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http://jtlu.org. 4 . 1 [Spring 2011] pp. 2544 doi:
10.5198/jtlu.v4i1.170
New urbanism or metropolitan-level centralization?A comparison
of the inuences of metropolitan-level and neighborhood-level urban
form characteristics ontravel behavior
Petter NssAalborg University, Denmark a
Abstract: Based on a study in the Copenhagen Metropolitan Area,
this paper compares the inuences of macro-level and micro-level
urbanform characteristics on the respondents traveling distance by
car on weekdays. e Copenhagen study shows that metropolitan-scale
urban-structural variables generally exert stronger inuences than
neighborhood-scale built-environment characteristics on the amount
of car travel.In particular, the location of the residence relative
to the main city center of the metropolitan region shows a strong
eect. Some local scalevariables oen described as inuential in the
literature, such as neighborhood street pattern, show no signicant
eect on car travel whenprovisions are made to control for the
location of the dwelling relative to the city center.
Keywords: Residential location; travel; regional accessibility;
centrality; neighborhood characteristics; rationales
1 Introduction
In the United States, research into relationships between
landuse and transport during recent years has been largely
focusedon the inuence of local-scale urban structural conditions
ontravel behavior, comparing traditional suburban residential
ar-eas with areas developed according to the so-called New
Ur-banism or Transit Oriented Development principles (e.g.Cervero
1989; Krizek 2003). Oen, studies comparing thetravel behavior of
residents living in dierent kinds of built en-vironment do not take
the location of the investigated neigh-borhoods into consideration.
For example, among 38 researchstudies reviewed in a recentAmerican
articleCao et al. (2009),only six included variables indicating the
locationof theneigh-borhood relative to the city center, and one of
these stud-ies was actually European. According to Boarnet and
Crane(2001, 49),a relatively limited geographical scale (not
muchmore than a census tract) was, when their bookwas
published,typical of virtually all recent American empirical
research intorelationships between built environment
characteristics andtravel.
In a European context, research into relationships betweenland
use and travel has focused much more strongly on thelocation of the
residence relative to the main metropolitan
[email protected]
center and sub-centers within the metropolitan-scale
spatialstructure. Studies in a number of cities in dierent
Europeanas well as Asian and South American countries have
shownthat residents living close to the city center travel less
than theirouter-area counterparts and carry out a higher proportion
oftheir travel by bicycle or on foot (e.g. Mogridge 1985; Nss2006b;
Nss et al. 1995; Zegras 2010).
Based on a study in the Copenhagen Metropolitan Area,this paper
compares the inuences of macro-level and micro-level urban form
characteristics on respondents traveling dis-tance by car on
weekdays. e main results of the Copen-hagen Metropolitan Area study
have been published else-where Nss (2005, 2006a,b, 2009b) and will
therefore onlybe presented briey here. e same applies to the
theo-retical background and the research methods used. esehave been
described in detail in the above-mentioned pub-lications and in a
separate paper in which the CopenhagenMetropolitan Area study is
used as a reference case for a dis-cussion of the ontological,
epistemological and methodologi-cal basis of research into
relationships between residential lo-cation and travel (Nss 2004).
e present paper concen-trates on a comparison of the eects
ofmetropolitan-scale and
is also includes the issue of residential self-selection, which
has beenexamined in detail in Nss (2009a) and hence will not be a
focus of thepresent paper.
Copyright 2011 Petter Nss.Licensed under the Creative Commons
Attribution NonCommercial License 3.0.
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neighborhood-scale urban form characteristics on the amountof
car travel, and an explanation of why the former variablesturn out
to be more inuential.
In the next section, the theoretical background of the studyis
presented, followed by a section about the case urban
region(Copenhagen Metropolitan Area) and the research methods.e
empirical results are presented in sections 47.
2 Theoretical background
e so-called activity-based approach (Fox 1995; Jones
1990;Vilhelmson 1999) is a useful conceptual framework for
ourstudy. According to this approach, nearly all travel activity
isderived from the need orwish to carry out other, stationary
ac-tivities. Activities are carried out to fulll physiological
needs(eating, sleeping), institutional needs (work, education),
per-sonal obligations (childcare, shopping), and personal
prefer-ences (leisure activities) (Vilhelmson 1999, 178). In
recentyears, this view has been challenged by theorists who
considertravel in late modern society to be increasingly a purpose
in it-self, rather than an instrument to move from one place to
an-other (Steg et al. 2001; Urry 2000). is may be true to
someextent for vacation and leisure trips, but the activity-based
ap-proach remains, inmyopinion, a useful tool for understandingand
analyzing daily travel behavior.
Hgerstrand (1970) distinguishes between three types
oftime-geographical restrictions on human activities: capabil-ity
constraints, coupling constraints, and authority or steer-ing
constraints. Together, the dierent types of restrictionsconstitute
a signicant limitation on peoples use of time andon the spatial
distribution of their activities, in particular forworkforce
participants and pupils on workdays and school-days. Hence, the
scope for free activities on weekdays farfrom home is limited, in
particular for those who do not havea private motor vehicle at
their disposal. erefore, there willbe distance decay in the
attractiveness of facilities (Maddi-son et al. 1996). e impact of a
remote residential locationin terms of longer traveling distances
is therefore likely to becounteracted to some extent by a lower
frequency of trips, atleast for non-compulsory activities.
Based on Vilhelmson (1999, 181), trips can be classiedinto
dierent categories, depending on how xed or exiblethey are in time
and space. Bounded trips are those under-taken in order to reach
activities for which both the time andgeographical location are xed
and cannot freely be deviatedfrom, e.g. journeys to work or school.
According to Vilhelm-son, the majority of trips on weekdays belong
to this category.Non-bounded trips are those where the time of the
activity
is exible and the location may vary. A heterogeneous
inter-mediary group includes trips where the time of the activity
ismore or less xed but the location may vary, and trips wherethe
location is more or less xed but the time may vary. eextent of
space-time xity varies substantially between indi-viduals. For
example, although the journey to work has a highdegree of xity
formost workforce participants, someworkers(e.g. service mechanics
and builders) work at dierent places,and the duration of work at
the same location may also vary.
For some facility types, we almost always choose the
closestfacility, because the various facilities aremore or less
equal (e.g.post oces) or have regulated catchment areas (e.g. local
gov-ernment oces). But for other facilities, qualitative or
sym-bolic dierences within each facility category may cause peo-ple
to travel beyond thenearest facility to amore attractive onefarther
away. For cinemas and a number of other recreationalfacilities,
many types of shops, and not the least workplaces,a number of
features other than proximity are also importantwhen choosing among
facilities.
Despite decentralizing trends, most cities still have a
higherconcentration of workplaces, retail businesses, public
agen-cies, cultural events, and leisure facilities in the
historical ur-ban center and its immediate surroundings than in the
periph-eral parts of the urban area. For residents in the inner and
cen-tral parts of the city, the distances to this concentration of
fa-cilities will be short. Downtown is usually also close to the
ge-ographical center of gravity of theworkplaces and service
facil-ities that are not themselves located to the city center
(Nielsen2002). erefore, the average distance to the peripheral
work-places and facilities will also be shorter for those living
close tothe city center. Local-area densities are usually also
higher inthe inner parts of cities than in the peripheral suburbs.
Witha higher density of residences or workplaces in the local
area,the population base for various types of local service
facilitieswill increase. Hence, the average distance from
residences tolocal services will also be shorter. Inner-city
residents couldthus be expected, on average, to make shorter daily
trips thantheir outer-area counterparts to both local and more
special-ized facilities, and a high proportion of destinations
might beeasily reached on foot or by bicycle.
A large number of empirical studies conducted during thelast
couple of decades have concluded that the amount oftravel and the
proportion travel by car are higher amongsuburbanites than among
inner-city residents. is relation-ship holds true when controlling
for demographic and so-cioeconomic variables, and also in the cases
where controlhas been made for transport attitudes or residential
prefer-ences (Fourchier 1998; Mogridge 1985; Newman and Ken-
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New urbanism or metropolitan-level centralization?
worthy 1989; Nielsen 2002; Nss 2006b, 2010; Nss andJensen 2004;
Nss et al. 1995; Schwanen et al. 2001; SteadandMarshall 2001;
Zegras 2010).
Local-scale urban design principlessuch as street
pattern,availability of sidewalks and bicycle paths, etc.as well as
aes-thetic neighborhood qualities can inuence the attractivenessof
dierent travel modes and can for some travel purposes alsoaect trip
destinations. As mentioned above, interest in suchcharacteristics
has been at the core of American studies of theinuence of the built
environment on travel behavior. Forexample, in their inuential book
Travel by Design, Boarnetand Crane (2001, 37) mention the following
six urban fea-tures as urban form and land use measures that might
inu-ence travel behavior: density (residential or employment,
ormore complex accessibility measures); extent of land use mix-ing;
trac calming; street pattern; jobs-housing balance (orland use
balance); and pedestrian features such as the avail-ability of
sidewalks. Handy et al. (1998) point to the fact thatmany
neighborhood-scale studies focus on non-work trips, es-pecially
shopping, and how built environment characteristicscan potentially
reduce car travelpartly by encouragingwalk-ing as a substitute for
driving, and partly by facilitating shortercar trips than would be
necessary if a certain facility type werenot available in the
neighborhood. e built-environmentfeatures that can encourage
walking include objective factors(e.g., how close the facility is
to the dwelling, or the avail-ability of sidewalks) as well as more
subjective factors (e.g.how pleasant and safe the walking route is
perceived to be).Mixed land use is generally considered more
conducive to re-ducing car dependency than monofunctional
residential zon-ing. is includes local employment opportunities;
the con-cept of local jobs-housing balance has been a prevailing
plan-ning ideal since the 1970s (Cervero 1989; Roundtable
2008;Weitz 2003).
It is generally assumed that the greater the number of
desti-nation choices available within the neighborhood, the
greaterthe likelihood that a destinationwithin the
neighborhoodwillbe selected. However, asHandy et al. (1998, 10)
point out, theavailability of a greater number of choices outside
the neigh-borhood, may also increase the likelihood that a
destinationoutside the neighborhood will be selected.
Empirical studies suggest that neighborhood characteristicssuch
as higher residential densities and the presence of mixedland uses
do promote walking as a travel mode in connec-tion with non-work
activities (see Boarnet and Crane 2001;Chatman 2005; Handy et al.
2005; Handy and Clion 2001;Handy et al.1998;Rajamani et al.2003). e
impacts in termsof reduced vehiclemiles traveled are, however,
generally found
to be quitemoderate. Moreover, inmany of the studies of
rela-tionships between local built-environment characteristics
andtravel behavior, no eort has been made to control for the
lo-cation of these neighborhoods within the metropolitan
struc-ture, i.e. in relation tomajor concentrations of workplaces
andservices. To some extent, a higher local jobs-housing balancehas
been found to reduce commuting distances among the res-idents of
the areas where new jobs have been established andamong the
employees of businesses in the areas where newhousing has been
added (Frank and Pivo 1994; Nowlan andStewart 1991). However, this
does not necessarily mean thathigher local jobs-housing balances
have reduced commutingdistances at a metropolitan scale. Employment
growth in pre-dominantly residential suburbsmay result in longer
commutesfor those employees who are not local residents. If the
work-places in question are specialized and recruit employees froma
wide catchment area, this eect may well outweigh any re-duction in
commuting distances among the local residents.
3 Case area andmethods
e Copenhagen Metropolitan Area (population: approxi-mately 1.8
million) is one of the largest urban areas in north-ern Europe and
a major node for international air and railtransport. Although it
now encompasses several smaller citiesthat previously were largely
autonomous, the CopenhagenMetropolitan Area is today a conurbation
functioning largelyas a single city and as a continuous job and
housing market.
According to some authors, historical urban cores have lostmuch
of their dominant position during the past 30 to 40years. For
example, Sieverts (1999) holds that cities can nolonger be tted
into a hierarchic system according to cen-tral place theory and
should, instead, be understood as net-works of nodes, where there
is a spatially more or less equal,scattered distribution of labor
with spatial-functional special-izations. Such net-like cities or
city regions have polycen-tric rather than monocentric or
hierarchic center structures.However, the Copenhagen Metropolitan
Area does not tthis description (which may also be of limited
validity in awider European context; cf. Newman and Kenworthy
1989;Nielsen and Hovgesen 2006). e inner city of Copenhagenstill
has an unchallenged status as the dominant center of thecity
region. e central municipalities of Copenhagen andFrederiksberg,
making up only 3.4 percent of the total area ofthe Copenhagen
Metropolitan Area, are home to a third ofthe inhabitants and an
even higher proportion of the work-places. is implies that people
whose residences are locatedclose to downtown Copenhagen travel, on
average, consider-
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ably shorter distances to most facility types than those
whoreside in the outer suburbs (Nss 2006b). e center struc-ture of
Copenhagen Metropolitan Area could be character-ized as hierarchic,
with downtown Copenhagen as the maincenter, the central parts of ve
formerly independent periph-eral towns now engulfed by the major
conurbation as second-order centers along with certain other
concentrations of re-gionally oriented retail stores, and more
local center forma-tions in connection with, among others, urban
rail stationsand smaller-size municipal centers at a third
level.
e study was carried out using a combination of qualita-tive and
quantitative research methods. Apart from a regis-tration of urban
structural conditions, the data collection in-cluded a large travel
survey among inhabitants of 29 residentialareas (1932 respondents),
a more detailed travel diary investi-gation among some of the
participants of the rst survey (273respondents), and qualitative
interviews with 17 households.e questionnaire surveys and the
interviews were all carriedout during the period from June to
September 2001, avoidingthe main holiday month of July. e chosen
residential areas(Figure 1) include a wide range of urban
structural situations,varying in their location relative to
downtown Copenhagenand lower-order centers, as well as in their
density, composi-tion of housing types and availability of local
green areas.
e qualitative interviews were (apart from a single
case)conducted in the homes of the interviewees, usually
lastingbetween 90 minutes and two hours. Nine interviewees
werechosen from three inner-city areas (C1, C2, and C4 in Fig-ure
1) and eight were recruited from two outer-suburban ar-eas. Among
the latter areas, one is located close to an urbanrail station
(La2) whereas the other suburban interviewee area(S4) has very poor
accessibility by public transport. All in-terviewswere
audio-recorded and subsequently transcribed intheir entirety. e
interviews were semi-structured, focusingon the interviewees
reasons for choosing activities and theirlocations, travel modes
and routes, as well as the meaning at-tached to living in or
visiting various parts of the city. As animportant tool for the
analysis an interpretation scheme wasdeveloped. By requiring the
research team to make writteninterpretations of each interview in
light of each of the de-tailed research questions, the
interpretation scheme made usread and penetrate the transcribed
interview texts more thor-oughly than we would probably have done
otherwise.
e questionnaires included questions about respondentstravel
behavior, activity participation, socioeconomic charac-teristics,
residential preferences, and attitudes to transport
andenvironmental issues. e main survey included questionsabout the
distances traveled via each mode on each day over
the course of one week. e travel diary investigation cov-ered
the four-day period from Saturday morning to Tuesdaynight, and
included detailed questions about each trip made(purpose, length,
travel time, and travel mode). e concen-tration of respondents in a
limited number of selected loca-tions allowed for an in-depth
accounting of contextual condi-tions in each of the chosen areas.
is enabled us to record alarge number of urban structural
characteristics of each resi-dential address within the selected
areas and to include thesecharacteristics as variables in the
investigation. In total, 38 ur-ban structural variables were
measured for each respondent,includingve variablesmeasuring the
locationof the residencerelative to theoverall center structure in
themetropolitan area,eleven variables indicating the location of
the residence in re-lation to rail-bound public transport, seven
variables measur-ing the density of the local area and the
neighborhood, twelvevariables measuring dierent aspects of service
facility accessi-bility in the proximity of the dwelling, two
variables measur-ing the availability of local green recreational
areas, and onevariable indicating the type of local street
structure.
In addition to the urban structural variables, a number
ofindividual characteristics of the respondentswere recorded. Inthe
subsequentmultivariate analyses, 17 demographic, socioe-conomic,
attitudinal, and other non-urban-structural vari-ables were
included as control variables:
1. Four variables describing demographic characteristics ofthe
respondent and the household to which he or shebelongs (sex, age,
number of household members belowseven years of age, number aged
717 years)
2. Seven variables describing socioeconomic characteris-tics of
the respondent (workforce participation, stu-dent/pupil, pensioner,
personal income, drivers license,two dichotomous variables for type
of education)
3. One attitudinal variable (index for transport-related
res-idential preferences)
4. Five other control variables indicating particular
activ-ities, obligations or social relations likely to
inuencetravel behavior during the period of detailed travel
reg-istration.
I consider these control variables to be the most relevant among
thoserecorded. Analyses have also been carried out with a larger
number of con-trol variables. e eects of the main urban-structural
variables remainfairly stable across these various analyses. See
Nss (2009a) for a discussionof the appropriateness of dierent
control variables in studies of relation-ships between land use and
travel.
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New urbanism or metropolitan-level centralization? Figur 1
Omtrentlig lokalisering af de planlagte undersgelsesomrder, vist
med bl cirkler.
P1
T5
T4
T1T3
C5 C1 C6
H1
S1
C2
Li1
Li2
La2La1
C4P2
S2
C3R1
R3
R4
R5
R6
S3
T6
S4
T2
R2
Figure 1: Location of the 29 investigated residential areas.
Scale 1:750 000.
A large number of travel behavior indicators were recordedfor
each respondent: total traveling distance, traveling dis-tances by
car, bus, train, and non-motorized modes, and theproportions of the
total distance traveled by car, public trans-port, and
non-motorized modes. Each of these eight aspectsof travel behavior
was recorded for the weekdays (Monday-Friday), the weekend
(Saturday-Sunday) and for the week asa whole, resulting in a total
of 24 travel behavior variables.In addition, annual driving
distances of any cars belonging tothe household were recorded, as
well as ights and other long-distance holiday trips. Due to space
constraints, in the follow-ing we shall concentrate on a single
travel behavior variable:the distance traveled by car on weekdays.
However, the rel-
ative strengths of the inuences of dierent urban
structuralvariables on the remaining travel behavior variables are
fairlysimilar to the strength of their inuence on traveling
distanceby car on weekdays (Nss and Jensen 2005, 353-371).
First, simple bivariate correlations between traveling dis-tance
by car and each urban structural variable will be shown.Second,
results of analyses where each of these relationshipshas been
controlled for the inuences of the 17 non-urban-structural
variables as well as the location of the dwelling rela-tive to the
city center ofCopenhagenwill be presented. ere-upon, material from
our qualitative interviews will be usedin order to explain why
metropolitan-level urban structuralvariables exert a stronger
inuence on travel behavior than
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neighborhood-scale urban characteristics do. Finally, the
re-sults of an analysis including only the fourmost inuential
ur-ban structural variables and the 17 non-urban-structural
con-trol variables will be presented.
4 Bivariate correlations
As mentioned in the introduction, neighborhood-scale
streetpattern is an urban structural variable oen used in
Americanstudies investigating relationships between urban built
envi-ronment and travel behavior. Compared to the curvilinearand
cul-de-sac street patterns typical for suburban neighbor-hoods
planned according tomodernist principles, grid-shapedstreet
networks facilitate more direct access to local destina-tions and
can thus bring a larger number of local facilitieswithin acceptable
walking (or biking) distance (Handy et al.1998). Street patterns in
the neighborhood were recordedin the Copenhagen Metropolitan Area
too. Among the 29investigated residential areas, nine were located
in neighbor-hoods characterized by a (more or less) grid-shaped
street pat-tern, whereas the remaining 20 residential areas were
locatedin neighborhoods characterized by other kinds of street
pat-terns (curvilinear streets or some sort of hierarchical street
lay-out based on recommended driving speeds, with cul-de-sacaccess
roads and separation of motorized and non-motorizedtrac). Figure 2
shows an example of investigated neighbor-hoods with street
patterns belonging to the grid and the non-grid categories.
In line with what has been found in several American stud-ies
(e.g. Cervero 2003) (e.g. Cervero, 2003; Frank, 2003), theamount of
car travel is lower among residents living in neigh-borhoods
characterized by grid-shaped than by other types ofstreet patterns.
As can be seen in Figure 3, mean traveling dis-tance by car during
the weekdays from Monday through Fri-day is only 86 km among the
respondents living in a neigh-borhoodwith grid-shaped street
pattern, compared to 153 kmamong the respondents living in local
areas with other types ofstreet
However, the correlation between street pattern andamount of car
travel does notnecessarily reect any causal rela-tionship.
Admittedly, grid-like street patternsmay oer betterconnectivity
between dierent locations within the neighbor-hood and may thus
facilitate shorter local traveling distances,especially compared to
cul-de-sac-based street patterns. eshorter local traveling
distancesmay also be conducive to non-motorized travel, since
people who rely on their own musclesfor transportation are usually
sensitive to travel distance andoen change to motorized modes if
the distance exceeds a
Figure 2: Examples of investigated residential areas with grid
andnon-grid street patterns. Amager North (top) andHolmene
(bottom).
comfortable level. Nevertheless, the dierences in
travelingdistances resulting from the local street patterns are
probablynot very large, since the investigated neighborhoods are
them-selves of a limited size. Arguably, the location of the
neigh-borhoods relative to concentrations of workplaces and
otherfacilities matters more. Figures 4 and 5 provide some
prelimi-nary indication of this. Here, the respondents have been
sub-divided into four groups of approximately equal size,
depend-ing on how far from the Copenhagen city center they live.
Aswe can see from Figure 4, the respondents living far from thecity
center travel, on average, considerably farther by car thantheir
counterparts living in the inner distance belts, especiallythose
living less than six kilometers away from the city cen-ter. Among
the latter group of respondents, the mean trav-eling distance by
car over the ve weekdays is 66 km, com-pared to 176 km among
respondents living more than 28 kmaway from the city center of
Copenhagen. Figure 5 showsmean trip distances for journeys to
work/education as well
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New urbanism or metropolitan-level centralization?
Street pattern in the neighborhood of theresidence
Non-grid street patternGrid street pattern
Mean
trave
ldist
ance
byca
rMon
day-
Frida
y(km
)
200
150
100
50
0
Figure 3:Mean traveling distances by car during the
weekdays(Monday-Friday) among respondents living in
residentialareas with grid and non-grid street patterns. N = 1810,
ofwhich 603 in grid-type and 1207 in other street
environ-ments.
as several other trip purposes. e same pattern as for over-all
traveling distances by car are evident for the various cate-gories
of trips: suburbanites tend to make longer trips thaninner-city
dwellers do. On average, our respondents (includ-ing both workforce
participants and non-participants) made3.4 journeys to work or
education during the week, 3.5 shop-ping/errand trips, 1.0 trip for
bringing/picking up children,1.3 visiting trips and 2.4 leisure
trips. Since journeys to workare, on average, longer than trips for
any of the other purposes,these gures imply thatmuch of the
dierence between inner-city dwellers and suburbanites in overall
traveling distances(and traveling distances by car) is attributable
to the longercommuting distances among respondents living in the
outerparts of the metropolitan area.
Table 1 shows the bivariate relationships of each of the 38urban
structural variables with the respondents distance trav-eled by car
on weekdays, as well as partial correlations of eachurban
structural variable with the distance traveled by car,
Trips longer than 100 km have been excluded. Trip distances for
jour-neys to work are based on workplace locations given by
workforce partic-ipants who responded to the main survey, with
distances measured alongthe road network by means of the GIS
program ArcView. Trip distancesfor other purposes are based on the
travel diary survey (273 respondents).e gures displayed in the
diagram for non-work trips are weighted aver-ages of trip lengths
during weekdays and weekends.
Trips home from any of these destinations are not included in
thesegures.
Distance from the dwelling to the city center ofCopenhagen
(km)
over 2815 - 286 - 15below 6
Mean
trave
lingd
istan
ceby
carM
onda
y-Frida
y(km
)
200
150
100
50
0
Figure 4:Mean weekday (Monday-Friday) traveling distances by
caramong respondents living within dierent distance inter-vals from
the city center of Copenhagen. N = 1810, vary-ing from 405 to 530
per distance belt.
Below 6 6 to 15 15 to 28 Over 280
5
10
15
20
25
sub-title
Journey to work (N = 1234) Shopping/errand (N =
585)Bringing/picking up children (N = 123) Visiting trips (N =
264)Leisure trips (N = 421)
Distance from dwelling to the city center of Copenhagen (km)
Mea
n tr
ip le
ngth
(km
)
Figure 5:Mean one-way trip lengths for dierent purposes
amongrespondents living within dierent distance intervals fromthe
city center of Copenhagen.
controlling for the location of the dwelling relative to the
citycenter ofCopenhagen and17non-urban-structural variables.
ese control variables are: sex, age, number of children younger
thanseven years of age in the household, number of children aged
717 in thehousehold, personal income, possession of a drivers
license for car, whetheror not the respondent is aworkforce
participant, whether or not the respon-dent is a student, whether
or not the respondent is a pensioner, whether or
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.
Table 1: Relationships between various metropolitan-scale and
neighborhood-scale urban structural variables and the respondents
travelingdistance by car on weekdays (Monday- Friday).
Bivariatecorrelations(Pearsons r)
Partialcorrelations
Location of the dwelling relative to the metropolitan-level
center structure:Location of the dwelling relative to downtown
Copenhagen (non-linear function) 0.233***Linear distance along the
road network from the dwelling to downtown Copenhagen
0.201***Logarithmic dist. along the road network from the dwelling
to the closest second-order center 0.210*** 0.092***Linear dist
along the road network from the dwelling to the closest
second-order center 0.177*** 0.056*Logarithmic dist. along the road
network from the dwelling to the closest regional shopping mall
0.166*** 0.019Location of the dwelling relative to rail
stations:Logarithmic dist. along the road network from the dwelling
to the closest urban rail station 0.199*** 0.072**Linear dist.
along the road network from the dwelling to the closest urban rail
station 0.164*** 0.062*Linear dist. along the road network from the
dwelling to the closest well-serviced junction station 0.159***
0.027Linear dist. along the road network from the dwelling to the
closest junction station 0.151*** 0.053*Linear dist. along the road
network from the dwelling to any rail station 0.169***
0.097***Residential location less than 500 m away from closest
urban rail station (yes = 1, no = 0) 0.094*** 0.019Residential
location less than 1000 m away from closest urban rail station (yes
= 1, no = 0) 0.124*** 0.016Residential location less than 500 m
away from any rail station (yes = 1, no = 0) 0.089***
0.022Residential location less than 1000 m away from any rail
station (yes = 1, no = 0) 0.102*** 0.007Residential location less
than 500 m away from closest junction station (yes = 1, no = 0)
0.113*** 0.034Residential location less than 1000 m away from
closest junction station (yes = 1, no = 0) 0.117*** 0.036Density in
the surroundings of the dwelling:Density of inhabitants & jobs
in the local area of the dwelling (inhab.+jobs within a radius of
800 m) 0.203*** 0.071**Population density in the local area of the
dwelling 0.191*** 0.048Job density in the local area of the
dwelling 0.188*** 0.075**Density of inhabitants and jobs in the
narrowly demarcated residential area 0.231*** 0.053*Population
density in the narrowly demarcated residential area 0.234***
0.054*Dwellings per hectare in the narrowly demarcated residential
area 0.230*** 0.050*Job density in the narrowly demarcated
residential area 0.120*** 0.019Availability of service facilities
in the proximity of the dwelling:Combined index for availability of
service facilities in the proximity of the dwelling 0.234***
0.107***Index for availability of shopping opportunities in the
proximity of the dwelling 0.217*** 0.095***index for availability
of primary schools, kindergartens and crches in the proximity of
the dwelling 0.192*** 0.084***index for availability of
public-sector oces in the proximity of the dwelling 0.175***
0.075**Number of grocery stores within 1.5 km distance from the
dwelling 0.204*** 0.077**Number of special commodity stores within
1.5 km distance from the dwelling 0.171*** 0.065*Linear dist. along
the road network from the dwelling to the closest grocery store
0.164*** 0.075**Linear dist. along the road network from the
dwelling to the closest post oce 0.122*** 0.044Linear dist. along
the road network from the dwelling to the closest town hall
0.160*** 0.070**Linear dist. along the road network from the
dwelling to the closest primary school 0.156*** 0.065*Linear dist.
along the road network from the dwelling to the closest
kindergarten 0.139*** 0.01Linear dist. along the road network from
the dwelling to the closest crche 0.233*** 0.144***Local green
recreational areas:Availability of a recreational area of at least
10 hectares within 0.5 km distance from the dwelling 0.041
0.043Availability of a recreational area of at least 10 hectares
within 1 km distance from the dwelling 0.041 0.042Local street
pattern:Grid structure (1) or other street patterns (0) 0.189***
0.004
-
New urbanism or metropolitan-level centralization?
All the bivariate correlation coecients have the expectedsigns:
Travelingdistances by car arehigher among respondentsliving:
peripherally in relation to various types of centers in thecenter
structure of Copenhagen Metropolitan Area; in areaswith a modest
supply of local facilities; far away from urbanrail stations; and
in areas where population density and work-place density are low.
ere is also a (rather weak) tendencytoward increased car travel
among respondents who have pooraccess to local green recreational
areas.
In line with the impression le by Figure 4, we nd a quitestrong
bivariate correlation between distance traveled by carand location
of the dwelling relative to the city center ofCopenhagen,
especially when the distance to the city center istransformed by
means of a non-linear function (r = 0.233).Some local-scale urban
structural variables also show strongcorrelations with the amount
of car travel, notably densitieswithin the narrowly demarcated
residential area, measured ei-ther as population density (r
=0.234), density of dwellings(r = 0.230) or as a combined
population and job densityvariable (r = 0.231). ere are also
correlations of similarstrength between the amount of car travel
and, respectively,the distance from the dwelling and the distance
to the closestcrche provider (r = 0.233) and a combined index for
avail-
not the respondent has completed advanced studies (more than six
years fol-lowing the standard nine years of primary school) in a
technical eld suchas engineering or economics, whether or not the
respondent has a short ormedium-long education (less than six years
following primary school) asa tradesman or industrial worker,
transport-related residential preferences,whether or not the
respondent hasmoved to the present dwellingwithin theprevious ve
years, employment-related trips during the investigated
week,overnight stays away from home more than three nights, number
of days atthe workplace or school during the investigatedweek, and
regular transportof children to school or kindergarten.
e city center was dened as the City Hall Square. Based on
the-oretical considerations as well as preliminary analyses of the
empiricaldata, the location of the residence relative to the city
center of Copen-hagen was measured by means of a variable
constructed by transform-ing the linear distance along the road
network by means of a non-linearfunction. is function was composed
of a hyperbolic tangential func-tion and a quadratic function,
calculated from the following equation:AFSTFUN = ((EXP(centafs0.18
2.85)) EXP( (centafs0.18 2.85))) / (EXP(centafs0.18 2.85) + EXP(
(centafs0.18 2.85))) (0.00068( centafs 42)(centafs 42) 2.8) where
ASTFUN = thetransformed distance from the dwelling to the city
center and centafs = thelinear distance along the road network,
measured in km.
Crche describes childcare for children under the age of three
years.
ability of service facilities in the proximity of the dwelling(r
=0.234).
As can be seen in Table 1, all urban structural variablesshow
statistically signicant bivariate relationships with theamount of
car travel (p < 0.001) except the two variablesindicating
availability of green outdoor areas in the proxim-ity of the
dwelling. Local street pattern (see Figure 4) showsa fairly strong
bivariate correlation with distance traveled bycar (r = 0.189), but
not as strong as with the location ofthe dwelling relative to the
city center of Copenhagen. Cartravel is also quite strongly
correlated with logarithmic dis-tance from the dwelling to the
closest second-order center,local-area density of inhabitants and
jobs, and logarithmic dis-tance from the dwelling to the closest
urban rail station.
5 Controlling for non-urban-structural variablesand the location
of the dwelling relative to thecity center of Copenhagen
Controlling for the location of the dwelling relative to the
citycenter of Copenhagen as well as for the
non-urban-structuralvariables (Table 1, right column) considerably
weakens thecorrelations of many of the neighborhood-scale urban
struc-tural variableswith car travel. is is particularly true for
streetstructure; the correlation between it and the amount of
cartravel becomes insignicant when we control for residential
e service index was constructed as a weighted sum of z-scores
forthree other indices also included in the analysis: an index for
availability ofshopping opportunities near the residence, an index
for availability of pri-mary schools, kindergartens, and crches in
the environs of the dwelling,and an index for availability of
public-sector oces in the proximity of thedwelling. e weighting
between the sub-indices was based on data fromNorwegian (Vibe 1993,
35) and Danish (Christensen 1996, 9) nationaltravel surveys on the
frequencies of dierent trip purposes. Given a weightsum of 100,
this implied the following weights: Shopping opportunities,60;
schools, kindergartens and crches, 27; and public oces, 13. e
threelatter indices were constructed as follows: e shopping
opportunity in-dex was constructed by adding the z-scores for
number of grocery storeswithin 1.5 km of the dwelling, number of
special commodity stores within1.5 kmof the dwelling, anddistance
from the dwelling to the closest grocerystore (with the sign of the
z-score changed for the latter factor in order tomake a high index
value signify a high accessibility for all the sub-elementsof the
index). e index for availability of primary schools,
kindergartensand crches was based on the measured distances from
the dwellings to theclosest facilities in these categories, with a
weighting based on considera-tions about the number of years the
children spent in each type of institu-tion and the propensity of
parents to follow their children to the varioustypes of
institutions. Again, the sign of the index value was changed in
or-der tomake a high index value signify high accessibility. e
index for avail-ability of public-sector oces was constructed in a
similar way, based on theassumption that residents go to the town
hall to make use of public servicesabout as oen as they visit the
post oce.
-
.
location relative to the city center. In this study, all the
inves-tigated residential areas having a grid-like street pattern
are lo-cated in the inner part of theCopenhagenMetropolitanArea,no
more than 9 km from downtown. e correlations of cartravel with the
location of the dwelling relative to lower-ordercenters as well as
with the various local-area and residential-area density variables
are alsoweakened, but these correlationsare still statistically
signicant, at least when density is mea-sured within a local area
larger than the narrowly demarcatedresidential area.
e weakened correlation coecients of severalneighborhood-scale
variables reect the fact that thereare considerable internal
correlations between the dierenturban structural variables.
Population densities, workplacedensities, accessibility to public
transport, and availabilityof service facilities near the residence
are generally higherthe closer to the city center of Copenhagen the
residenceis located (see Table 2). Many of the bivariate
correlationsbetween neighborhood-scale urban structural variables
andcar travel are therefore attributable to the location of
theseneighborhoodswithin the overall urban structure, rather thanto
the proximity of each residence to local service facilities.
e various service index variables also have statistically
sig-nicant correlations with the amount of car travel when
non-urban-structural variables and the location of the dwelling
rel-ative to the city center are taken into account. e correla-tion
between car travel and the distance from the dwelling tothe closest
crche is especially strong. However, proximity toa crche is hardly
an important determinant of the amount ofcar travel among the
respondents. Only aminority among therespondents follow children to
and fromcrches, and the aver-age length of such trips is
considerably shorter than, for exam-ple, journeys to work. Rather,
this variable serves as a proxyfor other urban-structural
conditions, notably proximity to asecond-order or third-order
center wheremany other facilitiesthan crches are available.
Moreover, as there is a strong cor-relation between the distance
from the dwelling to the clos-est crche and the distance to the
city center, the former vari-able may steal some of the eect of the
latter variable whenboth are included in the same analysis. ese two
variablesare equally strongly correlated with the amount of car
travelwhen control is made for non-urban-structural variables,
andthe theory-blind way that the statistical soware used in
this
Among the respondents of our main survey, 62 percent reported
tripsto a workplace or place of education at least four times
during the week ofinvestigation, whereas only 12 percent reported
regularly bringing childrento and from kindergarten, childcare, or
school. In addition, mean commut-ing distances are more than four
times as long as trips from home to kinder-garten/crche.
research carries out the multivariate calculations implies
thatthere is always a risk that a theoretically less-well- founded
vari-able may mask some of the relationship between a
dependentvariable and a theoretically more plausible independent
vari-able.
Needless to say, the location of the dwelling relative to
thecity center of Copenhagen has not been included in the ta-ble,
as this is one of the control variables. However, if we in-stead
use the variable showing the second-strongest relation-shipwith car
travel when controlling for non-urban-structuralvariables (i.e. the
combined index for availability of service fa-cilities in the
proximity of the dwelling) as the urban struc-tural control
variable, the correlation coecient between theamount of car travel
and the location of the dwelling relativeto the city center of
Copenhagen is still as high as 0.132, witha level of signicance of
0.000.
With the exception of proximity to crches, the
local-scaleurban-structural variables tend to become less strongly
corre-lated with car travel when the analysis includes control for
thelocation of the dwelling relative to the city center of
Copen-hagen. In the next section, we shall draw on qualitative
inter-views data that may help explain why this is so. We also
note(in the right column of Table 1) that some of the service
in-dex variables are more strongly correlated to car travel is
morestrongly correlated to some of the service index variables
thanto local area density or to the location of the dwelling
relativeto second-order centers. In the analyses presented in
Section5, we have still preferred to include the latter variables
insteadof any of the service indexes. is is because we believe
thatthe service indexes act, to some extent, as proxy variables
forthe latter variableswhich are, in our view, more appropriateas
the centers include not only concentrations of service fa-cilities
but also concentrations of workplaces, and oer highaccessibility by
public transport.
6 Rationales inuencing travel behavior
Why does travel behavior in the Copenhagen MetropolitanArea
depend more on metropolitan-scale than on local-scalebuilt
environment characteristics? Material from the qual-itative
interviews illuminates some important rationales onwhich people
base their travel behavior. e relative impor-tance of
metropolitan-scale and neighborhood-scale built en-vironment
characteristics to travel behavior depends, in par-ticular, on
peoples rationales for choosing the locations of theactivities in
which they participate.
e interviewees choices of locations for their activities aremade
as a compromise between two competing desires: the
-
New urbanism or metropolitan-level centralization?
Table 2:Mean values for some urban structural characteristics of
the respondents residences, grouped into four distance intervals
from the citycenter of Copenhagen. N = 1932 respondents of the main
survey.
Urban structural factor Distance interval from the city center
of Copenhagen28 km(N=457)
Distance from residence to downtown Copenhagen (km) 3 9.7 21.9
41.8Distance from residence to closest second-order urban center
(km) 2.2 5.7 7.6 10.1Distance from residence to closest urban rail
station (km) 1.4 2.3 3 11.2Local area population density
(inhabitants/ha) 85 24 14 10Local area workplace density (jobs/ha)
66 11 7 5Distance from residence to closest grocery store (km) 0.13
0.51 0.97 0.68Number of grocery stores within 1.5 km distance from
the dwelling 150 18 12 8Number of specialized stores within 1.5 km
distance from the dwelling 218 16 15 15Distance from residence to
closest primary school (km) 0.51 0.85 1.5 1.2Distance from
residence to closest kindergarten (3-5 years) (km) 0.31 0.52 0.65
0.77Distance from residence to closest crche (0-3 years) (km) 0.32
0.72 1.08 2Distance from residence to closest post oce (km) 0.74
1.09 1.56 0.98Distance from residence to closest town hall (km) 2.8
2.8 4 3.7Proportion of residences with a green recreational area of
at least 10 ha within 1km distance (%)
36 60 55 42
desire to limit travel distances and the desire for the best
fa-cility. Depending on the trip purpose, the balance betweenthese
desires may vary somewhat. Our interviews suggest thateach resident
establishes an individual threshold value for thelongest acceptable
travel distance within each category of des-tination.
Among the dierent travel purposes, travel related to em-ployment
and higher education, along with visits to friendsand relatives,
are the purposes for which the longest distancesare accepted.
Because the workplace or school/university isusually visited each
weekday, while long trips for social pur-poses are far less
frequent, journeys to work or education ac-count for the largest
proportionof the travel distance onweek-days. e acceptable travel
distance to work or education ap-pears to be greater for travelers
with more specialized workqualications, those withmoremobility
resources at their dis-posal, and those living further from the
largest concentrationsof work and education opportunities.
For example, one interviewee, a computer engineer now liv-ing in
the peripheral suburb ofUvelse, told that his present jobwas chosen
without much consideration of the distance fromtheir dwelling at
that time:
No, I just thought that I wanted employment,and then we would
have to see where to go. And itdid not matter where the workplace
was located.(Male computer engineer living in the suburb ofUvelse,
30 years old.)
Similarly, an economist living in the same suburb reportedthat
he preferred to commute all the way to downtownCopenhagen instead
of nding a less-challenging job in a mu-nicipality closer to the
residence:
Surely, I would like something closer to home,but there are no
such [relevant] jobs available tome here in the vicinity. en it
would have to beif I were interested in working in a municipal
ad-ministration. ... But that would be such a smallworkplace, and I
simply want some more chal-lenges. ... Yes, for sure, mostwork
opportunities foreconomists are in the city of Copenhagen.
(Maleeconomist, living in the suburb of Uvelse, 38 yearsold.)
One of the interviewees living in the inner-city area of
Fred-eriksberg reported that both his own and his wifes work-places
were chosen primarily because they found the jobs in-teresting.
Both had quite specialized job skills (civil engineerand
pharmacology researcher). Due to the central location oftheir
dwelling, they still succeeded in nding satisfactory jobswithin a
moderate distance from home. Asked whether theywould have taken
these jobs if theworkplaceswere located fur-ther away, for example
in Roskilde (40 km away), the intervie-wee replied:
You see, we have never made long journeys towork. ... It would
of course depend on the situa-
-
.
tion. If it was not possible to get a job closer tohome, yes.
... Roskilde is pretty easily accessible, butyou see ... I dont
think that I would be willing todrivemore than half an hour or so
to get there, I re-ally dont. (Male civil engineer, living in the
inner-city area of Frederiksberg, 43 years old.)
Since primary schools, kindergartens and well-stocked gro-cery
stores can usually be found closer to the residence thancan jobs
matching specialized work qualications, the thresh-old values for
acceptable distances to such facilities are usuallyshorter than for
workplaces and higher education opportuni-ties.
Distance limitation is included as an important (butnot theonly)
rationale for most interviewees choices of locations fordaily-life
activities. e desire to limit travel distances may begrounded on
dierent reasons, oen in combination, such assaving time, saving
money, personal physical limitations withrespect to walking and
bicycling, and the desire to support thelocal community and
maintain local social contacts.
Along with distance limitation, a desire for the best facil-ity
(judged against the instrumental purpose of the trip) is themost
important rationale for the interviewees choices amongdestinations.
In a way, this is the most fundamental rationale,as the trips would
simply not occur if no suciently attractivefacility existed that
might be visited. In practical locationalchoices, distance
limitations and the desire for the best facil-ity must be weighed
against each other. What is consideredthe best facility will vary
with the purpose of the trip andwith the individual characteristics
of the person in question.For workplaces, factors like job duties,
qualication require-ments, wages, and work environment are
relevant. For spe-cialized jobs, the catchment area from which
employees arerecruited typically includes considerable parts of the
region.
Factors inuencing the perceived quality of shops include,among
others, the selection of goods available, prices, and pos-sibly the
availability of parking. When living in the periphery,the local
grocer is oen used only for emergency purchases,e.g. when there is
no coee le in the house. Among those liv-ing in the central parts
of Copenhagen, local shops are oenwell- stocked and are relied on
to a higher extent for ordinaryshopping. Among kindergartens, the
reputation of the insti-tution (pedagogy, etc.) and perhaps also
the ethnic composi-tion of the children may be inuential
factors.
e tendency to choose a facility other than the closest oneis
indicated in Figure 6, where lengths of tripsmade by parentswhen
transporting their children to kindergarten or crcheare compared to
the distances from dwellings to the closestsuch facilities.
Although parents usually do not choose (nor
are they oered places for their children in) kindergartens
andcrches located very far away from the family dwelling, the
fa-cilities actually used are, on average, located more than
fourtimes farther from the home than the nearest kindergartensand
crches. We still see a clear tendency toward longer tripsto such
facilities among respondents living farther from thecenter of the
metropolitan area, reecting the higher densityof facilities in the
inner city than in the outer suburbs.
Below 6 6 to 15 15 to 28 Over 280
1
2
3
4
5
6
7
8
Mean distance to closest kindergarten and creche (N = 1932)Mean
trip distance for bringing children on weekday mornings (N =
33)
Distance from dwelling to the city center of Copenhagen (km)
km
Figure 6:Mean trip distances for trips with the purpose of
trans-porting children on weekday mornings (Monday-Tuesday)among
travel diary survey respondents living within dif-ferent distance
intervals from the Copenhagen city center,compared to distances
from home to the closest kinder-gartens and crches among main
survey respondents livingin the same distance intervals. N = 33
travel diary tripsand 1932 main survey respondents.
e destinations of visiting trips are dened entirely by
thetravelers family relations and circle of acquaintances. Whenit
comes to leisure trips, the choice among facility categoriesdepends
strongly on the interests and lifestyle of the person inquestion,
but quality dierences within each facility categorymatter as well.
For example, when planning an outing, a forestthat is distant but
larger and more beautiful may be preferredto a local forest.
e trip distances shown in Figure 6 are based on the follow-up
traveldiary survey, in which 273 of the original 1932 respondents
participated.Among a total of 231 visiting trips carried out during
the period Satur-dayTuesday, only trips on Monday and Tuesday were
included, and onlytrips carried out as the rst trip of the day,
thus including only trips origi-nating at the residence. When
measuring average distances from dwellingsto the closest
kindergarten and crche, the distances have been weighted inorder to
take into account the fact that trips to bring children to
kinder-gartens are carried out about twice as frequently as trips
to bring them tocrche.
-
New urbanism or metropolitan-level centralization?
Besides emphasizing the possibility of choosing the
instru-mentally best facility, the atmosphere and aesthetic
quali-ties of the destination are important to many of our
intervie-wees. In particular, this applies to trips such as visits
to restau-rants, cinemas, theaters, and other cultural facilities,
as well asto shopping (particularly when purchasing non-grocery
com-modities). In contrast, peoples choices of locations in whichto
seek employment are inuenced to a much lesser extent bythe
atmosphere of the district where the workplace is lo-cated.
In quotidian travel, some trips are more fundamental, andtheir
characteristics more xed, than other trips. Oen, suchtrips are part
of a trip chain. Other travel purposes are thenlinked to this
fundamental trip. For example, by choosinga well-stocked store
along the route followed anyway on theway home from work, the
rationale of distance limitation canbe combined with the rationale
of choosing the best facility.is can, to some extent, compensate
for the longer distancesto shops typical of residences in the
outskirts of the city. iskind of adaptation is very common among
our interviewees.
Formost travel purposes, our respondents and
intervieweesemphasize the possibility of a choice of facilities
over proxim-ity. is means that the amount of travel is inuenced to
agreater extent by the location of the residence in relation
toconcentrations of facilities than by the distance to the
closestsingle facility within a category. is is particularly
evidentfor workplaces and places of higher education, but also
forcultural and entertainment facilities, specialized stores and,
tosome extent, grocery stores. For leisure activities, the
atmo-sphere and the aesthetic qualities of a destination may
alsoplay a role.
us, formost travel purposes, our interviewees donot nec-essarily
choose the closest facility, but rather travel a bit fartherif they
can then nd a better facility. ey tend to emphasizea rationale of
choosing the best facilities above a rationale ofminimizing the
friction of distance. is is especially true inregard to workplaces.
Travel distances therefore depend moreon the location of the
dwelling relative to large concentrationsof facilities than on the
distance to the closest facilities. Peo-ple who live close to the
city center can access a large num-ber of facilities within a short
distance from the dwelling andtherefore do not have to travel long
distances, even if they arevery selective as to the quality of the
facility. Since the largestconcentrations of workplaces, as well as
other facilities, are inthe city center and the inner districts of
the city, the above-mentioned circumstances imply that the amount
of quotidiantravel is inuenced by how far away the interviewees
live from
the city center rather than by the distance from their
dwellingto lower-order centers.
Table 3 summarizes the impacts of the rationales identiedin the
qualitative interviews on relationships between residen-tial
location and travel . ese eects are attributable to theability of
the rationales to inuence the relationships betweenresidential
location and concentrations of facilities at both lo-cal and
metropolitan scales. e rationale of distance limi-tation has been
divided into two aspectslimitation of geo-graphical distance and
limitation of time usagebecause theyappear to inuence travel
behavior dierently.
e relationship between the amount of travel and the dis-tance
from the residence to the main center of the urban re-gion is
strengthened, in particular, by the rationale of beingable to
choose the best facility (judged against the instrumen-tal purpose
of the trip). e rationales of limiting geographicaldistance and
limiting travel time also contribute to this rela-tionship, to some
degree, because the regions largest concen-tration of facilities
serves as a local concentration of facilitiesfor a large number of
inner-city residents in the regions majorcity and because the urban
center approximates the geograph-ical center of gravity even for
themore peripheral destinationsthat mightfrom a rationale of
time-savingbe chosen bycar drivers who want to avoid congested
streets. e rationaleof enjoying atmosphere and aesthetic qualities
also increasesthe inuence of the distance between the residence and
down-town on the amount of travel. e relationship between theamount
of travel and the distance from the residence to localfacilities is
based, rst and foremost, on the rationale of limit-ing geographical
distances; however, it is also aected by therationale of saving
time, as the local facilities will oen be theones that can be
reached most quickly.
Onemight perhaps imagine that the rationales of
inner-cityresidentswould dier from those of suburban residents.
How-ever, no clear dierences of this type emerged. e rationaleswere
fairly similar across residential locations, but the need
toemphasize one rationale at the cost of another was much
lesspresent among inner-city residents than among suburbanites.
Because their trips are oen short, inner-city residents alsomake
a higher proportion of trips by bicycle or on foot. is,and the
generally higher level of public transport service inthe inner
city, helps to reduce the amount of car travel amonginner-city
dwellers.
e rationales appear to be of a high generality across cultural
and so-cial contexts. For example, very similar rationales for
location of activi-ties were found in a study of residential
location and travel in HangzhouMetropolitan Area, China (Nss
2009a).
-
.
Table 3:e contributions of the various rationales for location
of activities to the relationships between residential location and
travel.
Rationales for activitylocation
Frequency of occurrence Inuence on the relationshipbetween the
amount of traveland the distance from thedwelling to the main
centerof the metropolitan area
Inuence on the relationshipbetween the amount oftravel and the
distance fromthe dwelling to local facilities
Limitation of geographicaldistances
Emphasized by allinterviewees, in particularthose without a
car.resholds for acceptabledistances vary betweenactivity types and
betweenindividuals
Contributes to some extentto this relationship, bothbecause the
facilities indowntown Copenhagen arethe closest opportunities
forinner-city residents, andbecause of the shortage offacilities in
the periphery
Contributes strongly to thisrelationship by increasingthe
likelihood of choosinglocal facilities rather thanmore distant
ones
Limitation of timeconsumption
Emphasized by allinterviewees, but thresholdsfor acceptable
timeconsumption vary betweenactivity types and
betweenindividuals
May induce some car driversto choose, e.g., suburbanshopping
malls instead ofcentral-city shops.Contributes nevertheless tosome
extent to therelationship between thedistance from the residenceto
downtown and theamount of travel, due to thefunction of the urban
centeras geographical point ofgravity
Contributes to thisrelationship because it willusually take a
short time togo to local facilities. Butbecause travel speeds
willoen be higher when goingto e.g. a more distantshopping mall
with ampleparking space, the inuenceof this rationale is not
asstrong as the inuence of therationale of limitinggeographical
distances
Wish for the best facility(judged against theinstrumental
purpose of thetrip)
Emphasized by allinterviewees, but itsimportance varies
betweenactivity types and betweenindividuals
Contributes strongly to thisrelationship by increasingthe
likelihood of traveling tothe large concentration offacilities in
the inner parts ofthe metropolitan area, butalso because of
downtownsrole as a point of gravity forall peripheral
destinations.
Contributes to a certainweakening of thisrelationship by
increasingthe likelihood of choosingdistant facilities rather
thanlocal ones
Enjoying atmosphere andesthetic qualities
Emphasized by manyinterviewees, primarily fornon-bounded
trips
Contributes to thisrelationship by directing ahigher number of
non-worktrips to the historical urbancore
May contribute to a certainweakening of thisrelationship by
makingrespondents bypass facilitiesin local centers where
theatmospheric qualities arelower than in the downtownarea
-
New urbanism or metropolitan-level centralization?
7 Multivariate analyses with mainurban-structural variables
andnon-urban-structural control variables
Based on theoretical considerations, the information from
thequalitative interviews about the interviewees rationales for
se-lecting activity locations, and preliminary analyses of the
cor-relations of individual urban-structural variables with
travelbehavior, the following four urban-structural variables
wereincluded in the main statistical analyses of this study:
Location of the residence relative to the Copenhagencity
center
Location of the residence relative to the closest second-order
center
Location of the residence relative to the closest urban
railstation
Density of inhabitants and workplaces in the local
areasurrounding the residence
ese urban-structural variables oer the greatest explanatorypower
for interpreting travel behavior variables. Limiting thenumber of
urban-structural variables to these four avoidedproblems of
multicollinarity.
Table 4 shows the results of amultiple regression analysis
offactors potentially inuencing the distance traveled by car
onweekdays, including the four above-mentioned urban struc-tural
variables and the seventeen non-urban-structural con-trol variables
used in the analyses presented earlier. Eectsmeeting the required
signicance level are evident for all foururban-structural
variables. e eect of the location of the res-idence relative to
theCopenhagen city center is, however, con-siderably stronger
andmore certain (= 0.130, p = 0.0001)than the other three urban
structural variables (absolute val-ues ranging from 0.048 to 0.062,
with p values ranging from0.07 to 0.08).
e strong eect of the location of the dwelling relative tothe
city center does not, of course, imply that the city center it-self
(i.e. CityHall Square) is the destination of a large number
If only variables meeting a signicance level of 0.05 are allowed
to beincluded in the model, the location of the dwelling relative
to the closestsecond-order center is excluded. e standardized
regression coecientsand p-values of the remaining three urban
structural variables are thenas follows: Location of the residence
relative to downtown Copenhagen: = 0.133, p = 0.0000; local area
density: = 0.088, p = 0.0048;logarithmic distance from the
residence to the closest urban rail station:= 0.067, p =
0.0067.
of trips. e trip destinations reected in the eect of
residen-tial location relative to the city center are the numerous
work-places and other facilities concentrated in and around the
citycenter. In this sense, distance to the center is a proxy for
othercharacteristics. e important point is that the areas
centrallocation supports the high concentration of facilities in
thisparticular part of the metropolitan area. Centrality implies
ahigh concentration of facilities, and vice versa.
Based on the results shown in Table 4, Figure 7 shows (bymeans
of black dots) how the expected traveling distancesamong
respondents living within each of the 29 residential ar-eas varies
with the distance from the residential area to the citycenter of
Copenhagen when controlling for the non-urban-structural variables
in the regression model. Expected traveldistance by car over the ve
weekdays is nearly four times aslong (187 km) in the most
peripheral area investigated thanin the most central area (50
km).
In addition, the triangles in Figure 7 illustrate the
relation-ships between traveling distances by car and the distance
fromthe residential area to the city center of Copenhagen whencar
ownership and attitudes toward car traveling are added tothe other
control variables. Several studies have shown thatcontrolling for
car ownership and attitudes toward car travelreduces the eects of
urban-structural variables. As can beseen, this also applies to the
Copenhagen case. ere is stilla fairly strong and statistically
certain eect of residential lo-cation on the amount of car travel
(p = 0.000), with pre-dicted traveling distances twice as long in
the outer suburbs asin the inner city. Moreover, our ndings show
that car own-ership and, to some extent, transport attitudes are
both inu-enced by residential location (see Nss 2009b for a
thoroughaccount). Treating car ownership and attitudes to car
travelas exogenous control variables not inuenced by urban
struc-ture will, therefore, lead to an underestimation of the
impactsof residential location on travel. As long as
socio-demographicvariables and transport-related residential
preferences have al-ready been controlled for, it is my opinion
that car ownershipand attitudes to car travel should not be
included as additionalcontrol variables. I therefore consider the
black dots in Fig-ure 6 to provide a more appropriate
representation than thetriangles of the inuence of residential
location on travelingdistances by car.
As can be seen in the diagram, expected amounts of cartravel in
some of the residential areas are higher or lower thanwhat would be
the case if location relative to Copenhagenscity center was the
only urban-structural variable inuencingthe amount of car travel.
For example, two residential ar-eas located about 35 km from the
city center have consider-
-
.
Table 4: Results from amultivariate analysis of the inuence of
various independent variables on the distance traveled by car (km)
onweekdays.Only variableswith a level of signicance of 0.15or lower
are included. N = 1564 respondents from29 residential areas
inCopenhagenMetropolitan Area. Adjusted R2 = 0.272.
Unstandardizedcoecients
Standardizedcoecient
Level ofsignicance(p-value,
two-tailed test) Std. error
Occupational trips during the investigated week (yes = 1, no =
0) 85.65 9.54 0.21 0.0000Index for residential location preference
(1 = preference for residential loca-tion facilitating public
transport, walking or biking, 0 = no such preferenceexpressed)
47.14 7.22 0.143 0.0000
Possession of a drivers license for car (yes = 1, no = 0) 63.22
11.06 0.131 0.0000Location of the residence relative to
downtownCopenhagen (non-linear dis-tance function, values ranging
from 0.66 to 3.80)
17.28 4.32 0.13 0.0001
Personal annual income (1000 DKK) 0.088 0.019 0.119
0.0000Overnight stays away from home more than three nights (yes =
1, no = 0) 53.56 13.41 0.087 0.0001Long technical or economic
education (yes = 1, no = 0) 44.96 12.02 0.086 0.0002Workforce
participation (yes = 1, no = 0) 26.73 9.18 0.07 0.0037Short or
medium-long education as a tradesman or industrial worker (yes =1,
no = 0)
29.47 10.61 0.064 0.0056
Density of inhabitants and workplaces within the local area of
the residence(inhabitants. + workplaces per hectare)
0.168 0.093 0.062 0.0699Logarithm of the distance (meters) from
the residence to the closest second-order urban center (log values
ranging from 2.49 to 4.46)
24.73 14.11 0.055 0.0799
Sex (female = 1, male = 0) 17.88 7.82 0.054 0.0224Logarithm of
the distance (meters) from the residence to the closest urbanrail
station (log values ranging from 1.90 to 4.47)
14.18 8.03 0.048 0.0776
Number of household members below 7 years of age (p >
0.15)Age (deviation from being middle-aged, logarithmically
measured) (p > 0.15)Number of days at the workplace or school
during the investigated week (p > 0.15)Number of household
members aged 7 17 (p > 0.15)Regular transport of children to
school or kindergarten (yes = 1, no = 0) (p > 0.15)Pensioner
(yes = 1, no = 0) (p > 0.15)Student/pupil (yes = 1, no = 0) (p
> 0.15)Has moved to the present dwelling less than ve years ago
(yes = 1, no = 0) (p > 0.15)Constant 135.91 49.85 0.0065 e
number of days a person is present at the workplace or place of
education is directly related to the number of weekly trips.
eweekly number of working hours was tried as an alternative control
variable, but this variable, too, showed a statistically
non-signicanteect. Working hours were slightly negatively
correlated with the distance from the dwelling to the city center,
and including workinghours among the control variables therefore
yielded a slightly stronger eect of residential location relative
to the city center on the travelingdistance by car.
-
New urbanism or metropolitan-level centralization?
6040200
200
150
100
50
0
Control for sociodemographics, residential preferences,car
ownership and car attitudesControl for sociodemographics and
residential preferences
Figure 7: Average expected travel distances by car (km) over the
veweekdays for each of the 29 investigated areas. e blackdots are
based on the actual values of each urban-structuralvariable in the
regression model, with socioeconomic vari-ables, demographic
variables, and residential preferenceskept constant at mean values,
cf. Table 4. (N = 1564 re-spondents, p = 0.0000.) e blue triangles
are based on aregression analysis that includes, in addition to the
variablesin Table 5, car ownership and attitudes toward car
driving(N = 1476, p = 0.0000).
ably lower expected traveling distances by car than the
otherperipheral residential areas. is reects the fact that
boththese areas are located near second-order centers (the townsof
Hillerd and Kge) and are also fairly close to urban railstations.
Conversely, expected car usage in one residential arealocated about
20 km from the city center is clearly higher thanin the other
residential areas located at similar distances fromthe city center.
is reects the fact that the area in questionhas a particularly low
local-area density and is located far fromthe closest second-order
center and the closest urban rail sta-tion.
8 Concluding remarks
Our study shows that metropolitan-scale
urban-structuralvariables generally exert stronger inuences
thanneighborhood-scale built-environment characteristicson
traveling distances by car during weekdays. In particular,the
location of the residence relative to the main city center ofthe
metropolitan region shows a strong eect on the amountof car travel.
We also nd that the amount of weekdaycar travel is aected by the
location of the dwelling relativeto the closest second-order center
and to the closest urbanrail station, as well as the density of
population and jobswithin the local area (a two-square-kilometer
zone aroundthe residential area). Compared to these four
variables,the eects of local-scale urban characteristics are
generallyweaker. For example, when we control for the location
ofthe dwelling relative to the city center, density measured atthe
level of the narrowly demarcated residential area is not asstrongly
correlated with traveling distances by car as densitymeasured
within a larger geographical area. Similarly, thedistance from the
dwelling to the closest facility within acategory is generally less
important to traveling distancesthan proximity to concentrations of
facilities. Since thehighest concentrations of service facilities
and workplacesare found in the central and inner parts of the
metropolitanarea, inner-city residents generally have better
possibilitiesof nding suitable jobs, shopping opportunities, and
leisurefacilities without having to travel long distances.
e nding that metropolitan-scale built-environmentcharacteristics
exert a stronger inuence on travel behav-ior than
neighborhood-scale characteristics is not limited toweekday car
travel. Similar results have been obtained for totaltraveling
distance onweekdays andweekends, and for the pro-portion of total
distance traveled by car (Nss 2006b; Nssand Jensen 2005).
e obvious interpretation of these results is that the
fourhigher-level urban-structural variables inuence travel
behav-
An examination of 485 respondents who had moved from one
resi-dence within the Copenhagen Metropolitan Area to another
during theprevious ve years gives additional support to this claim.
ese respondentswere askedwhether they, according to their own
judgment, had experienceda change in their amountof travel due to
themove. ephrasingof theques-tion was: If you have moved has moving
from your latest to your presentresidence caused any changes in
your amount of travel? e answer alter-nativeswere: a) Yes, moving
has had the consequence that I now travelmoreb) Yes, moving has had
the consequence that I now travel less c)No, movinghas not led to
any changes in my amount of travel worth mentioning. eanswers to
these questions show a clear tendency toward increased
travelwhenmoving outward (Wald= 33.259, p = 0.0000) and decreasing
whenmoving closer to the city center (Wald= 22.147, p =
0.0000).
-
.
ior through the accessibility of various types of facilities.
Be-cause the variables measuring accessibility to dierent
facili-ties only capture the travel purposes associated with the
facil-ity categories in the respective indices, their eects are
weakerthan the eects of the variables representing the location of
theresidence relative to themain centers of themetropolitan
area.Accessibility indices that include a greater number of
facilitycategories generally exhibit a stronger relationship with
travelbehavior. us, traveling distances by car are more
stronglycorrelated with the index for availability of shopping
oppor-tunities near the dwelling than with the index for distance
tothe closest grocery store, and stronger with the combined in-dex
for availability of service facilities near the dwelling thanwith
the index for shopping opportunities.
Arguably, an equal or even higher statistical power of
de-termination (adjusted R2) might have been obtained by re-placing
the variables measuring distances to various types ofcenters with
measures of accessibility at local, district, andmetropolitan
scales Bhat and Guo (2007); Krizek (2003).However, as guidance for
urban planning, it is probably moreinteresting to know how the
location of the dwelling relativeto various types of centers aects
travel behavior than to knowthe relationship between travel
behavior and, for example, themean opportunity distance.
e moderate eect of local-area density on traveling dis-tances by
car should not lead us to believe that neighborhood-scale density
is unimportant to travel. Apart from inuencingthe provision of
local services and public transport, local areadensities add up to
the overall density of the city. e higherthe population density of
the city as a whole, the lower willbe the average distance between
the residences and the down-town area. In this way, local area
densities indirectly inuencethe urban-structural variable that,
according to our studies, ex-erts the strongest inuence on the
travel behavior of individu-als and households, namely the location
of the residence rela-tive to the city center.
Interestingly, any relationship between the local-level
streetstructure and traveling distance by car disappears with the
in-troduction of statistical control for the location of the
resi-dence relative to downtown Copenhagen. is gives rise to
asuspicion that the corresponding relationship seen in
researchcarried out in the United States might reect the location
ofthe residential areas rather than the shape of the local
streetnetwork. In most of the American studies that have attacheda
great importance to the shape of the local street pattern,control
for the location of the residential area relative to
thehigher-level center structure seems to be missing.
e results of this study are in line with the ndings of anumber
of studies in other cities in Europe and Asia, as notedin the
introductory section. e Copenhagen study nd-ings are also in
accordance with evidence from some Ameri-can studies, such as
(Ewing and Cervero 2001) and (Zegras2010), both of which found
regional accessibility to be moreimportant than local
built-environment characteristics to thenumber of vehicle miles
traveled. A clear dierence in theamount of car travel between
suburban/rural residents andresidents living close to the Central
Business Districtalsoaer control for socioeconomic and attitudinal
factorswasalso found in a study by (Zhou and Kockelman 2008).
e lesson for spatial planners aiming to facilitate more
en-vironmentally friendly travel patterns in city regions is that
ur-ban containment ismore conducive to this end than the
devel-opment of new suburbanneotraditional housing areas. In
par-ticular, densication close to themain center of
themetropoli-tan area contributes to a reduction in traveling
distances andencourages the use of travel modes other than the
private car.From the perspective of sustainable mobility,
metropolitan-level centralization is thus more favorable than
decentralizeddevelopment according to New Urbanist principles.
Today,fortunately, many European and American cities have
consid-erable opportunities for inner-city densication and
regener-ation due to the strong deindustrialization processes that
havebeen going on during recent decades in mostWestern cities.
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