交通灯控制系统外文翻译.docx
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交通灯控制系统外文翻译.docx
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交通灯控制系统外文翻译
本科生毕业设计(论文)
外文文献翻译
毕业设计题目:
交通灯智能控制系统
学院:
信息科学与工程学院
专业班级:
测控技术与仪器0703班
学生姓名:
王欣
指导教师:
桑海峰
2011年3月19日
外文原文
IntelligentTrafficLightControl
MarcoWiering,JellevanVeenen,JillesVreeken,andArneKoopmanIntelligentSystemsGroup
InstituteofInformationandComputingSciencesUtrechtUniversity
Padualaan14,3508TBUtrecht,TheNetherlands
email:
marco@cs.uu.nl
July9,2004
Abstract
Vehiculartravelisincreasingthroughouttheworld,particularlyinlargeurbanareas.Thereforetheneedarisesforsimulatingandoptimizingtrafficcontrolalgorithmstobetteraccommodatethisincreasingdemand.Inthispaperwestudythesimulationandoptimizationoftrafficlightcontrollersinacityandpresentanadaptiveoptimizationalgorithmbasedonreinforcementlearning.Wehaveimplementedatrafficlightsimulator,GreenLightDistrict,thatallowsustoexperimentwithdifferentinfrastructuresandtocomparedifferenttrafficlightcontrollers.Experimentalresultsindicatethatouradaptivetrafficlightcontrollersoutperformotherfixedcontrollersonallstudiedinfrastructures.
Keywords:
IntelligentTrafficLightControl,ReinforcementLearning,Multi-AgentSystems(MAS),SmartInfrastructures,TransportationResearch
1Introduction
Transportationresearchhasthegoaltooptimizetransportationflowofpeopleandgoods.Asthenumberofroadusersconstantlyincreases,andresourcesprovidedbycurrentinfrastructuresarelimited,intelligentcontroloftrafficwillbecomeaveryimportantissueinthefuture.However,somelimitationstotheusageofintelligenttrafficcontrolexist.Avoidingtrafficjamsforexampleisthoughttobebeneficialtobothenvironmentandeconomy,butimprovedtraffic-flowmayalsoleadtoanincreaseindemand[Levinson,2003].
Thereareseveralmodelsfortrafficsimulation.Inourresearchwefocusonmicroscopicmodelsthatmodelthebehaviorofindividualvehicles,andtherebycansimulatedynamicsofgroupsofvehicles.Researchhasshownthatsuchmodelsyieldrealisticbehavior[NagelandSchreckenberg,1992,WahleandSchreckenberg,2001].
Carsinurbantrafficcanexperiencelongtraveltimesduetoinefficienttrafficlightcontrol.Optimalcontroloftrafficlightsusingsophisticatedsensorsandintelligentoptimizationalgorithmsmightthereforebeverybeneficial.Optimizationoftrafficlightswitchingincreasesroadcapacityandtrafficflow,andcanpreventtrafficcongestions.Trafficlightcontrolisacomplexoptimizationproblemandseveralintelligentalgorithms,suchasfuzzylogic,evolutionaryalgorithms,andreinforcementlearning(RL)havealreadybeenusedinattemptstosolveit.Inthispaperwedescribeamodel-based,multi-agentreinforcementlearningalgorithmforcontrollingtrafficlights.
Inourapproach,reinforcementlearning[SuttonandBarto,1998,Kaelblingetal.,1996]withroad-user-basedvaluefunctions[Wiering,2000]isusedtodetermineoptimaldecisionsforeachtrafficlight.Thedecisionisbasedonacumulativevoteofallroadusersstandingforatrafficjunction,whereeachcarvotesusingitsestimatedadvantage(orgain)ofsettingitslighttogreen.Thegain-valueisthedifferencebetweenthetotaltimeitexpectstowaitduringtherestofitstripifthelightforwhichitiscurrentlystandingisred,andifitisgreen.Thewaitingtimeuntilcarsarriveattheirdestinationisestimatedbymonitoringcarsflowingthroughtheinfrastructureandusingreinforcementlearning(RL)algorithms.
Wecomparetheperformanceofourmodel-basedRLmethodtothatofothercontrollersusingtheGreenLightDistrictsimulator(GLD).GLDisatrafficsimulatorthatallowsustodesignarbitraryinfrastructuresandtrafficpatterns,monitortrafficflowstatisticssuchasaveragewaitingtimes,andtestdifferenttrafficlightcontrollers.Theexperimentalresultsshowthatincrowdedtraffic,theRLcontrollersoutperformallothertestednon-adaptivecontrollers.Wealsotesttheuseofthelearnedaveragewaitingtimesforchoosingroutesofcarsthroughthecity(co-learning),andshowthatbyusingco-learningroaduserscanavoidbottlenecks.
Thispaperisorganizedasfollows.Section2describeshowtrafficcanbemodelled,predicted,andcontrolled.Insection3reinforcementlearningisexplainedandsomeofitsapplicationsareshown.Section4surveysseveralpreviousapproachestotrafficlightcontrol,andintroducesournewalgorithm.Section5describesthesimulatorweusedforourexperiments,andinsection6ourexperimentsandtheirresultsaregiven.Weconcludeinsection7.
2ModellingandControllingTraffic
Inthissection,wefocusontheuseofinformationtechnologyintransportation.Alotofgroundcanbegainedinthisarea,andIntelligentTransportationSystems(ITS)gainedinterestofseveralgovernmentsandcommercialcompanies[Ten-TexpertgrouponITS,2002,WhitePaper,2001,EPA98,1998].
ITSresearchincludesin-carsafetysystems,simulatingeffectsofinfrastructuralchanges,routeplanning,optimizationoftransport,andsmartinfrastructures.Itsmaingoalsare:
improvingsafety,minimizingtraveltime,andincreasingthecapacityofinfrastructures.Suchimprovementsarebeneficialtohealth,economy,andtheenvironment,andthisshowsintheallocatedbudgetforITS.
Inthispaperwearemainlyinterestedintheoptimizationoftrafficflow,thuseffectivelyminimizingaveragetraveling(orwaiting)timesforcars.Acommontoolforanalyzingtrafficisthetrafficsimulator.Inthissectionwewillfirstdescribetwotechniquescommonlyusedtomodeltraffic.Wewillthendescribehowmodelscanbeusedtoobtainreal-timetrafficinformationorpredicttrafficconditions.Afterwardswedescribehowinformationcanbecommunicatedasameansofcontrollingtraffic,andwhattheeffectofthiscommunicationontrafficconditionswillbe.Finally,wedescriberesearchinwhichallcarsarecontrolledusingcomputers.
2.1ModellingTraffic.
Trafficdynamicsbareresemblancewith,forexample,thedynamicsoffluidsandthoseofsandinapipe.Differentapproachestomodellingtrafficflowcanbeusedtoexplainphenomenaspecifictotraffic,likethespontaneousformationoftrafficjams.Therearetwocommonapproachesformodellingtraffic;macroscopicandmicroscopicmodels.
2.1.1Macroscopicmodels.
Macroscopictrafficmodelsarebasedongas-kineticmodelsanduseequationsrelatingtrafficdensitytovelocity[LighthillandWhitham,1955,Helbingetal.,2002].Theseequationscanbeextendedwithtermsforbuild-upandrelaxationofpressuretoaccountforphenomenalikestop-and-gotrafficandspontaneouscongestions[Helbingetal.,2002,JinandZhang,2003,BrouckeandVaraiya,1996].Althoughmacroscopicmodelscanbetunedtosimulatecertaindriverbehaviors,theydonotofferadirect,flexible,wayofmodellingandoptimizingthem,makingthemlesssuitedforourresearch.
2.1.2Microscopicmodels.
Incontrasttomacroscopicmodels,microscopictrafficmodelsofferawayofsimulatingvariousdriverbehaviors.Amicroscopicmodelconsistsofaninfrastructurethatisoccupiedbyasetofvehicles.Eachvehicleinteractswithitsenvironmentaccordingtoitsownrules.Dependingontheserules,differentkindsofbehavioremergewhengroupsofvehiclesinteract.
CellularAutomata.Onespecificwayofdesigningandsimulating(simple)drivingrulesofcarsonaninfrastructure,isbyusingcellularautomata(CA).CAusediscretepartiallyconnectedcellsthatcanbeinaspecificstate.Forexample,aroad-cellcancontainacarorisempty.Localtransitionrulesdeterminethedynamicsofthesystemandevensimplerulescanleadtochaoticdynamics.NagelandSchreckenberg(1992)describeaCAmodelfortrafficsimulation.Ateachdiscretetime-step,vehiclesincreasetheirspeedbyacertainamountuntiltheyreachtheirmaximumvelocity.Incaseofaslowermovingvehicleahead,thespeedwillbedecreasedtoavoidcollision.Somerandomnessisintroducedbyaddingforeachvehicleasmallchanceofslowingdown.ExperimentsshowedrealisticbehaviorofthisCAmodelonasingleroadwithemergingbehaviorsliketheformationofstart-stopwaveswhentrafficdensityincreases.
CognitiveMulti-AgentSystems.AmoreadvancedapproachtotrafficsimulationandoptimizationistheCognitiveMulti-AgentSystemapproach(CMAS),inwhichagentsinteractandcommunicatewitheachotherandtheinfrastructure.Acognitiveagentisanentitythatautonomouslytriestoreachsomegoalstateusingminimaleffort.Itreceivesinformationfromtheenvironmentusingitssensors,believescertainthingsaboutitsenvironment,andusesthesebeliefsandinputstoselectanaction.Becauseeachagentisasingleentity,itcanoptimize(e.g.,byusinglearningcapabilities)itswayofselectingactions.Furthermore,usingheterogeneousmulti-agentsystems,differentagentscanhavedifferentsensors,goals,behaviors,andlearningcapabilities,thusallowingustoexperimentwithaverywiderangeof(microscopic)trafficmodels.
Dia(2002)usedaCMASbasedonastudyofrealdriverstomodelthedrivers’responsetotravelinformation.Inasurveytakenatacongestedcorridor,factorsinfluencingthechoiceofrouteanddeparturetimewerestudied.Theresultswereusedtomodeladriverpopulation,wheredriversrespondtopresentedtravelinformationdifferently.Usingthispopulation,theeffectofdifferentinformationsystemsontheareawherethesurveywastakencouldbesimulated.Theresearchseemspromising,thoughnoresultswerepresented.
Atrafficpredictionmodelthathasbeenappliedtoareal-lifesituation,isdescribedin[WahleandSchreckenberg,2001].Themodelisam
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