ARTICLE IN PRESS
Transport Policy 16 (2009) 281–292
Contents lists available at ScienceDirect
Transport Policy
journal homepage: www.elsevier.com/locate/tranpol
Evaluation of voluntary travel behaviour change: Experiences from
three continents
Werner Brög a, Erhard Erl a,, Ian Ker b, James Ryle c, Rob Wall c
a
Socialdata GmbH, München, Germany
CATALYST, Perth, Australia
c
Sustrans, Bristol, UK
b
a r t i c l e in fo
abstract
Available online 28 October 2009
The past 20 years has seen a rapid growth across the world in the number, range and scale of voluntary
travel behaviour change (VTBC) initiatives. These so-called ‘soft’ measures have challenged the
assumption that modal shift is only possible through ‘hard’ system-based measures, or through
regulation. Among the most high-profile VTBC initiatives is a household-based behaviour change
technique known as Individualised Travel Marketing. This dialogue marketing approach was developed
by Socialdata (under the brand name IndiMarks) in response to its own research suggesting that a lack
of information and motivation, and incorrect perceptions of the alternatives to the car, were significant
barriers to modal shift. IndiMark has been applied in more than 100 pilot and nearly 150 large-scale
projects, targeting a total of more than three million people on three continents. A key factor in this
widespread take up has been the consistent use of a detailed evaluation design, employing travel
behaviour surveys before and after the IndiMark intervention, using Socialdata’s KONTIVs survey
method. This well-established design uses a self-administered, mail-back questionnaire, coupled with
motivation by post and telephone to encourage high response rates (typically between 60% and 80%)
helping to provide reliable data on mobility behaviour. This paper reviews the development of the
IndiMark technique and the key features of its evaluation using the KONTIVs survey method. It draws
on this experience to address key challenges in the evaluation of VTBC initiatives, and to identify the
common threads of an integrated approach which might strengthen the case for all soft measures.
& 2009 Elsevier Ltd. All rights reserved.
Keywords:
Travel behaviour change
Individualised travel marketing
Evaluation methods
1. The growing importance of ‘soft’ policies
1.1. Potential for behavioural change
Levels of car dependency across the developed world have
grave and growing consequences for the environment and health,
and for the many local communities blighted by road traffic. At
the same time, delays caused by road congestion are estimated to
cost business billions of pounds every year (Eddington, 2006). The
global environmental and social costs of greenhouse gas emissions from personal road transport are also high (e.g. Foley and
Fergusson, 2003).
Since the 1970s, Socialdata has conducted in-depth research into
the reasons for an individual’s mode choice for each trip (Brög et al.,
1976). The research uses face-to-face interviews to identify the
awareness, perception and choice barriers preventing individuals
from using non-car modes for actual trips. These analyses – echoing
findings from many academic studies (e.g. Anable and Gatersleben,
Corresponding author.
E-mail address: Erhard.Erl@socialdata.de (E. Erl).
0967-070X/$ - see front matter & 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.tranpol.2009.10.003
2005; Steg, 2005; Wall et al., 2008) – have made it possible to
differentiate clearly between people’s subjective and objective
situations and, with this information, to determine the opportunities for travel behaviour change to environmental-friendly modes.
This research (VDV and Socialdata, 1993) showed that in
German cities in 1990, 81% of all trips were made by modes other
than public transport (PT), and 19% by PT modes. Nearly a quarter
of all trips (24%) used another mode because there were
constraints to using PT. As these constraints could be because
the car is used for business reasons or to carry a heavy load, these
trips are likely to have limited potential for change. A further 32%
of trips would have required system improvements, such as the
provision of an adequate bus connection or improved service
frequencies, before a switch could be made.
However, for the remaining 25% of trips there were only
subjective reasons preventing PT use. For these trips, a voluntary
behaviour change approach (using so-called ‘soft’ measures)
appeared to be a solution to achieve modal shift without the
need for ‘hard’ measures such as system improvements, pricing or
changes in land use policy. This research showed for the first time
that soft measures could activate large potentials for travel
behaviour change, often on the same scale as system measures.
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1.2. A personal approach to voluntary travel behaviour change
The findings of this research provided an important underpinning for the growth, across Europe and elsewhere, in policies
and measures focusing on voluntary travel behaviour change
(VTBC). The late 1980s onwards saw the development of a wide
range of interventions aiming to influence the travel mode choices
of individuals and organisations through awareness-raising,
marketing and education.
Based on its own research, Socialdata pioneered Individualised
Travel Marketing (ITM) – under the brand name IndiMarks – as a
technique for changing personal travel behaviour. The IndiMark
process uses direct contact with households to identify and meet
their individual needs for support, and to motivate people to think
about their day-to-day travel choices. This conscious consideration is an important precursor to change in a type of behaviour
that is notoriously habitual (e.g. Matthies et al., 2002).
The IndiMark process begins with personal contact, either by
telephone or on the doorstep, with households in the target area.
This initial contact enables the target population to be segmented
into three main groups: existing regular users of sustainable travel
modes; non-regular users who are interested in receiving
information on alternatives to the car; and those who are not
interested in taking part.
Most of the ITM campaign focuses on households in the
‘interested’ group. They receive a TravelSmarts (the brand name
for the IndiMark travel behaviour programme, delivered in the UK
by Sustrans in partnership with Socialdata) order form enabling
them to choose from a range of local travel information materials
and other services, provided by the local authority, public
transport operators and other partners. The requested items are
assembled into personalised packs and hand-delivered to households who requested them.
Households that are not regular users of sustainable travel
modes are also offered a range of further services to enable them
to try these out. These services include home visits, conducted by
a local bus driver or other local travel expert, and the offer of small
incentives such as a test ticket for local bus services, a cycle trip
computer or a pedometer. Regular users are offered a reward to
reinforce their travel behaviour together with a personalised
information pack if required (similar to the interested group).
It is this highly customised, dialogue-based approach, together
with the focus on households rather than major destinations such
as workplaces, that sets IndiMark apart from other VTBC
measures. The success of IndiMark schemes in Europe, Australia
and North America has spawned a range of similar VTBC
techniques also focusing on households, known alternatively as
personal travel planning (PTP).
This paper describes the development of the IndiMark
technique on three continents (Section 2) and reviews experiences
in the evaluation of its effectiveness in changing travel behaviour
(Section 3). Drawing on this, Section 4 explores the evaluation
challenges faced across the range of VTBC schemes and addresses
some common concerns in the debate around their effectiveness.
Section 5 makes the case for a more integrated approach to the
evaluation of VTBC schemes, using behavioural surveys alongside
traffic counts and other output-based, marketing indicators.
2. From promoting public transport to reducing car trips
2.1. Origins of IndiMark and its evaluation
IndiMark was pioneered by Socialdata as a tool for promoting
PT in the late 1980s. An overview of large-scale PT projects in
Germany – where IndiMark was first developed – and elsewhere
in Europe is given in Table 1.
Fig. 1 shows that in the German PT projects there was an
average increase of 33 PT trips per person per year among the
Fig. 1. Results of IndiMarks for German PT projects.
Table 1
PT projects in Europe.
Country
Projects
Locations
Target population (people)
PT increasea(rel. change) (%)
Germanyb
Austriac
Swedend
Switzerlande
UKf
59
23
25
5
6
45
15
19
2
1
1,007,000
228,200
163,800
20,800
286,000
+ 19
+ 13
+10
+10
+6
a
Big variations due to different PT-shares.
Augsburg, Bergheim, Berlin, Bielefeld, Calw, Chemnitz, Dessau, Dotzheim, Dransfeld, Dresden, Duisburg, Düren, Düsseldorf, Erfurt (2), Erkrath, Freiburg/Umland, Fürth,
Halle (8), Hannover, Hilden, Karlsruhe, Kassel, Köln (2), Leipzig (2), Ludwigshafen, Magdeburg (2), Markgröningen, München, Neukirchen-Vlyn, Potsdam, Remseck,
Rohrbach, Rosenheim, Rostock (2), Saarbrücken, Saarbrücken-Region, Stuttgart (3), Sillenbuch, Sachsen-Anhalt, Viernheim, Warburg, Wesel, Wiesbaden (2), WiesbadenBiebrich, Wiesloch, Zwickau; without Nürnberg.
c
Baden, Bruck, Brunn, Eisenstadt, Korneuburg, Linz (6), Maria Enzersdorf, Mödling, Osttirol, Purkersdorf, Salzburg, Schwechat, Tulln, Wien (4), Wolkersdorf.
d
Ahus, Dalvik, Gävle (2), Gothenburg (2), Hällviken, Helsingborg, Jönköping (3), Karlstad, Linköping, Lundby, Malmö (3), Norrköping, Njurunda, Skanetrafiken,
Sundsvall, Trelleborg, Umea, Uppsala, Vellinge.
e
Basel (4), Bern.
f
Hampshire; only one project was evaluated.
b
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283
target groups. The control groups in these same projects showed
an increase of five PT trips per person per year. This means that
the net increase in PT trips attributable to IndiMark was 28 trips
per person per year (relatively + 22% in the target and + 3% in the
control groups).
The same pre-test post-test control group design has been
applied in nearly all IndiMark projects. Results take account of
factors such as weather and infrastructure changes, allowing the
effect of the ITM intervention to be isolated with reasonable
confidence.
2.2. Evaluation of the ongoing Nürnberg IndiMark programme
The value of the pre-test post-test control group design is
evident from work in the City of Nürnberg, Germany (population
approximately 500,000), which uses IndiMark to promote PT use
on an ongoing basis. This work started in 1996 with a small
project of some 5000 people. Several projects have been
conducted in each subsequent year. Since 2006, early target areas
have been revisited and by the end of 2007 a total of 600,000
people had participated in the programme. All projects were
successful in increasing rates of PT use and, moreover, there is
evidence that attitudes to PT across the city have also been
changed1 (Brög, 2006).
Each Nürnberg project has been evaluated separately, but in
2004 all previous evaluation surveys were synthesised to provide
an overview of programme results to date. This analysis
summarised in Fig. 2 showed an overall increase in PT use of
13% (fully supported by counts and calculated against control
groups), along with – although not targeted – a reduction in caras-driver trips of 3%.
That means that there were 20 additional PT trips per person
per year, resulting in additional revenue of approximately h2.5
million for the operator (net). Furthermore, there was a reduction
in road transport carbon dioxide (CO2) emissions of approximately
14,400 ton per year; a saving in external costs of approximately
h6m per year and positive effects on life expectancy in Nürnberg
due to increased walking to and from stations (Brög, 2006).
*)
calculated on trips per year
Source: Brög (2006)
Fig. 2. Consolidated behaviour change 1996–2004: Nürnberg, Germany.
2.3. Sustainability of behaviour change
Fig. 3. Long-term changes in mode choice in Dalvik, Sweden.
There is increasing evidence that behaviour changes generated
by IndiMark are sustained over time. This may be explained by its
focus on enabling voluntary change which helps to make people’s
lives easier, rather than denying them choice. In addition, the
dialogue into which households enter is likely to promote central
processing of messages, as opposed to peripheral processing
(Petty and Cacioppo, 1986), leading to longer-lasting behaviour
change.
Repeat travel surveys conducted up to four years after the
large-scale TravelSmart programme in South Perth have shown
that the behaviour change achieved by the original VTBC
interventions – a 14% reduction in car-use – has been ‘locked in’
with a 13% reduction in car-use measured three and four years
after the intervention (James and Brög, 2002).
Long-term monitoring of travel behaviour has also been
undertaken in the Swedish city of Dalvik following a PT IndiMark
campaign. Three post-intervention surveys – the last one five
years after the ITM – illustrated the stability of public transport
usage increases (Socialdata Sverige, 2002) (see Fig. 3).
1
Travel behaviour research has been conducted continuously in the city since
1989 together with evaluation surveys for each project.
2.4. IndiMark with system improvements
While effective in its own right, the case for IndiMark is even
stronger when it is combined with transport system improvements (‘hard’ measures). Eight projects have been carried out in
recent years where system improvements and ITM have been
combined. Improvements to the system – as illustrated by control
group data – lead to an average increase of 23 additional PT trips
per person per year. However, when ITM was delivered alongside
system improvements, the average increase in PT trips per person
per year was more than doubled (see Table 2).
Applications of TravelSmart in Australia, Europe and North
America are summarised in Table 3 and discussed in Sections 2.5–
2.8 below.
2.5. Car-use reduction in Perth, Western Australia
The VTBC process was applied as a tool for promoting public
transport in the 1990s and has since been developed to reduce car
use by promoting all environmental-friendly modes.
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Table 2
IndiMarks with (rail) system improvements.
Projects
11
a
Locationsa
9
Target population (people)
Increase of PT TRIPS per person per year
156,600
Only system
System and IndiMarks
+ 23
+ 48
Baunatal, Karlsruhe, Köln, München (2), Nürnberg (2), Remseck, Saarbrücken, Stuttgart, Portland.
Table 3
TravelSmarts in Australia, Europe and North America.
Perth
Other Australiaa
UKb
Other Europec
USAd
Canadae
Projects
Locations
Target population (people)
24
10
24
7
12
6
1
4
12
6
9
1
408,500
338,800
304,800
47,000
47,500
4000
Car reduction (rel. change) (%)
11
12
12
13
8
10
a
Brisbane (4), Melbourne (3), Adelaide (2), Townsville.
Bristol (4), Cramlington, Doncaster, Frome, Gloucester (3), London, Lancaster (2), Nottingham (2), Peterborough (3), Preston (2), Sheffield, Worcester(3).
Other countries: Austria, France, Germany, Sweden.
d
Bellingham (2), Bend, Cleveland, Durham, Eugene, Portland (2), Sacramento, Salem, Seattle (2).
e
Vancouver.
b
c
The first application of this extended IndiMark approach was in
Western Australia. In 1997 the Government of Western Australia
commissioned Socialdata to undertake an ITM pilot project
covering 400 households in the City of South Perth. This project
reduced car trips by 10% and increased use of other modes
(walking, cycling and public transport) without constraining
mobility (Socialdata, 1991). As noted above, further travel surveys
one and two years after the project showed that these changes
had been sustained (James and Brög, 2002).
more than 600,000 people have been targeted in 24 pilot and
large-scale projects since 2001. The average car-use reduction
(measured in car trips per person per year) achieved is 12%, but for
single projects the range varies between 6% and 13% (Parker et al.,
2007). The largest UK project targeted a population of 120,000
people in Preston and Lancaster. This project was in 2006 winner
of the CIVITAS demonstration city award.3
TravelSmart projects have also been implemented in four other
European countries and have delivered an average car-use
reduction of 13% (see Table 3).
2.6. Further development of TravelSmart in Australia
2.8. TravelSmart in North America
The success of the small-scale project in South Perth – and the
evidence from a detailed cost-benefit study (Ker and James, 1999)
– laid the foundations for a large-scale project in South Perth in
2000. This extended IndiMark to a population of 35,000 people. Of
17,500 households in South Perth, 15,300 were identified with a
contact name, address and telephone number. Direct contact was
made with 94% of these and 55% (of the 15,300) chose to
participate in the IndiMark programme. The TravelSmart approach has since been delivered to a total of over 400,000 people
in the Perth area, achieving a reduction of car trips of 11% in total,
varying between 4% and 14% across individual projects.2
Following the pioneering work in Perth, Socialdata has
implemented projects in other Australian cities including the
biggest single project ever realised in Brisbane with 180,000
people (Ker, 2008).
2.7. TravelSmart in Europe
In the UK, the IndiMark concept has been applied in partnership with Sustrans under the TravelSmart programme. A total of
2
Reports on TravelSmart programmes undertaken in Australia are available as
follows: Western Australia: http://www.dpi.wa.gov.au/travelsmart; Queensland:
http://www.transport.qld.gov.au/travelsmart; Victoria: http://www.travelsmart.
vic.gov.au/.
There have been 18 TravelSmart projects in ten locations in
North America. Even in the car-dominated US, TravelSmart has
proven to be a successful approach to reducing car use. The range
of car reduction varies considerably – between 2% and 11% – with
an average reduction of 8% (Socialdata America, 2006).
3. Evaluation of behaviour-change interventions
3.1. Indicators of behaviour change
Measuring the effects of marketing interventions on behaviour
is a considerable challenge, not least because of the many
uncontrolled factors that may have an influence on people’s
actions (Brög and Ker, 2008). There are several evaluation
methods available and each has advantages and disadvantages.
As such, when measuring the success of a travel behaviour-change
3
CIVITAS stands for CIty-VITAlity-Sustainability. With the CIVITAS Initiative,
the European Commission aims to generate a decisive breakthrough by supporting
and evaluating the implementation of ambitious integrated sustainable urban
transport strategies. The CIVITAS Awards honour the most ambitious European
CIVITAS Member Cities, which have shown their commitment by introducing
innovative transport policies or activities aimed to achieve better and cleaner
urban transport.
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project, it is preferable to use a variety of evaluation methods. If
multiple methods all point in the same direction and show
qmore-or-less the same magnitude of change, one can be reasonably confident in the results.4
There are three main measures that should be considered in
evaluating a behaviour-change intervention.
Marketing indicators. These include the number and type of
information requests, and quantitative feedback from residents
throughout the project. For example, where one of the desired
changes is to increase PT use, researchers might consider the
number of stop-specific bus timetables requested by participating
households. While it is quite possible that some households may
request information that they do not go on to use, it is unlikely
that thousands of households will order specific, address-based
timetables that they are not interested in and which they will
never use. Very often in traditional direct marketing, such
indicators are the only measures of success. A reliance on
information requests alone may be too courageous, but they are
nonetheless reliable, precise and easy to measure indicators
which should not be ignored.
In addition, it should be borne in mind that enduring
behaviour change is likely to be based to at least some extent
on attitude change (e.g. Petty and Cacioppo, 1986). TravelSmart
evaluations have provided thousands of comments from participants which suggest that attitude change does indeed occur as a
result of the intervention. These qualitative data, whilst not
constituting ‘‘proof’’, are arguably as important as changes
measured in counts or surveys.
External Indicators. These include measured public transport
patronage. In the TravelSmart programme in Western Australia,
for example, bus boarding data are collected and the ability to
gather and use such data will be enhanced by the recent
successful introduction of a comprehensive Smart Card ticketing
system (SmartRider). Parker et al. (2007, p. 127) supports the value
of this type of ‘‘robust corroborative data’’. This form of
monitoring, however, is not without its challenges, most notably
isolating the effects of the VTBC initiative from those of other
influences.
Behavioural Indicators. The effectiveness of travel behaviourchange projects can also be evaluated by measuring changes in
the mobility patterns of residents by conducting extensive preand post-intervention travel surveys. Data from these surveys for
TravelSmart projects describe mode share, activities and travel
time, and analysis of these data shows the mode shift from car-asdriver trips to environmentally-friendly modes. As with external
indicators, it is important to measure changes in behavioural
indicators against a control group to account for background
(uncontrolled) factors (Parker et al., 2007). This issue is discussed
further in Section 3.3.
In recent years, most emphasis in the evaluation of VTBC has
been on behavioural indicators, often to the exclusion of marketing and external indicators. Whatever methods are used, acceptance of the results will be highly dependent upon comprehensive
and consistent documentation of processes and outcomes.
and robust evaluation of the effects of dialogue marketing on
travel behaviour is of critical importance. This means that:
(a) A design has to be used which is fully developed and has
already proven its capability to provide reliable and valid
results.
(b) Data on individuals’ mobility behaviour should be collected;
traffic counts, patronage data and so on will not be enough.
(c) Information about mobility from all household members
should be collected because, for example, one household
member might be motivated to change from car to another
mode and another household member might then decide to
use the available car (instead of another mode).
(d) Data on all trips should be collected; not only trips within the
target area.
Socialdata has developed, applied and continuously improved a
research design based on the above criteria and which seeks to
ensure data quality and high response rates. This is known as the
KONTIVs Design: a mail-back technique using a one-day diary for
all household members (Brög, 2000). The survey instrument
collects information on individual activities performed at all outof-home destinations on a nominated travel day and this provides
an account of how, where and why respondents travelled (or did
not travel). It has been applied in more than 15 countries with
consistently high response rates and reliable and valid results.
3.3. Behaviour change in context
Behaviour change initiatives do not happen in isolation. Almost
by definition, such schemes are funded because there is a
sympathetic policy environment. There is also evidence that
increased fuel costs have had a systematic impact on travel
behaviour (e.g. Warren (2008)). This requires that, wherever
possible, we also measure the travel behaviour of people or
households similar to those subject to the intervention so that the
impact of factors external to the intervention can be identified and
appropriate adjustments made to the measured outcomes in the
intervention group. This poses a problem for interventions that do
not attempt to measure change over the whole target population,
as there is a potentially high degree of self-selection in the
intervention group that makes them different from any potential
control group.
The only group that, in principle, meets all of these criteria
(subject to sampling variability) would be a random sample from
the population of the intervention area itself. This is particularly
so with a very large-scale application that covers a large and
demographically diverse area. In practice, however, even those
who are not part of the target population are highly likely to be
influenced by the intervention itself. As a result, it is often most
appropriate to draw the control group from a geographically close
but distinct residential area that is likely to be subject to similar
background influences to the target area.
3.2. KONTIVs Design
4. Challenges in the evaluation of VTBC
An international demonstration project initiated by the UITP
(International Association of Public Transport) with scientific
leadership from Socialdata (UITP, 1998) showed that a detailed
4.1. Background
4
One cannot rule out the possibility that all evaluation methods are biased in
the same direction, but the likelihood of this decreases as the number of
evaluation methods increases.
285
Getting people to change their (often habitual) travel behaviour on a large scale and at a reasonable cost is undoubtedly a
challenge. However, in the context of global climate change, rising
fuel costs, and obesity and other health problems associated with
sedentary lifestyles, the rewards from achieving real and
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sustained behaviour change are potentially very great (AGO,
2006).
It has to be expected that the VTBC programmes reporting the
most positive outcomes should also be subject to the highest
levels of external scrutiny. However many practitioners in the
field have been taken aback by the degree of criticism levelled at
specific evaluation methods (Stopher,2003), and by the apparent
paucity of robust alternatives (see, for example, Roth et al., 2003).
A welcome contribution to this debate has been provided by a
recent, comprehensive study of ‘personal travel planning’ in the
UK and elsewhere (Parker et al., 2007). This report highlights
many of the evaluation challenges facing VTBC practitioners. The
next section explores these issues in more detail and attempts to
make a contribution to some of the most common arguments in
the debate around the effectiveness of VTBC.
4.2. Significance of results
Most of the debate on sample size has concerned sampling
error estimation (Richardson et al., 2003). In practice, the error
due to systematic factors (e.g. response rate) can be many times
larger than sampling error. This is a serious problem, because
there are always two types of errors to be considered in empirical
studies: random errors (sampling-related); and systematic errors
(design related).
Since there are always systematic errors, the calculated
random errors are always based on an incorrect assumption (that
there are no systematic errors). But the real problem of these two
types of errors is that random errors can be calculated exactly, but
not corrected, whereas systematic errors can be corrected through
survey design changes but cannot be precisely calculated.
A master calculation undertaken for the Dutch National Travel
Survey (MON) can illustrate this (see Table 4). The MON 2005
achieved a net sample of 66,500 respondents at a response rate of
72% and with a gross sample of 92,350 people (using KONTIVs
Table 4
Random and systematic errors (MON).
Net responses
Response rate
Gross sample required
Trips per person per day
Random statistical error
Systematic (response rate) error
66,500 respondents (net)
40%
72%
166,250
92,350
3.4
3.1
70.02 trips per person per day
+ 0.30 trips per person per day
Source: Own calculations.
Design). The trip rate per person and day (calculated after a
detailed analysis of non-response and non-reported trips) was 3.1.
With a response rate of only 40%, a gross sample of 166,250 would
have been needed to get 66,500 respondents. Using the speed of
response technique (described below), the trip rate for the first 40%
can be calculated as 3.4 trips per person and day. The random error
of this survey would be 70.02 trips. The systematic error (of only
one factor: non-response) is fifteen times higher (see Table 4).
4.3. Non-response and non-reported trips
One technique of understanding, calculating and correcting
systematic errors is through the speed of response technique
(Brög and Meyburg, 1981). In a mail-back survey, the number of
trips per person per day can be analysed according to the response
rate reached in the different phases of a survey (speed of
response). This allows different response rates to be simulated
and the result for the variable of interest to be estimated for a full
response. This approach has been used to analyse the likely
shortcomings of a survey that achieved only a 25% response rate
(Brög and Erl, 1999). Fig. 4 shows trip rate index on the y-axis
(where 100 corresponds to the expected value for the total sample
and is the lower bound of the y-axis) and the response rate on the
x-axis. This survey showed an over-estimation of trips per person
per day (index= 121) with a 25% response rate compared to the
expected trip rate for the total sample of 2.9 trips per person per
day (index =100).
The speed of response technique has been applied in analysis
of the Netherlands National Travel Survey: the only long-running
continuous travel survey in the world (Ministerie van Verkeer en
Waterstaat, 2007). As a consequence, a specific self-validating
design has been developed. This design can correct for nonreported items, non-reported trips and non-response and is not
dependent on external data. Fig. 5 shows the effect of response
rate on the trips per person taking into account survey nonresponse and non-reported trips. If we look at the upper response
curve we see a curve following in principle the one presented in
Fig. 4, just flatter: The non-response effects have been reduced.
This is a result of systematic analysis of these non-response
effects and continuous improvements of techniques to reduce
them.
The lower response curve is another valuable addition. It
shows the percentage of non-reported trips by response rate. This
is important because it is often argued that later respondents do
not have lower mobility but do not report all their trips correctly.
The curve shows that this effect exists, but only to a very small
Source: Brög and Erl (1999)
Fig. 4. Trip rate by response rate: Vienna 1993 (response rate 85%).
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Source: Ministrie von Verkeer an Waterstaat (2007)
Fig. 5. Trip rate and non-reported trips by response rate: Netherlands. (MON—Mobiliteitsonderzoek Nederland).
Table 5
Sample sizes (respondents and response rate).
Brisbane North
Victoria Park
Perth-Midland Line
Before
Combined response rate panela
After cross section
1309 (76%)
905 (80%)
868 (72%)
825 (65%)
780 (70%)
345 (60%)
1381 (79%)
766 (79%)
920 (72%)
Source: Own surveys.
a
% response rate of before survey % response rate of after survey.
Table 6
Reduction of car tripsa.
Panel
Cross section
Brisbane North (%)
Victoria Park (%)
Perth-Midland line (%)
11
12
7
13
14
11
Average (%)
10
13
Source: Own surveys.
Note: All reductions in car use are highly significant at least at a significance level
of more than 95%; the difference in the average car reduction between Panel and
Cross Section is highly significant.
a
Calculated with a constant level of mobility.
degree. In principle, non-reported trips have a similar proportion,
irrespectively of the response rate.
4.4. Panel surveys
Many travel behaviour-change interventions have adopted a
cross-sectional survey approach to estimating ‘before’ and ‘after’
travel behaviour. With small samples, this can cause problems
with statistical reliability when examining differences between
the two surveys, but these problems are less severe for larger
samples.
It has often been suggested that a panel survey approach, using
the same people for both surveys, would be more suitable (Stopher
et al., 2009).5 However, panel surveys have their own problems that
are more systematic and less amenable to treatment by statistical
analysis (e.g. aging, attrition, survey fatigue).
While it has been argued that panel surveys require lower
samples sizes (Richardson et al., 2003), some of the parameters
enabling smaller sample sizes also give rise to survey designs
which are more difficult to undertake. For example, panel survey
data is more difficult to obtain (with full control of other biases)
than repeated cross-sectional data (Richardson, 2002). Acquiring
repeated data from the same respondents is challenging and a
reduced response rate in the post-intervention survey can lead to
sampling bias (Parker et al., 2007).
One example of a panel survey in Melbourne (2004/05) used to
estimate the effect of a VTBC initiative had a low initial response
rate (49%: 1346 out of a sample of 2772 households) for the preintervention survey and substantial further loss by the time of the
post-intervention survey. Furthermore, in the post-intervention
survey only 682 of the 881 households that responded to the preintervention survey had the same composition as in the earlier
data collection exercise (Richardson, 2005). At the individual level,
5
On request from the editors we have included cross-references to other
papers in this issue so that there is a means for readers to be aware of related
papers in the special issue.
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Table 7
Sample sizes of experimental response rates (respondents and response rate).
Original panela
Simulated panel
Before
Combined response
rate panel
2988 (75%)
1753 (44%)
2172 (63%)
1103 (32%)
Source: Own surveys.
a
Armadale, Cambridge, Marangaroo (Perth).
Table 8
Reduction of car tripsa.
Original panel
Perth Suburbsb
8%
Simulated (lower)
response rate
6%
Source: Socialdata Australia (2006).
Note: The original result for car reduction is significant at a level over more than
99%.
a
b
Calculated with a constant level of mobility.
Armadale, Cambridge, Marangaroo (Perth).
people may exhibit substantially different travel behaviours at
different times for reasons that are purely idiosyncratic and not
related to the intervention being investigated.
Table 5 summarises evaluations of three TravelSmart projects
(Brisbane North, Victoria Park, and Midland Line, Perth) using
both panel and repeated cross-sectional (random) surveys.
In Table 6 we compare the results of the panels with the crosssectional surveys. We see that the behaviour changes captured in
the cross-section were always greater than in the panel
(Socialdata Australia, 2007a, 2007b, 2008a).
This provides evidence that people who are flexible, move
more, and change their behaviour more readily may be underrepresented in panels, which tend to focus on stability and statusquo. Behaviour changes measured in panels therefore seem to be
smaller than in reality.
This conclusion is supported by current research into travel
behaviour change projects for which panel evaluation has been
selected. In all three surveys, the response rate for the preintervention survey was at least 70% (average of 75%) and the
response rate for the post-intervention surveys always above 80%.
Thus the combined panel response was always over 60% (e.g. 75%
for pre-intervention survey, 82% for post-intervention survey and
therefore 62% for combined surveys).
Using the speed of response technique, it was possible to
simulate the typical pre- and post-intervention panel which has a
response rate in the low forties (pre-) and the low seventies (post-):
a combined response rate of about 30% (Socialdata, 2006; see
Table 7). In terms of the analysis above, the selection worked even
more in favour of stability. This is clearly reflected in the results
(see Table 8). Behaviour changes in a panel with a combined
response rate of about 30% are smaller than in a panel with a
combined response rate of over 60% (Socialdata Australia, 2006).
4.5. Odometers
In the recent behaviour change literature, it has been suggested
that use of odometers would be a promising tool to improve the
reliability of intervention evaluations (e.g. Seethaler and Rose,
2009; Bonsall, 2008). The main arguments to support the use of
odometers are that respondents in surveys are self reporting and
might want to report what they perceive as desirable results, and
that meters are more precise and statistically valid. However,
there are some counter arguments.
For example, even in water conservation projects (where meter
readings often provide the only means of evaluation), the readings
are reported readings. Even where independent meter readers
have been used, experience from five projects has shown that
such operatives make mistakes, are sloppy, or may not read the
meter at all, simply making the results up (Socialdata Australia,
2006b).
In an odometer reading project these problems may be even
greater, if data are self-reported by a household member.
Respondents may wish to give answers that they imagine
researchers would wish to hear and this would be much easier
than in a diary-based survey involving all household members.
This risk alone may rule out self-reported odometer readings as a
viable option. Bonsall (2008) has, for this reason, suggested using
odometer readings taken by third parties as part of statutory
annual vehicle inspection and re-registration procedures—but
this many not be feasible.
In addition, the odometer-reading projects tend to achieve very
low participating rates (e.g. Stopher et al., 2007a) and need a strict
regime to be kept by the households. Adding to the problems of
low participation, in any given wave, about 25% of households will
fail to provide odometer readings (Stopher et al., 2007a). At best
this increases the required sample size, but in conjunction with
low recruitment rates it may cause systematic bias that cannot be
addressed by larger samples. In a recent validation survey on
odometer readings conducted by Socialdata Australia (2008b)
(recruitment rate 80%), about 60% of the readings reported were in
doubt (collected at the wrong time; wrong day; wrong car;
reading invented; later readings lower than earlier ones; or
distances travelled of several thousand kilometres a day).
Finally, even if the odometer reading is correct, it tells us
nothing about the type of travel, its frequency, or the trip purpose.
4.6. Projects using GPS
It has been suggested that geographical positioning systems
(GPS) can provide a means of directly measuring travel and, by
inferential means, mode use (Stopher et al., 2007b; Stopher et al.,
2009). However, this is as yet unproven in at least four key areas:
The robustness of the algorithms for inferring mode use;
The potential for the measurement actually to influence travel
behaviour—for example, a person ‘equipped’ with a GPS device
might be more aware of their travel behaviour (as distinct from
it being habitual) and use transport more efficiently while
carrying the device;
The participants are well aware that every movement is
registered and will find ways to omit trips which they want
to keep confidential. Moreover, if such concerns become
widespread fewer people may be willing to participate in such
exercises;
Even now the willingness to participate in such an exercise is
low, the projects are expensive, and yet do not achieve
representative samples. This is even more critical because the
sample sizes of such projects are traditionally very small
(Stopher et al., 2007a).
A recent experiment following the Brisbane TravelSmart
projects (Socialdata Australia, 2006a; 2007a) attempted to test
the willingness of target persons to participate in a GPS survey.
On offer were two alternatives for a GPS survey: carrying the
GPS tool for one week; or repeating this exercise three times at
two year intervals. Target persons were survey respondents from
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Table 9
Response rates.
Survey experimenta
Original response rate (one sample day) (%)
Participate again (one week) (%)
Participate again (three times, two years intervals) (%)
Sample size of experimental survey (persons)
GPS experimentb
76
72
52
79
30
27
1606
3408
Source: Own surveys.
a
b
Karlsruhe, Germany.
Brisbane, Australia.
Table 10
Reported mobility differences by willingness to participate.
Survey experimenta
Baseline survey
Trips per person/day
(in base year)
Main mode (%)
Walking
Bicycle
Motorcycle
Car as driver
Car as passenger
Public transport
TOTAL
2.9
GPS experimentb
Willing to
participate again
3.0
Willing to participate
three times
3.1
Baseline survey
3.1
Willing to
participate again
3.4
Willing to participate
three times
3.5
22
15
1
38
12
12
21
16
1
39
11
12
21
16
1
39
12
11
8
1
0
64
18
9
6
1
0
72
17
5
5
1
0
74
16
4
100
100
100
100
100
100
Source: Own surveys.
a
b
Karlsruhe, Germany; for the car driver share there is no significant difference the three groups shown.
Brisbane, Australia; for the car driver share there are highly significant differences between the 3 groups observed.
the TravelSmart pre- and post-intervention surveys (KONTIVs
Design). In order to control for the tendency of survey respondents
to participate again, the Brisbane experiment is contrasted with an
experiment in Karlsruhe, where survey respondents were asked to
participate in the same survey again for one week and repeat
surveys in two-year intervals. All results are calculated for people
aged between 14 and 75 years. They show that the well known
effects of repeated surveys are evident in the GPS experiment: not
everyone who says ‘yes’ will actually participate, but we can be
sure that everyone who says ‘no’ will not. The results shown in
Tables 9 and 10 suggest that researchers must be very alert to
biases introduced by recruiting, especially for GPS-type projects.
group results being contaminated to a degree (which always
makes the changes look smaller than they really are).
Control group evaluation adds substantially to the cost of a
travel behaviour change project. In practice, the change in the
control group has consistently been small, largely because the
time between ‘before’ and ‘after’ measurement is short (one year)
and designed to remove the strongest seasonality impacts. Whilst
this may change with rapid change in fuel prices and volatile
economic conditions, other more general indicators of car use
(e. g. road traffic volumes and public transport patronage) may be
able to provide the necessary baseline information.
5. Integrated evaluation
4.7. Use of control groups
Discussions around the use of control groups often follow an
established pattern. There is agreement that a control group
should be used; there is agreement that it is often very difficult to
identify an (ideal) control group; and then lamentation when the
(unavoidably) non-ideal control group is not ideal. Many papers
could be written on the subject of control groups alone, but a few
practical observations based on more than 300 evaluation surveys
(large scale and pilots, which included in most of the cases a
control group) illustrate the key issues.
The most important factors to be controlled in travel surveys
are: season (including holidays), weather, special events, infrastructure changes, and prices. It is generally possible to find a
reasonable control group for each of these factors.
But the most problematic single factor is diffusion. Effective
behaviour-change projects can generate enormous momentum
and it is often difficult (sometimes impossible) to avoid control
The focus on evaluation through measurement of travel
behaviour instead of concept and tools means that large
proportions of the project money are spent on research instead
of on behaviour change (in projects using the IndiMark approach,
the evaluation consumes between 25 and 75% of the total budget).
As experience in the field develops, an increasing number of
VTBC programmes are seeking to broaden the mix of tools used for
monitoring and evaluation. One of the first to use all three types of
indicator (as defined in section 2.1) was in the town of Cambridge,
Western Australia (Socialdata Australia, 2003). Cambridge is a
suburb in Perth, on the coast. The project was conducted in 2002
with a base population of 24,000 people.
Behavioural indicators. The project saw a 7% reduction in car-as
driver trips and a 17% increase in use of sustainable modes
(walking, cycling, PT).
Marketing indicators. Of the base population, 90% were
successfully contacted and 58% agreed to join a dialogue about
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their day-to-day mobility. (Fig. 6 summarises other important
features of the project conduct.)
There were 20 different marketing items on offer, plus 175
different stop related timetables. Total orders of these (each item
is individually recorded) amounted to 44,030. These were
delivered in 3,360 individual packages: 95% using bicycles (and
trailers) and 85% within one week from the initial order. This is an
important indicator because, according to the principles of
dialogue marketing, participants should become actively involved
and should receive only those materials that they specifically
request.
Bus drivers conducted 282 home visits with an average
duration of 32 minutes (ranging from 10 to 90 minutes) and
92% of visited households rated this service as being positive
(cycling home visits had an average duration of 34 minutes and a
98% positive rating).
These factors are, for marketing people, arguably just as
important as the measured behavioural changes measured and
in conjunction with those behavioural measures they make a
strong case for the effectiveness of the intervention. This
effectiveness is further evidenced by hundreds of unprompted
comments from participants, some examples of which are
presented below.
General:
Public
transport:
‘Every little helps, especially as I get older I need
the encouragement.’
‘The phone discussion and personal visit helped
me to overcome some of my unconscious
misconceptions about public transport and
clarify my resistance i. e. didn’t know about
tickets, how to validate and purchase, where bus
routes went and about safety on trains and
walking home after dark. It was useful to talk
about these things.’
‘It gave me the confidence to use a bus that goes
near my home. In 8 years it was the first time.’
‘It’s good to get people used to public transport
as there are various advantages like meeting
people and doing some walking to get to the
transport.’
Home visits: ‘Our bus driver made us feel that the transport
system belongs to everyone; that there is a
strong desire to make it work for everyone; that
we are more connected in City Beach than we
generally feel.’
‘An efficient and knowledge based visit. My bus
driver was an expert and really inspired me to
make an effort to get our and enjoy the travel
available.’
Walking:
‘I will use my car less and walk more.’
‘I think in general the initiative has made me
walk more e.g. instead of driving to school I
walk.’
Bicycle:
‘Just bought it back into awareness, and made
me get my bike out.’
‘A good idea; for me the local visit to my house
and the incentive on discount for bike service
actually got me onto the road.’
External indicators. the above findings are supported by longterm independent monitoring of bus ticketing data, showing a
Source: http://www.dpi.wa.gov.au/mediaFiles/ts-Cambridgebusdata.pdf
Fig. 6. Travel Behaviour Change Program—Cambridge, Western Australia.
sustained 23% increase in patronage four years after the
TravelSmart programme (Fig. 7).
It is notable that this evaluation happened under ceterisparibus conditions. In other words, there was no change in
population size, social structure, or PT supply in this period of
time.
Furthermore, there is (since 2003) a quality control procedure
in place which means that at least 50% of households are
contacted by telephone after they have received their delivery.
In a limited number of cases, the behavioural surveys are also
supplemented by in-depth interviews. These have shown, for
example, that in South Perth (Socialdata Australia, 2008a) the
information about and perception of non-car modes has improved, although the transport system has not been altered.
Interviews in Portland (USA) have also shown that a combination
of ‘hard’ policy (provision of nearby light rail) and ‘soft’ policy
(IndiMark) not only changed behaviour, but also led to perceived
changes in context, giving people more subjective choices over
their travel behaviour (Socialdata America, 2006).
Despite some challenges in obtaining bus patronage data of the
required scope and quality, similar integrated evaluations are
being adopted in all UK TravelSmart programmes.
6. Conclusions
Voluntary travel behaviour change (VTBC) programmes aim to
reduce car-as-driver trips without investment in physical infrastructure or transport services, or regulation of transport activity
(including pricing). The principal techniques used in VTBC
programmes with households differ in a number of ways:
whether they attempt to engage the whole population of the
target area: the means of identifying those who participate
actively: the methods of participation; and the methods used to
assess the extent of behaviour change.
VTBC is almost unique among transport initiatives in that it
has been developed from a strong theoretical and observational
basis, through a series of interventions using experimental design
(generally a pre-test post-test control group design), and has been
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291
Fig. 7. Long-term monitoring of bus patronage in Cambridge, Western Australia.
subject to comprehensive monitoring and evaluation of outcomes,
widely documented in the public domain.
Reported estimates of travel-behaviour change achieved by
IndiMark have consistently been in the range of a 5% to 15%
reduction in car-as-driver trips. This consistency, repetition of results
from successive applications, and the cumulative sample size now
achieved can be seen to have successfully countered any doubts
about effectiveness based on the method of evaluation. Confidence
in these estimates is further enhanced by consistently high survey
response rates, which minimise the effect of non-response bias.
Further development is required to realise the potential for
direct or indirect measurement of car-as-driver trips as an
alternative to surveys, to provide robust estimates of the primary
travel behaviour outcome. This might be achieved through
measurement related to household vehicles rather than to
individual members of households. However, such approaches
do not automatically overcome issues related to sampling and
non-sampling errors associated with surveys.
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