Sustainable Data Construction and CLS-DW Stacking for Traffic Flow Prediction in High-Altitude Plateau Regions

Wu Bo, Xu Gong (), Fei Chen, Haisheng Ren (), Junhao Chen, Delu Li and Fengying Gou
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Wu Bo: School of Engineering, Tibet University, Lhasa 850001, China
Xu Gong: School of Engineering, Tibet University, Lhasa 850001, China
Fei Chen: Intelligent Transport System Research Center, Southeast University, Nanjing 211189, China
Haisheng Ren: Intelligent Transport System Research Center, Southeast University, Nanjing 211189, China
Junhao Chen: School of Engineering, Tibet University, Lhasa 850001, China
Delu Li: School of Engineering, Tibet University, Lhasa 850001, China
Fengying Gou: School of Engineering, Tibet University, Lhasa 850001, China

Sustainability, 2025, vol. 17, issue 16, 1-27

Abstract: This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions.

Keywords: traffic flow prediction; deep learning; constrained least squares; plateau mountain roads with sharp curves (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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