The intersection of two elevated high-traffic tracks, solved by an unharmed roundabout, causes traffic jams of hundreds of meters and more than 6 minutes of maximum waiting time. Because of the roundabout preference system, it can happen that the inlet flow through a high-traffic branch is interrupted by vehicles from another branch with less intensity, which has priority because it is on its left. To solve cases like this it is often necessary to improve the infrastructure, for example by building an intersection at different level, which is an important economic investment. Sometimes traffic can be improved through a Smart Roundabout, which learns how to regulate traffic through Deep Reinforcement Learning.
Deep Reinforcement Learning
To train an intelligent system like this, we developed a roundabout model capable of simulating its actual behavior in different traffic situations. The traffic lights at the roundabout are then modeled and an Agent is put to control the traffic light phases. The agent receives the queue length information on each branch and decides which traffic lights should turn red at any given time. From time to time, you receive a reward if you manage to reduce the maximum waiting time of the roundabout.
At first, the Agent manipulates the phones randomly, and receives penalties for it. Over time, learn the optimal way to operate traffic lights at all times, depending on the traffic situation. This agent, trained in the roundabout model, will be able to improve traffic at the actual roundabout, and continue learning.
- Improved maximum waiting time at roundabout
- Economic solution to new infrastructure implementation alternatives
- Possibility to implant the system in other roundabouts
The longest rush hour waiting time is reduced from 440 seconds to 285 seconds, by 35%