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

1 Talk 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

�  Decision Value – To individuals, businesses, government 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? �  Data – Quantitative as well as qualitative

¡  Dissolved oxygen, ¡  pH, ¡  … 30+ measurable quantities of interest

�  Access – ¡  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

Impr

ovem

ent p

oten

tial a

s %

of s

yste

m in

effic

ienc

y

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 Transport Security Planning Food . . . Science Health ICT

City

Responsibility

Department

Responsibility

Project

Responsibility

Task

Responsibility

Typically lacking holistic view

Ope

rati

onal

Sys

tem

s Before


14

D o

IT

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

Ope

rati

onal

Sys

tem

s

C i t y M a n a g e m e n t Analytics, Insight, Visualization, Control Center, etc.

Water Energy Transport Security Planning Food . . . Science Health . . .

D o

W

D o

E

D o

T

D o

S

D o

P

D o

F

D o

. . .

D o

S

D o

H

. . .

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

Uni

que

valu

e re

aliz

ed

Use of Smarter Cities capabilities

ManageData 1

AnalyzePatterns 2

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

16 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015 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 pe

rcep

tion

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 20 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015

Static Deterministic Observable Instantaneous Propositional

“Classical Planning”

Dynamic R

epla

nnin

g/

Situ

ated

P

lans

Partially Observable

Con

tinge

nt/C

onfo

rman

t P

lans

, Int

erle

aved

ex

ecut

ion

Durative

Tem

pora

l R

easo

ning

Continuous

Num

eric

Con

stra

int

reas

onin

g (

LP/IL

P)

Stochastic

MD

P P

olic

ies

PO

MD

P P

olic

ies

Sem

i-MD

P P

olic

ies

Slide by Subbarao Kambhampati 21 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015

Underlying System Dynamics

Traditional Planning

Opt

imiz

atio

n M

etric

s

Any (feasible) Plan

Shortest plan

Cheapest plan

Highest net-benefit

Multi-objective

PSP Planning

Slide by Subbarao Kambhampati 22 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015

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

Com

petit

ive

Adv

anta

ge

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


Peek into the Future - Amsterdam

http://citydashboard.waag.org/ 36 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015

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 ¡  WEKA Tutorial:

÷  Machine Learning with WEKA: A presentation demonstrating all graphical user interfaces (GUI) in Weka.

÷  A presentation which explains how to use Weka for exploratory data mining. ¡  WEKA Data Mining Book:

÷  Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)

÷  http://www.cs.waikato.ac.nz/ml/weka/book.html ¡  WEKA Wiki: http://weka.sourceforge.net/wiki/index.php/Main_Page

�  Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 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


Bus Location No information today

Will be available (GPS)

Will be available (GPS)


Bus Condition No information today




Analytics (Bus Selection Decision Support)


Will be available (Transit)

No information but in plan


Last –mile Connectivity to/ from nearest stop



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.

64 Tutorial 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 68 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015

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

�  Decision Value – To individuals, businesses, government 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? �  Data – Quantitative as well as qualitative

¡  Dissolved oxygen, ¡  pH, ¡  … 30+ measurable quantities of interest

�  Access – ¡  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.

Stakeholder

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. 87 Talk 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

Japanese

French

Russian

German Italian

Spanish

Portuguese

Arabic

Traditional Chinese

Simplified Chinese

Hindi

Romanian

Korean

Multumesc

Turkish

Teşekkür ederim

English

Dr. Biplav Srivastava, [email protected]://www.research.ibm.com/people/b/biplav/


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

Engineering