Emission Aware Multi-Metric Reinforcement Learning for Adaptive Traffic Signal Control in Sri Lanka

Abstract

Urban intersections in Sri Lanka experience growing congestion and greenhouse gas emissions, while signal control remains fixed-time and insensitive to demand variability. An adaptive traffic signal control framework based on reinforcement learning (RL) is proposed. The control task is formulated as a Markov decision process with state features comprising queue length, waiting time, braking events, and estimated emissions. A Deep Q-Network (DQN) is trained in the SUMO-TraCI microsimulation under static and dynamic demand scenarios. Multiple reward formulations are examined, including emission-only and multi-metric designs with normalized, weighted components. Experiments show that emission-only rewards yield unstable learning, increased queuing, and no measurable emission reduction, indicating the limitations of single-metric optimization. A balanced reward combining queue length and braking achieves the most favorable trade-off, reducing average delay by up to 22% and CO2 emissions by 15% relative to fixed-time baselines. The framework is further instantiated on OpenStreetMap-derived models of Sri Lankan intersections (Borella, Kollupitiya), demonstrating applicability to local geometries and operating conditions. The results indicate that emission-aware RL controllers can outperform fixed-time operation and offer a viable pathway for deployment to support congestion mitigation and emission-reduction objectives.