# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
## 4 Case Analysis
### Experimental Process and Result
In the CRITIC weights, the weights of card-swiping passenger volume and boarding and alighting volume both  
Figure 14: Entropy weight method weight comparison (\(x\)-axis: indicator code). Figure 15: Comparison of critic weights (\(x\)-axis: indicator code). exceed 20%, leading significantly compared to other indicators. Based on the obtained weights \(w_{j}{}^{(1)}\) and \(w_{j}{}^{(2)}\), the total weights \(w_{j}\) can be calculated. The results are shown in Table 8.  
Comparing the total weight rankings of the two routes, it can be observed that the total weights of card-swiping passenger volume and boarding and alighting volume are significantly higher than other indicators. The total weights of card-swiping passenger volume are 39.265 and 38.77, while the total weights of boarding and alighting volume are 20.65 and 22.43, respectively. Therefore, we can conclude that according to the CRITIC-entropy weight evaluation model, card-swiping passenger volume is a key indicator in the evaluation system for monitoring the operational status of the bus network and can be used to calculate the bus network's resilience. (2) Bus route resilience analysis  
In this example analysis, data processing and calculations are performed using Python and related packages. First, the data of each indicator is smoothed using the STL (seasonal and trend decomposition using Loess) time series decomposition method for indicators with significant seasonality and periodicity, such as card-swiping passenger volume and boarding and alighting volume. STL is a versatile and robust method that stands for \"Seasonal and Trend decomposition using Loess,\" where Loess refers to a robust regression algorithm. One of the advantages of this algorithm is that the seasonal component can vary over time with controllable changing rates. STL provides a separate multiplicative trend for the seasonal part, which allows for increasing amplitudes when using an additive model. Additionally, STL is robust to outliers, meaning that outliers do not significantly affect the seasonal and trend components. Figure 16 shows the trend and seasonal variations of card-swiping passenger volume for some routes (Route 1, Route 139, Route 101, and Route 116) after STL decomposition. Figure 17 represents the trend and seasonal variations of passenger boarding and alighting at the Bawangfen East stop and Langjiayuan stop after applying the STL decomposition method. By observing the trend of each indicator after applying STL decomposition, it can be noticed that there is a significant decrease followed by an increase between time scales 25 to 65, which is influenced by the rainfall from August 12 th to 13 th. This pattern represents a complete resilience cycle. The declining trend after time scale 80 is mainly due to the decrease in commuting demand from citizens during weekends. This analysis focuses mainly on the variations within the resilience cycle. The next step is to determine the time when each indicator reaches a steady state and the corresponding steady-state value within the resilience cycle. This involves extracting a subset of data for each indicator within the \([t_{L},t_{E}]\) timeframe. The augmented Dickey-Fuller (ADF) test is then applied to this subset using an autoregressive process with drift