## 1 Introduction  
An urban road network is a complex network composed of a large number of nodes (intersections) and road segments. It not only ensures the daily life of residents and visitors and industrial production but also supports key issues such as post-event rescue and evacuation, material allocation, and so on. However, the components of the road network are closely related, and the overall distribution range is wide. It is easily affected by unexpected events such as natural events, man-made events, traffic accidents, etc., which lead to the degradation of some key node functions, the decline of local road capacity or congestion, and even the paralysis of the road network due to the effect of cascade failure [1, 2]. Previous studies show that as long as 5% or 10% of the important nodes fail at the same time, the whole network will be paralyzed [3]. Therefore, the research on the identification and classification of the key nodes in the urban road network is of great significance to the management and control of the key nodes of the road network, the layout of emergency resources, improving the invulnerability and reliability of the road network, and ensuring the safe and effective operation of the road network after the events. At present, scholars at home and abroad have done a lot of research on the identification of key nodes of road networks from different angles, and their methods are basically derived from graph theory and data mining based on graph theory. The difference of nodes is reflected by mining the important attribute information of nodes [4; 5]. For example, node connectivity [6; 7], betweenness [8; 9], aggregation coefficient [10], eigenvector [11; 12], and so on are all measures of network topology attributes [13]. But the traffic dynamics characteristics of the nodes in the road network are not reflected with these indicators. Therefore, some scholars have proposed to use node peak-hour traffic flow (the traffic volume in the hour of the day when the traffic volume is at its peak) [14], traffic load degree (the ratio of the actual traffic density of the road to the density under the maximum capacity) [15], flow ratio (the ratio of traffic flow to planning and design traffic flow at a certain time), and saturation (ratio of maximum traffic volume to maximum capacity) [1; 2] to characterize the traffic operation characteristics of the road network. Because it is difficult for a single index to accurately reflect the importance of road network nodes as a whole [16; 17], a multi-index comprehensive identification method has gradually emerged to reflect the importance of road network nodes from different angles [14; 15; 16; 17; 18], but too many indicators will also increase the complexity of calculation. Some methods need to determine the classification parameters in advance, and seldom consider the correlation between indicators, which affects the rationality and credibility of the results. For example, the FCM clustering method [14], self-organizing competition neural network clustering method [15], and adaptive clustering method [19] are all divided by a certain clustering function according to the characteristics of the data field. These methods are suitable for large-grained network cluster structures. The network of core and periphery structures can only be roughly divided, and there may be one-sidedness according to a certain clustering function [19]. In addition, some multi-index comprehensive evaluation methods, such as grey relationship method (GRA) [20], principal component analysis (PCA) [21], cosine similarity method (CSM) [22], TOPSIS method [17; 18], and so on, take into account the correlation information between indicators, and only the order of node importance can be given. However, they require an artificial hierarchy of importance. The classification and grading results are highly subjective. The problems such as spatial heterogeneity of road networks are not taken into account. A detailed description of these methods is shown in Table 1.