IF-MABAC Method for Evaluating the Intelligent Transportation System with Intuitionistic Fuzzy Information

Abstract

Intelligent transportation system (ITS) is the development direction of the future traffic system. ITS can effectively employ the existing traffic facilities and ensure the safety of traffic, urban traffic, and public security management for effective control in order to satisfy people’s travel demand. Therefore, the results of the system in-depth understanding and objective evaluation are very necessary. And it is frequently regarded as a multiattribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is required to tackle it. Depending on the conventional multiattributive border approximation area comparison (MABAC) method and intuitionistic fuzzy sets (IFSs), this article designs a novel intuitive distance-based IF-MABAC method to assess the performance of financial management. First of all, a related literature review is conducted. Furthermore, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria are decided objectively by utilizing the maximizing deviation method. Afterwards, relying on novel distance measures between intuitionistic fuzzy numbers (IFNs), the conventional MABAC method is extended to the IFSs to calculate the final value of each enterprise. Therefore, all enterprises can be ranked, and the one with the best environmental behaviors and awareness can be identified. Eventually, an application for evaluating the intelligent transportation system and some comparative analyses have been given. The results illustrate that the designed algorithm is useful for assessing the performance of financial management.