Decision analysis with known and unknown weights in the complex N-cubic fuzzy environment: An application of accident prediction models

The component of Pakistan's road safety management (RSM) systems that appears to be the least reliable is the evaluation of road safety measures. Road safety initiatives' daily operations, such as allocating specific financial resources and incorporating measures for road safety into the fabric of culture, are only sometimes observed by governments. When this happens, the analysis usually concentrates on issues related to the infrastructure and the enforcement of laws; thorough evaluations of road safety initiatives are incredibly uncommon. Road authorities, practitioners, and architects of road safety depend on prediction tools, often known as accident prediction models (APMs). These instruments are employed to assess safety concerns, pinpoint areas for improvement, and calculate the expected safety consequences of these modifications. The goal of this research is to use the complex N-cubic fuzzy set (CNCFS), an innovative and practical tool for decision making that excels at handling imprecise or ambiguous data in real-world decision-making processes, in the context. This study also proposes a novel entropy approach to multi-attribute group decision-making issues in RSM. We also investigate the assessment of accident forecasting models in RSM to demonstrate the feasibility and efficacy of the suggested strategy. Further, the advantages and superiority of the proposed strategy are explained using the experimental data and comparisons with known and unknown weights obtained by the entropy method. The study's conclusions demonstrate that the suggested approach is more workable and compatible with other current strategies.