smart systems in the 21st century digital...

Smart Systems in the 21st Century Digital Economy

Irving Wladawsky-Berger [email protected] www.irvingwb.com

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Industrial Economy Information-based Digital Economy

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Cloud Computing

Information-based Intelligence

Internet of Things

Universal, Mobile Internet

A Perfect Storm of Disruptions

Social Networks

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Information-based Digital Economy Leverage science and technology to achieve major advances in people-centric, services-based sociotechnical systems

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The World’s Physical & Digital Infrastructures are Converging

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AI, Data Science and Big Data Extracting Insights from Massive Amounts of Data to Help Make Better Decisions

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!Statistical, brute force approach based on analyzing vast amounts of information using powerful computers and sophisticated algorithms

!Scales very nicely: the more information you have, the more powerful the computer, the more sophisticated the analytical algorithms . . . the better the results

!Originated in science, especially high energy physics

!Data mining in 1990s

!Deep Blue (1997)

!Watson (2011)

Information-based Intelligence

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Everything is now connected and smart

Smart transportation

Smart water management

Smart energy grids

Smart healthcare

Smart food systems Smart oil fields

Smart regionsSmart weather

Smart countriesSmart supply chains

Smart cities

Smart retail

© 2014 IBM Corporation

Era of Smart….Innovative technologies enabling the transformation of systems, infrastructure and citizen services

• Extensive broadband, wireless and mobile adoption

• New connectivity demands of workers, citizens and customers 24/7 any place e.g. smart work hubs & connected homes

• New wave of intelligent systems for industry and cities including M2M, V2X, robotics and RFID

• Engaging and Understanding empowered citizens through social media

• Big Data & Predictive Analytics is changing decision making

• Cloud models are changing the economics and scaleability of citizen services


EventsIncidents

EventsIncidents

EventsEvents

Incidents

Leverage Information

Anticipate problems through analytics

Coordinate resources and response using actionable intelligence

InsightData Actions

Being Smarter means turning DATA into INSIGHTS - which drives timely ACTIONS


IBM Research: Industries and Solutions Strategy

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Smart Transportation Initiatives IBM Research Eleni Pratsini - [email protected] Mahmoud Naghshineh- [email protected]

mailto:[email protected]




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Data-driven Decision Support for Airline Operations

Benefits

Potential use in other settings with multiple dependent tasks and predictable, time-dependent root causes of delay (shipping and logistics, hospitals, manufacturing, construction)

SolutionChallenge A probabilistic flight delay

model Model combines

scheduled crew and aircraft dependencies with historically significant delay patterns at airports

Delay model is coupled with a cost model, so the cost of possible future disruptions and mitigating actions can be quantified

Provides a decision support tool for airline operations controllers

Ops controllers can identify and evaluate actions that could mitigate future schedule disruptions well before they happen

Allows ops controllers to reduce disruption costs over time

Flight delays and cancellations cost the airline industry 10-20 billion USD per year

Recovering a small fraction of this lost revenue would yield a huge savings for airlines

Can we anticipate disruptions and mitigate their financial impact before they happen?



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Click to edit Master title styleMega Traffic Simulator

Benefits

Large-scale agent-based traffic simulator which allows what-if simulation of traffic under various conditions - Emergency response, road construction plans, regulations…

SolutionChallenge

Large-scale agent-based traffic simulator developed on scalable simulation engine supporting parallel execution environment Provide what-if simulation with numerous different scenarios for comparison and optimization

Dramatically reduce the cost for social experiments in the real city by executing computational experiments Utilizing HPC and Cloud environment, the city planner can easily analyze many cases in real time More effective evacuation planning to respond a wide-scale disaster

Estimate what happens on the city traffic flow and what is the optimal decision making if a certain traffic regulation is taken a new transportation is introduced, or a wide-scale disaster occurs.

IBM Mega Traffic Simulator

IBM eXtensible Agent eXecution InfraStructure

Driver Agent

Vehicle



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Highly Scalable Multi-Modal Traffic Simulation Platform

Benefits

Platform extendible for supporting various what-if scenarios of multi-modal transportations such as car parking strategy, car and bike sharing schemes.

SolutionChallenge

For prescriptive analysis towards smarter transportation, various factors need to be considered not only for private cars but also for public transportations (e.g. transportation network, pricing mechanism, scheduling, etc)

Build a high performance and accurate multi-modal traffic simulation platform named M3 runnable on a parallel and distributed systems.

M3 is based on multi-agent model by taking account into all the entities in a city such as private cars, taxies, buses, trains, pedestrians, bikes, etc so that users can change their behavior and underlying infrastructure at micro-level

City governments or public transportation operators can use the M3 platform to conduct and assess various what-if scenarios by changing entities’ behavior and the underlying infrastructures in the city in a near real-time manner.

M3 : Multi-Modal Traffic Simulator

Origin -Destination

and Departure Time

Preferences(Time, Cost, Transport

Mode, , ..)

Network (Road, Bus Route, Train

Route)

Time Tablefor Public

Transportation

Congestion Data (ex. Jam length, Average

Speed per road) / CO2 emission

# of Passengers and Travel Time for Public

Transportation Multi-Modal Journey Planner

New or Delete Route / Bus Lane / Road

Mobile Data

Journey Time and Trajectory for

Individuals

New Time Table

Census

Query / Journey Plan

Evaluation with Open Data in Dublin



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Real-time data on bus

positions

Historical model of bus movements

Bus Bunching prediction and

correction

Predictive analytics for bus bunching and schedule optimization

Benefits

Reusability Asset deployed in Miami Dade Transit

SolutionChallengePredictive analytics for bus bunching detection – when and where are buses likely to bunching up to an hour in advance. Corrective actions for operational decision making (changes in dispatch) Schedule optimization to reduce systematic bunching events

Operational benefits of higher transit vehicle utilization and load balancing, higher perceived level of service. Optimization leads to significant revenue savings by reduction in operational costs.

Public transport operators have operational challenges to provide reliable service to their customers. Bus bunching is a phenomenon when multiple buses arrive at a stop at the same time. Leads to lower revenue, lower level of service and longer wait times for passengers

Data patterns

and insights

Schedule optimization



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USDOT Smarter Travel Project for the City of Dubuque

Benefits

Proposed solution can be deployed in other medium size cities in the US which encompass the majority of US cities.

SolutionChallenge

Platform can be used to gather travel patterns of citizens and used to optimize bus routes and other transportation modes.

Obtain insight about citizens’ travel patterns and increase ridership of public transit in the city of Dubuque, Iowa. Optmize bus routes and schedules to satisfy demand while controlling operating costs and increasing rider satisfaction

Gather travel data from GPS via a smartphone app, travel diary from volunteers, aggregated cell phone data Generate travel model and O/D matrices. Formulate objective function for optimization problem and solve



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Click to edit Master title styleDecision Support System Optimizer for Road Transport Incident Management

Benefits

Tools for real-time command centers :Use real-time data to empower transportation operators to better manage incidents and planned and unexpected events

SolutionChallengeRoad traffic operations have access to considerable real-time data but very few resources to manage incidents Traffic operators can

handle routine traffic well During ncidents, analytics

are needed to assist operators to make the best choices

The Decision Support System Optimizer (DSSO) is a suite of integrated analytics from incident detection to impact prediction to optimal response. DSSO builds on existing systems for routine operations to add value when it is most needed

Dramatically reduced time-to-detection of incidents

Enables operators to prioritize their efforts using incident impact prediction

Crucial advice to operators to handle multiple incidents in parallel

Incident impact predicted over space and time highlighting maximal queue length expected Queue length is a key KPI for road traffic operations

Singapore Land Transport Authority and GrandLyon (France) are involved in DSSO pilots


Innovative leadership in Rio de Janeiro transformed incident management

• Initial focus - prevent deaths from annual flooding and mudslides now expanded to all emergency response issues

• Advanced weather forecasting enables up to 49 hour early warning of potential issues and triggers proactive processes

• 30 city departments integrated into processes • Increased efficiency in resource deployment

and coordinating all agencies in response • Inspired Mobile Command Centers for each of

the 20+ FIFA World Cup Football venues


Smarter Transportation, Shenyang, China

Built a state-of-the-art traffic management system by integrating real-time data from various applications to operate the 3rd-Ring road - a highway that surrounds Shenyang

City Benefits

• Improved Traffic Efficiency - integration of real time data around the road

• Advanced Management - close cycle management of emergency plans

• Better Information Service - dynamic road guidance with short term travel time prediction


Insights from Internet of Things (Machines, Mobile, Social) can enhance city planning, operations and citizen services

Real-time Sensored Cars - Eindhoven Pervasive braking combined with weather data identifies snow & ice issues Smartphone application notifies citizens of issues Identifies road maintenance needs early, saving costs Managed taxi fuel consumption by at least 10% IBM, Nokia & university partnership

Big Cities + Big Data

• Informatics capabilities are exploding – Storage, transmission, analysis

• Proliferation of static and mobile sensors

• Internet of things

Global network traffic, 30% CAGR

• The world is urbanizing • Cities are the loci of consumption,

economic activity, and innovation Cities are the cause of our problems and the source of the solutions

Steven E. Koonin, PhD, Director

October 16, 2014

The Promise of Urban Science

New York City as a Living LabThe Center for Urban Science and Progress (CUSP) is a unique public-private research center that uses New York City as its laboratory and classroom to help cities around the world become more productive, livable, equitable, and resilient. CUSP observes, analyzes, and models cities to optimize outcomes, prototype new solutions, formalize new tools and processes, and develop new expertise/experts. These activities will make CUSP the world’s leading authority in the emerging field of “Urban Informatics.”

Urban Data Sources: Acquire, Integrate, UseNovel Technologies

• Visible, infrared and spectral imagery

• RADAR, LIDAR • Gravity and magnetic • Seismic, acoustic • Ionizing radiation, biological, chemical

• …

Sensors

• Personal (location, activity, physiological)

• Fixed in situ sensors • Crowd sourcing

(mobile phones, …) • Choke points (people,

vehicles)

Organic Data Flows

• Administrative records (census, permits, …)

• Transactions (sales, communications, …)

• Operational (traffic, transit, utilities, health system, …)

• Social media (Twitter, Facebook, blogs, …)

The CUSP PartnershipNational Laboratories

•• Brookhaven •• Lawrence Livermore •• Los Alamos •• Sandia

Industrial Partners

• IBM • Microsoft • Xerox • Cisco, Con Edison, Lutron,

National Grid, Siemens • AECOM, Arup, IDEO

University Partners

• NYU (multiple schools) • The City University of New York • Carnegie Mellon University • University of Toronto (Canada) • The University of Warwick (UK) • IIT-Bombay (India) City & State Agency Partners

• The City of New York

• Metropolitan Transportation Authority • Port Authority of NY & NJ

Buildings City Planning Citywide Administrative Services Design and Construction Economic Development Environmental Protection Finance Fire Department Health and Mental Hygiene Information Technology and

Telecommunications Parks and Recreation Police Department Sanitation Transportation

Projects for the City & State• Economic Mapping • Greener Greater Buildings Plan • MTA Bus Driver Optimization • MTA Origin/Destination Study • NYPD event analysis • Parks Attendance & Utilization • Parks Tree Census • Property Ownership Records Assessment • Public Health • School Property Use Assessment • Taxi Visualization • Transit Operations • …and more

Overview • Data from yellow cabs 2009-2013 is almost 800 million

trips; nearly impossible to manage, explore, visualize, and analyze with existing tools

Objective & Goal • Build scalable, usable tools that can be used by experts

and non-experts • Work with relevant city agencies on development &

deployment of the technology Status

• Initial deployment of TaxiVis at NYC Taxi & Limousine Commission and Department of Transportation

Freire, Silva, Vo, et al.

Analysis of Massive Taxi GPS Data

NYC Taxi Rides by Day in 2011

Taxis as City Sensors


TaxiVis: Interactive Visual Exploration of NYC Taxi Records


The book identifies ways in which vast new sets of data on human beings can be collected, integrated, and analyzed to improve urban systems and quality of life while protecting confidentiality. Sponsored by CUSP, the American Statistical Association, its Privacy and Confidentiality subcommittee, and the Research Data Centre of the German Federal Employment Agency.

Editors: Julia Lane, American Institutes for Research; Victoria Stodden, Columbia; Stefan Bender, The German Federal Employment Agency; Helen Nissenbaum, NYU

Chapter Authors Steve Koonin, CUSP; Frauke Kreuter, U-MD and Richard Peng, Johns Hopkins; Alessandro Acquisti, Carnegie Mellon University; Robert Goerge, UChicago; Helen Nissenbaum, NYU; Kathy Strandberg, NYU; Paul Ohm, Colorado; Victoria Stodden, Columbia; Alan Karr, National Institute of Statistical Sciences and Jerry Reiter, Duke University; John Wilbanks, Sage Bionetworks/Kauffman Foundation; Cynthia Dwork, Microsoft; Alexander Pentland, et al., MIT; Carl Landwehr, George Washington University; Peter Elias, University of Warwick.

Privacy, Big Data, and the Public Good: Frameworks for Engagement

CUSP Educational ProgramsTrain professionals who will understand how cities function and

the potential of urban informatics. CURRENT • Master of Science in Applied Urban Science & Informatics • Advanced Certificate in Applied Urban Science & Informatics • Executive education, “Boot Camps” for City and other professionals

FUTURE • PhD in Urban Informatics (projected launch: fall 2015) • Global Executive M.S. in Urban Analytics and Innovation • Dual/joint degrees (NYU & Academic Partners) • Distance and online learning modules

1/10/2015 About Us

http://www3.imperial.ac.uk/digital-economy-lab/partnernetworks/dce/about_us 1/4

WHAT IS DIGITAL CITY EXCHANGE?

Digital City Exchange is a fiveyear Digital Economy multidisciplinary research programme at ImperialCollege London. Researchers are exploring ways to digitally link utilities and services within a city,enabling new technical and business opportunities.

The programme of research focuses on harnessing next generation digital systems to combine andrepurpose city data: ultimately, transforming the planning and use of cities.

Digital City Exchange is a major research programme within Imperial College London’s Digital EconomyLaboratory initiative.

[PODCAST]: An introduction to the Digital City Exchange programme, and the research aims. FeaturingDCE Principal Investigator, Prof David Gann, DCE CoInvestigator Dr Andy Davies and Former DCEProgramme Coordinator Dr Claire Thorne (July 2012)

> Back to Digital City Exchange> Back to Digital Economy Laboratory> RCUK Digital Economy programme> DCE Advisory Board portal

DIGITAL CITY EXCHANGE

ABOUT USPEOPLE AND PARTNERSRESEARCHGET INVOLVEDIMPACT 2014 / 2013 / 2012 / 2011

UNITING RESEARCHERS

Faculty of EngineeringDEPARTMENT OF CHEMICALENGINEERINGDEPARTMENT OF CIVIL ANDENVIRONMENTAL ENGINEERINGDEPARTMENT OF COMPUTINGDEPARTMENT OF ELECTRICALAND ELECTRONIC ENGINEERING

Imperial College Business SchoolINNOVATION ANDENTREPRENEURSHIP GROUP

Return to the Imperial homepage

Search Imperial PeopleFor: Prospective Students Students Alumni Staff Business Media

Digital Economy LabHome About Us News and Events Research Publications Teaching Contact Us

exchange [ikscheynj]An act of giving one thing and receiving another (esp. of the same type of value) in return.n.Any organisation, association or group which provides or maintains a marketplace wherecommodities can be traded; or the marketplace itself.To give in return for something received; trade.v.

integrating data | transforming services

1/10/2015 About Us

http://www3.imperial.ac.uk/digital-economy-lab/partnernetworks/dce/about_us 1/4

WHAT IS DIGITAL CITY EXCHANGE?

Digital City Exchange is a fiveyear Digital Economy multidisciplinary research programme at ImperialCollege London. Researchers are exploring ways to digitally link utilities and services within a city,enabling new technical and business opportunities.

The programme of research focuses on harnessing next generation digital systems to combine andrepurpose city data: ultimately, transforming the planning and use of cities.

Digital City Exchange is a major research programme within Imperial College London’s Digital EconomyLaboratory initiative.

[PODCAST]: An introduction to the Digital City Exchange programme, and the research aims. FeaturingDCE Principal Investigator, Prof David Gann, DCE CoInvestigator Dr Andy Davies and Former DCEProgramme Coordinator Dr Claire Thorne (July 2012)

> Back to Digital City Exchange> Back to Digital Economy Laboratory> RCUK Digital Economy programme> DCE Advisory Board portal

DIGITAL CITY EXCHANGE

ABOUT USPEOPLE AND PARTNERSRESEARCHGET INVOLVEDIMPACT 2014 / 2013 / 2012 / 2011

UNITING RESEARCHERS

Faculty of EngineeringDEPARTMENT OF CHEMICALENGINEERINGDEPARTMENT OF CIVIL ANDENVIRONMENTAL ENGINEERINGDEPARTMENT OF COMPUTINGDEPARTMENT OF ELECTRICALAND ELECTRONIC ENGINEERING

Imperial College Business SchoolINNOVATION ANDENTREPRENEURSHIP GROUP

Return to the Imperial homepage

Search Imperial PeopleFor: Prospective Students Students Alumni Staff Business Media

Digital Economy LabHome About Us News and Events Research Publications Teaching Contact Us

exchange [ikscheynj]An act of giving one thing and receiving another (esp. of the same type of value) in return.n.Any organisation, association or group which provides or maintains a marketplace wherecommodities can be traded; or the marketplace itself.To give in return for something received; trade.v.

integrating data | transforming services

Eric Yeatman - [email protected] David Stokes - [email protected]


12/29/2014 Industry Partners

http://www3.imperial.ac.uk/digital-economy-lab/partnernetworks/dce/people_partners/industry_partners 2/2

Digital City Exchange is working to identify potential business opportunities by conducting realworldtrials with industrial partners and engaging with the SME community.

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Dr David StokesProgramme CoordinatorDigital City Exchanget: +44 (0)20 7594 5218e:[email protected]

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Digital City Exchange - Industrial Partners

Key Challenge in the 21st CenturyLeverage technology, science and innovation to make major improvements in the productivity and quality of people-centric systems, organizations and the very way the world works

Smart Systems in the 21st Century Digital Economy

Irving Wladawsky-Berger [email protected] www.irvingwb.com

smart systems in the 21st century digital...

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