## 4 Experiment and Results
### Analysis of Experimental Outcomes Under Varying Traffic Volumes  
Taking into account the significant variation in traffic flow at different times of the day on urban roads, this study not only validates the optimization effect of the model during peak hours but also compares and analyzes the performance of the intersection signal control strategy under three traffic intensities: 2400 pcu/h, 3600 pcu/h, and 4800 pcu/h. These traffic intensities correspond to vehicle arrival rates of 40 pcu/min, 60 pcu/min, and 80 pcu/min, respectively, simulating off-peak traffic conditions. By adjusting the green signal duration to 3 s and the yellow light duration to 2 s, the system can more effectively respond to varying traffic flow conditions.  
As depicted in Table 9, the average vehicle waiting time increases with traffic flow, with the actuated signal timing scheme experiencing the greatest rise and the Rainbow DQN_AM-based scheme showing the least. The Rainbow DQN_AM-CO2 Reduction scheme achieves the optimal optimization effect under all three traffic intensities, with waiting times of 4.38 s, 6.88 s, and 11.46 s, respectively. At traffic volumes of 2400 pcu/h and 3600 pcu/h, the D3 QN-CO2 Reduction scheme performs better than the Rainbow DQN_AM-Time scheme, with waiting times of 4.85 s and 7.45 s, respectively. At the highest traffic intensity, the optimization effectiveness of the timing schemes using the DQN and D3 QN algorithms significantly declines, while the scheme based on the Rainbow DQN_AM algorithm demonstrates better robustness. Furthermore, the proposed scheme in this paper demonstrates the best optimization performance under high traffic intensity, reducing average waiting times by 18.08%, 45.66%, and 68.09% compared with the Rainbow DQN_AM-Time, DQN-CO2 Reduction, and Webster signal timing schemes, respectively.  
As shown in Table 10, the average vehicle CO\({}_{2}\) emissions increase overall with rising traffic flow. Consistent with the trend in vehicle waiting time, the Rainbow DQN_AM-CO2 Reduction scheme achieves the best optimization results for average vehicle CO\({}_{2}\) emissions across all three traffic intensities, with values of 175.18 g, 180.53 g, and 192.02 g, respectively. In the 2400 pcu/h and 3600 pcu/h scenarios, the proposed scheme reduces CO\({}_{2}\) emissions by 1.73% and 1.41%, respectively, compared with the next best scheme,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Control Method**} & \multicolumn{3}{c}{**Average Waiting Time (s)**} \\ \cline{2-4}  & **2400 pcu/h** & **3600 pcu/h** & **4800 pcu/h** \\ \hline Webster signal timing & 13.51 & 20.51 & 35.91 \\ Actuated signal timing & 10.89 & 18.34 & 39.26 \\ DQN-CO2 Reduction & 7.73 & 10.11 & 21.09 \\ D3 QN-CO2 Reduction & 4.85 & 7.45 & 22.70 \\ Rainbow DQN_AM-Time & 4.87 & 7.69 & 13.99 \\ Rainbow DQN_AM-CO2 Reduction & 4.38 & 6.88 & 11.46 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average vehicle waiting time for different traffic intensities.  
D3 QN_CO2 Reduction. In the 4800 pcu/h scenario, emissions are reduced by 6.26% compared with the next best scheme, Rainbow DQN_AM-Time. In medium and low traffic intensity scenarios, the optimization performance of the D3 QN-CO2 Reduction and DQN-CO2 Reduction models, which are based on the same reward function, is inferior to that of the Rainbow DQN_AM-CO2 Reduction scheme. As traffic intensity increases, the performance gap in model optimization widens further. Under high traffic intensity, the proposed model reduces average CO2 emissions by 9.42% and 13.16% compared with D3 QN-CO2 Reduction and actuated signal timing schemes, respectively.  
Under different traffic volume conditions, the Rainbow DQN_AM-CO2 Reduction model demonstrated significant advantages in both the algorithm and reward function compared with classical DRL algorithms and reward functions. This confirmed its effectiveness in reducing the average vehicle waiting time and average CO2 emissions at signalized intersections under mixed traffic conditions.