Inspiration

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What it does

The Car-kill-ator allows the user to easily input their commute to and from two separate locations. Our website then imports the current day, time, and speed-limit to predict the likelihood that a driver will crash on their designated path. This overall percentage is composed of several percentages that describe the crash chances for smaller road segments. These segments are displayed on a map, and list theoretical distance traveled and sped on the roads.

How we built it

  • found appropriate data sets
  • created a model to determine the risk factor of driving on road segment using machine learning
  • designed a webpage that inputs a google maps trip to predict the crash probability of a driver
  • use multiple APIs from Google and other sites to obtain road information necessary for the calculations
  • use server requests calling as well as python function calling to obtain necessary data

Challenges we ran into

  • how to choose what data to use
  • Information within databases was inconsistent
  • time-constraint of the competition lead to abandonment of certain parameters (weather most prominently)
  • limited prior knowledge with many of the needed language
  • consistent color gradient to probability depiction

What we learned

  • Hackathon is pretty cool
  • It is important to make a detailed and structured plan from the beginning
  • Have a basic understanding of multiple programming languages is necessary
  • Asking for mentorship could have been beneficial
  • Our understanding of programming languages is not enough

What's next for Car Crash Probability Calculator

  • Use of more parameters to more accurately predict the crash probability
  • Possibly include more data set to allow more calculation
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