Inspiration
Parking is a big problem at UCR. Commuters often do not have spots to park in if they do not come in very early. We want to visualize the congestion to learn when commuters should show up to class. This lets commuters optimize their schedules by showing the best places to park and best times to park.
What it does
R'Park visualizes parking data provided by the TAPS API. It constantly learns and improves its predictions for parking availability through the week. This lets commuters better manage their time so they can spend more time on things that matter rather than on finding a spot.
How we built it
We built the front end with HTML. The website uses the HighCharts library to plot the data. It receives the data through API calls to the TAPS parking API. The back end is built off JavaScript, Python and Flask. The JavaScript handles the button interaction and generating the plot from the data. The Python handles data processing, API calls and calculations for our data. Flask is used to host a local web server to handle GET requests from our JavaScript to provide data to generate the plots.
Challenges we ran into
A lot of edge cases for optimal parking spot calculations. Setting up Flask and learning about bypassing CORS protection for local debugging. Working with HighCharts to plot the data we like.
Accomplishments that we're proud of
Getting GET requests to work locally. Successfully synthesizing both front end and back end to create a presentable project within 12 hours without prior experience.
What we learned
Flask, HighCharts, GET requests, web APIs, using Python backend and HTML/CSS/JavaScript frontend.
What's next for R'Park
Hopefully setting up some Machine Learning like Logistic Regression to figure out correlations between different features and the parking information, e.g. correlation for weather and parking availability, midterm/finals schedules, certain UCR events, seasons of the year, certain quarters, etc.
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