Improving decision reliability in transport safety engineering through a machine learning-based model

Ensuring defensible policy inferences and robust decision reliability is fundamental for multi-criteria decision-making activities, particularly in transport safety engineering. This study developed a hybrid preference function-nested and machine learning-embedded decision model, i.e., EXPROM II-K-means with a linear discriminant analysis, to provide a robust support system for policy setting and decision making. The proposed model incorporates a refined nonparametric preference function into the EXPROM II method to alleviate cognitive burden on decision makers while improving the flexibility of preference articulation. Meanwhile, linear discriminant analysis, a supervised machine learning-based dimensionality reduction algorithm, is embedded to reduce data dimensionality and project features into axes that maximize class separability, which simplifies the data structure, filters noise, and emphasizes informative attributes. This transformation improves the clustering performance of K-means clustering, which yields clearer and more actionable patterns in high-dimensional or noisy datasets. A case study on transport safety engineering across the Asia-Pacific Economic Cooperation countries demonstrates the reliability, scalability, and effectiveness of the model in guiding resource allocation and strategic prioritization. The proposed framework offers a practical, interpretable, and intelligent support tool to manage complex decision tasks with enhanced stability and reliability.