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As cities face growing pressure to manage transportation networks, infrastructure and public services using smart-sensing systems, new research led by 91��ɫ suggests there may be a more effective – and economical – way to collect data.

The , published in Transportation Research Part C: Emerging Technologies, shows that municipalities may not need new infrastructure to become “smarter” and explores how to use the city itself as the sensor.

Urban planners depend on data to support decisions ranging from parking management and road maintenance to traffic operations and environmental monitoring, explains Elham Heydari-Gharaei, the study’s first author and 91��ɫ PhD alum. Collecting that information across an entire city, however, can require expensive networks of fixed sensors.

“It struck me that while municipalities spend millions installing and maintaining fixed sensors, thousands of vehicles are already travelling through urban streets every day,” says Heydari-Gharaei, who is now an assistant professor at Toronto Metropolitan University. “That made me wonder: What if the city itself could become the sensor?”

By strategically coordinating vehicles equipped as mobile sensors, bicycles, maintenance vehicles, municipal fleets and shared mobility services could gather and share information as they move through urban areas.

“The key challenge is coordinating those vehicles efficiently, so the data remains reliable while keeping costs manageable,” says Heydari-Gharaei. “By solving that coordination problem, cities can collect reliable, up-to-date information while making better use of existing resources.”

In response, the research team developed algorithms to help planners evaluate operational scenarios. They assessed factors such as sensing frequency, fleet size and service routes, while scaling solutions for large urban networks.

One finding that stood out emerged from a Toronto case study. Researchers found that only about one-quarter of Toronto’s Bike Share fleet could monitor the entire permit parking network if equipped with sensors and strategically routed. The result highlights how optimization can significantly reduce the resources needed for city-wide monitoring.

Mehdi Nourinejad, associate professor at the and Heydari-Gharaei's doctoral supervisor, says the implications for mobile sensing systems could enhance data retrieval for infrastructure inspection, environmental monitoring, traffic management and other smart-city applications.

“Cities already have thousands of vehicles moving through their streets every day and the key is to develop optimization methods that determine how those vehicles should be routed and deployed to collect reliable data,” says Nourinejad. “If cities can collect reliable information using resources they already operate, they may be able to improve services without major new expenditures.”

The findings could also help address financial barriers experienced by municipalities seeking to expand data collection without making large investments in smart-city infrastructure.

Reliable urban data, says Heydari-Gharaei, supports everyday decisions, from helping drivers find parking to monitoring road conditions, air quality and public infrastructure. Better data, says Heydari-Gharaei, can support better planning, reduced congestion, improved sustainability and more responsive public services.

“As sensing technologies continue to improve, there is enormous potential to rethink how cities gather information without continuously investing in new infrastructure,” says Heydari-Gharaei.

During the project, 91��ɫ researchers collaborated with Professor Matthew Roorda from the University of Toronto, who contributed expertise in freight transportation and urban mobility.

With files from Alex Huls

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