Smart Parking Finder with Prediction - ParkWise

Inspiration

Finding parking in busy areas is often more frustrating than the drive itself. We noticed how much time people waste circling streets or parking lots, especially in cities and crowded campuses. This not only causes stress but also contributes to traffic congestion and unnecessary fuel consumption. The idea behind ParkWise came from wanting to make everyday commuting smoother by solving a very common but overlooked problem.

What it does

ParkWise helps users find available parking spots in real time and predicts future availability using past data. Instead of guessing or driving around, users can see nearby parking options on a map and get recommendations on where they are most likely to find a spot. The system learns from historical trends, such as peak hours and location patterns, to improve its predictions over time.

How we built it

We built the frontend using React to create a responsive and interactive user interface. For the backend, we used Node.js to handle APIs and manage real-time data flow. MongoDB was used to store parking data, including timestamps and location information.

To display locations and availability visually, we integrated Mapbox for map rendering and geolocation features. For the prediction system, we implemented a basic time-series model that analyzes historical parking data and identifies patterns based on time and usage trends. This allows the system to estimate the likelihood of finding parking in specific areas at different times.

Challenges we ran into

One of the biggest challenges was working with real-time and historical data together. Ensuring that predictions stayed relevant while updating live availability required careful data handling. Another challenge was simulating realistic parking data since access to real datasets was limited.

We also faced difficulties integrating maps smoothly with dynamic data updates, especially when multiple users interact with the system simultaneously. Optimizing performance while keeping the UI responsive took extra effort.

Accomplishments that we're proud of

We’re proud of building a system that combines real-time functionality with predictive insights in a meaningful way. The ability to visualize parking availability on a map and provide intelligent suggestions makes the project feel practical and usable.

Another accomplishment is successfully implementing a working prediction model, even with limited data, and integrating it into a full-stack application.

What we learned

Through this project, we learned how to design and build a full-stack application that handles both real-time and historical data. We gained experience working with mapping APIs and learned how to structure geospatial data effectively.

We also explored the basics of time-series prediction and understood how data patterns can be used to solve real-world problems. Beyond technical skills, we improved our ability to break down a complex problem into manageable parts.

What's next for Smart Parking Finder with Prediction - ParkWise

In the future, we plan to improve the accuracy of predictions by using more advanced machine learning models and larger datasets. Integrating IoT sensors or public parking APIs could make the data more reliable and real-time.

We also want to add features like parking reservations, pricing insights, and user-based reporting. Expanding the platform to support smart city infrastructure and partnerships with parking providers could turn ParkWise into a scalable, real-world solution.