## 4 Results  
Using the data output from SUMO, the average speed of each road section in the Shanghai urban road traffic network in every ten-minute time interval is selected as a measure of the network function, and the waiting time in every ten-minute time interval is used to calculate the socio-economic loss. Since the Shanghai urban road traffic network in SUMO is a directed graph, we have to process it into an undirected graph in order to facilitate the visualization of the data. The speed of a road closed due to a vandalism event is set to zero, and the speed of the same road section in both directions is averaged to obtain the average speed of each road section interval in the network every ten minutes. The average speed of the whole network during the ten-minute interval can be obtained by summing up the average speed of all the road sections during the ten-minute interval and averaging them. The horizontal axis represents the time and the vertical axis represents the average speed of the whole network, so we can draw a graph of the dynamic change in the average speed of the whole network over time. The total waiting time in the network can be obtained by summing up the waiting time of all the sections in each ten-minute interval, and the socio-economic loss of the network can be calculated by Equation (12). With the horizontal axis representing the time and the vertical axis representing the socio-economic loss of the whole network, a graph of the dynamic change in the socio-economic loss of the whole network with time can be drawn. By fitting these two curves with a polynomial, we can obtain the network-wide average speed and socio-economic loss distribution in time under the scenario of a bad traffic accident. The network-wide average speed and the time distribution of the network-wide socio-economic loss for the disruptive event scenario are shown in Figures 4 and 5, respectively. The blue dashed lines in the two figures indicate the processed simulation experimental data, and the red solid lines indicate the polynomial fitted curves. Figure 3: Simulation experiment flow. The time distribution of the average speed of the whole network shown in Figure 3 can be divided into five phases: the initial smooth phase (0-t1), the disruption phase (t1-t2), the post-disruption smooth phase (t2-t3), the recovery phase (t3-t4), and the post-recovery smooth phase (t4-t5). Before the damage event (initial smooth phase), the whole network is in a normal operation state, and the average speed of the whole network is in a smooth and slightly fluctuating state. After the disruptive event (disruption phase), the average speed of the whole network starts to fluctuate and drop. When the drop reaches its lowest value (post-damage stabilization phase), a new stabilization phase is reached. However, at this time, the capacity and level of service are low and the network average speed is in a very low, slightly fluctuating state. After the roadway network gradually starts to take recovery measures (recovery phase), the network average speed gradually increases and reaches a new phase (post-recovery smooth phase) at a specific value. It is worth pointing out that since we are simulating a short recovery period, the remaining traffic congestion has an impact on the average speed of the whole network, and the average speed of the post-recovery smooth phase is slightly lower than that of the initial smooth phase. Similarly, the time distribution of the socio-economic loss of the whole network shown in Figure 4 can be divided into five phases: initial smooth phase (0-t1), disruption phase (t1-t2), post-disruption smooth phase (t2-t3), recovery phase (t3-t4), and post-recovery  
Figure 4: Average speed time distribution of the whole network. Figure 5: Network-wide socio-economic loss time distribution. smooth phase (t4-t5). Before the disruptive event, the entire network is in a normal operating state, with a slight extension of the waiting time due to the gradual increase in vehicles and a gradual increase in socio-economic losses.