## 3 Case Study
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Number of** \\ POIs for** \\ **Higher** \\ **Education** \\ **Institutions** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **GOvernment** \\ **Agencies** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Mall POIs** \\ **Mall POIs** \\ **Hospital POIs** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **General** \\ **of POIs** \\ **of POIs** \\ \end{tabular} &
\begin{tabular}{c} **Total Number** \\ **Sorting** \\ \end{tabular} \\ \hline Wenchuan Highway & 10 & 11 & 2 & 2 & 25 & 1 \\ Inner Ring & 16 & 2 & 0 & 0 & 18 & 2 \\ Elevated Road & & & & & & \\ Shanghai Ring & 6 & 2 & 1 & 5 & 14 & 3 \\ Expressway & & & & & & \\ Humin Road & 7 & 0 & 4 & 1 & 12 & 4 \\ Caoan Highway & 11 & 0 & 0 & 0 & 11 & 5 \\ Longwu Road & 0 & 10 & 0 & 0 & 10 & 6 \\ Zhaojiabang Road & 2 & 2 & 3 & 2 & 9 & 7 \\ Dalian West Road & 9 & 0 & 0 & 0 & 9 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the ranking of road sections according to the number of POIs (partial). be considered as the impact of the failure of this road segment on the total network wait time. To better represent the socio-economic loss to road users caused by the road section failure, we propose the concept of \"value of time\". Using the average monthly salary of Shanghai residents in 2021 as a benchmark, which is RMB 9580, and the average hourly wage of Shanghai residents in 2021 is USD 8.17, considering the current exchange rate. The concept of the value of time links the roadway waiting time and socio-economic loss, providing a conduit to calculate the socio-economic loss from the SUMO experimental data. Consequently, we obtain the formula to calculate the socio-economic loss from each road section failure, as shown in Equation (12). \[\text{SEL}=\text{VoT}\times(\text{T}_{i}-\text{T}_{i-1}) \tag{12}\]  
where SEL represents the Social-Economic Loss (SEL) and VoT represents the value of time (VoT), which is USD 8.17/h. \(T_{i-1}\) represents the total waiting time of the whole network in the previous time interval. \(T_{i}\) represents the total waiting time of the whole network in the latter time interval. The resulting as shown in Table 5, the ranking method ranks the socio-economic loss impacts of the failures generated by the 50 critical roads selected in the failure model as the order of the recovery model, and it restores them sequentially in the recovery process. In the simulation experiment, disruptive events sequentially occur on the road sections in the order identified in the failure mode at specific time intervals. These events cause the target road sections to lose capacity and service level. Subsequently, recovery measures are implemented sequentially in the order identified in the recovery mode at certain time intervals to restore the capacity and service level of the target road sections. The detailed simulation experiment process is as follows. During the initial hour of the simulation experiment, traffic flows are gradually generated randomly and dispersed throughout the network, slowly reaching a balanced and stable state [19]. Based on the preliminary experiments, it takes approximately 50 min for the traffic flow to disperse throughout the Shanghai urban road traffic network and reach a stable operational state. To avoid interference with subsequent experiments, we allow the traffic flow to run freely in the network for the first hour. After one hour, a road is closed every 10 min according to the sequence established in the failure model, simulating the occurrence of a disruptive event that eliminates the roadway capacity and service level. Simultaneously, the software is set to allow all road users to re-route every 10 min based on road conditions, simulating the process of road users re-routing to the shortest alternative routes upon learning of the incident. After all 50 critical roads identified in the failure mode have sequentially failed, the simulation experiment is allowed to continue running