# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
## 5 Conclusions and Outlook
### Outlook  
This study is based on real multi-source data and utilizes various mature theories and algorithms to model, analyze, and validate the resilience of bus networks. It has obtained research results of high theoretical and practical significance. However, there are areas for improvement and further development in the study. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Stop Name** & \(\mathbf{C_{C}}^{{}^{\prime}}(\mathbf{v_{i}})\) & **Section** & \(\mathbf{C_{B}}^{{}^{\prime}}(\mathbf{e_{l}})\) \\ \hline Xibahe Station & 0.07737 & Liaguangying Intersection West Station–Dongzhimen Hub Station & 0.14812 \\ Dongzhimen Hub Station & 0.07713 & Laiguangying Intersection West Station–Xibahe Station & 0.14753 \\ Laiguangying Intersection West Station & 0.07692 & Xima Community Station–Taiping Street Station & 0.14407 \\ Yuquan Yingqiao North Station & 0.07633 & Taiping Street Station–Zengguang Road East Station & 0.14239 \\ Beach Crossing West Station & 0.07611 & Yuanda Road Station–Jiaojiafen Intersection East Station & 0.13564 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The top five nodes and lines by resilience value. The research findings of this study are oriented toward bus network managers and operators, and the data used are collected from the bus operation process. In the future, if passenger data can be integrated (such as passenger location and passenger origin-destination pairs), conducting bus network resilience analysis from the perspective of passengers will enhance the generality and versatility of the research outcomes. Although the study attempted to incorporate emergency events other than adverse weather conditions for analyzing the resilience of bus networks, it is regrettable that due to the limited time span of the available bus data, only a few emergency events related to adverse weather conditions could be matched with the multi-source bus data for that period. In future research, if it is possible to acquire multi-source bus data that match various types of emergency events, it would enable the validation of the proposed bus network resilience analysis model in different emergency scenarios. This would allow for a comparative analysis of the resilience of bus networks under different types of emergency events, which would yield more meaningful research results. The real bus data used in this study are sourced from Beijing Public Transport Group and Beijing Municipal Traffic Operation Monitoring and Dispatching Center. Therefore, all the case analyses are based on the bus network in Beijing. While Beijing's bus network ranks among the top five in terms of network density and scale in the national bus system, it is essential to consider the uniqueness of bus networks in different cities. In future research, if it is possible to obtain multi-source integrated data for bus networks in different cities, it would optimize this study from the perspectives of universality and applicability. Additionally, it would allow for a comparative analysis of the different responses of bus networks in different cities to emergency events, thereby making the research findings more significant in terms of theory and practicality. Conceptualization, L.H. ; data curation, H.H. ; formal analysis, H.H. ; funding acquisition, L.H. ; investigation, L.H. and Y.W. ; methodology, L.H. and H.H. ; project administration, L.H. ; resources, L.H., H.H. and Y.W. ; software, H.H. ; supervision, L.H. and Y.W. ; validation, H.H. and Y.W. ; writing--original draft, L.H. and H.H. ; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 52172311. Not applicable. Bus daily passenger flow data can be obtained from the corresponding author.