An IT view of Smarter Cities
Jurij Paraszczak for Smarter Cities Global Team Director Industry Solutions and Smarter Cities IBM Research [email protected] With many thanks to the Research Smarter Cities team
2009 IBM Corporation
The city a system of systemsSystems 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 ?EDUCATION TRANSPORTATION SOCIAL SERVICES SAFETY UTILITIES HEALTHCARE COMMUNCATION
+$EDUCATION TRANSPORTATION SOCIAL SERVICES SAFETY UTILITIES HEALTHCARE COMMUNCATION2 15 September 2010
2008 IBM Corporation
OverviewSmarter 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 insightsTaking advantage of our deep scientific and engineering capabilities in IBM ResearchAsset Management Pipes, Roads, Wires, Bldgs, etc. Resource Optimization Water, traffic, energy etc.
System of Systems
People Motivation & Inclination
Jobs Comfort Lifestyle City water, energy, buildings & transport Safety & Security
City Needs 2008 IBM Corporation
IBM Research: Smarter City Global engagements
PNW SmartGrid Traffic Agency West Coast
Dubuque Water, Energy
Dublin Traffic, Water, Energy Bornholm Energy
Stockholm Traffic Beijing Energy Beijing Traffic Shenyang Water, Carbon Tokyo Integ. City Delhi Energy Traffic
NY Bldgs, Emer Security DC WASA Water
Moscow Nanotech
PA Bldgs Texas River Basin
Ranaana Water
Smarter City Activity
Rio Emerg. Natural Resources
Singapore Traffic Water
Melbourne Sydney Energy & Energy LifeScience
2008 IBM Corporation
Analysing Cities
Who wants what when and where
2009 IBM Corporation
Who spends what in cities ?IBM assessment from top 50 cities by population 3 City types identifiedMature Large Mature Medium Cities in Transition
City Budgets in Aggregate50 Cities Budget : $561BCities In Transition $161B Mature Large $285B(19 Cities/198M People)
(15 Cities /217 M People)
Mature Medium $115B(16 Cities/59M People)
Each city type has different focusMature 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
IBM Smarter Cities ChallengeThe 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 todaySee http://smartercitieschallenge.org/
2008 IBM Corporation
Observations in working around the world with Cities Key issues includeAbility 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
We are targeting the following city domains
Building Energy
Traffic & Transportation
Water availability & purity
Safety 2008 IBM Corporation
Underlying Science and Engineering
From paper to models
2009 IBM Corporation
Developing the Research which underlies Smarter CitiesWe view the Smarter City through this structure
Solutions
Emerging area: Human interaction with Smarter City Business Decisions
Data
Models
Optimization
Infrastructure Technologies & Tools
Core Technologies
2008 IBM Corporation
Understanding disconnects: A warning and a simple example of a common problem
2008 IBM Corporation
Using mathematics and models to drive the business activity - for example, traffic managementOperational/ Transactional Insights System wide control Road Usage Optimization, GHG emission models Operational/ Charge collection Transactional only - disconnected operational dataBusiness Development
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
Transaction data from the management of payments Little automated use is made of real-time traffic data2008-10
2008-12?
2009-15? 2008 IBM Corporation
Advanced Analyticsis the use of data and models to provide insight to guide decisions
AnalyticsData
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
Models
Insight
Decision executed automatically or by people 2008 IBM Corporation
Managing Traffic in Stockholm
Stockholm Traffic
2008 IBM Corporation
Stockholm Road Charging
40 Gantries with 18 ingress pointsApprox 320K entries/exists per day
2008 IBM Corporation
Charging to reduce traffic
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 2008 IBM Corporation
Analysing Traffic
2008 IBM Corporation
Notional Information Stream computingSupply Chain fortocritical paradigm shift represents a Decision-making action! Transforming the Information Supply Chain reduce the time toAnalytical Modeling & Information
Time to Action
Elapsed Time to ActionAnalytical Modeling & Information Operational Reports Bus Process & Event Mgmt Reports Ad-hoc QueriesDashboards Planning Scorecarding
WAREHOUSE DATA INTEGRATION OPERATIONAL DATA STORES
DATAMARTS
SOURCES
20
2008 IBM Corporation
Infosphere Streams in Stockholm - why models are important
Traffic SpeedSlow/stop Moderate Average Good Fast >140 Km/hr
Bouillet, Riabov, Verscheure 2008 IBM Corporation
Predicting Traffic
2008 IBM Corporation
Traffic Prediction Tool (TPT) background and motivationThe 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
InductiveFixed loop
locations, Traffic camera sparse in the network
GPS device
Smart phone
Infrared laser radar Passive infrared ultrasonic sensor Historical origin-destination trip tables 2008 IBM Corporation
Traffic Prediction Tool (TPT) Model: stochastic model used to predict traffic in Singapore Issue: real-time is too lateLittle 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
IBM Innovation: forecast the futureIBMs TPT provides a layer of intelligence by using sensor data in sophisticated algorithms that create relevant insights from the raw datablack = actual red = incident
blue = forecast4000
resultsrr r rrr r r rr r r r rr r rr r r r r r rr rr r r
volume
3000
TPT accurately forecasts future traffic conditions, including incidents
tool screenshot
1000 0
2000
50
100
150
200
250
time
Current FocusTraffic Operations: Variable Message Sign setting; traffic signal timing, ramp metering
Future UseTraffic 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 2008 IBM Corporation
Agent Based Analytics and prediction
2008 IBM Corporation
Large-scale Agent-based Traffic Flow Simulator
IBM Mega Traffic Simulatorbase dataMap data
inputRoad network
IBM Mega Traffic SimulatorDriver Behavior Model Driver Agent
outputCO2 emissionLink A
Traffic census
OrigindestinationAgent Space
VehicleJava Virtual Machine Agent Agent Agent Agent Agent Agent Agent Agent Agent Agent Simulation Space Messaging Handler Scheduler Agent Manager Memory Manager Thread Managerthread thread thread thread thread thread
Link B
Link C
CO2 emission for each link 2k cars/hour 3k cars/hour 0.5k cars/hour
Driving log
Driver Model
Agent Agent Agent Agent Agent
Message Queue
Communication Manager
IBM Zonal Agent-based Simulation Environmenttraffic volume for each link
Traffic 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
Application of the simulator: What-If AnalysisThe 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 if we introduce congestion tax.
2k cars/day
If Condition1 Then 32k cars/day 49k cars/dayHow the total emission would change if we introduce a new traffic policy?
If Condition2 Then Current traffic statusWhat is the appropriate information providing service to minimize traffic congestion?
If Condition3 Then How the traffic policy and citydesign should be in the aging society?
What is the proper traffic policy to solve traffic congestion, green issues....
If Condition4 Then 2008 IBM Corporation
Water Infrastructure Management
DC WASA Water
2008 IBM Corporation
Analytics Driven Asset Management (ADAM)
Insight, Foresight and Prescriptions Descriptive, Predictive and Prescriptive Analytics
Maintenance Planning Maintenance Scheduling Replacement Planning Condition Assessment Failure Cause Analysis Failure Prediction Usage Analysis Customer Analysis
ADAM Data
Data
Operational, Failure, Usage, Condition, Customer, LocationEAM / SCADA Scada, Sensors, Inspection, Metering SystemsAsset Management Work Management Service Management Inventory / Contract Procurement Management
Enterprise Asset Management
2008 IBM Corporation
Assets
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 anomaliesWater Pipes Sewer Pipes Hydrants Valves Catch Basins Water Meters Waster Water Capacity Water Customers Sewer Customers 1200 Miles 1800 Miles 9000 24,000 36000 130,000 370MGallons / day 600,000 1,600,000
All from conventional historical and log data! 2010 International Business Machines Corporation 30
ADAM for Water Utilities V1.0Work Management Predictive Maintenance Usage/ Revenue Optimization
Spatio-Temporal Manual Scheduling
Failure Pattern and Cause Analysis
Customer Segmentation
Automated spatial schedules
Failure Risk based PM Optimization
Usage Anomaly Detection
Automated Task level rolling scheduling
Failure Prediction
Non-Revenue Water, Energy Optimization
Dynamic Mobile Work Management
Replacement Planning
Usage & Revenue Forecasting
Advanced Reporting EAM
Predictive Analytics GIS Data
Optimization Water Usage Data
2008 IBM Corporation
Examples of Advanced Reporting Catch Basin Work Orders
Catch Basin
Temporal Analysis of Work Order PatternsSpatial Distribution of annual work
Work classification vs Problem code visualization
Catch basic problem code distribution 2008 IBM Corporation
Use casesADAM V1.0 Use cases Manual Map Based Schedule Construction Semi-Automated Route Completion Multi-crew automated scheduling Ongoing R & D
Task Level Scheduling
Dynamic Re-Scheduling using GPS data 2008 IBM Corporation
IBM Research: Smarter City Global engagementsDublin Traffic, Water, Energy
Smarter City Activity
2008 IBM Corporation
Smarter Cities Technology Centre Dublin
2008 IBM Corporation
TransportationDeveloping 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
2011 IBM Corporation
Dublin Bus Demonstration
2011 IBM Corporation
City FabricPlatform for gathering and analyzing Dublin city data,. Working with Dublin City on an Open Innovation Platform for CitiesBackground Governments are seeking to spawn & exploit innovation & promote awareness through better access to data of citizens interest Deploying significant common infrastructure for IBMs 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 intuitivelyAdvanced City Technology Platform Open Innovation PlatformMulti-City & International Collaboration Open Collaborative Research Common Standards & Definitions
Presentation
Data
2011 IBM Corporation
Managing Public Safety in NYC and Chicago
NY City + Chicago Public Safety
2008 IBM Corporation
Safety and Security ManagementChicagos Virtual Shield ProgramImplemented 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 ZoneImplemented 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
2008 IBM Corporation
Statistical modeling, machine learning & pattern recognition are key technologies to enable Smart Safety and SecurityStatistical Modeling is the key to handling changeBackground Subtraction Algorithm
Blob Tracking Algorithm
Object Classification Algorithm
Color Classification Algorithm
Machine learning enables recognition of person attributes 2008 IBM Corporation
Selected Research & Technical Challenges
Handling crowded scenes
Federated / Partitioned Architectures
Finer grained analysis of objects
Analytics at the edge 2008 IBM Corporation
Managing Energy in Buildings
NY Bldgs,
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 citys 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 thatAssess 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 2008 IBM Corporation
Modeling Approach
2008 IBM Corporation
Dashboard Example (Energy Use & Greenhouse Summary, GIS Energy Intensity Map)
K-12 Schools
2008 IBM Corporation
The Benefit of AnalyticsIdentify anomaly that can lead to failure of equipment and wasted energy, and take corrective actions for faultsStatistical Analysis (SPC, CUSUM, Time Series Model, Data Mining..)
Identify underperforming buildings with respect to peer buildings and identify the root causesMultiple Regression Modeling
Accurately estimate heat loss (gain) through walls, roofs, windows, and develop retrofit plansHeat 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 energyForecasting Model
And others 2008 IBM Corporation
The Role of People in Cities
Dubuque
2009 IBM Corporation
IBM Research: Smarter City Global engagements
Dubuque Water, Energy
2008 IBM Corporation
Green Dubuque CICERO: Citizen centric Intelligence & Resource Optimization
2008 IBM Corporation
Participants Compete IBM provides the platformPilot 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 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 providesCloud platform and software that aggregates and maps usage Provides metrics and competition information Tracks all usage helping development of behavioural models
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 2008 IBM Corporation
Whither Weather
2008 IBM Corporation
The opportunity and challenge of combining modelsWeather models and resulting damage prediction for Electric Utilities IBM Weather Prediction System DEEP THUNDER - accurate to 2 km x 2 km areaA 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
2008 IBM Corporation
IBM uses advanced weather forecasting technologies to predict power demand and outages - Deep Thunder our unique world class weather prediction technologiesWeather causes damage and outages Outages require restoration (resources) Restoration takes time, people, etc. Build stochastic model from weather observations, storm damage and related dataOutage location, timing and response Wind, rain, lightning and duration Demographics of effected area Ancillary environmental conditions
Weather prediction
Power Line Damage prediction
Work crew requirement prediction
Restoration time prediction
2008 IBM Corporation
13 March 2010 Noreaster Deep Thunder Impact Forecast
Actual Outages (Repair Jobs)
Estimated Outages (Repair Jobs) 2008 IBM Corporation
Approach to Urban Flood ForecastingPrecipitation Estimates
Weather Prediction and/or Rainfall Measurements
Analysis of Precipitation
Flood Prediction
Actual Flood Impacts
Refine Sensor Network and Model Calibration
Model Calibration Impact Estimates 2008 IBM Corporation
Integrating Systems
2008 IBM Corporation
IBM Research: Smarter City Global engagements
Rio Emergency Management
2008 IBM Corporation
RIO Operations CenterAllows diverse agencies to share emergency information and plan coordinated responsesPart 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 includeData 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 2008 IBM Corporation
SummaryIBM 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 resourceThe Smarter Cities approach enables this transition
2008 IBM Corporation