## 8 Performance Analysis
### The Evaluation of Different Travel Start-Destination Time Cost  
Figure 8 shows the comparison of AEV travel time for DRT-A* with CA* in different start-destination nodes. We can observe that the travel time with our DRT-A* algorithm is lower than or equal to the CA* algorithm. The DRT-A* algorithm can replan the original path based on updating real-time traffic information so that the traveling time estimation is much more accurate than the CA* algorithm. Furthermore, our DRT-A* algorithm considers the TFD constraint of the road segment in the path-planning, which can alleviate traffic congestion and reduce travel time. Each path can arrive at the destination, which verifies our proposed algorithm's convergence. The average performance deviation between our proposed method and the CA* approach in terms of travel time is 2.87 min. Figure 9 shows the computation time for each of the instances. It can be observed that the computation time with our DRT-A* algorithm is lower than the CA* algorithm. The DRT-A* algorithm improved the average performance by 27.8% compared to the CA* algorithm, and the computation time for each instance is very close, with a difference of less than 8% between the maximum and minimum computation times. The results demonstrate the efficiency of the DRT-A* algorithm in finding optimal paths for AEVs in urban traffic networks while reducing travel time and computation time. Figure 8: Travel time comparison between CA* and DRT-A*. Figure 9: Computing time. Figure 10a shows the comparison of AEV charged time and queued time for DRT-CS with CA* in different start-destination pairs. The charged time obtained here is when the charging rate is 70 kWh. We can observe that the queue time with our DRT-A* algorithm is lower than or equal to CA* algorithm in start-destination #3_to_#21, #11_to_#35, #4_to_#40, #18_to_#2 and #2_To_#9. The DRT-CS algorithm can select the charging infrastructure based on updating the number of queued vehicles and the service status of charging slots so that the queue time estimation is much more accurate. The travel path planned by the CA* algorithm takes more time compared to the DRT-A* algorithm in start-destination #3_to_#21, #11_to_#35 and #2_To_#9, which causes AEVs to consume more charge. As a result, when all AEVs are charged to 90% of their battery capacity, the charging time will be longer to compensate for the higher energy consumption. In DRT-CS, we can observe that there is no queued time in start-destination #33_to_#7,#3_to_#12, and the charged time is also relatively short. This is because the vehicle chooses to charge at the ES, and the charging capacity does not reach 90%. The average performance deviation between our proposed method and the CA* approach in terms of charging time and queuing time is 6.11 min and 4.85 min, respectively. Figure 10b shows the charged cost of the vehicle. The unit charging price of charge station is RMB 1.1, and the unit charging price of ES is RMB 2.8. In the travel of start-destination #3_to_#21, #11_to_#35 and #2_To_#9, when the AEV's battery is charged to 90% capacity, AEV will require more charge with CA* algorithm compared to the DRT-CS algorithm. As a result, the AEV needs to pay for more charge with the same unit charging price. The mobile charging service provided by ES for AEV does not require queued time compared with charge station. However, due to the length limitation of ES, the charged capacity of vehicles may not reach 90%, and the charged cost will be higher than charge station. Figure 11a shows the charged time of the vehicle at different charged speeds in the selected charging infrastructure. We can observe that the charged time required in ES in start-destination #3_to_#7 and #3_to_#12 is less than the minimum charging time in charge station, because the charging capacity in ES is less than that in charge station. Choosing a charging slot with a faster charging rate reduces the charging time required but increases the charging cost. Therefore, the vehicle can select a charging slot based on its individual needs.