Considering Spatial Structure of the Road Network in Pavement Deterioration Modeling

TL;DR

This paper demonstrates that incorporating the spatial structure of road networks using graph neural networks enhances the accuracy of pavement deterioration predictions, leveraging extensive Texas pavement data.

Contribution

It introduces a GNN-based approach for pavement deterioration modeling that explicitly accounts for spatial dependencies in road networks.

Findings

GNN models outperform traditional models in prediction accuracy.

Spatial structure consideration improves deterioration forecasts.

Large-scale pavement data supports the model's effectiveness.

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.