It may be hard for residents to believe, but the City of Toronto has one of the most advanced traffic systems that was available on the market.
Back in 1992, anyway.
That’s when the first Split Cycle Offset Optimization Technique (SCOOT)-driven traffic control system was installed in Toronto. Designed to optimize traffic signal operations based on real-time traffic conditions, it was a boon at the time but is sorely in need of an update – which is why the city’s transportation services division is running two pilot projects designed to choose a technology to replace them, manager Barb Gray told IT World Canada during the company’s recent Technicity conference.
“There’s no more capacity to build additional roads,” she says. “At the same time, the city growing, and we want the economic investment, commercial development, and residential development that brings. So we need to actively manage the network that we have, and technology provides tremendous opportunity to do that.”
Unlike SCOOT, which uses vehicle detection loops to measure traffic and falls back on a predetermined Urban Traffic Control (UTC) schedule as a failsafe, both of the city’s current pilots utilise artificial intelligence (AI) in some fashion.
Installed in 10 locations, the American InSync system relies on a video camera feed, while using machine learning to detect the number of vehicles approaching an intersection and relay that data to its associated traffic signals.
Meanwhile, the Sydney Coordinated Adaptive Traffic System (SCATS), used in Australia, has been installed in 12 locations, and uses radar detection to measure traffic flow along an intersection while adjusting signals accordingly.
“We’re piloting two so that we can see which one is a better investment for our capital programs moving forward,” Gray says.
Though she cannot yet comment on the department’s preference, both systems are adaptive, which was its primary consideration, she says.
“Say the Rogers Centre empties out and you need a lot of throughput for an hour, but don’t need it to stay that way,” Gray says. “As the traffic starts to dissipate then the green time will go back to its normal set.”
The city has also piloted the use of drones, scanning congestion at special events from a bird’s eye view and analyzing how traffic behaves when they close a particular street or intersection, and is working closely with its emergency services and transit departments to help emergency and public vehicles navigate the city more easily using smart signal technology, she notes.
The city’s smart cities working group is also examining the impact of autonomous vehicles on traffic, Gray says.
“Curb space becomes a very important commodity (with autonomous vehicles),” she says. “Because if you have fleets of autonomous vehicles, the space where somebody can park, do their shopping, and leave becomes a shrinking resource, and the need for curb space where vehicles can pick up and drop off passengers becomes more significant.”
The division has already begun collaborating with provincial transportation authority Metrolinx on an initiative to test autonomous technology on Toronto streets, she adds.
Its two technology-driven traffic reduction pilot projects began at the end of November and are expected to last for the next year.