Analysis of Factors Influencing Driving Safety at Typical Curve Sections of Tibet Plateau Mountainous Areas Based on Explainability-Oriented Dynamic Ensemble Learning Strategy

Xinhang Wu, Fei Chen (), Wu Bo (), Yicheng Shuai, Xue Zhang, Wa Da, Huijing Liu and Junhao Chen
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Xinhang Wu: School of Engineering, Xizang University, Lhasa 850000, China
Fei Chen: Intelligent Transport System Research Center, Southeast University, Nanjing 211189, China
Wu Bo: School of Engineering, Xizang University, Lhasa 850000, China
Yicheng Shuai: School of Engineering, Xizang University, Lhasa 850000, China
Xue Zhang: School of Engineering, Xizang University, Lhasa 850000, China
Wa Da: School of Engineering, Xizang University, Lhasa 850000, China
Huijing Liu: School of Engineering, Xizang University, Lhasa 850000, China
Junhao Chen: School of Engineering, Xizang University, Lhasa 850000, China

Sustainability, 2025, vol. 17, issue 17, 1-28

Abstract: 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.

Keywords: plateau mountain roads; horizontal curve types; contributing factors; safety studies; explainability-oriented dynamic ensemble learning strategy; Sustainability of Plateau Transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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