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New division focuses on the future of traffic

It will apply expertise in areas such as computer vision, deep learning, AI and big data analytics to improve traffic flow for cities

Transport networks will be one of the most important pieces in smart cities, says McBride
Transport networks will be one of the most important pieces in smart cities, says McBride

One of the initial projects will conduct research on freight data

New sources of data are only just being properly understood

Traffic systems technology developer Miovision has created a new division focused on leveraging transportation data and smart traffic technology to take cities closer to embedding intelligence in urban infrastructure.


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, artificial intelligence (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.


The United Nations projects that 66 per cent 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 pieces in smart cities of the future.”


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 US 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.


“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.


He added: “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 it is using real-world data, not a simulation.”


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