## 4 Conclusions  
1. Urban complex road network model was constructed based on the complex network theory. The node quantitative indexes were selected from the local attribute, global attribute, and functional attribute of the urban complex road network. A grade identification method of a critical node in the road network based on multi-attribute evaluation correction was established, which provides the basis for the emergency guarantee and recovery of the key nodes after the event. However, the value of weight in the comprehensive evaluation method will affect the evaluation result. In future research, subjective and objective determination of optimal weight will be considered to make the model more accurate.  
Figure 15: Ranking of the first-grade nodes.  
2. The road network within the second Ring Road of Beijing was taken as an example. The analysis showed that the distribution of weighting connection degree of nodes on the whole road network generally presented the structure of \"peripheral high connectivity-central low connectivity\". If we identify the key nodes according to the highly weighted connection value of the nodes, most of the key nodes are on the expressways in the road network peripherally. The betweenness of nodes presented a certain structural characteristic of the \"hierarchical circle layer\" in spatial distribution. If it was identified according to the betweenness of nodes, most of the key nodes were located on the trunk road in the central area of the road network. Most of the nodes carrying large traffic volumes in the whole road network were the expressways on the outer edge of the road network and the intersections of the trunk roads in a certain range on both sides of the northwest-southeast line. 3. The first-grade nodes identified by the method in this paper were all the intersections among expressways and trunk roads. Compared with the self-organizing competitive neural network clustering method, the proposed method is more scientific and reasonable, and more accurate and effective in node identification. According to the spatial distribution pattern of first-grade and secondary-grade nodes, the \"five vertical and five horizontal\" pattern of the important traffic corridor of the regional road network was formed. Preventive measures such as monitoring and early warning (Traffic Control Centers), project resilience improvement (Road Management and Maintenance Department) and emergency function guarantee (Transportation Authority) should be implemented the event prevention resilience of the first-grade key nodes of the road network before an emergency occurs. Emergency traffic organizations can guide on-site vehicles to flow into the secondary or tertiary road network nodes and their connected roads, to avoid the cascade failure effect of the road network nodes caused by the failure of important nodes.  
Conceptualization, C.L.; Formal analysis, H.Y.; Funding acquisition, C.L. and H.Y.; Investigation, Y.S.; Methodology, C.L.; Project administration, X.G.; Software, H.Y., Y.S. and L.W.; Supervision, L.W. and X.G.; Validation, L.W.; Visualization, Y.S.; Writing--original draft, C.L., H.Y. and Y.S.; Writing--review & editing, C.L. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2019 EEEVL0501), National Natural Science Funds of China (Grant No. 51908025), and Natural Science Foundations of Hebei Province, China (Grant No. E2019202470, G2018202059), Tianjin Science and Technology Plan Project (Grant No. 19 YFZCSN01200) and Key R & D projects in Hebei Province (Grant No. 20375506D).  
Not applicable.  
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We thank five anonymous reviewers for their comments. The first authors are the main contributors to this paper.  
The authors declare no conflict of interest.