flow through the Digitised Links.

For each possible O-I-D pair 25 random visits are created for each of two visit duration bands. Behaviours

are selected and then locations that match the Behaviour Route Record (BRR). The passenger/visitor is then

routed through the BRRs. Finally a visibility score is applied.



In order to illustrate this process an example is given below. This is based on Manchester Airport (terminal 1).

This is a type A passenger (departing from the airport on a trip). Full details of the route taken are shown in

Table 5.2. The O-I-D is as follows:

Start point (O) – Entrance point

Entry is at entry 1 and node ID 1208229

Zone I

Middle Point (I) - Passport control

Passport Control 1 – node ID 1184540

Zone H

End point (D) – Departure gate

End is departure gate 1 node ID 1208234

Zone F



Type A

Sheet_ID_start 13023002

Node_ID_start 1208229

Point_number_start 1

Entry _ Name 1 Entry 1

Zone_start I

Sheet_ID_middle 13023002

Node_ID_middle 1184540

Point_number_middle 112

Middle _ Name 1 Passport Control 1

Zone_middle H

Sheet_ID_end 13023002

Node_ID_end 1208234

Point_number_end 6

Entry_ Name 2 Departure Gate 1

Zone_end F

Start point: Entry 1 – Node 1208229

Finish point: Passport Control 1 – Node 1184540

The record is matched against a behaviour study ID for < 15 minutes. The selected record is B508. The

duration of the visit from entering the airport to reaching passport control is 14 minutes – this is the time spent

in the departure area. There are two attractors as the passenger proceeds to passport control.

Attractor 1 = 01 (check-in desk) for 10 mins

Attractor 2 = 16 (passport control) for 3 mins – this is the END point and has already been selected

For Attractor 1, there are 5 count-locations in this area that are labeled check-in desk

Select 1 at random from the 5

Attractor selected – node ID 1184536 (point 108)

Chart 5.2 illustrates how the passenger moves from the entry point to the check-in desk and then to passport

control.



Connector locations also need to be allocated so that the passenger can move from zone to zone.

The full allocation of visits for this randomly created BRR is shown in Table 5.3 (below).

Journey from Journey to

Sequence Zone Node Point

number Zone Node Point

number Dur-

ation Journey

type Conn-

ector 1 I 1208229 1 I 1184536 108 Walk 2 I 1184536 108 I 1184536 108 10 Wait 3 I 1184536 108 H 1192432 204 Walk 1 4 H 1192432 204 H 1184540 112 Walk 5 H 1184540 112 H 1184540 112 3 Wait

Start at Passport Control 1 – Node 1184540

Finish at Departure Gate 1 – Node 1208234

The record is matched against a behaviour study ID for < 2 hours. The selected record is B003. The duration

of the visit from leaving passport control to the departure gate is 26 minutes – this is the time spent in the

departure area. There are three attractors as the passenger proceeds to the departure gate.

Attractor 1 = 02 (departure board) for 2 mins

Attractor 2 = 09 (Seating area) for 15 mins



Attractor 3 = 17 (Gates) – this is the END point and has already been selected. It is assumed that

the passenger stays here until boarding the flight

For Attractor 1 (departure board), there are 9 count-locations in this area that are labelled ‘departure board’:

Select one at random from the 9


For Attractor 2 (seating area), there are 9 count locations in this are labeled ‘seating’ (some overlap with

departure board and there are procedures in place to prevent the same location from being selected):

Select one at random from the 9


Then proceed to Departure Gate 1

Chart 5.4 illustrates how the passenger moves from passport control to the departure gate.

The full allocation of visits for this randomly created BRR is shown in Table 5.5 (below).

Journey from Journey to



Sequence Zone Node Point

number Zone Node Point

number Dur-

ation Journey

type Conn-

ector 1 H 1184540 112 G 1192431 203 Walk 1 2 G 1192431 203 G 1184551 123 Walk 3 G 1184551 123 G 1184551 123 2 Wait 4 G 1184551 123 G 1184557 129 Walk 5 G 1184557 129 G 1184557 129 15 Wait 6 G 1184557 129 F 1192430 202 Walk 1 7 F 1192430 202 F 1208234 6 End

Tables 5.6 and 5.7 show the time spent at attractor locations in departures and arrivals.

Departing passengers Check-in visitors

ID 1 2 1 2

Duration

type Length < 2 hrs 2+ hrs <=30 mins > 30 mins

Average

number of

attractors: Check-in 1.4 2 2.6 3.8

Dep. hall 3.7 5

Time spent in airport (mins): Total 92 174 18 61

Check-in 7 20 18 61

Security 9 12

Departure 42 95

Gate area 34 47

Domestic arrivals International arrivals Arrival visitors

ID 1 2 1 2 1 2

Duration

type Length

<=30

mins

>30 mins

<=30 mins

>30 mins

<=30 mins

>30 mins

Attractors: Arrival Hall

1.0 1.5 1.2 1.7 2.4 3.4

Time spent in airport (mins): Total

9

32

20

58

16

55

Baggage

Claim

2 7 3 12

Arrival Hall

7 25 17 46 16 55



The final stage is creating matches to frames whilst on each route. This procedure uses the digitised links

identified from the maps (see Chapter 4). This feeds into the creation of file 1 and file 4 outputs (see Chapter

9). Table 5.8 (below) shows the number of O-D routes identified in each airport. The number of journeys

through each link and the number of frame link records are also shown.

All uses Frame link records matched

O-D Routes Journeys Total Ave. per journey

Aberdeen 85 4250 452,390 106

East Midlands 215 10750 449,301 42

Edinburgh 202 10100 7,301,843 723

Gatwick North 1406 70300 1,179,012 17

Gatwick South 1167 58350 1,013,623 17

Glasgow 420 21000 5,189,859 247

Heathrow T1 4740 237000 354,308,364 1495

Heathrow T3 1000 50000 22,060,030 441

Heathrow T4 780 39000 75,568,249 1938

Heathrow T5 3085 154250

Luton 205 10250 3,147,145 307

Manchester 1586 79300 12,057,480 152

Stansted 930 46500 3,252,141 70

It is apparent that at a relatively small airport like Aberdeen there are comparatively few O-D routes – only 85

were identified. This compares with 4,740 links at Heathrow Terminal 1 and 3,085 at Terminal 5, which

indicates the complexity of these environments. The number of frame link records also varies in line with the

size and complexity of the different airports. Heathrow Terminal 1 yields 1,495 contacts while Terminal 4 yields

1,938.



Imputation, a method for dealing with missing data, has been used in the models developed for other

environments. It is particularly important for airports because the travel survey under-represents airport

passengers. It is apparent that anyone who is away on holiday when the interviewer calls, or who is going to

be away when the GPS meter is due for collection, will not be recruited to the study.

Since Year 3 of the survey this problem has been largely overcome by the introduction of a recontact

programme. Respondents from the travel survey who were identified as being likely to have flown were sent

a follow-up questionnaire. This group was supplemented by the use of respondents who had previously

been interviewed on the National Readership Survey. Data is collected by means of PDAs that are

despatched to potential respondents.

Table 6.1 shows the number of airport passengers (flyers) and visitors contacted in each year of the survey.

About half the interviews achieved in Years 1-5 have come from the recontact sample, demonstrating the

importance of this enhancement to the methodology. Visitors are split into two groups – those who went

inside the terminal building and those who only came into contact with the outside of the terminal building,

presumably because they were dropping off friends/relatives.

Airport Flyers Visitors

Departing

(A)

Arriving (E

)

Interchange Total Workers Inside

terminal

building

Outside

Original sample

Year 1 85 58 143 57 62 190

Year 2 77 55 1 133 80 94 126

Year 3 48 40 88 43 107 105

Year 4 57 42 99 33 70 85

Year 5 44 18 62 69 86 165

Recontact sample

Year 3 104 63 167 1 4

Year 4 170 120 290 4 1

Year 5 42 29 71 3

Total 627 425 1 1053 282 424 679



Passengers and other visitors are treated separately for modelling purposes. Airport workers are not

modelled.

Table 6.2 shows the number of flyers, airport workers and other visitors who were included in the Year 1-5

sample. The number of contacts is roughly in line with the size of individual airports; the number of achieved

interviews with passengers departing the airport ranging from 4 in Aberdeen to 128 in Heathrow.

Airport Flyers Visitors

Departing

(A)

Arriving

(E)

Interchange Total Workers Inside

terminal

building

Outside

ABERDEEN 4 3 7 2 9

BIRMINGHAM 32 23 55 13 34

EDINBURGH 40 30 1 71 12 18 15

GLASGOW 34 23 57 11 21 47

GATWICK 99 57 156 40 39 53

HEATHROW 128 80 208 95 177 185

LUTON 37 20 57 1 9 57

MANCHESTER 54 38 92 32 47 68

EAST MIDS 11 10 21 5 7 16

STANSTED 52 30 82 33 37 48

OTHER 136 111 247 53 54 147

Total 627 425 1 1053 282 424 679

Table 6.3 shows the weekly population of airport travellers achieved from the travel survey compared with

CAA data.



Weekly Population (000s)

GPS travel survey 7 day CAA

target

% of target

ABERDEEN 4 53 8.4%

BIRMINGHAM 53 133 40.1%

EDINBURGH 74 123 60.5%

GLASGOW 45 111 41.0%

GATWICK 151 440 34.4%

HEATHROW 185 573 32.2%

LUTON 57 125 45.4%

MANCHESTER 103 303 34.0%

EAST MIDS 14 67 20.6%

STANSTED 85 189 44.8%

OTHER 217 478 45.3%

Total 989 2594 38.1%

Overall, survey respondents make up 38.1% of the required number of passages. The shortfall needs to be

allocated on the survey as otherwise there will not be enough actual contacts in the probability model.

Weights are used to factor the travel survey data up to the population. Airport visits outside the nine day

data collection period were excluded and weekend flights received a weight of ½ to reflect the fact that

two weekends are included in the data collection period.

Year 1 data was imputed after year 1 fieldwork (not shown here). The imputation was repeated for years 2-5.

Imputation is required to correct for the under-representation of air passengers in the travel survey. This is also

required to increase the efficiency of the models. In this respect it is important to have a robust

methodology and the following criteria were applied:

Only those qualifying will be imputed (someone who never makes an air trip should not be

imputed).

The imputation should be made to each airport individually to bring it up to target.

Where possible the new data-set of actual and imputed travellers should reflect demographic

profiles.

A representative dataset produces a better quality probability model and improved survey outputs of

contacts with frames by respondents.



Data from the first year of the survey were imputed but this process was not repeated until data were

available for Years 1-5. The use of a relatively large dataset meant that the imputation process was more

accurate.

• Weekly audiences include actual year 1-5 flyers plus year 1 imputed flyers allocated in 2010.

• The difference in the number of weekly airport passengers from weighted travel survey data

compared with the CAA average weekly targets needs to be imputed.

Including imputed data from Year 1 caused the weighted sample to increase from 38% of the CAA target

to 57%.

It should be noted that the imputation task outlined here was carried out to identify additional sample

records for the probability model. Imputed data was not used in routing passengers through the airport.

Table 6.4 shows the weekly CAA targets for each airport, the actual data from the travel survey, previously

imputed Year 1 data and the coverage achieved with the imputed Year 1 data. This is compared with

population data. The table shows that the coverage before imputing year 2-5 data ranges from 26.0% for

Aberdeen to 81% at Edinburgh,

2012 7 day

CAA target Actual Y1-5

Y1 prev

imputed

Coverage with

Y1 imputed

Pop. target to

impute

ABERDEEN 53 4 9 26.0% 40

BIRMINGHAM 133 53 30 62.5% 50

EDINBURGH 123 74 25 81.0% 24

GLASGOW 111 45 24 62.5% 42

GATWICK 440 151 97 56.5% 192

HEATHROW 573 185 102 50.0% 286

LUTON 125 57 13 56.1% 55

MANCHESTER 303 103 74 58.5% 126

EAST MIDS 67 14 17 45.7% 36

STANSTED 189 85 34 63.0% 70

Total 2117 771 425 56.5% 921

Year 1 respondents were not included as data had already been imputed for this part of the sample.

Only respondents who had flown from the airport in the past year qualify to be included in the Year

2-5 imputation.

Claimed frequency of return flights was used as a weighting variable for selection

o Those making more trips are more likely to be imputed



o Claimed travel frequency (1 vs 2+ return trip flights per year) is used to stratify the selection

towards more frequent travellers

Demographic data collected on the Route travel survey and the CAA passenger survey was used

to identify and correct shortages in cells

o Age (x 3) , gender, social grade (x 4)

o Incidence of Business and Leisure

o Region of residence (x 11)

Note: The CAA calculation of region is based on the start point for the journey rather than home address.

Region has been recalibrated using distance matches to approximate this variable.

A Network Optimisation algorithm was developed to identify the demographic groups with the largest

shortfalls and to target these as a priority within the imputation.

Events are imputed in sequence with the demographic profiles being reset to allow the model to approach

targets effectively. The tolerance level for successful imputations for a demographic group compared with

the population was +/- 5%. The demographic profiles were checked after the imputation and if they were

outside these limits the program would be re-run.

Table 6.5 (in two parts) shows the demographic targets (%) for age, sex, social grade and region.

Airport Name Weekly

population Male Female 16-34 35-54 55+ AB C1 C2 DE

ABERDEEN 52,558 61.3 38.8 31.3 46.9 21.9 36.6 35.8 23.3 4.4

BIRMINGHAM 13,2521 48.1 51.9 24.6 33.5 41.9 33.5 37.3 18.5 11.3

EDINBURGH 122,696 46.8 53.2 30.3 39.9 29.9 47.6 33.1 13.6 5.7

GLASGOW 110,511 51.1 48.9 30.0 47.0 22.9 29.7 41.1 21.1 8.2

GATWICK 439,830 55.4 44.6 34.5 37.8 27.7 39.0 42.3 11.0 7.6

HEATHROW 573,292 56.2 43.8 37.6 40.2 22.2 49.3 37.1 8.7 4.8

LUTON 125,013 49.2 50.8 36.6 35.0 28.4 37.7 34.4 17.2 10.7

MANCHESTER 303,411 53.4 46.6 27.2 42.9 29.9 25.0 37.1 23.7 14.2

EAST MIDS 66,922 46.5 53.5 23.4 35.3 41.3 27.4 30.4 19.9 22.4

STANSTED 189,082 53.2 46.8 45.0 33.5 21.5 29.4 46.6 15.2 8.8



Airport Name North

East

North

West

Yorks/

Hum-

ber

West

Mids

East

Mids

East

Anglia

South

West

South

East

Lon-

don Wales

Scot-

land

ABERDEEN 0.3 0 0 0 0 0 0 0 0 0 99.7

BIRMINGHAM 0.2 1.2 1.8 61.4 20.0 1.2 6.1 5.3 0 2.8 0.1

EDINBURGH 0.6 0.1 0 0.1 0 0 0 0 0 0 99.2

GLASGOW 0.2 0.4 0.1 0 0 0 0 0 0 0 99.2

GATWICK 0.2 0.8 0.9 2.1 2.6 12.1 6.6 40.7 32.6 1.3 0.1

HEATHROW 0.5 1.1 1.9 3.6 4.7 12.5 10.9 24.0 37.6 2.7 0.4

LUTON 0.3 0.6 1.2 4.9 9.4 35.7 2.9 16.2 28.0 0.6 0.3

MANCHESTER 2.5 55.9 23.1 6.6 4.8 0.1 0.3 0.3 0 4.7 1.8

EAST MIDS 0.3 1.6 16.0 16.3 62.2 1.4 0.6 1.0 0 0.4 0.1

STANSTED 0.3 0.7 1.3 1.9 4.7 44.4 2.7 8.3 34.8 0.8 0.2

Private Travel Public Travel Other Business Leisure

ABERDEEN 93.3 4.8 1.9 54.1 45.9

BIRMINGHAM 81.8 17.6 0.7 16.9 83.1

EDINBURGH 79.9 19.6 0.6 35.6 64.4

GLASGOW 89.6 10.4 0.0 30.2 69.8

GATWICK 66.8 32.9 0.2 14.2 85.9

HEATHROW 64.9 34.8 0.3 31.2 68.8

LUTON 73.8 25.8 0.3 16.3 83.7

MANCHESTER 88.1 11.8 0.2 15.7 84.3

EAST MIDS 92.8 7.1 0.1 8.4 91.6

STANSTED 62.9 37.0 0.2 16.1 83.9

N.B Travel mode to the airport and Business / leisure were not used as controls in the imputation

It should be noted that targets are based on weighted populations but the imputation itself works on

unweighted data. Consequently, the end results will not always match the targets exactly.

Table 6.7 shows the number of respondents (actual and imputed) in the sample and shows the profile

alongside CAA universe data. This can be compared with Table 6.4, which presented the same data prior

to imputation. The imputation has worked well with all targets inside the criteria set of +/-5%.



Population

7 day users

(000s)

Air trips Weighted

respondents

(000s)

% of target

HEATHROW 573 569 566 98.8

GATWICK 440 414 430 97.7

MANCHESTER 303 281 315 104.0

STANSTED 189 192 188 99.5

BIRMINGHAM 133 121 138 103.8

EDINBURGH 123 118 122 99.2

LUTON 125 124 121 96.8

GLASGOW 111 115 115 103.6

EAST MIDS 67 80 65 97.0

ABERDEEN 52 49 54 103.8

OTHER 478 341 305 63.8

Total 2594 2404 2419 93.3

Table 6.8 shows the regional profile for visitors to Heathrow before and after imputation. There are some

inaccuracies in this comparison due to the way the CAA treats journey data. This is based on the location

from which the passenger started the final leg of the trip to the airport. So, if a passenger travels from home

the start point is the home address. However, if he/she stays in a hotel overnight the starting point is taken as

the location of the hotel.

This information is not available from the travel survey and is of little use for the purposes of an audience

model. In order to get round this, distances have been recalibrated using distance matches. Although it is

an approximation Table 6.8 suggests that the regional profile after imputation is broadly similar to the CAA

data.



Target profile

from CAA

Original Travel

Survey After imputation Difference

Residence Start of journey

North East 0.5 0.2 0.3 0.0 -0.5

North West 1.1 0.3 1.2 0.0 -1.1

Yorkshire 1.9 0.4 2.1 0.0 -1.9

West Midlands 3.6 3.4 4.9 4.7 1.0

East Midlands 4.7 2.0 5.2 5.1 0.4

East Anglia 12.5 11.9 13.1 13.1 0.6

South West 10.9 9.3 10.8 8.5 -2.5

South East 24.0 30.6 23.4 23.4 -0.6

London 37.6 36.1 34.5 44.9 7.3

Wales 2.7 1.2 2.4 0.5 -2.2

Scotland 0.4 4.7 2.4 0.0 -0.4

For all airports with a large enough sample (all except Aberdeen and East Midlands), the distribution of

imputed travellers by day and day-part has been selected based on the distribution of actual travellers to

that airport. Table 6.9 shows the sample profile before and after imputation by day and day part.

Day Actual

traveller % Imputed

visit %

Actual +

imputed

%

Total

sample Day

Part

Actual

traveller

%

Imputed

visit %

Actual +

imputed

%

Total

sample

1 7.7 10.8 9.7 55 1 15.9 11.9 13.4 76 2 11.1 12.7 12.1 69 2 22.1 15.0 17.6 100 3 9.1 8.9 9.0 51 3 13.0 10.5 11.4 65 4 11.5 11.1 11.3 64 4 14.4 13.0 13.5 77 5 14.4 9.7 11.4 65 5 9.1 15.5 13.2 75 6 15.9 11.6 13.2 75 6 10.1 13.6 12.3 70 7 14.4 9.1 11.1 63 7 5.3 7.8 6.9 39 8 5.8 9.4 8.1 46 8 10.1 12.7 11.8 67 9 10.1 16.6 14.2 81 Total 208 361 569

Days 8 and 9 represent the second weekend over which the GPS meter is carried by respondents.

Day parts are defined as follows:



Weekdays Weekends

Day Part 1 06.00-09.59 Day Part 5 06.00-09.59

Day Part 2 10.00-15.59 Day Part 6 10.00-15.59

Day Part 3 16.00-18.59 Day Part 7 16.00-18.59

Day Part 4 19.00-05.59 Day Part 8 19.00-05.59

Airport visitors – those whose purpose is to see off or meet and greet travelling passengers - need to be

considered separately. Only those who enter the terminal building are of interest for the airport model. There

is no outside data source that can provide weighting targets so it is assumed that targets can be taken from

the travel survey. No imputation is required.

Airport visitors have been coded according to whether or not they visit the terminal building. This is estimated

based on a manual interpretation of the GPS signal.

Table 6.10 shows the number of weighted and unweighted visitors to each airport based on those who

enter the terminal building. The profiles are quite similar suggesting that there are no major imbalances in

the data.

Visitors inside terminal building

Unweighted Weighted

(‘000s)

ABERDEEN 2 2

BIRMINGHAM 13 14

EDINBURGH 18 19

GLASGOW 21 20

GATWICK 39 39

HEATHROW 177 181

LUTON 9 13

MANCHESTER 47 60

EAST MIDS 7 7

STANSTED 54 63

OTHER 37 32

Total 424 449



Another source of information relating to respondents’ travel behaviour is the CAPI questionnaire that is

administered as part of the recruitment interview. This can be used to identify candidates for imputation. In

order to investigate the accuracy of this data it has been compared against external data sources. Key

questions asked were as follows:

Q38. Have you travelled by air in the last 12 months, whether on business, holidays or for personal

reasons?

Q39. How many return flights have you taken in the last 12 months from British Airports for holiday,

pleasure or business? Please think of one complete return trip as one

Q40. Which airports have you flown from in the last 12 months?

Q41. For each of these can you say how many times have you used these airports in the last 12

months, please think of each return trip as one complete occasion?

Table 7.1 shows the percentage of respondents who say that they have flown within the last twelve months.

The responses are compared with data from wave 5 of the TouchPoints multimedia survey. TouchPoints is

carried out by Ipsos MORI on behalf of the IPA and is an important reference source for media data. It can

be seen that the 44.5% of those interviewed on the travel survey claimed to have flown in the past twelve

months compared with 49.2% of TouchPoints respondents. This is encouraging as it suggests that the two

datasets are consistent.

The National Readership Survey (NRS) was consulted as another respected information source. This also

collects data on the percentage of respondents who have flown, but this is only asked about with reference

to the last three years. Some 55% of NRS respondents claim to have flown in the last three years which,

although the question is different, is also comparable with data from the travel survey.

Unweighted Y1-

5 resps.

Population

(‘000s) % TouchPoints 5 %

Yes 15,512 22,799 44.5 49.2

No 18,653 28,467 55.5 50.8

Total 34,165 51,266 100.0 100.0

Further comparisons with TouchPoints 5 are presented in Table 7.2. This shows the proportion of respondents

who have travelled on business by air in the last year, the percentage who have travelled for holiday/leisure

purposes and the total who have travelled.

It can be seen that the percentages travelling on business (5.9% on the travel survey and 8.5% for

TouchPoints 5) are comparable, although this suggests that the travel survey may under-represent business



travellers. However, the equivalent question on the NRS indicates that 5.3% have travelled on business in the

past year, which is closer to the figure taken from the travel survey. These surveys all use different methods of

sampling and data collection and so cannot be expected to match exactly. The important thing is that the

results should not be vastly different.

Table 7.2 also shows that the percentage travelling for holiday/pleasure was 42.5% compared with 45.8%

on TouchPoints 5.

In addition Table 7.2 shows distributions of the number of flights taken for business or holiday/leisure purposes

and in total. It is apparent that frequent flyers are represented in the sample – 2.6% claim to have made

three or more business trips in the last twelve months, 10.7% holiday/leisure trips and 13.2% any type of trip.

Return flights in the last year for business

Unweighted Y1-

5 resps.

Population

(‘000s)

% TP5 %

0 31,914 48,217 94.1 91.5

1-2 1,221 1,698 3.3 8.5)

3+ 1,030 1,352 2.6 )

Total 34,165 51,266 100

Return flights in the last year for holiday/pleasure

Unweighted Y1-

5 resps.

Population

(‘000s)

% TP5 %

0 19,373 29,467 57.5 54.2

1 7,024 10,463 20.4 45.8)

2 3,965 5,854 11.4 )

3+ 3,803 5,483 10.7 )

Total 34,165 51,266 100

Return flights in the last year



Unweighted Y1-

5 resps.

Population

(‘000s)

% TP5 %

0 18,742 28,601 55.5 52.4

1 6,754 10,091 19.8 47.6)

2 3,939 5,851 11.5 )

3+ 4,730 6,724 13.2 )

Total 34,165 51,266 100

Table 7.3 shows weighted and unweighted data relating to airports visited in the last year. It can be seen

that weighting is not having sizeable impact on the data, suggesting that the sample upon which it is based

is reasonably representative. A catchment area of 100km for users has been used, any travelling to or from

Birmingham (not one of the measured airports) has been excluded.

Note that modelling is only carried out for ten agreed airports, the rest are moved into the ‘Other‘ category.

In addition, on some of the tables on the CAPI questionnaire, East Midlands was missed from the CAPI script

so the ‘Other’ responses to the airport visit include some from this airport.

Weekly Population

Unweighted Weighted

(‘000s)

% of total

HEATHROW 4,159 5,925 19.0

GATWICK 4,288 6,049 24.0

STANSTED 1,728 2,432 12.7

LUTON 1,094 1,541 9.2

BRISTOL 791 1,181 7.8

MANCHESTER 2,785 4,175 29.8

BIRMNGHAM 1,299 1,972 20.0

EDINBURGH 640 956 12.1

GLASGOW 829 1,214 17.5

ABERDEEN 152 243 4.3

EAST MIDLANDS 958 1,419 26.0

Other 2,722 4,048



Table 7.4 shows the number of return trips from Heathrow broken down by region based on those who have

travelled by air in the last twelve months. Not surprisingly residents of London and the South East have a

higher frequency of use. London residents make an average of 2.69 trips via Heathrow while those in the

South East make 2.91 trips. The equivalent for other parts of the country is c1.8 return trips.

Number of return

trips in past 12m London South East East of England Other regions

1 58.7% 59.2% 71.4% 73.6%

2 20.0% 19.4% 13.8% 16.3%

3 7.3% 7.4% 6.7% 4.2%

4 4.4% 3.9% 2.0% 2.4%

5 2.5% 2.1% 1.8% 0.5%

6-10 3.8% 4.7% 3.2% 1.5%

11-25 2.1% 2.2% 0.8% 1.2%

26+ 1.2% 1.0% 0.2% 0.3%

Average 2.69 2.91 1.89 1.81

Annual users (‘000s)

1,758

1,451

694

2,051

Table 7.5 shows annual airport trips collected from the CAPI-questionnaire and compares this against

external data from the CAA. The key comparison is between the total number of annual flyers from the CAPI

questionnaire and the number of passengers per year reported by the CAA. The table also shows the

percentage of the CAA data accounted for. The fact that data from the CAPI survey is typically 80-90% of

the CAA data indicates that the information collected on the survey is quite robust.



CAPI claimed flight information CAA external

Unique annual

flyers (‘000s)

Ave. annual

return trips per

flyer

Total annual

flyers (x2)

Total

passengers per

year

% of target

HEATHROW 5,925 2.36 26,082 29,893 87.3

GATWICK 6,049 1.74 20,564 22,934 89.7

STANSTED 2,432 1.81 8,600 9,859 87.2

LUTON 1,541 1.78 5,422 6,519 83.2

BRISTOL 1,181 1.79 4,206 4,817 87.3

MANCHESTER 4,175 1.73 14,736 15,821 93.1

BIRMNGHAM 1,972 1.67 6,400 6,910 92.6

EDINBURGH 956 2.73 5,092 6,398 79.6

GLASGOW 1,214 2.23 5,316 5,762 92.3

ABERDEEN 243 2.86 1,400 2,741 51.1

EAST MIDLANDS 1,419 1.66 4,726 3,490 135.4

Other 4,048 1.87 20,704 20,168 102.7

Table 7.6 (in two parts) shows airports flown from in relation to region of residence. In general it is apparent

that region of residence is strongly related to airports used. It also demonstrates that the catchment area for

Heathrow is somewhat greater than the other airports. This is evidenced by the fact that relative high

percentages of Heathrow passengers have travelled from outside London and the South East.

Respondent GOR Heathrow Gatwick Stansted Luton Manchester

East Anglia 12% 14% 39% 35% 1%

East Midlands 6% 3% 6% 11% 5%

London 30% 27% 28% 27% 0%

North East 2% 0% 1% 0% 4%

North West 3% 1% 2% 1% 50%

Scotland 4% 2% 2% 2% 3%

South East 24% 37% 14% 15% 1%

South West 9% 8% 3% 3% 1%

Wales 3% 2% 0% 1% 4%

West Midlands 5% 3% 3% 3% 8%

Yorkshire 3% 2% 3% 2% 23%



Respondent GOR Birmingham Edinburgh Glasgow Aberdeen East Midlands

East Anglia 1% 3% 2% 4% 1%

East Midlands 18% 1% 1% 0% 50%

London 1% 2% 1% 1% 0%

North East 0% 2% 1% 1% 0%

North West 1% 1% 2% 3% 8%

Scotland 2% 86% 89% 80% 0%

South East 3% 2% 2% 4% 0%

South West 6% 2% 2% 3% 0%

Wales 3% 0% 0% 1% 0%

West Midlands 63% 1% 0% 2% 18%

Yorkshire 2% 0% 0% 1% 23%

Questions about trips to airports where the respondent was not planning to fly were also asked on the CAPI

questionnaire.

Q42. Are there any Airports that you have been to in the last 12 months but which you have not

flown from, i.e. visited to pick up or drop off other people?

Q43. Frequency of Visit for specific airports

Table 7.7 shows the number of respondents in each region who claimed to have visited an airport but not

to have flown from it. Note that East Midlands airport has not been specified within this question so is not

included in this table. Further on in the process, when modelling, respondents stating ‘Other’ but living close

to the airport have been used to estimate use.



Q.42 Are there any Airports that you have been to in the last 12 months but which you have not

flown from?

Resp. % Weighted (‘000s) %

HEATHROW 1,662 4.9 2,377 4.6

GATWICK 1,080 3.2 1,562 3.0

STANSTED 661 1.9 953 1.9

LUTON 441 1.3 627 1.2

BRISTOL 307 0.9 446 0.9

MANCHESTER 809 2.4 1,231 2.4

BIRMNGHAM 503 1.5 765 1.5

EDINBURGH 232 0.7 360 0.7

GLASGOW 241 0.7 367 0.7

ABERDEEN 71 0.2 110 0.2

Other 2,752 8.1 4,076 8.0

At any airport, a claimed traveller in the past year could also be a visitor. However, distance is likely to play a

part and those from further away are not likely to visit each airport. An analysis of the distance travelled for

airport visitors in the travel survey suggests the following rule for the maximum distance an airport traveller is

likely to travel:

• Large and international airports – Heathrow, Gatwick, Manchester, Stansted, Luton – 200 km

distance

• Medium airports – Birmingham, Edinburgh, Glasgow – 100 km distance cut-off

• Small airports – Aberdeen, East Midlands – 50 km distance cut-off

An example allocation from Heathrow is shown in Table 7.8. Eligible sample frame for past year respondent

visits increases from 5% (GPS visitors plus claimed visits) to 16% (adding GPS and claimed flyers who are

within distance).



Category Year 1-5

respondents % of total

Actual GPS visitor 108 0.3

Not GPS but claimed visits 1,632 4.8

Not a claimed visit but a GPS flyer within distance 181 0.5

Not a claimed visitor but a claimed flyer and within distance

3,616 10.6

Ineligible 28,628 83.8



Probability models have been developed for all the out of home media measured on Route. These involve

calculating the probability that each respondent will come into contact with each medium in a defined

time period. This is calculated from an analysis of behaviour and demographic data.

The airport model is different because there is enough data to model each of the ten selected airports

individually. Separate models have been developed for passengers and airport visitors. This passenger

model requires the full imputed data and is based on two datasets:

1. The respondent actual and imputed journey file. The imputation brings the weighted journeys close

to the CAA weekly journeys

2. The CAPI respondent file with claimed journeys and annual frequency. The total weighted annual

frequency of journeys is close to the CAA annual target

Each respondent on the travel survey who claims to have travelled by air in the past twelve months is

assigned a probability. This represents their chance of travelling in any given week. Over multiple weeks the

probability of having travelled increases. Other factors that are taken into account in the calculation of

individual probabilities are distance from the airport, actual flights in a week, the number of claimed flights in

the past twelve months and other respondents in the same or neighbouring zones. A combination of travel

zone clusters, CAPI/ Self-Completion Questionnaire (SCQ) data and respondent visits has been created to

define use.

The NBD has been fitted around these zone clusters to assign actual/virtual Expected Values (EV) and

probabilities. Frame contacts are matched for actual and virtual visits for file 4 audience-frame file creation.

These contacts are used to help create a file 1 and 7 frame population output of frame contacts.

Distance has proved to be an important predictor of travel in other environments, for example indoor and

outdoor shopping centres. Airport travel, however, is less dependent on this variable and, in addition, is

characterised by a low frequency of visiting. The number of respondents on the travel survey who visited an

airport was 1628; of whom 489 were actual flyers. This represents 4.8% of all respondents.

Travel zone clusters are created as a convenient way of assigning survey respondents to different

geographic locations. The Year 1-5 sample has 1,341 clusters with between 10 and 45 respondents in

each. Airports were modelled individually and clusters were defined relative to each of the ten airports that

were modelled.

Clusters with no actual visitors are excluded (i.e. clusters where none of those interviewed had travelled from

an airport in the past year). Clusters with eligible respondents but no actual contacts were collapsed with an

adjacent zone where there were survey respondents. After collapsing every zone contained at least one



respondent. The maximum distance to the cluster is 266 km (Glasgow). Table 8.1 shows the number of

clusters after collapsing, each of which contained at least one actual or imputed GPS trip.

Number

of "initial"

eligible

clusters

% of

eligible

clusters

Number of

collapsed

clusters

Average

number of

clusters

collapsed

Heathrow 1047 78% 307 3.4

Gatwick 928 69% 245 3.8

Stansted 621 46% 116 5.4

Luton 451 34% 84 5.4

Manchester 605 45% 166 3.6

Birmingham 373 28% 68 5.5

Edinburgh 179 13% 54 3.3

Glasgow 190 14% 56 3.4

Aberdeen 55 4% 19 2.9

East Midlands 258 19% 53 4.9

Table 8.2 illustrates the process of collapsing zones for the Heathrow model. Clusters nearer to the airport are

more likely to be eligible (have at least one past year claimed flyer). Following this principle some 294

clusters were removed from the Heathrow sample while only 7 of the 297 clusters within 50 km of Heathrow

are excluded.

Distance to

Heathrow

(km)

Excluded clusters Eligible clusters

0-50 7 2.4% 290 27.7% 50-100 7 2.4% 159 15.2% 100-150 24 8.2% 147 14.0% 150-200 39 13.3% 126 12.0% 200-250 44 15.0% 81 7.7% 250-300 89 30.3% 111 10.6%

300+ 84 28.6% 133 12.7% Clusters 294 1047 Average

distance

(km)

285 164

Clusters were collapsed based on geographic proximity to one another. The cluster with most others joining

is centred in Manchester – 400km from Heathrow. The average cluster distance was 27km and the

maximum 117km. Some 4% of collapsed clusters have five or more clusters within them. There were 778

respondents in clusters that did not contain any travellers who were going to Heathrow.



Table 8.3 shows the number of collapsed clusters that contain 1, 2, 3 etc initial clusters.

Number of

initial

Clusters

Number of

Collapsed

Clusters %

Average

distance to

Collapsed

cluster (km) 1 88 28.7% 0 2 86 28.0% 8 3 44 14.3% 7 4 27 8.8% 13 5 18 5.9% 13

6-10 29 9.4% 15 11-15 10 3.3% 25 16-20 3 1.0% 24 21+ 2 0.7% 21

The calculation of the negative binomial distribution (NBD) follows the same principles as in other

environments. The NBD is a curve that describes respondent contacts within a group. The following are

important parameters in the calculation of the NBD:

No_resps = the number of eligible respondents

P(0) – the probability of no contact = 1-(no_visitors/no_resps)

Avge() – average contacts per respondent = no_visits/no_resps

Curve variable cc - =avge()/ log(p(0))

Then iterate to calculate value apara

Kpara = avge/ apara

Only eligible clusters and respondents with SCQ information are included here. Any travel zone cluster with

zero GPS visits is ineligible. The model takes as its input the probabilities assigned to individuals and uses this

information to calculate reach and frequency. Separate bespoke models have been developed for the

ten airports included in the research.

The NBD has been used with the travel zone clusters and eligible respondents. Actual/imputed airport trips

drive the development of the model. Key variables in the analysis are:

Number of airports – always 1 for the airport model as each is modelled separately

Number of respondents

Number of airport visits

Number of eligible respondents

Pairs of Outward/ Return trips are treated as one overall journey. If there is only one of an outward/return pair,

a second can be allocated later to represent the total journey (halving the probability generated).



The standard NBD curve is used and the calculation is based on an iterative process to calculate curve

parameters a and k with constant variable c.

Calculate a share factor for all eligible past year claim respondents using actual visits and

frequency of claimed to help distribute an expected weekly contact across all records – labelled

an Expected Value (EV) – EVs within a cluster cumulate the number of weekly visits.

Probability is derived from the EVs

Table 8.4 shows the average probabilities for each airport for actual and virtual contacts. The average

probability for actual contacts is somewhat higher than those for virtual contacts. Conversely, the number of

virtual records is much higher than the number of actual contacts as the requirement is to spread the

contacts out across different geographies and demographic groups.

Actual contacts Virtual contacts

Records Average probability Records Average probability

Total 3076 0.135 33894 0.037

ABERDEEN 80 0.172 292 0.058

EDINBURGH 164 0.103 1336 0.039

GLASGOW 163 0.165 1792 0.045

LONDON GATWICK 671 0.119 8684 0.041

LONDON HEATHROW 948 0.171 8246 0.035

LUTON 193 0.143 2298 0.036

MANCHESTER 442 0.102 5512 0.034

NOTTINGHAM EAST MIDS 119 0.095 2078 0.033

STANSTED 296 0.106 3656 0.033

A rule of thumb applied to other environments is:

Ratio of virtuals to actuals – whilst in some ways it is good to have a lot of virtuals, this does generate

a lot more records and this can cause problems within the delivery system. A ratio of 10 virtuals to 1

actual has been output on roadside and this ratio is also used for airports.

Average probability of actual versus virtual contacts. Actuals have a much higher average

probability. A rule of having between 10% and 20% of the total contact audience (probability x

number of records) being generated by virtual contacts has been used on other environments and

has been also been adopted for airports.

There are no external statistics available on the number of airport visitors who are not flying from each airport.

For all practical purposes this information can be taken from the travel survey.

Tables 8.5 to 8.8 show the distribution by day and day part for passengers departing each airport, arrivals

and airport visitors who are not travelling themselves. Day parts are defined as follows:

Weekdays Weekends

Day Part 1 06.00-09.59 Day Part 5 06.00-09.59



Day Part 2 10.00-15.59 Day Part 6 10.00-15.59

Day Part 3 16.00-18.59 Day Part 7 16.00-18.59

Day Part 4 19.00-05.59 Day Part 8 19.00-05.59

Day number


ABERDEEN 8.6% 11.1% 25.3% 1.9% 14.1% 29.7% 9.3% 53

EDINBURGH 15.7% 10.6% 22.4% 8.1% 18.9% 21.1% 3.2% 94

GLASGOW 10.4% 18.1% 20.6% 5.9% 15.7% 22.2% 7.1% 90

GATWICK 7.4% 4.7% 20.5% 12.6% 21.1% 15.9% 17.8% 414

HEATHROW 9.6% 10.5% 11.5% 14.4% 20.3% 16.6% 16.9% 530

LUTON 9.1% 14.5% 11.8% 14.5% 19.6% 12.2% 18.4% 107

MANCHESTER 8.0% 4.2% 17.9% 18.1% 15.1% 16.9% 19.8% 302

EAST MIDS 14.9% 13.9% 20.7% 20.4% 13.9% 9.3% 6.9% 63

STANSTED 13.9% 7.6% 21.7% 14.7% 13.1% 14.2% 14.8% 158

Total 9.7% 8.6% 17.3% 13.8% 18.3% 16.7% 15.7% 1811

Day number


ABERDEEN 4.4% 12.3% 14.6% 8.9% 22.9% 22.0% 14.9% 54

EDINBURGH 5.2% 8.3% 15.6% 10.8% 26.9% 15.1% 18.0% 95

GLASGOW 8.0% 6.8% 17.2% 14.5% 21.8% 13.8% 17.9% 93

GATWICK 9.2% 21.4% 22.8% 10.2% 10.9% 15.5% 10.0% 312

HEATHROW 10.4% 20.4% 6.8% 13.9% 13.4% 16.2% 19.0% 474

LUTON 8.4% 6.5% 18.4% 8.6% 11.9% 25.1% 21.0% 96

MANCHESTER 12.0% 12.7% 11.2% 13.4% 24.2% 7.0% 19.5% 242

EAST MIDS 16.0% 9.1% 9.7% 3.1% 23.4% 20.4% 18.3% 64

STANSTED 12.7% 14.1% 10.1% 10.7% 22.6% 9.6% 20.2% 147

Total 10.1% 15.7% 13.2% 11.7% 17.4% 14.7% 17.2% 1577



Day part


ABERDEEN 23.6% 25.5% 26.3% 4.8% 4.9% 0.0% 9.9% 4.9% 53

EDINBURGH 28.6% 26.2% 12.1% 6.9% 2.5% 11.5% 8.5% 3.7% 94

GLASGOW 22.8% 26.4% 15.3% 7.0% 6.8% 6.7% 9.6% 5.4% 90

GATWICK 19.6% 34.7% 12.6% 20.9% 5.7% 2.3% 1.5% 2.6% 414

HEATHROW 22.8% 31.8% 12.0% 13.3% 7.8% 4.0% 1.5% 6.9% 530

LUTON 11.1% 30.8% 14.0% 20.5% 3.4% 10.3% 1.9% 7.9% 107

MANCHESTER 25.7% 23.7% 5.6% 32.8% 2.6% 4.6% 2.3% 2.6% 302

EAST MIDS 7.9% 31.9% 19.3% 12.1% 3.8% 9.0% 5.2% 10.8% 63

STANSTED 19.6% 21.9% 3.5% 33.5% 5.7% 6.9% 2.8% 6.1% 158

Total 21.4% 29.4% 11.3% 19.5% 5.5% 4.9% 2.9% 5.0% 1811

Day part


ABERDEEN 18.9% 10.3% 18.5% 35.6% 2.1% 0.0% 1.6% 13.0% 54

EDINBURGH 27.5% 25.1% 17.8% 16.0% 3.0% 0.0% 0.7% 9.8% 95

GLASGOW 15.3% 27.7% 20.6% 21.6% 5.8% 1.3% 0.0% 7.7% 93

GATWICK 9.9% 10.4% 21.2% 27.9% 6.2% 4.8% 4.8% 14.8% 312

HEATHROW 13.8% 10.5% 24.0% 20.9% 1.3% 10.4% 6.5% 12.7% 474

LUTON 1.8% 24.7% 19.4% 39.2% 0.0% 1.7% 5.3% 7.9% 96

MANCHESTER 3.7% 16.7% 14.9% 40.0% 2.5% 8.2% 5.8% 8.3% 242

EAST MIDS 1.7% 19.0% 12.5% 41.6% 0.0% 3.7% 6.5% 15.0% 64

STANSTED 2.3% 15.5% 18.4% 37.0% 2.0% 8.3% 6.6% 9.9% 147

Total 10.2% 15.0% 20.0% 28.9% 2.8% 6.4% 5.1% 11.5% 1577

The number of GPS visitors (entering the terminal building) determines the number of weekly contacts (see

Table 8.9). The number who claim to have visited the airport in the past year determines the number of

virtual contacts that are required. Eligible past-year visitors are drawn from:

• Those visiting in the survey week identified in the travel survey.

• Non-flyers who claim to have visited an airport.

• Flyers from an airport who are within a certain distance of the airport (GPS visitor distribution is used to

set a limit on the distribution).

The distribution of visitors who enter/do not enter a terminal building can be used to segment further on

possible use.

Visitors to each airport are modelled separately. Travel zone clusters are collapsed to represent the

probability model as accurately as possible.



GPS visitors Past year

allocated

visits

Weighted population Travel zone

clusters in

NBD

Inside

terminal

building

Outside only Weekly

(‘000s)

Annual

(‘000s)

ABERDEEN 2 9 86 2 109 2

BIRMINGHAM 13 34 843 14 1002 9 EDINBURGH 18 15 533 19 643 11

GLASGOW 21 47 434 20 523 13

GATWICK 39 53 2617 39 2568 30

HEATHROW 177 185 3439 181 4036 97

LUTON 9 57 578 13 636 8

MANCHESTER 47 68 1723 60 2038 34 EAST MIDS 7 16 342 7 430 5

STANSTED 37 48 1313 32 1410 20

Total 370 532 11908 386 13397

The final distribution of probabilities is shown in Table 8.10

Actual contacts Virtual contacts

Records Average probability Records Average probability

Total 357 0.210 3710 0.059

ABERDEEN 2 0.151 26 0.068

EDINBURGH 18 0.133 207 0.057

GLASGOW 21 0.230 147 0.080

GATWICK 39 0.099 833 0.050

HEATHROW 177 0.240 1178 0.063

LUTON 9 0.125 203 0.069

MANCHESTER 47 0.153 587 0.066

EAST MIDLANDS 7 0.187 105 0.049

STANSTED 37 0.309 424 0.048

Tables 8.11 and 8.12 show the distribution of departures by day of week and day part.

Departures Day number




ABERDEEN 0.0% 0.0% 0.0% 0.0% 0.0% 20.7% 79.3% 2

EDINBURGH 13.0% 16.3% 26.8% 5.7% 22.6% 7.9% 7.7% 19

GLASGOW 15.6% 27.4% 10.3% 17.6% 6.8% 22.2% 0.0% 20

GATWICK 22.4% 20.8% 11.5% 0.0% 18.5% 8.2% 18.6% 39

HEATHROW 12.7% 13.8% 15.4% 17.0% 15.4% 12.0% 13.8% 181

LUTON 4.4% 24.7% 23.1% 0.0% 0.0% 22.6% 25.2% 13

MANCHESTER 16.0% 8.2% 13.3% 15.9% 4.3% 17.7% 24.6% 60

EAST MIDS 8.1% 6.9% 60.0% 12.5% 12.5% 0.0% 0.0% 7

STANSTED 18.8% 18.4% 11.5% 0.0% 34.6% 11.2% 5.6% 32

Day parts are defined as follows:

Weekdays Weekends

Day Part 1 06.00-09.59 Day Part 5 06.00-09.59

Day Part 2 10.00-15.59 Day Part 6 10.00-15.59

Day Part 3 16.00-18.59 Day Part 7 16.00-18.59

Day Part 4 19.00-05.59 Day Part 8 19.00-05.59

Arrivals Day part


ABERDEEN 23.6% 25.5% 26.3% 4.8% 4.9% 0.0% 9.9% 4.9% 53

EDINBURGH 28.6% 26.2% 12.1% 6.9% 2.5% 11.5% 8.5% 3.7% 94

GLASGOW 22.8% 26.4% 15.3% 7.0% 6.8% 6.7% 9.6% 5.4% 90

GATWICK 19.6% 34.7% 12.6% 20.9% 5.7% 2.3% 1.5% 2.6% 414

HEATHROW 22.8% 31.8% 12.0% 13.3% 7.8% 4.0% 1.5% 6.9% 530

LUTON 11.1% 30.8% 14.0% 20.5% 3.4% 10.3% 1.9% 7.9% 107

MANCHESTER 25.7% 23.7% 5.6% 32.8% 2.6% 4.6% 2.3% 2.6% 302

EAST MIDS 7.9% 31.9% 19.3% 12.1% 3.8% 9.0% 5.2% 10.8% 63

STANSTED 19.6% 21.9% 3.5% 33.5% 5.7% 6.9% 2.8% 6.1% 158

Total 21.4% 29.4% 11.3% 19.5% 5.5% 4.9% 2.9% 5.0% 1811



File 4 links probability output with behaviour records and contacts. The file 4 output comprises the

respondent-frame records representing contact with frames over different multiple time periods. The

probability model generates airport contacts using information from the travel survey. Contacts are based

on actual visits or on virtual visits for respondents who are possible users over a longer time frame.

An O-I-D visit is generated together with a Behaviour Route Record (BRR) for each actual/virtual contact

record. This gives a series of contacts with frames for that BRR together with a VA score. Then:

For actual visits, one Link Route is selected randomly using the Distance Weight in each BRRP. The

VA for that Link Route is added along all BRRPs.

For virtual visits, all Link Routes are used. The Distance Weight is used to ‘mist’ frame contacts so they

are spread more widely. In effect, the distance weight is multiplied by the probability output to

reduce contact probabilities.

Table 9.1 shows the number of frame contacts by type for each airport.



Actual Records Virtual Records

Airport

Frame

Con-

tacts

%

Ave.

Prob-

ability

Aver-

age VA

Frame

Con-

tacts

%

Ave.

Prob-

ability

Ave. VA Visit

Ratio

Aberdeen 3010 2.2% 0.168 0.626 13120 0.7% 0.047 0.671 4.4

East Midlands 2355 1.7% 0.096 0.785 41883 2.3% 0.032 0.787 17.8

Edinburgh 12175 9.0% 0.104 0.700 122101 6.7% 0.030 0.627 10.0

Gatwick North 8841 6.5% 0.120 0.698 191345 10.4% 0.022 0.747 21.6

Gatwick

South 6552 4.8% 0.118 0.704 120336 6.6% 0.026 0.733 18.4

Glasgow 3873 2.9% 0.172 0.618 60233 3.3% 0.031 0.616 15.6

Heathrow

Terminal 1 10055 7.4% 0.181 0.580 128915 7.0% 0.023 0.552 12.8

Heathrow

Terminal 3 7087 5.2% 0.162 0.545 99044 5.4% 0.023 0.557 14.0

Heathrow

Terminal 4 13993 10.3% 0.179 0.608 135136 7.4% 0.023 0.608 9.7

Heathrow

Terminal 5 38400 28.3% 0.177 0.658 425210 23.2% 0.023 0.671 11.1

Luton 6710 4.9% 0.142 0.689 97273 5.3% 0.027 0.699 14.5

Manchester 12595 9.3% 0.103 0.771 195190 10.7% 0.024 0.780 15.5

Stansted 10098 7.4% 0.119 0.702 202369 11.0% 0.018 0.713 20.0

Total 135744 100% 0.149 0.665 1832155 100% 0.024 0.679 13.5

A weekly population for each Origin-Interchange-Destination (O-I-D) route is calculated from the overall CAA

2012 figures. The calculation takes into account all the different routing permutations. It includes

differentiation between International and Domestic audiences where this information is available.

25 x 2 Behaviour Route Records (BRRs) have been randomly generated for the O-I-D. ROTS (Realistic

Opportunity to See) and visibility scores for contacts with frames have also been generated. The proportion

of splits into the two time groups can be calculated from the travel survey. An average of the visibility across

these frames can be calculated for the O-I-D route and all O-I-D routes added to give an overall average

audience for frames on each route. This average can then be used to produce a total audience.

O-I-D routes are distributed by day-parts across the overall audience for the airport rather than simulating

contacts within the travel survey.

Table 9.2 shows the average weekly audience at each airport and the distribution of frames based on the

weekly file 1 audience. For example, at Heathrow 4.8% of the total audience is made up from frames with

fewer than 1,000 passages per week while 12.0% is from frames with more than 100,000 weekly passages.



For Edinburgh, Gatwick and Luton, frames accounting for more than 60,000 weekly passages represent

more than 40% of the total audience compared with 25.2% averaged across all airports. Not surprisingly,

the average frame audience for Edinburgh (58504), Gatwick (52634) and Luton (48339) is greater than the

overall average (40745).

Frames by weekly file 1 audience

Airport Av aud-

ience < 1000 1 -

10,000 10-

20,000 20-

40,000 40-

60,000 60-

100,000 >

100,000 Total

frames

ABERDEEN 21333 8.5% 16.9% 23.7% 42.4% 8.5% 0.0% 0.0% 59

EDINBURGH 58404 6.8% 4.2% 7.6% 20.3% 19.5% 16.9% 24.6% 118

GLASGOW 22704 5.8% 19.8% 33.7% 25.6% 7.0% 7.0% 1.2% 86

GATWICK 52634 0.0% 0.0% 7.1% 28.6% 21.4% 35.7% 7.1% 14

HEATHROW 39949 4.8% 25.5% 12.8% 19.1% 13.3% 12.5% 12.0% 392

LUTON 48339 3.0% 10.6% 15.2% 22.7% 7.6% 33.3% 7.6% 66

MANCHESTER 47241 1.1% 11.8% 10.8% 14.0% 30.1% 26.9% 5.4% 93

EAST MIDS 24661 4.8% 23.8% 4.8% 57.1% 9.5% 0.0% 0.0% 21

STANSTED 40359 0.0% 4.3% 17.4% 47.8% 8.7% 17.4% 4.3% 23

Total 40745 4.7% 17.9% 14.7% 23.1% 14.4% 15.0% 10.2% 872

Table 9.3 shows the distribution of the average weekly frame audience by dimension code (i.e. the type of

frame). This shows the extent to which the average weekly audience for larger panels exceeds that of

smaller panels. In this context, 96 sheet panels have an average weekly audience of 94,981 while the

equivalent for 48 sheets is 84,710. This can be compared with weekly audiences of 23,806 for escalator

frames, 22,216 for irregular small scrolling frames and the overall average of 40,745.



Dimension

code Frame type Frames

Average

weekly

audience

4 Escalator Frame 10 23,806 10 6 sheet static 19 26,736 10 6 sheet scrolling 84 29,932 15 48 sheet 3 84,710 16 96 sheet 5 94,981 17 Irreg small static 358 43,216 17 Irreg small scrolling 70 22,216 17 Irreg small digital 312 44,157 18 Irreg large static 11 67,004

Total 872 40,745

Chart 9.1 provides an example of a frame with a high file 1 audience. It relates to a frame in Heathrow

Terminal 4 that is in the departure hall opposite the security desk, so most passengers will have contact. It is

a digital frame with an irregular shape and a weekly audience of 200,000.

.



A second frame, also in Heathrow Terminal 4 provides an example of a frame with a low audience (Chart

9.2).

This is near to Gate 7 with only a limited cone of vision. It is a small frame with an irregular size (0.9m x 0.3m)

and a weekly audience of 364.



Mixed frame volumes - national campaigns

All R12.01 formats - cover build %

Weeks

100

frames

200

frames

400

frames

600

frames

1000

frames

All

frames

1 2.6 3.3 3.6 3.6 3.7 3.7

2 4.7 5.9 6.4 6.5 6.6 6.7

3 6.5 8.1 8.8 9.0 9.1 9.2

4 8.1 10.0 11.0 11.1 11.3 11.4

8 12.8 15.9 17.4 17.7 17.9 18.2

13 16.9 20.8 22.8 23.1 23.5 23.8

26 23.3 28.1 30.7 31.1 31.5 31.9

52 29.4 34.5 37.1 37.5 37.9 38.3

0

10

20

30

40

50

0 13 26 39 52

Co

ver

bu

ild %

Weeks

100 frames 200 frames 400 frames

600 frames 1000 frames All frames

Weeks

100

frames

200

frames

400

frames

600

frames

1000

frames

All

frames

1 2.6 4.4 7.8 9.8 18.2 26.4

2 2.9 4.9 8.7 10.9 20.2 29.2

3 3.2 5.4 9.4 11.9 22.0 31.8

4 3.5 5.8 10.2 12.8 23.7 34.2

8 4.4 7.4 12.8 16.1 29.9 43.0

13 5.4 9.1 15.9 20.0 37.1 53.3

26 7.8 13.5 23.6 29.7 55.3 79.4

52 12.3 22.0 39.0 49.3 91.9

132.

5



Frames in each airport

All R12.01 formats - cover build %

Weeks

Aberd

een

Edinbu

rgh

Glasg

ow

East

Midls Luton

Stanste

d

1 0.1 0.2 0.2 0.1 0.2 0.3

2 0.2 0.4 0.4 0.3 0.4 0.7

3 0.3 0.5 0.5 0.4 0.6 1.0

4 0.3 0.7 0.7 0.5 0.8 1.2

8 0.4 1.1 1.2 0.9 1.3 2.1

13 0.5 1.5 1.6 1.3 1.8 2.9

26 0.6 1.9 2.2 2.1 2.6 4.3

52 0.6 2.2 2.7 2.7 3.2 5.3

0102030405060708090

100110120130140

0 13 26 39 52

Avg

Fre

q

Weeks

100 frames 200 frames 400 frames

600 frames 1000 frames All frames

0

2

4

6

8

10

0 13 26 39 52

Co

ver

bu

ild %

Weeks

Aberdeen Edinburgh Glasgow

East Midlands Luton Stansted

Manchester

Weeks

Gatwick

South

Gatwick

North

Heathr

ow T1

Heathr

ow T3

Heathr

ow T4

Heathr

ow T5

1 0.4 0.5 0.3 0.3 0.3 0.6

2 0.8 0.9 0.5 0.6 0.5 1.2

3 1.2 1.3 0.7 0.9 0.7 1.6

4 1.5 1.7 0.9 1.1 0.9 1.9

8 2.6 2.9 1.4 1.8 1.3 3.0

13 3.6 4.0 1.9 2.4 1.7 3.9

26 5.2 5.7 2.5 3.2 2.1 5.1

52 6.4 7.0 3.0 3.9 2.5 5.9



All R12.01 formats - 500 frames cover build %

Weeks Aged 15+

Men 15-34

Men 35+

Women 15-34

Women 35+

ABC1 C2DE FT

Men FT

Women AB

FTW

Business air

travellers

1 3.6 4.1 4.0 3.2 3.3 5.0 2.1 4.9 4.3 7.3 9.9

2 6.6 7.3 7.2 5.8 6.0 8.9 3.8 8.8 7.8 12.7 17.4

3 9.1 10.0 9.8 8.0 8.4 12.1 5.3 12.1 10.7 17.0 23.5

4 11.3 12.2 12.1 10.0 10.5 14.9 6.7 14.9 13.4 20.7 28.7

8 18.0 19.0 19.1 16.1 16.9 23.2 11.0 23.3 21.4 31.4 43.2

13 23.7 24.5 25.0 21.2 22.4 30.1 14.7 30.0 28.1 39.7 54.4

26 31.8 32.4 33.3 28.9 30.5 39.8 20.5 39.5 37.8 51.0 69.3

52 38.0 38.6 39.7 35.0 37.0 47.3 25.4 46.5 45.4 59.4 80.2

0

5

10

15

0 13 26 39 52

Co

ver

bu

ild %

Weeks

Gatwick South Gatwick North Heathrow T1

Heathrow T3 Heathrow T4 Heathrow T5

Heathrow



All R12.01 formats - 500 frames cover build %

Weeks DP 1 DP 2 DP 3 DP 4

1 0.9 1.1 0.6 1.2

2 1.6 2.0 1.2 2.3

3 2.2 2.8 1.7 3.2

4 2.8 3.5 2.2 4.1

8 4.8 5.9 3.9 7.1

13 6.7 8.2 5.5 9.9

26 10.0 12.2 8.4 14.9

52 13.5 16.3 11.8 20.3

0

10

20

30

40

50

60

70

80

90

0 13 26 39 52

Co

ver

bu

ild %

Weeks

Aged 15+ Men 15-34 Men 35+

Women 15-34 Women 35+ ABC1

C2DE FT Men FT Women

AB FTW Business air travellers



0

5

10

15

20

25

0 13 26 39 52

Co

ver

bu

ild %

Weeks

All airport day-part profile

DP 1

DP 2

DP 3

DP 4



Definitions

Airport exteriors comprise frames that are on the roads leading up to an airport, in a car park outside the

terminal, at a drop off area or close to the entrance to the terminal. They are usually on the roadside and

can be seen by vehicles/pedestrians. Contacts with these frames may occur for airport passengers as they

approach or depart from the airport or visitors who are picking up or dropping off passengers. Visitors may or

may not enter the terminal building. There may also be vehicular contacts by people who are in the area of

a frame but who may not be making a trip or travelling with passengers.

These frames have their own environment code (16) and need to be treated in isolation. It is apparent that

this environment has much in common with roadside media but cannot be treated in exactly the same way

because of differences between those who simply pick-up/drop off passengers and those who park and

enter the terminal building. The solution has been to develop a hybrid model that draws on the work that

had been done to create roadside and airport models.

For airport users, behaviour should see a slow audience build over many weeks. For non-airport users, the

build should be similar to roadside – i.e. faster.

Visits generated by the airport interior model have the following characteristics:

The contact probability is based on an airport visit.

A sub-model for interiors identifies frame contacts whilst at the location.

This sub-model can be replaced by GPS frame contacts as this environment is outside.

Respondents visiting the airport terminal

Actual airport visits generate frame contacts based on GPS second by second contacts in the same way as

roadside. These GPS records can be cumulated using the five second rule. Visibility adjustments can be

applied for vehicular and pedestrian audiences.

These data records can be used as a basis for modelling imputed visits. Actual visits are randomly matched

to create imputed and virtual visits with similar characteristics. Frame contacts can be allocated from the

actual visit.

Respondents who do not visit the airport terminal

This is similar to the roadside model. GPS roadside contacts that are not in the Airport Interior model are

identified. These are integrated into the probability model created for roadside media. This includes visitors

who do not enter the terminal building and those driving past but excludes airport interior visitors. The two files

are then added together.

Airport interior model

Weekly audiences for airport visits are available from the airport interior model based on passengers and

visitors who enter the terminal building. For actual visits, the probability of a frame contact is calculated

together with the visibility based on the GPS contacts. This probability and visibility can be applied to the total

weekly audience.



For roadside exteriors direct GPS contacts that are not airport interior visits are calculated together with

visibility adjustments and this is applied. The final step is to add the two audience counts together.

Table 10.1 shows the distribution of airport exterior frames by airport, media owner frame type by dimension

code. More than half the total are at Heathrow, which reflects the size and the number of terminals at that

airport.

JC Decaux operate some 80% of these frames the main types being 1- six sheet static and scrolling which

together account for nearly half the total.

Note that Eurotunnel frames are discarded as these are not

currently included in any of the models.

Table 10.2 shows the location of airport exterior media within each airport. Frames are split fairly evenly

between roadside and other locations. Indeed a number of terminals only have roadside media. More

than a quarter of these frames are in the vicinity of Heathrow Terminal 4.

Airport Frames %

01 - Heathrow 133 52.8% 02 - Gatwick 13 5.2% 03 - Stansted 12 4.8% 04 - Luton 4 1.6% 05 - Glasgow 12 4.8% 06 - Aberdeen 22 8.7% 07 - Edinburgh 20 7.9% 08 - Manchester 10 4.0% 09 - East

Midlands 14 5.6%

99 – Eurotunnel 12 4.8% Total 252 100%

Frames %

Media Owner 4 - JC Decaux 191 79.6% 32 - EYE 49 20.4%

Frame type by dimension code 1 - Static 172 71.7% 10 - 6 sheet 52 21.7% 15 - 48 sheet 21 8.8% 16 - 96 sheet 7 2.9% 17 - irregular

small 49 20.4%

18 - irregular

large 43 17.9%

2 - scrolling 68 28.3% 10 - 6 sheet 58 24.2% 15 - 48 sheet 4 1.7% 17 - irregular

small 6 2.5%

Grand Total 240 100%



Airport / Terminal Car-park Entrance Roadside Total Aberdeen 6 9 7 22

East Midlands 3 11 14

Edinburgh 2 10 8 20

Gatwick North 5 5

Gatwick South 8 8

Glasgow 1 4 7 12

Heathrow T1 6 10 15 31

Heathrow T3 9 15 24

Heathrow T4 2 22 43 67

Heathrow T5 2 9 11

Luton 1 3 4

Manchester T1 2 2

Manchester T2 3 3

Manchester T3 2 3 5

Stansted 4 8 12

Total 28 80 132 240

As outlined above, the modelling process generates additional data in two ways: by imputing trip data to

respondents who have a reasonable likelihood of travelling and by creating virtual trip records. In both

cases real visits control the profiles of imputed and virtual respondents.

Table 10.3 shows the number of actual contacts made by real respondents, the number where the

imputation process has added a return trip, the number where other visit data has been imputed and the

number of virtual visits. The ratio of virtual to real/imputed data is about 16:1. A relatively large number of

virtual visitors are required to supplement real and imputed respondents in order to create enough records

to maximise the efficiency of the model.



Actual visits Virtual

visits

Real Return

added

Imputed Modelled

ABERDEEN 7 1 66 171

EDINBURGH 87 7 84 1388

GLASGOW 71 4 96 1599

LONDON

GATWICK

226 35 476 8841

LONDON

HEATHROW

550 84 506 8879

LUTON 54 13 148 2191

MANCHESTER 166 15 346 5317

NOTTINGHAM 27 1 74 1480

STANSTED 102 20 212 3526

Total 1290 180 2008 33392

An example frame and audience calculation is outlined in this section. This is based on Frame Number

1235463575 at Gatwick Airport.

Gatwick Frame – 1235463575

1. Airport interior contacts

Number of frame contacts – 110, average VA = 0.789

Airport terminal weekly total= 489,000

Visits in GPS sample = 271,000 (55% of total)

Total ROTS contacts with frame (including repeat visits) = 123,000 (45% of available)

Weekly audience of terminal visits



= 123K/271K x 489K x 0.789

= 175,000

2. Roadside contacts (excluding airport visits)

Number of frame contacts – 264, average VA = 0.681

Weighted weekly contacts = 335,000

Weekly audience of terminal visits

= 335K x 0.681

= 229,000

3. Total File 1 audience

= 229,000+ 175,000

= 404,000 (44% based on airport terminal visit)

Table 10.4 shows the File 1 Weekly Audience Summary by Location

All frames

Location Frames Average weekly

Audience Average %

airport visit Car-park 28 82251 68.1% Entrance 80 55316 75.1% Roadside 132 121302 49.0% Total 240 94750 59.9%

Heathrow Airport

Location Frames Average weekly

Audience Average %

airport visit Car-park 19 101843 71.9% Entrance 47 54847 78.7% Roadside 67 132215 40.8% Total 133 100535 58.6%

Table 10.5 shows the File 4 calculations split by the two contact types. The ratio of virtual to actual contacts

is roughly 10:1 for the airport interior model. This is consistent with the models developed for other media



measured by Route. The ratio of virtual to actual contacts for the roadside component of the model is 70:1.

This reflects the fact that roadside contacts are coming from a wider catchment area. This is particularly true

for Heathrow where the ratio rises to 95:1.

Interior model Roadside exterior contacts Airport name Actual

frame

contacts

Virtual frame

contacts Ratio V:A Actual

frame

contacts

Virtual frame

contacts Ratio V:A

Stansted 1247 14875 11.9 695 13110 18.9 Luton 488 5274 10.8 177 2653 15.0 Heathrow 15174 126436 8.3 8560 816886 95.4 Gatwick 1575 20343 12.9 1367 35355 25.9 East Midlands 422 6611 15.7 280 5256 18.8 Manchester 1169 12255 10.5 676 19478 28.8 Aberdeen 578 1620 2.8 219 2813 12.8 Edinburgh 1842 15339 8.3 465 10819 23.3 Glasgow 952 8588 9.0 870 30951 35.6 Total 23447 211341 9.0 13309 937321 70.4

Table 10.6 shows the average probabilities for actual and virtual frame contacts for both models. It is

apparent that probabilities are lower for airport interiors. The exception is Heathrow where virtual contacts

have a much higher number of roadside GPS contacts. The interior model is producing a slower cover build

via the probabilities.

Interior model Roadside GPS

Airport name Actual

frame

contacts

Virtual frame

contacts Actual frame

contacts

Virtual

frame

contacts Stansted 0.272 0.038 0.387 0.050 Luton 0.148 0.038 0.365 0.056 Heathrow 0.223 0.043 0.401 0.042 Gatwick 0.114 0.039 0.355 0.049 East Midlands 0.126 0.032 0.365 0.047 Manchester 0.117 0.037 0.352 0.048 Aberdeen 0.151 0.057 0.485 0.060 Edinburgh 0.109 0.041 0.378 0.051 Glasgow 0.145 0.046 0.416 0.053 Total 0.196 0.042 0.394 0.043

Table 10.7 shows the average visibility scores by model type and airport. Roadside GPS visibilities are taken

directly from the overall Route roadside model. Interior visibilities are based on the airport interior model



described in this document. The airport interior model shows consistency between actual and virtual

contacts.

Interior model Roadside GPS

Airport name Actual

frame

contacts

Virtual frame

contacts Actual frame

contacts

Virtual

frame

contacts Stansted 0.532 0.528 0.484 0.661 Luton 0.753 0.789 0.761 0.642 Heathrow 0.592 0.593 0.496 0.464 Gatwick 0.839 0.832 0.736 0.656 East Midlands 0.768 0.783 0.760 0.669 Manchester 0.634 0.636 0.702 0.654 Aberdeen 0.680 0.653 0.556 0.574 Edinburgh 0.708 0.700 0.735 0.568 Glasgow 0.657 0.664 0.465 0.508 Total 0.628 0.636 0.547 0.482

Weeks 50

frames 200

frames

1 2.92 4.51

2 4.97 7.52

3 6.58 9.85

4 7.92 11.75

8 11.78 17.18

13 14.99 21.55

26 20.18 28.16

52 25.74 34.52

0

10

20

30

0 13 26 39 52

Re

ach

%

Weeks

Airport exterior cover build over time

R1401

0

14-025805-02 | Version 1 | Internal / Client Use Only | This work was carried out in accordance with the requirements of the international

quality standard for Market Research, ISO 20252:2012, and with the Ipsos MORI Terms and Conditions which can be found at

http://www.ipsos-mori.com/terms. © Ipsos MORI 2015.

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communication and relevant media planning.

Ipsos Connect is a specialist division within Ipsos, one of the world’s largest market research agencies. Ipsos has

offices in 87 countries, generating global revenues of €1.669,5 million (2 218,4M$) in 2014.

Neil Farrer

Chief Data Science Officer

Ipsos Connect

[email protected]

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