# Considering Spatial Structure of the Road Network in Pavement Deterioration Modeling

arXiv: https://arxiv.org/abs/2508.02749 · HTML: https://mlsanity.com/paper/2508.02749/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Evaluating Pavement Deterioration Rates Due to Flooding Events Using Explainable AI](https://mlsanity.com/paper/2507.01056/) (2025)

## Built on by (most cited first, 3 of 3)

- [Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks](https://mlsanity.com/paper/2511.02957/) (2025)
- [Developing an AI Assistant for Knowledge Management and Workforce Training in State DOTs](https://mlsanity.com/paper/2603.03302/) (2026)
- [ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenance](https://mlsanity.com/paper/2603.14107/) (2026)

## Abstract (arXiv)

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.