Rapid.Tech

Imagine being able to instantly plan a high speed rail route between two locations with instant insight into projections.

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Inspiration

We were originally inspired by the research using slime molds to create optimal paths through cities. After some more ideating we expanded our idea to simulating what it would be like to add high speed trains between any city in the US.

What it does

Rapid.Tech is a simulation tool with the. goal of allowing anyone to learn more about high speed rail in an interactive way. Rapid.Tech Is built to allow users to input two different locations and get back the cost of the train being implemented, an optimal path for it to take between the two locations, and the time that it would take for trip between the two locations.

How we built it

We used Googles API and WorldPop's Census data to figure out pathing, population data, and location information. As well as these we used apps like Replit to help get us started, allow for simultaneous programming, and help with coding problems.

Challenges we ran into

We had many difficulties that we had to learn from throughout our 24 hours. At the start we barely understood what an API was, and had to get some help in understand how to use them. We also had to completely change our programming location after trying to upload a cache full of lots of census data caused replit to stop working. This eventually lead to most of our hopes for the project becoming much harder to reach than initially expected.

Accomplishments that we're proud of

We are proud of our quick learning of topics such as APIs, and our ability to implement and use the different API tools to allow for Rapid.Tech to do much more than we originally expected. We are also proud to have learned more how to use AI not as a tool to answer questions for us, but allow us to improve and create things that would have taken much more time in the past.

What's next for Rapid.Tech

We are hoping to fix our project and get it into a more working state. As well as this we want to host it on an actual website that will allow for other users to access Rapid.Tech. In the future we would like to expand Rapid.Tech to allow us to teach more people about high speed rail.

This project was made by Isabelle Hageman, Evan O'Leary, and Reece Whitaker for the March 2025 Revolution UC. The project was initially worked on over a span of 24 consecutive hours with mentorship from upperclassmen and mentors.

For this project, Google Dynamic Mapping API was implemented. The API was utilized using free and limited usage. The project also utilized machine learning to quicken the coding process (largely due to the 24 hour timer) and fill in gaps of our current knowledge.

We'd like to thank KineticVision and Major League Hacking for their informational sessions on APIs and Machine Learning Usage during the Hackathon, as well as Revolution UC and all their other sponsors for hosting the event.

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