research and education from a smart campus transit laboratory

USDOTRegionVRegionalUniversityTransportationCenterFinalReport

ReportSubmissionDate:October15,2009

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NEXTRANSProjectNo006OY01

ResearchandEducationfromaSmartCampusTransitLaboratory

By

MarkR.McCord,PrincipalInvestigatorProfessorofCivilandEnvironmentalEngineeringandGeodeticSciences

[email protected]

and

RabiG.Mishalani,Co‐PrincipalInvestigatorAssociateProfessorofCivilandEnvironmentalEngineeringandGeodeticSciences

[email protected]

and

PremGoel,Co‐PrincipalInvestigatorProfessorofStatisticsOhioStateUniversitygoel.1@osu.edu

DISCLAIMER

PartialfundingforthisresearchwasprovidedbytheNEXTRANSCenter,PurdueUniversityunderGrant

No.DTRT07‐G‐005oftheU.S.DepartmentofTransportation,ResearchandInnovativeTechnologyAdministration(RITA),UniversityTransportationCentersProgram.Thecontentsofthisreportreflecttheviewsoftheauthors,whoareresponsibleforthefactsandtheaccuracyoftheinformationpresented

herein.ThisdocumentisdisseminatedunderthesponsorshipoftheDepartmentofTransportation,UniversityTransportationCentersProgram,intheinterestofinformationexchange.TheU.S.Governmentassumesnoliabilityforthecontentsorusethereof.

USDOTRegionVRegionalUniversityTransportationCenterFinalReport

TECHNICALSUMMARY

NEXTRANS Project No 006OY01Technical Summary - Page 1

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NEXTRANSProjectNo006OY01 FinalReport,October2009


Introduction

Forapproximatelyadecade,membersoftheprojectteammonitoredOhioStateUniversity(OSU)

campusbusesservingfourmillionpassengersannuallywitha“homemade”GPS‐basedautomaticvehiclelocation(AVL),communications,andinformationsystemcalledBLIS(BusLocationandInformationSystem).Wesuppliedregular,system‐wideperformancereportstoOSU’sTransportation

andParking(T&P)CampusAreaBusService(CABS),respondedtospecialrequestsfromCABS(generallyresultingfromcustomercomplaintsaboutservice),andconductedresearchstudiesthatexploitedtheBLISdatawearchived.Theseresearchandoutreachactivities,alongwiththeBLISarchiveddata,formed

thefirstgenerationoftheOSUCampusTransitLab(CTL).

ThroughajointeffortofT&P,theOSUCollegeofEngineering,OSU’sDepartmentofCivilandEnvironmentalEngineeringandGeodeticScience,andCleverDevices,Inc.,BLISisbeingreplacedwithan

advanced,commercial‐grade“SmartBus”system.CleverDeviceshasequippedlargepublicbusagencieswithsuchsystems,butthisisthecompany’sfirstimplementationforacampusbusservice.

Thissubstantiallyupgradedassetandthepartnershipssurroundingitoffertheopportunitytodevelop

theCTLintoaunique,valuable,andrecognizedlivinglabthatcansimutaneouslysuportinnovativepublictransportationresearch,education,andoutreach.Obtainingthisstatuswillrequiresustained

developmentthatproducesbenefitstothemultiplecollaboratingstakeholdersalongthewaywhilekeepingthemawareofthelongtermpotential.Toassistinthissustaineddevelopment,itisnecessarytoconductamulti‐facetedeffortthatimplementsandmanagestheunderlyingphysicalandinstitutional

infrastructureoftheSmartBussystemwhilesimultaneouslyproducingresearch,educational,andoutreachresultsthatexploitSmartBusdataandtheCTL.

Findings

Duringthereportingperiod,we

• contributedtotheimplementationofthe“SmartBus”systemonOSUcampusbuses:This

systemisnowinstalled.Bugsarestillbeingidentifiedandeliminated,butthesystemis


functionallyinanoperationalmode,andearlyindicationsofuserandoperatorsatisfactionappeargood.

• developedprocessestomakeSmartBusautomaticvehiclelocation(AVL)andautomatedpassengercounter(APC)dataavailableinusefulformatsforresearch,education,andoutreachapplications:Furtherrefinementswillberequiredforfutureapplications,butwenowhavethe

meanstopre‐processlargequantitiesofAVLandAPCdataintoformatsthatcanbeusedbymultipleusersforavarietyofpurposes.

• pre‐processedafirstwaveofAVLandAPCdataandusedthedataformultipleapplications:We

usedoursoftwaretopre‐processSmartBusdataintodatathatservedasinputtoproduceempiricalmeasuresusedintheprojectreportedonhereandsupportedseveraltasksonaFederalTransitAdministrationproject.

• conceivedofandvalidatedinnovativemeasuresindicatingbuspassengertravelpatternsthatarederivedformAVLandAPCdataandwhichcanbemonitoredonanongoingbasisincollaborationwithOSUT&P:APCdatacanbeusedtoestimatebuspassengerorigin‐destination

(OD)flows.BecausetheAPCdataarereceivedonaregular(daily)basis,theODflowsandmeasuresthatcanbederivedfromtheestimatedODflowsandtheAVLdata(e.g.,ODflowsbytime‐of‐dayandday‐of‐week,passengertripdistancedistributions,expectedtimeonbus

conditionalonboardingoralightingstop)canbemonitoredovertime.ThroughfieldtestsandfamiliaritywithCTLroutes,wepartiallyvalidatedthemeasuresweproducedfromtheSmartBusdata.

• conductedmultipleresearchstudiesrelatedtotheuseofbusAVLandAPCdata:WeimprovedamethodwehadpreviouslydevelopedformatchingAVL‐basedbustrajectoriestobusschedules

byincorporatingconsiderationsofbusoperatingpolicies.EmpiricalresultsusingCTLdatademonstratedthesuperiorityoftherefinedmethod.WealsodevelopedaninnovativemethodologicaldesignthatweappliedtoempiricaldatacollectedonaCTLroutetoassessthe

performanceofaneasy‐to‐implementprocedureforestimatingbuspassengerODflowsfromavailableAPCdata.Wefoundthattheprocedureworkedsurprisinglywellinourstudy.Inaddition,webuiltasimulationtoolbasedononeoftheCTLroutesandappliedthetoolto

comparetheperformanceofdistance‐basedtotime‐basedAVLdatasamplingintermsoftheaccuracyofestimatingbusdwelltimes.Wefoundthatdistance‐basedsamplingperformedmarkedlybetterthantime‐basedsamplinginthisapplication.

• implementednewmodulesfocusedontheCTLAVLandAPCdataintwotransportationcourses:Onecourseisalarge(over100students),requiredcourseforCivilEngineeringstudents,themajorityofwhomarenon‐transportationmajors.Thesecondcourseisasmaller(approximately

15students),electivecourseforgraduatestudentswithatransportationfocus.ThenewmodulesandquantitativeexercisesusingCTLdataexposedthestudentstotheCTLandtotheadvantagesofAVLandAPCtechnologiesbyaddressingpracticalapplicationsinafamiliarand


observablesetting.TheapparentbenefitstothestudentsandtotheinstructorsmotivateustodevelopadditionalwaystouseCTLdataandapplicationsincourses.

• conductedafirstwavesurveyofcampustransitbususers’andnonusers’perceptionsofOSU’sCABS:Themotivationforconductingthesurveywastodevelopbenchmarkinformationforassessingchangesinperceptions,attitudes,andawarenessofOSUbustransitservicethatmay

beattributabletotheimplementationoftheSmartBussystem.Responseratesbydemographicgroupwereverygood,andweplantoconductthesecondwaveafteruseoftheSmartBus‐basedpassengerinformationsystemknownasTRIP(TransportationRouteInformation

Program)enterssteadystate.Nevertheless,someofthefirstwavesurveyresults,suchasperceptiontowardthevariouselementsofCABSbydemographicgroup,arealreadyproducinginformationofinteresttoT&Padministrators.Moreover,otherresults,suchastherecognition

ofthepositiveimpactofabussystemontheenvironmentandonreducedtraffic,andthedifferencesinthisrecognitionamongdifferentdemographicgroups,areofgeneralinteresttothetransitandmultimodaltransportationcommunity.

Recommendations

Webelievethatthemulti‐thrustapproachweundertookduringthereportingperiodwasproductiveincontributingtothesustaineddevelopmentthatwillestablishtheOSUCampusTransitLab(CTL)asaunique,recognized,andvaluableinfrastructureforresearch,education,andoutreach.Additional

developmentwillcontinuetoberequired,andwebelievethatitwouldbebeneficialtoproceedinasimilarlymulti‐facetedapproachdevotedto

• developingthemeanstocollect,process,andmakeAVLandAPCdataaccessibletomultipleresearchersandeducatorsonaroutinebasis,

• usingthedatatosupportmultiplepublictransportationrelatedresearchandeducational

activitiessponsoredinsideandoutsideofNEXTRANS,

• conductingadditionalresearchstudiesrelatedtoimprovedbustransitplanningandoperationsthatcanoccurthroughinnovativeusesofthesedata,and

• ongoingmonitoringofthebussystemincollaborationwithOSUT&Ptoprovidebenefitstomajorstakeholders(e.g.,toT&P,intermsofbetterunderstandingoftheserviceitisproviding,andtoCleverDevices,intermsofdevelopingnewproductsthatcanbederivedfromits

technologies),groundresearchandeducationalprojectactivitiesinactualoperations,andfosterthegenerationofnewresearchideas.


ContactsFormoreinformation:

MarkR.McCordPrincipalInvestigatorCivilandEnvironmentalEngineeringandGeodeticSciencesOhioStateUniversitymccord.2@osu.eduRabiG.MishalaniCo‐PrincipalInvestigatorCivilandEnvironmentalEngineeringandGeodeticSciencesOhioStateUniversitymishalani@osu.eduPremGoelCo‐[email protected]

NEXTRANSCenterPurdueUniversity‐DiscoveryPark2700KentB‐100WestLafayette,[email protected](765)496‐9729(765)807‐3123Faxwww.purdue.edu/dp/nextrans

NEXTRANSProjectNo006OY01


By

MarkR.McCord,PrincipalInvestigatorProfessorofCivilandEnvironmentalEngineeringandGeodeticSciences

[email protected]

and

RabiG.Mishalani,Co‐PrincipalInvestigatorAssociateProfessorofCivilandEnvironmentalEngineeringandGeodeticSciences

[email protected]

and

PremGoel,Co‐PrincipalInvestigatorProfessorofStatisticsOhioStateUniversitygoel.1@osu.edu

ReportSubmissionDate:October15,2009

2

DISCLAIMER

PartialfundingforthisresearchwasprovidedbytheNEXTRANSCenter,PurdueUniversityunderGrant

No.DTRT07‐G‐005oftheU.S.DepartmentofTransportation,ResearchandInnovativeTechnologyAdministration(RITA),UniversityTransportationCentersProgram.Thecontentsofthisreportreflecttheviewsoftheauthors,whoareresponsibleforthefactsandtheaccuracyoftheinformationpresented

herein.ThisdocumentisdisseminatedunderthesponsorshipoftheDepartmentofTransportation,UniversityTransportationCentersProgram,intheinterestofinformationexchange.TheU.S.

Governmentassumesnoliabilityforthecontentsorusethereof.

ACKNOWLEDGMENTS

TheauthorsacknowledgetheinvaluableinstitutionalsupportofMs.SarahBlouch,directorofOSU’s

TransportationandParkingServices(T&P),thetechnicalassistanceofMr.ChrisKovitya(T&P)andMr.MatthewBarber(DepartmentofCivilandEnvironmentalEngineeringandGeodeticScienceIT),and

severalhelpfuldiscussionswithProf.NigelWilson,Mr.JohnAttanucci,andDr.JinhuaZhaoofMassachusettsInstituteofTechnology,Prof.MarkHickmanofUniversityofArizona,andProf.PeterFurthofNortheasternUniversityrelatedtothegeneraldevelopmentandpotentialoftheCampus

TransitLabandtomethodologicalaspectsaddressedinthisreport.Prof.MarkHickmanisamemberoftheprojectadvisorycommittee.TheauthorsacknowledgethecollaborationwithandcontributionsofProf.YoramShiftanofTechnion,IsraelInstituteofTechnology(andUniversityofMichiganandTheOhio

StateUniversity,asVisitingProfessor)onthe“perceptionsandattitudessurvey”researchactivityofthisproject.TheyalsothankGraduateResearchAssistantsMr.ChengChen,Mr.YuxiongJi,Mr.JinjianLiang,Mr.PingboLu,Mr.BrandonStrohl,Ms.FanyuZhou,andMr.HongleiZhu.Inaddition,theauthors

acknowledgethefinancialsupportofferedbytheUTCprogramandTheOhioStateUniversity.Theviewsandopinionscontainedinthisreportarethoseoftheauthorsanddonotrepresenttheviews,opinions,

orpoliciesofanyotherindividuals,agencyorgroup.

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TABLEOFCONTENTS

LISTOFFIGURES 5

LISTOFTABLES 6

CHAPTER1.INTRODUCTION,PROBLEM,ANDAPPROACH 7

1.1.Introduction 7

1.2.Problem 8

1.3.Approach 8

CHAPTER2.METHODOLOGY 9

2.1.SmartBusinfrastructuredevelopment 9

2.2.Datapre‐processing 10

2.3.APCandAVL‐basedresearchandoutreachactivities 10

2.3.1.MatchingAVLdatatobusschedules 11

2.3.2.PerformanceassessmentofODestimationfromAPCdata 13

2.3.2.1Introduction 13

2.3.2.2TheIPF‐with‐nullbaseprocedure 14

2.3.2.3Designofempiricalstudy 16

2.3.3.Developmentandapplicationofbusoperationssimulation 20

2.3.3.1.Simulationstructure 21

2.3.3.2.Point‐to‐pointtraveltime 21

2.3.3.3.Dwelltime 22

2.3.3.4.Specialpointdelay 23

2.3.3.5.Validation 23

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2.3.3.6.Applicationofthesimulationprogram 24

2.3.4.Developmentofoutreachproducts 25

2.4.EducationalUseofCTL 27

2.5.Perceptionsandattitudessurvey 30

CHAPTER3.FINDINGS 33

3.1.Infrastructuredevelopment 33

3.2.Datapre‐processing 33

3.3.APCandAVL‐basedresearchandoutreachactivities 34

3.3.1.MatchingAVLdatatobusschedules 34

3.3.2.PerformanceassessmentofODestimationfromAPCdata 35

3.3.3.Developmentandapplicationofbusoperationssimulation 38

3.3.4.Developmentofoutreachproducts 39

3.4.EducationaluseofCTL 45

3.5.Perceptionsandattitudessurvey 46

3.5.1.Travelmodebehavior 46

3.5.2.Perceptionsandevaluationanalysis 47

CHAPTER4.CONCLUSIONS 52

REFERENCES 57

5

LISTOFFIGURES

Figure2.4‐1:StatementofCampusTransitLab‐basedassignmentintroducedinOSUcourse

CE570:IntroductiontoTransportationEngineeringandAnalysis,WinterQuarter2009 28

Figure3.3.3‐1:Averageabsolutedwelltimeerrors,acrosssimulationreplicationsandbusstops,asafunctionofspatialsamplingintervalortemporalsamplinginterval 39

Figure3.3.4‐1a:ExpectedpassengertraveltimebyCLSboardingstopdeterminedfromAPC‐derivedODmatricesandAVLdata 44

Figure3.3.4‐1b:ExpectedpassengertraveltimebyCLSalightingstopdeterminedfrom

APC‐derivedODmatricesandAVLdata 44

6

LISTOFTABLES

TABLE2.3.2‐1:Summaryof“procedures”usedtoproducetriplevelODvolumes 19

Table2.5‐1:CTLtransportationfirstwavesurveyresponserates 31

Table2.5‐2:Responseratescomparisonwithothersurveys 32

TABLE3.3.2‐1:Pair‐wiseperformancecomparisonsbetweenprocedures(numberoftrips

inwhicheachoutperformstheothers) 36

TABLE3.2.2‐2:RelativeperformanceRPsummariesacross10trips 37

Table3.3.4‐1:NormalizedODflowmatrixforOSUCampusLoopSouthrouteproducedfrom

1003APC‐derivedtriplevelmatricesusingtheIPF‐with‐null‐baseprocedure 41

Table3.3.4‐2a:Dissimilaritymeasuresforday‐of‐weekpairsof7‐10AMODmatrices 42

Table3.3.4‐2b:Dissimilaritymeasuresforday‐of‐weekpairsof2‐5PMODmatrices 42

Table3.3.4‐2c:Dissimilaritymeasures:7‐10AMvs.2‐5PMODmatricesonsamedayofweek 42

Table3.5‐1:Transportation‐to‐campusmodechoicesofsurveyrespondents 47

Table3.5‐2:Summaryofresponsesonperceptionandevaluationquestions 49

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CHAPTER1.INTRODUCTION,PROBLEM,ANDAPPROACH

1.1.Introduction

Approximatelyadecadeago,a“homemade”GPS‐basedautomaticvehiclelocation(AVL),communications,andinformationsystem–referredtoasBLIS(BusLocationandInformationSystem)atthattime–wasdevelopedfortheOhioStateUniversity(OSU)CampusAreaBusService(CABS),which

carriesfourmillionpassengerridersannually.BLISwasdevelopedandimplementedthroughacollaborationamongthreegroups:plannersandoperatorsatCABS,facultyandresearchersinCivilandEnvironmentalEngineering,andfacultyandresearchersinElectricalandComputerEngineering.The

thirdgroupwasprimarilyinterestedinthehardwareandtheintegratingsoftware,thesecondgroupwasmostlyinterestedineffectiveuseofthecollecteddataforserviceplanning,design,andmonitoring,andthefirstgroupwasinterestedinimprovedservicethroughtheprovisionofpassengerinformation

andtheidentificationandavoidanceofserviceanomalies.

Assuch,OSUresearchersacquiredfirsthandexperiencewiththeapplicationofautomateddatacollectionsystemstopublictransportationatatimewhensuchapplicationswereintheirinfancy,and

theplannersandoperatorsacquiredfirsthandexperiencewiththevalueofresearchinextractingmeaningfulanddecision‐worthyinformationfromthecollecteddata.Thisongoingcollaborationdemonstratedthevalueofexaminingin‐situoperationstosupportresearchandthevalueofusing

researchtoimproveoperations.Moreover,thepotentialvalueofthesoftandhardinfrastructureresultingfromthiscollaborationinsupportingeducationalactivitiesbecameapparent.Ineffectthis

infrastructurewasalreadyservingasa“livinglab,”wheretheCABSoperationwaslargeenoughtobeabletogeneralizefrom,yetaccessibleenoughtoallowexperimentation,whetherthroughtherelianceontheautomaticallycollecteddataormanualfieldobservations.Thus,OSU’sCampusTransitLab(CTL)

wasformed.

ThishistoricalandmutuallybeneficialcollaborationallowedthecasetobemadeforinternalOSUinvestmentfrombothoperatingandacademicunitstoreplacetheincreasinglydegradingBLISwitha

commercialgradesystemprovidedbyanexternalcontractor.GiventheimportanceofCTLinsupportingpractice‐groundedresearchandeducation,partofthisinvestmentisnowservingascost‐shareonthisproject.CTLisalsoservingasacriticalinfrastructureforotherexternallyfundedresearch.

SeveraldesirablecharacteristicsrenderedCTLofgreatinteresttoCleverDevices,theexternalcontractor.WhileCleverDevicesisanindustryleaderthathasdesignedandinstalledtheinformationtechnologiessystemsfortransitagencyinmajormetropolitanareas,thecompanyhadnotinstalledits

technologiesonauniversitysystem.Inadditiontobeingabletoenteranewmarketsector,CleverDeviceswasparticularlyinterestedincollaboratingwithOSUinthismajorinformationsystemsupgrade

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becauseitrecognizedthevalueofengagingintheCTLenvironment,ofhavingproximitytouniversityresearch,andofgainingaccesstostudentswitheithershortorlongtermemploymentinterests.

TherevivalofCTLthroughnewtechnologiesandnewcollaborations–whichinvolvetheDepartmentof

CivilandEnvironmentalEngineering,theDepartmentofStatistics,theTransportationandParkingServices(whichoperatesCABS),andCleverDevices–isnotonlycriticallyenhancingtheresearchand

educationopportunitiessupportedbyCTL,butitisalsogeneratinguniqueopportunitiesforoutreach.

1.2.Problem

TheoverarchingobjectiveofthisprojectistoassistinthedevelopmentoftheOSUCampusTransitLab

(CTL)asaunique,recognized,andvaluableinfrastructureforresearch,education,andoutreachbothatTheOhioStateUniversityandincollaborationwithotheruniversities.Thespecificobjectivesofthisyearweretoassistintheimplementationofthe“SmartBus”systemonOSUcampusbuses,todevelop

processestomakethedataavailableinusefulformatsforresearch,education,andoutreachapplications,tobeginprocessingthedataformultipleuses,tousedataforspecificapplications,andtoconductasurveyofcampustransitbususersandnonusersthatwouldprovidebenchmarkinformation

forassessingchangesinperceptionsandawarenessofOSUbustransitservicethatmaybeattributabletotheimplementationoftheSmartBussystem.

1.3.Approach

ToassistindevelopingtheCTLintoanoperationalandvaluableresearch,education,andoutreachinfrastructure,ourapproachconsistedofamixofactivitiesthatcanbedividedintothefollowingthrusts.

Thrust1:Develophardware,software,communicationsprotocols,andinstitutionalarrangementsthatwillallowdatacollectedfromtheSmartBussystemtobeprocessedandusedonaroutinebasisfor

research,education,andoutreachtasks

Thrust2:Pre‐processdatacollectedfromtheSmartBussystemforuseinresearch,education,andoutreachtasks

Thrust3:Investigateresearchquestionsanddevelopoutreachproductsinvolvingtheuseofautomaticvehiclelocation(AVL)andautomaticpassengercounter(APC)datainbustransitplanningandoperations

Thrust4:IncorporatetheuseofCTLdataintoeducationalactivities

Thrust5:ConductasurveyoftheOSUcommunitytoprovideinsightsongeneralandOSUspecificattitudestowardtransitandrealtimeinformationsystemsandthatcanprovidebenchmarkdatatofar

aneventualinvestigationofchangedattitudesafterimplementationoftheSmartBussystem

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CHAPTER2.METHODOLOGY

Themultiplethrustandsub‐thrustsrequireddifferentmethodologicalapproaches.Wedescribethesemethodologicalapproachesbyeachthrustandsub‐thrustinthissection.

2.1.SmartBusinfrastructuredevelopment

Weneededtoaddressseveralitemstodeveloptheunderlyinglaboratoryinfrastructurethatwouldallowdatatobecollectedandeventuallyusedforongoingresearch,education,andoutreachactivities.

MultiplesensorsystemsneededtobeinstalledonCABSbuses,protocolstotransferdatafromthedatawarehouseoftheOSUTransportationandParking(T&P),theoperationaluserofthedata,totheprojectteamneededtobearranged,serversystemstoautomaticallytransfertherawdatafilesforprojectuse

neededtobespecified,andsoftwaretopre‐processthedataintoeasy‐to‐useformatsneededtobedeveloped.

Theplanningdesign,andinstallationofthenewsetofintegrateddatacollection,communication,

travelerinformation,andenunciationforCABSbyCleverDevicestookplaceoverafairlylongperiodthatstartedbeforeproposingandbeforetheapprovalandcommencementofthisproject.Nevertheless,asubstantialpartoftheeffortbythisproject’sresearchteamhasbeendedicatedto

continuingwithandfollowingthroughonthisprocess,especiallygiventhatthecost‐sharecontributionbyOSUtothisprojecttakestheformofpartiallyinvestinginthenewsystemforthepurposeofensuringtheavailabilityoftheCTLinfrastructuretosupporttheresearch,education,andoutreachactivitiesof

thisproject.Thiseffortinvolvedbi‐lateralandmulti‐lateralmeetingsanddiscussionswithTransportationandParkingServicesandCleverDevicestoensurethattheCTLcapabilitiesareachieved.

Insomecases,thisledtojointlymakingsystemspecificationdecisions.

TheOSUSmartBussystemcollectsandrecordsAVLdataataveryhighfrequencyinthedailylog‐filesandAPCdataatalowerfrequencyinthebus‐statefiles.Bothsetsoffilesarestoredonabus’son‐board

dataunits(OBDU).ThesefilesaredumpedviaawirelesschannelontotheserversintheTransportationandParking(T&P)datawarehouseafterthebusarrivesatthedepot.Thesefilescouldcontainrecordsforadayormoreonallroutesthebusservedsincethetimeofthelastdatadump.Otherevents,such

asradiocommunicationsandlivetransmissionofbuspositionstothecontrolcenteronaverylowspace‐timeresolution,arestoredinseparatefiles.Thelivetransmissionofbuspositionsareusedinupdatingtheforecastsofnextbusarrivaltimeoneachstop,whichalsouseshistoricaldataontravel

times.

WeworkedwiththeT&PITsectortoestablishaprotocolforautomatictransferofallsmartbusdatatotheCTLserversonanoperationalbasis.Thisinvolvedseveralmeetingsbetweentheprojectpersonnel

andT&Ptounderstandthedatadefinitionsandstructuresofvariousfiles,expectedsizeofthesefileson

10

aroutinebasis,andtheformatsinwhichthedatacouldbetransferredmachine‐to‐machine.ThisinteractionhasalsoallowedustohavemeaningfuldiscussionwithITpersonnelinCivilEngineeringto

developtheprocessor/storageneedsoftheCTLserveranddeterminehowfundsfromotherprojectsandsourcescouldbeleveragedtospecaserverthatcanservetheneedsoftheCTLandofotherdataactivitiesandmaximizetheeffectivenessofequipmentinvestments..

2.2.Datapre‐processing

ThedatadownloadedfromthebusestotheT&PserversandthentransferredtoCTLserversmustbepre‐processedintoaformthatcanbeeasilyaccessedformultipleapplicationsbyvarioususers,

includingresearchersandundergradandgradstudentsindifferentcourses.Toaddressthisneed,wedeterminedthedatafilesthatwouldbeusedtoaccomplishthespecificapplicationsdiscussedinSections2.3,and2.4andtheinputformatsexpectedbytheinvestigatorsaddressingtheapplications.

Theseinputfileswouldinfluencethedesignofthepre‐processingsoftware.Wehavedeveloped,tested,andrefinedMATLABcodestotransformthedataintotheseformats.

Particularlyimportantwasourneedto“project”thelatitude‐longitudevehiclepositioninformation

containedinthedatarecordsontoadescriptionofeachtransitroute,sothatabusrecordcouldbeassociatedwithalineardistancefromsomereferencelocationdenotingthebeginningoftheroute.Weexploitedprojectionlogicandcodeswedevelopedinpreviouseffortsfora“home‐made”automaticbus

locationsystem,aswellaspreviouslyspecifiedwaypointstodefinetheroutestructurethatreceivestheprojections.However,theseprojectionswerenotaccurateatsomelocationsalongtheroute,sincethesmartbusAVLdatahassubstantiallygreaterspace‐timeresolutionthanouroldsystem.Weneededto

refinetheprojectionlogicandcodesandspecifyadditionalwaypointstoallowmoreaccurateprojectionsoftheSmartBusdata.WeintendtofurtherimprovetheprojectionmethodforallCABS

routesusingmuchmorepreciseshapefilesofthecampusarearoadspreparedbytheFranklinCountyEngineer’sofficeandprovidedbytheOSUFacilitiesOperationsandDevelopmentOffice.

Inadditiontodevelopingthehardware,software,andcommunicationsinfrastructureforfutureuse,we

wantedtobeginprocessingSmartBusdatathisyeartoinitiateitsincorporationintoresearch,education,andoutreachactivities,whilewesetupautomaticdatatransferandpre‐processingprotocols.Inthisthrust,wedeterminedseveralactivitiesthatwouldusetheSmartBusAVLandAPC

data.ActivitiessupportedfromthisprojectaredescribedinSection2.3.Otheractivities,supportedfromothersources,aredescribedinSection3.2.WethenusedthesoftwarewedevelopedtoprocesstheSmartBusdatafortheseactivities.

2.3.APCandAVL‐basedresearchandoutreachactivities

WeaddressedvariousresearchquestionsandbegandevelopingseveraloutreachproductsthatinvolvetheuseofAPCandAVL.

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2.3.1.MatchingAVLdatatobusschedules

TheadoptionofAutomatedVehicleLocation(AVL)technologyinpublictransportationprovidesthe

capabilitytoamassrichandlargedatasetsthatcouldbeusedinvariousplanningandoperationsfunctions.However,manyAVLsystemsaredesignedforreal‐timeapplications.Asaresult,thedataarenotnecessarilywellarchivedforuseinoff‐lineanalyses(Furthetal2006).EvenwhenAVLdataarewell

archived,specificanalysistoolsmustbedevelopedtoconverttherawdatatomeaningfulmeasures.OneofthechallengesinthedevelopmentofeffectiveanalysistoolsistomatchthevehicletrajectoriesderivedfromAVLdatatoschedules(Furthetal2006).Whiletheproblemappliestobothbusandrail

applications,thisstudyfocusesonAVLdatainthecaseofbusservice.

Itisimportanttodistinguishbetweentwotypesofservices.Forinfrequentbusservice(i.e.,typicallyheadwaysgreaterthantenminutes),passengerstendtotimetheirarrivalsatthestopaccordingtothe

schedule.Twocommonmeasuresoftransitreliabilityinsuchacaseareon‐timeperformanceandthevariabilityofscheduledeviations,wherescheduledeviationisdefinedasthedifferencebetweenactualbusarrivaltimeandscheduledarrivaltime.On‐timeperformancecanbequantifiedbytheproportionof

scheduledeviationsthatfallwithinanon‐timewindowaroundthescheduledarrivaltime.Thevariabilityinscheduledeviationscanbequantifiedbythestandarddeviationofthedeviations.Toproducethesetwomeasures,thescheduledtripthatabusfollowsmustbeidentified.

Forfrequenttransitservice,passengersdonotgenerallybasetheirarrivalsonaschedule.Rather,theytendtoarriveatthestopsrandomly.Onecommonmeasureoftransitreliabilityinthiscaseisheadwayadherence.Headwayistheelapsedtimebetweenthedeparturetimesoftwoconsecutivebusesata

specificstop,andheadwayadherencerelatestotheregularityofheadways.Toanalyzeheadwaysonaroute,busesservingtheroutemusthavereliablyfunctioningAVLcapabilities.Otherwise,itwouldbe

difficulttotellwhetherlargeheadwaysbetweentwoconsecutivebusesareresultingfrombusbunchingoranabsenceofAVLdata.Identifyingthescheduledtripsthebusesarefollowingcanhelpdistinguishbetweenthesetwocasesandassessreliabilitymoreaccurately.

IfallbuseshavereliablyfunctioningAVLcapabilities,thenumberofAVL‐identifiedbustripswouldequalthenumberofscheduledtrips,assumingthattheschedulednumberoftripsisprovided.InthiscasetheactualtripscouldbematchedtothescheduledtripsbysortingtheAVL‐identifiedtripsbytimeandthen

matchingthemtothescheduledtripsone‐to‐one.However,thisapproachfailsinthreerealisticsituations.FirsttheAVLcapabilitycouldbreakdownonsomebuses,resultinginmissingbustriptrajectories.Second,insomecasesbusdriversmustinitiatetheAVLcapabilityontheirbusestheyatthe

beginningofarun,andthedriversmayfailtodosoattimesThird,congestionorincidentscouldresultinthecancellationofsomescheduledbustripsortheintroductionofnewtrips.ThesesituationscouldresultinthelossofadirectcorrespondencebetweentheAVL‐identifiedtripsandthescheduledtrips,

renderingthematchingbythisorderingmethodinfeasibleorhighlypronetoerrors.

AmoresophisticatedmatchingmethodcouldbebasedoncalculatingthedeviationofanAVL‐identifiedbustriptrajectoryfromallscheduledtrips,andthenmatchingtheAVL‐identifiedtrajectorytothe

12

scheduledtripthatproducesthelowestdeviation.TheshortcomingofdoingsoisthattwoormoreAVLidentifiedtripsmaybematchedtothesamescheduledtrip.Asaresult,onlyoneofthesetripsremains

(e.g.,theoneclosertothescheduledtrip)andtheotherwouldhavetobedisregarded.

Inlightoftheabovedifficulties,priortobeginningthisproject,theprojectteamdevelopedamethodthatdoesnotrequireequalityinthenumberofAVL‐identifiedandscheduledtripsandthatguarantees

thatoneAVL‐identifiedtripismatchedtoonlyonescheduledtripwithouthavingtodisregardanyotherAVL‐identifiedtrips.Themethodweoriginallydevelopedsufferedfromashortcomingthatarisesundercommonlyapplicableconditions.Therefore,underthisprojectwefurtherdevelopedthemethodto

addressthisshortcomingandarriveatamorerobustsolution.

Briefly,ourmethodisbasedonanoptimizationassignmentformulationwiththefollowingproperties:

• OneAVL‐identifiedbustripcanonlybematchedtoonescheduledtrip,

• OnescheduledtripcanonlybematchedtooneAVL‐identifiedtrip,and• Ameasureoftotalmatcherrorisminimized.

Theobjectiveistominimizethetotalmatcherrorandsatisfythefirsttwopropertieslistedabove.The

matcherrorcouldbedefinedinseveralways.Inthisstudy,thematcherror ofassigningtheithbus

tripinthespot‐checktabletothejthtripinthetimetableisconsideredtobetheweightedsumofthe

absolutevalueofthedifferencebetweenthebuscrossingtimesandscheduleddeparturetimes,wherethesumistakenoverallstopsasfollows:

(2.3.1‐1)

and,

=totalmatcherrorasdefinedabove,

i=indexrepresentingthebustripinthespot‐checktable,

j=indexrepresentingthetripinthetimetable,k=indexrepresentingthebusstop,

N=indexrepresentingthenumberofbusstopsalongtheroute,

=scheduleddeparturetimeatbusstopkonthejthtripinthetimetable,

=crossingtimeatbusstopkontheithtripinthespot‐checktable,and

=weightfactoratstopkassociatedwiththesignof .

Theterm“crossingtime”isusedtoindicatethepresenceofthebusatthestop,whichisestimated

usinglinearinterpolationbetweenthelastAVLsignalbefore(upstreamof)thebusstopandthefirstsignalafter(downstreamof)thebusstop.Thetablethatincludesthecrossingtimesatstopsalongtherouteisreferredtoasthespot‐checktable.Onerowinthespot‐checktableincludesthecrossingtimes

atallstopsforoneAVL‐identifiedbustrip.Thetablethatincludesthescheduleddeparturetimesatall

13

stopsalongtherouteisreferredtoasthetimetable.Onerowinthetimetableincludesthescheduleddeparturetimesatallstopsalongtherouteforonescheduledtrip.

TheempiricaldatausedthusfaristhelowresolutionAVLCTLdataobtainedfroma“home‐made”

systempreviouslyimplementedonseveralOSUbuses.NowthatthehighresolutionAVLCTLdataisbecomingreadilyavailablefromtheSmartBussystem,the“crossingtime”wouldeasilybereplacedby

eitherthearrivalordeparturetimes.Thesimulation‐basedapplicationresearchactivitydescribedbelowaddressestherelationshipbetweenAVLresolutionandtheaccuracyofinformationthatcouldbederivedregardingthebehaviorofbusesatstops.

Thematchingproblemisformulatedasanetworkassignmentsolvedusinganintegerprogramthat

minimizesthedeviationsoftheAVL‐identifiedbustripsfromthescheduletripsundercertainconstraintsthatguaranteethepropertieslistedabove(Jietal.2009).ThebasicHungarianalgorithm

(Hillieretal.2001)isusedtosolvethisoptimizationbyarrivingatauniquesolution.Inpreviousvalidationsofthisformulation,thesolutionwasfoundnottoberobust.Underthisproject,thenatureofthesolutionwasinvestigatedandsourceoftheresultinglackofrobustnesswassuspectedtoberelated

tothespecificiationoftheweightfactors indefiningthematchingerrorinEquation(2.3.1‐1).

Theunderlyingassumptionofthisformulationisthatbusdriversalwaystrytomeettheschedule.Inthedefinitionofmatchingerror,anegativedifferencebetweenthecrossingtimeandscheduleddeparture

timerepresentthecasewherethebusarrivesearlytoastop,andapositivedifferencerepresentsthecasewherethebusarriveslate.Ingeneral,ifdriversholdattimepointswhenarrivingearly,early

arrivalsatstopsarelesslikelythanlatearrivals.Ingeneral,underthisoperatingcondition,higherweight

factors shouldbeusedforearlyarrivalsthanforlateones.

WeconductedanempiricalstudyononeoftheCTLroutes(theCampusLoopSouth,whichis8.3kmlongserving19stops).Atotalof1,726AVL‐identifiedbustripsarematchedtothescheduleusingthe

developedassignmentmethodundertwosetsofassumptionsregardingtheweights ofEquation

(1).Inthefirstcase,equalweightingisspecifiedtoearlyandlatearrivalsinthetotalmatcherrorof

Equation(2.3.1‐1).Inthesecondcase,theweightsassociatedwithearlyarrivalsarespecifiedtobetwicethoseoflatearrivalsgiventheholdingpolicyineffectontherouteunderstudy.

2.3.2.PerformanceassessmentofODestimationfromAPCdata

2.3.2.1.Introduction

Origin‐destination(OD)flowsconstituteoneofthemostfundamentalsetsofinformationusedinplanningandoperatingtransportationsystems.However,ODflowshavealwaysbeendifficultandcostlytoobtain(Chan,2007;Furth,etal,2006).TheincreasinguseofAutomaticPassengerCounters(APC)in

bustransitsystemsisyieldingcomprehensivepassengerboardingandalightingdataanon‐goingbasisacrossthetransitnetworkwhich,althoughusedforotherpurposesatthetransitagencies,offerthepotentialtodetermineODflowsonafrequentandcomprehensivebasis.APCdataprovidethenumbers

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ofpassengersboardingatthevarious(origin)busstopsandalightingatthevarious(destination)stops.Assuch,theAPCdataprovideinformationrelatedtotheODflowsbetweenpairsofstops.This

informationcanonlybeconsideredindirectinformation,however,sincethepassengerscountedasboardingatastopcouldhavealightedatmultipledownstreamstops,andthepassengerscountedasalightingatastopcouldhaveboardedatmultipleupstreamstops.Still,theindirectinformation

providedbyboardingandalightingdatacanconceivablybehelpfulindeterminingODflows.

UsingboardingandalightingdatatoestimatebustransitODflowsisnotanewconcept(e.g.,SimonandFurth,1985;Ben‐Akiva,etal.,1985),butitisnowofgreaterpracticalinterestbecauseoftheavailability

ofAPCdata.InaFederalTransitAdministrationproject,weareinvestigatingthepotentialofestimatingbuspassengerorigin‐destinationflowsfromAPCdata.TohelpguideourFTA‐sponsoredefforts,wedesignedandconductedaNEXTRANSstudyinwhichweinvestigatedtheperformanceofasimple

procedurefordeterminingroute‐levelODflows(theflowsfromboardingstopstoalightingstopsonabusroutewheretransfersbetweenroutesarenotconsidered).Specifically,weinvestigatedtheperformanceoftheIterativeProportionFitting(IPF)methodusedwitha“null”basematrix.Becauseit

onlyrequiresAPCdataandaspecificationoftheboardingandalightingstopsasinputs,the“IPF‐with‐null‐base”procedurecanbeeasilyimplementedforanyroutewhereAPCdataarecollected.However,the“non‐informative”natureofthenullbasematrixmaybeconsideredtoosimplistictoproducegood

results,andwewishedtoquantitativelyassestheempiricalperformanceofthisapproach.OurstudyconsistedofcollectingtrueODflowsonOSUbustrips,producingODflowsusingtheIPF‐with‐nullprocedureforthesametrips,andcomparingthetwosetofODflowsusinganinnovativeapproachthat

allowsameaningfulinterpretationoftheresults.Detailsonthemethodologyareprovidednext.

2.3.2.2.TheIPF‐with‐nullbaseprocedure

TheIPFprocedure,whichhasbeenreferredtobyavarietyofnames,hasbeenwidelyusedintransportationandotherfields(Ben‐Akiva,etal.,1985).WhenappliedtobuspassengerODestimation,theIPFprocedureusestheboardingandalightingvolumestotransformaninputbaseODmatrixintoan

outputODmatrix,wherethesumoftheODflowsfromaboardingstoprtoalldownstreamstopsequalstheinputboardingvolumeatstopr,andthesumoftheODflowstoanalightingstopsfromallupstreamstopsequalstheinputalightingvolumeats.

TheIPF‐producedODflowsareproportionaltobasematrixODflows,withproportionalityconstantsfor

eachrow(boardingstop)andforeachcolumn(alightingstop).Letting denoteflowsbetweenorigin

(boardingstop)randdownstreamdestination(alightingstop)sthatareproducedbytheIPFprocedure

usingasinputsbaseODflows ,andagivensetofboardingandalightingvolumes,theoutputIPF

flowsaresuchthat:

(2.3.2‐1)

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where and areproportionalityconstantsforboardingstoprandalightingstops,respectively,

whichchangefromiterationtoiterationoftheIPFprocedureuntilconvergenceisachieved.

TheIPFprocedurewasfirstsuggestedbyDemingandStephan(1940),inthecontextofestimationforacontingencytable,asapossiblesolutiontotheconstrainedoptimizationproblemconsistingoffinding

estimate ofanODflowmatrixthatminimizesthechi‐squareddistancefromagiven(or

observedfromasmallsurvey)basematrix ,suchthattheboardingandalightingvolumes

determinedfromtheestimatedmatrixareequaltotheobservedboardingandlightingvolumes.Thatis:

, (2.3.2‐2)

subjecttotheconstraints

(2.3.2‐3a)

, (2.3.2‐3b)

where, representsaspecified(measured)vectorofboardingvolumes,and

representsaspecified(measured)vectorofalightingvolumes.However,Stephan

(1942)showedthattheIPFprocedureprovidesanapproximatesolutiontothisoptimizationproblem.

TheconvergenceoftheIPFprocedurewasfirstprovedbyFeinberg(1970).Mosteller(1968)pointedout

thattheODflowsestimatedfromtheIPFprocedure,startingwithabasematrix and

subjecttoagivensetofboardingandalightingtotals,retaintheinteractionstructureofthebasematrix,inthattheoddsratiosofthebasematrixandthedeterminedmatrixarethesame,i.e.,

(2.3.2‐4)

InthecontextofdeterminingbusrouteODflows,itcanbeshown(FurthandNavik,1992)thattheestimatesproducedfromtheIPFprocedureusinganullbasematrixasinputareequivalenttothose

thatareproducedfromaspecialcaseofamethodgivenbyTsygalnitsky(1997),whereitisassumedthatanypassengeronboardwhenthebusarrivesatastopisequallylikelytoalightatthatstop.

Asseenfromtheabovesummary,inadditiontotheboardingandalightingvolumes,whichcanbe

collectedfromanAPCtechnology,thebasematrixisanessentialinputtotheIPFprocedure.AbasematrixcanbeconsideredtobethebestODmatrixavailabletotheplannerthatcouldbeusedastheset

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ofstartingODflowvaluesintheiterativeprocedure.Itcouldbedevelopedfromhistoricaldata,aplanningmodel,expertopinion(althoughelicitingexpertopinionfortheextremelylargenumberofOD

pairsinatransitsystemwouldbeoperationallydifficult),orsomecombinationofthesesources.Ifthe

basematrix isconsistentwithboardingandalightingvolumesusedintheconstraints(2.3.2‐3a)and

(2.323‐3b),theoptimalsolutiontotheaboveproblemis and Since

boardingandalightingvolumesarestrictlydeterminedfromthetrueODflows,itfollowsthatifthetrueODflowmatrixisusedasthebasematrix,theIPFsolutionisthebase(true)matrix.Ofcourse,ifthe

basematrixreflectsdifferentboardingandalightingvolumesthantheobservedinputs,theoutputmatrixwilldifferfromthebasematrix.

Intheabsenceofanyinformativebaseinformation,anullODmatrixreflectingequalflowsacrossOD

paircanbeusedasinputbasematrix.Thatis:

(2.3.2‐5)

Itfollowsfromconstraints(2.3.2‐3a)and(2.3.2‐3b)andequation(2.3.2‐4)thatifthebasematrixQ0isreplacedbyascalarmultiple,theoptimalsolutionwouldremainunchanged.Therefore,foroperationalpurposes,thenullbasecanbearbitrarilyconstructedtoconsistofunitflows(q0rs=1)forallfeasibleOD

pairsrs.Forcomputationalreasons,theIPFprocedurewillbemorecomputationallyefficientiftheaverageflowisconsideredinthenullbaseforeachODpair,i.e.,q0rs=∑rbr/N=∑sas/N,forallfeasibleODpairsrs.(TospecifyfeasibleODpairs,itisassumedthattravelersdonotboardandalightatthe

samestopandonlytraveldownstreamalongtheroute.)Alternatively,inthecontextofa“normalized”ODmatrix,wherethematrixprovidestheproportionoftotalflowtravelingfromaspecifiedorigintoaspecifieddestination,thebasematrixentriescanbesettoq0rs=1/N,whereNisthenumberoffeasible

ODpairs.ThenormalizedODmatrixcanbeinterpretedastheprobabilitythatarandompassengertravelsfromthespecifiedorigintothespecifieddestination.Inthissense,thenull(normalized)basematriximpliesthatanyfeasibleODpairisequallylikelytobetheonetraveledbyarandompassenger.

Thenullbasematrixcan,therefore,beconsidereda“non‐informative”priordistributioninBayesianterminology(Berger,1985).

2.3.2.3.Designofempiricalstudy

Inourempiricalstudy,wecollectedtrueODpassengerflowsandthecorrespondingboardingandalightingvolumesforeachofasetofbustrips.WethenusedtheIPFproceduretocalculatetheODmatrixforeachbustrip,usingthetrip‐levelboardingandalightingvolumesandanullbasematrixas

inputs.ThequalityoftheflowsproducedwasassessedbycomparingeachdeterminedbustripODmatrixtothecorrespondingobservedtrueODmatrix.Toputtheperformanceinperspective,theperformanceofotherapproachesusedtoproduceODmatriceswasalsoassessed.Someofthe

approacheswouldbeexpectedtoperformworse,andotherswouldbeexpectedtoperformbetterthantheIPFprocedureusingthenullbasematrix.

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Itcanbeshownthatthesolutiontotheoptimizationproblemdefinedbyobjectivefunction(2.3.2‐2)andconstraints(2.3.2‐3a)and(2.3.2‐3b)doesnotchangeifthebaseODflows,theboardingvolumes,

andthealightingvolumesarealldividedbythetotalvolume,excepttheproblemisconvertedtothedeterminationofthenormalizedODmatrix.(Asmentionedabove,the“normalized”ODmatrixisthematrixthatprovidestheproportionofpassengertrips,ratherthanthenumberofpassengertrips,using

theODpair.ItisformedbydividingtheODmatrixindicatingthenumbersofODtripsbythetotalnumberoftripsinthematrix.)ConsideringnormalizedmatricesfocusesthecomparisonondeterminingODpatternsintheformofproportionsorprobabilitiesandcontrolsforanyeffectofvolumeonthe

analysis.Therefore,webasedourcomparisonsonnormalizedODmatricesforeachbustrip.

WeadaptedaproceduredescribedbySimonandFurth(1985)tocollectdataontentripsofOSU’sCampusLoopSouth(CLS)busroutebetween8and10a.m.onweekdaysduringthewinterquarter

(JanuarythroughmidMarch)of2009.TwodatacollectorsrodeCLSbuses,withonepersonstationednearthefrontdoorandonestationednearthereardoor.Thedatacollectorsdistributedcardsindicatingtheboardingstoptopassengersastheyboardedthebusandcollectedthecardsasthe

passengersalighted.Byfilingthecardscollectedaccordingtothealightingstopandbustrip,thecardscouldbeusedtodetermineboththeempiricalODflowsandthecorrespondingempiricalboardingandalightingvolumesforthevariousbustrips.ThisapproachallowedustocollectODflowsonallorigin‐

destinationpairsonabustripwithonlytwodatacollectors.

CLStravelsinalooppattern,servingtwentystops,fourofwhicharelocatedina“WestCampus”parkinglot.(AtthetimethatdatawerecollectedforthestudydescribedinSection2.3.1,CLSservednineteen

stops.Atwentiethstopwasrecentlyaddedtotheroute.)Forthepurposesofthisstudy,thefourWestCampusstopswereaggregatedintoasinglepseudo‐stop,whichweconsideredasthefirstboarding

stopfortheensuingbustripandthelastalightingstopforthejust‐completedbustrip.(Becauseofserviceprovidedbyotherbusroutesandthetrippatternsderivedfromcampusactivities,itisrarethatapassengerwouldboardupstreamoftheWestCampusparkinglotforadestinationdownstreamofthe

lot.Only6ofthe702passengertripswereobservedwithsuchanODpattern,andtheywereomittedfromtheempiricaldatausedinthestudyreportedhere.)Inthisway,foreachofthe10trips,thedataconsistedofvolumesfor18boardingstopsand18alightingstopsandODflowsforeachofthe153

feasibleODpairs.

Weuse todenotethematrixoftrue(normalized)ODflowsfortripjand toindicatethetrue

(normalized)flowbetweenboardingstoprandalightingstopsontripj.UsingtheempiricalboardingandalightingvolumesfortripjwiththeIPFprocedureandanullbasematrixQnullasinput,we

determinedatrip‐levelODmatrix withelements foreachtrip.

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Forcomparisonpurposes,weconsideredtrip‐levelODflowmatricesproducedbyother“procedures.”WesummarizetheseproceduresinTable2.3.2‐1anddescribethembrieflyhere.Wemotivateand

explainthematricesfurtherinMcCordetal.(2009).

• Asdiscussedabove,thenullmatrixQnullrepresentsa“non‐informative”estimateofthenormalizedODflows,whereitisassumedthatarandompassengeronthetripwasequallylikely

totravelonanyofthefeasibleODpairs.

• TherefinednullmatrixQref‐nulljrefinesQ

nullbyusingtheboardingandalightingdataontripj.Specifically,ifnopassengersboardedatastopontripj,therecouldbenoODflowtoanyofthe

downstreamdestinationsonthetripand,similarly,therewouldbenoflowontripjtostopsthathadnorecordedalightingvolumeonthetrip.InQref‐null

j,zeroprobability(proportion)isassignedtoallsuchODpairs,andtheequalprobabilitiesarerecalculatedbasedonthereducednumber

offeasibleODpairs.

• ThematrixQIPFj(Q

null)producedfromtheIPF‐with‐nullbaseprocedurehasbeendiscussedabove.

• Asalsoexplainedabove,weobtainedtrueODflowsfortenempiricaltrips.Torepresentthe

resultsproducedfromanon‐boardsurvey(with100%sample),weproducedthenormalizedflowsfromthissetofODflows.Resultsfromon‐boardsurveyswouldbeusedtopredictflowsonfuturetrips.Assuch,whenconsideringestimatingtheODflowsontripj,weheldoutthe

truetripjflowswhenformingQon‐boardj.

• TheonboardsurveyshouldprovideabetterestimateoftheODflowsthanwouldthenullmatrix.Therefore,wewouldexpectthatthematrixQIPF

j(Qon‐board)producedfromtheIPF

procedureusingtheon‐boardsurveymatrixasinputwouldperformbetterthanthematrixproducedwhenusingthenullmatrixasinput.

• Aspresentedabove,QtruejrepresentsthematrixoftruenormalizedODflowsontripj,wherethe

trueflowswereobtainedbythedataobtainedinourdatacollectioneffort.

WeproducedtheODmatricesdeterminedbythedifferent“procedures”summarizedinTable2.3.2‐1

foreachofthetentripsforwhichwecollectedempiricaldata.Wethencomparedthesematricestothetruetrip‐levelnormalizedODmatrices.ToassesstheperformanceofproceduremindeterminingthetruenormalizedODmatrixQtrue

jontripj,wecomputedtwodifferentscalarmeasuresof

performance ,i=1,2.

PerformancemeasureP1consistsofthesumofthesquareddifferencesbetweenthenormalizedODflowsproducedbyproceduremandthetruenormalizedODflows:

(2.3.2‐6)

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where, isthetrue(observed)normalizedODflowontripjfromboardingstoprtoalightingstops

and isthenormalizedODflowfromrtosontripjdeterminedbyprocedurem.LargervaluesofP1

representpoorerperformance.

TABLE2.3.2‐1:Summaryof“procedures”usedtoproducetriplevelODvolumes

Procedure Notation Description

Null EqualprobabilitiesacrossalltheoreticallyfeasibleODpairs,zerootherwise;constantacrossalltrips.

Refinednull(R‐null)

EqualprobabilitiesacrossallAPC‐determinedfeasibleODpairs,zerootherwise;determinedfromboardingandalightingvolumesontripj.

IPF‐with‐nullbase(IPF‐null)

IPFprocedureusingAPCdataontripjasinputsandthenullmatrixforabase.

On‐boardsurvey(OBS)

NormalizedflowsbasedonallobservedtripODflowsexcludingthoseoftripj.

IPFwithOBS(IPF‐OBS)

IPFprocedureusingAPCdataontripjasinputsandtheon‐boardsurveymatrixforabase.

True Observed(true)normalizedODflowsfortripj.

ThesumofsquareddifferencesP1isacommonlyusedmeasureofperformanceincomparingvectorsin

generalapplications,butitdoesnotincorporatethespatialnatureofODflows.Forexample,assigningflowsfromanorigintoanerroneousdestinationclosetothecorrectdestinationmaybeconsideredlessonerousthanassigningtheerroneousflowstoadestinationfartheraway.

Toincorporateaspatialdimensioninmeasuringperformance,wedevelopedasecondmeasureP2basedonpassengerdistancestraveled(PDT)derivedfromtheODmatrices.Specifically,weusedtheroutedistancebetweenorigin‐destinationpairsandthe(normalized)ODflowsQ[m]

jfortripjtoproducethe

distributionofPDTvaluesfortripjandformedthecumulativedistribution ofthesePDT

values.Thematrix oftrueODvolumesfortripjprovides ,thetruecumulative

distributionofPDTfortripj.Thesecondmeasureofperformance isthentheabsolutevalueofthe

areasbetweenthetruecumulativedistributionfunctionandthedistributionfunctionderivedfromthetriplevelODmatrixobtainedfromprocedurem:

. (2.3.2‐7)

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LikeP1,smallervaluesofP2indicatebetterperformance.However,P2incorporatesspatialconsiderationsthroughitsuseofdistance.Ontheotherhand,P2wouldnotreflectalargeerrorin

estimatedflowforanODpairwithagivendistancethatiscompensatedbyanerrorofsimilarmagnitudeintheoppositedirection(e.g.,anunderestimatecompensatedbyanoverestimate)foranODpairwithsimilardistance.Therefore,weconsideredbothmeasuresP1andP2inassessingempirical

performance.

BothP1andP2canbeusedtorepresentordinalperformanceoftheprocedures.Thatis,aprocedure

withsmallervalueof performsbetterthanaprocedurewithlargervalueindeterminingtheOD

flowsontripjaccordingtothesumofsquareddifferencesmeasureP1,andsimilarlyforthepassengerdistancetraveledmeasureP2.Toprovideamoremeaningfulquantificationoftheperformanceofthe

proceduresindeterminingODflowsontripj,wedefinedameasureofrelativeperformance .For

tripj,RPquantifiestheimprovementintheODmatrix,accordingtothereductioninperformancemeasureP1orP2,producedbyproceduremfromthatofthenullmatrix,asaproportionofthecorrespondingperformancemeasureforthenullmatrixonthattrip.Comparisonsaremadewith

respecttothenullmatrix,sincethenullmatrixisthemostbasicestimateoftheODflows,whereonly

thestructureoftheroute(yieldingthefeasibleODpairs)isused.Inthisway,wedefined as:

(3.2.2‐8)

AccordingtotheRPmeasure,therelativeperformanceofthenullmatrixQnullwouldbezeroforanytrip,andtherelativeperformanceofthetruematrixQtruewouldbeoneforanytrip.Thus,RPvaluesclosetozerowouldindicatelittleimprovedperformance,relativetowhatcouldbeproducedfromthenull

matrix.RPmeasuresclosetoonewouldindicatearelativelylargeimprovementinperformance.

2.3.3.Developmentandapplicationofbusoperationssimulation

Giventhecomplexityofactualbusoperations,certainproblemsarenotpossibletocharacterizeand

solveanalytically.Aneffectivealternativeinsuchsituationsistheuseofsimulation.Ourinterestindevelopingacampuslabmotivatesustobeabletoinvestigatetransitoperationsundercomplex,realisticconditions.Therefore,wearedevelopingabusoperationssimulationtool.Thesimulationisbus

specificandreflectsthestochasticnatureofoperations.Thisyear,wederivedtheactualparametersofafirstversionofthesimulationtoolfrompreviouslycollectedCTLdataandpartiallyvalidatedresultswithadditionaldatacollectedthisyear.Wethenusedthisversionofthebussimulationtooltoassess

theeffectofAVLsamplingontheaccuracyofbusdwelltimes,animportantvariablefortransitplanningandoperationsthatcanbeestimatedfromAVLgenerateddata.ThissectiondescribesthedevelopedsimulationanditsapplicationtotheAVLsamplinganalysis.

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2.3.3.1.Simulationstructure

ThebussimulationmodelisbasedontheOSUCampusLoopSouthrouteandispartitionedintothree

components:point‐to‐pointtraveltimesoverspace,dwelltimesatbusstops,anddelaysatspecialpoints.Point‐to‐pointtraveltimeisthetimeabusneedstotransverseacertainspacethatdoesnotincludebusstopsorsomespecialpoints.Dwelltimeisthetimeintervalbetweenthearrivalofabustoa

stopanditsdeparturefromthatstop.Delayatspecialpointisthedelaycausedbycertainlocationsalongthebusroutesuchassignalizedandun‐signalizedvehicularandpedestrianintersectionsthatformbottlenecksintheroadwaynetwork.Atthispoint,onlydelayscausedbymajorsignalizedintersections

havebeenincorporatedinthesimulationtool.

Thethreecomponentscollectivelydeterminethebustrajectory.Spacealongtherouteisrepresentedbycontiguoussections,wheretheboundariesbetweensectionssignifyeitherabusstoporaspecial

point.Furthermore,sectionsaresubdividedintosmallcontiguoussegments(asegmentlengthof5metersisusedasadefault).Foreachsection,apoint‐to‐pointtimeissimulated.Asimulatedbusmovesalongasectionaccordinglyuntilitencountersastoporaspecialpointattheendofasection.Adwell

timeoraspecialpointdelayissimulatedatthatpoint.Asimulatedbuscomestoastopatbusstops(toreflecttheoperatingpolicyinplaceontheOSUbussystem)butnotnecessarilyataspecialpoint.Bothdwelltimeandspecialpointdelaysareaddedtothetraveltimewhenthebuscrossestheboundaryand

justbeforeitstartstraversingthenextsection.Inwhatfollowsthesimulationofpoint‐to‐pointtraveltimes,dwelltimes,anddelaysaredescribed.

2.3.3.2.Point‐to‐pointtraveltime

Point‐to‐pointtraveltimeisthetimeabusneedstotransverseacertainsectionalongtheroutethatdoesnotincludebusstopsorspecialpoints.Apoint‐to‐pointtraveltimeissimulatedforeachsection

basedonempiricaldata.Atime‐basedsimulationisadoptedwherebyabusspeedisgeneratedfollowingacertaintime‐step(atime‐stepequaltoonesecondisusedasadefault)andthenadvancedalongtheroutebasedonthegeneratedspeed.Ageneratedspeeddependsonthepreviouslygenerated

speedandempiricalhistoricaldataobservedonthesegmentwherethebusislocatedattheinstantofthesimulatedevent.

Thedependencebetweentwoconsecutivesimulatedspeedsiscapturedthroughconstraintssetonthe

accelerationofthebus.Thedefaultmaximumaccelerationissetat+2m/s2andthedefaultminimumaccelerationissetat–4m/s2.Thatis,ifthemostrecentlysimulatedspeedisv,thenthespeedsimulatedonetime‐steplatermustfallwithintherangeofspeedsdefinedbythemaximumandminimum

accelerationsandthepreviousspeedv.Giventhispermissiblespeedrange,tosimulatethecurrentspeed,aspeedisrandomlydrawnfromtheconditionalspeeddistributionderivedfromempiricalAVL‐baseddataforthesegmentwherethesimulatedbusislocated.Theconditioningisperformedonthe

determinedspeedrange.

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Thesimulatedbusisthenadvancedalongtherouteinaccordancewiththetime‐stepandthenewlygeneratedspeed.Atthatpointtheprocessrepeatsitself.Thatis,forthenewlocation,aspeedrangeis

determinedfromthepreviousspeedandtheaccelerationconstraints,andthenewspeedissimulatedbydrawingfromtheempiricallyderivedconditionalspeeddistributionforthesegmentonwhichthenewlysimulatedbuspositionislocated.

2.3.3.3.Dwelltime

Dwelltimeisthetimeintervalbetweenthearrivalandthedepartureofabustoandfromabusstop,respectively.Thesimulationgeneratesdwelltimesfromsetsofstop‐specificdwelltimevalues

estimatedfromempiricaldata.TheempiricaldwelltimesareestimatedfromtheCTLAVLdatausingoneofseveralpossiblemethods.TheinputstothesemethodsincludethelatestAVLsignalupstreamofthestopandtheearliestsignaldownstreamofthestop.Thesignaldataincludelocation,timeandspeed.

Giventheoperatingpolicyinplace,busesmustcometoacompletestopatthebusstopeveniftherearenopassengerswishingtoalightorboard.Thisstoppingbehaviorcouldbecapturedinoneoftwoways:(i)ThebusrunsataconstantspeedfromthelocationofthelastupstreamAVLsignalbeforethe

stopuntilitarrivesatthebusstopatwhichpointthespeeddropstozeroinstantaneously;ittheninstantaneouslychangesitsspeedfromzerotosomeconstantspeeduntilitarrivesatthelocationofthefirstdownstreamAVLsignal.Or,(ii)thebusdeceleratesfromacertainspeedfromthelocationof

thelastupstreamAVLsignaltoaspeedofzerowhenarrivingatthebusstop;andthenacceleratesfromaspeedofzerowhendepartingthestoptoacertainspeedatthelocationofthefirstdownstreamAVLsignal.Thesetwoscenariosarereferredtoasthe“without‐acceleration”and“with‐acceleration”

models,respectively.Inbothcases,thetrajectoryofthebusisprojectedfromthelocationandtimepointofeitherAVLsignaltothestop.Thedwelltimeisthencalculatedassimplythedifferencebetween

theprojecteddepartureandarrivaltimesatthestop.

Inthewith‐accelerationcase,thespeedoutofthefirstsignalisassumedconstantfollowedbyadefaultaccelerationof–2m/s2suchthatthebuscomestoafullstopatthelocationofthebusstop.Ifthis

constraintcannotbemetunderthisprojectionassumption,thebusisassumedtofollowanaccelerationfromthegivenspeedatthelocationoftheupstreamsignalthatensuresafullstopatthelocationofthatstop.Similarly,whenprojectingfromthedownstreamsignalaconstantspeedisassumedgoinginto

thesignalprecededbyadefaultaccelerationof+2m/s2iftheconstraintofzerospeedatthestopcanbemet.Otherwise,thenecessaryhigheraccelerationiscalculatedsuchthatthebusachievesthegivenspeedatthelocationofthedownstreamsignal.

Inbothaccelerationmodels,speedinformationateitherAVLsignalisrequired.TheinstantaneousspeedsreportedwitheachAVLsignal(alongwithlocationandtime)arepronetohighmeasurementerrors.Therefore,aformofsmoothingisadopted.Morespecifically,historicalbusspeedvaluesalong

therouteareaveragedwithinthecontiguoussegmentsconstitutingeachsection(asdefinedabove).Assuch,eachroutehasaspeedlook‐uptablewithsomespeedvalueassociatedwitheachsegment.(Inamoregeneralimplementation,thelook‐uptablecouldbetimeperiodspecific.)

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Threealternativeaveragespeedsareadoptedingeneratingthreelook‐uptablesforabusroute:averageinstantaneousAVLspeed,harmonicaverageofinstantaneousAVLspeed,andaveragearc

speed.Inthefirstcase,theaverageinstantaneousAVLspeedisthesimpleaverageofthehistoricalinstantaneousAVLspeedsfallingwithineachsegment.Inthesecondcase,theharmonicaverage,insteadofthesimpleaverage,isadopted.Sincespeedsofzerocannotbeusedincalculatingthe

harmonicaverage,thereportedzerospeedscouldeitherbeignoredorreplacedwithaverylowspeed.Inthisstudy,zerospeedsarereplacedwith0.2m/s.Inthethirdcase,firsthistoricalarcspeedsarecalculatedfromthelocationandtimeofconsecutivepairsofAVLsignalsnotseparatedbybusstopsor

specialpoints.ThelocationofthecalculatedarcspeedissettobethemidpointofthetworespectiveAVLsignallocations.Thelook‐upspeedforasegmentisthendeterminedasthesimpleaverageofthehistoricalarcspeedswhoselocationsfallwithinthatsegment.

Theestimateddwelltimesbasedoneachofthesixcombinationsofspeedlook‐uptablesandaccelerationmodelsconstitutethehistoricaldwelltimesbasedonwhichthedwelltimedistributionisdetermined.Itisoneofthesesixempiricallyderiveddistributionsthatisdrawnfromwhensimulating

thedwelltimesinthesimulator.

Whenthebusissimulatetobeatastopwhereaholdingpolicyisineffect,onceadwelltimeissimulated,theresultingdeparturetimeiscomparedtothescheduleddeparturetime.Ifthecalculated

departuretimeislessthanthescheduleddeparturetimebymorethanashortthreshold,thenthesimulatedbusishelduntilthescheduledeparturetime.Therefore,inderivingthedwelltimedistributionsforeachsegmentfromwhichdwelltimesaredrawn,onlyempiricaldwelltimesthatdonot

reflectanybusholdingareconsidered.(Thatis,onlyempiricaldwelltimesthatcorrespondtobusdeparturetimeslargerthanscheduleddeparturetimesareconsidered.)

2.3.3.4.Specialpointdelay

Similartothesimulationofdwelltimes,specialpointdelaysarealsosimulatedbygenerateddelaysfromempiricallyderiveddistributionsforeachspecialpoint.Inthismodel,majorintersectionsalongthe

routearetreatedasspecialpoints.Asinthecaseofdwelltimes,specialpointdelaysneedtobedeterminedtoproducetherespectivedistributionstosamplefrominthesimulation.ThesedelaysarecalculatedfromtwoAVLsignals,oneupstreamandonedownstreamofaspecialpoint,alongwiththe

speedlook‐uptablesinamannersimilartothatusedinthecalculationofthedwelltimes.However,whileinthecaseofdwelltimesthebusisconstrainedtocometoafullstopatthelocationofabusstop,thisconstraintisnotappliedinthecaseofthespecialpoints,giventhatbusesdonotalwaysstopat

thesespecialpoints.

2.3.3.5.Validation

Thethreespeedlook‐uptables,coupledwiththetwoaccelerationmodels(usedtocalculatethedwell

timesandspecialpointdelaysthatare,inturn,usedtoderivetherespectivedistributionsthatgeneratethedwelltimeandspecialpointdelaycomponentsofthesimulator)resultinatotalofsixpossible

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simulationscenarios.Weconductedavalidationexercisewherewecomparedthesimulatedbustraveltimesbetweentwodeparturesfromconsecutivebusstopsundereachofthescenariostoobserved

traveltimes.Morespecifically,wecomparedthemeanandvarianceofsimulatedandactualtraveltimesandidentifiedthescenariothatmostcloselymatchedtheactualtraveltimes.

2.3.3.6.Applicationofthesimulationprogram

Weenvisionusingthesimulationprogramtoaddressmultiplequestionsofinterestthatwillariserelatingtobusoperations.Thispastyear,wewantedtoapplythesystemtoassesstheabilityofasimplemethodtoestimatebustimesatastopasafunctionofdatafrequency.Thetimethebusis

stoppedatabusstopisanimportantmeasureofperformanceforoff‐lineplanningandrealtimeoperationsofbussystems.Understandingstoppedtimepatternsallowsplannerstounderstandwheredriversrushtomakeuptimeorwherethereisexcesstimeontheroutesothatthebuscanwaittoget

backonschedule.Wewillcallthesestoppedtimes“dwelltimes”forthisstudy,althoughdwelltimesareoftenmeasuredasthedurationoftimefromwhenthebusdoorsopenafterarrivingatastopuntiltheyclosebeforedepartingthestop.Thedwelltimeswewillconsiderwillalsoincludeanytimethebus

maywaitatthestopwithdoorsclosedafterallpassengershaveboardedandalighted.Thisextrastoppedtime(referredtoas“holdingtime”)wouldgenerallyoccuratpredeterminedtime‐points(whicharestopsdesignatedforpossibleholding)whenadriverisaheadofscheduleatthattime‐point.As

discussedabove,notethatduetothestructureofsimulatingdwelltimeswhereholdingissimulatedseparately,onlydwelltimeswherenoholdingistakingplaceareusedinempiricallyderivingthedwelltimedistributionsfromwhichthetimeabusspendsstoppedatastopbeforeholdingissimulated.

Ourmethodofestimatingdwelltimeswasmotivatedbyourexperienceswiththeprevious,“home‐made”AVLsystem.Likemanysystems,locationdatainoursystemweretransmittedandrecordedona

limitedbasistoreducecommunicationcosts.Time‐stampedlocationsweretobecommunicatedevery100metersoftravel,orevery3minutesofelapsedtime,whicheveroccurredfirst.Suchdatawouldnotallowadefinitivedeterminationofthebusdwelltimes,andwewishedtoestimatethetimesto

understandbehaviorofdwelltimes–includingthespatial(acrossstops)andtemporal(acrosstimeatthesamestop)variabilityinthedwelltimes–forthebussystemsoastounderstandtransitoperationsbetterandtocalibrateoursimulationprogram.

Asasimplemeansofestimatingdwelltimes,weassumedthatwecoulddevelopa“lookup”tablethat

providedtheaveragespeedforabustotraverse80‐mlongspatialintervals,whereintervalswerenon‐overlappingand,takentogether,spatiallycoveredtheentireroute.Giventhese“lookup”speeds,thelocationofanyAVLsignal,andthelocationoftheconsideredbusstop,thetimethatwouldelapse

betweenthetimethebussentanAVLsignalupstreamofthestopandthetimethatthebuswouldarriveatthestopcouldbedetermined.AddingthistimetothetimeoftheAVLsignalwouldyieldan“arrivaltime”estimateatthestop.Similarly,thetimethatwouldhaveelapsedbetweenthetimethat

thebusdepartedfromthestopandthetimethatitsentadownstreamAVLsignalcouldbedetermined.Subtractingthistimefromthetimeofthesignalwouldyielda“departuretime”estimatefromthestop.Subtractingthearrivaltimefromthedeparturetimewouldproducetheestimateddwelltime.

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Weobtainedthe“lookuptable”speedsbyrunningthesimulationmodelmanytimes,assumingthattime‐stampedAVLlocationsweregeneratedatsomespatialortemporalsamplingfrequency(see

below),determiningtheaveragespeedbetweenapairofsignalsbydividingthedifferencebetweenthelineardistancesbythedifferencebetweenthecorrespondingtimestampsforthepairofsignals,andassociatingthisspeedwiththemidpointofthesignaldistances.Then,weusedtheharmonicmeanofall

suchgeneratedspeedsin80‐mspatialcellsasthespeedscorrespondingtothecellsthatwereusedtoestimatearrivalanddeparturetimes.

Inoursimulation,weassumedthatsignalsweregeneratedeitherwithaspatialsamplingintervalofy1metersoratemporalsamplingintervalofy2seconds.Wewouldgeneratethetruelocationandtimeof

thevehicleataveryfineresolutionbyusingthesimulationprogram.Wethensampledthegeneratedlocationsandtimesofthevehicleatthespecifiedsamplingintervalanddeterminedthelastsampledupstreamsignalbeforeastopandthefirstsampleddownstreamsignalafterastop.Basedonthese

sampledsignals,weusedthe“lookuptable”speedsdescribedabovetoestimatethearrivalanddeparturetimesatthestopand,consequently,theestimateddwelltimeatthestop.Thesimulationgeneratedtruedwelltimesatthestops.Weusedtheabsolutevalueofthedifferencebetweenthe

simulatedandtruedwelltimestoproduceameasureofperformanceforthedwelltimeaccuracy.

Weran1000replicationsofthesimulation,wherethereplicationsgeneratedconsecutivebustripcoveringtheentireroute.Wesampledthesimulatedtruelocationsandtimesin10‐mspatialincrementsand3‐sectemporalincrements.TheresultsarepresentedinSection3.3.3.2.

2.3.4.Developmentofoutreachproducts

InadditiontoconductingresearchactivitiesrelatedtotheuseofAPCandAVLdata,wewishtoexploitthedataandtheresultsofourresearchinvestigationstoproducequantitativeinformationthatwewilluse,incollaborationwithOSUTrafficandParkingServices(T&P),tomonitorperformanceoftheOSU

busservice.InSection2.3.2wedescribedtheuseoftheIterativeProportionalFitting(IPF)proceduretoproduceorigin‐destination(OD)flowsfromtheboardingandalightingdatarecordedbytheAPCsystem.(Wearepresentlyinvestigatingandrefiningothermethodsforfutureuse.)WeintendtoproduceOD

matricesforOSUT&Ponanongoingbasis.Inthisfirstyeareffort,wewantedtoapplythecodeswedevelopedinourFTA‐supportedprojectinabatchmodetoproducemultipleODmatricesusingtheSmartBusAPCdatacollectedontheCampusLoopSouth(CLS)routeandsynthesizethemultiple

matrices.

InadditionweidentifiedvariousapplicationsthatrelyontheODestimatestodevelopandmonitortravelpatterns.WeintendtoestablishbenchmarkpatternsdevelopedfromtheseapplicationsandmonitorthepatternsovertimeincollaborationwithOSUT&P.Inthisfirstyear,wedevelopedseveral

conceptsandproducedpreliminaryresultsusingthefirstwaveofSmartBusdataontheCLSroute.

OneconceptwedevelopedisderivedfromanaspectweareaddressinginourFTAproject.InthatprojectwearedevelopingmethodstoautomaticallyindicateperiodsofhomogeneousODflowsfromtheAPCdata.IntheNEXTRANSprojectreportedonhere,weborrowedconceptsfromthesemethods,

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whicharestillunderdevelopment,totestexogenouslyspecifiedperiodsforsimilarityofODpatternsontheCLSroute.WeexploitedthecodeswearedevelopingintheFTAprojecttodeveloptheabilityto:

• automaticallysegmenttheroute‐levelODflowmatricesproducedfromtheAPCdatainto

specifiedperiods

• aggregatetheroute–levelODmatricesintonormalizedperiod‐levelODmatrices(matricesindicatingtheproportionofpassengersusingthespecifiedODpairs

• calculate“dissimilarity”measuresbetweenpairsofaggregatednormalizedODmatricestoindicatematricesthataresimilartoeachotherandmatricesthatareverydifferent.The

measureusedhereisbasedonthechi‐squaredstatisticforapairofODmatrices,dividedbythedegreesoffreedom,whichisequivalenttoCramer’smeasureofassociationbetweentwoprobabilitydistributions,describedin3.5.Thedissimilaritymeasuresaredefinedsothatgreater

valuesindicatelessassociation.

Thedissimilaritymeasurescanberecalculatedandmonitoredovertimetodeterminechangesintravelpatterns.OurgoalinthisfirstyearwastousethefirstwaveofAPCdatatodemonstratetheconceptontheCLSroute.

InadditiontodeterminingperiodsofsimilarODpatterns,wewereinterestedindevelopingtheability

tomonitorthedistributionofbuspassengertripdistances,wherethetripdistancesarederivedfromtheAPC‐derivedODmatricesandthedistancesbetweenstoppairs.WecollectedtrueODdataduringWinterandSpringquartersusingthetechniquedescribedinSection2.3.2.Fromthesedata,wenoticed

ahigherpercentageofshort(lessthanonemile)bustripsinWinterquarterthaninSpringquarter.Thehypothesisisthatthebetterspringweatherenticesmorepeopletowalktheseshorterdistances,rather

thanridethebusforsuchtrips.WewishedtovalidatethatourAPC‐derivedODestimatesreflectedthischangeintravelpattern.Ifitdid,wewouldhavemorefaithinourabilitytousetheAPC‐derivedODmatricestomonitorthepassengerdistancedistributionsovertime.WeusedtheIPF‐with‐nullbase

procedure(seeSection2.3.2)toproduceaggregateWinterquarterandSpringquarterODmatricesfromtheobservedboardingandalightingvolumesandinvestigatedwhetherthesematricesexhibitedthereductioninshorttripsobservedinthetrueODflowdata.

ThefinalconceptexploitingtheODestimatesthatwedevelopedthisyearwasinspiredbythecourse

assignmentdevelopedforCE570(seeSection2.4below).Specifically,wedevelopedthemeanstocombinetheestimatedODflowsderivedfromtheAPCdatawiththebustraveltimeanddwelltimeinformationcontainedintheAVLdatatodeterminetheexpectedtimethatapassengertravelsonthe

bus,conditionalonthepassenger’sboardingstop,andtheexpectedtraveltimeonthebus,conditionalonthepassenger’salightingstop.ThegoalofthefirstyeareffortwastodemonstratetheconceptwiththefirstwaveofAPCandAVLdatacollectedandtoprepareforbenchmarkingandongoingmonitoring

inthefuture.WethereforeproducedthesetimesfromtheSmartBusAPCandAVLdataontheCLSroute.

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2.4.EducationaluseofCTL

TheinclusionoftheSmartBussystemintheCTLhasbegunprovidingauniqueinfrastructureforresearchandoutreachprojectsandhas,therefore,providedanimportanteducationalexperiencefor

severalgraduateandundergraduateresearchassistants.SeveralofthesestudentsarewritingthesesorMSreportsrelatedtotheCTL.TheCTLcanalsobeusedtoenhancecoursework.AlthoughtheSmartBussystemhasonlyrecentlybeeninstalled,wesoughtwaystoincorporatethedataandinformationwe

havebeencollectingandwillcontinuetocollectinexistingcourses.WeidentifiedtwoOSUcourses,CE570:IntroductiontoTransportationEngineeringandAnalysisandCE873:UrbanTransportationDemandAnalysis,inwhichwecouldusedataandresultsproducedfromourfirstyeareffortsdescribedabove.

CE570isacourserequiredofallundergraduateCivilEngineeringstudents.Someofthesestudents

choose“transportation”astheirmajorareaofspecialization,butthevastmajorityofstudentschooseotherareasofCivilEngineeringastheir“majorarea.”Formostofthesestudents,CE570istheonlytransportationcoursetakenintheirundergraduateprogram.Thecoursecoversmultipletopicsin

transportationengineering.However,thevarioustopicsarecoveredataleveldeepenoughthat,inadditiontolearningbasicconceptsandterminology,studentsareexpectedtoconductmathematicalandlogicalanalysissoastogaininsightsfordesign,planning,oroperations.Thecourseisofferedonce

eachyearandhashadrecentenrollmentsofapproximately100studentsperoffering.

ApreviouslyexistingmoduleofCE570coveredtheestimationofexpectedtraveltimesforapublictransportationsystem.Calculationshadbeenconductedanalyticallyforasystemwithdedicatedright‐of‐way.Tosupplementthismodule,wedevelopedanassignmentinwhichstudentsusedtheODdata

describedinSection2.3.2andspeciallycollectedbustravelanddwelltimedatatodetermineempiricalexpectedpassenger“linehaul”times(timesaboardthebus).Thestudentsweregivenstop‐to‐stop

expectedtraveltimesfortheOSUCampusLoopSouth(CLS)route,expecteddwelltimesatthestopsontheroute,andtheaveragenumbersofpassengerspertripwhoboardedintheWestCampusareaandalightedateachdownstreamstop.(Theseaveragenumbersofpassengerswerederivedfromthe

passengerODmatricesasdescribedinSection2.3.2.).ThestudentswerethenrequestedtocalculatetheexpectedtimesforapassengerboardingattheOSUWestCampusareatoarriveateachmaincampusbusstop,theprobabilitythatapassengerboardingatWestCampuswouldalightateachofthe

maincampusstops,andtheexpectedtimeonthebus(linehaultime)forarandompassengerboardingatWestCampus.Tomotivatetheassignment,thecampustransitlab,asitexistedatthetimeandasitisenvisioned,waspresentedinalectureformattothestudents,andtheadvantagesofAVLandAPC

technologiesfordatacollectionwereemphasized.ThestatementoftheassignmentispresentedinFigure2.4‐1.

CE873isagraduatelevelcoursedevotedtothetheoryofdiscretechoicemodelingasappliedtotransportationchoices.Thiscourserequiresstatisticalandeconometricanalysis,mathematical,logical,

anddomainspecificanalysis,andcomputerworkwithmodelestimationsoftware.Thecourseemphasizestheuseofthebinary,multinomial,andnestedlogitmodelsformodelingdiscretechoice.GraduatestudentsmajoringinCivilEngineering,CityandRegionalPlanning,and,occasionally,

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Geographytakethiscourse.CE873isarequiredcourseforstudentsenrolledintheDualMastersDegreeinUrbanTransportationPlanningprogram.The“dualdegreeprogram”isaspeciallydesigned

programinwhichacceptedstudentscanreceiveM.S.degreesinCivilEngineeringandinCityandRegionalPlanninginlesstimethanitwouldtaketopursuethesedegreesseparately.CE873isanelectivecourseforthestudentsnotenrolledinthe“dualdegreeprogram,”whomakeupthemajorityof

theclass.Thecourseisofferedeveryotheryearandhashadrecentenrollmentsofbetween10and15studentsperoffering.

Figure2.4‐1:StatementofCampusTransitLab‐basedassignmentintroducedinOSUCourseCE570:

IntroductiontoTransportationEngineeringandAnalysis,WinterQuarter2009

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Figure2.4‐1(continued)

Althoughmostoftheexamplesandassignmentsdiscussedinthecoursepertaintotransportationmodeanddestinationchoice,otherusesofdiscretechoicemodelsarepresented.Inadditiontoaseriesof

modelestimationassignments,studentsdevelop,withthehelpoftheinstructor,a3‐4weekprojectinwhichtheyestimatelogitmodelstogaininsightsonsomeproblemoftheirchoosing.Theprojectsaregenerallyperformedingroups.Duringtheofferinginthispastyear,agroupoftwostudentsestimated

binarylogitspecificationstoinvestigatetheeffectofdifferentfactorsontheperformanceofthebuspassengerODestimationdescribedinSection2.3.2.Specifically,thestudentsdeterminedthe

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percentilesoftheP2measurepresentedinSection2.3.2fromthemultipleroute‐levelODmatricesproduced.Theythenestimatedbinarylogitmodels,wherethedependentvariablewasabinary

indicationofwhethertheP2measurefortheestimatedODmatrixwasgreaterorlessthanthespecifiedP2percentilevalue.Theindependentvariablesinvestigatedconsistedofthedifferentbases(nulloron‐boardsurvey)usedintheIPFprocedure,thetripvolume,thedayoftheweek,andthetimeofday.

2.5.Perceptionsandattitudessurvey

Atwo‐wavesurveyoftheOSUcommunitywasdesigned,andthefirstwavewasundertakentostudy

factorsthatinfluencetransportationchoicesandtravelersatisfaction,ingeneral,andtodevelopinsightsonindividualpreferencesandperceptionsoftransportationoptionsasimpactedbytheprovisionof

passengerinformation.OSU’sCABSandtheSmartBussystemareusedasacasestudy.Thefirstwaveofthesurveytookplacebeforetheprovisionofreal‐timepassengerinformation,andtheresultsprovidebenchmarkdataforinvestigatingpossiblechangesinperceptionsandattitudesresultingfromthe

implementationoftheSmartBussystem,whicharetobecapturedbydatacollectedinthesecondwaveofthesurvey.

Duringtheplanningphase,wemadeseveraliterationsonpossiblestudydesigns,theissuestobe

addressedinthestudy,andmodesofadministeringthesurveyquestionnaire.Giventherelativehighcostsforconductingthesurveyviainterceptmodes,itwasdecidedthatawebbasedsurveywouldbethemostcost‐effectivewaytoobtainthedataofinterest.AcontractwassignedwiththeOhioState

UniversityStatisticalConsultingService(SCS)toimplementoursurveydesignon‐line.First,thepilotversionofthequestionnairewasimplementedinordertotesttheformatandthewordingofthe

questions.Basedonthefeedbackfrompotentialsubjects,wefinalizedthesurveyquestionnaire.SCSthencodedthefinalversionforonlineimplementation.SCSobtainedarandomsampleofe‐mailaddressesofundergraduateandgraduatestudentsfromtheOSUOfficeoftheRegistrarandofthe

facultyandstafffromtheOfficeofHumanResourcesforinvitingthesampleofsubjectstoparticipateinthesurvey.

Thequestionnaireconsistedof9demographicquestions,10‐13questions(thenumberdependsona

subject’sresponseoncertainquestions,whichwouldthenpromptfollow‐upquestions)dealingwithsubject’smodeoftransportationtoandoncampus,and14questionsabouthisorherperceptionsandevaluationofCABSservice,safety,andexternalities,suchasCABS’roleincontributingtoreductionof

trafficoncampusormakingthecampus“green”.Inall,therewereupto36questionsthatarespondentcouldanswer.Itwasestimatedthatasubjectwouldrequirenomorethan8minutestocompletethesurvey.

Weencounteredsomedelaysinadministeringthesurvey.Theresearchstudyinvolvesresponsesfromhumansubjects,andthesurveyprotocolrequiredapprovalbyTheOSUInstitutionalReviewBoard(IRB)forhumansubjectresearch.Theapplicationforapprovalrequiredtheprojectinvestigatorstocomplete

theCITItrainingbeforetheapplicationcouldbesubmitted.Theresearchprotocoldescribingtheprocesstobefollowedtoensurethattheprivacyoftherespondentswouldbeprotectedwassubmittedfor

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approvalonOct.30,2009.Thesubmittedprotocolalsoincludedthee‐mailmessagetobesenttotheinvitedsurveyparticipantsandtheweb‐basedquestionnaire.Theresearchteamreceivedanexemption

fromcontinuedoversightbytheboardonNov7,2008.SCSadministeredthesurveysoonafter.Subjectsweregivenapproximatelysixweekstocompletethesurvey.ThesurveyresponseratesbycategoryofparticipantsareprovidedinTable2.5‐1

Table2.5‐1:CTLtransportationfirstwavesurveyresponserates

Tounderstandtheadequacyoftheseresponserates,wecomparethemwithresponseratestoother

OSUsurveys.Recently,asurveywasconductedregardingattitudesandperceptionsofOSUundergraduatestudentsonglobalwarming.Arandomsampleof24900undergraduatestudentswas

selectedand3570respondedtothissurvey.The14.3%responserateissimilartotheresponserateofundergraduatestudentsinoursurvey.Responseratesfromfaculty,staff,graduatestudents,andundergraduatestudentsforoursurveyandotherOSUsurveysareprovidedinTable2.5‐2,whereitcan

beseenthatourresponseratesarecomparabletotwoothersurveysthatweredevotedtoinformationtechnology.Inaddition,oursurveyhadsimilarundergraduateresponseratesasarecentsurveydevotedtoTransportationandParkingissuesatOSU,butsubstantiallyhigherresponseratesfromfaculty,staff

andgraduatestudents.(ItshouldbenotedthatundergraduatestudentsformedthedemographicgroupmostrelatedtotheissuesoftheTransportationandParkingsurvey.)Thus,webelievethatoursurveysfindingsandconclusionshaveahighdegreeofvalidity.

Thedatacollectionprocesswasfairlysmooth.AttheendofDecember2008,SCSprovideduswithadatadictionaryandtherawresponsedata,withnoidentifiersonrespondentsornon‐respondents.

Group Surveyed Responses ResponseRate

Faculty 4480 1233 27.52%

Staff 4479 1758 39.25%

GradStudents 2994 571 19.07%

UGStudents 5999 837 13.95%

Overall 17,952 4,399 24.5%

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Table2.5‐2:Responseratescomparisonwithothersurveys

GroupFall2008CTL

TransportationSurvey

OIT2009CIO

TechnologyPollQuestionnaire

OIT2008CIO

TechnologyPollQuestionnaire

T&P2008

COTA‐CABSSurvey

Faculty 27.52% 27.0% 26.0% 10.5%

Staff 39.25% 37.2% 33.1% 21.4%

GradStudents 19.07% 19.9% 23.2% 8.2%

UGStudents 13.95% 13.2% 17.2% 13.4%

Overall 24.5% 23.4% 24.3% 13.8%

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CHAPTER3.FINDINGS

Wepresentthefindingsbythrustinthissection.

3.1.Infrastructuredevelopment

Aftertheplanninganddesignprocesswascompleted,theinstallationoftheintegratedtechnologiesforCABSbyCleverDevicescommencedinlatesummerof2008andcontinuedforapproximatelyoneyearbeforemostoftheissuesandbugswereaddressed.All28busesintheCABSfleetarefullyequipped

withthenecessaryhardwareandsoftware,thebusdriversaretrainedtousetheon‐boarddisplay,thereal‐timecommunicationofAVLdatatotheoperatingcenterison‐going,thebusarrivaltimeforecastingalgorithmisfunctioning,andelectronicmessagessignsat10majorstopsareinfull

operation.AsofAutumnquarter2009,thesystemwithitsintegratedcomponentsisforthemostpartoperatingreliably,anduseofthepassengerinformationcomponentissharplyincreasing.

InitiallyweobtainedSmartBusdataforonebusononerouteforoneday.Thedata,whichwassupplied

in.csvformat,allowedustounderstandthedatastructuresandthesizeoffilesthatcouldbeexpectedtobeproducedonadailybasis.Weobtainedabetterunderstandingofdatastorageneedsforthelargeamount(>1TB)ofSmartBusdatathatwillbegeneratedinthenexttwoyears.Wearenow

finalizingthedetailsthatwillmakeupourserverorder.TheiterationsweundertookthispastyearwerealsohelpfulinhelpingtoestablishtherelationshipsbetweenITpersonnelinT&PandinCivilEngineeringthatwillberequiredtoimplementtheautomaticdatatransferprotocols.

Thepre‐processingsoftwareseemstoworkwellandthecomputationaltimesforaday’sworthofdataareverylow(intheorderofsecondstotensofseconds).Wewillneedfurtherrefinementswhenwe

startpre‐processingdataforotherroutes,buttheexperiencewegainedbyworkingontheCampusLoopSouthrouteshouldreducethetimespentonthelearningcurveinthefuture.Thevalueofthedataproducedisdemonstratedintheuseofthedatainthefollowingsections.

3.2.Datapre‐processing

WeweresuccessfulinusingthecodeswedevelopedtoprocessasmallportionofSmartBusAPCand

AVLdataintoformatsthatcanbeexploitedbymultipleusers.Specifically,weprocessedthedataforuseintwoaspectsofourFederalTransitAdministration(FTA)project,foroutreachtasksdescribedinSection2.3.4,andfordevelopmentoffutureAPC‐andAVL‐relatedresearchandeducational

investigations.Weareanticipatingthatthedatatransferredonaroutinebasiswillbepre‐processedonaregularbasisandstoredintoadatabaseforaccessbythemultipleusers.

Asdescribedabove,inourFTAprojectweareinvestigatinganddevelopingmethodstoestimatebuspassengerorigin‐destination(OD)flowsfromAPCdata.Weweresuccessfulinprocessingthefirstwave

ofSmartBusAPCdataintoformatsthatcouldbeusedinourFTAprojectsforvariouspurposes:

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• toserveasasetofinputstodifferentODestimationmethodsforcomparisonofoutputs

• todevelopandtestmethodsthatautomaticallydetermineperiodsofhomogenousODpatterns

• todetermineinputstosimulationprogramsthatareusedtocomparetheaccuracyofthedifferentODestimationmethods

AsecondthrustofourFTAprojectistoexplorethepotentialofusingbusAVLdatatoindicatetraffic

conditionsonsurfacestreets.Inthatproject,wedevelopedanapproachtodetectindicationsofrecurringcongestionfromAVL‐derivedbusspeedsandusedAVLdatapreviouslycollectedfromour“home‐made”AVLsystemtovalidatethismethodontheCampusLoopSouthroute.Afterovercoming

the“projectionproblem”mentionedinSection2.1,werecentlyprocessedafirstwaveofSmartBusAVLdataintoaformthatcanbeusedbytheresearchersontheFTAprojecttoconfirmthepromisingresultswiththisnewsetofdata.Ifthesevalidationtestsaresuccessful,wewillextendourempirical

scopetoincludeotherOSUbusroutesusingSmartBusAVLdatathatwearenowcollectingandwhichwewillbecollectinginthefuture.

Insection2.3.4wedescribedvariousmeasuresderivedfromODflowmatricesthatwewishtomonitorincollaborationwithOSUTrafficandParkingServices.Weusedoursoftwaretoprocessthefirstwave

ofAPCdataintoformatsthatalloweddeterminationofthesemeasures,asexplainedinSection3.3.4.

ThedatausedintheeducationalcontextsdiscussedinSections2.4and3.4weregeneratedfromthefield‐baseddatacollectioneffortdescribedinSection2.3.2.Inthefuture,wewishtogeneratethedatafortheseandothercourseassignmentsfromtheSmartBusAPCandAVLdata.Weweresuccessfulin

processingthefirstwaveofSmartBusdataintoformatscompatiblewiththeformatsusedintheexercises.

3.3.APCandAVL‐basedresearchandoutreachactivities

3.3.1.MatchingAVLdatatobusschedules

InSection2.3.1wedescribedapotentialimprovementtothemathematicalformulationoftheapproach

wehadpreviouslydevelopedtomatchAVL‐basedbustrajectoriestobusschedules.Inthisrevisedformulation,weightsonthedeviationsbetweenempiricalandscheduledtimesatabusstopare

adjustedtoreflectthepresenceofaholdingpolicythatwasineffectontheCTLrouteforwhichweobtainedAVLdata.

Weconductedanempiricalstudyoftheequal‐weightformulation(whichignorestheholdingpolicy)to

therevisedformulationusingAVLdatafrom1,726CTLbustrips.Comparedtotheresultsproducedintheequalweightcase,therevisedformulationproduced81fewermismatchesoftrajectoriestoschedules.

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Inadditiontovalidatingtheexpectedimprovementofferedbyourrevisedformulation,wealsoexaminedtheconditionsthatareparticularlypronetoproducematchingerrorwhenequalweightsare

assumedasameanstoidentifyfurthermethodologicalrefinements.Theoccurrenceofmatchingerrorsresultingfromtheassumptionofequalweightsbecomesmorepronouncedwhenexcessivedelaysinbusoperationsoccur.Undersuchconditions,thelikelihoodofearlybusarrivalstostopsismuchlower

thanthelikelihoodoflatearrivals,whereasusingequalweightswouldimplyequallikelihoods.Whenthediscrepancybetweenthetwosetsoflikelihoodsincreases,thechancesofencounteringmatchingerrorsincreasesaswell.

Inourempiricalstudy,wearbitrarilyspecifiedtheweightssothattheydifferedbyafactoroftwo.Theempiricalresultsdemonstratethateventhisratherarbitraryspecificationcanimproveperformanceappreciably.However,ouranalysismotivates,additionalanalysisfocusingonthesensitivityofthe

improvedperformancetotheweightingandthedevelopmentofanoperationalmeanstospecifytheweightsinamoremeaningfulmanner.

3.3.2.PerformanceassessmentofODestimationfromAPCdata

InSection2.3.2,wepresentedthemethodologyusedtoassesstheperformanceoftheeasy‐to‐implement,butrathersimplistic,IPF‐with‐nullbaseprocedureofestimatingpassengerODflowsfromAPCdata.OurassessmentisbasedonusingthesumofsquareddifferencemeasureP1andthe

cumulativepassengerdistancetraveled‐basedmeasureP2toquantifythedifferencebetweenthenormalizedODflowmatricesproducedbytheIPF‐with‐nullbaseprocedureandthetruenormalizedODflowmatrices,calculatingtherelativeperformanceRPdefinedinequation(2.3.2‐8),andcomparing

thesequantifiedmeasurestothoseobtainedwhenusingtheotherprocedureslistedinTable2.3.2‐1.

InTable3.3.2‐1,wepresentthenumberoftrips(outofthe10tripsforwhichwecollectedempirical

data)forwhichaprocedurefromTable2.3.2‐1outperformedanotherprocedurefromthetablebyperformancemeasuresP1andP2.Notsurprisingly,theODmatricesproducedfromtheIPF‐with‐nullbaseprocedure(IPF‐null)outperformedthenullmatrix(Null)ortherefinednullmatrix(R‐null)forall10

trips.Wealsoseethat,accordingtoP1,theODmatrixproducedbytheIPF‐with‐nullbaseprocedureoutperformedtheon‐boardsurveymatrix(OBS)forall10trips.Ontheotherhand,wenotethatwhenusingP2,thematricesdeterminedfromtheon‐boardsurveyperformedbetterthantheIPF‐with‐null

basematricesonall10trips,highlightingthevalueofusingmultiplemeasuresforsummarycomparisons.Similarly,itissurprisingthat,accordingtoP1,thematricesproducedwhenusingtheIPF‐with‐nullbaseprocedurewerebetterthanthoseproducedwhenusingtheIPFprocedurewiththe

supposedlybetterbasedeterminedfromtheon‐boardsurvey(IPF‐OBS)for3ofthe10trips.Again,whenusingP2,theresultsaremoreinlinewithintuition:usingthebetterbaseproducedbetterresultsonall10trips.

Giventheseresults,onemightbelievethatP2isamoreappealingmeasurethanP1.However,whencomparingtheresultsproducedbythenullmatrixtothoseproducedbytherefinednullmatrix,itisseenthatmeasureP1producesmoreintuitiveresultsthanP2.Comparedtothenullmatrix,therefined

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nullmatrixusesadditionalinformation,namely,theboardingandalightingdata.Therefore,onewouldexpecttherefinednullmatrixtobebetterthanthenullmatrix.Thisisthecaseforall10tripswhen

usingP1,butforonly4ofthe10tripswhenusingP2.TheseeminglyparadoxicalresultthatthenullmatrixdoesbetterthantherefinednullmatrixwhenmeasuringperformancebyP2butnotbyP1canbeexplainedwhenlookingcloselyatthespatialpatternofthetruetripsintheempiricaldata.

TABLE3.3.2‐1:Pair‐wiseperformancecomparisonsbetweenprocedures(numberoftripsinwhicheachoutperformstheothers)

Procedure Null R‐null IPF‐null OBS IPF‐OBS

BasedonperformancemeasureP1(sumofsquareddifferences)

Null – 0 0 0 0

R‐null 10 – 0 0 0

IPF‐null 10 10 – 10 3

OBS 10 10 0 – 0

IPF‐OBS 10 10 7 10 –

BasedonperformancemeasureP2(passengerdistancetraveled)

Null – 6 0 0 0

R‐null 4 – 0 0 0

IPF‐null 10 10 – 0 0

OBS 10 10 10 – 0

IPF‐OBS 10 10 10 10 –

Table3.2.2‐1illustratesthat,unsurprisingly,usingtheIPF‐with‐nullbaseprocedureclearlydidbetter

thanusinganullmatrixorarefinednullmatrixasanestimateoftheODmatrix,andthattheODmatricesproducedfromtheIPFprocedureweremostlyimprovedwhenusingabetterbase.WhetherthematricesproducedwhenusingtheIPF‐with‐nullbaseproceduredidbetterthanthoseproduced

directlyfromanon‐boardsurveyisnotclearfromthetable:Theyperformedbetter10of10timesaccordingtoP1but0of10timesaccordingtoP2.Inaddition,thetableshowsthat,accordingtoboth

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measures,usingtheon‐boardsurveytoproduceabasefortheIPFprocedurealwaysperformedbetterthanusingtheon‐boardsurveydirectly.

InTable3.2.2‐2,weshowRPsummariesacrossthetentripsbyprocedurebasedoneachofthetwoperformancemeasures.(Asdiscussedabove,theRPofthenullmatrixiszeroforalltrips,andtheRPofthetruematrixis1;therefore,neitherofthesemeasuresisincludedinthetable.)Usingtherefinednull

matrix–i.e.,usingtheboardingandalightingdatatopossiblyimprovethenullmatrix–producedanimprovementofatmost19%ofthepossibleimprovement,whereasusingthesimpleIPF‐with‐nullbaseprocedurewiththeboardingandalightingdataimprovedperformancebybetween60%and89%.

TABLE3.2.2‐2:RelativeperformanceRPsummariesacross10trips

Procedure Average Minimum Maximum

RelativeperformancemeasureusingP1(sumofsquareddifferences)

R‐null 0.10 0.03 0.16

IPF‐null 0.70 0.60 0.89

OBS 0.33 0.21 0.48

IPF‐OBS 0.74 0.55 0.88

RelativeperformancemeasureusingP2(passengerdistancetraveled)

R‐null –0.04 –0.21 0.19

IPF‐null 0.68 0.60 0.78

OBS 0.80 0.72 0.87

IPF‐OBS 0.89 0.83 0.92

Comparingacrossprocedures,theRPresultsindicatethatusingtheIPF‐with‐nullbaseproceduremarkedlyimprovedperformancecomparedtousingthenullmatrixortherefinednullmatrixdirectly

(i.e.,withouttransformingthesematriceswiththeIPFprocedure).Usingthebetterbaseobtainedfromtheon‐boardsurveyasinputtotheIPFprocedureimprovedperformancefurther,butthemarginal

improvementislessmarked.AsmentionedabovewhendiscussingtheresultsinTable3.2.2‐1,directlyusingthematrixobtainedfromon‐boardsurveytodeterminetheODflowsperformedbetterthanusingtheresultsproducedfromtheIPF‐with‐nullbaseprocedureaccordingtothepassengerdistance

traveledmeasureP2.However,accordingtoTable3.2.2‐2,theimprovementinperformancewasslight.WhenconsideringthesumofsquareddistancesmeasureP1,thedecreaseinperformance,comparedtothatproducedbytheIPF‐with‐nullbaseprocedure,isofgreatermagnitude.

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3.3.3.Developmentandapplicationofbusoperationssimulation

Thepreliminaryvalidationofthesimulationrevealedthatthesimulationscenariowherethedwelltime

andspecialpointdelaydistributionsarederivedfromdwelltimesanddelayscalculatedusingtheharmonicaveragespeedlook‐uptablesingeneralresultedinthemoreaccurateresultsintermsofmatchingtraveltimesalongtheroute.

AsdiscussedintheapplicationportionofSection2.3.3,wedeterminedtheabsolutevalueofthedifferencebetweentheestimatedand(simulated)truedwelltimeforeachsimulationreplication,foreachofthe19stopsontheroute,andforeachspatialortemporalsamplingintervalconsidered.We

averagedtheseabsolutevaluesacrossthereplicationsandstopsforagivensamplingintervaltosummarizethedwelltimeestimationerrorforthesamplinginterval.

WepresenttheseresultsinFigure3.3.3‐1.Thecurveontheleftofthefigurecorrespondstothespatial

samplingintervalindicatedontheleftverticalaxis,whereasthecurveeontherightcorrespondstothetemporalintervalindicatedontherightverticalaxis.Wearrangedtheheightsoftheleftandrightverticalaxestocorrespondtoequalquantitiesofdatageneratedbythecorrespondingspatialand

temporalsamplingintervals.Forexample,thespatialsamplingintervalof100montheleftaxiswouldgenerate83.2AVLpointsperbustriponthe8320meterlongroute.The100mheightontheleftaxiscorrespondstoa26secheightontherightaxis,sincea26secsamplingratewouldproducethesame

numberofAVLsignalsonanaveragebustrip,wheretheaveragerunningtimeofthetripis36minutes.

Toillustratetheinterpretationofthefigure,considera100‐mspatialsamplingrate.Enteringtheleftcurve(thatproducedwhensimulatingspatialsampling),thecorrespondingaverage(absolute)dwell

timeerrorisapproximately4seconds.Enteringthecurveproducedwhensimulatingtemporalsampling(therightcurve)atthesameheight(i.e.,atthetemporalsamplingintervalof26seconds,which

correspondstothesamenumberofAVLpointsgeneratedperbustrip),theerrorisapproximately16seconds.Thespatialsamplingapproachproducedmuchsmalleraverageerrorthandidthetemporalsamplingapproach(4seconds,comparedto16seconds).Theshifttotherightofthetemporalsampling

curveindicatesthatthespatialsamplingapproachoutperformsthetemporalsamplingapproachforall“equivalent”intervals.(Wearepresentlyinvestigatingtheslightnonmonotonicbehaviorofthetemporalsamplingcurveatrelativelylargevaluesoftemporalsampling.)

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Figure3.3.3‐1:Averageabsolutedwelltimeerrors,acrosssimulationreplicationsandbusstops,asafunctionofspatialsamplingintervalortemporalsamplinginterval

3.3.4.Developmentofoutreachproducts

Asexplainedabove,weprocessedthefirstwaveofSmartBusAPCandAVLdataintoinformationthatcouldbeusedtoproducequantitativeproductsandmeasuresthatweplantomonitorincollaboration

withOSUTrafficandParking(T&P).Thispastyear,wealsodevelopedpreliminaryversionsoftheseproductsandmeasures,whichwewillsoonpresenttoT&P.Wereportontheseresultsinthissubsection.

WiththehelpofresearchassistantsfromourFTAproject,weusedthedataprocessedintheNEXTRANS

projecttoproduce1003triplevelorigin‐destination(OD)flowestimatesonOSU’sCampusLoopSouth(CLS)route.Weaggregatedtheseintoanormalizedmatrixthatprovidestheprobabilitythatapassengerchosenatrandomfromthissetof1003tripsusedthedesignatedODpair.Thenormalized

matrixinpresentedinTable3.3.4‐1.

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Toinvestigatethepotentialofusingthemeasurediscussedinsection2.3.4toinvestigatethesimilarityofODmatrices,weconsideredamorning,7‐to‐10AMperiod,anafternoon,2‐to‐5PMperiod,and

determinednormalizedODmatricesfromthe1003APC‐derivedtriplevelmatricesthatfellintheappropriateperiodbydayoftheweek.InTable3.3.4‐2a,weshowthedissimilaritymeasurevaluesbetweentheODmatrixforthemorningperiodofonedayoftheweekandtheODmatrixforthe

morningperiodofanotherdayoftheweek.InTable3.3.4‐2b,wepresentthedissimilarityvaluesforday‐of‐weekpairsfortheafternoonperiods.Largervaluescorrespondtomoredissimilarmatrices.Incomparingthevaluesinthetwotables,wenoticethatthevaluesobtainedwhencomparingFriday

afternoonmatricestotheotherafternoonmatricesarenoticeablylargerthanwhencomparinganyotherpair,indicatingthatthegreatestday‐of‐weekdifferenceinpassengertrippatternisassociatedwithFridayafternoon.

Tosupporttheuseofthedissimilaritymeasure,wedeterminedthevalueofthemeasurebetweena

matrixproducedinthemorningonagivendayoftheweekandtheafternoonmatrixforthesamedayoftheweek.AlargeproportionofmorningCLSridersparkintheremoteWestCampuslotandridethebustomaincampus.Intheafternoon,theytravelfrommaincampustotheWestCampuslot.Assuch,

theexpectationisthattheODpatternswouldbeverydifferentforthesetwotimesofday,andthedissimilaritymeasurewould,therefore,belarge.Thedissimilaritymeasuresdeterminedinthisway,presentedinTable3.3.4‐2c,areindeedmuchlargerthanthoseintheprevioustwotables,supporting

thevalidityofthismeasure.

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Table3.3.4.‐1:NormalizedODflowmatrixforOSUCampusLoopSouthrouteproducedfrom1003APC‐derivedtriplevelmatricesusingtheIPF‐with‐null‐baseprocedure

Table3.3.4.‐1(continued)

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Table3.3.4‐2a:Dissimilaritymeasuresforday‐of‐weekpairsof7‐10AMODmatrices

Table3.3.4‐2b:Dissimilaritymeasuresforday‐of‐weekpairsof2‐5PMODmatrices

Table3.3.4‐2c:Dissimilaritymeasures:7‐10AMvs.2‐5PMODmatricesonsameday‐of‐week

AsmentionedinSection2.3.4,wearealsoproposingtomonitorthedistributionofbuspassengertripdistancesovertime,wherethedistributionisproducedfromtheAPC‐derivedODmatricesandthedistancesbetweenstops.Tosupporttheabilityofmonitoringthisdistribution,wevalidatedtheability

oftheAPC‐derivedODmatricestocapturethedecreaseintheproportionofshortbustripsfromWintertoSpringquarterswhichwehadobservedinthetrueODdata.Weproducedquarter‐specificOD

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matricesusingtheIPF‐with‐nullbaseproceduredescribedinSection2.3.2andboardingandalightingdataobtainedfromthemanual,onboardproceduredescribedinthatsection.BasedonthetrueOD

datacollected,theproportionofpassengertripsthatwerelessthanonemilewas5.5%lowerinSpringquartertheninWinterquarter.Usingthematricesproducedfromtheboardingandalightingdata,theSpringquarterproportionwas3.5%lowerthantheWinterquarterproportion.Giventhesimplicityof

theIPF‐with‐nullbaseprocedure,weconsiderthistobefairlygoodagreementandencouragingofthepotentialtomonitortripdistancedistributionsfromtheAPCdata.

ThefinalODmatrix‐derivedmeasurethatwedevelopedthisyearwastheexpectedpassengertimeonthebus,conditionalonboardingoralightingstop.Figures3.3.4‐1aandb,wepresenttheexpected

passengertimeonthebusbyboardingandalightingstop,respectively.Whenconsideringthetimebyboardingstop(Figure3.3.4‐1a),wenoticethehighesttime,asexpected,fromthefirstboardingstop(stop4,whichisanaggregationoftheremoteWestCampusparkinglotstops).Weseeaclusterof

mostlydecreasingtimesforboardingstops5through9,whicharestopsthatprogressivelyapproachCentralCampus,andasecondclusterofapproximatelysimilartimesfortheremainingstops,servingCentralCampus.

TheclustersoftimesinFigure3.3.4‐1bforalightingstops4‐10,11‐12,13‐20,and21(whichisthe

aggregatedWestCampusparkinglotalightingstop)aresimilarlycompatiblewiththesectionsofcampusservedbytheCLSroute.Morespecifically,inthemorningthemostcommonoriginfordestinationsuptostop10isstop4(thewestcampusparkinglot).Thus,traveltimesprogressively

increaseforalightingstops5through10.Thetraveltimefunctionplateausatalightingstops11and12.Thisresultisconsistentwiththeexpectationthatsometripsdestinedtothesestopsaremorelikelyto

beoriginatingfromstopscloserthanstop4,resultinginlowertraveltimesforsometravelers(whilethoseoriginatingfromstop4havelongertraveltimes).Traveltostops13through20mostlyreflectslocalshorttrips,astravelersdestinedtothesestopsfromtheremotestop4aremuchmorelikelyto

takeanalternativeroute(CampusLoopNorth)whichreachesthesestopsmorequickly;thus,thelowertraveltimesassociatedwithalightingatthesestops.Finally,travelersheadingtostop21(theWestCampusparkinglot)areengagedinsubstantiallylongertripsbecauseoftherelativelylongdistance

fromstop20to21.

ThecompatibilitybetweenthepreliminaryresultsproducedandourknowledgeofthecharacteristicsoftheCTLroutegivesusconfidencethattheapproachcanproducemeaningfulmeasures,whichcanbemonitoredthroughtime.WewillsoonbepresentingtheseconceptsandpreliminaryresultstotheOSU

T&Pdirectorandoperationsstaff.

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Figure3.3.4‐1a:ExpectedpassengertraveltimebyCLSboardingstopdeterminedfromAPC‐derivedODmatricesandAVLdata

Figure3.3.4‐1b:ExpectedpassengertraveltimebyCLSalightingstopdeterminedfromAPC‐derivedODmatricesandAVLdata

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3.4.EducationaluseofCTL

AsdescribedinSection2.4,CTLdataandresultswereincorporatedintwoOSUtransportationcoursesinthispastyear–alargecourse(CE570)requiredofallCivilEngineeringundergraduatestudents,

includingbothtransportationmajorsandnon‐majors,andagraduatecourse(CE873)forstudentsinterestedintransportationsystemsfrommultipledepartments.

Therewere105studentsenrolledinCE570thispastyear.Thelectureusedtodescribethecampuslabandtheassignmentconsumedapproximately30minutes.Thelecturewasgivenattheendofthe

quarter,andtheextratimerequiredwasobtainedfromthe“slack”builtintothecoursetoaccommodatespecialtopicssuchasthis.

IncorporatingtheCTLmaterialintothecoursewasconsideredsuccessful.Moststudentsreceivedfullcreditontheassignment,indicatingthatthematerialwassuccessfullypresentedandreceived.

Nevertheless,therewereenoughstudents,eventhosewhoperformedwellinotheraspectsofthecourse,whomademistakesthatweresufficientlysimilarinnaturetoillustratealackofunderstandingonaspecificconcept–namely,theneedtointegratevariouscomponentsofpassengertraveltimeand

conditionalmathematicalexpectationstoformtheunconditionalexpectationofpassengertraveltimefromoriginstoptodestinationstop.Thisconceptisimportanttounderstandingtheanalyticalmethodcoveredandisnotspecifictotheempiricalcomponentintroducedforthefirsttimethispastyear.In

thisway,incorporatingtheCTLcomponentwasvaluableinhighlightingthispreviouslyunnoticedrelativedifficulty.(Wecallthisa“relative”difficulty,sincemostofthestudentsdidnotseemtohavetroublewiththisconcept.)

Therewere13studentsinCE873thispastyear.TwoofthesestudentsundertooktheCTL‐basedprojectdescribedinSection2.4.Theestimationresultsproducedintheprojectmostlyrevealedwhatwehadalreadyfoundinourfocusedinvestigationdescribedinsection3.3.2.Specifically,theresults

showedasignificantimprovementwhenusingtheIPFprocedurewiththeboardingandalightingdataratherthansimplyusingtheboardingandalightingdatathroughthe“refinednull”matrix,andafurthersignificantimprovementwhenusingthematrixderivedfromtheonboardsurvey,ratherthanthenull

matrix,asthebaseintheIPFprocedure.Theresultsalsorevealedsomeaspectswehadnotconsidered.Whereastheresultsdidnotindicateaday‐of‐weekeffect,theydidshowaslighttime‐of‐dayeffect:Matricesproducedfrommorningtripswereslightlybetterthanmatricesproducedfromafternoontrips.

Theresultsalsoindicated,althoughweakly,thatmatricesproducedfromtripswithhighervolumesperformedbetterthanthoseproducedfromtripswithlowervolumes.Wewillconsidertheseresults,producedinaneducationalcontext,inourfutureresearch.

Thestudentswhoundertooktheprojectseemedtogaintheintendedinsightsintomodelestimation

andinterpretationasmuchas,ormoreso,thanthosewhoundertookotherprojects.Toobtaintheresultssummarizedabove,thestudentsestimatedmultiplespecifications,wheretheresultsofonespecificationwereusedtoinformsubsequentspecifications.Thistypeofopen‐endeduseofthemodel

isoneoftheprimaryobjectivesoftheextendedproject.

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Inadditiontotheimpactonthetwostudentswhoundertookthisproject,wewerehappywiththemoregeneraleducationalimpactofthisproject.Thestudentteamspresentthesetting,design,andresultsof

theirprojectsorallytotherestoftheclassandtheinstructor.Intheiroralpresentation,thestudentsinvolvedwiththeCTLprojectdidanexcellentjobofcommunicatingtheconceptoftheCTL,thedatacollectioneffort,andthevariouscomponentsoftheODestimationprocedureintheiroralpresentation.

ThiswasthefirsttimemostofthestudentsintheclasswereexposedtotheCTL.AprerequisitecourseforCE873exposesthestudentstotheIPFprocedureinadifferentcontext.TheCE873projectallowedthestudentstoseethisprocedureappliedinapracticalapplication(estimatingbuspassengerorigin‐

destinationflows)usingempiricaldatacollectedinalocalsetting(theOSUcampus)withwhichtheyarefamiliar.Thistypeofreinforcementisconsideredparticularlyvaluable.

3.5.Perceptionsandattitudessurvey

Inthissection,wereportonsomeinterestingsurveyresponsestotravelbehavior,perception,andevaluationquestions.Allthereportedresultsarebasedonanalysisthatindicatesstatisticalsignificancewhereapplicable.Ourfindingsonafewperceptionsandevaluationissuesaresimilartothoseobtained

inthe2008OSUTransportationandParkingServices(T&P)survey,whichwasconductedforaverydifferentpurpose.However,oursurveyproducedresultsthatweconsiderofinteresttothegeneraltransitcommunitythatwerenotaddressedinthe2008T&Psurvey.Therefore,adescriptionofthe

methodologyandfindingswillbepresentedinapaperunderpreparationforpossiblepublication.

Approximately30%and4%,respectively,oftheundergraduateandgraduaterespondentsliveon‐campus.Thusanoverwhelmingproportionofoursubjectpopulationcommutestocampus.Inaddition,

therespondentsrepresentacross‐sectionofthecampuscommunity,whichisspatiallydistributedacrossthelargeOSUcampus(consistingofacoresurroundingbyspread‐outareas)aswellasvarious

academicandadministrativegroups.

3.5.1.Travelmodebehavior

Someinterestinghighlightsaboutthetravelmodebehaviorofourrespondentsareasfollows:

• Approximately60%ofrespondentsneveruseCABS,andapproximately30%rideCABSoccasionally,whereasonly10%rideCABSregularly.

• Approximately90%oftherespondentshaveacarintheColumbusarea.

• MostoftherespondentsdroveacartocampusasshowninTable3.5‐1,whilethoselivingon‐campuswalkedtotheircampusdestination.

• Approximately69%oftherespondentswhodonothaveacarinColumbusconsiderCABSis

valuableorhighlyvaluabletotheirtravelneedscomparedtoapproximately36%ofthosewhohavecars.

• Approximately49%oftherespondentswerefamiliarwithoneormoreroutesonCABSservice,

whereasapproximately45%knewthatCABSexisted,butwerenotfamiliarwithanyofitsroutes.Approximately6%didnotknowthatCABSexisted.

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• Approximately23%oftherespondentswhoneveruseCABSwerefamiliarwithoneormoreroutesonCABSservice,ascomparedtoapproximately85%oftherespondentswhouseCABS

onlyoccasionallyand99%oftherespondentswhouseCABSregularly.

Table3.5‐1:Transportation‐to‐campusmodechoicesofsurveyrespondents

TravelMode

Drivealone Shareacar COTA CABS Bike Walk

% 67 7 4 3 5 13Counts 2825 286 176 133 190 539

3.5.2.Perceptionsandevaluationanalysis

Thesurveycontainedfourteenstatementsdesignedtoelicitrespondents’attitudestowardCABS.Therespondentswereaskedtorespondtoeachquestionusinga5‐pointscale,labeledas1:Stronglydisagree,2:Disagree,3:Neutral,4:Agree,and5:Stronglyagree.Thestatementsareparaphrased

below:• EQ1‐HavingCABSservicereducestheamountofcartrafficoncampus...• EQ2‐ProvidingbusservicearoundcampusshouldbepartofOSU'seffortstopromoteagreen

campus…• EQ3‐CABSoffersservicethatisvaluabletomytravelneeds…• EQ4‐IfeelsafewalkingtoCABSstops…

• EQ5‐IfeelsafewaitingforCABSbuses…• EQ6‐IfeelsaferidingCABSbuses.• EQ7‐CABSbusdriversareprofessional…

• EQ8‐CABSbusesarecomfortable…• EQ9‐CABSroutesarereasonable…• EQ10‐MytraveltimetoreachmydestinationusingCABSisreasonable…

• EQ11‐MywaitingtimeforCABSbusesisreasonable…• EQ12‐AccessinginformationaboutCABSservice(e.g.,routes,frequencyofservice,hoursof

operation)iseasy…

• EQ13‐CABSisreliable…• EQ14‐Overall,IamsatisfiedwithCABS…

Wenotethattwelveofthesestatementscanbeclassifiedintothreeperceptioncategories:

• Category1:EnvironmentalIssues(EQ1‐2)• Category2:SafetyIssues(EQ4‐6)

• Category3:CABSServiceQualityIssues(EQ7‐13)

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TheothertwostatementsconcernthevalueofCABStoindividualtravelneeds(EQ3)andanoverallevaluationofCABS(EQ14).

ResponseratestoEQ1‐3wereveryhigh(greaterthan75%).Theotherstatements–whichrelatetospecificaspectsofCABS,suchassafety,CABSservicequalityissues,andoverallevaluationofCABS–receivedmuchlowerresponserates,sincemanyoftherespondentsuseCABSrarelyornotatall.The

distributionofthe5‐pointresponsesacrossthefourteenstatementsissummarizedinTable3.5‐2.Eachstatementisassociatedwiththreerowsinthetable.Thefirstrow,inwhichEQ#appears,liststhefivepossiblerespondentresponses.Thesecondandthirdrowsprovide,respectively,theproportionand

numberofindividualsrespondingtothestatement.

Someoftheinterestingobservationsthatcanbemadebasedonthistableandfromaninvestigationofnumericalassociationbetweenindividualresponses(describedinmoredetailbelow)arethefollowing:

• CABS’valuetoindividualtravelneedsreceivedalowerratingthandidotherevaluationitems.35%ofrespondentsdonotbelieveCABSisvaluabletotheirtravelneeds(thosewhochoose1or2),while

only39%believeCABSisvaluable(thosewhochoose4or5).• CABSreceiveditshighestratinginresponsetoitscontributiontopromotingagreencampus.Only

3%ofrespondentsdonotrecognizeCABS’roleinpromotingagreencampus(thosewhochoose1or

2),while90%recognizesucharole(thosewhochoose4or5).• Responsestostatementsaboutsafetyissues(EQ4,5,6)arecloselyassociatedwitheachother;thatis,

anindividualrespondentislikelytoprovideasimilarratingtoallthreeofthesestatements.Amongthesethreeissues,safetyofwalkingtoaCABSstopandsafetyofwaitingforaCABSbushave

strongerassociationthandothetwootherpairs.

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Table3.5‐2:Summaryofresponsesonperceptionandevaluationquestions

EQ1 1 2 3 4 5 EQ8 1 2 3 4 5

0.02 0.08 0.18 0.42 0.3 0.01 0.05 0.27 0.47 0.21

75 285 623 1492 1067 27 120 671 1179 535

EQ2 1 2 3 4 5 EQ9 1 2 3 4 5

0.02 0.01 0.07 0.36 0.55 0.01 0.06 0.24 0.49 0.21

79 47 257 1394 2130 35 138 587 1225 512

EQ3 1 2 3 4 5 EQ10 1 2 3 4 5

0.11 0.24 0.26 0.2 0.19 0.03 0.09 0.27 0.44 0.16

375 805 883 678 642 72 228 659 1075 401

EQ4 1 2 3 4 5 EQ11 1 2 3 4 5

0.01 0.04 0.19 0.41 0.34 0.04 0.12 0.32 0.4 0.12

39 112 537 1157 944 92 282 778 977 284

EQ5 1 2 3 4 5 EQ12 1 2 3 4 5

0.01 0.05 0.2 0.4 0.33 0.03 0.12 0.29 0.4 0.17

37 136 558 1103 908 70 299 759 1023 432

EQ6 1 2 3 4 5 EQ13 1 2 3 4 5

0.01 0.02 0.17 0.39 0.41 0.02 0.04 0.27 0.47 0.2

27 41 449 1055 1106 38 96 676 1173 494

EQ7 1 2 3 4 5 EQ14 1 2 3 4 5

0.01 0.03 0.24 0.43 0.29 0.01 0.04 0.26 0.5 0.19

30 81 597 1073 716 38 95 669 1282 498

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Inaddition,wehavefoundthefollowinggroupedbypertinentcategory:

SatisfactionwithCABS’service

• PeoplewhousedCABSmoreoftenaremoresatisfiedwithCABSservice.• PeoplewhocometocampusbyCABS,COTA(theColumbusareatransitservice)orwalkaremore

satisfiedwithCABSservicethanthosewhotravelbycarorbike.• Peoplewhospentmorethanonehour/dayontheInternetareslightlymoresatisfiedwithCABS’

informationaccessibilitythanthosewhospentlessthanonehour/dayontheInternet.

CABS’contributiontoenvironmentandtrafficreduction

• MorethanhalfofthepeoplewhosaidCABShadlittlevaluetotheirtravelneedsnevertheless

expressedanappreciationofCABS’positiveenvironmentalcontributionandofitscontributiontoreducingtrafficoncampus.

• UndergraduatestudentshadaslightlylowerappreciationofCABS’positiveenvironmental

contributionthandidtheothergroups.• ThefrequencyofusingCABShadlittleimpactonpeople’spositiveappreciationofCABS’

environmentalcontribution.• PeopleappreciatedCABS’positiveenvironmentalcontributionmorethanCABS’trafficreduction

contribution.

• Peoplewhouseorhaveusedmetropolitanpublictransportation(MPT)appreciatedCABS’positivecontributiontotheenvironmentandtotrafficreductionmorethanthosewhodonotorhavenotusedsuchpublictransportation.

CABSusage

• PeoplewhousedorareusingMPTweremorelikelytouseCABSoncampus.• ConditioningonCABSusers,whetherornotapersonusesMPTdidnotaffectthedistributionof

frequencyofusingCABS.• PeoplewhocametocampusbyCABS,COTAorwalkedweremorelikelytouseCABSwhileon

campusthanthosewhocametocampusbyothermodeslikethecarorbike.

• PeoplewhocametocampusbybikeweremorelikelytouseCABSwhileoncampusthanthosewhodrovetocampus,butlesslikelytouseCABSwhileoncampusthanthosewhocametocampusbyCABS,COTA,orwalked.

CABSsafety

• PeoplefeelsaferwhenridingCABSthanwhenwalkingtoaCABSstoporwhenwaitingforaCABSbus.

• PeoplefeelequallysafewhentheyarewalkingtoaCABSstoporwaitingforaCABSbus.• WhilewaitingforaCABSbus,alongerwaitingtimetendstomakepeoplefeellesssafe.

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Overallevaluation

Theresponsestotheoverallevaluationitem(EQ14)arecloselyassociatedtoEQ9,10,11,13.Thus,a

personislikelytogivehigheroverallevaluationtoCABSifheorsheappreciatesthereasonablenessofCABSroutes,traveltimes,waitingtimes,andreliability.

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CHAPTER4.CONCLUSIONS

Despitethedelaysencounteredinimplementingthislarge“SmartBus”systemandexploitingtherich

dataitgeneratesformultipleacademicpurposes–or,perhaps,inlightoftheseeminginevitabilityofsuchdelays–wehavemadesignificantprogressinthispastyeartowarddevelopingaCampusTransitLab(CTL)thatcanserveasaflagship,livinglabforresearch,educationandoutreach.Furthermore,we

haveproceededwithvariousresearchactivitiesresultingininsightfulfindingsofvaluetoresearchers,transitplanners,andtransitoperators.T&PpersonnelhavetoldotheruniversitiesofthebenefitsoftheSmartBussystemtoitsoperations.Theyhavealsoemphasizethevaluetheyarederivingfromtaking

advantageofthesystemtodeepentheirpartnershipwiththe“academicside”oftheuniversity.(Theconferenceinwhichmanyoftheseremarksweremadepubliclyoccurredaftertheendofthereportingperiodforthisyear’sproject.Wewillreportontheconferenceinlaterreports.)

IttookmuchlongerthanexpectedforOSUTransportationandParkingServices(T&P)tocompletethebiddingprocess,selectthecontractorandthenworkwiththecontractortoinstallandtesttheintegratedinformationtechnology“SmartBus”systemonOSU’sCampusAreaBusService(CABS).

Testingidentifiedmanystartupglitches,asmightbeexpectedwithaprojectofsuchamagnitudeandcomplexity.Furthermore,someelementscontinuetobeimplementedandrefined.Forexample,justrecently,afterthecloseofourreportingperiod,butbeforesubmissionofthisreport,T&Pintroduced

thetextmessagingcapabilityoftheCABStravelerinformationsystemreferredtoas“TransportationRouteInformationProgram”(TRIP),http://tp.osu.edu/cabs/trip.shtml.And,while10electronicmessage

signsareinstalledandareinoperationatthemajorstopsonthesystem,anothersetofsignsareplannedforinstallationinthenearfuture.TheformalannouncementofTRIPwasoriginallyplannedtobemadeinSpring2009,butdoingsowaspostponeduntilAutumn2009inlightofthedelaysin

addressingtheglitches.Infact,anoteontheTRIPsitestillstatesthatit“iscurrentlygoingthroughtesting,andbusinformationdisplayedmaynotbeaccurate.”

Inaddition,giventhecomplexitiesofusingautomateddatacollectionsystems,ithastakentheproject

teamlongerthananticipatedtoobtainapreliminarysetofreliableAVLandAPCdatathatcanbeusedforresearch,education,andoutreach.Furthermore,developinganunderstandingoftheAVLandAPCdata,andvalidatingitwiththegroundtruthcollectedbyourteamonselectedbustripsaddedtothe

effortinimplementingthesoftinfrastructuretosupportresearch,education,andoutreach.TheformatsofthedatafrombusAVLandAPCsensorsareprimarilydrivenbyoperationalneeds.Asaresult,thedatasetsareoftennotavailableinareadilyusableformforotherthanoperationalneedsand

containfieldsthatarecumbersometointerpret.Delaysandtime‐consumingiterationsseeminevitablewhenimplementingalargeprojectinanewenvironment.Indeed,wehavebeenpresentwhenT&Ppersonnelhavetoldotheruniversitiesthattheyshouldexpectunforeseendelaysiftheyplanto

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implementsuchasystem.Wewouldsimilarlyconcludethatobtaining,processing,andusingdatafromsuchasystemforresearch,education,andoutreachwilllikelyencounterunanticipateddelays.

Wehavemadeprogressindevelopingthemeanstotransferandstoredataautomaticallyandonanongoingbasisandin“workingoutthebugs.”Weareespeciallypleasedwithourabilitytoprocessthedatawehavereceived.Throughacombinationofconceptualdesignandtrialanderror,wehave

successfullyprocessedAPCandAVLdataintoageneralformthatcanbeusedbymultipleusersforavarietyofapplications.Therearestillseveralfieldsthatareyettobedeciphered,andthereexistsomeelementsofinformation(e.g.,theroutethebusisserving)thatwebelievemustbepresentinthedata

structurebuthaveyettoincorporateinanautomaticfashion.(Forthetimebeing,wehavedevelopedsomewhatlabor‐intensivemeansofidentifyingthebusroutefromthedata.)Wewillbeclarifyingmanyoftheseissuesinthenextiterationofinteractionswiththecontractorinthenearfuture.

Wehaveworkedaroundpresentstart‐updifficulties,andtheAPCandAVLdatawehavecollectedandprocessedthisyearhavebeenvaluableinseveralways.WehaveconvertedthedataintopreliminaryindicatorsofOSUbuspassengeractivitythatwewillmonitorincollaborationwithOSUTrafficand

ParkingServices(T&P).Wehavealsoprocessedthedataintoformatsthatcanbeusedinthecourseexerciseswepilotedwithmanuallycollecteddatainthispastyear.And,thedatahavesupportedandenlightenedmultipleaspectsofaFederalTransitAdministrationprojectweareconducting.

TheconceptualdevelopmentoftheeducationalandoutreachactivitiesweproducedthispastyearwereimportantstepstowardbroadeningtheuseandappealoftheCTL.WeimplementedCTL‐basedconceptsintotwoverydifferentcourses.Thecourseinstructorsbelievethattheabilitytointroducethe

conceptsinapracticalsettingonasystemthatis“justoutsidethedoorsoftheclassroom”offersauniqueandvaluablemeansofexposingthestudentstotheadvantagesofAVLandAPCtechnologiesfor

bustransitplanningandoperations,andofadvanceddatacollectiontechnologiesfortransportationsystemsingeneral.UsingempiricalCTLdatainquantitativeexercisesisalsovaluableasanunderstandablemeansofreinforcinggeneralprinciplespresentedincourses.Althoughwemanually

collectedthedataintheexercisesthispastyear,nowthatwecanproducethedatafromtheAVLandAPCsystemsinareliableandrepeatablemanner,wewillbeusingSmartBusdatainthefuture.Moreover,thesuccessoftheseeducationaleffortsismotivatingustodevelopandimplementadditional

CTL‐basededucationalmodulesintheupcomingyear.

ToindicateourprogressinunderstandingandprocessingtheSmartBusdata,wementionedaboveourabilitytoproduceAPC‐andAVL‐derivedmeasuresofpassengeractivitywhichwewilluseformonitoring

servicewithT&P.Equallyimportantwasourdevelopmentandvalidationofthesemeasures.Toourknowledge,monitoringmeasuresofdissimilarityinorigin‐destination(OD)flowmatrices,tripdistancedistributions,andexpectedbuspassengertraveltimeconditionalonboardingoralightingstophavenot

beenproposedpreviously.Indeed,themeasuresareallderivedfromODflowmatrices,whichpreviouslycouldnotbeproducedonaroutinebasisbeforetherelativelyrecentavailabilityofspatiallyandtemporallyextensiveAPCdata.BecauseoftheongoingandspatiallyextensiveAPCdatathatare

nowavailable,thesematricescannowbeproducedformonitoringpurposes,andweseetheCTLas

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meanstodemonstratethevalueofdoingso.SincetheAPC‐derivedODmatricesareonlyapproximationsofthetrueODmatrices,thequalityoftheapproximationswouldaffectthequalityof

themeasureswedeveloped.Therefore,thecorrespondenceoftheempiricallyestimatedmeasuresto“groundtruth”datawecollectedmanuallyandtoourknowledgeofgeneralpassengeractivityontheOSUcampussystemisnoteworthyasapreliminaryvalidationoftheabilitytoproducemeaningful

measuresonanongoingbasis.

WealsoconductedmultipleresearchactivitiesrelatedtotheCTLthatmakeimportantcontributions.ThemethodwehaddevelopedtomatchAVLdatatobusschedulesareunique,andtherefinementswe

madeappeartohavegeneratedinterestintheacademiccommunity.Therefinedapproachdesignedtoincorporatetheeffectsofbusholdingoperatingpoliciesandresultingbehaviorintoourmathematicalformulationproducedmuchimprovedresults.Thematchingproblembecomesmorecomplicatedwhen

morecomplexreal‐timeinterventionsareinvolved.Examplesincludeshort‐turningandscheduleswapping(viaovertaking)inmid‐route.Suchinterventionsneedtobeidentifiedusingadditionalinformationforthematchingmethodtoproducereliableresults.Incorporatingsuchinformationinthe

formulationoftheassignment‐basedmatchingmethodwouldbeavaluableextensionofinteresttoboththeresearchandpracticecommunities.

OurempiricalstudyonthequalityofODmatricesdeterminedfromaprocedurethatcanbereadily

implementedtotakeadvantageofexistingAPCdataisimportant.Theempiricalresultswerederivedfromonlytenbustripsononeroute.However,sincethereareveryfewempiricalstudieswheretrueODinformationisobtained,ourscanbeconsideredtobelarge.Itwassurprisingthattheseemingly

simplisticIPF‐with‐null‐baseprocedureproducedresultsofsimilarqualitytothoseobtainedfromanon‐boardsurvey.Sinceon‐boardsurveyshavetraditionallybeentheprimarymeansofdirectlyobtaining

ODmatrices,thisroughlyequivalentperformanceindicatesthatmuchcouldbegainedfromusingreadilyavailableAPCdata,evenwhenapplyingthesimpleIPF‐with‐null‐baseprocedure.Inaddition,itwasfoundthattheresultsoftheIPFprocedureweremarkedlyimprovedwhenusingtheon‐board

surveyderivedmatrix,ratherthanthenullmatrix,asabase.Theresultisastrongindicationthatcombiningon‐boardsurveyinformationwithincreasinglyavailableAPCdatacanleadtoODmatricesthataremarkedlybetterthanthosepresentlyavailable.

Inaddition,webelievethatthedesignofthestudy–throughtheuseofmultiplemetrics,thedevelopmentoftheintuitivelyappealingrelativeperformancesummary,andthedirectcomparisontootherreferenceprocedures–makesanimportantmethodologicalcontribution.Thedeveloped

methodologyshouldbeusedinnextstepsonthisprojectwithmoreextensiveempiricaldataandonotherroutestoinvestigateiftherelativelygoodperformanceoftheIPF‐with‐nullbaseprocedureweobservedisdependentonspecifictravelpatternsorroutestructure.Othermethodsaimedat

estimatingODflowsthataremoregeneraland,thus,morecomplexthantheIPFprocedureareworthinvestigating,andwearedevelopingtheseconceptsinaFTAsponsoredprojectforapplicationsonlargerurbantransitsystems.WewillapplythoseconceptstoAPCdataonOSUroutesbeingstudiedin

CTLasastepping‐stoneinthisregard.ThistypeofcoordinatedsupportactivityhighlightstheroleofCTL

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asatestbedthatcansupportmultiple,yetdistincteffortsandserveasalaunchingpadwhereprovenmethodscanbeappliedtolargersystems.

Wealsobelievewehavedevelopedausefultoolinourbusoperationssimulation.Whileadditionalvalidationisunderway,theinitialvalidationresultsindicatethatthesimulatoriscapturingCABSoperationsrealistically.Especiallyoncefurthervalidationiscompletedandpossiblerefinementsare

developed,weexpecttousethesimulationframeworktoaddressmultipleresearchquestionsonthisprojectinthefuture.Forexample,thissimulationwouldbeeffectiveinevaluatingvariousapproachestoscheduledesign.Thereisare‐emerginginterestinheadway‐basedschedules,whichcannowgain

tractioninlightoftheprevalenceofreal‐timeAVLsystems.Oursimulationtoolwouldalsobevaluableininvestigatingandevaluatingtheperformanceofvariousreal‐timeoperationsstrategies,whichautomatedmonitoringandcommunicationssystemsarealsomakingfeasible,

OurapplicationthisyearofthesimulationprogramtocompareAVLsignalsamplingonadistance‐basedapproachtosamplingonatime‐basedapproachintermsoftheaccuracyindwelltimeestimationshowedthesuperiorityofthedistance‐basedapproach.Whetherinthecontextofprovidingreal‐time

passengerinformationorinsupportingoff‐lineanalyses,samplingisacriticalcomponentofAVLsystems.However,toourknowledge,suchcomparisonsarenotavailableintheliterature.Dwelltimeestimationwouldonlybeonecomponenttoconsider,ofcourse,butitwasilluminatingtonotethe

extenttowhichthedistance‐basedapproachoutperformedthetime‐basedapproach.Wearepresentlydevelopingabehavioralexplanationofthisresult,andweareinvestigatingthesimulation‐basedresultsinfinerdetail–forexample,comparingdistance‐totime‐basedsamplingperformanceseparatelyat

stopswithlongdwelltimesandatstopswithshortdwelltimes,orinvestigatingtheimpactofthecharacteristics(length,variabilityintraveltime)ofthesectionsimmediatelyupstreamordownstreamof

thestoponthecomparativeperformance.

Finally,oursurveyresultsareprovinginformative.Theoriginalintentofthesurveywastoserveasabenchmark(the“before”case)forperceptionsandattitudesinabefore‐andafter‐implementationof

theSmartBusandpassengerinformationsystem.Westillintendtouseitassuch.However,theresultsareprovidinginterestinginformationonattitudesofusersandnonuserstowardCABS,theOSUbussystem.Webelievesomeofourfindings,suchasperceptiontowardthevariouselementsofCABSby

demographicgroup,willbeinterestingtoT&Padministrators.(WewillreportinthefutureonourdiscussionofthesurveyresultswithT&P,whichoccurredafterthecloseofthisreportingperiod.)However,someoftheresults–suchastherecognitionofthepositiveimpactofabussystemonthe

environmentandonreducedtraffic,andthedifferenceinthisrecognitionamongdemographicgroups–areofgeneralinteresttothetransitandmultimodaltransportationcommunity.

Insummary,webelievethatthemulti‐thrustapproachweundertookthispastyearwasproductivein

leadingtotheestablishmentoftheOSUCampusTransitLab(CTL)asaunique,recognized,andvaluableinfrastructureforresearch,education,andoutreach.Moreprogressisrequired,andwebelievethatitwouldbebeneficialtoproceedisinasimilarlymulti‐facetedapproachdevotedto:

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• developingthemeanstocollect,process,andmakeavailableAVLandAPCdataonaroutinebasis,

• routinelymakingthedataavailabletomultipleusers,

• usingthedatatosupportmultipleresearchactivities(sponsoredinsideandoutsideofNEXTRANS),neweducationalactivities,andongoingbussystemmonitoringincollaboration

withOSUT&P,

• conductingseveralresearchstudiesrelatedtoimprovedbustransitplanningandoperationsthatcanoccurthroughinnovativeusesofthesedata.

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