ITU -人工智能赋能城市元宇宙 人工智能时代的城市应用场景 交通与出行 AI-Enabled Citiverse Use Cases for Cities in the Age of AI – Transport and Mobility

AI.EnabledCitiverse:UseCasesfor

CitiesintheAgeofAI

TransportandMobility

Foreword

Thispublicationwasdevelopedwithintheframeworkofthe

GlobalInitiativeonAIandVirtual

Worldsand–DiscoveringtheCitiverse

,whichisaglobalmultistakeholderplatformlaunchedbytheInternationalTelecommunicationUnion(ITU),theUnitedNationsInternationalComputingCentre(UNICC),andDigitalDubai,andsupportedbymorethan70internationalpartners.

TheInitiativeadvancesthedevelopmentoftheAI-enabledcitiverse,whereartificialintelligence,spatialintelligence,digitaltwins,andimmersivesystemsconvergetodeliverreal-worldimpact.Itaimstoensurethatthistransformationisinclusive,trustedandinteroperable,andthatitservespeople,citiesandcommunities.

Byconnectingcities,governments,industry,academia,andtheUNsystem,theInitiativesupportsthetransitionfromvisiontoimplementation–empoweringleaderstoharnessthesetechnologiestoimprovequalityoflife,strengthenresilience,anddrivesustainableandinclusivedevelopment.

Acknowledgements

ThedevelopmentofthisdeliverablewasledandcoordinatedLouisaBarker(IDC).Thedeliverableisbasedonthecontribution,supportandparticipationofSteffenBraun(FraunhoferInstituteforIndustrialEngineering),VanessaBorkmann(SRHUniversityDresden),PetrSuska(OICTPrague),TaishaFabricius(ESRI),JenniferSchooling(AngliaRuskinUniversity),EvaHolzova(BrnoCity),MichalLakomski(CityofPoznan),RicardoGoncalves(MunicipalityofFundão),JukkaAlander(ForumViriumHelsinki),BrandonBranham(CityofPeachtreeCorners),ChristophSchubert(CityofLeipzig),AndreiaRosaCollard(RegionalGovernmentofMadeira),DavidWardenSime(InvantageLtd),ScottDickson(BoldDigitalMediaPtyLtd.),CarlosSousa(UrbanEconomyForum),FabioCarbone(NorthamptonUniversity),AdeniyiTinubu(HudersFieldProperty),JoeAppleton(BizzTech),AleksanderOrlowski(GdanskUniversityofTechnology),SegunWilliams(LagosState),CarloCapua(CityofFortWorth),GintarėJanušaitienė(MinistryofTransportandCommunications,Lithuania),GraceQuintana(MetroBogota),AnnaLisaBoni(CommunediBologna),AlisonBrooks(IDC),JohnApostolidis(CityofToronto),LeonidasAnthopoulos(UniversityofThessaly),FabriceKlein(PortofBordeaux),AndrewSchroeder(DirectRelief),KanikaKalra(WHO),QueenNdlovu(QPDroneTech,SouthAfricaFlyingLabs).

TheauthorsextendtheirsincerethankstotheExecutiveCommitteeoftheGlobalInitiativeonAIandVirtualWorlds–DiscoveringtheCitiverse:H.E.MrHamadAlMansoori(DirectorGeneral,DigitalDubai),H.E.MsAngellahJasmineMbelwaKairuki(MinistryofInformation,CommunicationandInformationTechnology,Tanzania),H.E.MrWilliamKabogoGitau(MinistryofInformation,CommunicationsandtheDigitalEconomy,Kenya),FelipeFernandoMacíasOlvera(MunicipalityofQueretaro,Mexico),ManuelBarreiro(AstonGroup),Karl-FilipCoenegrachts(Open&AgileSmartCities(OASC)),HyoungJunKim(ITU-TStudyGroup20“InternetofThings,digitaltwinsandsmartsustainablecitiesandcommunities”),JaakkoMustakallio(CityofTampere,Finland),PaulaLlobetVilarrasa(CityofValencia,Spain),SameerChauhan(UnitedNationsInternationalComputingCentre(UNICC))andJeongKeeKim(WorldSmartSustainableCitiesOrganization(WeGO)).

TheauthorsalsothanktheSteeringCommitteeoftheGlobalInitiativeonAIandVirtualWorlds–DiscoveringtheCitiversefortheircontinuedsupport:OkanGeray(DubaiDigitalAuthority),BertrandLevy,TeppoRantanen(CityofTampere,Finland),PaolaCecchiDimeglio(HarvardUniversity),ErnestoFaubel(EuropeanDigitalInfrastructureConsortium(EDIC)onLocalDigitalTwins),MartinBrynskov(OASC),AnishSethi(UNICC),AnaMariaMeshkurti(AMVSCapital)andRolandvanderHeijden(CityofRotterdam,TheNetherlands).

Theauthorsalsoextendtheirgratitudetothecontributingorganizationsalongwiththeirrepresentatives:CristinaBueti,YiningZhao,ChiaraCo(ITU)andFrancaVinci(UNICC).

Disclaimers

Theopinionsexpressedinthispublicationarethoseoftheauthorsanddonotnecessarilyrepresenttheviewsoftheirrespectiveorganizations,ExecutiveCommitteemembersorSteeringCommitteemembersoftheInitiative.Thefindingspresentedinthisreportarebasedonacomprehensivereviewofexistingliteratureandvoluntarywrittencontributionssubmittedbyadiverserangeofstakeholders.

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©DigitalDubai,UNICCandITU

AI.EnabledCitiverse:UseCasesforCities

intheAgeofAI

TransportandMobility

Tableofcontents

Foreword ii

Acknowledgements ii

Abbreviationsandacronyms viii

ExecutiveSummary ix

1Introduction 1

2Transportandmobility 2

2.1Thematicareadescription 2

2.2Transportandmobilityusecases 3

Usecase1:Trafficoptimizationandsimulation(Horizon1) 3

Usecase2:Predictivetransitscheduling(Horizon1) 7

Usecase3:MunicipalParkingSpaceOptimization(Horizon1) 11

Usecase4:Multimodalemergencyevacuationsimulation(Horizon1) 16

Usecase5:Immersivemobility-as-a-service(Horizon2) 20

Usecase6:XRmobilityplanning(Horizon2) 24

Usecase7:Metaverseforcivicmultimodalplanning(Horizon3) 28

Usecase8:PersonalisedXRcommuterpods(Horizon3) 31

AbouttheGlobalInitiativeonAIandVirtualWorlds–DiscoveringtheCitiverse 34

References 37

vi

vii

Listoffiguresandtables

Figures

Figure1:Overallusecaseoverviewandhorizonmapping 1

Figure2:Transportandmobilityusecaseoverviewandhorizonmapping 2

Tables

Table1:Risklevel:Trafficoptimizationandsimulation 5

Table2:Risklevel:Predictivetransitscheduling 9

Table3:Risklevel:MunicipalParkingSpaceOptimization 13

Table4:Risklevel:MultimodalEmergencyEvacuationSimulation 18

Table5:Risklevel:Immersivemobility-as-a-service 22

Table6:Risklevel:XRmobilityplanning 26

Table7:Risklevel:Metaverseforcivicmultimodalplanning 31

Table8:Risklevel:PersonalisedXRcommuterpods 33

viii

Abbreviationsandacronyms

AI

Artificialintelligence

API

Applicationprogramminginterface

AR

Augmentedreality

CCTV

Closed-circuittelevision

GAI

Generativeartificialintelligence

GenAI

Generativeartificialintelligence

GIS

Geographicinformationsystem

GPS

Globalpositioningsystem

ICT

Informationandcommunicationtechnology

IoT

InternetofThings

MERS

MiddleEastRespiratorySyndrome

MR

Mixedreality

SDG

SustainableDevelopmentGoal

TOPIS

TransportOperationandInformationService

VR

Virtualreality

XR

Extendedreality

ix

ExecutiveSummary

Movementisthelifebloodofacity,andyetcongestion,pollution,ageinginfrastructureandunequalaccesstotransportremainamongthemostpersistentchallengesurbanleadersface.ThisreportexamineshowAI-enabledcitiverseandrelatedtechnologiesaregivingcitiesnewtoolstoaddressthosechallenges:simulatingtrafficinrealtimebeforeinterventionsaremade,optimisingtransitnetworksbeforedelaysaccumulate,planningevacuationsbeforeemergenciesstrike,anddesigningmobilityinfrastructurewithcommunitiesbeforeconstructionbegins.ItisoneoffivethematicusecasereportsthatcollectivelyconstitutetheAI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI.Together,theyprovideapracticalreferenceforAI-enabledcitiverseimplementationacrossmajorurbandomains.Intendedforcityleaders,policymakersandurbaninnovationpractitioners,itprovidesaconciseoverviewofapplicationsthroughwhichAI-enabledcitiverseandrelatedtechnologiescanimprovetrafficmanagement,enhancepublictransportoperations,optimizeinfrastructureuse,strengthenemergencypreparedness,supportmultimodalwayfinding,andenablemoreinclusiveandparticipatorymobilityplanning,spanningtrafficoptimizationandsimulation,predictivetransitscheduling,municipalparkingspaceoptimization,multimodalemergencyevacuationsimulation,immersivemobility-as-a-service,XRmobilityplanning,andcivicmultimodalplanning.

Adigitaltwinofacity'sroadnetworkorapredictivetransitschedulingsystemisonlyasvaluableasthereductionincongestionitdelivers,theemissionsitsaves,orthecommuterwhoarrivesontime.Usecasesareexaminednotonlyintermsoftheirtechnologicalcomposition,butthroughthelensoftheirrelevancetoaccessibility,safety,operationalefficiency,sustainabilityandimplementationreadiness.

Whoshouldusethisreport?

Thisreportisintendedfor:

•mayorsandcityleaders;

•nationalministersandseniorpolicymakers;

•nationalregulatoryauthorities;

•cityadministratorsandpublicsectorleadershipteams;

•policyadvisersandurbanstrategyteams;

•digital,innovation,andtransformationoffices;

•publicofficialsresponsiblefortransport,mobility,infrastructure,servicedelivery,andcivicengagement;

•consultancyfirmssupportingtechnical,commercial,andstrategicdecision-making.

x

Howcanthisreporthelp?

Thisreportisintendedtohelpreaders:

•understandtherangeofusecasesthroughwhichtheAI-enabledcitiversecansupporttransportandmobility;

•explainwhythoseusecasesmatter,howemergingtechnologiestranslateintopracticalimprovementsintransportoperations,publicsafety,accessibilityanduserexperience,andwheretheirrelevanceliesforcitiesandcommunities;

•connectlong-termmobilitytransformationgoalswithpracticalimplementationchoices;

•assessusecasesinrelationtopublicpurpose,safety,sustainability,feasibility,scalabilityandimplementationrisk;

•supportmoreresponsible,inclusiveandfuture-readyapproachestotransportplanning,mobilitymanagementandservicedeliver.

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

1

1Introduction

The

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

providesaconsolidatedoverviewofnearly50usecasesspanningfivethematicareas.Figure1presentstheoverallusecaselandscapeandhorizonmapping.Ithighlightstheinterconnectionsbetweendomainsanddemonstrateshowemergingtechnologiescanbeappliedacrossmultipleaspectsofurbanlife.Withinthisbroaderframework,thisreportfocusesonthethematicareaoftransportandmobility.Itwilldiscusskeyenablingtechnologies,implementationimpacts,andcasestudiesrelatedtothisthematicarea.ThemethodologyforusecaseselectioncanbefoundinAI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI:Introduction.

Figure1:Overallusecaseoverviewandhorizonmapping

Source:AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI:Introduction,2026

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

2

2Transportandmobility

Figure2:Transportandmobilityusecaseoverviewandhorizonmapping

Source:AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI:TransportandMobility,2026

2.1Thematicareadescription

CitiesaregrapplingwithhighlevelsofGHGemissionsandairpollutionlinkedtotransport.Estimatessuggestthaturbanareasareresponsiblefor70percentofglobalC02emissions,withtransportandbuildingsbeingamongthelargestcontributors(IPCC2022).Manycitiesarealsofacinghighlevelsoftrafficcongestion,ageinginfrastructure,andgrowingpopulations.

ThetransportandmobilitythematicareafocusesonleveragingAI-enabledcitiversetotransformthewaypeopleandgoodsmovethroughcities.Usecaseswillspan:

•Publictransportation:Includingenhancingtheaccess,sustainability,efficiency,customerexperienceandmodalshareofpublictransportfromrailwaystobusnetworks.

•Activetransportandmicro-mobility:Increasingopportunitiesandreclaimingurbanspacesforsafeandattractivewalking,cyclingandmicro-mobilitysuchasscooters.

•Privatevehicles:ReducingtrafficcongestionandsafetyandenablingEVadoptionthroughbetterurbaninfrastructuresystems.

•Urbanlogisticsandfreight:Optimizingthemovementofgoodswithincitiesthroughsmarterandmoreintelligentsystemsfromsupplymanagementtolast-miledelivery.

•Transportationhubs:transformingstations,cityportsandairportsintoseamlesslyintegratedmultimodaltransporthubs.

ThethematicareawillprioritisetransportandmobilityusecasesthatsupporttheimplementationoftheSDGS,includingTarget11.2“By2030,provideaccesstosafe,affordable,accessibleandsustainabletransportsystemsforall,improvingroadsafety,notablybyexpandingpublictransport,withspecialattentiontotheneedsofthoseinvulnerablesituations,women,children,personswithdisabilitiesandolderpersons.

Toachieveintelligentandsustainabletransportationandmobilitysystems,aparadigmshiftisrequired.EmergingtechnologiessuchasAR,VR,digitaltwinsandAIwillbeessentialforthistransformation.Thesetoolscanbeappliedacrossallphasesoftransportationoperations,from

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

3

planninganddesigntoreal-timemonitoringandmanagement,enablingdata-drivendecisionmaking.

2.2Transportandmobilityusecases

Usecase1:Trafficoptimizationandsimulation(Horizon1)

Description

Virtualworldtechnologiessuchasdigitaltwinscanbeusedtocreateareal-timesimulationoftrafficflowsacrosscities.Citystakeholderssuchastransportplanners,trafficmanagementauthorities,andpublicsafetydepartmentscanusethisplatformtomonitormobilitypatterns,modeldifferenttrafficscenarios,andadjustinfrastructureoperationstooptimizeflowandenhancesafety.IoTdevicessuchassensorsandconnectedtrafficsignalsfeedlivedataintothedigitaltwin,enablingcontinuousupdatestothevirtualmodel.AIcanbeleveragedtopredictcongestionhotspots,dynamicallyadjusttrafficsignaltimings,andrecommendreroutingstrategiestominimizedelays,reduceemissions,andimprovetravelexperience.

Impacts

1)Reducedtrafficcongestion:Real-timedataanalysisandadaptivesignalcontrolhaveledtosmoothertrafficflowanddecreasedtraveltimes.

2)Enhancedpublicsafety:Improvedmonitoringandrapidresponsecapabilitieshavecontributedtoareductionintrafficaccidentsandfatalities.

3)Environmentalbenefits:Optimizedtrafficflowhasresultedinlowervehicleemissions,contributingtobetterairquality.

4)Informedurbanplanning:Trafficoptimizationandsimulationcanprovidevaluableinsightsforinfrastructuredevelopmentandpolicymaking.

5)Economicbenefits:Reducingtraveltimecanleadtooverallincreasesineconomicproductivity,throughreducecommutetimesandimprovingaccesstolocalbusinesses,retailandrecreationalareas.

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

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Keybeneficiaries

•Commutersandresidents

•Emergencyservices

•Urbanplannersandpolicymakers

•Environmentalagencies

Keytechnologies

•Digitaltwin:A3Dsimulationplatformthatmodelsthecity'sinfrastructureandenvironment.

•AI:Analysetrafficdatatopredictcongestionandoptimizesignaltimings.

•C-ITS:Enablesvehicle-to-infrastructure(V2I)andvehicle-to-vehicle(V2V)communicationforcoordinatedtrafficmanagement.

•IoTSensors:Collectreal-timedataontrafficconditions,vehiclespeedsandenvironmentalfactors.

SDGalignment

•SDG3:Target3.6By2020,halvethenumberofglobaldeathsandinjuriesfromroadtrafficaccidents.

•SDG9:Target9.1Developquality,reliable,sustainableandresilientinfrastructure,includingregionalandtransborderinfrastructure,tosupporteconomicdevelopmentandhumanwell-being,withafocusonaffordableandequitableaccessforall.

•SDG11:Target11.2By2030,provideaccesstosafe,affordable,accessibleandsustainabletransportsystemsforall,improvingroadsafety,notablybyexpandingpublictransport,withspecialattentiontotheneedsofthoseinvulnerablesituations,women,children,peoplewithdisabilitiesandolderpeople.

•SDG11:Target11.3By2030,enhanceinclusiveandsustainableurbanizationandcapacityforparticipatory,integratedandsustainablehumansettlementplanningandmanagementinallcountries.

•SDG11:Target11.6Strengtheneffortstoprotectandsafeguardtheworld’sculturalandnaturalheritage.

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

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Risklevel

Table1:Risklevel:Trafficoptimizationandsimulation

Riskattribute

Riskrating

Explanation

Publicsafety

Low

Medium

High

Thesystemenhancessafetythroughimprovedtrafficmanagementandemergencyresponsecapabilities.

Stakeholder

acceptance

Low

Medium

High

Publicandinstitutionalsupportisstrongduetovisibleimprovementsintrafficconditions.

Dataprivacy

andsecurity

Low

Medium

High

Handlingofreal-timedatarequiresrobustcyber-securitymeasurestoprotectagainstbreaches.

Financial/

operational

Low

Medium

High

Significantinvestmentisneededforinfrastruc-

tureandmaintenance,butlong-termbenefitsaresubstantial.

Implementedin:

Seoul,RepublicofKorea;Aachen,Germany;Zurich,Switzerland;Boston,USA;Dubai,UAE;Singapore

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

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Casestudy:AI-poweredtrafficoptimizationusingdigitaltwinsinSeoul

Context

Seoul,RepublicofKorea,isoneofthemostdenselypopulatedcitiesintheworld,hometomorethan9millionresidentswithinitsmetropolitanboundary.

1

Thecityhaslonggrappledwithseveretrafficcongestion,risinggreenhousegasemissions,andtheneedformoreefficientandresilientpublicinfrastructure.Historically,Seoul’strafficmanagementreliedonsiloedsystemsandmanualcontrolprotocols,whichwereinsufficienttomeetthegrowingcomplexityofmobilitydemandsinamegacity.TheCOVID-19pandemicandincreasingincidentsofextremeweatheraddedurgencytothecity'seffortstomodernizeitstransportationecosystem.Inresponse,SeoulimplementedapioneeringdigitaltwinsolutioncalledtheSmartTrafficManagementSystem,designedtosimulate,predict,andcontrolurbantrafficpatternsinrealtime.Thisdigitaltwinintegrateswiththecity'sTransportOperationandInformationService(TOPIS)andispoweredbyAI,IoTsensors,andgeospatialmappingtechnologies.

2

Objective

TheprimarygoalofSeoul’sdigitaltwininitiativeistoreducecongestionandimprovepublicsafetybypredictingandmanagingtrafficconditionsmoreeffectively.Itaimstoprovideareal-timemodelofthecity’stransportnetwork,whichcanhelpoptimizetrafficsignaltimings,rerouteemergencyvehicles,andsupportlong-terminfrastructureplanning.

Keyobjectivesinclude:

•Reducingaveragecommutetimesacrosscorecorridors.

•Improvingsafetybyreducingtraffic-relatedfatalitiesandemergencyresponsetimes.

•Loweringcarbonemissionsthroughbettertrafficflowandmodeshift.

•Supportingpredictiveplanningtopreventfuturebottlenecks.

•Facilitatingcross-agencycoordinationanddatasharinginrealtime.

3

Solutionapproach

TheprojectisspearheadedbytheSeoulMetropolitanGovernmentincollaborationwithtech-nologypartnersincludingLGCNS,MORAI,andacademicinstitutionssuchasKAIST.Seoul’ssolutionisbuiltonacity-scaledigitaltwinknownasthe“S-Map”thatmirrorstrafficconditionsusingdatacollectedfrommorethan5000sensors,1200CCTVcameras,andGPS-enabledvehicles.Keytechnologiesinvolvedinclude:

•AIandMachineLearningtoanalysetrafficdata,detectanomalies,andprovidepredictiveanalytics.

•Digitaltwinsforreal-timesimulationandvisualizationoftrafficpatternsundervariousscenar-ios.

•IoTDevicessuchassmarttrafficlightsandspeeddetectorsthatfeeddataintothedigitalplatform.

•GISMappingtocorrelatetrafficflowswithspatialinfrastructureanddemographics.

•EmergencyRoutingAlgorithmstoprioritizeambulancesandfiretrucksincongestedareas.

TheplatformisaccessibletocityofficialsviatheTOPISdashboardandusedbymultipleagenciesforcoordination.Whileitcurrentlyoperatesin2D,developmenttowardsa3Dsimulationmodelisunderwayforadvancedvisualizationofmultilevelinfrastructuresuchasbridges,tunnels,andundergroundexpressways.

4

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

7

(continued)

Casestudy:AI-poweredtrafficoptimizationusingdigitaltwinsinSeoul

Results

•Traveltimesdecreasedby15–25percentinkeydistrictsfollowingtheintroductionofAIsignaloptimization.

•Emergencyvehicleresponsetimesimprovedbyupto20percentduetoadaptiverouting.

•Vehicleemissionsdecreasedbyanestimated11.3percentintrialzonesasaresultofreducedidling.

•Real-timetrafficpredictionswith92percentaccuracyallowedpre-emptiveadjustmentstosignalsystems.

•ThedigitaltwinhasbecomeafoundationaltoolforSeoul’sfutureplanstointegrateautono-mousvehicles.

•Thepublicdashboardhasenhancedtransparencyandtrustamongcitizens.

•AccordingtoSeoul’sDigitalFoundationDivision,citizensatisfactionwithtransportservicesincreasedby18percentpost-implementation.

5

Conclusion

Seoul’sAI-powereddigitaltwindemonstrateshowreal-timesimulation,AIanalyticsandIoTintegrationcantransformurbantrafficmanagement.Byenablingpredictivecontrolandcross-agencycoordination,theSmartTrafficManagementSystemdeliveredmeasurablereductionsincongestion,emissions,andemergencyresponsetimes.Asothermegacitiesfacesimilarmobilitychallenges,Seoul’sexperienceoffersablueprintforleveragingdigitaltwintechnologytocreatemoreefficient,resilient,andcitizen-centrictransportecosystems.

Usecase2:Predictivetransitscheduling(Horizon1)

Description

DigitaltwinscanbeusedtosupportAI-poweredpredictivetransitschedulingbysimulatingandvisualizingreal-timepassengerdemand,trafficconditions,andoperationalscenariosacrosscity-widetransportnetworks.Citystakeholderssuchasfleetoperatorsandtransportplannerscanusetheseplatformstodynamicallyadjusttransportschedules,optimizevehicledispatching,andreduceservicegapsbasedonliveandforecasteddata.IoTsensorsacrossvehicles,stations,androadnetworkscanfeedreal-timeinformationintodigitalreplicasofthetransitsystemtoenablecontinuousperformancemonitoring.AIcouldbeleveragedtoanticipatesurgesindemand,recommendfleetadjustments,andoptimizeresourceallocation.

Impacts

1)Improvedpunctuality:AImodelshelpalignscheduleswithreal-worldconditions,reducinglatenessandmissedtransfers.

2)Operationalcostreduction:Busesaredeployedmoreefficientlybasedondynamicdemandmodelling.

3)Increasedpassengersatisfaction:Ridersexperiencemoreconsistentwaittimesandreliableservice.

4)Loweremissions:Optimizedroutingandidlingreductioncontributetoenvironmentalimprovements.

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5)Improvedaccessibility:TransportplannerscanuseAI-poweredpredictivetransitschedulingtoplanandtestroutesfordisabledpeopletoimproveaccessibilityacrossthenetwork.

Keybeneficiaries

•Commuters,includingshiftworkersandstudents

•Publictransportauthoritiesandfleetmanagers

•Municipalenvironmentalandmobilitydepartments

Keytechnologies

•Digitaltwins:CanbeusedtosupportAI-poweredpredictivetransitschedulingbysimulatingandvisualizingreal-timepassengerdemand,trafficconditions,andoperationalscenariosacrosscity-widetransportnetworks.

•AI:Forecastdemandusinghistorical,real-time,andcontextualdata.

•Fleetmanagementsystems:IntegrateAIoutputsintodispatchandrouting.

•IoTsensors:Monitorlocation,vehiclehealth,andoccupancylevels.

•OpendataAPIs:ShareinformationwiththepublicandMaaSplatformsfortransparentaccess.

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SDGalignment

•SDG9:Target9.1Developquality,reliable,sustainableandresilientinfrastructure,includingregionalandtransborderinfrastructure,tosupporteconomicdevelopmentandhumanwell-being,withafocusonaffordableandequitableaccessforall.

•SDG11:Target11.2By2030,provideaccesstosafe,affordable,accessibleandsustainabletransportsystemsforall,improvingroadsafety,notablybyexpandingpublictransport,withspecialattentiontotheneedsofthoseinvulnerablesituations,women,children,peoplewithdisabilitiesandolderpeople.

Risklevel

Table2:Risklevel:Predictivetransitscheduling

Riskattribute

Riskrating

Explanation

Publicsafety

Low

Medium

High

Optimizingschedulesandreducingcrowdingimprovesusersafety.

Stakeholder

acceptance

Low

Medium

High

Passengersandoperatorsgenerallywelcomeimprovementsinreliability.

Dataprivacyandsecurity

Low

Medium

High

Passengertraveldatamustbeanonymizedandprotected.

Financial/

operational

Low

Medium

High

Upfrontinvestmentininfrastructureandtrainingisrequired.

Implementedin:

Singapore;Madrid,Spain;Helsinki,Finland;Zurich,Switzerland;Boston,USA

AI-EnabledCitiverse:UseCasesforCitiesintheAgeofAI

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Casestudy:AI-poweredpredictivetransitschedulinginMadrid

Context

Madrid,thecapitalofSpain,hasapublictransportnetworkthatservesmorethan4milliondailycommutersacrossbuses,metroandsuburbanrail.Whilethesystemisoneofthemostexten-siveinEurope,growingdemand,trafficcongestion,andsustainabilitygoalshavecreatednewchallenges.

Thecityfacessignificantvariationintransportdemandduetocommutingpatterns,specialevents,andweatherconditions,leadingtoinefficienciesinbusschedulingandfleetmanagement.TheEmpresaMunicipaldeTransportes(EMTMadrid),responsibleforoperatingmorethan2000buseson200+routes,recognizedtheneedformorere