exploring bicycle route choice behavior with space … · exploring bicycle route choice behavior...
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Exploring Bicycle Route Choice
Behavior with Space Syntax Analysis
Zhaocai LiuZiqi Song,Ph.D.
AnthonyChen,Ph.D.Seungkyu Ryu,Ph.D.
DepartmentofCivil&EnvironmentalEngineeringUtahStateUniversity 1
Motivation
• Cyclingcanimproveurbanmobility,livabilityandpublichealth,anditalsohelpswithreducingtrafficcongestionandemissions.Understandingtheroutechoicebehaviorofcyclistscanpromotebicycletransportation.
• Cyclists’routechoicebehaviorisinfluencedbymanyfactors.Travelers’cognitiveunderstandingofthenetworkconfigurationhasbeenoverlookedbypreviousstudies.
• Spacesyntaxtheorycananalyzetravelers’cognitiveunderstandingofthenetworkconfiguration.
• Thecombinationofspacesyntaxtheoryandotherbicycle-relatedattributescanprovidebetterexplanatorypowerinmodelingcyclists’routechoicebehavior.
2Asaresult,wewanttoexploretheapplicationofspacesyntaxinmodelingbicycleroutechoicebehavior.
SpaceSyntaxTheory
•IntroducedbyHillerandHanson1984.•Originallyusedinarchitecturetomodeltheinfluenceofthespacestructureofabuildingonthemovementofpeopleinit.•DevelopedattheSpaceSyntaxLaboratoryatUniversityCollegeLondon.•Hasbeenappliedinurbanplanning,transport,socialinteractionandspatialeconomics
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ApplicationsinTransportation
Theprocedureoftraveldemandestimationwithspacesyntax:
1) Representthenetworkwithagraphbyso-calledaxialanalysis
2) Measureconfigurationthroughtopologicaldistanceinthegraph,withoutmetricweighting
3) Predicttrafficflowdistributionbasedontheconfigurationalmeasurements.
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AxialAnalysis
UnitSpace:Axialline
Definition:Thestraightroadsegmentthroughwhichtripmakersfindtheirextentofvisibility.
AxialMap:Urbanspacesuchasroadsandstreetsaremodeledbyaxiallines.
DualGraph:Eachaxiallineisrepresentedasanode,andtheintersectionsbetweenaxiallinesarerepresentedaslinks.
(a)Roadnetwork(b)Axialmap(c)Graphrepresentation
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AccessibilityofUnitSpace:Integration
6
1.MeanDepth(MD)
SpaceSyntaxtypicallydescribethetopologicalconnectionsofunitspacethroughthenotionofdepthanalysis.
Whenmovingfromonespacetoitsconnectedspace,thereisatransitionofspace.Inspacesyntax,thetransitionofspace,whichisalsocalledsteporturn,istheunitofmeasurementof“distance”.
Thedistancefromonespacetoanotherspaceiscalleddepth.Themeandepthfromonespacetoallotherspacecanrepresenttheconnectivityofthespaceinthesystem.
3
1
2
1Step
1Step
1
(1,2) 1(1,3) 2
1 2 1.52
dd
MD
==+
= =
2
(2,1) 1(2,3) 1
1 1 12
dd
MD
==+
= =
( , )
1
( , ) the steps between space and
i kk
d i kMD
k
d i k i k
¹=-
=
å1
1Step
2
3
AccessibilityofUnitSpace:Integration
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2.RelativeAsymmetry(RA)
Whenaspaceisdirectlyconnectedtoallotherspaces,ithasthelowestmeandepth.Inspacesyntax,weconsiderthisspacehasthehighestsymmetricity.
Whenaspaceneedtotravelthelongesttopologicaldistanttoreachotherspaces,ithasthehighestmeandepth.Inspacesyntax,ithasthelowestsymmetricity.
1( 1)( ) 11
nMD lowestn-
= =-
1
2
3
n
32
1
1Step
n
n
1
2
Depth(k-1)
Depth11
2
3k
1(1) 2(1) ( 1)(1)( )1 2n nMD highest
n+ + + -
= =-L
( )( ) ( )1 2( 1)
/ 2 1 2
kk
k k
MD MD lowestRAMD highest MD lowest
MD MDn n
-=
-- -
= =- -
AccessibilityofUnitSpace:Integration
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3.RealRelativeAsymmetry(RRA)
Therelativeasymmetries(ofunitspaces)oftwodifferentsystemcannotbecompared,becausethesizeofasystem(nvalue)alsoinfluencestheaccessibilityoftheunitspaces.Thus(Hillieretal.1984)proposedafactorDn torelativise theRA.
222 log 1 13
( 1)( 2)n
kk
n
nnD
n nRARRAD
æ öæ + öæ ö - +ç ÷ç ÷ç ÷è øè øè ø=- -
=
AccessibilityofUnitSpace:Integration
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4.Integration
Itdescribeshowclosely(ordistantly)thespaceistopologicallyaccessiblefromallotherspaceswithinagivensystemaddressingitssymmetricity andsize.
1kIntegrationRRA
=
GlobalIntegrationandLocalIntegration
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•GlobalIntegration:itmeasureshowcloselyordistantlyeachspaceisaccessiblefromallotherspacesofasystem.
•LocalIntegration:integrationanalysisisrestrictedatalowerdepthofconnectivitytodeterminetheaccessibilityofthespaceatalocalorneighboringlevel.Forinstance,inanintegrationradius-3analysis,onlythespacethatarethreedepthsawayareconsidered.
AngularSegmentAnalysis
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•IntroducedbyTurner(2001)•Axiallinesarebrokenintosegments•Stepbetweentwoconnectedsegmentsisweightedbasedontheanglebetweenthem.
(a) 1.0 step between line 1
and 2
(b) 0.5 step between line 1
and 2
(c) 𝜃/90 step between line 1
and 2
TravelDemandEstimation
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•Spacesyntaxhasbeenusedtomodeldifferentmodeoftransportationincludingvehicle,metro,bicycleandpedestrian.
•Globalintegrationßà Vehiculartraffic•Localintegrationßà Pedestriantraffic
•Regressionanalysisisusedtocalibratethecorrelationbetweenintegrationandactualtrafficvolume.
No. Source Studyarea R-square Remarks1 Hillier1998 BalticHousearea 0.773 Pedestrian2 Hillieretal.1987 Bransbury 0.6413 Hillier1998 Santiago 0.54 Pedestrian4 Hillieretal.1987 Islington 0.536 Pedestrian
5 Eisenberg2005 Waterfront,Hamburg 0.523 Pedestrian
6 Peponisetal.1997 SixGreektowns 0.49 Pedestrian
7 Karimietal.2003 CityIsfahan 0.607 Vehicular
8 Peponisetal.1997 Buckhead,Atlanta 0.292 Vehicular
9 Paul2009 CityofLubbock,Texas 0.18 Vehicular
SpaceSyntaxinModelingBicycle
•Limitedworkshavebeendone(i.e.,Raford etal.2007;McCahill andGarrick2008;Manum andNordstrom2013)•Theresultsarenotasidealasexpected.•Bicycletrafficisatransportationmodethatfallssomewherebetweenvehicularandpedestriantraffic.Aspecificprocedureandproperspacesyntaxmeasurementneedstobedetermined.•Otherbicycle-relatedattributesalsoneedtobeconsidered.
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Methodology
Bicycle-relatedAttributes
(1) Linkcognition:representedbyspacesyntaxmeasurements(2)Segmentbicyclelevelofservice(BLOS):evaluatedbasedon
HCM(2010)(3)Motorvehiclevolume(4)Linkpollution:estimatedbasedonanonlinearmacroscopic
modelofWallaceetal.(1998)(5)Presenceofbicyclefacilityonalink(6)Averageslopeofterrainonasegment
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Methodology
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StatisticalModeling
Linearregressionwasusedtoanalyzetherelationshipbetweenbicyclevolumeandvarioussegmentattributes.
𝑌" = 𝛽% + 𝛽'𝑋'" + 𝛽)𝑋)" +⋯+ 𝛽+𝑋+"where𝑌"=thebicyclevolumeonlink𝑎𝑋+" =thevalueofexplanatoryvariable𝑚 onlink𝑎.𝛽+ =modelcoefficientforvariable𝑚.
CaseStudy
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BicycleCountsinSaltLakeCity
Date:Sep15th(Tue),16th(Wed),17th(Thu),19th(Sat),and20th(Sun).Location:19intersectionsDuration:2hourseachday,5-7pmonweekdays,12-2pmonweekends
Statistic All Counts Weekday Weekend
Number of counts 95 57 38
Minimum 2 7 2
Maximum 161 129 161
Median 47.0 47 42
Mean 54.8 54.1 55.9
Standard deviation 35.7 31.3 41.8
SummaryStatisticsfor2-hourBicycleCounts
CaseStudy
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SpaceSyntaxAnalysis
Globalintegrationandlocalintegrationwithametricradiusof3kilometers(1.86miles)werecalculatedusingsegmentanalysis.
(a) Global Integration
(b) Local Integration
5.1 to 1069.27 5.1 to 2962.79
CaseStudy
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SupplementaryData
• Motorvehiclevolume:annualaveragedailytraffic(AADT)datafromUDOT.
• Dataaboutspeedlimitandnumberoflanes:UDOT• Bicyclelanedata:SaltLakeTransportationDivision• Terrainslopedata:digitalelevationmodel(DEM)from
UtahAutomatedGeographicReferenceCenter(AGRC)• Otherdata:estimatedbasedonHCM(2010)
CaseStudy
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RegressionAnalysis
Localintegrationismoreappropriateinmodelingbicycletraffic.
ModelVariable
Coefficients
GlobalIntegrationModel LocalIntegrationModel
Constant
18.877
(0.325)
7.056
(0.332)
IntGa
0.001
(0.617) -
IntLa -
0.010
(0.005)
R-square 0.016 0.396
F-statistic
0.261
(0.617)
10.502
(0.005)
CaseStudy
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RegressionAnalysis
Thecombinationoflocalintegrationandmotorvehiclevolumesprovidesastatisticallysignificantmodelwhichhasmoreexplanatorypower.
Othermodelsareeitherstatisticallynon-significantorunreasonable.
Model1 Model2 Model3
Constant
264.884
(0.075)
137.917
(0.333)
18.200
(0.039)
IntLa
0.014
(0.002)
0.012
(0.002)
0.013
(0.001)
BSega
-20.242
(0.242)
-8.295
(0.648)
Motva
-0.001
(0.497)
-0.001
(0.191)
-0.001
(0.041)
PSega
-4526.53
(0.068)
-2107.57
(0.352)
BikeLa
0.298
(0.970) -
Slopea
4.978
(0.034) -
R-square 0.731 0.588 0.547
F-statistic
4.986
(0.011)
4.635
(0.015)
9.055
(0.003)
Summary
• Localintegration,whichdescribestheaccessibilityataneighboringlevel,ismoreusefulinmodelingbicycleroutechoice.
• Spacesyntaxtheoryispromisinginanalyzingcyclists’cognitiveunderstandingofthenetworkconfiguration.
• Thecombinationofspacesyntaxmeasurementandotherbicycle-relatedattributescanimprovetheexplanatorypoweroftheregressionmodel.
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