## 4 Experiment and Results
### Training Results

Table 7: Statistical significance analysis of optimization indicators. Figure 8: The heatmap of optimization effects of different signal control strategies: (**a**) Average vehicle waiting times; (**b**) average vehicle CO\({}_{2}\) emissions. average waiting time due to the low-emission nature of the electric vehicles themselves. Additionally, the Rainbow DQN_AM-CO2 Reduction scheme shows standard deviations of 0.56 for average vehicle waiting time and 1.06 for average CO2 emissions, significantly outperforming the Rainbow DQN_AM-Time scheme, which uses the same algorithm but with a different reward function. These results indicate that a reward function considering both waiting time and CO2 emissions helps the agent learn a more reliable control strategy. By analyzing the data on average waiting time and average CO2 emissions, it is evident that in the simulation scenario using real traffic flow data, the proposed Rainbow DQN_AM-CO2 Reduction model demonstrates clear superiority. Compared to the DQN and DQN models, it reduces average waiting time by approximately 17.16% and 14.22%, respectively, and decreases average CO2 emissions by about 6.94% and 6.39%. To provide a comprehensive comparative analysis of the model, the results of this study are critically compared with those of related literature. Given that reinforcement learning models are influenced by various factors such as state space and reward functions, which can lead to differences in outcomes, we selected a study [35] as a comparison due to its similarities in state space, reward function, and other aspects with the proposed model. The model in [35] considers a mixed traffic scenario with Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), using a D3 QN framework with a CNN module. The state space includes vehicle position, speed, and acceleration, while the reward function considers average vehicle waiting time and CO2 emissions with fixed weight parameters. A comparison of the optimization results between the proposed model and the model in [35], in relation to the optimization of signal timing with actuated signal control, is shown in Table 8. It is evident that, in the scenario based on real traffic data, the proposed model outperforms the D3 QN_CNN model in terms of optimization. This suggests that the Rainbow DQN algorithm, as an integrated model, demonstrates superior performance in real-time signal optimization compared with the D3 QN algorithm. Furthermore, the ACmix module introduced in this study enhances the model's processing capabilities more effectively than the CNN module used in [35]. Additionally, compared with the fixed weighting of the reward function in [35], the dynamic adjustment of the queue  
Figure 9: The boxplot of optimization effects at different signal control strategies: (**a**) Average vehicle waiting time; (**b**) average vehicle carbon dioxide. length coefficient in the proposed model also contributes to the improved optimization performance. However, it is important to note that there are still some differences between the intersection scale, entry lane configuration, and traffic data used in [35] and those in this study. Furthermore, the mixed traffic scenario in [35] focusing on CAVs and HVs differs from the mixed traffic scenario with gasoline and electric vehicles in this study. Therefore, this comparison serves only as a reference. Comparative Analysis of Different CO\({}_{2}\) Emission Factors for Electric Vehicles and Electric Vehicle Proportions  
With the gradual development of electric vehicles (EVs), the proportion of EVs is expected to change in the future. At the same time, the CO\({}_{2}\) emission factors for electric vehicles published by the government vary annually. This section explores the model performance under different CO\({}_{2}\) emission factors for electric vehicles and varying proportions of electric vehicles, based on real traffic flow data.