Intelligent transportation systems optimisation using Non-Linear Diophantine Fuzzy Bonferroni Mean Dombi operators: A case study

Abstract

Intelligent transport systems (ITS) are going to dominate the landscape of traffic control in the foreseeable future. In order to meet the travel requirements of individuals, ITS can efficiently use the available traffic infrastructure, guarantee the protection of transportation and urban traffic, facilitate public safety monitoring, and provide efficient control mechanisms. The term “Multi-criterion Group Decision-Making” (MCGDM) refers to a circumstance in which numerous criteria must be considered before making a decision. Hence, it is important to develop novel Non-linear-Diophantine fuzzy Bonferroni-mean operators to effectively find the optimal alternative based on specific criteria for Intelligent Transportation Systems. Numerous non-linear-Diophantine fuzzy Bonferroni-mean-Dombi Aggregation-Operators are introduced (Non-linear-Diophantine Fuzzy-Weighted-Averaging (N-LDFWA) operators and Non-linear-Diophantine Fuzzy-Weighted-Geomatric (N-LDFWG) operators), employing the Bonferroni-Mean-Dombi T-norm and T-conorm. In order to examine the reliability and efficiency of the present method, a real-life scenario of ITS is used. The execution performed for the evaluation of the intelligent transport system and some comparisons are also provided.