# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
## 2 Related Literature
### Resilience Research in Transportation
[PERSON] and [PERSON] summarized the concepts used to evaluate the performance of transportation systems in disasters and identified seven characteristics of transportation system performance, including resilience, robustness, reliability, risk, vulnerability, survivability, and flexibility [42]. As the last two concepts are less used compared to the first five in existing research, this paper focuses on reviewing system resilience, robustness, reliability, risk, and vulnerability. Measurement methods for the resilience of transportation networks. The measurement methods of traffic network resilience are used to provide calculation methods for the resilience of traffic network systems, in order to accurately calculate their resilience. These methods can be classified into optimization models, topological models, simulation models, probability models, fuzzy logic models, and data-driven models. Optimization models are primarily used to address two issues in traffic network resilience: solving traffic assignment problems, such as user equilibrium (UE) or system optimum (SO) problems, and optimizing the utility of resources for mitigation, preparedness, response, and recovery. [PERSON] et al. constructed a stochastic optimization model of traffic networks based on resilience, using traffic flow, link capacity, time constraints, and recovery speed ranges as constraints; repair start time and progress as decision variables; and minimum maintenance and flow costs and maximum resilience as objectives [43]. Many researchers also use topological indicators corresponding to the topology model to measure resilience. Unlike optimization models, topological methods usually have explicit expressions [44; 45]. Some are based on the calculation of shortest paths, while others are determined by the node degree distribution [46]. Simulation models are not frequently used in assessing traffic network resilience. Some studies simulate faults in traffic network systems to test changes in topological metrics [47]. [PERSON] et al. first quantified the resilience of traffic network systems using fuzzy logic [48]. With the development of data collection and storage technologies, data-driven measurement methods have been applied in many fields of resilience research. Unlike the above methods, data-driven methods do not examine the intrinsic mechanisms of traffic network systems but directly select recorded data that reflect the system's performance changes in different scenarios to evaluate its resilience. [PERSON] used passenger travel times and passenger volume as system performance indicators to study the resilience of subway networks [49]. [PERSON] et al. used passenger flow data from Shanghai's rail transit system to quantify the impact of the epidemic on urban rail transit systems using the LSTM model and verified the impact of urban control strategies on rail system resilience [50]. In summary, there are various methods to measure the resilience of transportation networks, and different methods have different advantages based on different transportation network systems. This article provides a review of the six most common methods, among which optimization models, topological models, and data-driven methods are more prevalent. Based on this, the article selects topological models and data-driven methods to study the resilience of bus networks in terms of measurement methods for transportation network resilience.