URide

URide Features

Inspiration

Becoming independent isn’t easy. University life throws a lot at you; figuring out your schedule, handling finances, balancing social life, and making sure you actually show up to things on time. The thing is, no one likes being forced into networking or being told, “You should go make friends.” That’s awkward and unnatural.

We wanted to build something that helps people become more independent without making it feel like a chore. That’s where URide comes in. It’s not just about getting a ride, it’s about making life easier, saving money, and, without even realizing it, helping students build habits of responsibility. Riders commit to scheduled rides due to their low cost, holding themselves accountable to their plans. Drivers not only make money but also maintain consistency by showing up when others are relying on them. And with the events feature, students can casually find themselves in gym meetups, study groups, or weekend soccer games without the pressure of “forced socializing.”

This is how independence should work. Not through pressure, but through organic opportunities that make life better while helping students develop routines, financial awareness, and a sense of accountability. URide was designed to be that low-key life hack; something that saves you money, helps you be more responsible, and gets you where you need to go without the hassle of unreliable transit or expensive rides..

What it does

URide is a university-exclusive ridesharing platform that connects student drivers with riders who need a lift. It helps students:

Find affordable rides to and from campus without overpaying for Uber. Earn money as a driver by filling up empty seats. Build consistency and responsibility through scheduled commitments. Join and organize community events, from study sessions to gym meetups, to make campus life more connected. URide also focuses on safety and trust. Every user is verified with a university email, and we built in in-app messaging and ratings to keep things secure. Plus, we integrated AI-driven features that provide financial insights and ride optimization, making the platform more than just a way to get from point A to point B.

How we built it

This was our first time working with Next.js, and we quickly realized how much more efficient it is compared to a traditional React setup. We used TypeScript for extra security and built the backend with both TypeScript and FastAPI (Python) to handle ride-matching logic and API requests. Authentication was done through Firebase, which worked fine locally… until we tried deploying it (more on that later).

For location and route optimization, we used Google Maps API, making it easier for drivers and riders to find the best routes. One of the coolest parts was building an AI chatbot using Naive Bayes and Pandas pipelines, which helps students with ride info and financial tips. Hosting and deployment started locally, but due to some unexpected hardware failures, we had to pivot and use Vercel to preview and deploy our platform.

This project pushed us to learn API integrations, customize request handling, train AI models, and troubleshoot authentication problems. It was a crash course in building a full-stack app while constantly adapting to unexpected challenges.

Challenges we ran into

This project had its fair share of roadblocks. The biggest issue was deployment. Everything ran smoothly on our local machines, but when we moved to Vercel, Firebase authentication completely stopped working. It turned out that the API had permission restrictions that weren’t an issue locally but broke everything once hosted. That set us back quite a bit.

On top of that, our original plan was to host everything locally, but then our PC (the "potato computer") completely gave up on us. This forced us to find a quick solution, which ended up being Vercel’s preview feature.

Another big challenge was ride-matching logic. Getting it to efficiently match drivers and riders based on route and availability took way longer than expected, but when it finally worked, it was a huge relief. Training the AI chatbot was also tough because it required a lot of computing power, which slowed down development at times.

Accomplishments that we're proud of

Despite the challenges, we’re really proud of what we pulled off. Successfully implementing the ride-matching system was a huge moment for us, especially after struggling with it for so long. Seeing our AI chatbot actually work and provide insights was another big win.

On a technical level, we learned how to use Next.js, integrate APIs, set up authentication, customize backend requests, and train AI models. These are all skills that we’ll take with us beyond this project.

What we learned

One of the biggest lessons we learned was that deployment needs to be tested early. If we had checked how Firebase worked on Vercel sooner, we could have saved a lot of time troubleshooting it at the last minute. Another takeaway was that hardware limitations can seriously slow you down. Training an AI model or running a server requires power, and without a solid machine, things can crash unexpectedly.

What's next for URide

If we had more time, we would focus on:

Integrating a full payment system with Stripe, PayPal, and Apple Pay Fixing authentication issues so everything works smoothly on any host, and integrating UCalgary Authentication Hosting on a dedicated server for better performance Improving the AI chatbot to provide more helpful and personalized insights

Note: Demo Link is kinda messed up due to deployment issues, but final pres. copy is functional