Fine-Grained Pavement Performance Prediction Based on Causal-Temporal Graph Convolution Networks

Pavement performance prediction is the foundation of maintenance decisions, which is the key problem of infrastructure management. Most prediction methods focus on section-based and annual deterioration on pavement, while it is hardly supporting daily and preventive maintenance plans. To fill in gaps in pavement forecasting for refined maintenance, this paper introduces a prediction model which is fine-grained on both temporal and spatial scales. Due to the coupling effects and action delay of multiple environmental factors, it is difficult to fathom and model the detailed deterioration process of pavement. Another problem is there are rare refined pavement datasets opening to public for research. Therefore, we establish a high-frequency and real-world pavement dataset and causal discovery is brought in to explicate the inner mechanism of the process. The proposed model first applies Partial Mutual Information from Mixed Embedding (PMIME) method for causal discovery, obtaining a causal graph and impact delays between factors and pavement performance. Based on this, we use an advanced pavement performance prediction model called Causal-Temporal Graph Convolution Network (CTGCN), combining the Graph Convolution Networks (GCNs) and the Long Short-Term Memory models (LSTMs) to capture causal features and temporal features simultaneously. We validate CTGCN model using collected datasets with two predictive time lengths. The experimental results prove that CTGCN model has better performance in both prediction accuracy and robustness than the state-of-art baseline. Dataset and more information are available at https://github.com/wowocai/CTGCN-dataset.