Trying to take advantage of his closeness to the Appathon venue, Tim tried walking. However, on his way he was swept up in the scaffolding of modern city construction, trapped within the hustle and bustle and smells and noise between the narrowed sidewalks and the enclosing fence. If he had know, he would have chosen a different route or even taken an Uber.

What it does

Leveraging city-wide IoT sensors, social media likes, and crowd sourced content, zenWalk recommends the most pleasant and efficient available routes tailored to the individual.

How we built it

We pulled down historical data from CityIQ and ran regression models to find the optimal routes for pedestrian traffic and CityIQ's video feeds to capture photos. Moreover, we integrated with external services just as Google's Map API and a Predix Edge Device.

Challenges we ran into

Interpreting both the APIs and the data was difficult. Managing errors and outliers and completing repetitive and boilerplate tasks such as standing up services proved time consuming to delivering a working prototype provided the time constraints.

Accomplishments that we're proud of

We have a prototype that works with and was inspired by the CityIQ dataset.

What we learned

Business domain knowledge is immensely valuable.

What's next for zenWalk

Pitching and partnering with future looking cities

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