A meta-analysis to evaluate an effectiveness of mobility management inJapan and comparison to the case of EU countries
Ayako Taniguchi
University of Tsukuba
Haruna Suzuki, Satoshi Fujii
Tokyo Institute of Technology
Abbreviations:
MM: Mobility management
TFP: Travel feedback (/feed-forward) program
1. INTRODUCTION
Since the end of the 1990s, “mobility management” (MM) has attracted
increased attention from both transportation policy makers and researchers in
EU countries, Australia, the U.S., and Japan, as a “soft” measure that isdesigned to change car use behavior. Although the definitions of MM may
differ slightly by country, all definitions share the idea of motivating individuals
to voluntarily change to more sustainable transport modes by providing
detailed travel information and incentives and by using marketing techniques
focusing on personal travel behavior1).
In a typical MM program, participants report their travel behavior to MM
implementers and/or request information that can help them to change theirtravel behavior. The MM implementers then provide feedback such as CO2
emission estimates for specific cars, advice on reducing car use, andindividualized information on public transport. Examples of such programs
include the Individualized Marketing,2) TravelSmart,3) Travel Blending,4) 5) Thinkof Wise Ways to Use Cars,6) 7) and the personalized travel planning in the UK
reported in the Smarter Choices document.8) Here, we refer to these behavior
modification programs as “travel feedback programs” (TFPs), as detailed
below.7) 9)
For EU countries, the European Platform of Mobility Management (EPOMM)
website (http://www.epommweb.org/) helps transportation management
practitioners implement mobility management. The website provides numerous
reports of case studies and ideas for implementing MM measures. The
European Conference of MM (ECOMM) has also been held yearly since 1997.
The website and conference suggest the maturity of MM practices in the EU.
As part of MM, TFPs have been implemented in many EU cities, including
London, Bristol, Göteborg, and Baunatal.
In Japan, the first TFP pilot study was implemented in 1999 as a soft measure
to ease traffic congestion. By 2000–2002, there were several studies of TFPs,
but these were more experiments by transportation researchers than largescale practical policies. According to a meta-analysis of ten TFP cases©Association for European Transport and contributors 2006

implemented in Japan before 2002 by two of the present authors10),
households that participated in TFPs reduced their car use by 19% on average.
It should be noted that transportation demand management (TDM) has alonger history than MM in Japan. One TDM measure, “park & ride,” has beenfrequently implemented to reduce traffic congestion in Japan. In addition, roadpricing and zonal car restrictions have also been discussed as TDM measures,
but they have not been implemented in any Japanese cities. In the Japanesecontext, measures such as park & ride, road pricing, and zonal car restrictions
are now referred to as TDM, while communication-based transportation
measures such as TFP are referred to as mobility management (MM). In thispaper, we use “mobility management” to denote communication-basedtransportation measures that attempt to induce voluntarily behavioral changes
from car use to other modes of travel.
Because TFPs are the most frequently used MM measure in Japan, this study
focused on TFPs to evaluate MM effects in Japan. We report on the history,
current practical situation, and characteristics of MM in Japan; we also
quantitatively analyzed the effectiveness of TFPs by extending Fujii andTaniguchi’s previous analysis to 2003.
We will then compare the result of meta-analysis of TFPs in Japan with theresults of past research of TFPs in EU countries reported. For the comparison,
we especially focus on cases of “Smarter Choices” by Department For
Transport, UK (2004)8) as cases in EU to clarify the differences and similarities
between EU countries and Japan, which would be helpful to design better MM
utilizing TFPs that would be practically useful for many courtiers in all over theworld.
What is a “TFP”?
Among several types of individualized MM communication practices such as
personal conversation, workshops, school education, and TFPs, it is TFPs
(e.g., individualized marketing2), travel blending4) 5) 11), personalized travel
planning 8)) that seem to be most effective in terms of aggregate-level effects
such as regional transportation mode shares, regional amounts of CO2
emission reductions, and number of passengers on public transportation. Thisis because TFPs can potentially target all households or individuals in a
specific area or organization, if the program budget is sufficient. On the other
hand, it is not feasible to invite that many people to workshops or to have
conversations with each person in an entire area. Education in schools is
another important approach for travel-behavior modification, but it takes a longtime (dozens of years) before it has an aggregate-level effect.
In all TFPs, participants receive feedback or feedforward information12). This
feedback may include information on behavioral consequences, such as CO2
emissions from car use. Feedforward information may include travel
information (e.g., time tables or maps related to alternative travel options forcommuting or shopping).

©Association for European Transport and contributors 2006

TFPs differ with respect to location, technique, and procedure10) (see Table 1)and have been implemented for three main settings: residential areas, schools,
and workplaces. TFPs in residential areas typically target the daily travel
behavior of any household member, but TFPs for schools and workplaces
typically target commuting. TFPs may be implemented in schools as part of theschool curriculum.
_____________
Table 1_____________
TFPs use several techniques based on whether the goal is to motivatechanges in travel-behavior, to request plans for changing travel behavior, torequest goal-setting regarding a change in travel behavior, or to provide
customized information. For example, individualized marketing does notprovide motivational support2), while travel blending does4) 5) 11). A TFP thatinvolves planning may ask participants to plan how they will change their travel
behavior. For example, Fujii and Taniguchi (2005) proposed a TFP,
implemented in several cities in Japan9) 12), that requires participants to form abehavioral plan for changing their travel behavior. After requesting a behavioral
plan from the participants, the participants were also asked to set a goal
regarding how much they would change their travel behavior. The final issue iswhether the TFP provides customized information. Typical TFPs such as travel
blending and individualized marketing provide customized information, butsome less elaborate TFPs do not. For example, a TFP implemented in Obihiro,
Japan, provided participants with non-customized information about the bus
service and requested that they made a behavioral plan for how to use the bus
more often13).
TFP procedures also differ. For instance, individualized marketing involves twoor three contacts to survey travel behavior and intentions to change behavior,and provide customized information as necessary2). Travel blending involves
four contacts4) 5): to motivate a behavior change, to conduct a travel diarysurvey, to provide customized comments, and to provide additional customizedcomments. A less elaborate TFP may have only a single stage. For instance, a
TFP in Obihiro, Japan13), provided participants with a single questionnaire andnon-customized information. The questionnaire included a request thatparticipants formulate a behavioral plan for changing their travel behavior.
2. CURRENT SITUATION OF MM IN JAPANAt the time of writing in 2006, there is awareness in transport planning
agencies, urban planning boards, and local governments in Japan thatproblems due to motorized vehicles are becoming detrimental to society andthat reductions of personal vehicle use are urgently needed. However, it is also
recognized that past measures such as increased road construction, traffic
control, and TDM measures (e.g., park & ride) cannot sufficiently ease thesituation. Mobility management measures such as TFP are expected to breakthe deadlock in the traffic congestion situation. In this section, we briefly©Association for European Transport and contributors 2006

describe the history of MM (see Figure 1) and the current situation with respect
to TFPs.
Communication-based mobility management measures, referred to as TFPs in
this paper, were first introduced from overseas to Japanese transportation
researchers and policy makers in 1998. One case that was modeled fromoverseas was the Travel Blending Program implemented in Adelaide,
Australia14). At that time in Japan, travel demand management (TDM)
measures such as park & ride, road pricing, and zonal car restrictions were
expected to be effective in reducing car use. Such TDM plans reflected the
difficulty of implementing “hard measures” such as transportation capacity
expansion and or new transportation systems partly because of low public
acceptance and partly because of budgetary restrictions. Under thesecircumstances, the Road Bureau of the Ministry of Land, Infrastructure, andTransport (MLIT) of the Japanese government established a bounty system forpromoting TDM measures.
In 1999, the first pilot TFP study in Japan was implemented in Sapporo,
Hokkaido prefecture. This project was followed by a larger-scale TFP
experiment in that area in 2000 funded by the road section of the Hokkaido
Development Agency, a regional bureau of the MLIT. At the same time, thefirst experiment attempting to induce voluntarily travel behavior changes byrequesting people to develop plans for changing their own behavior was
implemented in Osaka9). Later TFP and MM measures adopted this techniqueof requesting a behavior plan, which has repeatedly proven effective inchanging behavior10) 2000 to 2002, several other MM measures were
implemented, but more as experiments by transportation researchers than aspractical policies.
In 2001, the first MM research group was organized by researchers andpractitioners as one of the specialty groups of the International Association of
Traffic and Safety Sciences (IATSS). This research group implemented several
MM experiments to test the effectiveness of several communicative techniques
and programs in terms of participant attitudes and behavior changes. The
IATSS research group held an MM workshop for the Japanese Society of Civil
Engineers (JSCE) in 2003. Because the IATSS research group was focusedon research, the JSCE workshop objective was to balance discussion ofacademic research on MM techniques with practical discussion on MM
implementation. In 2005, this workshop was upgraded to an official committee
composed of a wider group of JSCE members.
As mentioned above, the MLIT has tried to promote TDM measures to reduce
traffic congestion and promote public transport. However, coercive
implementation of TDM measures such as road pricing and zonal car
restrictions often prove difficult because of low public acceptance. Less
coercive TDM measures such as park-and-ride programs may be politicallyfeasible but are typically less effective. In addition, installing high-occupancy
vehicle (HOV) lanes is not possible for most roads in Japan because of thelimited number of lanes. Several case studies of TDM have suggested to theJapanese government that the effectiveness of TDM may be limited, although©Association for European Transport and contributors 2006

TDM may be successful in special situations such as places where park andride can be promoted. On the other hand, MM measures that induce voluntarychanges are not as politically difficult because these measures are noncoercive. Furthermore, such measures can, in theory, be implemented
wherever there is travel behavior to change. Therefore, if MM measures suchas TFPs prove successful in changing people’s travel behavior, public sectors
such as MLIT will more actively seek wider implementation. Indeed, several
early studies implemented by IATSS and JSCE research groups have shown
the effectiveness of such measures10). Based on these findings, the Kinki
Regional Council of Transportation stated in 2004 that “We should widely
implement ‘mobility management’ that promotes voluntary travel behavior
changes through direct communication with travelers.15)”.
This is the first official commitment to mobility management by a Japanese
government agency. Following this statement by the Kinki Regional Council on
Transportation, the concept of MM and TFP has been rapidly disseminatedamong numerous sections of government, including the central government,
and more practical applications are underway.
The JSCE published a handbook for MM in 200516). The handbook was editedby the JSCE committee on MM and includes information on practicalprocedures for MM and TFP, points to consider, and many examples of tools
used in past cases. As illustrated in Figure 1, MM measures in Japan rapidly
increased in 2005 after publication of this handbook.
Since 2005, ministries and local governments have prepared various budgets
for MM programs such as subsidies to reduce car use in favor of commutertransport so as to reduce CO2 emissions by personal vehicles and promote
public transport.
In July 2006, the first Japanese Conference on Mobility Management(JCOMM) was held by JSCE and MLIT. Over 300 participants attended the 1st
JCOMM, giving 64 oral and poster presentations. The JCOMM will be held
annually. As illustrated above, MM is rapidly gaining strength as a politicalmeasure in Japan.
_____________
Figure 1_____________

3. META-ANALYSIS TO EVALUATE TFP IN JAPAN
Survey Items
To analyze the state and effectiveness of TFPs, we collected reports andresearch papers on TFPs in Japan issued before December 2005. As shown in
Table 2, we identified 31 cases of TFP. We then created a database that
included information on the type of procedure, techniques, information, andmeasures for evaluating travel behavior change for each case.
©Association for European Transport and contributors 2006

_____________
Table 2_____________

Table 3 shows the basic survey items in the literature that we reviewed, suchas location and sample size. Table 4 shows the types of communication
techniques used in our meta-analysis. In Table 3, the TFPs are classified bytheir type and number of steps. A simple TFP consists of a pre-survey andquestionnaire intended for communication on such subjects as behavioral
planning or advice and information. A standard TFP consists of feedback on a
behavioral change added to the components of the simple TFP. A one-shot
TFP consists of only a questionnaire intended to collect information andcommunicate advice.
_____________
Table 3__________________________
Table 4_____________
Measures for evaluating attitude and behavior change
To evaluate the TFPs, we had to measure the effects. However, comparing theeffects among all the cases was difficult because different measurements wereused for each case study. Thus, in this study, we defined measurements forbehavior change as indicated below and used these measurements for ouranalysis.
1) Behavioral measurements
In this study, we calculated the rate of change in behavioral measurements
reported before and after a TFP intervention. If several behavioral
measurements were reported, we selected one based on the following order ofpriority: (1) distance of trips, (2) time duration of trips, and (3) frequency of trips.
In projects that had a control group, the rate of change in behavior was
calculated by comparison with the control group.
Based on the above-mentioned procedures, we calculated the rate of change
in car use and public transport use, defined as “car use change” and “PT use
change.” Note that if one of the behavioral factors mentioned above was notreported, we considered that the effect on behavior was not measured.
2) Psychological measurements©Association for European Transport and contributors 2006

We used measurements of intention to reduce car use and to increase public
transport (PT) use (or specific bus/train use) to assess the impact of TFP
intervention on psychological factors. We calculated differences inmeasurements of behavior intentions before and after the TFP intervention andthen calculated the rates of difference to create a scaled range ofmeasurements. These measurements are referred to here as “car use
intention change” and “PT use intention change.”

General analysis of the samples
Between 1999 and 2005, 31 TFPs were reported in Japan. Figure 2 showstheir locations. As illustrated by this figure, many TFPs have been
implemented in the Osaka and Kobe areas, which are part of the Kinki region;these projects may have been inspired by the 2004 endorsement of TFPs by
the Kinki Regional Council of Transportation15), mentioned above. By type oftarget group and number, 18 TFPs were implemented in residential areas,targeting 4,407 people; 10 were implemented in schools, targeting 869 people;
and four were implemented in workplaces, targeting 917 people. The target
population refers to the number of people who participated in all the TFP
procedures. Regarding the type of TFP, standard TFPs were more frequently
adopted than one-shot and simple TFPs in schools, but simple TFPs were
implemented more than the other types in residential areas.
_____________
Figure 2__________________________
Figure 3_____________
Figure 3 shows the percentage of TFPs that adopted the respective techniques.
According to Figure 3, 83.9% of the cases provided motivational information.
Within these cases, the motivational information dealt with the environment in
93% of cases, health in 58% of cases, and availability of specific public
transport resources in 33% of cases. Furthermore, the “behavioral plan” and“feedback on CO2” techniques were also frequently adopted in the TFP cases
studied here.
Dataset for meta-analysis of TFP effectivenessTo analyze TFPs in Japan, we prepared the following datasets. Some of the
projects shown in Table 2 differed in terms of the experimental interventions
conducted. Thus, to investigate TFP effectiveness in terms of attitude andbehavior changes caused by the various experimental interventions, wecreated a dataset in which observations were composed of experimental
conditions rather than projects. The dataset was composed of 60 experimental©Association for European Transport and contributors 2006

conditions of the 31 projects. We call this dataset the experimental-conditionbased dataset.
Some TFPs were implemented as “experiments” to investigate more effective
design of TFPs. Therefore, some experimental conditions were implemented
for the purpose of comparison with other conditions to demonstrate theeffectiveness of the conditions that were supposed to be superior. Therefore,
when actually implementing these TFPs, the inferior method would not be
adopted. Thus, we prepared another dataset called the project-based dataset.
In this dataset, psychological and behavioral measurements to denote the total
effectiveness of a TFP, Y, were estimated as follows:
Y = (s1E1+ s2E2+.....+ sJEJ) / (s1 + s2+ ... + sJ)
where sj (j = 1, ..., J) is the sample size of segment j, and Ej is the TFP
effectiveness for segment j. Ej is the effectiveness by the optimal TFP
intervention for segment j. For example, when there are two experimental TFP
interventions implemented for segment j and the effectiveness is a and b (< a),Ej is b.
Average TFP effects for residential areas
The TFP analyses were divided by those implemented in residential areas,
workplaces, and schools, because the effects likely differ by situation. As
mentioned above, our study set contained only nine cases for schools and fivecases in workplaces; among these, even fewer cases had control groups.
Because the data from TFPs implemented for schools and workplaces might
be insufficient for our analysis, we used only data from TFPs implemented inresidential areas.
According to an experimental-condition-based dataset, 32 experimental
conditions out of 11 cases were available to analyze “car use change,” and 28
experimental conditions out of 11 cases were available to analyze “PT use
change.” Regarding psychological measurements, 30 experimental conditions
out of 9 cases were available to analyze “car use intention change,” and 20
experimental conditions out of 8 cases were available to analyze “PT use
intention change.”_____________
Table 5_____________
Table 5 shows the mean (%) and standard deviation (STD) for each of the
behavioral and psychological measurements calculated using theexperimental-condition-based dataset. The table also lists those values forresidential area TFP experiments having control groups. The residential TFP
experiments, including those with and without control groups, indicated a 7.3%
reduction in car use, 68.6% increase in public transport, and 10.4% and 7.5%
increase in intentions to limit car use and increase PT use, respectively.
Because the data for projects without a control group may have been
©Association for European Transport and contributors 2006

influenced by unexpected factors such as seasonal or weather factors, thesemeans may contain effects other than TFP intervention. The means of the TFP
experiments that had control groups indicated a 12.1% reduction in car use
and 38.6% increase of public transport as well as a 9.6% increase and 0.3%
reduction in intention to reduce car use and use PT, respectively.
As mentioned above, the experimental-condition-based dataset includes
observations that were conducted for comparison with other conditions
assumed to be superior to demonstrate the “superior intervention” technique.
Therefore, the means listed above may be underestimated. Thus, we
calculated the mean effectiveness of TFP interventions in residential areasusing the project-based dataset. These results indicated a mean reduction in
car use of 19.2% and an increase in PT use of 31.7% for the nine TFPs with
control groups.

Available TFP techniques for residential areas and workplaces
Among the 31 TFPs shown in Table 2, we focused on 14 residential andworkplace cases where changes in car use were reported except for theearliest pilot study for residential area implemented in Sapporo in 1999 and theearliest pilot study for workplace implemented in Kanazawa in 2001. Amongthe 14 TFPs, all provided information on motivating behavior modification.
Almost all of the TFPs (11 of the 14) described techniques for providing
customized information. In addition, almost all (11 out of 14) requested thatparticipants create a behavioral plan for how to change their travel behavior.
The effectiveness of such measures has been empirically demonstrated in a
TFP experiment in Japan9). The average car use reductions of 18% forresidential areas and 9% for workplaces were mainly induced by TFPs that
included motivational support, customized information, and requests forbehavioral-plan formation.
A technique that some but not all TFPs adopted was to request thatparticipants set a behavior-change goal. In 7 out of 14 TFPs, participants wereasked to set a behavioral-modification goal (Himeji, 2004; for the areas of
Kawanishi-Inagawa in 2003; Keihanshin in 2004; Miki in 2004; Osaka in 2004;
Suita in 2003; and Suzurandai in 2004). In all these cases, before making abehavioral plan for how to reduce their car use, participants filled out aquestionnaire specifying the percentage by which they would reduce their car
use. In two cases (Miki in 2004; Suzurandai in 2004) participants were also
asked to specify the percentage by which they would increase their public
transport use. The average car use reduction for seven TFPs that requested
such goal setting was 20%, whereas that for seven TFPs without explicit goal
setting requests was 10%. The average increase in public transport use for six
TFPs requesting goal setting was 76%, whereas that for six TFPs without such
requests was 25%. These results imply that the technique of asking
participants to set behavior-modification goals is promising.

©Association for European Transport and contributors 2006

4. Comparison of Effects for TFP between EU countries and Japan
TFPs were implemented and reported in EU countries and Australia8) 17). The
Leading consultant companies TFPs in EU countries include Socialdata, basedin Germany and Steer Davies Gleave, based in UK. There are somedifferences on concept and techniques between these two companies.
Therefore we summarize the respective effectiveness of TFPs implemented bythese two companies.
Distinct characteristics of Socialdata‘s TFP projects are in “Marketing” method.
In the TFPs, the targeted people are divided into several groups by theirinterests regarding travel behavior change and their actual travel behavior
such as frequency of public transport use. Different communicative procedures
are used for different groups. It was reported that the effect sizes of TFP which
objective was to reduce car trips were 14% - 4% reduction of car driver trips forthe cases implemented in Germany, Australia, Sweden and USA8).
Steer Davies Gleave seems to attach importance to community development
through TFP projects. They request participants to fill out the travel diary andprovide personalized information according to the diary in order to aware
participants themselves problems due to occur their over use of car. It wasreported that effect sizes of TFPs were 15% - 9% reduction of car driver trips
for the cases in Australia and USA8). Note that these impacts were on
participants completing before and after travel diaries.
Then, we focused the cases of TFPs in UK from among cases in several
countries, because a number of TFP cases were implemented in UK and they
were well documented17).
Table 6 shows the percentage change of trips in each projects achieved bypersonalised travel planning as smarter choices in UK17). “Smarter choices”
was implemented by Department for Transport (DfT) as an experimental
project of mobility management in 2003-2004. “Personalised travel planning” is
one of the type of method in smarter choices, and the effects was reported inresidential areas, workplaces and schools as 7 cases, 6 cases and 2 cases
respectively.
_____________
Table 6_____________
Note that percentage change in residential area except for York means adifference between target and control group, projected across whole of target
population. Therefore, the effect of the residential area can not be directlycompared with those of in Japan. The effects of York and workplaces mean
the change amongst recruits, not across the target population. These might becomparable with those of in Japan shown at table 2.

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Department for Transport also reported in the final report of “smarter choices”
that the effectiveness of their TFPs for aggregated level in urban areas is 1 to
5 % reduction in car driver trips, which was calculated on assumptions that
proportion of population targeted is 15 to 30% and reduction in car driver trips
per person who participated in 7 to 15%8).
As mentioned in section 3, the average effect of residential TFP in Japan was
12.1% reduction of car use comparing with control group, and the average
effect of the optimal intervention of TFP that was calculated using projectbased dataset was 19.2% reduction of car use amongst recruits. These
numbers of car use reduction per person who participated due to TFPs were
not largely different from those for UK, that is 7 to 15 % reduction.
Yet, as the report by Department for Transport 8) indicated, effectiveness TFPshould be assessed for target population. Therefore, proportion of participants
in TFPs is important as well as car use reduction for the participants. However,proportion of participants to TFPs in Japan have not always well assessed anddocumented. This is because Japanese TFPs that have been implemented so
far were rather experimental or pilot project than enforcement of transportation
policy. Therefore, it is difficult to compare the effects in UK which projectedacross whole of target population and those of in Japan. However, with goodprospect for the participation in Japan, it can be considered that the effects ofaggregated level in Japan may be as much as those in UK. For instance, If weassume the participant rate were to be 15 to 30% as assumed in UK as well,the effect of aggregated level in residential TFP across whole of target
population would be 2.9 to 5.8% reduction of car use trip.
This isapproximately same as the estimate for UK. Furthermore, if we can expect
participant rate to be 50% or more, the aggregated level effect would be 9.6%
or more than 9.6%. These numbers are also same as that for TFPs bysocialdata (4-14%) and Steer Davies Gleave (9-15%).

4. CONCLUSIONSTFP in Japan and Difference in its EffectivenessThis paper has outlined the history, current practical situation, andcharacteristics of MM in Japan and has detailed the results of a meta-analysisof the quantitative effectiveness of TFPs as MM methods in Japan. In TFPs forresidential areas, the analysis indicated reduced car use and increased use of
public transport. The analyses showed project-based mean reductions in car
use of approximately 19% and increases in PT use of approximately 32%. The
numerical analyses of the effects of TFPs in this study all focused on
behavioral changes of participants in TFPs at the disaggregated level. This isbecause almost all TFPs in Japan have been implemented as experiments thathave small population sizes and are thus not sufficient for evaluations of
aggregate-level effects. Still, the effect size of MM measures should be
evaluated at the aggregate level from the viewpoint of practical transportation
policy. Therefore, larger-scale MM measures should be implemented in Japan.
©Association for European Transport and contributors 2006

With respect to TFP techniques, the results indicate that asking participants to
set behavior-modification goals is effective in increasing the extent of
behavioral change.
The use of MM measures has evolved rapidly in Japan and various TFPs havebeen and are being implemented. While a number of government policy
makers and planners have come to believe in the effectiveness of MM, others
still wonder if MM measures are worthwhile. Therefore, proof of theeffectiveness of these techniques in reducing vehicle traffic and promoting
public transportation must be disseminated to these skeptical governmental
officials. Further meta-analyses like that in this study should also be conductedto accumulate data on the effectiveness of MM and TFPs and further clarify thebest techniques.
Then, we compared the effectiveness of TFPs between Japan and EU
countries. The comparison analysis did not find large difference between
Japan and EU courtiers.
Differences of Practical Situations
In this section, we will describe differences of practical situation regarding MM
and TFPs, while partially focusing on UK as a EU country.
One of the differences of practical situation of MM between UK and Japan is arole of researchers in University. In UK, consultant companies, governments
and NPO play important role to implement and sophisticate TFPs. Otherwise,in Japan, researchers of Japan society of civil engineering (JSCE) had
introduced the concept and technique of MM, and carried out under JSCE’s
initiative until at least in 2005. In UK, researchers in university were usually
responsible mainly for the monitoring or evaluation of MM, governments were
responsible for organization and implementation of MM. It is presumed that
there might be both merits and demerits for both situations regarding
researchers.
Personalised travel planning (PTP) in UK is a same concept of TFP. However,
PTP (i.e. TFP in UK) seems not to use the technique of “requesting behavioral
plan formation”. Behavioral planning is a technique being confirmed its
effectiveness scientifically in the field of applied psychology. PTP in UK would
potentially work better in combination behavioral planning. The reason why
such a scientific technique was used in Japan might be that MM in Japan waslead by scientific committee and researchers. They try to apply scientificknowledge into practice8), and it might be the merit of being lead byresearchers.
To expand implementation of mobility management, the evaluation of thespillover, multiple and cancel effects in aggregate level is also required. In UK,
governments had spent enough budgets for evaluation8) 17). They said “To do
the political decision, EVIDENCE for evaluation is essential”. It is the point
lacking of this evidence-based approach in large scale implementation of MM©Association for European Transport and contributors 2006

in Japan. This might be the merit of being lead by national government like in
UK.
However, in UK, the measurements to evaluate PTP were different in eachproject, thus there were cases which comparison between each project had
some difficulty. To implement large scale MM led by government, it might beessential to standardize the measurements for appropriate evaluation. Setting
a control group and random sampling are needed for appropriate evaluation.
For this accurate and scientific evaluation, it is effective to work together withacademic researchers. This might be the merit of being lead by academic
researchers.
Lastly, we consider that the more advanced point of EU countries including UK
than Japanese situation are as below;- Platform of travel information infrastructure
In Japan, there is a few except for metropolis to develop public transport
information enough to encourage people to use such as map andtimetable of public transport, or search system for transit in website.
Expansion and improvement of travel information may be fundamental
to induce voluntary behavior change.
- Platform to exchange the information between stakeholders
Although the first Japanese Conference On Mobility Management washeld on July, 2006, there is a few opportunity to exchange theinformation of MM in Japan other than the conference. In EU countries,
there is many type of conference concerning MM, further opportunity
may be also required in Japan.
- Implementation of large scale MM projects
There is some project funded by EU or government such as CAMPARIE,CIVITAS, MAX, PTP in EU countries. In Japan, there is not enough fundand scheme to carry out large scale MM project. This also issue to morefine transport policy.
In Japan, we have already started discussion for establishing these platforms
and implementing large scale MM. After Japanese platform for mobility
management is successfully established in the near future, It would be desired
to develop an international platform to exchange further practical and politicalinformation with respect to MM in all over the world.
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8) Cairns, S., Sloman, L., Newson, C., Anable, J., Kikbride, A., Goodwin, P.
(2004) Smarter choices: Changing the way we travel, Department forTransport, UK.
9) Fujii, S., Taniguchi, A. (2005) Reducing family car use by providing traveladvice or requesting behavioral plans: An experimental analysis of travel
feedback programs, Transportation Research D, 10 (5), pp. 385–393.
10)Fujii, S. & Taniguchi, A. (2006) Determinants of the effectiveness of travel
feedback programs: a review of communicative mobility management
measures for changing travel behavior in Japan, Transport Policy, 13 (5),pp. 339-348
11) Ampt, E. (2003) Voluntary household travel behavior change—Theory andpractice, Presented at the 10th International Conference on Travel
Behaviour Research, Lucerne, Switzerland.
12) Gärling, T., Fujii, S. (in press) Travel Behavior Modification: Theories,
Methods, and Programs Resource paper for the IATBR conference, Kyoto,
Japan.
13) Taniguchi, A. and Fujii, S. (in press) Promoting public transport using
marketing techniques in mobility management and verifying theirquantitative effects, Transportation.
14) Harata, N., Makimura, K., (1998) Approach for smoother transport inEurope and U.S. — Strategies for sustainable mobility —, Road andtransportation economics, ’98-4, pp.35-47. (in Japanese)
15) Kinki Regional Council of Transportation (2004) On a preferable mobility in
Kinki region, Kinki Regional Bureau of Transportation. (in Japanese)16) The committee of mobility management in Japanese Society of Civil
Engineering (2005) A hand book of mobility management, JapaneseSociety of Civil Engineering.
17) Department for Transport (2004) The document of Personalised Travel
Planning, End of Programme Conference.

©Association for European Transport and contributors 2006

Figure 1. Chronology of Mobility Management in Japan
Figure 2. Locations of each MM case study in Japan
Figure 3. Percentage of TFP techniques used by the case studies
Table 1. Common features (and possible differences) of travel feedback
programs
Table 2. All reports and research papers related to MM measures implemented
in Japan and brief summaries of those cases
Table 3. Basic survey items from the reviewed literature
Table 4. Definition of types of communication techniques used
Table 5. Mean and standard deviation (STD) for each behavioral andpsychological factor
Table 6. Percentage change of trips in each projects achieved by personalised
travel planning as smarter choices in UK©Association for European Transport and contributors 2006

note: The number of cases shown in above figure includes the number ofcases of which details were not presented or published in any conference orjournals and were not, therefore, included in Table 2.
Figure 1. Chronology of Mobility Management in Japan©Association for European Transport and contributors 2006

Figure 2. Locations of each MM case study in Japan©Association for European Transport and contributors 2006

100%
90%

83.9%

80%
70%
60%

64.5%
58.1%

50%

48.4%

45.2%

40%

32.3%

30%

19.4%

20%

9.7%

9.7%

feedback on
caloriecustom ized Trave
Inform ation

10%
0%

face-to-facecom m unication

m ap ofpublic
transport

feedback on CO2

custom ized Advice

goalsetting

m otivational
Inform ation

behavioralplan

Figure 3. Percentage of TFP techniques used by the case studies©Association for European Transport and contributors 2006

Table 1. Common features (and possible differences) of travel feedback
programs 12)
Location

Technique

Procedure

residential area (for all

motivational support

single stage

trips)

request goal setting

multistage (travel diary

workplace (manily forrequest plan formation

survey, feedback)commute)customized information

school (mainly forcommute)©Association for European Transport and contributors 2006

Table 2. All reports and research papers related to MM measures implemented
in Japan and brief summaries of those cases(1) Residential area TFPs
Place

SapporoSapporo(Ainosato)

Sapporo(Ebetsu)

Takasaki City

Kanazawa

Hitachi City

Osaka City

Suita CityYear
(implemented)(1999)(2000)(2000)(2000)(2001)(2002)(2001)(2002)

Sampl
e
size

Technique66a) motivationb) no planc) provide
individualized
information

120a) motivationb) no planc) provide
individualized
information

349a) motivationb) no planc) provide
individualized
information

Procedure
1) travel diarysurvey2) feedback on
travel behavior
3) travel survey4) feedback on
travel behavior
change1) travel diarysurvey2) feedback on
travel behavior
3) travel survey4) feedback on
travel behavior
change1) travel diarysurvey2) feedback on
travel behavior
3) travel survey4) feedback on
travel behavior
change1) simple travel
survey2) provide trial
ticket3) travel survey

Method ofmeasurement

Car usechange(%)

Publictransportusechange(%)

before/after
designwithout
control
group

-8.39

11.36

before/after
designwithout
controlgroup-26.089.93

before/after
designwithout
control
group

-8.95

6.06

before/after
design with
control
group

―

―

91

a) Trial ticket

19a) motivationb) no planc) provide
individualized
information

1) travel diarysurvey2) feedback on
travel behavior
3) travel survey

before/after
design with
controlgroup+12.00―58a) no planb) provide
individualized
information

1) travel diarysurvey2) feedback on
travel behavior
3) travel survey

before/after
designwithout
control
group

―

―

106

a) no planb) provide
individualized
information

1) travel diarysurvey2) feedback on
travel behavior
3) travel survey

before/after
design with
control
group

―

―

a) planb) customized
travel information

1) simple travel
survey2) provide
customized
information
3) travel survey

before/after
design with
control
group

―

―

422

©Association for European Transport and contributors 2006

KawanishiInagawa area

SapporoMihara Town

Suzuran-dai(HyogoPrefecture)

Miki City

Himeji City

Keihanshin
area

Obihiro City

Ryugasaki
City(2003)(2003)(2003)(2004)(2004)(2004)(2004)(2004)(2005)

312a) motivationb) plan with goal
settingc) provide
individualized
information

1) simple travel
survey2) individualized
information withbehavioral plan

before/after
design with
controlgroup-27.02

68.97

50a) motivationb) planc) provide
individualized
information

1) simple travel
survey2) individualized
information withbehavioral plan

before/after
design with
controlgroup-11.7872.01―a) motivationb) provide group
advicec) integrated map

1) provide
information
2) questionnaireanswers toquestionnair
e

―

―

210

a) motivationb) plan with goal
settingc) provide nonindividualized
information

1) area-specificinformation withbehavioral plan

before/after
design with
controlgroup-18.7450.79

48a) motivationb) plan with goal
settingc) provide nonindividualized
information

1) area specificinformation withbehavioral plan

before/after
design with
controlgroup-26.0931.61

103a) motivationb) plan with goal
settingc) provide
individualized
information

1) simple travel
survey2) individualized
information withbehavioral plan

before/after
design with
controlgroup-12.803.771560a) motivationb) plan with goal
settingc) provide nonindividualized
information

1) simple travel
survey2) feedback on
travel behavior
with behavioral
plan

before/after
design with
controlgroup-26.92257.28410a) motivationb) plan1) request
creation of
behavioral plan2) travel survey

experimenta
l group /controlgroup
design

―

29.41

153

a) motivationb) planc) provide
individualized
information

1) simple travel
survey2) individualized
information withbehavioral plan

before/after
design with
control
group

-6.00

20.60

“―” in the column of car use and public transport use change denotes “not reported”.

©Association for European Transport and contributors 2006

(2) Workplace TFPs

Place

Kanazawa
City (several
workplaces)

ToyonakaCity(1workplace)

Suita City
(1 university)

Himeji City(3
workplaces)

Osaka
Prefecture(severalworkplaces)Year(implemented)(2001)(2003)(2003)(2004)

Sampl
e
size

Technique

Procedure
1) travel diarysurvey2) feedback on
travel behavior
3) travel survey4) feedback on
travel behavior
change1) simple travel
survey2) individualized
information withbehavioral plan3) travel survey4) feedback on
travel behavior
changeMethod ofmeasurement

Car usechange(%)

Publictransportusechange(%)

before/after
designwithout
control
group

-0.10

29.00

before/after
designwithout
control
group

-6.10

―

106

a) motivationb) no planc) provide
individualized
information

79a) motivationb) planc) provide
individualized
information

133a) motivationb) plan withgoal settingc) provide
individualized
information

1) travel diarysurvey2) individualized
information withbehavioral plan

before/after
designwithout
controlgroup-16.50―99a) motivationb) planc) provide
individualized
information

1) simple travel
survey2) individualized
information withbehavioral plan

before/after
design with
controlgroup(measuringcommutingtrips)

-8.80

14.81

1) simple travel
surveya) motivation
2) individualizedb) plan with
before/after
information withgoal setting
design with
(2004)
500
behavioral plan
-15.06c) provide
control3) travel survey
individualizedgroup4) feedback on
information
travel behavior
change“―” in the column of car use and public transport use change denotes “not reported”.

©Association for European Transport and contributors 200643.76

(3) School TFPs

Place

SapporoKanazawa

SapporoSapporoYear(implemented)(2000)(2001)(2002)(2002)

Technique

Procedure

127a) motivationb) no planc) provide
individualized
information

1) class & travel
diary survey2) feedback on
travel behavior3) class & travel
surveybefore/after
designwithout
controlgroup-18.46

3.7639a) motivationb) no planc) provide
individualized
information

1) travel diarysurvey2) feedback on
travel behavior
3) travel survey

before/after
design with
control
group

16.88

―

292

a) motivationb) plan orindividualized
informationc) provide
individualized
information

before/after
designwithout
control
group

2.86

79.65

97

a) motivationb) planc) provide
individualized
information

before/after
designwithout
control
group

12.08

17.05

before/after
design with
controlgroup-14.60

―

―

―

―

―

―

―

―

―

before/after
designwithout
control
group

13.33

49.35

Kanazawa(2002)

71

Izumi City

(2002)

135a) motivationb) plan

Toyonaka(2003)

91a) motivationb) planHadano(2004)(2005)

Publictransportusechange(%)

Sampl
e sizea) no planb) coordinatorc) provide
individualized
information

Fuji

Car usechange(%)

Method ofmeasurement1) travel diarysurvey2) feedback on
travel behavior
3) travel survey4) feedback on
travel behavior
change1) travel diarysurvey2) feedback on
travel behavior
3) travel survey4) feedback on
travel behavior
change1) simple travel
survey2) individualized
advice bycoordinator3) travel survey1) travel diarysurvey2) workshop in
class
―

164

a) motivationb) plan1) simple travel
survey2) workshop inclass3) travel survey

81a) motivationb) planc) provide
individualized
information

1) simple travel
survey2) workshop inclass3) travel survey“―” in the column of car use and public transport use change denotes “not reported”.

©Association for European Transport and contributors 2006

Table 3. Basic survey items from the reviewed literature
Item
1
2

Classification

Place & Year
Sample size

(household,individual)

TFP types

One-Shot TFP
Simple TFPStandard TFP

Location

schoolworkplace,residential area

Location where the project was carried out

Individualized marketingwith or without

Individualized Marketing provides different communication toindividuals based on their travel activity such as times ofpublic transport use or intention to reduce car use.

Controlgroupwith or without

―

3
4

5
6

DetailsA length of the project and place where each project was
carried out

Method offirst contact

Mail, Post, Visit,
Organization

7
Method ofsampling
8
Responsibleorganization
9

Publicsubscription,
Randomsampling,
Throughassociation
Government(central, local),Institute,Company

The number of samples based on the final number ofquestionnaires collected
The “Standard TFP” is composed of four steps: (1) firstcontact, (2) providing feedback and/or feedforward
information that is customized based on the individual
information obtained in the first step, (3) observing how travel
behavior changes, and (4) providing feedback information onchanges in travel behavior. The “Simple TFP” is composed of
the first two steps of the standard TFP. The “One-Shot TFP”
has only one intervention (step 2).

For the “Mail” method, the experimenter checks addresses
beforehand and then mails the information. By the “Post”
method, information is dropped directly into participant’s
mailbox without checking addresses first. For the “Visit”
method, the experimenter visits households in person andhands out information. By the “Organization” method,
information is distributed through an organization such as acompany, club, or school.
For “Public subscription” sampling is done through a public
media outlet such as the Internet or a magazine. By “Random
sampling” samples are randomly obtained from a specificdatabase. “Through association” is the method of selecting
participants through their association with others or an
organization.
The organizations that carried out the project10

Object ofproject

The project aim, such as the promotion of a specific type of
public transportation

11

Reference

The documents or websites referenced©Association for European Transport and contributors 2006

Table 4. Definition of types of communication techniques used
Techniques

Details

1

Motivational information on
health, environment, local
transportation2

Feedback on travel activity(including CO2 emissions
and calorie consumption

3

Goal setting

4

Personal advice

5

Customized
information

6

Behavioral plan7

Face-to-facecommunication

8

Map distribution

Promoting changes in attitudes toward car and/or public transport use byproviding information about the benefits of public transport and personal& social demerits of car use (e.g., as related to health, the environment,
and/or local transportation).
Providing individualized feedback on time of car use and quantity of CO2
discharges and/or on the consumption of calories by various travel
behaviors based on travel survey
Requesting a “numerical target” such as the desired percentage of car
use reduction

travelProviding specific personal advice for behavioral change based on
information such as current travel behavior or mailing address or trip
purpose.
Providing customized travel information such as public transport map or
timetable for public transport resources near an individual’s home orworkplace.
Activating intention by requesting that respondents make detailed plansfor how to change their travel behavior
Giving people the opportunity to talk with somebody regarding behavioral
changeFor example, at school, in a workshop, or with an advisor
Distributing route maps of public transportation and/or of the town center©Association for European Transport and contributors 2006

Table 5. Mean and standard deviation (STD) for each behavioral andpsychological factor
Behavioral measurement
Car use change

PT use change

Psychological measurement
Car use intention
changePT use intention
changeCondition

n

mean
%

SD

n

mean
%

SD

n

mean
%

SD

n

mean
%

SD

All residential area
TFP experiments

32

-7.3

22.6

28

68.6

137.7

30

10.4

13.5

20

7.5

11.4

Residential area
TFP experimentswith control groups

20

-12.1

21.5

18

38.6

63.0

22

9.6

15.2

10

-0.3

4.0

©Association for European Transport and contributors 2006

Table 6. Percentage change of trips in each projects achieved by personalised
travel planning as smarter choices in UK

Project

Residential

targetpopulation

Numberrecruited

2,758
5,000
2,000
1,900

18

Bracknell
Bristol
Cramlington
Nottingham-Lady Bay
-The Meadows

Quedgeley
Sheffield
York
Bracknell
Cambridge

10,000
3,210
5,100
8,000
713

167
34
713

-Addenbrooke NHS

Workplace

5,000
22,500
5,470
2,500
373

97
1149
132

Walking

Cycling

-4
-5

2
4

1
1

-5
-5
-5
-5
-16

4
5
4
4
10

0
1
1
0
1

-St mary's-Meadowside

1,355-Chatsmore-Davison High- Forest School-Inkerbourne-Millais-Our Lady of Sion
-Sackville Community-St Andrews]

2 *
1 *
*
1 *
1 *
1 *
2 *
5 **

-18
-27
5
-22

-3
-2
-1

West Sussex

Bus

†
†
†
†

397

-AspinSchoolCar
driverno evidence of modal sift-Combined council

DurhamOldhamWinchester
Worcester
KnaresboroughPercentage change achieved

3
-1
4

0
4
-2

0
-1
-2

1355
-11
increased
constant
-1
-8
-19.5
-5
-10

* difference between target and control group, projected across whole of target population** % change amongst recruits, not across the target population† mode shift achieved across those recruited

This table is made by being abstracted in the document distributed on
Department for Transport (2004).

©Association for European Transport and contributors 2006