## 5 Conclusions  
Consequently, we showed that leading autonomous vehicles became more worthwhile with respect to the DRL policy, mobility, and energy with their higher AV penetration  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**AV Penetration Rate**} & \multicolumn{3}{c}{**Average Reward**} \\ \cline{2-3}  & **Proposed Hyperparameter** &
\begin{tabular}{c} **PPO with Adaptive KL** \\ **Penalty Hyperparameter** \\ \end{tabular} \\ \hline
20\% & 43,863.63 & 36,656.48 \\
40\% & 81,232.67 & 63,854.03 \\
60\% & 101,690.6 & 93,752.99 \\
80\% & 123,731.6 & 101,102.62 \\
100\% & 135,611.7 & 113,793.09 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A comparison of the proposed hyperparameter and the PPO with an adaptive Kullback-Leibler (KL) penalty hyperparameter over the AV penetration rates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**AV Penetration Rate**} & \multicolumn{2}{c}{**Average Speed (m/s)**} & \multicolumn{2}{c}{**Average Reward**} \\ \cline{2-3}  & **Leading Autonomous Vehicle Experiment** & **Leading Manual Vehicle Experiment** & **Leading Manual Vehicle Experiment** & **Leading Manual Vehicle Experiment** \\ \hline
20\% & 7.31 & 6.79 & 43,863.63 & 39,709.56 \\
40\% & 7.46 & 6.90 & 81,232.67 & 75,080.93 \\
60\% & 7.67 & 7.14 & 101,690.6 & 87,551.11 \\
80\% & 7.74 & 7.51 & 123,731.6 & 116,557.88 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of the leading autonomous vehicle experiment and the leading manual vehicle experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**AV Penetration Rate**} & \multicolumn{2}{c}{**Average Speed (m/s)**} & \multicolumn{2}{c}{**Average Reward**} \\ \cline{2-3}  & **Leading Autonomous Vehicle Experiment** & **Leading Manual Vehicle Experiment** & **Leading Manual Vehicle Experiment** \\ \hline
20\% & 7.31 & 6.79 & 43,863.63 & 39,709.56 \\
40\% & 7.46 & 6.90 & 81,232.67 & 75,080.93 \\
60\% & 7.67 & 7.14 & 101,690.6 & 87,551.11 \\
80\% & 7.74 & 7.51 & 123,731.6 & 116,557.88 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of the leading autonomous vehicle experiment and the leading manual vehicle experiment.  
rates. Additionally, the traffic flow performed better than with a higher AV penetration rate. Full automation traffic performed better than other AV penetration rates. Full automation traffic showed an improvement in the average speed by 1.27 times compared with the entire manual vehicle experiment. Furthermore, the leading autonomous vehicle experiment increased the average speed 1.07 times compared with the leading manual vehicle experiment. Therefore, the leading autonomous vehicle experiment outperformed the leading manual vehicle and entire manual vehicle experiments.  
In summary, the leading autonomous vehicles performed much better compared with the other experiments. Our major contributions are the advanced DRL-based PPO hyperparameters that enhanced an effective performance of the mixed-traffic environment in the urban network with different AV penetration rates. The proposed method becomes more effective with a higher AV penetration rate. Furthermore, traffic management agencies and researchers could apply this proposed method to mitigate traffic congestion due to stop-and-go behavior. In our future work, we will consider the comprehensive turning (left-turn, right-turn, and lane change) and the disturbance factors by adopting a more hybrid deep machine learning method. Furthermore, we will try to compare with other AI approaches to evaluate the advantages of the proposed method.  
The authors jointly proposed the idea and contributed equally to the writing of the manuscript. [PERSON] designed the algorithms and performed the simulation. [PERSON], the corresponding author, supervised the research and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.