WhatAffectsMillennials’Mobility?PARTII:TheImpactofResidentialLocation,IndividualPreferencesandLifestylesonYoungAdults’TravelBehaviorinCalifornia

March2017

AResearchReportfromtheNationalCenterforSustainableTransportation

Dr.GiovanniCircella,UniversityofCalifornia,DavisFarzadAlemi,UniversityofCalifornia,DavisKateTiedeman,UniversityofCalifornia,DavisRosariaM.Berliner,UniversityofCalifornia,DavisYongsungLee,GeorgiaInstituteofTechnologyDr.LewFulton,UniversityofCalifornia,DavisProf.PatriciaL.Mokhtarian,GeorgiaInstituteofTechnologyProf.SusanHandy,UniversityofCalifornia,Davis

AbouttheNationalCenterforSustainableTransportationTheNationalCenterforSustainableTransportationisaconsortiumofleadinguniversitiescommittedtoadvancinganenvironmentallysustainabletransportationsystemthroughcutting-edgeresearch,directpolicyengagement,andeducationofourfutureleaders.Consortiummembersinclude:UniversityofCalifornia,Davis;UniversityofCalifornia,Riverside;UniversityofSouthernCalifornia;CaliforniaStateUniversity,LongBeach;GeorgiaInstituteofTechnology;andUniversityofVermont.Moreinformationcanbefoundat:ncst.ucdavis.edu.DisclaimerThecontentsofthisreportreflecttheviewsoftheauthors,whoareresponsibleforthefactsandtheaccuracyoftheinformationpresentedherein.ThisdocumentisdisseminatedunderthesponsorshipoftheUnitedStatesDepartmentofTransportation’sUniversityTransportationCentersprogram,intheinterestofinformationexchange.TheU.S.GovernmentandtheStateofCaliforniaassumesnoliabilityforthecontentsorusethereof.NordoesthecontentnecessarilyreflecttheofficialviewsorpoliciesoftheU.S.GovernmentandtheStateofCalifornia.Thisreportdoesnotconstituteastandard,specification,orregulation.AcknowledgmentsThisstudywasfundedbyagrantfromtheNationalCenterforSustainableTransportation(NCST),supportedbyUSDOTandCaltransthroughtheUniversityTransportationCentersprogram.TheauthorswouldliketothanktheNCST,USDOT,andCaltransfortheirsupportofuniversity-basedresearchintransportation,andespeciallyforthefundingprovidedinsupportofthisproject.AdditionalfundingforthedatacollectionforthisprojectwasprovidedbytheUCDavisSustainableTransportationEnergyPathways(STEPS)program.Theauthorsaregratefulforthissupport.Allerrorsoromissionsaretheresponsibilityoftheauthors,andnotthefundingorganizations.TheauthorswouldliketosincerelythankDanielSperling,RamPendyala,KayAxhausen,JoanWalker,ElisabettaCherchi,CinziaCirillo,DavidBunch,GilTal,KariWatkins,ScottLeVine,DeborahSalon,LauraPodolsky,EricGudz,FreshtaPirzada,AliaksandrMalokin,GouriMishra,CalvinThigpen,AlvaroRodriguezValencia,SimonBerrebi,AliceGrossman,AliEtezady,AtiyyaShaw,SarahMooney,RubemMondaini,AnissBahreinian(CaliforniaEnergyCommission),KatieBenouar,MelissaThompson,SoheilaKhoii,MohammadAssadi,DillonMiner,NicoleLongoria,PatrickTynerandDavidChursenoff(Caltrans),JohnOrr,ElisabethSanfordandGuyRousseau(AtlantaRegionalCommission),DavidOry(MetropolitanTransportationCommission),MikeAlba(LinkedInCorp.),KenLaberteaux(ToyotaMotorCorp.),andNataliaTinjacaMora(CamaradeComercioBogota,Colombia)fortheirthoughtfulcommentsandcontributionsduringthesurveydesign,datacollectionanddataanalysis.

WhatAffectsMillennials’Mobility?PARTII:TheImpactofResidentialLocation,IndividualPreferencesandLifestyleson

YoungAdults’TravelBehaviorinCalifornia

ANationalCenterforSustainableTransportationResearchReport

March2017

GiovanniCircella,InstituteofTransportationStudies,UniversityofCalifornia,Davis

FarzadAlemi,InstituteofTransportationStudies,UniversityofCalifornia,Davis

KateTiedeman,InstituteofTransportationStudies,UniversityofCalifornia,Davis

RosariaM.Berliner,InstituteofTransportationStudies,UniversityofCalifornia,Davis

YongsungLee,SchoolofCityandRegionalPlanning,GeorgiaInstituteofTechnology

LewFulton,InstituteofTransportationStudies,UniversityofCalifornia,Davis

PatriciaL.Mokhtarian,SchoolofCivilandEnvironmentalEngineering,GeorgiaInstituteofTechnology

SusanHandy,InstituteofTransportationStudies,UniversityofCalifornia,Davis

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TABLEOFCONTENTSEXECUTIVESUMMARY....................................................................................................................iIntroduction...................................................................................................................................1TheMobilityofMillennials.............................................................................................................4TheCaliforniaMillennials’Dataset.................................................................................................8

DataCleaningandRecodes......................................................................................................12Geocoding................................................................................................................................14WeightingandRaking..............................................................................................................19IntegrationofAdditionalLandUseDatafromOtherSources.................................................22FactorAnalysis.........................................................................................................................24

AdoptionofTechnology,IndividualAttitudesandMobilityChoicesofMillennialsvs.GenXers29InvestigatingMillennials’AttitudestowardsTransportationandTechnology........................33TravelBehaviorandtheAccessibilityofthePlaceofResidence.............................................46AdoptionofMultimodalTravelBehavior.................................................................................49

VehicleMilesTraveled.................................................................................................................55DependentVariable:Self-ReportedWeeklyVMT....................................................................56ExplanatoryVariables...............................................................................................................56Results......................................................................................................................................58

CarOwnership,VehicleTypeChoiceandPropensitytoChangeVehicleOwnership..................63VehicleTypeChoiceModel......................................................................................................67PropensitytoModifyVehicleOwnership................................................................................73

ConclusionsandNextStepsoftheResearch...............................................................................76References....................................................................................................................................81ListofAcronymsUsedintheDocument......................................................................................85

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WhatAffectsMillennials’Mobility?PARTII:TheImpactofResidentialLocation,IndividualPreferencesandLifestylesonYoungAdults’TravelBehaviorinCaliforniaEXECUTIVESUMMARYYoungadults(“millennials”,ormembersof“GenerationY”)areincreasinglyreportedtohavedifferentlifestylesandtravelbehaviorfrompreviousgenerationsatthesamestageinlife.Theypostponethetimeatwhichtheyobtainadriver’slicense,oftenchoosenottoownacar,drivelessiftheyownone,andusealternativenon-motorizedmeansoftransportationmoreoften.Severalexplanationshavebeenproposedtoexplainthebehaviorsofmillennials,includingtheirpreferenceforurbanlocationsclosertothevibrantpartsofacity,changesinhouseholdcomposition,andthesubstitutionoftravelforworkandsocializingwithtelecommutingandsocialmedia.However,researchinthisareahasbeenlimitedbyalackofcomprehensivedataonthefactorsaffectingmillennials’residentiallocationandtravelchoices(e.g.informationaboutindividualattitudes,lifestylesandadoptionofsharedmobilityisnotavailableintheU.S.NationalHouseholdTravelSurveyandmostregionalhouseholdtravelsurveys).Improvingtheunderstandingofthefactorsandcircumstancesbehindmillennials’mobilityisoftheutmostimportanceforscientificresearchandplanningprocesses.Millennialsmakeupasubstantialportionofthepopulation,andtheirtravelandconsumerbehaviorwillhavelargeeffectsonthefuturedemandfortravelandgoods.Further,millennialsareoftenearlyadoptersofnewtrendsandtechnologies;therefore,improvingtheunderstandingofmillennials’choiceswillincreasetheabilitytounderstandandpredictfuturetrendsatlarge.ThisstudybuildsonalargeresearcheffortlaunchedbytheNationalCenterforSustainableTransportationtoinvestigatetheemergingtransportationtrendsandtheimpactsoftheadoptionofnewtransportationtechnologiesinCalifornia,particularlyamongtheyoungercohorts,i.e.millennialsandthemembersoftheprecedingGenerationX.Duringthepreviousstagesoftheresearch,wedesignedadetailedonlinesurveythatweadministeredinfall2015toasampleof2400residentsofCalifornia,includingmillennials(youngadults,18-34in2015)andGenXers(35-50year-oldadults).Weusedaquotasamplingapproachtorecruitrespondentsfromeachagegroup(youngmillennials,oldermillennials,youngGenXers,andolderGenXers)acrossallcombinationsofmajorgeographicregionofCaliforniaandneighborhoodtype(urban,suburban,andrural). TheresultistheCaliforniaMillennialsDataset,acomprehensivedatasetthatcontainsinformationontherespondents’personalattitudes;lifestyles;adoptionofonlinesocialmediaanduseofinformationandcommunicationtechnology(ICT)devicesandservices;residential

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locationandlivingarrangements;commutingandothertravelpatterns;autoownership;awareness,adoptionandfrequencyofuseofvarioussharedmobilityservices;majorlifeeventsinthepastthreeyears;expectationsforfutureevents;propensitytopurchaseanduseaprivatevehiclevs.touseothermeansoftravel;politicalideas,andsociodemographictraits.Thisreportsummarizestheanalysesoftheresidentiallocation,travelbehaviorandvehicleownershipofmillennialsandGenXers.Inthisstageoftheresearch,weaugmentedtheCaliforniaMillennialsDatasetwithadditionalvariablesmeasuringlanduseandbuiltenvironmentcharacteristicsfromothersourcesincludingtheU.S.EnvironmentalProtectionAgency’sSmartLocationDataset,andthewalkscore,bikescoreandtransitscorefromthecommercialwebsitewalkscore.com.Weweightedthedatatocorrectthedistributionofcasesinthesample,andtoreducethenon-representativenessofthedata,basedontheregionofCaliforniawheretherespondentslive,theneighborhoodtype,theagegroup,gender,studentandemploymentstatus,householdincome,raceandethnicity,andpresenceofchildreninthehousehold.Weapplieddatareductiontechniquestosummarizetheinformationrelatedtotheindividualattitudesandpreferences.Todothis,weperformedaprincipalaxisfactoranalysisonthe66attitudinalvariablesthatwerecollectedinthesurvey.Atotalof17factorswereextracted.SeveralkeydifferencesareobservedinthedistributionofthefactorscoresacrossvariousgroupsofmillennialsandGenXers.Forexample,wefindlargedifferencesintheattitudinalprofilesofmillennialsandGenXersonattitudinaldimensionssuchasmaterialism,thepropensitytoadoptnewtechnologies,andthedegreetowhichindividualsfeeltheyarewell-establishedintheirlife.Forotherattitudinalfactors,e.g.thepro-environmentalpolicyattitudes,thedifferencesassociatedwiththelocationwhererespondentsliveareremarkablylargerthanthedifferencesobservedacrossagegroups:urbandwellersconsistentlyreportstrongerpro-environmentalpolicyattitudesthannon-urbanresidents.Wealsofindthaturbanmillennialsareheavyadoptersoftechnology,smartphoneappsinparticular,andonaverageusetheseservicesmoreoftenforvariouspurposes,includingaccessinginformationaboutthemeans(orcombinationofmeans)oftransportationtouseforatrip,findinginformationaboutpotentialtripdestinations(e.g.acafé,orarestaurant),ornavigatinginrealtimeduringatrip.Largedifferencesarealsoobservedintheadoptionofsharedmobilityacrossbothagegroupsandurbanvs.non-urbanpopulations;notsurprisingly,millennialstendtoadoptthesetechnologicalservicesmoreoftenthanGenXers,particularlyinurbanareas.Wefurtheranalyzedtherelationshipsbetweenaccessibilityandtheadoptionofmultiplemodesoftransportation(multimodality,and/orintermodality)amongthevarioussub-segmentsofthepopulation.Forthisanalysis,weclassifiedmillennialsintwogroupsofindependentanddependentmillennialsbasedontheirlivingarrangementsandhouseholdcomposition.Infact,theresidentiallocationwheredependentmillennialslivehaslikelybeentheresultoftheirparents’choices,andnotofthemillennialsthemselves.WecomparedthelevelofaccessibilityoftheplaceofresidenceandtheadoptionofmultimodaltravelofthesetwogroupsofmillennialswiththoseofGenXers.Accessibilityandmultimodalityareusually

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positivelycorrelated:residentsofmoreaccessibleneighborhoodsaremoreoftenmultimodaltravelers.However,millennials,andespeciallydependentmillennials,arefoundtomakethemostoftheirbuiltenvironmentpotential,eitherduetoindividualchoices,orthepresence(orlack)oftravelconstraints.Theyarelesslikelytobemono-driversandmorelikelytobemultimodalcommuters,eveniftheyoftenliveinneighborhoodsthatarelesssupportiveofsuchbehaviors.Ontheotherendofthespectrum,GenXersbyfarrelythemostoncars.Independentmillennialsmoreoftenchoosetoliveinaccessiblelocationsandtendtoadoptnon-motorizedandmultimodaltraveloptionsmoreoften.Weestimatedalog-linearmodelofthenumberofweeklyvehiclemilestraveled(VMT),usingbothapooledmodelfortheentiresampleandasegmentedmodelthatteststheeffectsofindividual,householdandlandusecharacteristicsontheVMTofmillennialsandGenXersseparately.Interestingly,themodelformillennialsexplainsthelowestamountofvarianceinthedata.Thisfindingsignalsthehigherheterogeneityandvariationamongthemembersofthisgroup,andtheincreaseddifficultyinexplainingtheirbehaviorsthroughtheestimationofeconometricandquantitativemodels.TraditionalbuiltenvironmentvariablessuchaspopulationdensityandlandusemixexplainalowerportionofVMTformillennialscomparedtoGenXers.Individualattitudesandstageinlife(currentlivingarrangementsandthepresenceofchildreninthehousehold)havelargereffectsonVMTformillennialsthanforGenXers.Wealsoinvestigatedtherelationshipsbehindcarownershipandthetypeofvehicleownedbyahousehold.Notsurprisingly,independentmillennialsthatliveinurbanareas,onaverage,ownfewercarsperdriverthanothergroups.Thisfindingcorroboratesthereducedneedsforacarindenser(andmoreaccessible)centralareas,wherealargerportionofindependentmillennialslive.However,suchaneffectmightbeshort-lived:manyoldermillennialswholiveinurbanareasreportthattheyplantopurchaseanewvehicleinthenearfuture.Thus,theirzero-orlow-vehicleownershipisprobablytheresultoftheirtransientstageofliferatherthanthelong-termeffectofpreferencestowardsvehicleownership.Duringfuturestagesoftheresearch,wedoplantostudyhowcarownershipvariesacrossdifferentgroupsofthepopulationthroughtheestimationofamodelthatinvestigateshowsociodemographiccharacteristics,individualpreferences,andlandusefeaturesaffectcarownership.Toinvestigatethepreferencetowardsthepurchaseofvariousvehicletypesamongdifferentgroupsofusers,wealsoestimatedamultinomiallogitmodel(MNL)ofvehicletypechoice,usingsociodemographictraits,builtenvironmentcharacteristics,andpersonalattitudesandpreferencesasexplanatoryvariables,fortheindividualsthatboughtorleasedavehiclethatismodelyear2010ornewer.Futurestagesoftheresearchwillfocusontheanalysisofadditionalcomponentsofmillennials’choices,includingcurrentresidentiallocation,futureaspirationstomodifyvehicleownershipandtravelchoices,theadoptionofsharedmobilityservices,andtherelationshipsbetweentheadoptionofsharedmobility,household’svehicleownership,andothercomponentsoftravelbehavior(e.g.thefrequencyofuseofothertransportationmodes).

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IntroductionYoungadults(oftenreferredtoas“millennials”,ormembersof“GenerationY”)areincreasinglyreportedtohavedifferentlifestylesandtravelbehaviorfrompreviousgenerationsatthesamestageinlife.Theypostponethetimetheyobtainadriver’slicense,oftenchoosetoliveinmorecentralurbanlocationsandchoosenottoownacar,drivelesseveniftheyownone,andusealternativenon-motorizedmeansoftransportationmoreoften.Severalpossibleexplanationshavebeenproposedtoexplaintheobservedbehaviorsofmillennials,includingtheirpreferenceformoreurbanlocations,changesinhouseholdcomposition,andsubstitutionoftravelforworkandsocializingwithtelecommutingandsocialmedia.ThebehaviorofmillennialshasanimportantroleinexplainingthechangesincartravelobservedinrecentyearsintheUnitedStatesandotherdevelopedcountries,wherethetotalvehiclemilestraveled(VMT)have,atleasttemporarily,“peaked”beforereboundingsharply,atleastintheUnitedStates,tonewrecordhighsinthefirsthalfof2016(FHWA,2016;Circellaetal.,2016a).Severalstudieshavestartedtoinvestigatethefactorsaffectingtheresidentiallocationandmobilitychoicesofmillennials.However,thedebateinthisfieldisstilldominatedbyspeculationsaboutthepotentialfactorsaffectingmillennials’behavior.Previousstudieshavebeenlimitedbythelackofinformationonspecificvariables(e.g.personalattitudesandpreferences,forstudiesbasedonNationalHouseholdTravelSurveydata),ortheuseofconveniencesamples(e.g.studiesonuniversitystudents).Certainly,theconnectedtech-savvymillennialsareapopularfigureinthemediaheadlines,andtheyareacommonpresenceinSanFrancisco,LosAngeles,oranyothermajorcityinthecountry.Notallmillennialsfitthisstereotype,though,andtherearelargemassesofyoungadultsthatstillbehaveinawaythatismoresimilartooldercohorts:theyarelikelytogetmarriedatayoungerage,oftenliveinsingle-familyhomes,drivealonefortheircommute,andraisetheirchildreninapredominantlysuburbanenvironment.Understandingthedifferentpatternsinlifestylesandbehaviorsamongthevarioussegmentsoftheheterogeneouspopulationofmillennials,andquantifyingtheirimpactontraveldemandandtheuseofvariousmeansoftransportation,isofextremeimportancetoresearchers,plannersandpolicy-makers.ThisstudybuildsonalargeresearcheffortlaunchedbytheNationalCenterforSustainableTransportationtoinvestigatetheemergingtransportationtrendsandtheimpactsoftheadoptionofnewtransportationtechnologiesinCalifornia,inparticularamongtheyoungercohorts,i.e.millennials.Duringthepreviousstagesoftheresearch,alargedatasetwascollectedwithacomprehensiveonlinesurveythatwasadministeredinfall2015toasampleof2400residentsofCalifornia,includingbothmillennials(youngadults,18-34in2015)andmembersoftheprecedingGenerationX(middle-ageadults,35-50).Weusedaquotasamplingprocesstoensurethatenoughrespondentsfromallagegroups(youngmillennials,oldermillennials,youngGenXers,andolderGenXers)weresampledfromeachcombinationofgeographicregionofCaliforniaandneighborhoodtype(urban,suburban,andrural),and

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controlledfordemographictargetsofthesampleforfivedimensions:gender,age,householdincome,raceandethnicity,andpresenceofchildreninthehousehold.TheresultistheCaliforniaMillennialsDataset,anunprecedenteddatasetwhichcontainsdetailedinformationontherespondents’personalattitudes,preferencesandenvironmentalconcerns;lifestyles;adoptionofonlinesocialmediaanduseofinformationandcommunicationtechnology(ICT)devicesandservices;residentiallocationandlivingarrangements;commutingandothertravel-relatedpatterns;autoownership;awareness,adoptionandfrequencyofuseofthemostcommonsharedmobilityservices(includingcar-sharing,bike-sharing,dynamicridesharingandon-demandrideservicessuchasUberorLyft);majorlifeeventshappenedinthepastthreeyears;expectationsforfutureeventsandpropensitytopurchaseanduseaprivatevehiclevs.touseothermeansoftravel;politicalideasandsociodemographictraits.Duringthisstageoftheresearch,webuiltontheCaliforniaMillennialsDataset,integratedthedatasetwithadditionaldataavailablefromothersources,andinvestigatedseveraltopicsrelatedtomillennials’mobilitychoicesandthechangingtrendsintraveldemandinCalifornia.Specifically,aspartofthestudy,wegeocodedtheresidentiallocationandtheprimarywork/studylocationreportedbyeachrespondentinthesample.Usingalsotheinformationfromthegeocodedresidentialandworklocationsoftherespondents,wedevelopedasetofqualitychecks,andfurthercleanedandrecodedtheinformationavailableinthedataset.Wematchedtherespondents’geocodedresidentiallocationwiththeinformationonthedominantneighborhoodtypeavailablefromanotherresearchprojectdevelopedatUCDavis.Further,wedevelopedasetofweights,usingbothcellweightsandtheiterativeproportionalfitting(IPF)rakingprocess,tocorrectforthenon-representativenessofthesampleintermsofdistributionbyregionofCalifornia,predominantneighborhoodtype,agegroup,gender,householdincome,studentandemploymentstatus,raceandethnicity,andpresenceofchildreninthehousehold.Basedonthegeocodedresidentiallocationoftherespondents,weintegratedthedatasetwithadditionalvariablesobtainedfromexternalsources.Theadditionalvariablesprovidedinformationonthecharacteristicsofthebuiltenvironmentintheplaceofresidenceandtravelaccessibilitybymode,frommultiplesourcesincludingtheU.S.EnvironmentalProtectionAgency(EPA)SmartLocationDataset,andthecommercialwebsiteWalkscore.com(whichalsocomputesabikescoreandtransitscore,inadditiontothebetter-knownwalkscore).Weappliedfactoranalysisasadatareductiontechniquetoinvestigatetherelationshipsrelatingthe66attitudinalvariablesavailableinthedatasetandtoextract17factorsthatmeasureattitudinalconstructsonseveraldimensionsofinterest.Wedevelopedanumberofanalysesusingtheinformationinthedataset,focusinginparticularontheimpactsoflandusecharacteristicsandthedifferentbehaviorsobserved,forexample,among“urban”millennialsvs.theothergroupsofyoungadultswholiveinsuburbanorruralareas,andthecorrespondinggroupsofGenXers.Thisreportsummarizesthefindingsfromthisstageoftheresearch.Intheremainderofthisreport,wefirstdiscussrecentstudiesthathaveinvestigatedseveralaspectsofmillennials’mobilityandcarownershipchoices.Wethenpresenttheinformation

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containedintheCaliforniaMillennialsDataset,summarizethedatacleaningandrecodingtasksthatwereperformedaspartofthisstageoftheresearch,describetheprocessthatwasusedtogeocodetheresidentialandworklocationsoftherespondents,theweightingprocessappliedtothedataset,andtheadditionaldatathatwereimportedfromexternalsourcesandthatwerematchedbasedonthegeocodedresidentiallocationoftherespondents,andpresenthowweappliedfactoranalysisonthe66attitudinalstatementsinthedatasettoextract17mainattitudinalfactors.ThefollowingsectionsinvestigatedifferencesamongmillennialsandthemembersoftheGenerationXthatliveinurban,suburbanandruralareas,startingfromtheuseofsocialmediaandsmartphoneappstocoordinatetravelalternativesandtoaccessinformationonthemeansoftransportationavailableforatrip,informationaboutpotentialtripdestinationsandreal-timetravelinformation,amongothers,andthenmovingtodiscussthedifferentattitudinalpatternsreportedbytheresidentsofvariousneighborhoodtypes,bygeneration.Wepresentseveralmeasuresofaccessibilityandinvestigatetheadoptionofmultimodaltravelamongdifferentgroupssegmentedbygenerationandneighborhoodtype.Thefollowingchapterpresentsasetofeconometricmodelsoftheindividuals’vehiclemilestraveled(VMT),whichwereestimatedasbothapooledmodel(fortheentiresample)andsegmentedmodelsformillennialsandGenXers.Themodelsallowidentifyingtheimpactsofindividualandhouseholdcharacteristics,stageinlife,landusecharacteristics,adoptionoftechnologyandpersonalattitudesontheamountofcartravelofmillennialsandGenXers.Wethenturnourattentiontocarownershipandvehicletypechoice,throughthecomparisonofthedifferentcarownershiplevelsfoundamongmembersofdifferentgenerationalgroupsthatliveinthevariousneighborhoodtypes.Weestimateadiscretechoicemodelofthevehicletypechoice,whichshedslightontheimpactofseveralgroupsofexplanatoryvariablesonthedecisiontobuyorleaseaspecifictypeofvehicles,anddiscussthedifferenttrendsinthepropensitytochangethelevelofvehicleownershipinthehousehold(e.g.propensitytobuyanewvehicle)observedamongthemembersofdifferentgenerationalgroupsthatliveinurbanvs.non-urbanlocations.Thefinalconclusionssummarizethefindingsfromthisstageoftheproject,andidentifydirectionsforfutureresearch.Theactivitiesdevelopedsofarinthisresearchprojectandthelargeamountofinformationthathasbeencollectedwillallowanumberofadditionalanalysesofpotentialinterestfortheresearchcommunity,plannersandpolicy-makers;thesewillbedevelopedduringthenextstagesofthismulti-yearresearchprogram.ThisPartIIReportbuildsonthePartIReporttitled"WhatAffectsMillennials’Mobility?PARTI:InvestigatingtheEnvironmentalConcerns,Lifestyles,Mobility-RelatedAttitudesandAdoptionofTechnologyofYoungAdultsinCalifornia”,whichprovideddetailedinformationonthemotivationsforthisstudy,previousstudiesfromtheliteratureonwhichthisresearchbuilds,thedatacollectioneffort,thecontentoftheonlinesurveythatwasusedinthestudy,thesamplingmethodologyandpreliminaryanalysisoftheCaliforniaMillennialsDataset.AdditionalinformationonthesetopicscanbefoundinthePartIprojectreport(seeCircellaetal.,2016b).

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TheMobilityofMillennialsMillennials(i.e.theyoungadultsborninthe1980sand1990s,whobecameadultsinthe21stcentury)areoftenreportedtobehavedifferentlyfrompreviousgenerationsatthesamestageinlife.Severalstudieshavediscussedthechangingtrendsinmillennials’lifestylesandmobilitydecisions.Millennialsarefoundtopostponethetimetheyobtainadriver’slicense,oftenchoosetoliveinmorecentralurbanlocationsandchoosenottoownacar,drivelesseveniftheyownone,andusealternativenon-motorizedmeansoftransportationmoreoften(Blumenbergetal.2012;Kuhnimhofetal.2012;Blumenbergetal.2015;McDonald2015;Circellaetal.2016b).Severalpossibleexplanationshavebeenproposedtoexplaintheobservedbehaviorsofmillennials,includingtheirpreferenceformoreurbanlocationsclosertothevibrantpartsofacity,changesinhouseholdcomposition,andthesubstitutionoftravelforworkandsocializingwithtelecommutingandsocialmedia.Inthisstudy,wefollowthedefinitionof“millennials”thatisconsistentwiththerecentstudiespublishedbythePewResearchCenter,whichidentifymillennialsastheindividualsbornbetween1981and1997(i.e.theywere18to34-year-old,asof2015).Thissegmentofthepopulationmayhavedifferentbehaviorsandlifestylesfromoldergenerations,evenwhilecontrollingforstageoflife,causingthemtotraveldifferently.Severalstudieshavestartedtoinvestigatethechangingtrendsinmillennials’mobility,andthefactorsthatarelikelytoaffecttheirchoices.Foranextensivereviewoftheliteraturethathasfocusedonmillennials’behavior,pleaserefertothePartIreportfromthisproject(Circellaetal.,2016b).Itisdifficulttoseparatethegenerationalcomponentofmillennials’behaviorsfromotherfactorsaffectingtheirmobilitychoices,includingthechangingeconomicconditionsandfluctuationsinfuelprices,trafficcongestioninlargemetropolitanareas,changesintheurbanformofAmericancities,householdcompositionandpersonallifestyles,theeventualsubstitutionofphysicaltripswithinformationandcommunicationtechnologies(ICT),astrongertendencytowardsmultimodality,andtheincreasedavailabilityofalternativetraveloptionsincludingnewsharedmobilityservicessuchascar-sharingandon-demandrideservices(e.g.thoseprovidedbytransportationnetworkscompanies,orTNCs,suchasUberorLyft,intheAmericanmarket)(Wachs,2013,Polzinetal.,2014;BuehlerandHamre,2014).Recentsociodemographicshiftsandmodificationsinhabitsandlifestylesincludemodificationsinhouseholdcomposition,livingarrangements,changesinpersonalattitudes,reductionin(andpostponementof)childbearing,andtheincreaseddiversityinthepopulation(Zmudetal.,2014).Theincreaseddiversityofthepopulation,inparticular,maycontributetodecreasingtheaverageVMTpercapitaofyoungergenerations:BlumenbergandSmart(2014)foundthat(similarlytootherstudies)immigrantsaremorelikelytocarpoolthanthosebornintheUnitedStates,eveniflargedifferencesexistdependingontheoriginoftheindividualsandtheplacewheretheywereraised.BlumenbergandSmart(2014)analyzed2000censusdataand2001travelsurveydata,andfoundthatthepercentageofforeign-borninacensustractispositivelycorrelatedwithcarpoolingrates.Shin(2016)examinedethnicenclavesinthe2012-2013CaliforniaHouseholdTravelSurvey,andfoundsimilarresults.Specifically,theauthorfoundthat

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immigrantsresidinginethnicenclaveshavehigherratesofhousehold-externalcarpoolingfornon-worktrippurposesthanimmigrantsresidingoutsideethnicenclaves.Thestudypostulatesthatethnicenclavesmayofferstrongersocialnetworks,whichmayaffectmodechoice(Shin2016).Millennials’behaviordiffersfromthatoftheiroldercounterpartsduetoacomplexcombinationoflifecycle,periodandcohorteffects,includinglifestyle-relateddemographicchanges,suchasshiftsinemploymentrates,delaysinmarriageandchildbearing(PewResearchCenter2014),andshiftsinattitudesanduseofvirtualmobility,whicharebelievedtobemorespecificoftheircohort(assuggestedbyMcDonald,2015).IntheiranalysisofNationalHouseholdTravelSurvey(NHTS)data,Polzinetal.(2014)showedthatmillennialsexhibitdifferenttravelbehaviorthanthepreviousgenerationsatthesameage–specifically,20-34yearoldsin2001drovemoremilesperyearthan20-34yearoldin2009-andidentifiedseveralfactorssuchasresidentiallocation,race,employmentandeconomicstatus,livingarrangements,licensurestatus,amongothers,thatareexpectedtoinfluencemillennials’mobility.McDonald(2015)alsoanalyzedNHTSdataandhighlightedthatallAmericanstraveledlessfrom1995to2009,butmillennialtraveldecreasedthemost.Thestudyindicatedthatdemographicshiftstypicalofthe18to34agegroupcouldexplain10-25%ofdifferencesobservedintravelpatterns.Theauthorconcludedthatanadditionalportion(35-50%)couldbeexplainedbyothervariablessuchaschangingattitudesorvirtualmobility,evenifshecouldonlyinferthisasNHTSdatadonotcontaininformationonthesevariables.Theremainingpercentageisattributedtothegeneraldeclineintravelacrossallgenerations(McDonald2015).Moderntechnologicalinnovationsfurthercontributetoreshapingtransportation.TheadoptionofICT,e.g.onlineshopping,telecommuting,etc.,isattributedanimportantroleinreshapingindividuals’relationshipswiththeuseoftravelmodesandorganizationofactivities(cf.Mokhtarian,2009;CircellaandMokhtarian,2017;Circellaetal.,2016a).Sharedmobilityserviceshavefurtherreshapedtransportationthroughtheintroductionofoptionsthatgiveusersincreasedmobilityandaccessibilitywithoutincurringthecostsofowningavehicle.Sharedmobilityservicesrangefromcar-sharingservices,includingfleet-basedservicessuchasZipcarorCar2Goandpeer-to-peerservicessuchasTuro,toridesharingservices,includingdynamiccarpoolingsuchasCarmaandon-demandrideservices(alsoknownasridesourcing)suchasUberandLyft,andbike-sharingservices.Sharedmobilityservicesmodifyanumberofkeyfactorsrelatedtotraveldecisions,includingtravelcost,convenienceandsecurity(Tayloretal.2015).Theadoptionoftheseservicescanaffectthelevelofautoownershipofahousehold,andcontributetoshiftingindividuals’preferenceawayfromcarownershipwithpotentialsizableimpactsondailyschedules,lifestyles,andevenresidentiallocation.Notsurprisingly,earlyadoptersofsharedmobilityservicesarepredominantlywell-educatedyoungindividualswholiveinurbanareas(Rayleetal.2014;Tayloretal.,2015;Bucketal.;2013).Theseservicesareparticularlypopularamongmillennials,whoareheavyusersofICTdevicesandaremoreopentothesharingeconomy(Polzinetal.,2014;Zipcar2013;Bucketal.,2013;Rayleetal.,2014).

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Thereiscontinuedinterestininvestigatingmillennials’travelpatterns(andthereasonsbehindtheobserveddifferenceswiththeiroldercounterparts),alsoinconsiderationofthelargesizeofthissegmentofthepopulation,andthelikelylargeeffectsthattheirchoiceswillhaveonfutureconsumerexpenditures,demandforhousing,andtraveldemand.Inarecentanalysisof1990,2001,and2009NHTSdata,Blumenbergetal.(2016)foundthattherewasasignificantdropindriving(PersonalKilometersTraveled-PKT)inthe2000s.Theyexaminednumerousfactorsincludingdrivers’licensure,employment,webuse,andtransitionstoadulthood,includinganumberofvariablestodescribestageoflife,suchaslivingwithparents,etc.TheauthorsfoundnostatisticalrelationshipamongthemajorityofthesevariablesandPKT.However,andnotsurprisingly,employmentwasconsistentlyandpositivelyassociatedwithPKT.TheyconcludedthatdecliningemploymentduringtheGreatRecessioncontributedsignificantlytothedeclineinyouthtravelbetween2001and2009(Blumenbergetal.2016).Duringthattime,unemploymentmorethandoubled.Theauthorsfoundthattheeffectofemploymentwas32%greateramongolder(ages27–61)thanyounger(ages20–26)adults.TheyinterpretedtheseresultstosuggestthateconomicfactorswereattherootofthedeclineinpersonaltravelintheU.S.duringthe2000s.Garikapatietal.(2016)analyzedolderandyoungermillennials,andfoundthatoldermillennialsarebecomingincreasinglyliketheirGenXcounterpartsatasimilarage.However,itisunclearifmillennialswilladapttothesametravelpatternsofthepriorgenerationsoriflingeringdifferenceswillremainintheirtravelandtimeusepatterns.Theissuehasimportantplanningimplications.Forexample,realestatesalesdatasignalanincreaseinthenumberofmillennialsmovingtomoresuburbandevelopments,evenifwitha“delayeffect”associatedwiththelatertimeinwhichmembersofthesegenerationsestablishnewhouseholds.Ifsuchatrendexpandsinfutureyears,withanincreaseinsuburbanliving,itislikelytobringimportantconsequencesintermsnotonlyofthedemandforhousing,butalsooffuturetraveldemand,andtheuseofvarioustransportationmodes.Ontheotherhand,thereportedpreferencesofmillennialsforurbanlifestyleshasbeenpromptinghopesforafurtherincreaseinthepopularityofcentralurbanneighborhoods,whichhavealreadygonethroughaprocessofprogressiverenewalandregenerationduringrecentyears(Wachs,2013).Millennials,withtheirlowerper-capitaVMTandautoownershiparecreditedbymanyasimportantactorsthatcanhelpplanningagenciesandregulatorsreachthemilestonesofreductioninVMTandGHGemissionsfromtransportationoftenincludedaspartofplanningprocessesalsoastheresultofenvironmentalregulations(asinthecaseoftheSustainableCommunityStrategiesmandatedinCaliforniabytheSenateBill375andrelatedregulations).Thisgoalisalsomirroredinthechangeshappeningintherealestatetrends,andchangingregulationsinmanyjurisdictions,forexamplethroughtherevisionofparkingrequirementsfornewdevelopmentsandchangesinzoningregulations.Further,millennialsaremorelikelytoliveinmulti-generationalhouseholdsthanpreviousgenerationsatthesameage,withadditionalimplicationsintermsoftheiraccesstoprivatevehiclesownedbyahousehold,andcoordinationoftravelpatternswithotherhouseholdmembers.FryandPassel(2014)foundthatby2012,24%ofyoungadultslivedinmulti-generationalhouseholds,upfrom19%in2007,and11%in1980.Thisshareishigheramongmen(26%ofmale25-34yearoldsliveinmulti-generationalhouseholds,comparedto21%of

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women).Theauthorsconcludethatthismaybeamanifestationofthedelayedentrytoadulthood(alongwithlatermarriageandchildbearing)(Fry&Passel2014),whichareallfactorsassociatedwithpotentialimpactsonindividualtravelbehavior(i.e.duetothedelayedlifecycleeffects).InastudyofAustraliandriver’slicensingtrends,DelboscandCurrie(2014)concludedthatfull-timeemploymentandthepresenceofchildreninthehouseholdwerestrongpredictorsoflicensingstatus,withhigherlicensingratesamongyoungadultswhoworkfull-time(inparticulariftheyhavechildren),comparedtopart-timeworkersandstudents.Theypositthatchangesinlivingarrangementsandstateoflifemaycausereducedorpostponedlicensureofyoungadults(Delbosc&Currie2014).Thesameseemstobetrueforcarownership:inanexaminationofmillennialcarownership,KleinandSmart(2017)usedeightwavesofdatafromthePanelStudyofIncomeDynamics.Theyfound,consistentwithpreviousliterature,thatyoungadultsownfewercarsthanpreviousgenerationsatthesamelifestage.Inparticular,theauthorsfoundthateconomicallyindependentyoungadults(i.e.thosethathavealreadyestablishedtheirownhousehold)ownmorecarsthanexpectedfortheirincomeandpersonalwealth,thereforepositingthateconomicfactorsarethemainoneslimitingyouthcarownership.Asyoungadultsbecomeeconomicallyindependentfromtheirparents,theircarownershipratestendtoincrease.Thisconclusionsseemstoimplythatrecentlyobserved“peakcar”trendmayreverseinfutureyears,themoretheeconomyrecoversandmoremillennials“leavethenest”(Klein&Smart,2017).Youngergenerationsmayprefermultimodalmobility,aswell.Vijetal.(2015)usedcross-sectionaltraveldiarydatafromindividualsintheSanFranciscoBayAreain2000and2012todevelopalatentclassmodeloftravelmodechoicebehavior.Theirfindingsindicateshiftsintheregiontowardsgreatermultimodality.Duringtheobservedperiod,motorizedvehiclemodesharesdecreasedfrom85%in2000to81%,whiletheproportionofthepopulationthatonlyconsidersprivatevehiclewhendecidinghowtotraveldeclinedfrom42%to23%.Theauthorsofthestudyconcludedthatchangesineconomicandsocialfactorsandlevelofserviceofdifferenttravelmodeshadamarginaleffect,butdidnotaccountfortheentiredeclineinvehiclemodesharesobservedfrom2000to2012.Further,theyfoundthatthemodalshiftsexistacrosstheentirepopulation,andwerenotlimitedtoanyonegeneration(Vijetal.2015).Manyofthetopicsmentionedaboveareinvestigatedaspartofthisstudy.Understandingthefactorsaffectingmillennials’choices,andtheirpotentiallong-termimpactsontraveldemand,isextremelyimportanttoplanningprocessesandpolicy-making.Still,previousstudieshavebeenlimitedbyeither(1)thelackofinformationonspecificvariables,suchaspersonalattitudesortheadoptionofnewtechnologiesandemergingmobilityservices,forstudiesbasedonNHTSorotherhouseholdtravelsurveysatthestatewideormetropolitanplanningorganization(MPO)level;or(2)theuseofnon-randomsamples,suchasconveniencesamplesdrawnfromspecificsegmentsofthepopulation,e.g.universitystudents.Thisstudyhasbeendesignedwiththeaimofovercomingsomeoftheselimitations.

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TheCaliforniaMillennials’DatasetThisstudybuildsonalargeresearcheffortundertakentoinvestigatetherelationshipsamongmillennials’residentiallocation,individualattitudes,lifestyles,travelbehaviorandvehicleownership,theadoptionofsharedmobilityservices,andtheaspirationtopurchaseanduseavehiclevs.useothermeansoftransportationinCalifornia,whichwasdesignedtoovercomesomeofthelimitationsfrompreviousstudies.Duringthepreviousstageofthisproject,whichwasalsoprimarilyfundedbytheNationalCenterforSustainableCaliforniaandCaltrans,arichdatasetwascollectedinfall2015withacomprehensiveonlinesurveythatwasadministeredtoasampleof2400Californiaresidents,includingmillennials(i.e.youngadults,18-34,in2015)andmembersoftheprecedingGenerationX(i.e.middle-ageadults,35-50).WeusedaquotasamplingapproachtorecruitrespondentsfromeachofthesixmajorregionsofCaliforniaandthreedominantneighborhoodtypes(urban,suburbanandrural),whilecontrollingforsociodemographictargetsincludinghouseholdincome,gender,raceandethnicity,andpresenceofchildreninthehousehold.TheresultistheCaliforniaMillennialsDataset,anunprecedenteddatasetwhichcontainsinformationontherespondents’personalattitudesandpreferences,lifestyles,adoptionofonlinesocialmediaandinformationandcommunicationtechnology(ICT),residentiallocation,livingarrangements,commutingandothertravel-relatedpatterns,autoownership,awareness,adoptionandfrequencyofuseofthemostcommonsharedmobilityservices(includingcar-sharing,bike-sharing,dynamicridesharingandon-demandrideservicessuchasUberorLyft),propensitytopurchaseanduseaprivatevehiclesvs.useothermeansoftravel,majorlifeeventsthathavehappenedinthepastthreeyearsandthatmighthaveinfluencedthecurrentlifestyles,residentiallocationandtravelbehavior,environmentalconcerns,politicalideasandsociodemographictraits.Theanalysisoftherichamountofdatacontainedinthisdatasetallowsustoaddressanumberofresearchquestionsthathavereceivedattentioninrecentyearsinthescientificandplanningcommunity.TheremainderofthissectionprovidessummaryinformationontheCaliforniaMillennialsDataset,andondatahandling,cleaningandtransformationthatwerecarriedouttoexpandandintegratethedatasetwithadditionalinformationavailablefromotherdatasources,inordertodeveloptheanalysisofinterestforthisresearch.Formoredetailedinformationonthesurveycontent,datacollectioneffortandsamplingstrategybehindthecreationoftheCaliforniaMillennialsDataset,pleaserefertothePartIprojectreport(Circellaetal.,2016b).Thedatacollectionprocesswasspecificallydesignedtoinvestigatetherelationshipsassociatedwiththebehavioralprocessesandmobility-relateddecisionsofyoungadults(millennials),andtoinvestigatetheimpactthatseveralgroupsofvariables,includingchangesinlifestyles,sociodemographictrendsandtheadoptionofemergingmobilityservices,haveonthetraveldecisionthisdynamicsegmentofthepopulation.Inaddition,thepresenceofacontrolgroupcomposedofmembersoftheolderGenerationXisusefultoallowcomparisonsacrossgenerationsinthestudy,usingthesamemethodologiesfordatacollectionandselectionofrespondentsfortheentiresample.

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ThesurveyusedtocollecttheoriginalinformationincludedintheCaliforniaMillennialsDatasetincludes11sections,whichcollectedinformationonvariablesrelevantfortheanalysisofmillennials’mobilityandotheremergingtransportationtrends:

a. Individualattitudesandpreferences,measuredthroughtheagreementwithagroupof66statementsonafive-levelLikertscale,for20dimensionsincludingsocialhabits,lifestyles,adoptionoftechnology,environmentalconcerns,exercise/physicalactivity,individualism,materialism,timeorganization,etc.;

b. Useofonlinesocialmedia(Facebook,Twitter,amongothers),andadoptionofICTdevicesandservices,e.g.frequencyofuseofsmartphoneappstobooktransportationservices,purchasetickets,checktrafficconditions,ordecidewhatmodeoftransportationtouse;ownershipandregularuseofvariousICTdevices;adoptionandfrequencyofuseofe-shopping;

c. Residentiallocationandlivingarrangements,includingtheself-reportedcharacteristicsoftheneighborhoodwheretherespondentslive,detailedaddress(orclosesttwo-streetintersectionnearthehomeaddress),informationabouttenancy,yearstherespondenthaslivedatthataddressed,andinformationabouttheotherpeoplewholivewiththerespondents(e.g.partner,parents,children/grandchildren,siblingsorotherrelatives,eventualroommates/flatmates,etc.);

d. Employmentandwork/studyactivities,includingdetailedinformationaboutoccupation,typeofjob(s),fieldofoccupation,studentstatus,workschedule,numberofhoursworkedintheaverageweekforthemainoccupationandforanyvolunteeringactivities;

e. Transportationmodeperceptions,includingperceptionsofdriving,publictransportationandactivemodes(walking,biking).Theseperceptionsincludecomfort,reliability,safety,cost,privacy,andabilitytomultitaskwhileusingthesemodesoftransportation,amongothers;

f. Currenttravelchoices,includingdetailedinformationonthetypicalusageofvariousmeansoftransportation(privatevehicle,carpool,shuttle,publictransportation,bike,etc.)forbothcommutesandleisuretrips.Thissectionalsocollectedinformationontheself-reportedcommutedistanceandaveragetimespentcommuting,thelocationofmaincommutedestination(workorschool),theactivitiesconductedwhiletraveling,andtherespondent’slongdistancetravelpatterns(measuredintermsofthenumberoflongdistancetripsmadebydifferenttravelmodesforeitherbusinessorleisurepurposes,duringtheprevious12months).

g. Awareness,adoptionandfrequencyofuseofthemostcommonsharedmobilityservices(includingcar-sharing,bike-sharing,dynamicridesharingandon-demandrideservicessuchasUberorLyft);thesectioncollectedinformationaboutthesharedmobilityservicesthatareavailablewheretherespondentlives(e.g.peer-to-peercar-sharingsuchasTuro,fleet-basedcar-sharingsuchasZipcar,on-demandrideservicessuchasUberorLyft,etc.)andhowoftentherespondentusestheseservices.WealsocollectedinformationonwhytherespondentusedUber/Lyft,howthisimpactedtheiralternative

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modechoice,e.g.thedecisiononwhethertousepublictransportation,orchosenottodrive,andwhateventuallylimitsorpreventstheuseofon-demandrideservices.

h. Driver’slicensingstatusandvehicleownership,includinginformationonwhetherarespondenthasadrivers’license,thetypeoflicensetheyhave,andthelegalagetoobtainalicenseintheplacewheretherespondentgrewup.Thissectionalsoincludesquestionsonthepercentoftimeacar(and/ormotorcycle)isavailabletotheindividual,thenumberofvehiclesownerbytheindividual’shousehold,anddetailedinformation(year,makeandmodel)ofthevehiclethatisusedmostoften.Thissectionincludeddetailedquestionsonthefactorsbehindtherespondents’decisiontopurchasethevehicle(usedornew).Finally,thissectioncollectedinformationonthenumberofmilesarespondenttravelsperweekbycarandbybike,thetypeofparkingavailableattheplaceofresidence(ifany),andiftherespondenthasapublictransportationpass.

i. Previoustravelbehaviorandresidentiallocation(andinformationonthemajorlifeeventsfromthepastthreeyears):thissectioncollectedinformationaboutthelifeeventsfromthepastthreeyears(e.g.movingtoanewcityorstate,buyingahome,beginningstudy,movinginwithapartner,havingchildren,etc.).Thissectionalsocollectedinformationonwhyaparticipantmayhavemovedandtheimpactofseveralfactorsonthischoice(e.g.birthofachild,qualityoftheschooldistrict,housingprice,parkingavailability,easeofwalkingandbikingetc.).Thissectionalsocollectedinformationonhowmuchparticipantstravelbyeachmodenowcomparedtothreeyearsago.

j. Expectationsforfutureevents(andpropensitytopurchaseanduseaprivatevehiclevs.touseothermeansoftravel),includingiftheparticipantsexpects/planstomove,and/orforeseechangesinthehouseholdcompositionintheirjobsorschooltheyattend.Thisincludesdataonhowparticipantsexpecttotravelinthreeyearsfromnow,comparedtohowtheycurrentlytravel,bymode.Finally,thesectioncollectedinformationontheinterestinpurchasinganewvehicle(andthetypeofvehicletheywouldconsiderpurchasingorleasing)and/orinjoiningorleavingacar-sharingprogram.

k. Sociodemographictraits,includinggender,age,USstateorforeigncountrywheretheindividualwasraised,politicalviews,householdsizeandcomposition,individualandhouseholdincome,educationlevel,parents’education,andnumberofdriversinthehousehold.

Duringthesurveydesign,weengagedseveralstakeholdersandworkedwithcolleaguesatotherresearchinstitutions,Californiastateandlocalagencies,andotherpartnerorganizations,toobtainfeedbackonthesurveycontentandimprovethesurveytool.Weextensivelypretestedthesurvey,andtriedtobalancethetrade-offbetweenthecomplexityofthecontentofthesurvey(andtheamountofinformationthatiscollected)andthetimerequiredtocompletethesurvey.WeadministeredthesurveytoasampleofmillennialsandmembersofGenerationXinCalifornia.Weusedaweb-basedopinionpaneltoinvitemembersofthesesegmentsto

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completethesurvey,andusedaquotasamplingapproachtoensurethatenoughresponseswereincludedfromeachgeographicregionofCaliforniaandneighborhoodtypewheretherespondentlives(classifiedinpredominantlyurban,suburbanandruralareas).SociodemographictargetswereusedtomakesurethatthesamplemirroredthecharacteristicsoftheCaliforniapopulationonfivekeysociodemographicdimensions:sex,age,income,raceandethnicity,andpresenceofchildreninthehousehold.Forthepurposesofthisstudy,wedividedCaliforniainsixmajorregions:

• MTC–MetropolitanPlanningOrganization(SanFranciscoBayArea);• SACOG–SacramentoAreaCouncilofGovernments(Sacramentoregion);• SCAG–SouthernCaliforniaCouncilofGovernments(LosAngeles/SouthernCalifornia);• SANDAG-SanDiegoAssociationofGovernments(SanDiego);• CentralValley(eightcountiesinthecentralSanJoaquinValley);and• NorthernCaliforniaandOthers(restofstatenotincludedinthepreviousregions).

Atotalof5,466invitationsweresentout,and3,018completecaseswerecollected.Thehighresponserateof46.3%isnotsurprisingconsideringthedatacollectionmethodusedforthisproject,andthehigherpropensityofopinionpanelmemberstorespondtosurveyinvitations.Afterexcludingseverelyincomplete,inconsistentorunreliablecases,afinaldatasetthatincludedapproximately2,400validcaseswasusedtocomputeinitialdescriptivestatisticsandotheranalysesreportedinthePartIreport(Circellaetal.,2016b).Whilethesamplingmethodusedtorecruittheparticipantsforthisstudy(basedontheuseofanonlineopinionpanel)andtheuseofanonlinesurveymightrepresentapotentialsourceofbiasfortheresearch,andcautionshouldbeusedingeneralizingtheresultsfromthestudytotheentirepopulationofCalifornia,theuseofthesamemethodologyfortherecruitmentofbothmembersofthemillennialgenerationandoftheprecedinggenerationXensuresinternalconsistencyinthecollectionofthedataandcreationofthedataset.Inotherterms,ifanysamplingandresponsebiasesaffectthestudy,itisreasonabletoexpectthatthesimilarbiasesaffectboththemillennialsandGenerationXsubsamples.Forthisreasons,evenifeventualbiasesarepresentinthedatacollectionandsamplingapproachusedfortheresearch,thecomparisonsbetweentheobservedbehaviors,andrelationships,betweenmillennialsandGenXerspresentedinthisreportremainvalid.ThedatacollectioneffortwasdesignedasthefirststepofalongitudinalstudyoftheemergingtransportationtrendsinCalifornia,designedwitharotatingpanelstructure,withadditionalwavesofdatacollectionplannedinfutureyears.Theresearchteamiscurrentlyworkingwiththefundingagency,inordertodefinetheplanforthefuturecomponentsofthelongitudinal(panel)study,alsothroughtheintegrationoftheinformationcollectedwiththissurveywithadditionaltraveldiariesandtraveldatacollectedwithGPS-basedsmartphoneapps.Further,infuturestagesoftheresearch,weplantoexpandthedatacollectionalsothroughotherchannels,alsothroughthecreationofapaperversionofthesurvey,inordertoexpandthetargetpopulationforthestudy,andreachspecificsegmentsofthepopulation,e.g.elderlyor

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peoplethatarenotfamiliarwiththeuseoftechnologyorwhodonothaveeasyaccesstotheinternetandwouldnotlikelycompleteanonlinesurvey.Also,weareconsideringcreatingaversionofthesurveyinSpanish,inordertobetterreachtheCaliforniapopulationofLatinosandincreasetheresponserateamongtheHispanicminority.DataCleaningandRecodesInordertoenforcestrictqualitycontrolinthecollectionofrespondents,wedevisedseveralmeasurestoidentifyandremoveproblematicorinconsistentcasesfromthedataset.Amongthestrategiesthatweredevelopedforpurposesofqualityassurance,weusedacommonqualityassurancepracticeintheformoftwotothree“trap”questions(dependingontheversionofthesurveythatwasadministeredtotherespondent)thatwereincludedinvarioussectionsofthesurvey.FurtherdetailsaboutthetrapquestionsthatwereusedandthestrategiesthatwereusedtoidentifyinconsistenciesinthedatasetcanbefoundinthePartIprojectreport(Circellaetal.2016b).Inadditiontotheuseoftrapquestions,wecheckedtheconsistencyofresponsesthroughoutthesurveythroughtheapplicationofseveralcriteria.Theconsistencychecksthatwereusedalsoincludedverifyingthespeedwithwhichrespondentsansweredthesurvey.Forexample,weremovedindividualswhofailedatrapquestionandalsocompletedthesurveyinaveryshorttime(below20minutes)asasignoflackofattentionduringthecompletionofthesurvey.Theaverageresponsetimeforthissurveywasapproximately35minutes.Therefore,itwouldhavebeenextremelydifficulttocompletethesurveyinlessthan20minutes.Additionalcriteriathatwereusedduringtheprocessofdatacleaningandrecodingarediscussedinthesub-sectionsbelow.Thesecriteriaincludedcheckinginternalconsistencyofacase,analyzingsurveyresponseoutliers,andinconsistenciesbetweentheinformationreportedbytherespondentinthemainbodyofthesurveyandinthescreenerfromtheopinionpanel.1InternalconsistencyAspartoftheinternalconsistencychecks,weidentifiedandcarefullyreviewedcasesthatwereconsideredsuspiciousaccordingtooneormoreofthefollowingcriteria:

• Flatliners:Individualswho“flatlined”oneormoresectionsthathadconflictingstatements(e.g.respondentswhoansweredyestobothstatements:“Iexpecttomoveinthenextthreeyears”and“Iexpecttostayinmycurrenthouseinthenextthreeyears.”)

• Locationalconsistency:Forexample,individualswhoprovidedthesameaddressforworkandhome,thoughtheyindicatedthattheydidnottelecommute,orindividualswhoperceivedneighborhoodtypeasextremelydifferentfromtheobjectivemeasuresthatweredeterminedusinggeocodedvaluesforthehomeaddress.

1Theopinionpanelusedashortscreener,whichcontainedonlyninequestions,torecruitandselectparticipantsforthestudy.

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• Travelpattern:Weassessedmodeavailabilityforcommuteandleisuretripsaccordingtothereportedlocation,tripdistanceandtimeofthecommutingtrips,andthroughthecomparisonofthegeolocatedworkandhomeaddresses.Wealsoevaluatedthecasesthatreportedfrequentuseofmultiplemodes,andinconsistencyinthereportedmulti-taskingactivitiesduringthemostrecentcommutetrip.

• Useofemergingtransportation:Respondentswhoreportedthattheyusedservicesthatarenotavailableintheareaswheretheylive(thesurveyexplicitlyaskedrespondentswhethertheyusedtheserviceintheirhometownorwhiletravelingawayfromhome),orrespondentswhoreportedthattheyusedmultipleserviceswithveryhigh(andunrealistic)frequencyovershortperiodsoftime(e.g.respondentthatusedZimride,Turo,ZipcarandUberveryfrequently,especiallyiflocatedinlocationswheretheseservicesarenotlargelyavailable).

• Householdcomposition:Severalquestionsinthesurveyaskedinformationrelatedtothehouseholdcompositionandlivingarrangement,allowingtheresearcherstoestablishwhetherthereportednumberofchildrenandnumberofadultsinthehousehold,andtheirageranges,areconsistentwiththeinformationreportedabouttheotherindividualsthatliveinthehousehold(intheprevioussectionCofthesurvey)

Casesthatfailedoneormorecriterialistedabovewere,inmostcases,removedfromthedataset,unlesssomevalidreasonsfortheinternalconsistencywereidentified.ResponseoutlierWereviewedcasesthatposeproblemsrelatedtooneormoreofthefollowingcriteria:

• Dailyactivitypatterns:individualswhoreportactivitiesthatareimplausibleorimpossible(e.g.watchingTVfor24hoursinoneday).

• Longdistancetrips:Individualswhoreportedextremelyhighnumberoflongdistancetripsforeitherbusinessorleisuretrips(over100miles).

• MoneyspentonUber/Lyft:IndividualswhoreportspendingveryhighmonthlyamountofmoneyonUbercomparedtotheself-reportedfrequencyofthisservice.

• Numberofcars:Respondentswhoreportveryhighorverylownumberofcarscomparedtotheirhouseholdsizeandstructureandthereportedcommutepattern(e.g.individualsthatreportthattheytraveldrivingaloneinacaronadailybasis,butthenreportthattheyliveinazero-vehiclehousehold).

• Vehiclemilestraveled:IndividualswhoreportedillogicalaverageweeklyVMTforcommutesandtravelpatterns(e.g.individualsthatlikelyreportedannualVMT,bymistake,insteadoftheweeklyVMT,orthatreportedzeroVMT,butthenreportedthattheydrivealonetowork/schoolintheircommutepattern).

Theinformationassociatedwiththecasesidentifiedthroughoneofcriteriaabovewaseitherremovedfromthedataset,orrecodedaccordingly(e.g.somevariablevalueswererecodedto“missing”),dependingontheseverityoftheproblemsthatwereidentified.

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InconsistencybetweentheSurveyandScreenerQuestionsWealsoidentifiedinconsistenciesbetweentheinformationreportedinthesurveyandtheinformationthatwasreportedwhenansweringthequestionsthatwereproposedinthescreenerusedbytheonlinesurveycompanytopre-screenrespondentsduringtherecruitmentofparticipantsforthestudy.WedesignedthescreenertoensurethatasamplethatisasrepresentativeaspossibleofthepopulationinthestateofCaliforniacouldbeassembledforthisstudy.Thescreenercollectedinformationonthefollowingvariables:gender,agegroup,Hispanicorigin,race,householdincome,Zipcodeoftheplaceofresidence,neighborhoodtype,presenceofchildreninthehousehold,andnumberofchildreninthehousehold.Inparticular,wecheckedtheconsistencyforthefollowingvariables:

• Gender:wecomparedthescreenerdatawiththesurveydata.• Agegroup:Therewereseveralcasesforwhichtheagewasnotconsistentwiththe

reportedgroups:inthiscasewecheckedthescreeneragegroupswiththesurveyresponse.

• Neighborhoodtype:Wecomparedtheperceivedandgeocodedmeasuresofneighborhoodtype(suburban,urban,rural)andindividualreviewedcasesthathaddifferencesinthereportedneighborhoodtype,toidentifythereasonsforthedifferentinformation.

• PresenceofChildren:WeassessedthepresenceofchildreninthehomegiventheresponsesinsectionCandsectionKofthesurvey,andcomparedthemtotheinformationprovidedinthescreener.

Inmostcases,theinconsistenciesaboveledtorecodingthescreenerdata,giventhatthesurveyinformationwasconsideredmoreaccurate,e.g.thescreenercansometimesbefilledbyothermembersofthehousehold.However,caseswithmoresevereinconsistencieswereremovedfromthesample.Werecodedsomeresponsesonacasebycasebasis,reviewingallanswersprovidedbyarespondent.Insomesituations,werecodedavariableto“missing”value,whentheinformationaboutthatvariablecouldnotbeassessedwithcertainty.Inthecaseofthescreenerinconsistencieswerecodedeitherthesurveyorthescreenerdependingonthecase.Alistofrecodeswaspreparedandimplementedinthefinaldataset.Afterassessingthecaseswhichpresentedsomeinconsistenciesorotherreasonsfornotbeingconsideredreliable,weretained2155casesinthedatasetusedfortheanalysesinthisreport,fromthemorethan3000casesthatwereoriginallycollected(andapproximately2400casesthatwereusedfortheinitialanalysesinthePartIreport).GeocodingTomaketheCaliforniaMillennialsDatasetrichwithvariousinformationfromexternaldatasources,wefirstgeocodedtheresidential,school,andworkplaceaddressesofindividualrespondentsbyemployingoneofthereliablegeocodingmethods,theGoogleMapsapplication

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programminginterface(API).Othergeocodingmethodswerealsoconsidered,includingtheESRIDesktopArcGISgeocodingtoolboxandtheESRIArcGISonlinegeocodingtool.Thesetoolsweretestedandusedininitialcomponentsofthegeocodingprocess.However,theywerenotusedinthefinalgeocodingprocess,becauseofsomelimitationsthatmadethemnotwellsuitedforthisproject.Inparticular,theDesktopArcGIStoolboxneedsastreetnetworkinaspecificformasaninputforgeocoding,andmostusersusetheUSCensustopologicallyintegratedgeographicalencodingandreferencing(TIGER)AddressRange-Featureshapefileastheinput.AlthoughtheUSCensushaveregularlyupdatedthisshapefile,itisfarfrombeingperfect.Forexample,thefirstandlaststreetnumbersofstreetsegmentsinthisfileareoftennotrecentlyupdated.Moreover,becauseArcGISisnotasearchenginesuchasGoogleandBing,ifaddressesaremisspelled,itsgeocodingoutcomesarenotasgoodasthosefromonlinesearchenginesthatoftensuccessfullyfindfulladdressesalsoincaseofpartialonesbasedonprevioussearchesandselectionsfromotherusers.Thispropertyalsocomeswithsomedisadvantages,though,astheGoogleMapsAPImightsometimesreturnwrongaddressesastheresultofthepredictionsoftheirsearchengine.Still,inthisproject,itwasfoundtobepreferabletousetheGoogleMapsAPI,withsomeadditionalqualitychecksthatwereperformedbytheresearchteamasapost-process,toverifythattheaddressgeocodedbyGooglereasonablymatchedtheoriginaladdressprovidedbytheuser.AsfortheArcGISonline,althoughESRIclaimsthatitsgeocodingoutcomesaremoreaccuratethanthoseobtainedbyemployingtheUSCensusshapefiles,ESRIdidnotexplicitlyrevealthecharacteristicsoftheirgeodatabase.Afterintensiveexperimentations,wefoundthattheoutcomeoftheArcGISonlinewasnotdiscernablybetterthanthatoftheDesktopArcGIStoolbox.Somerespondentsreportedinaccurate,partial,anderroneousaddresses,butmanyoftheproblematicaddressesappearedtobeformattedcorrectly,sotheresearchteamwasabletocleanandgeocodetheseaddressesthroughamultipleiterationgeocodingprocess.Fourtypesofaddresseswereidentifiedinthedataset,basedonthetypeofinformationprovidedbytherespondents:

1. Fulladdresseswithstreetnumbers;2. Intersectionsoftwocloseststreets;3. One-streetaddresses;and4. Onlythenameofcitiesand/orZIPcode2.

Eachtypeofaddresspresentsuniquechallengesthataffectthegeographicaccuracyandprecisionofgeocodes.Althoughmisspellsandtheomissionofsomeinformationinthestreetnamesareusuallyaneasyfix,someofthereportedfulladdressesdidnotexist(i.e.,thestreetnameisreal,butthereportedstreetnumberisnotfoundonthatstreet).Moreover,wefoundanontrivialnumberofcaseswithtwonearbystreetswhichactuallydonotcrosseachother:notallpeopleareabletocorrectlyremembertwointersectingstreetsnearbytheirresidential

2ThesurveyrequiredeachrespondenttoreportavalidZIPcode.Thus,respondentsthatdidnotfeelcomfortableaboutprovidingadditionalinformationabouttheiraddress,ataminimumprovidedinformationthatallowedtheresearchteamtoidentifythecityandZIPcodeinwhichtheylive.

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location,somerespondentsreportedtwostreetsthatareactuallyparallel(andsometimesevenfarfromeachother).Inaddition,specificruleshadtobedefinedtotreatcasesinwhichthesurveyparticipantsreportedonlyonestreetinsteadoftheirresidentialaddress.TheresearchteamhadtodevelopasetofrulestoassignthemostlikelyCensustracttotheserespondents’residential,study,andworkaddresses.Lastly,caseswithonlyinformationabouttheZIPcodehadthelowestqualityofinformation:ZIPcodeareasareoftenlargeenoughtocovervarioustypesofneighborhoods(e.g.theycanincludebothsuburbanandurbanneighborhoods).Asanonlinesearchenginethatisspecializedtoreturnreliableoutcomesevenwithincompleteandpartiallyincorrectkeywords,GoogleMapsAPIworksononeofthemostupdatedgeodatabasesandproducesarichsetofinformationonthequalityofgeocodes,whichuserscanusetoexaminegeocodingoutcomes.BecausethegeodatabaseofGoogleMapsAPIisincorporatedwiththesatelliteimagesofGoogleMaps,GoogleMapsAPIproducesaresultfromadirectsearch,insteadofgeographicreferencingbasedonthefirstandlaststreetnumbersofstreetsegments(whichishowtheDesktopArcGIStoolboxandtheonlineArcGISwork).Moreover,foreachquery,GoogleMapsAPIreturnsaddressesthatitfindsfromitsgeodatabaseandtypesofgeocodingthatituses:thus,GoogleMapsAPIpresentstwowaysofexaminingthequalityofageocode.First,userscancompareinputandoutputaddressesanddeterminehowsimilartheoutputaddressfromGoogleistotheinputaddress(alsoincaseofincompleteandpartiallyincorrectaddresses).Inaddition,twocategoricalvariableshelpusersdeterminehowreliableindividualgeocodingoutcomesare.Table1summarizesthenumberofcasesinthedataset,bythetypeofaddressthatwasreported(andgeocoded):1,858caseshadhighlyreliableaddresses(withfulladdressortwo-streetintersections),233weremoderatelyreliable(one-streetaddresses),and64werelessreliablecases(withonlycitynamesand/orZIPcodes).Table1.TypeofAddressesGeocodedintheDataset

Qualityofgeocodingofresidences NumberofcasesFulladdressesorintersectionsofclosesttwostreets 1,858(86.2%)One-streetaddresses 233(10.8%)CitynamesandZIPcodes 64(3.0%)Total 2,155(100%)

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Figure1.DistributionofmillennialsandGenXersinthedataset,basedontheirgeocoded

residentialaddressTheoutcomesofthegeocodingofresidentialaddresseshelpedtheresearchteamdeterminethetypeofneighborhoodwheretherespondentsliveinCalifornia.ThisprojectusestheneighborhoodtypedevelopedinanotherprojectfromresearchersatUCDavis,whichanalyzedandclusteredthe8,036censustractsinCaliforniabasedonthepredominantneighborhoodcharacteristics(Salon,2015).Theprojectclassifiedeachcensustractasbelongingtooneoffivecategories:CentralCity,Urban,Suburb,Rural-In-Urban,andRural.Becausegeocodeswithone-streetaddressesandwithcitynamesandZIPcodesdonotpresenttheexactlocationsofresidences,theresearchteamvisuallyinspectedthesecasestoseewhetherornottheirneighboringCensustractsalsohavesimilarneighborhoodcharacteristics.Ifboththeidentifiedcensustractandtheneighboringcensustractsshowthesametypeofland-usepatterns,eveninthecaseoflowqualityofthegeocodedlocation(i.e.one-streetaddressesorcitynamesand

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ZIPcodes),theresearchteamwasabletoassigntheneighborhoodtypewithagoodmarginofreliability.Incontrast,ifone’sownneighborhoodtypediffersfromthatofitsneighboringCensustracts,weusedtheperceivedneighborhoodtypesthattheindividualsreportedinthesurveytodeterminewhichtypesofneighborhoodstherespondentsarelikelytolivein.Figure2summarizesthedistributionofcasesinthedatasetbyneighborhoodtype.

Figure2.Distributionofcasesinthedataset,bygeocodedresidentialaddressand

neighborhoodtype

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WeightingandRakingInordertocorrectfornon-representativenessofthesample,andreplicatethedistributionofthepopulationofMillennialsandGenerationXlivinginCalifornia,weusedacombinationofcellweightinganditerativeproportionalfitting(IPF)(Kalton&Flores-Cervantes2003).Weusedcellweightstoweighoursampleonthreedimensions–agegroup(18-24,25-34,35-44,45-50),neighborhoodtype(Rural,Suburban,Urban),andregion(CentralValley,NorthernCaliforniaandOthers,SACOG,SANDAG,SCAG,SFMTC).Thisweightingprocesscompensatesfortheeffectsofthequotasamplingprocessusedinthedatacollectionandtheintentionaloversamplingofsomeregions.Weintentionallyunderrepresentedtheresidentsofmajormetropolitanareas,mainlyLosAngelesandtoalowerextentSanFrancisco,inthedatacollection,andoversampledindividualswholiveinotherareas(ruralcountiesandlesspopulatedregions),inordertocollectenoughrespondentsforeachregion,andbuildrobustanalysesforallsubsamples.Atthetimethestudywaslaunched,weenvisionedasampleofatleast700casesselectedamongthepopulationofCaliforniamillennialsforthisresearch.Thesizeofthesamplesizewaslaterincreasedthroughtherecruitmentofadditionalparticipantsinthestudy,andalsoacontrolgroupcomposedofmembersofGenerationX,whichwasnotincludedintheoriginalscopeoftheresearch,wasadded,furtherenrichingthediversityofrespondentsinthesample.Whileanyremainingsamplingbiascanlimitthevalidityofthegeneralizationoftheresultsfromthissampletothepopulationofinterest,themethodusedinthisstudyremainsveryvalidforcomparisonsamongthetwosubsamplesofmillennialsandmembersofGenX,whowererecruitedwiththesamemethodology.Thesamplingmethodthatcontrolledforthedistributionofeachsubsampleonseveralsociodemographictraitsandtheapplicationofweightsallowustobuildrobustanalysesofthesedata.Todevelopourbaselinepopulationthatwasusedtodevelopthetargetforthecellweights,weusedtheAmericanCommunitySurvey20141-yearestimatedatapairedwithresidentialneighborhoodclassificationdatafromSalon(2015).While,theresidentialneighborhoodtypesforCaliforniacensustractswerederivedfromSalon(2015),weaggregatedthefiveneighborhoodtypesdeterminedinthatstudytothreemajorneighborhoodtypes,whereRural-in-UrbanandRuralareaswereclassifiedas“Rural”andCenterCityandUrbanareaswereclassifiedas“Urban”.Suburbanareasweretreatedas“Suburban”consistentwiththefiveneighborhoodtypeclassification.WeusedtheACSdatatobuildacrosstabulationbasedonagegroupbyregionandneighborhoodtype.ThefinalsetofcellweightsweregeneratedbycomparingthecrosstabulationofsurveyrespondentsandthepopulationofCaliforniaresidentsages18to50.Inadditiontocell-weightingonthethreedimensionsdescribedabove,weusedmultipleroundsofiterativeproportionalfitting(IPF)rakingtomirrorthedistributionoftheCaliforniapopulationonseveraladditionaldemographictargets.Thisallowedustocorrectthedistributionsinthesamplebyassigningspecificweightstooursamplebasedonsixdimensions–race,ethnicity,presenceofchildreninthehousehold,householdincome,student/employmentstatus,andsex,whichwereusedastargetsintheIPFprocess.Weused1-yearestimatesofthe

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PublicUseMicrodata(PUMS)from2015tocreatethetargetsfortheCaliforniapopulationfrom18-50(U.S.CensusBureau2014).AtotalofthreeiterationsoftheIPFmethodwasappliedinthisprocess.ForthefirstroundofapplicationofIPF,weusedthecellweightsasthestartingweights,andweightedonhouseholdincome,student/employmentstatusandsex.Theannualhouseholdincomewasclassifiedinthreebroadcategories:Low(<$35,000),Medium($35,000-$100,000)andHigh(>$100,000).Student/Employmentstatuswasclassifiedthroughafour-levelvariable,wheretheparticipantmaybeunemployed,workonly,beastudentonly,orbebothastudentandworker.ThesecondroundofIPFusedtheweightsgeneratedbymultiplyingthecellweightsandthefirstroundofIPFandweightedtheseonRaceandEthnicity.Duetoissuesrelatedtooursamplesize,weconsolidatedtheracecategoriesinthedatasetasthreemainracegroups–White,Asian/PacificIslander,andOther.ForEthnicity,weusedthetwocategoriesofHispanicandNon-Hispanic.ThethirdroundofIPFusedtheresultsofthepreviousiterationsandweightedonGenerationandPresenceofChildren.GenerationwasdefinedasGenerationY/Millennials(individualswhowere18to34in2015),andGenerationX(individualswhowere35to50in2015).Thepresenceofchildreninthehouseholdwasmeasuredwithabinaryvariable(children,nochildren).Table2summarizesthedescriptivestatisticsforboththeunweightedandweighteddataset.Thenumberofweightedcasesineachgroupmaynotsumexactlyto2155duetoroundingeffects.

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Table2.DemographicStatisticsintheCaliforniaMillennialsDataset Weighted Unweighted

Numberofcases

Percentageoftotal

Numberofcases

Percentageoftotal

Total 2155 100% 2155 100%Gender Male 1043 48.4% 876 40.6%Female 1090 50.6% 1257 58.4%Transgender 9 0.4% 8 0.4%DeclinetoAnswer 13 0.6% 14 0.6%PresenceofChildrenintheHousehold HouseholdwithoutChildren 1018 47.3% 1089 50.5%HouseholdwithChildren 1137 52.7% 1066 49.5%HHincome Prefernottoanswer 142 6.6% 158 7.3%Lessthan$20,000 167 7.7% 207 9.6%$20,001to$40,000 357 16.6% 392 18.2%$40,001to$60,000 311 14.4% 374 17.4%$60,001to$80,000 294 13.6% 356 16.5%$80,001to$100,000 194 9.0% 236 11.0%$100,001to$120,000 225 10.4% 157 7.3%$120,001to$140,000 120 5.5% 81 3.8%$140,001to$160,000 133 6.2% 75 3.5%Morethan$160,000 213 9.9% 119 5.5%Age YoungerMillennials(18-24) 473 21.9% 385 17.9%OlderMillennials(25-34) 714 33.1% 830 38.5%YoungerGenerationX(35-44) 608 28.2% 613 28.4%OlderGenerationX(45-50) 361 16.7% 327 15.2%Ethnicity Hispanic 907 42.1% 501 23.2%Non-Hispanic 1248 57.9% 1654 76.8%Race Black/AfricanAmerican 88 4.1% 98 4.5%AmericanIndian/NativeAmerican 49 2.3% 40 1.9%Asian/PacificIslander 326 15.1% 332 15.4%White/Caucasian 1269 58.9% 1399 64.9%Other/multi-racial 422 19.6% 286 13.3%Education

Prefernottoanswer 8 0.4% 8 0.4%Somegrade/highschool 44 2.0% 42 1.9%Highschool/GED 242 11.2% 278 12.9%Somecollege/technicalschool 595 27.6% 642 29.8%Associate’sdegree 232 10.8% 242 11.2%Bachelor’sdegree 710 32.9% 686 31.8%Graduatedegree(e.g.MS,PhD,MBA,etc.) 227 10.5% 197 9.1%Professionaldegree(e.g.JD,MD,DDS,etc.) 98 4.5% 60 2.8%AverageHHsize 3.24

3.20

Average#ofVehiclesintheHH 1.88

1.80

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IntegrationofAdditionalLandUseDatafromOtherSourcesKnowingthelocationofwork/schoolandhomeaddressoftherespondentsenablesustointegrateourdatasetwithotherexistingdataincludingSmartLocationDatasetpreparedbytheU.S.EnvironmentalProtectionAgency(EPA),andotherlanduseaccessibilitymeasuresincludingthewalk,bikeandtransitscoresfromotherwell-establishedsources(e.g.Walkscore.com).TheSmartLocationDatabasesummarizesnumerousdemographic,employmentinformation,andprovidesvariousstatisticalanddeterministicbuiltenvironmentindicatorsestimatedatthecensusblockgroup(CBG)level(Ramsey&Bell2014)3.Thesedemographicandlanduseindicatorswerematchedtoindividuals’residentialandwork/schoollocationbasedonthegeocodedlocationoftheself-reportedaddress.ThebuiltenvironmentalattributesthataremeasuredintheSmartLocationDatasetcanbeclassifiedintofivemaincategories:

• Densityindices:TheSmartLocationDatasetprovidesdifferentmeasureofdensity,includingpopulation,housing,activityandtotalnumberofemploymentandemploymentbytypeforeachcensusblockgroup.

• Diversityindices:Differentmeasuresoflandusediversitywereestimatedforeachcensusblockgroup,includingjobtohouseholdbalances,entropyindicesfor5-tierand8-tieremploymentcategories,employmentandhouseholdentropybasedontripproductionandattractions,tripequilibriumindex,regionaldiversity,andhouseholdworkersperjob.

• Urbandesignindices:Theseindicesestimatedvariousurbandesignmeasuresincludingstreetnetworkdensityandintersectiondensitybyautomobile,pedestrianandmultimodalfacilities.Exampleofthesevariablesarenetworkorintersectiondensityintermsofauto-orientedlinkspersquaremileineachcensusblockgroup.

• Transitindices:UsingtheGoogletransitdata(particularlythelocationoftransitstopsandtheirregularschedule),theSmartLocationDatasetprovidesdifferentmeasuresoftransitavailability,proximity,frequencyanddensity.Thetransitvariablesarecomprisedofdistancefromthepopulation-weightedcentroidtothenearesttransitstop,theproportionofblockgroupwithinaquartermileorhalfmileofatransitstop,theaggregatedfrequencyoftransitserviceperhourduringtheeveningpeakperiod,andtheaggregatefrequencyoftransitservicepersquaremile.Thesetransitmeasuresareonlyestimatedfortheareasforwhichthecorrespondingtransitagenciesprovidedtherequiredinformation.

• Destinationaccessibilityindices:Theseindicatorsaredevelopedtomeasuretheaccessibilityfromcensusblockgrouptocensusblockgroup.Thesevariablesmeasurethenumberofjobsorworking-agepopulationwithina45minutescommutebycaror

3The2010CensusTigerLine/polygonswereusedindefiningblockgroupboundaries,whichwerelatermergedwiththeinformationobtainedfromtheotherdatasetsincludingthe2010Censusdata,theAmericanCommunitySurvey,theLongitudinalEmployer-HouseholdDynamics,InfoUSA,NAVTEQ,PAD-US,TODDatabase,andGoogleTransitFeedspecification(GTFS)database.

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transitfromacertainblockgroup.Inaddition,theEPASmartLocationDatasetincludesrelativemeasuresofaccessibilityforeachcensusblockgroupbasedonthecomparisonwiththeaccessibilityofthecensusblockgroupsthatarelocatedwithinthesamemetropolitanareas.

Furthermore,byusingthelatitudesandlongitudesofallhomesandworkplaces,wecanappendadditionalvariablesthatcapturethecharacteristicsofspecificlocationsandthatareavailablefromreliablepublicandprivatedatabases.Inparticular,Walkscore.comhasbeenknownforitscompositemeasureofwalkability,the“walkscore,”whichmanyscholarshavefoundausefulvariabletounderstandrelationshipsbetweenthebuiltenvironmentandnon-motorizedtravelpatterns.Whilenotperfect4,Walkscore.comprovidesthreemeasures—walkscore,bikescore,andtransitscore—thatcapturetheeasinessofusingvarioustravelmodesatspecificlocations.SinceWalkscore.comprovidesanAPIservice,theresearchteamwasabletoextractthethreescoremeasuresbasedonthelatitudesandlongitudesofthegeocodedresidentiallocationofeachrespondent.Thesemeasuresprovideagoodproxyofthesupply-sidecharacteristicsofvariousneighborhoodsacrossCalifornia.Withthegeographicgeocodesofhomes,schools,andworkplacesofallindividualsinthedataset,infuturestagesofthisprojectweplantofurtherenrichtheCaliforniaMillennialDatasetwithavarietyoftransitandland-usevariablesfromotherreliablesourcessuchasAllTransit.comandGoogle.TheAlltransit.cnt.orgwebsiteprovidesawidearrayofmatricesontheperformanceoflocalpublictransportationsystemsforindividualcensusblockgroups.Byemployingthegeneraltransportationfeedspecification(GTFS)datasetsthattransitagenciesmaintain,anddirectlycollectinginformationabouttransitservicesfromtheagencieswithoutGTFSdatasets,thewebsitereturnsarichsetofvariablesundersixcategories,suchasjobs,economy,health,equity,transitquality,andmobility.Inaddition,twoamongthevariousGoogleAPIservices,theGooglePlacesAPIandGoogleDirectionAPI,provideuniqueinformationthatweplantouseinfuturestagesoftheprojecttoanalyzethelocationchoiceandthemodechoiceofMillennialsandGenXers.TheGooglePlacesAPIprovidesthegeographiccoordinatesofadiversesetofbusinesses.AsusersandbusinessownerscanaskGoogletocorrectcriticalinformationsuchasopeningandclosingofbusinesses,GooglePlacesAPIprovidesthehighlyaccurategeographiclocationsofbusinessesbytype.TheGoogleDirectionAPIcalculatesthedistanceanddurationofatripfromanorigintoadestinationbyfourmodes–driving,transit,biking,andwalking–basedonrealisticcongestioninformationthatvariesbytimeofdaybyusingtheirarchivedtrafficdata.

4Walkscore.commeasuresitsscoresbasedontheaccessibilitytopublicplaces.However,thedefinitionofpublicplaceshasbeenquestioned,assomeplacesthatareclassifiedas“private”,butdoprovidefreeaccesstothepublicandthereforecouldqualifyforthedefinitionofpotentialdestinationsfortrips,arenotconsideredinthecomputationofthescores.

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FactorAnalysisInthissection,wediscussthevariabledimensionreductionmethodthatwasappliedontheattitudinalstatementsfromsectionsAandJofthesurvey.Theattitudinalvariablesweremeasuredaskingtherespondentsfortheiragreementwith66statementsusinga5-levelLikerttypescale(fromstronglydisagreetostronglyagree).The66attitudinalstatementsweredesignedtomeasuretheindividual’sattitudesrelatedto28pre-determinedunobservableconstructs,includingattitudestowardbiking,carownership,changesvs.routine,environmentalconcern,landuse,masculinity,roleofgovernment,multitasking,etc.Theseattitudinalconstructscanexplainvariabilityindecisionsaboutcarownership,travelmodechoice,residentiallocationandmanyotherdecisionsthatmadebydifferentsegmentofpopulation.Asdiscussedearlier,outof2155respondents191individualshavefailedinansweringcorrectlytooneofthetrapquestionsincludedinthesurvey.ThreetrapquestionswereembeddedinthesectionsAandGofthereport,tocontrolforthequalityoftheresponses.Informationrelatedtotheindividualswhofailedtwoormoretrapquestionswasautomaticallyremovedfromthedataset.Theremaining191casesthatfailedonlyonetrapquestionareexpectedtocontainlowerqualityinformation,whichcouldskewtheresultofthefactoranalysisandsignificantlychangethefactorextractionandloadingprocess.Hence,weonlyperformedthefinalfactoranalysisontheindividualswithhigherqualityoftheresponses,i.e.therespondentswhodidnotfailanytrapquestion(N=1964cases).5Thefirstandmostchallengingstepinfactoranalysisistodeterminethenumberoffactorstobeextracted.Thedefaultinmoststatisticalsoftwarepackagesistoretainallfactorswitheigenvaluesgreaterthan1.0orgreaterthanavalueclosetoone,e.g.0.7(asdiscussedbyJolliffe,1972).Ontheotherhand,VelicerandJackson(1990)showedthatusingthiscriterionmayleadtotoomanyextractedfactors.UsingaMonteCarlosimulation,theauthorsfoundthat36%ofthesamplesretainedtoomanyfactorsusingthiscriterion.Hence,alternativeapproaches(basedonmultiplecriteria)havebeenrecommendedtoidentifythenumberoffactors,includingscreetestplot,Velicer’sMAPcriteria,parallelanalysis,andmostimportantlytheinterpretabilityoftheextractedfactors.Basedonmultiplecriteriaincludingtheevaluationoftheeigenvalues,screeplot,strengthoftherelationship,andinterpretability,arangeforthenumberoffactorwasfirstidentified.Thenfactorsolutionswiththosenumbersweretestedtoseewhichsolutionproducesthebestoutcomeconceptuallyandnumerically.Asexpected,somevariableswerefoundtohavesmallloadingsonanyfactors(smallerthan0.29).Intheotherwords,somestatementsdidnotloadonanyfactorsinanymeaningfulway.Thesestandalonestatementseitherbelongstosinglestatementconstruct(e.g.“Ilikeridingabike”isagoodattitudinalvariablethatcanbeusedinisolationtopredictbicyclingbehavior)orperceiveddifferentlybyrespondents(e.g.statement5Wecomparedtheresultsfromafactoranalysisthatwasperformedonthefulldataset,whichincludedalsotheselowerqualitycases.Thecomparisonconfirmedthehigheramountofnoiseinthesolutionthatwasestimatedusingthefulldataset.

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usedforcapturingtheeffectsofpeerpressureareoftendifficulttobeusedinbehavioralresearchduetothereluctantattitudeofmostrespondentstoreportpeerpressure,andsocialdesirabilitybias).Additionally,somestatementswithweakfactorloadingswereincludedinfactorsmeasuringacompletelydifferentattitudinalconstruct.Forexample,attitudestowardmasculinity(ormachismo),whichweremeasuredbystatementsincluding“Itismoreimportantformenthanforwomentohaveahigh-payingcareer”and“Atwork,it’sperfectlyfineforwomentohaveauthorityovermen”,loadedwellinthefactorthatmeasuredthepro-environmentalpolicyattitudesofindividuals.This,whileisasignofanotherlatentattributeofindividuals(e.g.whichmeasuressomeconservativism,ortraditionalthinking),makestheinterpretabilityofthefactormorecomplicated,intermsoftheirrelationshipwithenvironmentalchoices,andtravelbehavior.Forthisreason,thosetwostatementswereremovedfromthefactoranalysis.Table3showsthe14-standalonestatementsthatareexcludedfromthefactoranalysis.Onecanusethesestandalonestatementsasanordinalorasastandardizedvariablefordescriptivestatisticsandasexplanatoryvariablesformodelingpurposes,evenifthestatementsarenotincludedinthefactoranalysis.Table3.StandaloneStatements

AttitudinalStatementsIwouldpaymoneytoreducemytraveltime.ItismoreimportantformenthanforwomentohaveAhigh-payingcareer.Atwork,itisperfectlyfineforwomentohaveauthorityovermen.IavoiddoingthingsthatIknowmyfriendswouldnotapprove.Backgroundmusic/radio/TVistoodistractingforme.Ilikestickingtoaroutine.ItrytomakegooduseofthetimeIspendcommuting.Ilikeridingabike.Ifeelpositivelyaboutthelevelofinvestmentoccurringinmylocalroadsandlocaltransit.TheairqualityintheregionwhereIliveconcernsme.Havingchildrenmeansyouhavetohaveacar.Individualsshouldgenerallyputtheneedsofthegroupaheadoftheirown.Itisprettyhardformyfriendstogetmetochangemymind.IamuncomfortablebeingaroundpeopleIdonotknow.Aftercarefulanalysisoftheresultsandexcludingthestandalonestatements,weperformedthefactoranalysisonthe52remainingstatements.Basedonmultiplecriteria,atotalnumberof17factorswereidentified.Thefollowingsubsectionssummarizethecriteriathatwereusedtodeterminetheoptimalnumberoffactors.

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Eigenvaluegreaterthanone(orvalueclosetoone)Table4showstheinitialeigenvaluesfordifferentnumberoffactors.Asindicatedinthistable,16factorshaveeigenvaluesgreaterthan1.00and10factorshaveeigenvaluesbetween0.99and0.7.Hence,theoptimalnumberoffactorscouldbeintherangebetween16and26.Table4.Eigenvalues

Factor InitialEigenvalues Factor Initial

Eigenvalues Factor InitialEigenvalues

1 4.88 21 0.81 41 0.482 3.66 22 0.80 42 0.463 2.84 23 0.77 43 0.444 2.69 24 0.75 44 0.445 2.21 25 0.74 45 0.436 1.81 26 0.72 46 0.427 1.64 27 0.68 47 0.408 1.57 28 0.68 48 0.409 1.51 29 0.65 49 0.3710 1.28 30 0.64 50 0.3511 1.26 31 0.63 51 0.3412 1.20 32 0.62 52 0.2113 1.12 33 0.59 14 1.10 34 0.59 15 1.03 35 0.56 16 1.01 36 0.55 17 0.91 37 0.55 18 0.90 38 0.54 19 0.84 39 0.53 20 0.84 40 0.50

Screetest(i.e.elbowrule)Thesecondcriteriaforchoosingthenumberoffactorswasthescreetest.Accordingtothiscriterionthepercentofvarianceexplainedbytheindividualfactorswould“leveloff”asthesolutionreachesthemostappropriatenumberoffactors.Beyondthisnumberoffactors,additionalfactorswouldaccountforrandomerrors.Thisruleshouldbeappliedtoafinalun-rotatedsolution.Usingall52statementsusedinthefactoranalysis,weplottedthechangesinvarianceexplainedbydifferentnumbersoffactor.Theresultindicatesthatthedesirablenumberoffactorscanbebetween10and17(wherethepercentofvarianceexplainedbyindividualfactorsstartedtoleveloff).StrengthoftherelationshipInthiscriterionwecheckedwhethertherotatedfactorloadingsaregreaterthan|0.3|.Toidentifynon-trivialfactorsthatcouldbeobtained,researchersusedifferentcut-offs.Some

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researchersusemorerelaxedcriteriasuchasacut-offof|0.2|,whichseemsverylow,andsomeothersuseverystringentcriteriasuchasacut-offof|0.7|.Inourstudy,weusedacut-offvalueof|0.3|.Interpretability“Variablesthatloadnear1areclearlyimportantintheinterpretationofthefactor,andvariablesthatloadnear0areclearlyunimportant.Simplestructurethussimplifiesthetaskofinterpretingthefactors”(BryantandYarnold,1995,page132-133).Thus,forsimplicitywecontrolledthatallloadedstatementsconceptuallyconveyasimilarcontent(construct).Asdiscussedearlier,forexample,wehadtoexcludethemasculinity/machismostatements,whichloadedonthepro-environmentalpolicyfactor(withnegativedirection):thesetwogroupsofstatementsseemtocaptureratherdifferentconstructs.Table5.ModeratelyCorrelatedFactors

Factororvariable FactororVariable CorrelationPro-environmentalpolicies Mustowncar -0.356

Pro-environmentalpolicies Responsivetoenvironmentaleffectandpriceoftravel 0.301

Usingtheabovecriteriaensurestherobustnessandvalidity(convergentvalidityanddiscriminantvalidity)ofthefactorsolution.Furthermore,duetoexistenceofcorrelationamongfactors(seeTable5forthemosthighlycorrelatedfactors,withcorrelationshigherthan|0.3|),wechoseanobliquerotation:obliquerotationmayshowsomelevelsofcorrelationamongfactors,whichisnotidealinstatisticalanalysis,butitcancaptureindividualfactorsthatarebettersupportedbythedata,becauseitallowstohavefactorsthatarenotorthogonaltooneanother.Thefactoranalysisextractionmethodthatwasusedforthefinalsolutionwasthemaximumlikelihoodmethod.Thismethodproducesparameterestimatesthataremostlikelytohaveproducedtheobservedcorrelationmatrixifthesampleisfromamultivariatenormaldistribution(asreportedintheIBM’sSPSSManual).Maximumlikelihoodallowsthecomputationofawiderangeofgoodnessoffitmeasuresandsignificancetests.Thegoodnessoffittestofthefinalfactorsolutionwasstatisticallysignificant,withavalueofchi-squareof1336.94,andanumberofdegreesoffreedomequalto578.TheresultsoffinalfactorsolutionarepresentedinTable6.

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Table6.FinalResultsoftheFactorAnalysisFactorsandLoadedstatements FactorLoadingPro-storeshopping Iprefertoshopinastoreratherthanonline. 0.998Ienjoyshoppingonline. -0.413Pro-environmentalpolicies Weshouldraisethepriceofgasolinetoreducethenegativeimpactsontheenvironment. 0.937Weshouldraisethepriceofgasolinetoprovidefundingforbetterpublictransportation. 0.841Thegovernmentshouldputrestrictionsoncartravelinordertoreducecongestion. 0.331VarietySeeking Iliketryingthingsthatarenewanddifferent. 0.592Ihaveastronginterestintravelingtoothercountries. 0.405Pro-exercise Theimportanceofexerciseisoverrated. -0.822Gettingregularexerciseisveryimportanttome. 0.587Pleasantcommute Mycommuteisstressful. -0.802Mycommuteisgenerallypleasant. 0.689Trafficcongestionisamajorproblemformepersonally. -0.544ThetimeIspendcommutingisgenerallywastedtime. -0.501Gettingstuckintrafficdoesnotbothermethatmuch. 0.305Pro-suburban Iprefertoliveinaspacioushome,evenifitisfartherfrompublictransportationandmanyplacesIgoto. 0.764

IprefertoliveclosetotransitevenifitmeansIwillhaveasmallerhomeandliveinamorecrowdedarea. -0.69

Iliketheideaoflivingsomewherewithlargeyardsandlotsofspacebetweenhomes. 0.428Iliketheideaofhavingdifferenttypesofbusinesses(suchasstores,offices,restaurants,banks,andlibrary)mixedinwiththehomesinmyneighborhood. -0.357

Responsivetoenvironmentaleffectandpriceoftravel TheenvironmentalimpactsofthevariousmeansoftransportationaffectthechoicesImake. 0.739

Iamcommittedtousingalesspollutingmeansoftransportationasmuchaspossible. 0.598ThepriceoffuelaffectsthechoicesImakeaboutmydailytravel. 0.532Toimproveairquality,Iamwillingtopayalittlemoretouseahybridorotherclean-fuelvehicle. 0.384

EstablishedinLife I’malreadywell-establishedinmyfieldofwork. 0.704I’mstilltryingtofigureoutmycareer(e.g.whatIwanttodo,whereI’llendup). -0.636Iamgenerallysatisfiedwithmylife. 0.387Longtermsuburbanite Ipicturemyselflivinglong-terminasuburbansetting. 0.819Ahouseinthesuburbsisthebestplaceforkidstogrowup. 0.568Ipicturemyselflivinglong-terminanurbansetting. -0.310Mustowncar Idefinitelywanttoownacar. 0.697Iamfinewithnotowningacar,aslongasIcanuseorrentoneanytimeIneedit. -0.500Carasatool

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Thefunctionalityofacarismoreimportanttomethanitsbrand. 0.579Tome,acarisjustawaytogetfromplacetoplace. 0.480Climatechangeconcerned Greenhousegasesfromhumanactivitiesarecreatingmajorproblems. 0.796Anyclimatechangethatmaybeoccurringispartofanaturalcycle. -0.656ItispointlessformetotrytoohardtobemoreenvironmentallyfriendlybecauseIamjustoneperson. -0.307

Technologyembracing HavingWi-Fiand/or3G/4GconnectivityeverywhereIgoisessentialtome. 0.609Gettingaroundiseasierthaneverwithmysmartphone. 0.492Learninghowtousenewtechnologiesisoftenfrustrating. -0.359Technologycreatesatleastasmanyproblemsasitdoessolutions. -0.310Monochronic(Pro-monotasking) It’sbesttofinishoneprojectbeforestartinganother. 0.518Iliketojuggletwoormoreactivitiesatthesametime. -0.346Time/modeconstrained Myschedulemakesithardorimpossibleformetousepublictransportation. 0.580IamtoobusytodomanythingsI’dliketodo. 0.443Mostofthetime,Ihavenoreasonablealternativetodriving. 0.388Pro-social Socialmedia(e.g.Facebook)makesmylifemoreinteresting. 0.505Peoplearegenerallytrustworthy. 0.442Ienjoythesocialaspectsofshoppinginstores. 0.323Materialism Iwould/doenjoyhavingalotofluxurythings. 0.441IprefertominimizethematerialgoodsIpossess. -0.412Forme,alotofthefunofhavingsomethingniceisshowingItoff. 0.387Iliketobeamongthefirstpeopletohavethelatesttechnology. 0.380Tome,owningacarisasymbolofsuccess. 0.316TheBartlettmethodwasusedforgeneratingthefinalstandardizedfactorscores.Theresultingscoresfromthismethodareexpectedtobeunbiasedand,therefore,moreaccuratereflectionsofthecases’locationonthelatentcontinuuminthepopulation.

AdoptionofTechnology,IndividualAttitudesandMobilityChoicesofMillennialsvs.GenXersTheanalysisoftheCaliforniaMillennialsDatasetallowsustoinvestigateseveraltrendsassociatedwiththepersonaltravel-relatedattitudesofmillennialsandtheirmeasuresoftravelbehavior,andcomparethemwiththeattitudinalandbehavioralpatternsobservedamongmembersoftheolderGenerationX.Inthispartofthereportwesummarizetheobservedtrendsin(1)theuseofmoderntechnologies,socialmediaandsmartphoneapplicationsfortravelschedulingpurposes,(2)thedistributionofattitudinalpatterns,asmeasuredbythefactorscoresthatwerecomputedforallrespondentsincludedinthedataset,and(3)measuresoftravelbehaviorandadoptionofsharedmobilityservices,averageaccessibilityintheplaceofresidenceandadoptionofmultimodaltravelamongvarioussegmentsofthepopulation.In

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particular,wefocusondifferencesobservedamongvariousgroupsofmillennialsvs.olderadults,basedonthelocationwhereindividualslive.Figure3showstheuseofsocialmediasuchasFacebooktocoordinatetravelfornon-workactivitiesbyagegroup(millennialsvs.GenerationX)andneighborhoodtype(urban,suburbanandrural)wheretheindividuallives.Notsurprisingly,millennialsaremoreinclinedtofrequentlyusesocialmediatocoordinatefortheirnon-workrelatedtravel,withurbanmillennialsbeinginparticulartheheaviestadoptersoftheseservicestocoordinatetheiractivities.

Figure3.Theuseofsocialmediatocoordinatetravelbyagegroupandneighborhoodtype

Millennialsalsoreportedthattheyusesmartphoneinconnectionwiththeirdailytravelmoreoftencomparedtotheiroldercounterparts.Thefollowingsetoffiguressummarizestheuseofsmartphonetochecktrafficconditions(Figure4),checkwhenabusortrainarrives(Figure5),decidewhatmodeorcombinationofmodestouse(Figure6),learnhowtogetto/explorenewplaces(Figure7),andnavigateinrealtime(Figure8).Inparticular,andconsistentwithexpectations,urbanpopulationsarefoundtousetheirsmartphonemoreoftenforalltheseactivitiesbothamongMillennialsandGenXers.

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Figure4.Useofsmartphonetochecktrafficandtoplanthetravelrouteordeparturetimeby

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Figure5.Useofsmartphonetocheckwhenabusortrainwillbearrivingbyagegroupand

neighborhoodtypeThedifferencesacrossneighborhoodtypesareparticularlylargefortheuseofsmartphonetechnologytocheckwhatmodesoftransportation,orcombinationsofmodes,touse,whichislikelytobeaneffectoftheavailabilityofmultipletraveloptionsindenserurbanareas.Inlatersectionsofthereport,wewillreturntodiscussingthemeasuresoftravelaccessibility,bymode,forthemembersofthevariousgenerations.Weplantofurtherinvestigate,infuturestepsoftheresearch,howtheuseofthesetechnologies,andthevariouslevelsofaccessibility

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intheareaswhereindividualslive,affecttheirtravelpatterns,anissueofsignificantimportancetoplanningprocesses.

Figure6.Useofsmartphonetodecidemeansoftransportationtousebyagegroupand

neighborhoodtype

Figure7.Useofsmartphonetolearnhowtogettoanewplacebyagegroupand

neighborhoodtype

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Figure8.Useofsmartphonetonavigateinrealtimebyagegroupandneighborhoodtype

InvestigatingMillennials’AttitudestowardsTransportationandTechnologyThissectiondescribesthedifferingattitudinalprofilesobservedamongmillennialsandmembersoftheGenerationXbytheneighborhoodtypetheyliveinusingthecomputedfactorscores.Personalattitudesandpreferencesarelikelytobeimportantfactorsaffectingindividualchoicesrelatedtohousing,travelandactivityscheduling.Still,todate,informationaboutindividualattitudes,preferences,andlifestylesisrarelycollectedintransportationsurveys.Inthissection,weexplorehowaverageattitudesdifferamongvarioussegmentsofthepopulationofmillennialsandGenXerswholiveindifferentneighborhoodtypes,withrespecttoseveralconstructsthatwereexploredintheattitudinalsectionofthesurvey,andthroughthefactoranalysispresentedinthepreviouschapter.Thenextsetoffigurespresentstheaveragefactorscores(and95%confidenceintervals)forvariousgroupsofindividuals,classifiedbyagegroupandneighborhoodtype(urban/non-urban)inwhichtherespondentslive.Itisimportanttoremindthereadersthat,asallfiguresinthissectionreportinformationforthestandardizedfactorscores(e.g.withzeromean,andvarianceequalto1),any(eventual)differencesacrossgroupsshouldbeevaluatedaccordingly.Forexample,ifagrouphasamoderatelypositiveaveragefactorscoreforthepro-environmentalpolicyfactorscores,thatmeansthattheindividualsthatbelongtothatgroup,onaverage,tendtohavestrongerpro-environmentalpolicyattitudes,comparedtotheaveragefortheentiresample(whosemeanforthisvariableiszero).

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Accordingly,thefigurespresentedinthissectionshouldnotbeinterpretedintermsofwhatindividualshaveacertainattitudinalcharacteristics(e.g.whatgroupsare“pro-environmentalpolicy”)but,rather,inrelativetermsasacomparisonacrossgroups(e.g.thefigureshelpanswerthequestion“aretheindividualsthatbelongtotheyoungergenerationsmorelikelytohavehigher“pro-environmentalpolicy”attitudesthanthosethatbelongtotheoldergeneration?Andwhatabouturbanvs.suburbanresidents?”).Similarly,inthosecasesinwhichallindividualsinthesampleeventuallyshareasimilarattitudetowardsatopic(e.g.positive“pro-environmentalpolicy”attitudes),thecomparisonacrossgroupsoftheaveragevaluesforthestandardizedfactorscoreshelpsdistinguishwhatgroupsofindividualstendtohaveevenstrongerattitudes(agreeevenmorethanothers)withsuchattitudinalconstruct.

Figure9.Average“pro-environmentalpolicy”factorscorebyagegroupandneighborhood

type(95%confidenceintervalsarereportedinthefigureforeachgroup)Figure9presentsthedifferencesintheattitudestowardpro-environmentalgovernmentpolicy,asmeasuredbytheaveragefactorscorethatwasextractedinthefactoranalysisforindividualsfrombothgenerationsthatliveinurbanvs.non-urbanareas:individualswithahigheraveragefactorscoretendtohavehigherdegreeofagreementwiththefollowingstatements:“We

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shouldraisethepriceofgasolinetoreducethenegativeimpactsontheenvironment.”,“Weshouldraisethepriceofgasolinetoprovidefundingforbetterpublictransportation.”and“Thegovernmentshouldputrestrictionsoncartravelinordertoreducecongestion.”Urbanrespondentsofallagesappeartobehighersupportiveofpro-environmentalpolicies,whilenon-urbanresidents’agreementwiththesestatementsappearstodeclineastheageofrespondentsincreases.Urbanresidents,acrossallagegroups,alsopresentmoreheterogeneityforthisattitudinaldimension,asshownbythelargerconfidenceintervalsaroundthemean.Next,Figure10showstheaveragevaluesforthevarietyseekingattitudinalfactorscore,byagegroupandneighborhoodtype.Thisfactorcapturesindividuallevelsofagreementwithstatementsconsistingof“Iliketryingthingsthatarenewanddifferent”and“Ihaveastronginterestintravelingtoothercountries”.Urbanrespondentshavehigherscoresacrossagegroups,particularlyintheagerangesof25to34and35to44.

Figure10.Average“varietyseeking”factorscoresbyagegroupandneighborhoodtype(95%

confidenceintervalsarereportedinthefigureforeachgroup)

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Again,muchlargervarianceisobservedamongurbandwellers,probablyasthecombinedeffectoftheheterogeneityassociatedwiththesegroupsofindividuals,aswellasthesmallersamplesizesthatareavailablefortheurbansubsamples.6Individualsinthehighestagegroup(45-50)arethosethathavethelowestvaluesforthisfactorscore.

Figure11.Average“responsivetoenvironmentaleffectsandpriceoftravel”factorscorebyagegroupandneighborhoodtype(95%confidenceintervalsarereportedinthefigurefor

eachgroup)Figure11reportstheresponsivenessoftravelerstopriceandenvironmentaleffectoftransportation.Thosethathaveahighervalueforthisfactorscoretendtoagreewiththefollowingstatements:“TheenvironmentalimpactsofthevariousmeansoftransportationaffectthechoicesImake”,“Iamcommittedtousingalesspollutingmeansoftransportationas

6Urbanresidentsincludevariousgroupsofindividualswithdifferentlifestyles,includinggroupsofindividualswhoareinatransientstageoftheirlife,youngerindividualswhoarestilldevelopingtheirtrainingandeducation,individualsthatlivewithotherroommatesandhousemates,temporaryresidents,professionalsandotherhighly-educatedworkers,youngcoupleswithnochildren,membersofminorities,etc.Theproportionoftemporaryresidents(andtenantswhorenttheirhousingunits)isusuallyhigherinurbanareas,andtheaverageturnoverofresidentsinahousingunitisfaster.Inaddition,awidevarietyofurbanneighborhoodsexist,eachwithdifferentcharacteristicsandvariouslevelsofaccessibilitybyvarioustransportationmodes.

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muchaspossible”,“ThepriceoffuelaffectsthechoicesImakeaboutmydailytravel”and“Toimproveairquality,Iamwillingtopayalittlemoretouseahybridorotherclean-fuelvehicle”.Thisfactorcapturesrespondents’willingnesstochangetheirtravelmodebasedonboththeenvironmentalimpactsoftransportationandgasprice.AsindicatedinFigure11,urbanrespondentsofallagegroupshavehigheraveragefactorscoresthannon-urbanrespondents.Interestingly,non-urbanrespondents’tendencytoagreewiththesestatementsappearstodeclinebyagegroup,withtheindividualsbetween35and50yearold(GenXers)agreeingtheleastwiththesestatements.However,amongurbanrespondents,theaveragefactorscoreappearsrelativelyconstantbyagegroup.Thismaysuggestthaturbanrespondentsofallagesviewtheenvironmentpositivelyandconsidertheenvironmentalimpactsoftransportation-relateddecisionaswellaspriceoffuelwhenamakingtransportationchoices.Thismaybealsoaffectedbytheavailabilityofmoreoptions(i.e.transitservices,bikelanesandshorterdistancesthatcanbecoveredwithvariousmodes).Further,thisattitudinalfactorscoremightsignalthebehaviorofindividualsthatmayeventuallyself-selecttoliveinanurbanneighborhoodtypeduetotheseunderlyingpreferences(e.g.theymovedtoanareathatbettermatchestheirpreferences).Figure12showsthedifferencesintheaverageclimatechangeconcernfactorscorebyagegroupandneighborhoodtype.Thosethathavehighervaluesforthisfactorscoretendedtoagreewiththestatement“Greenhousegasesfromhumanactivitiesarecreatingmajorproblems”,andtendedtodisagreewiththefollowingstatements:“Anyclimatechangethatmaybeoccurringispartofanaturalcycle”,and“ItispointlessformetotrytoohardtobemoreenvironmentallyfriendlybecauseIamjustoneperson.”Thepatternofresponsesissimilartothefactormeasuringtheagreementwiththegovernmentintervention,whereurbanrespondentshavealmostuniformlyhigherscoresforthisfactor,whilefornon-urbanrespondentsexpresslowerconcernforclimatechange,onaverage.Differencesbetweenurbanandnon-urbanrespondentstendtoincreasewithage.

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Figure12.Average“climatechangeconcerned”factorscorebyagegroupandthe

neighborhoodtype(95%confidenceintervalsarereportedinthefigureforeachgroup)Figure13reportstheaveragevaluesfortheestablishedinlifefactorscorebyagegroupandareaswheretherespondentslive.Thisfactorcapturesrespondents’opinionabouttheirlifestagethroughtheirlevelofagreementwiththestatements“I’malreadywell-establishedinmyfieldofwork”,“Iamgenerallysatisfiedwithmylife”,and“I’mstilltryingtofigureoutmycareer(e.g.whatIwanttodo,whereI’llendup).”Itisnotsurprisingtoseethatasindividualsbecomemoreestablishedintheirlife,theirlevelofsatisfactionincreases(althoughthisseemstocounteractthestereotypeoftheoptimisticmillennialgeneration,whothinkpositiveeveniftheyareinatransientstageoftheirlife,asoftenreportedbythemedia).

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Figure13.Average“establishedinlife”factorscorebyagegroupandneighborhoodtype

(95%confidenceintervalsarereportedinthefigureforeachgroup)Bothlifesatisfactionandstabilityincreasesbyage.BothyoungerandoldermillennialstendtohaveloweraveragescoresthanmembersofGenerationX.Thisisunsurprisinggiventhatthemillennialsareoftenunderemployedandinmanycasesstillnotindependent(livingwiththeirparents),butlargedifferencesareobservedbetweenyoungandoldmillennials,withtheurbanmillennialshavingthelowestaveragescoresforthisfactor.Alsoforthisfactor,muchlargervarianceisobservedamongurbandwellers,evenifthey,onaverage,havehigherscoresthantheirnon-urbancounterparts.Figure14reportstheaveragefactorscoreandconfidenceintervalforthelong-termsuburbanitelifestylefactorscore.Thosethathavehigherscoresforthisfactortendtoagreewiththefollowingstatement“Ipicturemyselflivinglong-terminanurbansetting”andtheytendtodisagreewith“Ipicturemyselflivinglong-terminasuburbansetting”and“Ahouseinthesuburbsisthebestplaceforkidstogrowup.”

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Figure14.Average“long-termsuburbanite”factorscorebyageandneighborhoodtype(95%

confidenceintervalsarereportedinthefigureforeachgroup)Ingeneral,inclinationtowardsuburbanitelifestyleislowerforindividuallivinginurbanneighborhoodcomparedtotheircohortlivinginsuburbanorruralareas.Notsurprisingly,GenXerswholiveinsuburbanareashavethehighestaveragescoresforthisfactor.Veryinterestingly,andsomewhatunexpectedly,though,thetrendamongmillennialsshowthatmanymillennialsstillseethemselvesliving“longterm”inasuburbanarea.Thisfindinghasextremelyimportantplanningimplications:ifconfirmedbyfuturedecisionsaboutresidentiallocation,thetrendwouldconfirmthatthehigherpreferenceforcentralurbanareasamongmillennialsmightbeonlyatransitionassociatedwiththeirstageinlife.Similarly,thehopeofmanypolicy-makersthatmillennialsmightcontinuetoembraceurbanlifestylesandcontinuetosupporttheregenerationofthecentralareasofcitiesalsoastheyagemightnotbefullysupported,withimportantimplicationsonthefuturedemandforhousingandtravel.Figure15presentstheaveragefactorscoreandconfidenceintervalforthe“mustownacar”factor.Thosethathavehighscoresforthisfactortendedtoagreewiththefollowingstatement:“Idefinitelywanttoownacar”anddisagreewiththestatement:“Iamfinewithnotowningacar,aslongasIcanuseorrentoneanytimeIneedit”.

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Figure15.Average“mustowncar”factorscorebyageandneighborhoodtype(95%

confidenceintervalsarereportedinthefigureforeachgroup)Exceptyoungermillennials,theurbanrespondentsofallagegroupstendtodisagreewiththisfactor,indicatingthattheyarelessinclinedtoownacar.Fornon-urbanrespondents,carownershipattitudesappeartobestrongerwithage.Ingeneral,membersoftheGenerationXhaveahigherpreferencetowardsowningacarthanmillennials.Theurbanpopulationinthecentralagegroups(25-34and35-44)havethelowestscoresforthisfactor,thussuggestingthatthesegroupsdonotrecognizelargeimportancetoowningacar,aslongastheycanaccesssufficientmobilityservicesthroughotherchannels.However,theratherhighscoresforthisfactoramongyoungmillennials(agroupthatisfoundtohavelowercarownershiplevels)seemstoconfirmthatformanyindividualsinthisgroup,carownershipisstillseenhashavingavalue,evenifthecurrentlowercarownershiplevelsmightbeassociatedwithtemporaryconditions,suchaslowerincome,studentstatusandlackofemployment.

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Figure16reportstheaveragefactorscoreandconfidenceintervalforthecarsasatoolfactorscore.Thisfactorcapturestherespondents’levelofagreementwiththestatements“Thefunctionalityofacarismoreimportanttomethanitsbrand”and“Tome,acarisjustawaytogetfromplacetoplace”.Alsointhiscase,theloweraveragefactorscoreforyoungmillennialswholiveinurbanareasseemstosuggestthattheirlowerlevelsofcarownershipareonlyatemporarystatus.

Figure16.Average“carasatool”factorscorebyageandneighborhoodtype(95%confidence

intervalsarereportedinthefigureforeachgroup)Figure17presentstheaveragefactorscoreandconfidenceintervalcapturingrespondents’inabilitytouseothertravelalternativesduetotheirtimeandtravelmodeconstraintsimposedbyeithertheirbusyscheduleorunavailabilityofdifferentoptionsfortraveling.Thisfactorisbasedonthethreeattitudinalstatements:“Myschedulemakesithardorimpossibleformetousepublictransportation,”“IamtoobusytodomanythingsI’dliketodo,”and“Mostofthetime,Ihavenoreasonablealternativetodriving”.

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Amongurbanresidents,oldermillennialstendtohavehigheraveragescoresforthisfactor,whileamongnon-urbanresidents,theoldermembersofGenerationXhavethehighestaveragescores.Thismaybeduetophysicalconstraintsorotherliferesponsibilities,suchashavingchildren.

Figure17.Averagetimeandmodeconstrainfactorscorebyageandneighborhoodtype(95%

confidenceintervalsarereportedinthefigureforeachgroup)Figure18reportstheaveragefactorscoreandconfidenceintervalfortherespondents’feelingsregardingtheadoptionoftechnology.Thisfactorcapturesthetechnologicalembracementconstructthroughthestatements“Learninghowtousenewtechnologiesisoftenfrustrating”(withnegativesign),“Technologycreatesatleastasmanyproblemsasitdoessolutions”(withnegativesign),“HavingWi-Fiand/or3G/4GconnectivityeverywhereIgoisessentialtome”and“Gettingaroundiseasierthaneverwithmysmartphone.”Respondentsshowedaclearpatternwithdistinctivefeaturesbetweenurbanandnon-urbandwellers.Forurbanresidents,thefactorscoreispositiveorclosetozero–indicatingeitherpositiveorneutralfeelingsabouttheroleoftechnologyacrossallagegroups.Fornon-urban

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residentstechnologyexcitementdecreaseswithage.Youngmillennials(18-24)havehigherenthusiasmabouttechnology,whileoldmillennials(25-34)haveslightlylowerpropensitytowardstechnology,andthemembersofGenerationXreportthelowestembracementoforrelianceontechnology.

Figure18.Average“technologyembracing”factorscorebyageandneighborhoodtype(95%

confidenceintervalsarereportedinthefigureforeachgroup)Thelastfactorscorethatisdescribedinthisreportismaterialism.Figure19showsthedifferencesintheaveragescoreforthisfactorbyagegroupandneighborhoodtype.Thosethathavehighervaluesforthisfactortendtoagreewiththefollowingstatements:“Iwould/doenjoyhavingalotofluxurythings”,“Forme,alotofthefunofhavingsomethingniceisshowingItoff”,“Iliketobeamongthefirstpeopletohavethelatesttechnology”,“Tome,owningacarisasymbolofsuccess”.Also,thosewithhighfactorscorestendtodisagreewiththestatement:“IprefertominimizethematerialgoodsIpossess”.Theaveragescoresforthisindextendtodecreasewiththeincreasingageoftherespondents.Youngmillennialsinbothurbanandnon-urbanareashavethehighestaveragescores,perhaps

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duetotheirinterestinhavingthelatestgadgets,ortheirstageoflife–wherefewhavechildrenormortgagesthatpreventthemfromacquiringorwantingtoacquirematerialgoods.OlderGenerationXmembershavethelowestaveragescoresforthematerialismfactor.Infuturestagesoftheresearch,itwillbeveryinterestingtoexplorehowthemembersofthefollowingGenerationZ(under18yearolds,asoftoday),willbehaveinfutureyears,comparedtothesegenerationsthatwearestudying.Inaddition,non-urbanrespondents(apartfromtheyoungmillennials)tendtohaveloweraveragevaluesforthisfactor,andthushavelowermaterialisticattitudes,thantheirurbancounterparts.

Figure19.Average“materialism”factorscorebyageandneighborhoodtype(95%confidence

intervalsarereportedinthefigureforeachgroup)

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TravelBehaviorandtheAccessibilityofthePlaceofResidenceInthePartIreportforthisresearchstudy(Circellaetal.,2016b),wediscussedanumberofobserveddifferencesinthetravelbehaviorofmillennialsvs.theoldercounterpartsbelongingtotheprecedingGenerationX.Amongtheobserveddifferences,theanalysisofthecollecteddatahighlightedthatmillennialstendtodriveless,andthisdifferencesholdsevenaftercontrollingfortheneighborhoodtypewheretherespondentslive.Further,alargerproportionofmillennialsreportnottohaveavaliddriver’slicenseatthetimetheycompletedthesurvey.Millennialsalsoarelesslikelytodriveduringtheircommute,andmoreoftenuseactivemodesoftransportation,includingwalkingandbiking,aswellasridingpublictransit.Amongtheindividualsthatphysicallycommutetoworkatleastoneperweek,millennialstendtomorefrequentlyengageintravelmultitasking(i.e.carryoutanactivitywhiletraveling)duringtheircommute,comparedtoGenXersinallregionsofCalifornia.Thehigheradoptionofmultitasking,whichcorrelateswiththelargeradoptionofICTdevicesamongmillennials,mightbeassociatedwithadifferentevaluationoftheutilityoftravelalternatives,andthereforeexplainatleastinparttheobserveddifferencesinmodechoice.Oneofthereasonsthatmaybebehindtheobserveddifferencesintravelpatternsbetweenmembersofthedifferentgenerationrelatestothecharacteristicsofthebuiltenvironmentoftheresidentiallocationandthework/schoollocationwhereindividualstravel.Forexample,thefollowingTable7andTable8respectivelyreporttheaveragefrequencyofuse(byday)ofon-demandrideservicessuchasUberLyftandofcar-sharingservicessuchasZipcarorTuro.Table7.AverageFrequencyofUseofUber/LyftbyGenerationandNeighborhoodType

Millennials(N=1157) GenerationX(N=998)

NeighborhoodType Rural 0.004 0.003Suburban 0.010 0.007Urban 0.056 0.039Note:Numbersinthetablemeasuretheaveragenumberofper-capitatripsperdaybyneighborhoodtype(ordinalfrequencycategoriesweretransformedintodiscretenumbersoftripstocomputethedatainthistable)Whilecarsharingservicesarecertainlymorerarelyusedthanon-demandrideservicessuchasUberorLyft,Tables7-8reportsomesimilartrends,withresidentsofdenser,morecentrallocationsusingtheseservicesmoreoftenthansuburbanorruralresidents.Inalltheseareas,millennialstendtousesharedmobilityservicesmoreoftenthanGenXers.Thus,consideringalsothedifferentdistributionsoftheurbanvs.non-urbanpopulationsofmillennialsandolderpeers,acompositeeffectmightexplaintheadoptionofthesetrends:notonlymillennialsaremorelikelytoadopttheseservicesthanolderpeers,holdingthecharacteristicsoftheneighborhoodconstant,butmillennialsarealsomorelikelytoliveinurbanareas.Thejointdecisionsoftheresidentiallocationwhereanindividualdecidestolive,andthetypeoftravel

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behaviortheyhaveisanimportanttopictoexploreinordertoinvestigatethereasonsbehind,andtheimpactsofmillennials’decision.Table8.AverageFrequencyofUseofZipcar/TurobyGenerationandNeighborhoodType

Millennials(N=1157) GenerationX(N=998)

NeighborhoodType Rural 0.00211 0.00010Suburban 0.00202 0.00070Urban 0.00984 0.00098Note:Numbersinthetablemeasuretheaveragenumberofper-capitatripsperdaybyneighborhoodtype(ordinalfrequencycategoriesweretransformedintodiscretenumbersoftripstocomputethedatainthistable)Tounderstandtheimpactofbuiltenvironmentalcharacteristics,weintegratedourdatasetwithotherinformationusingthegeocodedself-reportedresidentiallocationaddress.Figures20-22,presenttheaveragevaluesofsomeresidentiallocationaccessibilitymeasuresforthedifferentagegroupandbydifferentmodes.Thefiguresrespectivelypresenttheaveragewalkscore,bikescore,andtransitscoreofmillennialsandgenerationXbyneighborhoodtype.

Figure20.Walkscorebyagegroupandneighborhoodtype

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Figure21.Bikescorebyagegroupandneighborhoodtype

TheaveragescoresobservedacrosstheresidentiallocationofGenXersandmillennialsareverysimilarwithinaneighborhoodtype.Forexample,theaveragewalkscore(Figure20)foranurbanmillennialwas80.8,comparedto80.4foramemberofGenerationXinanurbanarea(thoughmoremillennialstendtoliveinsuchneighborhoods,thanGenXers).However,largedifferencesinthewalkscoresarefoundacrossneighborhoodtypes:forexample,millennialswholiveinsuburbanareashaveanaveragewalkscoreof51.7,comparedtoaveragewalkscoreof30.7inruralareas.Thedifferencesinthebikescores(Figure21)wereslightlylesspronounced,duethemorehomogenouscharacteristicsofbikeaccessibility(e.g.manysuburbanneighborhoodsandruralareasareratherbike-friendly),forexamplewithmillennials’bikescoresrangingfrom70(urban)to56.7(suburban)to51.2(rural).Transitscoresshowedasimilarpattern,thoughwithamoresignificantdropintheaveragescoresinruralareas.Urbanmillennialshadanaveragetransitscoreof60,whilesuburbanmillennialshadanaveragescoreof35,andruralmillennialshadanaveragescoreof22.

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Figure22.Transitscorebyagegroupandneighborhoodtype

TherewerenosignificantdifferencesintheaverageaccessibilitymeasuredbythesescoresbetweenmillennialsandGenerationX,withtheonlyexceptionofthewalkscoresforruralmillennialswhichwere2percentagepointshigherthanthoseforruralGenXers,suggestingthatmillennialsmayliveinslightlymorewalkableruralareas.However,forthemostpart,millennialsandGenXershaveaverageaccessibilityscoreswithinapoint.AdoptionofMultimodalTravelBehaviorAspreviouslydescribedinthisreport,inordertoenrichtheCaliforniaMillennialsDatasetwithlandusedataavailablefromothersources,wedevelopseveralmeasuresofaccessibilityusingtwomainsourcesofdatathatwereimportedbasedonthegeocodedresidentiallocationoftherespondents:theSmartLocationDatabase(SLD)developbytheUSEnvironmentalProtectionAgencyandWalkscore.com.SLDdataprovidelandusemeasuresondensity,diversity,design,accesstotransit,anddestinationaccessibilityattheCensus2010blockgrouplevel(Ramsey&Bell,2014).Wecomplementedthesedatawiththewalkscores,bikescores,andtransitscoresavailablefromthecommercialwebsitewalkscore.com,whichreflectmoremicro-levelbuiltenvironmentcharacteristicsavailableatafinerlevelofspatialdetailthanthecensusblock

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groupandarebasedonrecentlyupdateddatasources.7FortherespondentsthatprovidedavalidstreetaddresswecomputedaccessibilitymeasuresbasedonthecensusblockgroupfortheSLDmeasures,andonthelatitudeandlongitudeoftheresidenceforthescoresobtainedfromWalkscore.com Inthisanalysis,wefurtherclassifiedmillennialsintwogroups:theindependentmillennialswhodonotlivewiththeirparents,andthedependentmillennialswholivewiththeirparents.Weassumethatindependentmillennialshavemoreflexibilityinchoosingtheirresidentiallocation,butdependentmillennialsareaffectedbytheirparentsintheirresidentialchoiceandmodechoiceforvarioustrips.FortherespondentsthatprovidedavalidstreetaddresswecomputedaccessibilitymeasuresbasedonthecensusblockgroupfortheSLDmeasures,andonthelatitudeandlongitudeoftheresidenceforthescoresfromWalkscore.com.Further,foreachrespondentinthedataset,wecomputedseveralmultimodalityindicesusinginformationonthemode(s)thattheindividualusedfortheirlastcommutetour.8Weclassifyrespondentsbasedontheirmono-vs.multi-modalitystatusasmono-car(i.e.individualswhodrovealoneorcarpooledfortheirentirecommutetour),mono-transit(i.e.individualswhoonlyusedpublictransportationservicessuchasbus,commuterrail,andlightrailfortheentiretyoftheircommutetour),mono-walk(i.e.individualswhoonlywalkedtoworkorschool),mono-bike(i.e.individualswhoonlybikedtoschoolorwork),andmono-other(i.e.individualsthatexclusivelyusedothermodesoftransportation,e.g.on-demandrideservices,ferry,etc.fortheircommute).Wealsodefinedtwointer-modalindicesforindividualswhousedmorethanonemodesduringtheircommutetour:intermodal-car(anindexthatidentifiesindividualswhousedacarastheirmaincommutemodeinconjunctionwithothersecondarymodes)andinter-modalgreen(thatidentifiesindividualswhousedanynon-carmodeastheirprimarymodeoftransportation,combinedwithothersecondarymodes). Wecomputedtheseindicesforallrespondentsthatcommutetoworkorschoolatleastonceperweek,andhaveavalidgeocodedaddress.Thesampleavailableforthisanalysisconsistsof483independentmillennials,320dependentmillennials,and584GenXers.Figure23reportsthesummarystatisticsforthetwolargestmetropolitanareasofCalifornia,SanFranciscoandLosAngeles,comparingtheaverageforfouroftheeightmultimodalityindicesthatwerecreatedandtheaverageaccessibilitymeasuresforthethreegroupsthathavebeenidentified.

7Therearesomelimitationsintheuseofthewalkscorewhencomparingdifferentneighborhoods:forexample,manycommunitieswherethehomeownersmaintaintheparks,communitycentersandotheramenitiesgetlowscoresfromWalkscore.combecausethefacilitiesarenotconsidered“public”,eventhoughanyonewholivesanywherenearhasaccessandthecommunitiesarenotgated.Despitetheselimitations,thescoreprovidesausefulmeasureofaneighborhood’swalkability,withastandardizedscorethatcanbeeasilycomparedacrosslocations.8Additionalmeasuresofmultimodalitywerecomputedfornon-commuting/leisuretrips,butarenotfurtherdiscussesinthisreport,forbrevity.

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Figure23.Accessibilitylevelandadoptionofmultimodality,bygenerationalgroup,in(a)the

SanFranciscoBayArea(MTC);and(b)GreaterLosAngelesregion(SCAG)Inbothregions,9independentmillennialshavethehighestvaluesforallaccessibilitymeasures.Importantdifferencesareobservedamongdependentandindependentmillennials.Dependentmillennialstendtoliveinareasthathavethelowestlevelsofaccessibilitybynon-carmodes,

9Thetrendsinbothregionsaresimilar,withtheonlyexceptionthatlevelsofaccessibilitybynon-automodesarehigherinSanFrancisco/MTC,whilethepercentageofmono-carcommuters,inparticularamongGenXers,ishigherinLosAngeles/SCAG.

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probablyduetotheresidentiallocationchosenbyothermembersofthehouseholds(e.g.youngadultswhostillwiththeirparents).Independentmillennials,ontheotherhand,aremoreoftenfoundtoliveinlocationswithhigheraccessibility.Suchlocationsaremoreconducivetotheadoptionofgreenerandnon-autocommutemodes(and/ormayreinforcethepropensityofyoungadultstousesuchmodes),asmoreoftendonebytheindividualinthisgroup.Attheotherendofthespectrum,GenXersrelyheavilyontheuseofcarsfortheircommute.Interestingly,inbothregionsGenXersarefoundtoenjoybettertravelaccessibilitythandependentmillennials.Thisseemstosignalthatatleastsomedependentmillennialstendtodrivelessandhaveamoremultimodaltravelbehaviordespitelivinginneighborhoodsthatarelessconducivetomultimodalityandtotheuseofnon-automodes.Severalexplanationscouldbebehindthisfinding,includingtheimpactoflowerincomeandweakereconomicconditions(whichconstitutepotentialconstraintstomillennials’useofprivatevehicles),reducedfamilyobligations(e.g.millennialswholivewiththeirparentsarelesslikelytohavetheirownchildrentoescorttoschoolorextracurricularactivities,thereforetheyhavefewerconstraintsofthistype,andmorespaceforindividualchoices),and/orhigherpropensitytowardssuchbehaviors.Mostlikely,acombinationofthesefactorsisbehindthesepatterns. Table9,below,summarizestheaccessibilitymeasuresandmultimodalityscoresthatwerecomputedforthevariousregionsofCalifornia.Next,Figure24summarizestheadoptionofmultimodalbehaviorbyregionofCaliforniaandsub-segmentofthepopulation.Insummary,accessibilityandmultimodalityarepositivelycorrelated:residentsofneighborhoodswithbetteraccessibilityaremoreoftenfoundtobemultimodalcommuters.However,millennials,andespeciallydependentmillennials,arefoundtomakethemostoftheirbuiltenvironmentpotential,eitherduetoindividualchoicesorthepresence(orlack)oftravelconstraints. Theyarelesslikelytobemono-driversandmorelikelytobemultimodalcommuters,eveniftheyliveinneighborhoodsthatarelesssupportiveofsuchbehaviors.Thissuggeststhattheconnectionbetweenthebuiltenvironmentandtravelpatternsmaydifferbygeneration:infuturestepsoftheresearchweplantofurtherinvestigate(andmodel)therelationshipsbetweenaccessibilityandmultimodalbehavioramongthemembersofthedifferentgenerations,whilecontrollingforotherfactorsaffectingresidentialandtravelchoices.FurtherinformationcanbefoundinCircellaetal.(2017).

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Table9.AverageaccessibilitymeasuresanduseofcommutemodesbyregionofCaliforniaandgenerationalgroup

Samplesize(N)

Housingunits/acre

People/acre

Jobs/acre Walkscore Bikescore Transitscore Always

car

Othermode(transit,walking,biking)

Morethanonemode

CentralValley IndependentMillennials 73 2.5 7.0 1.4 37.0 53.7 27.8 74.4% 7.3% 18.3%

DependentMillennials 35 3.0 8.5 2.2 41.0 55.9 30.9 60.0% 22.9% 17.1%GenXers 82 3.2 8.8 2.1 42.7 56.8 29.8 83.6% 9.6% 6.8%

MTC IndependentMillennials 179 11.1 24.9 14.9 66.6 77.1 56.7 56.6% 17.1% 26.4%

DependentMillennials 67 5.9 16.0 3.7 53.4 61.7 44.1 56.7% 14.9% 28.4%GenXers 129 8.7 19.6 7.9 60.3 70.7 51.8 72.6% 10.6% 16.8%

NorthernCaliforniaandRestofStateIndependentMillennials 53 3.5 8.9 2.6 47.6 82.6 32.2 60.4% 18.8% 20.8%DependentMillennials 26 2.4 6.8 1.5 30.9 52.3 18.3 80.8% 0.0% 19.2%GenXers 48 2.3 5.6 2.3 36.2 86.2 17.0 81.1% 11.3% 7.5%

SACOG IndependentMillennials 90 4.1 9.2 3.7 48.8 79.3 32.2 76.8% 13.7% 9.5%

DependentMillennials 32 3.4 8.8 1.7 41.3 66.0 28.9 68.8% 6.3% 25.0%GenXers 95 3.3 8.3 5.7 42.0 73.8 33.4 82.2% 11.1% 6.7%

SANDAG IndependentMillennials 114 6.4 14.0 5.2 51.0 50.3 38.4 73.8% 8.4% 17.8%

DependentMillennials 43 4.5 11.6 2.3 44.2 41.2 33.1 62.8% 7.0% 30.2%GenXers 107 7.0 15.0 7.5 57.7 54.1 41.3 80.7% 5.3% 14.0%

SCAG IndependentMillennials 156 7.5 17.8 11.9 62.3 62.2 51.3 68.0% 9.5% 22.5%

DependentMillennials 61 4.4 13.8 2.5 48.0 55.2 34.6 68.9% 6.6% 24.6%GenXers 169 6.6 16.0 5.1 57.3 62.7 42.8 84.0% 6.4% 9.6%

Total IndependentMillennials 665 6.6 15.2 8.1 54.8 63.6 44.0 68.3% 11.9% 19.8%

DependentMillennials 264 4.3 12.0 2.5 45.3 54.5 34.8 64.8% 10.2% 25.0%GenXers 630 6.1 14.1 5.8 52.8 62.9 42.0 79.8% 8.7% 11.4%

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Figure24.Adoptionofmultimodalbehavior,byregionofCaliforniaandsub-segmentofthe

population

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VehicleMilesTraveled

Aspointedoutintheliterature,millennialsmaytraveldifferently,andfordifferentreasons,thanpreviousgenerations.Inthissection,weinvestigatethereasonsaffectingmillennials’vehiclemilestraveled(VMT)whilecomparingthemwiththecorrespondingpatternsobservedamongthemembersoftheprecedingGenerationX.Asobservedinmanypreviousstudies,millennialstendtopostponemarriage,householdcreation,andchildbearing,andtheyhavefewertotalchildrenthanpreviousgenerations(PewResearchCenter2014;McDonald2015).AllthesepatternsmightcontributetolowerVMT.HouseholdswithoutchildrentendtohavelowerVMTthanthosewithchildren(Santosetal.2011;LeVine&Jones2012).However,oldermillennialsmayalreadyexhibitpatternssimilartooldergenerations,indicatingthatmillennialsmay“drivelater,ratherthandriveless”(Garikapatietal.2016).Surveysofmillennialsreportthatthemembersofthiscohortseemtohavestrongerpreferencefordenseurbanareas(PewResearchCenter2014;Polzinetal.2014;BRS2013;Zmudetal.2014),andaremorecommittedtoenvironmentalcauses(Hanksetal.2008;Strauss&Howe2000),whichmayalsocontributetoreducingVMT(Ewing&Cervero2010).ThebuiltenvironmentisastrongdeterminantofVMT:numerousstudieshaveconnectedpopulationdensity,employmentdensity,andregionaldiversity(amongotherdimensionsofthebuiltenvironment)withvehiclemilestraveled.Vehiclemilestraveledseemstobemoststronglyrelatedtomeasuresofaccessibilitytodestinations.Moregenerally,residentsofmoretraditionaldenseurbanneighborhoodstendtodrivelessthanthosethatliveinlessdensesuburbanneighborhoods(Santosetal.2011;Ewing&Cervero2001;Caoetal.2009b;Cervero&Duncan2003;Cervero&Duncan2006).However,itisunclearhowthiseffectsmayaffectfuturetraveldemand,asmillennialsageandmovetoadifferentstageoflife.Inotherwords,theoftenreportedmillennials’preferenceforurbanareasandreduceduseofpersonalvehiclesmightbeatemporarytrend,associatedwiththeirstageinlife,andmaynotbealastingtrend(Myers2016).Anotherwell-studiedcorrelateofVMTisvirtualmobilityoradoptionofinformationcommunicationtechnology(ICT),whichisanotherfactoroftenindicatedaspotentiallyaffectingtheamountofindividuals’travelandmodechoice(Mokhtarian2009;Salomon&Mokhtarian2008;Contrino&McGuckin2006,CircellaandMokhtarian,2017).Millennialsaremorelikelytoadoptvirtualmobilityoptions,suchasonlineshopping,telecommuting,ride-sharing,andotherreal-timetransportationservices(Blumenbergetal.2012;Zipcar2013).McDonald(2015)suggestedthatthemillennialuseofvirtualmobilitymightexplainasignificantportioninthedropindrivingobservedamongthemembersofthisgeneration.Moregenerally,theubiquityofthesmartphoneadoptionalongwithincreasesinmobilityoptionshavecreatedaclassof“real-timeriders”(ITSAmerica2015)thatspansallcohorts.Still,millennialsarethefirstgenerationofso-called‘digitalnatives’(Prensky2001),havinggrownupwiththeinternet,andarelikelytobetheusersthatmostbenefitfromtheavailabilityofmoderntechnologiesandemergingtransportationtechnologies(includingthemodernsharedmobilityservices).This

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mayapplytofollowinggenerations,too,butisonlybecomingapparentwithmillennials(Lyons2014).Itisthusimportanttostudythecohorteffectsandexploretheimpactoftraditionalexplanatoryvariablessuchasthebuiltenvironmentandsocioeconomicfactorsandtheirlikelyeffectsonthetravelbehaviorofthemembersofdifferentgenerations.Inthissection,westudytheself-reportedVMTofmillennialsandGenerationX,andinvestigatetheimpactofmultipleexplanatoryvariables,includingsociodemographics,landusecharacteristicsandindividuals’attitudes,ontheself-reportedVMT.DependentVariable:Self-ReportedWeeklyVMT

Thefollowingsectionspresenttheresultsofamodelthatwasestimatedusingtheself-reportedweeklyVMTasthedependentvariable.TheweeklyVMTreportedbyindividualsrangedfrom0to1000,withameanof115milesperweek,andamedianof75.Informationforthisvariable,whichislikelytobeaffectedbysomelimitationstypicalofanyself-reportedmeasuresoftravelbehavior(e.g.eventualunder-reportingoftripsandVMT)wascollectedinasimilarwayforallrespondentsinthedataset.Weusedalog-transformationoftheVMTvariable,inordertoreducethedeviationfromthenormalityofthevariable.DuetothepresenceofcaseswithVMTequaltozero,andinordertoavoidtakingthelogarithmofzero,thefinaldependentvariablethatwasusedinthemodelwasln(VMT+1),asoftendoneinsimilarmodelsintheliterature.ExplanatoryVariables

Sociodemographic

Weusedseveralsociodemographicvariablesinourmodel.Thevariablesusedincludedbothindividualandhouseholdcharacteristics.Further,inordertoallownon-linearrelationshipsofVMTwithage,wealsoincludedaquadratictermfortheageoftherespondent(i.e.“squaredage”wasalsoincluded)inthemodel.Itisexpectedthatvehicletravelincreasesasadolescentsbecomeadults,peaksduringadultage(30-60years)whenemploymentandchildrearingresponsibilitiesaregreatest,andthendeclinesasindividualsretireandage(LeVine&Jones2012).Wealsotestedtheinclusionofageinsegmentedmodelstomodeltheeffectofageineachgeneration:millennialsandGenerationX.Weincludedoccupationoremployment,codedasstudentonly,workeronly,studentandworker,andunemployed.HouseholdincomewasalsoincludedasadeterminantofVMT.PreviousstudieshavefoundthatincomehasapositiveeffectonVMT(Brownstone&Golob2009;Rentziouetal.2012;Greeneetal.1995).Inthisstudy,weusedthreeannualhouseholdincomebrackets(respectively,lowerthan$35,000,$35,000-$100,000,andhigherthan$100,000)toallowincometohaveanonlinearrelationshipwithVMT.

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Builtenvironment

EwingandCervero(2010)summarizesthefindingsfromtheliteratureregardingtheeffectsofthebuiltenvironmentcharacteristicsonVMTandtravelbehavior.Inthisstudy,weusethegeocodedinformationontheresidentiallocationreportedbytherespondentstomatcheachcasewithadditionalinformationaboutthelocallandusecharacteristics,includingtheneighborhoodtypeasdeterminedinapreviousstudydevelopedbyresearchersatUCDavis(Salon2015).

Withinacensusblock,characteristicsmayvaryenoughthatresidentneighborhoodperceptionsandexperiencemayvary(Handy2002;Handyetal.2005;Bagleyetal.2002).Inaddition,notonlytheobjectivecharacteristicsofthebuiltenvironmentbutalsotheperceivedneighborhoodcharacteristicsarefoundtobegoodpredictorsoftravelbehavior(Handyetal.2006).Inthisstudy,weusedgrosspopulationdensity(people/acre),grossemploymentdensity(jobs/acre),jobdiversity,andtotalroadnetworkdensityasobjectivemeasuresofthelocalbuiltenvironmentcharacteristics.Bothpopulationandemploymentdensitywerepreviouslyfoundtobesignificant(Cervero&Murakami2010)inpredictingVMT.Inaddition,weusedregionaldiversity,basedonpopulationandtotalemployment,deviationoftheratioofjobs/popinacensusblockgroupfromtheregionalaverages,andtripproductionsandtripattractionsequilibriumindex.Thesevariablesaregoodmeasuresofthecharacteristicsofthelanduse.Wherethesemeasuresaremorebalanced,thelocalmixoflandusesisthoughttoreducetraveltimeanddistance(Cervero&Duncan2006).TechnologyAdoptionandUseofSocialMedia

Theadoptionofinformationcommunicationtechnology(ICT)hasbeenoftenreportedasapotentialfactoraffectingtravelbehavior,which,dependingonthelocalcontextandindividuals’characteristics,mayleadtosubstitutionof,generationof,modificationoforneutralitywiththeamountoftravel(SalomonandMokhtarian2008,CircellaandMokhtarian,2017).Inthisstudy,wecontrolledforseveralmeasuresofICTadoptionanduse.Inthefinalmodel,weuseavariablethatmeasuresthefrequencywithwhichtherespondentstelecommutefortheirworktoaccountforthepotentialimpactsoftelecommutingonweeklyVMT.NewmobilityservicessuchasUberandLyftmayfunctionsimilarly,generatingnewtripsandincreasingVMTorreplacingdrivingmiles(Tayloretal.2015;Hallock&Inglis2015;Shaheenetal.2015).Wecreatedvariablestoassesstherespondent’sfrequencyofusingnewsharedmobilityservices,including:on-demandrideservices(e.g.UberandLyft),carsharing(includingfleet-basedandpeer-to-peerservicessuchasZipcar,Car2GoandTuro),bikesharing,ridesharing(includingpeer-to-peercarpoolinganddynamicridesharingsuchasZimrideandcarpoolingthatarrangedviaFacebookorCraigslist).Inthestudy,wetransformedthefrequenciesofuseoftheseservices,whichwerereportedasordinalvariablesinthesurvey,intomonthlyfrequenciesbyassumingthat‘‘5ormoretimesaweek”canbeconsidered5timesaweek(5/7),“3-4timesaweek”canbeconsideredthreeandahalftimesaweek(3.5/7),‘‘1–2

58

timesaweek’’canbeconsidered1.5timesaweek(1.5/7),‘‘1–3timesamonth’’becomes2timesamonth(2/30),“lessthanonceamonth”becomes3timesperyear,and“Iuseditinthepast”(butnotanymore)isapproximatedtozero.LifestylePreferencesandIndividuals’Attitudes

Asdescribedinanearliersectionofthisreport,weappliedfactoranalysisasadatareductiontechniquetoanalyzetheattitudinalvariablesinthesurveyandcompute17factorsscores.InthefinalVMTmodel,weincludedthefollowingfactorscores:

a. Establishedinlife:Individualswhoscorehighlyonthisfactorstronglyagreedwithstatementsincluding“I’malreadywell-establishedinmyfieldofwork”andtheytendedtodisagreewiththestatement“I’mstilltryingtofigureoutmycareer(e.g.whatIwanttodo,whereI’llendup).”

b. Preferenceforsuburbanneighborhoods(pro-suburban):Individualswhoscorehighlyonthisfactortendedtoagreewiththestatementsthatemphasizedthepreferenceforlivinginspacioushomesthatwerefurtherawayfrompublictransitandlivinginalocationwithlargeyardsandlotsofspacebetweenhomes.

c. Responsivenesstotheenvironmentalimpactsandpriceoftravel:Individualswhoscorehighlyonthisfactortendedtoagreewiththestatements“TheenvironmentalimpactsofthevariousmeansoftransportationaffectthechoicesImake”,“Iamcommittedtousingalesspollutingmeansoftransportationasmuchaspossible”,“ThepriceoffuelaffectsthechoicesImakeaboutmydailytravel”and“Toimproveairquality,Iamwillingtopayalittlemoretouseahybridorotherclean-fuelvehicle”.Thisfactorcapturesrespondents’willingnesstochangetravelplansbasedonbothgaspricesandenvironmentalconcerns.

d. “Mustownacar”:Individualswhoscorehighlyonthisfactoragreedwith“Idefinitelywanttoownacar”anddisagreedwiththestatement“Iamfinewithnotowningacar,aslongasIcanuseorrentoneanytimeIneedit”.

e. Time/modeconstrain:Thisfactorcapturestheattitudeofrespondentswhomorelikelydrivebynecessityasopposedtobychoice.Thisistheamalgamationoffourattitudinalstatements.Thosethatloadedpositivelyontothisfactortendedtoagreewiththefollowingstatements“Myschedulemakesithardorimpossibleformetousepublictransportation,”“IamtoobusytodomanythingsI’dliketodo,”and“Mostofthetime,Ihavenoreasonablealternativetodriving”.Respondentswhoscoredhighonthisfactormayhavenoreasonablealternativetodriving.Thisfactorislikelystronglycorrelatedwiththebuiltenvironmentcharacteristicsandneighborhoodtypewheretherespondentslive.

Results

Weestimatedweightedlog-linearmodelsusingthelog-transformationoftheself-reportedmeasureofweeklyVMTasthedependentvariable.Table10summarizestheestimatedcoefficientsforapooledmodel,whichincludesbothmillennialsandthemembersof

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GenerationX(N=1801),andtheseparatemodelsformillennials(N=976)andGenerationX(N=825).Allmodelshaveverysatisfactorygoodnessoffit,withR-Squaredbetween0.448and0.517(adjustedR-Squaredbetween0.439and0.509).Therefore,themodelsareabletoexplainapproximately50%ofthevarianceofthedependentvariable,avaluethatisremarkableconsideringthemanysourcesofpotentialnoisethataffectindividuals’VMTandthatcannotbeusuallycapturedineconometricmodels.Interestingly,themillennials’modelhasthelowestgoodnessoffit(R-squaredof0.448,comparedto0.517fortheGenerationX’smodel).Thisconfirmsthelargerheterogeneityinmillennials’mobilitychoices,andtheincreaseddifficultyofpredictingtheirbehaviors.Inotherwords,whileitiseasiertopredictGenXers’weeklyVMT,usingtherichsetofvariablesavailableforthisresearch.Moresourcesofnoise(e.g.impactofunobservedvariables,and/ordifferencesinindividualtastes,habits,etc.)characterizethemillennials’group.Still,ourmodeldoesaremarkablejobinexplainingthevariationinmillennials’VMT,duetotheabundanceofvariables,suchaslandusecharacteristics,individualattitudes,andadoptionoftechnology,whicharenotavailableinotherdatasets.Thefollowingsub-sectionsdiscusstheimpactsofthevariousgroupsofvariablesthatwerecontrolledforintheVMTmodels.Table10.ResultsofthePooledandSegmentedModeloflog(VMT+1)

PooledModel Millennials GenerationX

B p B p B p

(Intercept) 0.487 0.302 -5.253 <.001

1.162 <.001

Occupation StudentOnly 0.437 0.004

0.818 <.001

-0.441 0.155

StudentandWorker 0.73 <.001

0.839 <.001

0.608 0.001

WorksOnly 0.687 <.001

0.742 <.001

0.711 <.001

Sex(Male) 0.271 <.001

0.205 0.014

0.305 <.001

Age 0.038 0.163 0.453 <.001

Age2 -0.001 0.138 -0.008 <.001

HouseholdIncome >$100k 0.291 <.001

0.462 <.001

0.342 0.004

$35k-100k 0.155 0.031

0.194 0.042

0.21 0.062

LiveswithParents -0.235 0.003

-0.176 0.083 -0.383 0.004

LiveswithChildren 0.206 0.001

0.314 0.001

CarAvailability(%) 0.027 <.001

0.026 <.001

0.029 <.001

TelecommutingFrequency -0.506 0.001

-0.693 <.001

PopulationDensity -0.007 <.001

-0.013 <.001

Diversity -0.557 <.001

-0.325 0.072 -0.9 <.001

FSpro-suburban 0.054 0.064 0.066 0.085

FSresponsive_env_price -0.055 0.047

FSestablished_in_life 0.107 0.001

0.091 0.057 0.066 0.122

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FSmust_own_car 0.106 <.001

0.119 0.003

0.073 0.082

FStime_mode_constrain 0.165 <.001

0.153 <.001

Uber/LyftFrequency -1.407 0.05

Observations 1801 976

825 R2/adj.R2 .480/.474

.448/.439 .517/.509

Socio-demographics

Inourpooledmodel,aswellasinthesegmentedmodels,variablessuchashouseholdincome,gender,presenceofownchildreninthehousehold,andoccupation/employmentstatuswereallfoundtohaveastatisticallysignificanteffectonanindividual’sVMT.Interestingly,theeffectsofage(whichiscontrolledforthroughtheuseofboththeAgevariable,andthequadratictermAge2,tocontrolfornon-lineareffectsofageontheamountofcartravel)arefoundtobesignificantinthepooledmodelandinthemillennialmodelonly.ThefindingsconfirmtheassumptionthattherelationshipbetweenVMTandagedoesnotfollowalinearrelationship.Inparticular,theestimatedmodelcoefficientspredictthat(aftercontrollingfortheeffectsofothervariables,suchasHHincome,presenceofchildren,etc.)theeffectsofageonVMTappeartopeakatage35.Similarly,whenseparatingthetwosegmentsofthepopulationinthedataset,boththeAgeandAge2termsarenotsignificantintheGenerationXmodel,suggestingthattheremainingdifferencesinVMTattributabletoageamongthisgrouparenegligible(i.e.forindividualsintheagegroup35-50,individualVMThasalready“peaked”,andtheremainingchangesinVMTareexplainedthroughtheimpactofothervariables).Acrossallmodels,malerespondentshadhigherVMTsthanfemalerespondents,thoughtheeffectwasmuchsmallerinthemillennialmodel.Inthepooledmodel,mendrove30%moremilesperweekthanwomen,allelseequal,whileinthemillennialmodel,mendrove24%moremilesthanwomen.AmongthemembersofGenerationX,mendrove33%morethanwomen.Thismayindicatethatgenderdifferencesaresmallerwithinthemillennialgeneration,aswomenhavesaturatedtheworkforce(andtherearesmallergenderdifferencesinlifestyles,income,etc.)andmenshareinmorehouseholdobligations.Forthemillennialmodel,individualsthatwerebothemployedandstudentshadhigherVMTsthanindividualsthatworkedonly,orwerestudents–goingtobothworkandschool,assumingthattheyareindifferentlocations,resultsinahigherVMT.IntheGenerationXmodel,thosewiththehighestVMTsonlyworkedandwerenotstudents.Householdcompositionisfoundtobeaveryimportantfactoraffectingtheamountofindividuals’cartravel.Inparticular,individualsthatlivewiththeirparentstendtodrivefewermilesperweekthanthosethathavealreadyestablishedtheirownhousehold.Veryinterestingly,thepresenceofchildreninthehouseholdisfoundtobeaveryimportantpredictorofVMTformillennials:youngadultsthathavetheirownchildrentendtodrivemore(startingfromalowerbaselinevaluefortheirgeneration,comparedtotheolderGenXers)toaveryremarkableextent.Theeffectofhavingtheirownchildrenlivinginthehouseholdisalso

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foundtohaveasignificanteffectinthepooledmodel,butisnotfoundtobeasignificantpredictorofVMTforthemembersoftheGenerationX.

Caravailability(measuredasthepercentoftimeacarisavailabletotheindividual)wasalwaysfoundtobepositivelycorrelatedwithvehiclemilestraveled.Inthepooledmodel,foreachadditionalpercentincrementofcaravailabilitythereisa3%increaseinVMT.Thisvariablewasusedinplaceofthetypicalcarsperhouseholdorcarsperlicenseddriverasamorepreciseestimateofvehicleavailability.BuiltEnvironment

Theestimatedcoefficientsindicatethat,asexpected,populationdensityisnegativelycorrelatedwithvehiclemilestraveledinboththepooledmodelandGenerationXmodel.Thisisconsistentwithearlierfindingsintheliterature(Ewing&Cervero2001).Inthepooledmodeltheeffectofdensityisasmall,butsignificant:anincreaseinaunitofpopulationdensity,reportedinpopulationperacrepercensusblock,resultsinadecreaseinVMTof0.07%.However,thisvariablewasnotfoundtobesignificantinthemillennialsmodel.Regionaldiversity,measuredasthecensusblockgroupdeviationfromjobstopopulationratiofromtheregion’s,wasnegativelycorrelatedwithVMTacrossallmodels.Forexample,inthepooledmodel,aunitincreaseinregionaldiversityresultedinaVMTdecreaseof43%.ThisvariablehasevenlargereffectsamongGenerationX.Overall,theimpactoflandusecharacteristicsappearstobelargeramongtheoldergroup.Millennials’VMTseemtobeaffectedtoalargerdegreebyothergroupsofvariablesthatwerecontrolledforinthemodel.TechnologyAdoption

Wecontrolledfortheadoptionoftechnologythroughseveralvariablesinthemodelestimation.Inthefinalmodel,weincludeavariablethataccountsfortheeffectofthefrequencyoftelecommuting(fortheindividualsthateitherworkorworkandgotoschool),whichwasfound(notsurprisingly)tohaveastatisticallysignificant,andnegative,effectonVMTinboththepooledandtheGenerationXmodels.Veryinterestingly,thefrequencyoftelecommutingwasnotfoundtobesignificantintheVMTmodelformillennials.Whethermillennialsadopttelecommutingornot,thisdoesnotseemtohaveasignificanteffectonVMT,perhapsbecauseofthepotentialsubstitutionofcommutetripswithcartripsdoneforotherreasons.

Wealsocontrolledfortheimpactoftheadoptionofnewsharedmobilityservices.Inparticular,inthefinalmodel,weincludedavariablethataccountedforthefrequencyofuseofon-demandrideservicessuchasthoseprovidedbyUberorLyft.Thefrequencyofuseoftheseserviceswasfoundtohaveasignificant(ata0.10levelofsignificance)andnegativeeffectonmillennials’VMT.Thissuggeststhatmillennialswhouseondemandrideservicestendtodriveless.Thedirectionofcausalityofthisfindingisunclearthough:theadoptionofon-demandrideservicesmightleadsomemillennialstodriveless(asaconsequenceoftheadoptionoftheseservices,and/orthesubstitutionoftripsthatwouldhavebeenotherwisemadebydrivingtheircar),orthereversemightbealsotrue:millennialsthathaveloweraccesstoaprivatevehicle

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(e.g.theyliveinzero-orlow-vehicle-owninghouseholds)mightadopttheseservicesmoreoften,asawaytocompensatefortheirlowerautoaccessibility.Thistopicwillbefurtherinvestigatedinfutureextensionsoftheresearch,throughtheapplicationoflatentclassanalysisandtheestimationoflatentclassmodelstoanalyzedifferentbehaviorsamongdifferentgroupsofusers,andtheestimationofbivariatemodelsthatcanjointlyestimateanindividual’samountofcartravel(e.g.VMT,orthefrequencyofuseofpublictransportation)andthefrequencyofuseofmodernsharedmobilityservices(includingon-demandrideservices,suchasUberandLyft).

PersonalAttitudesandPreferences

Weusedseveralfactorscoresthatwerecomputedinthefactoranalysisofattitudinalvariables,tocontrolfortheimpactofindividualattitudesandpreferencesontheindividual’samountofcartravel.Inparticular,thefactorscoresmeasuringtheindividuals’perceivedlackofalternativestodriving,theirdegreeofresponsivenesstotheenvironmentaleffectsandpriceoftraveloptions,thedegreetheyfeeltheyarewellestablishedinlife,thepreferencetoownacar(vs.accessingonewhenneeded),andthepreferenceforsuburbanneighborhoodswerefoundtohavesignificanteffectsandwereincludedinthefinalpooledmodel(andinseveralcasesalsointhesegmentedmodels).

AllattitudinalfactorscoreswerefoundtohaveanimportanteffectinexplainingindividualVMT.IndividualsthatreportedthattheydonothavereasonablealternativetodrivingwerefoundtoreporthigherVMT.Further,VMTwasfoundalsotoincreasewiththedegreebywhicharespondentfeelsestablishedinlife(aone-unitincreaseinthisfactorresultedinan11%increaseinVMT).Theseindividualslikelyhavemoreresponsibilities,havehighersocioeconomicstatusandarebetterestablishedintheircareers,resultinginhigherVMT.Formillennials,inparticular,thosethathaveaunithigherscoreforthisvariabledrive9.5%more,whilethisvariableisnotsignificantintheGenerationXmodel.

Attitudeswereimportanttocontrolforasaproxyforresidentialself-selection,whichisoftenaconfoundingfactorintherelationshipwithVMTandstructuralvariables.Interestingly,thefactorscoremeasuringthedegreebywhicharespondentisresponsivetotheenvironmenteffectsandpriceoftraveloptionswasfoundtobesignificantonlyinthepooledmodel(withtheexpectedsign).Theweaksignificanceofthisvariablemayindicatethatconsideringtheenvironmenteffectsoftraveloptionswhenchoosingonwhethertodrivemightnothavesizableeffectsonone’sVMT,orthatthiseffectisalreadycapturedbyanothervariable,suchasresidentialselectionorcaravailability,orchoosingtoownacaringeneral.

ThosewholikecarsanddefinitelywanttoownonearemorelikelytohavehigherVMTsthanthosewhodonotloadpositivelyontothatfactor–theimpactofthisvariableislargerformillennials(andisnotfoundtobesignificantintheGenerationXmodel).Similarly,thefactorscoreforthepro-suburbanattitudewaspositivelycorrelatedwithVMTinthepooledandintheGenerationXmodel.Anincreaseofoneunitinthisfactorscore(beingmore“prosuburbs”)isassociatedwithanincreaseof5.8%inVMT.ThefullanalysiscanbefoundinTiedemanetal.(2017).

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CarOwnership,VehicleTypeChoiceandPropensitytoChangeVehicle

Ownership

Morethan17.4millionvehiclesweresoldintheUnitedStatesin2015,breakingthepreviousrecordof17.3millionvehiclessoldin2000(HarwellandMufson2016).Therecentincreaseincarsaleshaspromptedspeculationsonwhetherthecarmarkethasdefinitelyreboundedafterthetemporarydecreaseincarsalesduringtheyearsofeconomicrecession,10thoughacertain“delay”effectmightalsobebehindtherecordvolumesofcarsalesin2015:vehiclessalesduringtheyearmighthavebeengrownalsobecausemanyconsumerspostponedthetimeofreplacementoftheirvehiclesduringtheeconomiccrisis.

Figure25.Averageratioofavailablecars(includingminivans,SUVs,pickuptrucks)per

householdmemberwithadriver’slicense

10Thediscussionabouttheapparent“peak”incarsalesobservedduringthepastfewyearshasalsobeenconnectedwiththeobservedpeakincartravelthatwasobservedduringtheearly2000s.ForamorecompletediscussionofthefactorsassociatedwiththeobservedchangesinpassengertraveltrendsintheU.S.seeCircellaetal.(2016b).

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Severalfactorsaffectvehicleownershiprate,suchasindividualandhouseholdcharacteristics,availabilityandaccessibilityofdifferentmodesoftransportation,thequalityoftravelofferedbycarvs.theotheralternatives,characteristicsofbuiltenvironment,individual’slifestylesandpersonalattitudesandpreferencestowardstheuseofcarsand/orothermodes.Inthissection,wefirstlookattwodifferentmeasuresofcarownershipusingthedataavailableforthisproject:(1)averageratioofavailablecars(includingminivans,SUVs,pickuptrucks)perhouseholdmemberwithdrivinglicense,and(2)averageratioofavailablevehiclestohouseholdmembers.Wethendevelopamodelofvehicletypechoiceandanalyzethefactorsthataffectthedecisiononwhatvehicletoown.Figure25presentsthedistributionoftheratioofcarsperhouseholdmemberswithadriver’slicensebyagegroupandneighborhoodtype.Asdiscussedearlier,millennialswholivewithparentsareexpectedtobehavedifferentlycomparedtomillennialswhodonotlivewiththeirparentsandtheyhavealreadyestablishedtheirindependenthousehold.Asshowninthegraph,exceptdependentmillennialswholiveinsuburbanareas,theaveragenumberofvehiclesperhouseholddriverdecreasesfromruralneighborhoodtosuburbanandurbanareas.Thiscouldbeduetohigheravailabilityandaccessibilityofdifferenttravelalternativesandhighercostofcarownershipinurbanareas.Further,andmoreinteresting,theratioofvehiclesperdriversissensiblylowerforonecategory:theindependentmillennialswholiveinurbanareashavethelowestratioofcarperhouseholddrivers.Thisgroupofindividualsistheonlyonethathasanaverageratioofcarsperhouseholddriversthatislowerthan0.8.Incontrast,allgroupsofGenXershavemuchhigherratiosofvehiclesperhouseholddrivers(whichforruralGenXersisapproximatelyequalto1).

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Figure26.Averageratioofavailablecars(includingminivans,SUVs,pickuptrucks)per

householdmember

Incontrasttotheratioofcarsperhouseholddrivers,theratioofcarsperhouseholdmembers(Figure26)variesinasmallrangeacrossalldifferentagegroupsandneighborhoodtypes.Theresultindicatesthatbothdependentandindependentmillennialswholiveinurbanneighborhoodshavelowercaravailabilitycomparedtotheirpeerswholiveinruralandsuburbanareas.Inthiscase,thelowerratioofcars/householdmembersisobservedamongdependentmillennials.Duringfuturestagesoftheresearch,wedoplantostudyhowcarownershipvariesacrossdifferentgroupsofthepopulation:theresearchteamiscurrentlyworkingattheestimationofcarownershipmodelsthatinvestigatehowvarioussociodemographiccharacteristics,individualpreferences,andlandusefeaturesaffecthouseholdcarownership.Further,animportantfocusoftheresearchhasbeenfocusedonwhataffectsthetypeofvehiclesthatindividuals(andthehouseholdsinwhichtheylive)prefertoown.Withcheapergasandastrongereconomy,consumersareflockingtonewandusedcarlotslookingfortheirnewcar.Recenttrendshavealsoshownaresurgenceinvehiclesalesforlargervehicles

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(includingSUVs,crossoversandpick-uptrucks).Inthispart,weexamineindividualswhoownatleastonevehicleinthehousehold,inordertobetterunderstandhowindividualattitudes,lifestyles,builtenvironmentcharacteristics,andsocio-demographictraitsaffectthetypeofvehicletheyown.Asalreadymentioned,therecenttrendsincarsalessomehowcontrasttheobservedtrendsinvehicleuseandsalesfromthepastfewyears,whichshowedanapparentpeakincarownershipanduseintheUnitedStatesaswellasotherdevelopedcountries(SchoettleandSivak2013;Kuhnimhof,Armoogum,etal.2012).Thistrendhasbeenevenstrongeramongyoungadults,ormillennials.Severalstudieshavereportedthatyoungadultstendtodelaydrivinglicensure,ownfewerornovehicles,anddrivelessevenwhentheyhaveaccesstoacarinthehousehold(McDonald2015;Polzin,Chu,andGodfrey2014;Blumenbergetal.2012).However,todate,therehavebeennostudiesthatspecificallyfocusedoninvestigatingthevehicleownershipandvehicletypechoiceamongyoungadults.Alargervarietyofvehicletypes,includingsedan,hatchback,two-seater,pick-uptruck,SUV,minivan,coupe,etc.arenowadaysavailableonthemarket.However,ratherlimitedknowledgeexistsonthemotivationsaffectingbuyersofthesevehicles,beyondtheimpactofpurchasepriceandvehiclecharacteristicssuchasnumberofseats,operatingcosts,etc.Ourstudyaimstocontributeclosingthisgapbyinvestigatingtheeffectsofindividualattitudesandpreferences,generationaldifferences,andindividualcharacteristicsonvehicletypechoice.Veryfewauthors,todate,haveinvestigatedtheimpactsofattitudesandpreferencesonvehicletypechoice(BaltasandSaridakis2013;ChooandMokhtarian2004).Furthermore,nostudyhassofarlookedatgenerationaldifferencesinvehicletypechoice.Sincethe1980s,researchershavebeenexaminingvehicletypechoice(BeggsandCardell1980;BerkovecandRust1985;ManskiandSherman1980;LaveandTrain1979).Inordertomodelvehicletypechoice,studiestypicallyuseeithermultinomiallogit(MNL)(ChooandMokhtarian2004;Kitamuraetal.2000;LaveandTrain1979;ManskiandSherman1980;FredManneringandWinston1985)ornestedlogitmodels(BerkovecandRust1985;F.Mannering,Winston,andStarkey2002).LaveandTrain(1979)usedMNLtoinvestigatethevehicletypepurchasedandfoundthatlargerhouseholdsthathavetwoormorevehiclesaremorelikelytochoosesmallercars(LaveandTrain1979).Moreover,theyfoundthatolderpeopleandhouseholdswithhighVMTtendtochooselargervehicles.Unsurprisingly,vehiclepricenegativelyaffectsthechoiceofeachtypeofvehicle(LaveandTrain1979).ManskiandSherman(1980)werethefirstresearcherstotrytomodelthenumberofvehiclesandvehicletypechoicesimultaneously(ManskiandSherman1980).InestimatinganMNLmodel,theauthorsfoundthatseatingandluggagespacepositivelyaffectthevehicletypechoice,andinparticularlargerone-vehiclehouseholdsandhouseholdswithlowincomearelesslikelytochoosevehicleswithhigheroperatingcosts(ManskiandSherman1980).Similarly,ManneringandWinston(1985)developedamultinomiallogitmodeltomodelthechoiceamong10vehicletypealternativesbasedonyear,make,andmodel(FredManneringandWinston1985).Theyfoundthatbrandloyaltyhasasignificanteffectonthechoiceofthehousehold’svehiclemake.SimilartothefindingsofLaveandTrain(1979),vehiclepurchasepriceandoperatingexpendituresnegatively

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affectthechoiceofavehicletype(FredManneringandWinston1985).Kitamuraetal.(2000)usedamultinomiallogitmodeltoinvestigatethechoiceofvehiclebodytype(e.g.4-doorsedan,2-doorcoupe,etc.)andfoundthatmalesaremorelikelytousepick-uptrucks,andyoungerindividualsweremorelikelytouseSUVs,pick-uptrucks,andsportscars(Kitamuraetal.2000).Unsurprisingly,largerhouseholdsaremorelikelytousevansorwagonsasthesetypesofvehicleshavelargerspaceandseatingcapacity(Kitamuraetal.2000).Eventhoughseveralresearchershaveexploredthefactorsaffectingahousehold’svehicletypechoice,theliteratureismorelimitedregardingtheimpactofindividualattitudes,preferences,andlifestylesonthischoice.Amongthestudiesthatinvestigatedtheimpactofattitudinalvariablesonvehiclechoice,ChooandMokhtarian(2004)foundthattravelattitudes,personalitytraits,andlifestyleshavesignificanteffectsonthevehicletypechoice(ChooandMokhtarian2004).Morespecifically,peoplewholiveinhighdensityareasaremorelikelytodrivemoreexpensivecars,suchasluxuryandluxurySUVs(ChooandMokhtarian2004),andadislikeoftravelispositivelyassociatedwithdrivingaluxuryvehicle(ChooandMokhtarian2004).BaltasandSaridakis(2013)developedamultinomiallogitmodeltomodelthechoiceof12mutuallyexclusivevehicletypealternatives(BaltasandSaridakis2013).Theywerethefirstresearcherstodemonstratethatthepurposeofcaruse,theconsumer’sinvolvementwithcars,andtheconsumer’sattachmenttocars,havesignificanteffectsoncartypechoice.Further,theirmodelshowedthatthepropensitytopurchaseasmallcarisstatisticallyrelatedtotheirrelianceonfriendsandfamilymembersforadvice.SimilartothefindingsofChooandMokhtarian(2004),BaltasandSaridakis(2013)foundthatthosewhopreferluxuryvehiclesaremorelikelytoliveinurbanareas.Despitetherecentinterestoftheliteratureininvestigatingthebehaviorofthemillennialgeneration,toourknowledge,nopreviousworkhasbeendoneinvestigatingthevehicletypechoiceofyoungadults.VehicleTypeChoiceModel

Forthisanalysis,weestimatedamultinomiallogitmodel(MNL)toexploretherelationshipamongvehicletypechoice(thedependentvariableinthemodel)andsocio-demographiccharacteristics,residentiallocationandlandusecharacteristics,andpersonalattitudesandpreferences.Weusedonlyasubsetofthedatainestimatingthismodel,foranumberofreasons.First,werestrictedtheanalysestotheindividualswhoindicatedthattherewasatleastonevehicleinthehouseholdandprovidedvalidyear,make,andmodelinformationfortheprimaryhouseholdvehicle.Second,inordertofocusontherespondentsthatcurrentlyownavehiclethatwaspurchasedbythemunderconditionsrathersimilartotheircurrentlivingconditions,andremovethepossiblebiasofrespondentswhoweregiftedcarsfromotherfamilymembersorpurchasedanoldcaroutofcontingencies(e.g.itwasoneofthefewavailableinalimitedpricerange)ratherthanchoosingitbasedonpersonalpreferencesandtastes,wenarrowedthesubsetofanalysistotheindividualswhoownedorleasedausedornewvehiclethatismodelyear2010ornewer.

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DependentVariable:VehicleType

Surveyrespondentswhoindicatedthattherewasatleastonevehicleinthehouseholdwereaskedaquestionabouttheyear,make,andmodelofthevehiclethattheyusemost.Toassigneachvehicletoavehicletype,weusedtheEnvironmentalProtectionAgency’s(EPA)FuelEconomyDatasetwhichprovidesvehicleclassificationdataforallconsumervehiclesfrom1984tothepresent(EPA2016).Wematchedeachcompleteyear,make,andmodel,withacorrespondingvehicleclassificationbasedontheinformationprovidedbytheEPAdataset.Byusingthevehicle’smodelyear,wewereabletotakeintoaccountmodelredesignsthatinsomecasesmovedvehiclesfromonevehicletypetoanother.Forexample,the1984HondaAccordisclassifiedintheEPAdatasetasasubcompactcarbutthe2016HondaAccordisclassifiedasamidsizecar11.TheEPAhasmorethan15differentvehicletypeswhenaccountingforthedifferentdrivetrainoptions.Aswedonothaveinformationaboutthetrimlevelordrivetrainofthevehiclemodelinoursurvey,weaggregatedsomevehicletypessuchasSportUtilityVehicle2WDandSportUtilityVehicle4WDinjustonecategoryregardlessofthespecifictrimlevelordrivetrainthateachvehiclehas.Forthisanalysisweusedsixdifferentvehicletypechoices:

1. Small/compact2. Midsize3. Large4. Luxury5. SUV6. LuxurySUV

Weexcluded“pick-up”trucksand“sportcars”fromtheanalysisduetothesmallnumberofpick-uptrucksandsportcarsownedbytherespondentsinoursample,andtheverydifferentcharacteristicsofthesevehicles,whichwouldhavesignificantlyincreasedtheheterogeneityofanyonevehicleclassification(ifthevehicleswereincludedinthatcategory).Asmallnumberofrespondentsthedatasetreportedthattheyownseveralvehiclesthatcanbeclassifiedas“crossovers”or“minivans”.ThesevehiclesweremergedintheSUVcategory,duetothesimilarsizeofthesevehicles,andthemanysimilaritiesandoverlapsamongthevehiclesthatbelongtothesecategories.Sociodemographics

Weincludedindividualandhouseholdsocio-demographicandsocio-economiccharacteristicsasexplanatoryvariables.Wecontrolledforagethroughtheuseofthe“age”variable.Tocontrolforthenon-linearityofageinthismodel,wealsoincludedan“age-squared”variable.Wealsocontrolledforhouseholdcompositionthroughseveralvariablesincludingthenumberofchildrenandadultsinthehousehold.Householdswithchildrenareexpectedtomorelikely

11Pleasenotethatonlymodelyear2010ornewerwereincludedintheanalysisofthispaper.Theexamplepresentedhereisonlyforexplanatorypurposesontheprocessthatwasusedintheresearch

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ownlargervehicles,vansandSUVs(thelasttwocategoriesaremergedunder“SUV”inthisanalysis)duetotheirincreasedseatingcapacityandcomfortforriders.Finally,ascustomaryinmodelsofthistype,wecontrolledfortheimpactofothersocio-demographicvariablessuchasgenderandhouseholdincome(expectinghouseholdincometobeanimportantdriverforthepurchaseoflargerandmoreexpensive/luxuryvehicles).ResidentialLocationandLandUseCharacteristics

Inadditiontocontrollingforthetraditionalsocio-demographicandsocio-economicvariables,wealsocontrolledforthecharacteristicsoftheresidentiallocationthroughtheuseofaninteractionterm,whichallowedtheimpactoftheannualhouseholdincometovaryforthehouseholdsthatliveinurbanneighborhoods(usingthenon-urbanHHsasthereferencecategory).Weexpectedthat,holdingallelseequal,thosewholiveinurbanneighborhoodswouldbemorelikelytoownsmall/compactvehiclesandlesslikelytoownSUVs.IndividualPreferencesandAttitudes

Aspreviouslydescribed,thesurveyincluded66separatestatementsthatwereincludedinthestudytomeasuretheindividual’sattitudesaboutanumberofdimensionsrelatedtotheenvironment,travel,adoptionoftechnology,multi-tasking,lifesatisfaction,landuse,theroleofgovernment,etc.fromwhichweextracted17attitudinalfactors.Weincludedthreefactorscoresasexplanatoryvariablesinthefinalvehicletypechoicemodel:

a. Utilitariancaruse(carasatool):Individualswhoscorehighonthisfactortendedtoagreewithstatementssuchas“Thefunctionalityofacarismoreimportanttomethanitsbrand”.

b. Establishedinlife:Individualswhoscorehighlyonthisfactorstronglyagreedwithstatementsincluding“I’malreadywell-establishedinmyfieldofwork”Theytendedtodisagreewiththestatement:“I’mstilltryingtofigureoutmycareer(e.g.whatIwanttodo,whereI’llendup).”

c. Individualswithmultipletransportationmodesavailableandnotimerestraints(Reversedtime/modeconstrained).Thiscapturesrespondentsthatfeelasthoughtheyhavemultipletransportationoptionsavailabletothemandarenotconstrainedbytime.Thosethatloadedpositivelyontothisfactortendedtodisagreeorstronglydisagreewiththefollowingstatements:“Myschedulemakesithardorimpossibleformetousepublictransportation,”(indicatingthattherescheduledoesNOTmakeithardforthemtousepublictransit)“IamtoobusytodomanythingsI’dliketodo,”(indicatingthattheyareNOTtoobusytodotheactivitiesthattheywouldliketodo)and“Mostofthetime,Ihavenoreasonablealternativetodriving”(indicatingthattheyDOhavereasonablealternativestodriving).Theserespondentsmayhavenoalternativetodriving.

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Results

Sinceourdependentvariable,primaryvehicletype,consistsofsixmutuallyexclusivecategories,wedevelopedamultinomiallogitmodelforvehicletypechoice.Asmentionedintheprevioussection,thesesixcategoriesare:Small/Compact,Midsizecar,Largecar,SUV,Luxury,andLuxurySUV.Thefinalmodelhasfivealternativespecificconstantsand22alternativespecificvariablesthatrepresentninedifferentvariables.Thetablebelowpresentstheestimatedcoefficients(withtherespectivep-valuesinparentheses).Therho-squaredvalueofthefinalmodelis0.252,whichisquitegoodforamodelofthistype.Incomparison,therho-squaredforthemarketsharemodelis0.116,whichindicatesthatthemodelwithonlytheconstantsexplainsabout12%oftheinformationinthedata,andthatourfullmodelisabletocontributesignificantlytoexplainingthechoiceofvehicletype,despitetheobviousdifficultiesassociatedwiththeheterogeneityinthechoicesofvehicletype,andimpactsofeventualunobservedvariablesthatmightaffectthechoiceofthevehicleoneowns.Inparticular,aspointedoutinpreviouspapersintheliterature,thechoiceofthevehicletobuyisusuallyachoicethatismadeatthehousehold,andnotindividual,level.Additionally,thechoiceofthevehicletobuyisaffectedbytheothervehicle(s)thatthehouseholdeventuallyowns(orplanstopurchaseinthenearfuture).Thus,thechoiceofthevariousvehiclesthatareownedbyahousehold(forhouseholdsthatownmorethanonevehicle)isajointchoice,andshouldbemodelassuch.Unfortunately,inthisdatasetweonlyhaveinformationonthenumberofvehiclesowned/leasedbyahousehold(and,therefore,weknowoftheeventualpresenceofothervehicles,inadditiontothe“primary”vehicle),butwedonothaveinformationaboutthetypeofvehiclesthatareowned,apartfromtheprimaryvehicle.Thissomehowlimitstheabilityofthemodeltopredictthechoicesofahouseholdthatmightdecide,forexample,toownaSUVandacompactcartofulfilltheirmobilityneeds.12Despitethislimitation,theestimatedmodelprovidessomeusefulinformationontherelationshipbetweenvariousgroupsofvariableandthetypeofprimaryvehiclethatanindividualowns.

12Inourdataset,theinformationaboutsuchaHHwouldbeincludedas“owninganSUVastheprimaryvehiclethatisusedmostoftenandanotherunknownvehicle”,oras“owningacompactcarastheprimaryvehiclethatisusedmostoftenandanotherunknownvehicle”.

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Table11.EstimatedCoefficientofVehicleTypeChoiceModel

DependentVariable:VehicleType

Small/Compact

Midsize LargeCars

SUV Luxury LuxurySUV

Age 0.059(0.013)

(base) 0.252(0.000)

0.222(0.000)

1.176(0.000)

Age2 -0.001

(0.045)(base) -0.003

(0.001)-0.002(0.000)

-0.014(0.000)

Female (base) 0.603(0.000)

Numberofchildrenunder18

yearsoldinthehousehold

-0.266(0.048)

(base) 0.488(0.000)

-0.521(0.014)

FSCarasatool 0.355(0.005)

(base)

FSTime/Modeconstraint

(reversed)

-0.35(0.007)

(base) -0.584(0.047)

FSEstablishedinlife

-0.242(0.067)

(base) 0.5(0.025)

Householdincome 0.109(0.084)

(base) 0.22(0.014)

0.479(0.001)

InteractionHHIncomewith

urbanneighborhoodtype

(base) 0.121(0.026)

-0.16(0.078)

Constant -0.911(0.000)

(base) -6.181(0.000)

-5.646(0.000)

-2.561(0.000)

-29.696(0.000)

Numberofobservations 529 Log-likelihoodat0 -947.84 Log-likelihoodatmarketshare -801.05 Log-likelihoodatconvergence -708.53 !"#% ('()*+,-(!"#% ) 0.252(0.200)!/0% ('()*+,-(!/0% ) 0.116(0.088)Note:p-valuesarereportedinparenthesesbelowtheestimatedcoefficientsThesocio-demographiccharacteristicsusedinthemodelprovideinterestinginsightintovehicletypechoice:weusedageandagesquaredtoallowforanon-linearrelationshipofthisvariablewiththechoiceofcertainvehicletypes.Asshowninthemodel,theprobabilitythatanindividualownsalargecar,SUV,orLuxurySUVincreaseswithage.Similarly,thosewhoareolderaremorelikelytoassociatedhigherutilitywithandownasmall/compactvehicle(probably,asaneffectoftheHHvehiclefleetcomposition,asdiscussedabove),eveniftoalesserdegree.ThosewithchildrenlivingathomearemorelikelytoownSUVs(and/orvans,whichwerealsoincludedinthiscategory)andlesslikelytoownsmall/compactandluxuryvehicles:parentsneedtheutilityofanSUVwhichisnotofferedbysmallervehicles.Parentsaremorelikelytoassociatevaluewiththeseatingspace,storagecapacity,andgeneralcomforttypically

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associatedwiththesevehicles.Alsolookingathouseholdincome,ourmodelshowsthathigherhouseholdincomehasapositiveimpactonthelikelihoodtoownasmall/compactvehicle,aluxuryvehicle,andaluxurySUV.Whiletheimpactofhouseholdincomeonluxurybrandvehicles(eithercarsorSUVs)isprettystraightforward,theimpactofhouseholdincomeonthelikelihoodtoownasmall/compactcarislikelyassociatedwiththejointchoiceofthemultiplevehiclesownedbymoreaffluenthouseholdsdescribedabove.Forinstance,inahighincome2car–2personhousehold,thesurveyrespondentmayhaveequalaccesstobothvehicles;however,onevehicleismainlyusedbythespouse,leavingtherespondentwiththeothervehiclewhoseinformationisreportedinthesurvey.WomenwerefoundtobemorelikelytoownluxurySUVs.ThisreaffirmsfindingsfromarecentEdmonds.comstudywhichfoundthatwomennowaccountfor41%ofnewluxuryvehiclepurchases(http://www.detroitnews.com/story/business/autos/2016/09/06/women-buying-luxury-vehicles/89936258/).Theinclusionoffactorsextractedfromtheattitudinalvariablesprovideimportantinsightsintofurtherunderstandingvehicletypechoicebehavior.Asdescribedinthemethodologysection,weincludedthreefactorsasexplanatoryvariablesinthemodel.Theinclusionofthe“Establishedinlife”factorwasanattempttocapturetheeffectofstageoflike(inparticular,arelevantvariabletocaptureyoungermillennials’behaviorsandlifestyles).Inthisinstance,thosewhohavehighervaluesforthisfactoraremorelikelytoownluxuryvehiclesandlesslikelytoownsmallorcompactvehicles.Thisresultisnotsurprising,consideringthatluxuryvehiclesareexpensiveandindividualswhoaremorecertainaboutlife(andperceivethattheyarelessinatransientandunstablestageoftheirlife)havearemorelikelytobeabletopurchasemoreexpensivevehicles.Thosewhorecognizehigher“utilitarian”valuetotheuseofacar(i.e.havehigher“carasatool”factorscores)aremorelikelytoownsmallorcompactvehicles.Small/compactvehicles,inmostcases,donotfillanichemarketandtheyaresimplyseenasawaytogetfromorigintodestinationwhileminimizingpurchaseandmaintenancecost;theyarenotascomfortableasluxuryvehiclesandtheydonotprovidethespaceofanSUV.LandUseCharacteristics

Theinteractiontermofhouseholdincomeandurbanneighborhoodtypewasincludedinthemodelasawaytoaccountforthedifferentbehaviorofurbanhouseholdsregardingthechoiceofthevehicletoown.13Inadditiontothebaseeffectofhouseholdincomeonthevehicletypechoicethatwasdiscussedearlier,wefindthat,notsurprisingly,individualswithhighhouseholdincomesthatliveinurbanneighborhoodsaremorelikelytoownluxuryvehiclesandlesslikelytoownluxurySUVs.Theseeffectsareintroducedinthemodelascorrectionstothebaseeffect

13Inadditiontotheimpactonthetypeofvehiclethatisowned,landusecharacteristicsareexpectedtoaffectthenumberofvehiclesthatareownedbyahousehold.Weplantoexplorethisrelationshipinfuturestepsoftheresearch,throughtheestimationofacarownershipmodelthataccountsfortheimpactofindividualandlandusecharacteristicsonthenumberofvehiclesownedbyahousehold.

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ofthehouseholdincomeonvehiclechoice,meaningthathigherincomehouseholdsthatliveinurbanareasarenotaslikelytoownaluxurySUVasthehigherincomehouseholdsthatliveinotherneighborhoodtypes(thoughtheyarestillmorelikelytochoosethesevehiclesthanlowerincomehouseholds),andtheyareevenmorelikelytoownaluxurycar(andnotanSUV)thanthehighincomehouseholdsthatliveinotherneighborhoods.MoredetailscanbefoundinBerlinerandCircella(2017).PropensitytoModifyVehicleOwnership

IntheCaliforniaMillennialDataset,wealsocollectedinformationabouttherespondents’self-reportedwillingnesstobuy/leaseavehicle(Figure27)andtheirpropensitytosell/getridoftheircurrentlyownvehiclewithinthenextthreeyears(Figure28).AsshowninFigure27,millennialsingeneral,andoldermillennialsinparticular,moreoftenreportthattheyaremoreinclinedtopurchase/leaseacarwithinthenextthreeyears,comparedwithotheragegroups.Thisisconsistentwithexpectations,becausemillennials,particularlymillennialswholivesinurbanneighborhood,havelowercaravailabilitycomparedtotheiroldercounterpart,whohasalreadyacquiredavehicleorhashigheraccessibilitytoacarotherwiseownedinthehousehold.Thistrendalsoconfirmsthatveryoftenmillennialsareinatransientlifestage,andtheirzero-orlow-carownershipmightbeonlyatemporaryfactor,subjecttochangeduringtheirnearfuture.Thefindingmayhave,inparticular,consequencesonthecarownershipstatusofurbanmillennials.Thisgroupofyoungadultsareoftenfoundtoliveindenseneighborhoodandnottoownacar.Highexpectationshavebeenposedonthisgroupineventuallycontinuingtotransformthefutureoftransportation,andeventuallyhelpinthetransitiontowardsmoresustainablemobility.However,thehighpropensitytopurchaseacarduringthenextthreeyearsoftherespondentsincludedinthisgrouprepresentapotentialthreattosomesustainabilitygoals,andsignalsthatprobablymostpartofthelowcarownershipstatusofthisgroupisnotlikelytolastastheseindividualsageandtransitioninthefollowingstagesoftheirlife.

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Figure27.Distributionofindividual’swillingnesstopurchase/leaseavehiclewithinthenext

threeyearsbyagegroupandneighborhoodtype

Figure28presentsindividual’spropensitytosell/replaceoftheircurrentlyownedvehiclewithinthenextthreeyears.Asindicatedinthegraph,carownershipdecreasesfromruraltosuburbanandurbanneighborhoodsamongbothmillennialsandGenXers.Interestingly,thepropensitytosellacarishigheramongmillennialswhoownacarandlivesinurbanneighborhoodcomparedtoboththeirolderpeerswholiveinthesameareas,andtomillennialswholiveinotherneighborhoodtypes.Incontrast,thewillingnesstoreplacetheircurrentvehiclesishigheramongthemembersofGenerationX,inparticularamongthosewholiveinruralneighborhood.Weplantofurtherinvestigatethetopicsthataresummarizedinthesefigures,throughthedevelopmentofmodelsofthepropensitytochangethelevelofvehicleownershipinthehousehold,andinvestigatethefactorsaffectingthesetrends.Thistopicandthetypeofvehiclethattherespondentswouldconsiderbuying,asalsoreportedinthesurveyareofpotentialinteresttoautomakersandplanningagencies.Theywilllikelyaffectfuturedemandforcarsalesanduse.Further,infuturestagesoftheresearch,weplantoinvestigatetherelationships

0% 20% 40% 60% 80% 100%

Urban

Non-Urban

Urban

Non-Urban

Urban

Non-Urban

Urban

Non-Urban45

to 5

035

to 4

425

to 3

418

to 2

4

Yes

No

Not sure

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betweentheadoptionofsharedmobilityservicesandthepropensityofrespondentstomodifytheirlevelofvehicleownership.14

Figure28.Distributionofindividuals’propensitytosell/getridoftheirvehiclewithinthenext

threeyearsbyagegroupandneighborhoodtype

14Thiswillprovideadditionalinformationonthelikelychangesincarownershipanduse,astheadoptionofsharedmobilityservicesbecomemorepopularinfutureyears.

0% 20% 40% 60% 80% 100%

Rural

Suburban

Urban

Rural

Suburban

Urban

Gen

XG

en Y

No, I don’t currently own a vehicle

No, I don’t plan to sell/get rid of any of the vehicles in my household

Yes, I plan to reduce the number of vehicles in my household

Yes, I plan to replace my current vehicle with another one

I am not sure

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ConclusionsandNextStepsoftheResearch

Millennialsincludeaverylargesegmentofthepopulation,whooftenareearlyadoptersofnewtrendsandtechnologiesthatlaterareadoptedbyothersegmentsofsociety.Thus,improvingtheunderstandingofthefactorsandcircumstancesbehindmillennials’mobilitychoicesisofoutmostimportanceforscientificresearchaswellasforplanningprocesses.Previousstudieshavehighlightedhowmillennialsoftenhavedifferenttastes,lifestyles,consumerandtravelbehaviorfromthoseofpreviousgenerationsatthesamestageinlife.Still,today’syoungadultsareina“transitional”stageoflife,inwhichtheyarebuildingthebasisfortheirfuturelife,familyandworkcareer.Thus,theircurrentchoicesareexpectedtobeasumoflifecycle,periodandgenerationaleffects:theircurrentbehaviorsarenotnecessarilygoingtolastasmillennialsbecomeolder,andtransitiontomorestablelifestages.Thisstudyinvestigatesmillennials’choices,throughtheanalysisofacomprehensivedatasetthatincludesinformationonmanyofthevariablesthathavebeenattributedaroleinaffectingnewtraveltrendsandadoptionofemergingtransportationservices.Thesevariablesweredifficulttocontrolinpreviousstudies,whichwereoftenlimitedbythelackofavailabilityofinformationonspecificvariables(suchasstudiesbasedontheanalysisofNHTSdata),ortheuseofnon-representativesamples(asinthecaseofconveniencesamples,e.g.collectedamonguniversitystudents).ThestudybuildsonanextensiveresearcheffortcarriedoutwiththecollectionoftheCaliforniaMillennialsDataset,anunprecedenteddatasetcollectedin2015,whichincludesinformationonindividualpreferences,lifestyles,adoptionoftechnology,carownershipandtravelbehaviorforapproximately2400residentsofCalifornia,includingbothmillennials(youngadults,18-34,in2015)andmembersofprecedingGenerationX(middle-ageadults,35-50).ThestudyallowstheinvestigationofseveralcomponentsoftheemergingtrendsintraveldemandandadoptionoftransportationtechnologyinCalifornia.Inthisstageofthestudy,wematchedtheinformationcontainedintheCaliforniaMillennialsDatasetwithadditionalvariablesofinterestincludinglanduseandbuiltenvironmentdataavailablefromothersources,basedonthegeocodedresidentiallocationoftherespondents.ThedataprovideawidevarietyoflanduseandaccessibilitymeasuresavailablethroughtheUSEPASmartLocationDatasetandthewalkscore,bikescoreandtransitscoreobtainedfromthecommercialwebsiteWalkscore.com.Usingthegeocodedinformationontheresidentiallocation,andtheinformationprovidedbytherespondentsinthesurvey,wecarefullycleanedandrecodedthedata,toimprovethequalityoftheresponsesandidentifyinternalandexternalinconsistenciesandpotentialoutliersthatmayleadtonoiseinthedata.Further,wedevelopedasetofweights,throughtheapplicationofbothcellweightsandtheiterativeproportionalfitting(IPF)rakingapproach,tocorrectthedistributionofcasesinthesample,andreducethenon-representativenessofthedatabasedontheregionofCaliforniawheretherespondentslive,neighborhoodtype,age,gender,studentandemploymentstatus,householdincome,raceandethnicityandpresenceofchildreninthehousehold.

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WedevelopedanumberofanalysestoinvestigatethecomplexrelationshipsbehindresidentiallocationandmobilitychoicesofCaliforniamillennialsandmembersofGenerationX.First,throughtheuseofdatareductiontechniques,weappliedafactoranalysisapproachtothe66variablesthatcollectedinformationontherespondents’attitudesandpreferencestowardsanumberofdimensions,includingtravelmodepreferences,adoptionoftechnology,environmentalconcerns,landusepreferences,etc.Weextractedasetof17factorsthatmeasuresthemainattitudinalconstructsonanumberoftopics,andcanbeusedintheanalysisofchoicesrelatedtotravelbehavior,residentiallocation,andcarownershipanduse.Weanalyzedtheattitudinalprofilesandindividualcharacteristicsformanysubgroupsofindividuals:notsurprisingly,millennialsthatliveinurban,suburbanorruralareasoftenmanifestratherdifferentattitudinalpatternsfromtheircounterpartsinolderagegroups.Wealsoanalyzedtheadoptionandfrequencyofuseofsmartphoneappsamongdifferentsociodemographicgroups:urbanmillennialsareheavyadoptersoftheseservices,andonaverageshowhigheradoptionofthesetechnologiesforvariouspurposes,includingaccessinginformationaboutthemeans(orcombinationofmeans)oftransportationtouseforatrip,findinginformationabouttripdestinationsornavigatinginreal-timeduringatrip.Largedifferencesarealsoobservedintheadoptionofsharedmobilityservicesamongurbanandnon-urbanpopulations:notsurprisingly,millennialstendtoadoptthesenewtechnologicaltransportationservicesmoreoftenthanthemembersofGenX,inparticularinurbanareas.Wefurtheranalyzedtherelationshipbetweenaccessibilityandadoptionofmultiplemodesoftransportation(multimodality,and/orintermodality)amongthemembersofvarioussub-segmentsofthepopulation.Forthisanalysis,wefurtherclassifiedmillennialsintwogroups,dependingontheirlivingarrangementsandhouseholdcomposition,identifyingtheindependentmillennials(whodonotliveanymorewiththeirparents,andhavealreadyestablishedtheirownhousehold),andthedependentmillennials(wholivewiththeirparents),asabetterwaytocontrolfortheresidentiallocationoftherespondents(astheresidentiallocationfordependentmillennialshaslikelybeenchosenbytheirparents,andnotbythemillennialsthemselves).WecomparedthelevelofaccessibilityoftheplaceofresidenceandtheadoptionofmultimodaltravelofthetwogroupsofmillennialswiththoseoftheoldermembersoftheGenerationX.Independentmillennialswerefound,onaverage,tohavethehighestvaluesforallaccessibilitymeasures.Further,importantdifferencesareobservedamongdependentandindependentmillennials:dependentmillennialstendtoliveinareasthathavethelowestlevelsofaccessibilitybynon-carmodes,probablyduetotheresidentiallocationchosenbyothermembersofthehouseholds(e.g.youngadultswholivewiththeirparents).Thissharplycontraststheresidentiallocationofindependentmillennialswhoaremoreoftenfoundtoliveinlocationswithhigheraccessibility.Centrallocationsaremoreconducivetotheadoptionofgreenerandnon-autocommutemodes(and/ormayreinforcethepropensityofyoungadultstousesuchmodesortoadoptmultimodaltravel).Attheotherendofthespectrum,GenXersrelyheavilyontheuseofcarsfortheircommute.Interestingly,atleastapartofdependent

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millennialsarefoundtodrivelessthantheirolderpeersinspiteoflivinginneighborhoodsthatarelessconducivetomultimodalityandtotheuseofnon-automodes.Thefindingssuggestthatahighercomponentoftheadoptionofmultimodalbehaviorsisassociatedwithmakingthesedecisionsbychoice,ratherthannecessity.Insummary,andnotsurprisingly,accessibilityandmultimodalityarepositivelycorrelated:residentsofmoreaccessibleneighborhoodsaremoreoftenfoundtobemultimodalcommuters.However,millennials,andespeciallydependentmillennials,arefoundtomakethemostoftheirbuiltenvironmentpotential,eitherduetoindividualchoices,orthepresence(orlack)oftravelconstraints.Theyarelesslikelytobemono-driversandmorelikelytobemultimodalcommuters,eveniftheyliveinneighborhoodsthatarelesssupportiveofsuchbehaviors.Thissuggeststhattheconnectionbetweenthebuiltenvironmentandtravelpatternsmaydifferbygeneration:infuturestepsoftheresearchweplantofurtherinvestigate(andmodel)therelationshipsbetweenaccessibilityandmultimodalbehavioramongthemembersofthedifferentgenerations,whilecontrollingforotherfactorsaffectingresidentialandtravelchoices.Inordertoinvestigatetheimpactsofvariousgroupsofvariablesonthemobilitychoices,andinparticularoncaruse,ofthemembersofthevariousgenerations,weestimatedalog-linearmodelofthenumberofweeklyvehiclemilestraveled(VMT).Weestimatedbothapooledmodelfortheentiresample,andasegmentedmodelthatallowedustocontrolfortheeffectsofindividual,householdandlandusecharacteristicsontheVMTofmillennialsandGenXers,separately.Allmodelshaveexcellentgoodnessoffit:however,andveryinterestingly,amongthethreemodelsthatarepresented,themodelformillennialsexplainsthelowestamountofvarianceinthedata.Thisfindingsignalsthehigherheterogeneityandtastevariationamongthemembersofthisgroup,andtheincreaseddifficultyinexplainingtheirbehaviorsthroughtheestimationofeconometricandquantitativemodels.Traditionalbuiltenvironmentvariablessuchaspopulationdensityanddiversityofhousing/jobsdonotexplainasmuchvariationinVMTformillennialsasforGenerationX.Attitudinalvariablesandvariablesmeasuringthestageoflifeoftherespondents(inparticular,thelivingarrangementsandthepresenceofchildreninthehousehold)explainmorevariationformillennialsthanGenerationX,confirmingthatmillennials’travelchoicesarebestexplainedbytheirattitudesandstageoflifethanbymoretraditionalvariablesusedinotherstudies.Weinvestigatetherelationshipofindividualsbelongingtothevariousagegroupswithcarownershipandthetypeofvehiclethatisownedinthehousehold.Notsurprisingly,independentmillennialsthatliveinurbanareasarefoundtoownfewercarsperdriverinthehousehold.Thisfinding,whichmatchesthereducedneedsforacarindenser(andmoreaccessible)centralareas,andthestereotypeofmillennialsthatmoreoftenprefertoownfewervehiclesandadoptothermodesoftransportationmoreoften,mightbeshort-livedthough.Manyoldermillennialswholiveinurbanareasactuallyreportthattheydoplantopurchaseanewvehicleinthenearfuture,thusconfirmingthattheirzero-orlow-vehicleownershipstatusisprobablytheresultoftheindividuals’transientstageoflife,ratherthanthelong-termeffect

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ofstrongpreferencestowardsvehicleownershipanduse.Duringfuturestagesoftheresearch,weplantostudyhowcarownershipvariesacrossdifferentgroupsofthepopulationthroughtheestimationofcarownershipmodelsthatinvestigatehowvarioussociodemographiccharacteristics,individualpreferences,andlandusefeaturesaffecthouseholdcarownership,andtheuseoflatentclassanalysis(andlatentclassmodeling)tofurtheridentifytheimpactoftasteheterogeneityamongdifferentgroupsofindividualswithregardwithvehicleownershipandtravelbehavior.Inordertoinvestigatethepreferencetowardsthepurchaseofvariousvehicletypesamongdifferentgroupsofusers,inthisstageoftheresearchweestimatedamultinomiallogitmodel(MNL)ofvehicletypechoice,usingsocio-demographiccharacteristics,residentiallocationandlandusecharacteristics,andpersonalattitudesandpreferencesasexplanatoryvariables.Wefocusedonindividualsthatboughtorleasedausedornewvehiclethatismodelyear2010ornewerforthisanalysis,inordertoavoidthenoiseassociatedwiththeeventualpresenceofvehiclesthatweregiftedtotheindividualbyotherfamilymembers,orvehiclesthatwerepurchasedoutofcontingencies(e.g.asinthecaseofoldervehicles,forwhichonlyfewavailableoptionsmightbeavailableinalimitedpricerange).Duringthenextstagesoftheresearch,weplantocapitalizeonthisambitiousresearchprogramfortheinvestigationofthemobilityofmillennialsinCalifornia.Inparticular,weplantofurtherinvestigatetheheterogeneityinthepopulationofmillennials(andolderadults)throughthedevelopmentofclusterorlatentclassanalysistoanalyzedifferentprofilesofpeople,andinvestigatetheproportionofmillennialsandGenXersthatliveinurbanareas,havedynamiclifestyles,areheavyusersofsocialmedia,ownzero(orfew)cars,usepublictransportation,andadoptnewtechnologies,andwhatdifferencesexistwiththeothersegmentsofthemillennialpopulation.Further,weplantoinvestigate(andmodel)therelationshipsbetweenaccessibilityandmultimodalbehavioramongthemembersofthedifferentgenerations,whilecontrollingforotherfactorsaffectingresidentialandtravelchoices,includinghouseholdsizeandcomposition,individualattitudesandlifestyles,andadoptionoftechnology.Wealsoplantoinvestigatetherelationshipsbehindtheadoptionofsharedmobilityservicesandothercomponentsoftravelbehavior,amongvarioussub-segmentsofthepopulation.Inparticular,weplantoevaluatetherelationshipsandlatentconstructsbehindtheadoptionofsharedmobilityservices,suchascarsharingoron-demandrideservicessuchasUberorLyft,andanalyzetheimpactofvariousfactorsaffectingtheuseoftheseservicesinvariousgeographicregionsandneighborhoodtypes,andamongdifferentsegmentsofthepopulation,throughtheestimationofmultivariatemodelsoftheadoptionandfrequencyofuseofeachtypeofsharedmobilityservices.Wewillinvestigatetheimpactofresidentiallocationandneighborhoodcharacteristicsonthesechoices,andestimatebivariatemodelstoexploretherelationshipsbetweentheadoptionofsharedmobilityservicesand:

a) Theuseofothertravelmodes,includingdrivingaloneandusingpublictransportation;b) Autoownership;and

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c) Theindividual’sreportedwillingnesstochangethelevelofautoownership,e.g.reducingthenumberofvehiclesinthehousehold,buyinganewvehicle,etc.

Further,thestudywillexploreheterogeneityintravelers’behavior,withrespecttotheadoptionofsharedmobilityservices,travelbehavior,individuallifestylesandtastes,asawaytoinvestigatedifferencesintheobservedrelationshipsamongvariousgroupsofindividuals.Thestudywillprovideimportantinsightsintotheimpactoftheadoptionofnewsharedmobilityservicesonothercomponentsoftraveldemand,VMTandautoownershipinvariousregionsofCaliforniaandlandusetypes,controllingforindividualcharacteristicsanddifferencesamongsegmentsofthepopulation.Finally,thedatacollectioneffortforthisstudywasdesignedasthefirststepofalongitudinalstudyoftheemergingtransportationtrendsinCalifornia,designedwitharotatingpanelstructure,withadditionalwavesofdatacollectionplannedinfutureyears.Infuturestagesofthisresearch,weplantoexpandthedatacollectionalsothroughotherchannels,eventuallyalsothroughthecreationofapaperversionofthesurvey,inordertoexpandthetargetpopulationforthestudy,andreachspecificsegmentsofthepopulation,e.g.elderlyorpeoplethatarenotfamiliarwiththeuseoftechnologyorwhodonothaveeasyaccesstotheinternetandwouldnotlikelycompleteanonlinesurvey.Also,weareconsideringcreatingaversionofthesurveyinSpanish,inordertobetterreachtheCaliforniapopulationofLatinosandincreasetheresponserateamongtheHispanicminority.Theanalysisoftheinformationcollectedthroughmultiplewavesofsurveywillprovidevaluableinformationonthelikelychangeshappeningintraveldemand,andwillprovideinsightsintotheimpactsoftheadoptionofanumberofnewtransportationservicesonfuturetransportationinthestate.

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ListofAcronymsUsedintheDocument

ACOP AmericanConsumerOpinionPanelCaltrans CaliforniaDepartmentofTransportationCEC CaliforniaEnergyCommissionEPA (UnitedStates)EnvironmentalProtectionAgencyFHWA FederalHighwayAdministrationGenX GenerationX(Middle-agedadults,35-50y.o.in2015)GenY GenerationY(Youngadults,18-34y.o.in2015)GHG GreenhouseGasHH HouseholdICT InformationandCommunicationTechnologyIPF IterativeProportionalFittingIT InformationTechnologyIRB InstitutionalReviewBoardITS InstituteofTransportationStudiesLDT LightDutyTrucksLTE LongTermEvolution(a4Gmobilecommunicationsstandard)LU LandUseMNL MultinomialLogit(Model)MPO MetropolitanPlanningOrganizationsMTC MetropolitanPlanningOrganization(SanFranciscoBayArea)NCST NationalCenterforSustainableTransportationNHTS NationalHouseholdTravelSurveySACOG SacramentoAreaCouncilofGovernmentsSANDAG SanDiegoAssociationofGovernmentsSCAG SouthernCaliforniaCouncilofGovernmentsSTEPS SustainableTransportationEnergyPathwaysSUV SportUtilityVehicleTDM TransportationDemandManagementTNC TransportationNetworkCompanyTRB TransportationResearchBoardUC UniversityofCaliforniaUCDavis UniversityofCalifornia,DavisUCLA UniversityofCalifornia,LosAngelesUSDOT UnitedStatesDepartmentofTransportationVMT VehicleMilesTraveled