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Eco Drop
AI-Powered E-Waste Collection Scheduler
A full-stack, AI-powered application that manages e-waste collection scheduling — users book pickups while administrators optimise routes, demand and carbon impact with machine learning.
Overview
Eco Drop pairs a modern React frontend with a FastAPI AI backend. It forecasts pickup demand, clusters collection hotspots, optimises routes and quantifies the carbon saved by each optimisation.
Problem Statement
E-waste collection operators schedule pickups reactively, driving inefficient routes with half-empty vehicles — wasting fuel, time and emissions budget.
Architecture
- ›Vite + React + TypeScript client with shadcn-ui and Tailwind CSS
- ›Leaflet map layer and Recharts analytics dashboard
- ›Supabase for auth and persistence
- ›FastAPI AI service for prediction, clustering and routing
- ›Ollama + LLaMA3 for strategy recommendations
- ›OpenCage geocoding for address resolution
Workflow
- 01User schedules a pickup with an address, resolved through geocoding
- 02Demand model forecasts volume per zone
- 03K-Means clusters requests into collection hotspots
- 04Nearest-Neighbour routing produces an optimised vehicle route
- 05Carbon calculator scores emissions saved and the LLM drafts a strategy note
Features
- ✦Demand prediction with a custom ML model
- ✦Hotspot clustering via K-Means pickup detection
- ✦Route optimisation using a Nearest Neighbour algorithm
- ✦Carbon optimisation with an emission calculator
- ✦LLM strategy recommendations powered by Ollama + LLaMA3
- ✦Geocoding through the OpenCage API
Technology Stack
Vite + ReactTypeScriptTailwind CSSshadcn-uiLeafletRechartsSupabaseFastAPIK-MeansOllama + LLaMA3
Challenges & Solutions
Raw pickup addresses were unusable for routing.
SOLUTION · Added an OpenCage geocoding pipeline with caching and fallback matching.
Naive routing scaled badly as requests grew.
SOLUTION · Clustered requests with K-Means first, then ran Nearest Neighbour inside each cluster.
Operators needed the 'why', not just the route.
SOLUTION · LLaMA3 generates a short strategy summary alongside every optimised plan.
Results
- →Shorter routes per collection run through cluster-then-route optimisation
- →Quantified carbon savings surfaced directly in the admin dashboard
- →End-to-end flow from citizen booking to optimised operator route
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