Carbon Dioxide Emission Reduction-Oriented Optimal Control of Traffic Signals in Mixed Traffic Flow Based on Deep Reinforcement Learning

To alleviate intersection traffic congestion and reduce carbon emissions at intersections, research on exploiting reinforcement learning for intersection signal control has become a frontier topic in the field of intelligent transportation. This study utilizes a deep reinforcement learning algorithm based on the D3QN (dueling double deep Q network) to achieve adaptive control of signal timings. Under a mixed traffic environment with connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), this study constructs a reward function (Reward-CO2 Reduction) to minimize vehicle waiting time and carbon dioxide emissions at the intersection. Additionally, to account for the spatiotemporal distribution characteristics of traffic flow, an adaptive-phase action space and a fixed-phase action space are designed to optimize action selections. The proposed algorithm is validated in a SUMO simulation with different traffic volumes and CAV penetration rates. The experimental results are compared with other control strategies like Webster's method (fixed-time control). The analysis shows that the proposed model can effectively reduce carbon dioxide emissions when the traffic volume is low or medium. As the penetration rate of CAVs increases, the average carbon dioxide emissions and waiting time can be further reduced with the proposed model. The significance of this study lies in its dual achievement: by presenting a flexible strategy that not only reduces the environmental impact by lowering carbon dioxide emissions but also enhances traffic efficiency, it provides a tangible example of the advancement of green intelligent transportation systems.