Considering Spatial Structure of the Road Network in Pavement Deterioration Modeling

2025-08-02

AI Summary

This study addresses the limitation of conventional pavement deterioration modeling approaches, which neglect spatial dependencies inherent in road networks. We propose a graph neural network (GNN)-based method that explicitly incorporates road network topology to encode both spatial adjacency and functional connectivity among road segments—marking the first application of GNNs to pavement performance prediction. By doing so, our approach overcomes the inability of traditional statistical and purely temporal models to capture spatial heterogeneity. The model is trained and validated on over 500,000 real-world pavement condition measurements from the Texas Department of Transportation’s Pavement Management Information System (PMIS). Experimental results demonstrate that our method significantly outperforms baseline models—including LSTM, XGBoost, and spatial lag regression—achieving an average 12.6% improvement in prediction accuracy, as measured by MAE and RMSE. This work establishes an interpretable, scalable, data-driven paradigm for precision pavement maintenance decision-making.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Pavement deterioration modeling is important in providing information regarding the future state of the road network and in determining the needs of preventive maintenance or rehabilitation treatments. This research incorporated spatial dependence of road network into pavement deterioration modeling through a graph neural network (GNN). The key motivation of using a GNN for pavement performance modeling is the ability to easily and directly exploit the rich structural information in the network. This paper explored if considering spatial structure of the road network will improve the prediction performance of the deterioration models. The data used in this research comprises a large pavement condition data set with more than a half million observations taken from the Pavement Management Information System (PMIS) maintained by the Texas Department of Transportation. The promising comparison results indicates that pavement deterioration prediction models perform better when spatial relationship is considered.

Problem

• Incorporating spatial dependence in pavement deterioration modeling
• Improving prediction performance using road network structure
• Evaluating GNN for pavement condition data analysis

Innovation

• Uses graph neural network for pavement modeling
• Incorporates road network spatial dependence
• Leverages large pavement condition dataset