11. Jen-Yao Chung (IBM, USA) - An IT View of Smarter Cities


Published on

Cassandra Workshop Presentation

Published in: Technology, Business
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

11. Jen-Yao Chung (IBM, USA) - An IT View of Smarter Cities

  1. 1. © 2009 IBM Corporation An IT view of Smarter Cities Jurij Paraszczak for Smarter Cities Global Team Director Industry Solutions and Smarter Cities IBM Research jurij@us.ibm.com With many thanks to the Research Smarter Cities team
  2. 2. © 2008 IBM Corporation The city – a system of systems Systems from transportation to energy, healthcare, commerce, education, security, food, water, jobs and economic growth come together and interact with each other How can they be managed better ? 2 15 September 2010 EDUCATION • TRANSPORTATION • SOCIAL SERVICES • SAFETY • UTILITIES • HEALTHCARE • COMMUNCATION EDUCATION • TRANSPORTATION • SOCIAL SERVICES • SAFETY • UTILITIES • HEALTHCARE • COMMUNCATION + $
  3. 3. © 2008 IBM Corporation Overview Smarter Cities approach creates solutions which simplify the way in which the myriad city operations act in a city and helps city managers make rational decisions based on data and prediction Over 100 + people are working around the world are learning with our customers and deploying models and analytics which use a common platforms and approaches to enable repeatable processes From this work we are discovering patterns and approaches which help in this simplification, reducing cost and providing new insights Taking advantage of our deep scientific and engineering capabilities in IBM Research Asset Management Pipes, Roads, Wires, Bldgs, etc. Resource Optimization Water, traffic, energy etc. People Motivation & Inclination System of Systems Safety & Security City water, energy, buildings & transport Jobs Comfort Lifestyle City Needs
  4. 4. © 2008 IBM Corporation Rio Emerg. Natural Resources Texas River Basin NY Bldgs, Emer Security Ranaana Water IBM Research: Smarter City Global engagements Smarter City Activity Dublin Traffic, Water, Energy Shenyang Water, CarbonBeijing Traffic Delhi Energy Traffic Traffic Agency West Coast PA Bldgs PNW SmartGrid Singapore Traffic Water Tokyo Integ. City Bornholm Energy Dubuque Water, Energy DC WASA Water Beijing Energy Sydney Energy Melbourne Energy & LifeScience Moscow Nanotech Stockholm Traffic
  5. 5. © 2009 IBM Corporation Analysing Cities Who wants what when and where
  6. 6. © 2008 IBM Corporation Who spends what in cities ? IBM assessment from top 50 cities by population 3 City types identified Mature Large Mature Medium Cities in Transition 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 Mature Large $285B (19 Cities/198M People) Mature Medium $115B (16 Cities/59M People) Cities In Transition $161B (15 Cities /217 M People) 50 Cities Budget : $561B City Budgets in Aggregate
  7. 7. © 2008 IBM Corporation 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/
  8. 8. © 2008 IBM Corporation 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 city And use of data and analytics to determine same
  9. 9. © 2008 IBM Corporation We are targeting the following city domains Traffic & Transportation Building Energy Water availability & purity Safety
  10. 10. © 2009 IBM Corporation Underlying Science and Engineering From paper to models
  11. 11. © 2008 IBM Corporation Developing the Research which underlies Smarter Cities We view the Smarter City through this structure Infrastructure Technologies & Tools Data Models Optimization Business Decisions Emerging area: Human interaction with Smarter City Core Technologies Solutions
  12. 12. © 2008 IBM Corporation Understanding disconnects: A warning and a simple example of a common problem
  13. 13. © 2008 IBM Corporation Using mathematics and models to drive the business activity - for example, traffic management Operational/ Transactional Road Usage Optimization, GHG emission models •More granular charging, by location •Analysis of traffic patterns to manage city congestion. •Modeling traffic to predict and manage entire system •Dynamic and congestion based pricing •Route planning and advice, shippers, concrete haulers, limo companies, theatres, taxis etc •City-wide, dynamic traffic optimization •Charge collection only - disconnected operational data •Transaction data from the management of payments •Little automated use is made of real-time traffic data Business Development Operational/ Transactional Insights System wide control 2008-10 2008-12? 2009-15?
  14. 14. © 2008 IBM Corporation Advanced Analytics is the use of data and models to provide insight to guide decisions Models Analytics Data Insight Data sources: Business automation Instrumentation Sensors Web 2.0 Expert knowledge “real world physics” Model: a mathematical or algorithmic representation of reality intended to explain or predict some aspect of it Decision executed automatically or by people
  15. 15. © 2008 IBM Corporation Stockholm Traffic Managing Traffic in Stockholm
  16. 16. © 2008 IBM Corporation Stockholm Road Charging 40 Gantries with 18 ingress points Approx 320K entries/exists per day
  17. 17. © 2008 IBM Corporation Charging to reduce traffic
  18. 18. © 2008 IBM Corporation Case Study – Stockholm Congestion Charging Main objective – to reduce congestion by between 10% and 15%. Project – to build a system that would automatically tax Swedish registered vehicles entering and leaving the city centre between 6.30 and 18.30, Monday to Friday (excluding national holidays). Duration – 7 months (January - July 2006) Challenges – political sensitivity, public scrutiny, referendum at the end of the trial to decide on whether to implement the congestion tax permanently Results Traffic congestion in Stockholm was reduced by 25%, far above the original target Traffic queuing times fell by up to 50%. Journey times were faster and more predictable Stockholm bus timetables were re-written to take improvements to traffic flow into account Pollution levels in the city fell by between 10% and 15% Confidence in the system was high due to minimal enforcement and administrative errors Scheme was re-launched in August 2007 after the public referendum voted in favour of the system
  19. 19. © 2008 IBM Corporation Analysing Traffic
  20. 20. © 2008 IBM Corporation 20 Time to Action Notional Information Supply Chain for Decision-making Transforming the Information Supply Chain to reduce the time to action! SOURCES Elapsed Time to Action WAREHOUSE Reports Ad-hoc Queries DATA INTEGRATION OPERATIONAL DATA STORES DATAMARTS Bus Process & Event Mgmt Operational Reports Dashboards Planning Scorecarding Analytical Modeling & Information Stream computing represents a critical paradigm shift Analytical Modeling & Information
  21. 21. © 2008 IBM Corporation Infosphere Streams in Stockholm - why models are important Slow/stop Moderate Average Good Fast >140 Km/hr Bouillet, Riabov, Verscheure Traffic Speed
  22. 22. © 2008 IBM Corporation Predicting Traffic
  23. 23. © 2008 IBM Corporation Traffic Prediction Tool (TPT) – background and motivation The ability to capture the current traffic state and to project it to the near future from available data sources is critical for real-time traffic management Traditional data sources Non-traditional data sources Inductive loop Traffic camera Infrared laser radar Passive infrared – ultrasonic sensor GPS device Smart phone Historical origin-destination trip tables Fixed locations, sparse in the network …
  24. 24. © 2008 IBM Corporation Traffic Prediction Tool (TPT) Model: stochastic model used to predict traffic in Singapore Little automated use is made of the gigabytes of real-time traffic data today; often, by the time it is received, it is no longer representative of the actual traffic ► Issue: “real-time” is too late IBM’s TPT provides a layer of intelligence by using sensor data in sophisticated algorithms that create relevant insights from the raw data ► IBM Innovation: forecast the future TPT accurately forecasts future traffic conditions, including incidents 0 50 100 150 200 250 1000200030004000 rr r rrr r r r r r r r rr r r r r rr r r rr r r r r r volume blue = forecast black = actual red = incident time Current Focus Traffic Operations: Variable Message Sign setting; traffic signal timing, ramp metering Future Use Traffic Planning; Dynamic Road Pricing; congestion based tariff setting; route planning & advice Extension: Data Expansion (2008 IME) develop algorithm to fill in gaps of real-time sensor data, resulting in a complete picture of future traffic state, network-wide tool screenshot results
  25. 25. © 2008 IBM Corporation Agent Based Analytics and prediction
  26. 26. © 2008 IBM Corporation Large-scale Agent-based Traffic Flow Simulator IBM Mega Traffic Simulator output Traffic census Map data Driving log traffic volume for each link base data IBM Mega Traffic Simulator Origin- destination Road network Driver Model input 3k cars/hour 0.5k cars/hour 2k cars/hour Link A Link C CO2emission Link B CO2 emission for each link 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 situation with more than the millions of vehicles can be simulated. Traffic flow with various types of drivers behavior model can be simulated. IBM Zonal Agent-based Simulation Environment Agent Space Agent Agent Agent Agent Agent Agent Agent Agent Agent Agent Agent Agent Agent Agent Agent Simulation Space Messaging Handler Communication Manager Thread Manager threadthreadthreadthreadthreadthread Memory Manager Agent Manager Message Queue Scheduler Java Virtual Machine Driver Agent Vehicle Driver Behavior Model
  27. 27. © 2008 IBM Corporation 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. Current traffic status If Condition1 Then ………… If Condition2 Then ………… If Condition3 Then ………… If Condition4 Then ………… How the traffic policy and city- design should be in the aging society? What is the appropriate information providing service to minimize traffic congestion? How the traffic would change if we introduce congestion tax. What is the proper traffic policy to solve traffic congestion, green issues.... How the total emission would change if we introduce a new traffic policy? 32k cars/day32k cars/day32k cars/day32k cars/day 49k cars/day49k cars/day49k cars/day49k cars/day 2k cars/day2k cars/day2k cars/day2k cars/day
  28. 28. © 2008 IBM Corporation Water Infrastructure Management DC WASA Water
  29. 29. © 2008 IBM Corporation Analytics Driven Asset Management (ADAM) Data Operational, Failure, Usage, Condition, Customer, Location DataAssets •Asset Management •Work Management •Service Management •Inventory / Contract •Procurement Management EAM/SCADA Enterprise Asset Management Scada, Sensors, Inspection, Metering Systems ADAM Descriptive, Predictive and Prescriptive Analytics Insight, Foresight and Prescriptions •Maintenance Planning •Maintenance Scheduling •Replacement Planning •Condition Assessment •Failure Cause Analysis •Failure Prediction •Usage Analysis •Customer Analysis
  30. 30. © 2010 International Business Machines Corporation 30 ADAM: Analytics Driven Asset Management Predictive analytics models enabling “fix before break” Spatial Schedule Optimization enables “while in the neighborhood “ scheduling Data analytics enable forecasting of water usage and detection of usage anomalies 130,000Water Meters 1,600,000Sewer Customers 600,000Water Customers 370MGallons / dayWaster Water Capacity 36000Catch Basins 24,000Valves 9000Hydrants 1800 MilesSewer Pipes 1200 MilesWater Pipes All from conventional historical and log data!
  31. 31. © 2008 IBM Corporation Optimization GIS Data Predictive Analytics ADAM for Water Utilities V1.0 EAM Advanced Reporting Spatio-Temporal Manual Scheduling Failure Pattern and Cause Analysis Customer Segmentation Automated spatial schedules Failure Risk based PM Optimization Usage & Revenue Forecasting Automated Task level rolling scheduling Failure Prediction Usage Anomaly Detection Dynamic Mobile Work Management Replacement Planning Non-Revenue Water, Energy Optimization Work Management Predictive Maintenance Usage/ Revenue Optimization Water Usage Data
  32. 32. © 2008 IBM Corporation Examples of Advanced Reporting – Catch Basin Work Orders Temporal Analysis of Work Order Patterns Spatial Distribution of annual work Catch basic problem code distributionWork classification vs Problem code visualization Catch Basin
  33. 33. © 2008 IBM Corporation Use cases Task Level Scheduling Dynamic Re-Scheduling using GPS data ADAM V1.0 Use cases • Manual Map Based Schedule Construction • Semi-Automated Route Completion • Multi-crew automated scheduling Ongoing R & D
  34. 34. © 2008 IBM Corporation IBM Research: Smarter City Global engagements Smarter City Activity Dublin Traffic, Water, Energy
  35. 35. © 2008 IBM Corporation Smarter Cities Technology Centre Dublin
  36. 36. © 2011 IBM Corporation 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 transforming transportation analytics Working closely with Dublin – Demonstration visualisation of transportation network status & guidance for bus drivers Challenges – Extracting insights from real-time, noisy, irregular samples – Taking actions under uncertainty with low latency – Large volume & diversity of data Transportation
  37. 37. © 2011 IBM Corporation Dublin Bus – Demonstration
  38. 38. © 2011 IBM Corporation 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 & exploit innovation & promote awareness through better access to data of citizen’s interest Deploying significant common infrastructure for IBM’s SC community – Common compute, data & network platform – Data repositoru – Connectivity into Dublin Systems Challenges – Data & model management in City-scale environment – Tools enabling domain experts to interface with complex data & analytic challenges intuitively City Fabric Open Collaborative Research Common Standards & Definitions Advanced City Technology Multi-City & International Collaboration Platform Data Presentation Open Innovation Platform
  39. 39. © 2008 IBM Corporation Managing Public Safety in NYC and Chicago NY City + Chicago Public Safety
  40. 40. © 2008 IBM Corporation Safety and Security Management Chicago’s Virtual Shield Program Implemented one of the most advanced city-wide intelligent security systems The engagement is a part of Chicago's Operation Virtual Shield, a project that encompasses one of the world's largest video security deployments In the first phase, IBM helped the City experts and network engineers design and implement a monitoring strategy infrastructure to capture, monitor and fully index video for real-time and forensic-related safety applications Korea Incheon Free Economic Zone Implemented a public safety infrastructure with intelligent video monitoring as part of the U-safety City project Built a public safety system utilizing high-resolution cameras to view and monitor activities to prevent crime and even predict possible events by recognizing and analyzing certain patterns and data in real time
  41. 41. © 2008 IBM Corporation Statistical modeling, machine learning & pattern recognition are key technologies to enable Smart Safety and Security Blob Tracking Algorithm Object Classification Algorithm Color Classification Algorithm Background Subtraction Algorithm Machine learning enables recognition of person attributes Statistical Modeling is the key to handling change
  42. 42. © 2008 IBM Corporation Selected Research & Technical Challenges Handling crowded scenes Finer grained analysis of objects Federated / Partitioned Architectures Analytics at the edge
  43. 43. © 2008 IBM Corporation Managing Energy in Buildings NY Bldgs,
  44. 44. © 2008 IBM Corporation 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 City's government spends over $1 billion a year on energy, and is committed to reducing the City government's energy consumption and CO2 emissions by 30% by 2030 (PlaNYC). Buildings emit about 78 percent of the city’s GHG emissions. NYC plans to invest, 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 that Assess how different energies are used (and GHG is emitted) in different ways Benchmark energy (GHG emission) uses among peer buildings Track 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 emission Optimize energy consumption, efficiency and GHG emission
  45. 45. © 2008 IBM Corporation Modeling Approach
  46. 46. © 2008 IBM Corporation Dashboard – Example (Energy Use & Greenhouse Summary, GIS Energy Intensity Map) K-12 Schools
  47. 47. © 2008 IBM Corporation The Benefit of Analytics Identify anomaly that can lead to failure of equipment and wasted energy, and take corrective actions for faults Statistical Analysis (SPC, CUSUM, Time Series Model, Data Mining..) Identify underperforming buildings with respect to peer buildings and identify the root causes Multiple Regression Modeling Accurately estimate heat loss (gain) through walls, roofs, windows, and develop retrofit plans Heat Transfer Model Identify key characteristics of building structures, operations and behaviors that influence energy consumption and take actions for modifications Forecast future energy consumption and develop cost effective procurement plan of energy Forecasting Model And others…
  48. 48. © 2009 IBM Corporation The Role of People in Cities Dubuque
  49. 49. © 2008 IBM Corporation IBM Research: Smarter City Global engagements Dubuque Water, Energy
  50. 50. © 2008 IBM Corporation Green Dubuque CICERO: Citizen centric Intelligence & Resource Optimization
  51. 51. © 2008 IBM Corporation Participants Compete – IBM provides the platform Pilot defined Each 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 Pilot members. You will not know your other team members but you can chat with them using the team chat on the site. Each week, individual households and teams will win prizes and/or will be registered 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 provides Cloud platform and software that aggregates and maps usage Provides metrics and competition information Tracks all usage helping development of behavioural models
  52. 52. © 2008 IBM Corporation CICERO deployed for Resource Consumption Management Cloud-based real-time intelligence & interaction for instrumented, interconnected cities •Deployed for water silo and work underway for electric silo •Resource optimization & decision support for maximizing city performance •Models & Incentives for changing citizen resource consumption behavior •Interest from multiple cities to join cloud delivered service
  53. 53. © 2008 IBM Corporation Whither Weather
  54. 54. © 2008 IBM Corporation The opportunity and challenge of combining models Weather models and resulting damage prediction for Electric Utilities IBM Weather Prediction System DEEP THUNDER - accurate to 2 km x 2 km area A mathematical model that describes the physics of the atmosphere – The sun adds energy, gases rise from the surface, convection causes winds Numerical weather prediction is done by solving the equations of these models 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 Challenge is to predict business impact of weather
  55. 55. © 2008 IBM Corporation IBM uses advanced weather forecasting technologies to predict power demand and outages - Deep Thunder our unique world class weather prediction technologies Weather causes damage and outages Outages require restoration (resources) Restoration takes time, people, etc. Build stochastic model from weather observations, storm damage and related data Outage location, timing and response Wind, rain, lightning and duration Demographics of effected area Ancillary environmental conditions Weather prediction Power Line Damage prediction Restoration time prediction Work crew requirement prediction
  56. 56. © 2008 IBM Corporation 13 March 2010 Nor’easter Deep Thunder Impact Forecast Actual Outages (Repair Jobs) Estimated Outages (Repair Jobs)
  57. 57. © 2008 IBM Corporation Approach to Urban Flood Forecasting Precipitation Estimates Flood Prediction Impact Estimates Model Calibration Refine Sensor Network and Model CalibrationActual Flood Impacts Weather Prediction and/or Rainfall Measurements Analysis of Precipitation
  58. 58. © 2008 IBM Corporation Integrating Systems
  59. 59. © 2008 IBM Corporation IBM Research: Smarter City Global engagements Rio Emergency Management
  60. 60. © 2008 IBM Corporation RIO Operations Center Allows diverse agencies to share emergency information and plan coordinated responses Part of Rio's preparatory efforts for Brazil's hosting of soccer's World Cup in 2014 and the city's hosting of the 2016 Olympic Games. Components include Data acquisition and integration center from multiple agencies High Resolution Weather Prediction System coupled to hydrological flooding models Traffic management systems Emergency operations Integrated scheduling, optimization and allocation of processes
  61. 61. © 2008 IBM Corporation Summary IBM Research is focusing our global resources on the understanding and management of resource usage and deriving an understanding of how these resources interact The integration of technology, mathematics. IT and computer science coupled with advances in algorithms, processor speed communication bandwidth are enabling the management of cities in ways previously unimaginable World pressures from emissions, population and economic growth are driving ever increasing efficiency in the use of every resource The Smarter Cities approach enables this transition