trajectory data mining - graph for trajectories07轨迹数据挖掘图

TrajectoryDataMiningDr.YuZhengLeadResearcher,MicrosoftResearchChairProfessoratShanghaiJiaoTongUniversityEditor-in-ChiefofACMTrans.IntelligentSystemsandTechnology

ParadigmofTrajectoryDataMiningYuZheng.TrajectoryDataMining:AnOverview.ACMTransactionsonIntelligentSystemsandTechnology.2015,vol.6,issue3.GraphforTrajectoriesInanetworksettingInafreespaceFindingSmartDrivingDirectionsACMSIGSPATIALGIS2010BestpaperawardKDD2012FeaturedbyMITTechnologyReviewDrivingDirectionBasedonTaxiTrajectoriesAtime-dependent,user-specific,andself-adaptivedrivingdirectionsserviceusingGPStrajectoriesofalargenumberoftaxicabsGPSlogofanenduserPhysicalRoutesTrafficflowsDrivebehaviorJingYuan,YuZheng,etal.DrivingwithKnowledgefromthePhysicalWorld.KDD2011.DrivingDirectionBasedonTaxiTrajectories8:00DriverA14:00DriverA14:00DriverBDrivingDirectionBasedonTaxiTrajectories14:00DriverB14:00DriverBLoguserB’sdrivingroutesfor1monthMotivationTaxidriversareexperienceddriversGPS-equippedtaxisaremobilesensorsGPSlogsimplythedrivebehaviorofauserHumanIntelligence+TrafficpatternsDrivebehaviorSystemOverview0OfflineMiningIntelligencemodelingDatasparsenessLow-sampling-rateChallengesJingYuan,YuZheng,etal.DrivingwithKnowledgefromthePhysicalWorld.KDD2011.OfflineMining

JingYuan,YuZheng,etal.T-Drive:DrivingDirectionsBasedonTaxiTrajectories.ACMSIGSPATIALGIS2010MiningTaxiDrivers’KnowledgeLearningtraveltimedistributionsforeachlandmarkedgeTrafficpatternsvaryintimeonanedgeDifferentedgeshavedifferentdistributionsJingYuan,YuZheng,etal.T-Drive:DrivingDirectionsBasedonTaxiTrajectories.ACMSIGSPATIALGIS2010ReportedbyMITTechnologyReviewTwice,featuredonceResultsMoreeffective60-70%oftheroutessuggestedbyourmethodarefasterthanBingandGoogleMaps.Over50%oftheroutesare20+%fasterthanBingandGoogle.Onaverage,wesave5minutesper30minutesdrivingtrip.MoreefficientDemoJingYuan,YuZheng,etal.T-Drive:DrivingDirectionsBasedonTaxiTrajectories.ACMSIGSPATIALGIS2010MininginterestinglocationsandtravelsequencesfromsocialmediaYuZheng,etal.MininginterestinglocationsandtravelsequencesfromGPStrajectories.WWW2009.YuZheng,XingXie.Learningtravelmendationsfromuser-generatedGPStraces.InACMTransactiononIntelligentSystemsandTechnology,2(1),2-1916Mininginterestinglocations,travelsequences,andtravelexpertsfromuser-generatedtravelroutesWhatisalocation?(geographicalscales)Theinterestlevelofalocationdoesnotonlydependonthenumberofuserswhohavevisitedthislocationbutalsolieintheseusers’travelexperiencesHowtodetermineauser’stravelexperience?Thelocationinterestandusertravelareregion-relatedarerelativevalue(Rankingproblem)ChallengesYuZheng,etal.MininginterestinglocationsandtravelsequencesfromGPStrajectories.WWW2009.MethodologyHITS(hypertextinducedtopicsearch)modelAuthority:aWebpagewithmanyin-linksHub:isapagewithmanyout-linksMutualreinforcementrelationshipTopic-related,notefficientforonlineserviceYuZheng,etal.MininginterestinglocationsandtravelsequencesfromGPStrajectories.WWW2009.Users:HubnodesLocations:AuthoritynodesTheHITS-basedinferencemodel

YuZheng,etal.MininginterestinglocationsandtravelsequencesfromGPStrajectories.WWW2009.

YuZheng,etal.MininginterestinglocationsandtravelsequencesfromGPStrajectories.WWW2009.DetectingInterestingTravelSequencesThreefactorsdeterminingtheclassicalscoreofasequence:Travelexperiences(hubscores)oftheuserstakingthesequenceThelocationinterests(authorityscores)weightedbyTheprobabilitythatpeoplewouldtakeaspecificsequence:AuthorityscoreoflocationA:AuthorityscoreoflocationC:Userk’shubscoreTheclassicalscoreofsequenceA

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