Inspiration
we got inspired by the frustration experienced from long and erratic winter commutes. Commutes that unexpectedly take longer than usual have a very real financial consequence, which we usually ignore because gas is filled up at the start/end of the week
What it does
Estimates the distance, time and cost of route options prior to departing on your commute. The user can then select the most financially responsible choice
How I built it
Using the Google Maps Routes and Directions API we made html requests to receive detailed data on multiple possible routes from point A to B. Then using published car efficiency statistics we stored in a database using Google Cloud Services we pulled that data into our Python program to calculate the estimated cost of each route and give recommendations to the user.
Challenges I ran into
There were issues syncing the database with the python script and hosting the script online.
Accomplishments that I'm proud of
Working together in a team with diverse skillsvand using our experience with software, UI and product design to find a real problem with a feasible solution
What I learned
How to collaborate and manage our time effectively to complete deliverables within strict time constraints
What's next for Swerve
Operationalize our prototype and releasing our app on the app store. Also improve the users experience by gathering post-trip data and utilizing machines learning with open source libraries such as TensorFlow to present predictive stats
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