## 6 Conclusions  
The work analyzed the operational and traffic interruption characteristics of highways to Tibet in extreme environments using data on actual traffic operations and interruptions. A comprehensive evaluation method was used to assess and quantify the status values of traffic interruptions, which were then graded using a clustering algorithm. 1. The climate, topography, and geology along the highway to Tibet were complex and changeable, which resulted in diverse transportation functions and traffic volumes of these Highways. Therefore, the causes and characteristics of traffic interruptions were distinct. Traffic accidents were the primary cause of interruptions along the Qinghai-Tibet Highway, with snow disasters being a secondary factor. In contrast, debris flows were the predominant cause of interruptions along the Sichuan-Tibet Highway, followed by snow accumulation and collapses. Nearly 50% of traffic interruptions were attributed to snow buildup along the Yunnan-Tibet Highway. The Xinjiang-Tibet Highway and Yunnan-Tibet Highway exhibited similar patterns of traffic interruption causes, with accumulated snow being the predominant influencing factor. 2. The Sichuan-Tibet Highway recorded the longest mean interruption duration and mileage among the four routes to Tibet, with its traffic interruption severity being the most significant. Each statistical value for the traffic interruption duration of the Qinghai-Tibet Highway was merely lower than that of the Sichuan-Tibet Highway. However, the Qinghai-Tibet Highway had a low mean interruption mileage, indicating mild traffic interruption severity. The Xinjiang-Tibet Highway showed greater mean interruption duration and mileage compared to the Yunnan-Tibet Highway. 3. The status values of the Sichuan-Tibet Highway were significantly higher than those of the other highways based on evaluation results. The Qinghai-Tibet Highway exhibited higher statistical values compared to the Yunnan-Tibet Highway and Xinjiang-Tibet Highway. The parameters ranked from highest to lowest weight in the comprehensive evaluation model corresponded to traffic interruption severity, mileage, and duration. 4. The model integrating the entropy weight-TOPSIS method and FCM could grade the traffic interruption states of the highways to Tibet. The four-level classification had a lower PE value in all schemes except for the two-level classification. The work analyzed the operational and traffic interruption characteristics of highways to Tibet in extreme environments using data on actual traffic operations and interruptions. A comprehensive evaluation method was used to assess and quantify the status values of traffic interruptions, which were then graded using a clustering algorithm. The key findings are summarized as follows. 5. Practical Applications and Contributions (1) Real-time decision making Through clustering algorithms, events can be categorized into different classes based on the interruption status values calculated using the interruption duration and impact range. Different categories of disruptions may correspond to different emergency response needs. The system can provide hierarchical decision support to managers based on the clustering results. Example: High-risk category (e.g., long-term road closure triggered by snowstorm): more resources (e.g., emergency rescue teams, heavy equipment) need to be deployed immediately for handling. Medium category (e.g., short-term weather-triggered interruptions): traffic warnings and recommended detours may be all that is needed. Low-risk category (e.g., minor landslides): only routine maintenance needs to be scheduled. 2. Development and Optimization of Traffic Recovery Plans With the clustering results, the system can help transportation managers develop more effective recovery plans. Different categories of disruptions may require different recovery strategies.