Inspiration

After living in the Boston/Cambridge area for some time, we began to notice the high rate of bicycling accidents that occurred day to day. In order to provide cyclists with a more informed sense of traffic safety, our team built CYCL so that users can monitor areas of high bicycling traffic accidents to become more aware and alert of their surroundings during their commutes.

What it does

CYCL uses pre-existing data about bicycle related traffic accidents to generate a predictive statistical model that we loaded onto a Raspberry Pi, and used a GPS to determine the user's location and determine the safety levels of biking in that area.

How we built it

First we used MATLAB and Python to create a statistical model of the data set. Then we used Node.js to interface with the GPS and various hardware models we created.

Challenges we ran into

Various challenges we ran into included difficulties involved in creating the outer casing for the device and mounting it on the handlebars in a sleek yet practical way. It took multiple testing trials to fully calibrate the device so that it shows the safety score during a real bike ride.

Accomplishments that we're proud of

We're proud of creating a hardware device even though majority of the team's background had never worked with the circuitry and learned much of it throughout the course of this hackathon.

What we learned

We learned how to handle problems in fast and efficient ways as well getting practice in Python, MATLAB, and Node.js.

What's next for CYCL

There is a lot more functionality that can be integrated into CYCL, as currently it only outputs a safety score when the cyclist is on the road. In the future with further testing, CYCL has a lot of potential to change it's user's behaviors.

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