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May 6, 2016 AT 10:00 am

Measuring potholes in NYC with a Raspberry Pi #piday #raspberrypi @Raspberry_Pi

Neat concept from Argo Labs.

Elevator pitch : We built a low-cost sensor platform to passively measure street surface quality using an accelerometer and added a camera to be able to literally see the “ground truth”. This data and its visualization, we contend, will significantly improve decision making & resource allocation around existing pothole repair operations in New York City and attain “Escape Velocity” instead of maintaining orbital velocity as Dr. Lucius Riccio elegantly describes it.

TL;DR

“290,210 potholes have been repaired this winter maintenance season”

reads the “The Daily Pothole” a Tumblr site maintained by the Department of Transportation. A comic strip illustrating the daily fill, spill and milling of the hard-working pothole crews provide comic relief to this age-old problem of cities. The Center on Municipal Government Performance describes street maintenance as the “most visible example of local government performance” and we agree. To that end, we describe the creation of SQUID – Street Quality Identification Device. Of course we needed an acronym!

The motivation for the device came from initial forays into measuring Bumpiness while riding the NYC Century Marathon. This semester at CUSP – we had an opportunity to take an excellent course by Arlene Ducao over at ITP titled “Quantified Self about Town” which delved into wearables, & iOT in an urban environment. At the same time Bob Richardson was teaching Civic Tech Management and he mentioned in passing how cool it would be if the city could measure all the potholes. All we needed to do was put the 2 together in our heads and be a tad naive.

The concept was simple – a device that could passively record ride quality but more importantly also take a picture so that the readings could be backed up with some visual evidence.

We began with the Tessel which I have described in detail while demo-ing a parking sensor but we soon found out that we needed something faster and more robust to deliver our vision. Enter the Raspberry Pi. It took some familiarization with the Pi and some soldering help (Thanks Justin!) but once we had the individual sensors emitting data – all we needed to do is work towards coherence.

That is easier said that done as this proves….

Read more.

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