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基于梯度提升决策树的高速公路行程时间预测模型（英文）.docx

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基于梯度提升决策树的高速公路行程时间预测模型（英文） Title:PredictingHighwayTravelTimewithGradientBoostingDecisionTrees Abstract: Highwaytraveltimepredictionhasvitalimplicationsfortransportationmanagement,routeplanning,anddriverdecision-making.ThispaperproposesapredictivemodelforhighwaytraveltimebasedonGradientBoostingDecisionTrees(GBDT).GBDTisapowerfulensemblelearningtechniquethatcombinesmultipledecisiontreestoimprovepredictionaccuracyandgeneralizationcapabilities.Throughtheintegrationofhistoricaltrafficdataandvariousrelevantfeatures,thismodelaimstoaccuratelyforecasthighwaytraveltime,providingvaluableinsightstodriversandtransportationauthorities. 1.Introduction: Highwaytraveltimepredictionservesasacriticaltoolinmoderntransportationsystems.Accuratepredictionsoftraveltimehelpinoptimizingrouteplanningandensuringsmoothertrafficflow.Traditionalapproaches,suchaslinearregressionmodels,havelimitationsincapturingthecomplexnonlinearrelationshipsbetweeninputvariablesandtraveltime.Consequently,thispaperproposesaGBDT-basedpredictivemodeltoaddressthesechallengesandprovidemoreaccuratepredictions. 2.RelatedWork: Previousstudieshaveutilizedvarioustechniquesfortraveltimeprediction,includingneuralnetworks,randomforests,andsupportvectorregression.However,thesemethodsmaysufferfromoverfitting,limitedcapabilitytocapturecomplexinteractions,orrequireextensivehyperparametertuning.TheGBDTapproachhasproveneffectiveinaddressingtheseissuesandhasbeensuccessfullyappliedinvariousdomains. 3.Methodology: 3.1DataCollectionandPre-processing: Toconstructaneffectivepredictivemodel,acomprehensivedatasetcontaininghistoricaltraveltimerecordsisrequired.Thisdatasetshouldincludevariablessuchasweatherconditions,timeofday,dayoftheweek,roadconditions,andtrafficflow.Datapre-processingtechniques,includingoutlierremoval,missingvalueimputation,andfeaturescaling,areappliedtoensuredataqualityandmodelefficiency. 3.2GradientBoostingDecisionTrees: GBDTisanensemblelearningmethodthatcombinesmultipledecisiontreestogeneratepowerfulpredictivemodels.It