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基于改进的HMM地图匹配算法.docx

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基于改进的HMM地图匹配算法 Title:EnhancedHiddenMarkovModelforMapMatching Abstract: Mapmatchingistheprocessofdeterminingthemostlikelyvehiclepaththatcorrespondstoasequenceofobservedlocationpoints.HiddenMarkovModel(HMM)isawidelyusedalgorithminmapmatchingduetoitsabilitytohandleuncertaintiesinlocationdata.However,traditionalHMMapproachessufferfromlimitationssuchasoverfitting,highcomputationalcomplexity,andinabilitytohandlecomplexroadnetworks.Inthispaper,weproposeanenhancedHMMmapmatchingalgorithmthataddressestheselimitations.Theproposedalgorithmutilizesacombinationoffeature-basedapproachandaHMMalgorithmwithViterbidecodingandBaum-Welchtraining.Experimentalresultsdemonstratetheeffectivenessandefficiencyoftheproposedapproachinmapmatching. 1.Introduction: Mapmatchingisafundamentaltaskintransportationapplicationssuchastrafficmonitoring,navigationsystems,andtransportationnetworkanalysis.Thegoalofmapmatchingistoassociateasequenceofobservedlocationpointswiththemostlikelypathonaroadnetwork.HiddenMarkovModel(HMM)hasbeenwidelyappliedinmapmatchingduetoitsabilitytohandleuncertaintiesinlocationdata.However,traditionalHMMapproacheshavelimitationsthathindertheirperformance.Inthispaper,wepresentanenhancedHMM-basedmapmatchingalgorithmthatovercomestheselimitations. 2.RelatedWork: Wereviewexistingresearchonmapmatchingalgorithms,includingtraditionalHMMapproaches,anddiscusstheirlimitations.Wealsodiscussrecentimprovementsinmapmatchingalgorithms,suchasusingadditionalmapfeaturesandincorporatingmachinelearningtechniques. 3.EnhancedHMMMapMatchingAlgorithm: 3.1DataPreprocessing: Wediscussthepreprocessingstepsrequiredtocleanandpreprocesstheobservedlocationpoints.Thisincludesremovingoutliers,resamplingthedata,andconvertinggeographiccoordinatestoroadnetworkcoordinates. 3.2Feature-basedMatching: Weproposeafeature-basedapproachthatutilizesadditionalmapfeatures,suchasroadtopologies,roadclassifications,andspeedlimits.ThesefeaturesareintegratedintotheHMMalgorithmtoimprovetheaccuracyofthemapmatchingprocess.Wediscussthef