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1、外文文献翻译IntelligentTrafficLightControlMarcoWiering,JellevanVeenen,JillesVreeken,andArneKoopmanIntelligentSystemsGroupInstituteofInformationandComputingSciencesUtrechtUniversityPadualaan14,3508TBUtrecht,TheNetherlandsAbstractVehiculartravelisincreasingthroughouttheworl
2、d,particularlyinlargeurbanareas.Thereforetheneedarisesforsimulatingandoptimizingtrafficcontrolalgorithmstobetteraccommodatethisincreasingdemand.Inthispaperwestudythesimulationandoptimizationoftrafficlightcontrollersinacityandpresentanadaptiveoptimizationalgorithmbas
3、edonreinforcementlearning.Wehaveimplementedatrafficlightsimulator,GreenLightDistrict,thatallowsustoexperimentwithdifferentinfrastructuresandtocomparedifferenttrafficlightcontrollers.Experimentalresultsindicatethatouradaptivetrafficlightcontrollersoutperformotherfixe
4、dcontrollersonallstudiedinfrastructures.Keywords:IntelligentTrafficLightControl,ReinforcementLearning,Multi-AgentSystems(MAS),SmartInfrastructures,TransportationResearch1IntroductionTransportationresearchhasthegoaltooptimizetransportationflowofpeopleandgoods.Asthenu
5、mberofroadusersconstantlyincreases,andresourcesprovidedbycurrentinfrastructuresarelimited,intelligentcontroloftrafficwillbecomeaveryimportantissueinthefuture.However,somelimitationstotheusageofintelligenttrafficcontrolexist.Avoidingtrafficjamsforexampleisthoughttobe
6、beneficialtobothenvironmentandeconomy,butimprovedtraffic-flowmayalsoleadtoanincreaseindemand[Levinson,2003].Thereareseveralmodelsfortrafficsimulation.Inourresearchwefocusonmicroscopicmodelsthatmodelthebehaviorofindividualvehicles,andtherebycansimulatedynamicsofgroup
7、sofvehicles.Researchhasshownthatsuchmodelsyieldrealisticbehavior[NagelandSchreckenberg,1992,WahleandSchreckenberg,2001].Carsinurbantrafficcanexperiencelongtraveltimesduetoinefficienttrafficlightcontrol.Optimalcontroloftrafficlightsusingsophisticatedsensorsandintelli
8、gentoptimizationalgorithmsmightthereforebeverybeneficial.Optimizationoftrafficlightswitchingincreasesroadcapacityandtrafficflow,andcanprev
