Signal Control

Signal control research aims to optimize traffic flow and network efficiency, primarily focusing on traffic light management and vehicle routing. Current efforts leverage machine learning, particularly reinforcement learning (RL) and multi-agent RL (MARL), often incorporating digital twin frameworks for improved data integration and real-time insights, as well as genetic programming for explainable model development. These advancements aim to address limitations of traditional methods, improve upon existing RL approaches by incorporating industry standards and real-world constraints, and ultimately lead to more efficient and sustainable transportation systems.