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Interactive map via mapbox
Inspiration
Introducing Go-Go Eco! We in Light Mode Bad have developed sustainable solution to commercial commuting. Go-Go Eco! is a web based carpool scheduler aimed at providing companies a way to keep their employees connected, we help their employees find a safe and easy ride to work while reducing their carbon footprint at the same time.
What it does
Our project is designed to facilitate access from any company to our website and SQL database, with the end goal of licensing one project to many clients. Essentially, Go-Go Eco! is an opt-in program, where our end users can choose to be a part of carpooling and even volunteer to drive.
How we built it
Using mysql, we developed a database that would transmit data-points to a greedy algorithm we designed that would allocate the best groups for the most efficiency routes for carpooling. This would then be projected onto a website made with the Django API and mapbox map builder.
Challenges we ran into
We struggled meshing many implementations together as many of us were unfamiliar with each other's fields.
Accomplishments that we're proud of
For Privacy reasons, there is an authentication system for employees which is stored in our database. Once logged into the system, employees have access to view the current driving schedule, including the driver’s designated for that day and the pick-up locations. All of the groups are color coordinated and the driver node is increased in size for legibility. There is also a drop down box showing all groups, the members of the group with their respective pickup order and the driver for the current day. Drivers rarely have to veer far from home. The pickup schedule is generated in a way to maximize occupancy, and spend the least amount of time commuting to the company headquarters, shown in green. All of the features are conveniently displayed on a three dimensional, interactive, easy on the eyes map. Go-Go Eco! strives to reduce the carbon footprint of commuting heavy companies, while maintaining efficiency and employee relations all at an extremely low cost of entry.
What we learned
A ton of us took on tasks outside our comfort zones and knowledgeable fields. This enabled us to capitalize on learning new assets with the assistance of peers.
What's next for Go-go Eco!
We hope to optimize our datasets and algorithm so that it runs optimally. We would also like to include more user-account control allowing for users to have a greater influence on their account on the app.
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