Marta C. Gonzalez Associate Professor of Civil and Environmental Engineering, Center ... ·...
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Marta C. Gonzalez Associate Professor of Civil and Environmental Engineering, Center for Advanced Urbanism
Transcript of Marta C. Gonzalez Associate Professor of Civil and Environmental Engineering, Center ... ·...
MartaC.Gonzalez
AssociateProfessorofCivilandEnvironmentalEngineering,
CenterforAdvancedUrbanism
AboutMe
BS & MSc in Physics (2002)
PhD in Physics 2003-2006
Postdoc 2006-2009Understanding IndividualMobilityPatterns,Nature,2008
Understanding theSpreadingPatternsofMobilePhoneViruses,Science,2009
CityScience
• Interdisciplinaryapproachtostudyingcities.
• Combinesmodelsandmethodsfromstatisticalphysics,machinelearning,andtransportationtounderstandurbansystems.
• Applicationsinurbanplanning,transportation/logistics,epidemiology,energy,etc.
Highlights• InvitedspeakerNationalAcademiesofSciences(NAS)oftheapplied
StatisticscommitteetospeakonData-drivenUrbanPolicy,December2016
• Invitedspeaker,NationalAcademiesofEngineering(NAE)GeneralAssemblyMeeting,onBigDatainCivilEngineering,April2015
• SelectedfortheNAS-TRBNCHRBreportMobilePhoneDatatounderstandHumanBehavior
• BestPaperAward,ACMSIGKDDConferenceonKnowledgeDiscoveryandDataMining,Chicago,August2013
• BestPaperAward,ACMSIGKDDConferenceonKnowledgeDiscoveryandDataMining,Beijing,August2012
Mobility
Energy
Environment
ICT
SupportSustainableUrbanPolicythroughScienceandEngineering
WhatareweusingTravelModelfor?Wehavedevelopedaportablepipelinetogenerateurbandemandmodelsfrommobilephonedataandweareinterestednowinitsapplicationsforbettercities.Examplesofcurrentprojects:
1-WithinBlocks:BuildingOccupancyModelsforEnergyModeling
2- Metro:HelpingtoplanthefirstmetroinRiyadh(SaudiArabia)
3- PlanningforElectricVehiclesintheBayArea
4- UnderstandingTraveltimeReliability5- UnderstandingAirqualityinBejing integratingAQSensorsandaTrafficModel
6-DistributingTravelDemandduringtheOlympicGames
Sponsors: MITIndustryLiaisonsponsors:
1. MITEnergy2. Infonavit (Mexico)3. MITEnvironmentalSolutionsInitiative4. Ford5. PhilipsLightingGrandChallenge6. Natl.Sc.Foundation(Portugal)7. Natl.Sc.Foundation(Singapore)8. KACST(SaudiArabia)
1. UNFoundation2. Bill&MelindaGatesFoundation3. SiebelInstitute4. CambridgeSystematics5. USDepartmentofTransportation
DetailsinCVon-line:http://humnetlab.mit.edu/wordpress/wp-content/uploads/2014/04/cv2016.pdf
MobilityoflowestIncomeGroup
Vision:DataScienceforInformedUrbanPolicy
DataMiningforSocialGoodWeinvestigate theinterplaybetween
Salary,CostofLand,TravelTime
TimeGeo:aspatiotemporalframeworkformodelingurbanmobilitywithoutsurveys (ShanJiang, Yingxiang Yang,DanieleVeneziano,Shounak Athavale,MartaC.Gonzalez),PNAS(2016)
Researchexample1,link:http://humnetlab.mit.edu/wordpress/wp-content/uploads/2016/03/PNAS-2016-Jiang-E5370-8.pdf
Stay&PassbyExtractionHome&WorkDetection
• MartaC.Gonzalez
1Sampledayofastudent
1) WitharoamingdistanceΔd1 (300mts.),weclusterspatiallycloselocationswithinΔd1 .
2) AtimethresholdΔt(10minutes) separatevariousstaypoints Si.
3) Homeisdefinedasthemostfrequentlyvisitedlocationduringnightsofweekdays&daysofweekendsoverthestudyperiod;Aphoneuser’s“work”isdefinedasthemostfrequentlyvisitedloaction workinghoursoftheweekday
d1
d7
d2d3 d5d6
d4
d8d9
(a)
s1 s2
s3
(b)
• MartaC.Gonzalez
TheModel(learnsspatio-temporalfeaturestocreatesynthetictrajectories)
FeaturesExtractedfromdataofActive Users
FlowchartoftheModel
CircadianRhythm MobilityRates PreferentialReturn RankingofExplorations
Comparisonwithtraveldemandmodelsbasedontravelsurveys
TheTimeGeo modelingframeworkofurbanmobilitywithoutsurveysPNAS (August), 2016Link:
ModelsResultsModeled Trajectoriesofactive(smartphone) user
Modeled Trajectoriesfromsparse(mobilephone)dataofasampleuser (withprevious locationsused).
Note:Onsparsedata“nextlocation”predictionwithmachinelearningmethodsfail.
Note:Thismechanisticmodeldoesnotusehistoricdatafortraining;itcanbeenriched withexistingmethods to“predictnextlocations”(work inprogress forKDD’16)
DetectionofDifferencesinmobilitybyincomegroupinBogota
(usingmobilephonedata)
Link:http://humnetlab.mit.edu/wordpress/wp-content/uploads/2016/03/bogotatrb_2017.pdf
ComparisonofTotaltripsCDRmodelvs.SurveymodelofSteerDaviesGleaves
Extractionoftravelersbyincomegroup
Comparisson of Mobility Diversity
𝐷" =−∑ 𝑝"' log 𝑝"'+
',-log 𝑘"
NodeDiversity
Obs: Lowincomegroupvisitmorefocuseddestinationsthanthehighincomegroups
Commuting conditions by income group
Obs:Lowincomegroupsufferconsiderablymorefromcongestiondespitesmallerdifferencesincommutingdistancewithintheurbancore.Opportunity:Focusedinfrastructureplanningorridesharingplanning.
DemandManagementforLargeEvents
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Yanyan XuPhD.PostdoctoralAssociateDepartment ofCivilandEnvironmentalEngineering, MITEmail: [email protected]
Link:
Link:http://humnetlab.mit.edu/wordpress/wp-content/uploads/2016/03/Demand_management.pdf
Venues,Airbnb,hotels,BRT&Metro
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(a) DataIntegrated(b) Numberofaudiencesarrivevenuesandwhen?(useddata:Olympicsschedule,capacity
ofvenues)
CollectivedemandManagementTraveltime
Marginalcostperlink
Werankroutespertotalmarginalcost
RecommendationsofCarreductionperOriginandDestinationdivertedtotransitduringtheevent
Itispossibletoachievevehicledemanddecrease:~1.3%withatotaltraveltimedecrease:~10.5%inhighcontrastwithcarreductionbyplatenumber.
