Traffic signal control, which seeks to optimize the behavior of signalized intersections, is a focus of much research and analysis. By making intersections as efficient as possible, transportation engineers may minimize the impacts of congestion on dense areas. A paper by researchers from Gachon University in South Korea proposes a cooperative traffic signal control with a traffic flow prediction (TFP-CTSC) for multi-intersection spaces. Traffic flow prediction models simulate future traffic states from observed variables affecting current traffic conditions. The cooperative signals are designed to share information across intersections, allowing for behavior that is globally, rather than locally, efficient.

The proposed algorithm uses deep Q-network (DQN) reinforcement learning, which uses a neural network to select optimal actions based on the existing state of the environment and building off of previous saved experiences. The authors adapted DQN to create a TFP-CTSC algorithm that seeks to minimize the time spent waiting by vehicles within the network. Additionally, the TFP-CTSC algorithm considers the state of other nearby intersections to improve its predictions, which previously had not been incorporated in traffic signal learning algorithms.