EcoRoute AI

Smart navigation assistant that finds the most eco-friendly routes to cut CO₂ emissions and fuel costs.

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Inspiration

Climate change is the defining challenge of our generation. While using navigation apps daily, I noticed they prioritize speed above all else. A route that saves just 2 minutes might burn 20% more fuel due to highway speeds or aggressive traffic patterns. This inspired me to build a tool that empowers drivers to answer a simple question: "What if I could choose the 'Greenest' route as easily as the fastest one?"

What it does

EcoRoute AI is a smart navigation assistant that calculates and visualizes the carbon footprint of route options.

  • Eco-Scoring: It compares routes by distance, duration, and traffic patterns to estimate CO₂ emissions.
  • Vehicle Selection: Users can choose their car type (Sedan, SUV, Hybrid, or EV) to get personalized emission stats.
  • Smart Comparison: It highlights the "Eco Pick" 🌱, showing exactly how much CO₂ you save compared to standard routes.
  • Seamless Navigation: Provides 3 distinct geographic paths on an interactive map and lets users start navigation instantly via Google Maps.

How we built it

I built EcoRoute AI using a modern, scalable tech stack:

  • Frontend: Next.js 14 (App Router) for a fast, responsive interface.
  • Styling: Tailwind CSS v4 for a clean, premium design.
  • Mapping: Leaflet.js and OpenStreetMap for rendering interactive maps.
  • Routing Engine: OpenRouteService API to fetch real-world driving paths and geometry.
  • Geocoding: A custom server-side API wrapping Nominatim to convert city names to coordinates.
  • Algorithm: I developed a custom emission estimation logic in TypeScript that accounts for vehicle efficiency curves and traffic penalties based on EEA standards.

Challenges we ran into

  • API Limitations: The free tier of routing APIs often struggles to find "alternative routes" for long distances. I solved this by implementing a Multi-Path Strategy, parallel-fetching 3 distinct routes by forcing "via-points" to guarantee geographic diversity on the map.
  • Data Accuracy: Calculating emissions isn't linear. I had to research specific emission factors for EVs vs. SUVs to ensure the comparison was scientifically grounded and not just a visual gimmick.

Accomplishments that we're proud of

  • Creating a Robust Fallback System that ensures the app never crashes during a demo, even in remote locations like the deserts of Al Anbar.
  • Implementing the Vehicle Selector which dynamically updates calculations instantly—seeing emissions drop to near-zero for EVs is a powerful user moment.
  • Delivering a polished, production-ready UI with smooth animations and a professional look suitable for a hackathon winner.

What we learned

I learned a lot about Geospatial Engineering, specifically how to decode encoded polylines, handle map bounds automatically, and the importance of UX in encouraging sustainable behavior. I also deepened my knowledge of Next.js server-side API handling.

What's next for EcoRoute AI

  • Live Traffic Integration: Using real-time traffic data to dynamically adjust the "congestion penalty" score.
  • Gamification: Adding user profiles to track "Total CO₂ Saved" over time and awarding badges for green driving habits.
  • Public Transit: expanding the options to include buses and trains as even greener alternatives.

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