![Page 1: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/1.jpg)
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
![Page 2: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/2.jpg)
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.
![Page 3: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/3.jpg)
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
![Page 4: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/4.jpg)
[pageleftintentionallyblank]
![Page 5: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/5.jpg)
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
![Page 6: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/6.jpg)
i
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
![Page 7: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/7.jpg)
ii
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
![Page 8: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/8.jpg)
iii
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).
![Page 9: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/9.jpg)
1
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
![Page 10: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/10.jpg)
2
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
![Page 11: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/11.jpg)
3
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).
![Page 12: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/12.jpg)
4
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
![Page 13: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/13.jpg)
5
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).
![Page 14: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/14.jpg)
6
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
![Page 15: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/15.jpg)
7
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.
![Page 16: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/16.jpg)
8
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.
![Page 17: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/17.jpg)
9
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
![Page 18: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/18.jpg)
10
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
![Page 19: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/19.jpg)
11
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
![Page 20: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/20.jpg)
12
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.
![Page 21: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/21.jpg)
13
• 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.
![Page 22: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/22.jpg)
14
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
![Page 23: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/23.jpg)
15
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.
![Page 24: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/24.jpg)
16
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%)
![Page 25: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/25.jpg)
17
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
![Page 26: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/26.jpg)
18
ZIPcodes),theresearchteamwasabletoassigntheneighborhoodtypewithagoodmarginofreliability.Incontrast,ifone’sownneighborhoodtypediffersfromthatofitsneighboringCensustracts,weusedtheperceivedneighborhoodtypesthattheindividualsreportedinthesurveytodeterminewhichtypesofneighborhoodstherespondentsarelikelytolivein.Figure2summarizesthedistributionofcasesinthedatasetbyneighborhoodtype.
Figure2.Distributionofcasesinthedataset,bygeocodedresidentialaddressand
neighborhoodtype
![Page 27: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/27.jpg)
19
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
![Page 28: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/28.jpg)
20
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.
![Page 29: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/29.jpg)
21
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
![Page 30: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/30.jpg)
22
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.
![Page 31: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/31.jpg)
23
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.
![Page 32: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/32.jpg)
24
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.
![Page 33: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/33.jpg)
25
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.
![Page 34: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/34.jpg)
26
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
![Page 35: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/35.jpg)
27
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.
![Page 36: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/36.jpg)
28
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
![Page 37: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/37.jpg)
29
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
![Page 38: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/38.jpg)
30
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.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rural
Suburban
Urban
Rural
Suburban
Urban
Gen
XG
en Y
Seldom or never Sometimes Often Everytime or most often
![Page 39: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/39.jpg)
31
Figure4.Useofsmartphonetochecktrafficandtoplanthetravelrouteordeparturetimeby
agegroupandneighborhoodtype
Figure5.Useofsmartphonetocheckwhenabusortrainwillbearrivingbyagegroupand
neighborhoodtypeThedifferencesacrossneighborhoodtypesareparticularlylargefortheuseofsmartphonetechnologytocheckwhatmodesoftransportation,orcombinationsofmodes,touse,whichislikelytobeaneffectoftheavailabilityofmultipletraveloptionsindenserurbanareas.Inlatersectionsofthereport,wewillreturntodiscussingthemeasuresoftravelaccessibility,bymode,forthemembersofthevariousgenerations.Weplantofurtherinvestigate,infuturestepsoftheresearch,howtheuseofthesetechnologies,andthevariouslevelsofaccessibility
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rural
Suburban
Urban
Rural
Suburban
Urban
Gen
XG
en Y
Seldom or never At least once a year At least once a Month At least once a week Daily
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rural
Suburban
Urban
Rural
Suburban
Urban
Gen
XG
en Y
Seldom or never At least once a year At least once a Month At least once a week Daily
![Page 40: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/40.jpg)
32
intheareaswhereindividualslive,affecttheirtravelpatterns,anissueofsignificantimportancetoplanningprocesses.
Figure6.Useofsmartphonetodecidemeansoftransportationtousebyagegroupand
neighborhoodtype
Figure7.Useofsmartphonetolearnhowtogettoanewplacebyagegroupand
neighborhoodtype
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rural
Suburban
Urban
Rural
Suburban
Urban
Gen
XG
en Y
Seldom or never At least once a year At least once a Month At least once a week Daily
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rural
Suburban
Urban
Rural
Suburban
Urban
Gen
XG
en Y
Seldom or never At least once a year At least once a Month At least once a week Daily
![Page 41: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/41.jpg)
33
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).
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Rural
Suburban
Urban
Rural
Suburban
Urban
Gen
XG
en Y
Seldom or never At least once a year At least once a Month At least once a week Daily
![Page 42: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/42.jpg)
34
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
![Page 43: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/43.jpg)
35
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)
![Page 44: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/44.jpg)
36
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.
![Page 45: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/45.jpg)
37
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.
![Page 46: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/46.jpg)
38
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).
![Page 47: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/47.jpg)
39
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.”
![Page 48: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/48.jpg)
40
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”.
![Page 49: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/49.jpg)
41
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.
![Page 50: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/50.jpg)
42
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”.
![Page 51: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/51.jpg)
43
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
![Page 52: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/52.jpg)
44
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
![Page 53: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/53.jpg)
45
duetotheirinterestinhavingthelatestgadgets,ortheirstageoflife–wherefewhavechildrenormortgagesthatpreventthemfromacquiringorwantingtoacquirematerialgoods.OlderGenerationXmembershavethelowestaveragescoresforthematerialismfactor.Infuturestagesoftheresearch,itwillbeveryinterestingtoexplorehowthemembersofthefollowingGenerationZ(under18yearolds,asoftoday),willbehaveinfutureyears,comparedtothesegenerationsthatwearestudying.Inaddition,non-urbanrespondents(apartfromtheyoungmillennials)tendtohaveloweraveragevaluesforthisfactor,andthushavelowermaterialisticattitudes,thantheirurbancounterparts.
Figure19.Average“materialism”factorscorebyageandneighborhoodtype(95%confidence
intervalsarereportedinthefigureforeachgroup)
![Page 54: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/54.jpg)
46
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
![Page 55: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/55.jpg)
47
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
![Page 56: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/56.jpg)
48
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.
![Page 57: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/57.jpg)
49
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
![Page 58: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/58.jpg)
50
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.
![Page 59: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/59.jpg)
51
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.
![Page 60: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/60.jpg)
52
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).
![Page 61: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/61.jpg)
53
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%
![Page 62: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/62.jpg)
54
Figure24.Adoptionofmultimodalbehavior,byregionofCaliforniaandsub-segmentofthe
population
![Page 63: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/63.jpg)
55
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
![Page 64: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/64.jpg)
56
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.
![Page 65: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/65.jpg)
57
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
![Page 66: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/66.jpg)
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
![Page 67: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/67.jpg)
59
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
![Page 68: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/68.jpg)
60
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
![Page 69: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/69.jpg)
61
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
![Page 70: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/70.jpg)
62
(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).
![Page 71: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/71.jpg)
63
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).
![Page 72: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/72.jpg)
64
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).
![Page 73: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/73.jpg)
65
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
![Page 74: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/74.jpg)
66
(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
![Page 75: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/75.jpg)
67
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.
![Page 76: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/76.jpg)
68
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
![Page 77: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/77.jpg)
69
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.
![Page 78: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/78.jpg)
70
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”.
![Page 79: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/79.jpg)
71
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
![Page 80: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/80.jpg)
72
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.
![Page 81: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/81.jpg)
73
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.
![Page 82: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/82.jpg)
74
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
![Page 83: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/83.jpg)
75
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
![Page 84: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/84.jpg)
76
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.
![Page 85: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/85.jpg)
77
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
![Page 86: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/86.jpg)
78
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
![Page 87: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/87.jpg)
79
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
![Page 88: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/88.jpg)
80
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.
![Page 89: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/89.jpg)
81
References
Bagley,M.N.,Mokhtarian,P.L.&Kitamura,R.,2002.Amethodologyforthedisaggregate,multidimensionalmeasurementofresidentialneighbourhoodtype.UrbanStudies,39(4),pp.689–704.
Berliner,R.M.,andG.Circella.2017.“CalifornianMillennialsDriveSmallerCars:EstimatingVehicleTypeChoiceofMillennials.”Presentedatthe96thTransportationResearchBoardMeeting,Washington,D.C.,January2017.
Blumenberg,E.,A.Brown,K.Ralph,B.D.Taylor,andC.T.Voulgaris.2015.Typecastingneighborhoodsandtravelers:AnalyzingthegeographyoftravelbehavioramongteensandyoungadultsintheU.S.ProjectReporttotheFederalHighwayAdministration,September2015.
Blumenberg,E.,B.D.Taylor,M.Smart,K.Ralph,M.Wander,andS.Brumbagh..,2012.What’sYouthGottoDowithIt?ExploringtheTravelBehaviorofTeensandYoungAdults.ProjectReport,UniversityofCaliforniaLosAngeles,September2012,LosAngeles.
Blumenberg,E.,K.Ralph,M.Smart,andB.D.Taylor.,2016.Whoknowsaboutkidsthesedays?AnalyzingthedeterminantsofyouthandadultmobilityintheU.S.between1990and2009.TransportationResearchPartA:PolicyandPractice,93,pp.39–54.
Blumenberg,E.&Smart,M.,2014.Brothercanyousparearide?Carpoolinginimmigrantneighbourhoods.UrbanStudies,51(9),pp.1871–1890.
Brownstone,D.&Golob,T.F.,2009.Theimpactofresidentialdensityonvehicleusageandenergyconsumption.JournalofUrbanEconomics,65(1),pp.91–98.
BRS,2013.Americans’ViewsontheirCommunities,Housing,andTransportation,Washington,D.C.Availableat:http://uli.org/wp-content/uploads/ULI-Documents/America-in-2013-Final-Report.pdf(LastaccessedonMarch30,2017).
Cao,X.,Mokhtarian,P.L.&Handy,S.L.,2009a.Examiningtheimpactsofresidentialself-selectionontravelbehaviour:afocusonempiricalfindings.TransportReviews,29(3),pp.359–395.
Cao,X.,Mokhtarian,P.L.&Handy,S.L.,2009b.Therelationshipbetweenthebuiltenvironmentandnonworktravel:AcasestudyofNorthernCalifornia.TransportationResearchPartA:PolicyandPractice,43(5),pp.548–559.
Cervero,R.&Duncan,M.,2006.’WhichReducesVehicleTravelMore:Jobs-HousingBalanceorRetail-HousingMixing?JournaloftheAmericanPlanningAssociation,72(4),pp.475–490.
Cervero,R.&Duncan,M.,2003.Walking,Bicycling,andUrbanLandscapes:EvidenceFromtheSanFranciscoBayArea.AmericanJournalofPublicHealth,93(9),pp.1478–1483.
Cervero,R.&Murakami,J.,2010.Effectsofbuiltenvironmentsonvehiclemilestraveled:evidencefrom370USurbanizedareas.EnvironmentandplanningA,42(2),pp.400–418.
Circella,G.andP.L.Mokhtarian(2017)ImpactofInformationCommunicationTechnologyonTransportation,inHansonS.andG.Giuliano,eds.TheGeographyofUrbanTransportation,4thEdition,Chapter4,pp.86-109,NewYork:TheGuilfordPress.
Circella,G.,K.Tiedeman,S.Handy,F.Alemi,andP.L.Mokhtarian.2016a.WhatAffectsU.S.
PassengerTravel?CurrentTrendsandFuturePerspectives.WhitePaperfromtheNationalCenterforSustainableTransportation.UniversityofCalifornia,Davis,February2016;
![Page 90: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/90.jpg)
82
availableathttps://ncst.ucdavis.edu/wp-content/uploads/2014/08/06-15-2016-NCST_White_Paper_US_Passenger_Travel_Final_February_2016_Caltrans3.pdf(LastaccessedonMarch30,2017).
Circella,G.,L.Fulton,F.Alemi,R.M.Berliner,K.Tiedeman,P.L.Mokhtarian,andS.Handy.2016b.WhatAffectsMillennials’Mobility?PARTI:InvestigatingtheEnvironmental
Concerns,Lifestyles,Mobility-RelatedAttitudesandAdoptionofTechnologyofYoung
AdultsinCalifornia.ProjectReport,NationalCenterforSustainableTransportation.UniversityofCalifornia,Davis,May2016;availableathttp://ncst.ucdavis.edu/wp-content/uploads/2014/08/05-26-2016-NCST_Report_Millennials_Part_I_2016_May_26_FINAL1.pdf(LastaccessedonMarch30,2017).
Circella,G.,F.Alemi,R.M.Berliner,K.Tiedeman,Y.Lee,L.Fulton,S.Handy,andP.L.Mokhtarian.2017.“MultimodalBehaviorofMillennials:ExploringDifferencesinTravelChoicesBetweenYoungAdultsandGen-XersinCalifornia.”Presentedatthe96thTransportationResearchBoardAnnualMeeting,Washington,D.C.,January2017.
Contrino,H.&McGuckin,N.,2006.AnExplorationoftheInternet’sEffectonTravel,Citeseer.Delbosc,A.&Currie,G.,2014.Changingdemographicsandyoungadultdriverlicensedeclinein
Melbourne,Australia(1994--2009).Transportation,41(3),pp.529–542.Diana,M.&Mokhtarian,P.L.,2009.GroupingTravelersontheBasisoftheirDifferentCarand
TransitLevelsofUse.Transportation,36,pp.455-467.Ewing,R.&Cervero,R.,2010.TravelandtheBuiltEnvironment.JournaloftheAmerican
PlanningAssociation,76(3),pp.265–294.Ewing,R.&Cervero,R.,2001.Travelandthebuiltenvironment:asynthesis.Transportation
ResearchRecord:JournaloftheTransportationResearchBoard,(1780),pp.87–114.Frändberg,L.&Vilhelmson,B.,2011.Moreorlesstravel:personalmobilitytrendsinthe
Swedishpopulationfocusinggenderandcohort.JournalofTransportGeography,19(6),pp.1235–1244.
Fry,R.A.&Passel,J.S.,2014.Inpost-recessionera,youngadultsdrivecontinuingriseinmulti-generationalliving,PewResearchCenter,Social&DemographicTrendsProject;availableathttp://www.pewsocialtrends.org/2014/07/17/in-post-recession-era-young-adults-drive-continuing-rise-in-multi-generational-living/(LastaccessedonMarch30,2017).
Garikapati,V.M.,R.M.Pendyala,E.A.Morris,P.L.Mokhtarian,andN.C.McDonald,2016.ActivityPatterns,TimeUse,andTravelofMillennials:AGenerationinTransition?TransportReviews36(5),pp.558-584.
Goodwin,P.,2012.Peaktravel,peakcarandthefutureofmobility.Greene,D.L.,Chin,S.-M.&Gibson,R.,1995.Aggregatevehicletravelforecastingmodel,Oak
RidgeNationalLab.,TN(UnitedStates).Hallock,L.&Inglis,J.,2015.TheInnovativeTransportationIndex:TheCitiesWhereNew
TechnologiesandToolsCanReduceYourNeedtoOwnaCar.,Availableat:http://www.uspirg.org/sites/pirg/files/reports/Innovative_Transportation_Index_USPIRG.pdf(LastaccessedonMarch30,2017).
Handy,S.,Cao,X.&Mokhtarian,P.,2005.Correlationorcausalitybetweenthebuiltenvironmentandtravelbehavior?EvidencefromNorthernCalifornia.Transportation
![Page 91: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/91.jpg)
83
ResearchPartD:TransportandEnvironment,10(6),pp.427–444.Handy,S.,Cao,X.&Mokhtarian,P.L.,2006.Self-selectionintherelationshipbetweenthebuilt
environmentandwalking:EmpiricalevidencefromNorthernCalifornia.JournaloftheAmericanPlanningAssociation,72(1),pp.55–74.
Handy,S.L.,2002.TravelBehavior--LandUseInteractions:AnOverviewandAssessmentoftheResearch.InMahmassaniH.,ed.InPerpetualMotion:TravelBehaviorResearch
OpportunitiesandApplicationChallenges,Amsterdam:Pergamon.Hanks,K.W.Odom,D.Roedl,andE.Blevis.2008.Sustainablemillennials:attitudestowards
sustainabilityandthematerialeffectsofinteractivetechnologies.InProceedingsoftheSIGCHIConferenceonHumanFactorsinComputingSystems.ACM,pp.333–342.
ITSAmerica,2015.RiseoftheReal-TimeTraveler:Anexplorationoftrendsandinnovationin
urbanmobility.Kalton,G.&Flores-Cervantes,I.,2003.Weightingmethods.JournalofOfficialStatistics,19(2),
p.81.Klein,N.J.&Smart,M.J.,2017.Millennialsandcarownership:Lessmoney,fewercars.
TransportPolicy,53,pp.20–29.Kuhnimhof,T.,J.Armoogum,R.Buehler,J.Dargay,J.M.Denstadli,andT.Yamamoto.2012.
MenShapeaDownwardTrendinCarUseamongYoungAdults—EvidencefromSixIndustrializedCountries.TransportReviews,32(6),pp.761–779.
Lyons,G.,2014.Viewpoint:Transport’sdigitalagetransition.JournalofTransportandLandUse,8(2),pp.1-19.
McDonald,N.C.,2015.AreMillennialsReallythe“Go-Nowhere”Generation?JournaloftheAmericanPlanningAssociation,pp.1–14.
Metz,D.,2012.Demographicdeterminantsofdailytraveldemand.TransportPolicy,21,pp.20–25.
Metz,D.,2013.Peakcarandbeyond:thefourtheraoftravel.TransportReviews,33(3),pp.255–270.
Mokhtarian,P.,2009.Iftelecommunicationissuchagoodsubstitutefortravel,whydoescongestioncontinuetogetworse?TransportationLetters,1(1),pp.1–17.
Myers,D.,2016.PeakMillennials:ThreeReinforcingCyclesThatAmplifytheRiseandFallofUrbanConcentrationbyMillennials.HousingPolicyDebate,pp.1–20.
PewResearchCenter,2014.ComparingMillennialstoothergenerations.Availableat:http://www.pewsocialtrends.org/2015/03/19/comparing-millennials-to-other-generations/(LastaccessedonMarch30,2017).
Polzin,S.E.,Chu,X.&Godfrey,J.,2014.Theimpactofmillennials’travelbehavioronfuturepersonalvehicletravel.EnergyStrategyReviews,5,pp.59–65.
Prensky,M.,2001.DigitalNatives,DigitalImmigrantsPart1.OntheHorizon,9(5),pp.1–6.Puentes,R.,2013.HaveAmericansHitPeakTravel?ITFRoundTablesLong-runTrendsinCar
Use,152,p.91.Ramsey,K.&Bell,A.,2014.Smartlocationdatabase.Washington,DC.Rentziou,A.,Gkritza,K.&Souleyrette,R.R.,2012.VMT,energyconsumption,andGHG
emissionsforecastingforpassengertransportation.TransportationResearchPartA:PolicyandPractice,46(3),pp.487–500.
![Page 92: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/92.jpg)
84
Salomon,I.&Mokhtarian,P.L.,2008.Cantelecommunicationshelpsolvetransportationproblems?Adecadelater:Aretheprospectsanybetter.InD.A.Hensher&K.J.Button.,eds.HandbookofTransportModelling.Amsterdam,pp.519–540.
Salon,D.,2015.Heterogeneityintherelationshipbetweenthebuiltenvironmentanddriving:Focusonneighborhoodtypeandtravelpurpose.ResearchinTransportationEconomics,52,pp.34–45.
Santos,A.,N.McGuckin,H.Y.Nakamoto,D.Gray,andS.Liss.2011.Summaryoftraveltrends:
2009nationalhouseholdtravelsurvey.Shaheen,S.,N.Chan,A.Bansal,andA.Cohen.2015.SharedMobilityaSustainablityand
TechnologyWorkshop:Definition,IndustryDevelopmentandEarlyUnderstanding.UniversityofCaliforniaBerkeley.
Shin,E.J.,2016.UnravelingtheEffectsofResidenceinanEthnicEnclaveonImmigrants’TravelModeChoices.JournalofPlanningEducationandResearch,p.0739456X16663309.
Sivak,M.,2014a.HasmotorizationintheUSpeaked?Part4:Householdswithoutalight-dutyvehicle.TransportResearchInstitute,UniversityofMichigan,AnnArbor.
Sivak,M.,2014b.HasmotorizationintheUSpeaked?Part5:Updatethrough2012.Strauss,W.&Howe,N.,2000.Millennialsrising:Thenextgreatgeneration.NewYork:Vintage.Taylor,B.,R.Chin,M.Crotty,J.Dill,L.Hoel,M.Manville,S.Polzin,B.Schaller,S.Shaheen,D.
Sperling,M.Zafar,andS.Zielinski.2015.SpecialReport319:Betweenpublicandprivatemobility:Examiningtheriseoftechnology-enabledtransportationservices.
Tiedeman,K.,G.Circella,F.Alemi,andR.M.Berliner.2017.“WhatDrivesMillennials:ComparisonofVehicleMilesTraveledBetweenMillennialsandGenerationXinCalifornia.”Presentedatthe96thTransportationResearchBoardAnnualMeeting,Washington,DC.,January2017.
U.S.CensusBureau,2014.AmericanCommunitySurvey,2014AmericanCommunitySurvey1-YearEstimates.
Vij,A.,Gorripaty,S.&Walker,J.L.,2015.Fromtrendspottingtotrend’splaining:UnderstandingmodalpreferenceshiftsintheSanFranciscoBayArea.
LeVine,S.&Jones,P.,2012.OntheMove:MakingSenseofcarandtraintraveltrendsinBritain.,p.121.
Wachs,M.,2013.Turningcitiesinsideout:transportationandtheresurgenceofdowntownsinNorthAmerica.Transportation,40(6),pp.1159–1172.
Zipcar,2013.Millennials&Technology:ASurveyCommissionedbyZipcar(Powerpoint),Zipcar’smillennialstudy.
Zmud,J.P.,V.P.Barabba,M.Bradley,J.R.Kuzmyak,M.Zmud,andD.Orrell.2014.StrategicIssuesFacingTransportation,Volume6:TheEffectsofSocio-DemographicsonFuture
TravelDemand.
![Page 93: What Affects Millennials’ Mobility? PART II: The ... - STEPS€¦ · to a sample of 2400 residents of California, including millennials (young adults, 18-34 in 2015) and Gen Xers](https://reader033.vdocuments.us/reader033/viewer/2022060415/5f131a03f41cea0fab64668c/html5/thumbnails/93.jpg)
85
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