North East ink EES - Transport Model Peer Review Report … · 2020-02-07 · Infrastructure...
Transcript of North East ink EES - Transport Model Peer Review Report … · 2020-02-07 · Infrastructure...
NorthEastLinkAuthority:EnvironmentalEffectStatement(EES)forNorthEastLink
TransportModelPeerReviewReport
February 2019
Preparedfor:
Preparedby:
NorthEastLinkAuthority
Melbourne
Australia
LuisWillumsen
82WilliamCourt,HallRd
LondonNW89PB
NELTransportModelPeerReview
Contents1. INTRODUCTION........................................................................................................................................1
1.1. Theassignment............................................................................................................................................1
1.2. Thepeerreviewprocessinoutline.....................................................................................................12. THENORTHEASTLINKPROJECT........................................................................................................5
2.1. Context.............................................................................................................................................................52.2. NorthEastLink............................................................................................................................................6
2.3. Expectedimpact..........................................................................................................................................7
3. THETRANSPORTMODEL......................................................................................................................93.1. Introduction..................................................................................................................................................9
3.2. Zones,Networksandsegmentation.................................................................................................113.3. Tripgeneration.........................................................................................................................................12
3.4. Destinationchoice...................................................................................................................................13
3.5. ModeChoice...............................................................................................................................................143.6. Assignment.................................................................................................................................................16
3.7. Equilibration..............................................................................................................................................21
3.8. Growth..........................................................................................................................................................223.9. Modellimitations.....................................................................................................................................23
4. MODELREVIEW.....................................................................................................................................244.1. Introduction...............................................................................................................................................24
4.2. Mostrecentdatacollection..................................................................................................................24
4.3. Calibration/Validation...........................................................................................................................254.4. Convergence...............................................................................................................................................28
4.5. Parameters..................................................................................................................................................28
4.6. Forecastingassumptions......................................................................................................................304.7. Results...........................................................................................................................................................31
4.8. Realismofresults.....................................................................................................................................314.9. Sensitivityanalysis..................................................................................................................................32
5. DEALINGWITHUNCERTAINTY.........................................................................................................35
5.1. Approachadopted...................................................................................................................................355.2. Scenarioanalysis......................................................................................................................................35
5.3. Scenario1CAV..........................................................................................................................................365.4. Scenario2MaaS........................................................................................................................................37
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5.5. Scenario3CAV+MaaS...........................................................................................................................385.6. Results...........................................................................................................................................................38
6. CONCLUSIONS.........................................................................................................................................40
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1. Introduction
1.1. The assignment
NorthEastLinkAuthority(NELA)ispreparinganEnvironmentEffectsStatement(EES)inrespectoftheproposedNorthEastLink(NEL)project.IhavebeenretainedbyClaytonUtzonbehalfofNELAtoundertakeatechnicalpeerreviewofthestrategictransportmodelandassociatedstrategictransportmodellingreportspreparedfortheEES(TransportModellingReports).
IunderstandthattheobjectiveofmypeerreviewistoensurethattheTransportModellingReports:
a. adequatelyaddresstherelevantrequirementsoftheEESScopingRequirementsandthe"publicworks"declaration;and
b. aresuitabletorepresentthestrategictransporteffectsoftheNELproject.
Inthisregardmypeerreviewisexpectedtoassesstheprocess,methodologyandassessmentundertakeninpreparationoftheTransportModellingReportsincludinganyassessmentcriteriaappliedandassumptionsreliedupon.Inaddition,mypeerreviewistoidentifyanyadditionalmatterswhichshouldbeconsideredinordertoaddresstheEESScopingRequirements,'publicworks'OrderortootherwiseadequatelyassessthestrategictransporteffectsoftheProject.
1.2. The peer review process in outline
InundertakingthispeerreviewIhadto:
• Reviewbackgroundmaterialontheprojectitselfandotherinterventionsinthecity;
• Reviewtheappropriatestandardsapplicabletotransportdemandmodellingandforecasting;
• Reviewthescopeadoptedforthestrategictransportmodel
• ReadtheReportsidentifyingassumptions,methodologyandparametervalues;
• Contrastthesewithinternationalbestpractice;
• Reviewthedatacollectionplannedanddeliveredandhowitwasusedtocalibratethebaseyear.
• Visitthesitetogainfamiliaritywithconditionsandexpectations;
• Holdmeetingswithmodellersandforecastersthatpreparedthetrafficprojectionsinordertoclarifyissuesnotalwayspresentinreports;
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• Reviewtheassumptionssupportingthegrowthintrafficandwhetherthiscanbeconsideredrobustandacceptable;
• Specifyandreviewadditionalworkthatmaybenecessarytodeliverprojectionsofthenecessarystrengthtoprovidetherequiredconfidence;
Ireceivedanumberofdocumentsinpreparationforthepeerreview:
• NorthEastLinkBusinessCasedocumentsincludingExecutiveSummary,ModellingReport,OptionAssessment,TransportAssessmentandthe14chaptersofthefullreport.
• MultipleReports,NotesandSpreadsheetfromtheVeitchListerConsulting(VLC)including:
1 CommercialVehiclesNote:CVTechNoteRevB;
2 NELmodeldevelopmentreport(basecasemodelassumptions);
3 NELmodelspecificationspreadsheet(summaryofmodelassumptions);
4 Modelwidevalidation;
5 NELLocalAreaModelvalidation;
6 Backcastingsummaryreport;
7 ReviewofTravelForecastingMethodologies;
8 Modelcalibrationreports-genericcalibrationreports,explaininghowthemodelworks(40reports);
9 ValueofTravelTimeSavingsVTTSReport;
10 SpeedFlowrelationshipreview;
11 LocalareamodelcalibrationModelC(theversionusedfortheEES).
ItravelledtoMelbourneattheendofJulybeginningofAugusttoperformafieldvisit,travellingalongtherouteandareaofinfluencewithaconsultantfamiliarwiththemodelandproject.IalsohadextensivediscussionswiththeVLCmodellingteam.AsaresultofthesediscussionsIrequestedfurtherclarificationsandtheproductionofanoverallsummaryreport.AsIwantedtoexaminetheparametervaluescalibratedorchosenforthemodelIrequestedadetailedlistofthemandtheirsourceincludinganyusedinforecasting.AsIwasparticularlyinterestedintheassumedbehaviourinthetollchoicemodelasthishasbeenasourceofmiscalculationinthepast,Irequestedmoreinformationonthem.Ialsofoundthatthetreatmentofuncertaintyandriskdidnotgodeepenough,inparticulardealingwiththeadventofMobilityasaService(MaaS)and
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ConnectedandAutomatedVehicles(CAV).Asaresultoftheserequeststhefollowingfilesanddocumentsweresubsequentlyprovided:
12 TransportModellingSummary,therequesteddocumentincludingappendicesA,BandC;
13 ModelForecastingAssumptions;
14 DestinationChoiceParameters;
15 ModeChoiceParameters;
16 PTAssignmentParameters;
17 TrafficAssignmentParameters;
18 Tolldiversioncurves;
19 Notecommentingonparametervalues;
20 Memopresentingpossibleadditionalscenarios;
21 NELProjectDescriptionforEESSpecialistsUpdatedprojectdescriptionasprovidedtospecialists;
22 ModelledoutputsdepictingtraveltimesavingsasprovidedbyVLC.
FollowingmyinitialreviewofthematerialsreceivedIrecommendedaframeworktoprepareformalscenariostodealwithfutureuncertainty.ThiswasthenaddressedandreportedbyVLC1.
DuringtheperformanceofthispeerreviewIalsoconsideredanumberofseparatedocumentsstatingtherecommendedmodellingpracticeinAustralia,andVictoriainparticular.Theseincluded:
23 ATAPAustralianTransportAssessmentandPlanningGuidelines
24 TransportModellingGuidelinesVictoriaVolume2StrategicModellingVersionDraft3April2012
25 StrategicTransportModelElasticityGuidelines.DEDJTR2015
26 PresentationtoSenate,EconomicReferencesCommitteeInquiry:TollRoadsinAustralia
27 VictoriaStateGovernmentGuidanceonriskanduncertainty
28 InfrastructureVictoriaGuidanceonautomatedandzeroemissionsvehicles
1VLC(2019)AlternativeFutureScenarios–EmergingTechnologies.ReportdatedJanuary2019
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29 TransportInfrastructureVictoriaEvidenceReportonautomatedandzeroemissionsvehicles.
IalsoconsideredsimilardocumentsproducedintheUKDepartmentofTransport,inparticularWebTag,thatcomplementtheguidelinesabove.
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2. The North East Link Project
2.1. Context
IthasbeenarguedthatNorthEastMelbournelackssufficienthighqualityinfrastructureandthatthisresultsinheavytrafficusingwhatare,ineffect,arterialaccessroadstoresidentialareas.Moreover,thereisevidenceofnotjustcongestionbutalsosignificantvariabilityontraveltimestryingtobridgethisgapbetweentheEasternFreewayandtheM80.
TheNorthEastLinkwillcompletethe“missinglink”inMelbourne’sorbitalfreewaynetworkandestablishacontinuousfreeway-standardorbitalroadaroundMelbourne,betweenAltonainthewestandFrankstoninthesouth.
Figure1ContextfortheNorthEastLink.
Source:NELBusinessCase
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2.2. North East Link
TheproposednewNorthEastLinkwillbeginontheEasternFreewayatSpringvaleRoadbeforeconnectingviaanewroadwaytotheM80RingRoadatGreensborough.Themainroadwaywillextendapproximately11kilometresfromtheeasternendoftheM80totheEasternFreewayatBulleenandwillbetolled.ThenorthernsectionofthenewlinkwillrunatsurfacebeforedescendingintoacuttingnearWatsoniaRoadandintotunnelsatLowerPlentyRoad,andthentransitioningtoaviaductstructurejustnorthofKoonungCreektoconnecttotheEasternFreeway.ConnectionswillbeprovidedbetweenthefreewayandGreensboroughBypass,GrimshawStreet,LowerPlentyRoadandManninghamRoad.
Figure2TheNorthEastLinkSource:NELProjectDescriptionforEESSpecialists-SpecialistsIssue4
InfrastructureVictoriaidentifiedtheNorthEastLinkasahighpriorityinfrastructureprojectforthestateinits30-YearInfrastructureStrategy,releasedin2016.InfrastructureVictorianotedthatthelinkwillenhanceaccesstomajorsuburbanbusinessandemploymentcentres,improveorbitalroadconnectivityacrossMelbourneandboostthecapacityofthecity’sfreightnetwork.
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InOctober2017,theVictorianGovernment’sVictorianInfrastructurePlanconfirmedNorthEastLinkasoneofseveral‘catalyst’,state-shapinginfrastructureprojectsdesignedtostimulateeconomicgrowth,createjobsanddeliverpositive,long-termbenefitsforVictorians.
2.3. Expected impact
TheNorthEastLinkisexpectedtoprovidethemainmeansofconnectingtheM80inthenorthwiththeEasternFreewayabsorbingtrafficthatcurrentlyusessignalcontrolledlocalroadsandshorteningsignificantlythetraveltimebetweenthetwomotorways.AccordingtotheNELBusinessCasetheimpactswouldbe:
a. Significantreductionsintraveltimes,includingupto30minutesinreducedtraveltimebetweentheEasternFreewayandtheM80,anda40percentreductionintraveltimealongtheEasternFreeway
b. Significanttrafficreductionsacrossarterialroadsinthenortheast
c. 15,000fewertrucksonarterialroadsinthenortheast
d. Fasterandmorereliabletraveltimesforcross-cityandorbitalfreightmovements
e. Congestionreliefatthefivenorth-southbridgecrossingsoftheYarraRiver
f. TrafficreliefalongtheM1corridor,allowingittooperatemoreefficientlywithreducedtrafficvolumes
g. Upto30percentreductionintraveltimeforbusesalongtheEasternFreeway.
Moreover,theModellingReport2insupportoftheBusinessCaseshowsanestimateoftheimpactoftheprojectoncurrentYarrarivercrossingsscreenline;thisprovidesabroadperspectiveonhowdifferentelementsoftravelbehaviourarechangedbytheproject.Theresultsareshowninthefigurebelow:
2NorthEastLinkProject.AppendixR,TransportModellingReport,February2018
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Figure3ImpactofNELovercurrentYarrarivercrossingscreenlineSource:Figure25inAppendixRTransportModellingReport
Justremoving15,000trucksonarterialroadsshouldproduceenvironmentalbenefitsthatwhencoupledwiththeotherimpactswillbeverysignificant.Confidenceintheeventuationoftheseenvironmentalbenefitsdependsonthequalityandexpectedreliabilityofthetransportmodelusedtoforecastandestimatetheseimpacts.
Themainelementofmyownassignmentistopeerreviewthismodeltoprovideadditionalsupport,orotherwise,totheestimationofimpactsfortheEnvironmentEffectsStatement.
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3. The Transport Model
3.1. Introduction
TheNorthEastLinkAuthority(NELA)appointedVeitchListerConsulting(VLC)tosupplystrategictransportmodellingservicesfortheNorthEastLink(NEL)project.Thisincluded:
• setupandmodelpreparationfortheNorthEastLinktransportmodel;
• provideinputintotheoptionsassessment;
• preparationofthebusinesscaseforecastsforthepreferredproject;and
• inputintothereferencedesignandenvironmentaleffectsstatement(EES).
ThescopeforthisassignmentforVLCwastoestimatetheimpactoftheinclusionofthenewlinkonthetransportsystemusingastrategictransportmodel.Thismodelwasusedtoforecasttheimpactofthenewroadontravelpatternsacrosstwodistinctareas:MetropolitanMelbourne,definedbytheAustralianBureauofStatistics’GreaterCapitalCityStatisticalArea(GCCSA),andtheprojectstudyarea,whichincludespartoralloftheLocalGovernmentAreas(LGAs)ofBanyule,Boroondara,Darebin,Manningham,Maroondah,Nillumbik,Whitehorse,WhittleseaandYarra.ThereforethemodelprovidesimpactsformoreorlessthewholeofMelbourneandalsoamorelocalizedarea.ThestrategicmodelwasalsousedtoprovideinputstomicrosimulationmodellingofthelocalimpactoftheNELproject.
VLCuseditsZenithmodeltoinstrumentthistask.TheZenithmodelisaparticularapproachtoimplementaclassicfour-stagestrategicmodel3developed,calibratedandvalidatedbyVLC.ItisclaimedthatithasprovedmoreaccuratethanothermodelsappliedinAustralia,inparticularinrespectoftollroadforecasting4.ThemodeliseffectivelyimplementedinOmniTrans5,asoftwarepackagedevelopedinEuropeandusedinanumberofprojectsinternationally.Theclassicfour-stagemodelisillustratedinthefollowingfigure.
Thisistheapproachfollowedinternationallyinmostassessmentsofthiskindasitallowstheassessmentoflocalandcity-wideimpactsofaprojectofthisstrategicimportance.
3Ortúzar,J.dD.andWillumsen,L.(2011)ModellingTransportFourthEdition.JohnWiley&Sons,Chichester.4VLC(2018)TransportModellingSummaryReport.August2018,Table1.1,page4.5OmniTrans:http://archief.dat.nl/en/products/omnitrans/
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Figure4TheclassictransportmodelSource:AdapterfromOrtúzarandWillumsen(2011)
AstheNELprojectprogressed,themodelwasadjustedtoincludeuptodatedataandassumptions.Severalversionsofthemodelwereproducedasfollows:
ModelAwasusedinanearlyevaluationoftheNorthEastLinkproject.
ModelBwasdevelopedfortheNorthEastLinkprojectevaluation,incorporatingtheoptionsassessmentprocess.
ModelCwasdevelopedforuseinthepreparationofthebusinesscase.Ithasabaseyearof2016andcontainsupdatedassumptionsfromversion1.09ofthereferencecaseasprovidedbyTransportforVictoria(TfV).
ModelC2hasbeendevelopedforuseinEES.ItislargelyconsistentwithModelC,withthefollowingupgrades:
• Additionalobserveddatausedtoimprovethemodelvalidation;and
• UpdatedreferencedesignoftheNELproject.
TheZenithmodelwasrecalibratedin2014usingmodelparametersgeneratedfromthelatestavailableVictorianIntegratedSurveyofTravelandActivity,andvalidatedto2011
Transportnetwork Population,employmentlanduses
TripGeneration/Attraction
TripDistributionDestinationchoice
ModeChoice
Assignment
Traveltimes&costsFlowsonlinksandPTservices
Equilibrium
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trafficandpublictransportpatronageestimates.Themodelwasthenfurtherre-validatedto2016dataaspartoftheevaluationoftheNorthEastLinkproject.
TheZenithmodelfortheNELprojectconsidersfourtimeperiods:
• AMPeakfrom7:00to9:00
• Inter-peakfrom9:00to16:00
• PMPeak16:00to18:00
• Off-peak18:00to7:00,notreportedindetail.
Eachofthecomponentsofthismodelisnowdiscussedindetail.
3.2. Zones, Networks and segmentation
Themodelledareaisdividedintotravelzones.ThemodelusesatravelzonesystemthatwasoriginallydevelopedspecificallyforlargeinfrastructureprojectsinVictoria.ItisbasedonanaggregationoftheZenithSmallAreaTravelZoneSystem.Thereare3,477zonesacrosstheentiretravelzonecoveragespecificforthisstudy(asub-setofthezoneforthewholeofVictoria).
Thetwomainnetworks,roadandpublictransport,aremodelledwithasufficientlevelofdetailtorepresentwithreasonableaccuracytraveltimesandroutechoice.
Inthecaseofroadlinksfourdifferenttraveltime–flowformulationsareusedfor:
• Nonmanagedmotorway
• Managedmotorway
• Arterial
• Managedramps
Thesehavebeencalibratedusingexistingdataandvalidatedusingtrafficcounts.Oninspectionofthecurves6theyrespondedtothenormalexpectationsforthistypeofroad.Thereisnodetailedmodellingofjunctionsasisnormalpracticewhendealingwithstrategicmodelslikethisone.Detailedmodellingofjunctionsmayappearattractiveintermsofrepresentingcurrentconditionsbetter.However,therearetwocriticalproblemsassociatedwiththem.First,itisgenerallyaguesswhatthecharacteristicsofthesignaltimingswillbeinthefutureandthereforeresultscannotbeentirelyreliable.Second,detailedmodellingofjunctiondelaysislikelytocreateproblemsforconvergenceofthemodelthusweakeningconfidenceevenfurther,seeOrtúzarandWillumsen(2011),section11.4.3.
6TrafficAssignmentParameters.ExcelspreadsheetprovidedbyVLConrequestbytheauthor.
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Themodelconsidersthefollowingtripmakingsegmentation,ortrippurposes:
• Homebasedwork(whitecollar)
• Homebasedwork(bluecollar)
• Homebasededucation(primary)
• Homebasededucation(secondary)
• Homebasededucation(tertiary)
• Homebasedshopping
• Homebasedrecreation
• Homebasedother
Inmyview,thisismorethansufficienttocapturethedifferentaspectsoftravelchoiceinthestudyarea.
3.3. Trip generation
ThetripgenerationmodelwasaHomeBasedTripProductionModelfortheeightsegmentspresentedabove.SeparatepredictivemodelswereestimatedbyVLCandvalidatedforeachoftheabovetrippurposes.Eachpredictivemodelwasdevelopedusingthenumberoftripsrecorded(foreachtrippurpose)byeachhouseholdwhichtookpartintheVISTAsurvey.
Thehouseholdvariableswhichwereusedaspredictorswere:
• Householdsize;
• Numberofwhitecollarworkers;
• Numberofbluecollarworkers;
• Numberofdependantsaged0-17;
• Numberofdependantsaged18-64;
• Numberofdependantsaged65+;and
• Numberofcarsowned.
Theresults,arepresentedin7andappearreasonableinmyexperience.
TheNonHomeBasedTripProductionModelswereproducedforthefollowingtrippurposes,basedagainontheVISTASurveys:
7Paper4a–HomeBasedTripProductionModel.ZenithModelRecalibrationandValidationVersion3.0.1.May2014
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• WorkBasedWork(WBW)
• WorkBasedShopping(WBS)
• WorkBasedOther(WBO)
• ShoppingBasedShopping(SBS)
• ShoppingBasedOther(SBO)
• OtherNon-HomeBased(OHNB)
TripattractionsaremodelledseparatelyandincludedintheDestinationChoicemodel.
3.4. Destination choice
Thedestinationchoicemodel,sometimescalleddistributionmodel,isdescribedinDestinationChoiceModelZenithVictoria8.ThetypeofmodelusedisasinglyconstrainedGravityModelforalltrippurposes.Thismodelcanalsobeinterpretedasamulti-nomialLogitchoicemodelwheretheutilityfunctionis:
𝑉! = 𝛽𝐺𝐶!" + ln 𝐴! + 𝛼 ln 𝐺𝐶!" + 𝑈!
Where
𝑉! istheutilityfunctionfortripstodestinationd
𝐺𝐶!" isthegeneralisedcostoftravellingfromoriginotodestinationd
𝐴! istheattractivenessofdestinationd,forexamplethenumberofjobsatthatzone
𝑈! isadestinationspecificconstantthatcanbeadjustedtomakethemodelmorerealistic.
𝛼 𝑎𝑛𝑑 𝛽arecalibrationparameters
ThisisavariationonthestandardmodelasdescribedinOrtúzarandWillumsen.Itaddsthedestinationspecificconstantthatplaysarolesimilartokfactorsinrecognisingthatjustdestinationcharacteristicsandtheseparationprovidedbygeneralisedcostsisnotsufficienttoexplainthepatternoftripsinastudyarea.ThedestinationspecificconstantshavebeenestimatedforeachcombinationoftrippurposeandcarownershipandfourdifferentareasinMelbourne:CentralBusinessDistrict(CBD)core,CBDnon-core,CBDframeandCBDouterframe.Theseareasarealsousedlaterontoprovidedifferentestimatesofparkingcosts.Thisseemstobeasensiblevariationonthestandardmodel.
ThedestinationchoicemodelwasestimatedusingtheVISTAdataforeachtrippurposeandfourlevelsofhouseholdcarownership:nocar,one,twoor3pluscars.
8DestinationChoiceModelZenithVictoria-TechnicalNote8.March2013.
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Themodelshavebeenvalidatedagainstobserveddata,inparticulartheproductionofTripLengthDistributionsandsectortosectortravel(22sectors)movements.Thecalibrationseemssatisfactory.
TheNELmodelthenallocatestripstodifferenttimesofthedayfollowingtheobservationsoftheVISTAsurvey9.ThisallocationdoesnotdependonthecostsoftravellingbetweendifferentlocationsinthestudyareabutitdependsonthefourdifferentareasofMelbournementionedabove:CBDcore,non-core,frameandouterframe.
ItmustberecognisedthattheGravityModel,oranyotherpracticalDestinationChoicemodel,isprobablytheweakestsub-modelinthegroup.ThisisbecauseitistryingtoexplainverycomplexbehaviouraboutthechoiceofjobandresidenceessentiallyonthebasisofTripEnds(generationsandattractions)andseparation.Moreover,ithasbeenshownthatthecalibrationparameters𝛼 and 𝛽arerelatedtothesizeofthestudyareaandtheaveragegeneralisedcost.
Whenusedinforecastingmode,theGravityModelwillexaggeratethespeedwithwhichindividualswillbeabletochangejobs,homelocationsorpreferredshoppingareainresponsetoperhapssmallchangesincosts.Moreover,aspopulationsandcitiesgrowthevaluesof𝛼 and 𝛽shouldchange,otherwisetheGravityModeltendstoartificiallyincreasethenumberofshorttripsatthecostofsomeinevitablelongertripsinordertomaintaintheaveragegeneralisedcost;thisisnotexpectedtooccurinpracticeastriplengthisoftenobservedtogrowwithsize.
Despitetheseshortcomings,theGravityandLogitDestinationChoicemodelsarealmostuniversallyusedbutcaremustbetakentocontrolitsevolutionovertimetoavoidunreasonablemodelledresponses.
3.5. Mode Choice
AsshowninFigure3above,theimpactofNELonmodechoiceissomewhatlimited.ThemaininfluenceswouldbethemajorimprovementtotheEasternFreewaycorridorBusRapidTransit(BRT)system(notcapturedbytheYarrascreenline)andthegeneralreductionincongestioninarterialsclosetotheproject.
Nevertheless,VLChasdeployedamajorefforttoimprovethemodelmodetreatmentofmodechoice.TheimprovedmodelisaNestedLogitChoiceModel.Thisisafavouredstructureinmoststudiesaimingforanaccuraterepresentationofmodechoiceincomplexsystems10.ItisalsothestructurerecommendedbytheAustralianTransport
9TechnicalNote6Periodallocationandvehicleoccupancy,VLC10Ortúzar,J.deD.andWillumsen,L.(2011)ModellingTransport,FourthEdition.Section7.4
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AssessmentandPlanning(ATAP)Guidelines11.Thisisaveryflexiblestructurethatpermitscapturingtheessentialelementsofmodechoice.ThestructureadoptedandcalibratedinthiscasebyVLCisdepictedinFigure5:
Figure5TheNestedLogitModeChoiceStructureSource:ZenithVictoria–TechnicalNote7–ModeChoice
Themodelwascalibrated,again,usingtheVISTAsurvey.
Thecharacteristics(attributes)consideredinthemodelare:
• Car:Traveltime,fuelcosts,tollsanddestinationtype(thefourareasCBDcore,non-core,frameandouterframe)asaproxyforparkingcosts.
• WalkingandCycling:traveltime
• PublicTransport:walkingtime,feedermodetime,waitingtime,in-vehicle-time,numberoftransfers,fareandoff-streetparkingifavailableforrailtrips.
Inmyviewthemodelismorethansufficientlydetailedforthistypeofexercise.Themodechoicemodelhasthepotentialtoreflectinaveryrealisticmannerthenuancesofmodechoiceandreflectsamajoreffortinachievingthisaim.Iwouldhavefoundacceptableevenasimplersetofexplanatoryvariablesthanthisone.However,inthiscaseawell-developedmodechoicelikethisoneisusefulinidentifyingtheimpactoffuturepublictransportprojectsinMelbourneonthedemandlikelytousethenewNELfacility.ThisisanimportantcomponentofdeliveringareliableNELEES.
11AustralianTransportAssessmentandPlanning(ATAP)Guidelines.T1TravelDemandModelling.August2016,Section3.2.3
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3.6. Assignment
AsshownagaininFigure3,themostimportantbehaviouralresponseassociatedwiththenewlinkisexpectedandlikelytobeareassignmentofroutesintheareaofinfluenceoftheproject.AstheNELisplannedtobeatolledfacilitythisbringsanadditionalrequirementtoconventionalassignmenttechniques.TheaccuracyoftollroadforecastingasbeenquestionedinrecentyearsandIhavebeenpersonallyinvolvedinsomeoftheeffortstomakethisamorereliableforecastingexercise12.
VLCprovidessomeevidenceoftheaccuracyoftheirapproachwhenusingtheZenithmodelinTable1.113,reproducedbelow,fortollroadsinAustralia.
Table1ComparisonofforecastsandoutturnfortollroadswithZenithSource:Table1.1inTransportModellingSummaryReportAugust2018
VLCforecastswereproducedbeforetheschemesopenedbutnotinsupportofanybidforthem.Theywereproducedinasimilarcontextasthecurrentone,thatisindependentlyofanyprivatesectorpressure.TheseresultsgivesomeconfidencethatthemodelandtheapproachadoptedbyVLCcanachieve,andhaveachieved,greateraccuracythansomewellpublicisedfailuresbyinternationalconsultants.
TheRouteChoiceandAssignmentmodelisafairlystandardStaticTrafficAssignmentruntoequilibriumincorporatingaTollChoicemodel(separateforcarsandtrucks)torepresentthebehaviouralresponsetotolls.
12Willumsen,L.(2014)BetterTrafficandRevenueForecasting.MaidaValePress.AndmycontributiontothestudyandFinalReportonInitiativestoImproveTollRoadPatronageForecastingperformedforBITREinhttps://infrastructure.gov.au/infrastructure/infrastructure_reforms/files/GHD_Improving_toll_road_data_and_modelling_Stage_2.pdf13VLCTransportModellingSummaryReportAugust2018,page4.
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LinkdetailsincorporateLinkTypeandLength,FreeFlowSpeed,linkCapacity,TurnRestrictionsplusTollsandTollCaps(maximumtollpayable);alinkpropertyisalsousedtorepresentconstraintstotruckmovementsrecognisingthatnotalllinksareavailabletotrucksatalltimes.ThetollChoiceparametersaremeanttocapturethebehaviouralaspectoftollchoice.IncommonwithstaticassignmentdemandisrepresentedbyOrigindestinationtripmatricesfortheperiodmodelled.Apeak-hourfactorisusedwhenthemodelledperiodislongerthanonehour;thisisappropriate.
Ihavealreadycommentedonthereasonablenessofthespeed-flowrelationshipsandthefactthatjunctionsarenotmodelledindetail;Iconsiderturnpenaltiessufficientforamodelofthisscopeandcoverage.
ThekeyelementforthisprojectistheTollChoiceModel.Thisisnotmypreferredapproachtomodellingtheimpactoftollsonroutechoicebehaviour.However,asIrecognisein14theuseoftollchoicemodelsispracticallyunavoidablewhenthereisacomplexsetoftollroadsandatollingcapisapplicable.ThisisthecaseinMelbourneandthereforeIconsidertheapproachfollowedappropriate.
Asinmostmodellingcases,thekeyisinthedetailapplicationofatollchoicemodelandinthistheapproachadoptedbyVLCis,again,fitting.
VLCusesaLogittollchoicemodeltoestimatewhatproportionoftravellersfromeachorigintoeachdestinationwouldchoosetopayatolltosavetime.Themodelconsiderstworoutes:thebestuntolled(free)routeandrepresentativetolledroutes,estimatedasacombinationofonlytimeand(toll)money.Whenmorethanonetolledrouteispossible,VLCusesaNestedLogitformulation:
Figure6NestedLogitformulationwhenmorethanonetolledrouteispossible.Source:ZenithTechnicalNote-StaticTrafficAssignment-Methodology
14Willumsen,L.(2014)BetterTrafficandRevenueForecasting.MaidaValePress.
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Thisisasensibleapproach.Themodelalsoensuresthatnoproportionoftripmakersselectsatolledroutewhenthistakeslongerthanthebestfreeroute.
Inevitably,inthistypeofformulation,asmallproportionoftravellersisestimatedtoselectatolledrouteevenifitprovidesonlyasmallorevennotimesaving.Thistypeofbehaviourisobservedinpracticeforatleastacoupleofreasons.Oneofthemisthattolledroutesofferanadvantageintermsoftraveltimereliability,securityandqualityofridethatisnotactuallyreflectedinamodelthatconsidersonlytimeandmoney.Theotheroneisthatdriversnotfullyfamiliarwiththeuntolledalternativesmayoverestimatetheirtraveltimeandselectamoreobviousrouteeveniftolled.Itisimportant,however,toensurethatthisproportionisnotunreasonablyhigh.
ToensureVLC’stollchoicemodelwasreasonableIrequestedadditionaldetailsonitsparametersandassumptions.VLCprovidedtheseinacoupleofExcelspreadsheets,oneforcarsandanotheroneforcommercialvehicles.Ihavereviewedandusedthesespreadsheets,changedvaluestoexploretheirperformanceunderdifferentconditionsandadaptedtheoutputstomypreferences.
Themodeldistinguishedsixdifferentcaruserclasses:
1 CompanyCarSouth
2 Non-companyCarSouth
3 CompanyCarNorth
4 Non-companyCarNorth
5 AirportCar
6 CommercialVehicle
Thewillingnesstopaytollstosavetimeisrepresentedbytheratiooftheparametersinthemultiplyingtollvalueoverthatmultiplyingtimeresultinginan“ImpliedValueofTravelTimesSavings”,orImpliedValueofTime,asshowninthefollowingtableinAustraliandollarsof2008.
Table2ValuesofTime.Source:VLCprovidedspreadsheetAscanbeseen,AirportCarandCompanyCarSouthhaveequivalentaveragevaluesoftimeandthesearehigherthanforNon-companycars.Thisisareasonablerelationshipas
NonCompanyCarNorth
NonCompanyCarSouth
CompanyCarNorth
CompanyCarSouth AirportCar Commercia
lVehiclephitime -21.45 -15.786 -26.706 -20.412 -20.412 -45ImpliedValueofTime($/min) $0.82 $0.97 $1.14 $1.58 $1.58 $10.71
ImpliedValueofTime($/hr) $48.97 $58.47 $68.48 $94.50 $94.50 $642.86
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thosetravellingincompanycarsarelesssensitivetotolls(generallycoveredbytheemployer)andtripstotheAirportareparticularlysensitivetotraveltimeanditsreliability.
Theseimpliedvaluesoftimeareonthehighside,inparticularforcommercialvehicles,butprobablyreasonablecomparedtootherprojectsandcountriesbearinginmindtheaverageincomelevelsinAustraliaandVictoria.TheValuesofTimeforCommercialVehiclesweredeterminedfromobservationsoftheirelasticitiestotollchangeswhenthisoccurredinVictoria;theobservedelasticitieswereusedtoadjustthechoiceparameters.ThefinaltestisinthepropercalibrationofamodelinacontextwithmultipletollroadsinoperationasisthecaseinMelbourne.Anoverestimationofthevaluesoftimewouldresultinpoorvalidationvalues.ThisisnotthecaseoftheZenithmodelusedinthiscase.
Theresponsesimpliedbythese,andotherparametervalues,inthetollchoicemodelareillustratedinthefollowingfigure:
Figure7Proportionofdriverswillingtopayagiventollfora5minutesaving.Source:adaptedfromparametervaluesprovidedbyVLC.
Itcanbeseenthatthecurvesallowadispersionofroutesasevenforatolledoptionprovidedforfree(zerotoll)notalltravellersselectitevenifitsavesthem5minutes.Anotherwayofvisualisingthesametollchoicemodelistoconsidertheproportionusingatollroutewithacostof,say,AU$8.00fordifferenttimesavings.
0.000.100.200.300.400.500.600.700.800.901.00
0 200 400 600 800 1000 1200 1400 1600
Prop
ortio
nchoo
singto
ll
Toll(cents)
TollDiversionCurve(5minutetimesavingtollroute)
NonCompanyCarNorth NonCompanyCarSouth
CompanyCarNorth CompanyCarSouth
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Figure8Proportionofdriverswillingtopaya$8.00tollfordifferenttimesavings.Source:adaptedfromparametervaluesprovidedbyVLC.
Theproportionofdriverspayingatollfornotimesavingissmall,around10%inmostcases.Thisproportionincreasesfortimesavingsabove5minutesanditishigherforcompanycars(andAirporttrips)thanfornon-companycars.CommercialVehiclesareverysensitivetotimesavings.ThisislikelytoreflecttwoadditionalcharacteristicsaffectingCommercialVehicles:(1)theiroperatingcostsgoupverysharplywithstops(asexperiencedinurbanroads)andareloweronfreeflowingroads;(2)contractualconditions,forexample“justintime”contractsthatplacehighvaluetotraveltimereliability.
TheImpliedValuesofTravelTimeSavingsaregrownat1.55%peryearinrealtermstoaccountforthegrowthinpercapitaincomesinthearea.Thishasbeentakentoreflecttheexpectedevolutionofpercapitaincome.InordertoconsidertheinevitableuncertaintyaboutthisevolutionIwouldhavepreferredtogrowthisexplicitlyasafunctionofGDPpercapitaorIncomeperCapita.Thiswouldhavesimplifiedtestingthesensitivityofdemandmorerealisticallytoeconomicgrowth.However,itisnotconsideredapoorestimationofhowwillingnesstopayislikelytoincreasewithincomes.
0.000.100.200.300.400.500.600.700.800.901.00
0 2 4 6 8 10 12 14 16 18
Prop
ortio
npayingto
ll
TimeSaving(minutes)
TollDiversionCurve(Toll$8.00paid)
NonCompanyCarNorth NonCompanyCarSouth CompanyCarNorth
CompanyCarSouth CommercialVehicle
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TheassignmentprocessisruniterativelyandconvergenceisachievedusingtheMethodofSuccessiveAverages(MSA15).ThecriterionforconvergenceiswhentheRelativeGap(astandardmeasureofthedegreeofconvergence)islessthan1%.Iconcurthatthisshouldbesatisfactoryforthistypeofexercise.
3.7. Equilibration
Theclassictransportmodel,asdepictedinFigure4above,mustuseconsistentvaluesforkeyvariablesineachofitssub-models.Forexample,thetraveltimesforeachmodethatresultfromtheapplicationofMSAduringassignmentshouldbethesameusedinthemodechoicemodeltoensureconsistency.Astheclassicmodelisgenerallyappliedsequentiallyitisnecessarytodevelopagooditerativeprocesstoachievethisconsistency.
Therearetwokeyquestionsinaddressingthisissue.Thefirstonereferstothegeneralmethodtobefollowedtoachievethisconsistency.Afrequentlyusedapproachisto“feedback”theresults,saytraveltimes,fromassignmenttotheothersub-modelsandrepeattheprocessiterativelyuntiladegreeofconvergenceandconsistencyisachieved.Thesecondquestionishowfarupshouldthisfeedbackloopreach:shoulditcoverjustModeChoiceorshoulditalsoincludeDistributionandeventuallyTimeofTravelChoiceandTripGeneration?
Thefirstquestionisaninterestingoneasithasbeenshownthatdirectfeedbackoftimesandcostsmaynotbeaconvergentprocess,seeOrtúzarandWillumsen(2011)section11.3;itshowsthatevenforaverysimplesystemthereareconditionswhenconvergenceisnotachieved16.Indeed,abettermethodthandirectfeedbackofcostswouldbetoapplytheMSAapproachtothewholemodel.However,thisisseldomundertakeninpractice,apparentlybecauseitisconsideredmoredifficulttoimplement,andmostmodelstendtousedirectfeedbackwithalimitednumberofiterations.
15TheMSAconvergestoequilibriumoveranumberofiterations.ItisrobustbutnotasfastasFrank-Wolfeorothermorerecentlydevelopedalgorithms,seeOrtúzarandWillumsen(2011)Chapter11.16Anotherwaytodescribethisissueisthattheelasticityofdemandtochangesincostsandtimesshouldbedecreasingthehigherthemodelisintheclassicstructure.Inotherwords,changesindestinationshouldbelesssensitivetochangesincoststhanchangesinmodeandtheseinturnshouldbelesselasticthatchangesinroute.Asthesub-modelsaregenerallycalibratedseparately,itispossiblethatthisconditionisviolatedinpracticeandthuspreventingequilibration.Theissueisfurtherobscuredasoftenthesegmentationofdemandandthegeneralisedcostfunctionsaredifferentineachsub-model.
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Thesecondquestionisamorepragmaticone.ItisgenerallyrecognisedthatitwouldbeoflittlevaluetoextendthefeedbackeffecttoTripGenerationevenifthemodelhassomesensitivitytocongestion.ExtendingittoTripDistributionissometimesdoneandseemstobetherecommendedpracticeofGuidelinesforTransportModellingandEconomicAppraisalinVictoria(theseguidelines,producedin2017seemtobenolongeravailableinlate2018).However,asalreadydiscussed,theGravityModelgenerallyusedinTripDistribution,exaggeratestheresponseofchangingjobsorresidenceswhentravelcostschangethusmakingequilibrationamoredifficulttask.
TheVLCmodelfollowsnormalpracticeoffeedingbackdirectlythecostsfromassignmenttohighersub-models.However,themodelusesthecostsofthefirstiterationtorunDistributionandModeChoiceandfeedbackcostsofsubsequentiterationsonlytomodechoice.VLCcallsthis“dampened”or“singledistribution”approachincontrastwith“loopthroughdistribution”method.Inmyview,bothapproachesriskfailingtoachieveconvergence;the“loopthroughdistribution”methodrunsahigherriskandtoanextentVLCisrighttocalltheirapproacha“dampeningtechnique”.
VLCundertooka“backcasting”exercisetoshowthatitsapproachismoreconsistentwiththeobservedevolutionoftotalvehiclekilometres.TheresultsareconsistentwithmyexpectationandprovidefurthersupporttotheapproachadoptedbyVLC.
MostofthemodelsIhadbeentaskedtoreviewtreatthisissueinapragmaticbuttheoreticallypoorway.Thisdoesnotseemtohavedetractedmuchfromtheirabilitytoproducereliableforecasts.TheapproachadoptedbyVLCis,inaway,betterthanmostinthatitcontrolsordampenswildoscillationspresentwhendirectfeedbackondistributionandmodechoiceareimplementedtoaimforconvergence.
3.8. Growth
VLCisapplyingaclassicStrategicTransportModeltoNorthEastLinkandthereforedemandgrowthresultsdirectlyfromPopulation,EmploymentandotheractivitiesgrowthinVictoria.
Growthinthesekeydriversmustbeaccompaniedbyplannedchangesonthesupplyside,thatisplannedchangestothepublictransportandroadnetworksandservicesforeachoftheforecastinghorizons.
VLChasprovidedaveryextensivesetoftablesdescribingwhathasbeenassumedintheBaseCaseinrespectofgrowthandnetworkchanges.
IamnotinapositiontocommentonwhetherthelistofprojectsandexpectedgrowthisconsistentwithcurrentthinkinginVictoria.Icanconfirmthatthelistsarelongand
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apparentlyexhaustive;infactlongerandmoredetailedthanmanyotherstudiesofasimilarnatureandscope.
Thefollowingforecastinghorizonswereconsideredinadditiontothe2016baseyear:
• 2026BaseCase(noproject)
• 2026NorthEastLinkProjectscenario
• 2036BaseCase(noproject)
• 2036NorthEastLinkProjectscenario
3.9. Model limitations
Perhapsunusualinthistypeofworkthereportscontainaverygoodrecordofthemainlimitationsofthemodel.Theseareconsistentwiththelimitationsofmodelsofthisnature,seeAppendixAoftheTransportModellingSummaryReport.Themostrelevanthereare:
• ResultsaredependentonLandUseinputsandassumptions• Thereisnoexplicitmodelofpeak-spreading.Thismeansthatthemodel
overestimatespeakdemandandunderestimatesinter-peakandoff-peakdemand.Totaldemandshouldberoughlyunchanged.
• Imperfectmodellingofdelay(queueingbehaviour)underheavycongestion;delaysonat-leveljunctionsmaybeunderestimatedinfutureyears.
• Unconstrainedparkingcapacity.• ConsistencyoftravelbehaviourwiththeobservationsofVISTA.Behaviourchanges
overtimeasvaluesevolveinwaysthataredifficulttopredict.
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4. Model review
4.1. Introduction
InthissectionIconsidertheeffortsmadetocollectdatatocalibratethemodel,thelevelofcalibrationachieved,theparametersusedinthemodelandthereasonablenessoftheresultsassupportedbysensitivityanalysis.
IngeneraltermsmanyofthemodelparametershavebeencalibratedusingtheVictoriaIntegratedSurveysofTravelandActivity(VISTA07andVISTA09)recalibratedin2014andvalidatedusing2011trafficandpublictransportdata.Ithasbeenfurtherupdatedandre-validatedto2016data.Thisblendofdatafromdifferentyearsisnotunusual.Thetaskofcollectingafullsetofdataforasinglebaseyearisnotonlyformidablebutalsoextremelyexpensive.
4.2. Most recent data collection
AlocalsetoftruckmovementswasobtainedusingcameraOrigin-Destinationsurveys.Thesewereused,togetherwiththetollelasticitiesforcommercialvehiclesmentionedabove,toimprovethegoodsvehicletripmatricesandchoiceparameters.
Atotalof485trafficcountsfor“averageweekdays”duringschoolterm“reflecting”2016conditionswereusedforvalidationoftheupdatedmodel.Thequotesarepresentherebecauseinthesecasesoneusuallygetstrafficcountsobtainedindifferentdaysandweeksandaneffortismadetoachievetherepresentativenessofthedataforaverageweekdaysintermtime.
• Screenlinecountswereundertakenonsixscreenlinesinthestudyarea,againrepresenting2016conditions.
• Atotalof30surveylocationswereusedtoobtaincameraOriginDestinationdataintheEasternFreeway,akeyelementofdemandforNEL.
• Atotalof34peakand18inter-peaktraveltimesurveyswereundertakentoensurespeedsandtraveltimeswereaccuratelyrepresentedinthemodel.
• FlowswereadditionallyobtainedfromtollroadsinMelbourne,inparticularCityLinkandEastLink.Thisdataiscommercialinconfidenceandisnotdisplayedinthereports.
• PublicTransportpatronagedatawasalsoobtainedtovalidatethissub-model.
Thescopeandcoverageofthedatacollectedtovalidatethemodelis,inmyview,appropriatetothetask.
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4.3. Calibration/Validation
AppendixBoftheTransportModellingSummaryreportprovidesabundantevidenceoftheextentandaccuracyofthelocalvalidationfortheEESmodel(ModelC2).
Table3.1ofthatreportdisplaysasummaryofthevalidationexercise(intermsofcomparisonwithtrafficcounts)contrastedwithVicRoadsvalidationguidelines.Thisisreproducedbelow:
Table3ComparisonofvalidationofModelC2againstVicRoadsCriteriaSource:Table3.1TransportModellingSummaryreport
Thevalidationeffortextendsbeyondthesimplecomparisonoftrafficcounts.Journeytoworktripmatriceswerecontrastedagainstthe2016ABSJourneytoWorksurvey.AcomparisonattheLGAtoLGAlevelresultedinanR2of0.962showingagoodmatch.
ScattercomparisonoftrafficcountsagainstmodelledflowsshowedR2valuesabove0.9
formodelledperiodsandabove0.97fordailytotals.However,theresultspointtoaslightunderestimationofdailyflows(0.6%)andaslightlybiggerunderestimationofpeakflows(1.4%AMand1.2%PMpeak).Thisisnotunusualasitisextremelyunlikelythattotalflowswillbeperfectlymatched.
Themodelresultsonthebaseyearwerealsocomparedacrosssixscreenlinesshownbelow:
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Figure9Sixscreenlinesforvalidation.Source,Figure2.2fromVLC’sTransportModellingSummaryreport
Theresultsareshowngraphically.Forexample,fortheAMpeakperiod:
Figure9ScreenlinesvalidationAMPeak.Source,Figure4.8fromVLC’sTransportModellingSummaryreport
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AscanbeseenonlytwocasesarejustoutsidetheacceptableboundsandthisIconsideracceptableforamodelofthisnatureandcoverage.Indeed,theresultsforthePMpeakandthefulldaysarebetter.
VLCalsovalidatedthemodelagainstobservedtraveltimes.Thisisanimportantanddifficulttestasseveralfloatingvehiclerunsarenecessarytogetareasonablerepresentationoftraveltimesalongaroute,inparticularwhenjunctionsarecontrolledbytrafficlights.Thesecomparisonsarebestmadeusingplotsofcumulativetraveltimesalonglongerroutestoavoidtoomuchvariabilityonshorterlinks.Thereisanextensivesetofsuchplots,somedisplayingbettermatchthanothers.IconsiderthemostcriticalplotsthosealongtheGreensboroughRoad,RosannaRoad,BulleenRoadcorridor.Acoupleofexamplesareshownbelow.
Figure10Twocumulativetraveltimecomparisons.Source,Figure4.13and4.14fromVLC’sTransportModellingSummaryreport
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Thefiguresshowthedegreeofvariabilityofobservedtraveltimesandhowthemodelledtraveltimes,representinganaverage,reflectreasonablywelltraveltimesalongthecorridor.
Overall,IamsatisfiedthatthemodelusedtopreparetheEESforNorthEastLinkhasbeensufficientlywellvalidatedtobeusedforthatpurpose.
4.4. Convergence
Boththetrafficassignmentmodelandthecompletemodel,asdepictedinFigure3above,runsufficientiterationstoachieveconvergence,thatisconsistencyinthecostsamongthechosenroutesfromeachorigintoeachdestination(EquilibriumAssignment)andthecostsinthedemandmodels.
VLCusesfourdifferentindicatorsofassignmentconvergenceinthestudyfortheEES,RelativeGap,AverageAbsoluteDifference(AAD),theRelativeAverageAbsoluteDifference(RAAD)andthePercentageofLinkswithavolumechangeinsuccessiveiterationsoflessthan5%(PDiff).ThetargetsforthesemeasuresareRGAP<0.01,AAD<1,RAAD<1%andPDiffbetterthan95%.Naturally,themodeltakeslongertoconvergeundercongestedconditionsbutisstablemuchfasterforinter-peakandeveningoff-peakconditions.
VLCreportsthatthesetargetsareallmetforassignmentforbothpublictransportandvehiculartraffic.Thisisreassuringasitsupportstheassertionthatthemodelproducesconsistentresults.
ConvergenceforthecompletemodelhasnospecifictargetsinAustraliaorVictoria.Ingeneral,thisissueistreatedlookingatthePercentageRootMeanSquareofError(variationinsuccessiveiterations)ofthegeneralisedcostsastheyarewhatistargetedtobeconsistent.
VLCreportslevelsfor%RMSEforcostsanddailyflows;thesearearound0.50afterfouriterations.Iunderstandthatthisisthelevelofconvergenceachievedinallrunsandseemssufficientforthepurposeofthisexercise.
4.5. Parameters
Validationonabaseyearisnotenough.Apeerreviewerwouldliketobesatisfiedthattheparametersadoptedandcalibratedinthemodelarereasonable.Majordeparturesfromexpectationsbornefromlongstandingpracticemaybejustifiedbutthereviewerneedstobeconfidentthatthesedeparturesmaynotleadtobiasorsignificanterrorswhenthemodelisusedforforecastingmanyyearsaheadandundermoredemandingcongestedconditions.
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Theoriginalreportsprovideddidnotcoverthevaluesoftheseparametersinsufficientdetails.Therefore,Iaskedforamoredetailedaccountofalltheseparameters.Thiswasprovidedinasetofspreadsheets:
1 DestinationChoiceParameters;
2 ModeChoiceParameters;
3 PTAssignmentParameters;
4 TrafficAssignmentParameters;
Thefullmodelhasover1000parameterstoconsider.Mostofthemresultfrommodelcalibrationandestimationeffortsandtheymustbeseeninthecontextofacompletemodel,notnecessarilyoneparameteratthetime.Thereasonforthisisthatsomecombinationsofparametersmaybereasonableevenifoneortwoarebeyondtherangeofmyownpersonalexpectationsandexperience.
Ireviewedtheseandprovidedcommentsonthoseparametersthatturnedout,aftercalibration,tobeslightlyoutsidemyexpectedrange.VLCrepliedwithcommentsontheminaseparatenote.Thiswasausefulandtransparentdialoguethatenablemetoformanopinionoftheoverallqualityandconsistencyofthemodelparameters.
IhavealreadycommentedontherelativelyhighValueofTime,inparticularfortrucksinassignment.Thesevaluesmustbeinterpretedinthecontextoftollchoicecurves.VLCrespondedthattheyhavedonetwosensitivitytestsonthesecurves:
• Testone-2036NELprojectandtheimpliedVOTforCVtolldiversionofalowvalue~$100/hr;thisresultedinasignificantreductionofaround30%oftrucktrafficmid-blockineachdirectiononNEL.
• Testtwo-2036NELproject&halvingtheimpliedVOTforCVtolldiversionto~$320/hr;thisresultedinonlyaverysmallreductionintrafficmid-blockonNELofaround3%forLCVandHCVs
IconcurwithVLCthattheseresultsindicatethatNELCVforecastsarerelativelyinsensitivetotheValueofTimeandthatNELcanbeexpectedtoattractasignificantnumberofcommercialvehiclesawayfromlocalarterials.ThisisdueinparttothetimesavingsNELprovidesandthefreeflowingnatureofitstraffic,animportantfeatureforcommercialvehicles.
Theexchangeofviewsonotherparametersincludethefollowing:
Timeweightforcaraccesstotransit(3.85).ThisishigherthanusualasIwouldexpectavalueintherange1to2.5.VLC’sresponsewasthatthe“valueusewasestimatedbyanalysingmodechoicesintheVictorianIntegratedSurveyofTravelandActivity(VISTA)travelsurveys.Duringestimation,theparameterwasquitestable,i.e.fairly
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independentfromfunctionalformtested”.IacceptthisasreflectingsomeofthepreferencesinVictoriaforparticularmodes.
Timeweightforwaitingtime(0.85).Ifoundthisvaluelowerthanmyexpectation(range1.0to2.5).VLC’sresponsewas“thevalueusedwasestimatedbyanalysingmodechoicesintheVISTAtravelsurveys.Duringestimation,thevaluewasquitestable.VLChasfoundaverysimilarvalueusinghouseholdtravelsurveysinSydney.Thelowfactorheremightberelatedtothecalculationofwaittimeitself(whichisbasedonhalftheheadway)”.Iacceptthatusinghalfoftheheadwaymaybeoverstatingwaitingtimeasmanypeoplewouldaimtobeatthestop/stationafewminutesbeforetheserviceisdueandthereforewaitforlessthanhalfoftheheadway.
Transferpenalties.Thesewere,inmyviewratherhighvalues,above7to12minutesIwouldexpect.VLCagreedthat“thesearehigherthanisoftenassumed.Again,theywereestimatedbyanalysingmodechoicesintheVISTAtravelsurveys.Again,thevalueswereconsistentlyaroundthisorderofmagnitudeduringestimation.ItmayreflectthatpeopledonotlikeinterchanginginAustraliancities”.Iacceptthisasmyownexperienceismostlybasedoncitieswithdenserpublictransportsystemsthathavebeenoperatingforalongtimeandtravellersmaybeaccustomedtointerchange.
Overall,IamsatisfiedthatthecombinationofparametersresultsinadefensiblemodelthatseemsareliablesourcefortheestimationofimpactforanEES.
4.6. Forecasting assumptions
Thefollowingforecastinghorizonswereconsideredinadditiontothe2016baseyear:
• 2026BaseCase(noproject)
• 2026NorthEastLinkProjectscenario
• 2036BaseCase(noproject)
• 2036NorthEastLinkProjectscenario
VLChasprovidedtablesdescribingwhathasbeenassumedintheBaseCaseinrespectofgrowthandnetworkchanges.Theseareextensive,comprehensiveanddetailed.IamnotinapositiontocommentonwhetherthelistofprojectsandexpectedgrowthisconsistentwithcurrentthinkinginVictoria.Icanconfirmthatthelistsarelongandapparentlyexhaustiveandmoredetailedthanmanyotherstudiesofasimilarnatureandscope.
Sensitivitytestswereundertaken,reportedbelow,toensurethattheforecastsproducedusingtheseassumptionsandthemodelwerereasonable.Thisisanimportanttestinforecastingmode.
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4.7. Results
Theresultsarereportedextensivelyinthemaintextaswellasindetailedtables.ItisinterestingtounderstandwheretheadditionaldemandcomesfromandthisisshowninthefigurebelowextractedfromtheSummaryReport.
Figure11NELYarraRivercrossingvolumesattractedtotheproject.Source:Figure5.3fromSummaryReport
Thefigureshowsthatacrossthisscreenline,onethatdoesnotinterceptthenewBusRapidTransitfacility,shiftsinmodecontributeabout2%totheflowonNELandre-distributionoftripsanother10%.Themostsignificanteffectis,ascanbeexpected,thechangeinroutetousethetunnelandmanagedfreewayevenwhentolled;88%ofthedemandonNELisfromre-assignment.
VLCprovidesseveralfiguresdepictingfromwhichoriginstowhichdestinationstripsuseNELandtheyshowreasonablecaptureareasprovidingadditionalconfidenceinresults.
4.8. Realism of results
Itisalwaysdesirabletocheckwhethertheresultsofamodelarerealisticenoughtoprovideconfidenceintheresults.Realismisusuallytestedagainstexpectations.Someoftheseareintuitive.However,mostofthetimeitispossibletocomparetheimplicitelasticitiesinthemodelwithobservedelasticitiesobtainedfromobservations.Itisgenerallyrecognisedthatitisoftendifficulttoisolatedanelasticityintherealworldwhenmanyotherfactorsarechangingsocareshouldbetakenwhenextractingandinterpretingthem.
ThishasbeentheapproachadoptedbyVLCandithasusedanappropriatemeasureofelasticityεthatismorereliableacrossarangeofindependentvariables:
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∈=log 𝐷𝑒𝑚𝑎𝑛𝑑 𝑎𝑓𝑡𝑒𝑟 − log (𝐷𝑒𝑚𝑎𝑛𝑑 𝑏𝑒𝑓𝑜𝑟𝑒)𝑙𝑜𝑔 𝑣𝑎𝑙𝑢𝑒 𝑎𝑓𝑡𝑒𝑟 − log (𝑣𝑎𝑙𝑢𝑒 𝑏𝑒𝑓𝑜𝑟𝑒)
Thiscomparesthedemand,letsaytraffic,beforeandafterachangeinoneofthevaluesthatareassumedtoaffectdemand,forexamplefuelprice.
VLCreportsthemodelelasticitiestoanumberofindependentvariablechangesinthemodelandconcludesthatthemodelpassesarealismtests.RatherthanrepeattheresultshereIcommentonlyonsomeparticularvalues.
ThemodeldisplaysanelasticityofVehicleKilometrestravelledtofuelpricesof-0.33.Thisisatextbookvaluethatisoccasionallyobservable.Thereisaverylowelasticityforcartipstofuelcosts-0.06,asonecouldexpect.
Theaveragepublictransporttripselasticitytopublictransportfaresimplicitinthemodelis-0.20,abitlowerforpeakperiodsandhigherintheoff-peakascanbeexpected.ThisisslightlylowerthanLondonbutareasonablevalueintheacceptablerange.
Overall,Iconcurwiththeviewthatthemodelproducesrealisticresults.
4.9. Sensitivity analysis
ElevensensitivitytestswereundertakenfortheEESfor2036.
Thesewere:
• 1and2:Highandlowpopulationandlandusegrowthscenarios;• 3aproject-specificlandusescenario,assumingpersonsorbusinesseswhorelocate
totakeadvantageoftheproject.• 4and5:20%increase/decreaseinthetollpriceontheNELproject;• 6reducingwillingnesstopaytollsbycommercialvehicles(CV)byhalvingtheir
impliedvalueoftimesavings• 7:Extendingtheexistingnortheasttruckcurfewsto24-houroperation;• 8:AssessmentofanalternativeNorthEastLink/ManninghamRoadinterchange
layout;• 9:E6freewaycommencingattheM80RingRoadbetweenDaltonRoadandPlenty
Road,andterminatingattheHumeFreewaynorthofDonnybrookRoad;• 10:OuterMetropolitanRing(OMR)roadcommencingattheM80RingRoad
betweenDaltonRoadandPlentyRoad,andterminatingatthePrincesFreewayWestnorthofLittleRiverRoad
• InclusionofAlphingtonpapermillandtheGasandFuelredevelopmentsites.
Theresultsaresummarisedbelow:
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SensitivityTest%DifferenceNELVolumes
1 HighLandUse +5%
2 LowLandUse -5%
3 Projectspecificlandus Lessthan-1%
4 +20%TollPrice -4%
5 -20%TollPrice +4%
6Reducedwillingnesstopaycommercialvehicles
-2%forCVs
7 NorthEastTruckCurfew Lessthan1%
8 ManninghamRoadInterchange Lessthan-1%
9 E6FreewayProject +5%
10 OMRRoad 5%
11 Inclusionofredevelopments Lessthan-1%
Table4Sensitivitytests.SourceTable5.3TransportModellingSummaryReport
Thereisarelativelylowsensitivitytotollprice,consistentwithrelativelyhighImpliedValuesofTimeandthetimesavingsofferedbyNEL.Irecognisethatinthecaseoftollroadstherearenouniversalelasticitiesthancanbeusedforcomparison.Thisisbecausetollroadtrafficisheavilyinfluencednotjustbywillingnesstopayofpotentialusersbutmoresignificantlybythetimeonthealternativeroutes.Inthiscase,itisclearthatNELofferssignificanttimesavingstoamajorproportionofthedemandintheNorthEastofMelbourne.Thisexplainswhyanelasticityvaluetotollpricethatwouldappearlowcomparedwithotherexistingtollroadsisineffectlikelyinthiscase.
Allotherresultsinthesensitivitytestsarewithintherangeofmyownexpectations.
Ialsorequestedthegraphicaldepictionoftraveltimesavingsachievedbysomekeytrips.IreceivedfromVLCasetofgraphshowingdifferentrangesoftraveltimesavingestimatesfor2036.Thesearereproducedbelow.Thefiguresshowthattimesavingsofzerotofive
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minuteswillbeincurredfairlybroadlyacrossthenetwork,whichimpliesthescaleofgeneralroadnetworkdecongestionbenefitsprovidedbytheproject.Timesavingsoffiveminutesormoretendtobeconcentratedaroundtheprojectitself,withtheverylargetimesavings(over20minutes)situatedneareachendoftheproject.
Figure12AMpeaktraveltimebenefitsforcars,byoriginandtimesavingintervals(minutes),2036Source:SpreadsheetprovidedbyVLC
VLCalsoprovidedagraphshowingthedistributionofdailytimesavingsasfollows
Figure13Distributionofdailytimesavings(minutes),2036Source:SpreadsheetprovidedbyVLC
Whilethereisawidedistributionoftimesavingsthebulkwillbeachievedintherange2to10minutes.Nevertheless,therearemanyvehiclesbenefitingfromtimesavingsbetween15and30minutes.
TheseresultsgivemeadditionalassurancethatthemodelisbehavingaswellascanbeexpectedforthepurposesofanEES.
0to5mins 0to5mins
15+mins10to15mins
0%1%2%3%4%5%6%7%8%9%10%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Prop
otion
ofDa
iilyTime
Savin
gs
TimeSaving(mins)
DailyTravelTimeSavingsforNELUsers(cars)
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5. Dealing with uncertainty
5.1. Approach adopted
Therearethreemainsourcesofuncertaintyintrafficforecasting:datauncertaintyaboutthefuture,modeluncertaintyfromitscomponentsandparameters,andscenariouncertainty,disruptionsthatmaymaterialiseinthefutureandarenotaccountedforinthefuturedata.Ideally,thetreatmentofuncertaintymustcoverallthree.
VLChasadoptedanapproachthatcoversthesethreemainsourcesofuncertaintyusingthreecomplementarymethods.
ThefirstmethodistoundertakeadetailedsensitivityanalysisoftheprojectionsresultingfromtheapplicationofthemodelunderconditionsthatdifferfromtheCoreCase.Thisapproachhasbeenundertakeningreatdetailinthiscaseanditcovered,asreportedinTable4above,themostimportantelementsthatmayinfluenceresults.Theresultsaresatisfactoryandcomprehensive.
Asecondapproachistoprovide,indetail,howtheprojectedtrafficresultsfromacombinationofdifferentcontributions,forexamplechangesinroute,modeanddestinationaswellastheimpactfromlanduse.ThisapproachwasalsofollowedbyVLCasreportedaboveandprovidesanotherwayofunderstandingtheimpactoftheseelementsofthemodeluncertainty.VLChasprovidedseveral“waterfall”figuresshowinghowthesecomponentscontributetothefinalprojections.
Thethirdapproachdealwithuncertaintyduetopotentialtechnologydisruptionsthroughscenarioanalysis.Despiteitsdifficulty,thisisinmyview,themostappropriateapproachtodealingwiththistypeofuncertainty.
5.2. Scenario analysis
FollowinganexchangeofviewsVLCdecidedtoimplementaScenarioPlanningapproachtoconsiderthefuturedisruptioncausedbytwotechnologicalinnovations:MobilityasaServiceandConnectedandAutomatedVehicles(alsoknownasAutonomousVehicleswhentheyreachautomationlevels4and5).TheconceptofMobilityasaService(MaaS)includesawiderangeofon-demandservicesrangingfromsimplepay-as-you-goUber-likeservicetocomplexmulti-modalarrangementsperhapswithamonthlysubscription.ConnectedandAutomatedVehicles(CAV)willhaveanumberofimpactsprincipallyonsafety,willingnesstopayandfreewaycapacities17.
17 Willumsen, L. (2018) From When to What should happen to CAV and MaaS. Presented at the European Transport Conference 2018, Dublin.
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OthertechnologydisruptionslikeElectricVehicles(EV),theInternetofThings(IoT),DistantPresenceandArtificialIntelligencearelesslikelytoaffecttheimpactofNEL.
InfrastructureVictoriahasundertakenareviewofthepotentialimpactsofEVs,CAVsandMaaS18.ZeroEmissionVehiclesarealreadyprovidingbenefitsinVictoriaandelsewhere.Theyarebecomingcheapertorunandproducenoemissionsatthetailpipethusreducingtheimpactontheenvironment.Theywillnothaveamajorimpactontrafficandthereforetheyareunlikelytogenerateenvironmentaldisbenefits.
TheIoTwillprovideadditionalinformationontheperformanceandconditionofmajorinfrastructure,buildingsandequipmentthusreducingtheneedforregularinspectionandwillnotaffecttravelsignificantly.DistantPresenceislikelytoimprovebutstillmostmeetingsandnegotiationsarelikelytorequirephysicalpresencetobeeffective.
ArtificialIntelligencewillcertainlyaffecttheworldofworkbutitsimpactontravelisuncertain;Iexpectittohaveaverylowimpactontravel,inparticularthemovementofcommercialvehicles,asignificantsourceofenvironmentalbenefitsinthecaseofNEL.Therefore,themainconcernsareMaaSandCAVsandtheeconomy.
VLCthereforedecidedtodevelopandrunthreedifferentscenariosapplicabletotheforecastingyearof2036.ThisisasensibledatetoconsidertheimpactofCAVsthatbythenwillconstitutealowbutimportantfractionofthefleet.
5.3. Scenario 1 CAV
ThisconsiderstheimpactofCAVsontrafficandNEL.VLCrestricteditsanalysistoAutomationLevel4butthisisnotcriticalatthisstage.VehicleswithLevel4automationwilldrivethemselveswithouthumaninterventionwithintheir“operationaldomain”.Onlywhentheyventureoutsidetheiroperationaldomaintheywillrequirehumanintervention.ItisverylikelythaturbanareaslikeMelbourneandfreewayswillbetheirnaturaloperationaldomains.Inanycase,VLChasassumedthatCAVswillhavethefollowingcharacteristicsinthewholeareaofthemodel:
• Theywillconstitutethe20%ofthetraffic(VLCstatestheywillbe20%ofthefleetbutinpracticetreatthemas20%ofthetrafficasCAVwillcovermorekilometresperdaythanconventionalvehicles).Itisassumedthat20%ofthecartripsinthemodelwillshifttoCAVtripswithnewcharacteristics.
• HalfoftheCAVswillbeprivatelyownedandusedasabettercar.TheywillincurinadditionalVehicleKilometresTravelled(VKT)astheymaybesenttopark
18InfrastructureVictoria.AdviceonAutomatedandZeroEmissionsVehiclesInfrastructure.October2018.
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elsewhere,whatVLCcalls“deadrunning”.Theywillserve10%ofthepreviouscartrips.
• Theotherhalfwillbepartofataxilikefleetandservetheremaining10%ofcartrips.
• CAVuserswillbeabletoundertakeotheractivitieswhiletravellingandthereforetheirValueofTimewillbereducedbysome10%.
• WhentheCAVshareoftrafficis20%theywillincreasethecapacityoffreewaysby10%astheycankeepshorterheadwayssafely.TherewillbenoimpactonotherroadsasjunctionscontroltheircapacityandthereCAV’sadvantagewillbeminimalatthisshareoftraffic.
• VLCfurtherassumes,followingInfrastructureVictoria’sadvice,that20%ofthecarfleetwillbeelectricandthispenetrationwasadoptedforCAVsandconventionalvehicles.Inotherwords,20%oftheCAVfleetareEVsand20%oftheconventionalvehiclesarealsoEVs.
• VLCalsoassumesthatCAVswillgenerateadditionaltripsasthosepreviouslyunabletodrivewillbeabletousethem.VLCallowsa10%increaseindiscretionarytripsonthisaccount.Althoughitcouldbearguedthattherewillbesomeadditionaltripstoworkaswell,IconsiderthisareasonableandvalidapproximationtotheinductioneffectofCAVs.
TheseassumptionswerethenbuiltintothemodeltoestimatetheirimpactonNELin2036.
5.4. Scenario 2 MaaS
ThisistheMobilityasaServicescenarioanditfocussedontheimpactofride-sharingservices.Thisisavalidchoiceforanumberofreasons.Firstofall,ofallthepossibleMaaSservicesride-sharingistheonewiththegreatestimpactontrafficandcongestion.Second,thesingleuse,Uber-likeservicesarelikelytoincreasecongestion(becauseofemptyordeadrunning);therefore,policieswillputinplacetorestricttheirwidespreadgrowthandtointernaliseitsexternalities.
Thisscenariorequiredsomeadditionalassumptionsaboutthetypeofvehicleusedandmodalshifts.IndiscussionwithVLCthedecisionwastakentobasetheseontheprojectthattheInternationalTransportForum(ITF,partofOECD)undertookforAuckland,NewZealand.TheconditionsaresomewhatsimilartoMelbourneandcertainlymoreappropriatethanthoseofLisbon,DublinandHelsinki,theothercitiesstudiedbyITF.
Thefollowingassumptionswereadopted:
• Ride-sharingwillbeadoptedby20%ofthecurrentcartripsby2036.• Ride-sharingMaaSwillreduceprivatevehiclestripsasvehicleoccupancywillbe
higher.
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• 16%ofthesetripswillbeend-to-endinaMaaSvehicleand4%willuseMaaSasfeedertopublictransport,forexamplerailorBRT.
• Ride-sharingserviceswillbeprovidedbytwotypesofvehicles,16seatsminibusesand8seatstaxis.Theaverageoccupancyturnedouttobe6.7personpervehicleandtheaveragevehiclesizeis1.3passengercarunits(pcu).
• TherewillbeanincreaseinVKTduetotheneedtodetourtopickupanddroppassengersenroute.
ThereisalsoanimpactonwaitingandwalkingtimesbutthesedonotimpactdirectlyontheenvironmentalimpactoftrafficandNEL.
5.5. Scenario 3 CAV + MaaS
Inthisscenarioboththeimpactofride-sharingMaasandCAVsareincluded.InthiscasetheassumptionsareinpracticeasuitablecombinationofthoseofScenario2plustheideathatwhenMaaSservicesdonotrequiredriverstheiroperatingcosts,andthereforefares,willbesignificantlyreduced.Thiswillresultinadditionalindemandfortheseservices.Thiswashandledwiththeassumptionofa-0.02elasticityinthechangesofwhatisessentiallyapublictransportservicecapturingcartrips.
5.6. Results
Theresultsfromthisexerciseweregenerallyascanbeexpected.Scenario1producesanincreaseintrafficthatisnotbalancedbytheincreaseincapacityonfreeways.AsummaryofresultsisshowninthenextTable:
Table5ResultsfromScenarioanalysis
CAV MaaS CAV+MaaSPersontrips 25,813,000 26,118,000 25,813,000 26,118,000Difference -- 1% 0% 1%Personcartrips 19,086,000 19,453,000 15,501,000 15,604,000Difference -- 2% -19% -18%PersonPublicTransporttrips 3,221,000 3,166,000 3,812,000 3,755,000Difference -- -2% 18% 17%VehicleKilometresTravelled 223,072,000 235,073,000 208,271,000 218,310,000Difference 5% -7% -2%VehicleHoursTravelled 5,005,000 5,365,000 4,427,000 4,671,000Difference -- 7% -12% -7%ChangeinNELTraffic(2way) 9,000 -13,000 -6,000Percentchange 7% -10% -5%
ScenarioCoreCaseIndicator(Daily)
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TheimpactofCAVs(Scenario1)ispositiveintermsoftrafficonNELbutnegativeintermsoftheincreaseinVehicleKilometresandHourstravelled,plus5%and+7%respectively.Theincreaseinfreewaycapacityarenotsufficienttocompensatetheadditionalnumberofvehicletrips,inparticularemptyones.
Scenario2,ride-sharingMaaS,inturn,reducestrafficinNELbyabout10%buthaspositiveeffectsonthewholecitywitha19%reductionincartripsandan18%increaseinpublictransporttrips;thisresultsina12%reductioninhourstravelledand7%reductioninvehiclekilometrestravelled.
Scenario3,thecombinationofCAVandMaaS,resultsina18%reductionincartrips,17%increaseinpublictransporttripsanda7%reductioninhourstravelled.ThereductioninNELtrafficisonly5%.
Overall,IamsatisfiedthattheScenarioPlanningAnalysisundertakenbyVLCinthiscaseissufficienttoprovideabetterunderstandingofhowthesetechnologydisruptionsmightaffecttrafficinNELanditsareaofinfluenceandsatisfiestheadviceprovidedbyInfrastructureVictoria.
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6. Conclusions
Havingundertakenareviewofthemodel,assumptions,thecalibrationandvalidationtasks,itsparameters,thesensitivityandreasonablenesstestsandconvergencelevelsIconcludethatthemodelisappropriateforuseinthedevelopmentoftheEnvironmentalEffectsStatement.
Ihavebeenabletoobserveminordeparturesfrommyexpectationsinthemodel.However,indiscussionwiththemodellingteamtheseissueshavebeenclarifiedandIamsatisfiedthattheycorrespondtolocalconditionsandthatthemodel,overall,performswell.
ThemodelproducesreliableandconsistentresultsthatcanserveasasolidbasefortheEES.
Thetreatmentofuncertaintyusingdifferentapproaches,sensitivityanalysis,disaggregationofcontributorstotrafficandScenarioPlanningissufficientlythoroughanddetailedtogiveconfidencethattheseriskscanbetakenintoaccount.