Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Abstract

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope.

Full text





Sensors2021,21,2143.https://doi.org/10.3390/s21062143www.mdpi.com/journal/sensors
Review
EnablingTechnologiesforUrbanSmartMobility:Recent
Trends,OpportunitiesandChallenges
SaraPaiva
1
,MohdAbdulAhad
2,
*,GautamiTripathi
2
,NoushabaFeroz
2
andGabriellaCasalino
3,
*
1
InstitutoPolitécnicodeVianadoCastelo,4900‐367VianadoCastelo,Portugal;sara.paiva@estg.ipvc.pt
2
DepartmentofComputerScienceandEngineering,JamiaHamdard,NewDelhi110062,India;
gautami1489@gmail.com(G.T.);noushaba.feroz@gmail.com(N.F.)
3
DepartmentofComputerScience,UniversityofBariAldoMoro,70125Bari,Italy
*Correspondence:itsmeahad@gmail.com(M.A.A.);gabriella.casalino@uniba.it(G.C.)
Abstract:Theincreasingpopulationacrosstheglobemakesitessentialtolinksmartandsustainable
cityplanningwiththelogisticsoftransportingpeopleandgoods,whichwillsignificantlycontribute
tohowsocietieswillfacemobilityinthecomingyears.Theconceptofsmartmobilityemergedwith
thepopularityofsmartcitiesandisalignedwiththesustainabledevelopmentgoalsdefinedbythe
UnitedNations.Areductionintrafficcongestionandnewrouteoptimizationswithreducedeco‐
logicalfootprintaresomeoftheessentialfactorsofsmartmobility;however,otheraspectsmust
alsobetakenintoaccount,suchasthepromotionofactivemobilityandinclusivemobility,encour‐
agingtheuseofothertypesofenvironmentallyfriendlyfuelsandengagementwithcitizens.The
InternetofThings(IoT),ArtificialIntelligence(AI),BlockchainandBigDatatechnologywillserve
asthemainentrypointsandfundamentalpillarstopromotetheriseofnewinnovativesolutions
thatwillchangethecurrentparadigmforcitiesandtheircitizens.Mobility‐as‐a‐service,trafficflow
optimization,theoptimizationoflogisticsandautonomousvehiclesaresomeoftheservicesand
applicationsthatwillencompassseveralchangesinthecomingyearswiththetransitionofexisting
citiesintosmartcities.Thispaperprovidesanextensivereviewofthecurrenttrendsandsolutions
presentedinthescopeofsmartmobilityandenablingtechnologiesthatsupportit.Anoverviewof
howsmartmobilityfitsintosmartcitiesisprovidedbycharacterizingitsmainattributesandthe
keybenefitsofusingsmartmobilityinasmartcityecosystem.Further,thispaperhighlightsother
variousopportunitiesandchallengesrelatedtosmartmobility.Lastly,themajorservicesandap‐
plicationsthatareexpectedtoariseinthecomingyearswithinsmartmobilityareexploredwiththe
prospectivefuturetrendsandscope.
Keywords:smartmobility;sustainability;smartcities;smartservices

1.Introduction
Smartmobilityisanemergingconceptthatisincreasinglyalignedwithsustainable
worlddevelopment,takingintoaccountthe17sustainabledevelopmentgoalssetbythe
UnitedNations[1]for2030.Theconceptofmobility,andhowitwillarticulatewiththe
planningofcitiesandthelogisticsoftransportinggoodsandpeople,willexperiencedras‐
ticchangesinthecomingyears.Demographicgrowthcontinuestofollowanexponential
route,with6billionpeopleregisteredin1999,anapproximatenumberof7.7billionin
2020andapredictednumberofapproximately9billionexpectedfor2040[2].Thisgrowth
weseeincitiesallovertheworldwillnecessarilytranslateintotheneedfornewroute
optimizationalgorithmsforvehiclesandpeople,trafficmanagementtoreduceconges‐
tion,andgreateroptimizationinlogisticprocesses,amongothers.However,theconcept
ofsmartmobilitygoesfarbeyondsolvingtheseproblemssincefuturecontributionsare
expectedtorepresentdifferentiatingandtrulyinnovativesolutions[3].Thefocusonthe
sustainabilityofthesolutionsdeveloped,activetransport,theuseofenvironmentally
Citation:Paiva,S.;Ahad,M.A.;
Tripathi,G.;Feroz,N.;Casalino,G.
EnablingTechnologiesforUrban
SmartMobility:RecentTrends,
OpportunitiesandChallenges.
Sensors2021,21,2143.
https://doi.org/10.3390/s21062143
AcademicEditor:EnriqueAlba
Received:20February2021
Accepted:16March2021
Published:18March2021
Publisher’sNote:MDPIstaysneu‐
tralwithregardtojurisdictional
claimsinpublishedmapsandinsti‐
tutionalaffiliations.

Copyright:©2021bytheauthors.Li‐
censeeMDPI,Basel,Switzerland.
Thisarticleisanopenaccessarticle
distributedunderthetermsandcon‐
ditionsoftheCreativeCommonsAt‐
tribution(CCBY)license(http://crea‐
tivecommons.org/licenses/by/4.0/).

Sensors2021,21,21432of45


friendlyfuelsandengagementwithcitizensareaspectsthatshouldbepartofsmartmo‐
bilityinthecomingyears.Theimpacteddimensionswillthereforebevariedandinclude
sustainability,economyandliving,whichhasadirectimpactoncitizensandalsoongov‐
ernmententities[3].
Twokeyconceptsareatthebaseofwhatwillbetheevolutionofsmartmobilityin
thecomingyears:ononehand,thetransportofgoods,andontheother,theparadigm
shiftinthemobilityofpeoplethatwilltransitiontomobility‐as‐a‐service.Regarding
transportsofgoods,wealreadywitnessseveralrobotprototypesaroundtheworldthat
deliverbasicnecessitiestopeople’shomes,whichwillmeananadjustmenttohowbusi‐
nessesoperate(supermarkets,deliveryservices,amongothers).InternetofThings(IoT),
BigDataandArtificialIntelligence(AI)willplayafundamentalroleinnewsolutions.It
shouldbehighlightedthatthechangesthecomingyearswillbringwillpromoteinevita‐
blechangesinwhatwillbethejobsofthefuture.Regardingthetransportofpeople,trends
areevolvingtowardsamobility‐as‐a‐serviceparadigm,withadrasticreductioninthe
numberofprivatevehiclesthatwillbefilledbyelectric,shared,lighterandsmallervehi‐
clesandwithautonomousdriving.
Thispaperprovidesanexhaustiveliteraturereviewofseveralsolutionsacrossmul‐
tipledomainsthatarerelatedtosmartmobilityinsmartcities.Thepaperalsoprovides
anoverviewaboutwhatissmartmobilityandtherelatedopportunitiesandchallenges.
Further,thepaperhighlightstheenablingtechnologiesthatarebeingusedinorderto
deliversmartservicestocitizens.Lastly,thefuturetrendsandconclusionsarepresented.
1.1.NeedandImportanceofSmartMobility
Smartmobilityenablesinhabitantstonavigateandmovefreelywithinthesmartcity
surroundings.Improvedtrafficmanagement,theavailabilityofalternativeroutes(incase
oftrafficoremergencies),anddedicatedroutesandnavigationforessentialservices(such
asambulances,governmentvehicles,officialmovements)canbefacilitatedbysmartmo‐
bility.Suchmobilityservicesareneededtoprovidecongestionfree,environmentfriendly
andsustainablealternativesfortheinhabitantsandadministrationsalike.Withsmartmo‐
bilitysolutionswecanhavetwo‐foldadvantagesbothforcitizensaswellasadministra‐
tions,asshowninFigure1.

Sensors2021,21,21433of45



Figure1.Needandimportanceofsmartmobility.
1.2.PaperOrganization
Thispaperisorganizedintosevensections.Thesecondsectionpresentsrecentde‐
velopmentswithinthesmartmobilitydomainsandalsoidentifiestheresearchgapsand
openissues.Thethirdsectionpresentsadescriptionofsmartmobilityanditsroleinsmart
cities.Afteranoverview,wepresentthemainopportunitiesandchallengesfortheadop‐
tionofsmartmobilityinthecomingyears.Thefourthsectionpresentssomeapplications
andservicesofsmartmobilitysuchasMobility‐as‐a‐Service,TrafficFlowOptimization,
OptimizationofLogistics,AutonomousVehiclesandOutdoorNavigationTechnologies.
Thefifthsectionpresentsenablingtechnologiesforsmartmobilityandtheirroleinreal‐
izingsmartmobilitysolutions,namelythecontributionofInternetofThings,BigData
andArtificialIntelligence.Finally,wepresentfuturetrendsandmainconclusionsinthe
sixthandseventhsections,respectively.Anoverviewofthepaperorganizationisshown
inFigure2.

Sensors2021,21,21434of45



Figure2.Overviewonthepaperorganization.
1.3.MainContributions
Themajorcontributionsofthispaperinclude:
 Acomprehensivereviewofsmartmobilitysolutionsandrelatedservicesproposed
inthelastfewyears.
 Thecontextualizationofhowsmartmobilityisframedwithinsmartcitiesanditskey
benefitsandattributes.

Sensors2021,21,21435of45


 Discussionontheopportunitiesforsmartmobilitytobecomearealityinthecoming
yearsaswellasthemajorissuesandchallengesititsrealization.
 Meaningfulinsightsintothefutureofthemobility‐as‐a‐serviceparadigm.
 OverviewoftheEnablingTechnologiesthatwillsupportsmartmobilityservicesand
applicationssuchasAI,IoT,blockchain,geospatialtechnologyandbigdata.
 Futuretrendsforsmartmobility.
2.ResearchProgressinSmartMobilityandGapAnalysis
Thissectionprovidesadetaileddescriptionabouttheresearchprogressmadeinthe
fieldofsmartmobility.Wealsotrytoidentifytheresearchgapsintheexistingliterature.
Inordertoconductthesurvey,wehaveadoptedthestandardsystematicliteraturere‐
viewmethodology[4].Firstly,weperformedakeywordbasedsearchusingtheonline
researchpaperdatabasesScienceDirect(https://www.sciencedirect.com/accessedon15
January,2021)andIEEEXplore(https://ieeexplore.ieee.org/Xplore/home.jsp(accessed
on15January,2021)).Weprimarilyusedthekeyword“UrbanSmartMobility”tosearch
forarticles.Wereceived851resultsfromIEEEXploreand10,439resultsfromScience
Direct.Werefinedthesearchcriteriabyaddingtheterms“SmartCities”alongwith
smartmobility,whichreducedtheresultsto440(IEEEXplore)and8217(ScienceDirect).
Wefurtherrefinedthecriteriabyadding“Enablingtechnology”alongwiththeprevious
twokeywords,whichfurtherreducedtheresultstoatotalof2486papers.Next,refine‐
mentwasperformedonthebasisofpublicationyear.Weanalyzedthepapersfrom2011
to2020.Thisrefinementreducedthenumberofsearchresultsto2049.Wethenfiltered
thesearticlesbasedonthetitlecontentstoeliminatearticlesthatwereoutofthescopeof
thisreview.Finally,294articleswereleftandafurtherselectionhasbeenmadebasedon
theabstractcontents.Indeed,wereadtheabstractsofthese294articlestofurtherfilter
outarticleswhicharenotrelatedtoourprimefocus.Afterthisstep,186articleswere
left.These186articleswerereadthoroughlytounderstandtheirkeyproposals,discus‐
sionsandarguments.Finally,only81articles,whichcompletelymatchedourcriteria,
wereconsideredfortheanalysis.Thesearticleswerefurtheranalyzedanddiscussedto
identifytheirkeyfindingsandresearchgaps.Figure3showsthewordcloudofthere‐
sultsobtainedbythesearchqueryterms.

Figure3.Wordcloudofsearchqueryresults.


Sensors2021,21,21436of45


Figure4showsthestepsinvolvedinthereviewmethodologythathasbeenused:
1. ClassificationofArticlesonthebasisofthetype:wehavechosenonlypeerreviewed
research,reviewandsurveyarticlessatisfyingourcriteria;
2. IdentificationofPublicationYear:articlesfrom2011to2020onlywereanalyzedin
theliteraturereview;
3. ClassificationofArticlesonthebasisofkeywords:wehavecombinedthefollowing
keywords
a. SmartCity
b. UrbanSmartMobility
c. EnablingTechnology.
Oncethearticleswerefiltered,athoroughstudywasconductedtoidentifythecrux
andkeyfindingsofeachpaper.Thedetailsaboutthestate‐of‐the‐artarediscussedbelow.

Figure4.Stepsinmethodologyadopted.
2.1.ResearchBasedonSmartMobilityApplications
Thissectionspecificallydiscussesthearticlesontheapplicationsofsmartmobility
forthecitizensofthesmartcity.Thesepapersincludeframeworkbased,applicationand
deploymentbasedsystems.
In[5],theauthorshavepresentedasmartmobilityapplication“UTravel”,whichis
basedon“UniversalProfilingandRecommendation(UPR)”.TheUTravelapplicationis
basedoncontextawarenessanduserprofilingandrecommendstheoptimalpointsof
interest(POIs)dependingontheuser’slocation.Theapplicationhasbeendevelopedand
deployedonbothAndroidandiPhoneplatforms.Toevaluatetheperformanceoftheap‐
plication,theauthorshaveconductedreal‐worldaswellassimulatedexperimentations
andobservedthattherecommendationsprovidedbytheproposedsystemexhibithigh
precision,coverageandrecall.Atrafficcontrolsystembasedoncooperativeagentsispre‐
sentedin[6],whichaimsatreducingtrafficjamsatroadintersections.Formodellingroad
intersections,theauthorshaveusedsmartagents,viz.“Viewagents”(tocountcars),
“TrafficLightagents”(tocontrolthedurationoftrafficlights)and“Intersectionagents”
(tocontrolthedurationofaparticulartrafficlight).Thesystemhasbeendevelopedfor
settingswhereseveraltrafficlightsoperateincollaboration,i.e.,“Infrastructure‐to‐Infra‐
structure(I2I)”communication.Thesystemhasbeenimplementedusing“JavaAgentDe‐
velopmentFramework(Jade)”andvalidatedusing“SimulationofUrbanMObility
(SUMO)”.Theresultsshowthattheproposedsystemperformsbettertrafficcongestion
reductionthan“SmartTrafficLightsSystem(STLS)”.In[7],theauthorsintendtoevaluate

Sensors2021,21,21437of45


andidentifysmartmobilityactionstakingintoaccounttheirInformationandCommuni‐
cationTechnology(ICT)contentandobjectives.Theauthorshaveintroducedanovelac‐
tiontaxonomyinvolvingasystematicapproachtosmartmobilityandanalyzedtherole
ofICTinimprovingthequalityoflifeofpeople,increasingthepublicvalueandpromot‐
ingsmartmobilityactions.Asurveyhasbeenconductedthatidentifiesthreestagesof
smartmobilityactions,viz.“Starting”,“Intermediate”and“Mature”.Moreover,theau‐
thorsstatethatsmartcitizensarecrucialforimplementingaviable,successfulandsus‐
tainablesmartmobilityframework.Aframeworkfordevelopingandimplementingsmart
mobilityapplications,“SmartMobilityforAll(SMAll)”,isgivenin[8],whichleverages
theconceptof“microservicesorchestration”toimprovethehandlingofmultipledata
sourcesandthecreationofnovelservices.Theproposedsystemisaimedatassistingel‐
derlyand/ordisabledpersonstomoveinanurbansetting.Todothis,anetworkoftaxis
isdeployedtofulfiltheneedofeachapplicantandSMAllsynchronizestheinvolvedtaxi
operators,sometimescombiningcallsthatarenearbyinaneffectivemanner.Theauthors
maintainthatthedeploymentofSMAllresultsinimprovedqualityofservicewithlower
administrativeexpense.Tohandletheissuesregardingthesecurity,privacy,scalability
andmanagementof“SmartMobilityData‐market(BSMD)”,asix‐layerBlockchainarchi‐
tectureisproposedin[9].Thearchitectureenablestheexchangeofencrypteddatatothe
blockchainamonguserscomplyingwiththerulesofthetransactionprovidedbythedata
owner.TheauthorshaveassessedtheperformanceofBSMDovera370‐nodeblockchain
andestablishedthatBSMDsafeguardstheusers’privacyandsecuritybyofferingdata
accessmanagementcontrolandpreventingmessageinterceptionandspoofing.Within
theboundsofmobility,theauthorshaveclassifiedthreeimportantelementsinageneral
blockchain,viz.“sharedledger”,“peer‐to‐peernetwork”and“consensusmechanisms”.
SmartcontractsareaddedintoBSMDnodestocontrolaccesstothesharedmobilityin‐
formation.In[10],theauthorshavepresentedaframeworktoaidintheefficientdesigning
ofatrafficlightnetworkinanurbansettingtominimizetrafficjams.Theframework
“HITUL”assistsinthedecisionmakingoftrafficcontrolmanagementbydeterminingthe
idealtrafficlightschemesbyemployingmicro‐simulationsandbio‐inspiredmethods.The
authorshaveevaluatedHITULthroughacasestudyoftheSpanishcityofMalagaand
observedthatitisaneffectivetechniquetowardsminimizingtrafficjams.Moreover,the
authorshaveused“SimulatorofUrbanMobility(SUMO)”toobtainarrangementsof
practicalscenariosbasedonactualmobilitytrendsinacity.In[11],theauthorsattempt
todemonstratethespectrumofdevelopmentof“SpanishSmartCity”measureswitha
viewtomobilityandenvironmentalconcerns.Theauthorshaveconductedastudyin62
citiesof“SpanishSmartCitiesNetwork(RECI)”andprovidedasynergisticmapdisplay‐
ingacomprehensiveevaluationandadvancementofthecitiesonthebasisofdemo‐
graphicandsocioeconomicvariables.Thefindingsindicatethatsmartmobilityisavital
elementofsmartcitiesandsmartenvironmenthaspooroutcomesinSpanishcities.A
novelapproachtopredicttheoccupancyrateofcarparkingspacehasbeengiven[12],
whichispremisedondeeplearningwithRecurrentNeuralNetworks(RNNs).Theau‐
thorshavepresentedtwometaheuristictechniquestooptimizetheperformanceofthe
RNNdesign,onebasedonGeneticAlgorithms(GA)andtheotherbasedonEvolution
Strategy(ES).Theauthorshaveexaminedtheoccupancyratesof29carparksinBirming‐
ham,UKandobservedthattheproposedapproachismoreusefulandexceedstheper‐
formanceofavailablecompetition.
2.2.Taxonomy,SurveysandReviewBasedPapers
Ataxonomyfortheformulationofsmartcityservicesispresentedin[13].Forthis
purpose,theauthorshaveanalyzedthelatestrelevantworkscovering42servicespro‐
videdby9smartcitiesglobally.Theproposedtaxonomyiseight‐dimensionalandoffers
astandardvocabularytofacilitatecommunicationregardingtheservices.Thetaxonomy
isaimedataidingpolicymakersandresearchersinthefurtherimprovementofthearea.
Theauthorshaveincorporatedgeneraldefinitions,conceptsandillustrationsforeach

Sensors2021,21,21438of45


dimensionandspecifiedservice.In[14],theauthorhaspresentedanovelactiontaxonomy
concerninganextensivemethodologyrelatedtosmartmobility.Athoroughsurveyhas
beenperformedcovering114worksregardingtheinfluenceofsmartactionsonthequal‐
ityoflifeofcitizens,theexpectationsofstakeholders,regularlyappliedsmartmobility
measuresandtheroleofICTinsmartmobility.Thesurveyhasresultedintheidentifica‐
tionofthreesmartmobilityactionphasesandsixsmartmobilitygoals.Moreover,the
papernotesthatthoughICTisnotessentialfortheimplementationofsmartcityactions,
itisimportantwhensmartmobilityactionsbecomeincreasinglycomplex,integratedand
extended.Theauthorshaveproposedadistributedadaptationmethodforensemble‐
basedsystemsinasmartmobilitycontextin[15].Theyhavepresentedthe“Collective
AdaptationEngine(CAE)”thatcanaddressmultipleissuescollectivelyinafeasibleand
scalablemanner.Toassesstheproposedmodel,the“DeMOCAS”frameworkhasbeen
usedtosimulatethecontextofurbanmobility.ThefindingsindicatethatCAEsolvesur‐
banmobilitychallengesandpromotessustainablemobility.In[16],theauthorshavepro‐
posedtheapplicationofsystemdynamicstosimulatesubstitutesforconventionalhuman
mobility.Ananalysisofsixscenariosispresentedforattainingoptimizeddecision‐mak‐
ingwithinorganizations,definingtheprofilesoftravelersandachievingsustainable
smartmobilitywiththeeventualgoalofenhancingthecitizens’qualityoflife.Theanaly‐
sisisaimedatunderstandingtheprincipaldynamicinteractionsbetweenallthevariables
ofthesystemandmanagingtheircomplexity.Adetailedandrealisticstructurehasbeen
introducedin[17]todesignacomparativeanalysis,whichgaugescitiesbasedonthe
smartnessoftheirtransportframeworks.Theauthorshavegathereddatafrom26cities
globally,identified66indicatorsofsmartnessandobservedthatcitiessuchasSeattle,
LondonandSydneyhavethemostadvancedsmarttransportation,withLondonhaving
thebestemergencytransportfacilities,SingaporeandLondonhavingthebestpublic
transportfacilities,andParisandSeattlehavingthebestprivatetransportfacilities.In[18],
theauthorshaveanalyzedthelikelytransitionofpresentissuesofmobilitygovernance
withtheaimtosafeguardandincreasethepublicvalue.Fourcasestudieshavebeencon‐
ductedtostudydistinctmobilitygovernanceissuesandananalyticalstructurehasbeen
presentedtoevaluatethesmartmobilitygovernancenovelties.Furthermore,theauthors
assertthatthetransitioninmobilitygovernanceshouldbebackedwithtechnologicaltran‐
sition.In[19],theauthorshavefocusedonenhancingavitalelementofsmartmobility,
viz.positioninginasmartuniversitysettingactingasarepresentativeforasmartcity.An
indoorpositioningsystemintegratedwithanoutdoorpositioningsystemhasbeenpro‐
posedandimplementedtofacilitatepersistentindoorandoutdoornavigation.Theau‐
thorshaveimplementedtheproposedframeworkonthesmartuniversityplatform,
“SmartUJI”,atUniversitatJaumeI,Spain.Moreover,twomobileapplicationshavebeen
createdandimplementedbytheauthors—“SmartUJIAPP”(toprovidemap‐basedinfor‐
mationregardingvariouscampusservices)and“SmartUJIAR”(toprovidecommunica‐
tionwiththecampusviaanaugmentedrealityinterface).Theevaluationofthetwoap‐
plicationsindicatestheirusefulassistancetovisitors,facultyandstudentsinenhancing
spatialpositionandfindinguniversityfacilities.Theauthorshavespecifiedarangeof
mobilityindicatorstoassesssmarturbanmobilityin[20].Theyhavepresentedarelevant
quantitativemethodologythatisapplicabletoanycitygloballywiththeaimtobench‐
marksmartcities.TheauthorshaveselectedItaliancitiesfortheevaluationofsustainable
transportationtogathercrucialdata,andtheyobservedthatthecityofTurinisthebest
withregardtosmarttransport.Moreover,itisnotedthatthenortherncitiesofItalyhave
overallbetterrankingthanthesoutherncities.Theauthorhasfocusedonthetransfor‐
mationfromanautomobilecommunitytoamultimodalcommunityin[21],fosteredby
theadventofsmartmobilitypoweredbyICT.Thepaperhighlightsthreeoutcomesob‐
tainedfromquantitativeanalysisofdatafromtheGermanregionofRhine‐Main.These
includetransportpoverty,multimodaldivideandcriticalthinkingasfactorscontributing
tomistrusttowardsmultimodalmobility,andthepaperrecommendsachangeinthis
perspectivetoenhancethedebatesurroundingmultimodality.Elevenmetropolitancities

Sensors2021,21,21439of45


inItalyhavebeenstudiedbytheauthorsin[22]toassessthepossibilityanddegreeof
applyingthesmartcitymodelwiththeaimtoboosttheeffectivenessandlivingconditions
ofurbanareas.Theauthorshaveidentifiedkeysmartcitymeasuresandparametersand
groupedthemtounderlinetheimpactofthesmartcitymodelonthemobilitysystems
andobservedthatapoorstartingplacelimitsitsimplementation.Thepaperclassifiesthe
smartmobilityparadigmintothreegroups,“accessibility”,“sustainability”and“ICT”,
andapplies28parameterstolocatecontextswiththebestaccessibilityandsustainability.
Furthermore,itisindicatedthatICTisineffectivewhenthetransportframeworkisinad‐
equate.Adistributedsystemformobilitydatamanagementandsemanticimprovement
hasbeenproposedin[23],whichwouldbenefittheareasoftrafficmanagement,m‐health,
urbankineticsexaminationandemergencymanagement,amongothers.Theproposed
system,“SemanticMOVE”,enablesthesemanticmanagementofmobilethings,andoffers
understandingofthemobilitysemanticsaswellastheidentificationofpotentialuser
movements,behaviorsandactivities.In[24],theauthorshavefocusedontheimportance
ofamulti‐disciplinaryandcollectivemethodologytosmartmobility,whichwouldenable
theshifttoa“smartermobility”toimprovecitydevelopmentandcitizens’qualityoflife.
AstudyhasbeenconductedinBelgiumtoanalyzetheadvancementofsmartmobility
fromtechno‐centeredtouser‐centered.Theauthorsarguethatalthoughsolutionstosmart
mobilitychallengesaresoughtinnoveltechnologies,thesesolutionsarenotabsoluteand
smartmobilityoutgrowstechnologyandusers.In[25],theauthorhashighlightedthe
relationshipbetweensmartmobilityandsocialsustainabilityandclarifiedthedefinitions
ofthetwoconcepts.Thepaperindicatesthatthesocialsustainabilityofsmartmobility
dependsonthepathtakenbythelatter.Twodistinctscenarioshavebeengiven,thefirst
ofwhichinvolvesincreasedeaseofusepromotingtheprevalenceofcars,resultingin
socialdiscord,inequity,thescarcityofcardrivingandparkingspaces,etc.Thesecond
scenarioinvolvestheapplicationoftechnologiestoenableservicessuchasridesharing
andondemandrides,resultinginnegativeconsequencesontransportationworkersbut
apositiveimpactonsocialharmony,equityandavailability.Atheoreticalassessmentof
experimentalgovernanceispresentedin[26]throughtheanalysisoffundamentalliteral
andpracticalassumptionswithrespecttosmartmobility.Theauthorshaveselectedan
experimentalstudyofsmartmobilityinSwedentoexamineexperimentalgovernanceas
apolicytooltofacilitatetheobjectivesofpublicstakeholdersandassertthatthesestake‐
holdershaveaspecialroleinexperimentalgovernance.In[27],theauthorshavepre‐
sentedaquantitativeapproachforassessingtheurbanmobilityintheItalianareaofCa‐
gliariandrecommendedtheroadmaptoachievethefinestmobilityglobally.Thead‐
vantagesofsmartmobilityhavebeenanalyzedinCagliariandsimilarurbanareasby
choosingindicatorsbasedontherelevantdataoftheselectedcontexts.Thedataforthe
indicatorshavebeencollectedfromtheyear2014andclassifiedintogroups,anditisob‐
servedthatmobilitydatasharingisnotadequatesofar.In[28],theauthorhasexplored
theframeworkofcitizenengagementandefficiencyofJapaneseSmartCommunitiesto
contemplatethecollaborativedesignanddevelopmentofasmartmobilityframework.
Thepaperindicatestheanticipationoflittlefeedbackfromthecitizensandthedeploy‐
mentofICTtodirecttheparticipantsandmodifytheirbehavior.Itisobservedthatthe
currentsmartcityinitiativesareinanascentphasetocomprehendthattheobjectivesof
theadministration,focusedonenergysavingordevelopingalternateenergysources,
havelimitedICTdeploymentandtheICTrelatedprojectsarepoorlycoordinated.Aho‐
listicmethodologytomodeltheefficiencyofpublictransportfacilitiesisgivenin[29],
schemedasawholeinamulti‐stakeholdercontextfromanend‐to‐endperspective.The
authorsseektounderlinethekeyaspectsofthequalityofservicefromdistinctviewpoints
andhaveprovidedaholisticmethodologytomodellingthatsupportsthequalityofser‐
vicedesign,implementationandmonitoringinsmarttransport.Moreover,thepaperpre‐
sents“UCoMS”fortheanalysisofthequalityofservice,andtheproposedmethodology
hasbeenvalidatedintheItalianregionofApulia.In[30],theauthorseekstoanalyzethe
meaningof“smart”inthecontextofsmarturbanmobilityandtherelationshipbetween

Sensors2021,21,214310of45


smartnessandsustainability.Theauthorhasuncovereddiscordandinsufficiencyinthe
literaturepertainingtosmarturbanmobilityandhaspresentedandanalyzedthedefini‐
tionofsmarturbanmobility.Thepaperattemptstobridgethegapbetweentheconcepts
ofsmartandsustainableforthegrowthofurbanmobility.Acomprehensiveanalysisof
theroleplayedby“IntelligentTransportSystems(ITS)”inassistingurbansmartmobility
isgivenin[31].Atotalof71papershavebeenanalyzed,rangingfrom2006to2014,with
34%casestudiesand21%simulations.Thecasestudy‐basedworksanalyzethedeploy‐
mentofITSinurbancities,whilethesimulation‐basedworksanalyzetheinfluenceofITS
onurbanmobilityandmeasurecost,timeandenvironmentalimpacts.Thepaperaimsto
detectthegapsintheliteratureandobservesagenericinsufficiencyofquantitativeframe‐
works.Theauthorsrecommendaroadmapforfutureresearchbasedontheidentified
inadequacies.
In[32],theauthorhighlightsthefactorsthatlinkcitizenstodifferentfacilities,espe‐
ciallymobilityandICTframeworksintheSenegalesecityofDakar.Itisobservedthat
motorizedmethodsamountto40%whilenon‐motorizedmethodsamountto60%ofthe
overallmobility,andthepublicmobilitysectorislargelyinformal.Theauthorhasnoted
themeasurestakenbytheadministrationtobuildapositivesettingforICTdevelopment
anddeployment,includingE‐Infrastructure,E‐EducationandE‐Governance.
2.3.Regional,GovernanceandCitizenCentricPapers
AcasestudyinthePortuguesecityofLisbonhasbeenpresentedin[33],whiches‐
tablishesaperformanceassessmentofpassengerandcommercialvehicleredirection.The
resultsindicatethatredirectingvehiclesnotonlyreducestravelduration,butincreases
theroadeffectivenessintheurbangridsandthetrafficoutputintheanalysisofroutes.
Moreover,itisobservedthattheeffectivenessishigherattheroutelevel(variationof16–
32%intraveland4–13%indelaytime)thanatthenetworklevel(averagevariationof2%
intraveland6%indelaytimefor10%driverconformityrate).Theconceptof“Vehicular
SocialNetworks(VSNs)”hasbeengivenin[34],focusingontheimportanceofhighly
efficientandsecuresmartcitytransmissioninVSNs.Thepaperproposesausecaseon
trajectorydata‐analysis‐basedtrafficanomalydetectionforVSNsandhighlightsVSN‐re‐
latedresearchchallengesandpotentialsolutionstofacilitatetherealizationandextensive
applicationofVSNs.In[35],theauthorsaimtoestablishanindicatorforassessingthe
degreeofsmartmobilitysolutionsdeployedinurbanareas.Thepaperindicatesthatthe
insufficiencyofcomprehensiveknowledgerelatedtotheevaluationofcertainratingsof
theexistingliteratureandtheinordinatelygeneralanalysisofsmartmobilitychallenges
havepromptedtheneedfortheindicator.Theproposedindicatorisintendedforfacili‐
tatingtheanalysisofmobilityscenariosinaccordancewiththenotionofsmartcities,en‐
ablingcomparisonsinvariedurbanareasfortheidentificationofbestpracticestopromote
theadvancementofsmartmobility.Ananalysisof“MobilityasaService(MaaS)”has
beenpresentedin[36]toevaluateitspotentialimpactforcitypolicymakerswithregards
togovernanceandsustainability.ThepaperstressesthatMaaSisnotafixedcommodity
butaconceptualwayofprovidingservicestocustomers.Moreover,thepotentialriskto
mobilityandsocialsustainabilityduetooverdependenceonsingleoperatorsofnovel
servicesandthepotentialimpactofnovelservicesoncurrentservicesaregiven.In[37],
theauthorshavefocusedonevaluatingthesmartnessoftransportsystemsinthecitiesof
Ghanaandillustratingtherealizationofthenotion.ItisobservedthatGhanaiancitiesare
predominantlyreliantonroadsforthemobilityofpeopleandcargo,andtheswiftincrease
inthenumberofvehiclesaccompaniedbyinsufficientexpansionofroadnetworkshas
weakenedtheefficiencyofurbanareas.Thepaperimpliesthatthelackofsmartmobility
inthesecitiesendangerstheirsustainabilityand,asaresult,necessitatesinvestmentin
mobilitysystemstogetherwithincreasedreceptionandtechnologicalawarenessamong
thecitizens.Ageneralframeworkfortheimplementationoffogcomputingfeaturesina
“Vehicularad‐hocNetworks(VANET)”contextisintroducedin[38].Theproposedarchi‐
tecturehasbeenappliedintwoFogapplications,onefortheidentificationoftraffic

Sensors2021,21,214311of45


aberrations,andthesecondforpredictingbusarrivaltimetosupplypassengerinfor‐
mation.Theevaluationresultsindicatethattheoutcomesofthesetwoapplicationsare
akintothoseofferedbyCloud,theinformationofferedisfaster,reliableandreal‐time,
andtheoveralltrafficisreduced.In[39],theauthorshavediscussedthe“autonomic
transportmanagementsystem”,whichisanICTbasedsystemforthemanagementof
transport.Thepresentedapproachenablestheformationofa“P2P‐OverlayNetwork’on
theexistingnetworksandappliesIPv6alongwithmulticastandtransparentroutingfor
theeffortlessexpansionofthenetworktothemasses.Themethodologyenablesusersto
jointhenetworkusingtheirpersonaldevicesaswellastheintegrationofinfrastructures
suchastrafficmanagement,trafficlights,trains,etc.,intotheoverlaynetworkwithease.
AnanalysisofexistingIoTmethodsandnotionsconcerningsmartcitiesandsmart
mobilityispresentedin[40].Moreover,ananalysisofdifferentpropertiesandusesrelated
tosmartmobilityandreal‐timetrafficmanagementsystemshasbeengiven.Thepaper
identifiesandaddressesthemajorchallengesrelatedtosmartcitiesandsmartmobility,
suchasunequalgeographicadvancement,privacyconcernsandthelackofcollaboration.
Furthermore,significantgapshavebeenidentifiedinthedomainofsmartmobilityre‐
garding“VehicularAdhocNetworks(VANETs)”and“SmartTrafficLights”.In[41],the
authorshaveillustratedtheimplementationof“Service‐DominantBusinessModelRadar
(SDBM/R)”inthecontextofsmartmobility.Theproposedframeworkisaimedatdesign‐
ingmobilityrelatedbusinessmodelsforthemobilityoftravelersandcargoinacollabo‐
rativefashion.Inamulti‐stakeholderbusinesscontext,theauthorshavedesignedthesys‐
temasacentralelementinthedevelopmentofcomplexdigitalnoveltiesthatprovide
valuetousers.Asurveyhasbeenconductedtovalidatethemodelandtheinputfromthe
participantsindicatestherelevanceoftheproposedmodelanditspotentialforpractical
use.In[42],theauthorshaveexploredthecorrelationbetweenurbanintelligenceandsus‐
tainablemobilityformsforthemunicipalitiesinAustralia,toexplorewhethertheintelli‐
genceofcitiescontributestosustainablemobilityforms.Theimpactofgrowingbroad‐
bandinternetavailabilityonsustainablemobilitymodeisanalyzedusingamultivariate
multipleregressionmodel,anditisobservedthatgrowingbroadbandinternetavailabil‐
itydecreasestheuseofactive,publicmobilitymeans,whileincreasingtheuseofprivate
commutingmeans.Ananalysisoftheassociationbetweenthedeploymentofthesmart
citynotionandthesustainablemobilitynotionispresentedin[43].Theauthorshavealso
analyzedtheeffectofcarbondioxidereleasefromsmartcitycomponentsasadetermining
factorofmobility.TheUnitedNations’“ForFITS(ForFutureInlandTransportSystems)”
modelhasbeenusedtopredictthepossiblecarbondioxidereleaseresultingfromtheim‐
plementationoftheWarsawtransportsystem.Thefindingsindicatethatathorough
changeinthemobilityandenergydomainsisnecessarytoachievethereductiongoals
identifiedbythe“EuropeanUnion2011WhitePaperonTransport”.In[44],theauthor
hasfocusedonthenotionofsharedmobilityandhaspresentedananalysisofthepresent
literature.Thepaperobservesthatmostoftheliteratureisaimedatevaluatingtheeffect
ofsharedmobilityonfactorssuchasmodaltransition,congestion,transitclientele,vehicle
possessionandenvironmentalaspects,whilelittleconsiderationisgiventofemalesafety,
accessibilityandcomfortduringtravelling,revealingagrowinggenderparityinurban
mobility.
2.4.SummaryandResearchGapAnalysis
Thissectionsummarizesanoverviewofthestate‐of‐the‐artaboutsmarturbanmo‐
bilityandidentifiestheresearchgapsandopenissueswithinitsdomain.Foreaseofcom‐
prehension,Table1summarizestheworkscoveredintheliteraturereview.


Sensors2021,21,214312of45


Table1.Summaryofliteraturereview.
S.No.Paper(Year)Focus
1.Amoretti,M.etal.(2017)Smartmobilityapplication“UTravel”basedon“Universal
ProfilingandRecommendation(UPR)”.
2.Belbachir,A.etal.(2019)Trafficcontrolsystembasedoncooperativeagents.
3.Benevolo,C.etal.(2016)Novelactiontaxonomyinvolvingasystematicapproachto
smartmobilityandanalysisoftheroleofICT.
4.Mirri,S.etal.(2016)SmartMobilityforAll(SMAll)—Frameworkfordeveloping
andimplementingsmartmobilityapplications.
5.Lopez,D.etal.(2020)
Six‐layerblockchainarchitecturetohandletheissuesre‐
gardingsecurity,privacy,scalabilityandmanagementof
“SmartMobilityData‐market(BSMD)”.
6.Bravo,Y.etal.(2016)Framework“HITUL”forassistanceinthedecisionmaking
oftrafficcontrolmanagement.
7.Aletà,N.etal.(2017)
Spectrumofdevelopmentof“SpanishSmartCity”
measureswithaviewtomobilityandenvironmentalcon‐
cerns.
8.Camero,A.etal.(2018)
Predictionoftheoccupancyrateofcarparkingspaceprem‐
isedondeeplearningwith“RecurrentNeuralNetworks
(RNNs)”.
9.Cledou,G.etal.(2018)Taxonomyfortheformulationofsmartcityservices.
10.Dameri,R.P.(2017)Novelactiontaxonomyconcerninganextensivemethodol‐
ogyrelatedtoSmartMobility.
11.Bucchiarone,A.(2019)
CollectiveAdaptationEngine(CAE)—Distributedadapta‐
tionmethodforensemble‐basedsystemsinsmartmobility
context.
12.DelVecchio,P.etal.
(2019)
Applicationofsystemdynamicstosimulatesubstitutesfor
conventionalhumanmobility.
13.Debnath,A.K.etal.
(2014)
Realisticstructuretodesignacomparativeanalysis,which
gaugescitiesbasedonthesmartnessoftheirtransport
frameworks.
14.Docherty,I.etal.(2018)
Analysisofthelikelytransitionofpresentissuesofmobility
governancewiththeaimtosafeguardandincreasethepub‐
licvalue.
15.Torres‐Sospedra,J.etal.
(2015)
“SmartUJIAPP”and“SmartUJIAR”toenhancetheposi‐
tioningelementofsmartmobilityinasmartuniversityset‐
tingactingasarepresentativeforasmartcity.
16.Garau,C.etal.(2015)MobilityindicatorstoassesssmarturbanmobilityinItalian
cities.
17.Groth,S.(2019)
Transformationfromanautomobilecommunitytoamulti‐
modalcommunityfosteredbytheadventofsmartmobility
poweredbyICTinaGermanregion.
18.Battarra,R.etal.(2018)
Assessmentofthepossibilityanddegreeofapplyingthe
smartcitymodelwiththeaimtoboosttheeffectivenessand
livingconditionsofurbanareas.
19.Ilarri,S.etal.(2015)SemanticMOVE—distributedsystemformobilitydataman‐
agementandsemanticimprovement.
20.Papa,E.etal.(2015)
Multi‐disciplinaryandcollectivemethodologytosmartmo‐
bilitytoenabletheshifttoa“smartermobility”toimprove
thecitydevelopmentandcitizens’qualityoflife.
21.Jeekel,H.(2017)Relationshipbetweensmartmobilityandsocialsustainabil‐
ity.
22.Kronsell,A.etal.(2020)Theoreticalassessmentofexperimentalgovernancewithre‐
specttosmartmobilityinSweden.

Sensors2021,21,214313of45


23.Garau,C.etal.(2016)Quantitativeapproachforassessingurbanmobilityinthe
ItalianareaofCagliari.
24.Kudo,H.(2016)
FrameworkofcitizenengagementandefficiencyofJapa‐
neseSmartCommunitiestocontemplatethecollaborative
designanddevelopmentofasmartmobilityframework.
25.Longo,A.etal.(2019)
Holisticmethodologytomodeltheefficiencyofpublic
transportfacilitiesschemedasawholeinamulti‐stake‐
holdercontextfromend‐to‐endperspective.
26.Lyons,G.(2018)
Analysisofthemeaningof“smart”inthecontextofsmart
urbanmobilityandtherelationshipbetweensmartnessand
sustainability.
27.Mangiaracina,R.etal.
(2017)
Analysisoftheroleplayedby“IntelligentTransportSys‐
tems(ITS)”inassistingurbansmartmobility.
28.Mboup,G.(2017)
Highlightingthefactorsthatlinkcitizenstodifferentfacili‐
ties,especiallymobilityandICTframeworksintheSenega‐
lesecityofDakar.
29.Melo,S.etal.(2017)
CasestudyinthePortuguesecityofLisbonwhichestab‐
lishesaperformanceassessmentofpassengerandcommer‐
cialvehicleredirection.
30.Ning,Z.etal.(2017)
Conceptof“VehicularSocialNetworks(VSNs)”focusing
ontheimportanceofhighlyefficientandsecuresmartcity
transmissioninVSNs.
31.Orlowski,A.etal.(2019)Establishinganindicatorforassessingthedegreeofsmart
mobilitysolutionsdeployedinurbanareas.
32.Pangbourne,K.etal.
(2018)
Analysisof“MobilityasaService(MaaS)”toevaluateits
potentialimpactforcitypolicymakerswithregardstogov‐
ernanceandsustainability.
33.Peprah,C.etal.(2019)Evaluatingthesmartnessoftransportsystemsinthecities
ofGhanaandillustratingtherealizationofthenotion.
34.Pereira,J.etal.(2019)
Generalframeworkfortheimplementationoffogcompu‐
tingfeaturesin“Vehicularad‐hocNetworks(VANET)”
context.
35.Schlingensiepen,J.etal.
(2016)
“Autonomictransportmanagementsystem”—ICTbased
systemforthemanagementoftransport.
36.Faria,R.etal.(2017)AnalysisofexistingIoTmethodsandnotionsconcerning
smartcitiesandsmartmobility.
37.Turetken,O.etal.(2019)Implementationof“Service‐DominantBusinessModelRa‐
dar(SDBM/R)”inthecontextofsmartmobility.
38.Yigitcanlar,T.etal.(2019)Correlationbetweenurbanintelligenceandsustainablemo‐
bilityformsforthemunicipalitiesinAustralia.
39.Zawieska,J.etal.(2018)Analysisoftheassociationbetweenthedeploymentofthe
smartcitynotionandthesustainablemobilitynotion.
40.Singh,Y.J.(2020)Notionofsharedmobilityfocusingongenderparity.
Authorsin[5,6]primarilyfocusedondescribingthebenefitsofsmartcitiesforthe
usersandhowsmartmobilitycanbeusedasanaidtosolvetrafficcongestion,urban
planning,recommendationsystems,etc.In[7],itwasconcludedthatresponsiblebehavior
andtheattitudeoftheinhabitantshaveapositiveeffectonsmartmobilityinitiatives.The
securityaspectsofsmartmobilitywerediscussedin[8,9].Someofthekeypointswhich
werefoundmissinginthereviewedliteratureinclude:
● UserPrivacy
● DataIntegrationissues
● DataStandardizationissues
● Sensorcharacteristics
● Impactofexternalenvironmentonsensingcapabilitiesofsensors.

Sensors2021,21,214314of45


Inthispaper,wehaveprovidedaholisticreviewwhichcoverstheaboveaspectsin
additiontothosepreviouslydiscussed.Userprivacyconcernsandalackoftechnicaland
operationalknowhowrestrictcommonpeople,especiallyelderlypeople,womenandla‐
borerworkers,fromusingsmartmobilityservices[45].Severalprivacypreservingap‐
proacheshavebeendevelopedinrecentyearsfortheeffectiveadoptionandeaseofuse
ofmobilityservices[46,47].Thegovernanceissuesinsmartmobilityservices,including
consent‐baseddatacapturing,manipulationsandusage,dataintegrationandstandardi‐
zationchallenges,werecoveredin[48–51].Theimportanceofsensorcharacteristicsfor
sensingandcapturingthedataaboutthesubjectanditssurroundingsplaysavitalrolein
developingeffectiveandintelligentmobilityinitiatives[52–55].Theimpactofexternal
environmentsontheperformanceofthesensorsisanessentialfieldofstudy,asdiscussed
in[56,57].Thematerialsusedinthedevelopmentofsensorsalsoplayanimportantrole
indecidingtheirusageandlife[57].
Apartfromthese,afewopenissueswhicharestillprevalentinsmartmobilityin‐
clude:
● Enforcinguniformandubiquitousmobilitylaws,rulesandregulations
● Citizenparticipationinmobilityinitiatives
● Crowdsensinginsmartmobility
● Interoperability
● LegacyInfrastructuresetups
● AmicableCooperationbetweenpublic‐privatemobilityservicesplayers.
Withtherapidtechnologicaltransformations,theseopenissueswillsurelybead‐
dressedinyearstocome.Asthesmartcityinitiativeisslowlybecomingareality,exten‐
siveresearchisbeingconductedtohandlesuchopenissues[58,59].
3.SmartMobilityinSmartCities
3.1.Overview
Urbanmobilityisoneofthemajorcomponentsofasmartcitythatactsasacritical
factorbehindsmartandsustainabledevelopment.Today,nationsacrosstheglobeare
heavilyinvestingintransportinfrastructureasanapproachtoensuresmartandafforda‐
blemeansoftransportationtocitizens.Smartmobilityisoneofthekeydefiningfeatures
ofthesmartcity[60].Inmostdevelopingnations,whererapidurbanizationisincreasing
thedemandsofsmartandcleanermodesoftransport,theneedforsmartmobilityisever
increasing.Thekeymobilitychallengeinsuchcountriesdemandsthemassadoptionof
publictransportsystems.Thekeyattributesofthesmartmobilityconcept,asshownin
Table2,allowtheusersandotherstakeholdersclean,safeandefficienttravel.
Table2.Smartmobilityattributes.
AttributesSignificance
FlexibilityAllowsuserstochoosefromthemultiplemodesoftransportationtosuittheirneeds
usingsmartanddynamicnavigation.
EfficiencyProvidesefficientmobilityoptionswithminimumdisruptions,lowcostandmini‐
mumcommutetime.
IntegrationEnsuresend‐to‐endrouteplansindependentofthetransportationmodes.
SustainabilityPromotescleanerandsustainableoperationswithminimumemissions.
SecurityandSafetyTheefficientdatasharingandconnectivitymodelsensureroadsafety.
SocialBenefitsProvidesequalopportunitiestocitizenstousepublictransport.Ensuringqualityof
lifetoall.
AutomationFacilitatesautomationinallprocesses.
ConnectivityTheentitiesinthenetworkareconnected.
AccessibilityAffordabletoall.
UserExperienceTheefficientprocessesensureabetteruserexperience.

Sensors2021,21,214315of45


Theconceptofsmartmobilityencompassestheshiftfromthetraditionaltransporta‐
tionsystemtoMobilityasaService(MaaS),whereintelligentinfrastructureconnectsvar‐
iousstakeholdersandentitiestoprovideanefficient,intelligentandsustainablesolution
[61].Itincludesmultiplemodesoftransportationincludingondemandmobilitysolu‐
tions,electricvehicles,bikes,rapidmasstransitfacilities,walking,etc.Thekeyideabe‐
hindsmartmobilityistoensurequalityservicetothecitizensandatthesametimemini‐
mizingtheimpactonthesurroundingenvironment[62].Figure5presentssomeofthe
keybenefitsofintegratingsmartmobilitysolutionsinasmartcityecosystem.

Figure5.Keybenefitsofusingsmartmobilityinasmartcity.
RealTimeInformationSystems:Theuseofemergingtechnologiesintheexisting
transportationsystemsandthedevelopmentofsmartmobilitysolutionsproviderealtime
datacollection,monitoring,andmanagement.Thedatacollectedfromvariousconnected
entitiesresultinefficientandsmartinformationsystems.
PredictiveMaintenance:Further,themachinelearningandartificialintelligencecon‐
ceptscanbeutilizedforthepredictivemaintenanceofvariousprocessesinadvance.This
isfacilitatedbycontinuousdatacollectionandmonitoring.
IntelligentParkingManagement:Therealtimedatacollectedfromsensorsandother
connecteddevicescanbeanalyzedtoprovideusefulinsightsintotheavailabilityofpark‐
ingslotsatvariouslocations.
IntelligentTrafficManagement:Theuseofefficientdataanalyticscanhelpinthereal
timemonitoringandmanagementoftraffictoavoidanyjamsandcongestions.Further,
realtimenotificationscanbesenttotheconnectedvehiclesregardingthestateofparking,
routes,etc.
AutomatedTollCollection:Thesmartmobilitysolutionsprovidehasslefreemove‐
mentsattollplazasbyfacilitatingautomaticpayments.
IntegratedTicketingSystems:TheMobilityasaServiceconceptprovidestheintegra‐
tionofseverallocalservices,thusfacilitatingasmartticketingsystemtoprovideeasyand
multimodalservicestocitizens.
SmartSurveillanceandRoadSafety:Thecamerasandothersecuritydevicesforming
apartoftheconnectednetworkhelpinmonitoringthestateoftraffic,thusenhancing
roadsafety.

Sensors2021,21,214316of45


Theemergenceofconnectedtechnologieshasopenednewpossibilitiesforurban
planningandmanagement.Theeconomicalandhumancostassociatedwithroadmis‐
hapsisprofound.Today,intelligenttransportmanagementsystemshaveenabledthecon‐
ceptofSmartMobility,makingdrivingandcommutingsaferandmoreefficient.Thecon‐
nectivitymodelsforsmartmobilityincludethecommunicationbetweenthevehiclesand
theotherconnectedentities,asshowninFigure6.

Figure6.Theprimaryconnectedvehiclemodelsforsmartmobility.
VehicletoInfrastructure:Vehiclesareconnectedtothetrafficinfrastructuresuchas
trafficlights,tolls,parking,pedestriancrossings,etc.,toenablethesharingofrealtime
trafficinformationandpredictingtrafficjamsanddelays.
VehicletoVehicle:Vehiclesareconnectedtoothervehiclestoensureroadsafety.The
datasharedbetweenvehiclesfurtherhelpinavoidingcongestionandparkingmanage‐
ment.
VehicletoCloudStorage:Vehicletostorageconnectivityhelpsinrealtimeinfor‐
mationsharing,storageandprocessing.
VehicletoPedestrians:Thisconceptconnectivitymodelconnectsvehiclestopedes‐
triansviasmartdevicessuchasmobilephonesandwearablestofacilitatepedestrian
safetyandofferrealtimeefficientmobilityoptionsandsolutions.
VehiclestoOtherEntities:Thisensuresacomprehensiveconnectivitymodelthatin‐
tegratessmartmobilitywithothersmartcitycomponentsandprocesses.
3.2.Opportunities
Smartmobilitysolutionscanbringunprecedentedbenefitsforthesmartcityecosys‐
tem.Smarttrafficmanagementtointelligentlanduseplanningandmanagementcanbe
madepossiblewiththehelpofsmartmobilitysolutions.Cityplannerscanexploitthe
intelligentmobilityapproachestocomeupwitheffectiveandefficientdesignplanswhich
canberealizedinsustainableandenvironmentallyfriendlyways.Thereareseveralop‐
portunitiesassociatedwithsmartmobilityapproachesforthecityplanners,authorities,
usersandotherinhabitantsofthesmartcity[5–11]:
StrategicRoutePlanningandDevelopment:TheBigDatageneratedfromtheubiq‐
uitouslyconnectedIoTdevicescanbeharnessedtoextractbetterinsightsaboutthetraffic

Sensors2021,21,214317of45


andcityroutes.Thisinformationcanfurtherbeusedforstrategicrouteplanningandde‐
velopment.
BusinessMarketingCampaigns—IntelligentAdsPlacement:Businessescanusedig‐
italsignboardsandhoardingstoeffectivelyplacetheirmarketingadvertisementsatcrit‐
icallocations(wheretheycanbemadevisibletomaximumvehicles/traffic).
DigitalAdsandRevenueGeneration:Therudimentaryposterandbanner‐basedad‐
vertisementscanbereplacedwithdigitalcounterparts.Thishastwo‐foldbenefits.One,it
reducespaperwastage,andsecondly,sinceitcanbedynamicallyupdatedwithsimple
coding/programming,itconsumeslesstimeforsetupandupdates.
AlternateRouteManagementandIncentivizingCitizensforCooperatinginDecision
MakingandFollowingInstructions:lawmakerscantakereal‐timedata‐baseddynamic
decisionsfortrafficmanagement.Thisinformationcanbesharedwithcommutersto
avoidpossibletrafficcongestionsbysuggestingalternativeroutes.Governmentscanalso
incentivizethelaw‐abidingcitizenswhohelpinfollowingandenforcingtheselaws.This
stepcanpromotecitizencentricparticipativegovernance.
OpportunitiesforBuilders,BusinessesandManufacturers:Builders,businessesand
manufacturerscancomeupwithinnovativesolutionsforprovidingsustainableanden‐
vironmentfriendlyalternativesfortheclassicalapproaches.Thearea/techniquesfocused
startupscanexploitthisopportunityforgrowingtheirbusinesses.
CheaperandMultipleOptionsforTransportation:Withsmartmobilitysolutionsin
place,citizenshavetheoptionforabetterqualityofservice(QoS)intermsofcomparative
costs,improvedandmultipleoptionsfortransportationandhassle‐freecommutingexpe‐
rience.
ImprovedServiceability:Dataanalyticsapproachescanbeappliedtoprovidecitizen
centricservicestotheinhabitantsofthesmartcity.Sincethedecisionsandpoliciesare
datadriven,theycansurelyimprovetheQoS.
3.3.Challenges
Someofthemainchallengesthatarepresentedtosmartmobilitynowadaysinclude:
Infrastructure:Implementingsmartmobilitysolutionsinasmartcitysystemhas
highinfrastructuraldemandstoovercomethepressureonthesuboptimaltransportation
systemsinmostpartsoftheworld.Theincreasingpopularityofself‐drivingandelectric
vehiclesrequiresnetworkconnectivity,highbandwidthandelectricchargingstations.To
fullyutilizethepotentialofsmartmobility,thereisaneedtodeveloptheinfrastructure
thatcanrealizetheconceptssurroundingsmartsystems.
LastMileConnectivity:Oneofthemajorissuesinpublictransportationsystemsis
thelow‐costlastmileconnectivity.Forefficientsmartmobilitysolutions,thereisaneed
fordoor‐to‐doorconnectivityirrespectiveofthemodeoftransportation.
SecurityandPrivacy:Theneedforconnecteddevicesandtherapidgenerationof
richpersonaldataposeseriousprivacyconcernsrelatedtodatasharingamongstdevices
andusers.Moreover,duetotheconnecteddevices,theentirenetworkisvulnerableto
outsideattackandbreaches.
Governance:Theconceptofsmartmobilityhasextendedthescopeofconventional
transportationsystemstoincludeotherstakeholderssuchastechcompaniesandservice
providers.Theinclusionofthesenewactorsrequiresmodifiedpoliciesandrulesgovern‐
ingthesmartmobility.Theregulationsgoverningsmartmobilitysystemsarestilllagging
behind.Withnostandardsgoverningtheuseofsmartmobilitysolutions,themassadop‐
tionofthesesmartsolutionsisstillfarfromreality.Thereisaneedtoseamlesslyintegrate
theexistingtrafficlawstomeetthedemandsofsmartmobilitysolutions.
InitialAdoption:Oneofthemajorissueswithsmarttransportationiscreatingaware‐
nessamongstthepotentialusersforitsadoption.Themajorityofsolutionsareintheearly
stagesofdevelopment,whichdoesnotprovidesignificantproofoftheexactgoalsofthe
mobilitysolutions.

Sensors2021,21,214318of45


DynamicRoutingandTransportationMobility:Theefficientsolutionsrequiredy‐
namicroutingsystemstoestimatethetraveldemandsoftheusersandoptimizetheavail‐
ableresourcestoprovidethesolutions.Thisprocessrequiressophisticatedsoftwareand
technologicalsolutions.
NetworkManagementandMonitoring:Thesmartecosystemscomprisemillionsof
connectedentities,whichmakesthenetworkmanagementandmonitoringcomplexand
costly.
DataAcquisitionandIntegration:Thedataarecollectedfromheterogeneoussources
withdifferentsecurityandprivacyprotocols.Handlingthelargevolumesofrealtime
datageneratedfromtheconnecteddevicesisacomplextask.
LegalChallenges:Theinvolvementofmultiplestakeholderssuchaspaymentcom‐
panies,governments,cityadministrations,public‐privatetransportation,users,etc.,re‐
quiresawell‐definedlegalsystemandsupportpoliciesforsmartmobility.
4.SmartMobilityServicesandApplications
4.1.Mobility‐as‐a‐Service
TheemergenceoftheMobility‐as‐a‐Service(MaaS)paradigmisatrendthatisex‐
pectedinthecomingyearsintheareaofmobilityinsmartcities.Thecurrentrealityof
door‐to‐doorjourneysseemstohaveitsdaysnumberedasitdoesnotcomplywiththe
sustainabledevelopmentobjectivesandlagsbehindtheMaaSplatformsintermsofcosts
andtraveltime[63].TheobjectiveofMaaSplatformsistoprovideanalternativetothe
useofprivatetransportationwithseveralunderlyingconsequences,includingareduction
intrafficcongestionandvolumerestrictionsonurbantransportcapacity.Inthecoming
years,wewillwitnesstheemergenceofaglobalplatformthatintegratesvariousmodes
oftransport,withanon‐demandserviceandwhereinformationisavailableinrealtime
andinapredictivemannerinsuchawaythatallowstheprovisionofservicessuchas
multimodalroutes.Severalserviceshaveappearedinrecentyearsthatpromotethevision
oftheMaaSparadigm:carpooling(sharingthecarforagiventripinordertoprevent
severalpeoplefromtravelingtothesameplace),ridesharingcompanies(companiesthat
matchpassengersandvehicledrivers),bicycleande‐scooterssharingsystems(systems
thatallowtherentalofbicyclesorelectricscootersforashortperiodoftime)orcarsharing
(carrentalmodelforshortperiodsoftime).Therecentadvancesinautonomouscarsare
apromisefortheshort‐mediumfuturetohelpmaterializetheMaaSparadigm.Thiswill
putinperspectivetheneedforpeopletoownacar,bothineconomictermsaswellasin
benefitscomparedtousingon‐demandservicesthatareexpectedtohavemuchmoreaf‐
fordablecostswhentheuseofautonomousvehiclesbecomeswidespread.
Oneofthechallengesthatthecomingyearswillbringincludesthecreationofa
globalMaaSplatformintegratingseveralgeographicallydispersedMaaSplatformsand
heterogeneousintheirgenesis,whichcouldalsoleadtothecreationofunifiedstandards.
TwomaincharacteristicscanbepointedouttoaMaaSsystem[63][64]:(1)tobemulti‐
modal,aMaaSplatformmustnecessarilyincludedifferenttypesandmodesoftransport,
and(2)tobeuser‐centric,theresultofaMaaSplatformmustbeadaptedtotheneedsand
preferencesofeachuser,preferablycollectedinanimplicitandtransparentwayaccord‐
ingtowhatisthehistoryandpatternsoftheirmobility.
SomeofthechallengesthataMaaSwillhelpsolveinasmartcityinclude[63,64]:
 Highnumberofprivatecarswithinacity
 Littleattractivenessforcitizenswhenitcomestousingpublictransport
 Littleuseofactivemodesoftransport
 Lessadequatelocationofspecificplacesforbicycles,scootersandparkinglinksand
itslackofintegrationwiththetransportnetwork
 Lackofaccessibilitytotransportsystemsbypeoplewithdisabilitiesoroldpeople
 Lackofinformationinrealtimeforcitizens

Sensors2021,21,214319of45


 Lackofanintegratedpublictransportplatform,routessuitableforeachuser,on‐
demandparking,activemobilityandunifiedpaymentsystemsfortheentire
transportnetwork
 Lackofunderstandingofcitizens’mobilitypatterns
 Lackofinformationforcitizensabouttheconsequencesoftheirfeweractivehabits
inthecarbonscab
ThebenefitswecanexpectfromaMaaSinclude[63,64]:
 Aneasierwayforcitizenstoplan,bookandpayformobilityservices(whichwill
alsofacilitatecitizensabandoningtheprivatevehicle)
 Improvingtheefficiencyofthetransitnetwork
 Reducedcostsforcitizens
 Decreasedtrafficcongestion
 Reducingtheecologicalfootprint
 Predictingdemands
 Makingpersonalizedsuggestionstocitizens
 Allowingproviderstoplanaheadandmeetcitizens’needs
 Increasingconvenience,effectivenessandcustomersatisfaction
 Easeofpayment
 Revenuegrowthfortransportationserviceproviders.
4.1.1.MaaSImplementationChallenges
Theemergenceofthesmartcityconcepthasbroughtaparadigmshiftinurbanmo‐
bilitysystems.Today,withtechnologiessuchasInternetofThings(IoT),WirelessSensor
Networks,DataAnalytics,BigData,etc.,thetransportationsectorisseeinganewscope
forsignificantchangesandtransformations.Today,thetransportsectorhasbecomea
complexsystemwithservicesintegratedintooneplatformgovernedbytechnology.MaaS
describesatransportsystemthatintegratesvarioustransportmediumsalongwithother
relatedservicestoprovideaseamlessondemandexperiencetousers[65].Anefficient
MaaSsystemprovidessignificantpotentialtofacilitatebusinessandopportunities.
Today,mostcitiesfacechallengesintermsoftheirtransportationsystemsandother
relatedservicessuchasparking,trafficcongestion,etc.MaaSplatformshavehugepoten‐
tialtoprovidesolutionstoovercometheexistingchallengesoftransportationsystemsand
alsohaveapositiveimpactontheenvironmentbypromotingandsupportingrenewable
energyresources[66].However,despitethisutopianconceptofondemandseamlessmo‐
bility,therearestillmanychallengesandissuespertainingtothesuccessfulintegrationof
MaaSintheexistingsmartcityecosystems.Table3presentssomeofthemajorissuesand
challengesandthepotentialsolutionsintheimplementationofMaaSplatformsinasmart
cityecosystem.
MultipleServicesIntegration:MaaSfocusesonaggregatingvariousservicessuchas
publictransport,privatetransport,ridesharing,carpoolservices,payments,parkingsys‐
tems,etc.,onasingleplatformtoensureondemandseamlessmobilitytotheusers.How‐
ever,theheterogeneousnatureoftheseservicesalongwiththeirdifferentsafetyandpri‐
vacyrequirementsmakestheentireintegrationprocessdifficultandchallenging.
PaymentSystemIntegration:thecompleteandefficientimplementationofMaaS
platformsensuringaseamlessmobilityexperiencerequiresahasslefreepaymentsystem
thatallowsuserstomanagethevariousmobilityservicesthroughasingleplatform.Inte‐
gratingasinglepaymentsysteminvolvesmultiplestakeholdersatdifferentlevelsofhi‐
erarchies,suchasbankingsystems,transportcompanies,parkingsystems,vehicleown‐
ers,etc.Thisrequiressignificanttechnologicaldevelopmentinovercomingthesecurity,
safetyandprivacyrequirementsofmultiplestakeholdersandheterogeneousentitiesand
services.
MaaSSubscriptionModels:currently,theexistingtransportationsystemsandcom‐
paniesprovidetheiruserswithsubscriptionmodelsbasedonweekly,monthlyandyearly

Sensors2021,21,214320of45


services.Thesepackagesallowuserstoavailvariousservicesatlowercostsasperthe
termsandconditionsoftheparticularcompany.Implementingasimilarservicewiththe
MaaSplatformrequirescooperationandcoordinationamongstvariouscompanies,trans‐
portationserviceproviders,governments,privateplayersandotherrelatedservices.En‐
suringasinglesubscriptionmodelforaMaaSplatformisstillachallenge.
DataandInformationSharingAmountsServiceProviders:theimplementationofan
efficientMaaSplatformrequiresrealtimedatasharingamongstthevariousservicepro‐
viderstoensurearealtimeseamlessmobilityservicetotheusers.Thisposesseriousse‐
curityandprivacyconcernsasmanystakeholdersdonotwishtosharetheirpersonal
information,paymenthistories,transportationpreferencesandhabitswithothers.Addi‐
tionally,sinceMaaSintegratesmultipleservicesatdifferenthierarchicallevels,thepri‐
vacyconcernsandsecuritytechniquesvarysignificantly.AspertheGeneralDataProtec‐
tionRegulation(GDPR),businessesmustprotectthepersonalinformationofusers
(https://gdpr‐info.eu(accessedon3February2021)),andthereforeMaaSplatformsmust
alsocomplywiththedigitalprivacyoftheirusers.
LegalChallenges:MaaSplatformsfacilitatetheintegrationofmultipleservicesona
singleplatformandalsoprovideopportunitiesfornewbusinessmodels.Theinvolvement
ofmultipleservicessuchastransport,trafficsystems,parking,payments,ticketing,iden‐
titymanagement,datasharing,etc.,requiresstandardizedlawsandregulationsgovern‐
ingtheentireprocess.Currently,therearenolegalservicesandlawsthatregulatethe
entireMaaSplatform.
AdoptionChallenges:oneofthebiggestchallengesforthesuccessfulimplementa‐
tionofaMaaSplatformisthewillingnessofthevariousstakeholderssuchasusers,the
government,serviceproviders,etc.,toadopttheservicemodel.Althoughsmarttranspor‐
tationsystemsareslowlyandsteadilymakingtheirwayintothedailylivesofthepeople,
themassadoptionofplatformssuchasMaaSisstillfarfromreality.
Scalability:thescalabilityoftheMaaSplatformsfromalocalitytoacity,fromacity
toacountryandeventuallytotheentireworldrequiresrulesthatcanencompassthelaws
pertainingtovariousnations.Developingaservicemodelthatcanadapttomultiplecities
andcountriesandcanbereusedinmultiplejurisdictionsisstillachallenge.
TrustandCollaborationamongstStakeholders:theneedfortrustisoneofthemost
importantaspectsofasuccessfulcollaboration.MultipleentitiesinaMaaSplatformre‐
quireaclearunderstandingofthefinancial,societalandenvironmentalgainsofthecol‐
laboration.Thereareapprehensionsrelatedtotheneutralityandfairnessofthesystem
fromdifferentviewpoints.Fromtheuser’sviewpointtheremaybequestionsrelatedto
thequalityoftheserviceandvalueformoney,andtheserviceprovidersmayhavefears
relatedtothemannerinwhichthealgorithmsworkandtheservicesarepresentedtothe
users[67].
Table3.MobilityasaService(MaaS)implementationchallengesandpotentialsolutions.
SNoChallengesSolution
1MultipleServices
integration
Initiatinggoaldirecteddiscussionsamongstthevariousservice
providerstoensurecrossborderintegrations.
2PaymentSystem
Technologicalinterventionsbasedonemergingtechnologiessuch
asblockchain,canbeusedtoensurethesecurityandtransparency
ofthefinancialsystems.
3Subscription
ModelsinMaaS
Governmentandotherregulatingbodiesmustpromotecollabora‐
tionsthroughvariouspilotprograms.Customizedsubscription
modelsneedtobedevelopedtoenhancetheuserexperience.
4Dataandinfor‐
mationsharing
Datasharingmodelscanbedevelopedregulatingthegeneralprin‐
ciplesofdataandinformationsharingandensuringagreements
onthelevelsofdatasharingbetweenserviceproviders.

Sensors2021,21,214321of45


amongstservice
providers
5LegalChallengesThereshouldbegloballyrecognizedstandardsandlawsgovern‐
ingthevariousservicesprovidedundertheMaaSPlatform.
6AdoptionChal‐
lenges
TheMaaSplatformdevelopersmusttakemeasurestobuildtrust
amongsttheusersandcreateawarenessaboutthefinancial,socie‐
talandenvironmentalbenefitsofMaaS.
7Scalability
Internationalgovernmentalbodiesmustcollaborateforthedevel‐
opmentoflawsandstandardsgoverningthemobilityservicesand
tofacilitatejointventuresacrosscitiesandnations.
8
TrustandCollab‐
orationamongst
stakeholders
Thegoverningbodiesshouldensuretransparencyintheprocesses
bydraftingwell‐definedliabilitiesandmutualcontractualagree‐
mentsdocumentingtheagreed‐uponactionsandobjectives.
4.1.2.AnalysisofSecurityThreats
AutonomousvehiclesusetechnologiessuchasInternetofThings,WirelessSensor
Networks,ArtificialIntelligence,andMachineLearning,amongothers,tocollect,analyze
andsharedataandeventuallymakeinformeddecisions.Theseautonomousvehiclesform
apartofalargernetworkcomprisingthousandsofinterconnectedentities.Thesetypesof
connectedenvironmentsconsistingofheterogeneousdevicesarehighlydependentonthe
individualdevice’ssecurityprotocols.Thismakesautonomousvehiclesvulnerabletosev‐
eraltypesofsecuritythreatsandattacks.Theseattackscantakeplaceatvariouslevels
suchasthenetworklevel,devicelevelandsoftwarelevel[68].Table4showsthevarious
typesofvulnerabilitiesinautonomousvehiclesatdifferentlevels.
Table4.VulnerabilitiesinAutonomousVehicles[68].
LevelsTargetSystemsDisruptedServices
Sensors
Camera,GPS,Li‐
DAR,Radar,prox‐
imitysensors,ultra‐
sonicsensors
Parkingassistance,objectidentification,
navigation,collisionavoidance,traffic
signalidentification,cruisecontrol
DeviceAccesscontrolsys‐
tems
Anti‐theftsystem,keylessentrysys‐
tems,signaljamming,replayattack
SoftwareInVehicleprotocols:
LIN,CAN,FlexRayCommunicationsystem
Thevulnerabilitiesatdifferentlevelsposeserioussecurityandprivacyconcernsre‐
latedtotheuseofautonomousvehicles.Someofthemajorsecurityconcernsincludedata
security,networksecurity,vehiclesecurityandfinancialsecurity[69].
Themaintypesofsecuritythreatsinclude:
1.DataTheft:Autonomousvehiclesandself‐drivingcarsformapartofalargernet‐
workofconnectedentitieswhichcontinuouslysharedatatoprovideaseamlessuser
experience.Someofthedatageneratedthroughdifferententitiesconsistofpersonal
userinformationsuchasfinancialdetails,contactdetails,travelhabitsandhistory.
Withthistypeofpersonaldataatdisposal,therearevulnerabilitiesrelatedtodata
theftthatcanbeusedbyattackers.
2.IdentityTheft:Theconceptofautonomousvehiclesfocusesonthetransferofcontrol
fromthehumandriverstothevehicles.Insuchscenarios,theconnectedvehiclesbe‐
comeeasytargetsofcybercriminalsandhackers.Thesevehiclesarepronetoidentity

Sensors2021,21,214322of45


theftwherethehackerscanobtainthevehicleidentificationinformationandmisuse
it.
3.DeviceHijacking:Vehiclehijackingisoneofthebiggestthreatstoautonomousve‐
hicles.Theattackerscangaincontrolofthevehiclesystemandsoftwareandmodify
thealgorithmstoremotelycontrolthevehicle.Oncehijacked,thehackerscanmodify
theimportantfunctioningunitssuchasthenavigationcontrolunit,engine,brakes,
heatingsystems,communicationsystems,vehiclecameraandvisionsystems.
4.DenialofService:Attackerscantakeadvantageofthevulnerabilitiesoftheleast
secureddevicesonthenetworktogaincontroloftheentirenetworkandoverwhelm
theconnectedentities.Thesetypesofattackscanbelaunchedonanindividualnode
orvehicle‐to‐vehicleorvehicle‐to‐infrastructure,resultinginacommunicationsys‐
temdisruption.
6.PrivacyInfringement:Autonomousvehiclescontinuouslygeneratedatasuchasthe
vehiclelocation.Thesedataprovidepersonalinformationrelatedtotheuser’stravel
historyandnavigation,whichcanbemisusedtotracktheuser’scurrentlocationand
otherwhereabouts.
7.FinancialFraud:IncountriessuchasIndiawheretolltaxesareautomaticallyde‐
ductedusingRFIDtagsonthevehicles,thereareseriousconcernsrelatedtothefi‐
nancialsecurityofthelinkedbankaccounts.Anyinsecurenetworkordevicemay
resultinbankingfraudsandotherrelatedattackssuchasransomware.
4.2.TrafficFlowOptimization
Thecontinuousgrowthoftrafficisaproblemthatmanycitiesfacetoday,asaresult
ofplanningthatinmanycaseswascarriedoutmanydecadesago,whenthecongestion
experiencedtodaywasnotexpected.Populationgrowth,whichistakingplaceworld‐
wide,hashadadirectimpactonthenumberofvehiclesontheroadswithoutproper
monitoringofinfrastructuresastheycannotadaptasquicklyaswhatwouldbenecessary
toavoidtheproblemoftrafficcongestion[70].Thishighnumberofvehiclesthatresulted
inmajortrafficcongestionhasalsoresultedinexcessivefuelconsumptionandgasemis‐
sionsandanincreaseinthenumberofaccidents.Alltheseaspectshaveadirectimpact
onseveraldimensionssuchaseconomic,environmentalandliving[71,72].TheUSDe‐
partmentofTransportation(DoT)[73]pointsoutthreemainsourcesfortrafficcongestion:
(1)eventsthatsomehowinfluencetraffic,suchasaccidents,workingareasorbadweather
conditions;(2)fluctuationsinthetrafficdemand,whicharethecauseofnothavingasim‐
ilarandconstantpattern,and(3)existinginfrastructure,whichincludestrafficcontrol
devicesandalsophysicalbottlenecks.TrafficManagementSystems(TMSs)arethesys‐
temsresponsibleforpreventingtrafficcongestionandimprovingtrafficefficiency.Tothis
end,oneoftheircomponentsisthecollectionofinformationfromvariousheterogeneous
sourcessuchasvehicles,trafficlightsandsensorsplacedindifferentlocationsoftheroad
infrastructure.TheprocessingofthesedataisanothervitalcomponentofaTMSwiththe
purposeofpredictingpotentialproblemsinapredictiveorevenreal‐timemannerand
actinginatimelymannertoreducetrafficcongestionandallrelatedproblems[70].From
thevariousproposalsintheliteratureregardingTMSs,thefollowingcontributionscanbe
enumerated:speedadjustmentofthevehiclesothatitspendstheshortestpossibletime
stoppedatatrafficlight[74,75],thedetectionandpreventionoftrafficcongestion[76,77]
orrecommendationforalternativeroutes[71,77,78].
Someofthechallengesbeingfacedregardingtrafficflowoptimizationinclude[48]:
 Optimizationoftheuseofroadinfrastructure
 Integrationofdifferentdomains(air,landandsea)
 Conductingpredictivetrafficanalysis,withemphasisonmomentswhenagreater
flowofvehiclesisexpected
 Performingdataanalysisthatallowstrafficplanningandcontrolinrealtime

Sensors2021,21,214323of45


 Optimizingthelocationofelectriccarchargingstationstotheplaceswheretheyare
mostindemand
 Developingpredictivemodelsthatunderstandtheneedsofcitizensintermsofnew
mobilitymodels(includinge‐scooters,e‐bikes,carsharing,carpooling).
Accordingto[2],TrafficControlandOptimizationwillbeachievedinthecoming
yearsthroughanetworkofintelligentsensors,location‐basedapplicationsandanintelli‐
gentinfrastructurethat,actinginanintegratedmanner,cancontributetomakingtraffic
management,drivingandevenparkingmoreefficient.ICTsolutionswillbethefunda‐
mentalbasisforcontrollingandoptimizingtraffic,allowing,forexample,collisionalarm
andlane‐keeping‐systemsandalsointegratingprivatevehicleswithsmartstreets,traffic
lightsandTMSs.Thistypeofsolutionandintegrationwillcontributetothesafetyand
convenienceofallstakeholders.Fromthemomentthatcars,streets,trafficlightsandcon‐
trolsystemsareinterconnected,real‐timedataanalysiswillbepossibleandwillallow
trafficconditionstobeanalyzedandpreventiveactiontobetaken.Importantinformation
willthenbepossibletobeprovidedtouserstooptimizetheirroutesintermsoftimeand
inordertoavoidcongestionoraccidentsontheroad,adjustmentstobemadetothespeed
oftheirdrivingorinformationonnearbyparksorparkingspaces.Thisintegrationand
thepossibilityofreal‐timeaccesstoinformationandinstantcommunicationwithdrivers
willbereflectedintermsofsafety,lessaccidents,greaterefficiencyindrivingandplan‐
ningroutesandreducingcongestion,aswellastheemissionofgases.
4.3.OptimizationofLogistics
Urbanmobilityandthecurrentchallengeswefacearenotjustaboutthemobilityof
peopleandprivatevehicles.Wemustalsotakeintoaccountthemobilityofgoods,com‐
mercialtransportandurbanlogistics.Theseaspectsarealsoimportanttoachievesustain‐
ableandenvironmentallyfriendlymobility.Ifwepreviouslyreferredtotrafficcongestion
withagreaterfocusonprivatevehicles,wemustalsoconsidertheimpactthatcommercial
fleetshaveontrafficcongestion.E‐commerceisnowadaysaglobalrealitywithanincreas‐
ingtendencyofuseevenfortheeaseitbringstotheconsumer.However,thisbringsthe
needfordeliveries,whichhasamajorimpactonurbanlogisticsandalsotrafficconges‐
tion.Anotheraspectisrestaurantsandhomedeliveries,whichstartedtoincreaseinsize
withUber‐Eatsandothersimilarservices[79].Themobilityaspectassociatedwithbusi‐
nessesisthussomethingthatdeservesasmuchconcernasprivatemobility.Atthepresent
time,logisticsoperationsareverydefragmented,withlittleintegrationbetweenthevari‐
ousplayers,makinginefficientusethroughoutthesupplychain.Theseproblemswilltend
toworsenastheneedforthedeliveryofgoodsincreases,whichisexpectedtobealmost
certaingiventheincreaseine‐commercebusinesses,homemealdeliveries,amongothers.
Aswiththeincreaseinthenumberofprivatevehicles,theincreaseinthenumberofde‐
liveriesandlogisticsprocesseswillalsohavesignificantconsequencesfortheincreasein
fuel,energyandmaterialcosts,makingtheneedtooptimizetheseprocessesobvious[2].
Someofthechallengesbeingfacedregardingtrafficflowoptimizationinclude[63]:
 Vehiclesthatdelivernotbeingfullyfilledintermsoftheirtransportcapacity
 Inefficientrouteoptimizationtomakedeliveries
 Lackofcoordinationbetweentransportprovidersinordertoachieveanintegrated
waythatincludesseveralfleets
 Littleawarenessbyconsumersandsuppliersofthecarbonfootprintimplicationsof
thelargenumberofdeliveries
 Collisionofthedeliverytimeswiththehoursofgreatesttrafficcongestioninthecit‐
ies
 Deliveriesmadeusingvehiclesthatusefossilfuels
 Inefficientcoordinationofthedifferentmeansoftransport(sea,airandland)
 Useofprivatetransportbycitizenswhenshopping,insteadofusingpublictransport.

Sensors2021,21,214324of45


Accordingto[2],smartlogisticsisrelatedtotheintegrationofvehicles,productsand
loadunitsthatallowrouteandloadoptimization,thusreducingwasteinthesystemasa
whole.Thecurrentdataandsensorprocessingtechnologywillallowthecreationofsolu‐
tionsthataimtoincreasetheflexibilityandefficiencyofroad,air,trainandmarinefreight
throughtheintegrationofthevariousplayersinthelogisticprocessthatincludesindivid‐
ualvehicles,roadsandloadunits.Itisexpectedthatplatformsforfleetmanagementand
optimizedroutecalculationwillallowincreasedefficiencyintheoperationalizationoflo‐
gisticssystemsaswellastheplanninginvolvedintheprocess,reducingcosts,transport
withloadsthatdonotmakeuseofthefullcapacityofthevehicle,accidentsorevendam‐
agetotheproducts.Sensortechnologycancontributetothemonitoringandtrackingof
thelocationofproductsandfleets,whichallowsreal‐timechangestorouteswhenever
necessarytoavoidcongestionandreducetime.
4.4.AutonomousVehicles
Theautomationandrobotizationoftasks,withtheincreasingavailabilityofnewsen‐
sorsandsoftware,issomethingwecurrentlyassistinseveraldomains,fromindustryto
callcenters,applicationalsupporttoclients,amongmanyothers.Oneoftheapplications
ofautomationthatwecancallthemostambitiousistheAutonomousVehicle(AV).Ifwe
classifyAVsasambitiousitisbecausetheyinvolvepeopleandbecause,insomecases,we
maybetalkingaboutsituationsinwhichtheirphysicalintegrityisatstakeandevenlife
ordeathsituations.TheavailabilityofAVsisundeniablyagrowingtrend,andtodaywe
arewitnessinganexponentialevolutionuntilafewyearsfromnowwecanreachthecom‐
pleteautomationofvehicles.Thetechnologiesthatarepresentincitiestodayallowusto
anticipatethatinthecomingyearswewillhavefrankdevelopmentsregardingtheauto‐
mationofvehiclesforthedailycommutingthatallcitizensneedtoundertakefortheir
dailytasks[80].Thiswillundoubtedlyrevolutionizeurbanmobilityasweknowitina
verydrasticway.Theinfrastructuresofcitieswillhavetoundergosignificantchangesin
ordertoadapttothisnewreality.Theadventof5Gwillbeafundamentalpieceofthis
puzzle,withaspeed100timesfasterthan4G[81].TheAutomotiveVehicleReadiness
IndexreferstotheNetherlandsasthecountrymostprepared,intermsofinfrastructure,
toaccommodatefutureAVs,followedbySingaporeandtheUnitedKingdom.Threemain
characteristicscanbereferredtoasfundamentalwhenitcomestochanginginfrastructure
incities[81]:
 Lanemarking:thesemarkingsarenotthebestfortoday’svehicles.Itisanaspectthat
willnecessarilyhavetobeimprovedtothepointthattheycanbereadefficientlyby
machines.
 Roadsidesensors:thistypeofsensorshouldbeincludedinsidewalks,curbsand
lanes.Inthisway,itwillbepossibleforAVstobeawareoftheenvironmentthat
surroundsthemandthusactinapreventivewaytopossiblesituationsofdanger.
 Smartsignage:imagerecognitioniscurrentlyusedtoreadtrafficsigns.Inthefuture,
itisexpectedthatthesignalswillbeabletosendasignalthatcanbereadbymachines
andthusfacilitatethereadingofthesignalsbytheautonomousvehicles.
TheintroductionofAVsisconsideredtohavethefollowingconsequencesincities
[82]:
 Providingasaferandmorereliablemeansoftransport,
 Reducingthenumberofaccidents,
 Reducingtheneedforhumaninterventionduringdriving,
 Reducingtrafficcongestion
 Allowingelderlypeopleorpeoplewithdisabilitiestomaketheirliveseasier
 Eliminationoftrafficlightsasautonomousvehicleswillbeabletoefficientlysetpri‐
orities
 Reducing/eliminatingthetimespentsearchingforparkingspaces.

Sensors2021,21,214325of45


Onanotherhand,someofthechallengesfacingthewidespreadadoptionofAVsin‐
clude[43,82–84]:
 Perception,planning,controlandethics
 Dataprivacy
 Transmissionsecurity
 Processinglatency
 Energyefficiency.
4.4.1.LevelsofAutonomousDriving
TheSocietyofAutomotiveEngineers[85]definedsixlevelsofvehicleautomation
whichwereadoptedbytheU.S.DepartmentofTransportation.Thelevelsrangefrom0
(fullymanual)to5(fullyautomated),asdescribedinFigure7.
Level0(FullyManual—NoDrivingAutomation):mostvehiclestodayareatthis
levelwheretheyarefullycontrolledbythedriver.Theremaybesomedriversupport
systems,suchasanemergencybrakingsystemandsometypeofwarningtothedriver,
butthatisnotconsideredautomation.
Level1(DriverAssistance):thislevelischaracterizedbytheexistenceofsometype
ofautonomousactiononthepartofthevehicle,suchasacceleratingorbraking(cruise
control),butdriverinterventionisstillrequiredinmostsituations.
Level2(PartialDriverAutomation):thislevelisessentiallycharacterizedbytheex‐
istenceofAutonomousAdvancedDrivingAssistants(ADAS)thatcontroltheacceleration
andbrakingperformedbythevehicle,inlimitedscenarios.Thelevelofautomationisstill
lowasthedrivermustmaintainfullattentionastheymayhavetotakecareofdrivingat
anytime.
Level3(ConditionalDrivingAutomation):level3representsasubstantialtechno‐
logicaladvancesincethevehiclemustbeabletocontroltheenvironmentaroundit,such
asitsspeedbasedonthemovementofthecarinfront.Evenso,thedrivermustremain
alertincasethevehicleisunabletoperformsometasks.
Level4(HighDrivingAutomation):themainfeatureintroducedatlevel4istheabil‐
ityofthevehicletobeabletotakeoveralmostalldrivingindependentlyandtointervene
incasesomethinggoeswrongorthereisafailure.Inmostcases,driverinterventionisnot
necessary,butthedrivercanstilltakecareofthecar,iftheywish.
Level5(FullDrivingAutomation):level5representsfullvehicleautomationwhere
thereisnoneedtohaveasteeringwheelorpedalsinthecar.Thevehiclewillbeableto
goanywhereandmakeallthenecessarydecisions.

Figure7.Levelofautomationofautonomousvehicles.

Sensors2021,21,214326of45


4.4.2.VehicleSensors
SensorswillplayakeyroleinassertingAVsandintheconfidencethatcitizenswill
haveinthesevehiclesandthesafetytheycanoffer.Thesensorswillbeanessentialpart
ofvehicle‐to‐vehicle(V2V)andvehicle‐to‐infrastructure(V2I)communicationandwill
feedAdvancedDrivingAssistanceSystems(ADAS)intherecommendationsgivento
drivers.Examplesoftheserecommendationsincludewarningsforthedrivertostayin
thecentralpartoftheroad,avoidingdrowsinessordistractionwhenmanagingcruise
control,orinmorecomplexsituationsthatcrossinformationfromtheoutsideweather
withthedriver’scondition(calmer,moreaggressive),orwiththeirheartbeat,whichare
amongmanyotheraspectsthatcanbemonitoredinordertocollectasmuchinformation
aspossibletocreateasafedrivingenvironmentthatdrasticallyreducesthenumberof
accidents.
Inordertocollecttheinformationmentionedabove,itisnecessarytohaveseveral
sensorsinthevehicles,namelyenvironmentsensorswhichareresponsibleforcollecting
informationfromtheenvironmentsurroundingthevehicle[86].Withintheenvironmen‐
talsensors,therearecamerasandremoteradarsplacedinfrontofthevehicle,long‐range
radarsthatallowthedetectionofobjectsinfrontofthevehiclewhenitisathighspeed
andLidarLaserScannersthatallowthedetectionofobjectsatmediumdistances.Itis
normalforeachofthesesensorstobeplacedindifferentlocationsonthevehiclebecause
oftheeffectsoftime,thedetectionangleandthemaximumdistanceatwhichanobjectis
identified.Table5summarizesthevehicle’smainsensors,locationandtheirpurpose.

Sensors2021,21,214327of45


Table5.Vehiclesensors,locationanditspurpose.
SensorLocationPurpose
Long‐RangeRadarFront‐centerofthevehicle
EmergencyBrakingPedes‐
trianDetection
CollisionAvoidance
AdaptativeCruiseControl
LIDARFront,backand4corners
(diagonals)Environmentmapping
CameraFront,backand2sides
TrafficSignRecognition
LaneDepartureWarning
SurroundView
DigitalSideMirror
RearViewMirror
Short‐MediumRangeRa‐
dar
Front,backandrearcor‐
ners
CrossTrafficAlert
RearCollisionWarning
4.4.3.VehicleCommunicationProtocols
VehicularAd‐hocNetworks(VANETs)playakeyroleinintelligenttransportsys‐
tems(ITS).InVANETs,thedevelopmentofaroutingprotocolforeffectivevehicularcom‐
municationspresentsseveralchallenges,whichcanbehighlighted[87]:
 Saferouting:thesecurityofmessagesis,inallsystems,delicateandofenormous
importance.Inthisspecificcase,illegalmessagetamperingcanhaveveryserious
consequencesandevenhaveanimpactonlife‐or‐deathsituations;
 Reliablecommunication:linkrupturescansuddenlyhappenduetosomeVANETs’
characteristicssuchashighmobility,intermittentconnectivityorobstaclesincities,
andwhattodoincaseofpacketlossesisachallenge;
 Determiningtheoptimalpathfromasourcetoadestination,takingintoaccountthe
densityoftrafficandtheshortestdistance;
 Determiningaroutingstrategythatadaptstothetwodistinctenvironmentsof
VANETs:cityenvironmentandmotorways.
Theposition‐basedroutingprotocolisconsideredoneofthebestapproachesin
VANETs[87]andcanbeadoptedinanurbanenvironment(wherethebiggestchallenge
istheobstacles)aswellasonhighways.Intheliteratureandtakingintoaccountthechal‐
lengesofroutinginanurbanenvironment,therearemanyproposalsfortheadoptionof
thistypeofprotocolforcommunicationsV2VandV2I[88–92],withthevariouscategories
ofprotocolslistedinFigure8.

Sensors2021,21,214328of45



Figure8.Categoriesofprotocolsonvehicularcommunications.
4.4.4.EthicalIssues
Therecentpopularityofautonomousvehicleshasdrawntheattentionofautomobile
manufacturersfromallovertheworld.Autonomousvehiclesrepresentanymeansof
transportationthatarecapableofnavigatingtheroadswithnoorminimumhumaninter‐
ventionsandprovidepotentialsolutionstotheroadsafetyissuescausedbyhumanerrors
[93].Thetransitionfromapurelyhumanoperatedvehicletoafullyautomatedvehicleis
stillfarfromreality.However,manyautomobilecompaniesarealreadyofferingpartial
automationintermsofservicessuchasemergencybrakesapplication,assistedparking,
etc.Despitetheseveralbenefitsandtherisingpopularity,thedeploymentofthesevehi‐
clesontheroadposesseveralcriticalchallengesrelatedtotheoperations,technology,
legalaspectsand,mostofall,ethicalissues[94].

Sensors2021,21,214329of45


ThemainconcernsregardhowAVsshouldbehaveinextremetrafficsituations.In
theliterature,thisisknownasthetrolleyproblem,firstproposedin1967,whereamoral
choicemustbetaken,andtheAVshoulddecidewhethertokillfivepeoplebyallowing
thetrolleytoproceed,orkill“just”onepersonwhilestoppingthetrolley[95].Thisissue
hasbeendeeplydebatedastheMoralMachineExperiment(MME),butthereisstillno
solution.Fromthisresearch,itemergedthatcustomersarewillingtobuyAVsthatprior‐
itizethesafetyoftheirpassengers,whilsttheyagreeonothersbuyingAVsthatsacrifice
passengers’lifeforagreatergood[96].Thisisnomoreatechnologicalproblem,butPhi‐
losophy,LawandEthicsareinvolvedandtheindividualgoodisopposedtoagreaterone
[97].Thetrolleyproblemwasthefirstexampleofextremetrafficsituationsthatleadtoa
moralchoice.Severalsituationshavebeendiscussedintheliterature.Justtomentiona
few,oneregardswhethertokillachildoranelderlypersontoavoidanobstacle,or
whethertoinvadetheoppositecarriageway,thusinvolvingmorecarsinanaccident,to
avoidcollisionwithaheavygoodsvehiclethatsurelywouldstronglyinjuretheAV[98].
Fromatechnicalpointofview,thesechoicescanbeaddressedbyrulesthatembedthe
humandriverexperienceandreturnriskandgainprobabilities.However,firstofall,the
problemissocomplexthatitisnotpossibletoforeseeallpossiblesituations;moreover,
otherethicalandmoralissuesarise:thereistheneedtojudgeiftherulesareacceptable,
thenthesamerulesshouldbeadoptedbyallthecompanies,andfinallythemainissue
regardsthedamageresponsibility[98].Itisworthnotingthatwhilstinthescientificliter‐
aturethismoraldilemmahasbeenlargelydebated,andyetnoanswershavebeenfound,
industryreportsonthesametopicassumeapragmaticapproach.Indeed,theseextreme
trafficsituationsareconsideredtoberarecasesthatcannotbeavoided,anddamagesto
objectsandpeoplemustbeminimized[95].However,withthespreadofthefirstautono‐
mousvehicles,someaccidentshavealreadyhappened.Thisisthecaseofapedestrian
whowaskilledinArizonabyanUberself‐drivingcar(https://www.bbc.com/news/tech‐
nology‐54175359accessedon3February2021).
SincethistopicisverycriticalanditlimitsthedevelopmentofAVs,recently,ethical
guidelinesfortheautomotivesectorshavebeenreleasedbydetailingthefundamental
requirementsandpracticalrecommendationsforbothindustriesandpolicymakers[99].
Particularly,in2019sevenfundamentalrequirementswereproposedbytheHigh‐level
ExpertGrouponArtificialIntelligence,whichincluded“humanagencyandoversight”,
“technicalrobustnessandsafety”,“privacyanddatagovernance,transparency”,“diver‐
sity,non‐discriminationandfairness”,“societalandenvironmentalwellbeing”and“ac‐
countability”.Then,theguidelineshavepointedoutthatfiverightsunderliethesere‐
quirements,namely:therightto“self‐determinationandliberty”,“lifeandsecurity”,
“protectionofpersonaldata”,“equalityandnondiscrimination”andto“explanation”.
Table6summarizesthesevenrequirementsbyhighlightingtheirkeyconcernsandbrief
descriptions.Interestedreaderscanreferto[99]formoredetails.
Table6.Requirementsforautonomousvehiclesbasedonfundamentalrights.
RequirementsKeyConcernsDescription
Humanagency
andoversight
 Whichlevelofauton‐
omyshouldbeal‐
lowed?
 Isthestateofthedriver
suitableforobtaining
control?
 Howcanpedestrians
andpeopleoutsidethe
AVexercisetheirauton‐
omy?
AVsmustallowalevelofautonomyto
thehumandrivers.Theyshouldbeal‐
lowedtooverridethedecisionofthema‐
chineintelligence.
Autonomyrequiresdriverstobein‐
formed.Ontheotherhand,AVsshould
monitorthedriver’sstateandblockthe
autonomyiftheirstatecouldcauserisks
(e.g.,theyaredrunk,sleepy,etc.).Finally,

Sensors2021,21,214330of45


humansoutsidethevehicleshouldalsobe
abletopreservetheirautonomy.
Technicalrobust‐
nessandsafety
 HowtoprotectAVby
cyber‐attacksthataffect
vehiclesecurity?
 Howtoautomatically
manageemergencysit‐
uations?
AVsmustberobusttoexternalcybersecu‐
rityattacks(e.g.,Hijacking,Abuse,Pas‐
sivebehavioralattacks).Datacommunica‐
tionsbetweenvehiclesandserversmust
notrevealprivatedatathatcouldaffect
thebehaviorofthevehicle.
Moreover,AVsmustprovidean“auto‐
maticsafeconditionstate”formanaging
emergencysituationsandminimizingthe
risks.
Privacyanddata
governance
 Whichkindofdataare
stored?Whatistheir
aim?
 Isthedrivercorrectly
informedaboutdata
storage?
 Didtheyexplicitlyex‐
presstheirconsent?
 Aredatalawsre‐
spected?
Autonomousvehiclescontinuouslycap‐
tureinformationtoenhancetheirartificial
intelligencesystems.Thesedatacontain
informationrelatedtouserbehaviorand
travelhistory.Sharingthepersonaluser
datamustcomplywiththeGDPR.These
personaldataarefurthervulnerableto
theftsandmisuse.
Moreover,thetypeoftheinformationcol‐
lectedfromtheAVsinfluencestheircapa‐
bilities.Thus,thekindofdataandtheir
scopemustbespecified(e.g.,geolocalisa‐
tiondatafornavigation,biometricdata
foruserrecognitionanddriver’sstate
evaluation,driverbehavioraldatafor
analysis).Datastorageleadstothefollow‐
inglegalissues:transparency(privacy
policiesmustclearlydescribewhichdata
arecollectedandwhy),explicitconsent,
sharingwiththirdparties,compliance
withdataprotectionstandardsandregu‐
lations.
Transparency
 Cleardescriptionof
technicalinformation
behindtheAV
 Explainability
Transparencyisstrictlyrelatedtoprivacy
anddatagovernance.Themanufacturers
mustprovideinformationaboutdatacol‐
lectionandtheiruse.
Moreoverdriversmustbeawareofthe
AV’smechanismsforaccountability.
Transparencyalsoregardstherighttoex‐
planation.DriversneedtotrustAVs,thus
transparencyandcommunicationofthe
underlyingfunctionalitymustbeclearly
explainedinawayhumanscanunder‐
stand.ExplainableArtificialIntelligence
studiestheseaspectsthatarecrucialin
smartcitieswherehumansandmachines
continuouslyinteract[100]
Diversity,non‐dis‐
criminationand
fairness
 Intelligentsystems
learnfromavailable
dataandtheycanbebi‐
ased
Nodistinctionbetweenindividualsmust
beapplied.Thiscouldseemobvious,
sinceitisclearlystatedintheUniversal

Sensors2021,21,214331of45


 Nodistinctionbetween
individualsmustbeas‐
sured
DeclarationofHumanRights,butitis
alsowellknownthatsystemsbasedonar‐
tificialintelligencecanbebiasedbythe
datausedtotraintheirmodels.Ithasal‐
readyhappened,indifferentdomains,
that“intelligentsystems”havediscrimi‐
natedagainstsomeethnicgroups.
Societalandenvi‐
ronmentalwellbe‐
ing
 Whatwillbetheconse‐
quencesofanetimpact
ofintroducingAVs?
TheuseofAVsmustensureanincreasein
publichealthandmobility,reducethe
trafficflow,anddecreasecarbonemis‐
sions.
However,thereisuncertaintyregarding
thespreadinginusingAVs.Thiscould
causeanincreaseintotalpollutionand
congestion.TheintroductionofAVsmust
becombinedwithinfrastructurechanges
aimedtofacilitateandoptimizetheAV
experience.
Accountability
 Whowillberesponsible
foranymis‐happenings
onoroffroad?
 Whowillbepunished
andheldguiltyforacci‐
dentsandbreaking
laws?
 Howwilltheinsurance
companyhandleissues
relatedtoautonomous
vehicleclaims?
Aspreviouslydiscussed,theattributionof
liabilityandresponsibilityisanopenis‐
sue.
Whowillberesponsiblefortheactionsof
theautonomousvehicleinsituations
wheretheentirecontrolisinhandsofthe
vehicleitself?Isittheresponsibilityofthe
humanoperator,thecarcompanyorthe
algorithms?
Incaseofaccidents,AVscannotbere‐
sponsible,sincetheyarenotmoralagents.
Thefulldeploymentofautonomousvehi‐
clesontheroadneedswellestablished
lawsandregulationsgoverningtheliabil‐
ityandresponsibilities.
Moreover,oncethevehicle’scontrolisin
thehandsofanalgorithmorsoftware,
whatwillbethecriteriatodefinerisky
andsafedriving?Howwilltheinsurance
companieshandletheclaimsrelatedtoac‐
cidentsandroadsafetyissues?[101]
4.5.OutdoorNavigationTechnologies
Outdoornavigationrepresentsanimportantcomponentofoneoftheservicesthat
canbedeliveredtocitizensundersmartmobility.Inthissection,wementionsomeofthe
techniquesthatarebeingusedtoachievethatandserveasthebasistodeliverapplications
andservicestocitizensinordertoeaseoneofthemostcommontasksnowadays,namely
whenweareinunknownplacesoralsowhenwearetalkingaboutpeoplewithpermanent
ortemporarydisabilitiessuchasvisuallyimpairedpeople,autisticpeopleorpeoplein
wheelchairsforwhomnavigationrepresentsamajorissue.Oneofthemainandfirsttech‐
nologiesforoutdoornavigationistheGlobalPositioningSystem.Itisatthebaseofthe
guidancesystemsthatemergedincars,anditsmainlimitationisitslimitedaccuracy.GPS
technologywasfirstmadeavailablewithadeliberateerror—calledSelectedAvailability
(SA)—thatcouldcauseanerrorof100mforcivilianusage,whichhighlylimitedapplica‐
tionsthatneedtohaveanaccuratepositioning[102].AGPSreceiverisbasedonsignals

Sensors2021,21,214332of45


receivedfromseveralnon‐geo‐stationarysatellites.Thefactthatthenumberofsatellites,
aswellastheirposition,isvariabledirectlyaffectstheaccuracy[103–106].Evenafterthe
SAlimitationwasoverin2000,theaccuracyachievedwithonlytheuseofGPSislimited
forsolutionssuchashigh‐precisionagricultureorpedestriannavigationinlocationswith
tallbuildings[107].Tosolvetheselimitations,alternativeshaveemerged,suchasthose
basedondifferentialcorrectionsandwhichsupportmethodssuchasRTKorDGPS,which
stillhavethesignaldegradedorhaveinterruptionsdependingontherangeandquality
oftransmission[108].DGPScanbedefinedasareal‐timepositioningmethodthatmakes
useofatleasttwoGPSreceivers,oneinstalledatapointwithknowncoordinates(called
abaseorreferencestation)andtheothermovingalongagivenpathwhosecoordinates
needtobediscovered.Thebasestationcalculatesthedifferentialcorrectionsandtrans‐
mitsthemtothemobilereceiversothattheaccuracyofitslocationcanbeimproved.An‐
otherapproachistheuseofactivebeaconsthatcanuseradio,sonarorlaseranddetermine
thelocationofthepersonorobjectbasedonthetriangulationofthesignal.Thedelayin
receivingthesignalcausesinaccuracyinthelocationobtainedandtherearealsohigh
costsintheassociatedinfrastructure,whichrequirestheexistenceofmanybeacons[109].
Deadreckoningisanancientmaritimetermtodescribenavigationbasedonthestarting
positionandavelocityvectorcomposedofspeedanddirectionandhowlongthevelocity
ismaintainedtodeterminethenewposition[110].Thistechnique,appliedtopedestrian
navigation,measuresandanalyzesaperson’swayofwalkinginordertounderstandand
measuredisplacementinrelationtoaninitialpositionbasedonasetofsensorsthatallow
theextractionofspeedandaltitudeinformation.Thecalculatedpositioningisbaseden‐
tirelyontheinformationcollectedthroughsensors[111],theerrorbeingaccumulatedbe‐
causethecurrentpositioniscalculatedbasedonthepreviousone,andsoon.Thismethod
hasbeenusedinconjunctionwithGPStoimprovethedeterminationofauser’sposition
outdoors.Deadreckoningisalsobeingusedintermsofnavigationandvehiclelocation
[9],whereitispossibletoinstallodometerandopticalsensorsforwheeldirectiondetec‐
tionandachievealow‐costsolution,however,keepingtheproblemoferroraccumula‐
tion.Visualpositioningisatechniquethataimstoovercometheimprecisionofoutdoor
positioningobtainedwithpreviousmentionedtechnologiesusingvisualelementssuch
asmarkers,landmarkscombinedwithcameraimagescapturedbyasmartphone.Google
iscurrentlydevelopingitsVisualPositioningSystem(VPS)whichisintegratedwith
GoogleMapssothat,basedonaugmentedreality,navigationtakesonanotherdimension
whileusingthecameratoinspectthesurroundings,inferpositionandadjustpositioning
andnavigationrecommendations[112].Thisisachievedthroughconsiderablecomputa‐
tionalpowercombinedwithalargesetofdataonthebackendthatallowstherecognition
ofthepositionbasedontheimagecapturedbythecameraofthemobilephone,mostly
throughbuildingsandtouristspots.Thisapproachisfocusedontheuseofknownland‐
marks(monuments,etc.)thatcanthereforequicklyandunambiguously,oratleastwith
greatprecision,identifytheuser’scurrentposition.Someoftheadvantagesofthisap‐
proachinclude[112,113]:higheraccuracythanGPS,andthereforeanalternativeforloca‐
tionswithtallbuildings,addedvalueforblindpeople,whetheronthestreets,ataschool
orinanestablishment,usefulforsituationswhereapersonislostwithoutknowingtheir
location,addedvalueforcompanies,whocanusethissystemtoprovidenavigationin‐
formationtotheircustomerstotraveltotheirestablishment,eitheronfootorusingpublic
transport,theavailabilityofapplicationinindoorandoutdoorenvironments,highscala‐
bilityandlowcost.Likealltechnologies,thisonetoohasitslimitationsandchallenges,
whichincludedifficulty,insomelocations,tohavelandmarksthatcanbeusedtoaccu‐
ratelyidentifyaparticularlocation,difficultyinmappingtheinteriorofallbuildings,vul‐
nerabilitytoluminosity,causingshadowswithimpactontheidentificationofplaces,and
thepossibilityofpeopleorobjectsbeingabletoblockthecapturedimageandthuspre‐
venttherecognitionofthelocation.Passivemarkersarebeingusedtoprovideanopti‐
mizedroutethatgoesthroughseveralpointsofinterest.Thecurrentlocationcanbeob‐
tainedthroughpassivemarkersthatarestrategicallyplacednearpointsofinterestswhich

Sensors2021,21,214333of45


havebeenpreviouslymappedtoaSpatialMapGraph,usingmachinelearning[114].Pas‐
sivemarkers,inanoutdoorenvironment,mightberepresentedthroughQRcodesthat
areplacedinplacessuchaslamppostsortrees.Anexampleofvisuallandmark‐based
localizationisreportedin[115],wheretheauthorspresentastudyonlocationbasedon
geo‐referencedlandmarks,whoseimagesarecollectedbycamerasinordertoserveasan
inputtothelocationfunctionalitythatallowstheattainmentofthepreciselocationofmi‐
croaerialvehiclesinanurbanenvironment.Theuseofvision/landmark‐basedposition‐
ingisalsoreportedin[116],wheretheauthorsdescribetwodifferentwaysofacting:on
theonehand,whattheycall“singlesnapshot‐basedpositioning”,whichconsistsofob‐
taininganimageandcomparingitwithasetofimagespreviouslytakenandgeorefer‐
encedthroughanimagematchingalgorithm.Ontheotherhand,theypresentascenario
basedon“continuouslocalization”,whichiscombinedwithadeadreckoningcompass
allowinganaccuracyof10to15m.
5.EnablingTechnologiestoSupportSmartMobility
5.1.OverviewofEnablingTechnologies
Smartmobilityisagrowingtrendduetotheadventofseveraltechnologiesandcon‐
ceptsthat,whencombined,allowustothinkofsolutionsthatwilleffectivelymakeadif‐
ferenceinthefutureintermsofnewproductsandnewdataprocessingalgorithmsand
techniquesthatwillprovideusefulinformationtothecitizens,inordertoprovidemore
autonomyandmorewell‐beingintheirdailylives.Thereareseveralenablingtechnolo‐
giesthatcanfacilitatethesmoothadoptionofsmartmobility.Figure9showsthesetech‐
nologies[117–123].

Figure9.Enablingtechnologiestosupportsmartmobility.
Blockchain:Blockchaintechnologycanprovideaprivacypreserved,transparentand
trustlessarchitectureformobilityservicesforinhabitants.BlockchainbasedInternetof
Vehicles(IoV)architecturecanbecreatedforimprovedinteractionandcommunication
betweenvehiclesaswellasimprovedtrackingandmanagementoftrafficwithinsmart
cities.
SmartSensorsandIoT:Intelligentandenergyefficientsensortechnologycanbeef‐
fectiveforsensingandcollectingrealtimetrafficandmobilitydataofthevehiclesaswell
astheinhabitantstoprovideeffectivemobilitymanagement.Smartsensor‐basedstreet‐
lightsandtrafficsignalscanautonomouslymakeintelligentdecisionsonthebasisofreal
timesituationstofacilitatethesmoothmanagementoftraffic.

Sensors2021,21,214334of45


ArtificialIntelligence(AI):Onthebasisofthedatacollectedfromdifferentsources,
AIbasedalgorithmscanbedevelopedtoprovideautonomousdecisionsaswellasfuture
predictionsaboutdifferentmobilityrelatedservicesandentities,suchastheconditions
ofroads,traffic,andstreetlights.Additionally,datacentricAIalgorithmscanbeusedby
organizationsforcustomercentrictargetmarketing.
GeospatialTechnologies:Intelligentgeospatialtechnologiescanprovideaccuratein‐
formationforthetrackingandtracingofvehiclesandcitizens.Thesetechnologiescanalso
facilitatealternativeroutesmanagementincaseofemergenciesanddisasters.
BigData:BigDataformsthebackboneofthesmartcityecosystem.Theenormous
amountofdatageneratedfromthesensorsandIoTdevicescanprovidevaluableinfor‐
mationaboutthesubjectanditssurroundings.Thisinformationcanbeusedtoextract
valuableinformation.
CleanEnergy:Smartmobilitymustbecomplementedbycleanenergyalternatives.
Cleanenergysourcessuchassolarenergy,windenergy,hydroenergyandbiomass,etc.,
mustbeadoptedtoprovidezero‐emissionfuelstopowerthesmartcityecosystem.
Figure10showshowsomeoftheseconceptscancontributetosmartmobilityservices
andinfrastructures,startingfromthehardware‐levelgatheringdatabasedonanIoTlayer
composedofseveraldevicesonmultiplelevels;next,anaggregationlayerwithBigData
andthecreationofdatasetswithahugeamountofdata;andfinally,theprocessingofthat
informationusingAIthatwillalsoallowthepredictionoftrendsandsupportdecision
making.Allthiscombinedallowsthedeliveryofendservicestocitizenstoimprovemo‐
bilityinsmartcities.

Figure10.Contributionofsomeoftheenablingtechnologiestosmartmobility.
TheconceptofBigDataisanemergingtermthatisgainingmoreandmoreemphasis
giventhetechnologicalevolutionwearewitnessingandtheheterogeneityofsystemsand
sensorsthatemergeandwhichapplytovariousdomains.Atitsbase,thisconceptisin‐
trinsicallylinkedtotheexistenceoflargevolumesofdata,collectedfromvarioussources
and,therefore,heterogeneous.TherelevanceofBigDataonlyariseswheninformation

Sensors2021,21,214335of45


processingtechniquesareappliedtothesedatasothatpredictiveinformationandpattern
analysiscanhelptheminmakingdecisions.Thesourcesthatnowfeeddataintotheda‐
tasetsthatmakeupBigDataareimmenseandincludesmartphones,datafromsocialnet‐
works,vehiclesensors,GPSandlocationapplications,trafficlightsensorsandotherroad
locations,publicagencies,connectedcars,andcamerafeeds.
TheGeneralDataProtectionRegulationmayconstituteanobstacletotheuseand
adoptionofBigDatainsmartmobilityservices.Someexamplesinclude:
 Real‐timeinformationaboutthelocationcollectedfromsmartphonesthatcanbe
usedforvariousservicesonpeople’smobilitycanbeamajorbarrierintermsofpri‐
vacy;
 Thecollectionofimagesofpeopleobtainedfromservicesformappingandrecogni‐
tionpurposes;
 Theon‐boardunitsofthevehiclesmightbeassociatedwithplatenumberswhichare
consideredindirectidentifiers;
 Thelackofverificationoftheaccuracyandrelevanceoftheinformationcollected.
BigData,combinedwithAItechniques,canallowsignificantimprovementsinsev‐
eralaspectsanddimensionsofsmartmobility[124]:
 Portscalescanbeoptimizedifdataaresharedinadvanceinordertoimproveplan‐
ningandresourceallocation.InHamburg,forexample,electricvehiclesandreal‐
timenavigationareusedtoensurethesmoothflowoftrafficandthusreducecon‐
gestion;
 Trafficlightscanbecontrolledbasedontrafficflowifthenecessarydataarecollected.
Inthisway,trafficincitiescanbeoptimizedandcongestioncanbereduced.Thisis
anexamplecurrentlyinpracticeinthecityofHongKong;
 IoTsensorsandCCTVcamerascanalsobeusedfortrafficmanagement,thusreduc‐
ingcongestion;
 Opendataaboutmobilitycanbeofaddedvaluetocitizensthatcanbeawareofreal
timetrafficdataandplantheirdaywithmoreinformationsoitcanbemoreefficient;
 Parkingspacescanbemonitored,anddriverscanreduceparkingtimeandalsoCO2
emissions;
 On‐demandandmoreadaptivemodesofcapacityplanningandoperationscanbe
achievedusingdatafromsensors,camerasandvehiclessothatinformationtocom‐
muterscanbegivensotheycanplantheirroutesmoreefficiently;
 Fleetefficiencycanbeobtainedbycombiningreal‐timetrafficdatatoproduceroute
optimizationusingAItechniques,reducingwaittimesandoptimizingenergycon‐
sumption;
 Increasecitizens’securitybypreventinganomalydetectionandanticipatinginci‐
dents.
5.2.RoleofEnablingTechnologiesinSmartMobilityServicesandApplications
Inthissection,ananalysisismadeofthecurrentuseofenablingtechnologiesinthe
mainservicesandapplicationsintheareaofsmartmobilityaspreviouslydiscussedin
Section4.
5.2.1.RoleinMobility‐as‐a‐Service
InMaaSsystems,acentraloperatormadeavailablethroughanintermediatelayerof
thesystembecomesessentialtomanageallcommunicationbetweentransportcompanies,
passengersandotherstakeholdersofthesystem.In[125],theauthorsproposeablock‐
chain‐basedsolutionthateliminatesthisintermediatelayer,promotingtrustandtrans‐
parencyamongallstakeholdersandeliminatingtheneedforcommercialagreementsbe‐
tweenthevariousMaaSagents.In[126],easy,quickandtrustedtransactionsarecovered,
usingAIandblockchain‐enabledsmartcontracts.

Sensors2021,21,214336of45


TheuseofgeospatialtechnologieshasbigapplicabilityinMaaSsolutions,bringing
addedvaluetotheentiresystemandassociatedservices.Oneexampleisthemonitoring
ofcommuters.In[127],theauthorsexploitMulti‐accessEdgeComputing(MEC)topro‐
poseaMaaSsolutionthatcollectsandprocessesdatarelatedtothemobilityofcommuters
inordertobeabletorecommendoptimizedroutestakingintoaccounttheneedsand
preferencesofeachcommuter.Anotherstudy[128]alsousesMEC,IoTmessagingproto‐
colsandvirtualization(i.e.,digitaltwins)intheproposalofaframeworkthatcollectsdata
aboutcommutersinordertofeedthemonitoringofmobilitybythetransportplanning
solutions.
TheuseofBigDatainMaaSsystemsispresentintheaforementionedsolutionsthat
collectandprocessdatafromcommuters[127,128].Aspecificbigdataarchitectureispre‐
sentedin[129],motivatedbythespecificrequirementsofmobilityanalytics.
Withregardtocleanenergy,theentireconceptofMaaSrecommendstheprogressive
transitionfromprivatetopublicandsharedtransport,includingcar‐poolingandcar‐shar‐
ing.Thisparadigmshiftwillbringwithitaconsequentreductioninvehiclesandtheemis‐
sionofCO2intotheatmosphere.In[130],theauthorspresentastudyofwhenthetransi‐
tiontoMaaScouldhappenonalargerscaleandwhatimpactitwillhaveonthechoiceof
formsofmobility.
5.2.2.RoleinTrafficFlowOptimizationandOptimizationofLogistics
Trafficoptimizationisoneoftheprimaryneedsintermsofmobility.In[131],the
authorsproposeablockchain‐basedapproachfocusedonairtrafficoptimization,where
blockchaintechnologyisusedtoensureasecure,transparentanddecentralizedplatform.
Thetransportsector’scontributiontoCO2emissionsis23%,withtrafficcongestion
accountingfor¾ofthisvalue[132],hencetheoptimizationofthetrafficflow(privateand
alsofromthepointofviewoflogistics)isessentialifCO2emissionsaretobereduced.
Real‐timeanalysisoftrafficinformationisessentialtoefficientlymanageurbantraf‐
fic.In[133],theauthorsproposethemonitoringandanalysisoftrafficthroughunmanned
aerialvehicles(UAV)forreal‐timevideocollection,andartificialintelligencetechniques
fortheprocessingandidentificationofmovingobjects.In[134],theauthorsproposea
modelforcollectingandanalyzingtrafficpatterns,usingcamerasatintersectionsinorder
tohelpreducetrafficcongestion.Inordertoreducenetworkcongestion,theauthorspro‐
poseprocessingattheedge,withoutsendingtheBigDatasettothecloud.Processingis
performedusingdeeplearningtechniquesandalgorithms.Isitimportanttohighlight
thatthesetypesofapproachescanalsohelptosolveproblemsrelatedtotransportation
morerelatedtothelogisticssector.
TheuseofsensorsandanIoTnetworkfortrafficmonitoringandoptimizationisthe
resultofnumerouscontributionsintheliterature[135].In[136],asolutionisproposedto
optimizethetrafficflowatintersectionsbasedonultrasonicsensorsintegratedwitha
RaspberryPithatoperateontheroadlines,takingintoaccountthedensityofthetraffic.
TheauthorsstresstheimportanceofusingIoTtodriveforsmartersafetyandsaferdevices
ontheroad.
Theperformanceofthevariousstakeholdersofthesupplychains,including
transportlogistics,needstransparencysothatitcanbeaconnected,intelligentandeffi‐
cientsystem.In[137],theauthorsproposeanewapproachtotheuseofblockchainto
ensuretheintegrityoftheperformancemonitoringofthelogisticssector.Ontheother
hand,in[138],andgiventhelackofatraceabilitymechanisminthelogisticaltransaction,
aswellastheintegrityandsecurityoftheinformation,theauthorsproposeatraceability
algorithminlogisticaltransactionsbasedonblockchain.
5.2.3.RoleinAutonomousVehicles
Theadoptionofblockchaininautonomousvehiclesalreadyhasseveralproposalsin
theliterature.Oneofitsapplicationsistosupportasystemthatstoreseventsthathap‐
penedintheautonomousvehicleandthatcanbeusefulwhenthereisanaccidentbetween

Sensors2021,21,214337of45


vehiclesorinvolvingahumanbeing[139].Inthesesituations,responsibilitymustbede‐
cidedonthebasisofasystemthatmakesitpossibletounderstandthevariouseventsthat
havetakenplaceintheautonomousvehicle.AnotherapplicationoftheblockchaininAVs
isrelatedtocar‐sharingorcar‐poolingandtheguaranteeoftrustthatisintendedtoexist
amongeveryoneinvolvedinthistypeofsystem,whichisalsodirectlylinkedtothecon‐
ceptofMaaS[140].Blockchainandsmartcontractsarealsobeingusedtoensuretheau‐
thenticityandintegrityofthefirmwareupdateprocessbymanufacturersofthesystems
thatsupportAVs,whichareincreasinglysubjecttoattacks[141].
Geospatialtechnologiesaimattrackingandtracingvehiclesandpedestrians,thus
providingarangeofmobilityservices.In[142],asystemispresentedthattracksandmon‐
itorstheautonomousvehicleinrealtimeusingaFrequencyModulatedContinuousWave
(FMCW)radar.In[143],theauthorsproposeamodeltoaddresstheaccuracyoftracking
andvehiclestability.Themodelaimstoperformthepredictivecontrolthatintendsto
monitoranydeviationsinthetrajectoryinordertokeepthevehiclestable,evenathigh
speed.
OneoftherelevantaspectswithregardtoAVsisthechoiceofoptimaltrajectory.
Thischoice,in[144],ismadebytheauthorsusingBigDataminingandtheanalysisofreal
accidentdataaswellasreal‐timedatafromconnectedvehicles.In[145],theauthorspro‐
poseanautonomousaccidentdetectionsysteminrealtimebasedoncomputationalintel‐
ligencetechniques.ThestudyusesBigDataprocessingmethodologiestoanalyze2015
trafficflowdatafromIstanbulcollectedfromdifferentsensors,whicharealsointrinsically
relatedtohowautonomousvehiclesareconnectedtotheInternetofThings.
Autonomousvehiclesrepresentthefuture,inwhichvehiclesareexpectedtobecon‐
nectedandelectric.Thisevolutionisdirectlylinkedtoclimateandenvironmentalsustain‐
ability,takingintoaccountanEUdirectivethatrequiresa40%reductioninCO2by2030
comparedto1990.ThereductioninCO2emissionsfromvehiclesgoeslargelythroughthe
transitionforelectricvehicles[146].
Vision‐basedartificialintelligenceinautonomousvehiclesallowsasetoffeatures
includingthedetectionofobstaclesaswellasmechanismstoavoidthem[147].In[148],
theauthorsproposeawaytoincreasetheprocessingpowertoestimatethedistanceto
obstaclesusingComputeUnifiedDeviceArchitecture(CUDA)alongwithBelievePropa‐
gationAlgorithm(BPA).TheuseofAIforin‐vehiclevisionsystemstopowerAdvanced
DrivingAssistantSystems(ADAS)isexploredin[149].
Table7summarizes,foraneasierunderstanding,thestudiesthatapplyeachoneof
thetechnologiestothesmartmobilityserviceorapplication.
Table7.Summaryofstudiesthatapplyenablingtechnologiestomainservicesandapplicationof
smartmobility.
AutonomousVe‐
hicles
Optimizationof
Logistics
Mobility‐as‐a‐
Service
TrafficFlowOp‐
timization
Blockchain[139–141][137
,
138][125
,
126
,
140][131]
Geospatialtech‐
nologies[142,143][133,134][127,128][133,134]
BigData[144
,
145][134][127–129][134]
CleanEnergy[146][132][130][132]
ArtificialIntelli‐
gence[147–149][133,134][126][133,134]
IoT[142][145][135][136][128][135
,
136]


Sensors2021,21,214338of45


6.FutureTrendsonSmartUrbanMobility
Smartmobilitysolutionsarecatalyzingaparadigmshiftintheareaofmobilityman‐
agementinsmartcities.Severalinnovativeapproachesarebeingdevisedtoimprovethe
state‐of‐the‐artofsmartmobility.Afewimportantfactorstobeconsideredinclude:
 Mobilityindegradedvision
 Electricvehicles
 Alternatefuels
 Mobilitysolutionsinnaturalcalamitiesanddisasters
 Mobilityfordifferentlyabledcitizens
 Inclusive,environmentfriendly,sustainableandefficienttransportation
 IoTbaseddynamictrafficmanagement
 Transparentanddistributedtrafficmanagement
 Securityofcitizens,devicesandvehicles.
ArtificialIntelligenceisoneofthepioneeringtechnologiesbeingexploitedinrecent
yearstoprovidestate‐of‐the‐artmobilitysolutions.Deeplearning‐basedobjectdetection
andidentificationcanbeusedtoprovideimprovedvisibilityinconditionsofdegraded
vision(suchaslowlightareas,foggy,windyorrainyenvironments)[150–152].IoT‐based
roadconditionmonitoring,environmentmonitoring,andsurroundingmonitoringcan
provideunprecedentedinformationwhichcanbeusedtopredictfutureconditionstoaid
inthedesiredcourseofactions.Blockchaintechnologycanprovideadistributed,trans‐
parent,andimmutableVehicle‐to‐Everything(V2X)networkforeffective,securedand
privacypreservedmanagementofthesmartmobilityecosystem[153–156].Technology
forlowcarbonemissions,alternativefuels,electricvehiclesandimprovedbatteriescan
alsoaidintheprotectionoftheenvironmentandattainingsustainabledevelopmentgoals
forbetterenvironmentforabetterqualityoflife(QoL).Extensiveresearchwasconducted
inthefieldofcommunicationstoprovideconnectivityintimesofnaturalcalamityand
disasters[157,158].Affordablesatellite‐basedcommunicationtechnologiesarebeingex‐
ploredtoprovideconnectivityinextremeandunavoidableconditions.
7.Conclusions
Thelastfewyearshavealreadymadesomeadvancesandthefirststepstowardsthe
constructionoffuturesmartcitiesknown,namelyintermsofmobility.Theincreaseinthe
worldpopulationinevitablyhasconsiderableconsequencesonthewayurbanmobility
operates,notleastbecausecitieswereplanneddecadesorevenhundredsofyearsago,
takingintoaccountamuchsmallerpopulationandwhereinfrastructureneedswerevery
different.Achallengenowarisesinadaptingcitiestocurrentneeds.However,theevolu‐
tiontowardscitiesandsmartmobilitywillnotinvolvemakingmorespaceforvehiclesor
moreroads;rather,itwillundergoaculturalparadigmshiftinwhichpeoplewillhaveto
stopusingtheirprivatecarstoswitchtosharedtransport,whichmaythusreducenot
onlythenumberofvehiclesincirculation,butalsocontributetoreducingtheecological
footprint.Inadditiontothetechnologicalissueassociatedwiththischange,wealsohave,
andinaverymarkedway,anunderlyingculturalchangebecausethereisacertaincom‐
fortandfacilitationintheuseofourowncarsthatwillnotbeeasytochangefromone
momenttothenext.Citizensmustrealizethenegativeconsequencesfortheenvironment
ofmaintainingthecurrentstateandtheimpossibilityofmanagingsomanycarswithin
thecitieswiththehighestpopulationdensity,andwhiletheremustbeanefforttoraise
awarenessofthebenefits,wewillallgainfromthisculturalandparadigmshift.
Technologyanditsexponentialevolution,whichisexpectedtocontinueinthecom‐
ingyears,willsupportthetransitionfromSmartMobilitytoaMobility‐as‐a‐Servicepar‐
adigm,wherevehiclesharing,carsharing,andcarpoolingwillbringtheemergenceofa
newdimension.TheIoTandtheconnectivitythatwillbeabletobeestablishedbetween
vehicles,trafficlights,andpedestrians,amongothers,willallowtheevolutiontointelli‐
genttrafficmanagementsystemswithreducedcongestionandeventhenumberof

Sensors2021,21,214339of45


accidents,whichisthemainpremisethatLevel5AutonomousVehiclesintendtoachieve.
Tothisend,wewillhavethebigcontributionofBigDataandAIthat,together,willpro‐
cessgiganticvolumesofinformationcollectedfromtheIoTandwillallowservicestobe
madeavailabletocitizensthatwillmaketheirdailylivesmucheasierandwillallowpre‐
ventiveactionindecision‐makingwithregardtothemanagementofvehicle,fleetand
pedestriantraffic.
AuthorContributions:Conceptualization,M.A.A.,S.P.andG.T.;methodology,S.P.,G.C.andN.F.;
formalanalysis,M.A.A.,G.T.,G.C.;investigation,S.P.,G.C.andN.F.;resources,M.A.A.,S.P.,G.T.;
datacuration,N.F.,G.C.,G.T.;writing—originaldraftpreparation,M.A.A.,S.P.,G.T.;writing—re‐
viewandediting,N.F.,G.C.S.P.;supervision,M.A.A.,G.T.,N.F.;fundingacquisition,G.C.Allau‐
thorshavereadandagreedtothepublishedversionofthemanuscript.
Funding:Thisresearchreceivednoexternalfunding.
InstitutionalReviewBoardStatement:Notapplicable.
InformedConsentStatement:Notapplicable.
Acknowledgments:GabriellaCasalinoacknowledgesfundingfromtheItalianMinistryofEduca‐
tion,UniversityandResearchthroughtheEuropeanPONprojectAIM(AttractionandInternational
Mobility),nr.1852414,activity2,line1.
ConflictsofInterest:Theauthorsdeclarenoconflictofinterest.
References
1. UnitedNations.TransformingOurWorld:The2030AgendaforSustainableDevelopment.2020.Availableonline:https://sus‐
tainabledevelopment.un.org/post2015/transformingourworld(accessedon21January2021).
2. UnitedNations.WorldPopulationProspects.2020.Availableonline:https://population.un.org/wpp/Graphs/Probabilis‐
tic/POP/TOT/900(accessedon2March2021).
3. Gouveia,J.P.;Seixas,J.;Giannakidis,G.SmartCityEnergyPlanning.InProceedingsofthe25thInternationalConferenceCom‐
paniononWorldWideWeb—WWW’16Companion,Montréal,QC,Canada,11–16April2016;pp.345–350,
doi:10.1145/2872518.2888617.
4. Ruhlandt,R.W.S.Thegovernanceofsmartcities:Asystematicliteraturereview.Cities2018,81,1–23.
5. Amoretti,M.;Belli,L.;Zanichelli,F.UTravel:Smartmobilitywithanoveluserprofilingandrecommendationapproach.Per‐
vasiveMob.Comput.2017,38,474–489.
6. Belbachir,A.;ElFallah‐Seghrouchni,A.;Casals,A.;Pasin,M.Smartmobilityusingmulti‐agentsystem.ProcediaComput.Sci.
2019,151,447–454.
7. Backhouse,J.Ataxonomyofmeasuresforsmartcities.InProceedingsofthe13thInternationalConferenceonTheoryand
PracticeofElectronicGovernance,Athene,Greece,23–25September2020.
8. Mirri,S.;Prandi,C.;Salomoni,P.;Callegati,F.;Melis,A.;Prandini,M.Aservice‐orientedapproachtocrowdsensingforacces‐
siblesmartmobilityscenarios.Mob.Inf.Syst.2016,2016,2821680.
9. Lopez,D.;Farooq,B.Amulti‐layeredblockchainframeworkforsmartmobilitydata‐markets.Transp.Res.PartCEmerg.Technol.
2020,111,588–615.
10. Bravo,Y.;Ferrer,J.;Luque,G.;Alba,E.(2016,June).Smartmobilitybyoptimizingthetrafficlights:Anewtoolfortrafficcontrol
centers.InInternationalConferenceonSmartCities;Springer:Cham,Switzerland,2016;pp.147–156.
11. Aletà,N.B.;Alonso,C.M.;Ruiz,R.M.A.SmartmobilityandsmartenvironmentintheSpanishcities.Transp.Res.Procedia2017,
24,163–170.
12. Camero,A.;Toutouh,J.;Stolfi,D.H.;Alba,E.Evolutionarydeeplearningforcarparkoccupancypredictioninsmartcities.In
InternationalConferenceonLearningandIntelligentOptimization;Springer:Cham,Switzerland,2018;pp.386–401.
13. Cledou,G.;Estevez,E.;Barbosa,L.S.Ataxonomyforplanninganddesigningsmartmobilityservices.Gov.Inf.Q.2018,35,61–
76.
14. Dameri,R.P.ICTintensityinsmartmobilityinitiatives.InSmartCityImplementation;Springer:Cham,Switzerland,2017;pp.
85–108.
15. Bucchiarone,A.Collectiveadaptationthroughmulti‐agentsensembles:Thecaseofsmarturbanmobility.AcmTrans.Auton.
Adapt.Syst.(Taas)2019,14,1–28.
16. DelVecchio,P.;Secundo,G.;Maruccia,Y.;Passiante,G.Asystemdynamicapproachforthesmartmobilityofpeople:Implica‐
tionsintheageofbigdata.Technol.Soc.Chang.2019,149,119771.
17. Debnath,A.K.;Chin,H.C.;Haque,M.M.;Yuen,B.Amethodologicalframeworkforbenchmarkingsmarttransportcities.Cities
2014,37,47–56.
18. Docherty,I.;Marsden,G.;Anable,J.Thegovernanceofsmartmobility.Transp.Res.PartAPolicyPr.2018,115,114–125.

Sensors2021,21,214340of45


19. Torres‐Sospedra,J.;Avariento,J.;Rambla,D.;Montoliu,R.;Casteleyn,S.;Benedito‐Bordonau,M.;Huerta,J.Enhancinginte‐
gratedindoor/outdoormobilityinasmartcampus.Int.J.Geogr.Inf.Sci.2015,29,1955–1968.
20. Garau,C.;Masala,F.;Pinna,F.Benchmarkingsmarturbanmobility:AstudyonItaliancities.InInternationalConferenceon
ComputationalScienceandItsApplications;Springer:Cham,Switzerland,2015;pp.612–623.
21. Groth,S.Multimodaldivide:Reproductionoftransportpovertyinsmartmobilitytrends.Transp.Res.PartA:PolicyPr.2019,
125,56–71.
22. Battarra,R.;Gargiulo,C.;Tremiterra,M.R.;Zucaro,F.SmartMobilityinItalianMetropolitanCities:Acomparativeanalysis
throughindicatorsandactions.Sustain.CitiesSoc.2018,41,556–567.
23. Ilarri,S.;Stojanovic,D.;Ray,C.Semanticmanagementofmovingobjects:Avisiontowardssmartmobility.ExpertSyst.Appl.
2015,42,1418–1435.
24. Papa,E.;Lauwers,D.Smartmobility:Opportunityorthreattoinnovateplacesandcities.InProceedingsofthe20thInterna‐
tionalConferenceonUrbanPlanningandRegionalDevelopmentintheInformationSociety(REALCORP2015),Gent,Belgium,
5–7May2015;pp.543–550.
25. Jeekel,H.Socialsustainabilityandsmartmobility:Exploringtherelationship.Transp.Res.Procedia2017,25,4296–4310.
26. Kronsell,A.;Mukhtar‐Landgren,D.ExperimentalGovernanceofSmartMobility:SomeNormativeImplications.InShaping
SmartMobilityFutures:GovernanceandPolicyInstrumentsintimesofSustainabilityTransitions;EmeraldPublishingLimited:
Bingley,UK,2020.
27. Garau,C.;Masala,F.;Pinna,F.Cagliariandsmarturbanmobility:Analysisandcomparison.Cities2016,56,35–46.
28. Kudo,H.Co‐design,Co‐creation,andCo‐productionofSmartMobilitySystem.InInternationalConferenceonCross‐Cultural
Design;Springer:Cham,Switzerland,2016;pp.551–562.
29. Longo,A.;Zappatore,M.;Navathe,S.B.Theunifiedchartofmobilityservices:Towardsasystemicapproachtoanalyzeservice
qualityinsmartmobilityecosystem.J.ParallelDistrib.Comput.2019,127,118–133.
30. Lyons,G.Gettingsmartabouturbanmobility–aligningtheparadigmsofsmartandsustainable.Transp.Res.PartAPolicyPr.
2018,115,4–14.
31. Mangiaracina,R.;Perego,A.;Salvadori,G.;Tumino,A.Acomprehensiveviewofintelligenttransportsystemsforurbansmart
mobility.Int.J.Logist.Res.Appl.2017,20,39–52.
32. Mboup,G.Smartinfrastructuredevelopmentmakessmartcities—PromotingsmarttransportandICTindakar.InSmartEcon‐
omyinSmartCities;Springer:Singapore,2017;pp.871–904.
33. Melo,S.;Macedo,J.;Baptista,P.Guidingcitiestopursueasmartmobilityparadigm:Anexamplefromvehicleroutingguidance
anditstrafficandoperationaleffects.Res.Transp.Econ.2017,65,24–33.
34. Ning,Z.;Xia,F.;Ullah,N.;Kong,X.;Hu,X.Vehicularsocialnetworks:Enablingsmartmobility.IEEECommun.Mag.2017,55,
16–55.
35. Orlowski,A.;Romanowska,P.SmartCitiesConcept:SmartMobilityIndicator.Cybern.Syst.2019,50,118–131.
36. Pangbourne,K.;Stead,D.;Mladenović,M;Milakis,D.Thecaseofmobilityasaservice:Acriticalreflectiononchallengesfor
urbantransportandmobilitygovernance.InGovernanceoftheSmartMobilityTransition;EmeraldPublishingLimited:Bingley,
UK,2018;pp.33–48.
37. Peprah,C.;Amponsah,O.;Oduro,C.AsystemviewofsmartmobilityanditsimplicationsforGhanaiancities.Sustain.Cities
Soc.2019,44,739–747.
38. Pereira,J.;Ricardo,L.;Luís,M.;Senna,C.;Sargento,S.Assessingthereliabilityoffogcomputingforsmartmobilityapplications
inVANETs.FutureGener.Comput.Syst.2019,94,317–332.
39. Schlingensiepen,J.,Nemtanu,F.,Mehmood,R.,McCluskey,L.Autonomictransportmanagementsystems—Enablerforsmart
cities,personalizedmedicine,participationandindustrygrid/industry4.0.InIntelligentTransportationSystems–Problemsand
Perspectives;Springer,Cham,Switzerland,2016;pp.3–35.
40. Faria,R.;Brito,L.;Baras,K.;Silva,J.Smartmobility:Asurvey.InProceedingsofthe2017InternationalConferenceonInternet
ofThingsfortheGlobalCommunity(IoTGC),Funchal,Portugal,10–13July2017;pp.1–8.
41. Turetken,O.;Grefen,P.;Gilsing,R.;Adali,O.E.Service‐dominantbusinessmodeldesignfordigitalinnovationinsmartmobil‐
ity.Bus.Inf.Syst.Eng.2019,61,9–29.
42. Yigitcanlar,T.;Kamruzzaman,M.Smartcitiesandmobility:DoesthesmartnessofAustraliancitiesleadtosustainablecom‐
mutingpatterns?J.UrbanTechnol.2019,26,21–46.
43. Zawieska,J.;Pieriegud,J.Smartcityasatoolforsustainablemobilityandtransportdecarbonisation.Transp.Policy2018,63,39–
50.
44. Singh,Y.J.Issmartmobilityalsogender‐smart?J.Gend.Stud.2020,29,832–846.
45. Zhang,M.;Zhao,P.;Qiao,S.Smartness‐inducedtransportinequality:Privacyconcern,lackingknowledgeofsmartphoneuse
andunequalaccesstotransportinformation.Transp.Policy2020,99,175–185.
46. Deebak,B.D.;Al‐Turjman,F.AsmartlightweightprivacypreservationschemeforIoT‐basedUAVcommunicationsystems.
Comput.Commun.2020,162,102–117.
47. Oliveira,T.A.;Oliver,M.;Ramalhinho,H.Challengesforconnectingcitizensandsmartcities:ICT,e‐governanceandblock‐
chain.Sustainability2020,12,2926.
48. Dowling,R.Smartmobility:Disruptingtransportgovernance?InGovernanceoftheSmartMobilityTransition;EmeraldPublishing
Limited:Bingley,UK,2018.

Sensors2021,21,214341of45


49. Docherty,I.Newgovernancechallengesintheeraof‘Smart’mobility.InGovernanceoftheSmartMobilityTransition;Emerald
PublishingLimited:Bingley,UK,2018.
50. Putnam,D.;Kovacova,M.;Valaskova,K.;Stehel,V.Thealgorithmicgovernanceofsmartmobility:Regulatorymechanismsfor
driverlessvehicletechnologiesandnetworkedautomatedtransportsystems.Contemp.Read.LawSoc.Justice2019,11,21–26.
51. DeBlas,I.;Mediavilla,M.;Capellán‐Pérez,I.;Duce,C.Thelimitsoftransportdecarbonizationunderthecurrentgrowthpara‐
digm.EnergyStrategyReviews,2020,32,100543.
52. El‐Din,D.M.;Hassanien,A.E.;Hassanien,E.E.(2020).Informationintegrityformulti‐sensorsdatafusioninsmartmobility.In
TowardSocialInternetofThings(SIoT):EnablingTechnologies,ArchitecturesandApplications;Springer,Cham,Switzerland,2020;
pp.99–121.
53. Saponara,S.;Neri,B.(2016,April).Radarsensorsignalacquisitionand3DFFTprocessingforsmartmobilitysurveillancesys‐
tems.InProceedingsofthe2016IEEESensorsApplicationsSymposium(SAS),Catania,Italy,20–22April2016;pp.1–6.
54. Garcia‐Font,V.;Garrigues,C.;Rifà‐Pous,H.Acomparativestudyofanomalydetectiontechniquesforsmartcitywirelesssensor
networks.Sensors2016,16,868.
55. Adam,M.S.;Anisi,M.H.;Ali,I.Objecttrackingsensornetworksinsmartcities:Taxonomy,architecture,applications,research
challengesandfuturedirections.FutureGener.Comput.Syst.2020,107,909–923.
56. Jin,X.;Feng,C.;Ponnamma,D.;Yi,Z.;Parameswaranpillai,J.;Thomas,S.;Salim,N.Reviewonexplorationofgrapheneinthe
designandengineeringofsmartsensors,actuatorsandsoftrobotics.Chem.Eng.J.Adv.2020,4,100034.
57. Krech,M.;Trunk,A.;Groche,P.Controllingthesensorpropertiesofsmartstructuresproducedbymetalforming.ProcediaEng.
2017,207,1415–1420.
58. Nkenyereye,L.;Islam,S.R.;Bilal,M.;Abdullah‐Al‐Wadud,M.;Alamri,A.;Nayyar,A.Securecrowd‐sensingprotocolforfog‐
basedvehicularcloud.FutureGener.Comput.Syst.2021,120,61–75.
59. Sun,G.;Sun,S.;Sun,J.;Yu,H.;Du,X.;Guizani,M.Securityandprivacypreservationinfog‐basedcrowdsensingontheinternet
ofvehicles.J.Netw.Comput.Appl.2019,134,89–99.
60. Chourabi,H.;Nam,T.;Walker,S.;Gil‐Garcia,J.R.;Mellouli,S.;Nahon,K.;Pardo,T.A.;Scholl,H.J.Understandingsmartcities:
Anintegrativeframework.InProceedingsofthe201245thHawaiiInternationalConferenceonSystemScience(HICSS),Maui,
HI,USA,4–7January2012;pp.2289–2297.
61. Danesin,A.;Linares,P.Therelevanceofthelocalcontextforassessingthewelfareeffectoftransportdecarbonizationpolicies.
Astudyfor5Spanishmetropolitanareas.EnergyPolicy,2018,118,41–57.
62. Benevolo,C.;Dameri,R.P.;D’auria,B.Smartmobilityinsmartcity.InEmpoweringOrganizations;Springer:Cham,Switzerland,
2016;pp.13–28.
63. Mark,S.;Chen,H.;Teng,Y.;Edgar,E.;Koh,J.MobilityasaService.2018.Availableonline:https://www.eurobiz.com.cn/mo‐
bility‐as‐a‐service/(accessedon12December2020).
64. AI4CITIES.Mobility.2020.Availableonline:https://ai4cities.eu/challenges/mobility(accessedon12December2020).
65. Hietanen,S.MobilityasaService.NewTransp.Model2014,12,2–4.
66. Giesecke,R.;Surakka,T.;Hakonen,M.Conceptualisingmobilityasaservice.InProceedingsofthe2016EleventhInternational
ConferenceonEcologicalVehiclesandRenewableEnergies(EVER),MonteCarlo,Monaco,6–8April2016;pp.1–11.
67. Jittrapirom,P.;Caiati,V.;Feneri,A.M.;Ebrahimigharehbaghi,S.;AlonsoGonzález,M.J.;Narayan,J.Mobilityasaservice:A
criticalreviewofdefinitions,assessmentsofschemes,andkeychallenges.UrbanPlan.2017,2,13.
68. Ren,K.;Wang,Q.;Wang,C.;Qin,Z.;Lin,X.Thesecurityofautonomousdriving:Threats,defenses,andfuturedirections.Proc.
Ieee2019,108,357–372.
69. Parkinson,S.;Ward,P.;Wilson,K.;Miller,J.Cyberthreatsfacingautonomousandconnectedvehicles:Futurechallenges.IEEE
Trans.Intell.Transp.Syst.2017,18,2898–2915.
70. deSouza,A.M.;Brennand,C.A.;Yokoyama,R.S.;Donato,E.A.;Madeira,E.R.;Villas,L.A.Trafficmanagementsystems:Aclas‐
sification,review,challenges,andfutureperspectives.Int.J.Distrib.Sens.Netw.2017,doi:10.1177/1550147716683612.
71. DeSouza,A.M.;Yokoyama,R.;Boukerche,A.Icarus:Improvementoftrafficconditionthroughanalertingandre‐routingsys‐
tem.Comput.Netw.2016,110,118–132.
72. Karagiannis,G.;Altintas,O.;Ekici,E.Vehicularnetworking:Asurveyandtutorialonrequirements,architectures,challenges,
standardsandsolutions.Ieee.Commun.Surv.Tutor.2011,13,584–616.
73. US‐DoT.TrafficCongestionandReliability:TrendsandAdvancedStrategiesforCongestionMitigation.2015.Availableonline:
http://www.ops.fhwa.dot.gov/congestion_report/chapter2.htm(accessedon21January2021).
74. Rakha,H.;Kamalanathsharma,R.Eco‐drivingatsignalizedintersectionsusingv2icommunication.InProceedingsofthe2011
14thInternationalIEEEConferenceonIntelligentTransportationSystems(ITSC),Washington,DC,USA,5–7October2011;
pp.341–346.
75. Barba,C.;Mateos,M.;Soto,P.Smartcityforvanetsusingwarningmessages,trafficstatisticsandintelligenttrafficlights.In
Proceedingsofthe2012IEEEIntelligentVehiclesSymposium(IV),AlcaládeHenares,Spain,3–7June2012;pp.902–907.
76. Bauza,R.;Gozálvez,J.Trafficcongestiondetectioninlarge‐scalescenariosusingvehicle‐to‐vehiclecommunications.J.Netw.
Comput.Appl.2013,36,1295–1307.
77. Younes,M.;Boukerche,A.Efficienttrafficcongestiondetectionprotocolfornextgenerationvanets.InProceedingsofthe2013
IEEEinternationalconferenceoncommunications(ICC),Budapest,Hungary,9–13June2013;pp.3764–3768.

Sensors2021,21,214342of45


78. Doolan,R.;Muntean,G.M.EcoTrec—AnovelVANET‐basedapproachtoreducingvehicleemissions.IEEETrans.Intell.Transp.
2016,18,608–620.
79. Shukla,N.;Choudhary,A.K.;Prakash,P.K.S.;Fernandes,K.J.;Tiwari,M.K.Algorithmportfoliosforlogisticsoptimizationcon‐
sideringstochasticdemandsandmobilityallowance.Int.J.Prod.Econ.2013,141,146–166.
80. Petrovic,N.;Kocic,D.Adoptinglinearoptimizationtosupportautonomousvehiclesinsmartcity.InProceedingsofthe2019
27thTelecommunicationsForum(TELFOR),Belgrade,Serbia,26–27November2019;pp.1–4,doi:10.1109/TEL‐
FOR48224.2019.8971185.
81. Zoria,S.SmartCities:ANewLookattheAutonomous‐VehicleInfrastructure.2020.Availableonline:https://www.iot‐
forall.com/autonomous‐vehicle‐infrastructure(accessedon18January2021).
82. Yaqoob,I.;Khan,L.U.;Kazmi,S.A.;Imran,M.;Guizani,N.;Hong,C.S.AutonomousDrivingCarsinSmartCities:RecentAd‐
vances,Requirements,andChallenges.IEEENetw.2019,34,174–181.
83. Lin,L.;Zhu,J.J.PathPlanningforAutonomousCarParking.InProceedingsoftheASME2018DynamicSystemsandControl
Conference,Atlanta,GA,USA,30September–3October2018;pp.1–10,doi:10.1115/dscc2018‐9195.
84. Dave,R.;Boone,E.S.;Roy,K.EfficientDataPrivacyandSecurityinAutonomousCars.J.Comput.Sci.Appl.2019,7,31–36,
doi:10.12691/jcsa‐7‐1‐5.
85. SAE.TaxonomyandDefinitionsforTermsRelatedtoDrivingAutomationSystemsforOn‐RoadMotorVehicles.2018.Availa‐
bleonline:https://www.sae.org/standards/content/j3016_201806/(accessedon18January2021).
86. Taraba,M.;Adamec,J.;Danko,M.;Drgona,P.Utilizationofmodernsensorsinautonomousvehicles.InProceedingsofthe
2018ELEKTRO,Mikulov,CzechRepublic,21–23May2018;pp.1–5,doi:10.1109/ELEKTRO.2018.8398279.
87. Abbasi,I.A.;ShahidKhan,A.AReviewofVehicletoVehicleCommunicationProtocolsforVANETsintheUrbanEnvironment.
FutureInternet2018,10,14,doi:10.3390/fi10020014.
88. Jerbi,M.;Senouci,S.M.;Meraihi,R.;Doudane,Y.G.Animprovedvehicularadhocroutingprotocolforcityenvironments.In
Proceedingsofthe7thIEEEInternationalConferenceonCommunication,Glasgow,UK,24–28June2007;pp.3972–3979.
89. Liu,J.;Wan,J.;Wang,Q.Asurveyonpositionbasedroutingforvehicularadhocnetworks.J.Telecommun.Syst.Arch.2016,62,
15–30.
90. Lin,Y.W.;Chen,Y.S.;Lee,S.L.RoutingProtocolsinVehicularAdHocNetworks:ASurveyandFuturePerspectives.J.Inf.Sci.
Eng.2010,26,913–932.
91. Lee,K.C.;Lee,U.;Gerla,M.SurveyofRoutingProtocolsinVehicularAdHocNetworks.InAdvancesinVehicularAd‐HocNet‐
works:DevelopmentsandChallenges;InformationScienceReference(anImprintofIGIGlobal);IGIGlobal:NewYork,NY,USA,2009;
pp.113–175.
92. Bilal,S.M.;Khan,A.R.;Ali,S.Reviewandperformanceanalysisofpositionbasedroutingprotocols.Wirel.Pers.AreaCommun.
2016,1,559–578.
93. Brodsky,J.S.Autonomousvehicleregulation:Howanuncertainlegallandscapemayhitthebrakesonself‐drivingcars.Berkeley
Technol.LawJ.2016,31,851–878.
94. Pattinson,J.A.;Chen,H.;Basu,S.Legalissuesinautomatedvehicles:Criticallyconsideringthepotentialroleofconsentand
interactivedigitalinterfaces.Hum.Soc.Sci.Commun.2020,7,1–10.
95. Martinho,A.;Herber,N.;Kroesen,M.;Chorus,C.Ethicalissuesinfocusbytheautonomousvehiclesindustry.Transp.Rev.
2021,1–22,doi:10.1080/01441647.2020.1862355.
96. Holstein,T.;Dodig‐Crnkovic,G.;Pelliccione,P.Ethicalandsocialaspectsofself‐drivingcars.arXiv2018,arXiv:1802.04103.
97. Coeckelbergh,M.Responsibilityandthemoralphenomenologyofusingself‐drivingcars.Appl.Artif.Intell.2016,30,748–757.
98. Birnbacher,D.;Birnbacher,W.Fullyautonomousdriving:Wheretechnologyandethicsmeet.IEEEIntell.Syst.2017,32,3–4.
99. Lütge,C.;Poszler,F.;Acosta,A.J.;Danks,D.;Gottehrer,G.;Mihet‐Popa,L.;Naseer,A.AI4People:EthicalGuidelinesforthe
AutomotiveSector–FundamentalRequirementsandPracticalRecommendations.Int.J.Technoethics(Ijt)2021,12,101–125.
100. Alonso,J.M.;Mencar,C.BuildingCognitiveCitieswithExplainableArtificialIntelligentSystems.InProceedingsoftheCEX@
AI*IAConference,Bary,Italy,14–17November2017.
101. Hevelke,A.;Nida‐Rümelin,J.Responsibilityforcrashesofautonomousvehicles:Anethicalanalysis.Sci.Eng.Ethics2015,21,
619–630.
102. Kaplan,C.J.;Hegarty,E.D.UnderstandingGPS:PrinciplesandApplications;ArtechHouse:Worwood,MA,USA,2006.
103. Kourogi,M.;Sakata,N.;Okuma,T.;Kurata,T.Indoor/OutdoorPedestrianNavigationwithanEmbeddedGPS/RFID/Self‐con‐
tainedSensorSystem.InAdvancesinArtificialRealityandTele‐Existence.ICAT2006;Springer:Berlin/Heidelberg,Germany,2006.
104. Panzieri,S.;Pascucci,F.;Ulivi,G.AnoutdoornavigationsystemusingGPSandinertialplatform.Ieee/AsmeTrans.Mech.2002,
7,134–142.
105. Parkinson,B.W.;Spilker,J.J.GlobalPositioningSystem:TheoryandApplications;AmericanInstituteofAeronauticsandAstro‐
nautics:Reston,VA,USA,1996.
106. Wellenhof,B.H.;Lichtenegger,H.;Collins,J.GlobalPositioningSystem,5thed.;Springer:Berlin/Heidelberg,Germany,1992.
107. Lee,J.Globalpositioning/GPS.InInternationalEncyclopaediaofHumanGeography;Elsevier:Amsterdam,TheNetherland,
2009;pp.548–555.
108. Sejas,M.;Saatkamp,E.;Junior,J.PosicionamentoporDGPS:ExperimentosparaavaliaçãodastécnicasRDS,NTRIPeDGPS.
Rev.Bras.Geomática2013,1,8–16.

Sensors2021,21,214343of45


109. Lin,H.Y.;Lin,J.H.;Wang,M.L.Avisualpositioningsystemforvehiclenavigation.InProceedingsoftheIEEEConferenceon
IntelligentTransportationSystem,Vienna,Austria,16September2005.
110. Loewy,R.G.Avionics:AʺNewʺSeniorPartnerinAeronautics.AIAAJ.1999,37,1337–1354.
111. Zhang,M.;Yang,J.;Zhan,J.;Dai,Y.ADead‐ReckoningBasedLocalPositioningSystemforIntelligentVehicles.InProceedings
ofthe2019IEEEInternationalConferenceonPower,IntelligentComputingandSystems(ICPICS),Shenyang,China,12–14July
2019;pp.513–517.
112. WifiAttendance.VisualPositioningSystem—AllYouNeedtoKnow.Availableonline:https://www.wifiattend‐
ance.com/blog/visual‐positioning‐system/(accessedon20January2021).
113. Shi,T.;Wang,H.;Cui,W.;Ren,L.Indoorpathplanningforhex‐rotoraircraftwithlandmark‐basedvisualnavigation.InPro‐
ceedingsofthe201512thInternationalConferenceonFuzzySystemsandKnowledgeDiscovery,FSKD2015,Zhangjiajie,China,
15–17August2016;pp.340–344.
114. Anup,S.;Goel,A.;Padmanabhan,S.Visualpositioningsystemforautomatedindoor/outdoornavigation.InProceedingsofthe
TENCON2017—2017IEEERegion10Conference,Penang,Malaysia,5–8November2017;pp.1027–1031.
115. Wendel,A.;Maurer,M.;Bischof,H.Visuallandmark‐basedlocalizationforMAVsusingincrementalfeatureupdates.InPro‐
ceedingsofthe2012SecondInternationalConferenceon3DImaging,Modeling,Processing,Visualization&Transmission,
Zurich,Switzerland,13–15October2012;pp.278–285.
116. Steinhoff,U.;Omerčević,D.;Perko,R.;Schiele,B.;Leonardis,A.Howcomputervisioncanhelpinoutdoorpositioning.In
EuropeanConferenceonAmbientIntelligence;Springer:Berlin/Heidelberg,Germany,2007;Volume4794,pp.124–141.
117. Zan,T.T.T.;Gueta,L.B.;Okochi,T.Enablingtechnologyforsmartcitytransportationindevelopingcountries.InProceedings
ofthe2015IEEEInternationalConferenceonSmartCity/SocialCom/SustainCom(SmartCity),Chengdu,China,19–21December
2015;pp.170–174.
118. Moscholidou,I.;Pangbourne,K.ApreliminaryassessmentofregulatoryeffortstosteersmartmobilityinLondonandSeattle.
Trans.Policy2020,98,170–177.
119. Sigg,S.;Fu,X.Socialopportunisticsensingandsocialcentricnetworking:Enablingtechnologyforsmartcities.InProceedings
ofthe2014ACMInternationalWorkshoponWirelessandMobileTechnologiesforSmartCities,Philadelphia,PA,USA,11
August2014;pp.83–90.
120. Balakrishna,C.Enablingtechnologiesforsmartcityservicesandapplications.InProceedingsofthe2012SixthInternational
ConferenceonNextGenerationMobileApplications,ServicesandTechnologies,Paris,France,12–14September2012;pp.223–
227.
121. Akabane,A.T.;Immich,R.;Madeira,E.R.;Villas,L.A.iMOB:Anintelligenturbanmobilitymanagementsystembasedonve‐
hicularsocialnetworks.InProceedingsofthe2018IEEEVehicularNetworkingConference,Taipei,Taiwan,5–7December2018.
122. Ahad,M.A.;Paiva,S.;Tripathi,G.;Feroz,N.Enablingtechnologiesandsustainablesmartcities.Sustain.CitiesSoc.2020,61,
102301.
123. Bhushan,B.;Khamparia,A.;Sagayam,K.M.;Sharma,S.K.;Ahad,M.A.;Debnath,N.C.Blockchainforsmartcities:Areviewof
architectures,integrationtrendsandfutureresearchdirections.Sustain.CitiesSoc.2020,61,102360.
124. Basis.Mobility.2020.Availableonline:https://basis‐ai.com/industries/mobility(accessedon21January2021).
125. Nguyen,T.H.;Partala,J.;Pirttikangas,S.Blockchain‐BasedMobility‐as‐a‐Service.InProceedingsofthe201928thInternational
ConferenceonComputerCommunicationandNetworks(ICCCN),Valencia,Spain,29July–1August2019;pp.1–6,
doi:10.1109/ICCCN.2019.8847027.
126. Karinsalo,A.;Halunen,K.SmartContractsforaMobility‐as‐a‐ServiceEcosystem.InProceedingsofthe2018IEEEInternational
ConferenceonSoftwareQuality,ReliabilityandSecurityCompanion(QRS‐C),Lisbon,Portugal,16–20July2018;pp.135–138,
doi:10.1109/QRS‐C.2018.00036.
127. Campolo,C.;Cuzzocrea,D.;Genovese,G.;Iera,A.;Molinaro,A.AnOMALightweightM2M‐compliantMECFrameworkto
TrackMulti‐modalCommutersforMaaSApplications.InProceedingsofthe2019IEEE/ACM23rdInternationalSymposium
onDistributedSimulationandRealTimeApplications(DS‐RT),Cosenza,Italy,7–9October2019;pp.1–8,doi:10.1109/DS‐
RT47707.2019.8958677.
128. Campolo,C.;Genovese,G.;Molinaro,A.;Pizzimenti,B.DigitalTwinsattheEdgetoTrackMobilityforMaaSApplications.In
Proceedingsofthe2020IEEE/ACM24thInternationalSymposiumonDistributedSimulationandRealTimeApplications(DS‐
RT),Prague,CzechRepublic,14–16September2020;pp.1–6,doi:10.1109/DS‐RT50469.2020.9213699.
129. Vouros,G.;Glenis,A.;Doulkeridis,C.TheDeltaBigDataArchitectureforMobilityAnalytics.InProceedingsofthe2020IEEE
SixthInternationalConferenceonBigDataComputingServiceandApplications(BigDataService),Oxford,UK,3–6August
2020;pp.25–32,doi:10.1109/BigDataService49289.2020.00012.
130. Viet,R.D.;Molin,E.Mobility‐as‐a‐Service:Doesitcontributetosustainability?InProceedingsofthe2020ForumonIntegrated
andSustainableTransportationSystems(FISTS),Delft,TheNetherlands,3–5November2020;pp.192–197,
doi:10.1109/FISTS46898.2020.9264902.
131. Duong,T.;Todi,K.K.;Chaudhary,U.;Truong,H.DecentralizingAirTrafficFlowManagementwithBlockchain‐basedRein‐
forcementLearning.InProceedingsofthe2019IEEE17thInternationalConferenceonIndustrialInformatics(INDIN),Helsinki,
Finland,22–25July2019;pp.1795–1800,doi:10.1109/INDIN41052.2019.8972225.
132. InternationalEnergyAgency(IEA).CO2EmissionsfromFuelCombustion—Highlights;InternationalEnergyAgency:Paris,France,
2014.

Sensors2021,21,214344of45


133. Zhang,H.;Liptrott,M.;Bessis,N.;Cheng,J.Real‐TimeTrafficAnalysisusingDeepLearningTechniquesandUAVbasedVideo.
InProceedingsofthe201916thIEEEInternationalConferenceonAdvancedVideoandSignalBasedSurveillance(AVSS),Tai‐
pei,Taiwan,18–21September2019;pp.1–5,doi:10.1109/AVSS.2019.8909879.
134. Sreekumar,U.K.;Devaraj,R.;Li,Q.;Liu,K.Real‐TimeTrafficPatternCollectionandAnalysisModelforIntelligentTraffic
Intersection.InProceedingsofthe2018IEEEInternationalConferenceonEdgeComputing(EDGE),SanFrancisco,CA,USA,
2–7July2018;pp.140–143,doi:10.1109/EDGE.2018.00028.
135. Ramesh,K.;Lakshna,A.;Renjith,P.N.SmartTrafficCongestionmodelinIoT‐AReview.InProceedingsofthe20204thInter‐
nationalConferenceonElectronics,CommunicationandAerospaceTechnology(ICECA),Coimbatore,India,5–7November
2020;pp.651–658,doi:10.1109/ICECA49313.2020.9297631.
136. Talukder,M.Z.;Towqir,S.S.;Remon,A.R.;Zaman,H.U.AnIoTbasedautomatedtrafficcontrolsystemwithreal‐timeupdate
capability.InProceedingsofthe20178thInternationalConferenceonComputing,CommunicationandNetworkingTechnol‐
ogies(ICCCNT),Delhi,India,3–5July2017;pp.1–6,doi:10.1109/ICCCNT.2017.8204095.
137. Kuhi,K.;Kaare,K.;Koppel,O.Ensuringperformancemeasurementintegrityinlogisticsusingblockchain.InProceedingsof
the2018IEEEInternationalConferenceonServiceOperationsandLogistics,andInformatics(SOLI),Singapore,31July–2Au‐
gust2018;pp.256–261,doi:10.1109/SOLI.2018.8476737.
138. Liang,S.;Li,M.;Li,W.ResearchonTraceabilityAlgorithmofLogisticsServiceTransactionBasedonBlockchain.InProceedings
ofthe201918thInternationalSymposiumonDistributedComputingandApplicationsforBusinessEngineeringandScience
(DCABES),Wuhan,China,8–10November2019;pp.186–189,doi:10.1109/DCABES48411.2019.00053.
139. Guo,H.;Li,W.;Nejad,M.;Shen,C.‐C.Proof‐of‐EventRecordingSystemforAutonomousVehicles:ABlockchain‐BasedSolu‐
tion.IEEEAccess2020,8,182776–182786,doi:10.1109/ACCESS.2020.3029512.
140. Hasan,M.G.M.M.;Datta,A.;Rahman,M.A.PosterAbstract:ChainedofThings:ASecureandDependableDesignofAutono‐
mousVehicleServices.InProceedingsofthe2018IEEE/ACMThirdInternationalConferenceonInternet‐of‐ThingsDesignand
Implementation(IoTDI),Orlando,FL,USA,17–20April2018;pp.298–299,doi:10.1109/IoTDI.2018.00048.
141. Baza,M.;Nabil,M.;Lasla,N.;Fidan,K.;Mahmoud,M.;Abdallah,M.Blockchain‐basedFirmwareUpdateSchemeTailoredfor
AutonomousVehicles.InProceedingsofthe2019IEEEWirelessCommunicationsandNetworkingConference(WCNC),Mar‐
rakesh,Morocco,15–19April2019;pp.1–7,doi:10.1109/WCNC.2019.8885769.
142. Hwang,T.;Kim,S.;Kim,S.;Jang,G.;Park,J.;Park,S.;Matson,E.T.;Kim,K.AnApproachforReducingComputationalTime
forReal‐TimeAutonomousVehicleTracking.InProceedingsofthe201818thInternationalConferenceonControl,Automation
andSystems(ICCAS),PyeongChang,Korea,17–20October2018;pp.1760–1763.
143. Sun,C.;Zhang,X.;Zhou,Q.;Tian,Y.AModelPredictiveControllerwithSwitchedTrackingErrorforAutonomousVehicle
PathTracking.IEEEAccess2019,7,53103–53114,doi:10.1109/ACCESS.2019.2912094.
144. Najada,H.A.;Mahgoub,I.Autonomousvehiclessafe‐optimaltrajectoryselectionbasedonbigdataanalysisandpredefined
userpreferences.InProceedingsofthe2016IEEE7thAnnualUbiquitousComputing,Electronics&MobileCommunication
Conference(UEMCON),NewYork,NY,USA,20–22October2016;pp.1–6,doi:10.1109/UEMCON.2016.7777922.
145. Ozbayoglu,M.;Kucukayan,G.;Dogdu,E.Areal‐timeautonomoushighwayaccidentdetectionmodelbasedonbigdatapro‐
cessingandcomputationalintelligence.InProceedingsofthe2016IEEEInternationalConferenceonBigData(BigData),Wash‐
ington,DC,USA,5–8December2016;pp.1807–1813,doi:10.1109/BigData.2016.7840798.
146. Zaffiro,G.;Marone,G.SmartMobility:NewrolesforTelcosintheemergenceofelectricandautonomousvehicles.InProceed‐
ingsofthe2019AEITInternationalConferenceofElectricalandElectronicTechnologiesforAutomotive(AEITAUTOMOTIVE),
Turin,Italy,2–4July2019;pp.1–5,doi:10.23919/EETA.2019.8804575.
147. Mishra,S.K.;Das,S.AReviewonVisionBasedControlofAutonomousVehiclesUsingArtificialIntelligenceTechniques.In
Proceedingsofthe2019InternationalConferenceonInformationTechnology(ICIT),Bhubaneswar,India,19–21December2019;
pp.500–504,doi:10.1109/ICIT48102.2019.00094.
148. Choi,Y.K.CUDAImplementationofBeliefPropagationforStereoVision.InProceedingsofthe13thInternationalIEEEAnnual
ConferenceonIntelligentTransportationSystems,MadeiraIsland,Portugal,19–22September2010.
149. Davies,E.R.Computervision:Principles,Algorithms,Applications,Learning,5thed.;AcademicPress:Cambridge,MA,USA,2017.
150. Pandey,A.;Puri,M.;Varde,A.Objectdetectionwithneuralmodels,deeplearningandcommonsensetoaidsmartmobility.
InProceedingsofthe2018IEEE30thInternationalConferenceonToolswithArtificialIntelligence(ICTAI),Volos,Greece,5–7
November2018.
151. Mauri,A.;Khemmar,R.;Decoux,B.;Ragot,N.;Rossi,R.;Trabelsi,R.;Boutteau,R.;Ertaud,J.Y.;Savatier,X.DeepLearningfor
Real‐Time3DMulti‐ObjectDetection,Localisation,andTracking:ApplicationtoSmartMobility.Sensors2020,20,532.
152. Karballaeezadeh,N.;Zaremotekhases,F.;Shamshirband,S.;Mosavi,A.;Nabipour,N.;Csiba,P.;Várkonyi‐Kóczy,A.R.Intelli‐
gentroadinspectionwithadvancedmachinelearning;hybridpredictionmodelsforsmartmobilityandtransportationmainte‐
nancesystems.Energies2020,13,1718.
153. Bittencourt,L.F.;Diaz‐Montes,J.;Buyya,R.;Rana,O.F.;Parashar,M.Mobility‐awareapplicationschedulinginfogcomputing.
IEEECloudComputing,2017,4,26–35.
154. Vaa,T.;Penttinen,M.;Spyropoulou,I.Intelligenttransportsystemsandeffectsonroadtrafficaccidents:stateoftheart.Iet
Intell.Transp.Syst.2007,1,81–88.
155. López,D.;Farooq,B.Ablockchainframeworkforsmartmobility.InProceedingsofthe2018IEEEInternationalSmartCities
Conference(ISC2),KansasCity,MO,USA,16–19September2018;pp.1–7.

Sensors2021,21,214345of45


156. Sun,J.;Yan,J.;Zhang,K.Z.Blockchain‐basedsharingservices:Whatblockchaintechnologycancontributetosmartcities.Fi‐
nanc.Innov.2016,2,1–9.
157. Hank,P.;Müller,S.;Vermesan,O.;VanDenKeybus,J.Automotiveethernet:In‐vehiclenetworkingandsmartmobility.In
Proceedingsofthe2013Design,Automation&TestinEuropeConference&Exhibition(DATE),Grenoble,France,18–22March
2013;pp.1735–1739.
158. Schnieder,M.;West,A.Evaluationofalternativebatterychargingschemesforone‐wayelectricvehiclesmartmobilitysharing
systemsbasedonrealurbantripdata.InProceedingsofthe2019IEEE5thInternationalforumonResearchandTechnologyfor
SocietyandIndustry(RTSI),Florence,Italy,9–12September2019;pp.296–301.