# Identification of Critical Road Links Based on Static and Dynamic Features [PERSON]
## 2 Literature Review  
In recent years, the evaluation of road link importance has been mostly based on the construction of road network topology using the primal method for undirected road networks. Two primary research approaches are commonly used in this field: quantitative evaluation using importance indicators and assessing changes in network efficiency following road link failures. In terms of quantitative evaluation using importance indicators, some researchers combine complex network theory indicators and traffic information for quantitative analysis. [PERSON] et al. [8] introduced the concept of edge betweenness, which is based on betweenness centrality. This concept suggests that road links with higher edge betweenness have a greater transmission capacity and a more significant role in the network. [PERSON] et al. [9] proposed a node importance discrimination method based on local features, which considers the importance of neighboring nodes in addition to the node itself. [PERSON] et al. [10] introduced the minimum cut frequency vector to construct road link importance evaluation indicators for road link vulnerability identification evaluation between OD pairs. [PERSON] et al. [11] used the Fuzzy C-means (FCM) clustering algorithm to analyze the importance of road links by combining betweenness, PageRank, and traffic flow. [PERSON] et al. [12] combined node degree and node local similarity to evaluate the importance of road links. [PERSON] et al. [13] proposed a model for evaluating the importance of road links under different time delays by considering the spatio-temporal correlation between multi-order neighboring road links. [PERSON] et al. [14] proposed a method to identify the importance of road links in the \"effective impact area\" by considering that the failure of a road link mainly affects the surrounding neighboring areas and calculating the efficiency of the regional network in this way to reduce computation time. When dealing with smaller transportation networks, importance indicators can provide satisfactory results for quantitative evaluation. However, as the size of the network increases, the calculation of indicators based on complex network theory can become a time-consuming process and the evaluation results may not accurately reflect the actual traffic conditions. In terms of assessing variation in network efficiency following road link failures. Some researchers have employed traffic assignment theory to quantify the impact of road link failures on traffic network efficiency. The critical road links are then identified by comparing the change in traffic network operation efficiency before and after the failure of these links. [PERSON] et al. [15] proposed the network robustness index (NRI) measure to identify the importance of road links by combining network capacity, road network traffic flow, and topology. [PERSON] et al. [16] used the user equilibrium (UE) model to reassign network traffic flow after road link failure and identified the importance of road links by comparing the cost of travel on the network after the failure of different road links. [PERSON] et al. [17] combined link failure probability and comprehensive indicators of network efficiency and total travel time of the road network after traffic reassignment to evaluate the importance of road links. [PERSON] et al. [18] developed a model for link importance assessment that considers various factors such as road network structure, traffic demand, travel behavior characteristics, and multi-link failures by using the travel time of all users in the system as a measure of traffic network performance. The current research on link importance evaluation in traffic networks faces two main challenges. Firstly, most road network modeling is based on undirected graphs using the primal method [19], which maps intersections as nodes and connecting links as edges. The approach ignores the circulation relationship between road links and the time-varying characteristic of road link importance.