# Build EcoRoute: A smart City Waste and Circular Economy Engine.

> Source: <https://dev.to/ashomondi/build-ecoroute-a-smart-city-waste-and-circular-economy-engine-4aff>
> Published: 2026-08-25 12:01:25+00:00

**Build a full-stack, production-grade project during a hackathon**

When our team set out to compete in the **Smart Cities and Route Optimization** track, we didn't want to build just another static mapping app that plots static points. Municipal waste managment in fast-growing urban centers faces major operational challenges:

- Inefficient truck that burn unnecessary fuel
- Zero real-time visibility into bin fill levels
- Poor integration with post-collection recycling economies.

we decided to build **EcoRoute** an end-to-end platform that:

- Combines real-time waste monitoring
- AI fill prediction
- Go-powered route optimization
- Crowd-sourced citizen reporting
- A circular marketplace that traces recycled waste directly to retail products.
**The Problem**
Urban waste collection in many developing cities suffers from three core issues:
-
**Static, Inefficient Routes:** Refuse trucks follow rigid, predetermined routes regardless of whether bins are empty or overflowing. This waste driver time , spikes municipal fuel expenses and increases carbon emissions.
2.**Reactive Collection and Poor Citizen Feedback:** Bins overflow before cities notice. Citizens have no direct channel to report illegal dumping or missed pickups, leading to dalayed escalations.
3.**Disconnected Recycled Lifecycles:** Waste collection is treated as an endpoint rather than the start of a supply chain. Recyclers and consumers lack transparency regarding where recycled raw materials come from.
**The solution**
-
**Waste monitoring and AI predictions**
**Live Interactive Map:** Color-coded status markers show full bin locations in real-time.
**AI Fill Forecasting:** A lightweight microservice predicts tomorrows fill level per point so logistics teams can plan ahead proactively.
*EcoMarket Circular Economy:** A closed-loop marketplace where products made from recovered waste are traced back to the specific waste collection batch they originated from.

**Key Features**

**Waste Monitoring & AI Predictions**

**Live Interactive Map:** Color-coded status markers show full bin locations in real time.

-
**AI Fill Forecasting:** A lightweight microservice predicts tomorrow's fill level per point so logistics teams can plan ahead proactively.
-
**Resilient Architecture:** If the AI service drops, the backend gracefully degrades to historical baselines to prevent dashboard crashes (`500`

errors).

**Route Optimization & Driver Workflows**

-
**Nearest-Neighbor + 2-Opt Reordering:** Instantly re-sequences stops to cut total transit distance.
-
**Savings Delta:** Computes and saves before/after comparative metrics for distance (km), fuel (L), and time saved per collection run.
-
**Driver Execution UI:** Step-by-step mobile driver interface to log collections, mark completion, or flag failed pickups.

- *
*Community Reporting
**

- Citizens upload geotagged photos of illegal dumping or overflowing bins.
- Submitting a report automatically escalates the linked waste point to
`critical`

status, forcing immediate route re-calculation.

**EcoMarket & Provenance Ledger**

- Waste collected is weighed (kg) and broken down by category (PET plastics, metals, paper).
- Manufacturers convert these
**Recycling Material Batches** into commercial products sold on **EcoMarket**.
- Buyers click
**Trace Material Origin** to view the full lineage: `Waste Point`

→ `Collection Run`

→ `Recycling Batch`

→ `Finished Product`

, with simulated M-Pesa automated seller payouts.

**How It Was Built & Tech Stack**

We organized the project into a clean monorepo structure to accelerate parallel development:

**The Backend (Go 1.26 + PostgreSQL 16)**

The API core was built in** Go 1.26** using standard library routing (net/http) and pgx/v5 for PostgreSQL connectivity.

** Algorithms**: We implemented a local Nearest-Neighbor heuristic combined with 2-Opt edge swapping directly in Go to reorder stop arrays deterministically.

**The Frontend (React 19 + Leaflet)**

Built using **React 19, Vite, react-router 7**, and **Leaflet / react-leaflet.**

- Dynamic map layer rendering color-coded pins based on real-time fill percentages.
- Separate, role-tailored interface views for
**Admins, Drivers, Community Members, and Marketplace Seller
s.**

**The AI Prediction Service (Python)**

A lightweight Python service exposing a simple HTTP contract (POST /predict) that calculates expected fill trajectories based on recent historical fill velocity.

Building a full-stack monorepo with AI prediction, real-time map rendering, custom algorithms, and circular economic ledgers in 72 hours required deep trust and domain ownership:

Any comment and suggestion on the platform