Job number ; Title of document : Draft status 1
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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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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.
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.
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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 –
client-internal use -
131119-V1-PC
15 Jul
2013
Outputs from observational work at
Manchester Airport Paola Codini
1.4
Route - airports - Gatwick
behavioural study results –
client-internal use -
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
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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
Airport 1 (Sat) 2 3 4 5 6 7 (Fri) Total
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.
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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
Airport 1 2 3 4 5 6 7 8 Total
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;
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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.
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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
Airport 1 (Sat) 2 3 4 5 6 7 (Fri) Total
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.
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Day part
Airport 1 2 3 4 5 6 7 8 Total
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
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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).
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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
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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.
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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%
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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
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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
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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
Total Leisure Business
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.
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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.
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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
Total Leisure Business
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
Total Leisure Business
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.
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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
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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.
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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.
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% 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.
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Total (n=337) Business (n=124) Leisure (n=213)
Once respondents passed through the
Security/Passport Control, they were more likely
to go …
% Rank % Rank % Rank
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.
Total (n=114) Business (n=39) Leisure (n=75)
Once respondents passed through the
Security/Passport Control, they were more likely
to go …
% Rank % Rank % Rank
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
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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
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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.
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Blue – Arrival Gate Pink – Passport Control
Green – Baggage Claim Yellow – Arrival Hall
The following behaviour locations (shown in Table 4.2) were identified:
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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.
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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
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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):
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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.
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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
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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.
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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
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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.
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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
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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
Attractor selected – node ID 1184551 (point 123)
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
Attractor selected – node ID 1184557 (point 129)
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
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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
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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.
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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
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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.
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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.
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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
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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
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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%.
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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.
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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:
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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
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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
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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
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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
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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.
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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%
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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.
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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).
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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
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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
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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.
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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).
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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
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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
Airport 1 (Sat) 2 3 4 5 6 7 (Fri) Total
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
Airport 1 (Sat) 2 3 4 5 6 7 (Fri) Total
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
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Day part
Airport 1 2 3 4 5 6 7 8 Total
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
Airport 1 2 3 4 5 6 7 8 Total
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.
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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
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Airport 1 (Sat) 2 3 4 5 6 7 (Fri) Total
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
Airport 1 2 3 4 5 6 7 8 Total
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
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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.
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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.
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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.
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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.
.
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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.
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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
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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
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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
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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
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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
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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.
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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%
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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.
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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
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= 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
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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
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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
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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.
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