## 4 Conclusions  
Pavement icing is a critical factor affecting traffic safety and efficiency, and it is difficult to predict icing time due to the complexity of freezing. Support vector regression is a newly developed algorithm with the advantages of small-scale sample, high accuracy, and easier training. Thus, it is suitable for predicting icing time. This paper investigates the utility of RSM and self-adaptive ES-based PSO for calibrating SVR in order to obtain a hybrid improved model, applied for predicting icing time. A total of 188 sets of environmental data and the corresponding icing time collected onsite were divided into train set and test set by CV method using a train set percentage of 80%. The input variables of SVR were generated by RSM using the environmental data in train set, and the input variables were combined with corresponding icing time data to construct the original SVR model. The adopted improved PSO was used to optimize the penalty coefficient C and kernel function parameter \(\gamma\) to obtain the hybrid improved SVR model. To evaluate the performance of the proposed model, the test set was used to verify the accuracy of model through different indicators, including \(R^{2}\), \(MAE\), \(MAPE\), and \(MSE\). Besides that, the convergence speeds were used to compare the convergence capability between the hybrid improved SVR and PSO-SVR. It indicates that the convergence speed remains constant while the accuracy of SVR is greatly improved with the combined optimization of RSM and PSO.  
The proposed algorithm provides an alternative in predicting pavement icing time and it is helpful to the road transportation system. The algorithm considers the input variables and the model parameters and greatly enhances the prediction performance of the model with the combination of RSM and PSO, which signifies a new approach to improve SVR. Meanwhile, the calculation consumption and the complexity of the proposed model is relatively high compared with the SVR, without the application of optimization methodology, due to the iteration of PSO and the calibration of RSM, which hinders its application in long-term pavement icing time prediction. We are conducting research to reduce the calculation consumption of SVR while keeping the accuracy relatively high by simplifying the composition of PSO. Besides that, related work is also conducted to investigate the approach for the calibration of SVR input variables with simple composition.  
Figure 8: Fitness change.  
**Author Contributions:** Conceptualization, Z.D.; data curation, W.X.; formal analysis, L.S.; funding acquisition, Z.D., Q.Z. and Q.L.; investigation, W.X.; methodology, L.S., W.X. and Y.Y.; project administration, Q.Z. and Q.L.; resources, W.X., Q.Z. and Q.L.; software, L.S. and Z.D.; supervision, Y.Y.; validation, L.S., Q.Z. and Q.L.; visualization, Y.Y. and W.X.; writing--original draft, L.S.; writing--review and editing, Y.Y., Q.Z. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the project \"Early Warning and Rapid Processing Technology for Expressway Pavement Icing\".  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.