Combinative distance-based assessment method for decision-making with 2-tuple linguistic $q$-rung picture fuzzy sets

Multi-criteria group decision-making (MCGDM) approaches have a substantial effect on decision-making in a range of critical sectors, including science, business, and real-life research. These strategies also efficiently assist researchers in resolving challenges that may arise throughout their study activity. The current work's major purpose is to research and develop the combinative distance-based assessment (CODAS) approach by employing 2-tuple linguistic $q$-rung picture fuzzy sets (2TL $q$-RPFSs) as a background. The CODAS technique computes the distances from the negative ideal solutions and ranks the alternatives in increasing order. To compute the normal weights of attributes, the entropy weighting information process is used. Furthermore, two aggregation operators, namely the 2-tuple linguistic $q$-rung picture fuzzy Einstein weighted average and the 2-tuple linguistic $q$-rung picture fuzzy Einstein order weighted average, are introduced. Our inspiration for employing the notion of 2TL $q$-RPFSs is the ability of $q$-RPFSs to support a wide range of information and the significant qualities of 2-tuple linguistic term sets to handle qualitative data. Congested transportation networks may be made more efficient by leveraging digital transformation. Real-time traffic management is one solution to the problem of road congestion. As a result of connected autonomous vehicle (CAV) advances, the benefits of real-time traffic management systems have grown dramatically. CAVs can help manage traffic by acting as enforcers. To complement the extended approach, the proposed technique is used to select the best alternative for a real-time traffic management system. The performance of the suggested technique is validated using scenario analysis. The results show that the suggested strategy is efficient and relevant to real-world situations.