A spatiotemporal prediction method for the evolution of pavement distress in road networks

Existing pavement performance prediction models often struggle to capture complex spatiotemporal dependencies in road networks due to reliance on empirical rules and scenario-based calibration. This study proposes pavement graph network (PaveGNet), a spatiotemporal graph network framework designed to model fine-grained pavement distress evolution. It constructs a multi-node graph encoding topological correlations and time-based state transitions, while integrating exogenous factors such as temperature, precipitation, traffic, and maintenance works. Experiments demonstrate that PaveGNet performs effectively in predicting fine-grained indicators of distress evolution. The prediction error for distress evolution rate was significantly reduced, from 9.005% with the baseline spatial–temporal graph convolutional network model to 2.670%. Ablation experiments were conducted to verify the contribution of temporal interdependence, spatial correlation, and external variables in the proposed PaveGNet framework. The results demonstrate that all three components play essential roles in prediction, with external variables showing the most significant impact. To further assess the modular robustness, parts of the spatial and temporal learning modules were independently replaced. The results indicate that the prediction of distress evolution rate relies heavily on the originally designed learning components. Overall, this framework provides a more realistic and scalable solution for the spatiotemporal prediction of pavement distress evolution in road networks.