This paper proposes an integrated tree model architecture and a low-cost data construction method based on an improved Stacking strategy. It systematically analyzes the importance of safety indicators for mountainous sharp bends in plateau regions and conducts safety evaluation and optimization-strategy research for ten typical sharp-bend road segments in Tibet. In response to the challenges of traditional data collection in Tibet’s unique geographical and policy constraints, we innovatively use drone aerial video as the data source, integrating Tracker motion trajectory analysis, SegFormer road segmentation, and CAD annotation techniques to construct a dataset covering multi-dimensional features of “human–vehicle–road–environment” for mountainous plateau sharp-bend highways. Compared with similar studies, the cost of this dataset is significantly lower. Based on the strong interpretability of tree models and the excellent generalization ability of ensemble learning, we propose an improved Stacking strategy tree model structure to interpret the importance of each indicator. The Spearman correlation coefficient and TOPSIS algorithm are used to conduct safety evaluation for ten sharp-bend roads in Tibet. The results show that the output of the improved Stacking strategy and the sensitivity analysis of the three tree models indicate that curvature variation rate and acceleration are the most significant factors influencing safety, while speed and road width are secondary factors. The study also provides a safety ranking for the ten selected sharp-bend roads, offering a reference for the 318 Quality Improvement Project. From the perspective of indicator importance, curvature variation rate, acceleration, vehicle speed, and road width are crucial for the safety of mountainous plateau sharp-bend roads. It is recommended to implement speed limits for vehicles and widen the road-bend radius. The technical framework constructed in this study provides a reusable methodology for safety assessment of high-altitude roads in complex terrains.

The complex topography of China’s Tibetan Plateau mountainous roads, characterized by diverse curve types and frequent traffic accidents, significantly impacts the safety and sustainability of the transportation system. To enhance driving safety on these mountain roads and promote low-carbon, resilient transportation development, this study investigates the mechanisms through which different curve types affect driving safety and proposes optimization strategies based on interpretable machine learning methods. Focusing on three typical curve types in plateau regions, drone high-altitude photography was employed to capture footage of three specific curves along China’s National Highway G318. Oblique photography was utilized to acquire road environment information, from which 11 data indicators were extracted. Subsequently, 8 indicators, including cornering preference and vehicle type, were designated as explanatory variables, the curve type indicator was set as the dependent variable, and the remaining indicators were established as safety assessment indicators. Linear models (logistic regression, ridge regression) and non-linear models (Random Forest, LightGBM, XGBoost) were used to conduct model comparison and factor analysis. Ultimately, three non-linear models were selected, employing an explainability-oriented dynamic ensemble learning strategy (X-DEL) to evaluate the three curve types. The results indicate that non-linear models outperform linear models in terms of accuracy and scene adaptability. The explainability-oriented dynamic ensemble learning strategy (X-DEL) is beneficial for the construction of driving safety models and factor analysis on Tibetan Plateau mountainous roads. Furthermore, the contribution of indicators to driving safety varies across different curve types. This research not only deepens the scientific understanding of safety issues on plateau mountainous roads but, more importantly, its proposed solutions directly contribute to building safer, more efficient, and environmentally friendly transportation systems, thereby providing crucial impetus for sustainable transportation and high-quality regional development in the Tibetan Plateau.

The Qinghai–Tibet Plateau presents a unique challenge for infrastructure development due to its extreme geological and climatic conditions—high elevation, large diurnal temperature fluctuations, frequent freeze–thaw cycles, intense ultraviolet radiation, and seasonal precipitation. These factors greatly accelerate the weathering of rock materials, leading to aggregates with increased porosity, microcracking, and weakened mechanical properties. While the engineering implications of such degradation are evident, the underlying material science of weathered aggregates—particularly their microstructure–property relationships—remains insufficiently explored, necessitating further investigation to inform material selection and design. In this study, three representative types of weathered aggregates (silica-rich, carbonaceous, and alumina-rich), alongside unweathered natural aggregates, were examined through both macro-scale (density, water absorption, crushing value, abrasion resistance) and micro-scale (scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS)) analyses. To capture the material evolution, we introduced a simplified classification framework based on the Si/Al ratio and porosity and applied a gray entropy correlation model to quantify the coupling between microstructure and mechanical performance. Results show that weathering reduces the Si/Al ratio from 2.45 to 1.82, increases porosity from 4.2% to 12.7%, enlarges the average pore size to 0.85 μm, raises microcrack density to 1.40 μm/μm², and increases the proportion of connected pores to 68.2%. These microstructural degradations correlate with decreased aggregate density, increased water absorption (up to 8.0%), higher crushing value (27.4%), and abrasion resistance loss (26.0%). Based on these findings, a weathered aggregate classification and pretreatment strategy is proposed, offering a practical reference for engineers to improve material performance in high-altitude road construction.