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PAPERREF#8030Proceedings:EighthInternationalSpaceSyntaxSymposium

EditedbyM.Greene,J.ReyesandA.Castro.SantiagodeChile:PUC,2012.

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THEEFFECTSOFURBANFORMONWALKINGTOTRANSIT

AUTHOR: AyeZBILOkanUniversityDepartmentofArchitecture,Istanbul,Turkey

email:[email protected]

JohnPEPONISGeorgiaInstituteofTechnologyCollegeofArchitecture,Atlanta,UnitedStates

email:[email protected]

KEYWORDS: StreetConnectivity,LandusePatterns,WalkingforTransit,Atlanta,SpatialStructureofUrbanLayouts

THEME: MethodologicalDevelopmentandModeling

AbstractThisstudyanalyzesanonboard transitsurveyconductedby theAtlantaRegionalCommission inorder to

determinehowfarurbandensity,mixed landuses,and streetnetwork connectivityare related to transit

walkmode shares to/from stations. The data are drawnfrom all the stations ofAtlantas rapid transit

network(MARTA).Overall,theanalysespresentedinthisstudyconfirmthehypothesisthat localconditions

aroundMARTA rail stationsare significantly related to riders choice towalk to/from transit. The results

emphasizetheimportanceofincludingmeasuresofstreetconnectivityintransitorientedstudies.Itisshown

that street connectivity is stronglyassociatedwithwalkmode shareswhen controllingfor transit service

characteristicsas

well

as

population

density,

land

use

mix

and

personal

attributes.

The

research

findings

have several implications. They confirm that transit oriented policies are better supported by urban

developmentpoliciesand zoningand subdivision regulations thatencourage transitfriendlyurbanforms.

Findingsalsoaugmenttheknowledgebasethatsupportstransitorienteddevelopmentbyemphasizingthe

contributionofthespatialstructureofthestreetnetwork,overandabovetheimpactofsidewalkprovision

anddesignandpedestriansafety.

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Proceedings:EighthInternationalSpaceSyntaxSymposium.

SantiagodeChile:PUC,2012.

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OBJECTIVES

The aim of this study is to determine how far urban density, mixed landuses, and street network

connectivityarerelatedtotransitwalkmodesharescontrollingforsociodemographicattributesandtransit

service features. The underlying hypothesis is that environments that are connected so as to support

differentkindsofwalkingalsosupportpublictransportation.Usingtraveldatafromthe20012002Atlanta

RegionalOnBoardTransitSurvey,multivariateregressionequationsareestimatedwithin0.25,0.5,and1

mileradiiaroundMARTArailstationspredictingwalkmodeshares.Assuch,thisstudyaimstobuildupon

the growing literature onwalkmode choice by investigating towhat extent local conditions of station

environmentscontributetoanexplanationofvariations intransitaccess/egresswalkingshares,definedas

thenumberofriderswalkingfromwithinarangeasaproportionoftotalridership.Thisresearchrepresents

animportantcontributiontowardunderstandingtheextenttowhichstreetnetworkconnectivityinfluences

thechoicetowalkfortransitacriticaldimensionofoverallqualityoflife.

RESEARCHBACKGROUND

Previousstudieshaveusedvariousmeasuresofthebuiltenvironmenttocapturetheeffectsofurbanform

ontravelmodechoice,butmostoftheliteraturehasbeenframedaroundthreedimensionsofurbanform:

density,diversityoflanduseandstreetnetworkdesign.

Theprodensityargumentconsidersdensityasthemost importantfactoraffectingtravelchoices(Smith

1984;MarshallandGrady2005;BadoeandMiller2000).Aplethoraofrecentstudieshavesuggestedthat

compact developments with higher densities encourage nonmotorized travel by reducing the distance

betweenoriginsanddestinations;byofferingawidervarietyofchoicesforcommutingandabetterquality

of transit services; and by triggering changes in the overall travel pattern of households (Cervero and

Kockelman 1997; Krizek 2003; Ewing et al. 1994). Conclusions regarding the relative importance of

employmentandpopulationdensitiesoncommutemodechoiceprovidesomeevidencethattheprobability

of walking (both for work and nonwork trips as well as walking for commute) increases at higher

populationdensities (grosspopulationdensityat triporiginsanddestinations)andathigheremployment

densities(grossemploymentdensityatoriginsonly),controllingforavarietyofsociodemographicfactors

thatinfluencetransportchoice(FrankandPivo1994;ReillyandLandis2002;Chatman2003).Ontheother

sideofthedebate,otherstudiescontendthatanyassociationbetweenurbanformandtravelbehavior is

due to the intervening relationship between density and various factors such as income levels, auto

ownership rates, costandefficiencyof transit service,and the supplyandpriceofparking (Meyer1989;

ParsonsBrinkerhoffQuadeandDouglasInc.1996b,PushkarevandZupan1982;GomezIbanez1996).Thus,

it seems imperative that conclusions regarding density should be considered in conjunctionwith transit

serviceandsociodemographicattributes.

Recentstudiesexploringthelandusetransportationconnectionhaveverifiedhighlevelsoflandusemixat

thetriporiginsanddestinationsastheprimarydriverofmodechoice(BhatandPozsgay2002;Rodriguez

and Joo 2004; Schwanen andMokhtarian 2005). Studies regarding themeasurable impacts of landuse

characteristicsontravelhaveshownthattheproportionsoftripsbypublictransitandwalking increaseas

the intensityandmixingof landuses ishigher(Cervero1996;Cervero2002;CerveroandKockelman1997;

Frank and Pivo 1994). This is reflected in different trip generation rates and (sometimes)mode shares

attributedtodifferentlandusedevelopmentpatterns.Thus,itisarguedthatimprovingthediversityofuses

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in neighborhoods through flexible zoning can reduce automobile dependence and encourage walking

(Rajamanietal.2003).

Incontrasttothefocusontheeffectofdensityandlanduseontravelbehaviour,therehasbeenrelatively

lesserattentionontheimportanceattributedtostreetnetworkdesign.Forstreetnetworkdesign,prevalent

measuresofconnectivityhavebeenlimitedtoaveragemeasuresofstreetnetworks,suchasthenumberof

intersections,percentofgriddedstreets,andaverageblocksizesperarea.Acommonthemeofthisbodyof

research is that inordinate size of street blocks or the lack of a finegrained urban network of densely

interconnected streets fails topromotehigherwalking rates for transport (Kerretal.2007;Cerveroand

Gorham1995)and increasedproportionandnumberofutilitarianandnonworkwalk trips (Handy1996;

Moudonetal.2006;LeeandMoudon2006).Apartfromaveragemeasuresofstreetdensity,somestudies

haveinvestigatedtheunderlyingdifferencesofstreettypes,suchasthedistinctionsbetweentraditionalvs.

suburbanandgridvs.culdesac,toshowastatisticallysignificantrelationshipbetweenstreetdesignwitha

gridlikegeometryand increased frequencyofwalking trips (Shriver1997;GreenwaldandBoarnet2001;

Handy1992;Rajamanietal.2002;KhattakandRodriguez2005).

In spite of the burgeoning literature concernedwith street connectivity, conclusions about the relative

importanceof streetnetwork configuration inoverall travelbehavior remainsunclear.One reason is the

absence of commonly accepted measures that capture the internal structure of urban areas. The

significance of spatial structure in affecting pedestrian movement has been addressed through the

frameworkof configurationalanalysisof space syntax.Empirical studieshave shown that road segments

thatareaccessiblefromtheirsurroundingswithfewerdirectionchangestendtoattracthigherflows(Hillier

1996; Peponis andWineman 2002). From a point of view of this study, the key implication of previous

syntacticstudies isthatourunderstandingofhowstreetnetworks impactbehaviorsandperformancesof

differentkinds issignificantly improvedwhenweapplystrongerdescriptivemethodsandbettermeasures

ofspatialproperties.Asecond reason fortheweakexplanatorypowerofstreetnetworkdesign inurban

models is the absence of rich landuse and urban design data. Themodels employed by the broader

literatureonurbanformandpedestrianbehaviorhaveturnedtorelativelylargerunitsofanalyses,suchas

TrafficAnalysisZones(TAZs),censustracts,orblockgroups.Thesegrossgeographicunitsestimateaverage

regionalurbanformcharacteristics,failingtocapturefinegrainedlanduseanddesignaspectsessentialfor

understanding travel impacts of smallscale placeoriented projects.Anothermethodological dilemma of

studyingthetravelimpactsofstreetnetworkdesignisthemulticollinearitybetweenurbanfeatures.Clearly,

the foregoing findings point to the fact that urban formmeasures are interrelated since denser areas

typicallyhavehigher landusemixtures,onaveragehigherstreet intersectionsperareawithmoregridiron

streetnetworkpatterns(ParsonsBrinkerhoffQuadeandDouglasInc.1996a).

Thisstudyattempts toovercomesomeof themethodologicaldrawbacksunderlinedhere in twoaspects.

First,usingconnectivitymeasureswhicharesensitivetoboththesinuosityandthedensityofthenetwork,

the impactsof street layoutonwalkingareassesedmore rigorously,controlling for themulticollinearity

causedbyvariousotheraspectsofthebuiltenvironment.Second,thestatisticalmodelsdevelopedinclude

highlydisaggregatedataatthesegmentandparcellevelwithrespecttostreetnetworkdesignandlanduse

data.Thesesmallerunitsofanalysispreventtheunfairadvantageofdensitymeasures,generallymeasured

ataprecisemetricscale,overlanduseanddesignmeasures,computedthroughcoarserindices,anddetect

walkingimpactsofurbanformmoreclearly.Giventhecomplexityofthefactorsreviewedhereanyattempt

todevelopalternativebehavioraltheoriesandtoarriveatcomprehensiveexplanatorymodelswouldexceed

thescopeofthisstudy.Rather, thestrategy inthisresearch is to focusonsomeparticularregularitiesof

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interesthowfardostreetnetworksencouragemorepeopletowalktothestationasaproportionoftotal

ridership.

CASECONTEXTANDDATA

MARTAstationsarecharacterizednotonlybytheirowncharacteristics, includingthefrequencyofservice

andridership levels,butalsobythepropertiesofthesurroundingareas.Surroundingareasofstationsare

identifiedascirclesof0.25,0.5and1mileradiustojudgehowtheradiusdistancefortheanalysisaffects

results.Thisstudyreliesoncurrentlyavailabledatasourcesonsociodemographics,landusecompositions,

grossdensities,andstreetnetworksforsuchareas.

Definitionofthestudyarea

Figure1illustratesthegeographicallyaccuraterepresentationofMARTArailsystemoverlaidonthemapof

Atlanta.Asshown,thetransitsystem isboundedwithinmetroAtlanta;only4stations,namelyDunwoody,SandySprings,NorthSprings,andIndianCreek,liebeyondI285.

Figure1.RealgeometryofthesystemoverlaidonthemapofAtlantawithinI285.Thegreylinesrepresentroadswhiletheredlines

denotethefreewaysystem.

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Dependentvariable:proportionofriderswalking

Using traveldata from the20012002RegionalOnBoard Transit Survey, thewalkmode sharedatawas

extractedfromthetraveldataof individualriders(n=13,751). It istheratiooftotalwalktripstothetotal

ridershipbystation.Inotherwords, itrepresentsthepercentofwalking, includingbothaccessandegress

walkmodeshares.

Independentvariables

The independentvariablesemployed in themodelswere selected fromamultitudeof factors thatwere

showntobesignificantlyrelatedtomodechoicebytheliterature,andweregroupedintothefollowingsix

categories:

1. Connectivity:Themeasures of connectivity applied in this research have been developed atGaTech to allow for the

analysisofstandardGISbasedrepresentationsofstreetnetworksaccordingtostreetcenterlines(Peponisetal.2008).Theunitofanalysisistheroadsegment.Roadsegmentsextendbetweenchoicenodes,orstreet

intersectionsatwhichmovementcanproceedintwoormorealternativedirections.Figure2illustratesthe

newunitofanalysisbyclarifyingthedifferencebetweenroadsegmentsandlinesegments.

Figure2.Definitionoflinesegmentsandroadsegments.Source:Peponisetal.2008.

Metric reach captures thedensityof streetsand streetconnectionsaccessible fromeach individual road

segment. This ismeasured by the total street length accessible from each road segmentmoving in all

possibledirectionsup toaparametrically specifiedmetricdistance threshold.Directional reachmeasures

theextenttowhichtheentirestreetnetworkisaccessiblewithfewdirectionchanges.Thisismeasuredby

thestreetlengthwhichisaccessiblefromeachroadsegmentwithoutchangingmorethanaparametrically

specifiednumberofdirections. Figure 3 illustrates the twomeasures. In this researchmetric reachwas

computed for1,0.5and0.25milewalkingdistance thresholds.Directional reachwas computed for two

directionchangessubjecttoa10anglethreshold.Acompositeconnectivitymeasure(metricreachdivided

bythecorrespondingdirectionaldistance,subjecttoa10anglethreshold)wasalsoaddedtocalculatethe

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ratioofmetric reach to theaveragedirectionaldistanceassociatedwith it.Thiscompositevariable takes

highervaluesasstreetdensityincreasesandasaccesstostreetsbecomesmoredirect.Inotherwords,road

segmentsfromwhichmorestreet length isaccessiblewithinthewalkingradius,takingfewerturnstoget

everywhere,drawgreatervolumesofpedestrians.

Figure3.Diagrammaticdefinitionofsegmentbasedconnectivitymeasures.Source:Peponisetal.2008.

2. Accessibility:Sidewalkavailabilitymeasuring thepercentageofstreetswithsidewalkthatareaccessible topedestrians

withinwalkingrangesofstations.

3. Density:Populationdensity (people ingross acres)within1,0.5, and0.25mile radiiof stationswere established

usingUS2000censusdata.

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4. LandUse:Mixeduseentropyindex

1,basedonaformuladerivedfromCerveroandKockelman(1997),Cervero(2006),

and Greenwald (2006), was computed using parcelbased landuse data acquired from the database

developed at the Center for GIS at Georgia Tech for the SMARTRAQ program (Goldberg et al. 2006).

Separateentropyindiceswerecomputedfor0.25,0.5,and1mileradiiaroundeachMARTArailstation.

5. Transitservicefeatures:Transitservicefeatures,namelysupplyofparkandridefacilities

2,servicefrequency

3,feederbusservices

4,

andstationstructures5were included inordertocontrolforthe impactsoftransitoperationalanddesign

factorsonwalkinglevels.

6. Sociodemographics:A composite sociodemographic variable was developed to control for personal and household

characteristics.Autoownership relativizedbypercapita incomemeasures the ratioofautoownership to

percapitaincome(annualhouseholdincomedividedbyhouseholdsize).

MODELINGWALKINGASTRANSITACCESS/EGRESSMODECHOICE

We produced standard regressionmodels and reducedmodels forwalkmode shareswithin 1mile

rangetoidentifythestatisticalsignificancelevelsofallvariablesandtocapturetheuniquecontributionsof

connectivitymeasurestotheoverallmodel.Thestandardmodel includesall independentvariables.The

reduced model shows the extracted measures which are statistically significant at 5% level in the

standardmodel. Thenonurban form variableswere entered into the regression first to allow for the

evaluationofurbanformvariablesincontextrelativetootherfactorsaffectingtravelbehavior.Urbanform

measureswere thenadded into themodel respectively todemonstrate theeffectofaddingeach to the

model and to inferwhether some variables could be eliminated in the finalmodel without noticeably

increasingtheresidualsumofsquares.Whenmultivariateregressionsarerunforthreerangesseparately,

thecoefficientofdeterminationisfoundtobeconsiderablyhigherfor1milerange.Eventhoughtherelative

effectsizeofmetricreachisconsistentacrossallranges,ofamileappearstobeanoverlylimiteddistance

thresholdsinceitfailstocapturetheeffectsoflandusemix.Thus,resultsat1milerangearereportedhere.

Table 1 shows the results of standard regression models for 1mile radii including the connectivity

measuremetricreachasthestreetconnectivityvariable.

1

k

pp

entropyuseMixed

k

i ii

ln

ln

1 1

2numberofstationparkingspaces

3numberofinboundtrainsinampeakhour(7am9am)

4availabilityandnumberoffeederbusesarrivingatstation

5typesofstationstructure:atgrade,elevated,underground

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Fromtherelativeeffectsizesitisclearthattheprimaryfactorsinexplainingpredictabilityaremetricreach

and landusemix.This result indicates that thedecision towalk to/from transit is significantlyassociated

withthedensityofavailablestreetsandmixingoflanduseswithinalargersurroundingcontextofstations.

Somewhat surprisingly, the population density coefficient is positive but not significant. Thismight be

supportiveoftheargumentthatemploymentdensityexertsastronger influenceonthevariation inmode

choice forwalking(FrankandPivo,1994),andthatcombinedpopulationandemploymentdensitieshasa

greater degree of explanatory power overmode shares (Parsons Brinkerhoff Quade and Douglas Inc.,

1996a). Thus, future research should take into account employment density in addition to population

density.

Standard models also point to statistically significant associations between nonurban variables and

walkingshares.Consistentwiththeory,walkmodesharesaresensitivetotransitservicelevelsandpersonal

attributes.Thecoefficientonthefeederbusvariableindicatesthattheavailabilityoffeederbusservicesat

stationsisnegativelyassociatedwiththeproportionofwalking,withmorepeoplechoosingtoridethebus

to/fromstationsthantowalk.

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Multivariate regressionmodelsestimatedby including thecompositeconnectivitymeasure,metric reach

divided by the corresponding average directional distance based on metric reach (10), follow similar

patternswiththeearliermodelsincludingmetricreach.Table2reportstheresultsofstandardregression

model for1mileradii.Resultsreveal thataside fromstreetdensityand landusemix,spatialstructureof

urbanareasalsomattered.Thestandardizedcoefficientforthecompositeconnectivitymeasureispositive

and statistically significant.The signand significanceof the coefficient remains consistentevenafter the

inclusion of other urban formmeasures, controlling for nonurban form factors. This indicates that the

Table

1.Effecttestsformultivariateregressionsestimatingthe

proportionofwalkingwithin1mileb

ufferforallstations

consideredasasingle

set

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configurationofstreetnetworksatthescaleofanindividualareaisareasonablysignificantpredictorofthe

variation inwalkmode shares at stations.Moreparticularly, the composite connectivitymeasure,which

takes intoaccountbothstreetdensityandtheshapeandalignmentofstreetsas indexedbythedirection

changesneededtonavigatethesystem,isclearlyassociatedwithriderschoicetowalkfortransit.

Table 3 shows the results of reduced models by including metric reach (1mile) and the composite

connectivitymeasure,metric reach divided by the corresponding average directional distance based on

metricreach(10), for1mileradii.Comparisonsofcoefficientswithinthereducedmodelsprovideuseful

Table

2.Effecttestsformultivariate

testsformultivariateregressions

multivariateregressionsestimatingthe

regressionsestimatingtheproportionof

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insightsabouttheindividualcontributionofurbanformmeasures.Resultssuggestthattheprimaryfactors

in explaining predictability are connectivitymeasures and landusemix. Stationswith highermetric and

directionalaccessibilityaswellasmaximallymixeduseswithintheircatchmentareasattractmorewalkon

riders, evenwhen controlling for other factors. In fact, street network overpowers the effects of socio

demographic characteristics and transit features. Therefore it would appear that in addition to street

density,spatialstructurebasedondirectionalbiasisindeedimplicatedinthewayinwhichstreetnetworks

functiontosupportwalking.

Table3.Parameterestimatesandresidualplotsforthereducedmodelsbyincluding(a)metricreach(1mile)and(b)thecomposite

connectivitymeasure,estimatingtheproportionofwalkingwithin1milebufferforallstationsconsideredasasingleset.

(b) ReducedModel

totalriderswalked/total

ridershipperstation B t std

constant 0,15

servicefrequency 0,00 2,47 0,26

feederbusservices(no) 0,06 3,33 0,28

mixedlanduseindex 0,77 6,80 0,61

avg.metricreach(1mile)/

directionaldistance(10)0,02 3,91 0,42

N 37

R2 0,81

R2adjusted 0,79

std.error,Se

0,06

Prob>F 0,00

Numbersinbold=p

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DISCUSSION

Overall, the analyses presented here confirm the hypothesis that local conditions aroundMARTA rail

stations are significantly associated with increased transit access/egress walkmode shares. Statistical

models developed reveal thatmeasures of street network design and landusemix aremost strongly

associatedwithwalkingshares,whencontrollingforpopulationdensity,transitservicecharacteristics,and

personalattributes.Whilemixeduseneighborhoodsaroundstations increasetheoddsofwalkingto/from

transit,streetnetworkswithdenserandmoredirectconnectionsareassociatedwithhigherproportionof

walking shares among station patrons. Importantly, the results presented here also underscore the

significance of the spatial structure of street networks, specifically the alignment of streets and the

directionaldistancehierarchyengenderedbythestreetnetwork.Directionalaccessibilityplaysassignificant

aroleasmetricaccessibilityinaffectingtheproportionofriderswalkingfortransit.Thespatialstructureof

street network does notwork independently of landuse. On the contrary, based on the standardized

coefficientsestimatedinregressionmodels,streetnetworkandlandusemixhavecomparablyhighpositive

impactsontransitwalkmodeshares.

Apart from theorybuilding, this research alsoholds validity formorepractical implications.The findings

confirmthehypothesisthatwellstructuredanddifferentiatedstreetnetworksaffecttransitaccess/egress

walkmode shares.These resultsare likely toguide futureefforts to integrate subdivisionprovisionsand

regulationswith zoning regulations indeveloping currently sparse suburbanareas towardsdense transit

orientedurbanhubs.Traditionalmodelsestimatingdevelopmentimpactsarebasedontheconsiderationof

sociodemographic factors and transit service related features, but they do not take into account the

structuralqualitiesofstreetnetworks.Theevidenceinthisstudyconfirmsthepremisethatthedemandfor

public transportrelated walking is significantly influenced by the configuration of street layout. Thus,

incorporatingmeasuresofstreetdensityandmeasuresofdirectionalaccessibilityintransitorientedstudies

canleadtoenhancedmodelsofurbanformandfunction,which,inreturn,caninformspecificurbandesign

andurbanmasterplanningdecisions.Findingsalsosuggestthattransitorientedpoliciesarecompatiblewith

policiesaimedattheenhancementofhealthandthereductionofobesitythroughdailyphysicalactivity

walkingto/fromthestationcancontributeasignificantpartofthedailyactivityrecommendedbyHealthy

LivingGuidelines (USDepartmentofHealthServices1996).Finally findingsaugment the knowledgebase

thatsupportstransitorienteddevelopmentbyemphasizingthecontributionofthespatialstructureofthe

streetnetwork,overandabovetheimpactofsidewalkprovisionanddesignandpedestriansafety.

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