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KSCE Journal of Civil Engineering (2009) 13(3):169-178
DOI 10.1007/s12205-009-0169-6
− 169 −
www.springer.com/12205
Transportation Engineering
Direct and Indirect Effects on Weekend Travel Behaviors
Tae Youn Jang* and Jee Wook Hwang**
Received June 10, 2008/Revised 1st: October 16, 2008; 2nd: February 1, 2009/Accepted February 3, 2009
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Abstract
The purpose of this study is to determine the causal factors that influence travel behavior. In particular, the joint relationshipsbetween trips, activities and activity durations are researched by examining the effects of personal and household attributes. In order to meet this objective, the total, direct and indirect effects of individual attributes on travel behavior are estimated using structuralmodels. The statistical assumption and the interpretations of coefficients in classical causal analysis are similar to those in linear regression models. Specifically, the direct effects, as estimated coefficients, are only offered and interpreted. However, in travelbehavior that results from complicated relationships among trips, activities and individual attributes, the indirect effects amongfactors related to travel behavior cannot be disregarded. The indirect effects through other intervening factors, in addition to the directeffects, may cause the total effect of specified factors on travel behavior. If only the direct effect is considered for analysis, the causalrelationships among factors may not be able to be adequately understood. The advantage of using structural models is that they areable to estimate, in addition to direct structural effects, the indirect effects through other intervening factors. The study also assumesthat Saturday travel behavior has an effect on Sunday travel behavior. The subdivided indirect effects of the personal and householdattributes on trip generation, activity frequency, and activity durations are empirically analyzed in detail.
Keywords: causal relationship, direct and indirect effects, activity duration, structural equations model, travel behavior
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1. Introduction
1.1 Background
Activity-based analysis has received a great deal of attention
for its ability to effectively estimate various transportation policies
and thereby provide a fundamental understanding of travel
demand. Jones et al . (1990) stated that in some instances the
forecasts of trip-based models have proven to be inaccurate, and
this seems to be the result of a misspecification. Specifically,
these forecasts were based on an inappropriate representation of
travel behavior relationships, often due to a failure to recognize
the existence of linkage among and between trips and activity
participation. Because activity-based analysis approaches these
problems through a deeper understanding of travel behavior, it is
important to determine decision factors of travel behavior by
using activity-based analysis and to examine causal relationships
among various factors. These relationships are considered as the
effects on activity participation.
Several researchers have successfully determined the causal
factors on travel behavior, based on individual activities. The
studies are fulfilled for the relationship between the activity pat-
tern in the trip chaining behavior and individual characteristics
such as age, sex, working status, income, lifecycle, car ownership,
and residential location (Adler and Ben-Akiva, 1979; Kitamura,
1985; Jean-Claude and Isabelle, 1987; Golob and Hensher, 2007;
Hensher, 2007). By examining effects in the model system, Lu
and Pas (1999) tried to better capture the relationships between
socio-demographics, activity participation and travel behavior.
However, their research did not consider latent relationships
among elements. Lee and McNally (2006) summarized an in-
vestigation on the dynamic processes of activity scheduling and
trip linkage and used the ordinal regression models to find the
personal characteristics such as gender, marital status, and num-
ber of children that are pertinent to the scheduling horizon of
activity. By using analysis on inter-shopping duration, Bhat et al .
(2004) examined the determinants of the regularity and fre-
quency of the shopping activity participation behavior of in-
dividuals. In their study, the effect of the interaction between
household members on activity behavior was represented in the
form of simple factors such as marital status, spouse’s employ-
ment, and household structure. The use of the concepts of
activity time-use constraints has shed new light on many aspects
of activity-based analysis. Knowing how an individual allocates
time to different types of activities can identify the activity par-
ticipation of an individual. An aspect of an activity-based
approach is that it includes the concept of activity durations
according to activity purpose. One of the advantages of activity-
based analysis is that it is capable of explicitly incorporating the
*Member, Professor, The Research Center of Industrial Technology, Dept. of Urban Engineering, Chonbuk National University, Jeonju 561-756, Korea
(Corresponding Author, E-mail: jangty@chonbuk.ac.kr)
**Professor, Dept. of Urban Engineering, Chonbuk National University, Jeonju 561-756, Korea (E-mail: jwhwang@chonbuk.ac.kr)
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Tae Youn Jang and Jee Wook Hwang
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factor of time into the travel modeling process (Pas & Harvey
1997). Goulias (2002) mentioned that time was a finite and
critical resource that was consumed in the engagement of
activity and travel. Activity timing and duration are considered to
be important factors of activity-based travel modeling systems
(Steed & Bhat 2000; Kitamura et al . 2000; Pendyala et al . 2002,
2004). Recently, Krygsman et al . (2007) captured tour mode and
activity choice interdependences by logit model, and Ruiz and
Timmermans (2007) analyzed activity duration to resolve
scheduling conflicts.
As a result of the use of activity-based analysis, the relation-
ships between travel behavior and personal and household attri-
butes have been widely analyzed. It has also been suggested that
activity duration is a significant factor in the analysis of travel
behavior. However, previous studies are based on the direct
effects of personal and household attributes on travel behavior.
This present study offers an analysis of the joint relationships
between trips, activities and activity durations that influence
travel behavior. The study empirically emphasizes the total,
direct and indirect effects of personal and household attributes on
travel behavior in weekend. Especially, effects of Saturday travel
behavior on Sunday travel behavior is examined by a structural
equations model (SEM) technique. The SEM technique has
widely used for travel behavior research because of its flexibility
in finding relationship among the observed and latent variables.
The SEM technique is applied to panel trip diary data (Golob
1990), to trip generation (Golob, 1989), to vehicle usage and
driver allocation (Golob et al ., 1996), to causal relationship
among travel mode, activity and travel patterns (Jang 2003), and
to the impacts of personal characteristics and the spatial structure
on mode choice (Simma et al ., 2001). Roorda and Ruiz (2008)
use a structural equations model technique with latent variables
to explore short- and long-term dynamics in activity scheduling.
1.2 Objectives
Fig. 1 shows the concept and procedure used for this study.
Firstly, the study examines the total, direct and indirect effects of
personal and household attributes on travel behavior. Specifi-
cally, the effects of individual attributes on trips, activities and
activity durations are analyzed. The statistical assumption and
the interpretations of coefficients in classical causal analysis are
similar to those in linear regression models (Mueller, 1996). The
coefficients as direct effects of variables are only offered and
interpreted in those models. When using these models, there is a
difficulty in finding complex relationships between individual
attributes and travel behaviors. If the indirect effects are disre-
garded, the causal relationships influencing travel behaviors may
not be able to be adequately understood. Travel behavior can be
directly influenced by individual attributes and can also be
indirectly influenced through other intervening attributes. The
advantage of using structural models is that they are able to
estimate, in addition to direct structural effects, the indirect
effects from one or more intervening attributes. The procedure is
estimated by using a covariance structure model, which allows a
simultaneous estimation of a series of specified relationships.
Secondly, the causal relationships between the elements
influencing travel behavior are also studied. The study considers
a number of trips, activities, and activity durations as the elements
of travel behavior. It is assumed that activity durations influence
trip generation and activity frequency. Trips and activities depend
on the activity duration times. A number of trips may also be
dependent on activity frequency. That is, according to the trip
linkage, even if activity frequency increases, the number of trips
may not increase a great deal.
Thirdly, an analysis is carried out of the effects of Saturday
travel behavior on Sunday travel behavior. Individuals perform
mainly discretionary activities during the weekend, which may
have closer relationships to each other in terms of how they
affect travel behavior. It could be assumed that activity durations
affect activity frequency during the day. If the specified activity
in which the individual plans to participate cannot be performed
because he/she spends more time on other prior activities, he/she
will participate in that activity the next day.
2. Research Methodology
2.1 Travel Behavior Model
The purpose of constructing the covariance structure model is
to determine the degree of effect that personal and household
attributes have on travel behavior. Statistically, the task is to
explain the causal relationships between the observed variables,
as indicated by the covariance among these variables. The degree
of causal relationship is considered as the direct and indirect
effects. The development of the LISREL program (Joreskog and
Sorbom, 2002) has facilitated the objectives for this study. Eq.
(1) shows the structural equations based on Fig. 2. There are
three endogenous variables and two exogenous variables.
(1)
In equation, Y is a column vector of endogenous variables, X is
a column vector of exogenous variables measured as deviations
Y BY ΓX ξ + +=
y1
y2
y3
0 β 12 β 13
0 0 β 23
0 0 0
y1
y2
y3
γ 11 γ 12
γ 21 γ 22
γ 31 γ 32
χ 1
χ 2
ζ 1
ζ 2
ζ 3
+ +=
Fig. 1. Study Concept
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Direct and Indirect Effects on Weekend Travel Behaviors
Vol. 13, No. 3 / May 2009 − 171 −
from their means, B is a matrix of structural coefficients from
endogenous to other endogenous variables, Γ is a matrix of
structural coefficients from exogenous to endogenous variables,
and ζ is a column vector of error terms of endogenous variables.
Endogenous variables include a number of trips generated, a
number of activities, and activity durations according to purposes
on Saturday and Sunday respectively. Exogenous variables are
individual and household attributes. The structural model differs
from other multivariate analysis in the use of data of variance/
covariance or a correlation matrix. Covariance provides sound
results for reciprocal comparison among variables because it is
influenced by the estimated dimension. The correlation matrix
(the simple standardized variance/covariance) is calculated by
dividing the variance/covariance by the standard error. Since the
correlation matrix provides adequate results that assist the
understanding of the relationships among variables, which meets
the objectives of the study, it is utilized as the input data format.
2.2 Definition of Effect Analysis
The study applies the structural modeling process in order to
determine the direct and indirect effects among variables. In
describing the definition of effects in structural models, the direct
effect of an endogenous (or an exogenous) variable on another
endogenous variable is defined as the structural coefficient
(metric) linking the two variables (Mueller, 1996; Golob, 2003).
A particular indirect effect of one variable on another variable
through one or more intervening variables is defined by the pro-
duct of associated structural coefficients that link the variables in
the particular structural chain; the sum of all particular indirect
effects is defined as the total indirect effect. Finally, the sum of
the direct effect and the indirect effect is defined as the total
effect of an exogenous variable on an endogenous variable.
Fig. 2 shows the example of a possible path diagram used to
calculate the effect values. y1, y2 and y3 are the observed
endogenous variables and x1 and x2 are the observed exogenous
variables. γ s are the direct effects of exogenous variables on
endogenous variables and β s are the direct effects of
endogenous variables on endogenous variables.
For example, the effects of x1 and x2 on y3 are composed of y3
(total effect ), y3 (dirct effect ) and y3 (indirect effect ) as shown in
Eq. (2). The total effect on y3, considering only structural coeffi-
cients, is as shown in Eq. (3). The first parenthesis represents the
total direct effects and the second parenthesis represents the total
indirect effect, which contains the subdivided indirect effects.
The computational definitions of the direct and indirect effects of
an exogenous variable on an endogenous variable in a structural
equation model are based on the fact that the covariance between
two variables can be completely decomposed and written as
functions of the model-implied parameters. Table 1 shows the
process used to calculate the subdivided indirect effects on y3.
y3 (total effect ) = y3 (dirct effect ) + y3 (indirect effect ) (2)
y3 (total effect ) (3)
2.3 Basic Analysis on Variables
The study is based on an examination of weekend travel
behaviors of 436 workers who live in Jeonju City, Korea. Data
on their trips, activities and activity durations during the
weekend were surveyed by mail and interviews in June and July
of 2006. Originally, 1,000 persons are surveyed. Among them,
436 workers invest non-zero time for all weekend activities this
study classifies. Jeonju City has a population of approximately
620,000 and an area of 206.33 km² in 2006. Information about
all activities was collected during a designated 48-hour period in
a weekend. A policy, that enforces the week to consist of 5
working days and 2 non-working days, was introduced in Korea
on July 1, 2004. This policy has now been firmly established.
This policy therefore influences weekend travel behavior. It is
assumed that weekend discretionary activities have closer relation-
ships to each other than weekday obligatory ones. As shown in
Table 2, trips, activities and total duration times are classified
into endogenous variables. Activities and activity durations may
be the intervening variables which cause indirect effects.
Activity durations are the total time that an individual spends for
shopping, sport, and leisure. Exogenous variables are gender,
marriage, age, family members, income, education level and
whether there are children under the age of 12.
Table 3 shows a basic analysis of variables in a survey of 568
males and 116 females. There are no statistical differences
between males and females in Saturday trips and activities and in
Sunday trips. However, on Sunday females participated in more
activities than males, which is statistically significant at a level of
p=<0.05. In general, while total durations for sport and leisure
between the two days may be similar, Saturday shopping dura-
tions were longer than those on Sunday. Males spend more time
γ 31 γ 32+( ) γ 11β 21β 32 γ 21β 32 γ 11β 31 γ 12β 21β 32 γ 32β 32 γ 12β 31+ + + + +( )+=
Fig. 2. Definition of Effects between Variables
Table 1. Process for Subdivided Indirect Effects on y 3
Path Subdivided indirect effects
x1→ y1 → y2 → y3 γ 11β 21β 32
x1 → y2 → y3 γ 21β 32
x1 → y1 → y3 γ 11β 31
x2→ y1 → y2 → y3 γ 12β 21β 32
x2 → y2 → y3 γ 22β 32
x2 → y1 → y3 γ 12β 31
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Tae Youn Jang and Jee Wook Hwang
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shopping during the weekend because they may have less chance
to shop during weekdays due to their obligatory working activi-
ties. Married people make more trips and participate in more
activities than single people during the weekend. Married people
spend more time shopping and less time on leisure activities.
Youngest people have a tendency to take more trips and to
participate in more activities. The family is a significant factor
influencing Sunday trips and activities. As the size of the family
increases, shopping durations also increase. Household income,
education and whether a family has children show statistical
significances in testing differences among groups for trips and
activities. There are some exceptions but these variables also
show the statistical significances in most activity durations.
3. Results
3.1 Assessing Model Fit
When a covariance structure model is constructed for pro-
viding data, fitness indices are used in order to appraise whether
the data fits. Since indices need to be specified, those indices
with a strong point and those with a weak point on the hypothe-
sized model should be used for hypothesis tests. The chi-square
value and the probability value are calculated based on normality.
If the chi-square value is high, the model has a tendency to not fit
the data. The GFI (goodness of fit index) is not influenced by the
change of sample size and the violation of multivariate normal
distribution. If the sample size is over 200 and the value of the
GFI is at least 0.9, the model is able to find a fit for the data. The
Table 2. Variable Definition
Variable Definition
Endogenous variables
Trips
Activities
Shopping
Sport
Leisure
Saturday & Sunday
Number of trips
Number of activities
Shopping activity duration (min.)
Sport activity (walking, cycle, hiking, tennis, golf, etc.) duration (min.)
Leisure activity (church, movie, restaurant, meeting friends, tea time, etc.) duration (min.)
Exogenous variables
Gender
Marriage
Age
Education
Family
Income
Child
Male=1, female=0
Marital status (married=1, single=0)
Age of individual
Education level (under high school=1, college=2, graduate school=3)
Number of household members
Total household income (Korea currency :10,000 won, 0-200=1, 201-300=2, 300+=3)
Existence of children under 12 in the household (1, 0)
Table 3. Mean Values for Variables
Variable Freq.
Saturday Sunday
Trips Act.Shop.
(min.)
Sport
(min.)
Leisure
(min.)Trips Act.
Shop.
(min.)
Sport
(min.)
Leisure
(min.)
Gender Male
Female
568
116
3.28
3.38
1.90
2.07
239.3a
138.3
34.7
43.9
159.3
179.1
2.38
2.55
1.34b
1.59
058.6a
020.3
57.5a
17.5
185.4
156.0
Marital
Status
Married
Single
412
272
3.12a
3.55
1.75a
2.19
243.5a
189.9
39.2
31.8
133.5a
206.7
2.27b
2.01
1.28a
1.54
052.8
046.2
23.0
26.2
167.9b
199.4
Age
0-35
36-45
46+
280
236
168
3.62a
3.05
3.09
2.23ª
1.73
1.71
208.2c
211.8
260.0
32.2
44.7
31.1
215.5ª
103.9
157.0
2.66ª
2.18
2.31
1.54a
1.25
1.31
037.9a
088.1
017.6
28.9a
12.2
33.6
209.3a
147.2
179.1
Family
0-3
4
5+
248
300
136
3.40
3.29
3.12
2.04
1.85
1.88
215.8ª
194.9
293.9
28.1c
38.1
47.0
195.9ª
142.0
147.3
2.16b
2.49
2.67
1.25c
1.44
1.50
035.1c
051.8
074.1
25.8
26.3
17.0
169.2
186.7
187.0
Income
0-200
201-300
300+
348
212
124
3.29ª
2.98
3.83
1.90a
1.77
2.25
244.1ª
186.2
222.2
42.0c
25.2
38.8
168.8a
133.4
195.3
2.24ª
2.26
3.13
1.25a
1.34
1.84
023.7a
083.4
067.9
24.0
22.2
28.7
178.8a
157.3
224.4
Education
High-
College
Graduate
164
460
060
2.95ª
3.33
3.93
1.53ª
2.02
2.27
299.5ª
193.1
234.0
26.9b
41.7
20.0
104.6ª
181.3
177.6
2.00ª
2.53
2.60
1.14a
1.46
1.53
049.7b
043.9
100.0
15.0b
25.4
41.3
172.5
182.2
188.4
ChildYes
No
352
332
3.06a
3.54
1.75a
2.12
231.5
212.3
36.4
36.0
135.7ª
191.2
2.03ª
2.81
1.15ª
1.63
076.1ª
022.7
20.6
28.2
140.7a
222.5
a Significant at p=<0.01; b Significant at p=<0.05; c Significant at p=<0.1
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Direct and Indirect Effects on Weekend Travel Behaviors
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AGFI (adjust goodness of fit index) is the modified GFI. The
required acceptable AGFI is 0.9 or greater. The NFI (normed fit
index), frequently used to test fitness, should be between 0.0 and
1.0 and the required accepted value should be greater than 0.9.
An NFI of 0.9 means that the model has a 90% better fit than the
null model (Kelloway, 1998). The NNFI (non-normed fit index)
is in the range from 0.0 to 1.0 but rarely may be greater than 1.0.
The RMR (root mean square residual) indicates the average
discrepancy between the elements in the sample and hypothe-
sized covariance matrices. The values for the RMR range from 0
to 1.00. If fitness is good, then it has a value close to zero; it
should be less than 0.05 (Byrne, 1989).
Many of the indices are based on choosing a model as a
baseline and comparing the fit of theoretically derived models to
the baseline model. Rather than comparing against a model that
provides a perfect fit to the data, indices of comparative fit
typically choose a baseline model that is known to previously
provide a poor fit to the data. The most common baseline model
is the null model. The null model is the simplest model that
specifies no relationships between the variables composing the
model. An improved model makes progress by improving the fit
at the expense of simplicity. In Table 4, the null, initial and
modified models are compared in order to find an improved
model. The difference between models may be tested with the χ 2
test. The initial model is based on the concept of the study. The
modified model is the final model built by adding free para-
meters in order to improve the fit without changing the concept
of the study. A hypothesis test is performed based on the inverse
relationship between the fit and simplicity of the model in order
to find an improved model. A null hypothesis shows that the
increased fit is not significant at the expense of simplicity. An
alternative hypothesis is that the increased fit is significant at the
expense of simplicity. The difference between the two models is
tested with χ 2difference=χ 2A-χ
2B and . In Table 4, the
value of χ 2difference with ∆df of 7 is 174.31 and is greater than the
critical chi-squared statistic (χ 20.01,7=18.48). Therefore, null
hypothesis is rejected and the modified model is chosen. The
modified model has reasonable indices. If CN (critical N) is
generally over 200, it is accepted that the model satisfactorily
reflects the observed data. The modified model has 340.61.
3.2 Effects on Trips and Activities
Generally, the coefficients from modeling represent the direct
effects. If the direct effects are only considered for analysis as
they are in classical models, the causal relationship between
variables may not be able to be adequately understood. In
addition to the direct effects, Table 5 demonstrates the total and
indirect effects of individual attributes on trips and activities. All
values have statistical significance at a level of p<0.1. Because
males and married people show a positive sign in the direct
effects on trip generation, they tend to generate more trips during
the weekend. However, family, income, education and whether a
family has children, have negative effects on trips. Older aged
people tend to generate more Saturday trips and fewer Sunday
trips. In activities, income and education are positively associat-
ed with Saturday activities but others are negatively associated
with them. Age, family, income and education are positively as-
sociated with Sunday activities. Several attributes show greater
positive or negative total effects. Specifically, age, children, and
education are factors with a higher effect on Saturday trips and
df ∆ df A df B– =
Table 4. Assessing Fits
Model χ 2 df p-value GFI AGFI NFI NNFI RMR CN
Null 3,916.72 136 0.000 0.597 0.547 - - 0.194 27.05
Initial(A) 292.23 46 0.000 0.952 0.841 0.920 0.790 0.061 155.6
Modified(B) 117.92 39 0.000 0.980 0.922 0.968 0.920 0.048 340.61
A:B 174.31 7 0.000 - - - - - -
Table 5. Total, Direct and Indirect Effects of Attributes on Weekend Trips and Activities
Variable Gender Marriage Age Family Income Child Education
Saturday
Trips
0.029
0.021
0.008
0.013
0.026
-0.013
-0.109
0.002
-0.111
-0.091
-0.025
-0.066
0.051
-0.005
0.056
-0.141
-0.029
-0.112
0.101
-0.003
0.104
Activities
0.005
-0.022
0.027
-0.022
-0.005
-0.017
-0.122
-0.101
-0.021
-0.082
-0.025
-0.057
0.065
0.050
0.015
-0.126
-0.070
-0.056
0.120
0.103
0.017
Sunday
Trips
0.029
0.193
-0.164
0.023
0.076
-0.053
-0.098
-0.059
-0.039
0.069
-0.043
0.112
0.193
-0.065
0.258
-0.227
-0.041
-0.186
0.045
-0.010
0.055
Activities
-0.022
-0.197
0.175
-0.016
-0.054
0.038
-0.070
0.053
-0.123
0.064
0.074
-0.010
0.203
0.107
0.096
-0.193
-0.022
-0.171
0.035
0.019
0.016
1st value : total effect, 2nd value : direct effect, 3rd value : indirect effect
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activities, while income and children have a higher effect on
Sunday activities.
Some attributes show sign differences between the direct
effects and the total effects. These are age, income and education
for trips and gender for activities on Saturday. Family, income
and education for trips and age for Sunday activities also have
the same tendency. These sign differences result from the
indirect effects of the exogenous variables on the endogenous
variables through other intervening endogenous (or exogenous)
variable(s) such as activity durations. For example, age has a
marginal positive direct effect of 0.002 on Saturday trips but it
has a larger amount of negative indirect effect of -0.111 on
Saturday trips through other variables such as activity durations.
Ultimately, age has a negative total effect of -0.109 on Saturday
trips. Specifically, age influences Saturday trips more indirectly
than directly. So, indirect effects can not help being considered in
modeling.
3.3 Effects on Activity Durations
In Table 6, the total and direct effects on each activity duration
shows the same signs over all attributes even if some indirect
effects have different signs from some attributes. Several vari-
ables have larger indirect effects than direct effects on activity
durations, such as family on shopping duration and age on sport
duration. There are no subdivided indirect effects on activity
durations for Saturday sport and Sunday leisure. Males may
spend a longer time shopping during the weekend and spend
more time duration on Sunday sport and leisure than they do on
Saturday. Marriage is the major factor where more time is spent
on most activities but less time for Saturday leisure and Sunday
sport. Family has the same patterns in Saturday activity durations
as marriage. Furthermore, family has a positive effect on Sunday
shopping duration but has negative signs for Sunday sport and
leisure durations. Generally, age has negative effects on durations
of all activities except for Sunday sport duration. The higher
income person and the higher educated person are more likely to
spend more time for all activities, with the exception of Saturday
shopping. On Sunday, the positive effect of age on sport duration
is mainly shown as an indirect effect rather than a direct effect. It
is assumed that as age tend to make people spend more time for
Sunday sport time. The factor of whether a family has children
also has negative effects on all activity durations during the
weekend, with the exception of Sunday shopping duration.
3.4 Subdivided Indirect Effects on Trips and Activities
through Durations
The subdivided indirect effects on weekend trips and activities
through activity durations are manually calculated, based on Fig.
2 and Table 3. Table 5 demonstrated the indirect effects of
individual attributes on trips and activities. These indirect effects
are composed of the subdivided indirect effects. Table 7 shows
the subdivided indirect effects of attributes on Saturday trips
through activity durations and activity frequency. Gender has a
positive subdivided indirect effect of 0.033 through shopping
duration while it has negative subdivided indirect effects of
-0.003 and 0.002 through sport and leisure durations, respectively,
and -0.020 through activities. Therefore, the total subdivided
indirect effects by gender on Saturday trips are 0.008, which is
equal to the indirect effect of gender on Saturday trips, as shown
in Table 5. Males have a positive subdivided indirect effect on
Saturday trips through shopping duration, but are negatively
associated with the subdivided indirect effect through sport and
leisure durations. Furthermore, males negatively and indirectly
influence trips through activities on Saturday. The factors of
Table 6. Total, Direct and Indirect Effects of Attributes on Weekend Activity Durations
Variable Gender Marriage Age Family Income Child Education
Saturday
Shopping
duration
0.157
0.144
0.013
0.087
0.069
0.017
-0.040
-0.055
0.015
0.084
0.029
0.056
-0.094
-0.057
-0.037
-0.026
-0.067
0.041
-0.077
-0.049
-0.028
Sport
duration
-0.044
-0.044
0.000
0.071
0.071
0.000
-0.037
-0.037
0.000
0.023
0.023
0.000
0.057
0.057
0.000
-0.007
-0.007
0.000
0.012
0.012
0.000
Leisure
duration
-0.002
-0.007
0.005
-0.111
-0.103
-0.008
-0.015
-0.020
0.004
-0.188
-0.185
-0.003
0.064
0.071
-0.006
-0.119
-0.120
0.001
0.076
0.077
-0.001
Sunday
Shopping
duration
0.105
0.157
-0.052
0.009
0.016
-0.007
-0.109
-0.129
0.020
0.124
0.113
0.011
0.042
0.071
-0.029
0.160
0.104
0.055
0.057
0.058
-0.001
Sport
duration
0.220
0.240
-0.020
-0.039
-0.028
-0.011
0.030
0.008
0.022
-0.052
-0.055
0.004
0.137
0.146
-0.009
-0.101
-0.139
0.038
0.040
0.035
0.004
Leisure
duration
0.138
0.138
0.000
0.077
0.077
0.000
-0.153
-0.153
0.000
-0.025
-0.025
0.000
0.062
0.062
0.000
-0.260
-0.260
0.000
-0.029
-0.029
0.000
1st value : Total effect, 2nd value : Direct effect, 3rd value : Indirect effect
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Direct and Indirect Effects on Weekend Travel Behaviors
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income and education have opposite results to that of gender.
Age and children show negative indirect effects on trips through
all activity durations. The attribute that generally has the highest
positive or negative indirect effects on trips is that of leisure
duration. Males influence positively and indirectly Saturday
activities through shopping duration, but negatively influence
Saturday activities through sport and leisure durations. Age is a
negative indirect factor on Saturday activities. Marriage and
family have positive subdivided indirect effects on activities
through shopping and sport durations but have negative indirect
effects on activities through leisure duration. Income and educa-
tion are positively and indirectly associated with activities through
sport and leisure durations, but are negatively associated with
activities through shopping duration on Saturday.
Table 8 shows the subdivided indirect effects of attributes on
Sunday trips through Saturday and Sunday activities and dura-
tions. In total, family, income and education positively influence
Sunday trip generation. In the analysis of Sunday total, Sunday
trips are highly and positively influenced through Sunday dura-
tions and activities by income which shows the Sunday total
indirect effect of 0.249. Family and education also have a posi-
tive indirect effect on Sunday trip generation. In analysis of a
number of Sunday activities, with the exception of gender, mar-
riage and child, most attributes show positive subdivided indirect
effects on Sunday trip generation through activity generation
frequency. Especially, gender is the most negative and indirect
factor on Sunday trips through Sunday activity frequency. In
analysis of effects of personal and household attributes on
Sunday trip generation through Saturday travel behavior, gender,
income and education shows the positive value.
In Table 9, the subdivided indirect effects of personal and
household attributes on Sunday activity frequency through Sunday
activity durations are highly positive by gender and negative by
child in Sunday total. Generally, Saturday travel behavior may
not significantly influence Sunday activity generation. Specifically,
most indirect effects on Sunday activities are through Sunday
activity durations. Education shows higher subdivided indirect
effects on Sunday activities through Saturday travel behavior than
Sunday. In summary, gender, marriage, income, and education
show positive subdivided indirect effects on Sunday activities,
while age, family and children have negative indirect effects on
Sunday activities.
Table 7. Subdivided Indirect Effects of Attributes on Saturday Travel Behavior
Variable Gender Marriage Age Family Income Child Education
Saturdaytrips
Shopping duration 0.033 0.016 -0.012 0.007 -0.013 -0.015 -0.011
Sport duration -0.003 0.005 -0.003 0.002 0.004 -0.001 0.001
Leisure duration -0.002 -0.029 -0.006 -0.052 0.020 -0.034 0.022
No. of Activities -0.020 -0.004 -0.090 -0.022 0.045 -0.062 0.092
Total 0.008 -0.013 -0.111 -0.066 0.056 -0.112 0.103
Saturdayactivities
Shopping duration 0.031 0.015 -0.012 0.006 -0.012 -0.015 -0.011
Sport duration -0.002 0.004 -0.002 0.001 0.002 0.000 0.001
Leisure duration -0.002 -0.036 -0.007 -0.064 0.025 -0.042 0.027
Total 0.027 -0.017 -0.021 -0.057 0.015 -0.056 0.017
Table 8. Subdivided Indirect Effects of Attributes on Sunday Trips
Variable Gender Marriage Age Family Income Child Education
From
Sunday
Shopping duration 0.038 0.004 - 0.031 0.027 0.017 0.025 0.014
Sport duration 0.045 - 0.005 0.002 - 0.010 0.028 - 0.026 0.007
Leisure duration 0.069 0.042 - 0.077 - 0.013 0.031 - 0.131 - 0.015No. of Activities - 0.318 - 0.087 0.086 0.119 0.173 - 0.036 0.031
Sunday total - 0.165 - 0.050 - 0.021 0.124 0.249 - 0.167 0.037
FromSaturday
Shopping duration 0.005 0.002 - 0.002 0.001 - 0.002 - 0.002 - 0.002
Sport duration 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Leisure duration 0.000 - 0.005 - 0.001 - 0.009 0.004 - 0.006 0.004
No. of Activities - 0.003 - 0.001 - 0.015 - 0.004 0.007 - 0.010 0.015
Saturday total 0.001 - 0.003 - 0.018 - 0.012 0.010 - 0.019 0.018
Total - 0.164 - 0.053 - 0.039 0.112 0.259 - 0.186 0.055
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3.5 Effects among Trips, Activities and Activity Durations
The relationships between Endogenous variables are shown in
Fig. 3. On Sunday, activity frequency and leisure duration have
no statistical significance in direct effects on trips at p=<0.05.
However, leisure duration has a statistical significance in total
positive effect on trips. That is, some links are not significant
statistically but they may be still needed to calculate indirect
effects between variables. The explanation of the model is done
by effects of coefficients rather than by statistical significance.
Trips have positive total effects from activity frequency and
durations. Durations for shopping and sport positively and di-
rectly influence Saturday trips, which may mean that an indivi-
dual participates more in activities of a shorter time. However, on
Saturday, a number of trips decrease as leisure duration in-
creases. On Sunday, all activity durations show negative direct
effects on trips but the total effects become positive due to higher
positive indirect effects. All direct effects between activity dura-
tions show negative signs. Specifically, the duration increase of
one activity causes the decrease of other activity durations. On
Saturday, sport duration has a positive indirect effect of 0.037 on
shopping duration, which indicates that sport indirectly influ-
ences shopping through leisure in duration. This indirect effect
(0.037) of sport on shopping is calculated by the direct effect
(-0.112) of sport on leisure, multiplied by the direct effect (-0.320)
of leisure on shopping. It has been previously mentioned that
Sunday trips and activities may be indirectly influenced by Sat-
urday travel behavior. The direct effect from Saturday activities
to Sunday activities has a positive sign of 0.091. It is assumed
that the person that participates more in Saturday activities is also
active on Sunday.
3.6 Subdivided Indirect Effects of Saturday Travel Behav-
ior on Sunday Travel Behavior
Sunday trips are positively and indirectly influenced by all
activity durations of Saturday in Table 10. In particular, Sunday
trips are highly and indirectly influenced by Saturday leisure
duration as the value of 0.51. However, Saturday activities have
a major indirect effect on Sunday trip generation with the value
Table 9. Subdivided Indirect Effects of Attributes on Sunday Activities
Variable Gender Marriage Age Family Income Child Education
FromSunday
Shopping duration 0.035 0.004 - 0.028 0.025 0.016 0.023 0.013
Sport duration 0.061 - 0.007 0.002 - 0.014 0.037 - 0.035 0.009
Leisure duration 0.078 0.043 - 0.086 - 0.014 0.037 - 0.146 - 0.016
Sunday total 0.174 0.040 - 0.112 - 0.003 0.090 - 0.159 0.005
FromSaturday
Shopping duration 0.003 0.001 - 0.001 0.001 - 0.001 - 0.001 - 0.001
Sport duration 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Leisure duration 0.000 - 0.003 - 0.001 - 0.006 0.002 - 0.004 0.002
No. of Activities - 0.002 0.000 - 0.009 - 0.002 0.005 - 0.006 0.009
Saturday total 0.001 - 0.002 - 0.011 - 0.007 0.006 - 0.012 0.011
Total 0.175 0.038 - 0.123 - 0.010 0.096 - 0.171 0.016
Fig. 3. Total, Direct and Indirect Effects among Travel Behavior Variables
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Direct and Indirect Effects on Weekend Travel Behaviors
Vol. 13, No. 3 / May 2009 − 177 −
of 0.147. Furthermore, among Sunday activity durations, Sunday
activities have the highest indirect effects from leisure. The sub-
divided indirect effects are also manually calculated, based on
Fig. 2 and Table 1.
4. Conclusions
This study analyzed the total, direct and indirect effects of
personal and household characteristics on travel behavior using
structural models. Classic causal models only consider direct
effects, while, in addition to direct effects, the structural models
determine indirect effects through intervening attributes. The
structural model used in the study has a reasonable fit and
satisfactorily reflects the observed data. Based on the model
results, the study tried to determine the causal factors effecting
travel behavior. The effects of Saturday travel behavior on
Sunday travel behavior were also analyzed.
In the analysis of effects of personal and household attributes,
the sign differences are found between direct effects and total
effects. These sign differences are due to the indirect effects of
the exogenous variable on the endogenous variable through other
possible intervening endogenous (or exogenous) variable(s). For
example, age has a minimal positive direct effect on Saturday
trips, but it has a larger negative indirect effect on them through
activity durations. Specifically, age has more effects on a number
of trips indirectly than it does directly. In the analysis of effects
between travel behaviors, the trip generations are positively and
directly influenced by activity frequency and activity durations.
All direct effects between activity durations are negative. Sunday
sport and leisure durations show negative direct effects on trips
but the total effects are positive. Those people who participate in
more Saturday activities are assumed to be active on Sunday
because Saturday activity frequency positively influences Sunday
activities. Sunday trip generation is positively and indirectly in-
fluenced by all Saturday activity durations. In particular, the
major indirect effect factor on Sunday trips is Saturday activities.
Because travel behavior includes complicated relationships
among factors, this study suggests that indirect effects should be
reflected for an adequate understanding of travel behavior models.
From this study, a dependency is found in the relationships
between weekend travel behaviors. In the future, it will be nec-
essary to analyze the effects on travel behavior by using per-
iodical multi-day data for an improved understanding of travel
behavior.
Acknowledgements
This work was supported by The Research Center of Industrial
Technology at CBNU.
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