Inspiration

The issue is that in Switzerland alone everyday are used around 8 million disposable cups, enough to cover the distance between Basel and Vienna: 820km. Worldwide the usage elevates to 58 billion per year, which is equivalent to the woodcutting of 32 millions of trees, the consumption of 100 billion litres of water and the emission at the atmosphere of 6.4 million tones of CO2, and only every 400th of a cup is recycled.

What it does

It is a decentralise, digital deposit system for reusable coffee cups that will work at any train station, any time. We want coffee drinkers to be able to return the used cups at drop-off points that automatically and digitally credit back the deposit to the users.

How we built it

We can achieve this by identifying the cups using QR-Codes. We used the SBB API to obtain the name and other important information of the stations and specifically the database Passagierfrequenz that SBB offered us. From that we created a database about an approximation of the possible amount of cups that the passengers would use during their commute. We created and UI for different users to be informed about their respective interests all of the time.

Challenges we ran into

We experienced some difficulties with the creation and modification of the database with such a great number of instances.

Accomplishments that we're proud of

The identification and definition of the different states that the cup could be in, the mechanism with which we make the identification system, an intuitive UI for the user to register, have an historical of all the cups that she/he has returned and the credit obtain from those. Also representing with a map the current state of the database.

What we learned

How to make QR images, how to read them and process them. How to maintain the consistency of a big database. Create an intuitive and simplistic UI.

What's next for CupFlow

Better algorithms for the distribution of the cups, being able to predict where the people will buy they cup of coffee, and with more real data we could calculate more precisely these movements.

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