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Big Data in Transit and Rail

Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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Page 1: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

BigDatainTransitandRail

Page 2: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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BigDataOverview• WhatisBigData?

AnylargevolumeofdataStructuredorUnstructuredCoinedinearly2000’sHadoopisBigDataFramework

• The4V’sofBigData

Volume– theScaleoftheDataVelocity – AnalysisofStreamingDataVariety – DifferentFormsofDataVeracity – QualityofData

Page 3: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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BigDataVolumes

Page 4: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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BigDataVelocity• Certaininformationvaluedecaysovertime.Incident orequipmentfailuredatatodayisoflessusetomorrow.

• Railvehicle sensors generatemassivelogdatainrealtime• Ridershipbehaviorcanalsobecapturedinreal-timeANDstoredfortrendanalysis

Page 5: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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CybersecurityChallengesofBigDataThe“Elephant”intheRoom

• Hadoopisanopensource,java-basedprogrammingframeworkdevelopedforstoringandprocessinghugedatasets

• Aswithmostopensourcesoftware,Hadoopwasnotwrittenwithsecurityinmind

• Hadoopisfrequentlyusedwithmultiplevendorproductswhicharealsosecuritychallenged.

• TheCostofaDataBreachwithBIGDATAisnotquantifiablecurrentlybutassuredlywillbequitelarge.

• TheBiggertheData,theBiggertheRisk.• September2017:RecentsecuritybreachatEquifaxexposestheenterprisetolawsuitsestimatedtobeinthebillionsofdollars.

Page 6: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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UseCase– ElectronicFarePaymentSystems

• HopFastpass(Portland,OR/Vancouver,WA)– Account-based: real-timeprocessingofpaymenttransactionsandsystem

performanceinformation– OpenArchitecture:keysysteminterfacesbasedonpublishedApplication

ProgrammingInterfaces(APIs)

– InformationProtection:separatedatarepositoriesforcustomerPII andtransitusedata

• TypesofDataAvailable– Ridership:byindividual(anonymous),farecategory,agency,typeof

service,date/time,geolocation– SalesChannel:web,mobile,retailoutlet,transitstore,vendingmachine– CustomerData:collectedviasurveyslinkedtoanonymousHopcustomer

accounts(age,income,first/lastmile,#inhousehold,etc.)– OperationalData:equipmentfailure(responseandanalysis)

Page 7: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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• ServicePlanning&CustomerService

– IdentifyridershippatternsnotaccuratelycapturedbyridersurveysorAPCssuchasinter- andintra-agencytransfers

– Realtimeinformationforenhancedcustomerserviceandserviceplanning(i.e.crowdsourcingviamobile)

– Assetmanagementprovidesproactivedevicemaintenance– AccurateandsimplifiedNTD Reporting

• Third-partyIntegrations– IncentivizeTransitUse:gamifying(usetransitXX-timesearnsadiscountat

alocalretailer)– BikeShare:enhancedcustomerconvenienceandtoinfluencefirst/last

milemodechoice– CityParking:enhancedcustomerconvenienceandtoinfluencemode

choice– CongestionManagement:byknowingandinfluencingcustomerbehavior

UseCase– ElectronicFarePaymentSystemsHowMightDataBe Used?

Page 8: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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Account-basedArchitectureAllSystemsFeedIntoDataWarehouse

Internet

Internet (VPN) / TriMet LAN

Mobile Inspection Device

Mobile Website and Applications

$

Bank

Transit Store POS System

Device Monitoring Tool

TVM Back Office

Maintenance Management Tool

Reporting Tool

CRM Tool

Onboard Validators

Off-Board Validators

System / Device Development Outside of Contractor Scope

Payment Gateway

Data Warehouse

Reporting System

System Monitoring and Management

Application

Retail Network

TriMet MMIS

Financial Clearing and Settlement System

Maintenance Management

System

Retail Device

TriMet General Ledger

Fare Data (to ABP)

Customer Data (to CRM)

Financial Data

Device Management Data

Legend

Customer Relationship Management (CRM) System

Web Server

Customer Website

Institutional Website

Account Management and Processing System

Fareboxes (Not Integrated)

CAD / AVL

Ticket VendingMachines

Page 9: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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OtherBig Data OpportunitiesinTransitRailVehicleSensors- TheValueofNOW

Page 10: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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• SafetyandSecurity – CCTValertsenableoperationsandsecurityresponse

• OperatorReportCard– fortrainingandincidentinvestigation

• RealtimeMaintenanceandFailureAlerts – fortimelypredictivemaintenanceandimprovedservice

• AccurateVehicleLocation – enhancedcustomerserviceandagencyperformancemonitoring

OtherBig Data OpportunitiesinTransitHowMightDataBe Used?

Page 11: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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Trends ForBigDatainTransportation

• ThevalueofNOWmeansprocessinginreal-timeprovidesactionableinformationnotjusthistoricaldatafortrendanalysis

• ConvergenceofIoT,bigdata,andcloudservicesprovidedbyAmazonAWS,GoogleCloud,andMicrosoftAzureisenablingtransitagenciestoutilizecloudservicedashboards

• AgencieswithrecentbigdataInitiativesper2016APTA TCRPstudyinclude:

MBTA,SanJoaqin RTD,LACountyMTA,TriMet,PortAuthorityofAlleghenyCounty,CapitalMetro,UtahTransitAuthority,YorkRegionTransit,YumaCounty,andWMATA

• Thelistoftransitagencybigdataprojectswillcontinuetogrow

Page 12: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

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CH2MContactInformation

BrinOwen,VPPaymentSystemsCH2MSanFranciscoOffice- [email protected]

RajaKadiyala,VPIntelligentSystemsCH2MOaklandOffice- [email protected]

SusanHowardIntelligentSystemsCybersecurityAnalystCH2MPortlandOffice- [email protected]

Page 13: Big Data in Transit and Rail - railvolution.org · Veracity–Quality of Data. 3 Big Data Volumes. 4 Big Data Velocity •Certain information value decays over time. Incidentor equipment

ThankYou