By Luke Rosiak
A team of researchers today discussed technology that could dynamically control an urban street grid, rerouting traffic in the way most efficient for any given conditions. Described as an automatic architecture for real-time traffic, the street grid would learn from experience, remembering which strategies were most effective in the real world.
The scientists were part of a panel on artificial intelligence in transportation at the annual Transportation Research Board meeting, a gathering of 10,000 policymakers, professionals, and academics.
The system was inspired by the human body, a researcher said, and like humans, is fundamentally about "reflection, routine and reaction."
It would default to operating on a timeline and pattern that it has found to be most efficient for a given circumstance in the past. Sensors would monitor road conditions such as the weather and how heavy the traffic is and detect obstructions and accidents and their severity.
When a problem arose, it would respond by changing patterns in the smallest possible area--similar, the researcher said, to the way your body picks up a pen with its hands--no need to disrupt the legs. Similarly, as soon as that unit is freed up, the unit can begin maximizing some other aspect of traffic management, just as your hand could go on to do something else.
Specifically, the system's architecture divides the grid into small rectangular units to respond to conditions. Where it's necessary or most efficient, those units can then interact with neighboring units. That interaction can expand as necessary until many blocks are interconnected--in the maximally efficient way.
And it would evaluate how successful that strategy was, for use in the future.
While much of the math is in place, this is mostly all still theory, however.
One problem involves where to define the boundaries for each small unit, and another is how to detect incidents.
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