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Cubic, McMaster University Team for Next-Gen Transportation Tech

Cubic Transportation Systems and McMaster University in Hamilton, Ontario, have partnered to form the Centre of Excellence for Artificial Intelligence and Smart Mobility to further develop AI and machine learning in traffic management systems.

A digital illustration of a smartphone with a map on the screen. Surrounding the phone is a winding like, as if a road on a map, with a bike and mountains and a bus on it. There is also text next to the phone that reads: "Transportation Reimagined. Using data-driven technologies to ensure citizen safety and expand mobility options."
A partnership between Cubic Transportation Systems (CTS) and McMaster University in Hamilton, Ontario, is exploring new technologies around traffic prediction and a heightened understanding of vulnerable road users.

Cubic, a transportation and transit technology provider, has entered into a five-year partnership with the university to further develop technology products that include artificial intelligence and machine learning that takes into consideration the wide variety of users of transportation systems.

“At the center of everything we do is DEI — diversity, equity and inclusion,” said Ali Emadi, professor and research chair at McMaster University.

“That’s what makes us different from a lot of artificial intelligence centers in academia or in industry,” he added.

The project, known as the Centre of Excellence for Artificial Intelligence and Smart Mobility, is also exploring the notion of “human-centered design,” and populating design teams with researchers and technicians from across multiple backgrounds and expertise.

Using AI and machine learning, the collaboration is developing new technologies for traffic management — what Cubic officials call “smart mobility systems” — that will be able to predict congestion, and then offer mitigation solutions.

Such a system would tie in all aspects of transportation, whether that revolves around intersection safety and congestion prediction algorithms or simply urging people toward more active forms of mobility, explained Galen Chui, senior vice president for engineering and product organization at Cubic Transportation Systems.

“We still have to create some products that will actually physically be installed in the intersections,” said Chui, pointing to devices like “radar fusion cameras,” which couples a camera with a long-range sensor to provide insights from near-field to farther distances.

“This is to really improve the accuracy of predicting the length of traffic at an intersection, and how we can then tie that into the cabinet controllers that we have, that actually drives red-light, green-light signals and really to manage it at a network intersection perspective,” said Chui.

But at the heart of all of these technologies is human-centered design, and weaving this DEI ethos into the work in AI and machine learning, said Chui. “Whether it’s in smart mobility systems or smart sensors, to the automation and cloud performance lab.”

The first year of the Cubic and university partnership was focused on understanding the system and getting a diverse team up to speed on the industry, said Chui.

“Year two is all about productization. So, bringing some of those features, the congestion prediction … with that engine, how do you then tie that together with something I can therefore nudge a pedestrian, or a cyclist, or a motor vehicle to go a certain route based on certain attributes,” Chui added.
Skip Descant writes about smart cities, the Internet of Things, transportation and other areas. He spent more than 12 years reporting for daily newspapers in Mississippi, Arkansas, Louisiana and California. He lives in downtown Yreka, Calif.