Safety in numbers for cyclists – conclusions from a multidisciplinary study of seasonal change in interplay and conflicts

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ContentslistsavailableatScienceDirect

Accident Analysis and Prevention

j o ur na l h o me pa g e :w w w . e l s e v i e r . c o m / l o c a t e / a a p

Safety in numbers for cyclists—conclusions from a multidisciplinary study of seasonal change in interplay and conﬂicts

A. Fyhri

^a,∗

, H.B Sundfør

^a

, T. Bjørnskau

^a

, A. Laureshyn

^a,b

aDepartmentofSafetyandtheEnvironmentInstituteofTransportEconomics,NorwayGaustadalleen21,0349OSLO,Norway

bDepartmentofTechnology&Society,FacultyofEngineering,LTH,LundUniversity,Sweden

a r t i c l e i n f o

Articlehistory:

Received7December2015

Receivedinrevisedform2March2016 Accepted27April2016

Availableonline28May2016

Keywords:

Interplay Near-misses Videoobservations Trafﬁcconﬂicts Cyclistsafety

a b s t r a c t

InmanyEuropeancountries,itisapoliticalgoalthatfuturegrowthinlocaltravelshouldbeabsorbed bysustainabletransportmodes.Concernsthatincreasedwalkingandcyclingproducemoreaccidents havebeencounteredbythe“safetyinnumbers”(SiN)argument.AccordingtoSiN,themorewalk- ers/cycliststhereareinapopulation,thelowertheirrisk.SiNhasbeendemonstratedincrosssectional andlongitudinalstudies,butthemechanismsbehindtheeffecthaveyettobeproven.

Previousstudieshavemostlyreliedonregisterdata.Thecurrentstudy,carriedoutin2013and2014 teststheexistenceofthiseffectinamorecontrolledmanner.Thisisachievedthroughtheuseofthree datasets:(1)roadsidesurveydatawithcyclists,pedestriansandcardriversfromOslocarriedoutat threetimepointsinthecyclingseason(2)apanelstudycoveringthesametimeperiod,and(3)video observationsatfourdifferentlocationsinOslo.Byexploitingthenaturalseasonalvariationincycling frequency,andbyusingarepeatedmeasuresdesignwecanfurthercontrolforotherfactorssuggested toliebehindtheSiNmechanism,suchasdifferencesininfrastructureandtrafﬁcculture.

TheresultssuggestthatbicyclistsexperienceashorttermSafetyinNumberseffectthroughtheseason.

Eachindividualcyclistexperiencesfeweroccasionsofbeingoverlookedbycarsandfewersafetycritical situations(near-misses).Videoobservationdataconfirmthispattern.However,theSiNeffectseemsto becounteredbyanothermechanismtakingplaceatthesametime:theinfluxofinexperiencedandrisk- takingcycliststhroughtheseason.Thuscardriversandpedestriansalsoreporttofindthemselvesbeing surprisedbycyclistsintrafficlateintheseason.

1. Introduction

Acommonargumentagainstashiftfrommotorizedtonon- motorized travel is the concern about a potential increase in numbersof accidentsresulting from sucha policy.A common counterargumentisSafetyinnumbers.SafetyinNumbers(SiN)is usedtoexplainthenon-linearstatisticalrelationshipsbetweenthe numberofpedestrians(orbicyclists)andthenumberofinjuriesfor thesamegroup(Elvik,2009;Geyeretal.,2006;Jacobsen,2003).The mechanismhasbeenproven inanumberofcrosssectionaland longitudinalstudies,summarisedinaquiterecentmeta-analysis (ElvikandBjørnskau,2016).Theconcepthasbeensubjecttodebate, regardingitsexistence(BhatiaandWier,2011),itsmathematical characteristics(Brindle,1994;Elvik,2013;Knowlesetal.,2009) and alsorelated tothis, regarding a clearunderstandingof the mechanismbehindtheeffect.

∗Correspondingauthor.

E-mailaddress:[email protected](A.Fyhri).

The mechanism that has most frequently been proposed, is that motorists become more attentive, and change their behaviour,whenexposed tohighernumbersofpedestriansand cyclists(Jacobsen,2003).Anotherpossiblemechanismisimproved interplaybetweenroadusersgroupswhenroadusersacquireexpe- rience witheach other,and develop more correct expectations (Phillips etal.,2011).Stillanothersuggestedmechanismisthat thecyclistsandpedestriansenteringthepopulationatalaterstage maybemoreriskaverseandcautious(Fyhrietal.,2012).Ithas alsobeensuggestedthattheeffectcanbearesultofsaferenvi- ronmentalconditions,includingengineeringcountermeasuresor differencesinpedestriannormsandbehaviours(BhatiaandWier, 2011).However,thesehypotheseshaveyettobetested.Knowl- edgeaboutthesemechanismsisessential(BhatiaandWier,2011) andisnecessarytoadoptasafeactivetransportpolicyaimingata shifttoincreaseduseofsustainableurbantransport.

TheScandinaviancountries,andinparticularNorwayareinter- estingcasestotesttheSiNeffect,asthereisasubstantialseasonal variationinbicycleuse.Thecycleshareinwinterisintherangeof 1–2%ofalltrips,andrisesto8%insummer(Hjortholetal.,2014).

http://dx.doi.org/10.1016/j.aap.2016.04.039

0/).

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Pedestriansareamoresteadypresenceintrafﬁc.Infact,theshare ofpedestriansissomewhathigherinwinter,around22%,anddrops toaround18%insummer(probablyduetosomebicyclistsshifting towalkingwhenconditionsarenotgoodenoughforcycling).Inthe currentstudy,wewilltesttheattentiveness-mechanismbylooking atinterplayintrafﬁcasafunctionofseasonalvariationinbicycle use.

Theseasonalvariationissubstantial,meaningthateveryspring thereisadramaticincreaseinthenumberofbicyclesthatother roadusersareexposedtoeachsubsequentweek.Bystudyingcon- ﬂictsandinteractionsatthesamestudysites,itispossibletokeep aclosecontrolwithanyotherpotentialinﬂuencingfactors,and onlylookattheeffectofchangesintheshareofoneoftheroad usergroups.Inotherwords,thissituationcanbeusedasanexper- imentofwhethermotoristsbecomemoreattentive,andchange theirbehaviour,whenexposedtoanincreasingnumberofcyclists.

Trafﬁcaccidentsareoftenaresultofinadequateroaduserinter- action,butresearchontheimportanceofroaduserinteractionfor accidentsisratherlimited.Theimportanceofcorrectexpectations andtheabilitytopredictotherroadusers’behaviourhasnotbeen studiedmuch,despitethefactthatsuchabilitiesarevitalinorderto avoidaccidents(Bjørnskau,1994;Bjørnskau,1996;Rothengatter, 1991).

Whentheproportionsofdifferentroadusergroupschange,for instancethroughanincreaseinsofttransportmodes,interaction patternsmayalsochange.Bjørnskau(2016)hasdocumentedhow roaduserinteractioncanchangeovertimeasaresultofdynamic interplay.Oneexampleispedestriancrossings,wherecarsyieldto cyclistscontrarytothetrafﬁcrules(Bjørnskau,2016).Anotheris hownovicedriverschangetheiruseoftheheadlightsandadaptto thedominantpracticeofdipping,contrarytowhatisprescribedin drivereducation(Bjørnskau,1994).

Studyinginteractionamongroadusers,ratherthanbehaviour fromonesingleroadusergroup,createssubstantialmethodological challenges,whichmightbeonereasonforthescarcityofprevious controlledexperimentalstudies.InthecontextofSafetyinNum- bers,arelevantexperiencefromabicyclist’spointofviewisthat ofbeingoverlookedbyotherroadusers.However,whetherabicy- clistisoverlookedinagivensituationwilldependonthebicyclists’

ownbehaviourinthatsituationaswellasthebehaviourfromthe surroundingroadusers.

In order to overcome these challenges a multidisciplinary approachisneeded.Traditionalsurveysfunctionquitewelltopro- videvaliddescriptionsofdifferentroadusersperceptionsandown experiencesandcanalsotoacertainextentdescribeinteraction patterns(BjørnskauandFyhri,2012).Observationaltechniquescan functionwelltosupplementthepicture.Onepromisingapproach thathasgainedarenewedinterestinlateryearsistousesurro- gateaccidentmeasures,suchasconflictsandtorecordthesewith video.TheSwedishTrafficConflictTechnique(TCT)isoneamong severalsuchmethods(Hydén,1996;Laureshyn,2010),butisthe onlyonethathasbeenvalidatedwithstrongrelationfoundtothe numberofpolice-reportedaccidents(Svensson,1992).Themethod alsoexhibitsstrongprocessvalidity(similarityinhowconflictsto accidentsdevelop),andisespeciallyvaluableforthestudiesofvul- nerableroadusers’safetysincethisgroupisunder-representedin theaccidentstatistics(Transportstyrelsen,2012).

2. Objectives

Theobjectiveofthecurrentstudyistoinvestigateifinterplay betweenbicyclistsandcardriversimproveswhenmorebicyclists enterthestreetsthroughoutthecyclingseason.Inordertoinves- tigatethis, weusedata fromtwo datacollectionprocedures, a

combinedﬁeldandpanelsurveyofroadusersandvideoobser- vationofconﬂictsatselectedintersections.

Speciﬁcally,wehypothesizethat:

1.Thenumber oftimesbicyclistsarenotseenbycar driversis reduced,fromApriltoJuneandfromJunetoSeptember(survey data);

2.Thenumberoftimesbicyclistsarenotseenbypedestriansis reduced,fromApriltoJuneandfromJunetoSeptember(survey data);

3.Thenumberoftimescardriversaresurprisedbyabicyclistis reducedfromApriltoJuneandfromJunetoSeptember(survey data);

4.Thenumberoftimespedestriansaresurprisedbyabicyclistis reducedfromApriltoJuneandfromJunetoSeptember(survey data);

5.Thenumberoftimescyclistsareinvolvedinsafetycriticalsitu- ations(near-misses)withotherroadusersisreducedfromApril toJuneandfromJunetoSeptember(surveydata);

6.Thenumberoftrafﬁcconﬂictsbetweencardriversandbicyclists arereducedfromApriltoJuneandfromJunetoSeptember(video observations).

Wepresentthemethodology,resultsandinitialdiscussionsep- aratelyforeachdatacollectionprocedure,andprovideadiscussion synthesisingtheresultsfrombothproceduresattheend.

3. Surveydata

3.1. Method

Data were collectedin a seriesof ﬁeld surveysamong road usersinsomepreselectedstreetsandparkinglotsinOslo,Norway.

Thesurveyswereconductedatthreetime-pointsin2013:April (15th–29th), June (10th–21st)andSeptember (02nd–13th).The datacollectionperiodspannedovertwoweeksateachtimepoint.

Interviewswereconductedonweekdays,andduringdaytime.Most interviewswereconductedinthemorningandafternoon,during rushhours,inordertorecruitenoughrespondentsateachlocation.

Pedestriansandbicyclistswereinterviewedatthreedifferent locationsinOslo.Thelocationswereselectedsothatwewould recruit“average”roadusers,haveenoughtrafﬁc,andtoensurethat thoseinterviewedwouldhavehadsufﬁcientlylongtravelssothat theycouldhaveexperiencedinteractionswithotherroadusers.The interviewerswereinprincipleaskedtostopanypedestrianorbicy- clistsapproachingthem.However,asweweremostlyinterestedin bicyclists’perceptions,onsomedaystheinterviewerswereasked torecruittwiceasmanybicyclistsaspedestrians.Theinterview tookapproximately4–5mintocomplete,anddatawereregistered usingtabletPCs.Allwhoparticipated werepromisedaticketin drawforaprizeworth5000NOK(approx.600D).Interviewswere onlyconductedondayswithnorain.

Respondentswereaskedarangeofquestions,allregardingthe triptheyjusthadmade(orwereintheprocessofundertaking):

•Triplengthinminutes

•Numberoftimestheyhadexperiencedspeciﬁcsituationswith poorinterplay

•Assessmentofinterplaywithcarsandpedestrians(bicyclistsfor pedestrians)

•Experiencesofnear-misses

•Feelingofsafety

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Table1

Samplesizeforﬁeldandpanelsurveysforcyclistsandforﬁeldsamplesforcardriversandpedestrians.

Cardrivers Pedestrians Cyclists

Field Field Field Panel1AprilandJune Panel2JuneandSeptember Panel3April,JuneandSeptember

April 222 232 327 152 109

June 246 139 284 196

September 203 247 463

Total 671 618 1074 152 196 109

Table2

Samplecharacteristicsofbicyclists.Percent(exceptforage).

April June September

Mountainbike 44 34 37

“Hybridbike”(citybike) 39 38 33

Racerbike 5 7 9

Rentedbike 1 1 1

Classicalbike 10 19 19

Othertypes 1 1 1

5days/weekormore 73 72 73

2–4days/week 24 26 25

1day/week 2 1 1

1–3days/month 0 0 0

Rarely 0 0 1

Wholeyearbicyclist 46 33 36

Male 57 58 53

Meanage 44.6 43.8 43.1

N 212 288 480

Inaddition,backgroundquestionsaboutamountofcycling,sea- sonalvariationin cyclingandagewereasked.Theinterviewers registeredgender,bicycletypeandtypeofequipment.

Cardriverswereinterviewedatparkinglotsoutsidecommercial centresandatstreetsideparkinglotsinthecitycentre.

Respondents(bicyclists,pedestrians,andcardrivers)whocom- pletedtheinterviewwereaskedifwecouldcontactthemanew, andthosewhosaidyes,wereaskedtoleavetheiremailaddress.

Oneweekafterthefieldinterviewstherespondentsreceivedasur- veyathomewheretheywereaskedsomefurtherquestionsabout theirexperienceswithbeingintrafficduringthelastweek,and aboutinterplaywithotherroadusers.Inordertoestablishapanel surveydesign,thosewhocompletedthissurveyinOslo,wereasked ifwecouldcontactthemagainatthenextphaseofthesurvey(in JuneandSeptember).Forcardriversandpedestrians,onlythefield dataareanalysedinthispaper.Samplesizeforthethreefieldsam- plesandforthethreepanelsamplesofbicyclistsarepresentedin Table1.

3.2. Sample

Table2showsthesamplecharacteristicsoftheNorwegianbicy- clistsrecruitedintheﬁeldinApril,JuneandSeptember.

Notably,many of the respondents usemountain bikes.This shareisashighas44%inspring,andfallsto34%inmid-summer.

ThisistypicaloftheNorwegiancyclingpopulationwheremoun- tainbikesforawhilehasbeenthemostpopularcycletype,even forurbancyclists.Inaddition,wecanseethatmanyofthosewho areinterviewedarequiteaccustomedbicycleusers.Asmanyas 73%cycle“everyday”(i.e.ﬁveormoredaysaweek).Thisshareis quitestablethroughouttheseason.Still,theAprilsampleproba- blycontainsmoreexperiencedcycliststhantheothers,asthereis ahighershare(46%)whocycleallyearthanintheothersamples.

Thesampleshaveasomewhathighershareofmalesthanfemales, andarebiasedtowardsmiddle-agedparticipants(meanageranges from43.1to44.6;approximately4%areunder25yearsand3%are above65years).

Table3

Linearregressionanalysisofnumberoftimesbicyclistsarenotseenbycarson currenttrip.Standardizedparameterestimates(␤-values).

Bicyclists Gender

Age −0.82^*

Interviewplace Timeofday

Distance 0.16^***

Accustomedtoroute Mountainbike

Month −0.88^**

AdjR2 0.03

*p<0.1.

**p<0.01.

***p<0.001.

3.3. Results

3.3.1. H1:bicyclistsnotbeingseenbycars

Intheﬁeldsurvey,therespondentswereaskedtothinkabout thetriptheyhadmadetoday,andabouttheirencounterswithcars invarioussituations,atintersectionsetc.Thentheywereasked abouthowmanytimestheyhadexperiencedfourconcretesitua- tionsofpoorinterplaywithcars.Fig.1showsthemeannumberof timesbicyclistshaveexperiencedsituationswithpoorinterplayon thecurrenttripinApril,JuneandSeptember.

Aone-waybetweengroupsANOVAwasconductedinorderto exploretheeffectofseasonondifferenttypesofinterplaywith cars.Thenumberoftimesthecyclistsexperienceoverlookingsby acarfallsfromanaverageof0.47inAprilto0.27inJuneandto 0.25inSeptember(F(2,1070)=9,3,p<0.001).Posthoctests(Tukey HSD)revealedthatonlythefallfromApriltoJune wasstatisticallysignificant.Thenumberoftimesbicyclistsexperiencethatcars blocktheirroadwayisalsosignificantlyinfluencedbyseason(F(2, 1070)=8,9,p<0.001).Theposthoctests(TukeyHSD)againshowed thatonlythefallfromApril(M=0.55,SD=1.03)toJune(M=0.36, SD=0.77))wasstatisticallysignificant(p=0.01).Thereisnostatis- ticallysignificantchangeinthenumberoftimesbicyclistsareseen butnotrespected(i.e.thatcarshavenotyieldedatintersectionsor roundabouts).

Inordertocontrolforanyseasonalvariationthatmayexistin thesamplepopulation,weconductedamultipleregressionanal- ysis.Inthisanalysis,weincludednumberoftimesbicyclistshave experiencedtobeunnoticedbycarsoncurrenttripasadependent variable,andage,gender,interviewlocation,timeofday,distance cycled,knowledgeofpresentcyclingrouteandseasonaspredicted variables(Table3).

Theresultsoftheanalysisshowthatbothageandtraveldis- tancepredictwhetherbicyclistsareoverlooked.Theeffectofseason (month)isquitesubstantial(␤=−0.88).

Inthepanelsurvey,therespondentswereaskedtothinkback totheirlastweekintrafﬁc,theywereasked,“Thinkbacktoyour encounterswithcarslastweek.Imaginethatyouhavemet100 suchcardriversduringthepastweek.Approximatelyhowmany ofthesewillhave....”“notyieldedforyouatanintersection”etc.

(ﬁveitems).Responsesweretobegivenonaslidingscalewith11

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0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Felt 1 Felt 2 Felt 3 Felt 1 Felt 2 Felt 3 Felt 1 Felt 2 Felt 3 Felt 1 Felt 2 Felt 3 April June September April June September April June September April June September obviously have note seen

you

is placed in the roadway so you can not pass

not yielded at intersecon not yielded at roundabout

Fig.1. Meannumberoftimes(withupperandlowerconﬁdenceintervals)bicyclistshaveexperiencedpoorinterplayonthecurrenttripwithcardriversinApril,Juneand September.

Table4

Descriptivestatistics for cyclistsbeing overlooked bycars in April,June and September.

Mean SD N

April 16.86 17.91 86

June 13.60 19.40 86

September 11.40 13.30 86

intervalsrangingfrom“none”via10,20etc.to“all”.Themeansand standarddeviationsarepresentedbelow(Table4).

Inordertotestseasonaleffectofbicyclists’number ofover- looksfromcardrivers,aone-wayrepeatedmeasuresANOVAwas conducted.Thesamplefortheanalysiswere86outofthe109bicy- clists(somewereleftoutduetomissingdata)whohadresponded toallthreeofthehomesurveys(Panel3).Thenumberofover- looksdropsfrom16.9inAprilto13.6inJuneandfurtherto11.4 inSeptember.Therewasastatisticallysigniﬁcanteffectforsea- son(Wilks’Lambda=0.851,F(2,84)=7.36,p<0.001,multivariate partialetasquared=0.15.

3.3.2. H2:bicyclistsnotbeingseenbypedestrians

Inordertotestseasonaleffectofbicyclists’number ofover- looks from pedestrians, a one-way repeated measures ANOVA wasconducted.Theanalysisrevealednoeffectofseason(Wilks’

Lambda=0.986,F(2,84)=0.581,p=0.56).Uponcloserinspection, thereseemedtobeatendencyforanon-linearchangeinthenum- berofoverlooks.Apaired-samplest-testwasthereforeconducted tocompareoverlooksinAprilandJune,andinJuneandSeptember, respectively.

Table5showsthemeannumberoftimesbicyclistshaveexpe- riencednotbeingseenbyapedestrian,andthatapedestrianhas behavedunpredictablyinApril,aswellasthechangefromAprilto June,andfromJunetoSeptember.

Thereisnochangeinthenumber ofoverlooksfromAprilto June.Thereisastatisticallysigniﬁcantdropinthenumberoftimes bicyclistsare not seen bypedestrians from June toSeptember, t(172)=2.1,p=0.04).

Table5

NumberoftimesbicyclistshaveexperiencednotbeingseenbyapedestrianinApril, andchangefromApriltoJune,andfromJunetoSeptember.

April ChangefromApril toJune

ChangefromJune toSeptember

Notseenbypedestrian 22.33 0.37 −2.97^*

N 136 172

*p<0.05.

3.3.3. H3andH4:cardriversandpedestriansbeingsurprisedby bicylists

Thepedestriansandcardriverswereaskedhowmanybicyclists theythoughttheyhadseenonthecurrenttripandhowmanyof thesehadappearedsurprisingonthem(Table6).

For pedestrians,thenumberof bicyclistsencounteredareas expected, increasing through theseason. The number of times pedestriansaresurprisedisalsoincreasingfromJunetoSeptember.

Forcardrivers,thereisanincreaseinthenumberofbicycliststhey encounterfromApriltoJune.FromJunetoSeptemberthenumber ofbicyclesencountereddrops.Thenumberofsurprisesisrather steadywithasmallincreasefromJunetoSeptember.

Alinearregressionwasconductedusingnumberofsurprisesas dependentvariable,andamongotherthingsmonthasadummy variable(Table7).Exposure(numberofcyclistsmetonthecurrent trip)wasincludedasindependentvariables.

Theregressionmodelshowsthat,whencontrollingforexposure (numberofencounterswithcyclists),ageandgender,themonthly changeinnumberofsurprisesisnotstatisticallysigniﬁcant.

3.3.4. H5:near-missesbetweenbicyclistsandotherroadusers Thebicyclistswereaskediftheyhadbeeninvolvedin near- misses with a car or a pedestrian on the current trip. Fig. 2 showsthepercentageofbicyclistswhohavehadnear-misseswith cars/pedestriansforeachofthethreemonths.

Theshareofbicyclistswhohavehadanear-missesdropsfrom ApriltoJune,andthenincreasesfromJunetoSeptember.Thisholds forbothcarsandpedestriansascounterparts.

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Table6

Numberofcyclistsencounteredoncurrenttrip,numberoftimesbeingsurprisedbyacyclistforpedestriansandcardrivers.Mean.

Pedestrians CarDrivers

Numberofbicyclists Numberoftimessurprised Numberofbicyclists Numberoftimessurprised

April 6.4 0.44 4.8 0.34

June 7.2 0.49 6.3 0.31

September 9.1 0.77 5.9 0.42

6%

3% 4%

2%

4%

6%

0%

1%

2%

3%

4%

5%

6%

7%

s n a i r t s e d e p h t i W s

r a c h t i W

Bicycli sts' near-collision s

Fig.2. Bicyclistshavinghadnear-misseswithcarsandpedestriansoncurrenttrip inApril,JuneandSeptember.Percent.

In order to control for changes in the bicyclist population betweeneachinterviewperiod,wehaveconductedtwologistic regressionanalyses(stepwise).Priortoanalyses, wetested and conﬁrmed that all the independent variables were well below acceptablelevelsofmulticollinearity(bivariatecorrelationswere in the range 0–0.2). Bivariate correlations withthe dependent variablewerealsotested. Thehighest correlationwasbetween beingoverlookedandexperiencingnearmisses(r=0.2forcarsand r=0.21forpedestrians).Somevariableshadlowerthannormally recommendedbivariatecorrelationswiththedependentvariable, butwereincludedduetotheoreticalconsiderationsabouttheir potentialcontributiontoexplainingnear-misses.Atstep1month, gender,age,timeofdayanddistancecycledwasincluded.Atstep2 numberoftimesbeingoverlookedbycars/pedestriansoncurrent tripwasadded.

Fornear-misseswithcardriversthereisastatisticallysigniﬁcant reductionfromApriltoJune,butnochangetoSeptemberatstep1.

Timeofday(afternoonhavingalowerlikelihoodofnear-misses)is alsostatisticallysigniﬁcant.Whennumberofoverlooksisentered atstep2,theseasonaleffectisnotstatisticallysigniﬁcantanymore.

Fornear-misseswithpedestriansthereisasubstantialreduction from April to June, but this change is not statistically signifi- cant.Theincreasein near-missesfromApril (andfromJune)to Septemberisstatisticallysignificant.Ageisalsostatisticallysig- nificant(decreasedriskofnear-misseswithincreasingage).These effectsholdevenwhenwecontrolfornumberofoverlooksatstep 2.

Havingbeenoverlookedbycarsresultsinanincreasedlikeli- hoodofalsobeinginvolvedinnear-misseswithcars(Exp(B)=1.99).

Inthesamemanner,havingbeenoverlookedbypedestriansresults inanincreasedlikelihood ofalsobeinginvolved innear-misses withpedestrians(Exp(B)=1.88).

Thus,forboth typesofnear-misses,thereis aclearandsta- tisticallysigniﬁcantrelationshipbetweenbeingoverlookedbythe opposingroadusergroupandbeinginvolvedinanear-miss.

Thecardriversandpedestrianswerealsoaskediftheyhadexpe- riencedanear-missduringthelastweekinthepanelsurvey.Panel 1(ApriltoJune)andpanel2(JunetoSeptember)wereusedasunits ofanalysis.Inordertocalculateexposureweusednumberoftrips

.0000 .0100 .0200 .0300 .0400 .0500 .0600 .0700

r e b m e t p e S e n u J e

n u J li r p A

Risk of near-collision

Pedestrians vs cyclists Car drivers vs cyclists

Fig.3.Riskofnear-misseswithcyclistsduringthelastweekforpedestriansandcar drivers.Percent.

Table7

Linearregression,numberoftimespedestriansandcardriversaresurprisedbya bicyclist,baselineApril.

Pedestrians Cardrivers

June −0.02 −0.04

Sept 0.05 0.03

Gender −0.06 −0.05

Age −0.01 0.15^*

Numberofencounterswithcyclists 0.38^** 0.16^**

AdjR2 0.128 0.049

*p<0.01.

**p<0.001.

reportedduringlastweekandmultipliedwithanindexﬁgureof estimatednumberofcyclists(April=1,June=1.5,September=1.4).

Theindexﬁgurefornumberofcyclistsisderivedfromtwosources:

(1) The National Travel Behaviour Survey data (Hjorthol et al., 2014),subsampledrawnfromsoutheastNorway,meannumber oftripsperperson/day(N=3158)and(2)Bicyclecounters(induc- tiveloop)placedatfourdifferentlocationsinOslo(N=28725).Risk wascalculatedasoccurrenceofnear-misses/exposuretocyclists.

Fig.3showstheriskofnear-collisionswithacyclistforpedes- trians/cardriversinterviewedinAprilandJuneontheleftside,and inJuneandSeptemberontherightside.Notethatthemeannum- bersfortheleftsideJuneandtherightsideJunedifferssomewhat, sincetheyrepresentdifferent,butslightlyoverlapping,population samples(Panel1andPanel2,aspresentedinTable1).

Apaired-samplest-testwasconductedtocompareriskofnear- missesinAprilandJune,andinJuneandSeptember,respectively.

DatawereﬁrsttransformedusingtheFreeman-Tukeytransform forPoissondata(BisgaardandFuller,1994).

Thedropinriskfornear-misseswithcyclistsisstatisticallysig- niﬁcantfor bothcar driverst(30)=2.1, p=0.04)and pedestrians t(46)=1.8,p=0.07)fromApriltoJune. FromJunetoSeptember thereisanincreasedriskforcardriverst(44)=−1.9,p=0.06),and nochangeforpedestrians(Table8).

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Table8

Logisticregressionanalysesofnear-missesoncurrenttripwithcarsandpedestrians ascounterparts.Exp(b).

Cars Pedestrians

Step1 Step2 Step1 Step2

Month

June 0.38^** 0.46 0.36 0.43

September 0.65 0.85 1.89^* 2.17^*

Gender 1.19 1.29 0.90 1.00

Age 0.99 1.00 0.96^** 0.96^**

Timeofday

Midday 0.75 0.74 0.85 0.65

Afternoon 0.27^** 0.25^** 1.18 1.19

Distancecycled 1.17 1.05 1.09 0.91

#overlooks 1.99^*** 1.88^***

AdjR2(Nagelkerke) 0.06 0.13 0.07 0.18

*p<0.1.

**p<0.05.

***p<0.01.

4. Videoobservationsoftrafficconflicts(H6:conflicts betweenbicyclistsandotherroadusers)

4.1. Method

Behaviouraland conﬂictanalysesweredonebased onvideo observations. At each intersection, a camera covered with a weather-protectedboxwasmountedtoabuildinginordertohave agoodoverviewoftheintersection.Thevideowasrecordedwith relativelylowresolution(640×480pixels),whichdidnotallow recognisingindividualpersonsorreadingnumberplatesoncars, butwassufﬁcienttoseeandinterprettheroaduseractions.The videowassplitin30-minintervalsandstoredonamini-computer connectedtothecamera.

Video-recordingswereanalysedusingtheprogramT-analyst [10] developed at Lund University. The program was specifi- callydesigned to analyse roaduser interactionbased on video data. T-analyst efficiently managesa largenumber of detected eventsinlongvideo recordings,allowstolabelthemandafter- wardsfilterthem basedonthelabelling.Moreover,trajectories ofroad-userscanbeextracted,basedonwhichspecificparame- tersrelatedtointeractionbetweenroad-usersarecalculated.First, apre-screening ofthefootagebystudents tookplace,in which everypossibleviolationandconflictwasregistered.Thestudents’

instructionsweretomarkany“unusual”situationsuchasstrange route,congestion,“narrowcoming”,powerfulbraking,etc.Gen- erally,thenumberofpre-selectedsituationswasabouttentimes higherthanthefinalconflictcountandthereforetheriskofmiss- ingarelevantconflictatthisstageisjudgedtobelow.Afterwards theselectedeventswerereviewed,analysedandcategorizedby apersontrainedinusingtheSwedishtrafficconflicttechnique.A numberofvalidationstudiesofthetechnique(coveredinHydén, 1996) showed that trained observers agree very well on both detectingpotentialconflictsandinjudgingspeedsanddistances toestimatetheseverityofaconflict.Sinceinthisstudyweused objectivespeedsandtrajectoriesextractedfromvideo,thesub- jectivecomponentofjudgingaconflictbyahumanobserverwas furtherminimised.

4.2. Exposuremeasuresandrisk

Inordertobeabletocomparecyclistrisks,itisnecessaryto relatethenumberofobservedconflictstosomemeasureofactivity thatgeneratestheconflicts.Chapman(1973,p.99)definedexpo- sureas«amountofopportunityforaccidentsofacertaintypeina giventimeingivenarea».Inpractice,differentexposuremeasures areavailable,eachhavingprosandcons:

4.2.1. Trafﬁcﬂow

Cyclistcountsisthemostnaturalandeasytocollectmeasure ofcyclingactivity.Theproblemisthattherelationbetweenthe cyclistﬂowandtheriskitgeneratesisquitecomplex,sincethe numberofmotorvehicleshasadirecteffectonthefrequencyof thecyclists’interactionswiththemisnotreallytakenintoaccount (Ekman,1996).

4.2.2. Integratedmeasureofcyclistandmotorvehicleflows Thisseemsasareasonableapproachatfirstglance,butthere arenocommonlydefinedapproachesonhowtoaggregatethetwo measures(Hakkertand Baumeister,2002);shouldwemultiply, add,ormakesomekindoffactorisation?

4.2.3. Encounters

Anencounterbetweenamotorvehicleandacyclistcanbeseen asanelementaryeventintrafficthatmay,butnotnecessarilywill, turnintoaconflict/accident.Inthisrespectencountercorresponds besttothestatisticalconceptofabinominaltrialandtheriskcan beinterpretedastheproportionoftheencountersthatresultina conflict/accident(Elvik,2015).Themainchallengeisthatcounting theencountersisaquitedemandingtaskthatoftenhastobedone manually.

Inthecurrentpaperwetestedtheﬁrsttwotypesoftheexposure (cyclistcountsandcombinedmotorvehicle-cyclistmeasure).

4.3. Studysites

Thestudyisbasedonobservationsdoneatfourintersectionsin Oslo,Norway(Fig.4)

•SiteI.Toftesgate–Seilduksgata.Asmallintersectionincentral partofthecitywithonelaneineachdirectionformotortrafﬁc andcyclelanesonbothsideononeofthestreets.EstimatedADT 10.000vehicles.

•SiteII.Suhmsgate–Kirkeveien.Alargeintersectiononamain arterialstreet(apartofthesecondcity ring).Threelanesfor motortrafﬁcand cyclelaneonthemain streetineach direc- tion.Advancedstoplinesforthecyclists.EstimatedADT28.000 vehicles.

•SiteIII.Vogtsgate–MarcusThranesgate.Anotherintersection onthesecondcityring.Cyclelanesonthemainstreet,butonly ononesideoftheintersection.Tramlinegoingthroughtheinter- sectionontheminorstreet.EstimatedADT29.000vehicles.

•SiteIV.Mogata–Jutuveien–Stavangergata.Aroundaboutinres- identialpartofthecity.Oneincominglaneformotortrafﬁcin eachleg,cyclinglanesattwolegsmergingwiththemotortrafﬁc justbeforetheintersection.EstimatedADT15.000vehicles.

4.4. Videorecordings

Theoriginalplanwastoobserveeachsiteduring5workingdays between6:00and21:00inspring,summerandautumn.Themain bulkofthevideorecordingsweredonein2013,butsomecomple- mentaryrecordingsweredoneduringthespringof2014.Novideo wascollectedatMogata(SiteIV)forthespringperiod.Duetoa technicalfailure,autumnperiodatSuhmsgate(SiteII)contained onlyvideobetween6:00and11:00.Toextendtheobservationtime, thenumberofdaysanalysedwasdoubled.

4.5. Exposureestimation

Bicyclist,motorvehicleandencountercountswereperformed during 8 half-hour periods: 7:00–7:30, 8:00–8:30, 9:00–9:30, 10:00–10:30 in the morning and 14:00–14:30, 15:00–15:30,

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Fig.4.TheviewsofthestudiedintersectionsinOslo:(a)Toftesgate−Seilduksgata;(b)Suhmsgate–Kirkeveien;(c)Vogtsgate−MarcusThranesgate;(d)Mogata– Jutuveien–Stavangergata.

Table9

Exposure(numberofcyclists),conﬂicts,andriskofconﬂictatallfourintersections inApril,JuneandSeptember.

Exposure Sitesobserved I–III I–IV I–IV

Hours 180 300 275

Cyclists(C) 15,060 38770 46,513

Motorvehicles(MV) 225,198 413,459 390,422 (^C/h·^MV/h)/10⁶ 23.68 63.18 77.52

Conﬂicts alltypes 19 51 37

Risk Conﬂicts·10³/cyclists 1.26^* 1.32^* 0.80^* Conﬂicts·10⁶/(^C/h·^MV/h) 0.80 0.81 0.48

* Differenceinrisk(conflictspercyclist)isnotstatisticallysignificantfromspring tosummer,butstatisticallysignificantat95%fromsummertoautumn(twopro- portionZ-test).

16:00–16:30,17:00–17:30intheafternoon.Threeexposuremea- sureswereestimatedthenforeachsite:

•Cyclistnumber.Thetotalnumberofcyclistduringtheobserva- tionperiodwereobtainedusingavailabledailyvariationproﬁles forthesameorsimilarintersections.

•Combinedmotorvehicle–cyclistmeasure.Themeasurewascal- culatedasa sumof theproductsofhourlycyclist and motor vehicleflows(HakkertandBaumeister,2002).Again,toobtain thehourlyflowsfortheperiodswhennocountswereperformed, dailyvariationprofilesforcyclistsandmotorvehicleswereused.

5. Results

Foreachindividualintersection,thenumberofconflictswere toolow toproduceanystatisticallysignificantdifferences,even thoughthepatternofchangewasthesame.Table9summarizesthe exposure,numberofconflictsandriskofconflictforallofthefour

0,0 0,5 1,0 1,5 2,0 2,5

spring summer autumn

Relative index

Cyklister Motorfordon C x MF Risk per cyklist Risk per C x MF

Fig.5.Relativechangeinexposureandriskbasedonaggregatedresultsfromthe sitesI–IV(indexforApril=1).

intersections.Datafromeachindividualintersectionarepresented intheAppendix.Thenumberofconﬂictspercyclistdoesnotchange muchfromApriltoJune,butfallstowardsSeptember.Thedecrease inriskfromJunetoSeptemberisstatisticallysigniﬁcantat␣=0.05 level(two-proportionZ-test).

Asimilarpatterncanbeseenevenifthemotorvehicle×cyclist- measureisusedastheexposure.Itisnotpossible,however,totest statisticallytheriskchangesinceoneunitofsuchexposure(1motor vehicle×cyclist)isnotstrictlyspeakingatrialinstatisticalterms.

Fig.5belowshowstherelativechangeinthenumberofcyclists, motorvehicles, combined(motor vehicle×cyclist)measureand therisksbasedondifferentexposuredeﬁnitions.Onecanobserve thatthecyclistnumberisincreasingbothfromApriltoJuneand

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fromJunetoSeptember,whiletheamountofmotorvehiclesdoes notchangemuch.Asaresult,thecombinedexposuremeasurefol- lowsthecyclistnumberquiteclose,andthetworisksarealsovery similar.However,thisisratheracoincidenceandonecannotgen- eralisebysayingthatcyclistcountsisanequallygoodexposure measureasthecombinedmeasure,whichtakesintoaccountmotor vehicles.

6. Discussion

Thecross-sectional surveyresultsshowthat bicyclistsexpe- rienceanimprovedinteractionwithcardrivers(feweroverlook situations)fromApriltoJune,and afurtherimprovementfrom June toSeptember.Thepaneldata,where thesamepeopleare interviewedatthreedifferenttimepoints,showthesamepicture:

bicyclistsarelessofteninvolvedin situationswhereotherroad usersapparentlyhave notseen themlate inthecycling season thanearly.Thus, weconfirmourhypotheses1and2.However, whenlookingatthepicturefromtheotherside,cardriversand pedestriansdonotexperienceachangeinnumberoftimesthey aresurprisedbyabicyclistthroughtheseason(hypotheses3and 4).Theydo,howeverexperiencefewernear-collisions,confirming ourhypothesis5.Thevideoobservationdatashowsaquiteclear patternofincreaseofconflicts(butnotrisk)fromspringtosum- merandasubsequentdropinconflictsandrisklaterintheseason (hypothesis6).

Allinall,thesedatacanbeinterpretedinlightofaSafetyinNum- bersmechanismwherethesuddenincreaseofcyclistsinspringand earlysummerresultsinanincreaseofsituationswhereoverlook- ingandnear-misseshappen.Thissituationisthenfollowedbya situationwheretheotherroadusersgetusedtothepresenceof bicyclists,andthenlearntoexpectthemontheroads.Thisagain resultsinfewerconﬂicts.

Oneimportantlimitationwiththesurveydataisthattheyare self-reportdata, and hencewhat is observedis the roadusers’

interpretationofdifferentsituations.Wehavetriedtoeliminate theelementofinterpretationasmuchaspossible,byaskingthe respondentstoreportnumberoftimesconcretesituationshave happened,ratherthangivingassessmentsofthequalityofinter- play,andbelievethis tobea strengthofthedata.Eventhough respondentshavetroublewithrememberingtheexactﬁguresfor suchevents,webelievethatthesemeasures,whenaggregatedtoa group,giveavalidandreliableestimateofthequalityofinterplay amongroadusers.Stillwecannotruleoutthatthecyclists’inter- pretationofwhatisanoverlooking,oranear-misschangeswith increasedcyclingexperiencethroughtheseason.

Animportantadvantagewiththeﬁeldsurveydataisthatthe respondentsareclosetotherelevantsituationsintimeandspace.

Thismakesiteasierforthemtoremember“smallencounters”with otherroadusers,encountersthatareeasilyforgotteninanormal interviewsituationathome.

Aswehaveshown,notonlythenumberofcyclists,butalsothe compositionofthecyclistpopulationchangesduringtheseason, withmoreexperiencedandwellequippedcyclistsbeingdominant inspringcomparedtolaterintheseason.Fortheﬁeldsurveydata, wehavecontrolledforthisdifferenceintheregressionanalysis,by includingthemostrelevantbackgroundvariables.However,there willalwaysbeuncertaintyinvolvedinsuchstatisticalcontrol.The panelsurveydatahasanadvantageinthatwehavebettercon- trolofotherfactorsthatmightinﬂuencepeople’sresponses,thus strengtheningourconclusions.

Animportantfindingisthatthereisaclearandstatisticallysig- nificantrelationshipbetweenbeingoverlookedbytheopposing roadusergroupandbeinginvolvedinanear-miss.Thisfindingindi- catesthatourmeasureofpoorinterplay(beingoverlooked)hasa

certainecologicalvalidity,i.e.thatitfunctionsasasurrogatemea- sureforthemechanisminvolvedinproducingpoortrafﬁcsafety forbicyclists.Ontheotherhand,thevalidityofa“near-miss”asa safetysurrogatecanalsobequestioned.Therearesomeindications thatonlyverysevereincidentsshouldbeusedassafetysurrogates, whilemessy,butnotveryserioussituationscanontheoppositebe indicatorsofgoodsafety(astheykeeproadusersalert)(Svensson, 1998).Inthisstudywehadnocontrolonhowserioussituations theinterviewedcyclistperceivedas“near-misses”.

Astrengthofthisstudyisthatwecombinethreedifferentdata types(crosssectionalsurveydata,paneldataandobservationdata) tostudythesamephenomenon.Suchmethodtriangulationisoften calledfor,butisrarelyconducted.Thefactthatallthethreedata typespointinthesamedirectionbutthattherestillaresomedif- ferences,illustratestheimportanceofsuchanapproach.

Somequestionsstillremainunanswered.First,thepatternof changedifferssomewhatdependingonwhatmeasurewearelook- ingat.Thesurveydatashowsadropinnumberoftimescyclists areoverlookedbycarsfromApriltoJune,andafurtherdropto September.Thereisnochangeinnumberoftimescardriversare surprisedbycyclistsintheseason.Thelargestdropseemstobeat thebeginningoftheseason.Bothcardriversandcyclistsreporta dropinnear-missesfromApriltoJune,andasmallincreasefrom JunetoSeptember.Thevideodatashowsasomewhatdifferentpat- tern,withunchangedconﬂictriskfromApriltoJunefollowedbya dropfromJunetoSeptember(“delayedeffect”).

Oneexplanationforthisdiscrepancycouldbethattheexposure countsthatwereusedintrafficconflictanalysisbasedonvideodif- feredfromtheofficialcountsutilisedforcalculatingexposurefor cyclistsinthesurveydata.Theuseofdifferentexposuremeasures deservessomementioninthisrespect.Forthesurveydatawehave usedtwodifferentsourcesofinformationforcalculatingseasonal variationincyclinglevels(fortheanalysisofpedestriansandcar driversencounterswithcyclists):1)TheNationalTravelbehaviour data(forsoutheastNorway),showingasubstantialincreasefrom ApriltoJune,andasmalldecreasefromJunetoSeptemberand 2) Theroad authorities’ bicycle counters,showing a very large increasefromApriltoJune,andalargedroptoSeptember0F.¹As thesedatadifferedsubstantially,anditwasnotpossibletodeter- minewhatwastherightseasonalpattern,wedecidedtousean averageofthesetwo.Ourowncountsfromthevideoshowsadif- ferentpattern,withasubstantialincreasefromJunetoSeptember.

Itcouldbearguedthatweshouldhaveusedthesedatawhenesti- matingexposureinthesurvey.Asensitivityanalysisofthesurvey datausingexposureratiosfromthevideodatadidhowever,not changethepatternofdifferences.Also,asmentionedinSection 4.2,thenumberofcyclistshasseriouslimitationsasanexposure measure.Itismoretheoreticallyplausibletousethenumberof encountersbetweencyclistsandmotor vehicles(atleastin the observationalstudies),howevertodothesecountsmanuallyisa verylaborioustaskandsomeautomatedtool(likeautomatedvideo analysissoftware)isrequired.

Theﬁrstdatacollectionofthecurrentstudywasdoneimme- diatelyaftertheEastervacationinApril2013.InNorway,cycling levelsshiftdramaticallyfrombeforetoafterEaster,sincemany peopleusethisasakindofred-letterdaytobringouttheirbicycles afterwinter.Ideally,datacollectionshouldthereforehavestarted beforeEaster,inordertocapturethislargeinﬂuxofnewcyclists evenbetter.ThereasonforchoosingtheperiodrightafterEaster, wastobalancethetimeandcostsofdatacollectionwithaperiod

1Theseﬁguresarefrom2013,theyearthattheinterviewswerecollected.In2014, new(visiblecolumn)counterswereinstalled.Twooutoffourofthesecountersreg- isteredadropfromJunetoSeptember,oneregisterednochange,andonereported anincrease.

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withstillcomparablylownumbersofcyclists.Animportantnote regardingthe seasonalvariation istheinfluence ofweather on cyclinglevels.Asithappened,thefirstfewdaysafterEaster,when interviewscommenced,wererathercold,sothatthefirstcyclists mighthavedelayedtheirupstartofcyclingsomewhat.Still,future studiesshouldaimatmaximisingthechangesincyclinglevelseven morethanwemanagedbystartingthestudyperiodsearlier,oreven maybedoingwhole-yearstudies.Fortheremaininginterviewperi- ods,weatherconditionswerequitenormalforthesummerseason inNorway,i.e.around15–20^◦C,andmostlysunnyorlightclouds, withoccasional rainydays(wheninterviewswerepaused).The Septemberperiodhadnorain,andwassomewhatwarmerthan normal,withtemperaturesabove20^◦onsomedays.

Anotherunansweredquestionis“WhyistherenoSiNeffectfor cardriversandpedestrianswhen itcomestosurprises(H3and H4),butachangeinnear-misses(H5)?Aswesaw,thenumberof surpriseswasonlyrelatedtothenumberofencounters,anddid notchangethroughtheseason(i.e.therewasnolearningeffect).

Thenumberofinterviewedpedestriansandcardriversisfarlower thanthenumberofcyclists,sooneexplanationcouldbethatthis isjustmethodologicalartefact.Further,theanalysesrelyonthe respondents’ownassessmentsofnumbersofencounteredcyclists, whichisprobablyanumberwithalargeerrormargin.However, lookingatthecrudedata,weseethatpedestriansandcardrivers bothreportmoresurprisesinSeptemberthaninJune.So,evenif theownassessmentsofencounteredcyclistsweretobereplaced bymoreobjectiveﬁguresofcyclingnumbers,thetendencyofthe dataisinthewrongdirection.

One possible explanation could be because of a change in cyclingpopulationthatcountersthebeneﬁcialeffectofchanges in expectancies.As we have shown previously cycling through red was found to be more frequent in June than in April (de Goedeetal.,2014).Thissuggeststhat withincreasingnumbers ofcyclistsinNorway,theshareof‘risk-taking’orunexperienced cyclistsincreases.Thismayevencounteractandhideapotential SINeffect.Inotherwords,cardriversmightbecomemoreaware ofcycliststhroughouttheseason,butthebeneﬁtsofthismight becancelledoutbytheincreasedlevelofriskybehaviourbybicy- clists.Thisisonlyspeculations,andthequestionstillremainswhy theobservedchangeinnumberofnear-missesisnotprecededbya changeinnumberofsurprises.Isitamethodologicalartefactorisit relatedtosomeothermechanismcounteractingpositiveeffectsof increasednumbers?Furtherresearchshouldthereforeaimatlink- ingcardrivers’(andpedestrians’)experiencesofbeingsurprised bycyclistswithnumberofnear-misses,usingbettermeasuresof surprises,nearmisses,andexposure.

Aﬁnalquestionthatremainstobeansweredis‘Wholearns?’

Ifweare tobelievethesedata,interactionbetweencar drivers andcyclistsimprovesthoughtheseason.Wehavetakenthisasa proofofcardriversbecomingmoreusedtocyclists,andhencemore expectingofencounteringthemintraffic.However,itcouldalsobe thecasethatcycliststhroughinteractionwithcardriversbecome betteratreadingtrafficandfindingtheirplace,andtherebyless oftenfindingthemselvesinconflict-likesituations.Ourbackground data(Table2)showsthatthecyclistsinterviewedinSeptember arelesslikely tobewholeyearcycliststhanthose interviewed inApril,whichgivessomesupporttosucha contention.Future researchshouldaimattestingifcyclistsbecomemoreproficient withincreasedexperiencethroughtheseason,inthesamefashion asnovicedriversshowarapidlearningcurveintheirfirstmonths oftheirdrivingcarrier(SagbergandBjornskau,2006).

Inthispaperwehaveattemptedtotestthepredominantmech- anismoftheSafetyinNumbersphenomenon,thatanincreased number of cyclists in a given road environment results in an increasedattentivenessfromotherroadusers.Wehavefounda strongsupportforthismechanism,but wehavealsoseenindi-

cationsthat there aremore tosuchshifts thanjust changes in numbers.Morespeciﬁcally,wearguethatwithincreasingnumbers, differenttypesofbicyclistsalsoenterintothepopulation.Someof thesenewcyclistscanbelessexperiencedandmorerisktaking,as wehaveindicated.Ontheotherhand,someofthesenewcyclists canalsobelessrisk-takingthanthe“earlyadopters”.Theeffectsof thesepopulationdifferencesmightthusbothattenuateandaccen- tuatethepositiveeffectsofincreasedattentivenessfrommotorists.

AﬁnalverdictonSafetyinNumberscanthusnotbegivenjustyet.

AppendixA.

TableA1

TableA1

Exposure(numberofcyclists),conﬂicts,andriskofconﬂictatSiteIToftesgate− SeilduksgatainApril,JuneandSeptember.

Exposure Hours 75 75 75

Cyclists(C) 3889 6245 8485

Risk Conﬂicts·10³/cyclists 0.77 0.32 0.47 Conﬂicts·10⁶/(^C/h·^MV/h) 1.35 0.56 0.81

TableA2

TableA2

Exposure(numberofcyclists),conﬂicts,andriskofconﬂictatSiteIISuhmsgate− KirkeveieninApril,JuneandSeptember.

Cyclists(C) 5591 13,385 15,081

TableA3

TableA3

Exposure(numberofcyclists),conﬂicts,andriskofconﬂictatSiteIIIVogtsgate- MarcusThranesgateinApril,JuneandSeptember.

Cyclists(C) 5580 7960 11,228

TableA4

TableA4

Exposure(numberofcyclists),conﬂicts,andriskofconﬂictatSiteIV,roundabout Mogata–Jutuveien–StavangergatainJuneandSeptember.

June September

Exposure Hours 75 75

Cyclists(C) 11,180 11,719

Motorvehicles(MV) 115,867 128,792

(^C/h·^MV/h)/10⁶ 19.68 22.93

Conﬂicts alltypes 9 8

Risk Conﬂicts·10³/cyclists 0.81 0.68

Conﬂicts·10⁶/(^C/h·^MV/h) 0.46 0.35