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    Economic AnAlysis & Policy, Vol. 43 no. 3, dEcEmbEr 2013

    An Empirical Analysis of the Determinants of Passenger Rail Demand in Melbourne, Australia

    Albert Wijeweera1Southern Cross University

    Southern Cross Business School Gold Coast Campus, Southern Cross Drive,

    Bilinga, Qld 4225 (Email: albert.wijeweera@scu.edu.au)

    and

    Michael Charles Southern Cross University,

    Southern Cross Business School Gold Coast Campus, Southern Cross Drive,

    Bilinga, Qld 4225 (Email: michael.charles@scu.edu.au)

    Abstract: Considerable yet largely unexpected growth in passenger rail demand has occurredrecentlyinAustraliancapitalcities.Thisarticleuseshistoricaldata,togetherwithmoderntimeseriesmethods,toexamineempiricallythefactorsthatmighthavecontributedtogrowthinpassengerraildemandinMelbourne,Australia,andtogaingreaterinsightintotherelationshipsbetweenthevariousexplanatoryvariables.Acointegrationapproachisusedtoestimatethelong-runrailelasticities,whileanerrorcorrectionmodelisusedtoestimateshort-runelasticities.Thestudyfindsthattheshortrunrailelasticityistwiceaslowasthelong-runelasticity,althoughbotharehighlyinelastic.Theinelasticnatureofthedemandsuggeststhatafareincreasewouldnotleadtoasignificantdropinboardings,andhenceresultsinariseintotalrevenue.Inadditiontothefare,citypopulation,petrolpriceandpassengerincomeexertapositiveimpactonpassengerraildemand.

    I.InTroduCTIon

    unexpected growth in passenger rail demand has occurred recently inAustralian capitalcities.Forexample,Sydney,Australiaslargestcity,experiencedanincreaseof5.1millionannualrailpassengerjourneysfromtheyear2001/02to2006/07(BrookerandMoore2008),whilePerth,thecapitalofWesternAustralia,experiencedanincreaseinpassengerboardingsfrom35.7millionin2007to42.6millionin2008roughlya20percentincreasewithina

    1 Correspondingauthor:albert.wijeweera@scu.edu.au.

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    year.Melbourne,thecapitalofVictoriaandthesubjectofthisstudy,experiencedpatronagegrowthof47%between2004/05and2008/09(Gaymer2010).SincestategovernmentsinAustralia,whicharetaskedwithfundingheavilysubsidizedurbanrailservicesandassociatedinfrastructure,arebeingrequiredtodomorewithless,fundingurbanrailprojectswheretheyaremostneeded,andprovidingmoreservicesasrequired,isofcriticalimportance.Hence,itismorecriticalthaneverfortransportplannerstodevelopamoreinformedunderstandingoftheimpactofvariousfactorsonpassengerraildemand.Thisarticleuseshistoricaldata,togetherwithmoderntimeseriesmethods,toexamineempiricallythefactorsthatmighthavecontributedtogrowthinpassengerraildemandinMelbourne,andtogainagreaterinsightintotherelationshipsbetweenthevariousexplanatoryvariables.

    Thesequentialfour-steptripgenerationmodel,developedintheunitedStatesinthe1950s,hasregularlybeenusedforcontemporarytransportplanning.Itcomprisesofi)tripgeneration,ii)tripdistribution,iii)modalchoice,andiv)routeassignment(Gouliaset al.1990,Wardman1997).Withburgeoningprivatevehicleusedrivenbyinexpensiveautomobiles,lowaccesspricingandcheapfuel(Mees2000),themodelwassubsequentlyadoptedasthemaintoolforurbantransportplanning.Inrecentyears,however,ithasprovedincreasinglydeficientintermsofpredictingtheurbanraildemandspikesseeninAustraliancapitalcities.Inlightoftheinadequaciesofcurrentdemandestimationmethods,it isimportanttoascertainthefactorsthathavecontributedtotheriseinurbanrailpatronagefromotherapproaches.Here,ademandmodelestimatedbyemployingtimeseriesdatawillbeusedtogaingreaterinsightintothesematters.Thisdoesnotmeanthattraditionaltechniquesshouldbedispensedwith.Instead,thereisaneedtosupplementratherthanreplacethem,especiallysinceanapproachmoredirectlysuitedtoestimatingtherailpassengerdemandfunction,ratherthantransportdemandmoregenerally,isrequired.Sincethetimeseriesmethodisregularlyemployedforforecastinginfinanceandeconomicsfields,itsfunctionalitywillbetested,here,inthecontextofthepassengerraildemandofMelbourne.

    This study represents a pilot attempt to develop a time series technique that will beefficaciousfortestingthepassengerdemandfunctionofAustralianurbanrailtravel.ThestudythereforerepresentsanadditiontothemerehandfulofexistingAustralianstudiesemployinga comparable approach (see douglas and Karpouzis 1999, odgers and Schijndel 2011).Moderntimeseriestechniqueswillbeusedtoexaminetherelationshipbetweenpassengerraildemandanditsexplanatoryvariables,especiallysincepreviousstudieshavenotutilisedthesetechniquesintheestimationofpassengerraildemand.Inparticular,thatmosttimeseriesdataisnon-stationaryisnowwellknown.Ifthisisnottakenintoaccount,spuriousresultsandinvalidinferencesmayresult(Grangerandnewbold1974).Fromtheliteraturereview,notimeseriesstudyonurbanpassengerraildemandinAustraliatestedforstationarity,whichcompromisesthevalidityofthetechniquesdevelopedhitherto.Inaddition,cointegrationanderrorcorrectionmodelsallowtheresearchertoseparatebetweentheshort-runandlong-runelasticities(EngleandGranger1987).ThiswasalsolargelyneglectedinpreviousAustralianstudies.Withoutdoingthis,thereisthedangerofconfusingshort-runimpactswiththosethatwilloccurinthelong-runiftherelationshipbetweenkeyvariableschanges.

    Thearticleisdividedintofivemainparts.SectionIIprovidesabriefsynopticdiscussionoftherelevanttheoreticalandpertinentempiricalliteratureonthetopic.SectionIIIreflects

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    ondatadefinitions,datasources,andthemethodology,whileSectionIVexaminestheshort-runandlong-runpassengerrailelasticitiesobtainedfromtheestimation.ConcludingremarksarepresentedinSectionV.

    II.PrEVIouSSTudIESonPASSEnGErrAIldEMAnd

    Itisimportanttoreviewthepreviousliteratureontheuseofthetimeseriesapproachtoestimaterailpassengerdemand.Althoughthepresentstudywilllookexclusivelyaturbanrailserviceswithinamajormetropolitancentre,someofthestudiesreviewedestimatethedemandforinter-cityoratleastinter-regionalrailservices.Welookfirstattheinternationalstudies,andthenattwoAustralianstudiesthathaveemployedatimeseriesapproach.

    2.1 International Studies

    Jonesandnichols(1983)publishedthefirsttimeseriesstudyonpassengerraildemand.Theyemployedfour-weeklyuKdatafromthebeginningof1969tothemiddleof1977.Theauthorsemployedanordinaryleastsquaresmethodtoestimatethepassengerraildemandfunction,withseventeenlondon-basedroutesbeinginvestigated.Asingleequationframeworkwasemployed.Thiswaspreferredoveranostensiblysimultaneousmodelbecausetheauthorscontendedthatpriceisdeterminedbyrailmanagers,andthereforedoesnotchangefrequentlyenoughforittoberegardedasanendogenousvariable.Fortheestimation,adoublelogspecificationwasused.Bydoingthis,theestimatedcoefficientscoulddirectlybeinterpretedaselasticities.Theoutcomewasthatthemeanpriceelasticitywasdeterminedtobe0.64.Fromthis,onecanextrapolatethat,onaverage,a10percentincreaseinrailfarewouldreducepatronageby6.4percent.demandforpassengerrailservicesisthereforeinelastic.

    despitetheground-breakingnatureofJonesandnicholsstudy,someseriousstatisticalproblemsaffecttheirresults.Fowkesandnash(1991)pointedoutthatthedurbinWatsonstatisticsreportedaresignificantlylow.Thiscouldindicatethepresenceofserialcorrelationandpotentialstatisticalproblems.Theeconometricsliterature(e.g.,Farebrother1980,Breusch1978)makesitveryclearthati)iferrortermsareautocorrelated,theordinaryleastsquaresestimatorcannolongerberegardedasefficient,andii)thatanunbiasedestimatordifferentfromtheolSestimatorhasasmallervarianceandthusgreaterreliability.Jonesandnicholsfindingsmustbeusedcautiously.Forexample,thestudyfailstotakeintoaccountpossibleshort-runresponsesfromthemodel.Changesintheexplanatoryvariableswillthereforehavealimitedeffectintheshortrunbecausepassengers,onaccountofshort-termcommitments,willhavedifficultyinrespondingquickly.Indeed,thefulleffectofthesechangesondemandmaytakeseveralmonthstoeventuate.

    McGeehan(1984)estimatedtheraildemandfunctionforinter-urbantravelintherepublicofIreland.Todothis,quarterlydatafromthebeginningof1970totheendof1982wasused.FollowingJonesandnichols,McGeehanusedtheordinaryleastsquaresmethodandspecifiedthemodelinasingleequationsetting.YetMcGeehanusedthepassengermilesrunduringtheestimationperiodinsteadofticketsalesdatatorepresentdemand.Therationalebehindhischoiceofexplanatoryvariablesalsodiffered.McGeehancontendedthatrevenueperpassengermile

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    travelleddoesnotrepresentasatisfactoryproxyforthefare.Thisisbecausestrongdistancetapersareusedbymostoperators,sothefarechargedpermilefallsastripdistancesbecomelonger.2Ifrailpassengersthereforeswitchedfromlongjourneystocomparativelyshortones,therevenueperpassengermilewouldincrease,yetnoactualchangetothefarewouldhaveoccurred.AformulawasusedbyMcGeehantodetermineaweightedaveragefare.HealsousedtheIndexofIndustrialEarningstocontrolforthepassengerincomevariable,withtherationalebeingthatariseindisposableincomeincreasesthedemandfortravel(includingrail),eventhoughitincreasescompetitionfromothermodes,andprivatevehiclesinparticular.otherimportantexplanatoryvariableswereincluded,suchasprivatevehicleownershipandthreeseasonaldummies.overall,themodelsresultsaddweighttotheviewthatpassengerraildemandisinelastic.Sincepriceelasticitywasfoundtobe0.4,a10percentincreaseinrailfarewoulddecreaserailpatronageby4percent.

    Fowkes et al. (1985) used annual data (19721981) between tenmajor routes in theuKtoputtogetherapooleddatasampleconsistingoftimeseriesaswellascross-sectionaldata.railfareperjourney,carownership,employment,anddummyvariablestocapturetheintroductionofhighspeedrail(HSr)wereusedasexplanatoryvariables.Theresultssuggestthattherailfareexertsasignificantlynegativeeffect,andisalsoinelastic.ConsistentwithJonesandnichols,togetherwithMcGeehan,anyincreaseinpricewillleadtoanincreaseinrevenue.Yettherearesomeproblematicaspects.Asidefromthelimitationimposedbytheassumptionofnochangeinticketcoveragedata