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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.
2 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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.
3 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
4 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Examples
5 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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?
7 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
Key Idea: Can we make insights available when needed and help people make better decisions?
9 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Basics: Smart City
10 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
12 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
17 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Basics: AI
18 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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)
23 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Shades of Analytics
24 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
25 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
26 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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?
27 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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)
28 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Basics: Open Data
29 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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”
30 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
31 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
400+Data Catalogs of Public Data
As on 21 July 2015
32 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 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
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Data.gov.in (India)
As on 16 June 2015
35 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 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
37 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
38 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
39 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Still Confused on Semantics? Start with Linked Data Glossary
41 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
42 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
43 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Smarter Transportation
Details: Boston (2012), New York, (2014), India – Delhi, Bangalore (2011-2015)
44 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 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
45 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
46 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
47 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
48 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
49 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
• 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
51 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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/
52 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
New York: Single Taxi Ride
http://nyctaxi.herokuapp.com/
53 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Journey Planning with Open Data
54 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
55 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
56 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
57 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
58 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
59 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
60 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
61 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
62 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
IRL – Transit in Aug 2012
Key Points • SMS message from city • Event and location identified • Impact assessed • Impact used in search
63 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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.
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
Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
• 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
66 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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.
67 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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,
69 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Environment Pollution
Details: Singapore (2012-2013), Varanasi (2015-)
70 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Water Cycle (aka Hydrological Cycle)
Source: Economist, May 20, 2010
71 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Fresh Water: Supply and Demand Supply Demand
72 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
73 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
[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
74 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
75 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
Key Idea: Can we make insights available when needed and help people make better decisions?
76 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
77 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
78 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
79 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
80 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Creek Watch – Crowd Sourced Water Information Collection
As on 14 Oct 2014
81 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Location: http://creekwatch.researchlabs.ibm.com/call_table.php
~3120 data points in 4 years from around the world
As on 14 Oct 2014
82 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Health
Details: Africa (2014-), India (2013-)
83 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
84 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
IT Played a Major Role in Tackling Ebola
Crowd sourced
Online
National Government
International Bodies
85 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Ideas for Public Health in India
� Decision support to administration for tackling seasonal diseases
� Crowdsourced disease treatment recipes
86 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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"
88 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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!
89 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
(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.
90 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
91 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
92 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Cost-benefits for Different Approaches
* represents assumption made to compensate for missing data.
93 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
94 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
95 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
96 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
97 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Discussion
98 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
99 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
100 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
101 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
102 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
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
103 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Building a Technical Environment Problem Solving Community
104 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015
Thank You
Merci Grazie
Gracias Obrigado
Danke
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English
Dr. Biplav Srivastava, [email protected]://www.research.ibm.com/people/b/biplav/
105 Talk at IEEE Bangalore Workshop, Technologies for Planning and Acting in Real World Systems, Sep 4, 2015