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新增公共交通方式的选择行为分析-analysis on the choice behavior of new public transportation modes
AbstractTheaimofopeningnewpublictransportistoimprovetheseriesoftrafficandenvironmentalproblembroughtrelyingoncarchip.Toresearchonnewpublictransportmodechoicebyidentifyingthefactorswhichinfluencethechoicetonewtravelmodeandconfirmingthecausalrelationshipbetweenthefactorsandmodechoice.Itwillbehelpfultoimprovetheservicesofnewpublictransportdirectlyandeffectively,strengthenthepassengerflowofthenewpublictransportandprovidedesignbasisfortheimplementationofoperation.Thestudywasstartingwithtravelers’psychologicalperception,integratingthebehavioraleconomics,trafficpsychology,theoryofmathematicalstatisticsandotherscientificmethodstoanalysistheimpactfactors.AndmadeempiricalstudyofthecausalrelationshipbetweenthefactorsandthechoosebehaviorthroughtheRPSPsurveymethods,structuralequationmodelandtheBayesiannetworks.Accordingtothepsychologyandbehavioraleconomics,analyzedtheprocessandessenceofthepsychologicaldecision,constructedtheprospecttheoryframeworkoftransportmodechoiceandanalyzedtheimpactsfactorsandtheinfluencemechanism.ARPSPsurveyquestionnaireonthebasisofeffectfactorswasdesignedtosurveythetravelers’modechoicesalongtheproposedstreetcarinHaikou.Thereliability,correlationandfactoranalysisofthe886effectivedatawerecarriedonbySPSS,andtheresultshowsthereliabilityandvaliditywerehigh.Onthebasisofresultofthefactoranalysisandtheoreticalanalysis,5kindoflatentvariablesweredividedintopersonalbackgroundvariables,thenewmodefeatures,travelbackgroundaswellastheoriginaltravelcostandnewmodechoiceandastructuralequationmodelwasbuilt.Theresultsshowedthatthecostincreaseandtimereductionisthekeyfactorsinfluencingthetravelerstochooseanewtransport.Theresultofchoicebythecostincreasewasnegativeutilityandwhichabsolutevalueisgreaterthanthetimereductionfornewtransportchoice.Travelerincomes,theoriginaltravelcost,theoriginaltravelmodeandcarowningalsoarethemaininfluencefactors,andtheeffectsarepositive.Anditisworthnotingthattravelershavecarwillpromotethenewpublictransportmodechoose.IIAtlast,theBayesiannetworkamongt
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