Comparative Analysis of Deep Neural Networks and Graph Convolutional Networks for Road Surface Condition Prediction

Saroch Boonsiripant, Chuthathip Athan, Krit Jedwanna (), Ponlathep Lertworawanich and Auckpath Sawangsuriya
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Saroch Boonsiripant: Department of Civil Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
Chuthathip Athan: Mobinary Company Limited, Bangkok 10400, Thailand
Krit Jedwanna: Department of Civil Engineering, Faculty of Engineering, Rajamangala University of Technology Phra Nakhon, Bangkok 10300, Thailand
Ponlathep Lertworawanich: Department of Highways, Bureau of Road Research and Development, Bangkok 10150, Thailand
Auckpath Sawangsuriya: Department of Highways, Bureau of Road Research and Development, Bangkok 10150, Thailand

Sustainability, 2024, vol. 16, issue 22, 1-18

Abstract: Road maintenance is essential for supporting road safety and user comfort. Developing predictive models for road surface conditions enables highway agencies to optimize maintenance planning and strategies. The international roughness index (IRI) is widely used as a standard for evaluating road surface quality. This study compares the performance of deep neural networks (DNNs) and graph convolutional networks (GCNs) in predicting IRI values. A unique aspect of this research is the inclusion of additional predictor features, such as the type and timing of recent roadwork, hypothesized to affect IRI values. Findings indicate that, overall, the DNN model performs similarly to the GCN model across the entire highway network. Given the predominantly linear structure of national highways and their limited connectivity, the dataset exhibits a low beta index, ranging from 0.5 to 0.75. Additionally, gaps in IRI data collection and discontinuities in certain highway segments present challenges for modeling spatial dependencies. The performance of DNN and GCN models was assessed across the network, with results indicating that DNN outperforms GCN when highway networks are sparsely connected. This research underscores the suitability of DNN for low-connectivity networks like highways, while also highlighting the potential of GCNs in more densely connected settings.

Keywords: international roughness index (IRI); graph convolutional network (GCN); deep neural network (DNN); machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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