Urban Systems Collaborative Seminar | Jurij Paraszczak, An it view of smarter cities

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

© 2009 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 ?
EDUCATION • TRANSPORTATION • SOCIAL SERVICES • SAFETY • UTILITIES • HEALTHCARE • COMMUNCATION

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EDUCATION • TRANSPORTATION • SOCIAL SERVICES • SAFETY • UTILITIES • HEALTHCARE • COMMUNCATION

2 15 September 2010 © 2008 IBM Corporation

Overview
Asset
Smarter Cities approach creates solutions Management
Resource
Optimization
which simplify the way in which the myriad Pipes, Roads,
Water, traffic,
Wires, Bldgs,
city operations act in a city and helps city etc. System of
energy etc.
managers make rational decisions based on Systems

data and prediction

Over 100 + people are working around the
world are learning with our customers and
deploying models and analytics which use a People
Motivation &
common platforms and approaches to enable Inclination
repeatable processes

From this work we are discovering patterns
and approaches which help in this Jobs
simplification, reducing cost and providing Comfort
new insights Lifestyle

Taking advantage of our deep scientific and City water, energy,
engineering capabilities in IBM Research buildings & transport

Safety & Security

City Needs

IBM Research: Smarter City Global engagements

Dublin Traffic, Stockholm
Water, Energy Traffic
Dubuque
PNW Beijing
Water,
SmartGrid Bornholm Energy Shenyang
Energy
NY Bldgs, Energy Moscow Beijing Water, Carbon
Traffic Agency Emer Security Nanotech Traffic
West Coast Tokyo
PA Bldgs
DC WASA Integ. City
Texas Water Ranaana
River Basin Water
Delhi
Energy
Traffic
Singapore
Smarter City Traffic Water
Rio
Activity Emerg.
Natural Melbourne
Resources Sydney
Energy &
Energy
LifeScience


Analysing Cities

Who wants what when and where


Who spends what in cities ? City Budgets in Aggregate
50 Cities Budget : $561B
Cities In Transition Mature 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 Large
Mature 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


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/


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


We are targeting the following city domains

Building Energy

Traffic & Water
Transportation availability &
purity

Safety

Underlying Science and Engineering

From paper to models


Developing the Research which underlies Smarter Cities
We view the Smarter City through this structure

Solutions

Emerging area: Human interaction with Smarter City

Business
Data Models Optimization
Decisions

Infrastructure Technologies & Tools

Core Technologies


Understanding disconnects: A warning and a simple example of a
common problem


Using mathematics and models to drive the business activity - for
example, traffic management
Operational/ Transactional Insights System wide control

Road Usage
Optimization,
GHG emission
models

Operational/
• Charge collection • More granular • Dynamic and
Transactional
only - disconnected charging, by location congestion based
operational data pricing
• Analysis of traffic
• Transaction data from patterns to manage • Route planning and
Development
Business

the management of city congestion. advice, shippers,
payments concrete haulers,
• Modeling traffic to
limo companies,
• Little automated use predict and manage
theatres, taxis etc
is made of real-time entire system
traffic data • City-wide, dynamic
traffic optimization
2008-10 2008-12? 2009-15?

Advanced Analytics
is the use of data and models to provide insight to guide decisions

Analytics Data sources:
Business automation
Instrumentation
Sensors
Data Web 2.0
Expert knowledge
“real world physics”

Model:
a mathematical or
algorithmic
representation of
Models reality intended to
explain or predict
some aspect of it

Decision executed
automatically or
by people
Insight

Managing Traffic in Stockholm

Stockholm
Traffic


Stockholm Road Charging

40 Gantries with 18 ingress
points
Approx 320K entries/exists
per day


Charging to reduce traffic


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 Results
scrutiny, referendum at the end of the trial Traffic congestion in Stockholm was reduced
to decide on whether to implement the by 25%, far above the original target
congestion tax permanently 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

Analysing Traffic


Stream computingSupply Chain fortocritical paradigm shift
Notional Information
represents a Decision-making action!
Transforming the Information Supply Chain reduce the time to
Analytical Modeling
& Information

Time to Action Elapsed Time to Action
Analytical Modeling & Information
Operational Dashboards Planning Scorecarding
Reports
Bus Process
& Event Mgmt
Reports
Ad-hoc Queries

WAREHOUSE DATAMARTS

DATA INTEGRATION
OPERATIONAL DATA STORES
SOURCES

20

Infosphere Streams in Stockholm - why models are important

Traffic Speed
Bouillet, Riabov, Verscheure
Fast >140
Slow/stop Moderate Average Good © 2008 IBM Corporation
Km/hr

Predicting Traffic


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

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


Traffic Prediction Tool (TPT)
Model: stochastic model used to predict traffic in Singapore
► Issue: “real-time” is too late ► IBM Innovation: forecast the future
Little automated use is made of the gigabytes of real-time IBM’s TPT provides a layer of intelligence by using sensor
traffic data today; often, by the time it is received, it is no data in sophisticated algorithms that create relevant
longer representative of the actual traffic insights from the raw data

blue = forecast black = actual red = incident

4000
results
rr

3000
r
rrrr
r rr
r r
r rr

volume
r rr r r
r
r r rr TPT accurately

2000
rr

r forecasts future
r
1000
traffic conditions,
including incidents
tool screenshot
0 50 100 150 200 250
time

Current Focus Future Use Extension: 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; route in a complete picture of future traffic
timing, ramp metering planning & advice state, network-wide

Agent Based Analytics and prediction


Large-scale Agent-based Traffic Flow Simulator
IBM Mega Traffic Simulator

IBM Mega Traffic Simulator output
base data input Driver Behavior Model
Driver

CO2 emission
Road Agent
Map data
network

Traffic Origin- Vehicle
census destination Link A Link B Link C
Java Virtual Machine
Agent Space CO2 emission for each link
Agent Agent Agent
Driving log Driver Model
Agent
Agent
Agent
Agent
Agent
Agent
Agent
Agent
Agent
2k cars/hour
Agent Agent Agent

Simulation Space
Agent Manager
3k cars/hour

Scheduler
Memory Manager
Messaging
Handler

Message Queue Thread Manager
threadthread
thread
thread
thread
thread
0.5k cars/hour
Communication Manager

IBM Zonal Agent-based Simulation Environment
traffic 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


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 if
we introduce congestion tax.
2k cars/day
If Condition1 Then …
32k cars/day

49k cars/day How the total emission would
change if we introduce a new
traffic policy?

Current traffic status
What is the appropriate
information providing service to
minimize traffic congestion?

How the traffic policy and city-
What is the proper traffic policy to design should be in the aging
solve traffic congestion, green issues.... society?

Water Infrastructure Management

DC WASA
Water


Analytics Driven Asset Management (ADAM)

•Maintenance Planning
Insight, •Maintenance Scheduling
Foresight and Prescriptions •Replacement Planning

ADAM
•Condition Assessment
•Failure Cause Analysis
Descriptive, Predictive and •Failure Prediction
Prescriptive Analytics •Usage Analysis
•Customer Analysis

Data
Data Operational, Failure, Usage, Condition, Customer, Location

EAM / SCADA
Enterprise Asset Management Scada, Sensors, Inspection, Metering Systems
•Asset Management
•Work Management
•Service Management
•Inventory / Contract
•Procurement Management

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 anomalies

Water Pipes 1200 Miles
Sewer Pipes 1800 Miles
Hydrants 9000
Valves 24,000
Catch Basins 36000
Water Meters 130,000
Waster Water Capacity 370MGallons / day
Water Customers 600,000
Sewer Customers 1,600,000

All from conventional historical and log data!
© 2010 International Business Machines Corporation 30

ADAM for Water Utilities V1.0

Work Predictive Usage/ Revenue
Management Maintenance Optimization

Spatio-Temporal Failure Pattern and Customer
Manual Scheduling Cause Analysis Segmentation

Automated spatial Failure Risk based Usage Anomaly
schedules PM Optimization Detection

Automated Task level Failure Prediction Non-Revenue Water,
rolling scheduling Energy Optimization

Dynamic Mobile Replacement Usage & Revenue
Work Management Planning Forecasting

Advanced Reporting Predictive Analytics Optimization

EAM GIS Data Water Usage Data


Examples of Advanced Reporting – Catch Basin Work
Orders

Temporal Analysis of Work Order
Catch Basin Patterns

Spatial Distribution of annual work

Catch basic problem code
Work classification vs Problem code visualization distribution

Use cases
ADAM 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



Dublin Traffic,
Water, Energy

Smarter City
Activity


Smarter Cities Technology Centre
Dublin


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 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


Dublin Bus – Demonstration


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 & exploit
innovation & promote awareness through better Open Innovation Platform
access to data of citizen’s interest
Multi-City & Open
Deploying significant common infrastructure for Presentation International
Collaboration
Collaborative
Research
IBM’s 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-scale
environment
– Tools enabling domain experts to interface with
complex data & analytic challenges intuitively


Managing Public Safety in NYC and Chicago

NY City + Chicago
Public Safety


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


Statistical modeling, machine learning & pattern recognition are key
technologies to enable Smart Safety and Security
Statistical Modeling is the key to handling change
Background
Subtraction
Algorithm

Blob Tracking
Algorithm

Object
Classification
Algorithm

Color
Classification
Algorithm
Machine learning enables recognition of person attributes

Selected Research & Technical Challenges

Handling crowded scenes Federated / Partitioned Architectures

Finer grained analysis of objects Analytics at the edge


Managing Energy in Buildings

NY Bldgs,


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


Modeling Approach


Dashboard – Example (Energy Use & Greenhouse Summary, GIS
Energy Intensity Map)

K-12 Schools


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…


The Role of People in Cities

Dubuque



Dubuque
Water,
Energy


Green Dubuque
CICERO: Citizen centric Intelligence & Resource Optimization


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


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

Whither Weather


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

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) Weather
Restoration takes time, people, etc. prediction
Build stochastic model from weather observations, storm damage and
related data
Outage location, timing and response
Wind, rain, lightning and duration Power Line
Demographics of effected area Damage
Ancillary environmental conditions prediction

Work crew
requirement
prediction

Restoration
time
prediction


13 March 2010 Nor’easter Deep Thunder Impact Forecast

Actual Outages (Repair Jobs) Estimated Outages (Repair Jobs)


Approach to Urban Flood Forecasting

Precipitation
Estimates

Weather Prediction and/or Analysis of Precipitation
Rainfall Measurements Flood
Prediction

Refine Sensor Network
Actual Flood Impacts and Model Calibration

Model
Calibration
Impact
Estimates


Integrating Systems



Rio
Emergency
Management


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

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


Urban Systems Collaborative Seminar | Jurij Paraszczak, An it view of smarter cities

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