A Severity Grading Method for Traffic Interruptions Induced by Geological Hazards: A Case Study of Tibetan Highways

The complex geological environment and frequent natural disasters on the Qinghai–Tibetan plateau pose significant challenges to the reliability of the regional highway transportation network. In this paper, a severity grading method for traffic interruption caused by geological disasters on Tibet highways is proposed, based on principal component analysis (PCA) and spectral clustering (SC). Firstly, the causes of typical highway hazards, such as mudslides, subsidence, flood damage, collapse, and landslides, and their effects on traffic interruption were analyzed, and a classification system for these hazards was constructed. Subsequently, PCA was employed to extract the key factors affecting the severity of traffic interruption from multidimensional disaster data, thereby reducing data dimensionality and improving model interpretability. Based on dimensionally reduced data, the SC algorithm was used to classify the severity of traffic interruption, and a four-level classification system was established. The results show that high traffic volume, large affected areas, steep road gradients, and severe road hazards are the main factors leading to moderate and severe traffic interruption. The classification model proposed in this paper can provide a scientific basis for early warning and monitoring, emergency response, and optimal resource allocation for highway disaster management in Tibetan areas.