基于南京地铁AFC数据的大客流识别方法（英文）.docx

基于南京地铁AFC数据的大客流识别方法（英文）
Title:LargePassengerFlowDetectionMethodBasedonNanjingMetroAFCData
Abstract:
Withtherapiddevelopmentofurbanization,theissueoflargepassengerflowmanagementinurbanmetrosystemshasbecomeincreasinglyimportant.ThisstudyaimstoproposeamethodfordetectinglargepassengerflowsbasedonAutomatedFareCollection(AFC)dataintheNanjingmetrosystem.Theproposedmethodutilizesdataminingandmachinelearningtechniquestoanalyzeandidentifylargepassengerflows,providingmetrooperatorswithvaluableinsightsanddecision-makingsupport.
1.Introduction:
1.1Background:
Urbanmetrosystemsplayacriticalroleintransportationnetworks,offeringaconvenientandefficientmeansoftransportation.However,theincreasingpopulationdensityandurbanizationhaveledtoovercrowdingandlargepassengerflows,whichposechallengesformetrooperatorsintermsofpassengersafetyandsystemefficiency.
1.2ResearchObjectives:
TheobjectiveofthisresearchistodevelopamethodfordetectingandanalyzinglargepassengerflowsintheNanjingmetrosystemusingAFCdata.Byidentifyingpatternsandcharacteristicsoflargepassengerflows,metrooperatorscanmakeinformeddecisionsforcrowdmanagementandresourceallocationtoensurepassengersafetyandsystemefficiency.
2.Methodology:
2.1DataCollection:
TheAFCdatacollectedfromtheNanjingmetrosystemisutilizedastheprimarydatasetforanalysis.TheAFCdataincludesinformationsuchasentryandexittimestamp,stationID,andfarepaymentdetails.
2.2DataPreprocessing:
ThecollectedAFCdataundergoespreprocessingstepssuchasdatacleaning,filtering,andaggregationtoremovenoiseandirrelevantinformation.Necessarytransformationsandfeatureengineeringtechniquesareappliedtoobtainmeaningfuldatarepresentations.
2.3FeatureSelection:
Featureselectiontechniquesareemployedtoidentifythemostrelevantfeaturesthatcontributetothedetectionoflargepassengerflows.Statisticalanalysisandmachinelearningalgorithmsareusedtoevaluatetheimportanceofeachfeatureinpredictinglargepassengerflows.
2.4DataMiningAlgorithms:
Variousdataminingalgorithms,suchasclustering,classification,andassociationrule