Transportation Research Part D 10 (2005) 385–393
www.elsevier.com/locate/trd

Reducing family car-use by providing travel advice
or requesting behavioral plans: An experimental
analysis of travel feedback programs
Satoshi Fujii a,*, Ayako Taniguchi baDepartment of Civil Engineering, Tokyo Institute of Technology, 2-12-1, Ookayama,
Meguro-ku, Tokyo 152-8552, Japanb
Hokkaido Development Engineering Center, 11, South 1, East 2, Chuo-ku, Sapporo, Hokkaido 060-0051, Japan

Abstract
A ﬁeld experiment was conducted to look at the eﬀectiveness of a travel feedback program aimed at
reducing family car-use. The experiment focused on a travel feedback program that urged participants
to make behavioral plans, and compared it to a program that provided individualized information. The
results are used to discuss the psychological process of behavioral modiﬁcation, theoretically eﬀective interventions, and policy implications for implementing eﬀective travel feedback programs.
Ó 2005 Elsevier Ltd. All rights reserved.
Keywords: Car-use reduction; Travel-demand management; Mobility management; Travel-feedback programs

1. Introduction
To ease traﬃc congestion in urban areas and to reduce environmental problems resulting fromautomobile emissions, transport policy makers have begun to implement travel demand management (TDM) schemes. Such schemes have included toll roads, traﬃc restrictions, and reduced*

Corresponding author.
E-mail address: fujii@plan.cv.titech.ac.jp (S. Fujii).

1361-9209/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.trd.2005.04.010

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transportation fees. Fujii et al. (2001) labeled these schemes Ôstructural strategiesÕ for behavioral
modiﬁcation because they change the structure surrounding travel behavior, e.g., the service availability of various travel modes and systems that regulate travel behavior. Fujii et al. also discussed
psychological and behavioral strategies that inﬂuence individual awareness and address other,various psychological factors to encourage voluntary behavior change. Such approaches are
sometimes called soft measures in mobility management (Jones, 2003).
Psychological and behavioral strategies include providing speciﬁc information on public transport, travel campaigns, and travel education. Consider the following as a case in point. In one
program, participants report their travel behavior, and feedback information is provided, including information on the CO2 emissions their car produced, advice on how to reduce car-use, andindividualized information on public transport that may have been used as an alternative. Examples of such programs include Individualized Marketing (Brög, 1998), Travel Smart (Departmentof Transport, Western Australia, 2000), Travel Blending (Rose and Ampt, 2001), and the Travel
Feedback Program (Taniguchi et al., 2003). These behavior modiﬁcation programs (i.e., programs
that give participants feedback based on reported travel behavior) are referred to as travel feedback programs, in our discussion.
Travel feedback programs mentioned diﬀer among themselves in many ways, but they all share
a common feature: the participants in each program receive information that is designed to modify behavior, according to the reported behavior. Such feedback may be eﬀective by inducing
behavioral awareness—an essential element in modiﬁcation (Dahlstrand and Biel, 1997). This
feedback may also prompt participants to increase their knowledge of speciﬁc methods formodifying their travel behavior (Verplanken et al., 1997).
Although travel feedback programs have been demonstrated to be eﬀective in travel behavior
modiﬁcation in previous studies, there would seem to be room for improvement in terms of botheﬀectiveness and cost. One way to begin to eﬀect such improvements would be to examine scientiﬁc literature that has focused on behavior modiﬁcation. The objective of this study, then, was tocreate a foundation of basic knowledge by which travel feedback programs could be improved interms of cost and eﬀectiveness, by referring to psychological theories of implementation intention(Gollwitzer, 1996; Gärling and Fujii, 1999).
It has been demonstrated that people often fail to implement new behaviors, even when they
have developed such intentions and are, to some extent, motivated to change (Gärling et al.,
1998; Gärling and Fujii, 1999; Gillholm et al., 2000). However, to implement a desired behavior,
implementation intention is essential. Unlike behavioral intention (Fishbein and Ajzen, 1975) orgoal intention (Gollwitzer, 1996), which is merely an intention to implement a behavior without
any behavioral plan, implementation intention is an intention that includes information on when,
where, and how the behavior will be implemented. A behavioral plan made prior to actual implementation of the behavior is more eﬀective in increasing the implementation intention, as well as
increasing the probability that the behavioral intention will actually be implemented (Gärling andFujii, 2002). Thus, a strategy that provides individual information may not always stimulate participants to make behavioral plans. A more eﬀective way to modify travel behavior might be to
ask participants, directly, to devise a plan on how to modify their behavior, rather than merely
providing individualized information (Gillholm et al., 2000; Gollwitzer and Brandstätter, 1997).
A strategy that encourages participants to make behavioral plans would probably prove lesscostly than one that merely provides information based on reported behavior.

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387

Here a ﬁeld experiment is described that compares a travel-feedback program that asks participants to make behavioral plans, with a more conventional travel feedback program, i.e., one thatmerely provides individualized information (Brög, 1998; Rose and Ampt, 2001; Taniguchi et al.,2003). For the comparison analysis, we conducted a ﬁeld experiment in which the students at anelementary school, and their families, were targeted. This approach was taken because schools, inaddition to work places and residences, constitute one of the most important situations for implementing soft measures involving travel feedback programs. Consequently, eﬀective and practicalprograms for behavior modiﬁcation that can be implemented in schools are called for. Furthermore, since the travel feedback program was implemented as part of a school program, almost
all the students targeted, and their families, were expected to participate in the program. As aresult, the data from this experiment are to be preferred for comparison analysis, because biases
owing to diﬀerences in participation in the program can be ignored. This constitutes another reason for conducting the experiment in a school.

2. MethodTwo hundred and ninety-two, ﬁfth grade students (10 and 11 years old) from four homeroom
classes in a Sapporo city elementary school, and the members of their families, were used as subjects. The experiment began in September and ended in November. One hundred and ﬁfty-ﬁve
subjects from two classes received individualized information and advice on reducing family
car-use (referred to, from this point on, as the advice group). One hundred and thirty-seven subjects from the other two classes were asked to make behavioral plans with respect to methods of
reducing car-use (referred to as the planning group).1
Six weeks prior to experimental intervention, all subjects, that is, the students and their families,
answered a questionnaire that included questions on the frequency of car and public-transport use
over three consecutive days (Sunday, Monday, and Tuesday). They were also asked the number ofdays the family car was used each month. Four weeks before intervention, all students were given
lessons on the global warming issue, the role of CO2 in this problem, and the level of CO2 emission
from car-use.
Afterwards, all the families in the advice group were asked to ﬁll in a 3-day activity-travel diary.
Subjects logged starting times, ending times, types and locations for all their activities, as well as
the mode of transportation used to travel to and from these activities. After receiving their diaries,
we made diagnostic checklists—an example is shown in Fig. 1—for each family, in which their
3-day activity-travel patterns were graphically presented as a diagram, together with comments,
including proposals as to how the participantÕs activity-travel patterns might be modiﬁed to1

A control group is not established because the programs were implemented as actual educational programs for all
the ﬁfth-grade students in the school. For a more exact test of the eﬀects of various interventions, it would have been
preferable to set up a control group; it would not have been preferable, however, to create a situation in which somegroups of students received education about travel behavior and some groups did not in the same school. Despite thefact that we did not have a control group in the experiment, the comparison between experimental groups was expected
to produce appropriate data of diﬀerence between the cases of TFP with advice and TFP with a behavioral plan. Suchdata might be useful for designing more cost-eﬀective TFPs.

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Fig. 1. An example of a diagnostic checklist that was used with the advice group.

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389

reduce CO2 emissions. The comments were systematically drafted under a system established by
Taniguchi et al. (2003). The diagnostic checklist was then given to the household as an intervention to reduce car-use.
Each household in the planning group was asked to develop behavioral plans to modify theirhome-based car trip chains with the aim of reducing CO2 emissions. They were instructed todevelop behavioral plans to modify up to three of their car trip chains. A questionnaire asked
the families to describe their planned departure time, arrival time and travel mode for each trip,
and the location of each stop in their modiﬁed car trip chains. This intervention was on the same
day as the intervention for the advice group. One week after these interventions, subjects
answered an exact duplicate of the original questionnaire.

3. Results
Table 1 shows the frequency of trips by length and total trip durations for 3 days before andafter the experimental interventions, for both groups. A two (group; advice versus planning) by
two (wave; before versus after) analysis of variance (ANOVA) of total frequency of car use, withrepeated measurement on the last factor, indicated that a group eﬀect was signiﬁcant a wave eﬀect
was not.2 The interaction eﬀect was not signiﬁcant. This, in conjunction with Table 1, indicatesthat car trip frequency in the advice group was less than that in the planning group. The diﬀerence
between values before and after interventions was not signiﬁcant, and the results for the planninggroup did not diﬀer from those of the advice group.
Parallel ANOVAs of car-use frequency for diﬀerent trip lengths indicate that the interactioneﬀect between group and wave was signiﬁcant only for trips greater than 45 min.3 Table 1 showsthat the frequency of trips longer than 45 min for the planning group decreased from 0.72 to 0.37
after intervention. Separate t-tests demonstrate that the diﬀerence in trip frequency was only signiﬁcant for trips longer than 45 min in the planning group. Trips longer than 45 min were significantly reduced only in this group, and the frequency changes were far more pronounced than in
the advice group.
Table 1 also shows the estimated total duration of car trips over 3 days. This value was calculated based on the reported car trip frequency by diﬀerent ranges of trip length, using the rangeÕs
average trip length in the activity diary data used by Taniguchi et al. (2003) which also comesfrom Sapporo. Table 1 shows that the estimated total trip duration in the planning group fell,
while in the advice group, it grew. The reduction in the planning group was signiﬁcant but thegrowth in the advice group was not. The reduction in the total estimated trip duration in the planning group was 27.0% (from 144.6 min over 3 days, to 105.5 min). A two (group; advice versus
planning) by two (wave; before versus after) ANOVA of total car-use duration, with repeated2

Details of all the statistical tests reported in this paper are available at all times from the corresponding author.
The results did not change greatly when the frequency of trips in the ﬁrst wave for each trip length was included in
the ANOVAs as a covariate. This implies that the diﬀerence between groups in the ﬁrst wave cannot explain theinteraction eﬀect between wave and group.
3

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Table 1Frequency of car trips by trip length, and total trip duration for the 3 days prior to and 3 days after the experimental
intervention
Advice group (n = 155)
M
Frequency of car trips
Trip length 6 15 min
3 days before
3 days after
15 min < trip length 6 45
3 days before
3 days after
Trip length > 45
3 days before
3 days after
Total
3 days before
3 days after

Planning group (n = 137)
SD

M

SD

1.81
2.12

(2.56)(2.72)

2.36
2.80(2.99)(3.38)

1.55
1.32(2.24)(1.99)

1.74
1.56(2.41)(2.45)

0.50
0.65(1.31)(1.53)

0.72
0.37(1.29)(0.95)

3.86
4.09(3.48)(3.66)

4.81
4.74(3.83)(4.29)(201.27)(178.80)

144.63105.53(137.73)(125.22)

Estimated total duration of car trips over 3 days (min)3 days before
115.543 days after
122.77

measurement on the last factor, indicated a non-signiﬁcant group eﬀect, as well as a non-signiﬁcant wave eﬀect. The interaction eﬀect, however, was signiﬁcant. These results suggest that households in the planning group signiﬁcantly reduced total trip duration, as compared to those in theadvice group.
Fig. 2 shows the number of days of car-use before and after intervention for both groups. The
total fell in both, but was less pronounced in the advice group. T-tests indicate that the reduction
was not signiﬁcant. In the planning group, by contrast, total car-use days fell by more than two—from 19.75 to 17.46. This reduction proved signiﬁcant. Prior to intervention, total car-use in theplanning group was greater than that in the advice group, for a number of reasons. The diﬀerence
was signiﬁcant but the diﬀerence between the two groups after intervention was not.

20
19

(10.9)

18(10.7)
17(10.1)(10.7)

beforeafter

advice groupplanning group

16
15(standard deviation)

Fig. 2. Number of days of car-use during the month.

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A two (group; advice versus planning) by two (wave; before versus after) ANOVA of total caruse days per month with repeated measurement on the last factor indicated that the group eﬀect
was not signiﬁcant but that the wave eﬀect was. The interaction eﬀect was also signiﬁcant. Thus
households in the planning group signiﬁcantly reduced the number of days that their cars wereused as compared to households in the advice group.

4. Discussion
The experiment shows that subjects encouraged to make behavioral plans with respect to methods to reduce car-use actually made such reductions. The actual reduction was estimated to be
27.7% in terms of total trip duration, and 11.6% in terms of car-use days. By contrast, households
that merely received advice on how to reduce car-use did not make similar changes. These ﬁndings
diﬀer in relation to those of some previous studies that have suggested that travel feedback programs signiﬁcantly reduce car-use. The program used for the advice group in our experiment was
identical to that described by Taniguchi et al. (2003), which was found to have reduced car-use by
15%. Possible diﬀerences in our ﬁndings might be a result of the eﬀect of the reception of advice
being mitigated by an increase of car-use, due to a change of the other factors, such as season. The
ﬁrst wave of this experiment was carried out in September; and the second, in November. NovemberÕs monthly average temperature was 4.3 °C colder, which perhaps motivated people to use lessair conditioning.
The ﬁndings imply that the reduction in trip duration for the planning group was principally aresult of fewer long car trips; the interaction eﬀect of trip frequency between the group and wave
was signiﬁcant only for trips longer than 45 min. Subjects in the planning group may have done
more planning with regard to longer car trips.
Our results bear out the theoretical prediction grounded in the theory of implementation intention that argues that forming an intention to implement a behavioral plan (i.e., implementation
intention) is essential in the actual implementation of a behavior. Providing advice may be indirectly useful for subjects in the formation of implementation intention, but encouraging individuals to make behavioral plans provides a more powerful incentive, by directly urging subjects toform implementation intentions.
This study supports the notion that a travel feedback program urging users to make a behavioral plan to reduce car-use is more eﬀective than more conventional systems that provide advice,and that have been described in previous studies. These ﬁndings agreed with our predictions.
Needless to say, using these systems can be more cost eﬀective because they do not need to prepare
and oﬀer advice based on a travel diary.
However, it may be expected that programs that ask participants to formulate a behavioral
plan by themselves might be less motivating than programs that provide individualized information. As the experiment was implemented in an elementary school, diﬀerences in the participation
rates of these two programs could not be tested. However, previous studies demonstrate that theminimal eﬀort required to make a behavioral plan was eﬀective when it came to increasing behavior-intention consistency. It should therefore be possible to design an eﬀective questionnaire asking participants to make a behavioral plan that is simple enough not to prevent them fromparticipating in the program. For example, an experiment by Daito et al. (2003) showed that

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asking participants to plan a freeway route had the eﬀect of increasing the frequency with whichthat route was used. According to a pilot experiment, it took about 1 min, or less, to answer thequestion.4
The objective of here was to provide basic knowledge that may prove helpful when it comes todeveloping better travel feedback programs. Although the study may not be broad enough todemonstrate the usefulness and eﬀectiveness of programs that ask participants to formulate abehavioral plan on how to reduce car-use, the theoretical discussion, based on psychology, andthe empirical ﬁndings in the study that was set up to examine the theoretical hypothesis, suggestthat questions geared to asking participants to formulate a behavioral plan can be an eﬀective andrelatively cheap tool for travel behavior modiﬁcation.

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