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XX理工大學萬方科技學院本科畢業(yè)設計

附錄：

SafetyAssessmentofDriverOvertakingBehavioronTwo-LaneHighways

YaqinQIN1,JianXIONG1,XiujuanZHU1,andJianshiLI1

FacultyofTransportationEngineering,KunmingUniversityofScienceand

Technology<KUST>,Kunming,Yunnan650224,China;PH<86>

email:

ABSTRACT

Safetystudyofdrivingbehaviorstoreducetrafficaccidentsisofgreatsignificance.Theprimaryobjectivesofthestudyweretoinvestigatetherelationbetweendriverovertakingbehaviorsandtrafficsafety.Twelvedriversofdifferentproficiencywereselectedforanexperimentonadrivingsimulationsystemplatform.Asimulationsceneofovertakingonatwo-lanehighwaywassetup.Thesubjectsovertookinthevirtualscenariosatdifferentspeeds.Atotaloftwelveparameters-includingspeed,acceleration,timeofovertaking

process,thechangesofdistanceandotherparameters-wereextractedduringtheexperiments.ThesedatatogetherwiththeresultsofaDBQ<DrivingBehaviorQuestionnaire>wereanalyzedandevaluatedbythemultiplelinearregressionmethod.Theresultsshowedthatthreemotionparametershadastrongcorrelationwithdrivers’safety.Finally,thestudypresentsalinearmodelofsafetyassessmentofdriverovertakingbehaviorontwo-lane

highways.Themodelmayhelptoidentifysafeandunsafedriversandreducethenumberoftrafficaccidents.

INTRODUCTION

Two-laneruralroadsmakeupthemajorityoftheroadnetworkinmanycountries.In2009,China’sruralmileagereached3.2millionkm,andhighwaymileageofruralroadsaccounted1

XX理工大學萬方科技學院本科畢業(yè)設計

formorethanthree-quartersofthetotalmileage<Li,2009>.Ruralroadsarealsodominantintrafficfatalitystatistics.Lammetal.<2007>hasestimatedthatmorethan60%ofallfatalitiesintrafficoccurontwo-laneruralroads.Overtakingmaneuversonruraltwo-lanehighwaysisacommonphenomenon.Whendrivershavepotentialtoovertakeandthereissufficientspacetoovertakeontheroad,overtakingdemandswillbecreated.Intheprocessofovertaking,thedriverdetermineswhetherthereissufficientandadequatepassingsightdistanceandtimeheadwayofthe

opposinglaneandwhetherthereisanadequateinsertinggapofthesamelane.Thendriversdecidewhethertheovertakingshouldbeimplemented.Sinceovertakingconditionsanddriversbehaviorvaries,theovertakingprocessisverycomplex.Itisaffectedbyroad

conditions,alignment,sightdistance,vehicletype,speed,anddrivers,amongotherthings.Greenshieldsetal.<1935>wasthefirsttoestablishtheminimumrequirementsforsafe

passingunderaveragetrafficconditions.Bar-Gera,H.andShinar,D.<2005>haveshownthismaneuverisassociatedwithanincreaseincrashrisk,becauseitinvolvesdrivinginthelaneoftheopposingtrafficdirection.Atthesametime,Harris<1988>foundthatmostdriversareindeedawarethatovertakingisariskymaneuverbyself-reportratings.Atpresent,manyexistingstudies<seereviewsbyTang,2007;Geertje,etal.,2007;Wei,etal,2000;Rong,etal.,2007;Shao,etal.,2007>focusonovertakingmodeling.Afewstudiesassessingthesafetyofdrivingovertakingbehaviorsgenerallyexaminedrivergender,ageandothercharacteristics<David,etal.,1998>,orbyDBQquestionnairetosurvey<OzkanandLajunen,2005>.Itisdifficulttoobtaindrivers’real-timedirectperformanceofovertakingmaneuversduetothedangeroftheexperimentonarealroad.

Toassessdrivingbehaviorsinovertaking,weemployadrivingsimulator.Variousstudieshaveshownthatdrivingsimulatorscanprovidereliableobservationsofdrivers’behaviors<Blana,1996;Desmond,andMatthews,1997;VanderWinsumandBrouwer,1997;Ellingrodetal.1997.>.Inthisstudy,wefocusonindividualdifferencesinthesafetyof2

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overtakingmaneuverswithDBQandreal-timebehaviorsonadrivingsimulator.Theresultsofthestudyaredesignedtodistinguishunsafedriversfromotherkindsofdrivers.

METHOD

Participants

Twelvevolunteersparticipatedintheexperiment.Thesamplecomprisedsixmenandsixwomenbetween24and55yearsold<M=28.08years,S.D.=5.6>.Therewasanequalbalanceofmalesandfemales.Allhadavaliddriver’slicense.Themeannumberofyearsdrivingexperiencewas5.25yearsand,onaverage,driversdrove3.08hoursperweek.Allhadnormalorcorrected-to-normalvisualacuity,anddidnottakeanykindofmedicine.Apparatus

Afull-sizeadvanceddrivingsimulator<KMRTDS,developedbythesimulationlaboratoryofFacultyofTransportationinKunmingUniversityofScienceandTechnology,China>wasusedinthisstudy.Thesimulatedvehiclecab,anAxial,featuredallnormaldisplaysandcontrols<steering,brakes,andaccelerator>foundinavehicle.Differentdrivingscenarioswereprojectedontoa1500cyclescreen,withsoundeffectsofthevehiclesinmotion

broadcastedbytwo-channelamplifiers.Withtheoptimizedimageprocessingspeedofmorethan30framespersecondandthecalibrationforthespeedandvisual,KMRTDScanensurethereal-timeofthesystemandthefidelityoftheexperimentalscene.OutlookofthedrivingsimulatorisshowninFigure1.KMRTDScanprovideupto68motionparameterstoanalyzethebehaviorsofthevehicleanddrivers.

Experimentaldesign

Thepurposeofthisexperimentistoevaluatethesafetyofdrivingbehaviorsduringdriverovertakingatdifferentspeedsontwo-lanehighways.InChina,thelargestdesignspeedofatwo-lanehighwayis80km/h.Ontheotherhand,thesightdistanceofovertakingisdifferentwiththedifferentspeedsofexperimentalvehicles.Inthisexperiment,wesetthespeedofexperimentalpassedandoncomingvehiclesto30,37.5,45and60km/hrespectively.The3

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correspondingovertakingspeedsweresetat40,50,60and80km/h.Thesefourgroupsof

overtakingprocesseswererandomlydistributedinfoursectionsofequallengthonatwo-lanehighway.Allsubjectswilldriveonthesefoursectionsinthescenarios,andtherewere48observationsintheexperiment

Drivingscenarios

Theparametersofovertakingdrivingscenariosincludestaticanddynamicparameters.Thestaticparametersrefertotheroadalignments,trafficsigns,markingsandthesurroundingnaturalenvironmentandsoon.Thedynamicparametersindicatetheparametersofdynamicdrivingvehicles,includingthetriggeringmovementregionsofexperimentalvehiclesinthescenarios,speed,drivingroute,distancebetweenvehiclesandsoon

Staticscenarios

Thedesignofthestaticovertakingscenarioofthisexperiment<showninFigure2>isanapproximatelysquarescenario,composedoffourstraightsectionsofaruralsecondary

two-lanehighway.Totaldistanceis8.0km,with2.0kmlongoneachside,andtheturningradiusoftheconnectionis200meters.Thewidthofasinglelaneis4.5m<includingtheroadshoulder>,withnoisolationfacilitiesinthecenter.Bothsidesoftheroadaregrassand

randomlydistributedtreesandvillages,andeachsectionismountedwiththespeedlimitanddirectionalsigns.Thevisualfieldofthescenarioisopeninthattherearenoobstaclesoneitherside.Meanwhile,thefieldhasgoodclimateandnormaltrafficconditions,thatistosay,

nofog,norain,nosnowweatheranddryflatpavementconditions.ThestaticscenariodesignofexperimentisshowninFigure1

Dynamicscenarioshelp

InaccordancewithhighwaystandardsinChina,specificsectionwithovertakingsightdistanceinappropriatedistanceshouldbesetonatwo-lanehighwaybasedonneedand4

XX理工大學萬方科技學院本科畢業(yè)設計

terrain.PieandWang<2004>havestatedthelengthofthesectionshouldnotlessthan10%-30%ofthetotallengthoftherouteinnormalcircumstances.

Indynamicscenarios,theexperimentalvehiclesmustdriveaccordingtopre-determinedroutesandspeeds.Whentheleadingvehiclewhichthedrivermaneuveredreachedaspecificplace,theotherexperimentalvehicleswillbetriggeredtostartmovement.Theseplacesaretriggeringpoints.Thesettingofdynamicparametersofexperimentalvehiclesduring

overtakingprocesswasexplainedasfollows.

Figure1.Designofstaticscenario

Figure2isthedynamicscenariodistributionoftheovertakingprocess.Car0istheleadingcar,andCar1isthepassedcar,andCars2,3,4,5areoncomingcars.Inordertoincreasedrivingrealismandpreventthedriverfromovertakingaheadoftime,setfrontCar6and7aheadofCar2,3,4,5.Thesettingofdynamicparametersincludesstartingarea,speed,drivingtrack,andspaceofvehicles.OnceCar0reachedthetriggerpoint,allothervehicleswillbegintomoveatthesetspeedsandtracks.ThespaceofvehiclesinthedynamicscenarioSn_n+1meansthedistancebetweenCarnandCarn+1whenthemainCar0reachedthetriggerpoint.Thisdistanceisthemostdifficulttodetermine,butwecansetitbasedontheprescribedvalueofovertakingsightdistance

Sincethetimeheadwayisatimeintervalwhenthebumpersoftwotravelingcarspassthesametransectonaroadsection,itmainlydependsonthedistancebetweenthetwocarsandthespeedofthefollowingcar<Olsten,2005>.Observedinthestaticcondition,timeheadway5

XX理工大學萬方科技學院本科畢業(yè)設計

<calledH>canbedirectlyobtainedbymeasuringthemomentwhenthetwovehiclespassthroughthesametransect.

H=TB-TF<1>

Where,TB——Thetimewhentheleadingcarpassesthroughthedetector<s>.TF——Thetimewhenthefollowingcarpassesthroughthedetector<s>.Inadynamicovertakingprocess,thetimeheadwayisthequotientoftheheadwaybetweentwocarsandthespeedofthefollowingcar,namely:

H

3.6D

<2>Vf

Where,D——Headwayofleading-and-followingcar<m>

Vf——thespeedofthefollowingcar<km/h>

Liu<2007>foundthat,inamountainousareatwo-lanesecondaryhighway,whentheheadwaytimeoftheleadingandfollowingcarsisbelow3.1sandthespeeddifferencebetweenthetwocarsreachedabout20km/h,ademandtoovertakeisgenerated<>.Inthisexperimenttheheadwaytimewassetat3s.Inaddition,inordertomakefrontcarsdrivingontheroadseemmorereal,welettheCar1triggerinFigure3istheexperimentaldynamicscenarioafterloadedtrafficflow.

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advance,thatistakingS01=2D.Sn_n+1valueindifferentspeedlimitsectionswereshowninTable1.

Figure3.DynamicovertakingscenariosinKMRTS

Measurements

OvertakingBehaviorMeasurement

ThesubjectswererequiredtodriveintheKMRTSinordertodeterminethedriver

subjects’overtakingbehavior.Everysubjectfinishedfourtestsinthescenarios.Thereare48differentovertakingbehaviormeasurements.Whentheydrove,theyshouldincreasetheirspeedasclosetothespeedlimitaccordingtolimitspeedsignsinthesimulatedscenarios<including40,50,60,0KM/H,atotaloffourspeeds>,andthencompleteanovertaking

maneuverineachsectionaccordingtotheirpersonalexperienceandability.Butoncetheycompletedanovertakingmaneuverandonturningsections,therewasnolimitationonspeed.Duringtheexperiment,thedrivers’operationbehavior,drivingspeed,acceleration,

overtakingtime,distancestraveled,distancebetweentheovertakecarandoncomingcarsbeforeandafterovertakingwererecorded.Allthesedatawereusedtoevaluatethedrivers’drivingbehavior.

Self-reportmeasurement

ADBQwasintroducedasanself-reportquestionnairesurvey,whichwasadaptedbySullmanandothersbasedonReason’sDrivingBehaviorQuestionnaire<2002>.Thequestionnaireisdividedintothreedimensions,namely,violationsoftrafficrules,errorbehaviorsandoffensiveviolations,containingtwentyitems.Inthisquestionnaire,weusedtheLikert7

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five-pointscoringmethodtorecordthedrivers’scores.Thescoresaredescribedby

centesimalgrades.Aftercalculatingthemeanvaluesofalldriversubjects’totalscore,wecanconcludetheaverageintentofagroupondifferentdimensions.Inaddition,wecan

understandeachindividual’sattitudescoresdistributionsituationbythisprogressivemeanscale.

Determinationofbeginningandendingtimeofovertakingprocesses

Timingthebeginningandendingoftheovertakingprocessincludes:thetimewhenthedriverbeginstoovertake,beginstoreturntothepreviouslaneandcompletestheovertaking.Determinationofthesemomentsenablesustogetthetraveltimeanddistancetraveledatallstagesintheprocessofovertaking

Thedeterminationofthemomentwhendriversbegintoovertakecanbedividedintotwosituations:<1>Thetimeadriverturnsonaleftturningsignalbeforeovertakingand<2>ifthedriverdidnotturnonaleftturningsignal,thetimedeterminedbymathematicalmethods.Thisfixesthedrivingtrajectorybytwostraightlines.Timetoovertakecorrespondstotheintersectionoftwolinesfromthemomentthedriverbeginstoovertake.

Theendtimeoftheovertakingprocesswasthemomentthevehiclereturnstotheoriginallane.Weusedavideoplaybackwithstopwatchtorecordthemomentswhendriverscompletedtheovertakingmaneuvercompletelyandreturnedtotheoriginallane.The

momentwasdefinedasthetimeadrivercompletedovertaking.Basedonthestartandendtimeofthedriver’swholeovertakingprocess,wecanobtainthevehiclespeed,acceleration,steeringangleandothermotionparametersintheperiodofovertakinginthefollowinganalysisofexperimentalresults.

RESULTSANDDISCUSSION

Overtakingperformance

Theresultsoftwelvedrivers’operationbehaviorsandperformancesinfourspeedsduringtheovertakingareshowninTable2.

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Table2.Drivers’OvertakingBehaviorunderFourSpeeds

Table3.StatisticsofOvertakingBehavior

Ondifferentspeedlimitroads,driversubjectsallcompletedtheovertakingprocesssuccessfullyexceptfor80km/h.Amongthem,onthespeedlimit40km/hand60km/hsectionsallovertookCar2,butonthespeedlimit50km/hsection,driversubjectsNo.7,8,and

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12followedthefrontcarfortoolongatimeandovertooktoolate,sotheyonlyovertookCar3.Inthespeedlimit80km/hsection,duetothehigherspeed,driversubjectsNo.1,6,7,and11didnotcompletetheovertakingprocess,anddriversubjectsNo.8and9hadovertakenCar4andCar3.Whendriversturnedonaturningsignalduringtheovertakingprocess,driversubjectsNo.8and12didnothavethehabitofturnonaturningsignal,whichwouldaffectovertakingprocesssafety.

Tofurtherunderstandthedrivers’safetytrendsatdifferentspeeds,threeparameterswereselectedforfurtheranalysis.Thatis,Tp<thecompletingtimefortheentireovertakingprocess>,Sc<thedistancebetweenthemaincarandoncomingcarswhenreturnedtotheoriginallaneaftercompletedtheovertakingprocess>andVavg<averageovertakingspeed>.Table3showstheovertakingbehaviorparametersresultsofdriversubjectsatspeedlimit80KM/Handtheaveragespeedoffour.

InTable3,thenegativevalues<-16.74and-2.32>meanstherewasacollisionwith

oncomingcarsbeforecompletingovertaking,whilethevalue0.00indicatednotovertakinganyoncomingcarinthewholeovertakingprocess,namelytheovertakingprocesswasnotcompleted.Comparedtootherspeedlimits,itwasrelativelydifficulttocompletethe

overtakingprocessonthespeedlimit80km/hsection.Theaveragetimedriverscompletedtheovertakingprocesswas8.4s,shorterthantheaveragetimeofthethreeotherspeeds.

Exceptfordriver11,theaveragespeedofotherdriversintheovertakingprocesswas88km/h,ahighspeedinthetwo-lanesecondaryhighway,becausethespeedofthefrontcarandoncomingcarswas60km/h.Ifthedriverslightlyhesitatedorsloweddown,hewouldmissthebestopportunityforovertaking.Evenifthespacingbetweentheoncomingcarswaslargeenough,thedriverwouldnotriskanovertaking,especiallywithgreaterchancesofcollidingwiththeoncomingcar.

IntermsoftheaverageTop,Vague,Scofthetwelvedriversubjectsunderfourspeedlimitconditions,wefoundthatthecasesofsmallerTpandScandlargerVavgreflectedthe10

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conditionofthedriver’sovertakingstrategy.Thestrategyisthatdriverswillimprovetheirownsafetybyreducingtheconflictopportunitywithothervehicleswhentheyestimatetheovertakingconditions.Butatthispoint,thedriversmaybeinanunsafeovertaking.Forexample,wecanconcludethedriversubjectNo.4isasaferdriverratherthanNo.3.Self-reportonusualdrivingbehaviors

TheDBQquestionnaireresultsfortwelvesubjectswereanalyzedwiththeuseofa

Linkerfive-pointscoringmethod.Eachitemhasfiveselectionswhichgradesfrom1to5points.The20itemsscore100.Thehigherthescore,thesaferthedriver’sdrivingbehavior.ThescoresofDBQ<S_DBQ>arebetween77and95points.ThesubjectNo.3gotthehighestscore,whilesubjectNo.4thelowest

Furtheranalysisfromthescoresofthreedimensionsshowsthatsubjectshadgood

judgmentsabouterrorbehaviors<S_EJ:M=29.5,D=5>andforoffensiveviolations<S_AV:M=26.08,D=6>.Comparingthetwodimensions,therearemoreviolationsoftrafficrulesamongindividualdrivers<S_OR:M=28.42,D=9S_OR>.TheresultsareshowninFigure4

Itcanbestatedthattwelvesubjectsidentifiedaggressiveviolationsandmisjudgmentsasunsafedrivingbehaviors,butsomeoverlookedtrafficruleviolations.Ontheotherhand,withtheresultsofsubjectsovertakingperformanceandDBQquestionnaire,wefoundthatalthoughtheDBQreflectedtheirsubjectivedrivingexperience,therearestillgreat

differencesinreal-worlddriving.

SafetyassessmentMODELonovertaking

Toevaluatethesafetyofsubjectsintheovertakingprocess,amultiplelinearregressionanalysiswasusedtojudgetheirbehaviorscombinedwithdrivingbehaviorquestionnairescoreandtheirmotionparametersintheovertakingprocess.

WeselectedDBQquestionnairescore"y"asthedependentvariablefortheregression

equation,andidentifiedinitiallytwelvemotionparameterswhichprobablyinfluencedtheirsafetyasindependentvariables,withx1,x2,...x12indicatedinTable4.

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Selectionofinfluencingvariables

Table4.DescriptionofTwelveMotionParametersduringOvertaking

Weanalyzedthecorrelationamongtwelveinfluencingindependentvariablesin45of48observations<threeobservationswereredundant>.Thecorrelationcoefficientbetween

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variablesis

Table5.CorrelationCoefficientMatrix

showninTable5.FromtheorrelationcoefficientmatrixinTable5,thereisastrongcorrelationbetweensomevariables<thedatawith*>Modelingofmultiplelinearregression

Table6.ResultsofMultipleLinearRegressionAnalysis

Becausetherewerestrongcorrelationsbetweensomevariables,amultivariateregressionanalysisusingastepwisemethodwasperformedAftertheexcludedvariables,theoptimizedmodelincludingvariablesx8,x9andx10aretheinfluencingfactors,asshownin

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Table6

FromTable6,wecangetpredictorsinthemodel,whichareconstant,x8,x9andx10.Thereisnomulti-nonlinearityamongthepredictorsbecauseofVIF<5.Themodelisnotsogoodinthatthechi-squaregoodnessoffittestisweak.Itindicatedthatthereweresomeothernonlinearcorrelationsbetweenthem.Themodelofmulti-linearregressionobtainedisasfollows：

Y73.9280.137X80.046*X90.11*X10〔3

Thesethreeselectedvariablesshowedthatthesafetyofovertakingbehaviorisattributedtothespeedandsomedistancesduringovertaking.Thefasterthespeedis,thesaferthebehavioris。

CONCLUSION

Safetyassessmentofdrivingovertakingrequiresatleasttwothings:firstisamethodprovidingthedriver’sovertakingmaneuversandsecondamodelassessingandcontrollingforthesafetyofovertakingbehaviors.

Theproposeddriver’sovertakingsafetyassessmentmodelassumesthataveragespeedandsomedistancesarerelevanttothesafetyofovertaking.Thedistancesincludethe

distancesfromthepassingvehicletothepassedandoncomingvehiclewhentheovertakingended.Themodelreliesonastepwisemethodofmultiplelinearregressionanalysis,usingtwelveparametersinasimulationexperimentasindependentvariablesandDBQscoresasthedependentvariable.

Inourresults,themodelwascertainlysomewhatlimitedbytwofactors.Thefirstwastheselectionandquantityofsubjects,andthesecondwasthedifferencebetweendriving

simulatorsandon-roadexperiments.Concerningthefirstfactor,wemustbeawarethatDBQhassubjectiveresults.Moreover,therewere48observationvalues,thisexperimenthadonlytwelvesubjects.Weintroducedtwelveinfluencingfactorsunderinadequatedata;therewasgreaterinfluenceamongvariables.Allthesewillinfluencethevalidityofthemodeland14

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producesomeerrors.

Dealingwiththecomparabilityofthedrivingsimulatorandon-roadexperiments,

previousstudieshavemadesurethereareparallelobservations.Basedonthesestudies,wealsocanconcludethatwiththeadventofmorepowerfulgraphicsprocessorsandrenderers,simulatorsareincreasinglyappealingforstudyingandtrainingdrivers.

Thisworkwasafirstapproachtotheproblem.Infuturework,weplantoimprovethesafetyassessmentpredictionmodelbyincreasingthenumberofsubjectsandbyusingothermethodstobuildthemodel

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雙車道公路上駕駛員超車行為的安全性評估

秦亞琴雄健朱秀娟李建始

XX理工大學交通運輸工程學院,技術〔KUST,XXXX650224,中國;PH值〔860871-3802298;電子郵件：qyq_email@

摘要

安全駕駛行為的研究對減少交通意外具有偉大的意義。這項研究的主要目的是探討司機超車行為和交通安全的關系。十二個不同能力的司機被選定為駕駛模擬系統(tǒng)實驗平臺。成立一個雙車道的高速公路上超車的模擬場景。在虛擬場景以不同的速度進行超越。在這個實驗提取了十二個參數(shù),包括速度,加速度,超車過程中的時間,距離和其他參數(shù)的變化。這些數(shù)據(jù)連同一個DBQ〔違例駕駛行為問卷的結果,進行了分析和評價的多元線性回歸方法。結果顯示司機的安全與三個運動參數(shù)有一個密切的關系。最后,研究提出了一種線性模型的司機超車行為在雙車道高速公路的安全評估。該模型可以幫助識別司機的安全和不安全意識來減少交通事故的數(shù)量。

引言

在許多國家雙車道的農(nóng)村公路占據(jù)了絕大多數(shù)的公路網(wǎng)絡。在20XX,中國農(nóng)村里程達到3.2萬公里,高速公路在農(nóng)村公路里程占到總里程超過四分之三〔李,2009。農(nóng)村道路交通死亡人數(shù)的統(tǒng)計也占主導地位。拉姆等人〔20XX估計,超過60％的交通死亡發(fā)生在兩車道的鄉(xiāng)村公路上。農(nóng)村雙車道公路上的超車是一種常見的現(xiàn)象。當司機有潛在的超越意識和有足夠的超越空間,超車的情況將會發(fā)生。在超車的過程中,司機決定是否有足夠和充分的超車視距和車頭時距,以及是否有一個足夠的插入同一車道的差距。然后司機決定是否超車。由于超車條件和司機行為會有所不同,超車的過程是非常復雜的。它受道路條件、視覺距離、車輛類型,速度和司機,其他事情的影響。

Greenshieldsetal。〔1935年是第一個建立的最低平均流量條件下安全通過的要求。Bar-Gera,H.和Shinar,D.<2005>顯示這種操作是增加引起交通事故風險,因為它涉及到駕16

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駛在內(nèi)線的相反的方向的交通。同時,哈瑞斯〔1988發(fā)現(xiàn),大多數(shù)司機確實知道超車是危險動作的自評報告。目前,許多現(xiàn)有的研究<參見評審,2007;Geertje唐等人,2007;魏、等,2000;榮>等人,2007;Shao等人,2007>關注超車建模。一些研究評估安全駕駛的超車行為一般檢查司機的性別、年齡和其他特征<大衛(wèi),等人,1998>,或者通過DBQ問卷方式調(diào)查<主刀和拉尤寧就,2005>。很難獲得司機超車時的直接動作由于危險的實驗在一個真正的道路。

我們雇傭駕駛模擬器來評估駕駛行為的超車。不同的研究都表明,駕駛模擬器可以提供可靠的觀察駕駛員的行為<林松柏;1996年,德斯蒙德,和馬修斯,1997;VanderWinsum和Brouwer,1997;Ellingrodetal.1997年>。在本研究中,我們關注個體差異的安全演習DBQ超車和實時行為在駕駛模擬器。這項研究的結果被設計用來區(qū)分不安全的司機和其他類型的司機。

方法

參與者

十二個志愿者參加了這個實驗。示例包含6男6女24歲和55歲之間<M=28.08年,s.d=5.6>。有一個平等的男性和女性。都有一個有效的駕照。平均駕駛經(jīng)驗5.25年,平均每周3.08個小時開車。一切有正?；虺C正到正常視力,并沒有采取任何一種藥。

儀器

一個全尺寸的先進的駕駛模擬器<中國XX理工大學KMRTDS所開發(fā)的仿真實驗室的教員>是在這項研究中的應用。模擬車輛駕駛室,一輛夏利,普通顯示器的特色和控件<轉向、制動器、和加速器>中發(fā)現(xiàn)的車輛。不同的駕駛場景被投射到1500年周期的屏幕,車輛的音效在運動中由雙通道播放器放大。與優(yōu)化圖像處理的30幀/秒的速度和校驗和視覺KMRTDS確保了系統(tǒng)的實時和實驗場景的逼真度。駕駛模擬器的應用前景是圖1所示。KMRTDS可以提供多達68運動參數(shù),分析了車輛和司機的行為。

實驗設計

這個實驗的目的是評估司機超車在不同速度下雙車道高速公路上安全的駕駛行為。在17

XX理工大學萬方科技學院本科畢業(yè)設計

中國,最大的雙車道公路的設計速度是80公里/小時。另一方面,視線是超車的距離與不同速度不同實驗車。在這個實驗中,我們設定的實驗速度分別是30,37.5,45至60公里/小時迎面而來的車輛到。這個相應的超車的速度被設定在40、50、60和80公里/小時。這四組的超車過程被隨機分布在四個部分長度相等的兩車道的公路上。有48名實驗觀察者,所有的受試者將行駛于這四個部分的場景。

駕駛場景

超車的參數(shù)驅動的場景包括靜態(tài)和動態(tài)參數(shù)。靜態(tài)參數(shù)指的是道路的路線、交通標志、標記和周圍的自然環(huán)境。動態(tài)參數(shù)表示參數(shù)的動態(tài)駕駛車輛,包括引發(fā)運動區(qū)域的實驗車在場景中,速度、行駛路線、車輛等之間的距離。

靜態(tài)場景

靜態(tài)超車的設計實驗的場景<如圖2所示>是連續(xù)四個部分組成的一個農(nóng)村中學兩車道的公路的場景?？偩嚯x是8.0公里,兩邊長2.0公里,連接的轉彎半徑是200米。一條單行道的寬度為4.5米<包括路肩>,沒有隔離設施的中心。道路兩邊的是草和隨機分布的樹木和村莊,每一節(jié)都安裝了限速,指示標志。視野的場景是開放的,不存在任何障礙兩邊。與此同時,該領域具有良好的氣候和正常的交通狀況,也就是說,沒有霧,沒有雨,沒有雪的天氣和干燥的平坦路面條件。靜態(tài)場景實驗設計如圖1所示

動態(tài)場景有助于

在按照高速公路標準,在中國的特定部分根據(jù)需要和地形在適當?shù)木嚯x超車視距應設置雙車道公路。裴和小王<2004>已經(jīng)聲明部分的長度在正常情況下不小于總長度的10%-30%。在動態(tài)情況下,實驗車必須根據(jù)預先確定的路線和開車速度。當主要汽車司機操縱達到特定的地方,另一個實驗的車輛將被觸發(fā)開始運動。這些地方的觸發(fā)點。動態(tài)參數(shù)的設置的實驗車在超車過程是如下解釋。18

XX理工大學萬方科技學院本科畢業(yè)設計

圖1。靜態(tài)場景的設計

圖2是分布的動態(tài)場景超車過程。汽車0是領先的汽車,汽車1傳遞的是汽車和汽車2、3、4、5迎面而來的汽車。為了提高駕駛的現(xiàn)實主義和防止司機超車時間提前,設置前汽車6和7領先汽車2,3,4,5。設置動態(tài)參數(shù)包括起步區(qū),速度,行駛軌跡,和空間車輛。一旦汽車0達到觸發(fā)點,所有其他的車輛將開始移動在設定的速度和軌道?？臻g的車輛動態(tài)場景Sn意味著卡恩之間的距離和卡恩Sn+1的主要Car0到達觸發(fā)點。這個距離是最難以確定,但我們可以設置它基于規(guī)定的價值超車的景象距離。

由于時間的進展是一個時間間隔,當兩個保險杠行駛的汽車通過的路段上的同一斷面,它主要取決于兩車及以下汽車的速度〔Olstam,20XX之間的距離。在靜態(tài)條件下觀察,可直接獲得時間的進展〔稱為H通過測量的那一刻,當兩車通過同一斷面?zhèn)鬟f。

H=TB-TF〔1

TB——當時領先的汽車穿過探測器<s>。

TF——的時候,后車穿過探測器<s>。

在一個動態(tài)的超車過程中,車頭時距是智商進展之間的兩輛車及以下汽車的速度,即：

H3.6D<2>Vf

其中,D-領先的汽車的班次〔M

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XX理工大學萬方科技學院本科畢業(yè)設計

VF-以下汽車的速度〔公里/小時

劉〔2007發(fā)現(xiàn),在山區(qū)雙車道二級公路,當領導及以下汽車的進展時間低于3.1s和兩車之間的速度差異達到約20公里/小時,以需求時產(chǎn)生的。在這個實驗中進展的時間被設定在3s。此外,為了使陣線上道路行駛的汽車看起來更真實,我們讓汽車1觸發(fā)預先正在S01=2D。Sn+1值在不同的速度限制段表1所示。圖3是實驗性的動態(tài)場景加載后交通流。

圖3。在KMRTS動態(tài)超車的場景

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XX理工大學萬方科技學院本科畢業(yè)設計

測量

超車行為測量

為了確定司機受試者的超車行為受試者被要求開車去KMRTS。每一個主題完成了四個測試

場景。測量48個不同的超車行為。他們應該增加他們的速度,接近模擬場景限速〔包括40,50,60,80公里/小時,共限制速度標志4個速度,然后在每節(jié)完成超車他們個人的經(jīng)驗和能