Smarter Cities Research in Ireland 20120113

© 2010 IBM Corporation

IBM Research and Development - Ireland


Smarter Cities Research

Lisa Amini, PhD

Distinguished Engineer and Director IBM Research and Development – Ireland



1997: Copper

Interconnect Wiring

1998: Silicon-on-Insulator

1998: Microdrive 2002:

Millipede

2004: Blue Gene The fastest

supercomputer in the world

2006: 5-stage Carbon Nanotube

Ring Oscillator

2008: World’s First Petaflop Supercomputer

1948: SSEC

1956: RAMAC

1944: Mark 1

1957: FORTRAN

1964: System/360

1971: Speech Recognition

1967: Fractals

1970: Relational Database

1966: One-Device Memory Cell

1973: Winchester Disk

1979: Thin Film Recording

Heads

1980: RISC

Nobel Prizes:

Scanning Tunneling Microscope

High Temperature Superconductivity

1990: Chemically

Amplified Photoresists

1994: SIGe

1993: RS/6000 SP 1996,97: Deep Blue

1987: 1986:

A legacy of World-Class Research



IBM Research: 3 New Labs Established in 2010

IBM Research Labs 1998 - 2007

IBM Research – New Presence Since 2010

China

Watson Almaden

Austin

Tokyo Haifa

Zurich

India

Dublin

Melbourne

Brazil

! Natural Resources ! Smarter Devices ! Human Systems/Events ! Natural Resources

! Disaster management ! Healthcare/Life Sciences

! Smarter Cities ! Risk Analysis ! Exascale and Hybrid Computing



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!



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

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

Operations & Planning •  Leveraging mathematical programming for planning

in an uncertain world

Operations & Planning • Organising data and information to better engage

citizens, empower universities and enable businesses to help drive overall growth

Outline

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Continuous assimilation of real-time traffic data

Eric Bouillet, PhD

Research Staff Member, Analytics & Optimization Smarter Cities Technology Centre IBM Research and Development - Ireland



•  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.

Noisy GPS Data



Real-Time Geomapping and Speed Estimation

Matching map artifact

Estimated path

GPS probe

Estimated speed & heading



• Complex system & analytics challenges •  Data diversity, heterogeneity •  Data accuracy, sparsity •  Data volume

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

Routes & maps

Bus AVL (GPS)

Parking

capacity

Accessibility

SCATS Induction

loop

Timetables

CCTV

Car

Bike

1,000 buses 3,000 GPS / min

200 CCTV cameras

700 intersections 4,000 loop detectors 20,000 tuples / min

Our Dublin Experience (2011)



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.!

•  Pilot cities include Glasgow, UK and Graz, Austria!

•  FP7 EU-funded project starting September 2011!

•  Interactive, dynamic personal journey advisor!

•  Addresses complex, dynamic, multimodal transit network!

10:30

10:35

10:40

10:50

11:20

11:12

Best suggested route continuously updated based on changes in arrival departure times of buses and current position of subscriber

5

10

15

20

25−30

−20−10

010

2030

0

0.02

0.04

0.06

0.08

0.1

0.12

Delay in Minutes

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

Hour of the Day

5

10

15

20

25−30

−20−10

010

2030

0

0.05

0.1

0.15

0.2

Delay in Minutes

Journey Pattern 046A0001; Bus Stop 6059; from 7h to 23h. weekdays

Hour of the Day



Our Dublin Experience (2011)

• Complex system & analytics challenges •  Data diversity, heterogeneity •  Data accuracy, sparsity •  Data volume

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

Routes & maps

Bus AVL (GPS)

Parking

capacity

Accessibility

SCATS Induction

loop

Timetables

CCTV

Car

Bike

1,000 buses 3,000 GPS / min

200 CCTV cameras

700 intersections 4,000 loop detectors 20,000 tuples / min









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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



Angle of Arrival (AOA)

Timing Advance (TA)

Received Signal Strength (RSS)

The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may

Example of extracted trajectory over 1 week

!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.!

!

Mobile phones to detect human mobility and interactions



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!

Regional partitioning based on level of interaction



!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.!

!

!

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!

How travel demand changes over space and time



Modeling and predicting non-routine additive origin-destination flows in the city !

!

!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.!

!

Event duration! User stop!

Time!

Overlap time > 70%!

Estimated home location!

Attendance Inference!

How social events impact mobility in the city



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!









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Leveraging mathematical programming for planning in an uncertain world Susara van den Heever, PhD




• 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!

Overview



Design & long-term planning

Tactical planning

Operations planning

Time horizon

Real-time Hours Days Weeks Months Years

Dec

isio

n ag

greg

atio

n

Operations scheduling

Real-time control

Planning Levels



Time horizon


Dec

isio

n ag

greg

atio

n

Design & longterm planning

Tactical planning

Operations planning


Real-time control

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

Reservoir targets Production,

maintenance plans (e.g. leak detection)

Pump scheduling

Equipment set points

Examples of Decisions



Time horizon


Dec

isio

n ag

greg

atio

n

Design & longterm planning

Tactical planning

Operations planning


Real-time control

Reservoir targets

Pump scheduling

Equipment set points

Population growth

Long-term demand patterns

Energy costs, demand

Rainfall, renewable energy sources

Production, maintenance plans (e.g. leak detection)

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

Impact of Uncertainty



Reservoir!

*Based on Inniscarra network!

Example: Water treatment infrastructure*!

Water source!

Pumphouse!

Treatment plant!

Reservoir!Reservoir!

Pumphouse!

Reservoir!

Reservoir!

Network of pumps, treatment plant, pipelines, and reservoirs!



Inniscarra reservoir!

*Based on Inniscarra network!

Example: Water treatment infrastructure*!

Inniscarra dam!

Inniscarra pumphouse!

Inniscarra plant!

Curraleigh reservoir!

Chetwynd reservoir!

Carrshill pumphouse!

Carrshill reservoir!

Strawhall reservoir!

Network of pumps, treatment plant, pipelines, and reservoirs!

Long-term:!“What are the best investment

choices over the next two decades to optimize the network design?”!

!Mid-term:!

“What should the reservoir level targets be to best hedge against

uncertain demand?”!!

Short-term:!“How can we optimize our low-tariff

pumping?”!Current focus



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!









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Dublinked and Open City Data

Pol Mac Aonghusa

Smarter Cities Technology Centre IBM Research and Development - Ireland



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

2009, Data.gov.uk

Data.gov (US)

1993, SEC Online

2004, USG announces e-

Gov 2.0

Content Factual &

Static

>350 ‘Open City Data

Catalogs’ (data.gov)

2011+, Gov 3.0 City as an Enterprise ....

Activity

Time 2010,

Amazon, Google & MSoft

Content

Structure

Innovation

Aggregation & Efforts to

create linkage based on

Semantic Web

>25 Billion Triples on Linked Data Cloud

Innovation based on

Collaboration & Social

Innovation

35 Cities in Open Data Hackday, 12/2010

Ecosystem increasingly focused on long-term

sustainability

Publicdata.eu – LOD2 for Citizen study due 2014



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

IBM Connections Content Sharing & Collaboration Services

IBM Intelligent Operations Center (IOC) Integrated data visualization, real-time collaboration, deep analytics.

Dublin City

Enterprise Applications

IBM Enterprise Cloud Scalable compute, storage & network infrastructure

City 2 ..N

Enterprise Citizen

Open REST Web Services API

Contents & Catalog

Privacy & Security Knowledge Representation & Reasoning

Administration Monitor & Events

Semantic Query & Analytics

Open Innovation Portal

IBM Products & Services

Robust models to organize and represent resources and their context

Scalable privacy and security of resources Automated assimilation and sharing of resources

Composable resources for development, mash-up & visualization

Research Challenges include ..

IBM Research

Partners & People

Key

Efficient knowledge representation for continuous machine reasoning and diagnosis



Dublinked

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

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.

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



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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