Parameter calibration method for car-following models under snowy weather conditions: Integrating an informer time series encoder and physics-informed neural networks

Under snowy weather conditions, factors such as road conditions and weather conditions significantly affect vehicle car-following behavior. Traditional car-following models struggle to accurately capture driving characteristics on slippery roads. To address this, this paper proposes a parameter calibration method for car-following models under snowy conditions, considering factors including road adhesion coefficient and visibility. Five classical car-following models are selected for analysis: the GM model, the Gipps model, the Intelligent Driver Model (IDM), the Wiedemann model, and the Full Velocity Difference Model (FVDM). A systematic analysis is conducted on the key parameters to be calibrated for each model in snowy environments. To overcome the poor adaptability and low accuracy of traditional calibration methods, an adaptive parameter calibration framework combining an Informer time series encoder and a physics-informed neural network is proposed. This method extracts features of snowy environments using the Informer time series encoder and achieves dynamic optimization of model parameters via the physics-informed neural network algorithm, making it applicable to multiple car-following models simultaneously. Validation results based on the NGSIM dataset and real vehicle test data under snowy conditions show that the proposed method improves calibration accuracy by 12.7% under snowy scenarios compared to the traditional genetic algorithm, and exhibits strong generalization capability across different car-following models. This research can provide fundamental models for traffic simulation systems and enhance simulation accuracy.