ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenance

Abstract

Climate-vulnerable road networks require a paradigm shift from reactive, fix-on-failure repairs to predictive, decision-ready maintenance. This paper introduces ST-ResGAT, a novel Spatio-Temporal Residual Graph Attention Network that fuses residual graph-attention encoding with GRU temporal aggregation to forecast pavement deterioration. Engineered for resource-constrained deployment, the framework translates continuous Pavement Condition Index (PCI) forecasts directly into the American Society for Testing and Materials (ASTM)-compliant maintenance priorities. Using a real-world inspection dataset of 750 segments in Sylhet, Bangladesh (2021-2024), ST-ResGAT significantly outperforms traditional non-spatial machine learning baselines, achieving exceptional predictive fidelity (R2 = 0.93, RMSE = 2.72). Crucially, ablation testing confirmed the mathematical necessity of modeling topological neighbor effects, proving that structural decay acts as a spatial contagion. Uniquely, we integrate GNNExplainer to unbox the model, demonstrating that its learned priorities align perfectly with established physical engineering theory. Furthermore, we quantify classification safety: achieving 85.5% exact ASTM class agreement and 100% adjacent-class containment, ensuring bounded, engineer-safe predictions. To connect model outputs to policy, we generate localized longitudinal maintenance profiles, perform climate stress-testing, and derive Pareto sustainability frontiers. ST-ResGAT therefore offers a practical, explainable, and sustainable blueprint for intelligent infrastructure management in high-risk, low-resource geological settings.

Figures (8)

• Figure 1: Graphical Abstract. Overview of ST-ResGAT development and evaluation.
• Figure 2: Construction of the spatio-temporal graph representation used in ST-ResGAT. Pavement inspection records collected across multiple years are first organized as node features for each road segment. THe road network topology defines adjacency relationships among segments, forming a graph structure. Each year corresponds to a graph snapshot with identical topology but updated node attributes. A temporal window of historical snapshots is then combined to create a spatio-temporal feature sequence that serves as input to the proposed ST-ResGAT model for future pavement condition prediction.
• Figure 3: Architecture of the proposed Spatio-Temporal Residual Graph Attention Network (ST-ResGAT) for pavement condition prediction. The model receives temporal node features and the road network graph as input. Spatial dependencies among adjacent pavement segments are learned through multi-head Graph Attention Network (GAT) layer with residual connections to preserve original structural attributes. The resulting spatial embeddings across multiple time steps are aggregated using a Gated Recurrent Unit (GRU) to capture temporal deterioration patterns. The final spatio-temporal representation is passed through a regression head to estimate the Pavement Condition Index (PCI) for each segment, which can subsequently be used for maintenance prioritization.
• Figure 4: Actual vs. Predicted PCI scatter plot for all comparative models. The ResGAT model shows the tightest clustering along the diagonal identity line.
• Figure 5: (a) REC curve, further validating the high error tolerance and robustness of the ST-ResGAT predictions, (b) Taylor Diagram illustrating the standard deviation, root mean square error (RMSE), and correlation coefficient of the models relative to the ground truth observation point.