Adaptive Traffic Signal Control

Adaptive traffic signal control (ATSC) aims to optimize traffic flow and safety by dynamically adjusting signal timings based on real-time conditions. Current research heavily utilizes reinforcement learning (RL), often employing deep neural networks like Deep Q-Networks (DQNs) and Transformers, to learn optimal control policies, addressing challenges like partial observability and multi-agent coordination in complex networks. This field is significant for improving urban mobility, reducing congestion and emissions, and enhancing intersection safety, with ongoing work focusing on robustness to cyberattacks and resource-constrained deployments.