Policies_for_urban_form_and_their_impact_on_travel

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Urban Studies, Vol. 41, No. 3, 579603, March 2004

Policies for Urban Form and their Impact onTravel: The Netherlands Experience

T.SchwanenRegional Research Centre Utrecht (URU), Faculty of Geographical SciencesUtrecht University3508 TC UtrechtPO Box 80.115The Netherlands 31 30 254 [email protected]

Tim Schwanen, Martin Dijst and Frans M. Dieleman

[Paper first received, November 2002; in final form, July 2003]

Summary. This paper documents an evaluation of the consequences of the Netherlands national

physical planning policy for an individuals travel behaviour. Four components of this policy areconsidered: the concentrated decentralisation of the 1970s and 1980s; the strict compact-city

policy of the 1980s and 1990s; the A-B-C location policy; and the spatial retailing policy. Using

data from the 1998 Netherlands National Travel Survey, the article addresses the following

questions. Did physical planning reduce the use of the private car and promote the use of public

transport together with cycling and walking? Did physical planning lead to shorter travel

distances and times? The analysis suggests that national spatial planning has been most effective

in retaining high shares of cycling and walking in the large and medium-sized cities, in particular

for shopping trips. In terms of travel time, however, spatial policy seems to have been less

successful. The building of new towns and, more recently, the development of greenfield

neighbourhoods close to cities do not appear to have reduced commuting times. Alternative

strategies to promote the use of public transport, the bicycle and walking through the regulationof land use are discussed. Relaxing some of the present spatial planning controls is suggested to

reduce car use and travel times.

1. Introduction

Fuelled by general concerns about environ-mental problems, over the past two decades,

spatial planners have paid particular attentionto the impact of urban form on mode choiceand travel distance, especially for commut-ing. Compared with the US, where urbandevelopment is more often privately financedand less directed by national policies(Cervero, 1995), many European plannersand politicians have proposed or imple-mented measures to limit urban sprawl andpromote the development of compact urban

forms as a means of reducing energy con-sumption through transport (Hart, 1992;

Williams et al. 2000). Other important rea-sons for influencing and controlling the de-velopment of urban forms have been thepreservation of agricultural land, open spaceand environmentally valuable areas, and thepromotion of investment in existing built-upareas to halt neighbourhood decline.

The Netherlands has a long history of theintervention by spatial planners in the devel-opment of urban forms. In the 1970s and

Tim Schwanen, Martin Dijst and Frans M. Dieleman are in the Urban and Regional Research Centre Utrecht (URU), Faculty ofGeosciences, Utrecht University, PO Box 80.115, 3508 TC Utrecht, The Netherlands. Fax: 31 30 254 0604. E-mail:[email protected]; [email protected]; and [email protected]. The research reported here has been made possibleby grant 42513003 from the Netherlands National Science Foundation (NWO) to the Urban and Regional Research CentreUtrecht. Frans Dieleman was able to work on the theme of this article through a grant of the Royal Netherlands Academy of Artsand Sciences (KNAW) for the Netherlands Visiting Professorship at the University of Michigan, Ann Arbor.

0042-0980 Print/1360-063X On-line/04/03057925 2004 The Editors of Urban StudiesDOI: 10.1080/0042098042000178690


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TIM SCHWANEN ET AL.580

1980s, a policy of concentrated decentralis-ation of urban land use was implemented bydeveloping designated growth centres andprohibiting the growth of small rural settle-ments (Dieleman et al. 1999; Bontje, 2001).In the 1980s, a policy of compact urbangrowth was formulated; it has been imple-mented since the 1990s (Hayer and Zon-neveld, 2000). This policy was supplementedby policies of large investments in the re-newal of the urban housing stock and thestrict regulation of the location of retail fa-cilities with a ban on the development oflarge out-of-town shopping malls (Evers,2002). Additionally, the national government

has tried to channel new employment intonodes that were well served by public trans-port through the A-B-Clocation policy (Dijst,1997).

Because of planners intensive regulationof spatial development over a long period,the Netherlands provides an interesting casein which to evaluate the impact of nationalspatial planning policies on the travel behav-iour of individuals. This article documents an

evaluation of the impact of some of thenational spatial policies on the developmentof urban form and through this on travelpatterns in the Netherlands. Which policieshave stimulated the use of public transportand of short trips? In what respect did thesepolicies fail to promote walking, cycling andthe use of public transport and to reducetravel times and distances, and what were thelikely causes of the failure? We support our

arguments with empirical data from the 1998Netherlands National Travel Survey, supple-mented by results from our own recent workon travel behaviour and urban form(Schwanen, Dieleman and Dijst, 2001, 2003a,

2003b; Schwanen, Dijst and Dieleman, 2001,2002) and other studies on this theme.

The next section gives a summary of someof the main spatial policy measures of thepost-World-War II era in the Netherlands.Section 3 presents some empirical results on

the relationship between urban structure andtravel against the spatial policies imple-mented. In section 4, some conclusions aredrawn and some possible ways of making

spatial policies more effective in terms oftheir travel outcomes are discussed.

2. Physical Planning Controls on Urban

Dynamics in the Netherlands

A whole range of policy measures has beendesigned and put into action to regulate ur-ban spatial dynamics in the Netherlands dur-ing the past three decades. We brieflysummarise spatial planning controls on urbanexpansion and the compact-city policy, in-vestments in urban renewal and attempts toinfluence the location choice of firms andretail planning.

In the Second Physical Planning Memo-randum (MVRO, 1966), the national govern-ment took a powerful stand against suburbansprawl that was at that time threatening toengulf the Green Heart of the Randstad Hol-land. This is the (relatively) open space sur-rounded by the major cities in the westernpart of the Netherlands (Figure 1; Faludi andvan der Valk, 1990; Dieleman et al., 1999).The report proposed channelling suburbani-

sation into concentrated decentralisation.More specifically, new urban growth was tobe accommodated outside the existing citiesin a number of designated overspill centresreferred to as growth centres. The Nether-lands does not stand alone in this approach.Similar initiatives have been undertaken else-where in Europefor example, in England,Sweden and the Paris region (Cervero,1995). This policy was put into practice in

the late 1970s and early 1980s. It was verysuccessful in terms of concentrating subur-ban residential growth into the designatedgrowth centres and prohibiting the growth ofrural villages in the Green Heart (Faludi andvan der Valk, 1990).

Nevertheless, the policy of concentrateddecentralisation of urban growth was broughtto an end during the 1980s (Dieleman et al.,1999; Hayer and Zonneveld, 2000). Inner-city decline became a major issue on the

policy agenda. Many government officialsand academics blamed this decline on thepolicy of concentrated decentralisation(seefor example, MVROM, 1988). The


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URBAN FORM AND TRAVEL 581

Figure 1. Growth centres and VINEX locations in the western part of the Netherlands.

policy did indeed foster an unprecedentedwave of suburbanisation in the Netherlands,leading to substantial income differences be-tween city centres and the suburbs (Dielemanand Wallet, 2003). A new policy of compacturban growth was developed and put intopractice. Under this new policy formulated inthe Fourth Physical Planning MemorandumExtra (MVROM, 1991) and now being imple-

mented, urban growth is guided into(re)development locations within existing cit-ies (brownfield sites) and towards newgreenfield sites directly adjacent to the built-

up areas of the larger cities in the Netherlands(Figure 1). Leidsche Rijnthe greenfield de-velopment immediately to the west ofUtrechtis the largest of these VINEX loca-tions. Here, some 30 000 dwellings and10 000 jobs are being created. The compact-city policy also promotes the construction ofrelatively dense new residential neighbour-hoods in suburban settlements that kept ex-

panding and accommodating part of the urbangrowth outside the VINEX locations.

In the same period in which spatial plan-ning in the Netherlands changed track from


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concentrating suburbanisation in growth cen-tres to compact urban redevelopment, thenational government embarked on an exten-sive programme of urban renewal. Largesubsidies were given to upgrade the qualityof the housing stock, particularly in the oldcores of the largest cities. Oskamp and col-leagues (2002) show how, in 1985, of all theDutch housing stock, 19 per cent was still inthe category of in poor condition, whereasthis share was reduced to 1 per cent in 2000.The major cities of Amsterdam, Rotterdam,The Hague and Utrecht have benefited mostfrom the urban renewal policy, which hashelped to upgrade the old private rental hous-

ing stock in the urban cores. However, theurban renewal policy showed little concernfor retaining or attracting employment in thecore areas of the cities. This neglect hascertainly been one of the causes of the rapiddevelopment of the larger Dutch cities frommonocentric to polycentric functional units.The majority of jobs in many cities can nowbe found in new employment concentrationsoutside the old original city centres. The

policy of urban renewal has been reformu-lated in the Big Cities Policy that, notwith-standing its broader scope, has as one of itsmain aims the renewal and partial replace-ment of social housing estates built in the1950s and 1960s (van Kempen and Priemus,1999; van Kempen, 2000).

The Fourth Physical Planning Memoran-dum (MVROM, 1988) introduced a policyfor the location of firms, the A-B-C location

policy. This policy was explicitly formulatedto discourage the use of the private car and topromote the use of public transport togetherwith cycling and walking. A locations werecentrally located sites often close to mainrailway stations and hence readily accessibleby public transport. In contrast, B loca-tionstypically situated in developmentnodes outside the larger CBDs and the cen-tres of smaller urban settlementswere rea-sonably well connected to public transport

and readily accessible by car. C locationsusually had very good motorway access.Typical examples were business zones in theurban fringe area or alongside motorways.

The intention of the policy was to guide newemployment and public services as much aspossible towards A and B locations.

The A-B-C policy turned out to be veryhard to implement (MVandW, 1999). Localgovernment authorities often gave higher pri-ority to attracting new employment than tothe locational requirements of the new firms.Furthermore, B locations were often less ac-cessible by public transport than had beenpredicted. Most importantly, however, thegrowth in employment in the office sectorwas much larger than had been planned inthe 1990s and could not be accommodatedfully in A and B locations as originally

planned. As a result, the largest employmentgrowth in the 1990s occurred at C locations.The location policy has been modified as aresult of its malfunction and changes in pol-icy aims. The main aims of this renewed andmore broadly defined policy are not only toencourage the optimal use of different trans-port systems, as in the old A-B-C policy,but also to encourage economic develop-ment, spatial quality and the quality of the

living environment (MVROM, 2001).Finally, it is pertinent to discuss the very

specific position taken in the Netherlandswith regard to retail planning (Evers, 2002).One of the salient features of the deconcen-tration of urban land uses in many countriesis the growth of out-of-town hypermarketsand shopping malls. In the Netherlands na-tional legislation was enacted to prohibitsuch developments in 1973, because the es-

tablishment of out-of-town shopping mallswas seen as a threat to the vitality of towncentres and likely to generate extensive caruse. Netherlands retail policy has been highlyeffective in reaching its objectives. Manyshops are still located within the built-up areaof cities and towns, and within walking andcycling distance for local residents (Evers,2002). Nowadays, however, traditional retailplanning is under heavy pressure. In the cur-rent era of deregulation and privatisation, the

policy is being blamed for curtailing retailproductivity and competitiveness and cre-ating barriers for new firms to enter the retailmarket.


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In short, despite the failure of the A-B-Clocation policy, spatial planning has beenfairly successful during the past few decadesin guiding residential and retailing develop-ments towards the locations that had beenplanned. This success can be considered asubstantial achievement, given thedifficulties often encountered in implement-ing policies of compact urban growth andcontaining urban sprawl (Dieleman et al.,1999). The question remains, however, towhat extent these spatial policies have beensuccessful in terms of their travel-related ob-jectives. Have the policies led to largershares for public transport and walking and

cycling? Or to shorter travel distances andtimes?

3. Physical Planning and Travel Behav-

iour: A Confrontation

Using the 1998 Netherlands National TravelSurvey (NTS), we have analysed theinfluence of urban form on travel behaviourin the Netherlands. One-day travel-diary data

from male and female heads of householdshave been used. For these individuals, infor-mation is available about all their trips on asingle day together with their socioeconomicand demographic situation (Statistics Nether-lands, 1999).

The NTS also contains information abouta households residential location at the levelof the municipality (n550 in 1998). Twocategorisations of these municipalities have

been used to represent urban form. The firstclassifies the degree of urbanisation of theresidential environment into six categories(Figure 2); the second distinguishes fourtypes of daily urban systems: one monocen-tric (centralised) and three polycentric forms(Figure 3). Further details are provided inAppendix 1. All descriptive results presentedin the tables are weighted so that they arerepresentative for the whole Dutch popu-lation. In our evaluation of Netherlands na-

tional spatial planning policy of the past fewdecades, we focus on three main dimensionsof travel behaviour. Sub-section 3.1 reportsan assessment of how spatial policies and

urban forms may have influenced modalchoice, while 3.2 discusses travel distanceand time. The focus is not only on commut-ing, but also on shopping trips and to a lesserdegree on leisure travel.

3.1 Modal Choice

Attempts to influence modal choice throughspatial planning are usually based on thepremise that urban compactness induceshigher shares for public transport, cyclingand walking at the expense of the private car.The concept of compactness comprises sev-

eral interrelated urban form dimensions: highdensity, a land-use mix and short distances tothe urban core and suburban concentrationsof employment or retailing.

In line with the academic literature (Ca-magni et al., 2002; Cervero, 1996a, 1996b,2002; Frank and Pivo, 1994), we find that caruse for commuting falls and the share forpublic transport rises with the degree of ur-banisation. What is peculiar to the Nether-

lands is that so many commuters travel bybicycle or on foot. Ranking residential envi-ronments within and outside the RandstadHolland from high to low urbanisation showsthat the levels of cycling and walking as wellas bus/tram/metro use fall with decreasinglevels of urbanisation, whereas the share ofthe car-driver mode rises (Table 1). Thelarger share for mass transit reflects the factthat higher population densities and levels of

land-use mixing facilitate more extensivepublic transport systems. The greater use ofslow modes of transportcycling and walk-ingis a result of the fact that, in the moreurbanised environments, more activity loca-tions can be accessed within acceptablewalking and cycling (time) limits. The effectof the level of urbanisation on modal choiceremains strong after account is taken of per-sonal and household attributes; after carownership, the level of urbanisation is the

most important determinant of modal choice(Dieleman et al., 2002). Based on thesestatistics, we may conclude that the policiesof redeveloping brownfield sites, urban re-


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584 TIM SCHWANEN ET AL.

Table1.M

odalsplitbytrippurposeandlevelofurbanisationintheNetherlands,1998.

Randstad

RestoftheNetherlands

Threelarge

Medium-

Growth

Randstad,

More

Less

cities

sizedcities

Suburbs

centres

total

urbanised

urbanised

P

ercentageofcommutersusing

C

ardriver

37.4

44.4

60.8

56.8

50.6

52.7

64

.9

C

ycling

31.2

29.5

22.4

18.7

24.5

32.1

22

.6

W

alking

8.8

8.1

4.7

5.0

6.6

5.2

4

.0

T

rain

11.0

14.5

4.9

9.2

9.1

4.5

1

.9

B

us/tram/metro

14.9

4.8

3.7

7.9

7.9

2.3

1

.6

P

ercentageofshoppersusing

C

ardriver

25.6

28.4

44.0

43.9

35.6

40.3

52

.4

C

ycling

26.4

38.8

30.4

26.9

30.5

33.8

30

.0

W

alking

41.0

34.3

22.9

26.5

31.0

23.6

14

.7

T

rain

1.5

2.1

1.5

1.6

1.6

0.9

0

.3

B

us/tram/metro

12.2

2.9

1.9

4.2

5.5

1.8

0

.9


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Figure 2. Classification of municipalities in the Netherlands: degree of urbanisation.

newal and upgrading the inner-city housingstock have contributed to a modal split forcommuting in the large and medium cities inwhich walking, cycling and the use of publictransport predominate (Table 1).

In terms of influencing the modal split forshopping trips, the policies of creating com-pact cities and retail planning seem to have

been even more successful. As Table 1 indi-cates, the importance of walking and cyclingis even more pronounced for shopping thanfor commuting. Of the people engaging in

shopping in the three large cities and themedium-sized cities of the Randstad Hol-land, no less than 41 per cent and 34 per centrespectively travel on foot. These percent-ages stand in sharp contrast with the mere 14per cent for the least urbanised environmentsoutside the Randstad Holland. Furthermore,in the Netherlands many people use a bicycle

for shopping. In all residential environments,over a quarter of the population cycle to theshops; this percentage is highest in the me-dium-sized cities and more urbanised munic-


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Figure 3. Classification of municipalities in the Netherlands: urban structure. Source: Based on Van derLaan (1998).

ipalities outside the Randstad Holland. Asurvey of the relevant academic literatureindicates that these high shares of the slowmodes of transport are exceptional. Whileelsewhere the bicycle is rarely used for shop-ping, particularly in the US, there is someevidence that in some high-density, mixedneighbourhoods in the UK and the US shares

for walking do tend to mirror those for thelargest cities in the Netherlands. In general,however, the level of car use for shoppingseems to be much higher in urban areas in

these countries (seefor example, Frank andPivo, 1994; Handy and Clifton, 2001; Vanand Senior, 2000). Credit for this lower levelof car use in the Netherlands can undoubt-edly be attributed to the policies of stimulat-ing the development of compact and diverseresidential neighbourhoods and limiting re-tail sprawl.

While the strict compact-city policy of thepast decade has had beneficial effects interms of modal split, the impact of its prede-cessorthe policy of concentrated decentra-


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lisationseems to have been more mixed.As explained in section 2, in the 1970s andearly 1980s, national spatial planning fo-cused on the development of growth centresto accommodate suburban growth. A com-parison of the modal split in the growthcentres and the suburbs of Randstad Holland,which represent a more dispersed decentra-lisation pattern, shows that the inhabitants ofthe growth centres are somewhat less depen-dent on the private car for commuting. Thelevel of commuting by train is higher,reflecting the fact that almost all growth cen-tres are directly connected by rail to the mainemployment centres. However, the level of

bicycle use is lower in the growth centres,which seems to be related to the qualitativemismatch between residents and employmentin many new towns (section 3.2). With re-gard to shopping, the results again indicatethat the relatively good public transport con-nections between the new towns and themain urban centres lead to greater use of thebus/tram/metro. The share of the private carfor shopping trips in the growth centres is as

large as in the suburbs of Randstad Holland.Although conclusions about the effectivenessof spatial policy should be drawn from theseresults with caution, it seems that the impactof the policy of concentrated decentralisationon modal split is relatively limited.

There are also several aspects of spatialplanning policy that seem to have had lessfavourable consequences for modal choice.Focusing again on the more recent policy of

concentrating residential development withinand adjacent to existing built-up areas, it canbe argued that there are second-order effectsof a compact-city policy (Priemus et al.,2001). The more compact the city, the morelikely are some urban functions to relocate tothe suburbs. It may well be that the cram-ming (Counsell, 2001) of housing into thebuilt-up and redeveloped parts of Dutch cit-ies has accelerated the deconcentration ofemployment towards C locations situated

near the motorways (section 2). Such loca-tion behaviour will almost certainly haveresulted in greater car use, because theselocations are not well served by public trans-

port and neither are they suited to bicycletravel.

In addition, although in the more ur-banised environments the car is partly substi-tuted for by public transport, its higher sharesare sometimes reached at the expense ofcycling. Comparison of the medium-sizedand three large cities for shopping in Table 1provides some evidence for the substitutionof public transport for cycling. In otherwords, higher-quality public transport at thelocal level seems to compete not only withthe private car, but also with the bicycle(Dieleman et al., 2002). Similar evidencewas found for leisure travel by Dutch senior

citizens (people aged 50 or older and livingindependently in a one- or two-person house-hold) (Schwanen, Dijst and Dieleman, 2001)and in a comparative study of commutingbehaviour in 11 European cities (Schwanen,2002).

Furthermore, while the development ofhousing on brownfield sites within the exist-ing urban fabric may lead to less car travel, itremains to be seen whether the development

of compact greenfield sites adjacent to theexisting built-up areas of cities reduces theprobability of using a car. Some preliminaryevidence is available for people who haverecently moved to the newly built VINEXneighbourhood of Leidsche Rijn situated onthe fringe of the city of Utrecht (Figure 1).Dijst and colleagues (2000) found that, aftermoving to Leidsche Rijn, a shift from anyother means of transport to the private car

occurred more often than the reverse. Relo-cating to this greenfield development is thusassociated with higher levels of car depen-dence. This may be attributed to the fact that,in order to minimise operational deficits,most public transport services in these loca-tions are only made available after most resi-dential units have been completed. Inaddition, Leidsche Rijn has very good accessto motorways and the residents are to a largeextent young, two-earner couples and famil-

ies with a relatively high income. Thewomen in such households in particular relyon the private car for commuting to ease thecombination of paid labour with household


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maintenance activities (Hjorthol, 2000;Schwanen et al., 2003b). Furthermore, it ap-pears that policy-makers have incorrectly as-sumed that the new residents of LeidscheRijn would be employed in the city ofUtrecht and its surroundings. However, sinceLeidsche Rijn provides excellent access tojobs located in many parts of the RandstadHolland (van Ham et al., 2001), many resi-dents commute by private car to work loca-tions outside the Utrecht area.

3.2 Travel Distance and Time

Since the amount of energy used for trans-

port depends on distance travelled as well asmode choice, many previous studies havefocused on the impact of urban form ontravel distance. We have therefore also as-sessed the impact of spatial planning policieson the distance people travel per day forcommuting, shopping and leisure activities.However, since time use is a criticalelement for a thorough understanding oftravel behaviour (Kitamura et al., 1997,

p. 171), we have also considered how timespent on travelling varies with the character-istics of the built environment.

With respect to distance travelled, manyprevious studies have indicated that a higherdegree of urbanisation is associated withshorter travel distances in general as well asdistance travelled by private car (Nss et al.,1995; Kenworthy and Laube, 1999; Kockel-man, 1997). Compact urban structures thus

result in travel patterns that are efficient inthe sense that the distances travelled areshorter than in lower-density or more dis-persed urban areas (Camagni et al., 2002).Evidence for shorter daily travel distanceswith a higher level of urbanisation is alsoborne out for shopping in Table 2. For com-muting, the average distance travelled as acar driver is somewhat lower in the threelarge cities of Amsterdam, Rotterdam andThe Hague and in the more urbanised munic-

ipalities outside Randstad Holland than else-where.

The relationship between degree of urbani-sation and travel time appears to be more

complicated. For the US, Levinson and Ku-mar (1997) found that commuting times byprivate car first decrease with populationdensity, but then start to rise with the numberof persons per square mile above some den-sity threshold. In the Netherlands, traveltimes for car drivers tend to rise somewhatwith the level of urbanisation for shopping(Table 2) and leisure (not shown due to spacelimitations). For commuting, the results areagain a little more complicated. Neverthe-less, the average for the medium-sized citiesis higher than for the suburbs. In addition,the average for the Randstad Holland as awhole is higher than that for the rest of the

Netherlands. Furthermore, the difference be-tween the more urbanised and less urbanisedenvironments outside the Randstad Hollandis much smaller than in the case of commut-ing distance.

Because the results in Table 2 may partlyreflect differences in population compositionand the variation around the mean values isconsiderable, we have estimated four multi-level regression models with sociodemo-

graphic factors and the typology ofresidential environments as independent vari-ables. Multilevel regression modelling is anelaboration of standard ordinary least squaresregression. Compared with standard re-gression modelling, parameter estimates andt-statistics especially for variables at thehigher levels of analysis (the residential mu-nicipality) are improved, because residualvariance is not captured by one but by sev-

eral variance terms. Technical details areprovided in Appendix 2. The sociodemo-graphic variables included in the analysisreflect socioeconomic status (household in-come, education), transport availability (caravailability), life-cycle (age, household type)and role within the household (gender, inter-actions of gender and household type). Theywere selected on the basis of extensivepreliminary analysis. Their estimatedcoefficients (Table 3) are consistent with ex-

pectations and previous analyses. For in-stance, in line with numerous other studies(Johnston-Anumonwo, 1992; Turner andNiemeier, 1997), our models show that gen-


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589URBAN FORM AND TRAVEL

Table2.

Dailytraveldistan

ce(km)andtime(minutes)per

personasacardriver,bytrippurposeandlevelofurbanisation

oftheresidentialmunicip

alityintheNetherlands,1998

Commuting

Shopping

Distance

Time

Distanc

e

Time

Standard

Standard

Standard

Standard

Mean

deviation

Me

an

deviation

Mean

deviation

Mean

deviation

RandstadHolland

Threelargecities

39.6

44.2

53.2

40.8

14.9

21.0

31.5

32.6

Medium-sizedcities

43.9

50.6

56.6

45.4

15.5

21.4

29.6

23.5

Suburbs

41.9

45.9

52.5

40.8

15.7

21.9

28.1

25.0

Growthcentres

43.9

45.2

56.1

43.5

16.9

23.8

29.5

25.7

Total

42.0

46.2

53.8

42.0

15.7

21.9

29.3

26.8


Moreurbanised

38.7

49.0

48.6

42.7

13.8

18.3

27.2

23.0

Lessurbanised

41.4

49.1

49.5

42.9

16.3

26.0

27.4

24.3

Note:Dataareaverages

forpeoplewhodriveacartoworkorforshopping.


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590 TIM SCHWANEN ET AL.

Table3.

Multilevelregressionmodelsfortotaldailytravel

distanceandtimeasacardriv

erforcommutingandshopping

Commuting

Shopping

Distance

Time

Distance

Time

Coefficient

T-statistic

Coefficient

T-statistic

Coef

ficient

T-statistic

Coefficien

t

T-statistic

Fixedpar

t

Constant

2.9

89

42.7

8

3.2

71

61.9

8

1.9

75

34.9

3

2.76

1

81.9

5

Sociodemo

graphicfactors

Age(inyears)

0.0

02

1.6

9

0.0

04

4.9

3

0.0

04

4.3

8

0.00

7

11.6

4

Female

0.5

35

17.5

4

0.2

93

13.4

2

0.2

66

11.2

3

0.14

1

8.8

4

Singlefem

ale,working

12hperweek

0.1

71

3.0

3

0.1

13

2.3

5

0.2

58

4.0

1

0.23

7

3.7

2

Singlefem

ale,working012hperweek

1.1

80

3.7

3

0.8

19

3.5

1

Femalein

two-workercouple

0.2

39

6.3

2

0.1

33

4.8

6

Femalein

one-workercouple

0.3

47

4.6

0

0.0

99

1.3

2

Femalein

one-workerfamily

0.1

07

2.0

1

Singleperson,working

12hperweek

0.0

59

1.9

0

0.12

2

2.7

1

Singleperson,working012hperweek

0.3

70

1.8

0

0.3

27

2.1

3

Two-work

ercouple

0.1

67

5.0

2

One-workercouple

0.1

95

3.6

0

Zero-work

ercouple

0.1

11

2.5

8

Two-work

erfamily

0.08

1

3.7

1

One-workerfamily

0.0

75

2.8

9

0.0

41

2.1

0

0.1

04

2.3

8

Familywithadult(s)working012hperweek

0.2

90

1.7

1

0.2

73

2.1

7

Otherhouseholdtypes

0.0

59

2.5

2

0.0

41

2.4

2

0.0

70

2.3

3

Caravailabilityindex

0.2

29

8.9

4

0.1

32

6.9

1

0.1

14

3.7

4

Employed

for

30hperweek

0.2

37

8.4

0

0.1

73

8.3

1

Loweducationalattainment

0.2

28

10.0

9

0.1

42

8.4

9

0.0

28

1.3

4

Mediumeducationalattainment

0.1

41

6.7

8

0.1

07

6.9

5

Household

income

0.0

03

5.4

6

0.0

02

4.0

7

Household

incomeisunknown

0.0

22

0.8

5

0.0

27

0.0

2


13/26


Residentialenvironment

RandstadHolland,

threebigcities

0.1

36

2.5

5

0.0

69

2.0

5

0.1

22

2.3

7

0.12

1

3.7

1

RandstadHolland,medium-sizedcities

0.0

17

0.2

9

0.1

26

3.5

1

0.0

74

1.3

7

0.11

2

3.4

5

RandstadHolland,suburbs

0.0

08

0.2

7

0.0

57

2.9

1

0.0

50

1.6

4

0.01

4

0.6

9

RandstadHolland,growthcentres

0.0

72

1.3

0

0.1

39

4.1

1

0.0

33

0.6

4

0.06

2

1.9

8

RestNetherlands,moreurbanised

0.1

55

5.0

1

0.0

35

1.7

7

0.1

23

4.1

5

0.01

8

0.9

6

RestNetherlands,lessurbanised

0

0

0

0

Random

part

Variancetermforindividuallevel(2

e0)

1.0

73

85.9

0

0.5

89

86.0

5

1.0

41

24.0

7

0.57

0

24.0

7

Variancetermforhouseholdlevel(2

u0)

0.1

15

2.7

6

0.67

4

29.5

4

Variancetermformunicipallevel(2

v0)

0.0

24

6.0

9

0.0

06

3.7

0

0.0

11

3.0

0

0.00

0

0.0

0

Numberofobservations

15215

15215

12034

12034

2loglikelihoodatconstant

45819.8

36277.1

36347.6

29117.7

2loglikelihoodatconvergence

44492.9

35260.0

35985.3

28713.7

Chi-square

dmodelimprovement

1327.0

1017.2

362.2

403.9

Dependentvariable

LN(dailytravel

LN(dailytraveltime

LN(

dailytraveldistance

LN(dail

ytraveltime

distanceasacardriver

asacardriverfor

asacardriverfor

asacardriverfor

forcommuting)

commuting)

shopping)

shopping)


14/26


der is an important determinant of commut-ing distance and time, but its effect dependson household structure. Table 3 also showsthat travel patterns for shopping are to alesser degree dependent on sociodemo-graphic situation than commuting travel.

Regarding the residential environment, theestimated coefficients largely corroborate theobservations on the basis of Table 2. Both forcommuting and shopping travel, distance as acar driver tends to fall with the degree ofurbanisation within and outside RandstadHolland. For travel time, the conclusions arealmost opposite: travel time rises with urban-isation level for shopping, while for commut-

ing time the highest coefficients areassociated with the growth centres and themedium-sized cities of the Randstad Hol-land. Only small differences exist in traveltime between more and less urbanised mu-nicipalities outside Randstad Holland.

For travel modes other than the privatecar, the association between the degree ofurbanisation and travel time is similar to thatbetween distance and urbanisation level.

Whereas there is no discernible relationshipbetween time spent walking to shops andresidential environment, the average distanceand time travelled by bicycle are greater inmore urbanised environments (Table 4). Thesame is true for commuting and leisure travel(Dieleman et al., 2002; Schwanen, Dijst andDieleman, 2002). In contrast, travel distanceand time for public transport modes tend tobe lower in more urbanised environments.

This finding is related to the fact that, in thethree large cities and medium-sized cities,more people make use of public transportsystems at the city or daily urban systemlevel. Such systems operate at higher fre-quencies here than elsewhere in the Nether-lands.

What conclusions about the effectivenessof national spatial planning policies can bedrawn from Tables 24? A first conclusion isthat, despite the fact that the commute dis-

tance and time for car drivers vary with thetype of residential environment, the import-ance of urban form in influencing commutingbehaviour should not be overestimated. The

variation within residential environmenttypes is large, as the standard deviations inTable 2 show. Furthermore, the varianceterms estimated in the multilevel models inTable 3 can be used to determine the share ofthe municipal level in the total variation in adependent variable. These shares can be cal-culated by dividing the estimate for the resi-dential level in a model with only a constantincluded as an explanatory variable by thesum of all variance estimates in such a model(Snijders and Bosker, 1999). For the fourvariables analysed here, these shares are: 2.6per cent (commuting distance as a cardriver); 1.7 per cent (commuting time as a

car driver); 1.0 per cent (distance as a cardriver for shopping); 0.4 per cent (travel timeas a car driver for shopping). These resultsclearly illustrate that variations between trav-ellers within residential municipalities aremuch larger than differences between suchspatial units. Similar conclusions can befound in Herz (1983) and Weber and Kwan(2003).

The fact that travel times tend to be longer

in more urbanised environments is both apositive and a negative outcome of nationalphysical planning policy. On the one hand,the slightly greater travel times in more ur-banised environments depress car use. Thus,for shopping travel we might infer that thecombination of strict retail planning and theconcentration of housing development withinor adjacent to existing built-up areas hascontributed to travel patterns that are less

detrimental from an environmental perspec-tive than would have been the case had manylarge-scale shopping malls been developed inurban fringe areas. On the other hand, thelonger travel times in the more urbanisedenvironments may have important second-order effects (Priemus et al., 2001). It hasrepeatedly been shown that households tendto favour residential locations providinggood access, or limited travel time, to em-ployment locations (Brun and Fagnani, 1994;

McDowell, 1997) as well as shopping oppor-tunities (Lerman, 1976) and recreational ac-tivities (Guo and Bhat, 2002). Manyhouseholds might thus prefer residential


15/26


Table4.Averagedailytraveldistance(km

)andtime(minutes)perperson

forshoppingactivities,bytran

sportmodeotherthanthepriva

tecarandlevel

ofurbanisationin

theNetherlands,1998

Cycling

Walking

Train

Bus/tram

/metro

Distance

Time

Distance

Time

Distance

Time

Distance

Time

Standard

Standard

Standard

Standard

Standard

Standard

Standard

Standard

Mean

deviation

Mean

deviation

Mean

deviation

Me

an

deviation

Mean

deviationM

ean

deviation

Mean

deviation

Mean

deviation

RandstadHolland

Threelarge

cities

4.6

4.6

25.2

23.5

1.7

1.7

20

.7

20.3

32.9

22.7

73.9

43.8

10.0

7.9

46.6

42.3

Medium-sizedcities

4.4

3.6

23.3

18.6

1.7

1.7

21

.1

21.2

52.5

35.9

80.1

38.7

12.9

12.4

51.4

31.1

Suburbs

4.2

4.3

21.5

19.1

1.8

1.7

21

.6

21.5

60.6

60.4

89.6

59.3

18.4

13.3

50.7

32.5

Growthcentres

4.4

4.0

22.2

16.6

1.7

1.7

20

.3

25.4

38.6

27.5

61.6

26.5

20.6

17.4

54.4

36.8

Total

4.4

4.2

23.0

20.0

1.7

1.7

21

.0

21.4

48.1

44.2

79.3

47.9

12.4

11.2

48.4

39.6


Moreurbanised

4.4

3.9

22.4

19.5

1.7

1.7

20

.8

23.3

93.3

98.0

1

07.2

82.9

16.0

26.9

44.4

45.9

Lessurbanised

3.9

3.9

19.9

17.3

1.5

1.5

20

.8

27.9

49.4

36.2

69.9

35.3

22.0

16.4

47.9

28.5

Note:Da

taareaveragesforpeoplewho

goshoppinganduseagivenm

ode.


16/26


locations where travel to relevant activitiestakes less time and therefore choose to relo-cate to a lower-density environment if theywere given the opportunity to do so. In otherwords, a further suburbanisation of house-holds may be one of the longer-term conse-quences of the compact-city policy and thismay offset some of the short-term benefits,such as fewer car trips and car kilometrestravelled.

In short, we may conclude that spatialpolicies have probably only made a modestcontribution to an increase in travelefficiency in terms of travel distance andtime in certain parts of the urban system or in

travel behaviour in the country as a whole.Some additional empirical evidence can begiven to support this conjecture.

First, while comparisons of travel patternsin urban areas in different countries aredifficult because of large differences in typeof analysis and level of detail, some empiri-cal evidence about travel distance and time isavailable to put the above results in an inter-national perspective. This material suggests

that, notwithstanding its strong planning tra-dition, commuting patterns in the Nether-lands are not exceptionally efficient. A studythat compared average commuting distancesand times across metropolitan regions re-vealed that Amsterdam takes an intermediateposition in Europe (Schwanen, 2002). Forthe country as a whole, the evidence points ata less rather than a more efficient commute.The Dutch commute on average 46 minutes

per day, substantially more than in manyother countries in Europe (NRC Handels-blad, 2001). This comparatively long timeseems to result from a combination of longercommute distances (Jansen, 1993) and moresevere road congestion (Jansen et al., 2002;Bovy and Salomon, 2002). However, theNetherlands do differ from other west Eu-ropean countries in terms of the share of totaldistance that is covered by slow transportmodes. Whereas the share of walking and

bicycling in the total distance was 4 per centin 1990 for western Europe as a whole (Hart,2001), this share was 12 per cent for theNetherlands (Dieleman et al., 1999). Expla-

nations for this difference are, for instance,the pro-public transport and bicycle planningand less positive attitudes towards the car inthe Netherlands.

Secondly, the policy of concentrated de-centralisation does not seem to have resultedin more efficient commuting patterns. Tables2 and 3 indicate that, for car drivers, totalcommute distances and times are larger ingrowth centres than in other suburban partsof the Randstad Holland. These high aver-ages seem to be primarily related to thequantitative and qualitative mismatch be-tween workers and employment in the newtowns. In quantitative terms, the number of

jobs has long lagged behind the number ofhouses in these growth centres, turning theminto dormitory towns (van der Laan, 1998).Over time, however, the number of jobs inthese settlements has increased. Neverthe-less, many workers in growth centres resideelsewhere and many residents of these newtowns work elsewhere, suggesting that theskills and preferences of growth-centre resi-dents correspond less with the available jobs

than in other suburban communities (qualita-tive mismatch). For shopping, the differencesbetween growth centres and other suburbanlocalities are less pronounced, indicating thatover time retailing supply in the new townshas reached a mature state, comparable withthat in other communities.

Thirdly, a comparison of commuting be-haviour in monocentric and polycentric areasof the daily urban systems (DUS) in the

Netherlands provides little evidence of spa-tial planning efforts having created efficienttravel patterns. Rather, the reverse seems tobe the case. It has often been argued thatpolycentric urban areas have developed be-cause of households and employers prefer-ence for locations where congestion islimited (Anas et al., 1998; Carlino and Chat-terjee, 2002). To escape congestion, house-holds and their members periodically changejobs and/or residential location, so that they

can travel shorter distances, or use less con-gested routes. Employers move to lower-density environments for similar reasons.Nevertheless, there is a tendency among


17/26


Table 5. Average daily travel distance (km) and time (minutes) for commuting asa car driver, by type of daily urban system in the Netherlands, 1998

Commuting distance Commuting time

Standard Standard

Average deviation Average deviation

Centralised 37.6 46.1 49.5 41.1Decentralised 40.9 45.3 53.6 41.9Cross commuting 35.5 44.4 47.4 38.2Exchange commuting 42.9 54.2 53.3 50.7

Note: Data are averages for car-driving commuters.

firms to concentrate in suburban nodes of

development, or edge cities (Garreau, 1991),where they can benefit from agglomerationeconomies resulting from the presence andproximity of other firms. This line of reason-ing is known as the co-location hypothesis;Gordon and colleagues (1989a, 1989b and1991) and Levinson and Kumar (1994) pre-sent some empirical support with data from arange of US urban areas. However, the con-tention that polycentrism leads to more

efficient commuting patterns has been chal-lenged. Clark and Kuijpers-Linde (1994) andCervero and Wu (1998), for example, foundthat car travel times and distances are higherin polycentric than in monocentric areas.1 Bycomparing commuting behaviour in 26 urbanareas in the Netherlands, we have investi-gated how polycentrism is associated withlower commute times and distances for cardrivers in the Netherlands. The classification

of daily urban systems (DUSs) developed byvan der Laan (1998) and shown in Figure 3has been used for this.

By and large, commuting is less efficientfor car drivers in polycentric than in mono-centric regions (Table 5). Only in archetypalpolycentric regions consisting of several rela-tively self-contained concentrations of em-ployment and residences outside the old citycentre (cross-commuting DUSs) are averagecommute distances and times lower than in

traditional, centralised DUSs. However, thenumber of cross-commuting systems in theNetherlands is very small (Figure 3); mostpolycentric regions belong to the decen-

tralised type, where average commute times

and distances are much higher. Schwanen etal. (2003b) show that the differences be-tween monocentric and polycentric urban ar-eas persist after accounting for differences inthe sociodemographic variables, residentialdensity, urban size and other metropolitanstructure indicators.

In our view, spatial planning policies canbe held to account at least in part for therelatively large commute distances and times

in most polycentric-oriented urban regions inthe Netherlands. While residential growthhas been concentrated in central-city loca-tions and VINEX neighbourhoods, the A-B-Clocation policy has failed and much employ-ment was situated on car-accessible locationsthat were poorly served by public transportand could not be reached readily on foot orbicycle. As a consequence, a limited numberof new housing units have been built near

new or strongly growing employment con-centrations, many of which can be found at aconsiderable distance from existing residen-tial areas. Several reasons for this mismatchcan be given, including high land prices andthe relatively scarcity of land. This scarcityfor development is at least partly the result ofthe national governments sustained effortsto keep the Green Heart open. A chain ofevents has led this scarcity of land to becomeone of the causes of the severe distortions of

the Dutch housing market, which is currentlycharacterised by insufficient new construc-tion, extremely high prices in the owner-occupier stock and long waiting-lists in the


18/26


extensive social-rented sector.2 For manyhouseholds, it has become very difficult torent or buy a dwelling near the main bread-winners workplace location, or to find em-ployment at a short distance from thehouseholds residential location.

4. Conclusions and Discussion

The aim of this study was to assess theimpact of Dutch spatial policies on modalsplit and efficiency in travel distance andtime. Table 6 summarises the implicationsfor travel patterns of the main spatial policiesdiscussed in section 2: the policy of concen-

trated decentralisation of the 1970s and1980s; the strict compact-city policy of the1980s and 1990s; the A-B-C policy in the1990s; retail planning policy in the past threedecades. It should be stressed that Table 6does not present the results of formal tests ofthe impact of each of the policies mentionedon travel. Instead, the table reflects ourevaluation of how national spatial planninghas affected travel behaviour in the Nether-

lands through its regulation of the develop-ment of urban form. In addition, it is difficultto separate the impact of physical planningon travel patterns from other influences, suchas pricing measures impacting travel costs.However, we do feel that these tentativeresults illuminate some of the strengths andweaknesses of spatial planning policy andhence may have some relevance for policy-makers seeking to influence travel behaviour

through spatial-planning measures.As Table 6 indicates, the policy of concen-trated decentralisationthe development ofgrowth centreshas probably stimulated theuse of public transport, but has not led tomore cycling or walking. As a result, car useis only slightly lower in growth centres thanin suburban environments. Furthermore,travel distance and time are large in growthcentres. The strict compact-city policy, thesuccessor to the policy of creating growth

centres, seems to have had some positiveoutcomes in terms of travel patterns. Theempirical evidence presented indicates lowertravel distances as well as higher shares for

public transport and cycling and walking. Ithas been suggested, however, that the strictcompact-city policy might have some lessbeneficial effects in the longer term. It mayencourage the suburbanisation of householdsto lower-density environments because oftravel time considerations, which would re-sult in higher shares for and larger distancesby the private car. The A-B-C policy shouldhave led to more public transport use, butfew of the aims of (re)directing employmentgrowth have been met. The policys impacton travel behaviour seems to have beenslight; the only effect seems to have beenthat, as a result of developments in the vicin-

ity of large railway stations, the shares ofpublic transport for commuting at these loca-tions have increased slightly. Perhaps thegreatest success of Netherlands national spa-tial planning so far is that, thanks to spatialretail planning, walking and cycling forshopping still prevail and that, comparedwith other European countries, the use of thecar for shopping is relatively limited.

Having said that spatial planning policy

influenced travel behaviour in the Nether-lands, but only to a limited extent and notalways as intended, we may ask ourselveswhether further efforts should be made to tryto influence travel behaviour through spatialplanning. The discussants in the debate onthe role of planning can be broadly dividedinto two camps. There are the liberals, likeGordon and Richardson (1997), who arguethat market mechanisms result in efficient

settlement patterns in terms of travel andenergy use. In their view, land-use regula-tions and subsidies destabilise the market;direct investment in central cities and publictransport lead to inefficient land-use patterns.Then there are the regulators, like Ewing(1997) and Cervero (1998), who advocate theregulation of the development of urban formby planners to curtail the costs of urbansprawl, such as the growing use of the pri-vate car.

The discussion in the previous sections onphysical planning and travel patterns in theNetherlands does not provide a convincinganswer to the question whether spatial plan-


19/26


Table6.

Performan

ceofnationalspatialplanningpoliciesintheNetherlandsintermsoftravelefficiency

M

odalchoice

Stimulation

Stimulationof

Shortertravel

Shorter

Reduction

ofpublic

cyclingand

distance

traveltime

ofdriving

transport

walking

(privatecar)

(privatecar)

Policyofconcentrated

decentralisation(1970san

d1980s)

Strictcompact-citypolicy

(1980sand1990s)

A-B-Clocation

Policy

Retailplanning

/

policymadepositive

contribution;

policyhadlittle(neutral)effect;-policymadenegativecontribution.


20/26


ning helps to create travel patterns that aremore efficient in terms of travel distance andtime. The message from the preceding sec-tions is that the promotion of relatively com-pact urban structures can have positiveeffects on walking, cycling and the use ofpublic transport. However, strict designationof a small number of greenfield sites wheredeconcentration and urban growth are to beaccommodated is counter-productive, be-cause short travel times and distances wouldnot be the result. On the contrary, these latteraims might have been reached more readilyunder a regime of fewer land-use controls. Inthat case, the co-location of residences and

employment in close proximity to each othermight have occurred to a greater extent (Gor-don and Richardson, 1997; Levinson andKumar, 1994).

The argument the liberals usethat it isless rather than more land-use control thatmakes travel patterns efficientis very im-portant, given the fact that we are currentlyheading towards a network society, also ingeographical terms (Castells, 1996; Hayer

and Zonneveld, 2000). This type of society ischaracterised by highly diversified and spa-tially expanding activity and travel patternswhich have to be realised within tight timebudgets. The growth of the network societyseems to undercut the ideas of proximity andland-use control as ways to influence socio-spatial development. New spatialconfigurations emerge that can probably becharacterised better by nodes and flows than

in terms of land-use patterns. As a conse-quence, travel will increasingly be perceivedin time spent rather than distance covered(Dijst, 1999; Hayer and Zonneveld, 2000). Inour opinion, spatial planners have underesti-mated this temporal dimension of travel be-haviour in their policies. For instance, theplanning concept of the compact city usesgeographical proximity as the organisingprinciple and may become less relevant inthe light of the emerging network society.

Although a situation of more freedom inlocation choice behaviourthat is fewerrestrictions imposed by physical planningpoliciesmight result in time-efficient

settlement patterns, it will probably result ina dispersion of urban functions to lower-density environments and areas that are nowbeing used for agriculture, recreation andnature conservation. In relation to this disper-sion, the potential for public transport will bevastly reduced. This reduction will have anegative impact on the daily life of thosewho cannot afford a car because of financialconstraints or physical impairments; manypeople in this category belong to low-incomehouseholds, are female and/or have reachedretirement age.

Planners are thus facing a dilemma. Thebest way to resolve it, we believe, is to

develop a spatial planning approach combin-ing the best of both worlds. In the remain-der of this paper, we discuss an outline ofsuch an approach. It aims to optimise thecompetitiveness of public transport and theslow transport modes in terms of patronageand time efficiency. Although the approach istailored to the Dutch situation, we believethat the underlying principles have a widerapplicability, in particular to other countries

in north-west Europe.First, the use of public transport and walk-

ing and cycling can best be promoted bystimulating relatively compact urban areas.With respect to mode choice, Dutch spatialplanning has been most effective in theRandstad Holland (section 3). In terms oftravel time and travel distance, however, themore urbanised settlements outside the Rand-stad (at least 25 00050 000 inhabitants) per-

form quite well. These results lead us tothink that spatial policies aiming at the con-centration of land uses in smaller citieswould be highly beneficial from a travel-behaviour perspective. For the UK, Banister(1992) reached a comparable conclusion.

This national spatial policy is a necessary,but not sufficient, condition for improvingthe competitiveness of public transport. Thepolicy has to be supplemented by invest-ments in relatively high-speed public trans-

port systems, such as intercity trains at thenational level and light-rail and metro sys-tems at the conurbation level (Newman andKenworthy, 2000). These systems can


21/26


guarantee travel times which are competitivewith the private car (Bovy et al., 1990; vanden Heuvel, 1997). On the other hand, atthe local level, competition between thebicyclea cheap and flexible transportmodeand public transport should be pre-vented as far as possible. Rather, the comple-mentarity of the environmentally friendlytransport systems of the bus/tram/metro andthe bicycle should be promoted. The inte-gration of both systems could be improvedby allowing the transport of bicycles on pub-lic transport vehicles such as trams and met-ros. Furthermore, demand-responsive publictransport could replace line-haul local sys-

tems to guarantee public transport to all po-tential users.

The competitiveness of public transportmay also be improved by local land-use poli-cies focusing on nodes in the public transportsystem. In general, the attractiveness of pub-lic transport decreases rapidly beyond 500800 metres from a public transport stop. It istherefore necessary to encourage high-density, mixed-use development within 500

800 metres of a public transport stop(Cervero, 1996b). In our opinion, a system offinancial incentives would contribute to thedevelopment of such public transport nodes.Two types of incentive might be offered. Onthe demand side, firms and facilities wouldreceive an incentive when locating in publictransport nodes. The magnitude of this incen-tive would depend on the distance to the stopand the amount of land occupied. Short

walking distances and efficient use of space(high-rise buildings for example) would berewarded. In addition, incentives would beoffered to real-estate developers on the sup-ply side of the market. The more varied interms of land use a (re)developed publictransport node became, the greater the incen-tive the developers could receive. Becauseshort walking and cycling distances would bepromoted, these locational incentives wouldfavour not only the use of public transport

but also walking and cycling.Opponents of the regulations described

here may argue that this approach would notimprove the efficiency of travel times or

distances. A strict designation of new build-ing sites for commercial use at a small num-ber of public transport nodes would not becapable of providing sufficient opportunitiesfor households, firms and services to(re)locate to sites in close proximity to eachother. Hence, the best of both worlds plan-ning approach should also include a partialderegulation of the land and housing mar-kets.

According to the World Bank (1993), oneway of making the housing market workmore efficiently would be to select a largenumber of competitive new greenfield sitesfor development and equip these with the

necessary infrastructure provisions (such asroad and rail networks) far in advance of theconstruction of new housing and other landuses. Such a partial deregulation could yieldthree kinds of travel-related benefit. First, alarge supply of new building sites in publictransport nodes would improve the competi-tiveness of public transport, cycling andwalking as outlined above. Secondly, thesupply of high-quality public transport in

new residential areas before the arrival of thefirst new residents would offer better oppor-tunities for discouraging the development ofcar-based travel patterns. Finally, a largesupply of housing would encourage residen-tial mobility, which could result in moreefficient travel times and distances for bothpublic transport and private car users.

The combination of spatial policies at na-tional and local levels, investments in public

transport systems and a system of locationalincentives would give ample opportunities tohouseholds, firms and services to makeefficient locational choices. At the sametime, this spatial planning approach wouldprovide public-transport captives withsufficient opportunities for travelling in to-days society and preserve valuable culturaland natural landscapes.

Notes

1. A detailed review of these and other studiesis presented in Schwanen et al. (2002 and2003a).


22/26


2. The problematic situation of the Dutch hous-ing market can be illustrated by means of thenumber of housing units available per 1000inhabitants. For the Netherlands, this figureis 406, whereas the weighted mean for 9other European countries is 443 (Feddes andDieleman, 2000).

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Appendix 1

Degree of urbanisation is a composite variablewhich incorporates various dimensions of urbanform: urban size, residential density, distance tothe urban core, the degree of land-use mixing andprovision of infrastructure. The variable distin-


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guishes first between the Randstad Holland andthe Rest of the Netherlands, because residentialdensities in the former are much greater and morepublic transport infrastructure is available. Withinthe Randstad Holland, we discern from most toleast urbanised: three large cities (Amsterdam,Rotterdam, The Hague); medium-sized cities; andsuburbs. The growth centres are considered to bea separate category. The national governmentconsciously planned these settlements during the1970s and 1980s to become self-contained com-munities, in order to curb the decentralisation ofhouseholds and firms from the larger cities. How-ever, they turned into dormitory towns whoseinhabitants had to travel extensively to reach thedestinations they preferred to visit. Outside theRandstad Holland, a distinction has been drawnbetween more urbanisedand less urbanisedareas.

With regard to urban structure, we use aclassification of daily urban systems in four cate-gories based on van der Laan (1998), rather thandrawing a distinction between monocentric orpolycentric urban areas

(1) Centralised: these resemble monocentric sys-tems in which morning peak-hour commutingis primarily directed towards the core city ofthe DUS.

(2) Decentralised: many morning commuters areattracted to the suburban parts of the system,where much employment is located.

(3) Cross commuting: these structures resemblethe classic polycentric region consisting ofrelatively independent, self-contained devel-opment nodes. Suburban commuters tend towork in the suburbs; core-city residents oftenwork in the core city.

(4) Exchange commuting: these systems havemany reciprocal relationships between thesuburbs and the core city. Many suburbancommuters work in the city, while many cen-tral-city residents work in the suburbs.

Appendix 2

Multilevel models expand the random part of amodel; rather than summarising the residual vari-ance in a single random parameter as in ordinaryleast squares regression, more than one randomterm is estimated to accommodate the nestedstructure of the data. A random-intercept modelwith three levels of analysisfor instance, indi-viduals within households within municipalitiescan be written as (Goldstein, 1995)

yijk0iX1ijk (e0ijku0ijk0k)

where, yijk represents the dependent variable at thelowest level of analysis; 0 indicates the intercept;

X1ijk is an independent variable at the level of theindividual; and 1 a fixed regression; coefficientto be estimated. The parentheses in the equationindicate the random part of the model. The ran-dom term e0ijk refers to the individual, u0jk to thehousehold; and 0k to the municipality of resi-dence. These random terms are assumed to bemutually independent and to be normally dis-

tributedthat is, they have a mean of zero andcan be summarised by their variances 2e0,

2u0

and 2v0, respectively. The coefficients of multi-level models are estimated with maximum likeli-hood estimation procedures (more details inGoldstein, 1995).


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