## 3 Case Study  
Shanghai boasts a well-established transportation system, comprising a comprehensive transportation network on an extensive scale, which includes railroads, waterways, highways, airways, railways, and other transportation modes. In order to construct the Shanghai urban road traffic network required for this study, the entire Shanghai road network was downloaded from OpenStreetMap (OSM) and imported into ArcGIS 10.7 for processing. In order to simplify the complexity of the Shanghai urban road traffic network, ArcGIS software's road filtering function was utilized to retain three types of roads: highways, expressways, and trunk roads. The remaining roads were eliminated to yield a sparse Shanghai urban road traffic network, with each road assigned a unique OSMID. The urban road traffic network in the Simulation of Urban Mobility (SUMO) is depicted in Figure 2, encompassing 5933 nodes and 13,630 roads, with each road's maximum speed limit consistent with the actual road network. To more closely align with the real scenario, SUMO's batch traffic signal addition function was used, setting up default phase traffic signals at all the main road intersections within the Shanghai urban road traffic network. The Simulation of Urban Mobility (SUMO) platform offers a random Trips function, enabling users to generate a network-wide distribution of random traffic flows across any network. The probability of an edge in the network serving as the starting and ending edge of a trip can be determined by the road's speed limit. In this study, three types of urban roads are selected for the Shanghai urban road traffic network, each with distinct speed limits. The parameter for selecting the road section speed limit as the edge probability is set at 2, implying that the probability of a road section serving as the starting or ending edge is proportional to the square of the road section's speed limit. This approach yields the traffic flow throughout the Shanghai urban road traffic network. The timing of traffic generation and the magnitude of traffic flow are determined based on several pre-experiments. The principle for determining the timing of traffic generation and the magnitude of traffic  
Figure 2: Urban road traffic network of Shanghai in SUMO. flow is to approximate the real traffic flow of the Shanghai urban road traffic network. Under normal conditions, the traffic flow in the network runs smoothly without congestion. However, when a disruptive event occurs, the existing traffic flow leads to congestion due to re-routing, thereby magnifying the impact of the disruptive event on the urban road traffic network. Based on this, we set the traffic flow at 100,000 vehicles per hour, issued at equal intervals during each hour. For the vehicle following model and lane change model, we selected the default Krauss following model and the LC2013 lane change model. This research simulates scenarios where road sections are subjected to disruptive events, resulting in a loss of their capacity and service level. Building upon the resilience-based critical road identification framework for urban road traffic networks established in previous research, we integrate the actual urban road traffic network in Shanghai and apply the critical road identification framework to the Shanghai road network, thereby determining the failure mode and recovery mode in the simulation experiment. In the pre-accident identification framework, we select three attributes of the urban road traffic network: traffic attributes, topological attributes, and urban functional attributes. These are used to assess the criticality of each road segment in the network, and the failure mode is comprehensively determined based on the criticality of the road segments within the Shanghai urban road traffic network.