## 4 Results and Analysis  
The processing and analysis of the charging demand data, as well as the selection of charging station candidates, were conducted using Python. As the total number of charging demand points was 15,760 and the model settings allowed a maximum of 20 chargers per charging station, with a service rate uniformly distributed between 30 and 60 min, the number of charging station candidates was strategically designed to be 80. These 80 stations, in theory, have the capacity to serve up to about 25,600 charging demands (considering 20 chargers per station, times 80 stations, times a conservative estimate of 12 h during daytime, and assuming an average service time of 45 min). The initial solutions generated by the K-means algorithm are shown in Figure 3, where the yellow cross marks indicate the exact deployment locations of the charging stations. Points sharing the same color in the vicinity of each cross represent the corresponding demand points associated with those charging stations. In this study, the IKQT algorithm was adopted to enhance the overall service quality with respect to charging station deployment. The number of iterations in the IKQT was set to be 10, with both \(\alpha\) and \(\beta\) parameters selected to be 0.5. The final solution generated by the IKQT is shown in Figure 4.  
Table 1 summarizes the comparative influence factors that represent the overall service quality of the initial and final charging station deployments, along with their respective rates of change, as calculated using Equation (2). The values in this Table reflect the queuing theory simulation results for both the initial solution generated by K-means and the final solution obtained after the last IKQT iteration. Notably, not all influence factors exhibit a uniform trend towards improved service quality. Figure 2: Demand points’ distribution. Red dots are the input charging demand points for the IKQT, the location of each dot is the latitudes and longitudes of the service destinations in the service data. Figure 4: IKQT solution of charging station deployment. The colored dots represent the corresponding charging demand points for each charging station (indicated by yellow crosses), meaning that dots of the same color surrounding a yellow cross will go to that specific charging station. Figure 3: Initial solution of charging station deployment. The colored dots represent the corresponding charging demand points for each charging station (indicated by yellow crosses), meaning that dots of the same color surrounding a yellow cross will go to that specific charging station. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison Influence Factors of Service Quality** & **Initial** & **Final** & **Changing Rate** \\ \hline Max. sum of waiting time in one charging station (min.) & 69,347.03 & 59,980.14 & \(-\)13.51\% \\ Max. average waiting time in one charging station (min.) & 193.71 & 201.95 & 4.25\% \\ Max. number of unserved taxis in one charging station & 464 & 368 & \(-\)20.69\% \\ Total waiting time of all charging services (min.) & 544,487.60 & 509,590.38 & \(-\)6.41\% \\ Total number of unserved taxis in all charging stations & 1319 & 1071 & \(-\)18.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between initial and final solutions. Specifically, the IKQT solution demonstrates a reduction in the maximum total waiting time at a single charging station, the maximum number of unserved taxis at a single charging station, the total waiting time for all charging services, and the total number of unserved taxis across all charging stations, compared to the initial solution. However, the IKQT does not reduce the maximum average waiting time at a single charging station. Based on the values presented in Table 1, it is evident that the IKQT algorithm effectively optimizes the deployment of charging stations to enhance the overall charging service quality.