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


5.5. Scenario3CAV+MaaS...........................................................................................................................385.6. Results...........................................................................................................................................................38

6. CONCLUSIONS.........................................................................................................................................40


1

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;


2

• 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


3

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


4

29 TransportInfrastructureVictoriaEvidenceReportonautomatedandzeroemissionsvehicles.

IalsoconsideredsimilardocumentsproducedintheUKDepartmentofTransport,inparticularWebTag,thatcomplementtheguidelinesabove.


5

2. The North East Link Project

2.1. Context

IthasbeenarguedthatNorthEastMelbournelackssufficienthighqualityinfrastructureandthatthisresultsinheavytrafficusingwhatare,ineffect,arterialaccessroadstoresidentialareas.Moreover,thereisevidenceofnotjustcongestionbutalsosignificantvariabilityontraveltimestryingtobridgethisgapbetweentheEasternFreewayandtheM80.

TheNorthEastLinkwillcompletethe“missinglink”inMelbourne’sorbitalfreewaynetworkandestablishacontinuousfreeway-standardorbitalroadaroundMelbourne,betweenAltonainthewestandFrankstoninthesouth.

Figure1ContextfortheNorthEastLink.

Source:NELBusinessCase


6

2.2. North East Link

TheproposednewNorthEastLinkwillbeginontheEasternFreewayatSpringvaleRoadbeforeconnectingviaanewroadwaytotheM80RingRoadatGreensborough.Themainroadwaywillextendapproximately11kilometresfromtheeasternendoftheM80totheEasternFreewayatBulleenandwillbetolled.ThenorthernsectionofthenewlinkwillrunatsurfacebeforedescendingintoacuttingnearWatsoniaRoadandintotunnelsatLowerPlentyRoad,andthentransitioningtoaviaductstructurejustnorthofKoonungCreektoconnecttotheEasternFreeway.ConnectionswillbeprovidedbetweenthefreewayandGreensboroughBypass,GrimshawStreet,LowerPlentyRoadandManninghamRoad.

Figure2TheNorthEastLinkSource:NELProjectDescriptionforEESSpecialists-SpecialistsIssue4

InfrastructureVictoriaidentifiedtheNorthEastLinkasahighpriorityinfrastructureprojectforthestateinits30-YearInfrastructureStrategy,releasedin2016.InfrastructureVictorianotedthatthelinkwillenhanceaccesstomajorsuburbanbusinessandemploymentcentres,improveorbitalroadconnectivityacrossMelbourneandboostthecapacityofthecity’sfreightnetwork.


7

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


8

Figure3ImpactofNELovercurrentYarrarivercrossingscreenlineSource:Figure25inAppendixRTransportModellingReport

Justremoving15,000trucksonarterialroadsshouldproduceenvironmentalbenefitsthatwhencoupledwiththeotherimpactswillbeverysignificant.Confidenceintheeventuationoftheseenvironmentalbenefitsdependsonthequalityandexpectedreliabilityofthetransportmodelusedtoforecastandestimatetheseimpacts.

Themainelementofmyownassignmentistopeerreviewthismodeltoprovideadditionalsupport,orotherwise,totheestimationofimpactsfortheEnvironmentEffectsStatement.


9

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/


10

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


11

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.


12

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


13

• 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.


14

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


15

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


16

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.


17

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.


18

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


19

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


20

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


21

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.


22

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


23

apparentlyexhaustive;infactlongerandmoredetailedthanmanyotherstudiesofasimilarnatureandscope.

Thefollowingforecastinghorizonswereconsideredinadditiontothe2016baseyear:

• 2026BaseCase(noproject)

• 2026NorthEastLinkProjectscenario



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.


24

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.


25

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:


26

Figure9Sixscreenlinesforvalidation.Source,Figure2.2fromVLC’sTransportModellingSummaryreport

Theresultsareshowngraphically.Forexample,fortheAMpeakperiod:

Figure9ScreenlinesvalidationAMPeak.Source,Figure4.8fromVLC’sTransportModellingSummaryreport


27

AscanbeseenonlytwocasesarejustoutsidetheacceptableboundsandthisIconsideracceptableforamodelofthisnatureandcoverage.Indeed,theresultsforthePMpeakandthefulldaysarebetter.

VLCalsovalidatedthemodelagainstobservedtraveltimes.Thisisanimportantanddifficulttestasseveralfloatingvehiclerunsarenecessarytogetareasonablerepresentationoftraveltimesalongaroute,inparticularwhenjunctionsarecontrolledbytrafficlights.Thesecomparisonsarebestmadeusingplotsofcumulativetraveltimesalonglongerroutestoavoidtoomuchvariabilityonshorterlinks.Thereisanextensivesetofsuchplots,somedisplayingbettermatchthanothers.IconsiderthemostcriticalplotsthosealongtheGreensboroughRoad,RosannaRoad,BulleenRoadcorridor.Acoupleofexamplesareshownbelow.

Figure10Twocumulativetraveltimecomparisons.Source,Figure4.13and4.14fromVLC’sTransportModellingSummaryreport


28

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.


29

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


30

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:





VLChasprovidedtablesdescribingwhathasbeenassumedintheBaseCaseinrespectofgrowthandnetworkchanges.Theseareextensive,comprehensiveanddetailed.IamnotinapositiontocommentonwhetherthelistofprojectsandexpectedgrowthisconsistentwithcurrentthinkinginVictoria.Icanconfirmthatthelistsarelongandapparentlyexhaustiveandmoredetailedthanmanyotherstudiesofasimilarnatureandscope.

Sensitivitytestswereundertaken,reportedbelow,toensurethattheforecastsproducedusingtheseassumptionsandthemodelwerereasonable.Thisisanimportanttestinforecastingmode.


31

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:


32

∈=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:


33

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


34

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)


35

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.


36

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.


37

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.


38

• 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)


39

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.


40

6. Conclusions

Havingundertakenareviewofthemodel,assumptions,thecalibrationandvalidationtasks,itsparameters,thesensitivityandreasonablenesstestsandconvergencelevelsIconcludethatthemodelisappropriateforuseinthedevelopmentoftheEnvironmentalEffectsStatement.

Ihavebeenabletoobserveminordeparturesfrommyexpectationsinthemodel.However,indiscussionwiththemodellingteamtheseissueshavebeenclarifiedandIamsatisfiedthattheycorrespondtolocalconditionsandthatthemodel,overall,performswell.

ThemodelproducesreliableandconsistentresultsthatcanserveasasolidbasefortheEES.

Thetreatmentofuncertaintyusingdifferentapproaches,sensitivityanalysis,disaggregationofcontributorstotrafficandScenarioPlanningissufficientlythoroughanddetailedtogiveconfidencethattheseriskscanbetakenintoaccount.

North East ink EES - Transport Model Peer Review Report … · 2020-02-07 · Infrastructure...

Documents

Transcript of North East ink EES - Transport Model Peer Review Report … · 2020-02-07 · Infrastructure...