Informed Urban Transport Systems: Classic and Emerging Mobility Methods toward Smart Cities

473 Pages·2018·26.139 MB·English

by  Joseph Chow

INFORMED URBAN
TRANSPORT
SYSTEMS
INFORMED URBAN
TRANSPORT
SYSTEMS
Classic and Emerging
Mobility Methods
toward Smart Cities
JOSEPH Y.J. CHOW
Elsevier
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LIST OF FIGURES
Fig.1.1 OverviewofCanadianITSArchitectureversion2.0 6
Fig.1.2 DemonstrationofuseofMATSimtovisualizepublictransport
boardingsandalightingsinSingapore 7
Fig.1.3 Comparisonofpersonalcomputer,smartphone,andtabletsales 8
Fig.1.4 AvisionofMobility-as-a-Service 9
Fig.1.5 DistributionofBIXIbikeshareinTorontoin2012 14
Fig.1.6 Manheim-Florian-Gaudryframeworkfortransportsystems
analysis,withnewelementsingray 19
Fig.1.7 MATSimfunctionality 26
Fig.2.1 DemonstrationsofdataopportunitiesduetoadvancesinBig
Dataandsmartcities:(A)usingTwitterdatatotrackurban
mobilityduringHurricaneSandy;(B)real-timeCaliforniafreeway
trafficmonitoringfromCaltransPeMS;(C)repositoryofreal-time
andscheduledGTFSfeedsfromaroundtheworldincluding
Paris;(D)calldetailrecordsusedtomodeltravelpatterns;(E)Taxi
GPSdata 32
Fig.2.2 Atypologyofsmartcityfunctions 33
Fig.2.3 Columbussmartcitychallengeimplementationvision 35
Fig.2.4 IllustrationofconnectingIoTinfrastructurewithcities,users,
operators,andsoftwaredevelopers 36
Fig.2.5 Illustrationofbeatsthatformpulsesuniquetodifferent
locationsinacitydependingonthetypeofactivitydataused 39
Fig.2.6 Usinga(A)4-regionrepresentationofNYCand(B)illustrationof
astandardizedpacevectorprogressingovertimebefore,during,
andafterHurricaneSandy 41
Fig.2.7 (A)Originalillustrationofactivityprismsand(B)demonstration
ofspace-timeaquariumofspace-timepathsofAfricanandAsian
AmericansinPortland 44
Fig.2.8 (A)Minimumpathsand(B)velocityfieldisochronesforapoint
2.5mileseastofManchestercitycenterin1965 45
Fig.2.9 Constructingaleast-costpathfromacostvectorfield.(A)
Initialization,(B)leastcostpathwithaccumulatedcosttoj,(C)
temporallyreferencedleastcostpath,and(D)extractedpathas
aspace-timepolyline 47
Fig.2.10 Illustrationofafield-basedspace-timeprismfromMillerand
Bridwell(2009) 48
Fig.2.11 Illustrationoftwodifferentindividualvelocityvectors 48
Fig.2.12 (A)HouseholdtravelsurveydatafromGreaterTorontoArea;(B)
kerneldensitymapofthetrajectoriesat8a.m.;(C)travel
momentumfieldat8a.m.;(D)isometricviewsoftravel
momentumfieldin(C) 51
Fig.2.13 ExampleforExercise2.2 53
vii
viii ListofFigures
Fig.2.14 (left)Beijing,China,and(right)totaldailyreal-timeGPSlocations
from12,000taxisonNovember2,2012 54
Fig.2.15 SnapshotsofkerneldensityestimatedtaxipopulationTMF
in24h 55
Fig.2.16 Illustrationofvectorprojection 56
Fig.2.17 TMFsforExercise2.3 57
Fig.2.18 Projectedvaluesandtemporalprofile 58
Fig.2.19 Busrouteno.506fromTorontoTransitCommission 59
Fig.2.20 TemporalprofileofrouteprojectionsofTMFonTTCbusno.506 59
Fig.2.21 (A)TMFsforExercise2.4and(B)projectvaluesalongwith
temporalprofile 60
Fig.2.22 Magnitudeoftravelimpulsefieldbetween2011and2006TMFs
at8a.m 61
Fig.3.1 Supply-demandcurvesforatransportsystem 70
Fig.3.2 Equilibriumallocationoftravelersontwoparallelroutes 72
Fig.3.3 NetworkforExercise3.3 74
Fig.3.4 BeforeandafterscenariosofnetworkforBraess’Paradox
example 85
Fig.3.5 Illustrationoffreightassignmentof(A)path-basedcommodity
flows,andsimultaneously,(B)link-basedcyclictruckandrail
vehicularflows 94
Fig.3.6 SiouxFallsnetworkwithlinkIDsandlinkflows(ID/flow)underUE 96
Fig.3.7 IllustrationofnetworkassignmentinGreaterTorontoAreausing
TransCAD 98
Fig.3.8 Illustrationoftransitlinesoperatingovera4-nodenetwork 99
Fig.3.9 Samplenetworktoillustratecommonlinesproblem 100
Fig.3.10 SamplenetworkforExercise3.9 102
Fig.3.11 Samplenetworkwithupdatedlinkcostfunctions
forExercise3.10 106
Fig.3.12 Illustrationoftime-expandednetworkrepresentationofatransit
networkwiththreelines 108
Fig.3.13 Equilibriumlinkflowsforeachofthreepassengergroups 109
Fig.3.14 MILATRASmodelframework 110
Fig.3.15 Queuerepresentedbycumulativedeparturesfromhome
andarrivalsatwork 113
Fig.3.16 Bottleneckqueuewithoptimaltollappliedacrossoptimal
timeinterval 115
Fig.3.17 PlotofqueuesformedfromexampleinExercise3.11 116
Fig.3.18 (A)Assumeddowntowncityblocksettingand(B)dynamicflows
undersaturatedcondition 118
Fig.3.19 StudyareaindowntownToronto 121
Fig.3.20 Dynamicsofsearchfriction 124
Fig.3.21 Algorithmsto(A)runand(B)calibratethetaxiequilibriummodel 125
Fig.3.22 Relativespatialchangesinconsumersurplusfromsingle-ride
taximarkettosharedtaxivia(A)matchingbywaiting
and(B)matchingbydetour 127
ListofFigures ix
Fig.3.23 Smartcitiesmobilityprovisionrepresentedasatwo-sided
market 129
Fig.3.24 Illustrationofthesensitivityoftwo-sidedmarkettoplatform
pricing 130
Fig.3.25 Componentsofagent-basedstochasticuserequilibrium
obtainedbyday-to-dayadjustmentofuserandoperator
decisionsasatwo-sidedmarket 134
Fig.4.1 A(A)4-nodenetworkonwhich(B)auserconductsactivities 140
Fig.4.2 Additionaltripgeneratedwithoutanyadditionaleconomic
activity(Kangetal.,2013) 141
Fig.4.3 Disutilityofscheduledelaydependingontheflexibilityoflate
arrival 143
Fig.4.4 Activitysystemtoillustrateflexibilityofmodeltocapture
differentschedulingpreferences 153
Fig.4.5 ThreesolutionsofmHAPPbasedondifferentparameters
foruser1(A,B)anduser2(C) 155
Fig.4.6 SolutionsofexampleinChow(2014)forasinglehousehold
usingAlgorithm4.2,for(A)abaselinescenarioand(B)ascenario
wherediscretionaryactivityutilityisdoubled 161
Fig.4.7 Overviewofdecompositionapproachtoobtainingmarket
scheduleequilibrium 163
Fig.4.8 Cumulativediagramofarrivalsanddeparturesontolink(u,w)
forExercise4.4 165
Fig.4.9 ActivityandtransportsystemforExercise4.5(driving:{(0,7),
(0,6),(6,7)},transit:(8,9),walking:{(0,2),(0,8),(2,8),(2,7),(1,6),
(1,9)}) 168
Fig.4.10 Algorithm4.3convergenceforExercise4.5 169
Fig.4.11 Cumulativediagram(A)withoutlinkcapacityand(B)withlink
capacity 170
Fig.4.12 MATSiminputdata:(A)transitlineschedulefromGTFSoverlaid
onaroadnetworkfromOpenStreetMapsand(B)activityzones
correspondingtoNYMTChouseholdtravelsurvey 172
Fig.4.13 Schedulefilethatneedstobemodifiedforscenarioanalysis 173
Fig.4.14 Comparisonofdistributionofwelfaremeasureforusersofthe
systembeforeandafterheadwayreduction 174
Fig.4.15 Comparisonofdistributionof7a.m.–10p.m.ridershiponthe
AlinewithRegular(before)andReduced(after)headway 174
Fig.4.16 ComparisonofdistributionoftimeofdayarrivalsontotheAline
attheW.4thStation(Station13)beforeandafterheadway
reduction 175
Fig.4.17 Freightactivitysystem 179
Fig.5.1 IllustrationofastatechangeintheNguyen-Dupuisnetworkthat
leadstoachangeinobservedflows 186
Fig.5.2 BayesiannetworkmodelofLuxembourgcommutermode
choice 191
Fig.5.3 SamplenetworkforExercise5.1 196
x ListofFigures
Fig.5.4 Exampledial-a-rideproblemforExercise5.2 200
Fig.5.5 OCTAMNetworkandactivitylocationsofhouseholdsinOrange
County,CA,fromthe2001CaliforniaHouseholdTravelSurvey 203
Fig.5.6 ComparisonofHAPPoutputsbasedondifferentparameters 206
Fig.5.7 SampleGPSdatafromdrayagetrucksoutoftheSanPedroBay
Ports 207
Fig.5.8 ComparisonofinverseVRPresultsforindividualandfleetoftwo
vehicles 208
Fig.5.9 SamplenetworkforExercise5.3 209
Fig.5.10 ThirteenmajorairportsinCalifornia 211
Fig.5.11 Multiagentinverseoptimizationasafixed-pointproblem 214
Fig.5.12 IllustrationofAlgorithm5.2 215
Fig.5.13 TestnetworkwithnodeandlinkIDs 219
Fig.5.14 ConvergenceofAlgorithm5.2ontestnetwork 220
Fig.5.15 Outputdistributionofposteriorlinkcostsacrossthepopulation
of500simulatedagentsforlink1(top)tolink5(bottom) 221
Fig.5.16 Toynetworkusedforillustratingmethodology 227
Fig.5.17 Nguyen-Dupuisnetwork 228
Fig.5.18 ConvergenceofdualpricesusingAlgorithm5.2A 230
Fig.5.19 Trajectoryofsimulatedrouteobservationsinanonlinelearning
setting 231
Fig.5.20 Dualpricetrajectoriesbasedon300simulatedagentarrivals 231
Fig.5.21 Queensfreewaynetwork 232
Fig.5.22 Linkdualpricetrajectoriesobtainedfromonlinenetwork
learning 233
Fig.5.23 Half-hourintervalscreenshotsofGoogleMapsreal-timeshortest
pathqueries(Xuetal.,2017) 234
Fig.5.24 SnapshotsofmultiagentIOoutputdualpricesateveryhalfhour
withnonzeropricesrepresentedbyheavierarrows 235
Fig.6.1 Laplacedistributednoiseaddedtofunctionsofvaluesfrom
SandS0 242
Fig.6.2 IllustrationoffilteredincomedataaccordingtoE¼{1000,100} 246
Fig.6.3 Spatialdistributionandtraveltimesbetweenzonecentroids,
and20sampleODlocations 249
Fig.6.4 DistributionsoflocationnoisebyoriginzoneforE¼5 249
Fig.6.5 ComparisonofODdistributionsfororiginalandE-differentially
privatelocations 251
Fig.6.6 Frameworkfork-anonymousdiffusionmodels 253
Fig.6.7 (A)Originalflows,(B)outcomeassignmenttominimize
repeatingtheoriginalflows,and(C)theoutcomeassignment
tominimizerepeatingthecombinedflowsontheleft 254
Fig.6.8 Shiftofprobabilitiesfrommaximumprobabilityfororiginflows
tounconstrainedmaximumentropy 258
Fig.6.9 (A)Exampletour,with(B)truevehiclearrivaltimesand
passengertraveltimes,and(C)otherknownparameters 262
Fig.6.10 Diffusionof(0,2,1,3,5,4,6,0)into90tourswithΔ¼0.1 263
Fig.6.11 Ten-anonymousdiffusionof(0,2,1,3,5,4,6,0)usingAlgorithm
6.2withΔ¼0.10 264
ListofFigures xi
Fig.6.12 Comparisonofentropyvaluefornumberoftoursunderdifferent
tourgenerationmethods 264
Fig.6.13 Two-pathdiffuseddataobjectset 267
Fig.6.14 Two-anonymousdistributionofposteriorlink7dualpriceasa
functionofprior 267
Fig.7.1 Typesofproblemsbycomplexity 276
Fig.7.2 NetworkusedforExercise7.1 279
Fig.7.3 IterationsofPrim’sAlgorithmforExercise7.1 280
Fig.7.4 IllustrationoftheSteinerTreeProblem 281
Fig.7.5 SevenbridgesofKo€nigsberg 282
Fig.7.6 Illustrationofworst-casematchedodd-degreenodelengths 284
Fig.7.7 OptimalsolutiontoTSPusingintegerprogramming 284
Fig.7.8 EuleriantourobtainedusingAlgorithm7.2 285
Fig.7.9 Severaliterationsofannearestneighborheuristiconexample
fromFig.7.2 286
Fig.7.10 InstanceforExercise7.3 290
Fig.7.11 Optimalsolutionobtainedbyintegerprogramming 291
Fig.7.12 OptimalsolutiontoDARPexample 293
Fig.7.13 InstanceforExercise7.5 297
Fig.7.14 OptimalsolutionviaintegerprogrammingfortwoPvalues 298
Fig.7.15 Optimalsolutionstosetcoveringproblemwiths¼1,s¼2 301
Fig.7.16 Facilitylocationbasedon(A)nodes,(B)flow,and(C)itinerary
intercept 305
Fig.7.17 Solutionsto(A)relocationignoringqueuedelayand(B)
relocationwithqueuedelay 308
Fig.7.18 Illustrationoflineplanningproblem 309
Fig.7.19 InputdataforExercise7.11 314
Fig.7.20 IllustrationofabilevelprobleminEq.(7.20)withnosolution 318
Fig.7.21 Fourstepstosurrogate-basedNDP:(A)generatesample
solutions,(B)fitasurrogatemodeltothesamples,(C)ensure
feasibilityofsamplesolutions,(D)selectnewsolutionusing
surrogatemodel 319
Fig.7.22 NetworkusedforExercise7.12 320
Fig.7.23 TwolayersofAlgorithm7.6onExercise7.13 321
Fig.7.24 ExampleforExercise7.15 328
Fig.7.25 Lower(0,0,0)andupperbound(1,1,1)solutionsattherootnode 329
Fig.7.26 (A)Regionsforclassifyingsymbioticnetworkdesignstrategies
and(B)impactofnetworkexpansionandcontraction 333
Fig.7.27 ExamplenetworkforExercise7.16 334
Fig.7.28 Objectivespaceofthealternativesolutions 334
Fig.7.29 Examplewith(A)substitutabletollroadsand(B)complementary
tollroads 336
Fig.8.1 Examplesofstochasticvariables:(A)aprobabilitydensity
functionofarandomvariablewithatriangulardistribution
and(B)correspondingcumulativedistributionfunction
oftriangulardistribution 343
xii ListofFigures
Fig.8.2 Examplesofstochasticprocesses:(A)firstand(B)second
instancesofasimulation(t¼[0,5])andprojection(t¼(5,6])
ofageometricBrownianmotionwithμ¼0.1,σ¼0.4 350
Fig.8.3 ExpandingtheNPVdecisionspace(topregion)tofourother
regionsasopportunities 353
Fig.8.4 Distributionsofprofitundercommitment(solid)andoption
(dashed)investments 354
Fig.8.5 PlotofprojectvalueRandthecorrespondingoptionvalueV
fortwoprojects 357
Fig.8.6 (A)SiouxFallsnetworkwithcandidatelinksand(B)comparison
ofdeferralwithredesign(NetworkInvestmentDeferralOption
(NIDO))todeferralonlyasfunctionofσ 364
Fig.8.7 Illustrationofmarketswitching 365
Fig.8.8 Comparisonoftwotrajectories(Q1,Q2)fromaninitialpoint
Q0¼$0.75 368
Fig.8.9 (A)SingleOD,three-linkexamplewith(B)aplotofthebaselink
costfunctionsand(C)simulatedtrajectoriesofQovera2-year
horizon 375
Fig.8.10 ComparisonoftheCR-WeibulldistributionsforCityA(F )and
A
CityB(F ) 382
B
Fig.8.11 SimulationofHyyti€aetal.(2012)dynamicdispatchpolicy 383
Fig.8.12 Evaluationofmyopicandnonmyopicpoliciesrelativeto
CR-Weibulldistributions.(A)ScenarioA,(B)ScenarioB,
and(C)ScenarioC 384
Fig.B.1 Systemsengineeringprocess 416
Fig.B.2 Usecasediagramsfor(A)on-busfareticketingand(B)roadside
fareticketing 419
Fig.B.3 Aconceptualdiagramfortheroadsidebusticketingsystem 420
Fig.B.4 AnactivitydiagramwithswimlanesforPayFareusecasein
roadsideticketing 421
Fig.B.5 AdeploymentdiagramofthePT04TransitFareCollection
Managementusecase 422
Fig.C.1 Examplestatetransitiondiagram 427
Fig.C.2 Statetransitiondiagramofabirth-and-deathprocess,ofwhich
theM/M/1modelisone 430
Fig.C.3 ComparisonofthestateprobabilitiesforM/M/1andM/M/1/1
queues 434
Fig.C.4 Regionforaspatialqueueanalysiswithasingleshuttlebasedat
acentraldepot 435
Fig.C.5 Exampleofaqueuenetwork 437
Fig.D.1 Illustrationofisoutilitycurvesandresourceexpenditurebudget
(straightline) 440
Fig.D.2 Derivationofindividualdemandfunctionfromutilitycurves 441
Fig.D.3 ComparisonofCDFsofε 444
n
Fig.D.4 Nestedlogitexample 448
Fig.D.5 Illustrationofnonconvexityofrevenuemaximizationontwo
segmentswithp ¼$2 454
2