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Future Cities Conference´13 / Pól Mac Aonghusa - "Future Life and Services"

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    Future Cities Conference´13 / Pól Mac Aonghusa - "Future Life and Services" Future Cities Conference´13 / Pól Mac Aonghusa - "Future Life and Services" Presentation Transcript

    • IBM Research - IrelandFuture Life and ServicesShake up your CityPól Mac Aonghusa,IBM Research – Ireland © 2012 IBM Corporation
    • IBM Research - IrelandImagining Future Life: Interactions and Expectations 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). City + Citizen contributed content will become a core strategic economic resource – and the most scalable natural resource a City possesses. Global access to technology is already driving trends like ‘virtual citizenship’, ‘virtual employment’ & ‘social innovation’ Mobility, Openness & connection will matter more than presence & rigid structuresOn-demand interaction will increasingly be the norm for a global community of virtual citizeninnovators … who expect their experience of a City to be as simple as using an appliance © 2012 IBM Corporation
    • IBM Research - IrelandImagining Future Services: Interdependency and Complexity We have built a world of massive complexity and interdependency…. 24 Hours of Air Travel Global Trade Global Financial Nuclear Technology Markets….and along with progress, we have brought on massive risks we don’t manage well Pandemics Global Financial Crisis Nuclear Disasters © 2012 IBM Corporation
    • IBM Research - IrelandSome 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 © 2012 IBM Corporation
    • IBM Research - IrelandThree 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 © 2012 IBM Corporation
    • IBM Research - IrelandReasonableCity: Learning Systems to Help Diagnose the CityProblemHow can we provide City decisionmakers predict and diagnose events andanomalies in real-time from massive,rich, complex, heterogeneous anddynamic urban data?ApproachA system that identifies the nature andcause of changes and explain logicalconnection of knowledge across spaceand time • Identify a common (semantic) representation layer for causality Research Challenges detection and analysis • Identifying relevant data and information • Develop a reasoning engine that • Capturing and representing time-evolving interprets causality for diagnosis knowledge • Demonstrate the prototype with • Combining and correlating time-evolving DCC Data knowledge from heterogeneous data • Improve the flexibility of causality sources detection (through ML techniques) • Advanced fusion of data © 2012 IBM Corporation
    • IBM Research - Ireland Example … simplify decision making with smart systems that learnDiagnosing Cities: A Road Traffic Congestion Case © 2012 IBM CorporationFreddy Lecue, Anika Schumann, Marco Sbodio
    • IBM Research - IrelandPervasive Technologies Datasets as Digital Footprints Understand how people use the citys 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 © 2012 IBM Corporation
    • IBM Research - IrelandExample … interpreting human behavior to model demand• 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? 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 © 2012 IBM Corporation
    • IBM Research - Ireland …and uncertainty is everywhere! Water consumption Households Population growth Water shortages Increased costs Economic Water quality issues climateRenewable Rainfall energy Model accuracy Measurements patterns Water treatment and All these systems are River basins, coastal bays distribution networks connected in some and estuaries way – their IT solutions should be too! Equipment failure Climate Capacity shortages change Stress on water qualityIncreased investment and operating costs Floods And…solutions should address uncertainty for robust design, planning, and information sharing © 2012 IBM Corporation
    • IBM Research - Ireland 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 Valve Placementve Placement Optimal valve placement Resulting pressure reduction * Assistance from HRL on initial model © 2012 IBM Corporation
    • IBM Research - Ireland 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! © 2012 IBM Corporation
    • IBM Research - Ireland Working harder is not sustainable Cities require innovative approaches Join Us: http://www.ibm.com/ie/research © 2012 IBM Corporation