The impact of deep reinforcement learning-based traffic signal control on Emission reduction in urban Road networks empowered by cooperative vehicle-infrastructure systems

The critical challenges of climate change have made carbon emission reduction an urgent global priority, with urban transportation systems (UTS) being significant contributors. Rapid urbanization and increased traffic demand have intensified congestion and carbon emissions. However, existing traffic signal control methods often rely on large historical data and pre-set signal timings, making it struggle to adapt dynamically to reduce emissions effectively. This study proposes a novel traffic signal control method based on Deep Reinforcement Learning (DRL), integrated with Cooperative Vehicle-Infrastructure Systems (CVIS) and a doubly Day-to-Day (DTD) Dynamic Traffic Assignment model, aimed at improving traffic efficiency and reducing carbon emissions. The DTD model simulates the urban road network as the training environment, while CVIS provides real-time traffic flow data that informs the DRL model's optimization of signal timings. Furthermore, the DTD model adjusts traveler departure times and route choices for significant emission reductions. The proposed DRL-based method significantly improves signal control efficiency and carbon emission reduction under complex traffic conditions. A case study on the Sioux Falls network indicates that the DRL-based traffic signal strategy outperforms traditional fixed-time control strategies, achieving CO2 emission reductions of 21 % to 27 % in various scenarios, particularly excelling in the SV scenario. Notably, emissions on high-traffic Link 28 (SV) were reduced by up to 54.9 %. This study underscores the potential of DRL in low-carbon traffic management and provides practical insights for the sustainable development of future traffic systems, offering robust solutions for emission reduction and efficient traffic management in UTS.