Prediction of Road Rutting Depth Based on PSO-CatBoost

The rutting depth is one of the important indicators for evaluating the performance of road use. Accurately predicting the Rutting Depth Index (RDI) can timely detect and solve the problem of road damage, ensuring driving safety. This article constructs a multi influencing factor dataset for RDI prediction by integrating road detection, climate, and traffic flow data from the AB expressway in Shaanxi Province, China. by comparing the prediction results of four models, namely Random Forest(RF), XGBoost, CatBoost, and LightGBM, it was found that XGBoost and CatBoost has the highest accuracy on the test dataset. To further improve the predictive precision of above model, we utilized the Particle Swarm Optimization (PSO) technique to meticulously adjust the hyperparameters of both XGBoost and CatBoost. The experimental results show that using the PSO-XGBoost and PSO-CatBoost models to predict RDI, the R2 scores reached 0.8029 and 0.8135, respectively. Compared with the XGBoost and CatBoost models, the accuracy improved by 0.0655 and 0.022, respectively. This indicates that the application of PSO significantly enhances the predictive accuracy of both XGBoost and CatBoost models for road rutting prediction, with the PSO-CatBoost model demonstrating superior performance. Consequently, it provides valuable data for road maintenance strategies.