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Photo by Sean Kilcarr for Fleet Owner

Photo by Sean Kilcarr for Fleet Owner

Figuring out how to improve urban freight flow

One of the big challenges facing city planners in the U.S., much less around the world, is how to more accurately gauge urban freight flows so they can not only plan freight infrastructure investments better but also initiate more near-term traffic throughput improvements, too.

That’s where traffic systems technology maker Miovision, and its new division, Miovision Labs, come into play.

“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,” explained Kurtis McBride, CEO and co-founder of Miovision, in a statement.

“That brute force approach has become obsolete,” he believes. “In the next decade, cities will undergo rapid changes, and transportation networks will be one of the most important pieces of the smart cities of the future.”

Indeed, McBride noted that the United Nations projects that 66% of the world’s population will live in cities by 2050 – a shift he warned will put enormous pressure on urban traffic and infrastructure.

“Cities already struggle with moving people and goods within their cores,” McBride stressed. “Given the trends, cities want to adopt and harness technologies to manage growth. These demands combined with budget pressures require smarter approaches to traffic.”

Miovision is rolling out its Miovision Labs division at the annual Transportation Research Board meeting in Washington D.C. this week; a unit composed “technologists and product strategists” focused on the future of traffic, helping cities make sense of the vast amounts of traffic data now becoming available, McBride explained.

In one of its first research projects, he said Miovision Labs partnered with CPCS, a management consulting firm focused on transportation strategy, policy and economics, to conduct research on freight data.

The objective: 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.

“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,” noted 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,” he pointed out. “Most of these are just starting to be understood, and we’re going to figure out how to use them.”

Miovision Labs also plans to work with the University of Toronto on research around “conflict analysis,” which is a process designed to detect and rank the severity of “conflicts” or crashes at a particular street location.

Matthew Roorda, professor of civil engineering at the university, said real-world data collection for conflict analysis is very labor intensive, so transportation agencies rarely use it 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 – meaning crashes must occur in order for improvements to happen.

Instead, Minovision and the University of Toronto plan to tap vehicle tracking data to develop trajectories of real vehicles, pedestrians, and bicycles in order to guide such investments without the need to “wait” for crashes to happen.

“Our partnership will identify dangerous interactions and will give insights that can lead to better decisions about infrastructure,” Roorda said. “The important piece with this project is that is using real-world data, not a simulation.”

Those are all very ambitious plans, but freight could really benefit from insights developed from such “real-world” data streams.

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