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DemandforEmerging TransportationSystems ModelingAdoption,Satisfaction,and MobilityPatterns
ConstantinosAntoniou
ChairofTransportationSystemsEngineering, TechnicalUniversityofMunich,Munich,Germany
DimitriosEfthymiou ChairofTransportationSystemsEngineering, TechnicalUniversityofMunich,Munich,Germany
EmmanouilChaniotakis BartlettSchoolofEnvironment,EnergyandResources, UniversityCollegeLondon(UCL),London,UnitedKingdom
Elsevier
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Notices Knowledgeandbestpracticeinthis fieldareconstantlychanging.Asnewresearchand experiencebroadenourunderstanding,changesinresearchmethods,professional practices,ormedicaltreatmentmaybecomenecessary.
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Dedication ConstantinosAntoniou: ToMari-Elen,Maira,HarryandCecilia
DimitriosEfthymiou: ToAlexiaandVasilis
EmmanouilChaniotakis: ToIriniandZoi
Contributors MayaAbou-Zeid,DepartmentofCivilandEnvironmentalEngineering,American UniversityofBeirut,Lebanon
ArunPrakashAkkinepally,DepartmentofCivilandEnvironmentalEngineering, MassachusettsInstituteofTechnology,Cambridge,MA,UnitedStates
ChristelleAlHaddad,ChairofTransportationSystemsEngineering,Departmentof Civil,GeoandEnvironmentalEngineering,TechnicalUniversityofMunich, Munich,Germany
ConstantinosAntoniou,ChairofTransportationSystemsEngineering,Departmentof Civil,GeoandEnvironmentalEngineering,TechnicalUniversityofMunich, Munich,Germany
BilgeAtasoy,DepartmentofMaritimeandTransportTechnology,DelftUniversityof Technology,Delft,theNetherlands
MosheBen-Akiva,DepartmentofCivilandEnvironmentalEngineering,Massachusetts InstituteofTechnology,Cambridge,MA,UnitedStates
KlausBogenberger,DepartmentofCivilEngineeringandEnvironmentalSciences, BundeswehrUniversityMunich,Bavaria,Germany
EmmanouilChaniotakis,BartlettSchoolofEnvironment,EnergyandResources, UniversityCollegeLondon(UCL),London,UnitedKingdom
FrancescoCiari,PolytechniqueMontre ´ al,Montre ´ al,QC,Canada
AdamCohen,TransportationSustainabilityResearchCenter,UniversityofCalifornia, Berkeley,CA,UnitedStates
DimitriosEfthymiou,ChairofTransportationSystemsEngineering,Departmentof Civil,GeoandEnvironmentalEngineering,TechnicalUniversityofMunich, Munich,Germany
MengyingFu,BauhausLuftfahrte.V.,Taufkirchen,Germany
MaximJanzen,IVT,ETHZurich,Zurich,Switzerland
HarisN.Koutsopoulos,DepartmentofCivilandEnvironmentalEngineering, NortheasternUniversity,Boston,MA,UnitedStates
MasahiroKuwahara,ToyotaMotorCorporation,Toyota,Japan
CarlosLimadeAzevedo,DepartmentofManagementEngineering,Technical UniversityofDenmark,Lyngby,Denmark
DimitrisMilakis,InstituteofTransportResearch,GermanAerospaceCenter(DLR), Berlin,Germany
xiv Contributors
ToshiyukiNakamura,InstituteofInnovationforFutureSociety,NagoyaUniversity, Nagoya,Japan
TomokiNishigaki,DepartmentofUrbanManagement,KyotoUniversity,Kyoto,Japan
RaoulRothfeld,BauhausLuftfahrte.V.,Taufkirchen,Germany;ChairofTransportationSystemsEngineering,DepartmentofCivil,GeoandEnvironmentalEngineering,TechnicalUniversityofMunich,Munich,Germany
Jan-DirkSchmo ¨ cker,DepartmentofUrbanManagement,KyotoUniversity,Kyoto, Japan
StefanSchmo ¨ ller,DepartmentofCivilEngineeringandEnvironmentalSciences, BundeswehrUniversityMunich,Bavaria,Germany
RaviSeshadri,Singapore-MITAllianceforResearchandTechnology(SMART), Singapore
SusanShaheen,TransportationSustainabilityResearchCenter,Universityof California,Berkeley,CA,UnitedStates
AnnaStraubinger,BauhausLuftfahrte.V.,Taufkirchen,Germany
YannisTyrinopoulos,UniversityofWestAttica,DepartmentofCivilEngineering, Athens,Greece
NobuhiroUno,DepartmentofCivilandEarthResourceEngineering,Kyoto University,Kyoto,Japan
BertvanWee,TransportandLogisticsGroup,FacultyofTechnology,Policyand Management,DelftUniversityofTechnology,Delft,TheNetherlands
AkiraYoshioka,ToyotaMotorCorporation,Toyota,Japan
ZhanZhao,DepartmentofCivilandEnvironmentalEngineering,Massachusetts InstituteofTechnology,Cambridge,MA,UnitedStates
JinhuaZhao,DepartmentofUrbanStudiesandPlanning,MassachusettsInstituteof Technology,Cambridge,MA,UnitedStates
FangZhao,Singapore-MITAllianceforResearchandTechnology(SMART), Singapore
CezaryZiemlicki,SENSE,OrangeLabs,Paris,France
Abouttheeditors ConstantinosAntoniou isaFullProfessor andtheChairofTransportationSystems EngineeringattheTechnicalUniversityof Munich(TUM),Germany.Heholdsa DiplomainCivilEngineeringfromNTUA (1995),anMSinTransportation(1997)and aPhDinTransportationSystems(2004), bothfromMIT.Hisresearchfocusesonbig dataanalytics,modelingandsimulationof transportationsystems,intelligenttransport systems(ITS),calibrationandoptimization applications,roadsafety,andsustainable transportsystem.Inhis25yearsofexperience,hehasheldkeypositionsina numberofresearchprojectsinEurope,the UnitedStates,andAsia,whilehehasalso participatedinanumberofconsultingprojects.Hehasreceivednumerous awards,includingthe2011IEEEITSOutstandingApplicationAward.Hehas authoredmorethan350scientificpublications,includingmorethan110papersininternational,peer-reviewedjournals(includinginTransportation ResearchPartsA,B,andC,TransportPolicy,AccidentAnalysisandPrevention,andTransportGeography),240ininternationalconferenceproceedings,3books,andmorethan20bookchapters.Heisamemberofseveral professionalandscientificorganizations,editorialboards(Memberofthe EditorialBoardofTransportationResearch PartsAandC,AccidentAnalysisandPrevention, JournalofIntelligentTransportationSystems;Associate editorof EUROJournalofTransportationandLogistics,IETIntelligent TransportationSystems,andTransportationLetters),andcommittees(suchas TRBcommitteesAHB45 TrafficFlowTheoryandCharacteristicsand ABJ70 ArtificialIntelligenceandAdvancedComputingApplications, SteeringCommitteeofhEART TheEuropeanAssociationforResearchin Transportation,FGSVcommittee3.10“Theoreticalfundamentalsofroad traffic"),andafrequentreviewerforalargenumberofscientificjournals, scientificconferences,researchproposals,andscholarships.
Dimitrios isSeniorProgramManagerat AmazonandResearchAffiliateatTUM. BeforejoiningAmazon,hewasPostdoctoral ResearcherinTransportationSystemsEngineeringatTUMandSeniorConsultantin DataScienceatErnst&Young(EY).He holdsaPhDinTransportationSystems NTUA(2014),anMScandDICinTransport andBusinessManagementfromImperial CollegeandUCL(2010),andaDiplomain RuralandSurveyingEngineeringfrom NTUA(2008).Hisresearchfocuseson modelingtransportationsystems,demand forecasting,spatialeconometricmodels,and machinelearninginTransportation.HehasbeeninvolvedinconsultingprojectsinthefieldsofMobility,Banking,Telecommunications,CPG,and Shipping,andEuropeanandnationalresearchprojects.Hehasauthoredmore than30scientificpublicationsincluding19papersininternationalpeerreviewedjournals(includingElsevier’sTransportationResearchPartA:PolicyandPractice,TransportPolicyandJournalofTransportGeography),29in internationalconferenceproceedings,and2bookchapters.Heismemberof severalprofessionalandscientificorganizations.
Emmanouil(Manos)Chaniotakis isa Lecturer(AssistantProfessor)atMaaSLab, UCLEnergyInstitute,UniversityCollege London(UCL),UnitedKingdom.Heholdsa diplomainRuralandSurveyingEngineering fromAristotleUniversityofThessaloniki (AUTh),anMScdegreeinTransportation InfrastructureandLogisticsfromDelftUniversityofTechnology(TUDelft),andaPhD fromTechnicalUniversityofMunich (TUM).Hisresearchfocusesonmodeling andsimulationoftransportationsystems, includingconventionalandemergingtransportationsystems,demandmodeling,and machinelearningintransportation.HehasworkedonnumerousEuropeanand nationalprojectsintheareaoftransportmodelingandmachinelearning andhehasbeeninvolvedinconsultingprojectsforestablishmentofstrategic andoperationaltransportmodels,estimationofbehavioralmodelsaswellas theinvestigationofimpactsofnewmobilityservices.Hehasauthoredmore than30scientificpublicationsinpeer-reviewedjournals,conferences,and books.Heisamemberofseveralprofessionalandscientificorganizationsand afrequentreviewerformanyscientificjournalsandconferences.
ConstantinosAntoniou1,EmmanouilChaniotakis2, DimitriosEfthymiou1
1ChairofTransportationSystemsEngineering,DepartmentofCivil,GeoandEnvironmental Engineering,TechnicalUniversityofMunich,Munich,Germany; 2BartlettSchoolofEnvironment, EnergyandResources,UniversityCollegeLondon(UCL),London,UnitedKingdom Chapteroutline
1.Challengesofafast-changing
1.Challengesofafast-changinglandscape Mobilityisoneofthemostimportantaspectsofhumanactivity,withdirect andindirectimplicationsonthelifeofeveryindividual.Itcanbearguedthat mobilityhasbeenmostlystagnantforthesecondhalfofthe20thcentury,with cars/buses/railandtrucks/railbeingthedominantmodesforpassengerand goodstransport,respectively.Traditionally,inmostplacesaroundtheworld, transportationhavebeencentrallydeveloped,coordinated,andoperatedprimarilybylocalorregionalauthorities,reactingslowlytothechangingneeds. Existingdatacollectionprocessesandmodelingparadigmshavethusbeen adequateinmodelingandoptimizingthesemodes,supportingpolicy-makers andplannersinimprovingthequalityoflifeandminimizingthesocietal impactsofmobility.
Thechangesweobservecanbeencloseduponthe“threerevolutions”: shared,electric,andautomated.Wearecalledtoforecasttheimpactofthese revolutions(e.g.,highlyautomatedvehicles),whenthetechnologyisnotyet here,andthetrajectoriesfortransition(e.g.,fromafullyconventionalvehicle fleettoapartlyorfullyautomatedone)tobeunknownorexpectedtotake manyyears,evendecades.Similarly,othernewmodesappear(anddisappear) atablinkofaneye,withoutpriorinformationornotification,and typically withlittleornoregulationorcoordination(atleastinitially).These
uncertaintiesarenotonlyrelatedtothetechnologicalcharacteristicsandthe capacityofthevehiclesbutalsotothebusinessmodelsthatwillbecome widespread(e.g.,individuallyownedvs.shared).Evenforspecificmodeling extensions(e.g.,modelingautonomousvehicles’impacts),duringthislong transitionperiod,theunderlyingconditionswillbechangingconstantly,thus notleavingtimeforconventionalmodelsto“catch-up.”
Thesituationisexacerbatedwhendealingwithmorevolatilenewmodes. Forexample,Uber(arathernewphenomenon,founded10yearsago)is currentlygenerating14milliontripsdaily,1 whileitsChinesecounterpartDiDi isgenerating30milliontripsdaily.Uberhasextendeditsbusinessmodelfrom singlepassengertripstosharedtrips,whilerecentlydecidedtoalsooffer sharedbicycles,scooters,andevenhelicopterrides(operationstartedinNew YorkCityinMay2019).Respectively,theoBikesharedbikesystemflooded Europeancities(suchasMunichandZurich)in2017,onlytodisappearin 2018,amidprivacyandsidewalk-squattingcomplaints.Similarly,recently inauguratedsharedelectricbicyclesystemsinthenortheasternUSwere shuttered,afewmonthsafterthestartofoperation,duetosafetyconcerns. Whatcomplicatesthesituationevenfurtheristhat,besidesthenatureofthe modes,theirfunding,businessmodel,andownershipstatusvaries,aswell,as theseinitiativesaretypicallynotcontrolledbytheauthorities,butoriginate fromprivatecompanies(rangingfromstart-upstoestablishedentitieslikecar andaircraftmanufacturers).Theuncertaintyisalsogreat,asmanybusiness modelsaretriedatthesametime;e.g.,Airbusisdevelopingalargenumberof differenturbanairmobilityvehiclesinparallel,inordertocoverallpossible outcomes,whileUber(andsimilarcompanieslikeLyftandGrab)alsoexplore differenttypesofservices.
Mobilityofgoodsandpeopleisbecomingincreasinglymoreintertwined andhardertodistinguish.Peopleandpacketsareincreasinglyconsideredas mixeddemandpatterns.Passengerandfreighttransportchainsaregetting increasinglymoreentangled,withthepotentialforsynchronizationandcouse ofinfrastructure,and thus strongsynergiesandbenefits.Theideaisthatas thesamevehiclesandservicescanservethem,anintegratedtransportsystem canemergeonbothademandandasupplyside.Thus,insteadofdeveloping twoparallelsystems,eachbeingunderutilizedforamajorityofthedayand/or space,wecandevelopsystemsthatarecomplementingeachother,bringing alongbenefitsandefficiency.
Anotherbigchangethatisemergingisthemovefromaprimarilyplanebasedtransportationsystem(surface,plussomeundergroundandsome elevatedmodes)intoareallythree-dimensional(3D)situation.Commercialair dronesandurbanairmobility(Fuetal.,2019),aswellasurbanaerialcableways,thatseearevivalofinterest,e.g.,inMunich,butalsounderground
tunnels(e.g.,fromElonMusk’sBoringCompany)andhyperloopconcepts aboveandbelowground,respectively.Thistrendhasthepotentialtoincrease theavailablecapacityandfosterthedevelopmentofadditionalnovelsolutions, butofcoursemakestheapplicationofexistingmodelsverychallenging.
Asitismadeunderstood,thesenewmobilitymodesareemerging(and sometimesdisappearing)inahighpace.Transportationservicesprovisionis nowconsideredaprofitablebusinessoftenattractingstart-upsandotherprivatecompanieswhooperateatrapidpaces,withminimalwarning.The aforementionedexistingmodelingparadigmsarenotsufficientlyagileto respondtotheserapidlychangingconditions,whichoftenproposeand leveragefundamentallynewconcepts,suchasautonomy,connectivity, sharing,andthegig-economy.Theseallconstitutechangesthatrequireusto comeupwithmethodsthatcanfunctionwithinanenvironmentofradical transformationthatcompletelychangesthemobilitylandscape.Thishas immenselyincreasedthecomplexityoftransportationsystemsintermsof
1. Design: Fromaneed-baseddesign,weobserveachangetowardacreating needsprocess,whereemergingmodescompetitionandavailabilitydrives thecreationofadditionaldemand,whichwasnotpresentthelastfew years.Thishasbeenfacilitatedbyanumberoftechnologicaldriverssuch asthewidespreaduseofInformationandCommunicationTechnology (ICT).Thesedriversareessentiallychangingthepotentialofdeveloping transport-relatedservicesandgoodswhileinthesametimecreatingnew datasourcestobeexplored.
2. Coordination: Newactorschangethetraditionallyfollowedprocessesof transportationsystemmanagementandintroducenewandpossiblycontradictoryobjectives(welfarevs.profit).
3. Representation: Theactivitiesofplannershavebeensupportedbya numberofmodels,ofvaryingcharacteristics,includingresolution (microscopicandmacroscopic,butalsomesoscopic,i.e.,models comprisingmicro-andmacroscopiccomponents),butalsocommercial versusopen-sourceorgeneralpurposeversuscustom.Allthesemodels sharedonecommonattribute:rigidfunctionalforms,makingitextremely difficulttoextendandadaptthemtoeffectivelyincorporatingtheemerging modesanddata.Lately,modelingoftransportationsystemsmustdistinguishbetweendifferentformsofprivatecaruse(e.g.,carsharing,ridesharing,andridehailing)andotheremergingmodesoftransport(e.g.,kick scooterandsharedbicycles;autonomousvehicles).Additionally,the modelingparadigmchanges,withdynamicallydefinedsupply,whichis shapeduponthedemanditself(e.g.,availabilityofasharedvehicleinan areaisdefinedbythedemandoftripstothatarea).
4. DataAvailability: “Ifyoucannotmeasureit,youcannotimproveit,”as pertheaphorismattributedtoLordKelvin,andduringthisperiodwehave beenusingalimitedamountofdata(mostlypointdatafromloopdetectors,
andmorerecentlysomelimitedtraveltimeinformation).Duringthelast decade,anavalancheofrich,ubiquitousdataarebecomingincreasingly available,rangingfromsocialmediadatatotelecommunicationdataand fromfloatingcartovehiclestatusdata.
2.Howcanwerespondtothechallengesarising? Transportationandmobilityplanningneedtobecompletelyrethoughtto leveragechangesinasustainableandflexiblewayforward.Conventionaland newlyemergingmodelingtechniquesshouldbecombinedtobetterunderstand thesituationandevaluatescenariosofwhatthefutureoftransportationwillbe. Attemptstowardthisnewtransportationplanningrealityshouldnotoverlook emergingmodelingtools,emergingdatasources,multiactorsenvironment, rapidinnovationcycles,modelanddatatransferability,aswellasparticipatory planning.
Statisticallearningtechniques(suchasmachineorreinforcementlearning) andflexiblemodelshasbeenfoundtoyieldmoreaccurateresultsinsome cases,suchasshort-termtrafficprediction(Vlahogiannietal.,2005)andcar followingmodels(PapathanasopoulouandAntoniou,2015).Data-driven methodsextendthespectrumofvariablesincludedinananalysispotentially betterrepresentingthetransportationsystem(Dura ´ nRodasetal.,2019). However,allthesenewmodelingtechniquesneedtobecomparedtothe conventionallyusedmethodsasinsomecases,conventionalmethodsare foundtohavebetterpredictionperformance(e.g.,fordiscretechoicemodels, HensherandTon,2000).
CentraltothischangeofmobilityplanningforEmergingTransportation Systemsisthedatausedtopredictaspectsofadoption,satisfaction,anduse. Conventionaldatacollectionandmodelingapproachesareclearlyinsufficient intermsofcapturingtherapidlychangingmobilitylandscapeandrelated changesofgoodsandpassengermobility.Newdatacollectionmethodsare becomingestablished,rangingfromopportunisticsensors,suchasWi-Fiand Bluetoothdetectors,tosmartphone-basedapps[e.g.,FutureMobilitySurveys: Danafetal.(2019);meili: Prelipceanetal.(2018);nervousnet: Pournarasetal. (2015)]thatproviderichinsightsintothemobilitypatterns,butalsotrajectory dataandsatelliteimages(e.g., Efthymiouetal.,2018)and,even,videos.The factthatemergingandfuturetransportationsystemsareinmanycasesunrealized,andtheirexactcharacteristicsareyetunknown,maketheassumptions definedandthedatausedevenmoreimportant.Therecentlyavailabledata sourceshavebeenfoundtoproduceanimmenseamountofdata(Big Data BuckleyandLightman,2015;Chaniotakisetal.,2016;Readesetal., 2007)thatcouldpotentiallybeusedtoimprovetransportationsystems,firstin termsofidentificationandpredictionandsecondofoptimization.Onthesame time,pervasivesystemshaveallowedforseveralsupportingservicestorapidly
emergeandbewidelyused,suchastheconceptsofMobilityasaService (MaaS MatyasandKamargianni,2018)andvehiclesharing.
Thisbookaimsatservingasamediumforunderstandingdemandfor emergingtransportationsystems.Itcriticallyapproachesthepertinentliteratureinordertoestablishthenecessarybackgroundonthemodesthatare typicallyexplored,thefactorsthataffectuseandsatisfactionandtheimplicationsthatemergingmodesbringwithregardstosustainabilityandhuman well-being.Aimingattheestablishmentofasphericalevaluation,aspectsof themethodsanddatacommonlydeployedareexploredandapplicationsare discussed.
3.Structureofthisbook Thisbookisstructuredinthreemainparts:
PartI:Backgroundandcriticalreviewofthestate-of-the-art.Thispart comprisesthreechaptersprovidingacriticalreviewofthefactorsaffectingthe adoptionofestablishedandemergingmodesingeneral(Chapter2),an analysisofmobilityondemand,withanemphasisonitsinteractionswith publictransport(Chapter3)andaviewontheimplicationsofautomationon accessibilityandsocialinclusion(Chapter4).
PartII:Methods.Thispartcomprisesfourchapters,coveringdataaspects (Chapter5)andmethodologicalcomponentsforlocationplanningforone-way carsharingsystems(Chapter6),aswellastheanalysisofspatiotemporal structuresusingsmartcarddata(Chapter7),andmodellingrequirementsand conceptsforsharedautonomousvehicles(Chapter8).
PartIII:Applications.Thispartcomprisesfivechapters,withkeyapplicationscoveringpublictransport(Chapter9),vehiclesharingadoption (Chapter10),carsharing(Chapter11),smartmobilityplanning(Chapter12), andUrbanairmobility(Chapter13).
Introductoryandconcludingchaptersroundthebookup.
References Buckley,S.,Lightman,D.,2015.Readyornot,bigdataiscomingtoacity(transportationagency) nearyou.In:TransportationResearchBoard94thAnnualMeeting,number15-5156in TRB2015.
Chaniotakis,E.,Antoniou,C.,Pereira,F.,2016.Mappingsocialmediafortransportationstudies. IEEEIntelligentSystems31(6),64 70.
Danaf,M.,Atasoy,B.,deAzevedo,C.L.,Ding-Mastera,J.,Abou-Zeid,M.,Cox,N.,Zhao,F.,BenAkiva,M.,2019.Context-awarestatedpreferenceswithsmartphone-basedtravelsurveys. JournalofChoiceModelling31,35 50.
Duran-Rodas,D.,Chaniotakis,E.,Antoniou,C.,June2019.BuiltEnvironmentFactorsAffecting BikeSharingRidership:Data-DrivenApproachforMultipleCities. https://doi.org/10.1177/ 0361198119849908.TransportationResearchRecord.
Efthymiou,D.,Antoniou,C.,Siora,E.,Argialas,D.,2018.Modelingtheimpactoflarge-scale transportationinfrastructuredevelopmentonlandcover.TransportationLetters10(1),26 42. Fu,M.,Rothfeld,R.,Antoniou,C.,2019.Exploringpreferencesfortransportationmodesinan urbanairmobilityenvironment:munichcasestudy.TransportationResearchRecord.
Hensher,D.A.,Ton,T.T.,2000.Acomparisonofthepredictivepotentialofartificialneuralnetworksandnestedlogitmodelsforcommutermodechoice.TransportationResearchPartE: LogisticsandTransportationReview36(3),155 172.
Matyas,M.,Kamargianni,M.,2018.ThePotentialofMobilityasaServiceBundlesasaMobility ManagementTool.Transportation.
Papathanasopoulou,V.,Antoniou,C.,2015.Towardsdata-drivencar-followingmodels.TransportationResearchPartC:EmergingTechnologies55,496 509.EngineeringandApplied SciencesOptimization(OPT-i)-ProfessorMatthewG.KarlaftisMemorialIssue. Pournaras,E.,Moise,I.,Helbing,D.,2015.Privacy-preservingubiquitoussocialminingvia modularandcompositionalvirtualsensors.In:2015IEEE29thInternationalConferenceon AdvancedInformationNetworkingandApplications,pp.332 338.
Prelipcean,A.C.,Gidofalvi,G.,Susilo,Y.O.,2018.MEILI:atraveldiarycollection,annotation andautomationsystem.Computers,EnvironmentandUrbanSystems70,24 34.
Reades,J.,Calabrese,F.,Sevtsuk,A.,Ratti,C.,2007.Cellularcensus:explorationsinurbandata collection.PervasiveComputing,IEEE6(3),30 38.
Vlahogianni,E.I.,Karlaftis,M.G.,Golias,J.C.,2005.Optimizedandmeta-optimizedneural networksforshort-termtrafficflowprediction:ageneticapproach.TransportationResearch PartC:EmergingTechnologies13(3),211 234.
Chapter2 Reviewoffactorsaffecting transportationsystems adoptionandsatisfaction YannisTyrinopoulos1,ConstantinosAntoniou2
1UniversityofWestAttica,DepartmentofCivilEngineering,Athens,Greece; 2Chairof TransportationSystemsEngineering,DepartmentofCivil,GeoandEnvironmentalEngineering, TechnicalUniversityofMunich,Munich,Germany
Chapteroutline
1.Introduction
2.Transportationsystems
2.1Establishedtransportation systems
2.2Emergingtransportation systems 13
2.3Futuretransportationsystems 15
2.3.1Autonomousvehicles 15
2.3.2Urbanairmobility (“flyingtaxis”) 18
2.4Strengthsandweaknessesof thesystemsandmodes examined 19
3.Factorsaffectingtransportation systemsadoptionandsatisfaction 19
3.1Establishedtransportation systems 19
3.1.1Publictransport 19
1.Introduction 3.1.2Demandresponsive transit 20
3.1.3Taxi 21
3.2Emergingtransportation systems 21
3.2.1Carsharing,ridesharing, ridehailing,carpooling, andvanpooling 21
3.2.2Bikesharing 23
3.2.3Sharede-scooters 23
3.3Futuretransportationsystems 24
3.3.1Sharedautonomousvehicles 24
3.3.2Urbanairmobility
Thefactorsthatinfluencetheuseandadoptionoftransportationsystemscan beexaminedfromdifferentstandpoints,suchasorganizational,financial, legislative,technological,anduseracceptance.Allthesedifferentfactorsplay
12 PART|B Acriticalreviewof(emerging?)transportationsystems
aminorormajorroleintheuseofthelargevarietyoftransportationsystems andmodes.Theirunderstandingisofvitalimportanceforcreatingasustainabletransportationsystem.However,thevarietyoftransportationmodes andsystemsthatcurrentlyexistandthosethatwillemergeinthenearfuture makesthereviewofthesefactorsquitecomplicated.Inaddition,when analyzingtwoormoretransportationsystemsthatarecloselyrelated,asinthe caseofsharedmobility,overlapsandconflictsbetweenthesefactorsoften occur.Thus,theexaminationofeachtransportationsystemandmodeseparatelyhelpstoovercomethoseshortcomings.
Thepurposeofthischapteristopresentandanalyzethekeydeterminants, factors,andeventuallymotivatorsthataffecttheuse,adoption,andsatisfaction oftransportationsystemsfromthepointofviewoftheendusers,i.e.,commutersandtravelers.Thefocusisplacedonpassengertransportforurban, suburban,andperiurbancontexts.Thefindingsofthisreview,andmore particularlythesoundunderstandingofthedeterminantsinfluencingtheiruse, maybequiteusefulfortransportoperatorsandpolicymakerstobettertackle commuters’andtravelers’perceptionandtoplantheappropriatemobility managementactionsandpolicies.
Toassistthefactors’reviewanddiscussion,thetransportationsystemsand modeshavebeenclassifiedintothreebroadcategories:established,emerging, andfuture. Established referstothesystemsthatalreadyexist,suchaspublic transportandtaxi. Emerging referstoconceptsthathavebeenalready implementedinsomeareasandcontinuetoemerge,while future refersto conceptswhichhavereceivedattentionfromthetransportcommunityand industry,buttheyhavenotbeenimplemented(yet).Thesystemsandmodes thatfallintothesethreecategoriesarebrieflydescribedbelow.
2.Transportationsystems 2.1Establishedtransportationsystems Establishedtransportationsystemshavebeenlongexamineduponthe distinctionofprivateandpublictransportation.Privatetransportationusually referstomodesownedbytheuser,suchascar,bike,andwalk.Publictransport usuallyreferstothemodes,whichareoperatedbyanauthorityororganization.Thecharacteristicsofpublictransportvaryalotdependingonthemode (metro,bus,etc.),andithasbeenclaimedtobethemostviablesolutiontothe negativeeffectsofurbancongestion.Inmostcases,publictransportoperates onpredefinedschedulesandroutes.However,flexibleformsofpublictransportprovidingservicesmoreassociatedtodemandaredemand-responsive transit,alsoknownasparatransit.Itincludesserviceswhereatransitvehicle doesnotoperateonafixed-route,butpicksupanddropsoffpassengersat locationsinresponsetospecificservicerequests.
Taxiisoneofthemostwell-knowntraditionalsystems.Alongstanding debateexistsonthecategorizationoftaxisintermspublicorprivatetransport; thusitisreferredhereasaseparatetransportationsystem.Therearefour majormarketsegmentsinthetaxiindustry:hail,taxirank,prebook,and contract(Aarhaug,2016).Thehailandtaxiranksegmentsareuniquetothe industry,whiletheprebookandcontractsegmentsoverlaptosomeextentwith nontaxiindustries.Taxisareavitallinkinpublictransportsystems,functioninginaccordancewithpublicdemand.Theycanbeconsideredasauseful supplementtoconventionalpublictransport(Aarhaug,2016).Inmostbig citiesaroundtheworld(Beijing,Paris,Chicago,London)taxisaccountfor1% ofthemodalshare(StudyLib,2019).
2.2Emergingtransportationsystems Emergingtransportationsystemsrefertoconceptsandmodes,whichhave beenimplementedinvariousareas,butarecontinuouslyexpanded.Itshould benotedthatemergingdoesnotnecessarilymeansnew.Mostoftheseconceptshavebeenpresentfordecadesinsomecities(e.g.,bikesharingand carsharing);however,theirwidespreaddeploymenthasbeenintensifiedinthe pastfewyearswiththeassistanceofsomeenablingfactors,suchasthe developmentofinformationandcommunicationstechnologies(ICT).
Sharedmobilityisabroadtermthatincludesmanyformsofmobility,in whichtravelerssharevehiclesorrides.Itisaninnovativetransportation strategythatenablesuserstogainshort-termaccesstotransportationmodeson an“as-needed”basis(Shaheenetal.,2015).Carsharing,carpooling, vanpooling,bikesharing,ridehailing,ridesharing,andsharede-scootersare forms,practices,ormodelsofsharedmobility,andsomeofthemareoften confused.Althoughthereareclearsimilaritiesanddifferencesbetweenthese mobilitypractices,thefactorsaffectingtheirusearenotsodifferent.Thisis consideredinthenextsection(reviewoffactors).
Carsharingisapracticeoramodelofcarrental,wherepeoplerentcarsfor shortdistancesandforshortperiodsoftime.Althoughcarsharinghasalready alonghistoryintheurbanmobilitycontexts,itiscontinuouslyenhanced. Recently,moreflexiblecarsharingalternativeshaveemerged,suchasfreefloatingcarsharing,whereacarcanbepickedandparkedon(usually)any publicparkingspot,andpeer-to-peer(P2P)carsharing,whereprivatelyowned vehiclesareavailableforusebyothermembers.
Similartocarsharingisbikesharingforbicycles.Thenumberofbikesharingprogramshasincreasedrapidlyinrecentyearsandthereareover700 programsinoperationglobally,asof2015(Fishmanetal.,2015).Duetothe modeused(bicycle),bikesharingofferssignificantbenefitstotheendusers andthesociety.Itisthusexaminedseparately.
14 PART|B Acriticalreviewof(emerging?)transportationsystems
Ridehailingisanotherconceptthathasemerged,anditisbeingexplored thelastfewyears,affectingstronglythemarketshareoftaxis.Inridehailing,a riderhiresapersonaldrivertotakehim/herexactlywherehe/sheneedstogo. Intheinitialpracticeofridehailing,thetransportationvehicleisnotshared withotherriders,nordoesitmakeseveralstopsalongaroute.UberandLyft nowofferserviceswheresharingthevehicleispossible(ridesharing). Althoughridehailingexistsformanyyears,itspopularitycontinuouslyincreases(Statista,2018).Companiesofferingridehailingservices,suchasUber, Lyft,andGrab,increasetheirmodalshare.Forexample,almost10yearsafter itwasfounded,Ubertoppedthelistoftheworldleadingridehailingoperators inMay2018.Uberisatransportationnetworkcompanyoperatingin65 countriesandserving75millioncustomers(Statista,2018).Thesenewapproachesareexpectedtobecomethemainstream.
Anotherformofmobilitythatexistsformanyyearsandthatiscontinuouslyexpandingiscarpooling.Carpoolingallowstravelerstoshareavehicle withotherstoacommondestination.Itisnotpersonaltransportation,asthe spaceisshared,anditwillmakestopstopickupotherriders.Accordingto Shaheenetal.(2018),carpoolingisthesecondmostcommontravelmodeto workintheUnitedStatesafterdrivingalone,althoughintherecentyearsits modalsharedeclines(from19.7%in1980to9%in2016).Carpoolingcan includeseveralformsofsharingaride.Basedonthisestablishedform,others keepemerging,suchasflexiblecarpooling,whichisaformofadhoc, informalcarpoolingamongstrangers,anddynamiccarpooling,whichallows peopletoarrangeadhocridesondemand(orveryshortnotice)using smartphoneappsorawebsite.
Similartocarpooling,vanpoolingservesmorepassengers(usually7 15), whosharethecostofavan.Indicativecompaniesofferingvanpoolingservices arePandaBus(China),BerlKo ¨ nig(Berlin),andAlly(Germany),Jetty (MexicoCity).
Ridesharingisessentiallythesameascarpooling,butridesharingtendsto indicateamoreon-demandmobilityschemeanditdoesnotrequiretheriderto everbeadriver.Inaddition,carpoolingisabitmoreorganizedandpreagreed, thanridesharing.
Inadditiontotheabovesharedmobilitypractices,thischapterexamines onemoreemergingmode:sharedelectricscooters(e-scootersinshort). Althoughenteredthemobilitymarketplaceveryrecently,theelectricscooteris oneofthemicromobilitymodesthathaveanincreasingpresenceinmany countriesworldwide,suchasUnitedStates,China,Paris,Madrid,London,and Vienna(ShaheenandCohen,2019).Itconsistsofamoderntrendinshort distanceurbanmobility;itexploitsadvancedtechnologies;anditisenvironmentallyfriendly.Despiteitsbenefits,itsimpacttotrafficandroadsafetyis questionableandthereisusuallynoregulationapplyingtoit;therefore,new regulationisrequired.
2.3Futuretransportationsystems Futuretransportationsystemscanincludeanypossiblewaythatisbelievedit ispossibletobepresentinthefuture.Itbecomesobviousthataneffortto enumerateallpossiblevariantswouldleadtoanopen-endeddiscussionand, forthatreason,onlytwomajorinnovationsthatareexpectedtoinfluence urbanmobilityinthefuture:sharedautonomousvehicles(SAVs)andurbanair mobility(UAM),areextensivelydiscussed.Thereasonbehindthischoiceis thatthesetwoconceptsbothradicallychangethewaythattravelisperceived andarewidelystudiedinthepertinentliterature.Thereiscurrentlyalotof debateanduncertaintyregardingtheexacttimeframethatthesewillreacha criticalmass.However,thisisnotthepointhere,butrathertoexploretheir differentiationandpossibleaddedvalueintheurbantransportationlandscape ofthefuture.
2.3.1Autonomousvehicles Automatedvehiclesarevehicleswithsomelevelofautomationtoassistor replacehumancontrol.TheSocietyofAutomotiveEngineers(SAE)has defineddifferentlevelsofautomatedfunctionality,rangingfromnoautomated features(Level0)tofullautomation(Level5,commonlyreferredtoasselfdrivingorautonomousvehicles).Afteraninitialperiodofextendedhype, wearecurrentlyreachingapointwherethechallengesandopportunities associatedwithautonomousvehicles(AV)aregraduallybecomingbetter appreciated.Whilethebenefitsandproblemsrelatedtotheirintroductionare beingdebated,practicallyallmajortechnologycompaniesandcarmanufacturersinvestbillionsofdollarsannuallyinaracetogainacompetitive advantageinthisfield(Korosec,2018;Trivedi,2018).Althoughtheprecise solutioncharacteristicsareingeneraluncertain,theactionsoftheautonomous vehiclemanufacturersandtheirotherindustrialpartnerspointtowardthe initialdeploymentofautonomousvehiclesassharedautonomousmobility services.Forexample,BMWGroup,IntelandMobileyeaimtoproduce autonomousvehiclesby2021forthepurposeofridesharing(BMWGroup, 2016);GeneralMotorsplanstooperateautonomoustaxiservicesby2019 (Hawkins,2017);Fordalsoplanstointroduceautonomousridehailingor ridesharingservicesin2021(TheFordCompany,2016);VolkswagenGroup andHyundaiincollaborationwithAuroraInnovationsplantobeginautonomouson-demandservicesby2021(O’Kane,2018);DaimlerandToyotahave partneredwithUber(DaimlerAG,2017; Monaghan,2018);and,lastbutnot least,Waymohasrecentlystartedcommercialautonomousridesharingservice, availableinTempe,MesaandChandler,Arizona(LeBeau,2018). Reviewoffactorsaffectingtransportationsystems Chapter|2
TABLE2.1 Majorstrengthsandweaknessesoftransportationsystemsand modes.
System/ modeMajorstrengthsMajorweaknesses
Establishedtransportationsystems/modes
Public transport Reductionofroad congestion,economy savingsforcommuters, accessibletoall populationcategories, reductionofpollutants (Tyrinopoulosand Antoniou,2013)
Demandresponsive transit
Cost-efficient connectivityforrural populations,supporting citizenswithlimited mobility(Hunkinand Krell,2018)
TaxiPromptpick-upanddrop facility,convenience,24/ 7service
Emergingtransportationsystems/modes
CarsharingReducedvehicle ownership,lessparking requirements,reduced vehicletravel,cost savings(TCRPReport 108,2005)
BikesharingHealthbenefits,reduced roadcongestion, reducedfuel consumption,financial savings,lowcarbon emissions(Shaheen etal.,2010;QiuandHe, 2018;Yangetal.,2010; ShaheenandCohen, 2019)
Congestiononboardvehicles,lackof comfort,timeuncertainty,needfor transfers,lackofcontrol(Anwar,2009)
Relativelyhighcostofprovision,lackof flexibilityinrouteplanning,inabilityto managehighdemand(Ambrosinoetal., 2003);DRTnotrealisticallycostedor designedbasedonafullunderstandingof themarket(Enochetal.,2006)
Relativelyexpensiveespeciallyinpeak hours/periods
Lackofone-waytripoption(TCRPReport 108,2005)
Limitedpaymentoptions(debit/credit cards)imposebarriersforconsumerswho areunderbankedorunbanked, requirementforasmartphoneandhighspeeddatapackagestoaccessservices (ShaheenandCohen,2019);limiteduse ofhelmets,limitedcapacityofdocking stationsinheavytraffichours, unfamiliaritywithcyclingforfirst-time cyclistsleadingtoaccidents
Reducedtransitridershipandactivetravel (LavieriandBhat,2018);increased motorizedtravel(Rayleetal.,2016) 16 PART|B
RidehailingHigh-vehicleoccupancy, increasedconvenience, reducedvehiclemiles traveled(Lavieriand Bhat,2018)
TABLE2.1 Majorstrengthsandweaknessesoftransportationsystemsand modes. cont’d
System/ modeMajorstrengthsMajorweaknesses
Carpooling/ vanpooling
Reducedvehiclemiles traveled,reductionin fuelconsumption, reductioningreenhouse gas(GHG)emissions, costsavingsforpublic agenciesandemployers, convenience(Shaheen etal.,2018)
RidesharingCostsavingsforriders, reducedvehiclemiles traveled,reductionin fuelconsumption, reductioningreenhouse gas(GHG)emissions
Sharedescooters
Increasedmobility, reducedGHG, decreasedautomobile use,healthbenefits (ShaheenandCohen, 2019)
Futuretransportationsystems/modes
Autonomous vehicles
Majorcostreduction (duetoeliminationof drivercosts)(Litman, 2018);increased accessibility,e.g.,for segmentsofthe populationthatcannot drive(e.g.,young,old, disabled)(Meyeretal., 2017);possible reductioninemissions (GreenblattandSaxena, 2015)andincreasein capacity(Friedrich, 2015);safety improvements(Teohand Kidd,2017)
Lackoftrustleadingtoreducedsafetyand security(“strangerdanger”)(Olssonetal., 2019)
Lackoftrustleadingtoreducedsafetyand security,reducedtransitridershipand activetravel,increasedmotorizedtravel
Limitedpaymentoptions(debit/credit cards)imposebarriersforconsumerswho areunderbankedorunbanked, requirementforasmartphoneandhighspeeddatapackagestoaccessservices (ShaheenandCohen,2019);unfamiliarity withe-scootersforfirst-timeusersleading toaccidents
Mightleadtoextravehiclemilestraveled (VMT)e.g.,duetoextratripsandcruising insteadofparking(Litman,2018); increasedcybersecuritydanger(Petitand Shladover,2014)
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TABLE2.1 Majorstrengthsandweaknessesoftransportationsystemsand modes. cont’d System/ modeMajorstrengthsMajorweaknesses
Urbanair mobility
Drasticreductionof traveltimeinsomecases (Antcliffetal.,2016); effectivelyaddsathird dimensionand additionalcapacityfor urbanmobility
Requiresmassiveinfrastructure investments(e.g.,vertiports);notbeingon thegroundraisessafety/security/cost questions
2.3.2Urbanairmobility(“flyingtaxis”)
Asalreadydiscussed,sharedmobilityservicesareprovidinguserswithmore efficienttraveloptions,characterizedbylowerdemandforparkingspaces, lowervehicleownership,butalsoreducedenvironmentalimpactsresulting fromloweremissions(Baptistaetal.,2014).Furthermore,autonomousvehiclesmayoffersaferandmorecomfortabletransportationoptions,whichhas ledmostautomobilemanufacturerstoexploreandinvestinautonomousvehicles(Bimbraw,2015).Besidesthis,thereiscurrentlyanewthirddimension thatisactivelyexploredforurbanmobility,thatofthesky.
Whileseveralservicesexistthatuseconventionalaerialvehicles(suchas helicopters)toofferurbanservices,e.g.,VoominBrazil(Airbus,2018)and recentlyevenUberinNewYork,thereisamassiverecentmovementthataims touseautonomous,“unmannedaerialvehicles”(UAVs)toprovidesystematic transportationservicesincities.TheUSNationalAeronauticsandSpace Administration(NASA)isdevelopingaframeworkforthecoordinationofall actorsandstakeholdersintoafunctioningsystem(Tripphavongetal.,2018). Shamiyehetal.(2017),indicatingthatthetimeiscurrentlyripeforsuch services,duetoanumberoftechnologicaladvances,e.g.,inbatterystorage, electricalpowertransmission,anddistributedpropulsionsystems.Companies ofteninvestigatemultiplealternativeideas,suchas Airbus(2018),which exploresseveraldifferentvehiclearchitectures,suchasVahanaandCityAirbus aimingtoserveoneormultiplepassengers,respectively.
Thedevelopmentofthebusinessmodelsisalsoakeyaspectofsuch innovativeservices.Uberassumesavehicleforfourpassengers(UberElevate, 2016),while PorscheConsulting(2018) definestheentirechainofsuchservices,startingfromthefirstmile(accesstothevertiport),movingontothe vehicleboarding,thenthecoreflight,andthecorrespondingstagesatthe destination(deboardingprocessandlastmiletransfer).
Reviewoffactorsaffectingtransportationsystems Chapter|2 19
2.4Strengthsandweaknessesofthesystemsandmodesexamined
Table2.1 presentsthemajorstrengthsandweaknessesoftheabovetransportationsystemsandmodes.Thetablehasbeenenrichedwiththeappropriate references,whileforsomemodes,thestrengthsandweaknessesareobvious andnoreferenceshavebeenincluded.Thelatterarebasedonthepersonal opinionoftheauthors.
3.Factorsaffectingtransportationsystemsadoptionand satisfaction Thefactorsaffectingtheuse,adoption,andsatisfactionofthetransportation systemspresentedabovehavebeenidentifiedthroughareviewofmany researchandscientificarticles,butalsoreportsandstudies.
3.1Establishedtransportationsystems 3.1.1Publictransport
Thefactorsaffectingtheuseofpublictransportbypassengers(bothcurrent andfuture)dependonmanyaspectsandelements,suchasthemode(metro, bus,etc.),thetypeofuser(male,female,young,elderly,disabled,students, etc.),theareacovered(regional,urban,suburban,touristic),alsothetime (peak,off-peak).Thus,itisattemptedbelowtofindasmuchaspossiblea commongroundoffactors,combiningmanyoftheseaspects.
DeOn ˜ aetal.(2016) proposedamodelforpredictingtheintentionsof passengerstocontinuetousetransitservices,especiallylightrailtransit (LRT).Theiranalysisconcludedthatservicequalityismostlyexplainedby aspectsrelatedtocomfort,accessibility,andtimeliness,followedbyinformationandsafety.
TyrinopoulosandAntoniou(2008) conductedaresearchfocusingon passengers’perceptionontransitperformancewithanemphasisonthevariabilitybetweentransportoperatorsandthepolicyimplicationsofsuchdifferences.Theyexaminedfivetransitsystemsinthetwomajorurban conurbationsinGreece,AthensandThessaloniki.Forthetransitcompanies operatingbusandtrolleybusservicesinAthens,qualityofserviceisprimarily drivenbyqualityattributessuchasservicefrequency,informationprovision, waitingandin-vehicleconditions,accessibility,andtransfercoordination.The metroserviceoperatinginAthensdependsonthetransfercoordinationwith othermeansandinformationprovisionbecauseinAthensthehigh-quality servicesofmetroaretakenforgranted.InThessaloniki,thesolebustransit operatorshouldincludeinitspolicyplansimmediatecorrectivemeasures addressingtheservicefrequency,waitingtime,andvehiclecleanliness attributes.
EboliandMazzulla(2015) formulatedastructuralequationmodelto exploretheimpactoftherelationshipbetweenglobalcustomersatisfaction andthequalityofservicesofferedbyrail(regionalandsuburban)operatorsin NorthernItaly.Theyfoundthatinformation,cleanliness,andservicecharacteristicslikepunctualityandfrequencyofrunshavethehighestpositiveeffect onservicequality.
Passengercaranditsvariousforms(e.g.,carsharing)arethemajorcompetitorsofpublictransportinurbanareas.Inthisrespect, Tyrinopoulosand Antoniou(2013) estimatedprobitandstructuralequationmodelsandperformedadditionalstatisticalanalysistogaininsightintothekeyfactors affectingmodalchoicesofcommutersandthereasonsthatdiscouragethem fromusingpublictransportservices.Accordingtotheiranalysis,themain factoraffectingthepreferenceofcommuterstowardpassengercaristhe availabilityofparkingspace.Thefactorthatdiscouragesmostcommuters fromusingpublictransportiscrowdingfollowedbyserviceunreliability. Anotherinterestingfindingwasthathighfarelevelsdonotdiscouragecommutersfromusingpublictransport.
Basedontheabovereview,itseemsthatthemainfactorsinfluencingmost publictransportuseandsatisfactionareaccessibility,servicereliability, crowding,andsafety.Thefactorsdifferdependingonthetypeofmode.In particular,waitingandin-vehicleconditions(likecomfort)playadecisiverole forbustransit,whileformetrosystemsthekeyfactorsarepunctualityand informationprovision.
3.1.2Demandresponsivetransit Demand-responsivetransit(DRT)emergedinthe1970stoservetheniche marketofpeoplewithmobilitydifficulties.Sincethebeginningof1990s,it hasgrowninpopularityforseveralreasons,oneofwhichisthelackofthe adaptabilityofconventionalregularbusandtaxiservices.DRTtypically servesthegeneralpopulationinlow-densityand/orruralcommunities,aswell asthedisabledpopulation.
Ambrosinoetal.(2003) preparedareportthatconcernedprimarilywiththe potentialcontributionofadvancedpublictransportsystemstosustainable mobility,placingparticularfocusonDRT.Intheiruserneedsanalysis,they reportedthatthemainfactorsthatdriveDRTusers’adoptionincludethewide rangeofdestinations/coverage,easyaccesstoservices(walk,wait),accessibilitytocompleteandreliableinformation,easinessandspeedofbooking, reliabilityofserviceandarrivaltimes,andreasonablepricingstructure.
Aninterestingresearchwasconductedby Enochetal.(2006).They examinedmainstreamDRTschemesfromaroundtheworldthathavefailed withtheaimstoidentifythereasonsforfailureandtodrawlessonsthatcan helptopreventsimilaroutcomesoccurring.Severalreasonsoffailurewere recognized,suchaspoorplanning,insufficientstakeholdercommitment,
inflexiblefundingarrangements,lowfares,andmanyothers.Fromthepointof viewoftheendusers,asuccessfulDRTsystemissimple,isoflowcost,and provideslow-volumeschemesthatsucceededinconcentratingdemandintime andgeography oftenwithsemischeduledcoreroutes.
Concluding,DRThasimportantequityandaccessibilitybenefits.Inorder tobesuccessfulandwelladoptedbytheendusersitneedstobewell scheduled,ofreasonablecostandtargetedtoawidermarketandnotonly addressedtothemobilityimpairedpopulation.
3.1.3Taxi Taxis(orcabs)provideapubliclyavailableserviceandholdafavorableplace intheurbanmobilitycontexts.Thelackofregularschedules,routes,and stationsgivesthemasemiprivatecharacter.Stillakeycharacteristicoftaxisis theircloseinteractionwiththecustomers.Thischaracteristicmakesthefactors affectingtheuseoftaxisbycommutersevenmoresignificant.
Khanetal.(2016) conductedanempiricalresearchtodeterminethefactors affectingcustomersatisfactioninthetaxiserviceindustryinIndia.ThisindustryhasseenaverysignificantgrowthintherecentpastinIndiaandthere aremanyplayersoperatingthere.Theyfoundthatdriverprofessionalismand convenienceofbookingwerefoundtohaveasignificantimpactonoverall satisfaction.Theconditionofthecarseemstohavelessimpactonsatisfaction.
KumarandKumar(2016) applieddescriptivestatisticsandregression modelinginordertostudythefactorsinfluencingtheconsumerswhile selectingtaxiservices.Theyconcludedthatpriceconsciousness,coupon redemptionbehavior,andinnovativenessareinfluencingtheconsumersin theirselectionofcabservices.Intheiranalysis,theconsumerswhoareprice consciousarelikelytoredeemcouponswhilebookingcabs;theconsumersare interestedtoadoptfornewtechnologyliketheuseofappsforbookingtaxis, whiletheredemptionofcouponsisamotivatingfactorforconsumptionoftaxi services.
3.2Emergingtransportationsystems 3.2.1Carsharing,ridesharing,ridehailing,carpooling,and vanpooling
Asstatedabove,thesefivesharedmobilityschemesandpracticeshavesimilaritiesanddifferencesintheiroperation;however,thefactorsthataffecttheir useandsatisfactionarequitecommon.
Ballu ´ s-Armetetal.(2014) examinedthepublicperceptionandthepotentialmarketcharacteristicsofP2Pcarsharingthroughaninterceptsurvey conductedintheSanFranciscoBayArea.Theirstudyrevealedthatthetop threereasonsforusingP2Pcarsharingincludeconvenienceandavailability, monetarysavings,andexpandedmobilityoptions.Anotherinterestingfinding
22 PART|B Acriticalreviewof(emerging?)transportationsystems
isthatmanyrespondentscitedliabilityandtrustconcernsasprimarydeterrents.Finally,thelowawarenessofP2Pcarsharingwasevidentamongthe respondents.
TCRPReport108(2005)providesasubstantiveresourcewithconsiderable informationandusefultoolsforthedevelopmentandimplementationof carsharingservices.There,anumberofsuccessfactorswereanalyzedin detail.Oneofthemostimportantfindingsinthisreportistheneedtoprovide accesstoacarduringtheworkingday,sincemanyemployeesdrivetowork becausetheyneedacarduringtheworkingday.Theauthorsreferredtoa surveyconductedintheSanFranciscoBayArea,accordingtowhich11%of commuterscitetheneedforacarforworkasanimpedimenttocommutingby transit,bicycle,orcarpool(RIDESforBayAreaCommuters,2003).Providing carsharingatworkplacesmayhelptoeliminatethisbarrier,andmanyemployershaveintroducedcarsharingasapartoftheircommutetripreduction program.
LavieriandBhat(2018) investigatedridehailingexperience,frequency,and tripcharacteristicsthroughtwomultidimensionalmodelsestimatedusingdata fromtheDallas-FortWorthMetropolitanArea.Theirresultsrevealedthat people’sprivacyconcernsareakeydeterrenttopooledridehailingadoption. Furthermore,theirliteraturereviewrevealedinterestingfindingsderivedfrom otherstudies.Mostnotably,theeaseofpayment,theeasetocall,thelower cost,andtheshorterwaitingtimesarefrequentlycitedbyindividualsas reasonstouseridehailingrelativetotaxis.Inaddition,shortertraveltimesare identifiedastheprimaryreasontopreferridehailingoverpublictransitand activemodes(bicyclingandwalking),whilelimitedparkingatthedestination andavoidingdrivingwhileintoxicatedarethetypicalreasonsforpreferring ridehailingoverdrivingaprivatecar(Rayleetal.,2016;Alemietal.,2018a, 2018b;Zhengetal.,2018).
OnestudyofcasualcarpoolingintheSanFranciscoBayAreafoundthat convenience,timesavings,andmonetarysavingswerekeymotivatorsto carpool(Shaheenetal.,2016).Thisstudycitedalsootherstudiesthatderived thesameconclusions(Maltzman,1987;Renoetal.,1989;Beroldo,1990; BurrisandWinn,2006).Finally,onemorestudyfoundthatthetopreasonfor someonechoosingtobeariderwasthedesiretosaveonthecostofgasoline, followedbyapreferencetodootherthingsduringthedrive(Oliphant,2008).
BalachandranandBinHamzah(2017) conductedastudywiththeaimto identifythefactorsaffectingservicequalityandcustomersatisfactionof ridesharingservicesinMalaysia.Theiranalysisderivedthatreliability,price, promotionandcouponredemption,andcomforthavepositiveinfluenceon customersatisfaction.Fromallthevariablesexamined,comfortisthemost influencingfactor.
Theemployeesoflargefirmsareoneofthemostimportanttargetgroups ofridesharing. HwangandGiuliano(1990) performedawideliteraturereview andmadeasummaryontheeffectivenessofemployeeridesharingprograms.
