Abstract

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, and Japan, as a "'soft'' measure that is designed to change car use behavior, although MM is not always known to  any transportation policy makers and researchers in any countries. 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 behavior 19 . In a typical MM program, participants report their travel behavior to MM implementers and/or request information that can help them to change their travel behavior. The MM implementers then provide feedback such as CO 2 emission estimates for specific cars, advice on reducing car use, and individualized information on public transport. Examples of such programs include the Individualized Marketing," TravelSmart," Travel Blending," 5) Think of Wise Ways to Use Cars," 7) and the personalized travel planning in the UK reported in the Smarter Choices document. 89 Here, we refer to these behavior modification programs as "travel feedback programs" (TFPs), as detailed below. 7191 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 large-scale practical policies. According to a meta-analysis of ten TFP cases implemented in Japan before 2002 by two of the present authors Yo) , households that participated in TFPs reduced their car use by 19% on average. It should be noted that Travel demand management (TDM) has a longer history than MM in Japan. One TDM measure, "‘park & ride,’’ has been frequently implemented to reduce traffic congestion in Japan. In addition, road pricing and zonal car restrictions have also been discussed as TDM measures, but they have not been implemented in any Japanese cities. In the Japanese context, 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 this paper, we use "mobility management" to denote communication-based transportation measures that attempt to induce voluntarily behavioral changes from car use to o ther 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. The objectives of the paper are to report on the history, current practical situation, and characteristics of MM in Japan, and to analyze quantitatively the effectiveness of TFPs by extending Fujii and Taniguchi's previous analysis to 2003.
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 marketing ² and travel blending 4) 5) 11) that seem to be most effective in terms of aggregate-level effects such as regional transportation mode shares, regional amounts of CO 2 emission reductions, and number of passengers on public transportation. This is 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 long time (dozens of years) before it has an aggregate-level effect. In all TFPs, participants receive feedback or feedforward information 12 . This feedback may include information on behavioral consequences, such as CO 2 emissions from car use. Feedforward information may include travel information (e.g., time tables or maps related to alternative travel options for commuting or shopping). TFPs differ with respect to location, technique, and procedure 10) (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 the school curriculum.
Table l
TFPs use several techniques based on whether the goal is to motivate changes in travel- behavior, to request plans for changing travel behavior, to request goal-setting regarding a change in travel behavior, or to provide customized information. For example, individualized marketing does not provide motivational support 21 , while travel blending does 41 5) 11)  A TFP that involves 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 Japan 91 2 , that requires participants to form a behavioral 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 is whether the TFP provides customized information. Typical TFPs such as travel blending and individualized marketing provide customized information, but some 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 often 13). TFP procedures also differ. For instance, individualized marketing involves two or three contacts to survey travel behavior and intentions to change behavior, and provide customized information as necessary 21. Travel blending involves four contacts 41 5) : to motivate a behavior change, to conduct a travel diary survey, to provide customized comments, and to provide additional customized comments. A less elaborate TFP may have only a single stage. For instance, a TFP in Obihiro, Japan 13) , provided participants with a single questionnaire and non-customized information. The questionnaire included a request that participants formulate a behavioral plan for changing their travel behavior.
2. CURRENT SITUATION OF MM IN JAPAN
At the time of writing in 2006, there is awareness in transport planning agencies, urban planning boards, and local governments in Japan that problems due to motorized vehicles are becoming detrimental to society and that 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 the situation. Mobility management measures such as TFP are expected to be an option that may break the deadlock in the traffic congestion situation. In this section, we briefly 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 from overseas was the Travel Blending Program implemented in Adelaide, Australia 14) . 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 these circumstances, the Road Bureau of the Ministry of Land, Infrastructure, and Transport (MLIT) of the Japanese government established a bounty system (grants) for promoting 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, the first experiment attempting to induce voluntarily travel behavior changes by requesting people to develop plans for changing their own behavior was implemented in Osaka 9). Later TFP and MM measures adopted this technique of requesting a behavior plan, which has repeatedly proven effective in changing behavior 10) 2000 to 2002, several other MM measures were implemented, but more as experiments by transportation researchers than as practical policies. In 2001, the first MM research group was organized by researchers and practitioners 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 focused on research, the JSCE workshop objective was to balance discussion of academic 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, traveler information, staggered office hours, car pool matching and telecommuting may be politically feasible but are typically less effective because enough people may not always participate in such measures. Public transport fare reductions would be another option, but they may be again difficult because of lack of continuous monetary fund. In addition, installing high-occupancy vehicle (HOV) lanes is not possible for most roads in Japan because of the limited number of lanes. Several case studies of TDM have suggested to the Japanese government that the effectiveness of TDM may be limited, although TDM may be successful in special situations such as places where park and ride can be promoted. On the other hand, MM measures that induce voluntary changes are not as politically difficult because these measures are non- coercive. Furthermore, such measures can, in theory, be implemented wherever there is travel behavior to change. Therefore, if MM measures such as 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 measures' 90 . 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 disseminated among numerous sections of government, including the central government, and more practical applications are underway. The JSCE published a handbook for MM in 2005 16 . The handbook was edited by the JSCE committee on MM and includes information on practical procedures 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 s subsidies to reduce car use in favor of commuter transport so as to reduce CO 2 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 l st JCOMM, giving 64 oral and poster presentations. The JCOMM will be held annually. As illustrated above, MM is rapidly gaining strength as a political measure in Japan.

3. META-ANALYSIS TO EVALUATE TFP IN JAPAN
Survey Items
To analyze the state and effectiveness of TFPs, we collected reports and research papers on TFPs in Japan issued before December 2005. As s hown in Table 2, we identified 31 cases of TFP. We then created a database that included information on the type of procedure, techniques, information, and measures for evaluating travel behavior change for each case 1 . Table 2 Table 3 shows the basic survey items in the literature that we reviewed, such as location and sample size. Table 4 shows the types of communication techniques used in our meta-analysis. In Table 3, the TFPs are classified by their type and number of steps. A simple TFP consists of a pre-survey and questionnaire 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 and communicate advice.
Measures for evaluating attitude and behavior change
To evaluate the TFPs, we had to measure the effects. However, comparing the effects among all the cases was difficult because different measurements were used for each case study. Thus, in this study, we defined measurements for behavior change as indicated below and used these measurements for our analysis.
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 of priority: (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. Note that cases with a control group would be more preferable to cases without a control group for assessment of effectiveness of TFPs. Still, assessment without a control group would be possible by comparing before and after TFPs. 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 not reported, we considered that the effect on behavior was not measured.
2) Psychological measurements
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 (attitudinal factors). We calculated differences in measurements of behavior intentions before and after the TFP intervention and then calculated the rates of difference to create a scaled range of measurements. 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 shows their 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 Transportation¹¹ ), mentioned above. By type of target 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 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 CO 2 " techniques were also frequently adopted in the TFP cases studied here.
Dataset for meta-analysis of TFP effectiveness
To 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 and behavior changes caused by the various experimental interventions, we created a dataset in which observations were composed of experimental conditions rather than projects. The dataset was composed of 60 experimental conditions of the 31 projects. We call this dataset the experimental-condition-based 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 the effectiveness 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 = (s 1 E 1 + s 2 E 2 +.....+ s 1 E 1 ) / (s 1 + s 2 + ... + s 1 )
where s i (j = 1, ..., J) is the sample size of segment  j, and E i is the TFP effectiveness for segment j. Note that target population in some TFPs was divided into some segments, e.g. heavy car users and non-heavy car users, and different interventions were implemented for different segments. E i 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), E , 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 five cases 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 in residential 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 the experimental-condition-based dataset. The table also lists those values for residential 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 influenced by unexpected factors such as seasonal or weather factors, these means 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 2 . 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 areas using 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 and workplace cases where changes in car use were reported except for the earliest pilot study for residential area implemented in Sapporo in 1999 and the earliest pilot study for workplace implemented in Kanazawa in 2001. Among the 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 that participants 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 Japan 99 . The average car use reductions of 18% for residential areas and 9% for workplaces were mainly induced by TFPs that included motivational support, customized information, and requests for behavioral-plan formation. A technique that some but not all TFPs adopted was to request that participants set a behavior- change goal. In 7 out of 14 TFPs, participants were asked 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 a behavioral plan for how to reduce their car use, participants filled out a questionnaire 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.
4. CONCLUSIONS
This paper has outlined the history, current practical situation, and characteristics of MM in Japan and has detailed the results of a meta-analysis of the quantitative effectiveness of TFPs as MM methods in Japan. In TFPs for residential 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 is because almost all TFPs in Japan have been implemented as experiments that have 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. 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. TFPs were implemented and reported in EU countries and Australia 1)-5), 8)  According to the review results of TFPs in UK 89 , the effectiveness of such TFPs was reported to be 7 to 15 % reduction in car use trips for TFPs participants. On the other hand, as abovementioned in this paper, the average car use reduction by TFPs in Japan was 12.1% for TFP participants by comparison with with control group. The average car use reduction by optimized TFPs in Japan was estimated to be 19.2%. These numbers of car use reduction per person were not largely different from numbers for UK. The use of MM measures has evolved rapidly in Japan and various TFPs have been 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 the effectiveness of these techniques in reducing vehicle traffic and promoting public transportation must be disseminated to these skeptical governmental officials. In addition, there is s several issues as future studies; - The data in this study did not include how many people are involved in making the changes, because there were not enough cases reported. We also consider it is one of a future study. - In this study, because of the lack of the cases, we could not do satisfactory analysis concerning where and when we should do TFPs. This is another important future study. - Although cost benefit analyses of TFPs have not yet performed in Japan, Department for Transport in UK reported that the cost-benefit ratio of soft transport measures such as MM would be approximately 10. Note that we expect that TFPs with requesting to make a behavioral plan would be more cost-effective than TFPs without it that have been implemented in UK. In order to confirm such expectation, cost benefit analysis should be done in Japan as well. Soft measures such as MM would be effective to some degree whether it implements independently, however, there might be cases that soft measure does not work, for instance, targeting the people who have a strong car use habit. Therefore, it might be desirable to implement soft measures together with hard measures such as car restriction or infrastructure constructing. - Currently, Information Technology (IT) is rapidly advanced. These IT systems can automatically observed participants' travel behavior. Therefore it can minimize participants' reporting efforts and allowed us to provide appropriate feedback. To discuss applying IT to MM would be needed. Further meta-analyses like that in this study should also be conducted to accumulate data on the effectiveness of MM and TFPs and further clarify the best techniques. 1) Jones, P. (2003) Encouraging behavioural change through marketing and management: What can be achieved? Presented at the 10th International Conference on Travel Behaviour Research, Lucerne, Switzerland. 2) Brög, W. (1998) Individualized marketing: Implications for TDM. CD-ROM of the proceedings of the 77th Annual Meeting of Transportation Research Board. 3) Department of Transport, Western Australia (2000) TravelSmart: A cost-effective contribution to transport infrastructure. 4) Ampt, E., Rooney, A. (1999) Reducing the impact of the car—A sustainable approach, 23 rd Australasian Transport Forum, Perth, September 29–October 1. 5) Rose, G., Ampt, E. (2001) Travel blending: An Australian travel awareness initiative, Transportation Research, 6D, pp. 9–110. 6) Taniguchi, A., Hara, F., Takano, S., Kagaya, S. 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(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 and practice, Presented at the 10 th 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. (2006) Promoting public transport using marketing techniques in mobility management and verifying their quantitative effects, Transportation (electric version). 14) Harata, N., Makimura, K., (1998) Approach for smoother transport in Europe and U.S. - Strategies for sustainable mobility —, Road and transportation 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 , Japanese Society of Civil Engineering. 17) Taniguchi, A. and Fujii, S. (in press) An Analysis of Effects on Workplace Mobility Management Targeting Commuter Transport, CD-ROM of the proceedings of the 86th Annual Meeting of Transportation Research Board. 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 and psychological factor Figure 1.  Chronology of Mobility Management in Japan
Table 1. Common features (and possible differences) of travel feedback programs 12) Table 2. All reports and research papers related to MM measures implemented in Japan and brief
(2) Workplace TFPs