张侃侃单片机控制交通灯参考文献.docx

张侃侃单片机控制交通灯参考文献.docx

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张侃侃单片机控制交通灯参考文献

原文出处

AustralasianTransportResearchForumAdelaide

FUZZYLOGICTRAFFICSIGNAL

CONTROL

ZEESHANRAZAABDY

PREPAREDFOR

DRNEDALT.RATROUT

INTRODUCTION

Signalcontrolisanecessarymeasuretomaintainthequalityandsafetyoftrafficcirculation.Furtherdevelopmentofpresentsignalcontrolhasgreatpotentialtoreducetraveltimes,vehicleandaccidentcosts,andvehicleemissions.Thedevelopmentofdetectionandcomputertechnologyhaschangedtrafficsignalcontrolfromfixed-timeopen-loopregulationtoadaptivefeedbackcontrol.Presentadaptivecontrolmethods,liketheBritishMOVA,SwedishSOS（isolatedsignals）andBritishSCOOT（area-widecontrol）,usemathematicaloptimizationandsimulationtechniquestoadjustthesignaltimingtotheobservedfluctuationsoftrafficflowinrealtime.Theoptimizationisdonebychangingthegreentimeandcyclelengthsofthesignals.Inarea-widecontroltheoffsetsbetweenintersectionsarealsochanged.Severalmethodshavebeendevelopedfordeterminingtheoptimalcyclelengthandtheminimumdelayatanintersectionbut,basedonuncertaintyandrigidnatureoftrafficsignalcontrol,theglobaloptimumisnotpossibletofindout.

Asaresultofgrowingpublicawarenessoftheenvironmentalimpactofroadtrafficmanyauthoritiesarenowpursuingpoliciesto:

−managedemandandcongestion;

−influencemodeandroutechoice;

−improvepriorityforbuses,tramsandotherpublicservicevehicles;

−providebetterandsaferfacilitiesforpedestrians,cyclistsandothervulnerableroadusers;

−reducevehicleemissions,noiseandvisualintrusion;and

−improvesafetyforallroadusergroups.

Inadaptivetrafficsignalcontroltheincreaseinflexibilityincreasesthenumberofoverlappinggreenphasesinthecycle,thusmakingthemathematicaloptimizationverycomplicatedanddifficult.Forthatreason,theadaptivesignalcontrolinmostcasesisnotbasedonpreciseoptimizationbutonthegreenextensionprinciple.Inpractice,uniformityistheprinciplefollowedinsignalcontrolfortrafficsafetyreasons.Thissetslimitationstothecycletimeandphasearrangements.Hence,trafficsignalcontrolinpracticearebasedontailor-madesolutionsandadjustmentsmadebythetrafficplanners.Themodernprogrammablesignalcontrollerswithagreatnumberofadjustableparametersarewellsuitedtothisprocess.Forgoodresults,anexperiencedplannerandfine-tuninginthefieldisneeded.Fuzzycontrolhasproventobesuccessfulinproblemswhereexactmathematicalmodellingishardorimpossiblebutanexperiencedhumancancontroltheprocessoperator.Thus,trafficsignalcontrolinparticularisasuitabletaskforfuzzycontrol.Indeed,oneoftheoldestexamplesofthepotentialsoffuzzycontrolisasimulationoftrafficsignalcontrolinaninter-sectionoftwoone-waystreets.Eveninthisverysimplecasethefuzzycontrolwasatleastasgoodasthetraditionaladaptivecontrol.Ingeneral,fuzzycontrolisfoundtobesuperiorincomplexproblemswithmultiobjectivedecisions.Intrafficsignalcontrolseveraltrafficflowscompetefromthesametimeandspace,anddifferentprioritiesareoftensettodifferenttrafficflowsorvehiclegroups.Inaddition,theoptimizationincludesseveralsimultaneouscriteria,liketheaverageandmaximumvehicleandpedestriandelays,maximumqueuelengthsandpercentageofstoppedvehicles.So,itisverylikelythatfuzzycontrolisverycompetitiveincomplicatedrealintersectionswheretheuseoftraditionaloptimizationmethodsisproblematic.

Benefitsanddisadvantagesoffuzzysystems

Fuzzylogichasbeenintroducedandsuccessfullyappliedtoawiderangeofautomaticcontroltasks.Themainbenefitoffuzzylogicistheopportunitytomodeltheambiguityandtheuncertaintyofdecision-making.Moreover,fuzzylogichastheabilitytocomprehendlinguisticinstructionsandtogeneratecontrolstrategiesbasedonprioricommunication.Thepointinutilizingfuzzylogicincontroltheoryistomodelcontrolbasedonhumanexpertknowledge,ratherthantomodeltheprocessitself.Indeed,fuzzycontrolhasproventobesuccessfulinproblemswhereexactmathematicalmodellingishardorimpossiblebutanexperiencedhumanoperatorcancontrolprocess.Ingeneral,fuzzycontrolisfoundtobesuperiorincomplexproblemswithmulti-objectivedecisions.

Atpresent,thereisamultitudeofinferencesystemsbasedonfuzzytechnique.Mostofthem,however,sufferill-definedfoundations;eveniftheyaremostlyperformingbetterthatclassicalmathematicalmethod,theystillcontainblackboxes,e.g.defuzzification,whichareverydifficulttojustifymathematicallyorlogically.Forexample,fuzzyIF-THENrules,whichareinthecoreoffuzzyinferencesystems,areoftenreportedtobegeneralizationsofclassicalModusPonensruleofinference,butliterallythisnotthecase;therelationbetweentheserulesandanyknownmany-valuedlogiciscomplicatedandartificial.Moreover,theperformanceofanexpertsystemshouldbeequivalenttothatofhumanexpert:

itshouldgivethesameresultsthattheexpertgives,butwarnwhenthecontrolsituationissovaguethatanexpertisnotsureabouttherightaction.Theexistingfuzzyexpertsystemsveryseldomfulfilthislattercondition.

Manyresearchesobserve,however,thatfuzzyinferenceisbasedonsimilarity.Kosko,forexample,writes'Fuzzymembership...representssimilaritiesofobjectstoimpreciselydefinedproperties'.Takingthisremarkseriously,westudysystematicallymany-valuedequivalence,i.e.fuzzysimilarity.Itturnsoutthat,startingfromtheLukasiewiczwell-definedmany-valuedlogic,weareabletoconstructamethodperformingfuzzyreasoningsuchthattheinferencereliesonlyonexpertsknowledgeandonwell-definedlogicalconcepts.Thereforewedonotneedanyartificialdefuzzificationmethod（likeCenterofGravity）todeterminethefinaloutputoftheinference.Ourbasicobservationisthatanyfuzzysetgeneratesafuzzysimilarity,andthatthesesimilaritiescanbecombinedtoafuzzyrelationwhichturnsouttoafuzzysimilarity,too.Wecallthisinducedfuzzyrelationtotalfuzzysimilarity.FuzzyIF-THENinferencesystemsare,infact,problemsofchoice:

compareeachIF-partoftherulebasewithanactualinputvalue,findthemostsimilarcaseandfirethecorrespondingTHEN-part;ifitisnotunique,useacriteriagivenbyanexperttoproceed.BasedontheLukasiewiczwelldefinedmanyvaluedlogic,weshowhowthismethodcanbecarriedoutformally.

HypothesisandPrinciplesofFuzzyTrafficSignalControlTrafficsignalcontrolisusedtomaximizetheefficiencyoftheexistingtrafficsystems[6].However,theefficiencyoftrafficsystemcanevenbefuzzy.Byprovidingtemporalseparationofrightsofwaytoapproachingflows,trafficsignalsexertaprofoundinfluenceontheefficiencyoftrafficflow.Theycanoperatetotheadvantageordisadvantageofthevehiclesorpedestrians;dependonhowtherightsofwaysareallocated.Consequently,theproperapplication,design,installation,operation,andmaintenanceoftrafficsignalsiscriticaltotheorderlysafeandefficientmovementoftrafficatintersections.

Intrafficsignalcontrol,wecanfindsomekindofuncertaintiesinmanylevels.Theinputsoftrafficsignalcontrolareinaccurate,andthatmeansthatwecannothandlethetrafficofapproachesexactly.Thecontrolpossibilitiesarecomplicated,andhandlingthesepossibilitiesareanextremelycomplextask.Maximizingsafety,minimizingenvironmentalaspectsandminimizingdelaysaresomeoftheobjectivesofcontrol,butitisdifficulttohandlethemtogetherinthetraditionaltrafficsignalcontrol.Thecauseconsequence-relationshipisalsonotpossibletoexplainintrafficsignalcontrol.Thesearetypicalfeaturesoffuzzycontrol.

Fuzzylogicbasedcontrollersaredesignedtocapturethekeyfactorsforcontrollingaprocesswithoutrequiringmanydetailedmathematicalformulas.Duetothisfact,theyhavemanyadvantagesinrealtimeapplications.Thecontrollershaveasimplecomputationalstructure,sincetheydonotrequiremanynumericalcalculations.TheIFTHENlogicoftheirinferencerulesdoesnotrequiremuchcomputationaltime.Also,thecontrollerscanoperateonalargerangeofinputs,sincedifferentsetsofcontrolrulescanbeappliedtothem.IfthesystemrelatedknowledgeisrepresentedbysimplefuzzyIFTHEN-rules,afuzzy-basedcontrollercancontrolthesystemwithefficiencyandease.Themaingoaloftrafficsignalcontrolistoensuresafetyatsignalizedintersectionsbykeepingconflicttrafficflowsapart.Theoptimalperformanceofthesignalizedintersectionsisthecombinationoftimevalue,environmentaleffectsandtrafficsafety.Ourgoalistheoptimalsystem,butweneedtodecidewhatattributesandweightswillbeusedtojudgeoptimality.

Theentireknowledgeofthesystemdesignerabouttheprocess,trafficsignalcontrolinthiscase,tobecontrolledisstoredasrulesintheknowledgebase.Thustheruleshaveabasicinfluenceontheclosed-loopbehaviourofthesystemandshouldthereforebeacquiredthoroughly.Thedevelopmentofrulesistimeconsuming,anddesignersoftenhavetotranslateprocessknowledgeintoappropriaterules.SugenoandNishidamentionedfourwaystoderivefuzzycontrolrules:

1.operatorsexperience

2.controlengineer'sknowledge

3.fuzzymodellingoftheoperator'scontrolactions

4.fuzzymodellingoftheprocess

Zimmermannaddedthreesourcesmore

5.crispmodelingoftheprocess

6.heuristicdesignrules

7.on-lineadaptationoftherules.

Usuallyacombinationofsomeofthesemethodsisnecessarytoobtaingoodresults.As