A hybrid car-following behavior prediction framework for snowy weather based on stacking ensemble learning.

Authors

Zengjie Luo, Fan Geng, Zhaolei Zhang

Published in

PloS one. Volume 21. Issue 10. Pages e0361759. Epub Oct 30, 2026.

Abstract

To improve the accuracy and robustness of vehicle car-following behavior prediction on snowy highways, this paper proposes a hybrid prediction framework based on Stacking ensemble learning. The framework integrates a theoretically driven improved Intelligent Driver Model (IDM) with two state-of-the-art data-driven models-the Prediction-enhanced Intelligent Driver Model (P-IDM) and a Transformer-LSTM hybrid network-within a Stacking ensemble architecture. First, an Icy Condition Enhanced IDM (ICES-IDM) model is developed by introducing a road adhesion coefficient mapping function and a speed perception error coefficient to parameterize the classical IDM for snowy environments. Second, a P-IDM model is constructed by incorporating a self-attention-based preceding vehicle speed prediction module into the classical IDM framework to enhance responsiveness to sudden maneuvers of the leading vehicle. Third, a Transformer-LSTM dual-stream network (TL-Net) is designed to jointly predict car-following acceleration by parallel extraction of temporal attention features and recurrent neural network features. Within the Stacking framework, ICES-IDM and P-IDM serve as physics-driven base learners, TL-Net serves as a data-driven base learner, and XGBoost is adopted as the meta-learner to construct a hybrid acceleration prediction model. The model is trained and validated using naturalistic driving data collected in Norway-comprising a dataset of 311,908 observations that link vehicle measurements with weather, road surface, and lighting conditions, along with a five-month road record dataset from Narvik. Experimental results demonstrate that the proposed model achieves a root mean square error (RMSE) in acceleration prediction under snowy conditions that is approximately 28.1% lower than that of the standalone ICES-IDM model, 9.2% lower than that of the TL-Net model, and 4.2% lower than that of the conventional Stacking-IDM-LSTM framework. The model exhibits excellent generalization capability across varying snow intensity levels, providing theoretical support for vehicle car-following safety warning systems in snowy environments.

PMID:
42961920
Bibliographic data and abstract were imported from PubMed on 31 Oct 2026.

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