###### Abstract  
To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleration information into the state space. The reward function simultaneously considers two objectives: reducing vehicle waiting times and minimizing carbon emissions, with the vehicle queue length as a weighted factor. Additionally, an Acmix module, which integrates self-attention mechanisms and convolutional layers, is introduced to enhance the model's feature extraction and information representation capabilities, improving computational efficiency. The model is tested using an actual intersection as the study object, with a signal intersection simulation built in SUMO. The proposed approach is compared with traditional Webster signal timing, actuated signal timing, and control strategies based on DQN and D3 QN models. The results show that the proposed strategy, through real-time signal timing adjustments, reduces the average vehicle waiting time by approximately 27.58% and the average CO\({}_{2}\) emissions by about 7.34% compared with the actuated signal timing method. A comparison with DQN and D3 QN models further demonstrates the superiority of the proposed model, achieving a 15% reduction in average waiting time and a 6.5% reduction in CO\({}_{2}\) emissions. The model's applicability is validated under various scenarios, including different proportions of electric vehicles and traffic volumes. This study aims to provide a flexible signal control strategy to enhance intersection vehicle flow and reduce carbon emissions. It offers a reference for the development of green, intelligent transportation systems and holds practical significance for promoting urban carbon reduction efforts. Article  
11 December 202414 January 2  
and left-turn intersections at exit points [9; 10] have been proposed. However, these new intersection design methods often face the challenge of substantial engineering work in practical applications. The increasing complexity and time-variance of traffic systems have also led to the emergence of intelligent signal control methods, including those based on game theory [11], heuristic algorithms [12], and data-driven approaches [13]. Researchers have applied methods such as neural networks [14], particle swarm optimization [15], and genetic algorithms [16] to traffic signal control in an effort to enhance the system's global optimization capability. However, these algorithms face inherent issues, including low computational accuracy, difficulty in convergence, and slow convergence speeds. These limitations restrict their effectiveness in finding optimal traffic signal timing solutions and improving the throughput efficiency of road networks. Meanwhile, with the rapid development of data-driven methods, Deep Reinforcement Learning (DRL) has emerged as a powerful technology for traffic signal control. DRL combines reinforcement learning with deep neural networks, offering advantages such as high adaptability and strong learning capability. By leveraging trial-and-error learning, DRL can overcome the decision-making limitations of traditional models and find optimal strategies in high-dimensional environments. As a result, it has gradually become a popular method for optimizing intersection signals [17; 18]. For instance, [PERSON] et al. [19] proposed a signal optimization strategy based on a competitive double deep \(Q\)-network (3 RDQN) algorithm, which introduced an LSTM network to reduce the algorithm's reliance on state information, effectively improving intersection throughput. [PERSON] et al. [20] presented an intersection signal timing optimization method based on a hybrid proximal policy optimization (PPO) approach. By parameterizing signal control actions, this method reduced the average vehicle travel time by 27.65% and the average queue length by 23.65%. [PERSON] et al.