Context-aware knowledge graph reasoning for road maintenance decision-making

Abstract

The quality and efficiency of road maintenance greatly impact on transportation performance. There is an urgent need to automate the process of decision-making for road maintenance, especially for reactive maintenance. However, automating this process is challenging because (1) the expert knowledge required in this process is implicit and difficult to formalize and (2) the defect reports from frontline inspectors are in unstructured human language. This paper proposes a method based on natural language processing (NLP) and knowledge graph reasoning. This method combines the semantic understanding capability of NLP with the structural reasoning capability of the knowledge graph, enabling nuanced and logically rigorous road maintenance decisions. Real-world highway defect reports are used for validation. The experimental results demonstrate a significant improvement of the proposed model over baseline models, with up to 13.5% higher accuracy on the prediction of key tasks. This approach enables a traceable decision-making process, avoiding a black-box model.