Road icing significantly impacts traffic safety.The effective icing prediction and the proactive anti-icing measures are crucial for addressing this issue in a cost-effective manner.To enhance road driving safety and achieve precise prediction on road icing,the integrated model was proposed with principal component analysis (PCA),particle swarm optimization (PSO),and extreme learning machine (ELM) .Initially,due to 8 influencing factors,including relative humidity,atmospheric pressure,air temperature,wind speed,wind direction,road surface temperature,water film thickness,and deicer concentration,the PCA was carried out.Subsequently,the principal components of these factors were extracted.The PSO algorithm parameters,population size,and iteration times were set.The PSO algorithm was employed to optimize the input weights and hidden layer neuron thresholds of ELM model;thus the PCA-PSO-ELM road icing prediction model was constructed.Finally,the model was validated by using meteorological data from 3 road sections.The result indicates that the PCA-PSO-ELM icing prediction model achieves the average prediction accuracy of 95.85％,which significantly outperformings traditional ELM,BP neural network,and SVM models.Additionally,the model demonstrates high accuracy,precision,recall,and F1 scores across different road sections and time intervals,illustrating the excellent generalization capabilities.The PCA-PSO-ELM model not only guarantees the accuracy,but also improves the stability of road icing prediction,providing solid theoretical support for effectively addressing road icing issues.
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