Enhanced decision-making for urban climate change transportation policies using q-rung orthopair fuzzy rough fairly information aggregation

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2025-01-27 DOI:10.1016/j.ins.2025.121900

Hafiz Muhammad Athar Farid , Muhammad Riaz , Patrick Siarry , Vladimir Simic

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引用次数: 0

Abstract

Formulating and implementing effective transportation policies is of the utmost importance in the context of increasingly imperative urban climate change issues. Utilizing q-rung orthopair fuzzy rough fairly aggregation operators, this paper presents an innovative method for enhancing decision-making in urban transportation policy development. These operators provide a dynamic multi-attribute decision making (MADM) framework particularly for urban climate and transportation problems that are complex, ambiguous, and comprise multiple criteria. By incorporating “q-rung orthopair fuzzy rough sets” (q-ROFRSs) and fairly operations, this methodology provides a comprehensive and systematic method for evaluating and prioritizing sustainable transportation policies while taking into account the inherent ambiguity and imprecision in urban climate change data. This study makes a significant contribution to the field of urban climate change policy development by providing a novel decision-support instrument that improves the transparency, fairness, and efficacy of decision-making process. The findings highlight the significance of incorporating rough set techniques in addressing the complexities of urban climate change transportation policy development, ultimately leading to more resilient and sustainable urban environments.

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来源期刊

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.