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

19

Yanyan XuPhD.PostdoctoralAssociateDepartment ofCivilandEnvironmentalEngineering, MITEmail: yanyanxu@mit.edu

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.