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Smart Cities Thesis

Why Smart Cities?

Imagine a city that “thinks”

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Imagine, or rather, Re-Imagine:

A personal city guide and wiki : An AI assistant that knows the city better than your local chai or paanwaala. For example: Your smartphone tells where to get the bus from and when does the bus leave and what payment methods are accepted? All of this without juggling between 3 apps

Complaint department 2.0: Where your grievances don't just disappear into the void.

Plus one: Where a small addition to existing infra makes a big difference? (This is what we heard from Dr. Pramod in this talk -

Plus one thinking⁠

) For example: Surveillance cameras double as Traffic monitors

The bottom line: How not to solve city issues where we get away with slapping some tech on old problems, would entail reimagining our cities and how tech can reinforce them from the ground up.

If you want a formal definition of smart cities, below folks have done a good job:

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What exactly is a smart city? The Indian government does not want you to know It thwarted the Bureau of Indian Standards’ plan to fix clear criteria for defining a smart city, and came out with a fuzzy ‘Liveability Index’ instead. scroll.in⁠

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Where have all the ‘smart’ cities gone? Remember the scramble 7-8 years ago after Prime Minister Modi announced the Smart Cities Mission (SCM) in June 2015. More than 18 months were spent in selecting www.newindianexpress.com⁠

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The Emergence Of Smart Cities In The Digital Era Realizing the potential of Smart Cities will require public-private cooperation and security by design. www.forbes.com⁠

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If you are on the same page about this, please let us know by clicking the button below!

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While reading the doc, we request you to answer these questions and let us know your

Broken link⁠

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Do you feel we chose the right set of problems?

What are your thoughts on use of AI to solve specific challenges?

❓What We'll Explore

While

Opportunities and attempts to enhance information accessibility for citizens

Streamlining the grievance redressal process - looking beyond “better complaints”

Leveraging existing infrastructure with AI for smarter cities

🔑Key Personas

This thesis focuses on three primary personas:

Saamaj (Aam Aadmi)

Sarkar: Entities operating urban assets such as buses, roads, and public utilities, responsible for service delivery and infrastructure management.

Bazaar: Startups working in the urban service delivery and mobility space that would benefit from open city data and Smarter infrastructure

👏Recognizing Progress: The Initiatives to be Smart

Bangalore's "Smart City" efforts have made some progress:

Launched useful apps like Sahay 2.0 and BBMP Fix My Street for citizens to report issues.

Invested in infrastructure like CCTVs , bus PoS machines, Smart meters etc.

However, challenges remain:

Work overload: Government agencies are overwhelmed with their existing workload.

Recurring challenges: Cities face ongoing operational, maintenance, and administrative issues throughout the year.

Definitive Roles: Agencies are trying to be providers, facilitators, and developers simultaneously, which may lead to a lack of focus in any single area.

Slow tech adoption: For the reasons stated above along with multiple other issues compounding, we theorize that the other sectors are rapidly innovating, government agencies are falling behind in adopting new technologies

Note: These challenges are common in many cities, not just Bengaluru

The below image shows different apps related to Bengaluru:

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Do you Agree with the challenges mentioned??

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I have read this far!

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Tracks

Track 1 - Simplifying Information Access

Recognizing Progress: The initiatives to make Bengaluru smart have explored multiple approaches for smarter and tech-focused service delivery. They have launched impressive apps like Sahay 2.0 and BBMP Fix My Street. These platforms empower citizens to voice their concerns directly to the authorities. The government has also invested significantly in infrastructure, from CCTVs to PoS machines in buses, laying a foundation for data collection and analysis. However, the sheer operational burden on the organisations already is a hindrance to the adoption of tech forward initiatives. This is not just with Bangalore but with any other city, we took Bangalore just as an example.

Problem

Cities have tried various methods (info boards, apps) to share information about services.

Digital platforms are crucial, but must be user-friendly.

Challenges (using Bengaluru as an example):

Multiple agencies (BMTC, BMRCL) create separate apps.

These apps often have low adoption and poor maintenance.

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Opportunity: City wiki - Create a unified system where residents can easily find information: (For example)

Best transport routes

Real-time updates

Nearest stops

Key question: Instead of creating another app, can we develop standardized data sets for all city services, that multiple players innovate on??

Some reference information about how Singapore handles its data :

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DataMall | Land Transport Authority (LTA) Home datamall.lta.gov.sg⁠

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Loading beta.data.gov.sg⁠

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Where does AI come in?

Where does AI come in?

AI Capabilities

Description

Natural Language Processing (NLP)

Provides intuitive queries (e.g., "How do I get from Koramangala to Indiranagar?").

Personalized Recommendations

Offers suggestions based on user history and preferences.

Real-time Data Integration

Aggregates data from various sources for accurate, up-to-date information.

Predictive Analytics

Anticipates user needs and proactively offers relevant information.

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What Can we Do?

A Pilot for Bengaluru which could include

A Pilot for Bengaluru which could include

Steps

Description

Combine Basic Data

Gather information from various city services (water, electricity, roads, healthcare) to train an AI Agent

Create Multimodal Smart Assistant

Build an Assistant that can understand and answer questions about city services.

Send Timely Updates

Alert users about important issues in their area.

Listen to Users

Facilitate easy feedback to improve the system.

Work with Local Groups

Collaborate with organizations like eGov or Citizen Action Groups to provide information, test the system, and use city data for AI improvement.

Smart Infrastructure Updates

Develop an AI-powered system to understand and convert official announcements (PSAs) into a tech-friendly format for easy use by popular apps and services.

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For example, when a PDF is published about road closures in Bangalore, our AI will understand the content and create a standardized, easy-to-use data format. This means apps like Google Maps can quickly update their systems with the latest information, without manual input. Our goal is to bridge the gap between official announcements and real-time, user-friendly updates.

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Track 2 - Smart Grievance Redressal

Problem

The government has created multiple channels for grievance redressal, catering to various departments like BMTC for transportation and BWSSB for water, and despite unified efforts like apps like Sahaaya 2.0 etc, the problem often comes down to multiple organizations and departments having stake in the same issue, and they often suffer similar issues, but in silos.

Smart Grievance Redressal:

Multiple government channels exist for complaints (e.g., BMTC, BWSSB).

Unified apps like Sahaaya have been created, but lack traction because of lack of action Main issue: Multiple departments often responsible for same problem.

Improving the Process:

Proposal: Work with stakeholders (eGov, Janaagraha, Samagra, BPAC, municipalities) to understand:

Why do citizens perceive slow service delivery?

What's the full complaint lifecycle?

Which stages take longest and why?

Goal: Find ways to improve beyond just better complaint lodging.

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Exploring Efficiency: Understanding the Grievance Lifecycle

What could we do differently other than lodging better complaints that lead to an authority?

To further improve service delivery, we need to gain a deeper understanding of the current processes and public perceptions. We propose collaborating with industry stakeholders like eGov, Janaagraha, Samagra, BPAC and municipalities to answer the following key questions:

Why does the public think that service delivery is slow? What aspects of the process are not well understood by citizens?

What is the complete lifecycle of a grievance, from submission to resolution? Which stages take the most time and why?

What Can AI do?

What Can AI do?

AI Capabilities

Description

AI-Powered Assistant

Guides citizens to the right authority based on their complaints.

Unified Platform

Simplifies the complaint process across different departments.

Clear Communication

Provides detailed information on the documentation required for each type of complaint.

Accountability Enforcement

Implements a backend soft-nudge-based system to ensure accountability.

AI-Based Change Detection System

Develop tools to help citizens quantify improvements, such as an AI system that measures the progress of grievance resolution.

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Why the Report?

Why the  Report?

Actions

Expected Outcomes

Identify Bottlenecks

Pinpoint stages in the current system that cause delays.

Enhance Communication

Improve how the grievance resolution process is communicated to citizens.

Set Realistic Expectations

Provide citizens with realistic timelines and increase public satisfaction.

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(ex : citizen sees potholes- lodges complaint -authority does ad hoc fix- citizen can click a photo again to understand how much of the issue is resolved)

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Track 3 - Public Asset Optimization: The Power of "Plus One" Thinking

Problem

The government's investment in infrastructure is praiseworthy, laying a solid foundation for our cities. Now, we have a unique opportunity to enhance the value of this investment through intelligent upgrades. Instead of viewing infrastructure as serving only one purpose, what if we could add just one more function to each element? This small change could potentially lead to a significant increase in efficiency across the board

Goal: Maximize existing infrastructure through smart upgrades.

Examples:

BMTC Buses: Add QR counters for passenger tracking

Road CCTVs: Use AI for real-time traffic analysis

BBMP Workers' Phones: Track waste management in real-time

Solution

Action Plan:

Identify existing digital assets and new use cases

Create immediate feedback systems

Example

Example

Existing Asset

Possibilities

BMTC Buses

Equip with PineLabs QR for passenger counting to optimize routes.

Road CCTVs

Implement AI for real-time traffic pattern analysis and predictive management.

BBMP Worker’s Smartphones

Use for real-time tracking and updates on waste management coverage.

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A Day in the Life: The Koramangala to Indiranagar Journey

Let's envision how these enhancements could transform a simple bus journey:

Information Access: An AI-powered app provides real-time data on bus stop locations, schedules, and routes

Asset Tracking: Live updates on bus locations and estimated arrival times

Payment Info: Clear communication on accepted payment methods, including digital options

Capacity Management: Real-time data on bus availability and crowding levels for informed decision-making

What will we do?

Identify already deployed digital assets in everyday life, and map the untapped use cases (ex; the pine labs machines doubling as live trackers or examples mentioned above)

Setup immediate feedback loops based on the same

A bit of fun: What if we could use AI to play a prank on people who break traffic rules by spraying them with a harmless but stinky gas?

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Here’s How You Can Join:

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VOLUNTEER WITH US

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JOIN AS A PARTNER ORGANISATION

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JOIN OUR EVENTS

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SUBSCRIBE TO UPDATES

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Thought Partners/ Collaborators

WELL Labs is dedicated to creating solutions focused on water, environment, land, and livelihoods. They work on science-driven projects that address pressing issues like water scarcity, climate change, and sustainable resource management. They are leveraging AI and ML applications especially in fields of water to source information regarding water bodies, and their management through platforms like JALTOL. Through collaboration with various stakeholders, including governments, businesses, and communities, they aim to develop impactful and sustainable strategies to tackle these challenges. Learn more about their work

here⁠

WRI India is a leading organization dedicated to driving sustainable development through research, policy, and on-ground initiatives. Their work focuses on urban planning, climate change, energy, and sustainable transportation. They are exploring AI specifically in mapping resources like forestry, climate change,WRI India is leveraging AI across several key areas to address environmental challenges and promote sustainable development. In environmental monitoring and conservation, they utilize open-source AI to create global datasets for mapping forests and developing predictive models for forecasting reservoir water levels, which aids in effective water management.

here⁠

Proto is an innovative platform that enables users to earn by mapping and contributing real-life data about places they visit, from hidden cafes to local shops. By capturing insights beyond what traditional satellites or vehicles can provide, Proto allows users to build and monetize content with long-term potential. They are exploring AI to plug into their platforms that helps you catalog, share, and earn from your everyday actions and trips, turning them into valuable, interactive maps. Proto is about creating, contributing, and profiting from your unique insights. Learn more

here⁠

IIT Kharagpur - MUST Lab (Multimodal Urban Sustainability Lab) is a research Lab dedicated for working on sustainable transportation. they are exploring AI potentially to develop strategies and solutions to make urban transportation more sustainable. in the areas of a) Multimodal transport planning: Integrating different modes of transportation like walking, cycling, public transit, and private vehicles to create a balanced and efficient system, b) Travel demand management: Implementing policies and measures to influence travel behavior and reduce the demand for private vehicle usage.c) Mode choice modeling: Analyzing factors that affect an individual's choice of transportation mode and developing models to predict mode choice

Conclusion: Building on Our Successes

Some projects on these tracks below:

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Sthaan

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{CtAI} - City AI Engine

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Despite the overarching issues and sore thumbs, there are many initiatives taken in the recent times that have either contributed or have laid a strong foundation for urban development. By focusing on these key areas, we can elevate our cities further:

Enhance information accessibility through AI-driven solutions

Refine the grievance redressal process based on comprehensive understanding

Maximize existing infrastructure through AI integration

The future of our cities lies in collaborative innovation between citizens, government bodies, and technology experts. Together, we can build upon our achievements and create even smarter, more responsive urban environments that serve as models for the world.

Tentative Calendar

Tentative Calendar

Name

Tentative Timeline

Notes

Workshop to Bridge +1 Use cases

End of September

Open

Publish a thesis report like Adbhut India (AI) where everyone can align - How can Markets align to city

October / November

Open

Sector specific Mixers/Hackathon for the problems emerged from the report above

Open

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