“If you want to get home faster, drive slower,” and other counterintuitive lessons for reducing traffic jams
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Cars are like rice, says Baher Abdulhai, an engineering professor at the University of Toronto’s Faculty of Applied Science and Engineering. In fact, so intuitively do the grains of oryza sativa emulate car behaviour that pretty much every time he gives a speech describing the solution to traffic congestion, he brandishes two beakers of rice and a couple of funnels as part of a demonstration of his mantra that, if you want to arrive home sooner, you should drive slower.
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As counterintuitive as that may seem, his demonstration is pretty convincing. As proof, Abdulhai unceremoniously dumps the first beaker of rice into its funnel. As you’d expect, all the rice plummets down to the funnel’s narrow exit where, to no one’s surprise, the grains jam together like commuters getting ready for a punch-up on the 401. Needless to say, everything dribbles out of the funnel like molasses down a frozen lollipop.
On the other hand, when he meters the rice into the funnel — at a pace the director of Toronto’s Intelligent Transportation Systems Centre admits is practiced — the rice slows down not a whit as it races through the funnel’s exit, the same number of grains scurrying through the same space in about a third of the time. As metaphor for how just a few extra cars entering a roadway are needed to clog an otherwise free-flowing freeway, Abdulhai’s drive-slower-arrive-sooner demonstration is both brilliant and simple.
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Implementing that lesson is anything but simple, however. In fact, it requires the use of artificial intelligence. Yes, that artificial intelligence. But despite the controversy often surrounding learning computers these days, AI would seem the perfect tool to reducing traffic congestion.
Essentially, as I understand it, sensors feed the machine-learning computers all the parameters affecting traffic: the flow of the cars already on the highway, the speed in each lane, and, of course, the number of cars looking to join via, say, an on-ramp. Algorithms using Reinforcement Learning then proscribe how quickly the cars on the on-ramp are allowed to merge into traffic without disrupting its flow.
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Of course, simplistic forms of this metering are already in place on highways around the world (almost all the ramps onto L.A.’s most-travelled freeways have stoplights, for instance). What’s different here is that all this control is interactive. As Abdulhai explains it, the trick to consistent — and therefore rapid — traffic flow is all in how one section of the highway talks to the others.
There is not much use, for instance, in one ramp metering cars slowly if, á la his rice demonstration, if the two preceding ramps are dumping cars into the slow lane unrestricted. Similarly, it’s equally important that the traffic already on the highway be travelling at an optimal speed, as well — that controlled speed at which the professor meters out the rice in the second half of his demonstration. Put it all together, says a U of T thesis — Decentralized Coordinated Optimal Ramp Metering using Multi-agent Reinforcement Learning — and all that intelligent ramp-metering can reduce travel time by as much as 50 per cent.
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At least, that’s my understanding of the good professor’s dictums. But, if you want to get the message directly from the master, why not join us next Wednesday, April 19 at 11:00 a.m. when Driving into the Future puts on its latest expert panel, Traffic’s a mess: How do we fix it?
Not only will Abdulhai discuss his high-tech solution, but the CAA’s senior director of public affairs, Kristine D’Arbelles, will be detailing where traffic is at its worst — no surprise here, Toronto, Vancouver, and Montreal lead the pack — as well as how we compare with top U.S. bottlenecks (in the race for worst congestion, Toronto ranks a depressing seventh-worst in the world) and what the CAA sees as some practical, here-and-now solutions.
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Meanwhile, SNC-Lavalin’s Sabrina Martineau brings the infrastructure giant’s perspective on how the multi-modal mobility of the future will translate into fewer traffic jams — using “a digitally-enabled smart grid that allows vehicles to ‘talk’ to each other and infrastructure,” says the company’s Canadian VP & Practice Lead for Civil Engineering Services — as well as how we’ll control the traffic caused by ever-increasing urban populations by incorporating “congestion charges.”
Yes, whether it’s today, tomorrow, or ten years hence, we’re going to have more pay roads. For those already penning their angry e-mails, congestion pricing is a far better solution than the complete bans many cities around the world are contemplating. Indeed, “dynamic” congestion pricing technology would, like your electricity bill, vary depending on the time of day the service is utilized.
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We’ll also discuss a few real-world driving tips that really do help keep traffic flowing. Indeed, despite we Canadians’ preference to politely queue up in line for traffic restrictions, it turns out that it’s actually faster to use all lanes to filter past road work by forming two tightly-knit lines — called “zippering,” for obvious reasons — that mesh together in sequence. Yes, that bastard we all yell at who just cut to the front of the line is actually speeding up traffic.
Like I said, traffic is anything but simple. Or, considering Abdulhai’s drive-slower-to-arrive-sooner mantra, obvious.
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