# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 5 Experimental Results and Analysis
### The Other System Performance Based on Hybrid Control Path Planning  
The ST-ETA prediction results in a traffic distribution that is close to the traffic equilibrium status, and it can be assumed that path planning according to this distribution will achieve SO. To better demonstrate the changes brought by hybrid control path planning to the traffic system, we have performed three traffic assignment models, namely UE, SO, and our proposed ST-ETA, based on static networks and real-time networks. In static networks, the traffic flows can be assigned according to the maximum capacity of the road network. The real-time network updates the network capacity periodically, considering the traffic load of the previous period. Figure 9 illustrates the overall traffic distribution based on UE, SO, and ST-ETA for both static and real-time networks. The horizontal axis indicates the evacuation volume range and the vertical axis indicates the road sections count within that range. It can be seen that there are more high-traffic sections with traffic between 1000 and 2000 in UE. In contrast, SO and ST-ETA, which are aimed at system optimization, have fewer high-volume road segments and a slight increase in low-volume segments. This suggests that the adoption of SO and ST-ETA results in a more even loading of the roadway segments. This is more conducive to maintaining a steady state and coping with the recovery of emergency evacuation scenarios. Comparing Figure 9a,b, it can be seen that the SO and ST-ETA traffic distributions are close to each other in the static network scenario. This is due to the fact that ST-ETA is mapped based on the SO method. While in the dynamic network scenario, the ST-ETA histogram distribution is overall flatter, indicating a more balanced traffic distribution. Especially for the low and medium traffic sections, its road utilization is higher, which is more capable of alleviating the overall load of the high traffic sections. This proves that ST-ETA is better adapted to the balanced evacuation in the dynamic network of emergency scenarios. Next, to visualize the system performance of ST-ETA under dynamic scenarios, we calculate the total evacuation time, average traffic load, and road utilization of the system with the three assignment models, as shown in Figure 10. UE has the highest total evacuation time. This is because its iteration only considers the traveler's self-interest to find the optimal path, and fails to consider the impact of the process that travels in real-time cooperation and competition among ODs on the road network state. In the dynamic network scenario, the average traffic load of UE is 215 and the road utilization is 62.66%. The average traffic load of SO decreased by 4.88% and road utilization increased by 8.57% compared to UE. ST-ETA had the lowest average traffic load of 201 and road utilization of 68.86%.This is consistent with the results of Figure 7c,d of Experiment 5.2. The improvement in ST-ETA road utilization is obtained at the expense of the total scheduling time. While the system travel time is comparable to SO, the error from the absolute system optimum is within acceptable limits. Hybrid control path planning is used to advise multi-evacuation travelers to select paths in congested networks following the principle of system optimization. Specifically, ST-ETA predicts the volume of travelers' evacuation in advance to generate a traffic topology configuration. The hybrid control path planning prioritizes the road sections in the topology configuration during the routing decision, with the final path containing the maximum potential traffic. Figure 11 shows the paths using the Dijkstra shortest path planning method and the hybrid control path planning based on ST-ETA. Compared to the shortest path, the paths in Figure 11b use more road sections which come from the topology configuration obtained by ST-ETA prediction. Therefore, the hybrid control path planning contributes to the evolution of congested network to system optimization.