Post on 03-Jun-2018
8/12/2019 1-2-br (25)
1/15
PAPERREF#8030Proceedings:EighthInternationalSpaceSyntaxSymposium
EditedbyM.Greene,J.ReyesandA.Castro.SantiagodeChile:PUC,2012.
8030:1
THEEFFECTSOFURBANFORMONWALKINGTOTRANSIT
AUTHOR: AyeZBILOkanUniversityDepartmentofArchitecture,Istanbul,Turkey
email:ayse.ozbil@okan.edu.tr
JohnPEPONISGeorgiaInstituteofTechnologyCollegeofArchitecture,Atlanta,UnitedStates
email:john.peponis@coa.gatech.edu
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.
8/12/2019 1-2-br (25)
2/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:2
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
8/12/2019 1-2-br (25)
3/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:3
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
8/12/2019 1-2-br (25)
4/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:4
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.
8/12/2019 1-2-br (25)
5/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:5
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
8/12/2019 1-2-br (25)
6/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:6
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.
8/12/2019 1-2-br (25)
7/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:7
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
8/12/2019 1-2-br (25)
8/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:8
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.
8/12/2019 1-2-br (25)
9/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:9
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
8/12/2019 1-2-br (25)
10/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:10
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
8/12/2019 1-2-br (25)
11/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:11
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
8/12/2019 1-2-br (25)
12/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:12
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.
REFERENCES
Badoe,D.&Miller,E.(2000).Transportationlanduseinteraction:empiricalfindings inNorthAmerica,and
theirimplicationsformodeling.TransportationResearchPartD:TransportandEnvironment,5,235263.
Bhat, C.R. & Lockwood, A. (2004). On Distinguishing Between Physically Active and Physically Passive
EpisodesandBetweenTravelandActivityEpisodes:AnAnalysisofWeekendRecreationalParticipation In
theSanFranciscoBayArea.TransportationResearchPartA:PolicyandPractice,38(8),573592.
Bhat, C.R. & Srinivasan, S. (2005). A Multidimensional Mixed OrderedResponse Model for Analyzing
WeekendActivityParticipation.TransportationResearchPartB,39(3),255278.
8/12/2019 1-2-br (25)
13/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:13
Bhat,C.R.,&Pozsgay,M.A.(2002).DestinationChoiceModelingforHomeBasedRecreationalTrips:Analysis
and Implications for Landuse, Transportation,andAirQualityPlanning. TransportationResearchRecord:
JournaloftheTransportationResearchBoard,1777,4754.
Bhat,C.R.,&Zhao,H.(2002).TheSpatialAnalysisofActivityStopGeneration.TransportationResearchB,36
(7),593616.
Boarnet,M.G.&Crane,R.(2001).Travelbydesign:Theinfluenceofurbanformontravel,OxfordUniversity
Press,USA.
Boarnet, M.G.,&Sarmiento,S. (1998).CanLandUsePolicyReallyAffectTravelBehavior?AStudyofthe
LinkbetweenNonworkTravelandLandUseCharacteristics.UrbanStudies,35(7),11551169.
Cervero,R.&Gorham,R. (1995).Commuting in transitversusautomobileneighborhoods.Journalof the
AmericanPlanningAssociation,61,210225.
Cervero, R. & Kockelman, K. (1997). Travel demand and the 3Ds: density, diversity, and design.
TransportationResearchPartD:TransportandEnvironment,2,199219.
Cervero, R. (1996). Mixed landuses and commuting: evidence from the American Housing Survey.
TransportationResearchPartA:PolicyandPractice,30,361377.
Cervero,R. (2002).Builtenvironmentsandmode choice: towardanormative framework.Transportation
ResearchPartD:TransportandEnvironment,7,265284.
Cervero,R.(2006).Alternativeapproachestomodelingthetraveldemandimpactsofsmartgrowth.Journal
oftheAmericanPlanningAssociation,72,285295.
Chatman,D.G.. (2003).HowDensityandMixedUsesattheWorkplaceAffectPersonalCommercialTravel
andCommuteModeChoice.TransportationResearchRecord:JournaloftheTransportationResearchBoard,
1831,193201.
Ewing,R.H.,Haliyur,P.&Page,G.(1994).Gettingaroundatraditionalcity,asuburbanPUD,andeverything
inbetween.TransportationResearchRecord:JournaloftheTransportationResearchBoard,1466,5362.
Frank,L.&Pivo,G.(1994).RelationshipsbetweenlanduseandtravelbehaviorinthePugetSoundRegion.
FinalSummaryReport,preparedfortheWashingtonStateTransportationCommission.
Goldberg,D.,Chapman,J.,Frank,L.,Kavage,S.&Mccann,B.(2006).NewDataforaNewEra:ASummaryof
theSMARTRAQ Findings; Linking LandUse, Transportation,AirQuality andHealth in theAtlantaRegion.
SMARTRAQSummaryReportSmartTraqandSmartGrowth.
GomezIbanez,J.(1996).BigCityTransitRidersnip,Deficits,andPolitics:AvoidingRealityinBoston.Journal
oftheAmericanPlanningAssociation,62,3050.
Greenwald,M.J.(2006).Therelationshipbetween landuseand intrazonaltripmakingbehaviors:Evidence
andimplications.TransportationResearchPartD:TransportandEnvironment,11,432446.
8/12/2019 1-2-br (25)
14/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:14
Greenwald,M.J.&Boarnet,M.G.(2000).Builtenvironmentasdeterminantofwalkingbehavior:analyzing
nonwork pedestrian travel in Portland, Oregon. Transportation Research Record : Journal of the
TransportationResearchBoard,1780,3342.
Handy, S. (1992).Regional versus local accessibility:neotraditionaldevelopment and its implications for
nonworktravel.BuiltEnvironment,18,253267.
Handy, S. (1996).Understanding the link between urban form and nonwork travel behavior.Journal of
PlanningEducationandResearch,15,183198.
Hillier,B.(1996).Spaceisthemachine:aconfigurationaltheoryofarchitecture,CambridgeUniversityPress.
Kerr, J., Frank, L., Sallis, J.& Chapman, J. (2007). Urban form correlates of pedestrian travel in youth:
Differencesbygender,raceethnicityandhouseholdattributes.TransportationResearchPartD:Transport
andEnvironment,12,177182.
Khattak,A.J.&Rodriguez,D.(2005).Travelbehaviorinneotraditionalneighborhooddevelopments:Acase
studyinUSA.TransportationResearchPartA,39,481500.
Krizek,K.(2003).Residentialrelocationandchanges inurbantravel:Doesneighborhoodscaleurbanform
matter?JournaloftheAmericanPlanningAssociation,69,265281.
Lee, C.&Moudon, A. (2006). The 3Ds+ R:Quantifying land use and urban form correlates ofwalking.
TransportationResearchPartD:TransportandEnvironment,11,204215.
Marshall, N. & Grady, B. (2005). Travel demandmodeling for regional visioning and scenario analysis.
TransportationResearchRecord:JournaloftheTransportationResearchBoard,1921,4452.
Meyer,M.(1989).AToolboxforalleviatingtrafficcongestion,InstituteofTransportationEngineers.
Moudon,A.,Lee,C.,Cheadle,A.,Garvin,C.,Johnson,D.,Schmid,T.,Weathers,R.&Lin,L.2006.Operationaldefinitions of walkable neighborhood: theoretical and empirical insights. Journal of Physical Activity &
Health,3,99117.
ParsonsBrinkerhoffQuadeandDouglasInc.(1996a).TCRPReport16:TransitandUrbanForm.Washington,
D.C:TRB,NationalResearchCouncil.
ParsonsBrinkerhoffQuadeandDouglasInc.(1996b).Influenceoflandusemixandneighborhooddesignon
transitdemand.UnpublishedreportforTCRPH1project).Washington,D.C.:TransitCooperativeResearch
Program,TransportationResearchBoard.
ParsonsBrinkerhoffQuadeandDouglasInc.,CambridgeSystematics&CalthorpeAssociates.(1993).Making
theLandUse,Transportation,AirQualityConnection(LUTRAQ).Portland,OR:1000FriendsofOregon.
Peponis, J. &Wineman, J. (2002). Spatial structure of environment and behavior. In: Bechtel, R., and
Churchman,A.(ed.)Handbookofenvironmentalpsychology.NewYork:JohnWileyandSons.
Peponis, J., Bafna, S. & Zhang, Z.Y. (2008). The connectivity of streets: reach and directional distance.
EnvironmentandPlanningBPlanning&Design,35,881901.
8/12/2019 1-2-br (25)
15/15
Proceedings:EighthInternationalSpaceSyntaxSymposium.
SantiagodeChile:PUC,2012.
8030:15
Pushkarev, B. & Zupan, J. (1982).Where TransitWorks: Urban Densities for Public Transportation. In:
Levinson, H.S.,Weant, R.A . (ed.) Urban Transportation: Perspectives and Prospects.Westport, CT: Eno
Foundation.
Rajamani,J.,Bhat,C.R.,Handy,S.,Knaap,G.,&Song,Y.(2002).Assessingtheimpactofurbanformmeasures
innonworktripmodechoiceaftercontrollingfordemographicandlevelofserviceeffects.Paperpresented
atTRB
Annual
Meeting.
Reilly,M.&Landis,J.(2002).TheInfluenceofBuiltFormandLandUseonModeChoiceEvidencefromthe
1996BayAreaTravelSurvey.FinalReport,preparedforthe InstituteofUrbanandRegionalDevelopment
UniversityofCalifornia,Berkeley,WP4(1).
Rodriguez,D.&Joo,J.(2004).Therelationshipbetweennonmotorizedmodechoiceandthelocalphysical
environment.TransportationResearchPartD:TransportandEnvironment,9(2),151173.
Schwanen, T. & Mokhtarian, P.L. (2005). What affects commute mode choice: neighborhood physical
structureorpreferencestowardneighborhoods?JournalofTransportGeography,13,8399.
Shriver, K. (1997). Influence of environmental design on pedestrian travel behavior in four Austin
neighborhoods.TransportationResearchRecord:JournaloftheTransportationResearchBoard,1578,6475.
Smith,W. (1984).Mass transport forhighrisehighdensity living.Journalof Transportation Engineering,
110,521535.
U.S.DepartmentofHealthandHumanServices.(1996).PhysicalActivityandHealth:AReportoftheSurgeon
General. Atlanta, GA:U.S. Department of Health and Human Services, Centers for Disease Control and
Prevention,NationalCenterforChronicDiseasePreventionandHealthPromotion.