research and education from a smart campus transit laboratory
TRANSCRIPT
USDOTRegionVRegionalUniversityTransportationCenterFinalReport
ReportSubmissionDate:October15,2009
IL IN
WI
MN
MI
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NEXTRANSProjectNo006OY01
ResearchandEducationfromaSmartCampusTransitLaboratory
By
MarkR.McCord,PrincipalInvestigatorProfessorofCivilandEnvironmentalEngineeringandGeodeticSciences
and
RabiG.Mishalani,Co‐PrincipalInvestigatorAssociateProfessorofCivilandEnvironmentalEngineeringandGeodeticSciences
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
IL IN
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NEXTRANSProjectNo006OY01 FinalReport,October2009
ResearchandEducationfromaSmartCampusTransitLaboratory
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
NEXTRANS Project No 006OY01Technical Summary - Page 2
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
NEXTRANS Project No 006OY01Technical Summary - Page 3
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.
NEXTRANS Project No 006OY01Technical Summary - Page 4
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
ResearchandEducationfromaSmartCampusTransitLaboratory
By
MarkR.McCord,PrincipalInvestigatorProfessorofCivilandEnvironmentalEngineeringandGeodeticSciences
and
RabiG.Mishalani,Co‐PrincipalInvestigatorAssociateProfessorofCivilandEnvironmentalEngineeringandGeodeticSciences
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
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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
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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
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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
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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
14
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)
15
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
16
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.
17
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.
18
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)
19
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)
20
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.
21
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.
22
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
43
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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