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1、AMixtureModelClusteringApproachforTemporalPassengerPatternCharacterizationinPublicTransportAnne-SarahBriand,EtienneCome,MohamedK.ElMahrsi,andLatifaOukhellouˆUniversiteParis-Est,IFSTTAR,COSYS-GRETTIA,F-77447Marne-la-Valle,France´Email:anne-sarah.briand@ifsttar.fr,etienne.come
2、@ifsttar.fr,mohamed-khalil.el-mahrsi@ifsttar.fr,latifa.oukhellou@ifsttar.frAbstract—Smartcarddataprovideagreatnumberofinfor-andlocalauthoritiessinceitcanhelppredictandmanagemationthatareincreasinglyusednowadays.Inthefieldofinflow,measuretheadequacybetweentheexistingofferandtra
3、nsport,theyoffertheopportunitytostudypassengerbehavior,therealusage,andtaketheappropriatemeasurestoadaptleadingtoabetterknowledgeofpublictransitdemandandtotheobserveddemand.Thisgoalcanbeachievedbyusingtherebygrantingthetransportoperatorstheabilitytoadapttheirtransportofferan
4、dservicesaccordingly,bothinspaceandstatisticallearningtechniques,inparticularclusteranalysis:byintime.Inparticular,anaccuratecharacterizationofmobilitypartitioningpassengersintogroupsbasedontravelhours,itispatternsusingdataminingapprocheshasaverystronginterestpossibletoextra
5、ctgeneralpatternsdescribingdifferenttypesfortransportplanningpurposes.Thispaperaimstoproposeofusage(sporadicusage,typicalhome-workcommutebehav-atwo-levelgenerativemixturemodelthatpartitionspassengersior,etc.).Differentapproachestoclusteringpassengersusingaccordingtotheirtemp
6、oralprofiles.Usingthetimestampsofthepassengers’transactionsinthepublictransportnetwork,ticketinglogshavebeenpresentedintheliterature[1]–[4].thefirstlevelmodelsthepassengerspartitioningintoareducedHowever,mostpropositionsrelyonadiscreterepresentationsetofclusters,whereastheseco
7、ndlevelcaptureshowthetripsoftimeinwhichtripsareaggregatedoverpre-definedtimemadebyeachclusterofpassengersaredistributedovertime.slots(e.g.1hour).Thiscanleadtoissuesw.r.t.capturingtravelTheproposedapproachisappliedonrealticketingdatacollectedregularity.Forinstance,frequenttrip
8、smadeontheboundaryfromtheurbantransportnetworkofRennesMetropole(France).´Th
