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Inspiration

EarthBeats was born from a desire to make travel more meaningful, social, and sustainable. We saw how small businesses are often overlooked and how hard it is to connect with like-minded travelers on the road. At the same time, growing concerns over carbon emissions inspired us to create a solution that optimizes routes for lower emissions and rewards eco-friendly choices. By integrating social connectivity, AI-driven music personalization, and sustainability incentives, EarthBeats makes every journey engaging, rewarding, and greener.

What it does

EarthBeats is an AI-powered road trip planner that optimizes routes to minimize carbon emissions. Users input their start and end points, vehicle type, and travel preferences. The app suggests the most carbon-efficient route, considering factors like elevation changes and road smoothness. Users can earn "Community Currency" for choosing eco-friendly options, such as staying at sustainable hotels near by from the path to the destination or charging at EV-friendly stations. These rewards can be redeemed for discounts at participating eco-friendly businesses. Additionally, the app features an AI-powered social connection tool to match users with like-minded travelers and an adaptive music recommendation system based on mood detection.

How we built it

We developed EarthBeats using a combination of:

AI & Machine Learning: For route optimization and real-time carbon footprint calculations.
Hume AI: To analyze voice tone and recommend mood-based music.
Google Maps API: To assess road elevation, smoothness, and fuel efficiency.
Spotify API: To Log In to users account and automatically play music based on user's mood.
Gamification: Integrated "Community Currency" rewards system with participating businesses.
Social Matching Algorithm: AI-driven personality and interest-based trip-matching.

Challenges we ran into

Accurately calculating carbon emissions based on various factors, such as road incline and traffic patterns.
Developing a AI model for music recommendations that adapts to user emotions.
Ensuring the social matching feature creates meaningful connections for users.
Some new tech stack we're completely unfamiliar with like Flask - a Python backend framework, Next.js- a frontend framework based on React, and it also has backend capabilities, a lot of unfamiliar APIs like GoogleMaps, Spotify, Hume, and finally Tailwind CSS - It's a pain writing class names :(.

Accomplishments that we're proud of

Successfully integrating a reward system that incentivizes users to choose eco-friendly travel options.
Implementing AI route optimization for reducing carbon footprints.
Developing an emotional AI feature that personalizes music playlists based on user mood.
Creating a functional prototype that demonstrates the feasibility of the idea.

What we learned

How to integrate AI and geospatial data for sustainability-focused applications.
The importance of partnerships with eco-friendly businesses and organizations to make incentives attractive.
The potential for AI-driven emotional analysis in enhancing user experience.
Challenges and opportunities in gamifying sustainable behavior to drive adoption.
Overall, we gained a lot of valuable experience utilizing new technologies and integrating different tools into our project that we have not done before!

What's next for EarthBeats

Expanding the reward system by partnering with more eco-friendly businesses, EV stations, and sustainable hotels.
Refining AI-based carbon emission calculations with real-world data.
Enhancing the social matching feature to improve safety and compatibility in travel pairings.
Improving the AI-powered music recommendation system to provide an even more personalized experience using previous playlists, songs, and artists.
Launching a beta version and gathering user feedback to refine features before a full-scale release.
Launching a mobile application for user convenience.

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