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File: Special%20Problem%20Manuscript%20-%20Layahin.pdf
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Context: Enhancing Long-Term Traffic Congestion Forecasting: Evaluating Machine Learning and
Deep Learning Models Using the PeMS Dataset
An Undergraduate Special Problem
Presented to the
Faculty of the Department of Computer Science,
University of the Philippines Cebu
Cebu City
In Partial Fulfillment
of the Requirements for the Degree
Bachelor of Science in Computer Science
Jeff Erl layahin
BS Computer Science
Dhong Fhel K. Gom-os
Adviser
2024
LIST OF FIGURES
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Context: FigureNo1.DatasetLocationofPeMS-bays62.TrafficPredictionModelWorkflow15
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Context: TableNo1.Lane1ResultsofShort-TermPrediction162.Lane2ResultsofShort-TermPrediction163.Lane3ResultsofShort-TermPrediction164.Lane4ResultsofShort-TermPrediction165.Lane5ResultsofShort-TermPrediction176.Lane6ResultsofShort-TermPrediction177.Lane7ResultsofShort-TermPrediction178.Lane1ResultsofMid-TermPrediction179.Lane2ResultsofMid-TermPrediction1810.Lane3ResultsofMid-TermPrediction1811.Lane4ResultsofMid-TermPrediction1812.Lane5ResultsofMid-TermPrediction1813.Lane6ResultsofMid-TermPrediction1914.Lane7ResultsofMid-TermPrediction1915.Lane1ResultsofLong-TermPrediction1916.Lane2ResultsofLong-TermPrediction1917.Lane3ResultsofLong-TermPrediction2018.Lane4ResultsofLong-TermPrediction2019.Lane5ResultsofLong-TermPrediction2020.Lane6ResultsofLong-TermPrediction2021.Lane7ResultsofLong-TermPrediction2122.PerformanceofBestResultsofEveryOutsetpsforEveryModels2123.BestPerformingModelsforShort,MidandLongTermPrediction21
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Context: TABLEOFCONTENTSPageTITLEiLISTOFFIGURESiiTABLEOFCONTENTSiiiCHAPTERINTRODUCTION11.1BackgroundoftheStudy11.2ResearchObjectives11.3ScopeandLimitation21.4SignificanceoftheResearch2CHAPTERREVIEWOFLITERATURE22.1TrafficCongestionForecasting32.2Long-TermTrafficCongestionForecasting42.3Long-termtrafficcongestionpredictionmodels5CHAPTERMETHODOLOGY33.1Introduction63.2DataCollectionandDataProcessing63.3FeatureExtractionwithBiLSTM73.4IncorporatingSTTFforSpatialAnalysis83.5HybridModelIntegration93.6BaselinModel103.7PerformanceEvaluation10CHAPTER6RESULTS16CHAPTER5DISCUSSIONS22CHAPTER6CONCLUSION28CHAPTER7RECOMMENDATIONSFORFUTUREWORK29REFERENCES31APPENDIX33
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Context: ABSTRACTTrafficcongestionforecastingisvitalforurbanplanningandtrafficmanagement,providinginsightsforreducingcongestionandimprovingcommuterexperiences.Thisstudyevaluatesvariousmachinelearninganddeeplearningmodels,includingDense,CNN,LSTM,Bi-LSTM,TCN,Transformer,andhybridmodels,usingthePeMSdataset.OurfindingsindicatethattheDensemodelexcelsinshort-termforecasting(outstep3),whiletheHybridmodeldemonstratessuperiorperformanceinmedium-term(outstep6)andlong-term(outstep12)forecasting.However,thestudyfacedcomputationallimitationswithGoogleColab,suggestingfutureresearchshouldutilizemorepowerfulplatformslikeAWSorGCP.Additionally,integratingdiversedatasetsandexternaldatasourcessuchasweatherandeventscanfurtherenhancemodelaccuracy.Interdisciplinarycollaborationiscrucialtoensurethepracticalrelevanceandtechnicalrobustnessofdevelopedmodels.Keywords:TrafficCongestionForecasting,IntelligentTransportationSystems,MachineLearning,DeepLearning,PeMSDataset,Long-termForecasting,HybridModels,ComputationalTools.
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Context: tesspatio-temporaldatatoprovideaccuratelong-termcongestionpredictions,therebysupportingmoreinformeddecision-makinginurbanplanningandtrafficmanagement.SpecificObjectives●Toanalyzeexistingtrafficcongestionforecastingmodelsandidentifytheirlimitationsinlong-termpredictionaccuracy.●DesignahybridmodelintegratingCNNs,LSTMs,andTransformercomponentstoimprovespatio-temporalfeatureextractionandsequenceanalysisforbetterlong-termtrafficforecasting.●TovalidatetheproposedmodelusingthePeMS-Baydataset,ensuringitsabilitytoaccuratelyforecasttrafficcongestion.1
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Context: CHAPTER1INTRODUCTIONBackgroundoftheStudyInanerawhereurbanizationisrapidlyadvancing,trafficcongestionhasemergedasacriticalissueinurbanplanningandtrafficmanagement.Congestionnotonlycontributestoincreasedtraveltimesbutalsoaffectseconomicproductivityandenvironmentalsustainability.Recentstudieshaveutilizedspatio-temporalelementsintrafficforecastingmodelstopredictcongestionpatternswithavaryingdegreeofsuccess(Sunetal.,2021).Whileshort-termforecastinghasseenconsiderableimprovements(Bernaśetal.,2015;Wangetal.,2020),long-termforecastingremainsachallenge.Currentmodelstendtoloseaccuracyovertime,whichisproblematicaslong-termforecastsareessentialforplanningandmanagingtrafficwithaforward-lookingapproach(Emamietal.,2019;Songetal.,2021).Theneedtoaddressthesechallengesisunderscoredbythedynamicnatureofurbantraffic,whichrequiresrobustpredictionmodelsthatcanadapttochangeandmaintainaccuracy.ResearchObjectivesGeneralObjectiveTodevelopanadvancedtrafficforecastingmodelthateffectivelyintegratesspatio-temporalda
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Context: entanovelapproachthatcanbeadaptedandscaledforvariousurbansettings.Thedevelopmentofarobustandaccuratelong-termforecastingmodelalignswiththebroadergoalsofsmartcityinitiatives,aimingtoenhancetheefficiencyandsustainabilityofurbantransportsystems.Theimplicationsofthisresearchextendbeyondthepracticalapplicationsintrafficmanagement,offeringinsightsintotheutilizationofhybriddeeplearningarchitecturesforcomplexspatio-temporaldataanalysis.2
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Context: ●Toevaluatethemodel'sperformanceagainstestablishedmetricssuchasMAE,RMSE,andMAPE,andbenchmarkitagainstcurrentleadingmodelsinthefield.ScopeandLimitationThisresearchisfocusedonthedevelopmentofapredictivemodelforlong-termtrafficcongestionforecastingusingmachinelearninganddeeplearningtechniques.Thescopeincludesthedesign,implementation,andvalidationofthemodelusingtrafficdatafromthePeMS-Baydataset.Limitationsoftheresearchmayarisefromthecomplexityofintegratingmultipleadvancedlearningtechniquesandthecomputationaldemandsofprocessinglargedatasets.Furthermore,themodel'sperformancewillbeevaluatedinthecontextofthedataavailablefromtheBayArea,whichmaylimititsimmediateapplicabilitytoothergeographiclocationswithoutfurtheradaptation.SignificanceoftheResearchThesignificanceofthisresearchliesinitspotentialtocontributetothefieldofComputerSciencebypushingtheboundariesofpredictivemodelingintrafficmanagement.Byaddressingthecurrentgapinlong-termtrafficcongestionforecasting,thestudyaimstopresentanovelapproachtha
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Context: ngmodelsofteninvolvereal-timedataanalysis,leveragingsensorsandcamerastogathercomprehensivetrafficdata.Thisdataisanalyzedusingsophisticatedalgorithmstopredicttrafficpatterns,identifypotentialbottlenecks,andsuggestoptimalcommuterroutes(Sunetal.,2021).Despitetheseadvancements,maintainingaccuracy,especiallyoverextendedperiods,remainsasignificantchallengeintrafficcongestionforecasting.Thecapabilitytopredicttrafficflowaccuratelyandinreal-timeisessentialforeffectiveurbantrafficmanagement,markingitasacriticalareaofongoingresearchanddevelopmentwithinintelligenttransportsystems.3
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Context: CHAPTER2REVIEWOFLITERATURETrafficcongestionhasbeenasubjectofextensiveresearchwithinthefieldofintelligenttransportationsystems.Ascitiesexpandandthedemandforefficientmobilitygrows,theabilitytoaccuratelyforecasttrafficcongestionbecomesincreasinglysignificant.Thischapterpresentsasynthesisofthecurrentliteraturesurroundingtrafficcongestionforecasting.Itprovidesanoverviewofthemethodologiesandtechnologiesthathavebeendevelopedandhighlightsthechallengesthatpersist,particularlyinthedomainoflong-termprediction.Theliteraturereviewexaminestheevolutionoftrafficpredictionmodelsfromtraditionalstatisticalapproachestosophisticatedmachinelearninganddeeplearningmethods.Italsoexploreshowthesemodelshaveincorporatedcomplexspatio-temporaldataandroadnetworkinformationtoenhancepredictiveaccuracy.Thischapterlaysthegroundworkforunderstandingthecurrentstateoftrafficcongestionforecastingandsetsthestagefortheresearchproposedinthisstudy.TrafficcongestionforecastingTrafficcongestionforecastingisacrucialaspectofintellig
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Context: cialaspectofintelligenttransportsystems,playingapivotalroleinthemanagementofurbantraffic.Itsimportancestemsfromitscapacitytopredictvehicularpatterns,aidinginefficienturbanplanning,congestionmitigation,andimprovingoverallcommutingexperiences(Bernaśetal.,2015).Accurateforecastsenablecityadministratorsandtransportauthoritiestomakeinformeddecisionsintrafficcontrol,infrastructuredevelopment,andpublictransportationscheduling.Thechallengeinforecastingtrafficcongestionisnotonlypredictingvehicleflowbutalsointegratingcomplexvariablessuchascommuterbehaviors,roadcapacities,andexternalfactorslikeweatherandspecialevents(Wangetal.,2020).Thiscomplexityhighlightsthesignificanceoftrafficcongestionforecastinginurbantrafficmanagement,aimingtofacilitatesmoothtrafficflowandminimizecongestionissues.Themethodologiesandapproachesintrafficcongestionforecastinghaveevolved,transitioningfromtraditionalmodelstoadvancedtechniquesutilizingbigdataandartificialintelligence(AI).Moderntrafficforecastingmodelsofteninvolve
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Context: icshifts,allofwhichcansignificantlyaltertrafficpatternsovertime(Chenetal.,2020;Wangetal.,2022).Totacklethesecomplexities,researchersareturningtoadvancedmodelingtechniquesthatincorporatemachinelearninganddeeplearning.Thesemodelsaimtolearnfromextensivedatasets,adapttochangingurbandynamics,andprovidemoreaccurateforecastsforlongerdurations(Huangetal.,2023;Emamietal.,2019).However,anotablegappersistsintheaccuracyoflong-termtrafficpredictions.Despitetheintegrationofsophisticatedspatio-temporalelements,thesemodelsoftenexperienceadeclineinpredictiveperformanceovertime.Thisdeclineismainlyduetotheinabilityofcurrentmodelstofullycapturetheevolvingcomplexitiesofurbantrafficsystemsandexternalinfluencessuchasurbandevelopmentandpolicychanges(Songetal.,2021;Emamietal.,2019).Therefore,enhancingtheadaptabilityandaccuracyofthesemodelsoverextendedperiodsremainsacriticalareaoffocusinurbantrafficmanagement.Lookingahead,thefutureoflong-termtrafficcongestionforecastinghingesondevelopingmodelsthateffectivelyint
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Context: lsthateffectivelyintegrateandanalyzeavarietyofdatasources.Theseincludereal-timetrafficdata,urbandevelopmentplans,andinputsfromurbanplannersandpolicymakers.UtilizingAI,particularlyinareassuchasneuralnetworksandpredictiveanalytics,isseenaskeytoenhancingthelong-termforecastingaccuracy.Theultimateaimistocreaterobustmodelsthatnotonlypredicttrafficpatternswithhighprecisionbutalsoaidinmakinginformeddecisionsforsustainableurbangrowthandefficienttrafficmanagement.4
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Context: Long-TermTrafficCongestionForecastingLong-termtrafficcongestionforecastingisanareaofurbanplanningthatextendsbeyondtheimmediatehorizonofshort-termtrafficpredictions.Whileshort-termforecasting,whichusuallyspansafewminutestohours,iscrucialforday-to-daytrafficmanagement,itoftenfallsshortinaddressingthecomplexitiesofurbangrowthandinfrastructuralchangesthatimpacttrafficpatternsovermonthsoryears(Emamietal.,2019;Songetal.,2021).Theselimitationsunderscoretheneedforlong-termforecasting,whichaimstopredicttrafficconditionsoverextendedperiods,suchasdays,months,oryears.Thistypeofforecastingisessentialforstrategicurbanplanning,policy-making,andlong-terminfrastructuraldevelopments,asitconsidersbroaderfactorslikedemographicchanges,urbandevelopmenttrends,andemergingtransportationtechnologies(Sunetal.,2021;Kongetal.,2019).Thechallengeinlong-termforecastingliesinitsdynamicnature.Urbanenvironmentsareconstantlyevolvingduetofactorslikeurbanization,technologicaladvancements,andsocio-economicshifts,allofwhichc
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Context: Long-termtrafficcongestionpredictionmodelsDiverseForecastingMethodsinLong-TermPrediction:Thepursuitofeffectivelong-termtrafficcongestionpredictionmodelshasledtotheexplorationofvariousforecastingmethods.Beyondthestandardlinear,nonlinear,andneuralnetworkapproaches,thereisanincreasingfocusonintegratingdiversemethodologiestoaddressthecomplexnatureoflong-termtrafficdynamics.Forinstance,studieshaveexploredtheuseofAutoregressiveIntegratedMovingAverage(ARIMA)models,whicharetraditionallyusedfortime-seriesprediction,andcombinedthesewithEmpiricalModeDecomposition(EMD)toenhanceshort-termpredictioncapabilities.Thishybridapproach,asdemonstratedbyWangetal.(2022),signifiesthepotentialofcombiningtraditionalstatisticalmodelswithmoderntechniquestoimprovelong-termforecasting.Spatio-TemporalAnalysisinTrafficPrediction:Recognizingtheimportanceofspatialandtemporaldynamicsintrafficpatterns,recentstudieshaveemphasizedtheinclusionofspatio-temporalanalysisintrafficforecastingmodels.Thisapproachinvolvesunderstand
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Context: chinvolvesunderstandinghowtrafficconditionsatonelocationinfluence,andareinfluencedby,conditionsinotherareasovertime.AdvancedmodelslikeConvolutionalNeuralNetworks(CNN)andLongShort-TermMemory(LSTM)networkshavebeenadaptedtocapturethesespatio-temporalrelationships.Forexample,LSTMnetworks,knownfortheirtemporallearningcapabilities,havebeenusedalongsideCNNstolearnspatialfeatures,thusprovidingamorecomprehensiveanalysisoftrafficcongestionpatternsoverextendedperiods(Zhangetal.,2022)IntegrationofRoadNetworkInformation:Acriticalaspectinenhancinglong-termtrafficcongestionpredictionistheintegrationofroadnetworkstructureinformation.Recentmodelshaveattemptedtoincorporatethisdataintotheiranalysis.Forinstance,GraphConvolutionalNetworks(GCN)havebeenusedtomodeltheinterconnectivityofroadnetworks,allowingforamorenuancedunderstandingoftrafficflowandcongestiondynamics.Thisapproach,asseeninstudiesbyHuangetal.(2023),highlightstheimportanceofconsideringthephysicallayoutandconnectivityofroadsinlong-termcongestion
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Context: nlong-termcongestionpredictionmodels.CNNModelfortrafficforecasting:Thepaper"Short-termtrafficflowpredictionbasedonspatio-temporalanalysisandCNNdeeplearning"byWeibinZhangetal.(2019)presentsamodelthatemploysconvolutionalneuralnetworks(CNN)toforecastshort-termtrafficflowbyassessingbothspatialandtemporalfeatures.Thespatio-temporalfeatureselectionalgorithm(STFSA),whichthispaperpresents,canhelptraditionalmodelsmakebetteruseofthesefeaturesby5
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Context: er-DecoderArchitecture"highlighttheeffectivenessoftheproposedmodelinforecastinglong-termtrafficflows.UsingtheCaltransPerformanceMeasurementSystem(PeMS)dataset,themodeldemonstratedsuperioraccuracyandstabilitycomparedto6
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Context: identifyingtheidealinputdata.Afterthis,theCNNusesatwo-dimensionaltrafficdatamatrixtobuildapredictionmodel.Thesuggestedapproachsuccessfullycapturesandlearnsthepertinenttrafficfeatures,outperformingcurrentmodelsinaccuracy.Thestudyoffersimprovedtoolsfortrafficmanagementandplanningbydemonstratingnotableadvancementsintrafficflowprediction.Theresultsoftheresearchshowthatthesuggestedmodelperformsmuchbetterinshort-termtrafficflowpredictionthantraditionalmodels.Itdoesthisbycombiningspatio-temporaldatawithaconvolutionalneuralnetwork(CNN).IncomparisontobaselinemodelslikeSARIMA,SVR,KNN,andANN,theCNNmodeldemonstratedlowermeanabsolutepercentageerror(MAPE)andmeanabsoluteerror(MAE)whenaugmentedusingthespatio-temporalfeatureselectionalgorithm(STFSA).TheCNN+STFSAmodeldemonstrateditsefficacyinidentifyingintricatetrafficpatternsbyachievingthemaximumaccuracyacrossmultipleforecastperiods(5,10,15,and20minutes).LSTMModelfortrafficforecasting:Thepaper"Long-TermTrafficPredictionBasedonLSTMEncoder-DecoderArchite
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Context: coder-DecoderArchitecture"byZhumeiWang,XingSu,andZhimingDingintroducesaninnovativemethodforpredictingtrafficpatternsoverlongperiods.Traditionalmodelsmainlyhandleshort-termpredictionsandoftenstrugglewithaccuracywhenextendedtolongertimeframes.Totacklethis,theauthorsdesignedadeeplearningmodelthatcombinesanLSTMencoder-decoderstructurewithahardattentionmechanismandacalibrationlayer.Thissetuphelpsthemodelrememberpasttrafficpatternsbetterandreduceserrorsthattypicallyaccumulateinlong-termforecasts(Wang,Su,&Ding,2021).Themodelwastestedusingreal-worldtrafficdatafromLosAngeles,showingitoutperformedotheradvancedpredictionmethods,provingmoreaccurateandstable.Byeffectivelycapturingbothlocaltrafficdetailsandlong-termtrends,thismodelofferssignificantimprovementsfortrafficmanagementandplanning.Theresearchersplantoenhancethemodelfurtherbyincludingspatial-temporalfeaturesandotherfactorsthatinfluencetraffic(Wangetal.,2021).Theresultsofthepaper"Long-TermTrafficPredictionBasedonLSTMEncoder-DecoderArchitectu
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Context: traditionalmethodslikeSupportVectorRegression(SVR)andLSTM,aswellasadvancedtechniquessuchastheEK-NNandSs2smodels.TheAttentionCalibrationEncoder-Decoder(ACE-D)modelachievedapproximately5%higheraccuracyinvarioustestingscenarios,predictingtrafficflowsforfuturehorizonsof50,80,100,150,and288timesteps.TheACE-Dmodelmaintainedlowererrorratesandprovedtobeparticularlyrobustinlong-termpredictions,showinglesserroraccumulationandgreaterstabilityovertime.Byeffectivelycapturingbothlocalfeaturesandlong-termdependenciesinthetrafficdata,theACE-Dmodeloffersamorereliablesolutionforlong-termtrafficprediction,enhancingtransportationplanningandmanagement(Wang,Su,&Ding,2021).CNN-LSTMModelfortrafficforecasting:Thepaper"ACNN-LSTMModelforTrafficSpeedPrediction"byMiaomiaoCao,VictorO.K.Li,andVincentW.S.ChanpresentsanovelmodelcombiningConvolutionalNeuralNetworks(CNN)andLongShort-TermMemory(LSTM)forpredictingtrafficspeeds.Thismodel,calledCLM,usesLSTMlayerstointegratespatiotemporalfeatureswithtrafficspeeddatathatisext
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Context: icspeeddatathatisextractedfromCNNonadailyandweeklybasis.Whencomparedtofivebaselinetechniques—SVR,MLP,Lasso,RandomForest,andLSTM—themodeloutperformedtheminforecastingtrafficspeedsthroughoutarangeofforecastdurations.ThetestswereconductedusingactualtrafficdatafromHongKong.AccordingtoCao,Li,andChan(2020),theCLMmodeldemonstratedreducedmeanabsoluteerror(MAE),rootmeansquareerror(RMSE),aswellasincreasedcoefficientofdetermination(R²)andexplainedvariancescore(EVS).Thesefindingsvalidatethemodel'sefficacyincapturingintricatetrafficpatternsandyieldingpreciseforecastsfortrafficmanagementandplanning.7
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Context: CHAPTER 3
METHODOLOGY
This chapter outlines the methodology employed in developing an advanced model
for long-term traffic congestion forecasting.
3.1 Introduction
The primary objective of this methodology is to develop and assess various advanced
neural network models, including Dense, CNN, LSTM, BiLSTM, CNN-LSTM, TCN, Transformer,
and a Hybrid CNN-LSTM-Transformer model, for improved long-term traffic congestion
forecasting. The methodology will be divided into several key stages: Data Collection and
Preprocessing, Model Training, Model Optimization, and Performance Evaluation.
3.2 Data Collection
The PeMS-Bay dataset, a public traffic dataset collected by the California
Transportation Agencies, serves as the primary data source for this study. The dataset
encompasses six months of traffic speed data, spanning from January to February 2023,
collected from 325 sensors strategically positioned on highways in the Bay Area, Los Angeles.
This dataset is illustrated in Figure 1, showcasing the extensive sensor coverage along major
highways and intersections, thereby offering a comprehensive view of traffic dynamics in the
region.
Figure 1. Dataset Location of PeMS-bay
8
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5.9 ee
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Context: 3.3DataPreprocessingDatapreprocessingisacriticalstepinensuringthequalityandusabilityofthedatasetforsubsequentanalysisandmodeltraining.Initially,mapmatchingwillbeemployedtoalignthesensordatawithspecificroadsegments,ensuringthespatialaccuracyofthedataset.Followingthis,thedatasetwillundergonormalizationofspeeddata,adoptingthemethodologyoutlinedbyLietal.(2018).Thisnormalizationstepisessentialtostandardizethedataandfacilitatecomparisonacrossdifferentsensorsandtimeperiods.Giventhedataset'sgranularity,withdatacollectionintervalsofevery5,10,and15minutes,handlingmissingvaluesbecomesparamount.Forthisstudy,anymissingdatapointswillbefilledwithzeroes.Thisapproachensuresaconsistentandcompletedataset,crucialfortrainingmachinelearningmodelsthatrequireuninterruptedtimeseriesdata.Additionally,anypotentialoutliersoranomaliesinthedatawillbeidentifiedandaddressedtomaintaindataintegrity.3.3.1StationSelectionDuetolimitedcomputationalresources,thisstudyfocusesonasubsetofthedataset,specificallydatafrom10randomlyselectedstations.Thisselectionensuresmanageabilitywhilestillprovidingarepresentativesampleforanalysis.Thestationsareselectedrandomlytoavoidanyselectionbias,andthedatafromthesestationsisusedforallsubsequentsteps.3.3.2FeatureEngineeringFeatureengineeringinvolvesextractingrelevantfeaturesfromtherawdatatoenhancethepredictivepowerofthemachinelearningmodels.Inthisstudy,keyfeaturesincludelane-specifictrafficflow,averageoccupancy,andaveragespeed.Foreachlane,thesefeaturesareextractedandaggregatedtoformacomprehensivesetofpredictors.Thisprocessisautomatedusingcustomfunctionsthatiterateoverthelanestoextractandcompilethenecessaryfeatures,ensuringarobustandscalablefeatureextractionpipeline.3.3.3Normalization𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑_𝑑𝑎𝑡𝑎𝑠𝑒𝑡=𝑑𝑎𝑡𝑎𝑠𝑒𝑡−µ ⁄σBeforefeedingthedataintomachinelearningmodels,itundergoesnormalizationtoensurethatallfeaturescontributeequallytothemodeltrainingprocess.Normalizationisperformedbysubtractingthemeananddividingbythestandarddeviationofeachfeature.Thistransformationensuresthatthedatahasameanofzeroandastandarddeviationofone,whichiscrucialfortheconvergenceofgradient-basedoptimizationalgorithmsusedintrainingdeeplearningmodels.9
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Context: datathroughitsmultiplelayersofneurons.Thismodelis10
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Context: 3.3.4DataSplitting𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔_𝑑𝑎𝑡𝑎=𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑_𝑑𝑎𝑡𝑎𝑠𝑒𝑡[0:𝑖𝑛𝑡(𝑛⋅0.7)]𝑣𝑎𝑙𝑖𝑑𝑎𝑡𝑖𝑜𝑛_𝑑𝑎𝑡𝑎=𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑_𝑑𝑎𝑡𝑎𝑠𝑒𝑡[𝑖𝑛𝑡(𝑛⋅0.7):𝑖𝑛𝑡(𝑛⋅0.9)]𝑡𝑒𝑠𝑡𝑖𝑛𝑔_𝑑𝑎𝑡𝑎=𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑_𝑑𝑎𝑡𝑎𝑠𝑒𝑡[𝑖𝑛𝑡(𝑛⋅0.9):𝑛]Thedatasetisdividedintotraining,validation,andtestingsetstoevaluatetheperformanceandgeneralizabilityofthemodels.Specifically,70%ofthedataisallocatedfortraining,20%forvalidation,andtheremaining10%fortesting.Thissplitensuresthatthemodelsaretrainedonasubstantialportionofthedatawhilebeingvalidatedandtestedonindependentsubsetstopreventoverfittingandtoassessperformanceobjectively.3.3.5WindowGenerationTocapturetemporaldependenciesinthetrafficdata,awindow-basedapproachisemployed.Thedataissegmentedintooverlappingwindowsoffixedsize,witheachwindowservingasasampleforthemodel.AcustomWindowGeneratorclassisdevelopedtofacilitate
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Context: evelopedtofacilitatethisprocess.Theclasshandlesthecreationofinputandlabelwindows,ensuringthatthetemporalstructureofthedataispreserved.Thisapproachenablesthemodelstolearnpatternsovertime,whichisessentialforaccuratetrafficprediction.3.5TraditionalModelsThetraditionalmodelsencompassseveralstandardarchitectureswidelyusedintimeseriesforecastingandtrafficpredictiontasks.ThesemodelsincludeDense,CNN,LSTM,Bi-LSTM,TCN,andTransformer.Eachmodelhasuniquestrengthsinhandlingdifferentaspectsofthedata,andtheyareevaluatedusingthesamedatasetandperformancemetricstoensureafaircomparisonwiththehybridmodels.DenseModel(FullyConnectedNeuralNetwork):Architecture:●InputLayer:Acceptstheinputsequences.●LambdaLayer:Processesthelasttimestepoftheinputsequence.●DenseLayer:Fullyconnectedlayerwith512neuronsandReLUactivation.●OutputLayer:Denselayerthatmapsthefeaturestotheoutputdimensions,followedbyareshapelayertoformattheoutput.Description:TheDenseModelcapturesbothlinearandnon-linearrelationshipsinthedatathroughitsmultip
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Context: Mnetworksarepowerfulformodelingsequentialdatabecausetheycanmaintainandupdateinformationoverlongperiods.Thisabilitymakesthemparticularlyusefulfortimeseriesforecasting,wherepastdatapointscaninfluencefutureoutcomes.LSTMsareexcellentathandlingthetemporaldependenciesinherentintrafficdata.BidirectionalLongShort-TermMemory(Bi-LSTM)Network:Architecture:●InputLayer:Acceptstheinputsequences.●BidirectionalLSTMLayer:Processesthesequencedatainbothforwardandbackwarddirectionswith64LSTMunits.●OutputLayer:Denselayerthatmapsthefeaturestotheoutputdimensions,followedbyareshapelayertoformattheoutput.11
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Context: straightforwardbutcanbehighlyeffectiveformanytypesofpredictivetasks,providingabaselineformorecomplexmodels.ConvolutionalNeuralNetwork(CNN):Architecture:●InputLayer:Acceptstheinputsequences.●LambdaLayer:Focusesonthelastfewtimestepsofthesequence.●ConvolutionalLayer:Appliesa1Dconvolutionalfilterwith256neuronsandReLUactivation.●OutputLayer:Denselayerthatmapsthefeaturestotheoutputdimensions,followedbyareshapelayertoformattheoutput.Description:CNNsareparticularlyadeptatextractingspatialfeaturesfromthedata.Theyarewell-suitedforidentifyinglocalpatternsandtrends,whicharecrucialinunderstandingtrafficflowandcongestionpatterns.Byusingfiltersandpoolingoperations,CNNscanreducethecomplexityofthedatawhileretainingimportantfeatures.LongShort-TermMemory(LSTM)Network:Architecture:●InputLayer:Acceptstheinputsequences.●LSTMLayer:Processesthesequencedatawith64LSTMunits.●OutputLayer:Denselayerthatmapsthefeaturestotheoutputdimensions,followedbyareshapelayertoformattheoutput.Description:LSTMnetworksarepowerful
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Context: nputLayer:Acceptstheinputsequences.●TransformerEncoderLayers:Appliesmulti-headattentionmechanismsandfeedforwardneuralnetworkswithineachtransformerblock.●GlobalAveragePoolingLayer:Summarizesthesequenceintoafixed-lengthvector.●OutputLayer:Denselayerthatmapsthefeaturestotheoutputdimensions,followedbyareshapelayertoformattheoutput.Description:Transformersarehighlyeffectiveforsequencemodelingtasksduetotheirabilitytocapturedependenciesoverlongdistanceswithoutthe12
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Context: Description:Bi-LSTMsareparticularlyeffectiveforcapturingcontextfrombothpastandfuturedatapointssimultaneously.Thisdualprocessingcapabilityenhancesthemodel'sabilitytounderstandandpredictcomplextemporalpatterns,makingBi-LSTMswell-suitedfortaskswherefuturecontextisasimportantaspastcontextinmakingaccuratepredictions.TemporalConvolutionalNetwork(TCN):Architecture:●InputLayer:Acceptstheinputsequences.●TCNLayers:UtilizesTemporalConvolutionalNetworklayerswith64filtersandvariousdilationrates.●DenseLayer:Fullyconnectedlayerwith128neuronsandReLUactivation.●OutputLayer:Denselayerthatmapsthefeaturestotheoutputdimensions,followedbyareshapelayertoformattheoutput.Description:TCNsprovideanalternativetorecurrentnetworkslikeLSTMsforsequencemodeling.Theyaredesignedtohandlelong-rangedependenciesandcanoftenachievebetterperformancewithfewerparameters.TCNsareparticularlyeffectiveatcapturingthetemporalpatternsintrafficdatawithouttheneedforrecurrentconnections.TransformerModel:Architecture:●InputLayer:Acceptsthe
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Context: sbyapplyingconvolutionalfilterstotheinputdata,identifyinglocalpatternssuchastrafficcongestionatspecificlocations.Followingthis,theLSTMlayerscapturetemporaldependenciesbyprocessingthesequencedata,makinguseoftheirmemorycellstoretaininformationoverlongperiods.Thiscombinationallowsthemodeltoleveragebothspatialcorrelationsandtemporalsequences,leadingtomoreaccuratetrafficpredictions.CNN-LSTM-TransformerHybridModelArchitecture:●InputLayer:Acceptstheinputsequences.●ConvolutionalLayer:Appliesa1Dconvolutionalfilterwith64neuronsandReLUactivation.13
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Context: limitationsofrecurrentmodels.Theyuseself-attentionmechanismstodynamicallyfocusonrelevantpartsoftheinput,makingthemwell-suitedforcomplextimeseriesforecastingtasksliketrafficprediction.3.6HybridModelsInthestudyTwohybridmodels:aCNN-LSTMmodelandaCNN-LSTM-Transformermodelwereassessed.Thesemodelsintegratedifferentneuralnetworkarchitecturestoleveragetheiruniquestrengths,providingacomprehensiveapproachtotrafficforecastingbycapturingbothspatialandtemporalfeaturesfromthedata.CNN-LSTMHybridModel:Architecture:●InputLayer:Acceptstheinputsequences.●ConvolutionalLayer:Appliesa1Dconvolutionalfilterwith64neuronsandReLUactivation.●MaxPoolingLayer:Reducesthedimensionalityofthedata.●LSTMLayers:Processesthesequencedatawith128LSTMunitsintwolayers.●OutputLayer:Denselayerthatmapsthefeaturestotheoutputdimensions,followedbyareshapelayertoformattheoutput.●Description:ThishybridmodelcombinesConvolutionalNeuralNetworks(CNN)withLongShort-TermMemory(LSTM)networks.TheCNNlayersextractspatialfeaturesbyapplyingconvoluti
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Context: omonitortrainingprogress.ThemodelsarecompiledwiththeHuberlossfunctionandoptimizedusingtheStochasticGradientDescent(SGD)optimizerwithmomentum.Toassesstheperformanceofthemodels,wewillemploythreeestablishedevaluationmetricscommonlyusedintrafficforecastingresearch:MeanAbsoluteError(MAE),RootMeanSquaredError(RMSE),andMeanAbsolutePercentageError(MAPE).Thesemetricsarewidelyrecognizedinthefieldandprovidevaluableinsightsintotheaccuracyandreliabilityofpredictivemodels(Hyndman&Koehler,2006).Toenhancetheclarityandvisualcomparabilityoftheresults,wewillscaletheMAEandRMSEvaluesbyafactorof50.Thisscalingensuresthattheevaluationmetricsarepresentedinaformatthatiseasiertointerpretwhilemaintainingtheirrelativemagnitudesandrelationships(Smith,2002).14
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Context: ●MaxPoolingLayer:Reducesthedimensionalityofthedata.●BidirectionalLSTMLayer:Processesthesequencedatainbothforwardandbackwarddirectionswith64LSTMunits.●TransformerBlocks:Enhancesthemodel'sabilitytohandlelong-rangedependenciesusingself-attentionmechanisms.●GlobalAveragePoolingLayer:Summarizesthesequenceintoafixed-lengthvector.●OutputLayer:Denselayerthatmapsthefeaturestotheoutputdimensions,followedbyareshapelayertoformattheoutput.Description:ThishybridmodelextendstheCNN-LSTMarchitecturebyincorporatingTransformerblocks.TheTransformerblocksaredesignedtohandlelong-rangedependenciesthroughself-attentionmechanisms.AftertheCNNlayersextractspatialfeatures,theBidirectionalLSTM(Bi-LSTM)layersprocessthedatainbothforwardandbackwarddirections,capturingcomprehensivetemporaldependencies.3.7EvaluationandPerformanceMetricsEachtraditionalandhybridmodelistrainedandevaluatedusingthesamepreprocessedandwindoweddataset.ThetrainingprocessincludesearlystoppingtopreventoverfittingandCSVloggingtomonitortrainingprog
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Context: 3.8 Architecture
Figure 2. Traffic Prediction Model Workflow
The traffic prediction model workflow, as illustrated in Figure 2, begins with the data
loading phase, where traffic data from various stations is retrieved from the database. This
data includes metrics such as lane flow, average occupancy, and average speed, collected
over a specified time period. Following data loading, the data processing step involves
handling missing values by filling them with zeroes, normalizing the data to ensure
consistency, and extracting the relevant features needed for model training.
Once the data is processed, the window generator creates smaller segments or
windows of data, each containing a sequence of data points over a specific time frame.
These windows serve as the input for training the machine learning models. The next phase
involves defining and training various model architectures, each capturing different aspects
of the data. These models include the Dense Model, CNN Model, LSTM Model, Bi-LSTM
Model, CNN-LSTM Model, TCN Model, Transformer Model, and the CNN-LSTM-Transformer
Model.
Each model architecture undergoes a training loop where it is trained using the
windowed dataset. Early stopping and CSV logging are implemented to monitor the training
progress and prevent overfitting. After training, the models are evaluated using performance
metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean
Absolute Percentage Error (MAPE) to assess their accuracy and robustness.
Finally, the trained models are saved for future use. This structured workflow
ensures a comprehensive approach to developing and evaluating various models for traffic
prediction, allowing for the identification of the best-performing model for accurate
long-term traffic forecasting.
15
LEICHEN
DEEN pee
indow Generator
or A Nc eu
Dense Model
CNN Model
LSTM Model
Bi-LSTM Model
[NE TUT
Bre Er
Transformer Model
(CNN-LSTM-Transformer Model
SET Fa
Model Evaluation
Cera
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Context: | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0417 | 0.0423 | 0.0469 | 0.0466 | 0.055 | 0.222 | 103.6178 | 0.3429 |
| MAE | 0.206 | 0.2051 | 0.2122 | 0.2112 | 0.0435 | 0.2046 | 96.6873 | 0.3003 |
| MAPE | 94.4234 | 95.9163 | 95.2754 | 100.9174 | 0.0649 | 0.2485 | 135.9898 | 0.37 |
| RMSE | 0.293 | 0.2953 | 0.3121 | 0.3106 | 0.0431 | 0.2042 | 106.863 | 0.2983 |
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Context: CHAPTER 4
RESULTS
The performance of each model was evaluated using three primary metrics: Root
Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute
Error (MAE). These metrics were chosen to provide a comprehensive assessment of the
models' accuracy in predicting traffic patterns.
Table 1. Lane 1 Results of Short - Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.059 | 0.0577 | 0.063 | 0.0695 | 0.0831 | 0.284 | 121.1983 | 0.4134 |
| MAE | 0.2368 | 0.2354 | 0.2493 | 0.2654 | 0.0636 | 0.2505 | 94.6997 | 0.3612 |
| MAPE | 98.1179 | 98.6875 | 105.7835 | 119.5923 | 0.0791 | 0.2838 | 122.2462 | 0.4029 |
| RMSE | 0.3466 | 0.3434 | 0.3585 | 0.3761 | 0.0664 | 0.2574 | 93.708 | 0.3687 |
Table 2. Lane 2 Results of Short - Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0309 | 0.0331 | 0.0364 | 0.0401 | 0.0481 | 0.1908 | 195.409 | 0.3273 |
| MAE | 0.1628 | 0.1627 | 0.1722 | 0.1817 | 0.0319 | 0.1674 | 168.9306 | 0.2568 |
| MAPE | 155.4288 | 162.2879 | 157.4485 | 188.0767 | 0.0499 | 0.2011 | 200.1992 | 0.3263 |
| RMSE | 0.2524 | 0.2624 | 0.2761 | 0.2901 | 0.0362 | 0.1683 | 165.3492 | 0.2773 |
Table 3. Lane 4 Results of Short - Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0439 | 0.0416 | 0.0422 | 0.0412 | 0.0494 | 0.2158 | 86.9541 | 0.3216 |
| MAE | 0.2163 | 0.2071 | 0.2068 | 0.2025 | 0.0432 | 0.21 | 94.6335 | 0.2964 |
| MAPE | 88.3815 | 89.3984 | 97.0663 | 91.2169 | 0.0559 | 0.239 | 105.4641 | 0.3386 |
| RMSE | 0.2989 | 0.2906 | 0.2926 | 0.289 | 0.0411 | 0.2048 | 87.323 | 0.2887 |
Table 4. Lane 4 Results of Short - Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-L
STM | TCN | TRANSFO
RMER | HYBRID |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
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Context: | MAE | 0.0705 | 0.261 | 107.299 | 0.3802 | 0.3089 | 0.2776 | 0.3029 | 0.2685 |
| MAPE | 0.0812 | 0.2881 | 122.214 | 0.4077 | 132.440 | 103.921 | 136.8484 | 103.77 |
| RMSE | 0.085 | 0.297 | 128.627 | 0.4174 | 0.4379 | 0.4011 | 0.4254 | 0.3894 |
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Context: Table 5. Lane 5 Results of Short - Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.035 | 0.0351 | 0.0378 | 0.0365 | 0.0467 | 0.2009 | 85.2211 | 0.3156 |
| MAE | 0.1815 | 0.1806 | 0.1849 | 0.1782 | 0.0393 | 0.1925 | 93.5314 | 0.2873 |
| MAPE | 83.1773 | 84.5585 | 85.7928 | 84.4121 | 0.0498 | 0.2102 | 112.2476 | 0.3263 |
| RMSE | 0.2701 | 0.2709 | 0.2817 | 0.277 | 0.0374 | 0.1846 | 87.9247 | 0.2803 |
Table 6. Lane 6 Results of Short - Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0355 | 0.0339 | 0.0359 | 0.0351 | 0.0426 | 0.1943 | 72.0088 | 0.2982 |
| MAE | 0.1827 | 0.1775 | 0.1786 | 0.177 | 0.0362 | 0.1862 | 73.7934 | 0.2726 |
| MAPE | 72.1391 | 69.5507 | 70.5062 | 68.9648 | 0.0502 | 0.2092 | 77.1041 | 0.3245 |
| RMSE | 0.2698 | 0.2636 | 0.272 | 0.2681 | 0.0362 | 0.184 | 66.8132 | 0.2717 |
Table 7. Lane 7 Results of Short - Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0516 | 0.0512 | 0.0544 | 0.0537 | 0.0603 | 0.2313 | 213.5089 | 0.3567 |
| MAE | 0.2201 | 0.2175 | 0.2182 | 0.2176 | 0.0567 | 0.2306 | 191.9181 | 0.3432 |
| MAPE | 203.122 | 193.494 | 204.040 | 193.256 | 0.0644 | 0.24 | 234.1192 | 0.3695 |
| RMSE | 0.328 | 0.3272 | 0.3368 | 0.3352 | 0.0582 | 0.2329 | 225.794 | 0.3468 |
Table 8. Lane 1 Results of Mid- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0727 | 0.2646 | 106.842 | 0.386 | 0.093 | 0.0772 | 0.0882 | 0.0735 |
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Context: Table 9. Lane 2 Results of Mid- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0448 | 0.1928 | 189.382 | 0.31 | 0.0596 | 0.0446 | 0.057 | 0.0431 |
| MAE | 0.0465 | 0.1919 | 189.218 | 0.319 | 0.2217 | 0.1893 | 0.2127 | 0.1807 |
| MAPE | 0.0492 | 0.2001 | 202.798 | 0.322 | 225.597 | 205.555 | 207.6086 | 162.6 |
| RMSE | 0.0531 | 0.2136 | 212.125 | 0.335 | 0.3619 | 0.3113 | 0.352 | 0.3074 |
Table 10. Lane 3 Results of Mid- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0609 | 0.2508 | 96.2071 | 0.3548 | 0.0571 | 0.0505 | 0.0572 | 0.0482 |
| MAE | 0.0575 | 0.2394 | 94.3022 | 0.3454 | 0.2318 | 0.2229 | 0.2367 | 0.2181 |
| MAPE | 0.0519 | 0.2263 | 102.200 | 0.3273 | 94.2455 | 96.1922 | 95.3241 | 92.207 |
| RMSE | 0.0529 | 0.2303 | 99.5245 | 0.3301 | 0.3471 | 0.3243 | 0.3426 | 0.3152 |
Table 11. Lane 4 Results of Mid- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0558 | 0.234 | 103.638 | 0.344 | 0.0623 | 0.0568 | 0.0633 | 0.0527 |
| MAE | 0.0558 | 0.231 | 106.871 | 0.3444 | 0.2356 | 0.2311 | 0.2424 | 0.2212 |
| MAPE | 0.0592 | 0.2369 | 111.1363 | 0.3567 | 120.369 | 108.1711 | 121.8748 | 103.61 |
| RMSE | 0.0614 | 0.2433 | 122.132 | 0.3626 | 0.3694 | 0.3482 | 0.366 | 0.3354 |
Table 12. Lane 5 Results of Mid- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0466 | 0.2062 | 97.0455 | 0.3161 | 0.0501 | 0.0481 | 0.0532 | 0.0438 |
| MAE | 0.0464 | 0.203 | 92.1541 | 0.3164 | 0.208 | 0.2071 | 0.219 | 0.195 |
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Context: | MAPE | 0.0481 | 0.2041 | 91.0538 | 0.3228 | 87.5111 | 100.938 | 119.6853 | 92.240 |
| RMSE | 0.0479 | 0.2039 | 93.793 | 0.3219 | 0.3295 | 0.3229 | 0.3369 | 0.3084 |
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Context: | MAPE | 113.0106 | 113.8206 | 112.6898 | 117.0394 | 112.5488 | 103.933 | 132.9332 | 101.22 |
| RMSE | 0.4187 | 0.4031 | 0.3678 | 0.3828 | 0.3798 | 0.3572 | 0.3824 | 0.3516 |
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Context: Table 13. Lane 6 Results of Mid- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0489 | 0.2121 | 87.6774 | 0.3204 | 0.0525 | 0.0471 | 0.0518 | 0.0431 |
| MAE | 0.0474 | 0.206 | 85.8866 | 0.3166 | 0.2171 | 0.2074 | 0.214 | 0.1934 |
| MAPE | 0.047 | 0.2005 | 79.6221 | 0.3163 | 82.3698 | 78.4122 | 83.0729 | 72.358 |
| RMSE | 0.0479 | 0.2024 | 84.9838 | 0.3188 | 0.3323 | 0.3147 | 0.3299 | 0.3009 |
Table 14. Lane 7 Results of Mid- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0626 | 0.2389 | 181.982 | 0.3632 | 0.0659 | 0.0638 | 0.064 | 0.064 |
| MAE | 0.0608 | 0.2341 | 185.386 | 0.3586 | 0.2429 | 0.2409 | 0.24 | 0.2378 |
| MAPE | 0.0617 | 0.2312 | 199.948 | 0.3613 | 206.317 | 189.361 | 215.4275 | 187.08 |
| RMSE | 0.063 | 0.2336 | 194.7113 | 0.3664 | 0.3709 | 0.3674 | 0.3701 | 0.3673 |
Table 15. Lane 1 Results of Long- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0659 | 0.0671 | 0.083 | 0.0809 | 0.0852 | 0.0593 | 0.0794 | 0.0579 |
| MAE | 0.2366 | 0.2383 | 0.2693 | 0.263 | 0.2798 | 0.2193 | 0.2638 | 0.2118 |
| MAPE | 241.132 | 226.7211 | 248.7511 | 233.479 | 300.554 | 230.765 | 277.89 | 187.03 |
| RMSE | 0.3841 | 0.3899 | 0.4406 | 0.4304 | 0.4352 | 0.3692 | 0.4206 | 0.3648 |
Table 16. Lane 2 Results of Long- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0829 | 0.0769 | 0.0656 | 0.0708 | 0.0686 | 0.0601 | 0.0706 | 0.0585 |
| MAE | 0.295 | 0.281 | 0.2572 | 0.2652 | 0.258 | 0.2429 | 0.2695 | 0.2383 |
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Context: Table 17. Lane 3 Results of Long- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0752 | 0.0731 | 0.0718 | 0.0753 | 0.0778 | 0.0668 | 0.0773 | 0.0661 |
| MAE | 0.2755 | 0.2693 | 0.2615 | 0.2704 | 0.2663 | 0.251 | 0.275 | 0.2456 |
| MAPE | 133.262 | 137.909 | 140.439 | 145.526 | 145.677 | 131.701 | 145.838 | 125.97 |
| RMSE | 0.4055 | 0.3997 | 0.3941 | 0.4026 | 0.4154 | 0.3828 | 0.4072 | 0.3808 |
Table 18. Lane 4 Results of Long- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0613 | 0.0591 | 0.0573 | 0.0586 | 0.0618 | 0.0573 | 0.0638 | 0.0552 |
| MAE | 0.2404 | 0.2333 | 0.2237 | 0.2285 | 0.2323 | 0.2279 | 0.244 | 0.2187 |
| MAPE | 113.0647 | 111.0473 | 105.158 | 110.8312 | 105.787 | 110.0082 | 122.2279 | 100.68 |
| RMSE | 0.3655 | 0.3597 | 0.3523 | 0.3554 | 0.3674 | 0.3543 | 0.3719 | 0.3432 |
Table 19. Lane 5 Results of Long- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0649 | 0.0622 | 0.0595 | 0.0601 | 0.0652 | 0.0553 | 0.0644 | 0.0557 |
| MAE | 0.25 | 0.2414 | 0.2283 | 0.2315 | 0.2424 | 0.2236 | 0.2419 | 0.2237 |
| MAPE | 105.532 | 104.665 | 98.7492 | 101.973 | 102.354 | 89.6261 | 93.0246 | 80.810 |
| RMSE | 0.3722 | 0.3656 | 0.3552 | 0.3566 | 0.3717 | 0.3446 | 0.3699 | 0.3456 |
Table 20. Lane 6 Results of Long- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.0649 | 0.0622 | 0.0595 | 0.0601 | 0.0652 | 0.0553 | 0.0644 | 0.0557 |
| MAE | 0.25 | 0.2414 | 0.2283 | 0.2315 | 0.2424 | 0.2236 | 0.2419 | 0.2237 |
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Context: | MAPE | 105.5326 | 104.6656 | 98.7492 | 101.9737 | 102.3549 | 89.6261 | 93.0246 | 80.8102 |
| RMSE | 0.3722 | 0.3656 | 0.3552 | 0.3566 | 0.3717 | 0.3446 | 0.3699 | 0.3456 |
20
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Context: Table 21. Lane 7 Results of Long- Term Prediction
| Model | Dense | CNN | LSTM | Bi-LSTM | CNN-LS
TM | TCN | TRANSFO
RMER | HYBRI
D |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Loss | 0.074 | 0.0689 | 0.0656 | 0.0633 | 0.0686 | 0.0694 | 0.076 | 0.0713 |
| MAE | 0.2603 | 0.2511 | 0.2445 | 0.2405 | 0.2515 | 0.2517 | 0.2659 | 0.2541 |
| MAPE | 180.022 | 184.296 | 205.351 | 199.304 | 197.590 | 175.629 | 244.0392 | 213.02 |
| RMSE | 0.3953 | 0.3817 | 0.3684 | 0.3633 | 0.3781 | 0.3813 | 0.402 | 0.3861 |
Table 22. Performance of Best Results of Every Outsetps for Every Models
Table 23. Best Performing Models for Short, Mid and Long Term Prediction
21
OUTSTEP
3
OUTSTEP
6
OUTSTEP
12
Model
Loss
MAE
MAPE
RMSE
Model
Loss
MAE
MAPE
RMSE
Model
Loss
MAE
MAPE
RMSE
0.0359
0.1786
70.5062
0.272
87.6774
85.8866
79.6221
84.9838
0.0573
0.2237
105.1583
0.3523
Bi-LSTM
0.0351
0.177
68.9648
0.2681
Bi-LSTM
0.31
0.319
0.322
0.335
Bi-LSTM
0.0586
0.2285
110.8312
0.3554
CNN-LSTM
0.0426
0.0362
0.0502
0.0362
CNN-LSTM
0.0501
0.208
87.5111
0.3295
CNN-LSTM
0.0618
0.2323
105.7875
0.3674
0.1908
0.1674
0.2011
0.1683
0.0446
0.1893
205.5559
0.3113
0.0553
0.2236
89.6261
0.3446
TRANSFORMER HYBRID
72.0088 0.2982
73.7934 0.2726
77.1041 0.3245
66.8132 0.2717
TRANSFORMER HYBRID
0.0518 0.0431
0.214 0.1934
83.0729 72.3582
0.3299 0.3009
TRANSFORMER HYBRID
0.0638 0.0552
0.244 0.2187
122.2279 100.6894
0.3719 0.3498
OUTSEP Model Min Loss MAE MAPE RSME
3 DENSE 0.0309 0.1628 155.4288 0.2524
6 HYBRID 0.0431 0.1934 72.3582 0.3009
12 HYBRID 0.0552 0.2187 100.6894 0.3432
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Context: CHAPTER5DISCUSSIONSInthischapter,wedelveintothedetailedanalysisoftheresultsobtainedfromourvariousmodelarchitecturesforlong-termtrafficforecasting.ThefocusisonevaluatingtheperformanceofeachmodelusingkeymetricssuchasLoss,MeanAbsoluteError(MAE),MeanAbsolutePercentageError(MAPE),andRootMeanSquaredError(RMSE).Byexaminingthesemetricsacrossdifferentforecastinghorizons(outsteps),weaimtoidentifythestrengthsandweaknessesofeachmodelandunderstandtheirsuitabilityfordifferenttimeframesoftrafficprediction.Short-TermPrediction(Tables1-7)DenseModel:Performance:ConsistentlygoodacrossalllaneswithlowMAEandRMSEvalues.Itperformsslightlybetterinlaneswithfewervehicles(e.g.,Lane1)comparedtomorecongestedlanes.BestLane:Lane5,withanMAEof0.1815andRMSEof0.2701.CNN:Performance:ComparabletotheDensemodelwithslightvariationsinMAEandRMSE.Itexcelsinlaneswithmoderatetrafficvolumes.BestLane:Lane5,withanMAEof0.1806andRMSEof0.2709.LSTM:Performance:SlightlyhighererrormetricsthanCNNandDensemodelsbutstillperformswelloverall.Itc
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Context: formswelloverall.Itcapturestemporaldependencieseffectively.BestLane:Lane6,withanMAEof0.1786andRMSEof0.272.Bi-LSTM:Performance:ShowshigherMAEandRMSEcomparedtosingleLSTM,indicatingitmightbeoverfittingonsomelanes.BestLane:Lane4,withanMAEof0.2025andRMSEof0.289.22
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Context: f0.297.LSTM:Performance:Handlesmid-termpredictionsbetterthanshort-terminsomelanesbutoverallhighererrors.BestLane:Lane1,withanMAEof107.299andRMSEof128.627.23
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Context: CNN-LSTM:Performance:Excellentinspecificlanes(e.g.,Lane1)butoverallhigherMAEandRMSEinotherlanes.BestLane:Lane6,withanMAEof0.0362andRMSEof0.0362.TCN:Performance:Generallyhighererrorratesacrossalllanes,indicatingitmaynotbethebestfitforshort-termtrafficpredictions.BestLane:Lane6,withanMAEof0.1862andRMSEof0.184.Transformer:Performance:Higherrorsinsomelanes,showingmixedresults.Itmightneedfurthertuningforshort-termpredictions.BestLane:Lane6,withanMAEof73.7934andRMSEof66.8132.HybridCNN-LSTM-Transformer:Performance:Mixedresultswithhighererrormetricsinsomelanes.Thecomplexityofthehybridmodelmightneedmoredataortuning.BestLane:Lane6,withanMAEof0.2726andRMSEof0.2717.Mid-TermPrediction(Tables8-14)DenseModel:Performance:Maintainslowerrormetricsbutslightlyhigherthaninshort-termpredictions.BestLane:Lane2,withanMAEof0.0465andRMSEof0.0531.CNN:Performance:IncreasedMAEandRMSEcomparedtoshort-termpredictions,indicatingchallengesinmid-termforecasting.BestLane:Lane1,withanMAEof0.261andRMSEof0.297.LSTM:Performa
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Context: Bi-LSTM:Performance:Highererrorsinmid-termpredictions,similartoitsperformanceinshort-termpredictions.BestLane:Lane3,withanMAEof0.3454andRMSEof0.3301.CNN-LSTM:Performance:Stillperformswellinsomelanesbutshowsvariabilityinerrormetrics.BestLane:Lane3,withanMAEof0.2318andRMSEof0.3471.TCN:Performance:Consistentperformancewithmid-rangeerrors.BestLane:Lane2,withanMAEof0.1893andRMSEof0.3113.Transformer:Performance:Betterperformanceinmid-termpredictionscomparedtoshort-term,indicatingitsstrengthincapturinglongertemporaldependencies.BestLane:Lane3,withanMAEof0.2229andRMSEof0.3243.HybridCNN-LSTM-Transformer:Performance:Higherrorsinsomelanes,indicatingtheneedforfurtheroptimization.BestLane:Lane2,withanMAEof0.1807andRMSEof0.3074.Long-TermPrediction(Tables15-21)DenseModel:Performance:Maintainsareasonableperformancebutwithhighererrormetricscomparedtoshortandmid-termpredictions.BestLane:Lane4,withanMAEof0.2404andRMSEof0.3655.24
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Context: CNN:Performance:Increasederrorsinlong-termpredictions,showinglimitationsinhandlingextendedforecastinghorizons.BestLane:Lane2,withanMAEof0.281andRMSEof0.4031.LSTM:Performance:SimilartoCNN,highererrorsinlong-termpredictions.BestLane:Lane2,withanMAEof0.2572andRMSEof0.3678.Bi-LSTM:Performance:PerformsslightlybetterthanLSTMbutstillwithhigherrors.BestLane:Lane2,withanMAEof0.2652andRMSEof0.3828.CNN-LSTM:Performance:SimilarperformancetoBi-LSTM,indicatingthehybridapproachmayneedoptimizationforlong-termpredictions.BestLane:Lane4,withanMAEof0.2323andRMSEof0.3674.TCN:Performance:Consistentlyhigherrors,indicatinglimitationsinhandlinglong-termpredictions.BestLane:Lane6,withanMAEof0.2236andRMSEof0.3446.Transformer:Performance:BetterthanTCNandCNN,indicatingitssuitabilityforlong-termpredictions.BestLane:Lane4,withanMAEof0.2237andRMSEof0.3523.HybridCNN-LSTM-Transformer:25
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Context: TMsenhancesthemodel'sabilitytocapturebothspatialandtemporalfeatures,leadingtomoreaccurateshort-termtrafficpredictions.TheTransformermodel,however,performedpoorlywithveryhigherrorvalues,suggestingthatitstruggledwithshort-termpredictions,similartoobservationsintheliteratureregardingtheneedforlargedatasetsandfine-tuningforTransformermodels(Vaswanietal.,2017).Medium-TermForecasting(OUTSTEPS6)Inthemedium-termforecastinghorizon(outstep6),theHybridmodelintegratingCNN,LSTM,andTransformercomponentsachievedthelowestlossof0.0431onlane4.Thisresulthighlightsthestrengthofcombiningmultipleneuralnetworkarchitecturestocapture26
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Context: Performance:Mixedresultswithhighererrorsinsomelanes,indicatingtheneedforfurtherrefinement.BestLane:Lane4,withanMAEof0.2187andRMSEof0.3432.Short-TermForecasting(OUTSTEPS3)Fortheshort-termforecastinghorizon(outstep3),theDensemodelachievedthelowestlossof0.0309onlane2.ThisindicatesthattheDensemodel,despiteitssimplicity,ishighlyeffectiveforshort-termtrafficpredictiontasks.Thelowlossvaluesuggeststhatthemodelcancaptureimmediatetrendsandpatternsintrafficdataaccurately.ThisfindingisconsistentwithstudiessuchasZhangetal.(2019),whereaCNNmodelincorporatingspatio-temporalfeaturesdemonstratedsuperiorperformanceinshort-termpredictions.TheCNNmodelinourstudyshowedcomparableperformancewithslightlyhigherlossanderrormetrics,whichalignswithZhangetal.'sfindingsontheeffectivenessofCNNsfortrafficforecasting.TheCNN-LSTMhybridmodel,despitehavingaslightlyhigherloss,demonstratedsignificantlylowerMAE,MAPE,andRMSEvalues.ThissupportsthefindingsofCao,Li,andChan(2020),whoshowedthatcombiningCNNsandLSTMsenhancesthemodel'
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Context: fficdata.Themodel'sabilitytomaintainlowlossandrelativelylowerrormetricsoveranextendedperiodsuggestsitsrobustnessincapturinglong-rangepatternsandtrends.ThisfindingissupportedbyEmamietal.(2019)andSongetal.(2021),whoemphasizedtheimportanceofhybridmodelsinlong-termtrafficforecasting.TheTCNmodelalsoperformedwell,withalossof0.0553andthelowestMAPE,indicatingitseffectivenessinlong-termforecasting.ThisalignswiththestudybyBaietal.(2018),whichhighlightedthestrengthsofTCNsincapturinglong-rangedependenciesintime-seriesdata.TheTransformermodelcontinuedtostrugglewithhighererrormetrics,highlightingitslimitationsinthiscontext,asnotedbyVaswanietal.(2017)intheirworkonTransformermodelsrequiringextensivetuninganddataforoptimalperformance.27
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Context: bothspatialandtemporalfeaturesoveramedium-termperiod.TheHybridmodel'sperformanceisconsistentwiththefindingsofHuangetal.(2023),whoemphasizedtheeffectivenessofhybridmodelsincapturingcomplextrafficdynamics.TheDensemodelalsoshowedstrongperformancewithslightlyhigherloss,butlowerMAEandRMSE,indicatingitsrobustnessformedium-termforecasting.ThisalignswiththeobservationsbyWangetal.(2021)regardingtheeffectivenessofLSTMmodelsinmedium-termtrafficprediction,particularlywhencombinedwithotherarchitecturesinahybridmodel.TheLSTMmodel,however,performedpoorlywithhigherrorvaluesacrossallmetrics,whichmaybeduetoitslimitationsinhandlingmedium-termvariabilitywithoutadditionalfeatureintegration(Wang,Su,&Ding,2021).Long-TermForecasting(OUTSTEPS12)Forthelong-termforecastinghorizon(outstep12),theHybridmodelonceagaindemonstratedsuperiorperformancewiththelowestlossof0.0552onlane5.ThisfurtherreinforcestheeffectivenessoftheHybridmodelinhandlinglong-termdependenciesandcomplexinteractionswithinthetrafficdata.Themodel'sa
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Context: efindingsunderscoretheimportanceofselectingappropriatemodelsbasedonthespecificforecastinghorizon.Hybridmodelsshowsignificantpromise,particularlyformediumandlong-termforecasts,whiletheDensemodeliswell-suitedforshort-termpredictions.Theseinsightsprovideafoundationforfutureresearchandpracticalapplicationsintrafficmanagementandplanning,highlightingtheneedforadaptableandrobustforecastingmodelstailoredtodifferenttimeframes.28
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Context: CHAPTER6CONCLUSIONTheanalysisofthebestperformingmodelsbasedonminimumlossforeachoutstepprovidesvaluableinsightsintothesuitabilityofdifferentmodelarchitecturesforvaryingforecastinghorizons:Short-TermForecasting(Outstep3):TheDensemodelishighlyeffective,achievingthelowestloss.Itssimplicityandabilitytoquicklyadapttoshort-termtrendsmakeitareliablechoiceforimmediatetrafficpredictiontasks.Medium-TermForecasting(Outstep6):TheHybridmodelexcels,leveragingthecombinedstrengthsofCNNs,LSTMs,andTransformers.Thisintegrationallowsthemodeltohandlethemedium-termvariabilityintrafficdataeffectively.Long-TermForecasting(Outstep12):TheHybridmodelalsoprovestobethebestchoiceforlong-termforecasting,demonstratingrobustnessincapturinglong-rangedependenciesandmaintaininglowlossoverextendedperiods.Ourfindingsareconsistentwithpreviousresearch,indicatingthatCNN-LSTMmodelsaregenerallyrobustforshort-termpredictionswhileTransformermodelsexcelinlong-termforecasting(Zhangetal.,2019;Huangetal.,2023).Thesefindingsunderscoret
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Context: ther,events,andsocialactivities.Futurestudiesshouldintegrateadditionaldatasourceslikeweatherdataandeventschedulestoenhancethepredictivepoweroftrafficmodelsandbettercapturethedynamicnatureoftrafficflow.EnhancingModelComplexityandDepthFurtherexperimentationwithmorecomplexarchitectures,suchasattentionmechanisms,reinforcementlearning,andensemblelearningtechniques,couldimprovethe29
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Context: CHAPTER7RECOMMENDATIONSFORFUTUREWORKTheresultsandanalysisofthisstudyprovideasolidfoundationforfurtheradvancementsintrafficcongestionforecasting.However,severalareasforimprovementandfurtherresearchhavebeenidentifiedtoenhancetheaccuracyandapplicabilityoftrafficpredictionmodels.UtilizingMorePowerfulComputationalToolsGoogleColab,whileaccessible,haslimitationsinprocessingpowerandmemory.Futureresearchshouldutilizehigh-performancecomputingclustersorcloud-basedplatformslikeAWSandGCP,whichofferscalableresourcestohandlelargerdatasetsandmorecomplexmodels,therebyimprovingefficiencyandaccuracy.ExpandingtheVarietyofDatasetsTogeneralizefindingsandimprovemodelrobustness,futurestudiesshouldincorporateavarietyofdatasetsfromdifferentlocationsandcontexts.Includingdatasetsfromdifferentcitiesandcountriescanhelpdevelopmodelsthataremoreadaptabletodiversetrafficconditionsandidentifyregion-specificfactors.IntegratingAdditionalDataSourcesTrafficcongestionisinfluencedbynumerousfactorssuchasweather,events,andsocia
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Context: isciplinaryCollaborationCollaborationbetweencomputerscientists,urbanplanners,transportationengineers,andpolicymakerscanensurethatmodelsaretechnicallysoundandpracticallyrelevant.Interdisciplinarycollaborationcanalignmodelswiththeneedsofurbanplanningandtrafficmanagement.Byaddressingtheserecommendations,futureresearchcanbuildonthefindingsofthisstudytodevelopmoreaccurate,reliable,andversatiletrafficforecastingmodels.Theseimprovementswillcontributesignificantlytothefieldofintelligenttransportationsystems,ultimatelyaidinginthemanagementandmitigationoftrafficcongestioninurbanareas.30
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Context: performanceoftrafficpredictionmodels.Theseapproacheshaveshownpromiseinotherdomainsandcouldenhancetheabilitytocaptureintricatetrafficpatterns.ImprovingDataPreprocessingTechniquesFutureworkshouldfocusonimprovingdatapreprocessingtechniques,includingadvancedmethodsforhandlingmissingdata,detectingandcorrectingoutliers,andnormalizingtrafficdata.Enhancedpreprocessingcanprovidehigher-qualityinputsformodels,leadingtobetterperformance.ConductingLong-TermEvaluationsLong-termevaluationsoverseveralmonthsoryearswouldprovideabetterunderstandingofmodels'stabilityandrobustnessovertime.Thisiscrucialforlong-termtrafficmanagementandurbanplanning,whereconsistentperformanceisessential.ExploringTransferLearningTransferlearning,whichinvolvesleveragingpre-trainedmodelsonnewtasks,couldbebeneficialfortrafficforecasting.Futureresearchcouldexploretransferlearningtoadaptmodelstrainedononedatasettoanother,potentiallyreducingtheneedforextensivetrainingdataandcomputationalresources.EngaginginInterdisciplinaryCollabora
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Context: nofaheuristicANNmodelandahybridANN-PSOmodelinthetrafficflowmodellingofvehiclesatafour-waysignalizedroadintersection.Sustainability,13(19),10704.Graves,A.,&Schmidhuber,J.(2005).FramewisephonemeclassificationwithbidirectionalLSTMandotherneuralnetworkarchitectures.NeuralNetworks,18(5-6),602-610.Polson,N.G.,&Sokolov,V.O.(2017).Deeplearningforshort-termtrafficflowprediction.TransportationResearchPartC:EmergingTechnologies,79,1-17.Kong,Q.,Song,L.,&Wu,J.(2019).Urbantrafficcongestionpricingmodelwithconsiderationofcarbonemissionscost.Sustainability,11(4),1102Wang,X.,Zeng,R.,Zou,F.,Liao,L.,&Huang,F.(2022).STTF:AnEfficientTransformerModelforTrafficCongestionPrediction.NanyangTechnologicalUniversity,Singapore.Huang,X.,Jiang,Y.,Wang,J.,Lan,Y.,&Chen,H.(2023).Amulti-modalattentionneuralnetworkfortrafficflowpredictionbycapturinglong-shorttermsequencecorrelation.31
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Context: REFERENCESBernaś,M.,Płaczek,B.,Porwik,P.,&Pamuła,T.(2015).Segmentationofvehicledetectordataforimprovedk-nearestneighbours-basedtrafficflowprediction.IETIntelligentTransportSystems,9(3),264-274.Chen,C.,Liu,Z.,Wan,S.,Luan,J.,&Pei,Q.(2020).Trafficflowpredictionbasedondeeplearningininternetofvehicles.IEEETransactionsonIntelligentTransportationSystems,22(6),3776-3789.Emami,A.,Sarvi,M.,&AsadiBagloee,S.(2019).UsingKalmanfilteralgorithmforshort-termtrafficflowpredictioninaconnectedvehicleenvironment.JournalofModernTransportation,27,222-232.Goudarzi,S.,Kama,M.N.,Anisi,M.H.,Soleymani,S.A.,&Doctor,F.(2018).Self-organizingtrafficflowpredictionwithanoptimizeddeepbeliefnetworkforinternetofvehicles.Sensors,18(10),3459.Kong,F.,Li,J.,Jiang,B.,&Song,H.(2019).Short-termtrafficflowpredictioninsmartmultimediasystemforInternetofVehiclesbasedondeepbeliefnetwork.FutureGenerationComputerSystems,93,460-472.Olayode,I.O.,Tartibu,L.K.,Okwu,M.O.,&Severino,A.(2021).ComparativetrafficflowpredictionofaheuristicANNmode
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Context: dtrafficforecasting.TransportationResearchPartC:EmergingTechnologies.32
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Context: Hinton,G.E.,Osindero,S.,&Teh,Y.W.(2006).Afastlearningalgorithmfordeepbeliefnets.NeuralComputation,18(7),1527-1554.Goodfellow,I.,Bengio,Y.,&Courville,A.(2016).DeepLearning.MITPress.Bernaś,M.,Kamiński,B.,&Korczak,J.(2015).Trafficforecastingwithmachinelearningmodels.InternationalConferenceonDependableSystemsandNetworks.Wang,Y.,Li,Z.,Zhang,H.,Li,H.,&Wu,J.(2020).Areviewoftrafficcongestionpredictionusingartificialintelligence.IEEETransactionsonIntelligentTransportationSystems.Sun,Y.,Wang,Y.,&Li,Z.(2021).Real-timetrafficpredictionwithbigdata:Areviewofmethodsandapplications.IEEETransactionsonIntelligentTransportationSystems.Emami,P.,Yang,L.,&Jin,L.(2019).Long-termtrafficflowpredictionusingdeeplearningbasedspatio-temporalmodeling.IEEETransactionsonIntelligentTransportationSystems.Chen,X.,etal.(2020).Exploringurbanmobilitywithbigdata:Modelingandapplications.JournalofUrbanPlanningandDevelopment.Huang,H.,etal.(2023).Integratingmachinelearningandspatio-temporalanalysisforenhancedtrafficforecasting.
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Context: APPENDIX
Gantt Chart
33
Integrating Spatio-Temporal Data and Advanced Machine Learning for Long-Term Traffic Congestion Forecasting: A Hybrid Approach Using STTF and BILSTM Models
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"""QUERY: discuss the models with diagrams"""
Consider the chat history for relevant information. Use all information included. Use as much tokens as needed. Important: If you find information separated by a | in the context, it is a table formatted in Markdown.
Final Files Sources: Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 1, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 2, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 3, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 4, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 5, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 6, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 7, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 8, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 9, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 10, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 11, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 12, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 13, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 14, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 15, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 16, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 17, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 18, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 19, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 20, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 21, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 22, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 23, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 24, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 25, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 26, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 27, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 28, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 29, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 30, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 31, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 32, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 33, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 34, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 35, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 36, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 37, Special%20Problem%20Manuscript%20-%20Layahin.pdf - Page 38, SP.txt - Page 1
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FINAL ANSWER
Answer: Basierend auf den bereitgestellten Informationen aus dem Manuskript "Special Problem Manuscript - Layahin" wurde eine Vielzahl von Modellen zur langfristigen Verkehrsprognose bewertet. Die Modelle umfassen traditionelle Ansätze wie Dense (vollständig verbundene Netzwerke), CNN (Convolutional Neural Networks), LSTM (Long Short-Term Memory), Bi-LSTM (Bidirectional Long Short-Term Memory), TCN (Temporal Convolutional Networks), Transformer sowie hybride Modelle, die verschiedene Architekturen kombinieren.
Die Methodik der Studie gliedert sich in mehrere Schlüsselphasen:
1. **Datensammlung:** Verwendung des PeMS-Bay-Datensatzes, der Verkehrsgeschwindigkeitsdaten von Sensoren in der Bay Area, Los Angeles, beinhaltet.
2. **Datenverarbeitung:** Normalisierung der Geschwindigkeitsdaten, Umgang mit fehlenden Werten durch Auffüllen mit Nullen und Feature-Engineering zur Extraktion relevanter Merkmale.
3. **Modelltraining:** Verschiedene Modelle werden mit dem vorbereiteten Datensatz trainiert. Die Modelle werden in Bezug auf ihre Fähigkeit, Verkehrsmuster zu lernen und vorherzusagen, optimiert.
4. **Leistungsbewertung:** Die Modelle werden anhand von Metriken wie Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) und Mean Absolute Percentage Error (MAPE) bewertet.
Die Ergebnisse der Studie zeigen, dass verschiedene Modelle unterschiedlich gut für kurz-, mittel- und langfristige Vorhersagen geeignet sind. Das Dense-Modell zeigte eine hohe Effektivität bei kurzfristigen Vorhersagen, während das Hybridmodell, das CNN, LSTM und Transformer kombiniert, bei mittel- und langfristigen Vorhersagen überlegen war.
Die folgenden Diagramme (Figuren) wurden im Manuskript erwähnt:
- **Figure 1:** Standort der Datensammlung des PeMS-Bay-Datensatzes.
- **Figure 2:** Workflow des Verkehrsprognosemodells, der den gesamten Prozess von der Datensammlung über die Verarbeitung bis hin zur Modellbewertung darstellt.
Die spezifischen Ergebnisse für jedes Modell wurden in Tabellenform präsentiert, wobei jedes Modell anhand der genannten Leistungsmetriken bewertet wurde. Es wurden Tabellen für die Ergebnisse der kurz-, mittel- und langfristigen Vorhersagen bereitgestellt, die die Leistung jedes Modells in verschiedenen Verkehrsszenarien zeigen.
Zusammenfassend lässt sich sagen, dass die Studie einen umfassenden Ansatz zur Bewertung verschiedener Modelle für die Verkehrsprognose bietet und die Stärken spezifischer Modelle für unterschiedliche Vorhersagezeiträume aufzeigt. Die Ergebnisse legen nahe, dass hybride Modelle, die mehrere Architekturen integrieren, besonders vielversprechend für die Verbesserung der Genauigkeit von Verkehrsprognosen sind.
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