###### Abstract  
Pavement icing imposes a great threat to driving safety and impacts the efficiency of the road transportation system in cold regions. This has attracted research predicting pavement icing time to solve the problems brought about by icing. Different models have been proposed in the past decades to predict pavement icing, within which support vector regression (SVR) is a widely used algorithm for calibrating highly nonlinear relationships. This paper presents a hybrid improved SVR algorithm to predict the time of pavement icing with an enhancement operation by response surface method (RSM) and particle swarm optimization (PSO). RSM is used to increase the number of input data collected onsite. Based on that, the optimal SVR model is established by optimizing the kernel function parameters and penalty coefficient with the particle swarm optimization (PSO) algorithm. The hybrid improved SVR is compared with SVR, PSO-SVR, and RSM-PSO for coefficient of determination (\(R^{2}\)), mean absolute error, mean absolute percentage error, and root mean square error to check the effectiveness of PSO and RSM in optimizing SVR. The results show that the combination of two methods in the hybrid improved algorithm has a better optimization capability with \(R^{2}\) of 0.9655 and 0.9318 in a train set and test set, respectively, which outperforms PSO-SVR, RSM-SVR, and SVR. In addition, the \(R^{2}\) of the hybrid improved SVR and PSO-SVR both reach the optimal fitness value approximately at the iteration of 20, which suggests that convergence capacity remains relatively constant with the predictive accuracy being improved. c 2022 12 8109. 10.  
considered as an effective technique in pavement icing prediction, while the accuracy and cost of the sensor, as well as the difference between road construction material and sensor equipment, still demand further research. Thus, it appears necessary to propose different approaches to solve this problem. Recently, different icing prediction models have been proposed. In order to build an accurate prediction model, researchers investigate the impact of different environmental factors on pavement icing. It is suggested that pavement temperature, temperature, dew point, relative humidity, wind speed, and precipitation are commonly considered as the main factors in the process of freezing [7; 8]. Based on that, highway engineers and meteorologists have adopted numerical models to predict the formation of ice with advance knowledge of where and when ice will occur. This technique can provide valuable information for winter maintenance services with a good performance in different countries. However, most numerical models are empirical due to the complex properties of ice and this may not reflect the real ice formation process without an adequate database [9]. The disadvantages have hindered its usage in regions without complete systems to collect climatic information over the long term. The support vector machine (SVM) is a promising technique for small sample prediction. In the past research, it has been combined with an optimization method of differential evolution to forecast the road icing, and shows feasibility and effectiveness [10]. Besides that, different environmental factors that affect the freezing of pavements are considered to build the road icing prediction model by support vector classification (SVC), and the model can predict the pavement icing warning accurately [11]. While most published studies focus on the judgment of pavement icing conditions, the prediction of pavement icing time is seldom involved. Derived from SVM, support vector regression offers unique advantages over nonlinear regression analysis. It has been verified that the SVR model is valid in different areas, such as air pollutant concentration prediction [12], soil internal friction angle [13], and prediction of remaining service life of pavement [14]. The results in past research have demonstrated that SVR can predict the nonlinear problems with high accuracy and robustness [15].