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改进PSO优化RBFNN的短时交通流量预测方法.docx

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改进PSO优化RBFNN的短时交通流量预测方法 Title:ImprovedPSOOptimizationforShort-TermTrafficFlowPredictionusingRBFNN Abstract: Trafficflowpredictionplaysacriticalroleineffectivetrafficmanagement,congestionavoidance,andresourceallocationintransportationsystems.Inrecentyears,PSO(ParticleSwarmOptimization)hasbeenwidelyusedtooptimizeRBFNN(RadialBasisFunctionNeuralNetwork)modelsfortrafficflowpredictionduetoitsabilitytofindoptimalmodelparameters.However,theperformanceofthePSO-basedRBFNNmodelcanstillbefurtherimproved.ThispaperproposesanenhancedPSOoptimizationapproachforshort-termtrafficflowpredictionusingRBFNN,aimingtoenhancethepredictionaccuracyandrobustnessofthemodel. 1.Introduction Theaccuratepredictionoftrafficflowisofgreatsignificancefortrafficmanagementandefficientresourceallocationintransportationsystems.Traditionalstatisticalmethodsandmachinelearningalgorithmshaveshownlimitationsincapturingthecomplexandnonlinearpatternsoftrafficflow.RBFNNmodels,combinedwithPSOoptimization,havedemonstratedpromisingresultsintrafficflowprediction.However,existingPSO-basedRBFNNmodelsstillfacechallengesinachievinghigherpredictionaccuracyandrobustness. 2.RelatedWork ThissectionprovidesanoverviewoftheexistingliteratureontrafficflowpredictionandtheapplicationofPSOoptimizationinRBFNNmodels.Thelimitationsandchallengesofthecurrentapproachesarediscussed,highlightingtheneedforfurtherimprovement. 3.EnhancedPSOOptimizationforRBFNN ToimprovethepredictionaccuracyandrobustnessofthePSO-basedRBFNNmodel,severalenhancementsareproposedinthissection.Theseenhancementsinclude: 3.1HybridPSOAlgorithm AhybridPSOalgorithmisdesignedbyincorporatingalocalsearchmechanismbasedontheNelder-Meadalgorithm.ThislocalsearchmechanismaimstoenhancetheexplorationabilityofthePSOalgorithm,enablingittoescapefromlocaloptimaandfindbettersolutions. 3.2AdaptiveInertiaWeight AnadaptiveinertiaweightisintroducedtobalancetheexplorationandexploitationcapabilitiesofthePSOalgorithmdynamically.Thisadaptivemechanismadjuststheinertiaweightbasedontheevolutionofth