More Related Content
Similar to Future Cities Conference´13 / Pól Mac Aonghusa - "Future Life and Services"
Similar to Future Cities Conference´13 / Pól Mac Aonghusa - "Future Life and Services" (20)
More from Future Cities Project
More from Future Cities Project (20)
Future Cities Conference´13 / Pól Mac Aonghusa - "Future Life and Services"
- 1. IBM Research - Ireland
© 2012 IBM Corporation
Future Life and Services
Shake up your City
Pól Mac Aonghusa,
IBM Research – Ireland
- 2. IBM Research - Ireland
© 2012 IBM Corporation
By the middle of this Century, most of humanity will live in Cities that are
increasingly instrumented & interconnected. In 2013 we expect to generate >850 Exabytes
of Internet data. Most of it will be user contributed content (versus traditional enterprise sources).
Global access to technology is already driving trends
like ‘virtual citizenship’, ‘virtual employment’ & ‘social
innovation’
City + Citizen contributed content will become a core strategic
economic resource – and the most scalable natural resource a
City possesses.
Mobility, Openness & connection
will matter more than presence & rigid
structures
Imagining Future Life: Interactions and Expectations
On-demand interaction will increasingly be the norm for a global community of virtual citizen
innovators … who expect their experience of a City to be as simple as using an appliance
- 3. IBM Research - Ireland
© 2012 IBM Corporation
Imagining Future Services: Interdependency and Complexity
We have built a world of massive complexity and interdependency….
….and along with progress, we have brought on massive risks we don’t manage well
24 Hours of Air Travel Global Trade Global Financial
Markets
Nuclear Technology
Pandemics Global Financial Crisis Nuclear Disasters
- 4. IBM Research - Ireland
© 2012 IBM Corporation
Some Good News:
The future is already here -- it’s just not very evenly distributed*
• Linz, Austria
• Solar City, an entire district exclusively using solar power for energy.
• Milan, Italy, Southampton, UK, Salzburg, Austria
• Unified smart card access across services, i.e., bus, library, museum, bikes, and EV rental.
• Stockholm, Sweden
• Congestion charging and real-time information from taxi and lorry GPS, traffic and pollution sensors, transit
systems, and weather
• 20% reduction in city traffic, and halving the average travel time
• 40% reduction in GHG, such as CO2
• Barcelona
• EV innovation: >250 charge points, all with real-time status, free city parking and charging, 3% of parking spaces
reserved for EVs.
• Madrid
• Fully integrated its emergency management systems (fire, police, ambulance) with responsiveness increased by
25% (>8 minutes)
• Amsterdam
• Public/private platform to collaborate on aggressive goals:
• Municipal organizations climate-impact neutral before 2015
• 20% renewable energy by 2025,
• 40% reduction in CO2 emissions by 2025
*William Gibson
- 5. IBM Research - Ireland
© 2012 IBM Corporation
Three urgent challenges on the Future Cities roadmap
• Assimilate Data at Internet Scale
– Diversity, heterogeneity
– Accuracy, sparsity, resilience
– Volume, provenance, privacy
• Model Human Demand
– Understand how people use the city
infrastructure
– Infer demand patterns
• Factor in Uncertainty
– Operations and planning
– Organise and open data and
knowledge, to engage citizens,
empower universities and enable
business
- 6. IBM Research - Ireland
© 2012 IBM Corporation
ReasonableCity: Learning Systems to Help Diagnose the City
Problem
How can we provide City decision
makers predict and diagnose events and
anomalies in real-time from massive,
rich, complex, heterogeneous and
dynamic urban data?
Research Challenges
• Identifying relevant data and information
• Capturing and representing time-evolving
knowledge
• Combining and correlating time-evolving
knowledge from heterogeneous data sources
• Advanced fusion of data
Approach
A system that identifies the nature and
cause of changes and explain logical
connection of knowledge across space
and time
• Identify a common (semantic)
representation layer for causality
detection and analysis
• Develop a reasoning engine that
interprets causality for diagnosis
• Demonstrate the prototype with
DCC Data
• Improve the flexibility of causality
detection (through ML techniques)
- 7. IBM Research - Ireland
© 2012 IBM Corporation
Diagnosing Cities: A Road Traffic Congestion Case
Freddy Lecue, Anika Schumann, Marco Sbodio
Example … simplify decision making with smart systems that learn
- 8. IBM Research - Ireland
© 2012 IBM Corporation
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)
Applications
Improve city’s services
Optimize planning
Minimizing operational costs
Create feedback loops with citizens to
reduce energy consumption and
environmental impact
- 9. IBM Research - Ireland
© 2012 IBM Corporation
• Objectives
• What is the relationship between the on-
line and physical communities … and what
does it mean?
• Can we use this data to help adapt or
anticipate service demand?
• Can we characterise city spaces for
service operation and planning?
Example … interpreting human behavior to model demand
Applications
Help region and city to better plan or
adjust operations
Adjust service catchment areas
(e.g. hospital serviced neighbors)
Plan new transit systems to help
connecting areas with low
interaction
- 10. IBM Research - Ireland
© 2012 IBM Corporation
…and uncertainty is everywhere!
River basins, coastal
bays and estuaries
Households
Water treatment and
distribution networks
Water shortages
Increased costs
Water quality issues
Stress on water quality
Floods
Capacity shortages
Increased investment and operating costs
Population growth
Climate
change
Renewable
energy
Water consumption
Rainfall
patterns
Model accuracy Measurements
Economic
climate
All these systems are
connected in some
way – their IT
solutions should be
too!
Equipment
failure
And…solutions should address uncertainty for robust design, planning, and
information sharing
- 11. IBM Research - Ireland
© 2012 IBM Corporation
Valve Placement
ve Placement
Active and Robust Water Distribution Network Management
• Robust pressure management with uncertain demand profiles
– Optimization model* includes network hydraulics, and scales to very large urban networks
– Places valves and recommends outlet pressures to reduce leakage while satisfying customer
requirements
• Case study: Chapelizod District Metered Area (DMA) in Dublin
– Up to 44% reduction in average pressure
– Estimated 66 (16%) additional households can be served
Optimal valve placement Resulting pressure reduction
* Assistance from HRL on initial model
- 12. IBM Research - Ireland
© 2012 IBM Corporation
What could you do if you had access to all the public data of a city? Could you make the city run better,
faster, cheaper? What new economic opportunities would emerge? (Dublin City Manager, 2011)
Challenge
How can we make a ‘Future City’ as
consumable and accessible as email?
Observation
“After the telephone was introduced
more than a century ago, Kurzweil
says, it took 50 years for a quarter
of the American population to get
one. After the cell phone was
introduced, it took only seven
years.”
…. ‘Future Cities’ in < 7 years
Email was 40 years old in 2012!
- 13. IBM Research - Ireland
© 2012 IBM Corporation
Working harder is not sustainable
Cities require innovative approaches
Join Us: http://www.ibm.com/ie/research