Department of Mathematics, College of Science & Arts, King Abdul Aziz University, Rabigh, Saudi Arabia.
Department of Mathematics, Division of Science and Technology, University of Education, Lahore, Pakistan.
PLoS One. 2024 May 10;19(5):e0303139. doi: 10.1371/journal.pone.0303139. eCollection 2024.
Road traffic accidents (RTAs) pose a significant hazard to the security of the general public, especially in developing nations. A daily average of more than three thousand fatalities is recorded worldwide, rating it as the second most prevalent cause of death among people aged 5-29. Precise and reliable decisionmaking techniques are essential for identifying the most effective approach to mitigate road traffic incidents. This research endeavors to investigate this specific concern. The Fermatean fuzzy set (FFS) is a strong and efficient method for addressing ambiguity, particularly when the concept of Pythagorean fuzzy set fails to provide a solution. This research presents two innovative aggregation operators: the Fermatean fuzzy ordered weighted averaging (FFOWA) operator and the Fermatean fuzzy dynamic ordered weighted geometric (FFOWG) operator. The salient characteristics of these operators are discussed and important exceptional scenarios are thoroughly delineated. Furthermore, by implementing the suggested operators, we develop a systematic approach to handle multiple attribute decisionmaking (MADM) scenarios that involve Fermatean fuzzy (FF) data. In order to show the viability of the developed method, we provide a numerical illustration encompassing the determination of the most effective approach to alleviate road traffic accidents. Lastly, we conduct a comparative evaluation of the proposed approach in relation to a number of established methodologies.
道路交通事故（RTAs）对公众安全构成重大威胁，尤其是在发展中国家。全球每天平均有超过 3000 人死亡，这使其成为 5-29 岁人群中第二大常见死因。准确可靠的决策技术对于确定减轻道路交通事故的最有效方法至关重要。本研究旨在探讨这一具体问题。Fermatean 模糊集（FFS）是一种强大而有效的处理模糊性的方法，尤其是在 Pythagorean 模糊集无法提供解决方案的情况下。本研究提出了两种创新的聚合算子：Fermatean 模糊有序加权平均（FFOWA）算子和 Fermatean 模糊动态有序加权几何（FFOWG）算子。讨论了这些算子的显著特点，并详细阐述了重要的例外情况。此外，通过实施所提出的算子，我们开发了一种系统的方法来处理涉及 Fermatean 模糊（FF）数据的多属性决策（MADM）场景。为了展示所开发方法的可行性，我们提供了一个数值示例，包括确定减轻道路交通事故的最有效方法。最后，我们对所提出的方法与一些已建立的方法进行了比较评估。
