Pathr

Inspiration

We were inspired to build Pathr because overcrowded transit is something we experience every day as students. With thousands of people heading to the same place at the same time, most cars arrive with empty seats while buses are packed to capacity. Seeing a gap that public infrastructure and ride-hailing services cannot effectively address, we realized this problem could be solved by better utilizing trips that already exist without the need to build new infrastructure.

What it does

Pathr is a smart carpooling app that matches drivers and riders who are already traveling on similar routes at similar times. Users can enter their destination and time of travel, and Pathr will match them along the most optimal shared path while minimizing detours. Whether riders are on the same path to campus or heading towards Whistler, Pathr is there to get riders there fast and affordably. Pathr also has a billing system to compensate drivers for the riders they transport.

How we built it

We built Pathr with a strong focus on user experience. Our team used Lovable extensively to generate and iterate on the front end, allowing us to rapidly experiment with layouts and optimize the overall user experience. On the backend, we utilized JS, Gemini API, and Maps API to have the display routes display.

Challenges we ran into

One of the main challenges was integrating the Google Maps API and ensuring routing data worked reliably within our application. Additionally, implementing the pathing algorithm was technically demanding, as we needed to optimize routes and determine which drivers were the best matches based on direction of travel while minimizing detours. Balancing accuracy, efficiency, and simplicity within a limited time frame was a significant challenge.

Accomplishments that we're proud of

We’re proud that we were able to work on a problem we genuinely care about and experience firsthand as students. Building Pathr allowed us to focus on features we personally found interesting and impactful, which made the project both meaningful and motivating. Seeing an idea that directly affects our daily lives come together in such a short time was a rewarding experience for our team.

What we learned

We learned that pathfinding and route optimization are much more complex than they initially appear, especially when working with many users and possible routes. Implementing concepts such as Dijkstra’s algorithm highlighted the importance of optimization and pruning certain nodes to efficiently determine the best paths. We also learned how valuable tools like Lovable are for rapid iteration, allowing us to focus more time on solving core technical problems rather than building front-end components from scratch.

What's next for Pathr

We want to add more trust and safety features like user verification and ratings. We also want to refine the matching algorithm we have created after having real user data. Eventually we want to scale Pathr to a larger audience

Built With

• google
• node.js
• react
• supabase
• typescript