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”.
- Review of
different methodological approaches
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.
2.1
The impact of the land-use system on the transportation
system
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 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,
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. 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.
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.
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.
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,
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. 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 Urbanismadvocates
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.
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. 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.
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.
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. 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. 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.
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. 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. 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.
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. 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. 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.
- Some gaps in our knowledge and how to fill them?
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).
- Conclusions
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
in other cultures.
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