Direct and indirect effect on weekned travel behavior

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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: [email protected])

**Professor, Dept. of Urban Engineering, Chonbuk National University, Jeonju 561-756, Korea (E-mail: [email protected])



Tae Youn Jang and Jee Wook Hwang

− 170 − KSCE Journal of Civil Engineering

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




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





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




Vol. 13, No. 3 / May 2009 − 173 −

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





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


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




Vol. 13, No. 3 / May 2009 − 175 −

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


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


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





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


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




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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