SwachhFlow

An AI-powered waste collection optimization platform that combines intelligent route planning, real-time geo verified monitoring, and scalable hybrid infrastructure in urban sanitation management

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Inspiration

Urban waste management in large cities faces three recurring problems: inefficient routing, unbalanced truck loads, and lack of real-time verification. Garbage Vulnerable Points (GVPs) often remain uncleared due to poor visibility and accountability mechanisms.

We were inspired to build SwachhFlow as a data-driven sanitation intelligence platform that combines optimization algorithms, AI-based image verification, and real-time geospatial tracking to improve operational efficiency and transparency in municipal waste management systems.

What it does

SwachhFlow is an end-to-end intelligent waste collection management system that:

  • Optimizes truck routes using constraint-based routing
  • Balances waste load per vehicle
  • Enables real-time driver navigation with traffic data
  • Verifies garbage clearance using AI image recognition
  • Updates city-wide dashboard in real time
  • Scales dynamically using hybrid cloud infrastructure

The platform ensures operational efficiency, fuel savings, and transparency for municipal authorities.

How we built it

The system architecture consists of:

  • Route Optimization Engine using Google OR-Tools (Vehicle Routing Problem solver)
  • Driver App with Google Maps API for real-time navigation
  • Geo-tagged image capture for proof submission
  • AI verification using Gemini 2.0 Flash Vision model
  • MinIO for secure proof storage
  • OpenStreetMap + Leaflet for administrative and civilian dashboards
  • PostgreSQL for structured data management
  • Queue-based processing for handling simultaneous driver updates
  • Hybrid Infrastructure (Open-source primary layer + Azure fallback layer for peak traffic)

We designed the system to remain operational even during high traffic loads by dynamically shifting workloads to cloud infrastructure and reverting once stabilized.

Challenges we ran into

  • Designing balanced load distribution across trucks
  • Managing real-time concurrent updates from multiple drivers
  • Handling low-network environments in driver apps
  • Integrating AI-based image verification with geo-tag validation
  • Ensuring scalability without excessive cloud dependency

Each challenge was addressed using optimization modeling, caching mechanisms, queue systems, and hybrid cloud architecture.

Accomplishments that we're proud of

SwachhFlow contributes to:

  • Reduced fuel consumption
  • Lower carbon emissions
  • Improved operational transparency
  • Faster GVP clearance
  • Sustainable urban sanitation management

It aligns strongly with smart city initiatives and sustainable development goals.

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