EXPLAINING TRAVEL BEHAVIOR:
TOWARDS A NEW CONCEPTUAL FRAMEWORK
AND MODELING TECHNIQUE
Veronique VAN ACKER (corresponding author)
Geography Department, Ghent University
Campus “Sterre”
Krijgslaan 281, building S8
9000 Ghent
Belgium
Tel.: +32 9 264 46 97
Fax: +32 9 264 49 85
E-mail: veronique.vanacker@ugent.be
Frank WITLOX
Geography Department, Ghent University
Campus “Sterre”
Krijgslaan 281, building S8
9000 Ghent
Belgium
Tel. : +32 9 264 45 53
Fax: +32 9 264 49 85
E-mail: frank.witlox@ugent.be
ABSTRACT
Numerous studies attempt to explain travel behavior by various spatial, socioeconomic and socio-psychological characteristics. However, these studies do not
reflect on the theoretical question of why such a relationship should exist after all.
Answering this query involves combining theoretical frameworks from transport
geography and social psychology, which can be done through the concept of
lifestyle. Based on this, a new conceptual framework for travel behavior is
propounded. In order to analyze this framework, an appropriate modeling technique
should be used. Based on statistical reasons, this technique must account for two
kinds of interdependencies, i.e. interdependencies that exist between explanatory
variables causing indirect effects, and interdependencies that exist between different
data levels. Therefore, this paper suggests combining the analytical frameworks of
Structural Equation Modeling (SEM) and multilevel models.
1. INTRODUCTION
Living, working, shopping and recreating are spatially separated activities. In order
to participate in these activities and to achieve social and economic self-fulfillment,
people have to travel. Traveling also causes negative externalities such as congestion,
pollution and accidents. Policymakers try to control and reduce these travel
externalities through price or regulatory measures, or through more long term
measures such as urban planning and design. Illustrative are the New Urbanism
movement in the United States (e.g., Handy, 1996; Crane and Crepeau, 1998) and the
Compact City Policy in Europe (e.g., Jenks et al., 1996; Maat, 2001), both of which
1
aim at reducing car use and travel distances through urban planning. The basis idea is
that high-density and mixed-use neighborhoods are believed to be associated with
shorter trips and more non-motorized trips; hence, indicating that there exists a clear
relationship between urban form and travel behavior. Although numerous studies
exist that try to measure and explain the strength of this relationship (for a review,
see, e.g., Van Acker et al., 2007; van Wee, 2002; Ewing and Cervero, 2001), the
point is that there is little consensus in the conclusions. Some studies (e.g., Meurs
and Haaijer, 2001) indicate that various aspects of the urban form are linked with
travel behavior, while others (e.g., Schwanen, 2002) state the opposite.
In our view these inconsistent conclusions result from the fact that certain variables
are not fully taken into account. The models are usually controlled for socioeconomic differences among respondents. Still, within ‘homogeneous groups’ (i.e.,
respondents that are to be considered more or less identical in socio-economic terms)
there may be socio-psychological constructs, such as lifestyles, perceptions and
attitudes, which also have an impact on travel behavior. Far less is known about the
conceptual relationship that exists between spatial, socio-economic, and sociopsychological constructs on the one hand and travel behavior on the other hand.
Answering this query would involve combining and linking theories stemming from
transport geography (e.g., utility-maximizing theory, activity-based approach) and
social psychology (e.g., theory of planned behavior, social-cognitive theory).
The inconsistent conclusions occur also because of data interdependencies that are
not fully taken into account in the modeling approach. For example, only taking the
direct effects of the explaining variables on travel behavior into account, implies
2
neglecting the indirect effects caused by relationships among the explaining
variables. Moreover, several travel behavior aspects are related to each other (for
example, modal choice will determine travel distance and time), resulting in indirect
effects as well. Another example relates to interdependencies that exist between
several levels of data aggregation. This problem refers to a data structure of
individuals nested within households, households nested within neighborhoods, etc.
Because of this nested data structure, the individual’s characteristics depend on the
household’s characteristics, the household’s characteristics are related to the
neighborhood, and so on.
The present paper aims to contribute to the literature on travel behavior
conceptualization and modeling by taking a closer look at the above-mentioned
interdependencies. The paper is structured as follows. In Sections 2 and 3 the
relationship between spatial, socio-economic and socio-psychological characteristics
and travel behavior is explored. We analyze the different theoretical frameworks that
exist (section 2) and reveal the key variables in travel behavior research through an
extensive literature review (section 3). Using the output of both sections, we put
forward a new conceptual modeling approach in Section 4. This is the main novelty
and contribution of this paper. Our findings are contrasted with existing studies that
also try to account for interdependencies between variables (studies using structural
equation models) or between levels of data aggregation (studies using multilevel
techniques). This is done in Section 5. In Section 6 an appropriate methodological
framework is advanced that concurs with our conceptual model. This section
includes a theoretical discussion on travel behavior and how it can be analyzed by
3
multilevel structural equation models. Finally, in the last section, some conclusions
are drawn.
2. THE RELATIONSHIP BETWEEN SPATIAL, SOCIO-ECONOMIC AND
SOCIO-PSYCHOLOGICAL
CHARACTERISTICS
AND
TRAVEL
BEHAVIOR
Several studies try to measure the strength of the relationship between spatial, socioeconomic and socio-psychological characteristics and travel behavior. However,
almost none of these studies advance an overall theoretical framework that justifies
and explains this relationship. Clearly, such a theoretical justification cannot be
found in one comprehensive theory. Nevertheless, concepts from several theories can
contribute to the research debate. In this respect, Handy (2005) mentioned four
useful theories: (i) utility-maximizing theory, (ii) activity-based approach, (iii) theory
of planned behavior, and (iv) social-cognitive theory. The first two theories refer to
the mechanism determining travel behavior, whereas the latter two theories define
factors influencing travel behavior.
2.1. Utility-maximizing theory
McFadden (1974) introduced the concept of “utility-maximizing behavior” from
economics and psychology into travel behavior research. The theory focuses on
individual’s choice-behavior and states that individuals will choose the alternative
which offers the largest utility. In travel behavior research, these choices are discrete
(e.g., which destination, which travel mode) as opposed to continuous in consumer
4
behavior (e.g., how much spending on a purchase). Depending on the assumptions
made with respect to the distribution of the error terms, discrete choices can be
analyzed through, e.g., the well-known multinomial logit model. The probability of
choosing a particular alternative relates to the utility of this alternative relative to the
utilities of all alternatives in the choice set. Usually, utility is defined as a linear
function of the choice alternative’s attributes and this function generally includes
individual socio-economic characteristics as well. Hence, individual preferences are
expressed by this utility function. Although individual perceptions and attitudes are
recognized, utility-maximizing studies generally do not incorporate these individual
perceptions and attitudes. In practice, the theory is used for forecasting rather than
understanding the mechanism of travel behavior, and it is argued that perceptions and
attitudes are difficult to forecast. The utility-maximizing theory recognizes
relationships between short-term and long-term decisions. Daily travel behavior (e.g.,
modal choice, destination choice) can depend on long-term decisions such as car
ownership, residential and job location. Thus, the theory offers a conceptual model in
which relationships exist between several choices.
2.2 Activity-based approach
The activity-based approach can be considered as an extension of the utilitymaximizing theory (Goodwin and Hensher, 1978). Travel behavior is considered as
derived from the activities in which the individual wants to participate. Because
living, working, shopping and recreating are spatially separated, people have to
travel. Consequently, as noted by Chapin (1974), analyzing individual activity
patterns leads to a better understanding of travel behavior. Chapin (1974) assumes
5
that activity patterns are determined by (i) the propensity to engage in an activity,
and (ii) the opportunity to engage. The propensity to engage is function of individual
characteristics such as motivations and way of thinking, and roles and socioeconomic characteristics. The opportunity to engage refers to the individual’s
perception of (i) the availability of opportunities, and (ii) the quality of these
opportunities. In other words, it refers to the perceived environment in which
activities are performed. Objective characteristics of this environment are not taken
into account.
2.3 Theory of planned behavior
The theory of planned behavior explains behavior in terms of individual beliefs
(Azjen, 1991). These beliefs include behavioral beliefs, normative beliefs and control
beliefs, which respectively influence attitudes, subjective norms and perceived
behavioral control. Behavioral beliefs are beliefs about the probability of possible
results of a behavior. Those possible results are evaluated by the individual,
determining the individual’s attitude towards a behavior. An individual’s behavior
may depend on the approval of referent individuals (e.g. partner, friends, family, and
boss). This is referred to as normative beliefs. Subjective norms about a behavior are
determined by those normative beliefs and the individual’s motivation to go along
with those referent individuals. Control beliefs are beliefs about the probability that
particular factors will facilitate or constrain a behavior. Perceived behavioral control
is the result of those control beliefs taken into account the perceived power of each
factor facilitating or constraining a behavior.
6
The theory of planned behavior stresses the importance of individual beliefs.
External factors, such as urban form, are not explicitly taken into account. However,
particular urban form characteristics can be considered as factors facilitating or
constraining behavior (e.g., the presence of sidewalks will facilitate pedestrian trips).
Consequently, control beliefs may include individual perceptions of the urban form.
In other words, subjective perceptions of these factors are more important than
objective measurement of it. Moreover, the theory includes attitudes and social
norms.
2.4 Social-cognitive theory and ecological models
The social-cognitive theory postulates reciprocal relationships between the
individual’s characteristics, the individual’s behavior and the environment in which
this behavior is performed (Bandura, 1986). These reciprocal relationships may
differ in strength and may occur on different points in time. Comparable to the theory
of planned behavior, a distinction is made between objective and subjective factors.
The social-cognitive theory distinguishes environments and situations. Environments
are objective quantifiable factors which are external to the individual, whereas
situations refer to the individual’s perception of this objective environment. The
concept of self-efficacy is considered as fundamental. Self-efficacy refers to the
individual’s self-confidence to overcome constraining factors and to perform a
behavior. Moreover, a learning behavior appears, i.e. individuals learn that their
behavior has consequences and they expect the same results to occur again.
7
Ecological models are largely based on the social-cognitive theory. However,
ecological models underline the role of the physical environment besides the social
environment. These models acknowledge the multilevel data structure: a distinction
is made between the intrapersonal level, interpersonal level and the community level.
The concept of self-efficacy is important at the intrapersonal level, whereas the social
environment and its social norms refer to the interpersonal level. The community
level includes the physical environment (Handy, 2005).
3. KEY VARIABLES IN TRAVEL BEHAVIOR RESEARCH
Most travel behavior theories explain travel behavior by factors such as the
individual’s perceptions, attitudes and preferences. Urban form is generally
considered as a factor constraining travel behavior. In what follows we focus on the
empirical measurement of these key variables explaining travel behavior. Numerous
studies exist that measure the effects of urban form on travel behavior. Literature
reviews such as Ewing and Cervero (2001) or van Wee (2002) distinguish various
urban form characteristics, ranging from density and diversity measures to
neighborhood type and urban design features. The results are generally controlled for
socio-economic differences, and a limited number of studies take individual
perceptions, attitudes and preferences into account as well. Consequently, key
variables in these empirical studies refer to three components: (i) a spatial
component, (ii) a socio-economic component and (iii) a personality component.
3.1. Spatial dimension
8
Ecological models emphasize the existence of a physical environment in which the
behavior is performed. The scale of these environments ranges from the local
neighborhood to the metropolitan area. Here, we only focus on some frequently used
characteristics of the local neighborhood (density, diversity and design).
The effects of density on travel demand have long been acknowledged (e.g.,
Levinson and Wynn, 1963) and remain well-studied and understood. Higher
densities are associated with more public transport use, more walking and cycling,
and less car use. After all, public transport is organized more efficiently (more
routes, higher frequency of services) in high-density areas and car users may face
more congestion. Furthermore, travel distance and time is negatively associated with
density (Cervero and Kockelman, 1997; Kitamura et al., 1997; Stead, 2001; Dargay
and Hanly, 2004; Schwanen et al., 2004).
Several measures have been developed to estimate diversity: among others, a
jobs/housing ratio (Ewing et al., 1994; Boarnet and Sarmiento, 1996), an entropy
index to quantify the degree of balance across various land use types (Frank and
Pivo, 1994; Kockelman, 1997; Rajamani et al., 2003) or a dissimilarity index to
indicate the degree to which different land uses lie within one another’s surrounding
(Kockelman, 1997). The effects of more diversity on travel behavior are comparable
to the effects of higher densities.
Design can be characterized by a general classification of neighborhoods with a
standard suburban neighborhood and a neo-traditional neighborhood as extremes
9
(McNally and Kulkarni, 1997; Gorham, 2002). Standard suburban neighborhoods are
characterized by low densities, limited diversity, and a car-orientated design. As a
consequence, these neighborhoods are associated with more trips and more car use.
However, design can be characterized more specifically by site design, and dwelling
and street characteristics. Findings indicate that neighborhoods characterized by
small block sizes, a complete sidewalk system, the absence of cul-de-sacs and
limited residential parking encourage walking and cycling (Cervero and Kockelman,
1997; Hess et al., 1999; Stead, 2001; Rajamani et al., 2003). Meurs and Haaijer
(2001) noted that, although characteristics of the dwelling, street, and neighborhood
may influence modal choice, this is only true for shopping and social or recreational
purposes. Working trips can be less influenced by design characteristics.
3.2. Socio-economic dimension
The individual’s characteristics and its social environment (e.g., the household) are
important concepts within the social-cognitive theory. Empirical studies control their
results for various socio-economic and demographic characteristics of the individual
and the household.
Car ownership, and consequently car use, is lower among older persons (aged above
65 years). Moreover, if older persons travel by car, they travel shorter distances. Note
also that older persons not only travel because they want to participate in activities,
the travelling itself has socializing opportunities. Ride-sharing for non-work trips is,
therefore, found to increase by age (Boarnet and Sarmiento, 1996; Stead, 2001;
Rajamani et al., 2003; Dargay and Hanly, 2004; Schwanen et al., 2004).
10
Women travel more often by public transport, by bike or on foot, whereas car use is
higher among men. Because women are more relied to slow modes, they cannot
travel long distances (Simma and Axhausen, 2000; Stead, 2001; Schwanen et al.,
2002, 2004). This gender difference may be explained by, among others, the fact that
women earn lower wages and obtain different types of jobs than men (Madden,
1981; Hanson and Pratt, 1988). However, women remain responsible for most
household maintenance tasks. As a result, car use is higher and longer travel
distances are made for non-work trips by women (Boarnet and Sarmiento, 1996).
Educational level, employment status, and income are related, resulting in
comparable findings. For example, highly educated persons often obtain specialized
jobs which are concentrated in high-density office parks. As a result, they are more
involved in long-distance commuting and car use is higher (Boarnet and Sarmiento,
1996; Kockelman, 1997; Stead, 2001; Schwanen et al., 2002; Krizek, 2003; Dargay
and Hanly, 2004). However, the use of public transport, especially train use, is higher
if these office parks are located nearby a train station.
Household size is positively associated with car ownership. Because of intrahousehold decisions related to the activities of several household members, it may be
appropriate to own more cars. Households that own a car will use it more often.
Furthermore, because these households are more car dependent, they can travel
longer distances as well (Kockelman, 1997; Simma and Axhausen, 2000; Rajamani
et al., 2003; Dargay and Hanly, 2004). Comparable results are found with respect to
the number of employed persons in the household (Cervero and Kockelman, 1997;
11
Simma and Axhausen, 2000; Krizek, 2003) and the presence of children. However,
travel distances and times are longer for singles and couples, because they do not
have child care responsibilities (Boarnet and Sarmiento, 1996; Stead, 2001;
Dieleman et el., 2002; Schwanen et al., 2002; Dargay and Hanly, 2004).
As mentioned previously, car ownership influences car use: households owning a car
will use it. However, car ownership in itself is influenced by other socio-economic
variables, especially income. Car ownership is higher among high-income groups
(Boarnet and Sarmiento, 1996; Kockelman, 1997; McNally and Kulkanri, 1997;
Dieleman et al., 2002; Schwanen et al., 2002, 2004; Rajamani et al., 2003).
3.3 Socio-psychological dimension
The theory of planned behavior and the social-cognitive theory highlight the
existence and importance of perceptions and attitudes in explaining travel behavior.
These concepts refer to the socio-psychological characteristics of the individual. In
the 1970’s, various studies focused on perceptions and attitudes in travel behavior
(e.g., towards travel modes) (e.g., Hartgen, 1974; Golob and Recker, 1977; Dobson
et al., 1978). However, these studies did not include a spatial component. Recent
studies rediscover the importance of perceptions and attitudes, and measure the
relative influence of the spatial, socio-economic and socio-psychological component.
Attitudes towards urban form (Handy, 1996) and towards travel (Bagley and
Mokhtarian, 2002; van Wee et al., 2002) add explanatory power to models of travel
behavior that already include spatial and socio-economic variables. Moreover,
perceptions and attitudes are considered as the result of lifestyles. The study of
12
lifestyles in travel behavior research is, therefore, important (Kitamura et al., 1997;
Bagley and Mokhtarian, 2002).
4. A CONCEPTUAL FRAMEWORK FOR TRAVEL BEHAVIOR
Having analyzed the different theoretical frameworks that exist (section 2) and
discussed the key variables in travel behavior research through an extensive literature
review (section 3), we now focus on putting forward a new conceptual modeling
framework.
Figure 1 represents our conceptual model for travel behavior. Various concepts and
findings from the reviewed theories and the literature on travel behavior are included
within this model.
<insert Figure 1>
The individual’s lifestyle is considered as a key variable that determines travel
behavior. Lifestyle is a set of actions and habits that characterizes the individual’s
way of living, which indicates the person’s position in social contacts. This aspect
refers to the communicative character of lifestyle (Bourdieu, 1983; Ganzeboom,
1988). Lifestyle is determined by three dimensions (arrows 1 in Figure 1): (i) an
economic dimension (often measured by income), (ii) a cultural dimension (often
measured by education), and (iii) a stage of life dimension. For example, given that
parents have certain obligatory responsibilities towards their children, they lead
13
another, less adventurous lifestyle as singles. Identically, a high social-economic
status permits a more luxurious lifestyle.
Lifestyle influences habitual and spontaneous behavior. An adventurous lifestyle
permits more spontaneous behavior than, say, a cocooning lifestyle. Habitual and
spontaneous behavior is not only influenced by the individual’s lifestyle, but also by
the available spatial, travel and activity opportunities. For example, living in a highdensity area in which public transport is efficiently organized allows more
spontaneous trips by public transport. This relationship is indicated by means of
arrows 2 in Figure 1.
Spatial, activity and travel perceptions, attitudes and preferences are affected by the
individual’s lifestyle (arrow 3 in Figure 1). Perceptions refer to the way urban form,
activity and travel characteristics are considered, whereas attitudes include an
evaluation of these characteristics. Preferences are then formulated, based on
attitudes. This includes a ranking of different spatial, activity and travel
opportunities.
Spatial behavior, activity behavior and travel behavior are the result of habits and an
assessment between preferences and available opportunities (arrows 4 in Figure 1).
For example, an individual may prefer to travel by public transport. However, if no
public transport services are available, then the individual is obliged to choose
another travel mode. However, individuals are not constantly conscious of their
behavior and, therefore, behavior does not always result form rational decisions.
Consequently, the influence of habits must be mentioned. Initially, a specific
14
behavior is probably based on rational choices. Afterwards, the behavior is repeated
that often that it occurs without self-instruction.
Spatial behavior refers to all kinds of location decisions (e.g., residential location, job
location, trip destination location). Location decisions are not only influenced by
spatial preferences, but also by activity and travel preferences (arrows 5 in Figure 1).
For example, a household with public transport preferences will likely choice a
residential neighborhood with good public transport services.
Activity behavior includes the spatial and temporal activity pattern. Travel behavior
consists of travel-related decisions, such as modal choice, travel distances and times,
and combining trips into chains. Travel behavior is considered as derived from
location-decisions and activity patterns (arrows 6 in Figure 1).
The dotted arrows in Figure 1 refer to learning behavior: individuals can learn from
previous experiences. Consequently, habits, perceptions, attitudes and preferences
are not fixed in time.
5. HOW TO ACCOUNT FOR INTERDEPENDENCIES ?
The above literature review and conceptual model reveal the complexity of travel
behavior. Numerous variables are influencing each other, resulting in indirect effects
on travel behavior. This kind of interdependency can be analyzed using Structural
Equation Models (SEM). A second kind of interdependencies results from a nested
data structure. Data are collected at several levels: socio-economic data is collected
at the individual level and the household level, whereas spatial data is collected at the
neighborhood level and the metropolitan level. Moreover, individuals are nested
within households, and households within neighborhoods. This nested data structure
15
is best analyzed using a multilevel analysis. Accounting for both kinds of
interdependencies helps to better understand the complexity of travel behavior. In
what follows we briefly outline how both approaches can be implemented.
5.1 Structural equation models: accounting for direct and indirect effects
Structural equation modeling (SEM) is a research technique dating from the 1970s.
Most applications have been in psychology, sociology, biology and marketing. Its
use in travel behavior research dates from the 1980s (for a review, see Golob, 2003).
However, studies from the perspective of urban form are limited. Nevertheless, these
studies elucidate the importance of (i) the interdependencies among variables and (ii)
the resulting direct, indirect and total effects.
Bagley and Mokhtarian (2002) used a system of structural equations to test the
relationship
between
residential
neighborhood
type
and
travel
behavior,
incorporating socio-economic, attitudinal and lifestyle variables. Their analysis
indicated that attitudinal and lifestyle variables had the greatest impact on travel
demand and this in terms of total and direct effects. Residential neighborhood type,
however, had little impact on travel behavior. This fact suggested an indirect
relationship between urban form and travel behavior.
Simma and Axhausen (2003) estimated two models, for working and shopping trips
respectively. Socio-economic variables had stronger effects on travel behavior (car
ownership, modal choice and distance traveled) compared to spatial variables.
Moreover, they found several relationships between these endogenous variables as
16
well: car ownership had a strong impact on trips made by car and a strong negative
impact on trips made by other modes. Modal choice in turn had an impact on
distances traveled.
Kim (2003) analyzed the elderly mobility and found that this is mainly influenced by
personal and household characteristics. Urban form appeared to be an insignificant
factor. Indirect effects of personal and household characteristics were defined
through the mediator variable of urban form. Nevertheless, indirect effects remained
small.
A recent study by Van Acker et al. (2007) found that travel behavior is mainly
influenced by the individual’s social-economic status. Urban form plays only a minor
part. Moreover, they stressed the importance of indirect effects. For example,
although its direct effect is small, the indirect effect of the individual’s role within
the household was found to be strong because of an interaction with social-economic
status and urban form.
5.2 Multilevel analyses: accounting for a nested data-structure
Multilevel models are also known as random coefficient models, mixed models and
hierarchical linear models (Hox, 2002). Although the method is widely used in social
sciences, travel behavior studies applied this technique only recently. Comparable to
the above mentioned review of SEM-studies, a limited amount of multilevel studies
can be found in studies on the effects of urban form on travel behavior. However, the
few studies underline the importance of distinguishing a nested data structure.
17
Goulias and Kim (2001) constructed two-level multinomial logit models in order to
estimate the temporal, spatial, individual and household effects on activity and travel
patterns. Activity patterns were mainly determined by the individual and household
level. The household level characterized the distinction between non-work and work
activity patterns. This indicated a systematic coordination of tasks within the
household. At the individual level, a difference was found in activity patterns
characterized by short-work and long-work durations. Spatial characteristics were
found to influence travel patterns to a greater extent compared to activity patterns.
Snellen et al. (2002) used a two-level model as well. However, whereas Goulias and
Kim (2001) used two levels referring to the individual and its household, Snellen et
al. (2002) used two levels referring to the individual and its residential neighborhood.
However, they found only a modest effect of urban form on mode-choice decisions
for frequently conducted activities, such as working, shopping and leisure.
Schwanen et al. (2004) focused on working trips. They analyzed the influence of
metropolitan structure on commute behavior (modal choice, commuting distance and
time). Therefore, they estimated a four-level regression model (individual,
household, neighborhood and metropolitan region). The explained variation in modal
choice, as well as in commuting distance and time, is larger among individual
workers within neighborhoods and metropolitan regions than the variation between
such geographical units. Although significantly influencing commute behavior,
spatial characteristics explain only a limited part of the individual’s commute
behavior. Socio-economic and demographic variables are of greater importance.
18
Ma and Goulias (1996) estimated a four-level model as well (individual, household,
spatial context, temporal context) in order to explain activity and travel patterns. The
macro-levels are somewhat different than the levels used by Schwanen et al. (2004).
Since longitudinal data were used, the first macro-level refers to the temporal
context. This context includes two different scales, year-to-year and day-to-day
variations in activity and travel patterns. The second macro-level covers spatial
characteristics, assessed by accessibility measures and by residential location. Spatial
context was found to significantly affect the individual’s activity and travel pattern.
Nevertheless, these effects differ according to the employment’s status of the
individual. Activity and travel patterns of workers are more sensitive to accessibility
compared to non-workers.
6. TOWARDS A MULTILEVEL STRUCTURAL EQUATION MODEL
Multilevel analyses can be performed in a SEM framework. Consequently, both
interdependencies are taken into account.
A SEM model is represented by a series of simultaneously estimated structural (i.e.,
regression) equations. Hence, a SEM can include mediator variables. As a result,
direct, indirect and total effects can be distinguished. Because a variable can be an
independent variable in one equation but a dependent variable in another equation,
SEM makes a distinction between ‘endogenous’ and ‘exogenous’ variables.
Exogenous variables are not caused by any other variable. Instead, exogenous
variables influence other variables. In a graphical representation of a model, no paths
19
(symbolized by arrows) will point towards exogenous variables and paths will only
departure from exogenous variables towards other variables. Endogenous variables
are influenced by exogenous variables, either directly or indirectly (Kline, 2005;
Byrne, 2001; Raykov and Marcoulides, 2000).
A SEM can be composed of up to three sets of simultaneous models: (i) a
measurement model for the endogenous variables, (ii) a measurement model for the
exogenous variables, and (iii) a structural model (Golob, 2003). This full model is
known as ‘SEM model with latent variables’. Latent variables are constructs which
cannot be observed directly. Thus, latent variables must be defined in terms of
underlying variables which are believed to represent the latent variable. These
underlying and observable variables are called ‘indicators’ or ‘manifest variables’.
The measurement model, therefore, defines the relationship between a latent variable
and its indicators. The structural model represents the relationships between
exogenous and endogenous variables. This structural model is defined by the
matrices:
Y=BY+ΓX+ζ
with
[1]
Y = L x 1 matrix with endogenous variables
X = K x 1 matrix with exogenous variables
B = L x L matrix with regression coefficients relating endogenous variables
to other endogenous variables
Γ = K x K matrix with regression coefficients relating exogenous variables to
endogenous variables
20
ζ = L x 1 matrix with residues of the endogenous variables
The estimation of a SEM-model is usually based on matching the observed
covariance matrix with the model-based covariance matrix.
A ‘SEM with latent variables’ is seldom applied. No measurement models are
needed if all variables are observable. A SEM with observed variables is obtained by
conducting a path analysis (Golob, 2003).
Traditional statistical methods analyze a single unit only. Nevertheless, data may
have a hierarchical structure. For example, individuals nested within households, and
households within neighborhoods. Assume that data are only available on individuals
and households. Statistical methods assume independence over the N observations.
However, travel behavior of individuals may be influenced by household
characteristics, such as car ownership. Therefore, independence is assumed only over
the C households in this two-level SEM (Kline, 2005). Ignoring similarities among
individuals within households can result in biased estimates of the model’s
parameters, standard errors and fit indexes. The proportion of the total variation in
the individual’s travel behavior due to the variation between the households is
expressed by the interdependency index:

with
 B2
 B2   W2
[2]
 B2 = variation between households
21
 W2 = variation within households
If ρ has a value close to zero, then variation between households is limited. In these
circumstances, individual’s travel behavior is not influenced by household
characteristics and, consequently, data are independent. Nevertheless, values ranging
of 0.10 to 0.25 or higher indicate that data are not independent from each other and
multilevel analysis is needed (Torn and Bosker, 1999).
Multilevel SEM accounts for the nested data structure by assuming the existence of
one population (data-set) of individuals clustered in, for example, households.
Instead of developing a separate covariance matrix for each level, multilevel SEM
decomposes the individual data into two separate models for the within and between
household structure (Chung et al., 2004):
Within household model:
YWci = m + BW YWci + ΓW XWci + ζWci
[3]
Between household model:
YBc = BB YBc + ΓB XBc + ζBc
with
[4]
i = individuals i
c = households c
m = overall expectation for yci
22
The within household component contains the contribution of individual
characteristics on the individual’s travel behavior, whereas the between household
component accounts for the household contribution to the individual’s travel
behavior. Multilevel SEM seeks to decompose the variation in the endogenous
variables into variance components associated with each level in the nested datastructure. Moreover, it tries to explain the variation at each level simultaneously by a
set of exogenous and endogenous variables (Kline, 2005; Chung et al., 2004).
7. DISCUSSION AND CONCLUSION
To our knowledge, applications of a multilevel SEM remain scarce in travel behavior
research. The few exceptions are Chung et al. (2004) and Kim et al. (2004) but these
studies estimated two-level models referring to the individual and the household.
Consequently, both studies did not account for the influence of urban form. Due to
recent advances in software development, it is possible to include a third level which
refers to the neighborhood characteristics. Furthermore, both studies did not
underline the importance of indirect effects. Chung et al. (1994) only described direct
effects of individual and household characteristics on travel behavior, whereas Kim
et al. (2004) distinguished indirect effects but they did not underline its meaning.
The conceptual model proposed in this paper includes numerous relationships,
resulting in indirect effects on travel behavior. Within this framework, the estimation
of a SEM is appropriate. Moreover, the effects of a nested data structure must be
taken into account as well. Consequently, in further work, we would like to estimate
23
a multilevel SEM in order to put the conceptual model into practice. This multilevel
SEM will include data on the individual, the household and the neighborhood.
Another reason why SEM will be used in further research can be found in the
conceptual model. The presented conceptual model strongly emphasizes the
importance of lifestyle, perceptions, attitudes and preferences. However, these are
latent variables which cannot be observed directly. Therefore, data on indicators of
lifestyle, perceptions, attitudes and preferences will have to be collected. The latent
variables or constructs can be determined by a factor analysis, but relationships
between the constructs can be modeled in a SEM.
Because of possibilities to model indirect effects, to account for a nested data
structure and to estimate constructs, SEM is considered as useful to entangle the
complexity of travel behavior. Nevertheless, applications remain scarce. Further
research will, therefore, explore the use of SEM in travel behavior research.
Consequently, the presented conceptual model will be put into practice.
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Figure 1: A new conceptual model for travel behavior
33
4
Spatial perceptions, attitudes,
preferences
Economic
dimension
UNREASONED
INFLUENCES
BEHAVIOURS
REASONED
INFLUENCES
Spatial
behaviour
4
Spatial habits
1
6
5
Cultural
dimension
1
Lifestyle
3
4
Travel perceptions, attitudes,
preferences
5
1
Stage in
life
Travel
behaviour
Activity perceptions, attitudes,
preferences
4
Travel habits
6
4
Activity
behaviour
2
4
AVAILABLE OPPORTUNITIES
- objective spatial, travel and activity opportunities (car ownership, public
transport pass, …)
- objective socio-economic and demographic variables (gender, age, …)
- cognitive and physical skills (driving licence, knowledge, …)
4
Activity habits