Data driven solutions to transportation problems ebook pdf by louise.owens516

Data-DrivenSolutions toTransportation Problems

Editedby YinhaiWang UniversityofWashington

ZiqiangZeng

SichuanUniversity

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Contributors

NumbersinParenthesesindicatethepagesonwhichtheauthor’scontributionsbegin.

MatthewJ.Barth (11),DepartmentofElectricalandComputerEngineering;Collegeof Engineering-CentreforEnvironmentalResearchandTechnology(CE-CERT), UniversityofCalifornia,Riverside,CA,UnitedStates

KanokBoriboonsomsin (11),CollegeofEngineering-CentreforEnvironmental ResearchandTechnology(CE-CERT),UniversityofCalifornia,Riverside,CA, UnitedStates

XiChen (175),SchoolofTransportationScienceandEngineering,BeihangUniversity, Beijing,People’sRepublicofChina

Xiqun(Michael)Chen (201),CollegeofCivilEngineeringandArchitecture,Zhejiang University,Hangzhou,People’sRepublicofChina

GeGuo (247),InstituteofComputingTechnology,ChinaAcademyofRailway Sciences,Beijing,People’sRepublicofChina;DepartmentofCiviland EnvironmentalEngineering,UniversityofWashington,Seattle,WA,UnitedStates

MengLi (111),DepartmentofCivilEngineering,TsinghuaUniversity,Beijing, People’sRepublicofChina

HuipingLi (111),DepartmentofCivilEngineering,TsinghuaUniversity,Beijing, People’sRepublicofChina

LiLi (247),InstituteofComputingTechnology,ChinaAcademyofRailwaySciences, Beijing,People’sRepublicofChina

XiaoleiMa (175),SchoolofTransportationScienceandEngineering,Beihang University,Beijing,People’sRepublicofChina

XueweiQi (11),DepartmentofElectricalandComputerEngineering;Collegeof Engineering-CentreforEnvironmentalResearchandTechnology(CE-CERT), UniversityofCalifornia,Riverside,CA,UnitedStates

HaiyanShen (247),InstituteofComputingTechnology,ChinaAcademyofRailway Sciences,Beijing,People’sRepublicofChina

TianyunShi (247),InstituteofComputingTechnology,ChinaAcademyofRailway Sciences,Beijing,People’sRepublicofChina

XiaoqianSun (227),NationalKeyLaboratoryofCNS/ATM,SchoolofElectronicand InformationEngineering,BeihangUniversity,Beijing,People’sRepublicofChina

PengSun (247),InstituteofComputingTechnology,ChinaAcademyofRailway Sciences,Beijing,People’sRepublicofChina

xii Contributors

JinjunTang (137),SchoolofTraffic&TransportationEngineering,CentralSouth University,Changsha,China

SebastianWandelt (227),NationalKeyLaboratoryofCNS/ATM,SchoolofElectronic andInformationEngineering,BeihangUniversity,Beijing,People’sRepublicof China

YinhaiWang (1,51),DepartmentofCivilandEnvironmentalEngineering,University ofWashington,Seattle,WA,UnitedStates

GuoyuanWu (11),CollegeofEngineering-CentreforEnvironmentalResearchand Technology(CE-CERT),UniversityofCalifornia,Riverside,CA,UnitedStates

Yao-JanWu (81),DepartmentofCivilandArchitecturalEngineeringandMechanics, UniversityofArizona,Tucson,AZ,UnitedStates

ShuYang (81),DepartmentofCivilandArchitecturalEngineeringandMechanics, UniversityofArizona,Tucson,AZ,UnitedStates

ZiqiangZeng (1),DepartmentofCivilandEnvironmentalEngineering,Universityof Washington,Seattle,WA,UnitedStates,BusinessSchool,SichuanUniversity, Chengdu,People’sRepublicofChina

GuohuiZhang (51),DepartmentofCivilandEnvironmentalEngineering,Universityof HawaiiatManoa,Honolulu,HI,UnitedStates

MingqiaoZou (111),DepartmentofCivilEngineering,TsinghuaUniversity,Beijing, People’sRepublicofChina

ListofFigures

Fig.1.1 Data-driveninnovationprocessintransportationsystems. 5 Fig.1.2 Areader’sguidetothestructureanddependenciesinthisbook. 8 Fig.2.1 BasicoperationmodesforPHEV. 15 Fig.2.2 BasicclassificationofEMSforPHEV. Note:PMP,Pontraysgin’sminimum principle;MNIP,mixednonlinearintegerprogramming;DP,dynamic programming;QP,quadraticprogramming;RL,reinforcementlearning; ANN,artificialneuralnetwork;LUTs,look-up-tables;MPC,model predictivecontrol;AECMS,adaptiveequivalentconsumptionminimization strategy. 16 Fig.2.3 Flowchartoftheproposedon-lineEMS. 18 Fig.2.4 Timehorizonsofpredictionandcontrol. 18 Fig.2.5 Examplesolutionsofpower-splitcontrol. 20 Fig.2.6 EstimationandsamplingprocessofEA. 21 Fig.2.7 EDA-basedon-lineenergymanagementsystem. 22 Fig.2.8 SOCreferencecontrolboundexamples. 24 Fig.2.9 ExampletripalongI-210insouthernCaliforniausedforevaluation. 27 Fig.2.10 Populationinitializationfromthesecondpredictionhorizon(i.e., t 2). 28 Fig.2.11 Comparisonofcomputationtime. 29 Fig.2.12 SOCtrajectoriesresultedfromdifferentcontrolstrategies. 30 Fig.2.13 Box-plotoffuelsavingson30trips. 30 Fig.2.14 Fuelsavingsfortripswithdifferentduration,comparedtoB-I. 32 Fig.2.15 ResultantSOCcurvewhentripdurationis5000s. 32 Fig.2.16 SOCtrackwithknownorunknownchargingopportunity.(A)C-D.(B)S-A. (C)C.(D)S-L. 33 Fig.2.17 TaxonomyofcurrentEMS. 35 Fig.2.18 Graphicalillustrationofreinforcementlearningsystem. 39 Fig.2.19 Illustrationofenvironmentstatesalongatrip. 40 Fig.2.20 Convergenceanalysis(" ¼ 0.7; ¼ 0.5; ¼ 0.5). 43 Fig.2.21 4-Dslicediagramofthelearned Q table. 43 Fig.2.22 Fuelconsumptioningallon (bracketedvalues) andSOCcurvesbydifferent explorationprobabilities. 44 Fig.2.23 (A)Linearadaptivecontrolof ";(B)linearadaptivecontrolof " with chargingopportunity. 45 Fig.2.24 Optimalresultswhenavailablecharginggainis0.3(Cg ¼ 0.3). 45 Fig.2.25 Optimalresultswhenavailablecharginggainis0.6(Cg ¼ 0.6). 46 Fig.2.26 Fuelconsumptionreductioncomparedtobinarycontrol. 46 Fig.3.1 ThearchitectureoftheproposedANNmodel. 57 Fig.3.2 FlowchartoftheANNalgorithm. 59 Fig.3.3 Flowchartofthevideo-basedvehicledetectionandclassificationsystem. 60 Fig.3.4 Thesystemuserinterface. 60 Fig.3.5 Anexamplevideosceneanditsbackground.(A)Asnapshotofavideo scene;(B)extractedbackground. 62 Fig.3.6 Systemconfigurationandcomponentsofthevirtualdetector. 63 xiii

Fig.4.4

models:(A)firstmoment,Case1;(B)firstmoment,Case2;(C)second moment,Case1;(D)secondmoment,Case2;(E)thirdmoment,Case1;and

Fig.4.5 Percentile-basedtraveltimereliabilitymeasureusingthethreemixture models:(A)10thpercentiletraveltime,Case1;(B)10thpercentiletravel time,Case2;(C)50thpercentiletraveltime,Case1;(D)50thpercentile

Fig.3.7 AsnapshotoftheVVDCsystemwhenavehicleisdetectedandclassified. 65 Fig.3.8 ComparisonsbetweenobservedandestimatedBin1volumesat3-minlevel fordetectorofES-163R:_MN___2onMay13,1999. 67 Fig.3.9 Comparisonsbetweenobservedandestimatedbinvolumesat15-minlevel fordetectorofES-163R:_MN___2onMay13,1999. 67 Fig.3.10 Comparisonsbetweenobservedandestimatedbinvolumesat15-minlevel fordetectorofES-209D:_MN___2onMay10,2004. 68 Fig.3.11 Testsitesituations(A)NorthboundSR-99neartheNE41stStreet (B)SouthboundI-5neartheNE92ndStreet. 72 Fig.3.12 Errorinvestigations:(A)atruckoccupyingtwolanesismeasuredtwice; (B)amisclassifiedtruckwithacolorofthebedsimilartothebackground color. 75 Fig.4.1 Calculatingpercentilegivenadistribution. 90 Fig.4.2 Frameworkoftestinghypotheses. 92 Fig.4.3 Log-likelihoodsofthethreemixturemodelswithKlyingin[15,39]. Log-likelihoods(A)Case1and(B)Case2;AIC(C)Case1and(D)Case2; andBIC(E)Case1and(F)Case2. 93

Moment-basedtraveltimereliabilitymeasureusingthethreemixture

ofvariance,Case2;(I)standardizedskewness,Case1;and(J)standardized skewness,Case2. 95

(F)thirdmoment,Case2;(G)coefficientofvariance,Case1;(H)coefficient

traveltime,Case2;(E)90thpercentiletraveltime,Case1;(F)90th percentiletraveltime,Case2;(G)95thpercentiletraveltime,Case1;(H) 95thpercentiletraveltime,Case2;(I)bufferindex,Case1;(J)bufferindex, Case2;(K)planningtimeindex,Case1;and(L)planningtimeindex, Case2. 96 Fig.4.6 Frameworkofmeasuringtheaccuracyoftraveltimereliability. 98 Fig.4.7 Originanddestination,anditsshortestroutes. 103 Fig.4.8 Threepreferredroutes,casestudy. 103 Fig.4.9 Averagetraveltimesbypreferredroute. 104 Fig.5.1 Designofthestated-preference(SP)experiment. 116 Fig.5.2 TheinterfaceoftheSPexperiment. 117 Fig.5.3 Comparisonofthegenderratio. 118 Fig.5.4 Householdincomedistribution. 118 Fig.5.5 Departuretimedistribution. 118 Fig.5.6 Modesplit. 119 Fig.5.7 Frameworkoftheagent-basedchoicemodel. 119 Fig.5.8 Policyandscenarioanalysisframework. 125 Fig.5.9 Simulationnetwork(2ndringroadofBeijing). 125 Fig.5.10 Congestionchargesscenarios(I). 126 Fig.5.11 Congestionchargesscenarios(II). 127 Fig.5.12 AnillustrationofaVMSpanel. 128 Fig.5.13 AnSBOframeworkfortheVGSCproblem. 130 Fig.5.14 MapofTHIPwithlanduse. 131 Fig.5.15 RoadnetworktopologyofTHIP. 132 Fig.5.16 Convergenceprocessofthegeneticalgorithm:(A)Theevolutionprocess, (B)thestandarddeviationofpopulationingenerations,and(C)totaltravel timeofpopulationalonggenerations. 133 xiv ListofFigures

Fig.6.1 Demanddistributionoftaxitrips:(A)originsonweekday,(B)destinations onweekday,(C)originsonweekend,and(D)destinationsonweekend. 141 Fig.6.2 Hourlytaxitripdistributionfororiginsanddestinations:(A)weekdayand (B)weekend. 143 Fig.6.3 Clusternumbersunderdifferentparameters:(A)pick-uplocationsand (B)drop-offlocations. 144 Fig.6.4 Clusteringresultswithdefinedparameters:(A)pick-uplocationsand (B)drop-offlocations. 144 Fig.6.5 AcasestudyofashoppingcenterinHarbincity. 146 Fig.6.6 Traveldistanceoftrips.Weekday:(A)occupiedtripsand(B)nonoccupied trips.Weekend:(C)occupiedtripsand(D)nonoccupiedtrips. 148 Fig.6.7 Traveltimeoftrips.Weekday:(A)occupiedtripsand(B)nonoccupiedtrips. Weekend:(C)occupiedtripsand(D)nonoccupiedtrips. 151 Fig.6.8 Averagespeedoftrips.Weekday:(A)occupiedtripsand(B)nonoccupied trips.Weekend:(C)occupiedtripsand(D)nonoccupiedtrips. 153 Fig.6.9 Estimationresultsoftrafficdistributionusingentropy-maximizingmethod: (A)comparisonbetweenestimatedandobservedvaluesand(B)estimation errors. 158

Cumulativeprobabilitydistributionofdegreeandstrength:(A)degreeand strengthofoccupiedtrips,(B)degreeandstrengthofvacanttrips, (C)in-degreeandin-strengthofoccupiedtrips,(D)in-degreeandin-strength ofvacanttrips,(E)out-degreeandout-strengthofoccupiedtrips,and (F)out-degreeandout-strengthofvacanttrips. 160 Fig.6.11 Degree-strengthcorrelation:(A)occupiedtripsand(B)vacanttrips. 161 Fig.6.12 Correlationbetween kioutkjin and wij 162 Fig.6.13 Correlationbetweenstrength,clusteringcoefficientsandbetweenness: (A)occupiedtripsand(B)vacanttrips. 163 Fig.6.14 NetworkstructureofOTTNandVTTN:(A)occupied(EN ¼ 0.8259)and (B)vacant(EN ¼ 0.8032). 166 Fig.6.15 RegionalpartitionbasedonLouvainmethodinmainareaofHarbincity: (A)administrativedivisionsand(B)recognizedbyidentificationalgorithms. 167 Fig.6.16 Hourlyvariationoftripnumbersinaweek:(A)occupiedtripsand (B)vacanttrips. 168 Fig.6.17 Hourlyvariationofnormalized DV onweekdays. 169 Fig.6.18 ThresholdselectioninLorenzcurves:(A)originsand(B)destinations. 170 Fig.6.19 Identificationofhotspotswithtwodifferentcriteria:(A)densityoforigins, (B)hotspotsoforiginswith min,(C)hotspotsoforiginswith max, (D)densityofdestinations,(E)hotspotsofdestinationswith min,and (F)hotspotsofdestinationswith max. 172 Fig.7.1 Exampleofpublictransportationsmartcarddata. 179 Fig.7.2 ExampleoforiginalGPSdataoftheBeijingpublictransportationsystem. 182 Fig.7.3 HeatmapoftheplacesofresidenceofBeijingpublictransportation commutersinJune2015. 186 Fig.7.4 HeatmapoftheplacesofworkofBeijingpublictransportcommutersin June2015. 187 Fig.7.5 ClassificationofstopIDsbasedontheringroadswheretheyarelocated. 188 Fig.7.6 Comparisonofthetruevaluesandthepredictedvaluesthatareobtained usingtheRVMandSVMalgorithms. 192 Fig.7.7 Comparisonoftheconfidenceintervalofthepredictedvaluesthatare obtainedusingtheRVMalgorithmandthetruevalues. 193 Fig.7.8 Beijingpublictransportationnetworkspeedmap. 196 Fig.7.9 Analysisoftheridershipofroute51,300. 197 Fig.7.10 Ahistogramofbusheadwaysataparticularbusstop. 197

Fig.6.10

ListofFigures

force-directedalgorithmFruchtermann-Reingold,insteadofgeo-spatial

Notes:While nodeswithlowdegreeoccurfrequentlyinthenetwork,thefrequencyof nodeswithhigherdegreereducesfast.Onlyveryfewnodeshave exceptionallyhighdegrees.Thisstructuregivestheairtransportation networkitshub-and-spokeproperty.

Fig.9.6 Pairwisecorrelationoffourcentralities:degree,betweenness,closeness,and pagerank. Notes: Weobserveaweakcorrelationbetweenmostpairsonly. Particularly,thereisnostrongcorrelationbetweendegreeandbetweenness, whichimpliesthathighconnectivitydoesnotnecessarilyimplyhigh throughput.

Fig.9.7 Visualizingtherelativesizeofthegiantcomponentundernoderemoval accordingto100randomattacks. Notes:Globalairtransportationisresilient againstrandomattacks,ascanbeseenbytheclose-to-diagonalcurvesof randomattacks.

Fig.9.8 Comparisonofrobustnesscurves,visualizingtherelativesizeofthegiant componentundernoderemovalaccordingtodifferentnetworkmetrics. Notes: Betweennessandeigenvectorarethemosteffectiveattacking strategiesforglobalairtransportation.

Fig.7.11 (A)Spatialdistributionofbustraveltimereliability;(B)trendanalysisof bustraveltime. 198 Fig.8.1 AsystematicSBOframeworkfornetworkmodelingwithheterogeneous data. 205 Fig.8.2 SimulatedspatialdistributionofAMpeaktrafficflow. 210 Fig.8.3 Comparisonsofthesimulatedandmeasuredfreewaytrafficflow. (A) Vtfreeway.(B) Ktfreeway.(C) Qtfreeway . 212 Fig.8.4 Simulatedrelationshipsbetweenlink-basedandpath-basednetwork-wide statistics.(A) τ t vs. σ τ .(B) Kt vs. τ t and σ τ .(C) Qt vs. τ t .(D)Tripcompletion ratevs. σ τ . 213 Fig.8.5 ComparisonofsimulatedtriptraveltimewithhistoricalINRIXroutetravel time. 217 Fig.8.6 Individualobjectivefunctionsandempiricalcumulativedistributionof desirability. 219 Fig.8.7 Comparisonofmajorarterialaveragespeedsofmultipleobjective functions. 220 Fig.8.8 Comparisonofmultipleobjectivefunctions.(A)Network-wideaveragetrip traveltime.(B)Vehiclethroughput.(C)Tollrevenue. 222 Fig.9.1 Globalairtransportationnetworkfromopenflights. Notes: Airportsare visualizedas dots anddirectflightconnectionswith links.Intotal,wehave

visualizedthroughthecenterofthefigure;actualroutesmightbedifferent. 233

information. Notes: Distancesoflinksareminimizedforthepurposeof visualization.Thefigureexposeshowseveralnodesaggregateinto well-connectedclusters.Moreover,italsoexposeshowcertainnodesactas gatekeeperfortheaccessibilityofothernodestothenetwork. 233 Fig.9.3 AirportswithTop-Degreevaluesinglobalairtransportationnetwork. Notes: Allairportsarelocatedinthenorthernhemisphere,withastrongfocuson WesternEuropeandNorthAmerica. 235

Degreedistributionfortheglobalairtransportationnetwork.

236

3246airportsand18,890connections.Pleasenotethatallflightsare

Fig.9.2 Visualizationoftheglobalairtransportationnetworkusingthe

Fig.9.4

236

Fig.9.5 AirportswithTop-Betweennessvaluesinglobalairtransportationnetwork. Notes: Mostairportsarelocatedinthenorthernhemisphere.Comparedto high-degreenodes,wealsofindimportantnodesinSouthAsiaandOceania.

237

238

238 xvi ListofFigures

Fig.9.9 Air-sideaccessibilityofsixairportsintheglobalairtransportationnetwork. Notes: ThesourceairportsarelabeledinthecenterwiththeirIATAcodes. The concentriccircles reportthereachabilityofairportswithanincreasing numberofhops.Highlyconnectednodes,e.g.,AMS(AmsterdamAirport Schiphol),aremoreaccessibleandclosertootherairportsthanlow-degree nodes,e.g.,OGD(Ogden-HinckleyAirport,Utah,USA). 240

Fig.9.10 Communitiesintheglobalairtransportationnetwork. Notes:Eachcolor representsadifferentcommunity.Intotal,wehave31communities,where4 communitiescoverapproximately60%ofallairports.Aclearspatiallyinduceddistributionofcommunitiescanbeobserved. 241

Fig.9.11 AirlinenetworkofTurkishAirlines. Notes:Thenetworkcoversalarge numberofinternationalairports,almostallofthemareoperatedfroma singlehub:IST(IstanbulAtatuerkAirport).AfailureatISTisverylikelyto disruptthewholenetworkofTurkishAirlines. 241

Fig.9.12 AirlinenetworkofRyanair. Notes:Thenetworkconsistsofmanyhubnodes and,accordingly,afailureatasinglehubcanoftenbecompensatedforby otherairports.

Fig.9.13

DegreedistributionfortheairlinenetworksofTurkishAirlines(left)and Ryanair(right). Notes:Theleftdistributionhasveryfewhigh-degreenodes,

Fig.9.14 AnexampleofMultipleAirportRegion(MAR)fortheGreaterLondonarea. Notes:Sevenairportsservethecity,withdifferentcapacities,destinations, andaccessibility.ThemethodologyforcomputingMARsisusuallybasedon spatialdistances,oftenairportswithin120–150km.InFig.9.15,we visualizetheglobalMARswhichhaveatleastfiveairports.Pleasenotethat, sinceopenflights.orghasnopassengerdata,theregionscancontainairports withverylittleregularpassengertraffic.Wecanseethatthemajorityof MARsarefoundinWesternEuropeandNorthAmerica.Theair transportationsubsystemintheseareasismuchmoreresilientthaninother regions. 243

Fig.9.15 MultipleAirportRegions(MARs)intheglobalairportnetwork,with distancelessthan120km. Notes:OnlyMARswithatleastfiveairportsare shown.ThemajorityofMARsarefoundinWesternEuropeandNorth

temperaturesensor,5:tractiontransformeroilflowdevice,6:traction convertercurrent/voltagesensor,7:motortemperaturesensor,8:passenger cartemperaturesensor,9:smokeandfirealarmprobe,10:netpressure transformer,11:ATPspeedsensor,12:brakespeedsensor,13:semiactive controlaccelerationsensor,14:axistemperaturesensor,15:acceleration sensorforbogieinstabilitydetection,16:overvoltage/lightningprotection, 17:tractiontransformerprimarycurrentsensor,18:brakecontroldevice pressuresensor,19:cardoorsensor.

242

whiletherightdegreedistributionrevealslessconcentrationonafew selectedhubs. 243

America. 243 Fig.10.1 ISO-13374dataprocessingandinformationflows. 248 Fig.10.2 Sensordistribution.1:carinformationcontrollingdevicedisplayscreen,2: cabtemperaturesensor,3:wirelessdatatransmissiondevice,4:external

250 Fig.10.3 Datasourcesandtheirfusionprocessing. 252 Fig.10.5 Gearboxtemperatureanddifferencefusionresult. 257 Fig.10.4 Axistemperatureanditsdifference. 257 Fig.10.6 Tractionmotortemperatureanddifferencefusionresults. 258 Fig.10.7 Defectivedegreeofbearingbox,gearbox,andtractionmotor. 259 Fig.10.8 EMU’shealthindex. 261

xvii

ListofFigures

ListofTables

Table2.1 ClassificationofCurrentLiterature 17 Table2.2 RepresentationofOneExampleIndividual 22 Table2.3 ExampleFitnessEvaluationbyDifferentFitnessFunctions 25 Table2.4 AbbreviationsofDifferentSOCControlStrategiesComparedinThis Chapter 27 Table2.5 ComparisonsWithExistingModels 31 Table2.6 IncreasedFuelConsumption 35 Table3.1 FourLength-BasedVehicleCategoriesUsedbytheWSDOT 56 Table3.2 SelectedLoopDetectorsforExperimentalTests 66 Table3.3 StatisticalComparisonsofEstimationErrorsandCorrelation CoefficientsBetweenMeasuredandEstimatedBinVolumesatthe Intervalof3minforDifferentDaysatStationES-163R 69 Table3.4 StatisticalComparisonsofEstimationErrorsandCorrelation CoefficientsBetweenMeasuredandEstimatedBinVolumesatthe Intervalof3minforDifferentDaysatStationES-209D 70 Table3.5 SummaryofResultsforBothOfflineandOnlineTests 73 Table4.1 SummaryofDataSizeSelection 86 Table4.2 StatisticsofThreeDistributions 88 Table4.3 OptimalQuantityCaseStudies 99 Table4.4 CaseStudy1:23WeeksofData 99 Table4.5 CaseStudy2:23WeeksofData 100 Table4.6 CaseStudy3:23WeeksofData 100 Table4.7 TTRMeasuresandTheirAccuracy 105 Table5.1 SummaryofSelectedPersonalAttributes 128 Table5.2 BinaryLogitModelforDrivers’ResponsestoVMS 129 Table5.3 ComparisonofMinimumValuesofObjectiveFunction 132 Table6.1 DataSectionsofTaxiGPSDatainHarbinCity 140 Table6.2 ParametersEstimationResultsBasedonLMMethod 147 Table6.3 FittingParametersforTravelDistanceDistribution 150 Table6.4 FittingParametersforTravelTimeDistribution 152 Table6.5 FittingParametersforAverageSpeedDistribution 154 Table6.6 CalibratedParametersinEntropy-MaximizingModel 157 Table6.7 StatisticalResultofTwoTravelNetwork 164 Table6.8 CommunityDetectionResults 167 Table7.1 ExtractionofCommutingCharacteristics 185 Table7.2 NumbersofCommutersatPlacesofResidenceandWorkonEachRing RoadandTheirPercentageoftheTotal 189 Table7.3 ErrorsoftheRVMandSVMAlgorithm 192 Table8.1 Route-by-RouteValidationWithProbeVehicleTravelTimeStatistics 214 Table9.1 AnExampleofAirportEntityProvidedbyOpenflights 230 xix

Table9.2 AnExampleofAirlineEntityProvidedbyOpenflights 231 Table9.3 AnExampleofRoutesEntityProvidedbyOpenflights 232 Table10.1 ContributionofSystem1inSystemJoint 260 Table10.2 ContributionofSystem2inSystemJoint 260 Table10.3 ContributionofSystem3inSystemJoint 260 xx ListofTables

Preface

Inrecentyears,theincreasingquantityandvarietyofdataavailablefordecision supportpresentawealthofopportunityaswellasanumberofnewchallenges, inboththepublicandprivatesectors.Vastquantitiesofdataareavailable throughincreasinglyaffordableandaccessibledataacquisitionandcommunicationtechnologies,includingsensors,cameras,mobilelocationservices,etc. Whenthesearecombinedwithemergingcomputingandanalyticalmethodologies,theycanleadtomorethoroughscientificunderstandings,informeddecisions,andproactivemanagementsolutions.Asaresult,bigdataconceptsand methodologiesaresteadilymovingintothemainstreaminavarietyofscience andengineeringfields.

Duringthepastdecades,transportationresearchhasbeendrivenlargelyby mathematicalequationsandhasreliedonrelativelyscarcedata.Withthe increasingquantityandvarietyofdatabeingcollectedfromintelligenttransportationsystemsandothersensorsandapplications,thepotentialforsoliddatadrivenordata-basedresearchisincreasingrapidly.Nevertheless,todaythere arefewestablishedsystemsforsupportinggeneralbigdataanalyticsintransportationresearchandpracticalapplications.Mostcurrentonlinedataanalysis andvisualizationsystemsaredesignedtocomputeandvisualizeonetypeof data,suchasthosefromfreewayorarterialsensors,onanonlineplatform. Therefore,thoughthescopeandubiquityoftransportationdataareincreasing, makingthesedataaccessible,integrated,anduseablefortransportationanalysis isstillaremarkablechallenge.

Understandingdata-driventransportationscienceisessentialforenhancing anintelligenttransportationsystem’sperformance.Mostcommercialsystems areorientedtowardaspecifictransportationproblemoranalysisprocedure, andapproachtheproblemintheirown(oftenadhoc)way.Amatureframework foreffectivelyutilizingdataandcomputingresources,suchthatthesedatawill servetheneedsofusers,hasbecomeapressingneedinthefieldoftransportation.Thechallengesassociatedwithdevelopingthistypeofframeworkprimarilystemfromtheneedforstandardizedandefficientdataintegrationandquality controlmethods,computationalmodulesforapplyingthesedatatotransportationanalysis,andaunifieddataschemaforheterogeneousdata.

Thisbookconsistsof10chaptersprovidingin-depthcoverageofthestateof theartindata-drivenmethodologiesandtheirapplicationsinthe E-Scienceof transportation.Suchmethodsarecrucialforsolvingtransportationproblems

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Data driven solutions to transportation problems ebook pdf