## 3 Case Study
In the post-accident identification framework, we consider the impact of the damage caused by each road segment in the network according to the actual socio-economic loss induced by the disruptive event, and we determine the recovery mode based on the criticality of the road segments within the Shanghai urban road traffic network. To obtain real-time traffic attributes, we utilized the API interface provided by AMAP to calculate the real-time average speed data for driving on all road sections within a specific area. Specifically, we collected the real-time average speed data for all roads across the entire city of Shanghai at 18:00 during an evening peak hour on a weekday. This data was then matched with the corresponding road sections in the SUMO road network, allowing us to determine the real-time average speed for all roads within the studied network. Based on the relationship between traffic flow, speed, and density, and leveraging the classical Underwood model, we inferred the real-time traffic flow on each road section using the real-time average speed data. The road sections were subsequently ranked according to their traffic volume. The relationship between flow, speed, and density is shown in Equation (5), the equation for the Underwood model is shown in Equation (6), the equation of the traffic attributes in the pre-accident identification framework is shown in Equation (7), and the results of ranking the road sections according to traffic flow size are shown in Table 1.  
\[q=uk \tag{5}\]  
\[u=u_{f}e^{-\frac{k}{km}} \tag{6}\]  
\[R_{traffic}=rank(traffic\ volume) \tag{7}\]  
where \(q\) denotes the flow rate, \(u\) denotes the travel speed, \(k\) denotes the density, \(u_{f}\) denotes the free flow velocity of the road section, and \(k_{m}\) denotes the density corresponding to a large flow rate, i.e., the optimal density. For topological attributes, we modeled the road network using Python 3.0 software, utilizing the coordinates of the road section endpoints provided by the SUMO road network file. We computed the betweenness centrality of each road section in the Shanghai urban road traffic network as its topological attribute characterization. The formula for betweenness centrality is presented in Equation (8), and the formula for the topological attributes in the pre-accident identification framework is shown in Equation (9). The road sections are ranked according to the magnitude of betweenness centrality, and the results of the road section ranking are shown in Table 2.  
\[C_{b}(i)=\frac{2\sum_{j<k}\frac{g_{k}(i)}{\bar{s}_{jk}}}{(n-1)(n-2)} \tag{8}\]  
\[R_{topology}=rank(betweenness centrality) \tag{9}\]  
Here, \(C_{b}(i)\) represents the betweenness centrality of road section \(i\), \(n\) denotes the total number of nodes in the network, \(g_{jk}\) is the total number of shortest paths between nodes \(j\) and \(k\), and \(g_{jk}(i)\) indicates the number of those shortest paths that pass through road section \(i\). To assess urban functional attributes, we utilized the API interface provided by AMAP to extract the locations and attributes of all Points of Interest (POIs) of specific types within a defined area. Specifically, we focused on four key types of POIs across the entire city of Shanghai: higher education institutions, government agencies, shopping malls, and hospitals. We obtained their coordinate locations and attributes and mapped them onto the Shanghai urban road traffic network. Using the nearest neighbor analysis function of ArcGIS software, we matched each POI to its closest road section within the network. The number of POIs matched to each road section was then used as a measure of the urban functional attributes for that particular road section. In other words, the greater the number of POIs associated with a road section, the stronger its urban functional attributes. The formula for calculating the urban functional attributes in the pre-accident identification