Exploring the relationships between land-use system and travel behaviour concepts: some first findings
Veronique Van Acker*, Frank Witlox**
Key words useful for searching: Land-use/transportation
system, Travel behaviour, Attitude measurement, Structural equation
modelling. |
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Abstract The aim of this exploration is to try to summarise what is commonly
accepted when analysing the relationships between land use and transportation.
The interaction between land use and transportation composes the land-use/transportation
system. A large research body exists on the impact of land-use systems on
travel behaviour (for reviews, e.g., Handy, 2002;
Stead and Marshall, 2001;
Crane, 2000; Wegener
and Fürst, 1999).
In order to obtain a clear overview, a three-fold distinction has
been made based on type of variable included. Thus, three dimensions
in travel behaviour research have been found: (i) a spatial dimension,
(ii) a socio-economic dimension, and (iii) a behavioural dimension. Less is known about the reverse relationship, i.e., the impact of
the transportation system on location decisions of households and
firms (the land-use system). The greater part of this research utilizes
“accessibility” as an intermediate concept to measure the influence
of the transportation system on the land-use system. The presented literature
review enabled us to detect some gaps in the knowledge on the land-use/transportation
system. Understanding the interaction between land use and travel
behaviour involves having (i) data on land-use patterns; on the socio-economic
background of individuals; and on their attitudes, perceptions and
preferences toward land use and travel; and (ii) a methodology dealing
with potential multiple directions of causality. The first issue can
be achieved by combining empirical, revealed, and stated-preference
research. The second methodological question can be solved using structural
equation modelling (SEM). This is a modelling technique which can
handle a large number of independent and dependent variables, as well
as multiple directions of causality. These characteristics of SEM
seem useful in order to obtain an improved insight in the complex
nature of travel behaviour. |
* Department of Geography, Ghent University,
Krijgslaan 281 - S8, B-9000 Gent. Belgium
e-mail: Veronique.VanAcker@UGent.be (corresponding
author)
** Department of Geography, Ghent University,
Krijgslaan 281 - S8, B-9000 Gent. Belgium
e-mail: Frank.Witlox@UGent.be
Introduction
The land-use/transportation system (often
referred to as LUTS) has been subject of many research studies. The resulting
large body of literature on the LUTS reports on the impact of the land-use
system on travel behaviour on the one hand, and on the effects of the transportation
system on land use on the other hand. Both sets of impact are rarely incorporated
simultaneously in one study.
The research done by Mitchell and Rapkin
(1954) is considered to be one of the first studies
to deal with the exploration of the impact of the land-use system on travel
behaviour. Until now, knowledge of the travel consequences of land use has
certainly increased, but there is no consensus on the strength of this relationship.
Some studies (e.g., Newman and Kenworthy, 1989; Frank and Pivo, 1994; Ewing,
1994; Cervero and Kockelman, 1997;
Meurs and Haaijer, 2001) indicate that various aspects
of land use are linked with travel behaviour,
while others (e.g., Kitamura et al., 1997; Boarnet and Sarmiento, 1996;
Bagley and Mokhtarian, 2002; Schwanen, 2003)
found lower effects or virtually no effect at all. A possible explanation
may be that different research techniques have been applied, and that different
types of explanatory variables were included in the research. Based on a literature
review, a three-fold distinction may be made of this research.
The reverse relationship, the impact of
the transportation system on land use, has less been the subject of research.
Here the research mainly concentrated on accessibility and how it influences
land-use patterns. Hansen’s contribution (1959) on
the impact of accessibility to employment, population and shopping opportunities
may be considered ground-breaking.
The two approaches described above clearly
demonstrate the difficulty of the problem whereby initial focus, type of model,
and methodology used may differ. First, in Section 1, the problem is defined.
Section 2 gives an overview of different methodological approaches applied.
This overview helps to define the gaps in knowledge, presented in Section
3. Finally, suggestions for further research are made in order to attain a
better understanding of the land-use/transportation system.
The spatial distribution of activities,
such as living, working, recreating or education, implies that people have
to travel. Therefore, the land-use configuration is thought to be able to
generate particular travel patterns. Consequently, the theoretical foundation
for the impact of land use on travel behaviour can be found in the theory
of utilitarian travel demand (Lancaster, 1957). This theory postulates that the demand for travel
does not derive its utility from the trip itself, but originates from the
need to reach the locations where activities take place (van Wee, 2002).
This idea seems self-evident, but it remains important to stress the derived
nature of travel demand since this offers opportunities to influence travel
behaviour by designing specific land-use patterns (e.g., land-use patterns
which discourage car use).[1] On the other
hand, changes in the transportation system can alter location decisions of
households and firms, resulting in a land-use change. For instance, an investment
in transportation infrastructure changes the accessibility of a region, which
has an impact on housing values, economic development, and so forth. This
interaction between land use and transportation composes the land-use/transportation
system (LUTS).
(i)
The distribution
of land uses, such as residential, industrial or commercial, over the urban
area determines the locations of human activities, such as living, working,
shopping or education.
(ii)
To overcome the
distance between the locations of human activities spatial interactions or
trips in the transportation system are required.
(iii)
The distribution
of infrastructure in the transportation system allows spatial interactions
and can be measured as accessibility.
(iv)
The distribution
of accessibility in space co-determines location decisions and so results
in changes to land use.
Comparable to Wegener and Fürst (1999), Geurs and Ritsema
van Eck (2001) start their study on accessibility measures
with a description of the LUTS (Figure 2). They consider a similar interaction
between the land-use system and transportation system and go on to define
the concepts of these systems. According to them, the land-use system comprises:
(i) the spatial distribution characteristics of land uses, such as density,
diversity and design; (ii) the locations of human activities; and (iii) the
interaction between land uses and activities. The transportation system comprises:
(i) travel demand, i.e., the volume and characteristics of travel and movement
of goods, (ii) infrastructure supply, i.e., the physical characteristics of
infrastructure (e.g., road capacity, speed limits), the characteristics of
infrastructure use (e.g., distribution of traffic levels over time, the time-table
of public transportation), and the cost and price of infrastructure, vehicles
and fuels; and (iii) the interaction between travel demand and infrastructure
supply.
As with Geurs and Ritsema van Eck (2001),
most researchers define the transportation system as having a transportation
economic background in which supply of and demand for transportation are opposed
to each other (White and Senior, 1983; Cascetta, 2001;
Rodrigue, 2004). However, it is not sufficient simply
to travel demand and supply; the resulting travel behaviour of this interaction
needs to be stressed as well. This behavioural component is found in a definition
put forward by Korsmit and Houthaeve (1995). They distinguished
several ways to describe the transportation system: (i) by the infrastructure
network (e.g., the network of facilities for public transportation); (ii)
by the use of the facilities (e.g., expressed in number and type of movements);
and (iii) by the travel behaviour (e.g., modal choice).
Recently, a more technological approach
to the transportation system is given by Donaghy et al. (2004).
They define a transportation system as “a complex system composed of infrastructure,
logistics and information systems that manage and direct the actual movement
of vehicles, ships and airplanes”. Note that this definition only includes
the supply-side of transportation. Besides traditional infrastructure, such
as roads and train lines, logistics and information systems can also be considered
as infrastructure to guide travel trips.
1. 3 Definitions of the land-use system
Other definitions for the land-use system
than the one given by Geurs and Ritsema van Eck (2001)
are hard to find. A notable exception is made by Rodrigue (2004).
He makes a distinction between urban form, urban (spatial) structure land
use. Urban form refers to “the spatial imprint of an urban transportation
system as well as the adjacent physical infrastructures and activities”, whereas
urban (spatial) structure is defined as “the set of relationships related
to the urban form and its interactions of people, freight and information”.
In this way, urban form is the spatial and visible representation of the urban
structure, which consists of functional relationships. Although these definitions
make a clear distinction between urban form and urban structure, they over-emphasize
transportation. Land uses other than transportation also can influence urban
form and urban structure. According to Rodrigue (2004),
land use is defined as follows: “While the urban form is mostly concerned
by the patterns of nodes and linkages forming the spatial structure of a city,
urban land use involves the nature and level of spatial accumulation of activities.
The nature of land use relates to which activities are taking place, while
the level of spatial accumulation indicates their intensity and concentration.
Most human activities, either economic, social or cultural, imply a multitude
of functions, such as production, consumption and distribution. These functions
are occurring within an activity system where their locations and spatial
accumulation form land uses”.
A large body of literature on several aspects
of the LUTS exists, involving empirical and modelling studies. In this literature
review, the focus is on only empirical studies. Although empirical studies
do not easily lend themselves to establish the causality of relationships,
they have some advantages (Stead, 1999). First, empirical studies are based on real examples
or case studies and rely on fewer assumptions than modelling studies. Second,
they are often easier to interpret and transparent in approach, whereas modelling
studies are often seen as ‘black box’ exercises. Third, empirical studies
provide data for use in the construction or testing of models.
Research studies seldom consider the LUTS
in its totality. Most studies analyze the impact of the land-use system on
travel behaviour, whereas a smaller part of the research is concerned about
the reverse impact. This distinction is used to structure the current review.
Although the review was aimed to be international, most of the reported studies
originate from either the United States or Western Europe. Particular attention
was paid to spatial scale, method of analysis and, most of all, variables
considered.
In spite of the extended body of literature
on the impact of the land-use system on travel behaviour, a three-fold distinction
was found, in accordance with Naess (2003), based on
the type of variables included. Doing so, three dimensions in travel behaviour
research were distinguished: (i) the spatial dimension, (ii) the socio-economic
dimension, and (iii) the behavioural dimension.
2.1.1 The spatial
dimension in travel behaviour research
Hurst (1970) focused
on trip generation of non-residential land uses in the Central Business District
(CBD) in Perth, Scotland, and on some sites outside the CBD. Regression analysis
was used, with city size and density as explaining variables [2]
Within the CBD, higher rates of goods vehicle trip generation were found among
retail and office land uses compared with storage and industrial usage. This
fact, he concluded, reflects a differing relationship between intensity of
land use and travel volume.
A frequently quoted study in this respect
is Newman and Kenworthy (1989), who analysed 32 cities
on four continents. They found a significant negative statistical correlation
between residential density and transportation-related energy consumption
per capita. Their work has become very influential, however, but is not spared
from critism (e.g., Gordon and Richardson, 1989). During the 1990s, Kenworthy et al. (1999)
updated the original data. Cities in the USA, Canada, Australia and Asia were
added to the original dataset. A wide variety of data on land use and transportation
in 1960, 1970, 1980 and 1990 was collected. Recently, the collected data set
was supplemented with data on population, economy and urban structure in 1995.
Gordon et al. (1989) examined the LUTS for several cities in the United
States. The subject of their research was the influence of metropolitan spatial
structure on commuting time by car and public transit. Two regressions were
run, using density, economic structure, urban size, polycentricism, and income
measures as independent variables [3], plus the addition of carpooling in the regression
model for automobile commuting time. Polycentric and dispersed metropolitan
areas were found to facilitate shorter commuting times, and differentiation
among types of densities turned out to be important. Results for public transportation
and automobile were found to be similar. The addition of income in the regression
analysis indicated the limited interest of researchers for socio-economic
variables.
Six neighbourhoods in Palm Beach County,
Florida, were used by Ewing et al. (1994) to examine the impact of density, diversity, accessibility,
and percentage of multifamily dwellings on travel time for work trips and
non-home based trips [4]. Because study
samples were small and differences in travel behaviour could be due solely
to chance, analysis of variance was performed to test for significant differences.
Originally, more travel behaviour aspects were included, but only travel time
seemed to differ significantly across neighbourhoods. Then, those differences
were attempted to be explained by making use of the land-use variables mentioned
before. Households in sprawling suburbs were found to generate almost two-thirds
more vehicle hours of travel per person than comparable households in traditional
neighbourhoods. Additionally, sprawl dwellers were found to compensate for
poor accessibility by linking trips in multipurpose tours.
Since the 1990s, there has been a renewed
interest in the effects of neighbourhood design on travel behaviour. Neo-traditional
neighbourhood design developments received increasing attention as an alternative
community design to standard suburban developments.
Friedman et al. (1994) used data from the San Francisco Bay Area to examine
the relationship between neighbourhood type and modal choice. They distinguished
two neighbourhood types: standard suburban and neo-traditional neighbourhoods.
Despite the description of these types, it remains unclear which land-use
variables were taken into account. Higher total
household trip rates and automobile trip rates were found among residents
of standard suburban neighbourhoods. This difference is explained not only
by different neighbourhood design, but also by substantial income disparity
between both study groups (23%). As with Gordon et al. (1989),
income is the only socio-economic variable included in the research on the
LUTS. Hess et al. (1999) carried out research on pedestrian
volumes in 12 neighbourhoods around small commercial centres in the Puget
Sound Area, United States. The neighbourhoods studied are selected to be similar
in terms of population density, land-use mix and income. But they were also
selected to have very different neighbourhood design as measured by block
size and by the length and completeness of sidewalk systems. Urban neighbourhoods
with small blocks and extensive sidewalk systems were found to have, on average,
three times the pedestrian volumes of suburban sites with large blocks and
short, incomplete sidewalk systems.
Previous studies also compared two distinct
neighbourhood types: neo-traditional and standard suburban neighbourhoods.
However, such a binary categorization oversimplifies reality. McNally and
Kulkarni (1997) developed a methodology for identifying
a range of neighbourhood types by a clustering technique with transportation
network and land-use inputs. Land-use and socio-economic data for 20 neighbourhoods
from Orange County, California, were used. Land-use variables included several
aspects of the transportation network, accessibility measures and density
measures. [5] The only socio-economic
variable included was income, which was considered to be a proxy for the observed
socio-economic differences between the neighbourhood types. The hypothesis
that neighbourhood types display differences in travel behaviour was verified,
but it seems that those differences are explained primarily by income.
While most studies use cross-sectional
data, Krizek (2000) managed to use highly disaggregated
longitudinal data for the Puget Sound Area. This permitted him to carry out
a pre-test/post-test analysis of households’ travel behaviour before and after
they changed residential location. Density, street pattern and land-use mix
were used to explain travel distance (per trip, per tour), travel time (per
trip, per tour) and the percentage trips by transit, by bicycle or on foot.
These dependent variables also are used to gain also insight into trip
chaining. However, few changes in household travel behaviour after a move
were observed, suggesting that attitudes toward travel are more important
than land use.
Note that all of the above mentioned studies
are based on US evidence (except Hurst (1970) focussing on Perth, Scotland). Note also that some
studies stress the need to correct for socio-economic factors, but only a
few do so. Since the mid-1990s, researchers agree on the incorporation of
socio-economic and socio-demographic factors. Due to lack of data, however,
some recent studies still concentrate only on land-use variables. In this respect, Schwanen (2002)
carried out a cross-European comparison of 11 European cities. Three travel
behaviour indicators were examined: commuting distance, commuting time and
modal split for commuting. The effect of density, urban structure and city
size on these three travel behaviour indicators was investigated by variance
and regression analysis. Average commuting distances were found to be strongly
and negatively correlated with population density, while average commuting
time and modal split were associated more with the distribution of employment
and population across the urban area and with urban size.
It appears from these studies that measures
for density, diversity and design were analyzed frequently, mostly in relationship
to modal choice, travel volume and travel time. City size, urban structure
and accessibility are land use variables which were considered less often
to explain travel behaviour.
2.1.2 The socio-economic
dimension in travel behaviour research
Pas (1984) was one
of the first to mention the effect of socio-economic characteristics of travellers
on their daily travel-activity behaviour. He analyzed a much larger range
of socio-demographic variables than had been done before. Land-use variables,
however, were not included in his research.
2.1.2.1 Evidence from the U.S.A.
Frank and Pivo (1994) conducted research in the Puget Sound Area on the census
tract level. They tested the impact of density and diversity on the modal
choice for both work trips and shopping trips. Urban form measures were density
and land-use mix. Control variables included a limited number of socio-economic
variables and mobility constraints.[6] Statistical
methods were selected on the basis of the nature of the hypothetical relationship
being tested. The Pearson correlation was used to test the presence, strength
and nature of the linear relationships between urban form and modal choice.
The presence of a relationship between urban form and modal choice, while
controlling for non-urban form factors was analyzed by regression analysis.
Nonlinear relationships between urban form and modal choice were described
by cross-tabulation. Findings from this research indicated that density and
land-use mix are both related to modal choice, even after controlling for
non-urban form factors. Nevertheless, relationships between modal choice and
land-use mix remained relatively weak. Research at a smaller geographic unit
of analysis is thought to be more useful.
Another study in the Puget Sound Area was
carried out by Krizek (2003). Krizek (2000)
already reported on the changes in travel behaviour after a household had
moved to another residential location. [7] As in his
earlier research, regression analysis was done with density, land-use mix
and street pattern as independent land-use variables. Other travel aspects,
however, were chosen as dependent variables: travel distances (per person,
per vehicle), number of trips per tour and number of tours. As in his earlier
research, attention was paid to trip-chaining behaviour. Most socio-demographic
variables, as well as accessibility, had a statistically significant effect
on the travel changes. Households which had relocated to neighbourhoods with
higher accessibility reduced their vehicle miles travelled and increased the
number of tours.
Because of its widely used 1990 travel
survey, San Francisco Bay Area has become a well studied region. Kockelman
(1997) investigated the influence of urban form on
household vehicle kilometres travelled, automobile ownership and modal choice.
Instead of taking only density into account, which is relative easily to compute,
more complex measures of the built environment focussing on the intensity,
balance and mix of land-uses were used. After controlling for socio-demographic
variables[8], the results of regression analysis illustrated
the significance of measures of accessibility, land-use mixing and land-use
balance, computed for both trip origin and destination.
Cervero and Kockelman (1997)
focused particularly on the effects of density, diversity, and design on trip
rates and mode choice, mainly for non-work trips. The same socio-economic
variables as used by Kockelman (1997) were accounted for. Furthermore, housing tenure and
variables on transportation supply and services were added. [9]
Factor analysis was used to measure the relative influence of each dimension
as well as their collective impacts. Results indicated that density, diversity
and pedestrian-oriented design generally reduce trip rates and encourage non-auto
travel in statistically significant ways, though their influences appear to
be fairly marginal. Thus, it supports the belief of New Urbanism [10] advocates
that compact, diverse and pedestrian-oriented neighbourhoods can influence
travel behaviour.
Other evidence from North American studies
stems from research conducted by Boarnet and Sarmiento (1996) for Southern California,
Rajamani et al. (2003) for Portland, and Zhang (2004)
for Boston. Boarnet and Sarmiento (1996) studied
the demand for non-work travel. Both travel volume and travel distance for
car trips were modelled as a function of land-use and socio-economic variables
near the person’s place of residence[11] . Results of regression analysis showed little
influence of the land-use variables. In their study, the topic of residential
self-selection was dealt with to a limited degree. Residential self-selection
refers to the fact that households with an affinity for a certain travel mode
(e.g., walking or traveling by transit) may choose to reside in a
neighbourhood which facilitates the preferred travel mode (e.g., a high density
neighbourhood with walking or transit facilities). Residential location choice
was modelled as function of workplace location, preferences toward commuting,
non-work travel, and non-transportation location-specific amenities, land-use
characteristics and location-specific amenities which are not related to transportation
(e.g., school quality, municipal fiscal policy). Land-use characteristics
were found to be endogenous to residential location choice. This indicates
a first step into the research of preferences and attitudes toward transportation
and land use.
A GIS-based method was used by Rajamani
et al. (2003) to develop land-use measures at the neighbourhood
level. Whereas other studies used only a handful of simple land-use measures,
Rajamani’s database on the local built environment included a more extensive
set of variables. Land use was described based
on four categories: (i) land-use type and mix; (ii) accessibility; (iii) residential
density; and (iv) local street network. Besides socio-economic variables,
results were also controlled for trip characteristics. [12]
The results of a multinomial logit mode choice model indicated that mixed
uses promote walking behaviour for non-work activities. Locations easily accessible
by bicycle or on foot seemed to encourage walking and cycling for recreational
purposes. The analysis confirms the principles of the New Urbanism: traditional
neighbourhood street design seems to promote walking.
2.1.2.2 Evidence from the U.S.A. compared
to evidence from Europe and Asia
Whereas previous studies report on only
United States evidence, Gorham (2000) and Zhang (2004) compared data
for North American cities with non-American cities.
Gorham (2002) examined
whether similar neighbourhoods in San Francisco and Stockholm have common
travel behaviour characteristics. San Francisco represents a region that has
had minimal planning intervention, whereas Stockholm has had a tradition of
strong urban and regional planning. Neighbourhood type was the only land-use
variable taken into account, to which every respondent was assigned. Socio-economic
variables controlled for were lifecycle and income. A descriptive analysis
illustrated the differences between neighbourhoods according to trip generation,
trip distance, modal choice, trip duration and carbon budgets[13] . This is a larger set of travel aspects examined
before for the San Francisco Bay Area. Only for carbon budgets analysis of
variance (ANOVA) was performed to illustrate the significance of differences
among neighbourhoods. Results of the ANOVA-test suggested that there are similarities
in travel behaviour between equivalent neighbourhood types in the two regions.
Zhang (2004) estimated two sets
of discrete-choice models (for work and non-work trips) to analyse the influence
of land use on modal choice in Boston and Hong Kong. Three classes of explanatory
variables were considered: travel costs (time and monetary), traveller socio-economic
characteristics, and land-use variables. Each set of models contained a base
model and an extended model. The base model included variables typically considered
in the analysis of mode choice (e.g. travel time, costs and traveller socio-economic
variables). In the extended model, land-use variables were added into the
list of independent variables. Results showed that, for both work and nonwork
trip purposes, land-use explained additional variation in modal choice. Travellers
in both cities responded in the same way to costs of travel, personal and
family responsibilities and spatial constraints.
2.1.2.3 Evidence from Europe
Previous studies mainly show evidence from
the United States. However, evidence from Western Europe, especially Great
Britain and the Netherlands was found.
Stead (2001) was
one of the first to introduce socio-economic characteristics in the analysis
of the LUTS in Great Britain. His research concentrated on the impact on travel
distances. Data from national travel surveys and two local travel surveys
(Kent and Leicestershire) were analyzed by two main research methods. First,
multiple regression analysis was applied, allowing identification of the main
socio-economic and land-use variables associated with travel distance. Second,
case studies with similar socio-economic profiles but different land-use patterns
were described [14]. Results
indicated that the variation in travel patterns often owes more to socio-economic
reasons than to land-use characteristics.
Dargay and Hanly (2004) carried out research on the link between land-use variables,
socio-economic variables and modal choice and car ownership.[15] Land use was defined in terms of the characteristics of
the residential location of the individuals. Two logit models were constructed:
(i) a multinomial logit model for mode choice and (ii) a binomial logit model
for car ownership. Unlike Stead (2001), the estimation results strongly supported the importance
of the land-use variables considered on modal choice and car ownership.
Dieleman et al. (2002) explored the determinants of modal choice and travel
distance for different trip purposes by making use of the Netherlands National
Travel Survey. A wider set of purposes, generally used in mobility studies,
was considered: trips to work, for shopping and leisure activities. The residential
environment of the respondents was described by (i) the location of the municipality
within or outside the Randstad, and (ii) the urbanization level of the municipality.
Socio-economic variables were gathered at the disaggregated level of individual
respondents and their households. [16] Multivariate
statistical analyses found an almost equal importance of personal and land-use
characteristics for modal choice and distance travelled. However, these relationships
changed considerably when trip purposes were taken into account. For each
travel purpose, a multinomial logit model was constructed for modal choice,
as well as for travel distance. The three models explaining modal choice showed
the same pattern. Even after compensating for socio-economic variables, the
influence of residential environment on modal choice for work trips remained
high. The modal choice pattern for shopping trips and leisure activities was
found to be more or less the same. A different conclusion was made for distance
travelled. For work and shopping trips, the distance travelled by car depended
mostly on car ownership and income level. However, the model for leisure trips
showed fewer strong relationships and less clear patterns.
As in Dieleman et al. (2002),
Schwanen et al. (2002a) studied the impact of metropolitan structure on commuting
behaviour, especially mode choice, travel distance and travel time by car.
Multilevel regression modelling was used to deal with several levels of analysis,
ranging from the individual worker to the metropolitan region. Several land-use
variables were collected on the level of the metropolitan region and the residential
municipality, whereas more disaggregated data were found on the household
and individual level. [17] The analysis revealed longer commuting distances
and times by car in the majority of polycentric regions when compared to monocentric
regions. Furthermore, a limited set of spatial variables seemed to be useful
in the explanation of the variation in commute behaviour at the more aggregated
levels, whereas the largest part of the variation at the individual level
remained unaccounted for. Therefore, other additional and personal household
attributes are needed which probably relate to job characteristics, housing
tenure and attitudes towards commuting by car.
The greater part of previous studies concentrates
on distance travelled and modal choice. Travel time has received less attention.
Schwanen et al. (2002b) considers this as an unfortunate
oversight as people’s travel decisions are determined by time rather than
by distance. The joint effects of socio-economic and land-use variables were
determined in regression analyses. [18] However,
results needed to be corrected for selectivity bias because the decision to
travel for a trip purpose with a given mode is not unrelated to the decision
regarding to travel time. Therefore, Schwanen et al. constructed two types
of regression models. First, a participation model was used to estimate the
probability that someone travels for a trip purpose (work, shopping, leisure)
by a given mode (car driver, bicycle, walking, bus/tram/metro, train). This
likelihood was then transformed and incorporated in a second model, the substantial
model for travel time. Travel time was found to be influenced by socio-economic
variables and, to a lesser extent, the residential context.
Meurs and Haaijer (2001) tried to contact respondents from a former study (Tijdsbestedingsonderzoek
1990). In this way, a pre-test/post-test analysis could be carried out
in which three groups of respondents were distinguished: (i) those who did
not move, but for whom the spatial situation has
changed, (ii) those who did not move, but for whom the spatial situation has
not changed, and (iii) those who did move. Regression analysis was used to
examine the relationship between the spatial structure and mobility, in general,
and modal choice, in particular. [19]
The results indicated that certain aspects of land use do indeed have an impact
on mobility. These effects are particularly apparent in trips made for shopping
and social or recreational purposes. Commuter traffic, however, is largely
or almost entirely determined by personal characteristics. Modal choice is
influenced to a small degree by spatial characteristics, from about 10% for
car trips to 40% for journeys on foot
Previous studies have examined the direct
effects of urban form characteristics on travel behaviour. However, travel
is considered to be derived from the activities in which individuals and households
participate; thus, it cannot be understood independent of the activities that
cause it. Consequently, Maat and Arentze (2002) carried
out a survey on activity participation in 57 Dutch neighbourhoods. First,
they identified activity patterns based on activity frequency and duration.
Second, they retrieved the influence of the spatial context on these activity
patterns. Seven activity patterns were obtained by cluster analysis. Two different
approaches were used to examine how activity participation varies with land-use
variables, expressed by accessibility, and socio-economic variables. [20] The concept of accessibility was thought to
be useful because it takes into account both transportation costs, such as
distance or time, and the attraction of an activity. First, the effects on
duration and frequency per activity, as well as in total, were examined using
ordinary least square regression. Then, to avoid only testing only
separate effects, the clustered activity patterns were studied using multinomial
logistic regression. Unlike the socio-demographic variables, they found little
evidence that activity patterns vary across spatial characteristics.
Simma and Axhausen (2003)
report one of the few studies on the LUTS in Austria. The aim of their study
was to identify spatial factors which determine, or at least influence, travel
behaviour, especially mode choice for different trip purposes (work, shopping).
This research was carried out for only one province in Austria, but one which
covers a wide range of environmental settings. Land-use and socio-economic
variables were included in a Structural Equation Model (SEM). Socio-economic
variables taken into account were limited because spatial aspects were considered
to be more significant. [21] Nevertheless,
personal characteristics were found to be more important compared to the moderate
effects of the spatial structure.
Modal choice and travel volume remained
the most analyzed travel aspects in the studies cited above. However, travel
distance was a new travel aspect to be explored. Density, diversity and design
remained the most important land-use variables. Measures for design mainly
included aspects of the local street network, or several measures were combined
into a single neighbourhood type. Age, gender, household size, income and
level of education were frequently used socio-economic variables. Several
variables were sometimes combined into household type or lifecycle. Some studies
controlled their results for mobility constraints, which included variables
such as ownership of a car, a driver’s licence or a bus pass; accessibility
or the proximity of transportation networks or parking places.
2.1.3 The behavioural
dimension in travel behaviour research
In a third dimension, lifestyles, perceptions
and attitudes towards land use and travel are accounted for in addition to
the widely used land-use and socio-economic variables. Since the mid-1990s,
there has been some attention to this behavioural component of travel. This
new approach has been undertaken in especially North American and Dutch research.
Handy (1996) was
among the first to mention the importance of perceptions and attitudes towards
land use. She studied the influence of urban form of five neighbourhoods in
Austin, Texas, on pedestrian choices. Socio-economic variables and perceptions
towards urban form characteristics were also taken into consideration. [22] Correlation analysis revealed that individual
motivations and limitations are central to the decision to walk. Urban form
is rather a secondary factor in pedestrian choices. The results suggested
that urban form plays a greater role if the walking trip has a destination.
In this case, the most obvious aspect of urban form is the distance from home
to the destination.
Data from the San Francisco Bay Area remained a source
of inspiration. Kitamura et al. (1997) surveyed five neighbourhoods, which
were selected on the basis of density, diversity and rail transit accessibility.
First, socio-economic and neighbourhood variables were regressed against travel
volume by various modes. The researchers concluded that neighbourhood variables
add significant explanatory power when socio-economic differences are controlled
for. In particular, measures of residential density, accessibility of public
transportation, land-use mix and the presence of sidewalks are significantly
associated with trip generation by mode and modal split. Second, 39 attitude
statements regarding urban life, leisure activities and lifestyles were analyzed
into eight factors. Scores on these factors were introduced in the regression
models mentioned before. Assessment of the relative contribution of neighbourhood,
socio-economic and attitudinal characteristics revealed that each variable
type add some explanatory power to the models. However, the attitudinal variables
explained the highest proportion of the variation in the data.
Bagley and Mokhtarian (2002)
examined travel demand in the same five neighbourhoods as in Kitamura et al.
(1997), using a system of structural equations in which
land-use, socio-economic and attitudinal variables are included. In this way,
they incorporated not only a new set of variables, but they used a new research
technique as well. The survey included questions about attitudes towards several
transportation aspects, and lifestyle was examined using a list of more than
100 types of activities and interests. A nine-equation structural model system
was used as a conceptual model of the interrelationships. The nine endogenous
variables included two measures of residential location type, three measures
of travel demand, three attitudinal measures and one measure of job location.
They concluded that attitudes and lifestyles had much more impact on travel
demand than residential location type.
Schwanen and Mokhtarian published a series
of papers designed to enhance the understanding of the complex relationships
among residential location, commute behaviour and attitudes towards land use
and travel. They focussed on the concept of residential neighbourhood type
dissonance, or mismatch between preferred and actual type of residential location.
The basic question is simple: do mismatched individuals travel more like the
matched residents of the neighbourhoods they actually live in, or more like
the matched residents of the kind of neighbourhood they prefer to live in
? The former outcome suggests that the effects of the built environment outweigh
personal characteristics, the latter outcome suggests the converse. The series
of studies begins by exploring the role of attitudes toward land use and travel
in residential location choice (Schwanen and Mokhtarian, 2005c
Schwanen and Mokhtarian (2004) presented a model for
dissonance as a function of demographic and attitudinal characteristics. The
impact of dissonance on travel behaviour is then studied by three papers.
Non-commute trip frequencies (Schwanen and Mokhtarian, 2003)
and commute mode choice (Schwanen and Mokhtarian, 2005a)
were compared between matched and mismatched urban and suburban residents
and the role of dissonance in mode-specific distances for all purposes was
examined (Schwanen and Mokhtarian, 2005b). All studies
are based on data for three neighbourhoods in the San Francisco Bay Area and
take into account land-use and socio-economic variables, mobility constraints,
personality traits, lifestyle factors and attitudes towards land use and travel.
Preferences for travel modes, especially car and public
transportation, were studied by van Wee et al. (2002). Their research
attempts to answer four questions: (i) are there preferences for modes, (ii)
is there a relationship between preferences and neighbourhood characteristics,
(iii) have preferences for modes played a role in residential choices of households,
and (iv) do preferences for modes add explanatory power to models for travel
behaviour that include land-use, personal and household characteristics ?
Results reveal positive answers to all four questions. Their research was
carried out for three different neighbourhoods in the Dutch city Utrecht.
Neighbourhoods differed only in terms of attractiveness for travel by car,
bicycle or public transportation, whereas differences in household characteristics
and types of dwellings were limited. Techniques for analysis included cross-tabulations,
Chi-square test for significance and multivariate regression.
Whereas most studies point to a higher
significance of attitudes and preferences compared to land-use and socio-economic
variables, Naess (2005) concluded the reverse. Residential
location within the Copenhagen metropolitan area was found to affect travel
behaviour, especially travel volume and modal choice, even after controlling
for socio-economic and attitudinal variables. On average, living in a dense
area close to downtown Copenhagen contributes to less travel, a lower share
of car driving and more trips by bike or on foot. In particular, the length
and travel mode of journeys to work are affected by the location of the dwelling
relative to the city centre of Copenhagen. But also for a number of non-bounded
trip purposes, a centrally located residence facilitates less travel and a
higher share of non-motorized transportation. Furthermore, the respondents
emphasize the possibility to choose among facilities rather than proximity.
In this way, the amount of travel is influenced to a higher extent by the
residential location in relation to concentrations of facilities, rather than
the distance to the closest single facility within a category.
Research on this third dimension of travel
behaviour seems to add significant explanatory power to previous models about
the LUTS. However much counter-evidence exists, the greater part of the research
concludes that attitudes, lifestyles, perceptions and preferences toward land
use and transportation are important explanatory variables. Nevertheless,
this type of research still is in its infancy.
2.2 The impact of the transportation
system on locational decisions
The effects of the transportation system
on location decisions of firms and households are primarily studied by the
concept of ‘accessibility’, e.g., new transportation infrastructure influences
the accessibility of a place, which, in turn, influences location decisions
and land-use patterns.
The earliest of theses studies is the influential
study by Hansen (1959), in which he demonstrated for
Washington, D.C., that locations with good accessibility had a higher chance
of being developed, at a higher density, than remote locations. A similar
conclusion was drawn by Bruinsma and Rietveld (1997).
They performed a correlation analysis to study the strength of the relationship
between the accessibility of Dutch cities and the cities’ valuation as location
sites by firms. This relationship was found to be rather strong. Furthermore,
a regression analysis was carried out to explain this cities’ valuation. Among
the explaining variables were ‘location’, ‘infrastructure’ and ‘accessibility’.
The impact of the cities’ location in the road network on the valuation of
cities as location sites turned out to be considerably important.
Willigers et al. (2002) reviewed studies of the spatial effects of high-speed
rail infrastructure. They concluded that only simple measures for accessibility
have been used so far. Thus, they proposed further research into different
accessibility measures and accessibility as perceived by firms.
Recently, Mikelbank (2004)
analyzed the relationship between smaller road investments made by municipalities
and state departments of transportation and housing values in Columbus, Ohio.
A first database contained information on all single-family detached houses
sold in 1990. A second database included information on all accessibility-changing
road investments since 1978. Results indicated that past, current and future
road investments have distinct and significant impacts on house price.
However, there is also some counter-evidence.
Giuliano and Small (1993) observed that in the Los
Angeles metropolitan area, commuting cost has little impact on residential
location choice. The computed commuting time based on the observed jobs/housing
balance in the region does not compare to the observed commuting time.
Linneker and Spence (1996)
explored the regional development effects of the M25 London orbital motorway.
The M25 has affected levels of accessibility in Britain, which are thought
to influence regional development. They constructed a series of measures of
both regional development (e.g. differential employment shift, index for demand
for labour) and accessibility. Regression analysis also included a number
of other potential explanatory factors, such as industrial structure, congestion,
employment density and labour availability. However, a negative relationship
was found between accessibility and employment change. Areas which are highly
accessible are losing employment and vice versa, thus illustrating two types
of potential effects of improved accessibility. It may facilitate local firms
to expand their market areas by penetrating more distant markets, potentially
increasing employment in the area with improved accessibility.
On the other hand, it may facilitate expansion in the reverse direction
as stronger firms external to the area penetrate the area whose accessibility
has been relatively improved. Thus, any expansionary
developmental effects such as employment growth may occur in areas other than
those in which accessibility has largely been improved.
Remarks on the impact of the transportation
system on land use are made by Miller et al. (1998). Their review of
North American studies included studies
mainly on the impact of light rail, subway and commuter rail lines and stations
on residential density, employment density and property values, among others.
Four main observations could be made: (i) fixed, permanent transit systems
have the most significant effect, (ii) transit’s effects are measurable only
in the long term, (iii) transit’s effects on land and development markets,
not land values, must be considered, and (iv) transportation facilitates development
but does not cause development. Besides the small number of studies, most
of them suffer from methodological problems. In virtually no case did the
study design provide an adequately controlled ‘experiment’ to properly isolate
the impacts of transportation investments from other evolutionary factors
at work in the urban region.
3.1
Gaps in the research of the land-use/transportation
system as a whole
Research studies seldom consider the LUTS in its totality.
A large number of empirical studies on the impact of the land-use system on
travel behaviour exists. However, the reverse direction of impacts, the impact
of the transportation system on land use, has attracted much less attention
from researchers. One reason may be a difference in time scale: travel behaviour
can change easily, while land-use changes occur much more slowly. Thus, research
which wants to capture the impact of transportation changes on land-use patterns
must be carried out on the appropriate moment of time and not within a too
short period after the transportation changes (Miller et al., 1998).
Furthermore, land-use is subject to many other influences other than transportation,
such as population growth, economic development, changes in lifestyles, household
information, consumption patterns and production technology, and are therefore
difficult to isolate (Wegener and Fürst, 1999; Handy,
2002; Martínez, 2002).
3.2
Gaps in the research of the impact of the land-use system
on travel behaviour
Studies of the influence of the land-use system on travel
behaviour mainly focus on travel amount, travel distances and modal choice,
and recently, travel time. More complex aspects of travel behaviour, such
as trip-chaining and point-in-time, scarcely have been investigated. Trips
for different purposes have been examined, although commuting trips have been
the primary focus. Since they take up a large part of our travel behaviour,
recreational, shopping, visiting trips should be looked at more closely. Previous
studies offer a wide range of explanations of land-use and socio-economic
variables, on several scales of analysis. At present, researchers agree on
the inclusion of socio-economic variables. Furthermore, information about
perceptions, attitudes and lifestyles seems to add some explanatory power.
Since this kind of information is hard to find in empirical surveys, stated
preference is thought to be useful. Studies which include information about
attitudes and perceptions, do this either towards land use or either towards
transportation. Almost no studies were found that include this kind of information
for both land use and transportation at the same time.
Furthermore, land-use and socio-economic variables mainly
are observed only at the place of origin. Generally, studies do not take into
account these variables at the place of destination or in the course of the
trip. This fact could be interesting for further research, e.g., the provision
of public transportation at the place of destination can influence the decision
whether to travel by public transportation.
Principal component analysis, factor analysis, cluster
analysis and especially regression models are commonly used statistical techniques
in the research on the LUTS. As more types of variables are to be considered,
techniques must deal with several directions of interrelationships. As Bagley
and Mokhtarian (2002) and van Wee et al. (2002)
pointed out, structural equation models (SEM) can deal with these multiple
relationships, where the same variable that is the outcome (dependent variable)
in one set of relationships may be a predictor of outcomes (independent variable)
in other relationships. Therefore, it seems a useful research technique to
investigate the LUTS in its totality.
3.3
Structural Equation Modelling
SEM is a research technique dating from the 1970s. Most
applications have been in psychology, sociology, the biological sciences,
educational research, political science and market research. Applications
in travel behaviour stems from 1980. Golob (2003) gives
a review of the latter, although applications involving travel behaviour from
the perspective of land use (like Bagley and Mokhtarian, 2002;
Simma and Axhausen, 2003) were not included.
SEM is a confirmatory method guided by prior theories
about the structures to be modeled. As in traditional used regression analysis,
SEM captures the causal influences of the independent (explaining) variables
on the dependent variables. Furthermore, SEM can also be used to measure the
causal influences of independent variables upon one another, which is not
possible with regression analysis. This fact is considered very useful in
order to obtain better insights into the complex nature of travel behaviour.
A SEM can be composed of up to three sets of simultaneous equations (Golob,
2003): (i) a measurement (sub)model for the endogenous (dependent)
variables, (ii) a measurement (sub)model for the exogenous (independent) variables,
and (iii) a structural (sub)model, all of which are estimated simultaneously.
This full model is seldom applied. Generally, one or both measurement models
are dropped. SEM with a measurement and a structural model is known as ‘SEM
with latent variables’, whereas ‘SEM with observed variables’ consists only
of a structural model without any measurement models. Many standard statistical
procedures can be viewed as special cases of SEM. A measurement model alone
equals confirmatory factor analysis. Ordinary regression is the special case
of SEM with one observed endogenous variable and multiple observed exogenous
variables. In general, a SEM can have any number of endogenous and exogenous
variables.
A main reason why SEM is widely used is that it explicitly
takes into account measurement error in the observed variables (both dependent
and independent). In contrast, traditional regression analysis ignores potential
measurement error in all the explanatory variables included in a model. As
a result, regression estimates can be misleading. SEM makes also the distinction
between direct, indirect and total effects. Direct effects are the effects
that go directly from one variable to the target variable. Each direct effect
corresponds to an arrow in a path (flow) diagram. Indirect effects occur between
two variables that are mediated by one or more intervening variables. The
combination of direct and indirect effects determines the total effect of
the explanatory variable on a dependent variable. Advantages of SEM compared
to most other linear-in-parameter statistical methods can be summarized as
follows: (i) treatment of both endogenous and exogenous variables as random
variables with errors of measurement, (ii) latent variables with multiple
indicators, (iii) separation of measurement errors form specification errors,
(iv) test of a model overall rather than coefficients individually, (v) modeling
of mediating variables, (vi) modeling of error-term relationships, (vii) testing
of coefficients across multiple groups in a sample, (viii) modeling of dynamic
phenomena such as habit and inertia, (ix) accounting for missing data, and
(x) handling of non-normal data (Kline, 2005; Golob,
2003; Raykov and Marcoulides, 2000).
But, besides the benefits of SEM, a greater knowledge about the conditions
and assumptions for appropriate usage is required in order to obtain valid
outcomes and conclusions (Chin, 1998).
Theories on the reciprocal relationship between land use and transportation
address changes in locational decisions and travel behaviour of private actors
(households and firms) due to alternations in the transportion and land-use
system. This two-fold relationship is called the land-use/transportation system
(LUTS).
Research studies seldom consider the LUTS in its totality.
A large body of literature exists on the impact of land-use on travel behaviour.
Our literature review revealed
three dimensions in travel behaviour research: (i) a spatial dimension, (ii)
a socio-economic dimension, and (iii) a behavioural dimension. As more types
of variables need to be included in research on the LUTS, research techniques
must be able to deal with more potential relationships among those variables.
Because SEM can model the influences of independent variables upon dependent
variables and influences between independent variables, this research technique
is considered to be helpful in travel behaviour research. In this way, a distinction
can be made between direct effects and indirect effects of the independent
variables upon the dependent variable. Traditionally used techniques, such
as regression analysis, can measure only the direct effects. It can be useful
to compare the results of those traditionally used techniques (e.g., regression
analsyis) and more sophisticated techniques (e.g., SEM).
Evidence is based primarily on U.S. data.
Only from the late 1990s forward, were European studies undertaken, especially
in Great-Britain and the Netherlands. Research indicates that Europeans travel
half as many kilometres, consume half as much energy for transportation, and
emit half as much greenhouse gases as North Americans (Wegener, 2002).
The difference in travel behaviour may be the result of, among other factors,
the existence of a culture of historical cities (most of them dating from
the Middle Ages) and the tradition of spatial planning in Europe. However,
between European countries substantial differences in travel behaviour may
appear. For instance, Belgium’s spatial context differs from its surrounding
countries (e.g., the Netherlands) due to its lack of an established spatial
planning system. From 1998 forward, this lack appeared to diminish with the
approval of the Ruimtelijk Structuurplan Vlaanderen (1998). This plan contains
spatial principles which have been applied previously in other countries.
For instance, in the Netherlands the politics of deconcentrated centralization
(1970s and 1980s), the compact city (1980s and 1990s), and urban renewal (1970s
until 1990s) were already known. Although comparable data sets exist (national
and regional travel surveys, time use survey, and so forth), limited studies
with a Belgian setting could be found. Given its different spatial context,
this limitation is rather surprising.. Thus,
an exploration of Belgian data seems appropriate. Because research on
the behavioural dimension of travel behaviour has only been conducted in the
United States, it is important also to obtain
information about attitudes and preferences towards land use and transportation
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[1] Only
recently, the derived nature of travel has been questioned. Mokhtarian and
Salomon (2001) discuss the phenomenon of “undirected travel”. They hypothesize
that, under some circumstances, travel is desired for its own sake (e.g.,
touring, letting the dog out, balloon flight).
[2] City size was expressed in number of inhabitants, employees and jobs, whereas industrial and commercial floor space are density measures.
[3] Density was measured by residential and (commercial and industrial) employment density; urban size by number of inhabitants and surface; and economic structure included industrial and commercial economic structure.
[4] Density was measured by residential density and employment density; diversity included job-housing ratio.
[5] Transport network was described by number of cul-de-sacs, T- and X-crossroads and access points; accessibility was measured by access to residential, commercial and other land uses; and density was measured by density of single and multiple families, residential density, shopping density, general and office-commercial density, density of services, transportation density, population density and uncommitted density.
[6] Density was measured by residential and employment density. Socio-economic variables included household type, age and employment outside the home; mobility constraints included possession of a driver’s licence or bus pass, number of cars per household and number of cars at destination.
[7] Socio-economic variables included household income, number of vehicles, and number of adults, children and employees per household.
[8] Age, gender, ethnicity, household size, income, full- or part-time employment, professional occupation, car ownership and driver’s licence
[9] Transit service intensity, proximity of public transport and parking places described transportation supply and services.
[10] Supporters of the New Urbanism believe that the right neighbourhood design will encourage walking, thereby encouraging interaction and a greater sense of community, and discouraging automobile dependence (Handy, 1996).
[11] Socio-economic variables included: age, gender, ethnicity, level of education, income and the number of children under age 16 in the household. Population density, percentage of the street grid within a square mile radius of a person’s residence, density of total employment, retail and service employment were used to describe land use.
[12] Socio-economic variables comprised: age, gender, ethnicity, student status, employment status and presence of a physical handicap. Trip characteristics comprised level-of-service variables: travel time and travel cost.
[13] Carbon budgets were defined as “the product of the number of trips an individual makes per day, the distance per trip, the proportion of trips made by different modes, and a carbon emission factor for each of those modes … It represents the amount of carbon released into the atmosphere as the sum of transportation decisions that an individual has taken.” (Gorham, 2002)
[14] Land-use variables included: development density, diversity, distance from the urban centre, settlement size, provision of local facilities, proximity to the main transport network (main road network, railway station) and availability of residential parking. Socio-economic variables included: age, gender, household size and composition, working status, socio-economic status, possession of a driver’s licence.
[15] Land-use variables included: population density, urban size, accessibility to public transport and local amenities (e.g., shops and services). Socio-economic variables included: age, gender, income, household structure and employment status.
[16] Socio-economic variables included: income, household type, education and car ownership.
[17] Polycentrism, job density and development in number of jobs were used as land-use variables. Age, gender, household type, income, education, car availability are considered as socio-economic variables.
[18] City size, residential density, land-use mix and the structure of the urban system are used to identify the residential environment. Age, gender, education, car ownership and income are considered as socio-economic variables.
[19] Land-use included characteristics of the dwelling, the street, the neighbourhood and its position in the total urban area. Results were controlled for socio-economic variables, but, from their report, it remains unclear which variables specifically were accounted for.
[20] Socio-economic variables included age, gender, income, possession of a driver’s licence, availability of a car, number of cars per household (one or two), children < 6 years, children 6-12 years, household size and number of workers per household.
[21] Socio-economic variables included gender, employment status and number of children per household. Land-use was expressed by several accessibility measures, both at the municipality and household level, distance to the district capital, share of farms, working women and commuters, size of shop base and number of work places, supply of public transport and car.
[22] Urban form
was described by the neighbourhood transportation system, the level of public
transport service, the characteristics of residential streets, housing,
neighbourhood commercial areas and types of commercial establishments. Socio-economic
variables included: age, gender, number of inhabitants, average years residing
in the neighbourhood, number of vehicles per household, household size,
children under 12 years, income.