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

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3.4 Measurements ***Table 2 – about here*** Data was collected during the study in several stages. In the first stage, after volunteering (April-August, 2006), participants completed a web-based preliminary survey. This survey gathered data regarding several important pre-test factors including home to work daily travel routines, individual and household characteristics (gender, age, education level, income, family composition); work schedules (i.e. flexibility in departure from home and in starting work early/late, or ability to telework), family obligations (e.g. childcare or child chauffeuring duties), availability and use of alternative means of transport, attitudes towards alternative travel modes and regular use of travel information. The survey results can be requested from the authors. The second stage was the actual experiment, lasting 13 weeks (of which weeks 3-12 were with rewards). It consisted of tracking participant’s observed behavior. Detection equipment using in-vehicle installed transponders and electronic vehicle identification (EVI) as well as backup road-side cameras was installed at the exits from Zoetermeer to the A12 motorway and on other routes leaving the city. This equipment allowed detecting each and every car passage during the course of the day, minimizing the ability of participants to cheat by trying to access alternative routes. In addition, participants were instructed to fill in their daily webbased logbook. They recorded whether or not they had commuted to work (and if not, why not), which means of transport they used and at what slot time they made their trip. This information was used to gain insight into situations in which the participant was not detected by the EVI. In this paper we decided to focus on the logbook data. The main reasons were the completeness of the data which included not only car travel but also non-car travel. In addition, the logs provide a unique description of each days travel choice whereas detections could appear several times a day. Furthermore, the logbooks and detections were checked by the project’s back-office for consistency to avoid complaints and disagreements with participants regarding their eligibility for a reward. The logbook contained several entries: normal entries on working days about the choice of travel and abnormal entries (including situations like use of another car, holiday, illness, problems with the equipment etc). Only normal entries relating to working days were included in the analysis. Detection data is left for future research on dynamics of departure time choice. The third stage of the study was a posterior evaluation survey. In this survey questions were asked about the participant’s subjective experience during the course of the experiment. This dealt with their retrospective assessment of behavior adjustment (was it easy / difficult to adjust travel behavior and how much effort was involved in changing one's behavior). Other questions focused on support measures such as discussions with one's employer, colleagues and household members about flexible working times and household routines, practicing with behavior-change during the pre-test and purchasing of certain items. Questions were also asked regarding the use of travel information enabling a pre/post-test comparison that indicated a significant increase in usage of both traffic and public transport information.. Retrospective motivations to participate in the program were also inquired. One fact to be noted is that during the experiment disruptions occurred with the regional rail service and bus service replacements were not always adequately provided. In retrospect this was mentioned as causing participants some difficulty for using the public transport. At the same time that data was collected about the participants, a survey of non-participants was also carried out. It was based on a representative sample of Zoetermeer residents, regularly commuting to The Hague during the morning rush-hour, who did not participate in the experiment. The purpose was to determine whether the participants in the trial were 7
epresentative of the total population of rush-hour drivers. Similar questions were put to these respondents. This analysis (see Ben-Elia & Ettema, 2009) demonstrated that although the reward is the main motivation in potentially choosing to participate in a similar rewardbased scheme, lack of flexibility in daily schedules was the main reason to reject the scheme. 4. Results Responses appearing in the logbook were sorted into four distinctive and exclusive categories: rush-hour driving (RD), driving earlier (DE) or driving later (DL) than the rushhour, and non-driving (ND) which included all non-auto modes of travel (public transport, cycling, car pool) as well as teleworking. Since the rewards were provided on a weekly basis, the number of rush-hour avoidances within the week, could well be correlated. Daily responses were therefore assembled to weekly average shares (i.e. proportions). Weekly averages were further aggregated to treatment averages for statistical testing purposes in the following way: in the money group five repeated measurements (pre-test, three treatment levels, post-test); and in the Yeti group four repeated measurements (pre-test, credit, non-credit, post-test). The data analysis itself consisted of two stages. In the first stage (available from the authors by request), each of the four response categories was analyzed separately using GLM-repeated measures. In the second stage a mixed logistic regression (MLR) model was estimated based on the significant factors found in the first stage. The rationale behind using MLR was that the four response categories (RD, DE, DL, ND) attributed to each participant are in a sense a closed set of discrete choice alternatives and therefore correlated. The probability of choosing a discrete response (i.e. an alternative) is specified as the dependent variable and the independent factors explain this probability. Usually, the relationship between alternatives and explanatory factors is specified with an outcome function referred to as 'utility'. The greater the utility of an alternative is, the higher is the probability of a participant choosing it (Train, 2002). Simple logistic regression is unsuitable for analysis of repeated measurements (McFadden & Train, 2000). However the MLR model can accommodate this by specifying a panel data model (Revelt & Train 1998; Bhat, 1999;). We estimated the MLR model using the estimation program of NLOGIT 4.0 (Econometric Software Inc.,) and using share-based data with 1,000 random draws (see Train, 2000), for further details regarding drawing methods. Formally, the utility of person n of alternative i in response t and the probability (P) of person n choosing alternative i in response t are (eq. 1, 2): where P is the conditional probability that person n chooses alternative i out of a set of J alternatives, Y, is an indicator that i is chosen at response t, X is a vector of explanatory factors, , is a vector of fixed coefficients (including a constant), is a vector of random parameters with a distribution f (0 mean and a variance parameter ) and is a vector of independently, identically distributed (iid) Gumbel (or extreme-value type 1) error terms. The MLR model's main purpose is to estimate the composite effects on all four (correlated) response categories accounting for the sequential structure of the data. Each category has 8 (1) (2)
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Page 3 and 4: 1. Introduction Congestion on urban
Page 5 and 6: Fourth, do participants’ beliefs
Page 7: pages. Participants in possession o
Page 11 and 12: Mediators (between-subjects factors
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

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