Computer scientists at Nanyang Technological University in Singapore have developed a new intelligent routing algorithm that attempts to minimize the occurrence of spontaneous traffic jams—those sudden snarls caused by greedy merges and other isolated disruptions—throughout a roadway network. It’s both computationally distributed and fast, requirements for any real-world traffic management system.
“We assume that the traffic breakdown model has already been given, and the probability of traffic breakdown occurrence is larger than zero, and our goal is to direct the traffic flow so that the overall traffic breakdown probability is minimized,” Hongliang Guo and colleagues write.
So, the goal of the algorithm is this maximization, which reduces to a fairly tidy equation. It then becomes a machine learning problem. Now we’re taking the current traffic load, adding an unknown additional load that might enter the network at any time, and then coming up with probabilities of network breakdown at each of the network’s nodes or intersections.
Crucially, Guo and co. were able to come up with some mathematical optimizations that make this kind of calculation feasible in real-time.
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