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Exposure-based cycling crash, near miss and injury rates: The Safer Cycling Prospective Cohort Study protocol

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There are clear personal, social and environmental benefits of cycling. However, safety concerns are among the frequently cited barriers to cycling. In Australia, there are no exposure-based measures of the rates of crash or 'near miss' experienced by cyclists. A prospective cohort study over 12 months, with all data collected via web-based online data entry. Two thousand adults aged 18 years and older, living in New South Wales (Australia), who usually bicycle at least once a month, will be recruited from March to November 2011. In the 12 months following enrolment, cyclists will be surveyed on six occasions (weeks 8, 16, 24, 32, 40 and 48 from the week of the enrolment survey). In these survey weeks, cyclists will be asked to provide daily reports of distance travelled; time, location and duration of trips; infrastructure used; crashes, near misses and crash-related injuries. Information on crashes and injuries will also be sought for the intervening period between the last and current survey. A subsample of participants will receive bicycle trip computers to provide objective measurement of distance travelled. This study protocol describes the prospective cohort study developed to assess near misses, crashes and injuries among cyclists by time and distance travelled and by type of infrastructure used, with recruited participants entering data remotely using the internet. We expect to be able to calculate event rates according to exposure overall and for different infrastructure types and to report in-depth information about event causation.
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1
Exposurebasedcyclingcrash,nearmissandinjuryrates:TheSaferCyclingProspectiveCohort
Studyprotocol
Authors
RoslynG.Poulos1
JulieHatfield2,
ChrisRissel3
RaphaelGrzebieta2
AndrewSMcIntosh4
Affiliationsandcontactdetails
1SchoolofPublicHealthandCommunityMedicine,TheUniversityofNewSouthWales,Sydney
2052,Australia.
2TransportandRoadSafety(TARS)Research,TheUniversityofNewSouthWales,Sydney,New
SouthWales,Australia.
3SchoolofPublicHealth,SydneyMedicalSchool,UniversityofSydney,Sydney,Australia.
4RiskandSafetyScience,TheUniversityofNewSouthWales,Sydney,NewSouthWales,Australia.
Correspondenceto
RoslynGPoulos,SchoolofPublicHealthandCommunityMedicine,TheUniversityofNewSouth
Wales,Sydney2052,Australia;r.poulos@unsw.edu.au
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Exposurebasedcyclingcrash,nearmissandinjuryrates:TheSaferCyclingProspectiveCohort
Studyprotocol
Abstract
Introduction:Thereareclearpersonal,socialandenvironmentalbenefitsofcycling.However,
safetyconcernsareamongthefrequentlycitedbarrierstocycling.InAustralia,thereareno
exposurebasedmeasuresoftheratesofcrashor‘nearmiss’experiencedbycyclists.
Designandsetting:Aprospectivecohortstudyover12months,withalldatacollectedviaweb
basedonlinedataentry.
Participants:Twothousandadultsaged18yearsandolder,livinginNewSouthWales(Australia),
whousuallybicycleatleastonceamonth,willberecruitedfromMarchtoNovember2011.
Methods:Inthe12monthsfollowingenrolment,cyclistswillbesurveyedon6occasions(weeks8,
16,24,32,40,and48fromtheweekoftheenrolmentsurvey).Inthesesurveyweeks,cyclistswillbe
askedtoprovidedailyreportsofdistancetravelled;time,locationanddurationoftrips;
infrastructureused;crashes,nearmissesandcrashrelatedinjuries.Informationoncrashesand
injurieswillalsobesoughtfortheinterveningperiodbetweenthelastandcurrentsurvey.A
subsampleofparticipantswillreceivebicycletripcomputerstoprovideobjectivemeasurementof
distancetravelled.
Discussion:Thisstudyprotocoldescribestheprospectivecohortstudydevelopedtoassessnear
misses,crashesandinjuriesamongcyclistsbytimeanddistancetravelledandbytypeof
infrastructureused,withrecruitedparticipantsenteringdataremotelyusingtheinternet.Weexpect
tobeabletocalculateeventrateaccordingtoexposureoverallandfordifferentinfrastructuretypes
andtoreportindepthinformationabouteventcausation.
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Exposurebasedcyclingcrash,nearmissandinjuryrates:TheSaferCyclingProspectiveCohort
Studyprotocol
Background
Thereareclearpersonal,socialandenvironmentalbenefitsofcycling.Thesebenefitsincreaseas
individualscyclemoreandasmorepeoplecycle.[1]Personalbenefitsofrecreationalortransport
cyclingincludesubstantialhealthgains,[2]withasignificantreductioninallcausemortalityand
cardiovascularrisk.[3,4]Cyclingmayalsobeanimportanttoolinthefightagainsttheobesity
epidemic,withecologicalstudiesnotinglowerratesofobesityinregionswithhighratesof
cycling.[5]ThevalueofcurrentcyclingtotheAustralianhealthsystemhasbeenconservatively
estimatedat$227millionperannum.[1]
Cycling,andurbanenvironmentsthatsupportcyclingandwalkinganddiscouragecaruse,can
improvesocialinteractionsandincreasecommunityattachment,liveabilityandamenity.[6]Thereis
alsoevidencethatthemorecompact,permeableurbandesignsthatsupportcyclingandwalking
leadtocrimereductionthroughincreasedstreetactivityand‘naturalsurveillance’.[7]
Wherecyclingreplacesamotorvehicletripthereareenvironmentalbenefits,suchasreduced
carbondioxideemissionsandotherpollutants.[1]Additionally,anincreaseintransportcyclingmay
easetrafficcongestion,reducemotorvehiclecrashesandthuspotentiallyreduceroadtrauma
costs.[8]
ThetransportationcyclingrateinAustraliaislowcomparedtootherdevelopedcountriessuchas
theNetherlands,DenmarkorGermany.[5]Countrieswithhighcyclingratestendtohavebetter
cyclingfacilities,effectivetrafficcalmingmeasuresandwelldevelopedcyclinginfrastructure,
cyclingfriendlyurbandesign,restrictionsonmotorvehicleuseandcomprehensivetrafficeducation
abouthowtointeractwithcyclists.[9]
Cyclingspecificinfrastructureisgenerallyrecognisedasanecessarystartingpointtoattractnew
noncycliststocycling.[1]However,itisnotalwaysclearwhattypeofinfrastructurewillproducethe
maximaladoptionofcyclingwiththegreatestsafetybenefits.Forexample,introducingshareduse
pathsforpedestriansandcyclistsmaydecreasecyclistcrashrateswithmotoristsbutcouldincrease
pedestrianinjuryrates.[10]
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SignificantnumbersofcyclistsarekilledorseriouslyinjuredinAustraliaeveryyear,andthese
numbersappeartobeincreasing.Morethan15000bicyclistswereinvolvedinpolicereported
crashesintheAustralianstatesofVictoria,Queensland,SouthAustralia,andWesternAustralia
during20002004,[11]andthereweremorethan2000motorvehiclerelatedcrashesleadingto
hospitalisationsinbicyclistsinthestateofNewSouthWalesbetween1999/2000and2004/2005.
[12]Further,itislikelythatthesedataunderenumeratetheproblembecausemanylessserious
cyclingincidentsgounreportedtopolice,orbecausethedatadonotincludedeathsandinjuries
arisingfromfallsandcollisionsnotinvolvingmotorvehiclesanddonotincludecasualtiesreceiving
outpatienttreatment.RecentlypublisheddatafromVictoriareportsasignificantincreasein
emergencydepartmentandhospitaladmissionsforbicycleinjuriesfrom2001to2006andamarked
increaseinthenumberofcyclistssustainingsevereinjury.[13]However,theinterpretationofthese
dataisseverelylimitedbythequalityofdataintermsofcyclingexposureandcontributing
factors.[13,14]
Toreducetherisksassociatedwithcyclistssharingtrafficlaneswithmotorvehicles,strategiessuch
asbicyclelanes(i.e.,portionsoftheroadwaydesignedfortheuseofbicycles)andbicyclepaths(a
physicallyseparatepathwayforbicyclesorasharedusepathforbicyclesandpedestrians)have
beenbuilt.Therelativemeritsofthesedifferentstrategiesremaindebated,especiallyinurbanareas
inmajorcities,wherethisquestionisimportantduetolimitedavailabilityoflandandfinance.
Researchduringthe1970sintheUSAindicatedthatbicyclelanesmayhaveaprotectiveeffectfor
mostcollisiontypes,butanincreasedriskforcollisionsoccurringwhenthecyclistturnsleftinto
traffic(turningrightinAustralia).[15]Laterresearchfoundthattheriskofridingonthesidewalk
(includingbicyclepathsandfootpaths)washigherthantheriskofridingontheroadway,probably
becauseofblindconflictsatintersections.[16]EarlyresearchfromEnglandreportedthattherateof
injurywasgreateroncyclewayscomparedtolocalroadsandgridroads,butprimarilybecauseof
poordesignincludingvisibilityproblems,especiallyatintersections,sharpbends,steepgradients,
slipperybridges,loosegravelandmud.[17]Otherrisksincludedheadoncrashesbetweencyclists,
collisionswithdogs,andeyeinjuriesfromintrudingvegetation.[17]Thecontestednatureofthe
evidencesupportingvariousinfrastructuresolutionshasledforcallsintheinternationalliterature
formoreresearcharoundinfrastructureandforimprovedunderstandingaboutbehaviour,safety
andsafetyperception.[18]
Ifcyclingistobeencouragedasahealthpromotingpractice,orasasustainableformoftransport,
thenitisincumbentonauthoritiestoprovideanenvironmentthatminimisesrisk,tomanagepublic
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perceptionofriskandtopromotetheactivityappropriately.However,becauseofthelackof
exposuredatainAustralia,[13]actualcrashandinjuryratesrelatedtodistancecycled,durationof
travelorinfrastructureusedareunknown.
CyclingisbeingactivelypromotedbytheAustralianhealthandtransportsectorsandbygovernment
atalllevels,[1921]andinfrastructureisbeingprogressivelydevelopedacrossthecountry.
However,thereisverylittleknowledgeonwhichtobasedecisionmaking.Thisprojectistherefore
significantbecauseitmeetsanurgentandrapidlyincreasingdemandforevidencetosupportpolicy.
Thisstudyaimsto:
1. Developmeasuresofcyclistcrash,nearmissandinjuryratesforabroadlyrepresentative
populationofcyclistsfrommetropolitanandregionalNewSouthWales
2. Identifyfactorsthatcontributetocrash,nearmissandinjuryrates,forexample,human
factorsandroadenvironment.
3. Assesstherisksforcyclistsassociatedwithcyclingonroads,bicyclelanesandpaths.
4. Describethetype,locationandfrequencyofhazardsidentifiedbycyclists.
Methods
Overallstudydesign:Acohortstudyoftwothousandcyclists(18yrsandover)whoresideinNew
SouthWalesandrideatleastoncepermonthwillbeundertaken.Cyclistswillberecruitedvia
multiplechannelsincludingtheextensiveemaillistsofBicycleNSW(astatecyclingadvocacy
organisation),arangeofcommunitycyclingevents,bikeshops,mediapublicityandthroughwordof
mouthwithinthecyclingcommunity.Participantswillenrolandcompletethebaseline
questionnaireandallsubsequentfollowupquestionnairesviaasecurewebsite.Automatically
generatedemailsplusSMStextmessages(forthosewhoselecttextreminders)willbesentto
participantswhenfollowupquestionnairesaredueforcompletion.
Toincreasethelikelihoodofaccuratemeasurementofdistancesandtimetravelled,bicycletrip
computerswillbeofferedtoasubsetofenrolledcyclistswhorideatleastonceperweekandwho
reportnothavingtheirowntripcomputersonenrolment.Toensurethemosteffectiveuseof
limitedresources,cyclistswillnotbeofferedtripcomputersiftheyhavemorethanonebikeanddo
notidentifyridingonebikeatleast90%ofthetime,iftheirmainbikeisaBMXbikeas‘offroad’
cyclingisoutsidetheprimaryaimofthestudyoriftheirmainbikeisclassifiedas‘other’andmay
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notbecompatiblewiththebicycletripcomputer(e.g.,folding,recumbentorelectricbikes).
Participantswishingtoaccepttheofferofatripcomputerwillbeaskedtosupplythedataontyre
sizefromthewalloftheirbicycletyreandthediameteroftheirwheel.Tripcomputersare
individuallyprogrammedbyresearchstaffusingthetyrecircumferencereferencetableor
calculationinstructionssuppliedbythemanufacturer.Tripcomputerswiththemanufacturer’s
installationinstructionswillbesentbyposttoparticipants.Bicycletripcomputerswillbeoffered
untilresearchstocksareexhausted(estimatedtobearound400computers).
Pilottesting:Paperversionsofthequestionnaireswerepilotedwithcolleagueswhocycle(within
theUniversityandfundingpartnerorganisations)andmodifiedaccordingly.Electronicversions
wereagaintestedwithcolleagueswhocycle,priortoimplementation.
Baselinedatacollection:Atenrolment,demographicdata(e.g.,age,gender,education,
employmentstatus,income,drivinglicence,caraccess),cyclingexperienceandconfidence,self‐
identificationofcyclisttype(transportorrecreational),usualcyclinghabits(averagemonthly
distanceandhourscycled,useofinfrastructureandcyclingequipmentoverpast12months),history
ofcrashesandcrashrelatedinjuriesoverthelast12months,andattitudestoriskandsensation
seeking(usingpreviouslyvalidatedinstruments[22,23])willbecollected.
Followupdatacollection:Overthe12monthsfollowingenrolment,therewillbe6surveyweeks
(weeks8,16,24,32,40,and48fromtheweekofthebaselinesurvey).Atthestartofeachsurvey
week,participantswillbeaskedaboutcrashesandcrashrelatedinjuriesfortheinterveningperiod
betweenthelastandcurrentsurvey,andthentocompleteatraveldiaryfortheensuing7days.
Cyclistswillbeaskedtoprovidedailyreportsofdistancetravelled;time,locationanddurationof
trips;infrastructureused;crashes,nearmissesandcrashrelatedinjuriesexperienced.Toenablethe
calculationofrates,participantswithbiketripcomputerswillbeaskedtoprovidereadingsfromthe
computer,whileotherparticipantswillestimatetimeanddistancestravelled.Cyclistsmayenter
dataviathesecurewebsiteonadailybasis,orkeepahardcopyrecordoftheirtripsbyprinting
downa7dayPDFversionofthediary,andenteringtheirdataattheendoftheweek.
Ateachsurvey,severaladditionalquestionsonaspectsofcyclingwillbeasked.Thesewillinclude
aggressionexperiencedwhilecyclingwithotherroadusers(followupperiod1);infrastructure
preferencesandconcerningsafetyissues(followupperiod2);cyclingactivitiesandbeliefsinrespect
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ofsafety(followupperiod3);observationoftheroadrulesandlawsforcyclists(followupperiod
4);cyclingfortransport(followupperiod5);andcyclingwithchildren(followupperiod6).
Duringeachfollowupperiod,participantswillreceivereminderemails(oneonthedaypriortothe
commencementofthereportingperiod,ondays2to7andafinalreminderemailonday8).The
computerprogrammingallowsfor7consecutivedaysofreportingwithina14daywindow,after
whichaccesstothefollowupquestionnaireisblocked.Thisflexibilitywillallowcycliststhe
opportunityofcommencingtheirdiaryupto7daysafterthestartdateandstillcollecttheirdata
prospectively(e.g.,incaseswherecyclistsdonotaccesstheiremaildailyandthereforereceivelate
notificationoftheirstartdate)ortoallowsomeflexibilityinthenumberofdaysavailableinwhich
datacanbeenteredforthosecyclistswhohavekeptahardcopydiary.
Qualitativedatacollectionfollowingcrashreports:Allcyclistsreportingacrashoccurringduring
theirreportingweekwillbecontactedbytelephonetoansweradditionalquestionsaboutthe
locationoftheevent,thecircumstancesleadingtotheevent,behaviouralorenvironmentalrisk
factors,injuriessustained(type,severityandtreatment),andsuggestedwaysinwhichtheevent
couldhavebeenprevented.
Definitions:Crashesaredefinedascollisionsorfalls,basedonthedefinitionsgiveninthereviewby
Reynoldsetal(2009).[24]Acollisionisdefinedasaneventinwhichthebicyclehitsorishitbyan
object,personoranimal,regardlessoffault;andafallisdefinedasanevent(notcausedbya
collision)wherethebicycleand/orbikeriderlandsontheground.
Anearmissisdefinedasanunexpectedeventwhilecyclingthatcausesthecyclistoranotherparty
totakesuddenevasiveaction,andwithoutsuchactionthecyclistbelievesacrash(collisionorfall)
wouldhavehappened.
Forclarity,participantswillbeprovidedwithimagesanddefinitionsofallrelevantcycling
infrastructure,asshowninfigure1.
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Figure1Cyclinginfrastructuredefinitions.
Planneddataanalysis:Quantitativedataanalysiswillincludebasicdescriptiveanalysisofthe
baselineandfollowupdataandacalculationofeventrates.Eventrateswillbecalculatedusingthe
incidentnumberofevents(collision,fallsornearmisses)perhoursofcyclingorkilometrestravelled.
Theincidencerateofcrasheswillalsobecalculatedfordifferentformsofinfrastructure.
Multivariablegeneralisedlinearmodellingwillbeusedtoexplorehumanfactors(cyclist
characteristicssuchascyclingexperience,attitudesandbehaviour)aspredictorsofoutcome(event
rate).
Qualitativedatawillbeanalysedusing‘templateanalysis’.[21]Thismethodinvolvesthe
developmentofacodingtemplateorframeworkcomposedofcodesrepresentingthemesidentified
inthedatathroughmultiplereadingsofthetext.
Discussion
Thisstudyprotocoldescribesanoriginalresearchplanthatwillenablethecalculationofratesof
crashes,nearmissesandinjuriesrelatingtotimeanddistancecycledandthetypeofinfrastructure
usedforcycling.Thedeterminationofexposurebasedrateswillmakeasignificantcontributionto
knowledgeaboutcyclinginAustralia,astheonlymeasurescurrentlyavailablearebasedon‘per
head’ofpopulation.[13].Itwillalsoallowcomparisonsofcyclingrisktoothertravelmodesin
Australiaandinternationally.Additionalquestionsaboutcyclingexperience,attitudesand
behaviourwillenabletheexplorationofthesevariablesaspredictorsofoutcome.Thestudywillalso
helptoanswerquestionswhicharedebatednationallyandinternationallyaroundtherelativemerit
ofdifferentformsofinfrastructure.Inaddition,wewillrecordcrashesandinjuriesthatarenot
capturedintheavailabledatacollections,suchascrasheswhicharenotreportedtopoliceor
injurieswhichdonotrequirehospitalisation.Capturingqualitativeandquantitativedataonthese
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crasheswillprovideagreaterunderstandingofthesurroundingcircumstancesandislikelytomeet
theneedforadditionalinformationonsinglevehiclebicyclecrashes.[25]Itwillenableustobetter
understandcyclingriskandtoprovideevidencetosupportabestpracticesystemofcycling
infrastructureandeffectivepubliceducation.Thisshouldhelptopromotebettermanagementof
interactionsbetweenallusersoftransportfacilitiesinthefuture.
Strengthsandlimitations
Thisstudyisunique.Toourknowledge,itisthefirsttomeasureexposurebasedratesofcrashes,
nearmissesandcrashrelatedinjuriesforcyclistsinAustralia.Itwillbeconductedcompletely
online,supportedbyavarietyofonlineengagementandinteractionactivitiessuchastheSafer
CyclingStudyFacebookpage,aYouTubeclipaboutthestudyandregularemailand/ortextmessage
phonereminderstostudyparticipants.Thesearedesignedtomaintaincohortparticipationthrough
alldatacollectionstages.Inaddition,allcrashesrecordedindailyreportswillbefollowedupby
interviewtoexploreaspectsofthecrashthatarenotelicitedbythequantitativequestionsandto
provideacontextualunderstandingofthecrashevent.Finally,theprospectivenatureofthestudy
isexpectedtoreducerecallbias,andtheuseofbicycletripcomputersshouldreducemeasurement
error.
Thestudyislikelytohaveanumberoflimitations.Theseincludethepotentialforvolunteerandself
reportbias.Losstofollowupisalsolikely.However,asparticipantshavebeenaskedtosupplya
numberofdifferentcontactoptions(includingthoseofafriendorfamilymember),weexpecttobe
abletoidentifyparticipantsinwhomlosstofollowuphasbeentheresultofaseriouscycling
relatedinjury.
Acknowledgements
WethankDrSusanneMurphyforherassistanceinpreparingthismanuscriptforpublicationandthe
representativesfromourfundingpartnerorganisationswhohavecontributedtodevelopmentand
implementationofthisstudy.
Funding
ThisprojecthasbeenfundedundertheAustralianResearchCouncil’sLinkageProjectsfunding
scheme(projectnumberLP1000100597)withfinancialcontributionsfromtheRoadsandTraffic
10
AuthorityofNewSouthWales,SydneySouthWestAreaHealthService,BicycleNSWandWilloughby
Council.
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... Bicycling has numerous social, environmental, and health benefits (Poulos et al., 2012). Health benefits include the reduc-53 tion of chronic diseases, increased cardiovascular fitness, increased muscle strength and flexibility, improved mental health, 54 among others. ...
... Understanding the circumstances of near-miss events will facilitate the 84 appraisal of effective crash prevention strategies (Gnoni et al., 2013). The near-miss data enhance our understanding of 85 cycling safety by increasing the detail of information available for analysis (Poulos et al., 2012). A survey of cyclists con-86 ducted by Aldred (Aldred, 2016) revealed different types of near-miss events such as a cyclist's path being blocked, a driver 87 passing a cyclist too closely, and many others. ...
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Across the globe, many transport bodies are advocating for increased cycling due to its health and environmental benefits. Yet, the real and perceived dangers of urban cycling remain obstacles. While serious injuries and fatalities in cycling are infrequent, “near misses”-events where a person on a bike is forced to avoid a potential crash or is unsettled by a close vehicle-are more prevalent. To understand these occurrences, researchers have turned to naturalistic studies, attaching various sensors like video cameras to bikes or cyclists. This sensor data holds the potential to unravel the risks cyclists face. Still, the sheer amount of video data often demands manual processing, limiting the scope of such studies. In this paper, we unveil a cutting-edge computer vision framework tailored for automated near-miss video analysis and for detecting various associated risk factors. Additionally, the framework can understand the statistical significance of various risk factors, providing a comprehensive understanding of the issues faced by cyclists. We shed light on the pronounced effects of factors like glare, vehicle and pedestrian presence, examining their roles in near misses through Granger causality with varied time lags. This framework enables the automated detection of multiple factors and understanding their significant weight, thus enhancing the efficiency and scope of naturalistic cycling studies. As future work, this research opens the possibility of integrating this AI framework into edge sensors through embedded AI, enabling real-time analysis.
... In combination, these actions and interactions within the environment 109 contribute to crash risk. Traditionally, crash risk is assessed using historical data on 110 crash records, such as the UK's STATS19, combined with measures of exposure such as 111 traffic flows, which are combined in a multiple regression framework to identify risk factors 112 (Ambros et al., 2018 (Poulos et al., 2012;Fischer 116 et al., 2020). However, these data sources are often subjective and incomplete, making it 117 challenging to understand the underlying causes of hazards. ...
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Panoramic cycling videos can record 360{\deg} views around the cyclists. Thus, it is essential to conduct automatic road user analysis on them using computer vision models to provide data for studies on cycling safety. However, the features of panoramic data such as severe distortions, large number of small objects and boundary continuity have brought great challenges to the existing CV models, including poor performance and evaluation methods that are no longer applicable. In addition, due to the lack of data with annotations, it is not easy to re-train the models. In response to these problems, the project proposed and implemented a three-step methodology: (1) improve the prediction performance of the pre-trained object detection models on panoramic data by projecting the original image into 4 perspective sub-images; (2) introduce supports for boundary continuity and category information into DeepSORT, a commonly used multiple object tracking model, and set an improved detection model as its detector; (3) using the tracking results, develop an application for detecting the overtaking behaviour of the surrounding vehicles. Evaluated on the panoramic cycling dataset built by the project, the proposed methodology improves the average precision of YOLO v5m6 and Faster RCNN-FPN under any input resolution setting. In addition, it raises MOTA and IDF1 of DeepSORT by 7.6\% and 9.7\% respectively. When detecting the overtakes in the test videos, it achieves the F-score of 0.88. The code is available on GitHub at github.com/cuppp1998/360_object_tracking to ensure the reproducibility and further improvements of results.
... Surveys have been widely used as methods of studying bicyclists and pedestrians, particularly when faced with a lack of real-world data. Surveys, when composed carefully, can reliably and efficiently assess large populations of people and have been used to study a wide variety of topics including: perceived safety/comfort (Parkin et al. 2007;Chaurand and Delhomme 2013;Abadi and Hurwitz 2018), route choice (Sener et al. 2009) and crash history (Robartes and Chen 2018;Poulos et al. 2012;Yang et al. 2019). However, stated preference surveys have limitations, such as being subject to hypothetical bias where responses to hypothetical situations are not the same as they would be in real-world situations (Fitch and Handy 2018). ...
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Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on the rise. To better inform design for vulnerable road users, we need to conduct more studies to evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway designs and contextual settings. Previous research highlights the advantages of Immersive Virtual Environment (IVE) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of getting injured, are low-cost compared to on-road or naturalistic studies and allow researchers to fully control variables of interest. In this paper, we propose a framework ORCLSim, to support human sensing techniques within IVE to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies where we collect and analyze pilot data from five participants' physiological and behavioral responses in an IVE setting, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data is sensitive to road environment changes and real-time events, especially changes in heart rate and gaze behavior. Additionally, our preliminary data indicates participants may respond differently to various roadway settings (e.g., intersections with or without traffic signal). By analyzing these changes, we can identify how participants' stress levels and cognitive load is impacted by the simulated surrounding environment. The ORCLSim system architecture can be further utilized for future studies in users' behavioral and physiological responses in different virtual reality settings.
... Surveys have been widely used as methods of studying bicyclists and pedestrians, particularly when faced with a lack of real-world data. Surveys, when composed carefully, can reliably and efficiently assess large populations of people and have been used to study a wide variety of topics including: perceived safety/comfort (Parkin et al. 2007;Chaurand and Delhomme 2013;Abadi and Hurwitz 2018), route choice (Sener et al. 2009) and crash history (Robartes and Chen 2018;Poulos et al. 2012;Yang et al. 2019). However, stated preference surveys have limitations, such as being subject to hypothetical bias where responses to hypothetical situations are not the same as they would be in real-world situations (Fitch and Handy 2018). ...
Preprint
Full-text available
Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on the rise. To better inform design for vulnerable road users, we need to conduct more studies to evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway designs and contextual settings. Previous research highlights the advantages of Immersive Virtual Environment (IVE) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of getting injured, are low-cost compared to on-road or naturalistic studies and allow researchers to fully control variables of interest. In this paper, we propose a framework ORCLSim, to support human sensing techniques within IVE to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies where we collect and analyze pilot data from five participants' physiological and behavioral responses in an IVE setting, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data is sensitive to road environment changes and real-time events, especially changes in heart rate and gaze behavior. Additionally, our preliminary data indicates participants may respond differently to various roadway settings (e.g., intersections with or without traffic signal). By analyzing these changes, we can identify how participants' stress levels and cognitive load is impacted by the simulated surrounding environment. The ORCLSim system architecture can be further utilized for future studies in users' behavioral and physiological responses in different virtual reality settings.
... 20:208 Physical activity in the previous week was measured using the Active Australia Survey [35]. Participants were also asked what type of bicycle rider they most identified as [36]. Response options were: a) ' A low-intensity recreational bike rider -you like the fresh air and exercise, and cycle at an enjoyable pace'; b) ' A high-intensity recreational bike rider -you like to ride hard and fast'; c) ' A low-intensity transport bike rider -you are about just getting to places, and you travel at a more comfortable speed'; and d) ' A high-intensity transport bike rider -you are a fast rider who likes to keep up a fast pace throughout your journey' . ...
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We examined the public health consequences of unsafe and inconvenient walking and bicycling conditions in American cities to suggest improvements based on successful policies in The Netherlands and Germany. Secondary data from national travel and crash surveys were used to compute fatality trends from 1975 to 2001 and fatality and injury rates for pedestrians and cyclists in The Netherlands, Germany, and the United States in 2000. American pedestrians and cyclists were much more likely to be killed or injured than were Dutch and German pedestrians and cyclists, both on a per-trip and on a per-kilometer basis. A wide range of measures are available to improve the safety of walking and cycling in American cities, both to reduce fatalities and injuries and to encourage walking and cycling.
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A meta-analysis of studies of road accident reporting in official accident statistics made in 13 countries is described here. A rigorous comparison of reporting levels between countries is difficult because of differences in the definitions of reportable accidents, reporting levels, and data sources used to assess reporting levels. Based on 49 studies in 13 countries, it is concluded that reporting of injuries in official accident statistics is incomplete at all levels of injury severity. In rounded values, the mean reporting level in the countries included was found to be 95 percent for fatal injuries according to the 30-day rule, 70 percent for serious injuries (admitted to hospital), 25 percent for slight injuries (treated as outpatients), and 10 percent for very slight injuries (treated outside hospitals). Reporting levels vary substantially among countries, ranging from 21 to 88 percent for hospital-treated injuries. Reporting is highest for car occupants and lowest for cyclists. In particular, single-vehicle bicycle accidents are very rarely reported in official road accident statistics.