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2. The city a system of systemsSystems from transportation to energy, healthcare, commerce, education,security, food, water, jobs and economic growth come together and interactwith each otherHow can they be managed better ?EDUCATION TRANSPORTATION SOCIAL SERVICES SAFETY UTILITIES HEALTHCARE COMMUNCATION+ $EDUCATION TRANSPORTATION SOCIAL SERVICES SAFETY UTILITIES HEALTHCARE COMMUNCATION 2 15 September 2010 2008 IBM Corporation 3. OverviewAssetSmarter Cities approach creates solutions ManagementResource Optimizationwhich simplify the way in which the myriadPipes, Roads, Water, traffic,Wires, Bldgs,city operations act in a city and helps city etc.System ofenergy etc.managers make rational decisions based onSystemsdata and predictionOver 100 + people are working around theworld are learning with our customers anddeploying models and analytics which use aPeople Motivation &common platforms and approaches to enable Inclinationrepeatable processesFrom this work we are discovering patternsand approaches which help in thisJobssimplification, reducing cost and providingComfortnew insights LifestyleTaking advantage of our deep scientific and City water, energy,engineering capabilities in IBM Researchbuildings & transportSafety & SecurityCity Needs 2008 IBM Corporation 4. IBM Research: Smarter City Global engagementsDublin Traffic, StockholmWater, Energy TrafficDubuquePNWBeijingWater,SmartGridBornholmEnergy ShenyangEnergyNY Bldgs,Energy Moscow BeijingWater, CarbonTraffic AgencyEmer Security Nanotech TrafficWest CoastTokyo PA BldgsDC WASA Integ. CityTexas Water RanaanaRiver Basin WaterDelhiEnergyTrafficSingapore Smarter City Traffic WaterRio Activity Emerg.NaturalMelbourneResourcesSydney Energy & Energy LifeScience 2008 IBM Corporation 5. Analysing Cities Who wants what when and where 2009 IBM Corporation 6. Who spends what in cities ?City Budgets in Aggregate50 Cities Budget : $561B Cities In TransitionMature Large IBM assessment from top 50 cities by $161B $285B (15 Cities /217 M People) (19 Cities/198M People) population 3 City types identified Mature LargeMature Medium Mature Medium$115B Cities in Transition (16 Cities/59M People) Each city type has different focus Mature Large - safety & security Mature Medium - maintenance and resource management In Transition - focus on new state of art infrastructure and resource management systems 2008 IBM Corporation 7. IBM Smarter Cities Challenge The Smarter Cities Challenge is a competitive grant program awarding $50 million worth of technology and services over the next 3 years to 100 cities around the globe. These grants are designed to address the wide range of financial and infrastructure challenges facing cities today See http://smartercitieschallenge.org/ 2008 IBM Corporation 8. Observations in working around the world with Cities Key issues include Ability to engage with citizens and engage their opinions and support Management of public safety Scheduling of work and activities in the face of conflicting or completely non integrated activity. Dig patch Dig Understanding of movement of people and traffic in city Caused by Lack of understanding of details of what is happening in cityAnd use of data and analytics to determine same 2008 IBM Corporation 9. We are targeting the following city domainsBuilding Energy Traffic & WaterTransportationavailability & puritySafety 2008 IBM Corporation 10. Underlying Science and EngineeringFrom paper to models 2009 IBM Corporation 11. Developing the Research which underlies Smarter Cities We view the Smarter City through this structureSolutionsEmerging area: Human interaction with Smarter City Business DataModels Optimization DecisionsInfrastructure Technologies & Tools Core Technologies 2008 IBM Corporation 12. Understanding disconnects: A warning and a simple example of acommon problem 2008 IBM Corporation 13. Using mathematics and models to drive the business activity - forexample, traffic managementOperational/ TransactionalInsights System wide controlRoad Usage Optimization, GHG emissionmodels Operational/ Charge collection More granular Dynamic andTransactional only - disconnected charging, by location congestion based operational datapricing Analysis of traffic Transaction data from patterns to manage Route planning and DevelopmentBusiness the management of city congestion.advice, shippers, paymentsconcrete haulers, Modeling traffic to limo companies, Little automated usepredict and manage theatres, taxis etc is made of real-timeentire system traffic data City-wide, dynamic traffic optimization 2008-102008-12? 2009-15? 2008 IBM Corporation 14. Advanced Analyticsis the use of data and models to provide insight to guide decisionsAnalytics Data sources:Business automation Instrumentation SensorsData Web 2.0 Expert knowledge real world physics Model: a mathematical or algorithmicrepresentation ofModels reality intended toexplain or predictsome aspect of it Decision executed automatically or by peopleInsight 2008 IBM Corporation 15. Managing Traffic in StockholmStockholmTraffic 2008 IBM Corporation 16. Stockholm Road Charging40 Gantries with 18 ingresspoints Approx 320K entries/exists per day 2008 IBM Corporation 17. Charging to reduce traffic 2008 IBM Corporation 18. Case Study Stockholm Congestion ChargingMain objective to reduce congestion bybetween 10% and 15%.Project to build a system that wouldautomatically tax Swedish registeredvehicles entering and leaving the city centrebetween 6.30 and 18.30, Monday to Friday(excluding national holidays).Duration 7 months (January - July 2006)Challenges political sensitivity, publicResultsscrutiny, referendum at the end of the trialTraffic congestion in Stockholm was reducedto decide on whether to implement the by 25%, far above the original targetcongestion tax permanentlyTraffic queuing times fell by up to 50%.Journey times were faster and morepredictableStockholm bus timetables were re-written totake improvements to traffic flow intoaccountPollution levels in the city fell by between10% and 15%Confidence in the system was high due tominimal enforcement and administrativeerrorsScheme was re-launched in August 2007 after thepublic referendum voted in favour of the system 2008 IBM Corporation 19. Analysing Traffic 2008 IBM Corporation 20. Stream computingSupply Chain fortocritical paradigm shiftNotional Information represents a Decision-making action!Transforming the Information Supply Chain reduce the time to Analytical Modeling & InformationTime to ActionElapsed Time to Action Analytical Modeling & Information OperationalDashboards PlanningScorecardingReports Bus Process & Event MgmtReports Ad-hoc Queries WAREHOUSEDATAMARTSDATA INTEGRATION OPERATIONAL DATA STORESSOURCES 2008 IBM Corporation 20 21. Infosphere Streams in Stockholm - why models are important Traffic SpeedBouillet, Riabov, VerscheureFast >140Slow/stop Moderate Average Good 2008 IBM CorporationKm/hr 22. Predicting Traffic 2008 IBM Corporation 23. Traffic Prediction Tool (TPT) background and motivationThe ability to capture the current traffic state and to project it to the near future fromavailable data sources is critical for real-time traffic managementTraditional data sources Non-traditional data sources InductiveFixedlooplocations,Traffic camerasparse in the networkGPS deviceSmart phone Infrared laser radarPassive infrared ultrasonic sensor Historical origin-destination trip tables 2008 IBM Corporation 24. Traffic Prediction Tool (TPT)Model: stochastic model used to predict traffic in Singapore Issue: real-time is too late IBM Innovation: forecast the futureLittle automated use is made of the gigabytes of real-time IBMs TPT provides a layer of intelligence by using sensortraffic data today; often, by the time it is received, it is nodata in sophisticated algorithms that create relevantlonger representative of the actual trafficinsights from the raw datablue = forecast black = actual red = incident4000 results rr3000r rrrr r rr r rr rr volumer rr r r rr r rr TPT accurately2000rrrforecasts future r1000traffic conditions, including incidents tool screenshot 0 50 100150 200250timeCurrent Focus Future UseExtension: Data Expansion Traffic Operations: Traffic Planning; Dynamic(2008 IME) develop algorithm to fill in Variable Message Sign Road Pricing; congestion gaps of real-time sensor data, resulting setting; traffic signal based tariff setting; routein a complete picture of future traffic timing, ramp metering planning & advicestate, network-wide 2008 IBM Corporation 25. Agent Based Analytics and prediction 2008 IBM Corporation 26. Large-scale Agent-based Traffic Flow SimulatorIBM Mega Traffic SimulatorIBM Mega Traffic Simulator outputbase data input Driver Behavior Model Driver CO2 emission RoadAgent Map datanetworkTraffic Origin- Vehiclecensusdestination Link A Link B Link CJava Virtual Machine Agent SpaceCO2 emission for each linkAgentAgent Agent Driving logDriver ModelAgentAgent Agent AgentAgentAgent Agent AgentAgent 2k cars/hour Agent AgentAgent Simulation Space Agent Manager 3k cars/hour SchedulerMemory ManagerMessaging Handler Message QueueThread Manager threadthreadthread threadthread thread 0.5k cars/hour Communication Manager IBM Zonal Agent-based Simulation Environmenttraffic volume for each linkTraffic situation with more than the millions of vehicles can be simulated.Traffic situation with more than the millions of vehicles can be simulated.Traffic flow with various types of drivers behavior model can be simulated..Traffic flow with various types of drivers behavior model can be simulated 2008 IBM Corporation 27. Application of the simulator: What-If Analysis The simulator provides an experimental environment for traffic policy makers to perform what-if analysis concerning traffic in a large city. How the traffic would change ifwe introduce congestion tax.2k cars/day If Condition1 Then 32k cars/day49k cars/dayHow the total emission wouldchange if we introduce a new traffic policy? If Condition2 Then Current traffic status What is the appropriate information providing service to minimize traffic congestion? If Condition3 Then How the traffic policy and city-What is the proper traffic policy todesign should be in the agingsolve traffic congestion, green issues.... society? If Condition4 Then 2008 IBM Corporation 28. Water Infrastructure Management DC WASA Water 2008 IBM Corporation 29. Analytics Driven Asset Management (ADAM)Maintenance Planning Insight, Maintenance Scheduling Foresight and PrescriptionsReplacement Planning ADAMCondition AssessmentFailure Cause Analysis Descriptive, Predictive andFailure Prediction Prescriptive Analytics Usage AnalysisCustomer Analysis DataData Operational, Failure, Usage, Condition, Customer, LocationEAM / SCADAEnterprise Asset ManagementScada, Sensors, Inspection, Metering SystemsAsset ManagementWork ManagementService ManagementInventory / ContractProcurement Management Assets 2008 IBM Corporation 30. ADAM: Analytics Driven Asset ManagementPredictive analytics models enabling fix beforebreakSpatial Schedule Optimization enables while inthe neighborhood schedulingData analytics enable forecasting of water usageand detection of usage anomalies Water Pipes 1200 Miles Sewer Pipes 1800 Miles Hydrants9000 Valves24,000 Catch Basins36000 Water Meters130,000 Waster Water Capacity 370MGallons / day Water Customers 600,000 Sewer Customers 1,600,000All from conventional historical and log data! 2010 International Business Machines Corporation 30 31. ADAM for Water Utilities V1.0 WorkPredictiveUsage/ RevenueManagementMaintenance Optimization Spatio-Temporal Failure Pattern and Customer Manual Scheduling Cause AnalysisSegmentation Automated spatial Failure Risk basedUsage Anomaly schedules PM Optimization DetectionAutomated Task level Failure PredictionNon-Revenue Water,rolling scheduling Energy OptimizationDynamic MobileReplacementUsage & RevenueWork Management Planning Forecasting Advanced ReportingPredictive Analytics OptimizationEAM GIS Data Water Usage Data 2008 IBM Corporation 32. Examples of Advanced Reporting Catch Basin WorkOrders Temporal Analysis of Work OrderCatch BasinPatternsSpatial Distribution of annual workCatch basic problem codeWork classification vs Problem code visualization distribution 2008 IBM Corporation 33. Use casesADAM V1.0 Use cases Manual Map Based Schedule Construction Semi-Automated Route Completion Multi-crew automated schedulingOngoing R & DTask Level Scheduling Dynamic Re-Scheduling using GPS data 2008 IBM Corporation 34. IBM Research: Smarter City Global engagementsDublin Traffic,Water, EnergySmarter CityActivity 2008 IBM Corporation 35. Smarter Cities Technology CentreDublin 2008 IBM Corporation 36. Transportation Developing technology to continuously assess the state of the public transport system and provide personalized, real-time advice to riders and dynamic load-balancing opportunities to transit providers Background GPS & other sensor technologies are transformingtransportation analytics Working closely with Dublin Demonstration visualisation of transportationnetwork status & guidance for bus drivers Challenges Extracting insights from real-time, noisy, irregularsamples Taking actions under uncertainty with low latency Large volume & diversity of data 2011 IBM Corporation 37. Dublin Bus Demonstration 2011 IBM Corporation 38. City Fabric Platform for gathering and analyzing Dublin city data,. Working with Dublin City on an Open Innovation Platform for Cities Background Governments are seeking to spawn & exploitinnovation & promote awareness through better Open Innovation Platformaccess to data of citizens interest Multi-City &Open Deploying significant common infrastructure forPresentation International Collaboration Collaborative Research IBMs SC community Common Common compute, data & network platform Data Standards &Definitions Data repositoru Connectivity into Dublin Systems Platform Challenges Advanced City Technology Data & model management in City-scaleenvironment Tools enabling domain experts to interface withcomplex data & analytic challenges intuitively 2011 IBM Corporation 39. Managing Public Safety in NYC and ChicagoNY City + ChicagoPublic Safety 2008 IBM Corporation 40. Safety and Security ManagementChicagos Virtual Shield ProgramImplemented one of the most advanced city-wide intelligent security systemsThe engagement is a part of Chicagos Operation Virtual Shield, a project thatencompasses one of the worlds largest video security deploymentsIn the first phase, IBM helped the City experts and network engineers design andimplement a monitoring strategy infrastructure to capture, monitor and fully indexvideo for real-time and forensic-related safety applicationsKorea Incheon Free Economic ZoneImplemented a public safety infrastructure with intelligent video monitoring as partof the U-safety City projectBuilt a public safety system utilizing high-resolution cameras to view and monitoractivities to prevent crime and even predict possible events by recognizing andanalyzing certain patterns and data in real time 2008 IBM Corporation 41. Statistical modeling, machine learning & pattern recognition are keytechnologies to enable Smart Safety and Security Statistical Modeling is the key to handling change Background SubtractionAlgorithmBlob TrackingAlgorithm ObjectClassificationAlgorithmColorClassificationAlgorithm Machine learning enables recognition of person attributes 2008 IBM Corporation 42. Selected Research & Technical Challenges Handling crowded scenesFederated / Partitioned Architectures Finer grained analysis of objectsAnalytics at the edge 2008 IBM Corporation 43. Managing Energy in BuildingsNY Bldgs, 2008 IBM Corporation 44. i-BEE (IBM Building Energy and Emission) Analytics ToolSet Saving energy, improving energy efficiency and reducing greenhouse gas (GHG) emissions are key initiatives in many cities and municipalities and for building owners and operators.For example, New York Citys government spends over $1 billion a year on energy, and iscommitted to reducing the City governments energy consumption and CO2 emissions by 30% by2030 (PlaNYC). Buildings emit about 78 percent of the citys GHG emissions. NYC plans toinvest, each year, an amount equal to 10% of its energy expenses in energy-saving measures. In order to reduce energy consumption in buildings, one needs to understand patterns of energy usage and heat transfer as well as characteristics of building structures, operations and occupant behaviors that influence energy consumption. i-BEE is physics, statistics and mathematics based building energy analytics thatAssess how different energies are used (and GHG is emitted) in different waysBenchmark energy (GHG emission) uses among peer buildingsTrack energy consumption and its changes due the improvement actions (e.g., retrofits)Forecast future energy consumption (and GHG emission)Simulate impacts of various changes (improvements) on energy consumption and GHG emissionOptimize energy consumption, efficiency and GHG emission 2008 IBM Corporation 45. Modeling Approach 2008 IBM Corporation 46. Dashboard Example (Energy Use & Greenhouse Summary, GISEnergy Intensity Map)K-12 Schools 2008 IBM Corporation 47. The Benefit of AnalyticsIdentify anomaly that can lead to failure of equipment and wasted energy, andtake corrective actions for faultsStatistical Analysis (SPC, CUSUM, Time Series Model, Data Mining..)Identify underperforming buildings with respect to peer buildings and identifythe root causesMultiple Regression ModelingAccurately estimate heat loss (gain) through walls, roofs, windows, and developretrofit plansHeat Transfer ModelIdentify key characteristics of building structures, operations and behaviors thatinfluence energy consumption and take actions for modificationsForecast future energy consumption and develop cost effective procurementplan of energyForecasting ModelAnd others 2008 IBM Corporation 48. The Role of People in Cities Dubuque 2009 IBM Corporation 49. IBM Research: Smarter City Global engagements Dubuque Water, Energy 2008 IBM Corporation 50. Green DubuqueCICERO: Citizen centric Intelligence & Resource Optimization 2008 IBM Corporation 51. Participants Compete IBM provides the platform Pilot definedEach week, individual households and teams will have the chance to win prizes.Each week, you will be randomly assigned to a team made up of 3-5 other Pilotmembers.You will not know your other team members but you can chat with them using theteam chat on the site.Each week, individual households and teams will win prizes and/or will beregistered to win our mid-way and final prizes!Prize drawings take place at the end of week 6 and at the end of week 1 IBM providesCloud platform and software that aggregates and maps usageProvides metrics and competition informationTracks all usage helping development of behavioural models 2008 IBM Corporation 52. CICERO deployed for Resource Consumption ManagementCloud-based real-time intelligence & interaction for instrumented, interconnectedcitiesDeployed for water silo and work underway for electric siloResource optimization & decision support for maximizing city performanceModels & Incentives for changing citizen resource consumption behaviorInterest from multiple cities to join cloud delivered service 2008 IBM Corporation 53. Whither Weather 2008 IBM Corporation 54. The opportunity and challenge of combining modelsWeather models and resulting damage prediction for ElectricUtilitiesIBM Weather Prediction System DEEP THUNDER - accurate to 2 kmx 2 km areaA mathematical model that describes the physics of the atmosphere The sun adds energy, gases rise from the surface, convection causes windsNumerical weather prediction is done by solving the equations of thesemodels on a 4-dimensional grid (latitude, longitude, altitude, time)Solution yields predictions of surface and upper air Temperature, humidity, moisture Wind speed and direction Cloud cover and visibility Precipitation type and intensity 2008 IBM CorporationChallenge is to predict business impact of weather 55. IBM uses advanced weather forecasting technologies to predict powerdemand and outages - Deep Thunder our unique world class weatherprediction technologiesWeather causes damage and outagesOutages require restoration (resources) WeatherRestoration takes time, people, etc. predictionBuild stochastic model from weather observations, storm damage andrelated data Outage location, timing and response Wind, rain, lightning and durationPower Line Demographics of effected areaDamage Ancillary environmental conditionspredictionWork crew requirementprediction Restoration timeprediction 2008 IBM Corporation 56. 13 March 2010 Noreaster Deep Thunder Impact ForecastActual Outages (Repair Jobs) Estimated Outages (Repair Jobs) 2008 IBM Corporation 57. Approach to Urban Flood ForecastingPrecipitation EstimatesWeather Prediction and/orAnalysis of Precipitation Rainfall Measurements Flood PredictionRefine Sensor Network Actual Flood Impacts and Model CalibrationModelCalibrationImpact Estimates 2008 IBM Corporation 58. Integrating Systems 2008 IBM Corporation 59. IBM Research: Smarter City Global engagementsRioEmergencyManagement 2008 IBM Corporation 60. RIO Operations CenterAllows diverse agencies to shareemergency information and plancoordinated responsesPart of Rios preparatory effortsfor Brazils hosting of soccersWorld Cup in 2014 and the cityshosting of the 2016 OlympicGames.Components includeData acquisition and integrationcenter from multiple agenciesHigh Resolution WeatherPrediction System coupled tohydrological flooding modelsTraffic management systemsEmergency operationsIntegrated scheduling,optimization and allocation ofprocesses 2008 IBM Corporation 61. SummaryIBM Research is focusing our global resources on the understanding andmanagement of resource usage and deriving an understanding of how theseresources interactThe integration of technology, mathematics. IT and computer sciencecoupled with advances in algorithms, processor speed communicationbandwidth are enabling the management of cities in ways previouslyunimaginableWorld pressures from emissions, population and economic growth are drivingever increasing efficiency in the use of every resourceThe Smarter Cities approach enables this transition 2008 IBM Corporation
