The Joint Impact of Traffic Signal Control and Automated Vehicles on Traffic Efficiency, Safety and Emissions: A Deep Reinforcement Learning Approach

ABSTRACT Recent developments in intelligent transportation systems underscore the promise of combining deep reinforcement learning (DRL)‐based traffic signal control (TSC) with automated vehicles (AVs) to improve intersection management. This study analyses how integrating DRL‐based TSC systems with AVs affects traffic efficiency, safety and emissions under varying demand levels. By simulating realistic driving behaviours and using sophisticated statistical methods, the research finds that DRL‐based TSC significantly outperforms traditional fixed‐time and actuated systems, effectively reducing congestion, emissions and conflicts. Queue length analyses reveal that DRL‐based TSC provides substantial efficiency gains, further enhanced by AVs, which reduce congestion through improved driving automation. Notably, the short‐term benefits of DRL‐based TSC at low AV market penetration rates resemble the long‐term effects of conventional systems at high AV adoption. While fuel consumption improvements under low demand are modest compared to other adaptive systems, high‐demand scenarios show significant advantages of DRL‐based TSC, with AV integration further optimising flow and reducing stop‐and‐go patterns. Safety analysis indicates that DRL‐based TSC improves intersection safety, particularly at low AV penetration, with AVs dramatically reducing conflicts. Overall, combining DRL‐based TSC with AV technology holds considerable potential for advancing traffic management, safety and environmental outcomes in urban settings.