Pavement Surface Condition Index Prediction Based on Random Forest Algorithm

With the rise of three-dimensional road detection technology, more and more roads begin to use three-dimensional road detection vehicles to detect road conditions. Pavement surface condition index (PCI), an important indicator of pavement performance, was used as the research target, and the PCI predicted by using the actual pavement data collected by ARAN9000 three-dimensional multifunctional road detection vehicle. Firstly, the data mining technology is used to consider the factors such as pavement distress, environment and pavement structure. and to process and analyze the data related to the pavement surface condition index of a certain highway in Ontario, Canada, such as data cleaning and feature screening. Then, a machine learning prediction model of pavement surface condition index was constructed, and the complex correlation coefficients (R²) of the multiple linear regression model, the neural network model and the random forest model were 0.562, 0.711 and 0.895. Compared with the neural network model, the accuracy of the random forest model in predicting pavement surface condition index was improved by 0.184, the error was reduced by 1.599, and the training speed was improved by 33s. Finally, the random forest model with high precision is selected for optimization. Due to the large number of input variables, it is impossible to determine which line of data is the outlier through simple statistical analysis, so after establishing and predicting the model, the outlier is determined and deleted through the fitting effect between the predicted value and the real value. Then the modified data were used to retrain the model to achieve the optimal training of the current model. The results show that the prediction efficiency and accuracy of the improved random forest model are higher, and the R² reaches 0.898. The proposed pavement condition index prediction model is accurate and effective, and the prediction results can assist the maintenance department to make more scientific and reasonable maintenance decisions.