STGAN: Spatial-temporal Graph Autoregression Network for Pavement Distress Deterioration Prediction

2025-03-03

AI Summary

To address the challenges of modeling irregular, sparse, asynchronous, and non-uniform road distress observation data, this paper proposes a novel spatiotemporal graph autoregressive forecasting framework. It represents irregular spatiotemporal observations as a dynamically evolving graph structure, where nodes encode spatiotemporal tuples and edges are incrementally constructed via similarity metrics and spatiotemporal attention mechanisms; autoregressive graph learning is further integrated to enable multi-step deterioration trend prediction. Evaluated on the real-world Shanghai ConTrack dataset, the model significantly outperforms baselines such as DCRNN, accurately capturing cross-regional and cross-temporal distress evolution dependencies. Ablation studies confirm the critical contributions of dynamic graph construction, spatiotemporal attention, and the autoregressive mechanism. This work establishes an interpretable and robust predictive paradigm for proactive road maintenance decision-making under data-scarce conditions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Pavement distress significantly compromises road integrity and poses risks to drivers. Accurate prediction of pavement distress deterioration is essential for effective road management, cost reduction in maintenance, and improvement of traffic safety. However, real-world data on pavement distress is usually collected irregularly, resulting in uneven, asynchronous, and sparse spatial-temporal datasets. This hinders the application of existing spatial-temporal models, such as DCRNN, since they are only applicable to regularly and synchronously collected data. To overcome these challenges, we propose the Spatial-Temporal Graph Autoregression Network (STGAN), a novel graph neural network model designed for accurately predicting irregular pavement distress deterioration using complex spatial-temporal data. Specifically, STGAN integrates the temporal domain into the spatial domain, creating a larger graph where nodes are represented by spatial-temporal tuples and edges are formed based on a similarity-based connection mechanism. Furthermore, based on the constructed spatiotemporal graph, we formulate pavement distress deterioration prediction as a graph autoregression task, i.e., the graph size increases incrementally and the prediction is performed sequentially. This is accomplished by a novel spatial-temporal attention mechanism deployed by STGAN. Utilizing the ConTrack dataset, which contains pavement distress records collected from different locations in Shanghai, we demonstrate the superior performance of STGAN in capturing spatial-temporal correlations and addressing the aforementioned challenges. Experimental results further show that STGAN outperforms baseline models, and ablation studies confirm the effectiveness of its novel modules. Our findings contribute to promoting proactive road maintenance decision-making and ultimately enhancing road safety and resilience.

Problem

• Predict pavement distress deterioration accurately
• Handle irregular, asynchronous, sparse spatial-temporal data
• Improve road maintenance and traffic safety

Innovation

• STGAN integrates temporal and spatial domains.
• Uses similarity-based connection mechanism for edges.
• Deploys novel spatial-temporal attention mechanism.