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OverFlowLight is a real-time framework that prevents traffic gridlock by detecting queue overflow using multi-modal sensing and inserting dedicated overflow phases via a hybrid rule-based and RL controller. Deployed across 43 intersections, it reduces overflow incidents by 60.4% and increases network throughput by 18.2%.
The paper proposes an active inference controller for adaptive traffic signal control in noisy IoT environments, outperforming DQN in idle times and CO2 emissions under sensor occlusion and adverse weather conditions.
The paper introduces OracleTSC, a method using oracle-informed reward hurdles and uncertainty regularization to stabilize reinforcement fine-tuning of LLMs for traffic signal control. It demonstrates significant improvements in traffic flow metrics on the LibSignal benchmark using LLaMA-3-8B while maintaining interpretability.