A new tool that could help you get to the bottom of your traffic troubles

Traffic is already one of the biggest drains on a city’s economy — and it’s only going to get worse as cities house a bigger share of the world population. Congestion could cost the UK economy nearly £300 billion between now and 2030. The U.S. could waste $2.8 trillion over that same period.

You can already see the impact in cities like Atlanta. Some companies are moving their offices. Delivery firms are raising rates. Businesses in especially congested areas are struggling to find low-cost labor.

The key to finding a traffic solution is first finding the root of your traffic problems, and data is at the heart of that. Traditionally that data has been hard to collect, often requiring someone to stand on an overpass with a clipboard. And because that data collection is so resource hungry, the data often isn’t complete or totally accurate.

It will soon get easier for cities, however. As you’ll read in the press release below, Miovision is launching Miovision Labs, which is already working on several initiatives to pull different data sources together, giving cities a starting point to find answers. — Kevin Ebi


Miovision, a global leader in traffic systems technology, announced at the Transportation Research Board (TRB) annual meeting the creation of Miovision Labs, a new division within the company focused on leveraging transportation data and smart traffic technology to take cities closer to embedding intelligence in urban infrastructure.

The United Nations projects that 66 percent of the world’s population will live in cities by 2050. That shift puts enormous pressure on urban traffic and infrastructure. Cities already struggle with moving people and goods within their cores. Given the trends, cities want to adopt and harness technologies to manage growth. These demands combined with budget pressures require smarter approaches to traffic.

“For the last century, transportation infrastructure ‘progress’ has been all about building more roads and adding more lanes to try to move more people, cars, trucks and freight from Point A to Point B,” said Kurtis McBride, CEO and co-founder of Miovision. “That brute force approach has become obsolete. In the next decade, cities will undergo rapid changes, and transportation networks will be one of the most important piece in smart cities of the future.”

Miovision Labs is composed of a team of technologists and product strategists focused on the future of traffic. Its research will help cities make sense of the vast amounts of traffic data becoming available to cities and use that data to fuel smart city applications in traffic and beyond. The team will apply its expertise in communications, computer vision, deep learning, AI, big data analytics, and embedded device design to improve traffic flow for cities. Miovision Labs will also work with cities to explore potential uses of connected traffic signals and the distributed computing networks they enable.

In one of its first research projects, Miovision Labs has partnered with CPCS, a management consulting firm focused on transportation strategy, policy and economics, to conduct research on freight data. The objective is to study how new types of traffic data from passive sensors, video cameras, GPS, and other sources can be used to understand and improve how freight moves through urban and metropolitan areas. The vision of this project, sponsored by the Transportation Research Board’s National Cooperative Freight Research Program (NCFRP 49), is to eventually inform how planners and policymakers in the public sector can coordinate and collaborate with private firms in both the collection and use of new data types for streamlining urban freight flows.

“Typically, cities have manually counted trucks or done surveys about how commodities flow through and around their communities, but those methods were time intensive, prone to mistakes and only provided a partial picture,” said Donald Ludlow, managing director of CPCS’s U.S. operations. “Today we have a variety of new data sources from road sensors, vehicle data streams, image data, truck permits and more. Most of these are just starting to be understood, and we’re going to figure out how to use them.”

Miovision Labs will also work with the University of Toronto on research around conflict analysis. While not new in itself, conflict analysis has depended on human observation to detect and rank the severity of conflicts (or crashes) at a location, which is incredibly intensive work. Because real-world data collection for conflict analysis is so labor intensive, agencies rarely use conflict analysis to identify where infrastructure investments can be prioritized or to measure the impacts of infrastructure improvements. Instead, they typically wait for crashes to occur to identify high-risk intersections, streets, transportation facilities or anywhere conflicts between modes of transportation could occur. This necessitates crashes occurring in order for improvements to happen.

“Miovision has made great progress in vehicle tracking using computer vision and can provide trajectories of real vehicles, pedestrians, and bicycles,” said Matthew Roorda, professor of civil engineering at the university. “The partnership with the university will identify dangerous interactions that happened between these roadway users, and will give insights that can lead to better decisions about infrastructure. The important piece with this project is that is using real-world data, not a simulation.”

Third, Miovision Labs is working with the new World Bank-led Open Transport Partnership to change the way data companies collaborate with governments for the public good. The partnership will empower resource-constrained transport agencies to develop better, evidence-based solutions to traffic and road safety challenges. Miovision has committed to releasing traffic data, as it is made open by its customers, to support this work.

“The research being conducted through these partnerships represent important steps toward smart cities,” McBride said. “They combine new data sources and new analytical techniques that will eventually become core pieces of city operations and planning. Some companies talk about a top-down approach to smart cities, but that has never worked. Getting to the future won’t happen overnight, and these types of projects are critical to that progress.”

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