Machine learning embedded hybrid MCDM model to mitigate decision uncertainty in transport safety planning for OAS countries

Abstract

Providing defensible decisions is a prerequisite for methodologies of multi-criteria decision-making (MCDM) activities, and this is especially true for socio-economic analysis in public sector. This study proposes an all-in-one MCDM model with machine learning algorithms. The model integrates the method based on the removal effects of criteria (MEREC), combined compromise solution (CoCoSo), and density-based spatial clustering of applications with noise (DBSCAN), i.e., MEREC–CoCoSo–DBSCAN. In particular, the uniform manifold approximation and projection (UMAP) is implanted in DBSCAN to reduce the data dimensionality, and the k-nearest neighbors (KNN) algorithm is embedded to determine the inflection points (ɛ) and minPts in the data. This counters the inherent model failure of DBSCAN in dealing with high-dimensional data and eliminates the requirement for manual intervention in the model procedure, thereby fully avoiding potential human error and automating the computing process. A case study on benchmarking transport safety systems for member countries of the Organization of American States (OAS) demonstrates the reliability, adaptability, and efficiency of the proposed model. It moreover reflects its feasibility in resolving real-life socio-economic issues by offering valuable insights and potential solutions in economic investment and funding allocation in regard to transport safety strategy. Overall, this study provides government officials, managers, and policymakers with a valuable tool for handling MCDM activities in socio-economic development with considerable practicality and credibility.