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

Ben-Elia, E. and Ettema, D. (2011) Changing commuters’ behavior
using rewards: A study of rush-hour avoidance. Transportation
Research Part F Traffic Psychology and Behaviour, 14 (5). pp. 354-
368. ISSN 1369-8478
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The publisher’s URL is http://dx.doi.org/10.1016/j.trf.2011.04.003
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Changing commuters’ behavior using rewards: A study of
rush-hour avoidance
* (corresponding author)
Eran Ben-Elia * ,
Centre for Transport and Society
Faculty of Environment and Technology
University of the West of England
Frenchay Campus, Bristol, BS16 1QY, United Kingdom
eran.ben-elia@uwe.ac.uk
Dick Ettema
Urban and Regional research centre Utrecht
Faculty of Geosciences
Utrecht University
P.O. Box 80115
3508 TC, Utrecht, The Netherlands
d.ettema@geo.uu.nl
Key Words
Attitudes, behavior-change, congestion, habitual behavior, information, motivation, reward.
Abstract
In a 13-week field study conducted in The Netherlands, participants were provided with daily
rewards – monetary and non-monetary, in order to encourage them to avoid driving during
the morning rush-hour. Participants could earn a reward (money or credits to keep a
Smartphone handset), by driving to work earlier or later, by switching to another mode or by
teleworking. The collected data, complemented with pre and post measurement surveys,
were analyzed using longitudinal techniques and mixed logistic regression. The results
assert that the reward is the main extrinsic motivation for discouraging rush-hour driving. The
monetary reward exhibits diminishing sensitivity, whereas the Smartphone has endowment
qualities. Although the reward influences the motivation to avoid the rush-hour, the choice
how to change behavior is influenced by additional factors including gender and education,
scheduling considerations, habitual behavior, and cognitive factors regarding attitudes and
perceptions, as well as travel information availability factors.
1

1. Introduction
Congestion on urban roads throughout the European Union is increasing and is expected to
worsen as the demand for trip making increases and supply of road infrastructure remains
limited (European Commission, 2006a, 2006b). Loading of excess demand on the
transportation system has considerable external costs such as pollution, noise and road user
safety (Mayeres et al., 1996). Road overloading disrupts vehicle flow, increases the
frequency of incidents and magnifies the uncertainty of travel schedules (Lomax & Schrank,
2003). Congestion is a collective, synchronic phenomenon: massive commuting at a more or
less common time-frame (e.g. the morning rush-hour). Thus, shifting of commuters’
departure times to less congested times, before or after the rush-hour, change of transport
mode (from car to public transport) or change of work mode (working from home), should, in
theory, lead to considerable time savings, greater travel certainty and lower external costs of
congestion.
Transportation demand-based solutions (e.g. road pricing, promoting modal alternatives,
parking policy and land use planning policy) have been suggested to reduce congestion
(Shiftan & Golani, 2005). In this respect, transport economists have been arguing for the
implementation of road pricing as a first-best solution to efficiently alleviate congestion
externalities (Nijkamp & Shefer, 1998; Rouwendal & Verhoef, 2006; Small & Verhoef, 2007).
However, road pricing is controversial and its behavioral implications are not well
understood. As suggested initially by Vickrey (1969), optimal pricing requires the design of
variable tolls, making them quite complex for drivers’ comprehension (Bonsall et al., 2007;
Verhoef, 2008). In addition, road pricing raises questions regarding social equity (Giuliano,
1994), fairness and public acceptability (Eriksson et al., 2006) as well as economic efficiency
(Banister, 1994; Viegas, 2001).
Second-best schemes have been suggested to circumvent the difficulties in implementing
first-best pricing solutions (Small & Verhoef, 2007). In The Netherlands the notion of using
rewards to achieve desired outcomes in travelers’ behavior has been recently implemented
in the context of the Spitsmijden 1 program (Ettema et al., 2010; Knockaert et al., 2007), thus
far, the largest systematic effort to analyze the potential of rewards in the field as a policy
mean for changing commuter behavior. A pilot study (see section 3 for further details),
involving 340 participants and lasting over 13 weeks, was organized in the second half of
2006. Its objective was to investigate, in an empirical field study, the potential impacts of
rewards on commuters’ behavior during the morning rush-hour. Participants could earn a
reward (money or credits to keep a Smartphone handset which also provided real-time traffic
information), by driving to work earlier or later, by switching to another travel mode or by
teleworking. Initial results provided evidence of substantial behavior change in response to
the rewards, with commuters shifting to earlier and later departure times and more use of
public transport and alternative modes or working from home (Ettema et al., 2010).
The effectiveness of rewards to reinforce a desirable behavior (e.g. identification and loyalty,
work effort) is supported by a large volume of empirical evidence (Kreps, 1997; Berridge,
2001). However, in the context of travel and traffic behavior, rewards are poorly represented.
Punishments and enforcement (such as policing, felony detectors, fines etc.), have been
more widely documented than rewards (e.g. Rothengatter, 1992; Perry et al., 2002;
Schuitema, 2003). The relative salience of negative motivational means reflects, to a large
extent, a disciplinary bias. Given that travel behavior has been to the most part subjected
and influenced by microeconomic theories (McFadden, 2007), it is not surprising that the
behavioral rationale of many demand based strategies to manage traffic congestion is based
on negative incentives that associate, through learning, the act of driving with punishments
(such as tolls or increased parking costs).
1 translated literally as peak avoidance
2

The few examples where rewards have been applied in a travel context are short term
studies involving the use of a temporary free bus ticket as an incentive to reduce car driving.
To most parts, the results of these studies are inconclusive. For example, (Fujii et al., 2001;
Fujii & Kitamura, 2003) found that an incentive did encourage a change towards reducing
car driving; however the level of car driving returned to previous levels once the incentive
was stopped. In contrast, (Bamberg et al., 2002; Bamberg et al., 2003), found that habitual
behavior prevented substantial reductions in car use. It is not the scope of this paper to
debate which policy (pricing or rewards) is more effective. However there is substantial
evidence that people respond more favorably and are more motivated when rewarded rather
than punished (Kahneman & Tversky, 1984; Geller, 1989). Thus, the potential of rewards as
a base for traffic management policy is well worth considering if based on robust behavioral
foundations.
The main aim of this paper is to comprehensively analyze and explore the changes in
behavior during the course of the aforementioned pilot study and identify key factors that
influenced the response to the rewards. The rest of the paper is organized as follows:
Section 2 sets a number of theoretically driven research questions and hypotheses. Section
3 describes the experimental setup and methods. Results, based on a mixed logistic
regression analysis are presented in section 4. A discussion is presented in section 5,
followed by summary and conclusions in section 6.
2. Research questions & hypotheses
Several key questions are postulated: First, how effective are rewards as a means for
motivating travel behavior change? The literature does not provide a clear indication. One
view suggests that satisfying rewards contribute to higher rates of motivation (Cameron et
al., 2001; 1994). The other view propounds that rewards interfere and undermine intrinsic
motivation, deflecting motivation from internal to external causes and reducing the amount of
effort devoted to participate in activities (Deci, 1971; 1975; Lepper & Green, 1978). Theory of
Cognitive Evaluation (TCE) further asserts that the effect of reward will depend on how it
affects perceived self-determination and competence (Deci & Ryan, 1985).
Second, does the nature of the reward (monetary, in-kind) affect the willingness to change
travel behavior and its tenacity? People seem more receptive to large monetary rewards
compared to small ones (Gneezy & Rustichini, 2000; Gneezy, 2003). Moreover, a monetary
reward might be framed as a prospective gain. According to Prospect Theory (Kahneman &
Tversky, 1979), diminishing sensitivity to money can affect the perseverance of change.
Participants’ apparently have greater satisfaction and motivation is higher with gifts
compared to monetary rewards; however when asked, most people prefer receiving money
(Shaffer & Arkes, in press). In-kind rewards may therefore encourage behavior change
through a different cognitive path: the endowment effect. A Smartphone handset granted to
some participants may be regarded as an uncertain endowment. An endowment is not easily
relinquished, once given (Kahneman et al., 1991). The endowment effect may well motivate
to change behavior just in order to avoid the loss associated with the possibility to give up a
valued object. In this respect, the in-kind reward, unlike the monetary one may have affective
as well as motivational properties.
Third, to what extent do personal and social characteristics (e.g. gender, education level,
personal income, or household composition) sustain or diminish the potential impact of
rewards? The connection between socio-economic characteristics and travel choices is well
documented (e.g. Harris & Tanner, 1974; Ben-Akiva & Lerman, 1985; Axhausen & Gärling,
1992) In this respect income may well affect motivation in the case of the monetary reward.
Diminishing sensitivity could suggest that participants with higher incomes might be less
motivated to change behavior for a rather marginal monetary gain.
3

Fourth, do participants’ beliefs attitudes and norms influence their responsiveness to change
behavior? Several studies (e.g. Gärling et al., 1998; Gärling et al., 2001) suggest attitudes
towards travel alternatives, affect the choice of travel modes. The Theory of Planned
Behavior (TPB) (Fishbein & Ajzen, 1975; Ajzen, 1991) suggests a positive attitude towards a
certain behavior will influence a person’s intention to consciously engage in it. Rewards
which create a positive attitude with a certain behavior, will contribute to this behavior being
repeated. Another issue is that of personal norms that are self expectations or specific
actions in specific situations (Schwartz, 1977). They refer to feelings of moral obligations to
behave in a certain way (e.g. environmental friendly behavior). If a reward scheme is
regarded as congruent with the personal norms and expectations, it is more likely to
encourage behavior-change.
Fifth, are there situational factors (home and work-related) that affect the relative salience of
rewards as means for travel behavior change (here, rush-hour avoidance)? TBP stresses
the role of others’ attitudes, and the perceived situational control on influencing intentional
behavior-change. If a person perceives behavior changes as difficult, the probability of
repeating this action is relatively low. Scheduling constraints such as household obligations
(e.g. child care, children chauffeuring) and work organization have been found to influence
individuals’ responses to pricing schemes and limit their perceived effectiveness (Gärling &
Fujii, 2006). Participants with child care or children chauffeuring responsibilities on one hand,
or participants with inflexible working times, on the other hand, might have a limited ability to
change behavior even when motivated by the reward. Conversely, the support a person gets
from the household, workplace and from colleagues or friends that are also participating in a
reward based scheme may well contribute to one’s own participation.
Sixth, to what extent options chosen to avoid the peak are determined by habits? In the long
run habitual travel behavior, as asserted by Gärling et al. (2001) and Gärling & Axhausen
(2003), is quite relevant for promoting or discouraging a behavior change different from the
usual travel behavior. Theory of Interpersonal Behavior (TIB) (Triandis, 1977, 1980) stresses
the role of habit in behavior. With habitual behavior, decisions are made with a lesser degree
of consciousness which decreases the likelihood behavior will change in response to a
change in context. Habitual behavior is less intentional more automated and script based
(Ronis et al., 1989; Gärling & Garvill, 1993). Travel decisions (e.g. the drive to work) are an
example of habitual behavior as repeated decisions which loose intention and become
gradually routinized (Verplanken et al., 1997; Gärling et al., 1998).
Last, what is the role travel information plays in changing commuters’ behavior? Several
studies point out that availability of information has significant effects on travelers’ behavior
in the lab (Avineri & Prashker, 2006; Ben-Elia et al., 2008). For example in the case of routechoice,
Ben-Elia & Shiftan, (2010) found real-time travel information expedites learning in
unfamiliar environments and reduces initial exploration. At the same time, exposure to
information is also associated with more heterogeneity in choice behavior and in risk
attitudes. In this respect the Smartphone reward could well have instrumental value as it also
provides access to real-time traffic information. Information might motivate change of
behavior by facilitating the travel decision process and by reducing subjective effort and
difficulty increasing the perceived situational control.
3. Method
3.1 Participants
Using license plate recognition cameras, 2,300 cars, both privately owned and leased
company vehicles and traveling at least three times a week during the morning rush hour on
the busy stretch of the A12 motorway (about 15 km connecting Zoetermeer to The Hague).
The Dutch Department of Road Transport provided the names and addresses of the car
owners and they were approached by mail with an invitation to participate in the experiment.
4

A total of 341 commuters - 221 men and 120 women – chose to participate in the
experiment. Upon registration, the participants self selected one out of two types of reward.
The first type of reward was an amount of money (3-7 Euros and see next subsection) for
each day that the participant avoided driving during the morning rush-hour. In this case,
participants were provided with a realistic estimate of how much they could earn in the
course of the study. The second type comprised credits towards ultimately keeping a
Smartphone (called Yeti) at the end of the experiment. 232 participants (60% men),
selected a monetary reward (‘money’) and 109 (74% men) the Yeti reward. The Yeti’s
market value was around € 500 at the time. All the participants were inhabitants of the town
of Zoetermeer and the vast majority was working at the time in The Hague or its vicinities.
They are characterized by relatively high percentage of higher education, moderate to high
incomes and mostly families with children. Table 1 presents the descriptives of the
participants by group.
3.2 Procedure
***Table 1 about here***
The task and rules were communicated to the participants through the project's back office:
Participation had to be voluntary. The participants were to commute at least three times a
week from home to work. They had to have access to e-mail and the Internet. They were
requested to complete surveys completely and timely. They were made aware that their
movements by car would be recorded and had to agree to the installation of an on-board
transponder in their car. In addition it was explained that only the car in which a transponder
had been previously installed could be eligible for the reward. A travel log (i.e. logbook) was
to be filled in daily on a personal webpage on the projects’ internet site. Participants that
opted for the Yeti reward were also instructed to switch on the Smartphone during every car
trip, in order to get full and easy access to real-time travel information. All communication
was to be conducted via the project’s back-office which dealt with complaints or operational
problems. A weekly newsletter was also sent to participants’ homes providing further
information and clarifications. Participants’ earnings were shown on their personal webpage.
The earnings were updated once a week according to the relevant treatment schemes. The
monetary rewards were directly paid to participants’ bank accounts at the end of the working
week by bank transfer.
3.3 Design
Participants were instructed that they could avoid commuting during the morning rush-hour
(defined between 7:30-9:30 AM) either by shifting their departure times to earlier or later
times of travel, or by choosing other modes of travel (cycling, carpool, public transport), or by
working from home (teleworking). The experiment ran for a period of 13 weeks. The first two
weeks were without reward (pre-test). The data collected during the pre-test was used to
determine participants’ reference travel behavior and subsequent assignment to reward
classes. The final week (post-test) was also without rewards.
Those participants who opted for money were the subject of three consecutive reward
treatments lasting 10 weeks in total: a reward of 3€ (lasting three weeks), a reward of 7€
(lasting four weeks) and a mixed reward (lasting three weeks) of up to 7€ - of which 3€ for
avoiding the high peak (8:00-9:00) and an additional 4€ for avoiding also the lower peak
shoulders (7:30-8:00, 9:00-9:30). A counterbalanced (blocked randomization) design was
used to allocate participants randomly to 6 (that is 3! blocks) possible treatment orders
(referred to as scheme). A few exceptions were applied to couples using the same vehicle.
The scheme of treatments was communicated to the participants through their personal web
5

pages. Participants in possession of the Yeti could acquire credit during a period of five
consecutive weeks. If they earned enough credit relative to a known threshold they could
keep the Smartphone. This threshold was determined by their reward class (see below). The
other five weeks were without credits but participants could still have access to traffic
information. Participants were randomly divided between two schemes in relation to which of
the first or second set of 5 weeks credits could be awarded. They were also made aware of
their respective schemes.
Participants in possession of a Yeti also had 24 hour access to travel information via the
handset during 11 weeks: the credit treatment, the no-credit treatment as well as the posttest.
This information consisted of real-time travel times on the A12 motorway on the
Zoetermeer – The Hague corridor and an online map showing congestion levels on other
roads in the area. Information availability was not dependent on the reward itself. In contrast,
participants in the money group had access to information available to all other drivers: pretrip
through internet and media and en-route from variable message signs along the
motorway.
In addition to the treatments, each participant was also assigned to a reward class which
determined his/her maximum eligible reward. In essence, a participant could only earn the
reward as often as he/she was observed to drive in the morning peak during the pre-test.
Thus, a participant who would drive in the peak three times per week in the pre-test, could
only receive a reward for the third, fourth and fifth day in a week he/she avoided the peak,
whereas one who drove in the peak five times per week was eligible for any working day
he/she avoided the peak. This reward could be either the daily monetary reward or the
threshold number of credits needed to keep the Yeti. It should be noted that retrospectively
very few participants failed to meet their threshold. In order to avoid regret, it was also
decided at the end of the study to allow all the participants to keep their Yeti’s. Accordingly,
each participant was allocated into one of four possible reward classes. Once determined
these classes were fixed throughout the rest of the experiment. The majority of participants
belonged to classes A and B and the minority to classes C and D. Table 2 presents the
number of participants (by gender) in each class. In both groups women are more prevalent
in the classes with lower traveling frequencies. For a more detailed description of the
experiment’s design see the report (in English) of Knockaert et al., (2007) also available from
the authors by request.
Self selection of reward types by participants suggests by definition a quasi-experimental
design. Like random experiments, quasi-experiments share the same basic principles of
manipulation (cause precedes effect) and measurable associations (covariation). In contrast,
causation requires more effort as compared to random assignment there are more threats to
internal validity. In this we will follow the recommendations of Shadish et al., (2002) noting
possible threats. An analysis of threats to internal validity is described in section 4. Lack of a
control group also can contribute to validity problems. Several features in the design allow
improved control and reduce possible threats. First, the pre-test / post-test design is fostered
by additional measurements of stated behavior through the two surveys. Specifically threats
resulting from history and novelty can be assessed by comparing between the preliminary
survey and the pre-test. In addition, the measured factors from the surveys such as usual
behavior, constraints and support measures, can provide relevant mediators to the observed
behavior and verify if selection is a problematic issue. This is dealt in detail in Section 4.
Second, norm comparisons with traffic counts on the main A12 trajectory suggest other
drivers did not change behavior during this period. The sample is small enough not to have
any real impact on traffic flow. Since no significant change in traffic occurred during the 13
week observed period we can assert that any difference between observed behaviors with
treatments and without is likely to be related to the intervention. In retrospect, it is
acknowledged that random assignment and group control would have been the preferred
solution.
6

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)

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

for public transport alternatives to support their behavior are more likely to change behavior
by not driving ( the coefficients for both these factors are positive).
*** Table 3 – about here ***
***Table 4 – about here ***
As noted in section 3, this study is compromised of a quasi-experimental design. Based on
the recommendation of Shadish et al. (2002), we describe here the plausible threats to the
validity of the results. Threats to statistical inferences are not discussed here as we contend
that these are likely to be low given the conservative nature of the analysis method applied
which guarantees proper statistical identification of the measurable effects.
The two most plausible threats in our study are selection and history. Attrition is not an issue
since no dropouts occurred. Maturation is also not relevant given the short period of time
that the experiment was running. The issue of selection relates to a priori differences in the
money and Yeti groups which could compromise the results. In Table 5 we present a
statistical comparison of the differences between the self-selected groups by the factors that
are associated with the response in the MLR model. Most factors have no significant
difference, but gender, chauffeuring children and arrangements with employers regarding
flexible working times do. The latter which has the most significant difference was measured
in the posterior survey whereas the first two factors relate to pre-measurement. To contend
with the threats we estimated the effects moderated by group, specifying the MLR model
with group-specific coefficients for these three factors.
Regarding gender, it was barely significant in the model (p≈0.1). We tested moderating
effects for money and Yeti but this was found not to be significant. We can therefore
conclude that gender (woman) has a weak negative association with avoidance behavior.
Chauffeuring children is significantly and negatively associated with avoidance behavior
(p=0.02). We investigated if this might be moderated by gender but given the small group
involved of both men and women who have this constraint we could not identify with
confidence any significant moderating effect. Moderating by group also does not reveal
significant group differences in the MLR model. Therefore we can suggest that the negative
effect identified for chauffeuring on peak avoidance is probable.
The threat attributed to pre-arrangements regarding flexible work times requires more
attention (p

treatment (pre/post test) remains relatively valid. Practicing also has a weak negative effect
on rush-hour driving (p=0.07) which was only found relevant for the money group.
5. Discussion
Effectiveness of rewarding
***Table 5 – about here ***
The results demonstrate, that rewarding, at least in the short run, is effective as within a
short period of time of several weeks, the share of rush-hour avoidance substantially
increased. Thus in concordance with motivation theories (e.g. Cameron et al., 2001) rewards
do influence the motivation to avoid the rush-hour. Moreover, we also found that the decision
how to exercise this change of behavior, whether by driving at other times or by changing
transport or work modes seem to be determined by other factors unrelated to the type or
level of reward.
Nonetheless, it is difficult to conclude from a relatively short longitudinal study about the
impacts of rewards in the long run. Motivation theories suggest that if intrinsic motivation
kicks in, the change of behavior is more likely to be sustained. However, we observed in the
post-test, once rewards ceased, avoidance shares had dropped and participants had
returned more or less to their usual behavior of rush-hour driving (as observed in the pretest).
In this respect the results are similar to those obtained by Fujii & Kitamura (2003)
regarding free bus tickets. Therefore at first glance it seems the change was not sustained
for most of the participants. Notwithstanding, in the posterior survey less than 15% of
participants stated they had returned to their previous behavior. Unfortunately, we do not
have observations to corroborate this subjective evaluation. Further research is being carried
out in this respect (see section 7). We also do not posses sufficient (post-test) data to
conclude about the affective qualities of the rewards apart for the fact that the vast majority
of participants (in both groups) answered affirmatively to the question 'did you like the
reward' in the posterior survey.
Reward type and levels
We found that both types of reward (monetary and in-kind) have a significant and negative
effect on rush-hour driving. In the case of a monetary reward, diminishing sensitivity was
clearly noted. The 7€ treatment has the largest overall effect on RD; however the largest
marginal effect (the derivative) is associated with the 3€ treatment. Therefore, for practical
purposes, a moderate monetary reward seems to be sufficient to encourage a relatively
substantial change of behavior. In the case of the Yeti reward, the main effect is the credits
which had an effect similar to the 7€ reward. In this sense an in-kind reward, likely perceived
as an endowment, can be just as useful as the monetary reward. However, for practical
reasons, there may be difficulty in implementing an in-kind reward over a long period of time.
Though not statistically significant in comparison to pre-test levels, avoidance behavior was
also apparent without valid credits. This treatment had no extrinsic reward but travel
information was still accessible to Yeti users. Furthermore, it was evident that Yeti users
were more likely to drive later compared to participants in the money group. Two possible
explanations are possible for this different behavior. On one hand, Yeti users had higher
shares regarding support provided from employers. Thus, it is possible that pre-adjustments
were involved in choosing to depart later (especially during the pre-test). On the other hand,
the main advantage Yeti users had over the other group was 24 hour access to travel
information. This leads us to suggest that the decision how to change behavior is also
influenced by travel information availability (discussed later on).
12

Socio-demographic characteristics
As noted, this is hardly a novel assumption in travel behavior studies. Gender (marginally)
and education, were found to have an impact on the response to the rewards. It seems that
men (mainly in the money group) are more likely to avoid the rush-hour compared to women.
The lower motivation of women to avoid the rush-hour can be associated with many issues.
One idea that has been suggested in social mobility studies (Palma et al., 2009) is that
women are more constrained in time compared to men for various reasons, mainly
household tasks and child raising obligations. Dutch women quite often leave work early in
the afternoon to pick up children from nurseries (Schwanen, 2007). This limits their ability to
change their schedule - e.g. to start work later even when extrinsically motivated by a
reward. However, a larger sample is needed to clearly mark the causation between gender
and time-use behavior.
Education had a negative effect on behavior change (driving later). Participants (in the
money group) with higher education were less likely to drive later. Education is a known
proxy for latent income effects. Income is regarded as a key issue determining willingness to
pay for travel purposes as well as the value of travel time savings (Ben-Akiva & Lerman,
1985; Axhausen & Gärling, 1992). In the context of the money group, the significance of
higher education strengthens the notion of diminishing sensitivity in relation to the monetary
reward: participants with higher real income are likely to be less sensitive to a marginal
monetary gain compared to participants with lower incomes. As a result motivation to avoid
the rush-hour would be negatively associated with real income. Education did not appear to
be a relevant factor on the behavior of Yeti users, possibly because it is cognitively and
affectively appreciated as an endowment, rather than in monetary (how much it’s worth)
value.
Scheduling
This is a new territory of travel behavior research, lately identified by Gärling and Fujii
(2006). The results suggest that behavior change and more so the choice of behavior
change is associated with the ability or disability to change daily schedules. Both home
related and work related flexibilities are relevant. Family obligations, such as children
chauffeuring - a constraint associate positively with rush-hour driving - make it more difficult
for parents to change travel behavior. The ability to accommodate a flexible schedule and
the support provided by others are also significant factors. Participants that could start
working later or could telework were more likely to drive later. Participants reporting to have
received support from their employer with arranging flexible working times were also less
likely to drive in the rush-hour. We see these results as supporting evidence that flexibility,
especially at the work place is a key issue in promoting changes in travel behavior. Contrary
to our expectations, home-related support measures such as household arrangements did
not have a significant effect on behavior-change. A possible explanation to the effects of
scheduling is the extent of control over one’s actions and their outcomes. The Theory of
Planned Behavior (Ajzen, 1991) suggests perceived situational control is a key factor in
encouraging a conscience behavior-change. Thus flexibility in time-use promotes a sense of
self confidence and ability to contend with the schedule’s change.
Habitual behavior, experience and attitudes
As suggested by Theory of Interpersonal Behavior (Triandis, 1977, 1980) we found that
factors relating to habitual behavior play an important role in the choice how to change
behavior. This corroborates findings from other studies (e.g. Gärling et al., 2001; Gärling &
Axhausen, 2003). The effect of habitual behavior is well manifested in the significance of the
reward class, usual departure time, the preferred start of work time (in the case of shifting
driving times) as well as the use of other modes for commuting purposes (in the case of
switching mode). Participants with higher rush-hour commute frequencies during the pre-test
(reward class A, B) were relatively less likely to avoid the rush-hour compared to participants
13

with lower rush-hour frequencies (class C, D). Two potential explanations are put forward.
First, in terms of effort, one could argue that a similar relative response demands more rushhour
avoidances from frequent rush-hour drivers than from less frequent rush-hour drivers.
Hence, the effort involved is higher for high frequency drivers. This is in line with Garling et
al., (2004) and Cao and Mokhtarian (2005), who found that travelers prefer low effort
responses over high effort responses. A second explanation is that the added value of
additional rewards depends on the amount already gained, in the sense that the marginal
utility of reward decreases (i.e. diminishing sensitivity). Thus, the extra rewards gained by
high frequency drivers will have a lower impact on behavior. This is in line with the idea of
satisficing behavior described by Simon (1987). In the case of Yeti users, the effect of
reward class is weaker. This might be related to the affective qualities of the Smartphone
endowment i.e. avoiding the displeasure of having to give back the handy Smartphone
encouraged avoidance. In addition, real-time travel information may have been useful in
reducing perceived effort and promoting self confidence in the ability to manage with rushhour
avoidance.
It is also evident that a relation exists between the usual schedules (usual departure and
arrival times) and choice of behavior-change. The usual departure time was a decisive factor
affecting the choice to depart earlier whereas the preferred start of work time, a likely proxy
for the preferred arrival time, had a significant influence on both driving earlier and later.
Furthermore, previous experience using other transport modes was an important contributor
to the choice not to drive. That is, familiarity with an alternative seems to increase intrinsic
motivation. It appears that the choice of behavior-change is closely related to the perceived
gap between the usual behavior and the required change – the smaller the gap the more
likely is that the change will be exercised. One may argue that alternatives that are more
similar to the current behavior, will better meet the travelers’ preferences with respect to
characteristics of the travel mode and timing.
Contrary to the usual behavior, a behavior-change requires gaining of knowledge through
exploration and reinforced learning about the new situation. It is suggested that exploration
had an important role during the pre-test. Practicing avoidance behavior during the pre-test
was reported by almost a third of the participants( in both groups) and it is one explanation
for the dramatic drop in the pre-test shares of rush-hour driving compared to the usual
(stated) behavior recorded in the preliminary survey. This factor was also found (albeit
weakly) to increase the likelihood of decreasing rush-hour driving for the money group.
Recent findings in the context of route-choice behavior suggest information expedites
learning in the short-run whereas lack of information requires greater effort devoted to
exploration and learning (Ben-Elia et al., 2008; Ben-Elia & Shiftan, 2010). It is plausible that
the pre-test was devoted by participants for information acquisition.
Attitudes have been recently gaining attention in travel behavior studies (Gärling &
Axhausen, 2003). Moreover, perceptions and attitudes have been the focus of invigorating
attempts to improve choice modeling (Walker, 2001; Cherchi, 2009). As suggested by TPB,
attitudes and personal norms are significant factors in encouraging or discouraging a
conscious decision to change behavior. Our results support this assertion in that participants’
attitudes regarding alternative modes were a key factor in determining the choice of
avoidance behavior. Positive attitudes (defined as regarding a travel mode as a realistic
alternative), regarding public transport and cycling, discouraged driving (including at off-peak
periods) and encouraged mode switch away from the car. Conversely, perceptions regarding
the (high) effort involved in changing behavior decreased the likelihood of changing behavior
and were positively associated with rush-hour driving. We could not find real support for the
relevance of personal norms in the decision to avoid the rush-hour.
Information availability
Several studies confirm the key role that availability of travel information has on promoting
sensible travel choices (Mahmassani & Liu, 1999; Srinivasan & Mahamassani, 2003). Our
14

esults sustain this association in three ways. First, there appear to be significant betweengroup
differences in the behavior change. Yeti users had relatively higher shares of driving
later compared to higher shares of driving earlier in the money group. Yeti users’ main
advantage was the real-time access to travel information whereas the participants in the
money group had to search for the same information i.e. it involved more effort. However, as
noted this result could also be confounded by prior arrangements with employers over
flexible working times which were more dominant with the Yeti group. Hence, we cannot be
certain if the different response is attributed to the Yeti treatment or related to
prearrangements which facilitated driving later. However, the information explanation is
strengthened by the fact that moderating this effect by group was not significant.
Second, access of travel information, mainly traffic but also public transport information,
intensified during the course of the experiment (pre/post-test comparison). Thus, decisionmaking
in a changed environment apparently increased the need for information about the
outcomes of alternatives. Third, information availability is positively associated with not
driving or driving later. Participants who frequently accessed public transport information and
who were actively perusing information over public transport connections were more likely to
avoid driving altogether. In addition, participants with higher frequency of accessing traffic
information where more likely to choose driving later. It seems therefore that information
acquisition and choice of avoidance behavior are clearly related. However causality here is
uncertain as participants could also increase information acquisition for the alternative they
found is best.
6. Summary and conclusions
The main conclusion regarding the use of rewards in encouraging commuters to change
behavior is that it actually works. Rewards are effective extrinsic motivators for travel
behavior change - here rush-hour avoidance. The monetary reward was likely perceived as
a gain with diminishing sensitivity, whereas the Yeti should be regarded as an in-kind reward
which had added endowment and instrumental qualities. The rewards were able to sustain
the behavioral change throughout the experiment. Nonetheless, it is still an open question
whether the change would be sustained in the long run and without rewards. We do not have
enough post-test observations to provide an answer apart from subjective assessments by
the participants. A second conclusion that can be drawn from this research is that the reward
influences the magnitude of change – an increase or decrease in rush-hour avoidance.
However the choice how to avoid – driving at other times, switching to another mode of
transport or working from home, is determined by external factors relating to participants’
personal and social characteristics, scheduling flexibility, history and information availability,
Although already of some interest to the travel behavior research community these issues
deserve further attention in future research.
As a closing remark, following the success of the current study, application of reward-based
schemes is now taking place across The Netherlands. Although some concern, based on
traffic simulation models, indicated that too many people might start changing their
schedules to gain a reward (Bliemer & van Amelsfort, 2008), the evidence in the field does
not support this claim. Their effectiveness in mitigating congestion, especially in situations
involving temporary road maintenance or lane closures has been verified (Bliemer et al.,
2009). A recent survey of firms also has shown positive attitude amongst employers towards
the reward scheme (Vonk Noordegraaf & Annema, 2009). So far, the majority of the Dutch
public (apart for the public transport users who are ineligible and consequently grumbling)
and the government are quite content with the results. However as recently published in the
media the (last) government also wanted to advance a punishment policy through universal
kilometer road charging – a decision that stresses the importance of well-informed,
evidence-based, as well as behaviorally-sound public policy.
15

Acknowledgments
This study was undertaken as part of the Spitsmijden project, which was funded by
Transumo, the Ministry of Transport in the Netherlands, Bereik, RDW, NS, Rabobank, ARS
T&TT, OC Mobility Coaching, Vrije Universiteit Amsterdam, TU Delft, Universiteit Utrecht.
The modeling framework was discussed in the 5 th Discrete Choice Modeling Workshop
organized at EPFL (Lausanne, Swizterland) in August, 2009. The comprehensive comments
and suggestions of two anonymous reviewers are very highly appreciated.
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19

Table 1: Participants’ characteristics
Gender
Education level
Income €
(net person/month)
Household
composition
Cars / Household
Age (years)
20
Money Yeti
N % N %
man 140 60.3 81 74.3
woman 92 39.7 28 25.7
Secondary 24 10.4 9 8.3
Low vocational 9 3.9 5 4.6
Middle vocational 64 27.7 36 33.3
Higher education 134 58.0 58 53.7
4500 11 4.8 3 2.8
didn't answer 53 22.9 21 19.4
single 35 15.2 10 9.3
partner no kids 61 26.4 20 18.5
partner + kids 118 51.1 73 67.6
single parent 13 5.6 3 2.8
other 4 1.7 2 1.9
1 120 51.9 45 41.7
2 103 44.6 59 54.6
3+ 8 3.5 4 3.7
Mean 41.3 44.8
Median 42.5 45
Per.25 34 37
Per.75 49 51
Table 2: Reward classes* by gender and reward type (group)
Money Yeti
A B C D A B C D
Thresholds** 5 4 2 1 15 20 23 25
N
Men 83 33 13 11 34 27 13 7 221
62% 54% 57% 79% 72% 87% 59% 78% 65%
Women 51 28 10 3 13 4 9 2 120
38% 46% 44% 21% 28% 13% 41% 22% 35%
Total 134 61 23 14 47 31 22 9 341
* A: 3.5-5, B:2/5-3.5, C: 1-2.5, D: 0-1 trips/week.
** Money: maximum number of eligible rewards per week; Yeti: number of credits at the end of 5 weeks required to keep
the phone.

Table 3: Mean values of response and between-group differences
Money Yeti between-group
difference
Resp. Measurement N Mean s.d N Mean s.d t-stat Mann-
Whitney
U
Rush Preliminary Survey - S 232 89.9 15.44 108 89.8 13.25 0.03 12,153
Hour Pre test - R1 219 48.7 38.17 107 44.0 36.69 1.06 10,395
3€ - R2 229 22.4 28.66
7€ - R3 231 17.7 26.70
3-7€ - R4 230 17.8 26.88
No Credit - R5 109 31.0 31.26
Credit - R6 107 15.4 21.95
Post test - R7 225 47.3 38.91 101 37.6 37.64 2.22 9,808
Driving Preliminary Survey - S
Early Pre test - R1 219 22.7 33.03 107 22.0 33.06 0.20 11,489
3€ - R2 229 37.7 37.87
7€ - R3 231 41.8 38.05
3-7€ - R4 230 42.4 38.49
No Credit - R5 109 24.8 33.24
Credit - R6 107 33.8 37.12
Post test - R7 225 24.3 35.47 101 27.7 37.57 -0.78 10,768
Driving Preliminary Survey - S
Late Pre test - R1 219 10.1 22.29 107 20.3 31.32 -3.37 9,740
3€ - R2 229 17.7 27.64
7€ - R3 231 15.9 26.20
3-7€ - R4 230 15.9 27.15
No Credit - R5 109 24.1 32.19
Credit - R6 107 25.6 31.76
Post test - R7 230 15.9 27.15 101 19.1 31.87 -1.87 10,327
Not Preliminary Survey - S
Driving Pre test - R1 219 18.5 30.25 107 13.8 26.00 1.39 10,816
3€ - R2 229 22.2 30.70
7€ - R3 231 24.6 30.72
3-7€ - R4 230 23.9 32.73
No Credit - R5 109 20.1 28.50
Credit - R6 107 25.1 31.79
Post test - R7 225 15.6 29.02 101 15.6 30.28 0.02 11,086
21

Table 4: Results of mixed logistic regression model
Alt.* Parameter Est. S.E t-test p
RD Constant rush-hour driving 0.750 0.31 2.44 0.010
3€ reward -1.440 0.46 -3.12

Table 5: Between-group differences on Response-associated factors
Factor
23
Money
(N=231)
Yeti
(N=108)
Socio-demographics Gender (woman) 40% 26%
Alternative modes
Schedules
Difficulties
Support measures
travel information
High education 58% 53%
Other modes used for commuting 21% 19%
Public transport is realistic alternative 35% 32%
Cycling is realistic alternative 20% 14%
Usual departure time (hour.min) 7.52 7.57
Preferred start of work time (hour.min) 8.24 8.35
Start work later (days) 3.5 3.64
Telework (days) 0.46 0.58
Chauffeuring children duties 16% 27%
High effort perceived with changing behaviour 6% 9%
Problems with regional rain 33% 29%
p*
0.01
0.43
0.58
0.63
0.21
0.27
0.20
0.52
0.37
0.02
0.29
0.72
Arrangements with employer 34% 55%

Figure 1: Average response shares by group by treatment
24