Smarter Cities Research in Ireland 20120113

IBM Research and Development - Ireland

Smarter Cities Research

Lisa Amini, PhD
Distinguished Engineer and Director
IBM Research and Development – Ireland

© 2010 IBM Corporation


A legacy of World-Class Research

1944: 1948: 1956: 1957: 1964: 1966: 1967: 1970: 1971:
Mark 1 SSEC RAMAC FORTRAN System/360 One-Device Fractals Relational Database Speech Recognition
Memory Cell

Nobel Prizes:
1973: 1979: 1980: 1986: 1987: 1990: 1994: 1993: RS/6000 SP
Winchester Disk Thin Film RISC Scanning Tunneling High Temperature Chemically SIGe 1996,97: Deep Blue
Recording Microscope Superconductivity Amplified Photoresists
Heads

1997: 1998: 1998: 2004: 2006: 2008:
Copper Silicon-on-Insulator Microdrive 2002: Blue Gene 5-stage Carbon Nanotube World’s First Petaflop Supercomputer
Interconnect Millipede The fastest Ring Oscillator
Wiring supercomputer
in the world



IBM Research: 3 New Labs Established in 2010

!  Smarter Cities
!  Risk Analysis
!  Exascale and Hybrid Computing
Dublin
China
Zurich
Almaden Watson Haifa Tokyo

Austin India

Brazil
!  Natural Resources
!  Smarter Devices
Melbourne
!  Human Systems/Events
!  Natural Resources
!  Disaster management
!  Healthcare/Life Sciences
IBM Research Labs 1998 - 2007

IBM Research – New Presence Since 2010



Smarter Cities Technology Centre

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https://researcher.ibm.com/researcher/view_researchers.php



How can we help cities transform ?

1.  Sensor data assimilation!
–  Data diversity, heterogeneity
–  Data accuracy, sparsity
–  Data volume!
!

2.  Modelling human demand!
–  Understand how people use the city
infrastructure!
–  Infer demand patterns!

3.  Operations & Planning!
–  Factor in uncertainty!
–  Organise and open data and knowledge, to
engage citizens, empower universities and
enable business!



Outline

Sensor data assimilation
•  Continuous assimilation of real-time traffic data

Transportation
Understanding/Modeling human demand
•  Characterizing urban dynamics from digital traces

Operations & Planning

Water
•  Leveraging mathematical programming for planning
in an uncertain world


Multi-domain
•  Organising data and information to better engage
citizens, empower universities and enable
businesses to help drive overall growth



Continuous assimilation of real-time traffic data

Eric Bouillet, PhD
Research Staff Member, Analytics & Optimization



Noisy GPS Data

•  To become useful, GPS data has to be related to the underlying
infrastructure (e.g., road or rail network) by means of map
matching algorithms, which are often computationally expensive

•  In addition, GPS data is sampled at irregular possibly large time
intervals, which requires advanced analytics to reconstruct with
high probability GPS trajectories

•  Finally, GPS data is not accurate and often needs to be cleaned
to remove erroneous observations.



Real-Time Geomapping and Speed Estimation

GPS probe
Matching map artifact
Estimated path
Estimated speed & heading



Our Dublin Experience (2011)

•  Complex system & analytics challenges Parkin
g
•  Data diversity, heterogeneity capacit
y
•  Data accuracy, sparsity Timetabl
C
700 intersections ar
•  Data volume 4,000 loop detectors
es
20,000 tuples / min

Routes & SCATS
maps Induction
loop

1,000 buses Accessibi
3,000 GPS / min lity
Bus AVL
(GPS)
CC
TV
200 CCTV cameras

•  Active relationship with DCC
•  Deployed in Dublin’s DoT Bik
e



Actuating the city
•  Real-time, proactive traffic control!
•  Traffic control recommender!

•  Recommend actions under uncertainty!

•  Dynamic traffic light actuation strategy

•  Traffic Management Towards a Low Carbon Society!
•  Traffic (congestion) is a significant contributor to CO2 emissions!

•  We are building a method, system and tools for adaptively
influencing traffic in real-time to reduce carbon dioxide CO2-
and black carbon (BC) emissions caused by road transport in
urban and inter-urban areas.!
Journey Pattern 046A0001; Bus Stop 6059; from 7h to 23h. weekdays

Journey Pattern 046A0001; Bus Stop 2017; from 7h to 23h. weekdays

0.2

0.12 0.15

0.1
0.1
0.08

0.06 0.05

•  Pilot cities include Glasgow, UK and Graz, Austria!
0.04
0
0.02 5

0
5 10

10
1
15

15 1 0
1
20
10
20
30

20 20
30 0
1 :
0 Hour of the Day 25 −20
−10
0

•  FP7 EU-funded project starting September 2011!
10 −30

25 −20
−10
0
:
0 4
:
Delay in Minutes

Hour of the Day

3
: Best suggested route
−30
Delay in Minutes
0
5
0
3 0 continuously updated
5
based on changes in
arrival departure times

•  Interactive, dynamic personal journey advisor!
of buses and current
position of subscriber

•  Addresses complex, dynamic, multimodal transit network! 1
1
1
:
1
2
:
0
1
2



Understanding urban dynamics from digital traces

Francesco Calabrese, PhD



Pervasive Technologies Datasets as Digital Footprints

Understand how people use the
city's infrastructure!
!  Mobility (transportation mode) !
!  Consumption (energy, water, waste)!
!  Environmental impact (noise, pollution)!
!
Potentials!
!  Improve city’s services!
!  Optimize planning!

!  Minimizing operational costs!

!  Create feedback loops with citizens to
reduce energy consumption and
environmental impact!



Understanding Urban Dynamics

•  Research goals
•  Understanding human behavior in terms of mobility demand
•  Analyzing and predicting transportation needs in short & long terms

•  Outcome
•  Design adaptive urban transportation systems
•  Support urban planning and design

•  Examples of projects
•  How geography influences the way people interact
•  How travel demand changes over space and time
•  How social events impact mobility in the city



Mobile phones to detect human mobility and interactions

Angle of Arrival (AOA)

Timing Advance (TA)

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Example of extracted trajectory over 1 week
Received Signal Strength (RSS)

!F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, C. Ratti, Real-Time Urban Monitoring Using Cell Phones: a Case Study in Rome, IEEE
Transactions on Intelligent Transportation Systems, 2011.!
!



Regional partitioning based on level of interaction

Findings
•  Spatial cohesiveness of regions
!  State boundaries emerge in most of
the cases
!  Metropolitan areas (e.g. NYC, LA)
define new regions
!  Some states merge as level of
interaction is higher than expected

Applications
!  Help regional and city provides to
better plan or adjust their operations
!  Adjust service catchment areas
(e.g. hospital serviced neighbors)
!  Plan new transit systems to help
connecting areas with low
interaction

The Connected States of America. Can data help us think beyond state lines?, Time Magazine, 11 April 2011!



How travel demand changes over space and time

Origin Destination matrices are
used for transport planning!
!
!
Estimated from census data or
travel surveys!
•  Very costly, so rarely done in
developing countries, and quickly
outdated !
•  Only commuting!
!
!
Developed a new method making
use of mobile phone location data
to estimate ODs!
•  All travels (not only commuting)!
•  Real time monitoring!

!F. Calabrese, G. Di Lorenzo, L. Liu, C. Ratti, “Estimating Origin-Destination ﬂows using opportunistically collected mobile phone location data
from one million users in Boston Metropolitan Area”, IEEE Pervasive Computing, 2011.!
!
! © 2011 IBM Corporation


How social events impact mobility in the city
Modeling and predicting non-routine additive origin-destination ﬂows in the city !
!

Estimated
home
location!

Event duration! User stop!

Overlap time >
70%! Time!

Attendance Inference!

!F. Calabrese, F. Pereira, G. Di Lorenzo, L. Liu, C. Ratti, “The geography of taste: analyzing cell-phone mobility and social events”, In
International Conference on Pervasive Computing, 2010.!
!



Detecting and predicting travel demand

Applications!
•  Improving event planning & management!
•  Predicting the effect of an event on the urban transportation!
•  Adapting public transit (schedules and routes) to accommodate additional
demand!
•  Location based services!
•  Recommending social events!
•  Cold start problem!
!


Summary

•  In order to make city’s services more efﬁcient we need to understand how
people use the city infrastructure!

•  Pervasive technologies datasets allow to infer micro and macro behaviors of
a population!

•  Inferred demand patterns can be used to make services more adaptive and
efﬁcient!



Leveraging mathematical programming for
planning in an uncertain world

Susara van den Heever, PhD



Overview
• Design and planning of urban infrastructures!
–  Transportation
–  Water distribution and treatment
–  Energy

• “Standard” optimization approaches minimize costs while
meeting demand!
• Additional environmental objectives!
–  Minimize carbon footprint!
–  Meet pollution reduction targets!

• Additional challenge – capturing uncertainty, such as:!
–  Population growth and urban dynamics!
–  Rainfall !
–  Renewable energy sources!
–  Energy costs!



Planning Levels

Design & long-term
planning
Tactical
planning
Decision aggregation

Operations
planning

Operations
scheduling

Real-time
control

Real-time Hours Days Weeks Months Years
Time horizon



Examples of Decisions Plant & network design
(e.g. valve placement),
capacity expansion

Reservoir Design & longterm
targets planning
Production,
maintenance plans Tactical
(e.g. leak detection) planning

Operations
Pump planning
scheduling

Equipment
Operations
set points scheduling

Real-time
control

Time horizon



Impact of Uncertainty Plant & network design
(e.g. valve placement),
capacity expansion

Reservoir Design & longterm
targets planning
Production,
maintenance plans Tactical
(e.g. leak detection) planning Population growth

Operations
Pump planning
scheduling Long-term demand patterns

Equipment
Operations
set points scheduling
Energy costs, demand
Real-time
control
Rainfall, renewable energy sources

Time horizon



Example: Water treatment infrastructure*!
Network of pumps, treatment plant, pipelines,
and reservoirs!

Reservoir!
Pumphouse!
Reservoir! Treatment plant!

Pumphouse! Reservoir!

Water
Reservoir! Reservoir! source!

*Based on Inniscarra network!



Example: Water treatment infrastructure*!
Network of pumps, treatment plant, pipelines,
and reservoirs!

Carrshill Long-term:!
reservoir! the best investment
“What are Inniscarra
pumphouse!
Strawhall choices over the next two decades to
reservoir! optimize the network design?”! plant!
Inniscarra
!
Mid-term:!
Carrshill Inniscarra
“What should the reservoir level
pumphouse! reservoir!
targets be to best hedge against
uncertain demand?”!
!
Short-term:! Inniscarra
Chetwynd Curraleigh
dam!
“How can we optimize our low-tariff reservoir!
reservoir!
pumping?”!
Current focus

*Based on Inniscarra network!



Summary

• Design and planning of urban infrastructures under uncertainty !

• Ignoring uncertainty could lead to costly decisions!

• Traditional approaches to dealing with uncertainty!
•  Often require an expert to implement!
•  Scenario creation and analysis not obvious!

• Research towards generalized approach to aid!
•  Scenario creation!
•  Uncertainty and sensitivity analysis!



Outline

Sensor data assimilation
•  Continuous assimilation of real-time traffic data

Transportation
Understanding/Modeling human demand
•  Characterizing urban dynamics from digital traces


Water
•  Leveraging mathematical programming for planning
in an uncertain world

•  Organising data and information to better engage
citizens, empower universities and enable
businesses to help drive overall growth



Dublinked and Open City Data

Pol Mac Aonghusa



Opening the Data locked in our Cities is no longer an option
Open access to data and services coupled with ad hoc social innovation are only the beginning
Activity Ecosystem
Innovation increasingly
based on focused on
Aggregation Collaboration long-term
& Efforts to & Social sustainability Content
Content create linkage Innovation
Factual & based on
Static Semantic Web Publicdata.eu –
35 Cities in LOD2 for Structure
Open Data Citizen study
>25 Billion Hackday, due 2014
Triples on 12/2010
>350 ‘Open Linked Data
City Data Cloud
Catalogs’ (data Innovation
.gov)

2009, 2011+, Gov 3.0 Time
1993, SEC 2004, USG 2010,
.... announces e- Data.gov.uk City as an Enterprise
Online Amazon,
Gov 2.0 Data.gov (US) Google & MSoft


Open Innovation Portal (OIP) ! publish, organise, discover & consume the
information resources of a City
Research Challenges include ..

Open Innovation Portal Scalable privacy and security of
resources
Automated assimilation and sharing of
Administration Monitor & Events Contents & Catalog resources

Knowledge
Semantic Query & Robust models to organize and
Representation & Privacy & Security represent resources and their context
Analytics
Reasoning

Open REST Web Services API Efficient knowledge representation for
continuous machine reasoning and
diagnosis

Composable resources for
Enterprise Applications development, mash-up & visualization

IBM Intelligent Operations Center (IOC) IBM Connections
Integrated data visualization, real-time Content Sharing &
collaboration, deep analytics. Collaboration Services

IBM Enterprise Cloud
Scalable compute, storage & network infrastructure

Key IBM Research

Dublin IBM Products & Services
City Enterprise Citizen
City 2 ..N Partners & People


Dublinked

Excellent download statistics

The highest demand data sets are for water
telemetry reading (Water, Traffic, Planning)

Creating meaningful and accurate meta-
data is still a tedious and error prone task.

Enhanced support a priority for version 2.

Have provided a review of site usability &
function by student as input.

Will also provide analysis of data sets from
researchers.

http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/word-tree-of-dublinked-launch-open



How can we help cities achieve their aspirations?

"  Sensor data assimilation!
From noisy data!
! to uncertain information!
!

!
"  Modeling human demand!
! !Capturing uncertainty!
!
!
"  Operations & Planning!
! !Factoring in uncertainty!



Working harder is not sustainable

Cities require innovative approaches



Publications
•  The Connected States of America. Can data help us think beyond state lines?, Time Magazine, 11 April 2011!

•  F Calabrese, D Dahlem, A Gerber, D Paul, X Chen, J Rowland, C Rath, C Ratti, The Connected States of America:
Quantifying Social Radii of Influence, International Conference on Social Computing, 2011.!

•  F. Calabrese, G. Di Lorenzo, L. Liu, C. Ratti, “Estimating Origin-Destination flows using opportunistically collected
mobile phone location data from one million users in Boston Metropolitan Area”, IEEE Pervasive Computing, 2011.!

•  G. Di Lorenzo, F. Calabrese, "Identifying Human Spatio-Temporal Activity Patterns from Mobile-Phone Traces”, IEEE
ITSC, 2011!

•  F. Calabrese, Z. Smoreda, V. Blondel, C. Ratti, “The Interplay Between Telecommunications and Face-to-Face
Interactions-An Initial Study Using Mobile Phone Data”, PLoS ONE, 2011.!

•  D. Quercia, G. Di Lorenzo, F. Calabrese, C. Ratti, “Mobile Phones and Outdoor Advertising: Measurable Advertising”,
IEEE Pervasive Computing, 2011.!

•  F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, C. Ratti, “Real-Time Urban Monitoring Using Cell Phones: a Case
Study in Rome”, IEEE Transactions on Intelligent Transportation Systems, 2011.!

•  L. Gasparini, E. Bouillet, F. Calabrese, O. Verscheure, Brendan O’Brien, Maggie O’Donnell, "System and Analytics for
Continuously Assessing Transport Systems from Sparse and Noisy Observations: Case Study in Dublin”, IEEE ITSC,
2011!

•  A. Baptista, E. Bouillet, F. Calabrese, O. Verscheure, "Towards Building an Uncertainty-aware Multi-Modal Journey
Planner”, IEEE ITSC, 2011!

•  T. Tchrakian, O. Verscheure, "A Lagrangian State-Space Representation of a Macroscopic Traffic Flow Model”, IEEE
ITSC, 2011!


Smarter Cities Research in Ireland 20120113

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Smarter Cities Research in Ireland 20120113