Transportation
DOI 10.1007/s11116-013-9484-3

Rules for aggregated satisfaction with work commutes
Haruna Suzuki • Satoshi Fujii • Tommy Gärling • Dick Ettema •Lars E. Olsson • Margareta Friman

Ó Springer Science+Business Media New York 2013

Abstract In general trips frequently entail several stages varying in mode, duration, andother factors. In some way travelers aggregate their satisfaction with the stages to satisfaction
with the whole trip. In this paper we address the question of how this aggregation is made. We
use data from a Swedish survey measuring satisfaction with commutes to and from work andwith the stages of the commutes. We test several aggregation rules for their goodness of fit tothe observations. Our results show that a normatively correct averaging rule that takes into
account the relative durations of the stages out-perform heuristic aggregation rules such as thepeak-end, summation, and equal-weight averaging rules. We note that this does not exclude
that the heuristic aggregation rules apply to other trips than repetitive commute trips.
Keywords

Satisfaction with travel  Work commuting  Aggregation rule  Heuristic

Introduction
Utility maximization theory (McFadden 2001) dominates travel behavior analysis andmodeling. In this theory it is assumed that choices people make that maximize (random)
H. Suzuki
Yamaguchi University, Ube, Japan
S. Fujii
Kyoto University, Kyoto, Japan
T. Gärling
University of Gothenburg, Göteborg, Sweden
T. Gärling  L. E. Olsson  M. Friman (&)
The Service and Market Oriented Transport Research Group (SAMOT),
Karlstad University, 651 88 Karlstad, Sweden
e-mail: Margareta.Friman@kau.se
D. EttemaUtrecht University, Utrecht, The Netherlands

123Transportation

utility result in satisfaction with the outcomes of the choices. However, this assumed
correspondence between utility and satisfaction has been challenged. In recent conceptualizations (Ettema et al. 2010; Kahneman et al. 1997) a distinction is made between
experienced utility and decision utility. Experienced utility is the satisfaction with theoutcome of a choice (i.e., the degree to which it is liked or disliked), whereas decisionutility is the degree to which the outcome is desired when the choice is made. Empiricalresearch (e.g. Kahneman 2000; Kahneman and Sugden 2005) has convincingly shown that
due to lack of information and prevalence of cognitive distortions, experienced utility
frequently differs from decision utility.
Experienced utility appears theoretically to directly reflect satisfaction with the service
level of transport. If experienced utility differs from decision utility, in transport planningaiming at improving the service level it is therefore advisable to measure experienced
utility as a complement to decision utility inferred from choices. Such an argument is madeby Carse (2011) in an analysis of transport policy. It is also recognized in some recent
travel behavior research. For instance, Jakobsson Bergstad et al. (2011a) developed theSatisfaction with Travel Scale (STS) to measure the satisfaction with daily travel. Measures of satisfaction commonly entail both cognitive evaluations (e.g. quality) and affective
appraisals, that is evaluations based on feelings of good or bad (Oliver 2010). A drawback
is that the originally developed STS did not measure affective appraisals. Therefore, Ettema et al. (2011) proposed a revised STS that also assesses affective components. AbouZeid and Ben-Akiva (2011, 2012) and Abou-Zeid et al. (2012) have likewise developed
methods of measuring affective components of satisfaction with travel (referred to as travel
happiness) as well as how such measures can be integrated in a discrete-choice modelingframework.
The commute trip from home to work (and the reverse) frequently entails several stages
varying in mode, duration, and other factors. An example of a four-stage trip is (1) walking
to the car port (2) driving to a parking lot at the commuter train station (3) riding thecommuter train, and (4) walking from the train station to work. In some way travelers
aggregate their satisfaction with the stages to satisfaction with the work commute trip as awhole (Friman and Gärling 2001; Friman et al. 2001). Our aim in this paper is to investigate and model how this aggregation is made.
Another distinction due to Kahneman et al. (1997) is that between instantly experienced
utility (instant utility) and remembered utility. Instant utility is the smallest unit that isaggregated across time to a total utility. Assuming there are no interaction effects and notime discounting, a normative rule of aggregating is the sum of the instant utility (ordisutility) units. In which order stages are encountered in a multi-stage trip is assumed to
not matter. These assumptions would be fulfilled if aggregation is made for instant utilities
that are measured in such a way that possible position and order effects are captured by themeasure (i.e. if measurements are made in the same context). A measure of instant utility
would then, for instance, reflect that an otherwise identical stage has a different instantutility depending on its order in the sequence.
Several previous studies in different contexts have shown that aggregation is madeaccording to a peak-end rule that differs from the normatively correct aggregation rule (forreviews, see Fredricksen 2000; Kahneman 2000). For instance, the peak-end rule wasdemonstrated by Diener et al. (2001); Fredricksen and Kahneman (1993); Redelmeier et al.
(2003) by showing that remembered utility of an event sequence corresponds to theaverage of the peak experience in the sequence and the experience of the end of thesequence. Kahneman (2000) notes that the peak-end rule implies that the duration of asequence has no effect. This observation, referred to as duration neglect, lead to violation

123Transportationof transitivity of choices. In the controlled experiment conducted by Redelmeier et al.
(2003) participants experienced either a shorter sequence of painful stimuli or a longer
sequence identical to the first except that the last stimulus was less painful. When asked tochoose which sequence they would prefer to experience again, a majority of the participants chose the longer sequence with the less painful end.
The issue that we address in the present paper is how satisfaction with the stages of a workcommute is aggregated to satisfaction with the work commute as a whole. We draw an
approximate parallel to the relation between remembered utility (satisfaction with the work
trip as a whole) and instant utility (satisfaction with the stages of the trip), while recognizingthat also experienced utility of the stage is a remembered utility because stages themselves
consist of several components. Furthermore, in our empirical study we obtain retrospective
measures of satisfaction with the work commute trip and with the stages of the work commute
trip. For work commutes consisting of discrete stages varying in duration, weighing satisfaction with each stage with its duration and then summing would be the normatively correct
aggregation rule. The peak-end rule is a violation of this rule since it does not take into accountthe satisfaction with all stages, nor that the stages vary in duration. Summing the satisfaction
with each stage is another aggregation rule that takes into account the satisfaction with allstages but not their duration. Averaging the satisfaction with each stage is still anotheraggregation rule also taking into account satisfaction with all stages but not their duration.
It should be noted that our analysis is limited to the aggregation of the satisfaction withstages into an overall satisfaction with the trip. The analysis is however not applicable tothe evaluation of tours, which can be defined as a sequence of trips starting and ending at
the same location. For example, a commute tour would consist of a trip from home towork, being at work, and the trip from work back home. Satisfaction with such a commute
tour would not only depend on the trips made (consisting of consecutive stages), but also
on the satisfaction with the work activity (which consists of many subtasks). This aggregation process is beyond the scope of our study. In a survey of Swedish work commutersthat we have conducted, participants are only asked to report their satisfaction with eachstage of their commute to work and from work, respectively, as well as their satisfaction
with the commute to work as a whole and the commute from work as a whole, respectively.
Satisfaction is in both cases measured by the STS (Ettema et al. 2012). Friman et al. (2012)
showed by means of confirmatory factor analyses that the STS consists of a cognitive
evaluation, an affect dimension (positive activation) varying from excited to bored, andanother affect dimension (positive deactivation) varying from relaxed to stressed. The
results will be analyzed for all three dimensions of STS and for both the commutes to andfrom work. In the analyses of the results the peak-end rule will be compared to thesumming rule, the equal-weight averaging rule, and the duration-weighted averaging rule.
The remaining paper is organized as follows. In the next section a description is given of
how the survey data were collected. Then, the aggregation rules are specified mathematically. The estimation results are reported in the fourth section. The results and theirimplications are discussed in the fifth and final section.

SurveySampleA random sample of 4,430 individuals obtained from the Swedish tax register was mailed aquestionnaire to be answered. Usable answers were obtained from 996 of those who were123Transportation
Table 1 Background characteristic for participants in Stockholm, Göteborg, and Malmö
Stockholm(pop. 850,000)

Göteborg(pop. 510,000)

Malmö
(pop. 395,000)

Sample size

338

350

308

Response rate (%)

22.9

23.7

20.8

Women (%)

56.8

61.1

60.7

Non-response (%)

1.8

0.0

1.0

Mean age (M/SD)

43.5/13.2

42.7/13.7

42.2/13.1

Non-response (%)

3.8

2.9

2.6

Number of children (M/SD)

0.7/1.0

0.8/1.1

0.8/1.0

Non-response (%)

8.9

6.9

9.4

Household type (%)
Single household without children

24.5

25.7

20.4

Single household with children

7.1

2.6

7.8

Cohabiting household without children

36.4

33.7

36.4

Cohabiting household with children

29.9

37.7

33.8

Non-response (%)

2.1

0.3

1.6

Monthly household gross income in ’000 SEKa (%)
\42

34.3

40.9

39.6

42–64

29.6

32.9

26.9

[64

24.9

14.9

17.5

Non-response (%)

11.2

11.5

15.9

Driver’s license (%)

80.9

88.3

83.7

Non-response (%)

6.5

4.9

7.1

Type of owned car (%)

a

Small

22.3

26.6

30.8

Medium

39.6

51.2

45.5

Large

21.6

25.4

25.8

SUV or minibus

7.0

4.0

3.7

1 SEK was approximately equal to USD 0.15 at the time of the survey

contacted. After screening, the sample consisted of 713 work commuters (58.3 % female;age ranging from 20 to 65 with a mean of 41.2 years) living in the three largest urban areasof Sweden (Stockholm, pop. 850,000; Göteborg, pop. 510,000; Malmö, pop. 395,000). See
Table 1 for sample characteristics.

Questionnaire
The mail questionnaire had three consecutive modules consisting of questions about thework commute, overall subjective well-being, and socio-demographics. Here we only
describe the module with questions about the work commute. See Olsson et al. (2011) for a
detailed description of the survey and summary of descriptive statistics.
The most recent normal commute to work and from work was targeted in the questionnaire. Date, departure and arrival times, and whether being alone or accompanied
during the whole trip were first reported. The STS was then administered to assess123Transportation
Table 2 The ratings scales
comprising the satisfaction withtravel scale (STS)

Cognitive evaluation
Worst imaginable (-3)—best imaginable (3)Very low standard (-3)—very high standard (3)Worked very well (-3)—worked very poorly (3)Positive activation versus negative deactivation
Very tired (-3)—very alert (3)Very bored (-3)—very enthusiastic (3)Very fed up (-3)—very engaged (3)Positive deactivation versus negative activation
Very hurried (-3)—very relaxed (3)Very worried (-3)—very confident (3)Very stressed (-3)—very calm (3)satisfaction with both the commute to work as a whole and the commute from work as awhole (hence referred to as whole trip STS to work and from work, respectively). As
Table 2 shows, the STS consists of three numerical seven-point adjective scales ranging inequal steps from -3 to 3 measuring quality of travel, another three identical numericalseven-point adjective scales measuring positive activation versus negative deactivation,and three identical numerical seven-point adjective scales measuring positive deactivation
versus negative activation. The participants were asked to make a rating corresponding totheir experience during the trip. For instance, if they felt very stressed they were asked tochoose -3, if they felt very calm 3, or if they felt neither stressed nor calm 0. If they felt
only slightly stressed or slightly calm, they were asked to choose an appropriate number inbetween. The adjective scales were presented in a counter-balanced order.
After having answered the questions about the commute to work as a whole, at most fiveconsecutive intermediate stops of the commute to work were indicated. For each stage
defined by the stop, information was obtained about travel mode, duration in minutes,
degree of street congestion, degree of in-vehicle crowdedness for public-transit trips, andactivities performed during the trip. Thereafter, the STS was obtained for each stage (hencereferred to as stage STS). The same questions were then answered about the commute fromwork as a whole and about each stage of the commute.
MeasuresIndex measures of the cognitive evaluation, positive activation, and positive deactivation
dimensions of STS were constructed by averaging across the scale ratings. Table 3 reports
sample sizes, means, and standard deviations. Cronbach’s as reported in the table for eachmeasure suggest a satisfactory reliability ([0.70) (Nunnally and Bernstein 1994; Peterson
1994).

Specification of aggregation rules
The peak-end, summing, equal-weight averaging rule, and duration-weighted averaging
rules are specified as follows (where STSi denotes the STS values for stage i, I the set of all
stages (i [ I), n the number of stages, and ti the duration of stage i).

123Transportation
Table 3 Subsample size (n), means (M) of ratings on the different scales (see Table 2), standard deviations(SD), and Cronbach’s as for each STS dimension per stage and overall for commutes to and from work
Cognitive evaluationn

Positive activation

Positive deactivation

M

SD

a

N

M

SD

a

N

M

SD

A

Commute to work
Stage (1)

730

1.24

1.10

0.83

733

0.59

1.11

0.81

747

0.91

1.28

0.86

Stage (2)

366

0.99

1.12

0.86

365

0.36

1.10

0.82

372

0.77

1.23

0.85

Stage (3)

202

0.97

1.16

0.87

201

0.46

1.21

0.87

207

0.71

1.29

0.86

Stage (4)

76

0.94

1.25

0.91

76

0.55

1.19

0.82

78

0.71

1.39

0.89

Stage (5)

27

0.87

1.23

0.86

27

0.38

0.95

0.59

27

0.71

1.23

0.81

Whole trip

792

1.08

1.13

0.83

791

0.32

1.09

0.74

797

0.96

1.29

0.86

Commute from work
Stage (1)

673

1.07

1.13

0.87

673

0.43

1.13

0.82

673

0.98

1.23

0.89

Stage (2)

363

0.96

1.16

0.88

363

0.36

1.20

0.84

363

0.89

1.26

0.90

Stage (3)

185

1.11

1.21

0.91

185

0.46

1.21

0.85

185

0.91

1.32

0.92

Stage (4)

79

0.96

1.07

0.84

79

0.27

1.17

0.82

79

0.76

1.19

0.85

Stage (5)

30

0.68

1.14

0.88

30

0.20

1.02

0.73

30

0.53

0.91

0.82

Whole trip

794

1.07

1.13

0.83

794

0.32

1.09

0.73

801

0.97

1.28

0.86

Peak-end rule
The peak rule posits that the average of peak stage STS (i.e. PeakSTS) and last stage STS(i.e. EndSTS) determines whole trip STS. PeakSTS is given as
PeakSTS ¼ STSiðmaxÞ

ð1Þ

where i(max) denotes the sequential number of the stage excluding the last one whose STS
gives the largest deviation from the average STS. Note that this implies that the peak iseither the highest or lowest stage STS whichever is the largest. EndSTS is the STS of thelast stage given as
EndSTS ¼ STSn

ð2Þ

Thus the peak-end rule is given as
Peak  end rule ¼ SumðPeakSTS þ EndSTSÞ=2
Summing rule
The summing rule posits that summing of all stage STS (i.e. SumSTS) determines wholetrip STS. SumSTS is given as
SumSTS ¼

n
X

STSi

ð3Þ

i¼1

Equal-weight averaging rule
The equal-weight averaging rule posits that the average of all stage STS (i.e. AverageSTS)
determines whole trip STS. AverageSTS is given as123TransportationnP

AverageSTS ¼

STSi

i¼1

ð4Þ

n

Duration-weighted averaging rule
The duration-weighted averaging rule posits that the sum of all stage STSs weighted by the
duration of the stage relative to the total duration (i.e. Duration-WeightedSTS) determines
whole trip STS. Duration-weightedSTS is given asnPðSTSi  ti Þ
Duration-weightedSTS ¼ i¼1 P
n
ti

ð5Þ

i¼1

Estimation results
In tests of which aggregation rule gives the best fit to the data, only STS for trips with 3 and4 stages are used (n = 180 for STS to work, n = 166 for STS from work). Table 4 showsmeans and standard deviations of the rule-aggregated values defined as in Eqs. (1)–(5),product-moment correlations between these variables and whole trip STS, and the average
error (root mean square error or RMSE) between observed and rule-aggregated values. As
may be seen, the highest correlations and lowest RMSE are observed for the durationweighted averaging rule.
Hierarchical OLS multiple linear regression analyses were then conducted to investigate
how much the model fit would be improved by adding the more complex aggregation rules
to the peak-end rule. In the first step the peak-end rule was entered as an explanatory
variable. As can be seen in Table 5, all increments in variance (DR2) from zero are
significant at p \ 0.05 or less. In the next step, the summing rule is entered resulting insignificant increments in explained variance. This indicates that the summation model
outperform the peak-end rule. In the third step, the equal-weight averaging rule is entered.
Significant or marginally significant increases in variance are observed except for positive
activation for the from-work trip. In the final step the duration-weighted averaging rule isentered. The results again show that significant increases in variance are observed except
for positive activation for from-work trips. The regression analyses thus suggest that the
duration-weighted averaging rule gives a better fit to the data than the other aggregation
rules. A possible exception is positive activation for from-work trips where the varianceincrements failed to reach significance.

Discussion
In this paper we compared several rules by which people may aggregate satisfaction withthe stages of a work commute to satisfaction with the work commute as a whole. The STS(see Ettema et al. 2011, 2012; Friman et al. 2012) was used to measure three dimensions ofsatisfaction with travel, a cognitive evaluation dimension and two affective dimensions.
The data analyzed were obtained from a survey of work commuters in the three largest
urban areas in Sweden (Olsson et al. 2011).

123

123

172

175

175

175

EndSTSSumSTS

AverageSTS

Duration-WeightedSTS

156

157

157

157

EndSTSSumSTS

AverageSTS

Duration-WeightedSTS

0.89

1.00

3.36

1.30

1.43

0.95

1.06

3.38

1.11

1.45

0.97

1.00

3.21

1.20

1.28

0.98

0.98

3.15

1.17

1.19

0.77

0.73

0.72

0.45

.57

0.68

0.68

0.65

0.56

0.57

1.53

1.54

3.24

1.68

1.92

1.57

1.60

3.19

1.65

1.88

RMSE

157

157

157

156

157

174

174

174

172

174

0.22

0.32

1.04

0.53

0.47

0.37

0.48

1.51

0.62

0.62

M

1.05

1.02

3.38

1.26

1.50

1.07

1.06

3.41

1.24

1.44

SD

0.65

0.65

0.64

0.46

0.63

0.73

0.71

0.70

0.61

0.63

R

1.22

1.22

2.53

1.32

1.56

1.20

1.22

2.46

1.29

1.57

RMSE

161

161

161

160

161

177

177

177

174

177

n

0.83

0.90

2.96

1.13

1.29

0.74

0.76

2.45

0.80

1.07

M

1.08

1.08

3.58

1.23

1.40

1.08

1.08

3.59

1.26

1.39

SD

Positive deactivation

0.76

0.74

0.72

0.49

0.53

0.74

0.75

0.72

0.62

.69

R

1.56

1.57

3.19

1.65

1.90

1.52

1.54

3.05

1.60

1.75

RMSE

Ms and SDs are the rule-aggregated values, rs the correlations based on n observations between the rule-aggregated values and the overall STS. Root mean square error(RMSE) is calculated as Sqrt(Sum(Sqrt(observed value—rule-aggregated value)))/n

157

PeakSTS

Commute from work

175

PeakSTS

Commute to work

R

n

SD

n

M

Positive activation

Cognitive evaluation

Table 4 Means (M) and standard deviations (SD) of model-aggregated STS, product moment correlations with overall STS (r), and route mean squared error (RMSE)

Transportation

Transportation
Table 5 Increments in explained variance (DR2) in hierarchical multiple linear regression analyses
Cognitive evaluation
2

Positive activation

Positive deactivation

p

DR2

p

0.414

\0.001

0.486\0.001\0.001

0.079\0.001

0.081\0.001

0.019

0.019

0.011

0.056

0.008

0.083

0.035

0.001

0.035

0.001

0.029

0.021

0.323

\0.001

0.413\0.001

0.287\0.0010.230\0.001

0.042

0.002

0.231

\0.001

0.054\0.001

0.007

0.157

0.033

0.001

0.014

0.022

0.001

0.573

0.024

0.004

DR

p

DR

0.345

\0.0010.087

2

STS to work
Step 1
Peak-end rule
Step 2
Summing rule
Step 3Equal-weight averaging rule
Step 4
Duration-weighted averaging rule
STS from work
Step 1
Peak-end rule
Step 2
Summing rule
Step 3Equal-weight averaging rule
Step 4
Duration-weighted averaging rule

The results clearly refuted the peak-end aggregation rule which has been observed inseveral previous studies of how remembered utility is aggregated from instant utilities(Fredricksen 2000; Kahneman 2000). An exception is the study by Miron-Shatz (2009)
showing that a normatively correct duration-weighted aggregation rule outperformed thepeak-end rule. Thus, our results are consistent with Miron-Shatz’ results, which wereobtained for positive and negative affect associated with everyday activities performed
during a day. An important difference between our research as well as that of Miron-Shatz(2009), compared to the research demonstrating the peak-end rule (e.g. Kahneman 2000),is the smaller number of instant utilities over which aggregation was made. One likely
reason for using the peak-end rule is to overcome cognitive limitations such as difficulty toremember all the experienced events. In a similar vein Robinson and Clore (2002) argue
that affects induced by events in a sequence is only possible to later remember by
retrieving information about the experienced events. If time of occurrence or number of
events increases such that remembering the events become more difficult, different
selection biases would be expected due to simplification of memory retrieval. The peakend rule is one selection bias but there may be others depending on contextual factors.
Such selection biases may not always result in duration neglect (Ariely and Loewenstein2000).
The issue raised in this paper seems to have been largely neglected in research on travel
behavior. It is important then to ask whether our finding that the normatively correct(duration-weighted averaging) aggregation rule best fitted the data generalizes to othertypes of trips. As already implied, only if the stages (‘‘instant utilities’’) are few, this is
likely to be the case. Therefore, one should always consider the possibility, both in123

Transportation

research and applications, that the peak-end rule is a viable alternative. Another factor tonote is that in the survey we asked participants to evaluate normal commutes. Such trips
are likely to be more stable and would include few salient events. Therefore, ‘‘peaks’’ may
have a smaller impact for normal commutes than other trips. This may account for the factthat rules that incorporate all the stage STSs, such as the summing and equal-weightaveraging rules, explained the aggregation better than the peak-end rule. Furthermore, thedurations of each stage would be rather familiar. Therefore, duration-weights should beavailable to apply. Thus, we cannot claim that our results rule out that the peak-end rulewould explain the aggregation of STS of stages for trips that are not as repetitive as workcommutes. This is a question that future research should address.
Another short-coming of the present survey is that we measured both stage STS andwhole-trip STS by asking participants to recall the most recent (normal) work commute. In
future research it would be desirable to attempt to measure instant utilities for stages during
travel. Several methods for doing this have been developed (Stone et al. 1999). It should
however be noted that comparable results have been obtained with the Daily Reconstruction Method (Kahneman et al. 2004) requiring participants to judge affect associatedwith reconstructed episodes that occurred the previous day. Also the Event Reconstruction
Method (Schwartz et al. 2009) requiring participants to judge affect associated withspecified episodes may lead to comparable results for less recent episodes if these are
infrequent such that they stand out.
A remaining issue relevant for transport planning is what determines satisfaction withstages, in the present case stages of normal work commutes. Travel mode is likely to be themost important factor (Olsson et al. 2012). Walking and biking are popular because they
provide desirable physical exercise (Lawrence et al. 2006). Driving is preferred because ingeneral it is faster and flexible (Jakobsson Bergstad et al. 2011b). Speaking in favour ofpublic transit is that it allows for engagement in activities such as talking to others,
resulting in positive affects, and work or entertainment activities that reduce stress andboredom (Ettema et al. 2012). The emphasis of the present study is however on investigating how stage satisfactions are integrated, rather than on the factors that determine thesatisfaction with the stage. We finish by noting some implications for transport planning.
If a stage of a trip is experienced to enhance satisfaction, our results suggest thatsatisfaction with the whole trip would still suffer if this enhancement fails to apply to otherstages. An important practical message is then that no parts of a work commute should be
neglected in transport planning. Conversely, our results suggest that in order to increase
overall trips satisfaction, improvements in each stage will add to the overall trip satisfaction. Thus, not only increasing the peaks or avoiding the lows are important, but also
increasing the satisfaction of an average stage adds to a higher trip satisfaction. At the sametime, the duration-weighted aggregation rule suggests that improving the longest stage isthe most efficient way to increase trip satisfaction. For more complex trips, such as public
transport trips, this would require an analysis of the duration distribution of stage by
specific modes (e.g. walking/cycling to/from the public transport station, being in a bus ortrain) to determine on which modes policy should focus.

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Author Biographies
Haruna Suzuki is associate professor in Department of Civil and Environmental Engineering at Yamaguchi
University in Japan. Her research interests are in travel behavior and transportation measures, focusing on
subjective well-being, place attachment and behavior modification through communication.
Satoshi Fujii is a professor of transportation and behavior science in the Department of Urban Management
at Kyoto University and a special adviser for public policy to the cabinet of Japan. He has been engaged inapplied psychological research for behavioral modification of travel behavior.
Tommy Gärling is professor emeritus of psychology part-time affiliated with University of Gothenburg and
Karlstad University. His main research area is behavioral decision making with applications to economic
behavior, pro-environmental behavior and travel behavior.
Dick Ettema is associate professor in the Department of Human Geography and Planning of Utrecht
University in the Netherlands. His research interests include travel decision making and behavior change,well-being implications of travel and activity patterns and the influence of the built and social environmenton physical activity and active travel.
Lars E. Olsson is PhD of Psychology at the Service and Market Oriented Transport Research Group(SAMOT) at Karlstad University in Sweden. His main research area is consumer behavior with a focus onpro-environmental behavior, customer experience, daily travel, and subjective well-being.
Margareta Friman is professor of service research and director of the Service and Market Oriented
Transport Research Group (SAMOT), Karlstad University, Sweden. Her main research area includes
psychological processes underlying consumers’ thoughts, feelings (well-being), decisions, and behaviors.

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