A hybrid framework for asphalt pavement rutting prediction modeling and influencing factors analysis based on multilevel wavelet decomposition and transfer entropy

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Rutting prediction model is of great significance for asphalt pavement quality assessment and road management. As a typical complex system, the rutting evaluation of asphalt pavement is causaed by multiple factors. Meanwhile, the observation of rutting and each factors show different characterizes on different frequency bands. Therefore, the accuracy of predictive model depends on the time series decomposition and the selection of input factors. In light of this, this paper proposes a novel time series forecasting framework for rutting prediction, which consists of multilevel discrete wavelet decomposition (MDWD) based data processing module, multivariate transfer entropy (mTE) based feature selection module, and time series forecasting module. In the MDWD based data processing module, rutting and its influencing factors time series are decomposed into low-frequency band and high-frequency band by multilevel wavelet decomposition, which represent trend component and fluctuations component of original series, respectively. Each component is fed into mTE-based feature selection module, the statistically significant casual factors are selected by multivariate transfer entropy, which provides a novel insight for the effect of influence factors on rutting. Finally, the components in different frequency bands are predicted with the selected causal factors, and the predicted rutting is generated by wavelet reconstruction. To validate the performance of the proposed framework, we conduct some experiments with RIOHTrack data set. The performance comparison results with other prediction models demonstrate that the proposed framework is effective. Ablation study is set up for each module in the proposed framework, which shows that each module in the framework is indispensable.