# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 7 Conclusions  
In this study, we proposed a traffic light control method using a Deep Q-network with fuzzy logic, with the aim of reducing waiting times at intersections and increasing the efficiency of the method based on the state, where adjustments were made to consider the perspective of the individuals responsible for making traffic light control decisions. However, previous studies using the Deep Q-network have yielded suboptimal results due to the reward function, which causes the system to prioritize straight-moving vehicles, making left-turning vehicles wait excessively. Therefore, to solve this problem, we adjusted \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**With the Ambulance Prioritization System**} \\ \hline \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**Original DQN**} & \multirow{2}{*}{**Proposed**} \\  & **with Fuzzy Logic**[29] & & **Method** \\ \hline WT\({}_{\text{AVG}}\) (s) & 17.01 & 13.14 \\ \hline WT\({}_{\text{Total}}\) (s) & 0.04 & 0.00 \\ \hline \multicolumn{4}{c}{**Without an ambulance prioritization system**} \\ \hline \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**Original DQN**} & \multirow{2}{*}{**Proposed**} \\  & **with Fuzzy logic**[29] & & **Method** \\ \hline WT\({}_{\text{AVG}}\) (s) & 16.14 & 13.16 \\ \hline WT\({}_{\text{Total}}\) (s) & 65.24 & 67.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The experimental results show when there is an ambulance prioritization system and no ambulance prioritization system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**Average Total Waiting Time(s)**} \\ \hline \multirow{2}{*}{**Safety**} & \multicolumn{4}{c}{**Detection Distance(m)**} \\ \cline{2-5}  & 150 & 200 & 250 & 300 \\ \hline
3 & 12.87 & 12.98 & 13.14 & 13.53 \\ \hline
5 & 14.17 & 13.23 & 13.48 & 13.92 \\ \hline \multicolumn{5}{c}{**Total ambulance waiting time(s)**} \\ \hline \multirow{2}{*}{**Safety**} & \multicolumn{4}{c}{**Detection Distance(m)**} \\ \cline{2-5}  & 150 & 200 & 250 & 300 \\ \hline
3 & 0.24 & 0.24 & 0.00 & 0.00 \\ \hline
5 & 2.96 & 1.20 & 0.20 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The experimental results of the ambulance prioritization system with different safety durations and detection distances. the reward function to consider the waiting time in each lane. We conducted experiments in a variety of environments to test the flexibility of the method and account for the different lane conditions in real situations. Moreover, we implemented a system to prioritize ambulances to pass through the intersection first. By learning in the prototype environment and applying the model obtained from learning to various environments close to the real environment, the experimental results from using the proposed method were compared with a comparison target, which yielded better results for all environments and reduced the average total waiting time, average total waiting time for vehicles making a left turn, and average total waiting time per car by 18.46%, 40.36%, and 21.23%, respectively, in the prototype environment. Additionally, in the experiment using the ambulance prioritization system, an appropriate safety duration and detection distance of 3 s and 250 m, respectively, were determined, which resulted in a significant reduction in the ambulance waiting time. For this research, the proposed method was only applied to a four-way single intersection. In this study, the intersection environment was a single intersection that was not connected to other intersections. The intersection is considered isolated, and a larger number and variety of vehicles are required to ensure greater consistency with the real environment. Therefore, for future work, we plan to develop a system that can accommodate more situations by increasing the number of vehicles, diversifying vehicle types, and creating more interconnected intersections. Conceptualization, T.M. and S.W. ; methodology, T.M. and S.W. ; writing--original draft preparation, T.M. ; writing--review and editing, T.M. and S.W.