Ben-Elia, E. and Ettema, D. (2011) Changing commuters' behav- ior ...

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its own utility specification which is linear. Factors were entered into the utilities in a sequential manner whereby, non significant factors are dropped out and significant ones remain. Random effects (i.e ‘s) are specified (for statistical restrictions only for three out of four categories) as normally distributed error terms (with zero mean and unknown variance ) to better capture differences between respondents (i.e. heterogeneity) across the observations. In addition we allowed covariation between the random effects to account for inherent correlations between the unobserved factors in the model. This is also due to the nature of the similarity between the three driving alternatives (RD, DE, DL) relative to not driving (ND). Table 3 presents the treatments’ average measurements (also illustrated in Figure 1) and between group pre/post-test differences. Table 4, presents the coefficient estimates for the MLR model. As noted in Table 3, pre-measurement levels of RD were substantially higher than the pre-test and this difference is significant for both groups. Thus any significant change between the pre-test and other treatments is also expected to be significant relative to the pre-measurement level. Since around one third of the participants stated in the posterior survey that practicing with rush-hour avoidance during the pre-test assisted them to change their behavior, exploration with alternatives to rush-hour driving could be one way to explain the difference between stated and observed pre-test behaviors. However, since the pre-measurement is based on stated rather than observed behavior, to remain conservative we did not include it in the MLR analysis. In addition although the between-group analysis of RD, suggests that post-test differences are significant this is not confirmed in the more robust MLR. Consequently only the reward treatments were specified in the model whereby the coefficients reflect their effects relative to the pre-test. In terms of goodness of fit the model has a final log likelihood of -1,648.12 and the rhosquare is 0.22. A simple multinomial logistic regression model (without random effects) had a log likelihood of -1,678.24. The log likelihood ratio test shows this difference is significant ( 2 = 60.2, df=6, p
Mediators (between-subjects factors) included the design related factors (reward class and treatment scheme), and factors relating to the participants’ stated behavior derived from the two surveys. First, as neither the treatment scheme nor any of its interactions are significant we can conclude that the order of treatments had no effect on behavior. Therefore the order effect is discarded from the final model in Table 4. Second, among socio-demographic characteristics gender has a marginally significant effect on RD (p≈0.1) suggesting men tend to change behavior more than women. In the case of money, higher education has a significant and negative effect on DE. A possible explanation is that education as a proxy for income could well be masking an income effect. However testing of moderation by income is not possible due to the small groups involved and consequent loss of statistical power. Third, we find that factors relating to habitual behavior have significant results. The reward class, which relates to pre-test levels of driving at the rush-hour, has a negative association with behavior change. It was found that moderating the class effect by group proved significant. Participants, in both groups, associated with classes A, B (2.5 - 5 rush-hour trips at pre-test) were more likely to continue driving during the rush-hour compared to classes C and D (0-2.5 trips). In addition, the class coefficient for money is slightly larger than that of Yeti. The usual departure time has a negative association with DE: i.e. the earlier is the usual departure time - the more probable is a change of behavior by driving earlier. One may argue that similar factors that affect driving early in the non rewarded situation (such as household obligations) will still be at play during the rewarded period. The preferred start of work time, a likely proxy for the preferred arrival time, has a similar negative effect on DE but also a positive effect on DL. That means that participants driving later are those that are more accustomed to depart later in usual circumstances. Finally, the use of other modes for commuting has a positive effect on not driving. Fourth, concerning scheduling flexibility and constraints, a number of factors have been found to affect change of behavior. Child chauffeuring is positively associated with RD. Other constraints on early departure, such as childcare responsibilities, were not found significant. Conversely, participants who stated they had support from their employers with arranging flexible working times are less likely to drive during the rush-hour. These results demonstrate the relevancy of constraints and support measures as important factors that determine the probability to change behavior. The number of days (per week) that starting work late is possible has a positive effect on DL, a finding that suggests that participants with more flexible working schedules are more likely to drive later. Similarly but with a marginally significant positive effect (p
Page 1 and 2: Ben-Elia, E. and Ettema, D. (2011)
Page 3 and 4: 1. Introduction Congestion on urban
Page 5 and 6: Fourth, do participants’ beliefs
Page 7 and 8: pages. Participants in possession o
Page 9: epresentative of the total populati
Page 13 and 14: treatment (pre/post test) remains r
Page 15 and 16: with lower rush-hour frequencies (c
Page 17 and 18: Acknowledgments This study was unde
Page 19 and 20: Kahneman, D., Tversky, A. (1979), "
Page 21 and 22: Table 1: Participants’ characteri
Page 23 and 24: Table 4: Results of mixed logistic
Page 25: Figure 1: Average response shares b

Ben-Elia, E. and Ettema, D. (2011) Changing commuters' behav- ior ...

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