## 4 Performance Evaluation
In the following simulations, we set weight \(\omega\) to 0.6, which focused slightly more on the driving speed. Figure 6a,b show the average driving distance for AVs to reach their destinations under different MPRs in City1 and City2, respectively. The MPR values are 30%, 50%, 70%, and 90%, thus indicating the ratio of AVs in a mixed traffic environment. For instance, MPR 90 comprises 90% AVs and 10% HVs in a traffic environment. The AVs learn to optimize their routes to the destination depending on the traffic situation; the higher the MPR, the lower the driving distance of the AVs. Therefore, as the MPR increases, the number of AVs learning the optimal route increases, thereby decreasing the driving distance to the destination. It is evident that the performance difference between City1 and City2 was relatively small. This was because it was less dynamic due to the fewer intersections and decision zones. _Proposed_ yields increased the values in the approximate range of 12-25% in City1 and 7-15% in City2 in terms of the driving distance when compared with those of _Compare_ and _ds + ad_. This was because the number of detours decreased when the remaining driving distance from the destination was considered. The reward model of _Compare_ is expressed  
Figure 5: The driving speed and distance of AV depending on the weight value of the reward function. Figure 6: The average driving distance for the different MPRs: (**a**) in City1 and (**b**) in City2. in driving time, whereas the reward model of \(ds+ad\) is in terms of driving speed and accumulated driving distance. Therefore, _Compare_ and \(ds+ad\) are more likely to detour because they choose a shorter driving time and distance, regardless of the distance to the destination, when determining the route. In addition, the rewards for both _Compare_ and \(ds+ad\) include information on the driving time; however, the expressions for the reward are slightly different. _Compare_ is a single reward and \(ds+ad\) is a combination of driving time and distance. These have different weights for the driving speed and distance. _Compare_ balances the driving speed and distances, whereas \(ds+ad\) is more focused on driving speed than on driving distance. Therefore, \(ds+ad\) drives approximately 3-7% more of a distance than _Compare_ in City1 and City2. Furthermore, \(ds+nv\) exhibited the worst performance in terms of driving distance. This is because the \(ds+nv\) model has more detour routes than other methods as its model rewards driving speed and vehicle density without considering the driving distance. Table 2 presents the average driving speeds at which the AVs traveled to their destination in City1 and City2. _Compare_ attempted to drive quickly to minimize travel time, while the other three methods aimed to reduce the driving distance or vehicle density while maintaining a high speed. This meant that all four methods had information about the driving speed; thus, the performance difference was relatively small, i.e., about 1-6% in City1 and City2. The driving speed also increased as the MPR increased. This was because multiple AVs learn to drive along the optimal route based on traffic conditions. Despite City1 having a smaller scale, its AV speed is approximately 13-15% slower than that of City2 due to the higher vehicle density on its roads. Figure 7a,b show the average driving time for AVs to reach their destinations under different MPRs in City1 and City2. The figures illustrate that the driving time decreases as the MPR increases. This indicates that the higher the number of AVs, the better the traffic flow. Additionally, the driving time is affected by both distance and speed, with shorter distances and higher speeds resulting in shorter driving times. Therefore, _Proposed_, which performed best in terms of both driving distance and speed, showed performance improvements of about 18-28% in City1 and 7-17% in City2 when compared with those of the other methods.