Communications of the ACM
Machine Learning Techniques Aim to Reduce Traffic
Researchers at Tsinghua University in China have completed a study on how traffic signaling can be optimized using deep reinforcement learning, with implications for reducing traffic congestion.
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Researchers from Tsinghua University in China performed a study on how traffic signaling can be optimized using deep reinforcement learning, with implications for reducing traffic congestion.
Improving traffic efficiency is problematic due to the challenging tasks of producing a useful traffic flow model and then optimizing it.
The Tsinghua team accomplished the first task with a simplified model of an eight-lane intersection that had only red and green lights and only permitted straight-through traffic. They then deployed reinforcement learning algorithms to determine signaling actions that yielded the most systematic benefits, and they assessed them by quantifying the queuing length of traffic in both directions.
The algorithms attempted to minimize the length of traffic lines and decrease driver wait times, and their subsequent combination with deep-learning algorithms significantly shortened the computation time for finding optimized solutions. The researchers say the resulting deep reinforcement learning algorithms show potential, as they can significantly outperform conventional reinforcement learning algorithms.
During the course of a full day's simulation, more than 1,000 fewer vehicles came to a full stop with deep reinforcement learning, and they spent an average of 13 seconds less in traffic during peak hours.
From Engineering.com
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