## 2 Literature Review  
Adverse weather and geological hazards can severely affect road traffic operations, according to research on traffic interruptions [3, 4, 5, 6, 7]. For the grading of traffic interruptions, [PERSON] et al. [8] investigated various factors and their relationships with interruption accidents. They first introduced association rules mining, a data mining technique that uncovers potential associations among item-sets in a database. Additionally, a classic Apriori algorithm was proposed. [PERSON] et al. [9] used the AHP-Apriori algorithm to develop a traffic accident cause analysis model. The AHP algorithm is utilized to sort the factors contributing to accidents based on their degrees of importance. Then, the Apriori algorithm is used for correlation analysis of the main influencing factors selected. However, certain types of accidents are not considered as part of the frequent item-set due to their low frequency of occurrence. Both algorithms fail to reveal their association with other factors. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Highway Number** & **Highway Mileage (km)** & **Year of Construction** \\ \hline Qinghai–Tibet & G109 & 3901 & 2009 \\ Sichuan–Tibet & G318/G317 & 5476/2043 & 1954/1951 \\ Xinjiang–Tibet & G219 & 10,065 & 1956 \\ Yunnan–Tibet & G214 & 2961 & 1978 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic information of the four highways. Figure 1: Annual average daily traffic on the four highways to Tibet. Therefore, integrating additional data mining techniques, such as clustering analysis and anomaly detection, is important for better understanding rare events. The grading of traffic operation states commonly employs various clustering algorithms, including partition clustering, density clustering, hierarchical clustering, grid-based clustering, model clustering, and the fuzzy method. [PERSON] [10] proposed an FCM algorithm combining the K-means algorithm and Random Forest to develop a weighted improved FCM algorithm to evaluate the importance of traffic network nodes, which dynamically identifies the critical traffic nodes more accurately by determining the initial clustering centers using the K-means algorithm in order to reduce the number of iterations and to avoid the local optimal solutions. [PERSON] [11] proposed a clustering algorithm based on improved Gaussian Mixture Model (GMM) by analyzing the real-time monitoring data, such as road speed and flow rate, introducing the margin discrimination index, and adopting the improved mean-standard deviation algorithm to preprocess the data to determine the clustering centers. The results show that this method can efficiently and stably identify the state of urban road traffic. [PERSON] [12] used the entropy weight-TOPSIS method and K-means algorithm to determine the risk level of the target road and categorized the road into four risk classes according to the risk level. He et al. [13] discovered the potential relationships among the relevant factors of traffic interruptions by establishing a multi-dimensional attribute model of traffic interruption. Fuzzy correlation rules are used to explore the dependencies of the multi-dimensional attributes of interruption factors. [PERSON] [14] proposed a fuzzy cluster analysis method of traffic state to solve the problem of ambiguity and multifactorial influence of road traffic state, allowing for more accurate assessments the road traffic state. [PERSON] [15] proposed an FCM clustering algorithm based on information entropy weighting to solve the problem of urban traffic state identification. The authors determined the weights of evaluation indexes through information entropy theory and optimize the FCM algorithm by using the weighted Euclidean distance to improve clustering performance of the algorithm and the identification rate of the traffic state.