Safety Evaluation of Highways with Sharp Curves in Highland Mountainous Areas Using an Enhanced Stacking and Low-Cost Dataset Production Method

Xu Gong, Wu Bo (), Fei Chen, Xinhang Wu, Xue Zhang, Delu Li, Fengying Gou and Haisheng Ren ()
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Xu Gong: School of Engineering, Tibet University, Lhasa 850032, China
Wu Bo: School of Engineering, Tibet University, Lhasa 850032, China
Fei Chen: Intelligent Transport System Research Center, Southeast University, Nanjing 211189, China
Xinhang Wu: School of Engineering, Tibet University, Lhasa 850032, China
Xue Zhang: School of Engineering, Tibet University, Lhasa 850032, China
Delu Li: School of Engineering, Tibet University, Lhasa 850032, China
Fengying Gou: School of Engineering, Tibet University, Lhasa 850032, China
Haisheng Ren: Intelligent Transport System Research Center, Southeast University, Nanjing 211189, China

Sustainability, 2025, vol. 17, issue 13, 1-24

Abstract: 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.

Keywords: traffic safety evaluation; sharp-curved highway in highland mountainous areas; improved Stacking strategy; machine learning (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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