2025年人工智能與自動化在旅行、交通和旅游與酒店業(yè)中的崛起研究報告（英文版）-理特咨詢

2025

THERISEOFAI&AUTOMATIONINTRAVEL,TRANSPORTATION,TOURISM&HOSPITALITY

Howarethemovers&shakers

capitalizingondisruptions?

2

CONTENT

FOREWORD4

EXECUTIVESUMMARY6

1.MOSTDISRUPTIVEAI&AUTOMATION

APPLICATIONS8

2.THERISEOFAI&AUTOMATIONINMOBILITY10

3.REVOLUTIONIZINGRAILTRANSPORT

WITHAI&AUTOMATION14

4.THETRANSFORMATIONOFAVIATION

THROUGHAI&AUTOMATION18

5.AI&AUTOMATIONINLOGISTICS

&SUPPLYCHAIN22

6.ADVENTOFAI&AUTOMATIONIN

THEPORTS&MARITIMESECTOR26

7.AI&AUTOMATIONINTHETOURISM

&HOSPITALITYSECTOR28

8.TOP5CHALLENGESWITHIMPLEMENTING

AI&AUTOMATION32

CONCLUSION34

3

AUTHORS

PETTERKILEFORS

ManagingPartner,GrowthStockholm

FRANCOIS-JOSEPHVANAUDENHOVE

ManagingPartner,

GlobalPracticeLeader,Travel,Transportation,andHospitality(TTH)

Brussels

MICHAELZINTEL

ManagingPartner,TTHFrankfurt

MARTINGLAUMANN

Partner,InnovationStockholm

MICHAELMAJSTER

Partner,InnovationBrussels

SHOTAMITSUYA

Partner,InnovationToyko

MICKAELTAUVEL

Partner,TTHParis

JIMMILLER

Partner,TTHBoston

EDOUARDMOULLE

Partner,TTHParis

CAMILLABEVILACQUA

Partner,TTHDubai

NICHOLASNAHAS

Partner,TTHDubai

PAOLOCARLOMAGNO

Partner,TTHDubai

DANIELCHOW

Principal,TTHSingapore

OLIVERPILOT

Principal,InnovationLondon

HARSHITGARG

Manager,TTHDubai

FILIPFRANZéN

Consultant,TTHStockholm

OSCARBYLUND

Consultant,TTHStockholm

SOFIEDRUGGEENEROTH

BusinessAnalyst,InnovationStockholm

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FOREWORD

ThreeyearsafterthepublicationofourfirstViewpointonthestateoftheartofAIin

mobility,1

webelieveit

isnowtimetorefreshourperspectivewiththelatest

usecasesandadvancementsintheadoptionofAI

inthetravelandtransportindustry.Despitebeing

aroundforalmost60years,threeyearsintheareaof

AI—andcertainlytheselastthreeyears—feellikea

wholeera:justconsidertheeruptionofgenerativeAI

(GenAI)inourdailyliveslessthantwoyearsago—firstwithtext,followedbyimage,sound,andlatelyvideo

generation.NowisalsothetimewhenidentifyingAIusecasespurelycenteredonthebottomlinehasbecomemainstream,meaningthatthelow-hangingfruithas

longbeenplucked,leavingonlydeeper,morecomplexopportunitiesforAI-driveninnovation,growth,andnewbusinessmodelstotrulydrivesustainablevalueand

differentiateoverthelongterm.

PetterKilefors

ManagingPartner,Growth

ARTHURD.LITTLE

5

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REPORT:THERISEOFAI&AUTOMATIONINTRAVEL,TRANSPORTATION,TOURISM&HOSPITALITY

EXECUTIVESUMMARY

Thetravelandtransportationsectorhasundergonesignificant

transformationfollowingtheCOVID-19pandemic,withamarked

shiftindemandpatternsnecessitatingamovetowardmore

personalized,electrified,andintegratedmobilitysolutions.CentraltothistransformationisAI,whichhasemergedasacriticaldriverofinnovation,optimizingtransportnetworks,forecastingdemand,andenablingsustainable,personalizedcustomerexperiences.

ArthurD.Little’s(ADL’s)latestannual“CEOInsightsstudy,

conductedinlate2024,shedslightontheexpandinginfluenceof

AIandautomation.

2

Surveyingmorethan300globalCEOsacross

varioussectors,thefindingsrevealthatthesetechnologiesare

nowamongthemostimpactfultrendsdrivingfuturegrowth.Thismarksasignificantshiftfromthepreviousthreeyears,duringwhiche-commerceanddigitaltransformationdominated,reflectinga

movebeyondthesurvivalmodenecessitatedbythepandemic.

ThefocusisnowonAIasakeygrowthdriver.

However,widespreadadoptionofAIisnotwithoutitschallenges.

Thesurveyrevealsthatwhile98%ofCEOshaveinitiatedAIprojectsinatleastonedepartment,only13%havesuccessfullydeveloped

acomprehensiveAIstrategy.Thesurveyindicatesthatthe

transportationsector,alongwithtelecomandmanufacturing,is

leadingthewayinAIadoption.TheseindustriesareleveragingAItoforgenewbusinessmodels,distinguishingthemselvesfromsectorslikefinancialservicesandautomotive,whichremainfocusedon

enhancingoperationalefficiency.

TheneedtoprioritizeAIandautomationisevident—theyareessentialforsecuringthetransportationsector’sfuture.Astheindustryadjustspost-pandemic,theroleofAIinadaptingto

newconsumerbehaviors,enhancingoperationalefficiency,anddevelopinginnovativebusinessmodelsisincreasinglycritical.

Whileproductivity-relatedAIusecasesarenowwidespread,thekeychallengeliesinscalingtheseusecasesacrossentireorganizations.

ARTHURD.LITTLE

7

BeyondindividualproductivitytoolslikeChatGPTorDeepL,thetruevalueofAIisrealizedwhenusecasesarescaledholistically.However,manyAIsolutionsremainsiloedandfailtocreatewidespreadimpact,aseffectiveintegrationwithlegacysystems—stilldominantinmanyoperationsandindustries—posessignificantchallenges.AtADL,

wehavedevelopedamaturitymodel,illustratedacrossfourwaves,toprovideastructuredframeworkforunderstandinganddeployingAIatscaleacrosscorporatefunctions(seeFigure1).

ThisreportpresentsrealbusinessopportunitiesofAIuse

casesacrossspecificindustrysubsegments,focusingon

Waves0-3asoutlinedinFigure1.Weexploretheapplications

ofAIandautomationwithinsixkeysegmentsofthetravelandtransportationsector:(1)mobility,(2)rail,(3)aviation,(4)logisticsandsupplychain,(5)portsandmaritime,and(6)tourismand

hospitality.WefirstprovideanoverviewofthemostdisruptiveAIandautomationapplicationsacrosssegmentsandthendelveintopivotalareasforeachsegment,analyzinghowAIisdrivingtransformativechangesthroughouttheindustry.

Figure1.ADL’susecasematuritymodel

Waves

0

1

3

2

Individual/

personal

-Personal

research

forgeneralinformation

-Publicly

availabletools(e.g.,ChatGPT)

Newbusinessmodels

Createnewbusiness

modelsthatwouldnot

bepossiblewithoutAI

Increase

efficiency

…incore

functions(e.g.,R&D,customerservice,or

manufacturing)

Use

cases

Increase

effectiveness

…incore

functions(e.g.,createbetterproducts,

moreeffectivemarketing

&sales,suchasindividualproducts&individual

pricing)

…innon-corefunctions(e.g.,AI-basedautomationoftaxreturns,AI-basedcompliancechecksin

cybersecurityorESGreporting

Source:ArthurD.Little

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REPORT:THERISEOFAI&AUTOMATIONINTRAVEL,TRANSPORTATION,TOURISM&HOSPITALITY

1.MOSTDISRUPTIVEAI&

AUTOMATIONAPPLICATIONS

Beforedivingintotheapplicationswithinthesixkeysegments,thischapterhighlightsthe

mostdisruptiveAIandautomationapplicationsacrossthosesegments.

PREDICTIVEMAINTENANCE&ASSETOPTIMIZATION

AI-poweredpredictivemaintenanceleveragesreal-timedatafromsensors,historical

performancerecords,andmachinelearning

(ML)algorithmstomonitorandanticipate

assethealth.Thisapproachisrelevantnot

onlyinrailandportsbutalsoinmobility

andlogistics,addressingthehighfinancial

pressuresoninfrastructuremanagersand

operators.Railsystemsfacechallengesfrom

agingassets,resourcescarcity,andhigher

trafficvolumes,increasingthedemandfor

smarterapproachestooptimizethescarce

timeavailableformaintenance.Transitioning

fromplannedtopredictivemaintenancecan

improveassetuptime,reducemaintenance

costs,andfocuseffortspreciselywhereneeded.However,existingsolutionsoftenoperatein

silos,providingadvancedanalyticsonlywithinspecificareasandhaveyettoreplaceplannedmaintenanceatscale.

REAL-TIMEDISRUPTION

MANAGEMENT&RESILIENCE

AIsystemsexcelatreal-timemonitoringand

response,dynamicallyadjustingoperations

tominimizedisruptionsacrossallsegments,

includingrail,mobility,aviation,andportsandmaritime.Theincreasedlikelihoodofdisruptionsisdrivenbyagingassets,highertrafficvolumes,andthemultiplicationofoperators(e.g.,

liberalizationinrail),furtherexacerbatedby

climatechangeandextremeweatherevents.

Thesefactorsaddcomplexityduetothelargenumberofassets(e.g.,infrastructure,rolling

stock,personnel),competinginternaland

externalobjectives(e.g.,customerexperience,safety,capacity,punctuality),andtheneedtoconsideranincreasingnumberofdisjointed

decisionsanddatasources.AIprovidesthe

abilitytomanagethiscomplexitybyenablingsmarter,real-timedecisionsthathumansalonecannotmake,especiallywithoutprepreparedandtestedbusinesscontinuityplans.

ARTHURD.LITTLE

9

OPTIMIZATIONOF

URBANMOBILITY&

TRANSPORTATIONFLOWS

AI-drivensolutionsoptimizemobilityflows

incitiesbyintegratingreal-timedatafrom

sensors,vehicles,andinfrastructuretomanagetrafficandprioritizesustainablemodesof

transport.Thiscapabilityiscriticalacross

peoplemobilitysegments(rail,mobility,

aviation,tourism/hospitality)andcanextendtologistics(e.g.,transportasaservice).Theinterestinoptimizingtheflowofpeopleandgoodsiswell-documented(seeADL’s“BeyondMaaS—HowtoRealizethePromiseof

Mobility-as-a-Service”

3

).Withtheriseof

autonomousmobility,theimportanceof

managingflowsatthecityorregionallevelisbecomingincreasinglyclear.This“flowoptimization”capabilityservesasaback-endfunction,complementedbyfront-endcapabilitieslikepersonaltravelassistants.

DYNAMICRESOURCE

ALLOCATION&DEMANDFORECASTING

AItoolsanalyzehistoricalandreal-timedatatopredictdemand,optimizeresourceallocation,andmanageinventory.Thiscapabilityaddressesthechallengesofoverstocking,stockouts,andtheneedforpreciseplanninginsectorslike,butnotlimitedto,logisticsandhospitality.AWS

SupplyChain,forinstance,integratesdisparatedatasourcestocreateaccurateforecasts,

enablingbetterdecision-making.Similarly,

Duettoinhospitalitydynamicallyadjustsroompricingbasedondemandpatternsandlocal

eventstomaximizerevenuewhileensuringcompetitivepricing.

AIPROVIDESTHE

ABILITYTOMANAGECOMPLEXITYBY

ENABLINGSMARTER,

REAL-TIMEDECISIONSTHATHUMANSALONECANNOTMAKE

PERSONALIZED,

INTELLIGENTSYSTEMS

FORUSERENGAGEMENT

AIsystemscreatetailoreduserexperiencesby

analyzingindividualpreferences,behaviors,andreal-timeneeds.Thisisparticularlyimpactfulintravel,tourism,andmobility,wherepersonalizedexperiencesdriveloyalty.Examplesinclude

personalizedtravelrecommendationsfrom

AmericanAirlines,Uber’sAI-drivenroute

optimization,andchatbotsinhospitality,whichstreamlinecustomerinteractions.Thesesystemsnotonlyenhanceengagementbutalsofoster

trustbyprovidingrelevant,timely,andintuitiveservices.

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2THERISEOFAI&AUTOMATION

INMOBILITY

Inanerawheretechnologyisrapidlytransformingeveryaspectofourlives,AIandautomationare

makingsignificantstridesinvarioussectors,

notleastinthemobilityecosystem.These

innovationsnotonlyreshapehowwetravelbutalsoinfluencehowcitiesmanagetrafficandhowcompaniestransformtheiroperatingmodels.Forexample,theEUhasdevelopedrobustregulatoryframeworks,suchastheAIAct,togovern

high-riskAIapplicationswithincriticalmobilityinfrastructures,includingtrafficmanagementandautomatedtransportationsystems.This

frameworkensuresthatAIinnovationsare

compliant,ethical,andalignedwithEuropeanvalues,creatingasecureenvironmentforbothcompaniesandconsumers.

Theseregulatoryeffortsprovideafoundation

forAI-drivenmobilitysolutionstothrive,enablingsafe,efficient,andaccountableimplementationsthatprioritizeuserrightsanddataprivacy.

Additionally,cross-sectorcollaborationinitiativesledbyERTICO,

4

suchasmobilityasaservice

(MaaS),underscoretheEU’scommitmentto

cohesiveandinteroperableAIsystemsthat

advanceintelligent,accessible,andsustainabletransportationsolutionsforallusers.

AIANDAUTOMATION

AREMAKINGSIGNIFICANTSTRIDESINVARIOUS

SECTORS,NOTLEAST

INTHEMOBILITY

ECOSYSTEM

Thischapterdelvesintosixdistinctuse

cases(seeFigure2)thatshowcasehowAI

andautomationarecurrentlybeingappliedinthemobilityecosystem,eachillustratingthe

transformativeimpactofAIandautomationintheirrespectivedomains.Fromself-drivingcarstooptimizedtrafficsystemsandstreamlinedbusinessprocesses,theseexampleshighlightthepotentialofintelligenttechnologyto

enhancesafety,efficiency,andoverallqualityoflife.

Figure2.SelectedareasofAIandautomationapplicationsinmobility

Autonomousvehicles

Source:ArthurD.Little

Ride-sharing

&on-demand

services

Urbanplanning

&traffic

management

Automated

tenderwriting

Customerservice

Journeyplanning

ARTHURD.LITTLE

11

AUTONOMOUSVEHICLES

Waymo,aleaderinautonomousvehicle(AV)

technology,leveragesAIandautomationto

enhancethesafetyandefficiencyofitsself-

drivingfleet.Withacommitmenttohuman-

centricethicalstandards,Waymo’sapproach

alignswithEUpoliciesbyprioritizinguser

safetyandprivacyprotections.Thecompany’sadvancedAIsystemprocessesvastamountsofdatafromlightdetectionandranging(LiDAR)technology,radar,andhigh-definitioncameras,enablingreal-timedecisionsoncriticaldrivingfunctions,suchassteering,acceleration,andbraking,withminimalhumanoversight.This

capabilityhasbeenrigorouslytested,withthesystemaccumulatingover20millionmilesonpublicroadsandanadditional10billionmilesinsimulatedenvironments.Whilemeasuring

safetyimprovementswithAVsremains

challenging,Waymohasintroducedmethodsshowingsafetybenefitsinthreekeyservice

areas,includingan85%reductionininjury

crashesanda57%decreaseinpolice-reportedcrashescomparedtohumandrivers.

5

Waymo’sAIcontinuouslylearnsfrommillions

ofmilesdrivenindiverseenvironments.This

enablesthesystemtobetternavigatecomplextrafficscenarios,varyingweatherconditions,anddiverseroadtypes.Theseadvancements

haveledtothedeploymentof“WaymoOne,”afullyautonomousride-hailingserviceoperatinginselectcities,offeringasafeandreliable

transportationoptionforpassengers.

RIDE-SHARING&

ON-DEMANDSERVICES

Uber,aleadingride-sharingplatform,leveragesAIandautomationtostreamlineoperations

andenhancethecustomerexperience.Uber’s

advancedAIalgorithmsanalyzemassive

datasets,includingtrafficpatterns,rider

demand,anddriveravailability,toefficiently

matchriderswithdrivers.Bypredictingdemandspikesinrealtime,thesystemdynamically

adjustspricing(commonlyknownas“surge

pricing”),helpingbalancesupplyanddemand

whilereducingwaittimesforusers.

6

AstudyonUber’ssurgepricingfoundthatitincreasedtotalwelfareby2.15%ofgrossrevenue,whilerider

surplusroseby3.57%ofgrossrevenue

.7

Lyft,acompetitor,adoptedasimilarpricing

strategywithtwodistinctfeaturesinfluencingoverallfares:“PrimeTime”and“HappyHour.”

DuringPrimeTime,whendemandishigh,

ratescanincreaseupto200%ofnormalfares.Conversely,duringHappyHour,whendemandislow,faresdecreasebyasmuchas50%.

8

Inaddition,AI-drivenrouteoptimizationenablesUber’sdriverstotakethemostefficientpaths,minimizingtraveltimeandavoidingtraffic

congestion.Automationalsocontributes

significantlytosafetybyemployingreal-

timemonitoringsystemsthatanalyzedriver

behaviorandidentifyriskypatterns,suchas

suddenbrakingorspeeding.Theseinsights

providepersonalizedfeedbackandtrainingfordrivers,improvingoverallsafetystandards.ByleveragingtheseAIandautomationinnovations,Uberhasenhancedservicereliability,

reducedoperationalcosts,anddeliveredamoreseamlessandresponsiveride-sharingexperienceforbothdriversandpassengers.

URBANPLANNING

&TRAFFICMANAGEMENT

Copenhagen’s(Denmark)transportation

departmentisattheforefrontofintelligent

transportsystems(ITS),leveragingAIto

revolutionizeurbantrafficmanagement.

Theseadvancedsystemsutilizereal-time

datafromsensorsandcamerasacrossthe

citytomonitorandpredicttrafficconditions,enablingoptimizedtrafficflowandreduced

congestion.Throughautomatedtrafficsignalsanddynamicrouteguidance,Copenhagenis

significantlyminimizingdelaysandimprovingoverallmobilityforresidents.Alignedwith

EUregulatorystandards,thecity’sITS

implementationcomplieswiththeAIAct’s

directivesoncriticalinfrastructure,which

emphasizetransparency,ethicalpractices,

androbustdatasecurity.

9

Thisframework

ensuresthatCopenhagen’sAI-poweredtrafficsystemsarebotheffectiveandethicallysound,safeguardingcitizendatawhileenhancing

serviceefficiency.

Studiesshowthatintelligenttraffic

managementsolutionslikethoseinCopenhagencanreduceurbantraveltimesbyupto25%

.10

TRANSPORTATION

TOURISM&HOSPITALITY

,

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12

Moreover,thecity’sITSdeploymentincludes

advancedcontrollersat380intersections,

streamliningmovementforcyclistsandbuseswhilereducingenergyconsumptionbyathird.Withtargetstoreducetraveltimesforcyclistsby10%andforbusesbyupto20%,Copenhagenaimstoshiftmoreresidentstowardcyclingandpublictransport,fosteringagreenerandmoreefficienturbanenvironment.

AUTOMATEDTENDER

EVALUATION&WRITING

nPlandemonstrateshowitsinnovativeAI

capabilitiesautomatethetender-evaluation

processbyanalyzinghistoricaldata,industry

standards,andprojectrequirementsfor

highwayprojects.ThisAItoolconductsrigorousanalysisoftenders,uncoveringbothknownandhiddenriskswhileensuringconsistencyacrossevaluations.Bygeneratingaccuratetechnical

details,pricing,andcompliancedata,nPlan

enablescompaniestoreducemanualeffort

andfocusonhigh-levelstrategicdecisions

duringprojectbidding.Theabilitytoperformside-by-sideevaluationsusingquantitative,

unbiaseddataallowsfortheselectionofthe

bestcontractor,significantlystreamlining

andenhancingthetenderevaluationprocess

.11

AutogenAIprovidesanAI-driventoolfortenderwriting,claimingitcansaveupto70%ofbid

draftingtimethroughideagenerationand

storyboarding,boostproductivityby85%withfewerdocumentedits,andimprovesuccess

ratesbyupto241%throughscoringanalysis

andwinthemeintegration

.12

AnotherexampleisMonocubed,whichhelpedaclientdevelopabidsolutionautomatingadministrativetasks,suchasmanagingcostlists,vendorprices,andbidwriting.Thisresultedinanearly51%increaseinefficiencyanda43%increaseinproductivity

.13

CUSTOMERSERVICE

DeutscheBahn(DB)developedanAIchatbottoenhancecustomerservicebyinstantlyansweringqueriesaboutproducts,suchasBahnCardsandpromotionaloffers.UsingalargelanguagemodeltrainedspecificallyonDB’sservices,thechatbotrespondsinGerman,handlesFAQs,andprovidesreal-timeassistance24/7.

Thisimplementationledtoa50%reduction

inresponsetime,significantlyaccelerating

responsestocustomerinquiries;a30%

decreaseinsupportworkloadasautomated

responsesreducedtheburdenonhumanagents;andincreasedcustomersatisfactionand

engagementdueto24/7availability

.14

JOURNEYPLANNING

InpartnershipwiththeCityofSanJosé,

Texas,USA,VTApilotedanAI-poweredTransit

SignalPrioritysystem.Thesystemutilized

livebustrackingdataandMLtodynamically

adjusttrafficsignals,optimizinggreenlight

durationsforbusesandreducingsignaldelays—allwithoutrequiringadditionalhardware

installations.Thispilotachievedan18%-20%

reductionintraveltimesthroughdynamicsignaladjustmentsandafive-tosix-minutedecreaseinsignaldelay,significantlyimprovingon-timeperformanceandreliability

.15

KEYTAKEAWAYS

AIandautomationaretransformingthemobilityecosystem,significantlyenhancingsafety,

efficiency,andreliability,asdemonstratedbythecasestudies.Thesetechnologiesarereshapinghowwetravelandmanageurbanenvironments:

-Revolutionizingmobilityimprovessafety,efficiency,andreliabilityinareassuchas

AVs,ride-sharingservices,andurbantrafficmanagement.

-Enablingreal-timedataprocessingand

decision-makingenhancessystemresponsestodynamicenvironmentsanduserneeds,

resultinginbettertrafficflowandpersonalizedride-sharingexperiences,asshowninthecasestudies.

-IntegratingAIintobusinessprocesses

(e.g.,automatedtenderwriting)streamlinesoperations,reducesmanualeffortand

increasesaccuracyandconsistency,

demonstratingthebroadapplicability

andbenefitsofAIacrossindustries.

ARTHURD.LITTLE

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3.REVOLUTIONIZINGRAIL

TRANSPORTWITHAI&AUTOMATION

RAILOPERATORSARE

LEVERAGINGADVANCEDTECHNOLOGIESTO

ENHANCEOPERATIONSACROSSMULTIPLE

DIMENSIONS

Therailtransportationsectorisundergoing

aprofoundtransformationthroughthe

integrationofAIandautomation.Drivenby

increasingdemandsforefficiency,safety,

andsustainability,railoperatorsworldwide

areleveragingtheseadvancedtechnologiestoenhanceoperationsacrossmultipledimensions.Frompredictivemaintenancetooptimized

scheduling,AIisreshapingrailnetwork

functionality,whileautomationstreamlinesbothpassengerandfreightservices.

Thischapterhighlightsfivekeyareas(see

Figure3),whereAIandautomationaremakingsignificantstrides,revolutionizingtherail

industrytoaddressmodernchallengesandelevatepassengerexperiences.

PREDICTIVEMAINTENANCE&SAFETYENHANCEMENTS

DeutscheBahnhasintegratedAIintoits

predictivemaintenancestrategies,significantlyenhancingthereliabilityandsafetyofits

railnetwork.Akeyaspectofthisintegrationinvolvesasophisticatedplatformthatutilizesanetworkofsensorsinstalledthroughout

therailwayinfrastructure,includingtracks,switches,androllingstock.Thesesensors

continuouslygatherreal-timedataonvariousparameters,suchasvibrations,wearandtear,andtemperaturechanges.

TheuseofAIforpredictivemaintenanceisakeystrategytocounteractmaintenance

costincreasesandimprovefleetavailability.

DBannounceda€55million(~US$60million)

investmentinAIandroboticstorevolutionize

themaintenanceofitsIntercity-Express

(ICE)fleet.Attheheartofthisinitiativeisthe

E-Checksystem,whichusescamerasandAItoinspecttrainsinjustfiveminutes,identifyingissueslikeloosescrews,damagedcomponents,orareasrequiringrepair.

Figure3.SelectedareasofAIandautomationapplicationsinrailtransport

Predictive

maintenance&safetyenhancements

Source:ArthurD.Little

AIinscheduling

&traffic

management

Customerserviceimprovements

Energy-efficiency

&sustainability

efforts

CreatedbyIconi

Trainintelligent

detectionsystems

ARTHURD.LITTLE

15

Thistechnologysignificantlyenhances

efficiency,increasingmaintenancecapacity

by25%whilereducingdowntimeandimprovingfleetavailability.Additionally,collaborative

robots(cobots)arebeingintroducedto

automateroutinetasks,suchaswatersupplyandwastewaterremoval,freeingmaintenancestafftofocusonmorecomplexandspecializedrepairs.ByintegratingAIandrobotics,DB

aimstomodernizeitsoperations,address

workforcechallenges,andensurelong-termsustainability

.16

AIINSCHEDULING&

TRAFFICMANAGEMENT

TheEastJapanRailwayCompany(JR-East)operatesahighlyintricatescheduleof

upto10,000trainsdailyacrosstheTokyo

metropolitanarea.Previously,trafficcontrol

reliedheavilyonhumanresources,whichlimitedtheabilitytomanageoperationsefficiently

andsafely.Toovercomethesechallenges,JR-EastcollaboratedwithHitachitoimplement

theAutonomousDecentralizedTransport

OperationControlSystem(ATOS).Thissystem

automatestrainroutecontrol,allowingfor

real-timeadjustmentsbasedonpassengerflowandtrainconditions.Bydynamicallymanagingschedules,ATOShassignificantlyenhanced

operationalefficiency,reducingschedule

disruptionrecoverytimesby40minutes.Theseadvancementsensuretimelydeparturesand

greaterstabilityacrossthenetwork

.17

Beyondoptimizingtrainoperations,ATOSenhancestheexperienceofJR-East’s

14milliondailypassengersbyproviding

real-timeupdatesthroughelectronicdisplaysandmobileapplications.Thisintegration

oftechnologyensurespassengershave

accurateandtimelyinformationabouttrainschedulesandconditions.Thecombinationofimprovedreliabilityandcustomer-centriccommunicationshasestablishedJR-East

asagloballeaderinefficienturbanrailtransportation.

Similarly,theRATPGroupinParishasembracedinnovationtoaddressurbanmobilitychallenges.UsingAI,RATPhaslaunchedprojectstobetter

understandpassengerflowand