|Software apps and online services:|
The maintenance of city streets is the most visible indicators of a city government's performance.
Every year, US cities spend $ Billions on the maintenance and upkeep of roads. The methods used to survey streets range from:
- Citizens calling in potholes on an ad-hoc basis, or reporting via smartphone apps.
- “Windshield surveying” where some trained inspector assigns a score to the quality of a street based on his/her judgement and a training manual and scoring methodology that dates back to the '70s.
- Bulky, military grade equipment which provide an uber-high precision but only for a small sample of streets.
What's missing is a low-cost method to collect data about street quality for ALL streets in a city in a consistent and methodical manner that answers simple question:
In an age of autonomous vehicle futures, cities and municipalities need digital tools to ensure that their streets are well maintained at at-cost.
To this end, we created SQUID, a low cost data platform that integrates open source technologies to combine street imagery and ride quality data to provide a visual ground truth for all the city's streets.
In NYC's case: that's 6,000+ linear miles of streets!
In Fall, 2015 we conducted a pilot with the City of New York and their SCOUT Team and collected 400+ miles of data from a single vehicle in just over 1 week!
Imagine, if just 15 vehicles vehicles were used to collect street imagery, we could achieve a complete and up-to-date street condition surveys across the entire city in mere weeks!
SQUID is a project of ARGO Labs, a non-profit organization that builds, operates, and maintains data infrastructures to help cities deliver core public services better, faster, and cheaper.
SQUID started out with a hardware device, a $30 Raspberry pi with accelerometer and GPS sensors and a good chunk of grit and naive optimism to prove the initial hypothesis. After collecting our first dataset on the streets of New York, we set out to polish our "lump of clay".
In Spring 2016, we worked with the Mayor's Office of Innovation at the City of Syracuse!
Between April 14 - 28, 2016, we collected over 500 miles of street imagery in just 10 days with little to no human intervention, demonstrating the scalability of this approach to inspecting an entire city's street infrastructure.
In Summer, 2017 we worked with students from NYU's Center for Urban Science and Progress to use OpenStreetCam, an open-source mobile application purposefully designed to collect street imagery and repurpose SQUID for bike lane inspections giving birth to SQUID BIKE!
We plan to continue to exclusively use OpenStreetCam for SQUID. This allows cities to simply download an app and start recording street or bike-line condition data.
We are also currently building our automated Computer Vision approach to partially automate the detection of street defects such as cracks and large potholes.
We welcome feedback, ideas and support on how SQUID can be used to empower cities be proactive, open and transparent around street maintenance.
- New York Times, Why are the streets always under construction?
- Fast Company, A new cheap way to quickly map your city's potholes.
- FOX news video, Smart Cities: The pothole problem.
- Staten Island Live, A 21st century proposal for Street maintenance.
- Forbes Tech Blog, The Knight Foundation Funds 20 Media Tech Projects.
- Metro New York, Filling potholes faster.
Did you replicate this project? Share it!I made one
Love this project? Think it could be improved? Tell us what you think!