AI‑Driven Traffic Signal Control System to Reduce CO2 Emissions

Rapid urbanization and the resulting traffic congestion have made the reduction of transport‑related carbon‑dioxide (CO2) emissions a pivotal goal for sustainable‑city initiatives. This paper introduces an artificial‑intelligence (AI) traffic‑signal control system that exploits deep reinforcement learning (DRL) to achieve dynamic, emission‑aware phase scheduling at signalized intersections. Unlike conventional fixed‑time or actuated controllers, the proposed framework continuously perceives multi‑modal state information, queue length, average speed, arrival rates and projected emergency‑vehicle trajectories, through a digital twin fed by microscopic traffic simulation. A convolutional DRL agent, trained with proximal‑policy optimization and shaped by a composite reward that penalizes total CO₂, queue growth and delay while rewarding throughput, learns an adaptive policy that balances environmental and mobility objectives. In controlled experiments mirroring a mid‑size European arterial, the AI agent lowers cumulative CO2 emissions by 18% without sacrificing pedestrian service levels. The learned policy exhibits robust generalization under varying traffic demand patterns and mild sensor noise, suggesting strong transferability.