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Wednesday, September 14, 2016

Reducing Traffic with Reinforcement Learning

Our group looked at related problems in traffic and industry.    In Engineering.com

Machine Learning Techniques Aim to Reduce Traffic  by Michael Alba

It's a problem we can all relate to: sitting in traffic and waiting for a green light. While waiting, you may have even pondered how you would try to improve traffic efficiency—surely there's got to be some way for everyone to get to work on time.

But ponder no longer, because a team of engineers from Tsinghua University in China has handed the problem over to machines. The team’s recent study makes use of deep reinforcement learning algorithms to optimize traffic signaling, and its promising results suggest there may be a way to arrive on time after all.

Deep Reinforcement Learning

Let's be clear: traffic is a complex problem to solve, and traffic control engineers have long worked on improving efficiency. The difficulty arises because there are two distinct and challenging tasks involved—the first step is to create a useful model of traffic flow, and the next is to somehow find a way to optimize it.

The team modeled traffic flow using a simplified simulation of an eight-lane intersection, with only red and green lights (no yellows) and vehicles only allowed to go straight through (no right, left, or U-turns were permitted).

Using this simplified scenario, the team implemented reinforcement learning algorithms in order to determine signaling actions that were most beneficial to the system. This was evaluated by measuring the queuing length of traffic in both directions. By simulating different signaling situations, the algorithm aimed to minimize the length of traffic queues and therefore decrease driver wait time. ... " 

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