Technological Challenges in Managing and Operating a Smart City: Planning for a real-world

TECHNOLOGICAL CHALLENGES IN
MANAGING AND OPERATING A SMART
CITY: PLANNING FOR REAL WORLD
DR. BIPLAV SRIVASTAVA
A C M D I S T I N G U I S H E D S C I E N T I S T , A C M D I S T I N G U I S H E D
S P E A K E R
S E N I O R R E S E A R C H E R A N D M A S T E R I N V E N T O R ,
I B M R E S E A R C H – I N D I A
1Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015

Why This Talk? Main Messages
—  Sustainability is a key imperative of modern societies
—  Today, decision making is ad-hoc. We can change the
status-quo with automated decision techniques.
—  AI techniques like planning and optimization have
matured and have high potential to impact the world
—  But they need data which is not always available
—  Open data is often the most promising source to start
making quick impact
—  Eventual aim should be to scale innovations with
other data sources and reach production scale.

Acknowledgements
All my collaborators over last 5 years, and especially those in:
—  Government agencies around the world
¡  City: Boston, USA; New York/ New Jersey area, USA; Silicon Valley, USA; Dubuque, IA; Dublin,
Ireland, Stockholm, Sweden; Ho Chi Minh City, Vietnam; New Delhi, India; Bengaluru, India; Nairobi,
Kenya; Tokyo, Japan
¡  Country: India, Singapore
—  Academia
¡  India: IIT Delhi, IISc CiSTUP, IIIT Delhi, IIT BHU
¡  USA: Boston University, Wright State University, University of Southern California,
Arizona State University
¡  Vietnam: Ho Chi Minh University
—  IBM: Akshat Kumar, Anand Ranganathan, Raj Gupta, Ullas Nambiar, Srikanth Tamilselvam, L V Subramaniam, Chai Wah Wu,
Anand Paul, Milind Naphade, Jurij Paraszczak, Wei Sun, Laura Wynter, Olivier Verscheure, Eric Bouillet, Francesco Calabrese,
Tsuyoshi Ide, Xuan Liu, Arun Hampapur, Nithya Rajamani, Vivek Tyagi, Rauam Krishnapuram, Shivkumar Kalyanraman, Manish
Gupta, Nitendra Rajput, Krishna Kummamuru, Raymond Rudy, Brent Miller, Jane Xu, Steven Wysmuller, Alberto Giacomel, Vinod A
Bijlani, Pankaj D Lunia, Tran Viet Huan, Wei Xiong Shang, Chen WC Wang, Bob Schloss, Rosario Usceda-Sosa, Anton Riabov,
Magda Mourad, Alexey Ershov, Eitan Israeli, Evgenia Gyana R Parija, Ian Simpson, Jen-Yao Chung, Kohichi Kajitani, Larry L Light,
Lisa Amini, Marco Laumanns, Mary E Helander, Milind Naphade, Sebastien Blandin, Takayuki Osogami, Tony R Heritage, Ulysses
Mello, Wei CR Ding, Wei CR Sun, Xiang XF Fei, Yu Yuan, Bipin Joshi, Vishalaksh Agarwal, Pallan Madhavan, Ravindranath Kokku,
Mukundan Madhavan, Rashmi Mittal, Sandeep Sandha, Sukanya Randhawa, Karthik Vishweshvariah, Guruduth Banavar
For discussions, ideas and contributions. Apologies to anyone unintentionally missed.
Material gratefully taken from multiple sources. Apologies if any citation is unintentionally missed.

Outline
—  Motivating Examples
—  Basics
¡  Smart City
÷  Challenges
÷  Innovation needs – value desired
÷  Critical considerations different from other applications
¡  AI:
÷  Planning and Scheduling
÷  The different shades of analytics
÷  Open Data for Analytics: introduction and issues
—  Applications
¡  Transportation
¡  Environment Pollution - Water
¡  Health
—  Discussion

Examples

We All See Traffic Daily. An Illustration from Across the Globe
Source: Google map for New York City and New Delhi; Search done on Aug 20, 2010
Characteristics New York City,
USA
New Delhi,
India
Beijing, China Moscow, Russia Ho Chi Minh City,
Vietnam
Sao Paolo, Brazil
1 How is traffic pre-
dominantly managed
Automated control,
manual control
Manual
control
Automated control,
manual control
Automated,
manual control
Manual control Automated, manual
control, Rotation
system (# plate based)
2 How is data collected Inductive loops,
cops, video, GPS
Traffic
surveys, cops
Video, GPS, cops GPS, some video,
cops
Traffic surveys, cops Video, GPS, cops
3 How can citizens manage
their resources
GPS devices, alerts
on radio, web, road
signs (variable)
Alerts on
radio
alerts on radio,
road signs
(variable), mobile
alerts
GPS, radio, road
signs, mobile
alerts
Alerts on radio GPS devices, alerts
on radio, web
4 Traffic heterogeneity by
vehicle types(Low: <10;
Medium 10-25; High: >25)
Low High Low Low Medium Low
5 Driving habit maturity
(Low: <10 yrs; Medium:
10-20; High: > 20)
High Low Low Low Low Medium
6 Traffic movement Lane driving Chaotic Lane driving Lane driving Chaotic Lane Driving 6

Example –Traffic Management
—  Decision Value – To individuals, businesses, government
institutions
¡  Individuals Examples – Can I reach office on time? Where should I park if I take
my car?
¡  Govt Examples – How much overt-time does the city need to give today? Where
should I deploy my traffic cops today?
¡  Business Example – When should I service city’s buses?
—  Data – Quantitative as well as qualitative
¡  Volume – traffic count
¡  Speed on road
¡  City events
—  Access –
¡  Today, little and on city websites
¡  Facebook sites
Key Idea: Can we make insights available when needed and help
people make better decisions?

8
[India] Ganga – Local Ground Situation @ Varanasi (Assi/ Tulsi Ghats) + Patna
Photos of/ at Assi/ Tulsi Ghat, Varanasi on 25 March 2015 during 1700-1800 Hrs
Assi Ghat post recent cleanup Bathing on Tulsi Ghat
A nullah draining into Ganga
A manual powered boat
Photos at Gandhi Ghat, Patna on 18 March 2015 during 1700-1800 Hrs
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015

Example –River Water Pollution
institutions
¡  Individuals Examples – Can I take a bath? Will it cause me dysentery? What
crops should I grow?
¡  Govt Examples – How should govt spend money on sewage treatment for
maximum disease reduction? How should it inspect industries?
¡  Dissolved oxygen,
¡  pH,
¡  … 30+ measurable quantities of interest
¡  Today, little, and that too in water technical jargon
¡  In pdf documents, website

Basics: Smart City

What is a Smart City?
Smart city can mean one or more of the following:
—  As a resource optimization objective, it is to know and manage a
city's resources using data.
—  As a caring objective, it is about improving standard of life of citizens
with health, safety, etc indices and programs.
—  As a vitality objective, it is about generating employment and doing
sustainable growth.
A city leadership can choose among these or define their own objective(s)
and manage with measurements to pro-actively achieve it
11
See other FAQs at: https://sites.google.com/site/biplavsrivastava/research-1/intelligent-systems/scfaqs

15%
20%
25%
30%
35%
40%
15% 20% 25% 30% 35% 40% 45%
Economists Estimate, that the World’s Systems Carry Inefficiencies of up
to $15 Tn, of Which $4 Tn Could be Eliminated
System inefficiency as % of total
economic value
Improvementpotentialas
%ofsysteminefficiency
Education
1,360
Building & Transport
Infrastructure
12,540
Healthcare
4,270
Government & Safety
5,210
Electricity
2,940
Financial
4,580
Food & Water
4,890
Transportation (Goods
& Passenger)
6,950
Leisure /
Recreation /
Clothing
7,800
Communication
3,960
Global economic value of ...
System-of-
systems
$54 Trillion
100% of WW 2008 GDP
Inefficiencies $15 Trillion
28% of WW 2008 GDP
Improvement
potential
$4 Trillion
7% of WW 2008 GDP
Analysis of inefficiencies in the
planet‘s system-of-systems
How to read the chart:
For example, the Healthcare system‘s
value is $4,270B. It carries an estimated
inefficiency of 42%. From that level of 42%
inefficiency, economists estimate that
~34% can be eliminated (= 34% x 42%).
Note: Size of the bubble indicate absolute
value of the system in USD Billions
$54,000,000,000,000
$15,000,000,000,000
$4,000,000,000,000
42%
34%
This chart shows ‘systems‘ (not ‘industries‘)
Source: IBM economists survey 2009; n= 480

13
Cities are traditionally built and governed by independent departments
operating as domains of functions
C i t y
I n f r a s t r u c t u r e
D a t a
Water Energy TransportSecurity Planning Food . . . Science Health ICT
City
Responsibility
Department
Responsibility
Project
Responsibility
Task
Responsibility
Typically lacking holistic view
OperationalSystems
Before

14
DoIT
An integrated Smarter City Framework – a comprehensive management system
across all core systems, will anchor the vision to executable steps
I n f r a s t r u c t u r e
D a t a
City
Responsibility
Department
Responsibility
Project
Responsibility
Task
Responsibility
OperationalSystems
C i t y M a n a g e m e n t
Analytics, Insight, Visualization, Control Center, etc.
Water Energy TransportSecurity Planning Food . . . Science Health . . .
DoW
DoE
DoT
DoS
DoP
DoF
Do...
DoS
DoH
...
B u s i n e s s P r o c e s s e s a n d A p p l i c a t I o n s
Your
City
After

15
Smarter Cities solution paths leverage a similar approach
Uniquevaluerealized
Use of Smarter Cities capabilities
Manage 
Data1
Analyze 
Patterns2
Optimize
Outcomes
3
Integrate service
information to
improve department
operations
Develop integrated
view to improve
outcomes and
compliance

Leverage end-to-end
case management to
optimize service
delivery
Ç Improve service levels
È Reduce fraud and abuse
Ç Focus on the citizen
Ç Savings from overpayment
Ç Assistance with compliance
Ç Integrated case management
Ç Automation of citizen support
È Reduce operating costs

India’s 100 Smart Cities
Details: https://sites.google.com/site/biplavsrivastava/smart-cities-in-india

Comments on India’s 100 City Plans
—  A much-needed, much-delayed, start
¡  JNURM and earlier initiatives did not show impact
—  However selection criteria was non-technical
¡  Focus was on funding feasibility (center-state) and administrative
considerations
¡  No commitment on measurable improvement of any metric in any
city domain
—  Opportunity to impact India’s transformation
(theoretically)
¡  However, environment to try out India-specific, new innovations
needs to be created
¡  Focus has to be on improvement metrics; accountability for money
spent; quality outcomes

Basics: AI

Introduction to Planning & Scheduling
19

The Many Complexities of Planning
Environment
perception
Goals
(Static vs. Dynamic)
(Observable vs.
Partially Observable)
(perfect vs.
Imperfect)
(Deterministic vs.
Stochastic)
What action next?
(Instantaneous vs.
Durative)
(Full vs.
Partial satisfaction)
Slide adapted from Subbarao Kambhampati

Static Deterministic Observable Instantaneous Propositional
“Classical Planning”
Dynamic
Replanning/
Situated
Plans
Partially
Observable
Contingent/Conformant
Plans,Interleaved
execution
Durative
Temporal
Reasoning
Continuous
NumericConstraint
reasoning(LP/ILP)
Stochastic
MDPPolicies
POMDPPolicies
Semi-MDP
Policies
Slide by Subbarao Kambhampati

Underlying System Dynamics
Traditional Planning
OptimizationMetrics
Any (feasible) Plan
Shortest plan
Cheapest plan
Highest net-benefit
Multi-objective
PSPPlanning
Slide by Subbarao Kambhampati

Plans and Planning: Types of Applications
¡  Choose among pre-determined plans (static plan evaluation
and static monitoring)
¡  Need plans to be synthesized (dynamic plan evaluation and
static monitoring)
¡  Need plans to be synthesized and monitored during execution;
re-planning (dynamic plan evaluation and dynamic monitoring)

Shades of Analytics

Advanced AI Techniques (Analytics) like Planning & Machine Learning
make use of data and models to provide insight to guide decisions
Models
Analytics
Data
Insight
Data sources:
Business automation
Instrumentation
Sensors
Web 2.0
Expert knowledge
“real world physics”
Model:
a mathematical or
algorithmic
representation of
reality intended to
explain or predict
some aspect of it
Decision executed
automatically or
by people

Example: Talks
—  Are they useful? (Descriptive)
¡  Answering needs an assessment about the event
—  If it happens next time, how many will attend?
(Predictive)
¡  Above + Answering needs an assessment about unknowns
(e.g., future)
—  Should you attend? (Prescriptive)
¡  Above + Answering needs understanding the goals and current
status of the individual

Analytics Landscape
Degree of Complexity
CompetitiveAdvantage
Standard Reporting
Ad hoc reporting
Query/drill down
Alerts
Simulation
Forecasting
Predictive modeling
Optimization
What exactly is the problem?
What will happen next if ?
What if these trends continue?
What could happen…. ?
What actions are needed?
How many, how often, where?
What happened?
Stochastic Optimization
Based on: Competing on Analytics, Davenport and Harris, 2007
Descriptive
Prescriptive
Predictive
How can we achieve the best outcome?
How can we achieve the best outcome
including the effects of variability?

Real-World Applications of ICT Follow a Pattern
n Value (from Action, Decisions) – Providing
benefits that matter, to people most in need of, in a
timely and cost-efficient manner. Going beyond
technology to process and people aspects.
n Data + Insights – Available, Consumable with
Semantics, Visualization / Analysis
n Access - Apps (Applications), Usability - Human
Computer Interface, Application Programming
Interfaces (APIs)

Basics: Open Data

Open Data
—  Open data is the notion that data should not
be hidden, but made available to everyone.
The idea is not new.
—  Scientific publications follow this: “standing
on the shoulders of giants”
¡  Science stands for repeatability of results and
hence, sharing
¡  The scientific community asserts that open
data leads to increased pace of discovery.
(See: Ray P. Norris, How to Make the Dream Come True: The Astronomers' Data Manifesto, At
http://www.jstage.jst.go.jp/article/dsj/6/0/6_S116/_article, Accessed 2 Apr, 2012)
—  Governments are the new source for open
data
¡  Data.gov efforts world-wide; 400+
governmental bodies, including 20+ national
agencies, including India, have opened data
¡  In India, additional movement is “Right to
Information Act”

Not to Be Confused With Orthogonal Trend – Big Data
—  Volume
—  Variety
—  Velocity
—  Veracity
—  …
Cartoon critical of big data application,
by T. Gregorius.
http://upload.wikimedia.org/wikipedia/commons/thumb/b/b3/
Big_data_cartoon_t_gregorius.jpg/220px-
Big_data_cartoon_t_gregorius.jpg

400+Data Catalogs of Public Data
As on 21 July 2015

Data.gov (USA)
As on 16 June 2015
33
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems

City Level – Chicago, USA
34
As on 16 June 2015

Data.gov.in (India)
As on 16 June 2015
35

Peek into the Future - Amsterdam
http://citydashboard.waag.org/

Illustration of Levels
Source: http://5stardata.info/
Does Opening Data Make It Reusable? No
1
2
3
4
5

India: Right to Information Act
—  Any citizen “may request information from a "public
authority" (a body of Government or "instrumentality of State")
which is required to reply expeditiously or within thirty days.”
¡  Passed by Parliament on 15 June 2005 and came fully into force on 13
October 2005. Citation Act No. 22 of 2005
—  Lauded and reviled
¡  Brought transparency
¡  Also,
÷  Increased bureaucracy
÷  Shortcomings in preventing corruption
—  More information
¡  http://en.wikipedia.org/wiki/Right_to_Information_Act
¡  http://rti.gov.in

Data Quality in Public Data in India
—  Right to Information
¡  Not even 1*
¡  Information available to requester, but no one else
—  Data.gov.in
¡  2-3*
¡  Available in CSV, etc but not uniquely referenceable
—  Open data movements are moving to linked data
form for semantics

Semantics for Published Data
40
Classify data in public domain. Use schema.org as illustration.
¡  Select an area (e.g., food, news events, crime, customs, diseases, …)
¡  Build + disseminate the catalog tags via a website
¡  Encourage publishers to use meta-data tags and enable search
Catalog/
ID
General
Logical
constraints
Terms/
glossary
Thesauri
“narrower
term”
relation
Formal
is-a
Frames
(properties)
Informal
is-a
Formal
instance
Value Restrs. Disjointness,
Inverse, part-of…
Credits:
Ontologies Come of Age McGuinness, 2001
From AAAI Panel 99 – McGuinness, Welty, Uschold, Gruninger, Lehmann
Plus basis of Ontologies Come of Age – McGuinness, 2003

Still Confused on Semantics? Start with Linked Data Glossary

Open Data References
—  Concept
¡  Open Data, At http://en.wikipedia.org/wiki/Open_data,
¡  Open 311, At http://open311.org/
¡  Catalog of Open Data, At http://datacatalogs.org/dataset
¡  Data City Exchange: http://www.imperial.ac.uk/digital-city-exchange
—  India specific
¡  Open data report in India, At http://cis-india.org/openness/publications/ogd-report
—  Standards
¡  W3C, At http://www.w3.org/2011/gld/
¡  5 Star Linked Data ratings, At http://www.w3.org/DesignIssues/LinkedData.html
—  Applications and ecoystems
¡  Introduction to Corruption, Youth for Governance, Distance Learning Program, Module 3, World Bank
Publication. Accessed on June 15th 2011, At
http://info.worldbank.org/etools/docs/library/35970/mod03.pdf
¡  Dublinked, At http://dulbinked.ie

ML Reference
—  WEKA
¡  Website: http://www.cs.waikato.ac.nz/~ml/weka/index.html
¡  WEKATutorial:
÷  Machine Learning withWEKA: A presentation demonstrating all graphical user interfaces (GUI) in
Weka.
÷  A presentation which explains how to useWeka for exploratory data mining.
¡  WEKA Data Mining Book:
÷  Ian H.Witten and Eibe Frank, Data Mining: Practical Machine LearningTools and
Techniques (Second Edition)
÷  http://www.cs.waikato.ac.nz/ml/weka/book.html
¡  WEKAWiki: http://weka.sourceforge.net/wiki/index.php/Main_Page
—  Jiawei Han and Micheline Kamber, Data Mining: Concepts andTechniques, 2nd ed.
—  http://www.kdnuggets.com/2015/03/machine-learning-table-elements.html

Smarter Transportation
Details: Boston (2012), New York, (2014), India – Delhi, Bangalore (2011-2015)

Press on the IBM SCC Boston team work:
1. Boston Globe, June 29, 2012
http://www.boston.com/business/technology/articles/2012/06/29/
ibm_gives_advice_on_how_to_fix_boston_traffic__first_get_an_app/
(Alternative: http://bostonglobe.com/business/2012/06/28/ibm-gives-
advice-how-fix-boston-traffic-first-get-app/goxK84cWB9utHQogpsbd1N/
story.html)
2. Popular Science, 2 July 2012
http://www.popsci.com/technology/article/2012-07/bostons-ibm-built-
traffic-app-merges-multiple-data-streams-predict-ease-congestion
3. Others: National Public Radio (USA), and a range of local TV stations on
the work.
SCC Boston team with Mayor on June 27, 2012
Team at work – Source: Boston Globe article

Boston
Transporta+on
:
Before
State

GPS

Manual

Video

Road

Sensors

Lots
of
Instrumenta+on…
Not
enough
interconnec+on…
Unexploited
Intelligence…

Much
Data

Isolated
in

Silos

Mul+ple

Disconnected

Camera

Networks

Inaccessible

Data

Manual

Opera+ons

Insuﬃcient

Data

"  
Boston
is
forward-‐

thinking
&
progressive

"  
Boston
recognizes

climate
&
traﬃc
goals

are
interconnected

Boston
is
na)onally

recognized
for

innova)on


Ecosystem
Roadmap

Ci$zens

Sharing
Analyzing
Forward
Thinking
Consumer

Value

Unlocking

Smarter
Transportation
Ecosystem
Industry

Academics

Government

Induc$ve

Loop

Data

Applications
Platform
Data
Ideas
Pneuma$c

Tube

Data

Manual

Count

Data

Automated

Data

Transfer

Online

Access
to

Aggregated

Data

Privacy

Considera$ons

Ci$zen

Online

Access

Smarter

Traﬃc

Infrastructure

Environmental

Es$mates

Mul$ple

Visualiza$ons

City

Benchmarks

Exploit

Video

Camera

Advanced

Visualiza$ons

Exploit

More
Data

Sources

Advanced

Analy$cs

Deliverables

"  
Running
Prototype

"  
Recommenda+ons


Common Model
Standards Aligned,
Uniform format,
Uniform Error Semantics
Mapping to Source
Data
Transformation
Data Source
Metadata
A Snapshot of Common Model and
Mapping to Data Sources
Source Models

Result
1:
Publicly
Available
Data
for
Mul+ple
Consumers

"  

Many
data
sources,
various
loca+ons
&
+mes

"  

Stakeholders
can
access
data
easily
&
intui+vely

"  

Locate
available
data
sources

"  

Zoom
in
to
areas
of
interest

"  

Obtain
data

"  

Drill
down
to
traﬃc
paUerns

"  

Assess
environmental
factors

"  

See
what
happens
in
real
+me

Researchers

Prac++oners

Planners

Engineers

Residents


• 
Assign
different
traffic

light
paUerns
for

different
streets,
+mes

• 
Schedule
public
works

projects
to
minimize

traffic
impact

• 
Detect
changes
in

traffic
paUerns
to
drive

policy
changes

(parking,
lanes,
street)

• 
Assess
traffic
impact
of

new
landmarks

• 
Inform
businesses,

developers

Result
2:
Street
Classifica+on
Based
on
Traffic
Volume

Commuting
Going Home
Anomaly
Early-Bird
Night Owl Busy

Result
3:
Birds-‐Eye
View
of
City
Traﬃc
from
Aggregated
Data


New York: All Taxi Rides
taxi.imagework.com
NYC taxi trips
originate at various
NY airport terminals
(JFK and LGA) over
the holiday season
(Nov 15th to Dec
31st).
Data Source:
NYC Taxi &
Limousine
Commission Taxi
Trip & Fare Data
2013
Stats
173.2M Rows |
28.85GB
Tools
Hadoop | Mapbox |
Leaflet | jQuery | d3 |
polyline | MapQuest
Open Directions API
http://taxi.imagework.com/

New York: Single Taxi Ride
http://nyctaxi.herokuapp.com/

Journey Planning
with Open Data

Promoting Public Transportation: Before and After We Seek
Many cities around the world, and especially in India and emerging ones, are getting
their transportation infrastructure in shape.
–  They have multiple, fragmented, transportation agencies in a region (e.g., city)
–  They do not have instrumentation on their vehicles, like GPS, to know about their
operations in real-time
–  Schedule of public transportation is widely available in semi-structured form. They
are also beginning to invest in new, novel, sensing technologies
–  Cities give SMS-based alerts about events on the road.
Our approach seeks to accelerate time-to-value for such cities.
Kind of Information Today Available to
Bus User
With IRL-Transit+ Benefit
Bus Schedule (static) Available online and
pamphlets
Available from IT-enabled
devices( low-cost phones,
smart phones, web)
Increase accessibility
Bus Schedule Changes
(dynamic)
No information Infer from city updates Increase information
Analytics (Bus Selection
Decision Support)
No information Will be available (Transit) Increase information
Standardization of
information
No support Will be supported
(SCRIBE, Transit)
Increase information’s
interoperability

A Quick Review of Related Work
¡  Bay Area, USA has : http://511.org
÷  Multi-agency public authorities consortium, has advanced instrumentation
÷  It is the model to replicate
§  Google has state-of-the-art from any non-public organization. It has separate
services
¡  Maps for driving guidance
¡  Transit for public transport, more than 1 mode
¡  Gaps:
÷  Considers only time, not other factors like frequency, fare and waiting time
÷  Does not integrate across their services for different mode categories
÷  Does not publish their data
¡  Acknowledgement: We use their GTFS format to consolidate schedule data
§  Many experimental systems with capabilities less than Google,
¡  DMumbai: Go4Mumbai (portal)- A http://www.go4mumbai.com/
¡  Delhi: Disha on DIMTS (local agency) website by IIT-D, Mumbai Navigator by IIT-B; links no longer work
§  Shortest route finding algorithms from mapping companies

Journey Planning Problem
—  Invariant Inputs:
¡  The person
÷  has a vehicle (e.g., car), and
÷  can also walk short distances
¡  The city has taxis, buses, metros, autos, rickshaws
÷  Buses and metros have published routes, frequency and stops
÷  Autos and rickshaws can be available at stands, or opportunistically, on the road
÷  Taxis can be ordered over the phone
—  Input:
¡  A person wants to travel from place A to B
—  Output
¡  Suggest which mode or combination of modes to select
—  Observation: Using preferences over factors that matter to users to keep
commuting convenient, while making best use of available public and para-transit
commute methods

Background: Public Transportation
Schedule Information
—  Is widely available for public
transportation agencies around
the world
—  Gives the basic, static,
information about transportation
service
—  Usually in semi-structured format
with varying semantics
—  Can have errors, missing data
Delhi Bus and Metro Data

Multi-Mode Commuting Recommender in Delhi And Bangalore
Highlights
• Published data of multiple
authorities used; repeatable
process
• Multiple modes searched
• Preference over modes, time,
hops and number of choices
supported; more extensions, like
fare possible
• Integration of results with map
as future work; already done as
part of other projects, viz.
SCRIBE-STAT

Solution Steps
—  Use the widely available schedule information from individual operators
(agencies)
—  Clean and consolidate it across agencies and modes to get a multi-modal
view for the region
¡  Optionally: Convert it into a standard form
¡  Optionally: Enhance (fuse) it with any real-time updates about services
for the region
—  Perform what-if analysis on consolidated data
¡  Path finding using Djikstra’s algorithm
¡  Analyses can be pre-determined, analyses can also be user-created
and defined
—  Make analysis results available as a service
¡  On any device
¡  To any subscriber

Handling Dynamic Updates
—  Invariant Inputs:
¡  The person
÷  has a vehicle (e.g., car), and
÷  can also walk short distances
¡  The city has taxis, buses, metros, autos, rickshaws
÷  Buses and metros have published routes, frequency and stops
÷  Autos and rickshaws can be available at stands, or opportunistically, on the road
÷  Taxis can be ordered over the phone
—  Input:
¡  A person wants to travel from place A to B
¡  [Optional] City provides updates on ongoing events, some may affect
traffic
—  Output
¡  Suggest which mode or combination of modes to select
—  Observation: Using preferences over factors that matter to users to keep
commuting convenient, while making best use of available public and para-transit
commute methods
City Notifications as a Data Source for Traffic Management, Pramod Anantharam, Biplav Srivastava, in 20th
ITS World Congress 2013, Tokyo

Number of SMS messages for bus stops in Delhi
for 2 years (Aug 2010 – Aug 2012)*
• 344 stops
with updates
• 3931 total stops
* using Exact Matching

IRL – Transit in Aug 2012
Key Points
• SMS message from city
• Event and location identified
• Impact assessed
• Impact used in search

Increase Accessibility and Availability of Bus Information to Passengers
Kind of
Information
Today
Available to
Bus Users
With Solution
over Phone
Mysore ITS (for
reference)*
Benefit
Bus Schedule (static) Available online
and pamphlets
Available from low-
cost phones (Spoken
Web – Static)
Available online and
pamphlets
Increase
accessibility
Bus Schedule
Changes (dynamic)
No information
today
Will be available
(Spoken Web -
Human)
No information but in
plan
Increase
information
Bus Location No information
today
Will be available
(GPS)
Will be available
(GPS)
Increase
information
Bus Condition No information
today
Will be available
(Spoken Web -
Human)
No information today Increase
information
Analytics (Bus
Selection Decision
Support)
No information
today
Will be available
(Transit)
No information but in
plan
Increase
information
Last –mile Connectivity
to/ from nearest stop
No information
today
Will be available
(Spoken Web -
Human)
No information today Increase
information
Standardization of
information
No support Will be supported
(SCRIBE, Transit)
Some support due to
GPS
Increase
information’s
interoperability
* Opinion based on only public information; Accurate as of Jan 2014.
Spoken Web is an Interactive IVR technology. SCRIBE is a ontology models for city events.
64Tutorial on 27 July 2015 @ IJCAI 2015

A Flexible Journey Plan
Pushing the Boundaries: Information to Commuters to Reach Destination in All Eventuality
Pilots
running
in
Dublin,
Ireland

65
Docit: An Integrated System for Risk-Averse Multi-Modal Journey
Advising, Adi Botea, Michele Berlingerio, Stefano Braghin Eric
Bouillet, Francesco Calabrese, Bei Chen Yiannis Gkoufas, Rahul Nair,
Tim Nonner, Marco Laumanns, IBM Technical Report, 2014

•  Traffic simulation is a promising tool to do what-if analysis impacting traffic
demand, supply or every-day business decisions
•  What is the congestion if everyone takes out their vehicles?
•  What is the impact if buses daily failure rate doubles?
•  What happens if visitors constituting 20% of city traffic come for an event?
•  However, simulators need to be setup with realistic road network, traffic patterns
and decision choices
•  Open data is an important source for
•  Road network (e.g., Open Street Maps)
•  Creating pattern (e.g., vehicle
Origin-Destination pairs, accidents)
•  Framing and interpreting decision choices
Using Open Data with Traffic Simulation

New Delhi Area Selection
Area selected from openstreetmap.org with (top)
(bottom)(left)(right) co-ordinates as (28.6022)
(28.5707)(77.1990)(77.2522) for our experiment.

Office Timing Change Decision Choices
Last second of morning commute by different strategies

Traffic References
—  Tutorial on AI-Driven Analytics In Traffic Management, in conjunction with International Joint
Conference on Artificial Intelligence (IJCAI-13), Biplav Srivastava, Akshat Kumar, at Beijing, China,
Aug 3-5, 2013 (tutorial-slides).
—  Tutorial on Traffic Management and AI, in conjunction with 26th Conference of Association for
Advancement of Artificial Intelligence (AAAI-12), Biplav Srivastava, Anand Ranganathan, at Toronto,
Canada, July 22-26, 2012 (tutorial-slides).
—  Making Public Transportation Schedule Information Consumable for Improved Decision Making, Raj
Gupta, Biplav Srivastava, Srikanth Tamilselvam, In 15th International IEEE Annual Conference on
Intelligent Transportation Systems (ITSC 2012), Anchorage, USA, Sep 16-19, 2012.
—  Mythologies, Metros & Future Urban Transport , by Prof. Dinesh Mohan, TRIPP, 2008
—  A new look at the traffic management problem and where to start, by Biplav Srivastava, In 18th ITS
Congress, Orlando, USA, Oct 16-20, 2011.
—  Arnott, Richard and K.A. Small, 1994, “The Economics of Traffic Congestion,” American Scientist, Vol.
82, No. 5, pp. 446-455.
—  Chengri Ding and Shunfeng Song , Paradoxes of Traffic Flow and Congestion Pricing,

Environment Pollution
Details: Singapore (2012-2013), Varanasi (2015-)

Water Cycle (aka Hydrological Cycle)
Source: Economist, May 20, 2010

Fresh Water: Supply and Demand
Supply Demand
Source: Economist, May 20, 2010

Water Challenges
—  Increasing demand due to
¡  Population
¡  Changing water-intensive lifestyle
¡  Industrial growth
—  Shrinking supplies
¡  Erratic rains due to climate change
¡  Sewage / effluent increase
—  Poor management
¡  Below cost, unsustainable, pricing
¡  Delayed or neglected maintenance
Water is the next flash point for wars

[India] Ganga – Local Ground Situation @ Varanasi (Assi/ Tulsi Ghats) + Patna
Photos of/ at Assi/ Tulsi Ghat, Varanasi on 25 March 2015 during 1700-1800 Hrs
Assi Ghat post recent cleanup Bathing on Tulsi Ghat
A nullah draining into Ganga
A manual powered boat
Photos at Gandhi Ghat, Patna on 18 March 2015 during 1700-1800 Hrs

Value of Water Pollution Data
—  Government for business decisions
¡  Source attribution
¡  Sewage treatment
¡  Public Health
—  Individuals for personal decisions
¡  Bathing (Religious, Lifestyle)
¡  Recreation
¡  Community practices

Example –River Water Pollution
institutions
¡  Individuals Examples – Can I take a bath? Will it cause me dysentery? What
crops should I grow?
¡  Govt Examples – How should govt spend money on sewage treatment for
maximum disease reduction? How should it inspect industries?
¡  Dissolved oxygen,
¡  pH,
¡  … 30+ measurable quantities of interest
¡  Today, little, and that too in water technical jargon
¡  In pdf documents, website

Use-case: Individual
77
—  Name: which bathing site should
one use?
¡  Based on distance (cost of travel), risk of
disease, exposure to pollutants,
suitability to occasion
—  Total sites in Varanasi (ghats): 87
¡  Popular: 5
¡  #1 religious rites (puja):
Dashashwamedh Ghat
¡  Cremation (non-bathing) ghats: 2;
Manikarnika and Harishchandra Ghat
¡  Bathing ghats: All – cremation = 85
41.  Lali Ghat
42.  Lalita Ghat
43.  Mahanirvani Ghat
44.  Mana Mandira Ghat
45.  Manasarovara Ghat
46.  Mangala Gauri Ghat
47.  Manikarnika Ghat
48.  Mehta Ghat
49.  Meer Ghat
50.  Munshi Ghat
51.  Nandesavara Ghat
52.  Narada Ghat
53.  Naya Ghat
54.  Nepali Ghat
55.  Niranjani Ghat
56.  Nishad Ghat
57.  Old Hanumanana Ghat
58.  Pancaganga Ghat
59.  Panchkota
60.  Pandey Ghat
61.  Phuta Ghat
62.  Prabhu Ghat
63.  Prahalada Ghat
64.  Prayaga Ghat
65.  Raj Ghat built by Peshwa Amrutrao
66.  Raja Ghat / Lord Duffrin bridge /
Malaviya Bridge
67.  Raja Gwalior Ghat
68.  Rajendra Prasad Ghat
69.  Ram Ghat
70.  Rana Mahala Ghat
71.  Rewan Ghat
72.  Sakka Ghat
73.  Sankatha Ghat
74.  Sarvesvara Ghat
75.  Scindia Ghat
76.  Shivala Ghat
77.  Shitala Ghat
78.  Sitala Ghat
79.  Somesvara Ghat
80.  Telianala Ghat
81.  Trilochana Ghat
82.  Tripura Bhairavi Ghat
83.  Tulsi Ghat
84.  Vaccharaja Ghat
85.  Venimadhava Ghat
86.  Vijayanagaram Ghat
87.  Samne Ghat
1.  Mata Anandamai Ghat
2.  Assi Ghat
3.  Ahilya Ghat
4.  Adi Keshava Ghat
5.  Ahilyabai Ghat
6.  Badri Nayarana Ghat
7.  Bajirao Ghat
8.  Bauli /Umaraogiri / Amroha Ghat
9.  Bhadaini Ghat
10.  Bhonsale Ghat
11.  Brahma Ghat
12.  Bundi Parakota Ghat
13.  Chaowki Ghat
14.  Chausatthi Ghat
15.  Cheta Singh Ghat
16.  Dandi Ghat
17.  Darabhanga Ghat
18.  Dashashwamedh Ghat
19.  Digpatia Ghat
20.  Durga Ghat
21.  Ganga Mahal Ghat (I)
22.  Ganga Mahal Ghat (II)
23.  Gaay Ghat
24.  Gauri Shankar Ghat
25.  Genesha Ghat
26.  Gola Ghat
27.  Gularia Ghat
28.  Hanuman Ghat
29.  Hanumanagardhi Ghat
30.  Harish Chandra Ghat
31.  Jain Ghat
32.  Jalasayi Ghat
33.  Janaki Ghat
34.  Jatara Ghat
35.  Karnataka State Ghat
36.  Kedar Ghat
37.  Khirkia Ghat
38.  Shri Guru Ravidass Ghat[5]
39.  Khori Ghat
40.  Lala Ghat
Source:
http://en.wikipedia.org/wiki/
Ghats_in_Varanasi
Note: ghats are specialities of most cities along Ganga – Haridwar, Allahabad, Patna

Pollu+on
Example:
Leather
Tanneries
in
Kanpur,
India

•  > 700 tanneries in Kanpur
–  Employing > 100,000 people
–  Bringing > USD 1B revenue
•  Discharge water after leather processing to river or Sewage
treatment plants (STPs)
–  Requirement
•  Must have their own treatment facility
•  Or, have at least chrome recovery unit
–  But don’t due to costs which is a burden to main operations
•  Installation
•  Operations : electricity, manpower, technology upgrade, …
–  State pollution board is supposed to do inspections but doesn’t do effectively
•  Government’s STPs do not process chrome, the main pollutant
•  98 tanneries banned in Feb 2015 by National Green Tribunal; more
threatened

Analytics: Potential Use Cases
S.
No.
Stakehol
der
Use case Data Analytical
techniques
1 IT Identifying and removing outliers,
data validation
Sensor data Data mining (outlier
detection)
2 Individual Which bathing site to use? Sensor data, ghat
data
Rule-based decision
support
3 Individual/
Economy
What crops can I grow that will
flourish in available water?
Sensor data, crop
data
Distributed data
integration, co-relation
4 Institution Determine trends/anomalies in
pollution levels
Sensor data,
weather data
Time series analysis,
anomaly detection
5 Institution Attribute source of pollution at a
location
Sensor data,
demographics,
industry data
Physical modeling,
inversion, inspection
planning
6 Institution Sewage treatment strategy and
operational planning
Sensor data,
demographics
data, STP data
Multi-objective
optimization
7 Institution Promoting wildlife/ dolphins with
patrolling and monitoring
Sensor data,
wildlife data
Rule-based decision
support

India/Ganga – Very Little Data
Data.gov.in
https://data.gov.in/catalog/water-quality-data-river-ganga
Sr.
No.
Sta$on-‐Loca$on
Distance
in
Kms.

Dissolved
Oxygen

during
1986
(mg/
l)

Biological
Oxygen

Demand
in
1986

(mg/l)

Dissolved
Oxygen

during
2011
(mg/
l)

Biological
Oxygen

demand
during

2011
(mg/l)

1
Rishikesh
0
8.1
1.7
7.6
1.4

2
Hardwar
D/s
30
8.1
1.8
7.4
1.6

3
Garhmukteshwar
175
7.8
2.2
7.5
1.7

4
Kannauj
U/S
430
7.2
5.5
7.9
1.7

6
Kanpur
U/S
530
7.2
7.2
7.7
3.3

7
Kanpur
D/S
548
6.7
8.6
7.6
3.8

8
Allahabad
U/S
733
6.4
11.4
7.8
5.3

9
Allahabad
D/S
743
6.6
15.5
7.8
5.1

10
Varanasi
U/S
908
5.6
10.1
8
2.9

11
Varanasi
D/S
916
5.9
10.6
8
4.3

12
Patna
U/S
1188
8.4
2
7
1.8

13
Patna
D/S
1198
8.1
2.2
7.1
2.5


Creek Watch – Crowd Sourced Water Information Collection
As on 14 Oct 2014

Location: http://creekwatch.researchlabs.ibm.com/call_table.php
~3120 data points in 4 years from around the world
As on 14 Oct 2014

Health
Details: Africa (2014-), India (2013-)

Two Tales from (Public) Health
Cutting-edge Technical
Progress
•  Enormous improvement in our
understanding of diseases. E.g.,
Computational epidemiology
•  Enormous advances in treating
diseases are being made
÷  We are living longer - A baby girl born
in 2012 can expect to live an average of
72.7 years, and a baby boy to 68.1
years. This is 6 years longer than the
average global life expectancy for a
child born in 1990. (Source: WHO 2014
Health Statistics)
•  Data on disease outbreaks is
more available than ever before
thanks to open data movement
(E.g., data.gov, data.gov.in)
Stone-age Ground Reality
—  Half of the top 20 causes of deaths
in the world are infectious diseases,
and maternal, neonatal and
nutritional causes, while the other
half are due to noncommunicable
diseases (NCDs) or injuries. (Source:
WHO 2014 Health Statistics)
—  Worse – Indifference,
mismanagement in response to
communicable diseases - late
response to known diseases, in
known period of the year
¡  E.g.: Japanese Encephalitis (JE) has been
prevalent for ~3 decades in some parts of
India killing 600+ every year
¡  District level health experience is not
reused over time and in similar regions

IT Played a Major Role in
Tackling Ebola
Crowd sourced
Online
National Government
International Bodies

Ideas for Public Health in India
—  Decision support to administration for tackling
seasonal diseases
—  Crowdsourced disease treatment recipes

Case Study: Dengue (Mosquito-borne)
—  Overall cost of a Dengue case is US$ 828 (Sabchareon et al 2012).
—  From 9 countries in 1960s, it has spread to more than 110 countries now
—  Prevention methods
COMMUNITY
1.  Mosquito Coils & Candles: The use of mosquito coils, candles & vapor mats indoors and outdoors of homes to combat
mosquitoes.
2.  Window screens & Bed Nets: The use of window screens in homes and bed nets in bedrooms to keep mosquitos out.
3.  Insecticide Application: Application of insecticide to kill mosquitos that invade homes and surrounding areas.
4.  Larviciding at Home: Application of larvicide in homes to kill larvae that live in stagnant water breeding sites like small
ponds, gutters, cisterns, barrels, jars, and urns.
5.  Household/Community Cleanup: Organize cleanups within communities in the surrounding housing areas and
individual homes to recycle potential breeding sites like discarded plastic bottles, cans, old tyres, and any trash that can hold
water for mosquitoes to breed in.
GOVERNMENT
6.  Surveillance For Mosquitoes: Conduct periodical surveillance in hotspot areas and other communities to look for signs of
mosquitoes.
7.  Medical Reporting: To collate and compile reports of dengue cases and statistics to prioritize and focus dengue and vector
mosquito control efforts and actions for best results.
8.  Effective Publicity & Campaigns: To foster and champion effective campaigns amongst communities and create adequate
public awareness of combating dengue.
9.  Enforcement: Support and enforce the public and communities to practice effective dengue vector elimination under
existing laws and implement new laws as appropriate for public health.
10.  Insecticide Fogging: Conduct fogging in areas that have mosquitoes and dengue outbreak hotspots to kill adult mosquitoes.
11.  Public Education: Foster, promote, and participate in public education in schools and all possible public meeting places to
inform communities how to eliminate dengue vector mosquitoes, recognize early symptoms of the disease, and proper medical
care and reporting.
CORPORATE
12.  Education: To undertake community service initiatives and campaigns through marketing expertise and the media of TV,
radio, and newspapers.
13.  PR/CSR: To use public relations and customer service relations to reach communities on the fight against dengue.
14.  Adult Mosquito Traps: To provide adult mosquito traps and other measures within the work areas to protect employees
and workers from mosquitoes bites that transmit dengue.
15.  Mosquito Repellants: Provide mosquito repellants to employees and workers within the work areas for further protection.
16.  Mosquito Control Materials, Methods, and Agents: To provide the tools to the public and government that are
necessary for dengue mosquito vector control like pesticides, biocontrol agents, mosquito traps, repellants, and other means
to prevent dengue by eliminating the mosquito vectors.
WHO, 2013, Dengue Control. At http://www.who.int/Denguecontrol/research/en/, Accessed 21 June 2013.
Entogenex, 2013, Integrated Mosquito Management. At
http://www.entogenex.com/what-is-integrated-mosquito- management.html, Accessed 21 June 2013. 87Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015

So, Do We Control Dengue
Effectively? NO
Source: http://nvbdcp.gov.in/den-cd.html
Data for India
•  Increasing
number of
states every
year
•  No consistent
reduction of
cases
1"
10"
100"
1000"
10000"
100000"
C" C" C" C" C" C"
2008" 2009" 2010" 2011" 2012" 2013*"
Andhra"Pradesh"
Arunachal"Pradesh"
Assam"
Bihar"
Cha9sgarh"
Goa"
Gujarat"
Haryana"
Himachal"Pd."
J"&"K"
Jharkhand"
Karnataka"
Kerala"
Madhya"Pd."
Meghalaya"
Maharashtra"
Manipur"
Mizoram"
Nagaland"
Orissa"
Punjab"
Rajasthan"
Sikkim"
Tamil"Nadu"
Tripura"
UPar"Pradesh"
UPrakhand"
West"Bengal"
A&"N"Island"
Chandigarh"

Challenge: Prescribe Methods to Use for a
Hypothetical, Illustrative Area - Sundarpur
—  City is Sundarpur
¡  Made up of 10 districts
¡  10,000 people in each district.
—  Disease control
¡  Each district allocates $10,000 per annum to prevent disease.
¡  The city has a district-level health administrator per district and then an
overall citywide public health administrator.
—  What approach/ method should the district health officer use? What should
the city health officer recommend?
¡  a mix of control methods to produce the maximum reduction feasible.
¡  Default option is to do nothing. This is unfortunately followed a lot!

(ROI) Metrics
—  Expense for disease control
¡  $/person spent: How much money (in $) is spent for a given method divided by the population
of the region. Lower is better.
—  Impact of a disease control method
¡  Reduction: What is the magnitude of reduction in disease cases due to a method, expressed as
a percentage, in a time period (e.g., year, disease season)? Higher is better.
¡  Cases/ person: How many reported cases of a disease occurred in a time period divided by the
population of the region when a method was adopted? Lower is better.
—  Cost-effectiveness:
¡  Cases / $: how many cases were reported for a disease per dollar spent on controlling it in a
given time period? Lower is better.

Major Methods to Tackle Dengue
—  M1: Public awareness campaigns: to prevent
conditions conducive to disease propagation, to
improve reporting
—  M2: Chemical Control: Aerosol space spray
—  M3: Biological Control: Use of biocides
—  M4: Distributing equipments: bednets, insecticide-
treated curtains
—  M5: Vaccination against the disease

Dengue Control Case Studies from Literature
•  An approach
may use 1 or
more method(s)
•  They incur
different costs
per person
•  Their efficacy is
subject to
various factors
Still, can we
reuse these
results in new
areas?
Details:
Vandana Srivastava and Biplav Srivastava, Towards Timely Public Health Decisions to
Tackle Seasonal Diseases With Open Government Data , International Workshop on the
World Wide Web and Public Health Intelligence (W3PHI-2014), AAAI 2014

Cost-benefits for Different Approaches
* represents assumption made to compensate for missing data.

Prescription for Sundarpur
—  Best tactical option for administrators at Sundarpur (at district and the whole city
level)
¡  is O1_A1 since it brings the maximum reduction.
¡  If the administrators are interested to cover the maximum number of people in the given
budget, the best method is still O1_A1.
¡  If the administrators are interested to show maximum reduction in cases for a pocket of the
city (sub- district level which may be more prone to the disease), they may choose O4_A4 but it
costs maximum and thus can be perceived as taking resources away from the not- directed
areas.
—  Strategic option
¡  Select top-2 (O1_A1 and O2_A2), and try them in 5 districts each in one year. It hedges risk of
variability between Sundarpur and old location of previous studies.
¡  Based on efficacy, decide the single best option for Sundarpur in subsequent year.
¡  She may also use the vaccine option only when the disease outbreak is above certain
threshold.
Details:
Vandana Srivastava and Biplav Srivastava, Towards Timely Public Health Decisions to
Tackle Seasonal Diseases With Open Government Data , International Workshop on the
World Wide Web and Public Health Intelligence (W3PHI-2014), AAAI 2014

New Data Practices
—  Find correlation among methods (positive or negative)
¡  We assumed independence
¡  Needs: Historic Data, Experiment Design
—  Learn rate of return for approaches and methods (new combinations not
tried in health literature)
¡  Need: Collect data on efficacy of method individually
—  Find similarity among regions
¡  Data Need: Spatio-temporal modeling/ STEM
—  Multi-objective optimization
¡  Examples: Effectiveness of approach, Reduction of case, people coverage
¡  Needs: Data about approaches tried historically

Request to Medical Community on Data
—  Report both cost and effectiveness of approaches and
methods
¡  Overlooking one hampers reuse of results
—  Interact with AI community to learn and try mixed
approaches that reduce cost and improve overall
effectiveness
¡  All combinations cannot be tried on the ground due to practical
constraints
¡  Get more effective approaches rolled out faster targeted to new
regions

Planning Idea: Crowdsourced Health Treatment Plans
—  Human Information Sourcing
¡  Pros: Ease of acceptance (social), Easy to understand by humans
¡  Cons: Biased by contributors, possible incompleteness
—  Automated Generation
¡  Pros: Very efficient methods available
¡  Cons: Needs model of the world, goal specification
—  Idea: Bridge the two leveraging
¡  India’s educated crowd (sourcing, critiquing) on a social platform and
¡  new innovations in AI/planning on model learning and plan ranking to handling
uncertainty

Discussion

Smart City Challenges
—  From resource angle, decrease waste/ inefficiency while
improving service delivery to citizens
—  Problems are old but accentuated today by population
growth and reducing resources
—  Open Data, effectiveness of AI methods hold promise
—  Challenges
¡  Provide value quickly
¡  Use value synergies from different domains (e.g., health,
environment, traffic, corruption …)
¡  Grow to scale

Common Analytics Patterns,
Accelerated with Open Data
—  Correlation of outcomes, across
¡  Data sources in same domain
¡  Different domains
—  Return of investment analysis
¡  Money invested v/s Metrics to measure improvement in
domain
¡  Comparison of performance with history
¡  Comparison of performance with other regions

AI Planning Offers Innovation Opportunities
In talk, showed
—  Transportation
¡  Journey Planning (demand) – plan synthesis
¡  Route (supply) optimization – plan analysis
—  Environment
¡  Bathing – plan synthesis
¡  Source attribution – plan analysis
—  Health
¡  Public health – decision-theoretic optimization
¡  Treatment recipes – Crowdsourced planning

Employing All Data – Data Fusion
—  Open Data is one source
¡  Often easiest to get but with issues (e.g., at aggregate level, with gaps,
imprecise semantics)
—  Social is another promising data
¡  People are anyway generating it (People-as-sensors)
¡  However, social sites have varying data reuse permissions,
license costs, access limits
¡  Big data techniques already being used here
—  Use sensor data if available
¡  Internet of Things (IoT) and big data techniques are relevant
¡  Most prevalent in health, environment and transportation
—  Key is to release the fused data also for reuse

Building Community for Innovations
—  Multi-disciplinary
¡  In AI
¡  In Computer Science
¡  In science: domain (health, transport, …), techniques (CS, engg.) and
evaluation (public policy, …)
—  Multi-stakeholder
¡  Citizens
¡  Government
¡  Academia
¡  Business/ Industry
¡  Non-profits, …
—  Getting to scale is key

Building a Technical Environment Problem Solving Community

Thank You
Merci
Grazie
Gracias
Obrigado
Danke
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English
Dr. Biplav Srivastava,
sbiplav@in.ibm.com
http://www.research.ibm.com/people/b/biplav/

Technological Challenges in Managing and Operating a Smart City: Planning for a real-world

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