## 5 Discussion, Conclusions, and Limitations
### Conclusions and Limitations  
This paper is based on the Rainbow DQN algorithm and utilizes the Reward-CO2 Reduction function for training to optimize intersection signal timing. The conclusions are as follows:  
(1) Utilizing Rainbow DQN to explore real-time optimization of signal timing, the introduction of the Acmix module and self-attention mechanism significantly enhanced the model's computational efficiency and performance. To facilitate efficient learning of signal control strategies by the agent, vehicle acceleration was incorporated into the state space modeling. The action space was defined based on variable phase sequences, while the reward function was formulated according to the average vehicle waiting time and CO2 emissions. The calculation of CO2 emissions at the intersection takes into account both the specific power model for fuel vehicles and the energy conversion model for electric vehicles, making it more reflective of real-world conditions. (2) The model was validated using actual road intersection data, demonstrating the significant superiority of the Rainbow DQN_AM algorithm compared with signal timing based on DQN and D3 QN algorithms. This also verified the effectiveness of the Reward-CO2 Reduction reward function. The proposed scheme reduces average vehicle waiting time by 7.48% and average CO2 emissions by 4.37% compared with the Rainbow DQN_AM-Time scheme. It also achieves a reduction of approximately 27.58% in vehicle waiting time and about 7.34% in average CO2 emissions compared with the actuated signal timing scheme. (3) The impact of different CO2 emission factors of electric vehicles on the model is not significant. As the proportion of electric vehicles increases, the optimization effect of the proposed reward function in this study decreases. At medium and low proportions of electric vehicles, compared with the actuated signal timing, the Rainbow DQN_AM-CO2 Reduction method can reduce the average waiting time by more than 15% and decrease the average CO2 emissions by 4% to 7%. Under varying traffic intensities, the Rainbow DQN_AM-CO2 Reduction method adjusts its control strategy according to the traffic flow, reducing CO2 emissions at the intersection and improving traffic efficiency. In the 3600 pcu/h traffic volume scenario, the proposed method shows the best optimization effect, but as traffic volume increases, the optimization effect decreases to some extent. This study does have certain limitations. First, the research focuses solely on passenger cars and does not account for other types of vehicles. Future studies could include different vehicle types and categorize them based on their size and characteristics in the state input. Additionally, the model's state-space input only considers factors such as vehicle position, speed, and acceleration. Previous studies [56] have demonstrated the feasibility of incorporating turn signal information into the state space. In the future, this information could be encoded as one of the state inputs to enhance the model's adaptability at different intersections. Second, the model validation is primarily based on traffic flow data generated in a simulation environment and does not fully account for the complexities of real-world traffic scenarios, such as extreme weather conditions, unexpected events (e.g., traffic accidents), or fluctuations in pedestrian density. These factors may affect the model's performance in actual environments, and further real-world testing is needed for validation. Lastly, the training process of Rainbow DQN is highly dependent on computational resources, especially in large-scale traffic networks. This could pose significant real-time challenges during training and deployment. Therefore, future research should incorporate real-world traffic data to improve the model's generalization capability and explore more efficient algorithms to meet the demands of real-time optimization. Conceptualization, data curation, methodology, validation, and writing--original draft, J.L.