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.