## 1 Introduction
[PERSON] [16] argued that there were significant differences in the resilience performance of different transportation modes, and, therefore, when assessing traffic network resilience, the differences in transportation modes should be fully considered. Scholars have also conducted some studies on resilience assessment for metro networks. [PERSON] et al. [17] used network topology efficiency metrics to evaluate the resilience of the Shanghai metro network. [PERSON] et al. [18] assessed metro network performance through average network efficiency and used the cumulative loss of network efficiency before and after an attack as a measure of network resilience. To reflect the dynamic resilience of transportation networks, dynamic indicators such as traffic flow and operating speed should be considered. [PERSON] et al. [19] analyzed the spatiotemporal characteristics of metro passenger flows and used the recovery speed of passenger flow as an indicator of network resilience. [PERSON] et al. [20] conducted a quantitative analysis of the dynamic changes in metro network resilience under different events, based on network topology and passenger flow data. [PERSON] et al. [21] used a multi-stage resilience framework, combined with historical traffic data, to compare five metro systems--California Bay Area Rapid Transit, the London Underground, Singapore MRT, Queensland Rail, and Washington Metro. They found that the Singapore MRT exhibited the best resilience in the face of both random disruptions and targeted attacks. [PERSON] et al. [22] measured metro network resilience by calculating its absorption, resistance, and recovery capacities, incorporating various data such as passenger flow, train schedules, passenger travel behavior, etc. The recovery ability after disturbances is also essential for measuring traffic network resilience [23]. Optimizing the recovery process to minimize the recovery time is the key in formulating recovery strategies [24]. To enhance the resilience of transportation networks, [PERSON] et al. [25] proposed that the reasonable allocation of repair resources and the establishment of an effective restoration sequence were crucial for quickly recovering the damaged networks. [PERSON] et al. [26] proposed an optimization model with the goal of maximizing road network service resilience and obtained the optimal recovery strategy based on an optimization algorithm. [PERSON] et al. [17] used enumeration methods to study the optimal recovery strategy for the Shanghai metro network during emergencies, pointing out that establishing an effective restoration sequence could significantly improve the network's recovery speed. Additionally, [PERSON] et al. [27], considering factors such as the degree of node failure, the total amount of repair resources, and the number of repair teams, proposed a recovery strategy for damaged metro networks aimed at minimizing resilience loss. [PERSON] et al. [28] simulated the dynamic recovery process of metro networks after failures and proposed a dynamic restoration strategy model. In summary, although scholars have conducted considerable research on transportation network resilience in recent years, there are still some weaknesses. Firstly, none of the existing studies have explored the resilience of mountainous cities, which, however, are more vulnerable to extreme rainfalls due to their hilly terrains. To account for the influence of terrain, it is necessary to take the topographical factors into account when analyzing the resilience of mountainous cities. Secondly, regarding the disruption of extreme events to transportation networks, most studies randomly select the disrupted nodes. However, nodes should be chosen based on their disaster risk levels to be consistent with the truth and develop effective countermeasures. Lastly, most studies analyze the resilience of transportation networks based only on their physical topological structures, with little consideration given to the impact of passenger flows.