OSU RideConnect

Inspiration

Getting around OSU can be unpredictable, especially with CABS buses being overcrowded, delayed, or running off their posted schedule. We noticed how often students miss buses, show up too early, or spend unnecessary time waiting. That frustration inspired us to create a tool that gives OSU students real, accurate, and reliable bus information based on what’s actually happening on campus—not just what’s scheduled.

What it does

-OSU RideConnect predicts real bus arrival times using a mix of real-time GPS data and student-generated crowd information. The app lets students: -See accurate ETA predictions instead of relying on static schedule times

-Receive notifications when their bus is arriving

-View crowd levels so they know whether the next bus is packed or manageable

-Track their most-used routes and stops

-Reduce wait time and increase day-to-day efficiency

How we built it

We built OSU RideConnect using the builder with mostly Livable AI, and we did not write any manual code. Every feature was created by describing what we wanted through prompts, such as real time bus ETAs, crowd reporting, favorite stops, and notifications. Livable AI generated the screens, logic, and connections based on those prompts. We refined the app by adjusting layouts, linking components visually, and updating prompts as needed. The entire project was built through guided instructions inside the builder instead of traditional programming.

Challenges we ran into

One of the biggest challenges was dealing with inconsistent bus GPS behavior—data points jump or lag. Smoothing that into predictable patterns took experimentation. We also had to make crowd reporting simple enough that students would actually use it. Getting clean UI navigation and syncing real-time notifications reliably across devices also took multiple iterations.

Accomplishments that we're proud of

-We’re proud that RideConnect actually solves a real OSU problem in a way students immediately understand. Our prediction model became significantly more accurate over testing, and the UI feels fast and natural. We also built a fully functional cross-platform app within a short timeframe and created a system that can scale as more students use it.

What we learned

-We learned how messy real-time transit data can be and how important good filtering and prediction techniques are. We also learned the value of user-centered design—students want speed, clarity, and simplicity, not complicated dashboards. On the development side, we solidified skills in API design, mobile development, and real-time data syncing.

What's next for OSU RideConnect

-Next, we want to integrate a full heatmap of bus routes, add ML-driven peak-time predictions, and include a feature to notify users automatically when a bus is too full to board. We’d also like to expand beyond buses by adding scooter, carpool, and parking lot availability for a complete travel experience on campus. Our long-term goal is to make RideConnect the central transportation companion for every OSU student.

Built With

• lovable