Archive for June 2026

 
 

Safe Streets AI Lab – Project Proposal Template


Safe Streets AI Lab – Indiranagar Pilot

An AI-Enabled Urban Safety, Walkability and Environmental Exposure Assessment Initiative

Proposed By: Nexus3P Foundation
Project Location: Indiranagar, Bengaluru, Karnataka
Project Duration: 6 Months
Project Budget: ₹9.50 Lakhs (Maximum ₹10 Lakhs)
Implementation Model: Pilot Demonstration Project

1. Executive Summary

Indian cities have increasingly become vehicle-centric, often at the expense of pedestrian safety, accessibility, and quality of life. Even in premium urban neighbourhoods such as Indiranagar, pedestrians face multiple risks including damaged footpaths, potholes, poorly lit streets, encroachments, unsafe crossings, wrong-side driving, speeding two-wheelers, delivery riders using footpaths, signal violations, excessive noise, and deteriorating public spaces.

The Safe Streets AI Lab seeks to demonstrate how affordable digital technologies, artificial intelligence, citizen science, and community participation can generate actionable intelligence for improving pedestrian safety and urban livability.

The pilot will create a replicable framework that can subsequently be scaled to other parts of Bengaluru and other Indian cities.

2. Project Rationale

Current urban planning decisions are often based on fragmented complaints and limited data.

As a result:

  • Hazardous locations remain unidentified.
  • Pedestrian concerns remain under-reported.
  • Senior citizen mobility challenges remain invisible.
  • Urban environmental stressors are rarely measured together.
  • Municipal agencies lack prioritised intervention maps.

The project seeks to bridge this gap through evidence-based, technology-enabled urban diagnostics.

3. Project Objectives

Primary Objective

To create an AI-enabled, citizen-driven model for identifying, analysing, and mitigating pedestrian safety and environmental risks in Indiranagar.

Secondary Objectives

  1. Map pedestrian infrastructure deficiencies.
  2. Assess walkability for vulnerable populations.
  3. Document behavioural traffic risks.
  4. Generate actionable municipal recommendations.
  5. Create community ownership of public spaces.
  6. Develop a scalable methodology for replication.

4. Geographic Scope

The pilot shall cover selected roads, intersections and public spaces within Indiranagar including:

  • Residential streets
  • Commercial corridors
  • School zones
  • Metro station influence areas
  • Market areas
  • Public parks and community spaces

Approximate coverage:

  • 20–25 kilometres of streets
  • 2–3 square kilometres of urban area

5. Target Beneficiaries

Direct Beneficiaries

  • Pedestrians
  • Senior citizens
  • Women
  • School children
  • Persons with disabilities
  • Cyclists

Indirect Beneficiaries

  • Resident Welfare Associations
  • Schools and colleges
  • BBMP
  • Bengaluru Traffic Police
  • Urban planners
  • Civic technology organisations

6. Scope of Services

Component A – Baseline Planning & Stakeholder Engagement

Activities

  • Stakeholder mapping
  • Identification of survey zones
  • Engagement with RWAs
  • Meetings with local authorities
  • Development of field protocols

Deliverables

  • Inception Report
  • Stakeholder Register
  • Detailed Implementation Plan

Duration: Month 1

Component B – AI-Based Urban Hazard Mapping

Activities

Collection of geo-tagged data relating to:

  • Potholes
  • Broken pavements
  • Missing footpaths
  • Open drains
  • Obstructions
  • Encroachments
  • Poor lighting
  • Unsafe crossings
  • Traffic bottlenecks

Methodology

  • Smartphone-based field surveys
  • AI-assisted image classification
  • GIS mapping

Deliverables

  • Hazard Inventory Database
  • GIS Hazard Layer
  • Interactive Digital Maps

Duration: Months 1–3

Component C – Pedestrian Experience Assessment

Activities

Structured surveys among:

  • Residents
  • Students
  • Senior citizens
  • Domestic workers
  • Delivery personnel

Survey Sample

Minimum 500 respondents

Deliverables

  • Survey Dataset
  • User Perception Analysis Report

Duration: Months 2–3

Component D – Senior Citizen Walkability Assessment

Activities

Assessment of:

  • Footpath continuity
  • Surface quality
  • Crossing safety
  • Street lighting
  • Seating availability
  • Shade and thermal comfort

Deliverables

  • Senior Walkability Index
  • Priority Intervention Map

Duration: Months 2–4

Component E – Traffic Behaviour Analytics

Activities

Deployment of temporary monitoring cameras at selected locations.

Analysis of:

  • Wrong-side riding
  • Riding on footpaths
  • Signal violations
  • Dangerous turning movements

Deliverables

  • Traffic Behaviour Analytics Report
  • Violation Heat Maps

Duration: Months 2–4

Component F – Urban Environmental Exposure Mapping

Activities

Measurement and documentation of:

  • Noise exposure
  • Air pollution hotspots
  • Heat stress locations
  • Poorly lit streets

Deliverables

  • Environmental Exposure Atlas
  • Exposure Risk Maps

Duration: Months 3–4

Component G – Citizen Science Program

Activities

Partnership with schools and colleges.

Students participate in:

  • Street audits
  • Environmental monitoring
  • Hazard reporting

Deliverables

  • Youth Participation Report
  • Citizen Science Toolkit

Duration: Months 3–5

Component H – Public Dashboard & Knowledge Products

Activities

Creation of:

  • Online dashboard
  • Maps
  • Visual summaries

Deliverables

  • Public Dashboard
  • Communication Materials

Duration: Months 4–5

Component I – Policy Engagement & Advocacy

Activities

Presentation of findings to:

  • BBMP
  • Traffic Police
  • Ward Committees
  • Resident Associations

Deliverables

  • Safe Streets Report Card
  • Policy Recommendations Report
  • Stakeholder Workshop

Duration: Months 5–6

7. Project Deliverables

Technical Deliverables

  1. Inception Report
  2. Hazard Mapping Database
  3. GIS Street Safety Maps
  4. Pedestrian Survey Report
  5. Senior Citizen Walkability Index
  6. Traffic Behaviour Analytics Report
  7. Environmental Exposure Atlas
  8. Public Dashboard
  9. Safe Streets Report Card
  10. Final Project Report

Data Deliverables

  • 5,000+ geo-tagged observations
  • 500+ citizen surveys
  • 25 km walkability assessment
  • 20+ mapped risk hotspots
  • AI-generated violation datasets

8. Project Timeline

ActivityM1M2M3M4M5M6
Inception & Planning     
Hazard Mapping   
Citizen Surveys    
Walkability Audit   
Traffic Analytics   
Environmental Mapping    
Citizen Science   
Dashboard Development    
Reporting    
Dissemination     

9. Milestones

Milestone 1 – Project Mobilisation

End of Month 1

Outputs:

  • Inception Report approved
  • Stakeholder consultations completed

Payment: 15%

Milestone 2 – Field Data Collection

End of Month 3

Outputs:

  • Hazard mapping completed
  • Citizen surveys completed

Payment: 30%

Milestone 3 – Analytics & Index Development

End of Month 4

Outputs:

  • Walkability Index completed
  • Traffic analytics completed
  • Exposure mapping completed

Payment: 25%

Milestone 4 – Dashboard & Reporting

End of Month 5

Outputs:

  • Dashboard operational
  • Draft reports submitted

Payment: 15%

Milestone 5 – Final Dissemination

End of Month 6

Outputs:

  • Final report submitted
  • Stakeholder workshop conducted

Payment: 15%

10. Project Team

Core Team

Project Director

Overall oversight and stakeholder engagement

Project Manager

Day-to-day implementation

GIS & Data Specialist

Mapping and analytics

AI/Computer Vision Consultant

AI-based classification and analytics

Field Coordinator

Volunteer and survey management

Interns and Volunteers

Data collection and citizen engagement

11. Budget

Budget HeadAmount (₹)
Project Management1,50,000
Field Surveys & Data Collection1,20,000
GIS Mapping & Analytics80,000
AI & Computer Vision Analytics1,50,000
Citizen Science Program1,00,000
Dashboard Development75,000
Workshops & Consultations75,000
Communications & Design50,000
Travel & Logistics50,000
Contingency1,00,000
Total9,50,000

12. Expected Outcomes

Short-Term Outcomes

  • Evidence-based understanding of pedestrian risks.
  • Improved civic awareness.
  • Enhanced stakeholder collaboration.
  • Prioritised intervention plans.

Medium-Term Outcomes

  • Reduction in identified safety hazards.
  • Increased pedestrian-friendly infrastructure investments.
  • Improved urban governance decisions.

Long-Term Outcomes

  • Replicable Safe Streets Framework.
  • Expansion to other Bengaluru wards.
  • Adoption by municipalities across India.
  • Creation of a national Urban Environmental Exposure and Walkability Assessment model.

13. Sustainability & Scale-Up

The pilot is designed as a proof-of-concept.

Following successful implementation, the framework can be adapted for:

  • Other Bengaluru wards
  • Lucknow
  • Delhi NCR
  • Amritsar
  • Hoshiarpur
  • Smart Cities Mission locations

The project may also evolve into a recurring Urban Safety Index published annually by Nexus3P Foundation.

Can AI Help Save Pedestrian Lives in Our Cities?

The conversation around AI often focuses on saving lives through cancer detection, drug discovery, or autonomous vehicles. Yet one of the biggest opportunities may be much closer to home: helping ordinary pedestrians survive the daily chaos of urban streets.

Take Bengaluru. Even in relatively affluent areas like Indiranagar, a pedestrian faces a combination of hazards:

  • Potholes and broken pavements.
  • Encroached footpaths.
  • Delivery riders and couriers speeding on wrong sides.
  • Autorickshaws stopping unpredictably.
  • Poor street lighting.
  • Vehicles jumping signals.
  • Construction debris.
  • Stray animals.
  • Waterlogging during rains.

AI can help at four different levels.

1. AI as a “Pedestrian Guardian”

Imagine a smartphone app or smart glasses continuously scanning the environment.

It could:

  • Warn of approaching vehicles from behind.
  • Alert when a rider is coming the wrong way.
  • Detect open manholes, potholes, or broken footpaths.
  • Vibrate when a pedestrian is about to step into traffic.
  • Guide visually impaired or elderly users around obstacles.

Instead of reacting after an accident, AI would provide real-time risk alerts.

2. AI Mapping Every Hazard in the City

Every smartphone camera, dashcam, CCTV, and delivery vehicle can become a sensor.

AI can automatically identify:

The city would receive a continuously updated “pedestrian risk map” showing:

  • Potholes.
  • Missing footpaths.
  • Dangerous intersections.
  • Dark stretches.
  • Illegal parking.
  • Signal violations.
  • Streets with the highest accident probability.
  • Areas requiring urgent repairs.
  • Locations where streetlights are malfunctioning.

Instead of annual surveys, cities would have live intelligence.

3. AI-Powered Traffic Enforcement

Today’s traffic enforcement is largely manual

AI-enabled cameras can:

  • Detect wrong-side driving.
  • Identify red-light jumping.
  • Track repeated offenders.
  • Detect riding on footpaths.
  • Spot overspeeding near schools and markets.

Violations could be automatically documented and processed, increasing compliance without requiring thousands of traffic personnel.

4. AI for Urban Planning

The most powerful use of AI is not warning people about danger—it is removing the danger itself.

By analysing:

  • GPS traces,
  • pedestrian movement,
  • accident records,
  • CCTV feeds,
  • pollution levels,
  • lighting conditions,

AI can identify:

  • where zebra crossings are needed,
  • where footpaths should be widened,
  • where speed breakers are missing,
  • where signal timings are unsafe for elderly pedestrians.

The result is safer street design rather than simply safer behaviour.

5. AI for Senior Citizens

For older adults, who may have slower reflexes and reduced night vision, AI can be especially valuable.

A mobile assistant could:

  • Recommend the safest walking route rather than the shortest.
  • Avoid poorly lit roads.
  • Avoid roads with high traffic speeds.
  • Warn about uneven surfaces.
  • Share live location with family members during walks.

For a 70-year-old pedestrian, this could significantly reduce fall and collision risks.

The Bigger Question

The real challenge is not whether AI can identify potholes, rogue riders, or dangerous junctions. Technically, it already can.

The question is whether cities will use AI to prioritize pedestrians rather than vehicles.

For decades, urban technology has focused on moving more cars faster. The next generation of AI could instead focus on helping the most vulnerable road user—the person on foot.

In a city like Bengaluru, where a pedestrian often feels like an obstacle in the transport system rather than its primary beneficiary, that may be one of AI’s most life-saving applications.

The real question is not whether AI can do this.

The question is whether we are willing to use AI to prioritise pedestrians instead of vehicles.

Perhaps one of the most life-saving applications of AI won’t be in a laboratory or a hospital.

It may be on the footpath outside your home.

#AIForGood #UrbanInnovation #RoadSafety #SmartCities #PedestrianSafety #Bengaluru #PublicHealth #CitizenScience #Nexus3P #TechnologyForImpact