SyncUP Park

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  Testing our machine vision.

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Problem

T-Mobile employees spend up to 30 minutes of their work day trying to find parking wasting both company time, and money.

Inspiration

We believe that making unclear decisions leads to frustration, and unorganized patterns. The solution was to create an incredibly intelligent ecosystem that decides for everyone in an organized manner.

What it does

• Saves T-Mobile $761,250 per month on costs due to employee time spent on finding parking.

• Intelligently selects parking spots for the employees based on their first meeting location in their calendar, if they're disabled or not, current parking situation, past habits, and the habits of other employees.

• Opt-in service of sending a message early in the morning of where to park and when you should leave your home.

How We built it

We started with the user experience first and worked our way backwards to the technology that could support our original hypothesis.

We learned that 95% of users read text messages within 4-minutes of receiving them. Knowing this incredibly low barrier to entry, we heavily leveraged Twilio's SMS platform to engage our users and create a completely UI-less experience.

A combination of sensors (proximity, camera), devices (SyncUP Drive/GPS, Raspberry Pi), machine learning (backend/history of parking habits), and computer vision (backend/on-device) looks at the best outcome for future parking spots. It then texts the user which spot to take.

Accomplishments that I'm proud of

With a small team of two we were able to create a polished & fully working demo over a period of 36 hours.

What I learned

Teamwork and belief in your team members above all are the most important factors in successfully translating ideas to reality.

What's next for SyncUP Park

• Incorporating larger data sets for machine learning to understand habits, and be able to further minimize frustration.

• Using this technology to solve the problem of finding available meeting rooms.