Connected Car-Based Pavement Roughness Prediction Using CNN Model and Signal Decomposition Technique

Accurate pavement roughness assessment is critical for effective pavement management. Connected car data provides a cost-effective way to predict the pavement roughness based on the acceleration data. This paper proposes a novel prediction model integrating the variational mode decomposition (VMD) with convolutional neural network to predict road profile. A particle swarm optimization algorithm is employed to determine the optimal parameters of VMD. The decomposition is performed for the acceleration signal and its double integration item. The model is validated using the field measurement data. Its prediction performance is compared with that of artificial neural networks and a convolutional neural network (CNN) model utilizing discrete wavelet transformation. Furthermore, the predicted profile is validated by the ground truth values from both the spatial and frequency domains, with the international roughness index also being calculated. The results indicate that the proposed CNN model provides a promising estimation of the road profile across different roughness levels.