Software apps and online services
The maintenance of city streets is a most visible indicator 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 a trained inspector assigns a score to the quality of a street based on his/her judgement along with a scoring methodology that dates back to the '70s. (Pavement Condition Index)
- 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, 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 New York City's case, that is in excess of 6,000 miles of streets!
We worked with City of New York's Office of Operations and their SCOUT Team to collect 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 and ride quality data, 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 as a hardware device, a $30 Raspberry pi fitted with accelerometer and GPS sensors, naive optimism and a persistence 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".
The City of Syracuse, NY invites us to prototype with their Public Works Department.
Between April 14 - 28, 2016, we collected approximately 500 miles of street imagery (over 110,000 images) in just 10 days with little manual intervention, demonstrating the scalability of this approach to inspect an entire city's street infrastructure.
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 currently use OpenStreetCam as the primary method to collect street imagery and ride quality data. This approach allows cities and municipalities simply download an app and start collecting data.
We are currently building towards a 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.