In a nutshell: ARGO Labs bridges the worlds of established public administration and ex-citing civic data science to demonstrate how to deliver public services more efficiently, ef-fectively, and imaginatively. ARGO’s Street Quality Identification Device (“SQUID”) eas-ily mounts on vehicles to passively provide rich street quality data that ARGO will classify and map through a robust analytical pipeline.

[A]dvanced [R]esearch in[G]overnment [O]perations

S.Q.U.I.D. Pilot Proposal

Vision zero potholesFrom orbital to escape velocity

What would it take to get to zero potholes in New York City? That’s a bold aspirational goal, yet it helps clarify the opportunity SQUID presents. NYC’s current 311 based pothole locating system benefits from a low cost reporting techology (simple phone calls) yet at best can only ever operationalize reactions to existing potholes that citizens and city inspectors find. That paradigm makes it exceedingly tough to get ahead of ongoing road deterioration and what Lu-cius Riccio calls “Orbital Velocity.”

SQUID offers a new low cost opportunity to passively collect a comprehensive street quality dataset by leveraging two key technology trends. The dramatic growth in smart phones usage globally the past few years has driven accelerometer, camera, and gps components an order of magnitude cheaper. Modern image classification techniques benefit greatly from large datasets to “train” machine learning models. Together these trends position SQUID to provide the worlds first automatically updating citywide street quality map. By knowing not only where potholes are but where potential street defects will likely arise, NYC can achieve “Escape Velocity.”

Orbital Velocity

Escape Velocity

S.Q.U.I.D. OverviewAutomated Pothole Mapping

“Street maintenance is one of the a most visible examples of local government performance.” -Fund for the City of New York’s Center on Municipal Government Performance

ARGO has built a low-cost sensor platform to passively measure street surface quality using an acceler-ometer and added a camera to be able to literally see the “ground truth”.

ARGO has field tested SQUID in Brooklyn & Hoboken and built interactive dashboards for NYC Technology Development Corporation Executive Director Bob Richardson and Smart Cities author Anthony Townsend. ARGO has also demonstrated this technology to the Mayor’s Office of Operations in NYC and has discussed implementation in Los Angeles with the City’s Chief Data Officer, Abhi Nemani.

This data discovery will enable richer street quality maps and improved street maintenance operations. Fixing potholes today is a game of “whack-a-mole” as cities are reactive than proactive about fixing potholes. SQUID enables a comprehensive map of a city’s street surface quality, transforming the the existing paradigm.

As NYC Deputy Commissioner Canisi testified in Dep’t of Transportation v. Coppola, “the most time

used in pothole repair isn’t actually making the repairs, it’s the traveling.” Once they get to a complaint location they fix all the potholes on that street section. What if the adjacent area has street defects that no one bothered to report in? Road crews could be routed to another street far away and spend unneces-sary time in traffic. There is suboptimal situational awareness for a problem that the city is committing $310.1 million dollars to. Our approach offers a new way to tackle that enduring challenge.

Citizens can still report potholes via 311 yet the distributed SQUID sensor network can augment those community prioritized complaints with “ground truth” street quality information. Past and current alternative road imaging efforts use expensive, intrusive and ominous looking military grade sensors that overengineers the issue - knowing where the potholes are! ARGO built a “good enough” platform that enables improved decision making and optimized routing decisions to fix potholes faster. We welcome opportunities to pilot this system at scale in NYC.

SQUID Cobble Hill Experiment

Pilot ImplementationInitial experiment and long term goals

ARGO’s proposed initial city fleet vehicle pilot involves putting a dozen SQUID sensors in as many vehicles for a month and using that data to scope a comprehensive New York Citywide pothole map.

Logistics - SQUID mounts in the trunk of the Prius. The camera extends from inside the trunk and is positioned adjacent to the Prius’ backup camera.

Image 1 (L): SQUID fitted on the trunk of the PriusImage 2 (C): The camera placed adjacent to the Prius’ backup cameraImage 3 (R): SQUID mounting with the trunk closed

Wifi docking - In order to retrieve the data, we assume that all DOT cars return to a “home base” which has wifi connectivity. When SQUIDS “return-to-base,” they detect the wifi and transmit data to a central server. As an alternative, we could use 4G connectivity though the data transfer costs will not be entirely trivial (on the order of a hundred plus dollars a SQUID a month).

Goals - By the end of 2015, ARGO aims to build a comprehensive street surface quality map of NYC like the simulated one below and pipeline to automate the task of identifying street quality issues. More than data and analytics, the goal ultimately is better service delivery and we will measure progress by improvements in street quality.

Metrics - Measure pothole reporting to filling timeline and aggregate filling dfficiency. Measure cost sav-ings for city agencies and citizens using the roads. Measure overall street quality and citizen satisfaction with NYC’s roads.

Simulated NYC Pothole Map

Why SQUID is different

Device

Data

Decisions

Open source rasberry pi stack costing less than $200 a device.

Estimate a total of 1.5 to 2 million images total-ling 500 - 700 GB or $30 a month a device.

Analytical pipeline and street quality dash-board that ARGO develops with the City.

ARGO is currently pursuing 501(c)3 nonprofit status and is a mission focused organization. We’re in this to bridge the gap between what’s currently possible with today’s digital tools and established pub-lic administration practices. Furthermore, with this street quality mapping, ARGO has stayed highly focused on a low cost device that can work with the city’s existing vehicle fleet.

Unlike citizen reported pothole data, SQUID combines accelerometer with imagery data to provide both a much more robust method of tracking potholes and more comprehensive metric of street quality. And unlike highly engineered laser based system, SQUID offers a low cost sensor with the potential to passively collect data from the City’s vehicle fleet. That enables the City to not just have a one off map but a longitudinal dataset of street quality.

As former NYC transportation commissioner Lucius Riccio says, “Fixing potholes means the smart thing hasn’t been done, which is to do the work that prevents them in the first place.” We can strategically tackle that preventative maintenance question by exploring how today’s street defect SQUID data signa-ture correlates to what was observed 3 months ago or a year ago or longer as SQUID matures.

That process of research and refinement will lead to greater understanding of street quality and how to proactively manage potholes before they even arise. That’s the sort of strategy to achieve our big aspira-tional goal: New York, the first city in the world to achieve zero potholes.

Device

Data

Decisions

Team ARGOCivically savvy data scientists

Founding Team

We are graduate students at NYU’s CUSP (Center for Urban Science and Progress), an urban informat-ics academic program in New York City, with well over two decades of combined experience working in data intensive roles. We respect the past and are eager students of history.

We know how to distinguish a novel app from the genuinely new and understand that local context is critical for achieving real reform, as GK Chesterton eloquently elucidates in his parable of a fence. Our vision is to leverage the dramatic maturation of the digital revolution and work with local governments to tackle age-old wicked, civic challenges.

We have a deep appreciation for the panoply of factors -- political, economic, social, cultural -- that contextualize public problems and understand when specialization can be a barrier rather than a tool. We believe civic data science has created a new frontier for governmental operations and are excited to do our part to pioneer that potential.

Organizational Adviser

Dr. Neil Kleiman is a clinical professor at New York University, teaching gradu-ate-level courses on policy development, urban innovation, and new approaches to technology and big data at both the Wagner School of Public Service and the Center for Urban Science and Progress. Kleiman has spent 20 years building a career at the intersection of many sectors—policy, philanthropy, government and academia. He began his career as the founding director of the Center for an Urban Future, a 501c3 nonprofit based at 120 Wall Street in Lower Manhattan. He has written and edited over thirty policy reports, and his work has been featured in many media outlets including the New York Times, Wall Street Journal, USA Today, Chronicle of Higher Education and National Public Radio.

ARGO aims to pioneer a new public administration paradigm that is creative, adaptive and digitally native to better position cities everywhere to address pressing public challenges.