Varun AdibhatlaPatrick Atwater
Published © CC BY-NC-ND

SQUID: Street Quality IDentification

A low-cost approach to perform digital street condition surveys continuously by integrating street imagery with ride quality data.

AdvancedWork in progress5,272
SQUID: Street Quality IDentification

Things used in this project

Hardware components

Android device
Android device
×1
iPhone
Apple iPhone
×1
Bike Phone Mount
×1
Car windshield phone mount
×1

Software apps and online services

AWS S3
Amazon Web Services AWS S3
Images and telemetry data are streamed to an S3 bucket
OpenCV
OpenCV
We use this for our automated approaches to detect cracks and other street defects
open street maps
Used for data publishing and cleaning.
OSMnx - Python for Street Networks
Geoff Boeing's creation allows us to quickly convert raw GPS data to actionable maps really quickly and in an open manner.
Open Street Cam
We use Telenav's Open Street Cam as a primary source for data collection

Story

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Schematics

Digitizing Municipal Street Inspections - 2016 Data for Good exchange

"People want an authority to tell them how to value things. But they chose this authority not based on facts or results. They chose it because it seems authoritative and familiar." - The Big Short

The pavement condition index is one such a familiar measure used by many US cities to measure street quality and justify billions of dollars spent every year on street repair. These billion-dollar decisions are based on evaluation criteria that are subjective and not representative. In this paper, we build upon our initial submission to D4GX 2015 that approaches this problem of information asymmetry in municipal decision-making.
We describe a process to identify street-defects using computer vision techniques on data collected using the Street Quality Identification Device (SQUID). A User Interface to host a large quantity of image data towards digitizing the street inspection process and enabling actionable intelligence for a core public service is also described. This approach of combining device, data and decision-making around street repair enables cities make targeted decisions about street repair and could lead to an anticipatory response which can result in significant cost savings. Lastly, we share lessons learnt from the deployment of SQUID in the city of Syracuse, NY.

SQUID-Bike to digitally measure citywide Bike Lane Infrastructure

Urban bicycle usage has gained in importance across many cities with progressive transportation policies. According to [1], as of this paper’s publication, “there are more than 450,000 daily bike trips in New York City, up from 170,000 in 2005, an increase that has outpaced population and employment growth”. About one in five bike trips is by a commuter. Biking serves as an important transportation option to many around the world and we argue that the need for effective bicycle lane maintenance should be a top concern for municipalities.
Conventionally, street maintenance is an expensive, often inaccurate, and time intensive process that either uses subjective data prone to error or uses very precise data that is very expensive to collect citywide. There exists a clear need for cities to adopt a cost-effective and data intensive maintenance practice that can scale to the entire city and be performed
frequently. These needs have been explored through the Street Quality Identification also
known as the SQUID project [2] to develop standardized methods for digital street inspection.
This work extends the SQUID project and repurposes it for citywide bike lane measurements. In this paper, we describe the development of a data and analytics framework to measure bicycle lane quality using street imagery and accelerometer data obtained from an open source smartphone application, OpenStreetCam (OSC) [3] . This framework can be used in crowdsourced or situated settings with the overall purpose being the standardized measurement of citywide bike lane quality

Code

Streets Data Collaborative

Working together to ensure efficient public works and preparing for equitable autonomous futures.

Credits

Varun Adibhatla

Varun Adibhatla

1 project • 15 followers
Varun works at ARGO Labs, a civic data science org. that rapidly prototypes for cities by partnering with local gov around device, data & decision making.
Patrick Atwater

Patrick Atwater

0 projects • 0 followers

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