Inspiration
We noticed that the Food4Kids team spent a large amount of time requiring staff to manually type in addresses into Google Maps, then manually arrange the order until it displayed the most efficient route for volunteer drivers to take. We were reminded of the travelling salesman problem and decided to take up the challenge of solving this to save the organization time and manpower.
What it does
Our code takes in coordinates of latitude and longitude of an address from google sheets, then converts it into locations using the OpenRouteSource API, from which python calculates the minimum distance to travel to each location. This is a code that optimizes the routes for drivers. It then re-inputs the coordinates into OpenRouteSource, which automatically calculates the specific route for a driver (ex: turn left at King St, then turn right...) and creates a nice visual display similar to Google Maps.
How we built it
Python
Challenges we ran into
Finding the most optimizing distance and learning to use API's.
Accomplishments that we're proud of
Connecting Maps with python, html, and google sheets.
What we learned
We learned how to worth with APIs from scratch, modules such as requests, geopy.
What's next for Team #25
On the back end, we would like to add a feature that allows us to take in postal codes (like it did in main.py) into the spreadsheets instead of coordinates. We are aware that Food4Kids currently uses Google Maps for their drivers, and would love to complete this project using the Google Maps API so that drivers and staff both have an easy transition to navigate the new software. The use of OpenRouteSource is free and most easily accessible during our hackathon to show a proof of concept, while the Google Maps API requires a fee.
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