## 6 Experiment and Analysis
### Performance Evaluation  
In the conducted evaluation experiments, we undertake a comprehensive reconfiguration of the charging infrastructure by leveraging real-world charging station data while considering various constraints, including budgetary limitations. As the benchmark, we employ the existing layout of charging facilities, with all performance indicators normalized to 100%. Tables 2 and 3 present the performance outcomes of our PPO-Attention algorithm in comparison to several baseline algorithms across diverse urban road networks. Specifically, Table 2 showcases the results obtained for Stanford, California, United States;Queenstown, Central Region, Singapore; and Cambridge, United Kingdom, while Table 3 encompasses the outcomes for Rouen, France, and Culver City, California, United States. Table 2 reveals compelling findings for the Stanford region, wherein our algorithm achieves an astounding 426.38% increase in profit when contrasted with the established baseline configuration. This remarkable improvement markedly surpasses the outcomes attained by the other three baseline algorithms, which record profit increases of 228.09%, 329.98%, and 200.70%, respectively. Moreover, our devised solution effectively diminishes waiting time, travel time, and charging time by 80.81%, 41.57%, and 37.54%, respectively. Similarly, for the Queenstown, Central Region, Singapore area, our algorithm yields a remarkable profit escalation of 253.10% compared to the existing baseline, thus surpassing the performance of the other three baseline algorithms, which attain profit increments of 225.86%, 192.42%, and 192.42%, respectively. Furthermore, our proposed solution considerably reduces waiting time, travel time, and charging time by 85.15%, 2.03%, and 86.55%, respectively. Notably, it is evident that in this specific region, the genetic algorithm displays superior performance in terms of travel time and longest travel time, while our algorithm exhibits remarkable excellence across various other evaluation metrics. In the Cambridge, United Kingdom area, our algorithm's planning solution achieves a noteworthy comprehensive profit increase of 141.00%, surpassing the outcomes attained by the alternative baseline algorithms. Analogously, Table 3 showcases the impressive results for the Rouen, France and Culver City, California, United States regions, where our algorithm accomplishes comprehensive profit improvements of 248.67% and 221.04%, respectively, thereby outperforming the other baseline algorithms in each respective region. Furthermore, Table 4 consolidates the average outcomes obtained across all datasets. Remarkably, the PPO-Attention algorithm, rooted in real-world charging station layouts, achieves a remarkable average profit increase of 258.04%, while concurrently reducing the average waiting time by 73.40%, travel time by 18.47%, and charging time by 40.10%. When juxtaposed with the alternative baseline algorithms, our PPO-Attention algorithm emerges as the definitive frontrunner across all evaluation metrics. To enhance comprehension, Figure 4 provides visual representations of the planning outcomes derived from our PPO-Attention algorithm for diverse urban charging station layouts. These visual underscore the algorithm's ability to achieve a harmonious equilibrium by effectively considering various factors such as the prevailing distribution of charging stations, charging demand patterns, and budgetary constraints inherent to distinct urban road networks.