Funding Source: Analytics

Problem: The City of Clovis is located in eastern New Mexico and is the county seat of Curry County. Public works crews would assess the condition of their 260-mile road network manually by closing down sections of it and performing a visual inspection. This process was time-consuming, and the road closures were inconvenient for residents. Because of the technical jargon associated with pavement assessments, city officials could not adequately describe why they made particular pavement decisions to their resident.

Solution: In December 2019, the City partnered with RoadBotics to map their street conditions.
Roadway solution of RoadBotics works in 3 steps:
Using your smartphone, GoPro, 360 camera, or drone footage, collect visual data of the roads. The imagery collected will immediately appear on RoadWay, allowing to easily see what’s been covered and what still needs to be collected.
The road imagery is then fed through the artificial intelligence (AI) algorithms. RoadBotics technology is built on years of advanced research and development, and the algorithms have been precisely tuned to deliver objective and actionable data.
The imagery and ratings are mapped on RoadWay, the interactive online platform. Views can be customized to gain the insight needed to create preventative maintenance and construction plans.
The Embedded Maps function generates a few lines of code that can be copied and pasted into any web page, allowing to share an interactive map of the road network.

Problem: Major metropolitan areas face a housing shortage and affordability crisis. Paradoxically, there may be plenty of lands to build on. Disparate city and county records and varied zoning and municipal housing laws force developers to slowly and painstakingly dig through hundreds of millions of real estate records to find suitably-zoned land for development.

Solution:HEAVY.AI provides developers, builders, and third-party service providers a way to visualize housing and urban development data from multiple sources, all on a single platform. With accelerated analytics from the world’s fastest open-source SQL engine, they can cross-filter massive housing and urban development datasets any way they need—zoning, sub zoning, utilization, or construction activity—without down sampling, pre-aggregating, or writing SQL queries—all with millisecond response times in a seamless analytics experience.

Problem: Paris is creating a new generation of digitally enabled park benches that exploit the capabilities of the Internet of Things (IoT) and further extend Paris’ smart city initiatives.
The developers required a solution that was low power, low cost, and robust enough to last 10 years. The solution also enables Paris to create a second tier of custom analytics, with which it can develop its own questions and algorithms that address the particular information requirements of any situation. They can do this through a standard API that the software provides.

Solution: The project’s first step is pilot testing the technology, which uses sensors that are designed primarily for monitoring location and environmental data such as temperature and atmospheric pressure. The two-tier capabilities for analytics collected from these sensors can assist city planners.

Problem: Self-driving cars are cool but vehicle-to-infrastructure projects have more potential to make sidewalks and streets safer in the short term.

Solution: Columbus is building the infrastructure to test this communications platform that allows cars to communicate with other vehicles, traffic lights, and even crosswalks. The communications system will send vehicle-to-vehicle safety messages to drivers and collect non-personally identifiable data to the Smart Columbus Operating System. The CVE project will capture, relate, store, and respond to data generated by the communications infrastructure.

Problem: The streetlight project is part of the Smart City Cleveland plan. The city released its strategic plan in 2016 with five strategic focus areas: traffic, transit, sensing, grids, and communication. The project’s goal is to use technology to improve public safety and increase access to education and services by improving mobility, all while reducing greenhouse gas emissions.

Solution: The Cleveland Power Company has an online map that tracks the LED conversion progress. Telematics installed a T-Light Galaxy Network which provides web-based control of the light fixtures and the network. This wide-area network uses a single base station to cover an area of up to 20 km radius and monitors up to 50,000 lights.

Problem: Atlanta, like other metro centers, is striving to become a “smart city” via the latest technological innovations. Some of the city’s infrastructure is more than 100 years old, and it faces challenges that planners hope to resolve through an integrated technology approach. The smart city effort aims to improve city operations in five key areas: multimodal transportation, public safety, environmental sustainability, internal city operational efficiency, and citizen and business engagement.

Solution: The city’s smart initiative began in 2015 when it created a vision and plan for a comprehensive approach to technology application to city projects.
• The project team, called SmartATL, quickly realized that the deployment of Internet of Things (IoT) devices and the sharing of the resulting data would have a great impact. The SmartATL team identified key performance indicators [KPIs] for how well the city is doing in each of these areas. These KPIs will then drive what data is collected to establish baselines for how the city is currently performing.

Problem: Accurately cataloging and mapping of public infrastructure assets like signs, street lights, benches, trees, manholes, and fire hydrants is extremely tediours and and yet impossble sometimes manually. Asset inventory work can be very tedious and time-consuming. Finding a comprehensive, user-friendly asset management platform is difficult. The identification, tagging, sorting, mapping, and analysis of each asset image should be fast and intuitive. Storage of images and other data should be secure. Sorting and reporting need to be logical and flexible. Most of all, the platform needs to communicate an accurate, objective view of all the assets.

Solution: AgileMapper helps build meaningful maps and make data-driven decisions powered by artificial intelligence (AI). Starting by uploading existing visual data in the form of smartphone photos, 360 images, GoPro footage, and drone footage to the AgileMapper desktop website. To begin collecting new visual data, you can also download the AgileMapper app and using the camera on the smartphone to capture images of each asset.